how-gen-ai-affects-researchers/data/articles.csv

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"AR6WSBZ8","journalArticle","2023","Mesko, Bertalan","The ChatGPT (Generative Artificial Intelligence) Revolution Has Made Artificial Intelligence Approachable for Medical Professionals","Journal of Medical Internet Research","","","10.2196/48392","https://www.proquest.com/docview/2917629125/abstract/13D00F81FE2A494FPQ/1","In November 2022, OpenAI publicly launched its large language model (LLM), ChatGPT, and reached the milestone of having over 100 million users in only 2 months. LLMs have been shown to be useful in a myriad of health care–related tasks and processes. In this paper, I argue that attention to, public access to, and debate about LLMs have initiated a wave of products and services using generative artificial intelligence (AI), which had previously found it hard to attract physicians. This paper describes what AI tools have become available since the beginning of the ChatGPT revolution and contemplates how it they might change physicians’ perceptions about this breakthrough technology.","2023","2024-12-03 02:27:04","2024-12-03 02:27:04","2024-12-03 02:26:49","e48392","","1","25","","","","","","","","","","English","© 2023. This work is licensed under https://creativecommons.org/licenses/by/4.0/  (the “License”).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: e48392 Place: Toronto, Canada Publisher: Gunther Eysenbach MD MPH, Associate Professor Section: Viewpoints and Perspectives","","","","","Artificial; artificial intelligence; Artificial intelligence; Cardiology; chatbot; Chatbots; ChatGPT; computer generated; continuing education; conversation agents; conversational agent; curricula; curriculum; Design; digital health; future; generated text; generative; Generative; Generative artificial intelligence; Health care; Language model; large language model; Logos; medical education; Medical personnel; medical practice; Oncology; OpenAI; Physician; Physicians; Presentations; professional development; Professionals; Public access; Regulatory approval; Research; Social networks; technology; Technology","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LGINYFPR","journalArticle","2024","Demir-Kaymak, Zeliha; Turan, Zekiye; Unlu-Bidik, Nazli; Unkazan, Semiha","Effects of midwifery and nursing students' readiness about medical Artificial intelligence on Artificial intelligence anxiety","Nurse Education in Practice","","14715953","10.1016/j.nepr.2024.103994","https://www.proquest.com/docview/3075657852/abstract/13D00F81FE2A494FPQ/2","Background Artificial intelligence technologies are one of the most important technologies of today. Developments in artificial intelligence technologies have widespread and increased the use of artificial intelligence in many areas. The field of health is also one of the areas where artificial intelligence technologies are widely used. For this reason, it is considered important that healthcare professionals be prepared for artificial intelligence and do not experience problems while training them. In this study, midwife and nurse candidates, as future healthcare professionals, were discussed. This study aims to examine the effect of the artificial intelligence readiness on the artificial intelligence anxiety and the effect of artificial intelligence characteristic variables (artificial intelligence knowledge, daily life, occupational threat, artificial intelligence trust) on the medical artificial intelligence readiness and artificial intelligence anxiety of students. This study was planned and carried out as a relational survey study, which is a quantitative research. A total of 480 students, consisting of 240 nursing and 240 midwifery students, were included in this study. SPSS 26.0 and AMOS 26 package programs were used to analyse the data and descriptive statistics (frequency, percentage, mean, standard deviation) and path analysis for the structural equation model were used. No significant difference was found between the medical artificial intelligence readiness (p=0.082) and artificial intelligence anxiety (p=0.486) scores of midwifery and nursing students. The model of the relationship between medical artificial intelligence readiness and artificial intelligence anxiety had a good goodness of fit. Artificial intelligence knowledge and using artificial intelligence in daily life are predictors of medical artificial intelligence readiness. Using artificial intelligence in daily life, occupational threat and artificial intelligence trust are predictors of artificial intelligence anxiety. Midwifery and nursing students' AI anxiety and AI readiness levels were found to be at a moderate level and students' AI readiness affected AI anxiety.","2024-07","2024-12-03 02:27:04","2024-12-03 02:27:04","2024-12-03 02:26:50","103994","","","78","","","","","","","","","","English","©2024. Elsevier Ltd","","","","ProQuest","","Num Pages: 103994 Place: Kidlington, United Kingdom Publisher: Elsevier Limited","","","","","Activities of daily living; Anxiety; Artificial; Artificial intelligence; Candidates; Colleges & universities; Curricula; Decision making; Electronic health records; Goodness of fit; Health care; Health Professional; Knowledge; Medical personnel; Medical students; Midwifery; Midwifery education; Midwives; Nurses; Nursing; Nursing education; Obstetrics; Path analysis; Professionals; Quantitative analysis; Students; Threats; Vagina; Variables","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"F5IPEA5K","journalArticle","2024","Nguyen, Trung Tuan; Moore, Philip; Thanh, Dat Ha Vu; Pham, Hai Van","A Generative Artificial Intelligence Using Multilingual Large Language Models for ChatGPT Applications","Applied Sciences","","","10.3390/app14073036","https://www.proquest.com/docview/3037388983/abstract/13D00F81FE2A494FPQ/3","ChatGPT plays significant roles in the third decade of the 21st Century. Smart cities applications can be integrated with ChatGPT in various fields. This research proposes an approach for developing large language models using generative artificial intelligence models suitable for small- and medium-sized enterprises with limited hardware resources. There are many generative AI systems in operation and in development. However, the technological, human, and financial resources required to develop generative AI systems are impractical for small- and medium-sized enterprises. In this study, we present a proposed approach to reduce training time and computational cost that is designed to automate question–response interactions for specific domains in smart cities. The proposed model utilises the BLOOM approach as its backbone for using generative AI to maximum the effectiveness of small- and medium-sized enterprises. We have conducted a set of experiments on several datasets associated with specific domains to validate the effectiveness of the proposed model. Experiments using datasets for the English and Vietnamese languages have been combined with model training using low-rank adaptation to reduce training time and computational cost. In comparative experimental testing, the proposed model outperformed the ‘Phoenix’ multilingual chatbot model by achieving a 92% performance compared to ‘ChatGPT’ for the English benchmark.","2024","2024-12-03 02:27:04","2024-12-03 02:27:04","2024-12-03 02:26:51","3036","","7","14","","","","","","","","","","English","© 2024 by the authors.  Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 3036 Place: Basel, Switzerland Publisher: MDPI AG","","","","","Adaptation; Automation; Bloom; chatbot; Chatbots; ChatGPT; Datasets; generative AI; Generative artificial intelligence; Infrastructure; Language; language comprehension; Language model; large language models; multilingual language models; Multilingualism; Smart cities; Smart city; support systems; SWOT analysis; technological determinism","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"578MVBNR","journalArticle","2024","WANG, Weizheng; QIAO, Hong; LI, Xiaojun; WANG, Jingjing","User Willingness to Use Generative Artificial Intelligence Based on AIDUA Framework","Journal of Library and Information Sciences in Agriculture","","10021248","10.13998/j.cnki.issn1002-1248.24-0076","https://www.proquest.com/docview/3072296732/abstract/13D00F81FE2A494FPQ/4","[Purpose/Significance] Generative artificial intelligence (AI) technology has been widely used in many fields, and the application of this technology has become popular among researchers. However, there are few studies on the willingness of researchers willingness to accept generative AI. This leads to an insufficient understanding of the psychological mechanism, cognitive process and behavioral pattern of users' acceptance of generative AI, which limits the ability of theoretical innovation and practical exploration in user information behavior. This study focuses on researchers acceptance of generative AI. By studying the evaluation process of ChatGPT by college students, it explores the acceptance behavior of generative AI. At the same time, it verifies the applicability of the AIDUA model in the new context, and introduces the new variable of school identity, which further extends the AIDUA model. [Method/Process] Based on the cognitive assessment theory and the AI acceptance framework (AIDUA), this paper constructs a theoretical model of the intention to use generative artificial intelligence, and develops and empirically tests the theoretical model of the intention to use generative AI. Taking college students as the main research object, based on the maturity scale in authoritative literature at home and abroad, 8 variables and 29 observation variables such as social influence, hedonic motivation and anthropomorphism were designed. College students with experience in using generative AI were invited to participate in the questionnaire survey. SPSS26.0 was used to analyze the data from 294 valid questionnaires collected. SmartPLS 3.2.9 was used to construct a structural equation model to test the hypothesis, and the JN method was used to detect the regulatory effect. [Results/Conclusions] The study found that users went through three stages of decision making before using generative AI. The PLS-SEM results show that: 1) Social influence, hedonic motivation and anthropomorphism significantly affect performance expectancy and effort expectancy, and anthropomorphism is the strongest variable affecting performance expectancy and effort expectancy. 2) Performance expectancy and effort expectancy are significantly negatively correlated with negative emotions, while hedonic motivation has no significant effect on negative emotions. 3) Negative emotions are significantly negatively correlated with users' intension to use. 4) School identity moderates the relationship between effort expectancy and negative emotions. This study combines anthropomorphic research on college students' acceptance of generative AI, and provides a framework for the acceptance of generative AI. Researchers can use this framework to better study the acceptance of AI. This study has some limitations. In the future, we will focus on the following three aspects: 1) to evaluate the users' acceptance of generative AI in different usage scenarios. 2) to use samples of other groups to test the applicability of the model, such as civil servants, librarians, researchers and other groups. 3) to incorporate variables from other technology acceptance models into the model, such as ease of use and practicality.","2024","2024-12-03 02:27:04","2024-12-03 02:27:04","2024-12-03 02:26:51","36-50","","2","36","","","","","","","","","","Chinese","© 2024. This work is published under https://creativecommons.org/licenses/by-nc-nd/4.0/  (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 36-50 Place: Beijing, China Publisher: Agricultural Information Institute of Chinese Academy of Agricultural Sciences","","","","","ChatGPT; digital literacy; generative artificial intelligence; personification; user information behavior; willingness to use; 使用意愿; 拟人化; 数字素养; 生成式人工智能; 用户信息行为","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"J7PLXY3H","journalArticle","2024","Matijevic, Mirela Mezak; Pisker, Barbara; Dokic, Kristian","Constructing a Socio-Legal Framework Proposal for Governing Large Language Model Usage and Application in Education","Social Sciences","","","10.3390/socsci13090479","https://www.proquest.com/docview/3110687290/abstract/13D00F81FE2A494FPQ/5","Due to the fast-changing environments caused by artificial intelligence development, the socio-technical challenge in contemporary educational systems focuses on the need for more regulative measures guiding system stakeholders’ behavior. In fulfilling the present legal gap, enacted soft law regulation has been laid out, and a detailed systematic literature review was conducted in the paper presented. The specific methodological approach was selected to deal with two crucial research tasks: to reveal and recommend fundamental governing mechanisms regarding the use and application of generative artificial intelligence; more precisely, large language models in educational systems. Three systematically guided layers of quantitative and qualitative content analysis of central policy, legislation, and regulatory mechanisms in governing AI in education were extracted from the 142 Scopus Database and Web of Science research papers analyzed and presented. These research findings benefit policymakers, regulatory and legislative bodies, and agencies in constructing governing frames for using and applying generative artificial intelligence in education.","2024","2024-12-03 02:27:04","2024-12-03 02:27:04","2024-12-03 02:26:52","479","","9","13","","","","","","","","","","English","© 2024 by the authors.  Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 479 Place: Basel, Switzerland Publisher: MDPI AG","","","","","Chatbots; ChatGPT; Data integrity; Digital literacy; education; Education; Ethics; generative artificial intelligence; Generative artificial intelligence; governance; Infrastructure; Language; large language models; Large language models; Learning; Learning disabilities; legislation; National security; Personal information; policy; recommendation; regulation; Students; Teaching","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RIARA2KN","journalArticle","2024","Mansourian, Ali; Oucheikh, Rachid","ChatGeoAI: Enabling Geospatial Analysis for Public through Natural Language, with Large Language Models","ISPRS International Journal of Geo-Information","","","10.3390/ijgi13100348","https://www.proquest.com/docview/3120638333/abstract/13D00F81FE2A494FPQ/6","Large Language Models (LLMs) such as GPT, BART, and Gemini stand at the forefront of Generative Artificial Intelligence, showcasing remarkable prowess in natural language comprehension and task execution. This paper proposes a novel framework developed on the foundation of Llama 2, aiming to bridge the gap between natural language queries and executable code for geospatial analyses within the PyQGIS environment. It empowers non-expert users to leverage GIS technology without requiring deep knowledge of geospatial programming or tools. Through cutting-edge Natural Language Processing (NLP) techniques, including tailored entity recognition and ontology mapping, the framework accurately interprets user intents and translates them into specific GIS operations. Integration of geospatial ontologies enriches semantic comprehension, ensuring precise alignment between user descriptions, geospatial datasets, and geospatial analysis tasks. A code generation module empowered by Llama 2 converts these interpretations into PyQGIS scripts, enabling the execution of geospatial analysis and results visualization. Rigorous testing across a spectrum of geospatial analysis tasks, with incremental complexity, evaluates the framework and the performance of such a system, with LLM at its core. The proposed system demonstrates proficiency in handling various geometries, spatial relationships, and attribute queries, enabling accurate and efficient analysis of spatial datasets. Moreover, it offers robust error-handling mechanisms and supports tasks related to map styling, visualization, and data manipulation. However, it has some limitations, such as occasional struggles with ambiguous attribute names and aliases, which leads to potential inaccuracies in the filtering and retrieval of features. Despite these limitations, the system presents a promising solution for applications integrating LLMs into GIS and offers a flexible and user-friendly approach to geospatial analysis.","2024","2024-12-03 02:27:04","2024-12-03 02:27:04","2024-12-03 02:26:53","348","","10","13","","","ChatGeoAI","","","","","","","English","© 2024 by the authors.  Published by MDPI on behalf of the International Society for Photogrammetry and Remote Sensing. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 348 Place: Basel, Switzerland Publisher: MDPI AG","","","","","Accuracy; Algorithms; Artificial intelligence; Bridge foundations; Chatbots; code generation; Collaboration; Cutting tools; Datasets; Decision making; Deep learning; Design; Economic; Efficiency; Error analysis; Generative artificial intelligence; GeoAI; Geographic information systems; Geographical information systems; Geography; geospatial analysis; GIS; GIS democratization; Handling; Language; large language model; Large language models; Llama; Machine learning; natural language processing; Natural language processing; Query processing; Research & development--R&D; Semantics; Sentiment analysis; Spatial analysis; Styling; Task complexity; Usability; Visualization","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"R2QK4447","journalArticle","2024","Kumar, Yulia; Huang, Kuan; Perez, Angelo; Yang, Guohao; Li, J. Jenny; Morreale, Patricia; Kruger, Dov; Jiang, Raymond","Bias and Cyberbullying Detection and Data Generation Using Transformer Artificial Intelligence Models and Top Large Language Models","Electronics","","","10.3390/electronics13173431","https://www.proquest.com/docview/3103840384/abstract/13D00F81FE2A494FPQ/7","Despite significant advancements in Artificial Intelligence (AI) and Large Language Models (LLMs), detecting and mitigating bias remains a critical challenge, particularly on social media platforms like X (formerly Twitter), to address the prevalent cyberbullying on these platforms. This research investigates the effectiveness of leading LLMs in generating synthetic biased and cyberbullying data and evaluates the proficiency of transformer AI models in detecting bias and cyberbullying within both authentic and synthetic contexts. The study involves semantic analysis and feature engineering on a dataset of over 48,000 sentences related to cyberbullying collected from Twitter (before it became X). Utilizing state-of-the-art LLMs and AI tools such as ChatGPT-4, Pi AI, Claude 3 Opus, and Gemini-1.5, synthetic biased, cyberbullying, and neutral data were generated to deepen the understanding of bias in human-generated data. AI models including DeBERTa, Longformer, BigBird, HateBERT, MobileBERT, DistilBERT, BERT, RoBERTa, ELECTRA, and XLNet were initially trained to classify Twitter cyberbullying data and subsequently fine-tuned, optimized, and experimentally quantized. This study focuses on intersectional cyberbullying and multilabel classification to detect both bias and cyberbullying. Additionally, it proposes two prototype applications: one that detects cyberbullying using an intersectional approach and the innovative CyberBulliedBiasedBot that combines the generation and detection of biased and cyberbullying content.","2024","2024-12-03 02:27:04","2024-12-03 02:27:04","2024-12-03 02:26:54","3431","","17","13","","","","","","","","","","English","© 2024 by the authors.  Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 3431 Place: Basel, Switzerland Publisher: MDPI AG","","","","","Artificial intelligence; Bias; bias data generator; bias detection tokens; Bullying; Chatbots; CyberBulliedBiasedBot; cyberbullying detection; Datasets; intersectional cyberbullying; Large language models; multilabel text classification; synthetic cyberbullying data; transformer models; Transformers","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"BT3UFCG9","journalArticle","2024","Sahu, Arvind; Sahu, Atul","Revolutionary Applications of Generative AI in Higher Education Institutes (HEIs) and its Implications","Library Philosophy and Practice","","","","https://www.proquest.com/docview/3061354867/abstract/13D00F81FE2A494FPQ/8","In recent decades, there has been a notable transformation in educational procedures due to technological breakthroughs, particularly in artificial intelligence (AI). In recent times, there has been a noteworthy advancement and acceptance of generative artificial intelligence (AI) models, specifically exemplified by the emergence of Generative Pre-trained Transformers (GPT). Within the overarching category of Generative AI, various AI tools and technologies facilitate the production of computer-generated text, images, and other forms of digitized media. This paper comprehensively analyzes the concepts and implications of the discourse surrounding Generative AI. By adopting a position that advocates for the acceptance rather than the opposition of Generative AI, this study offers valuable insights for educators and researchers in higher education learners. The findings presented here contribute significantly to understanding Generative AI as a transformative force in reforming education. This study investigates the potential consequences of generative artificial intelligence (AI) technology on higher education, specifically focusing on the significant transformative shifts that may occur within higher education institutions (HEIs). This article examines three primary objectives: * The benefits and use cases of generative AI in higher education institutions (HEIs) * The influence or disruption of generative AI in the education sectors * The developing obstacles and opportunities associated with its implementation The authors contribute to the extant research study by presenting a comprehensive model elucidating the manifestation of generative artificial intelligence (AI) in higher education and its impacts on libraries. Additionally, they offer insightful recommendations for effectively managing this phenomenon. The paper's concluding discussion delves into the prospective ramifications of generative artificial intelligence (AI) within higher education institutions (HEIs), as well as the obstacles and risks that AI presents, particularly in the context of higher education.","2024-05","2024-12-03 02:27:04","2024-12-03 02:27:04","2024-12-03 02:26:54","1-9","","","","","","","","","","","","","English","© 2024. This work is published under https://creativecommons.org/licenses/by-nc/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 1-9 Place: Lincoln, United States Publisher: Library Philosophy and Practice","","","","","Chatbots; Critical thinking; Feedback; Generative artificial intelligence; Higher education; Libraries; Machine learning; Plagiarism; Student writing; Teachers; Teaching; Technology","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"Y44XPAYH","journalArticle","2024","Pan, Guixia; Ni, Jing","A cross sectional investigation of ChatGPT-like large language models application among medical students in China","BMC Medical Education","","","10.1186/s12909-024-05871-8","https://www.proquest.com/docview/3102479560/abstract/13D00F81FE2A494FPQ/9","Objective To investigate the level of understanding and trust of medical students towards ChatGPT-like large language models, as well as their utilization and attitudes towards these models. Methods Data collection was concentrated from December 2023 to mid-January 2024, utilizing a self-designed questionnaire to assess the use of large language models among undergraduate medical students at Anhui Medical University. The normality of the data was confirmed with Shapiro-Wilk tests. We used Chi-square tests for comparisons of categorical variables, Mann-Whitney U tests for comparisons of ordinal variables and non-normal continuous variables between two groups, Kruskall-Wallis H tests for comparisons of ordinal variables between multiple groups, and Bonferroni tests for post hoc comparisons. Results A total of 1774 questionnaires were distributed and 1718 valid questionnaires were collected, with an effective rate of 96.84%. Among these students, 34.5% had heard and used large language models. There were statistically significant differences in the understanding of large language models between genders (p < 0.001), grade levels (junior-level students and senior-level students) (p = 0.03), and major (p < 0.001). Male, junior-level students, and public health management had a higher level of understanding of these models. Genders and majors had statistically significant effects on the degree of trust in large language models (p = 0.004; p = 0.02). Male and nursing students exhibited a higher degree of trust in large language models. As for usage, Male and junior-level students showed a significantly higher proportion of using these models for assisted learning (p < 0.001). Neutral sentiments were held by over two-thirds of the students (66.7%) regarding large language models, with only 51(3.0%) expressing pessimism. There were significant gender-based disparities in attitudes towards large language models, and male exhibited a more optimistic attitude towards these models (p < 0.001). Notably, among students with different levels of knowledge and trust in large language models, statistically significant differences were observed in their perceptions of the shortcomings and benefits of these models. Conclusion Our study identified gender, grade levels, and major as influential factors in students’ understanding and utilization of large language models. This also suggested the feasibility of integrating large language models with traditional medical education to further enhance teaching effectiveness in the future.","2024","2024-12-03 02:27:04","2024-12-03 02:27:04","2024-12-03 02:26:55","1-9","","","24","","","","","","","","","","English","© 2024. This work is licensed under http://creativecommons.org/licenses/by-nc-nd/4.0/  (the “License”).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 1-9 Place: London, United Kingdom Publisher: BioMed Central Section: Research","","","","","Achievement tests; Artificial intelligence; Chatbots; ChatGPT; Clinical medicine; College students; Data collection; Educational technology; Health care; Knowledge; Language; Large language models; Males; Medical education; Medical personnel; Medical students; Nursing; Polls & surveys; Preventive medicine; Public health; Questionnaires; Smartphones; Teachers","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SJYAWB8D","journalArticle","2024","Luu, Rachel K.; Buehler, Markus J.","BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio-Inspired Materials","Advanced Science","","","10.1002/advs.202306724","https://www.proquest.com/docview/2956007626/abstract/13D00F81FE2A494FPQ/10","The study of biological materials and bio-inspired materials science is well established; however, surprisingly little knowledge is systematically translated to engineering solutions. To accelerate discovery and guide insights, an open-source autoregressive transformer large language model (LLM), BioinspiredLLM, is reported. The model is finetuned with a corpus of over a thousand peer-reviewed articles in the field of structural biological and bio-inspired materials and can be prompted to recall information, assist with research tasks, and function as an engine for creativity. The model has proven that it is able to accurately recall information about biological materials and is further strengthened with enhanced reasoning ability, as well as with Retrieval-Augmented Generation (RAG) to incorporate new data during generation that can also help to traceback sources, update the knowledge base, and connect knowledge domains. BioinspiredLLM also has shown to develop sound hypotheses regarding biological materials design and remarkably so for materials that have never been explicitly studied before. Lastly, the model shows impressive promise in collaborating with other generative artificial intelligence models in a workflow that can reshape the traditional materials design process. This collaborative generative artificial intelligence method can stimulate and enhance bio-inspired materials design workflows. Biological materials are at a critical intersection of multiple scientific fields and models like BioinspiredLLM help to connect knowledge domains.","2024-03","2024-12-03 02:27:04","2024-12-03 02:27:04","2024-12-03 02:26:55","","","10","11","","","BioinspiredLLM","","","","","","","English","© 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Place: Weinheim, United States Publisher: John Wiley & Sons, Inc. Section: Research Articles","","","","","bio-inspiration; Biological material; biological materials; Datasets; generative artificial intelligence; hierarchical structures; Knowledge; Language; large language models; mechanical properties; Mechanics; Sustainable materials","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"54Z57XRC","journalArticle","2024","Salvagno, Michele; De Cassai, Alessandro; Zorzi, Stefano; Zaccarelli, Mario; Pasetto, Marco; Sterchele, Elda Diletta; Chumachenko, Dmytro; Gerli, Alberto Giovanni; Azamfirei, Razvan; Taccone, Fabio Silvio","The state of artificial intelligence in medical research: A survey of corresponding authors from top medical journals","PLoS One","","","10.1371/journal.pone.0309208","https://www.proquest.com/docview/3096537093/abstract/13D00F81FE2A494FPQ/11","Natural Language Processing (NLP) is a subset of artificial intelligence that enables machines to understand and respond to human language through Large Language Models (LLMs)‥ These models have diverse applications in fields such as medical research, scientific writing, and publishing, but concerns such as hallucination, ethical issues, bias, and cybersecurity need to be addressed. To understand the scientific community’s understanding and perspective on the role of Artificial Intelligence (AI) in research and authorship, a survey was designed for corresponding authors in top medical journals. An online survey was conducted from July 13th, 2023, to September 1st, 2023, using the SurveyMonkey web instrument, and the population of interest were corresponding authors who published in 2022 in the 15 highest-impact medical journals, as ranked by the Journal Citation Report. The survey link has been sent to all the identified corresponding authors by mail. A total of 266 authors answered, and 236 entered the final analysis. Most of the researchers (40.6%) reported having moderate familiarity with artificial intelligence, while a minority (4.4%) had no associated knowledge. Furthermore, the vast majority (79.0%) believe that artificial intelligence will play a major role in the future of research. Of note, no correlation between academic metrics and artificial intelligence knowledge or confidence was found. The results indicate that although researchers have varying degrees of familiarity with artificial intelligence, its use in scientific research is still in its early phases. Despite lacking formal AI training, many scholars publishing in high-impact journals have started integrating such technologies into their projects, including rephrasing, translation, and proofreading tasks. Efforts should focus on providing training for their effective use, establishing guidelines by journal editors, and creating software applications that bundle multiple integrated tools into a single platform.","2024-08","2024-12-03 02:27:04","2024-12-03 02:27:04","2024-12-03 02:26:58","e0309208","","8","19","","","The state of artificial intelligence in medical research","","","","","","","English","© 2024 Salvagno et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/  (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: e0309208 Place: San Francisco, United States Publisher: Public Library of Science Section: Research Article","","","","","Artificial intelligence; Chatbots; Citation analysis; Cybersecurity; Editing; Electronic mail systems; Familiarity; Hirsch index; Journals; Language; Large language models; Medical ethics; Medical journals; Medical research; Natural language processing; Polls & surveys; Questionnaires; Research ethics; Scientific publishing; Scientific research; Social; Software; Survey research; Surveys; Training","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ALZUT5SH","journalArticle","2023","Thirunavukarasu, Arun James; Ting, Darren Shu Jeng; Elangovan, Kabilan; Gutierrez, Laura; Tan, Ting Fang; Ting, Daniel Shu Wei","Large language models in medicine","Nature Medicine","","10788956","10.1038/s41591-023-02448-8","https://www.proquest.com/docview/2850927560/abstract/13D00F81FE2A494FPQ/12","Large language models (LLMs) can respond to free-text queries without being specifically trained in the task in question, causing excitement and concern about their use in healthcare settings. ChatGPT is a generative artificial intelligence (AI) chatbot produced through sophisticated fine-tuning of an LLM, and other tools are emerging through similar developmental processes. Here we outline how LLM applications such as ChatGPT are developed, and we discuss how they are being leveraged in clinical settings. We consider the strengths and limitations of LLMs and their potential to improve the efficiency and effectiveness of clinical, educational and research work in medicine. LLM chatbots have already been deployed in a range of biomedical contexts, with impressive but mixed results. This review acts as a primer for interested clinicians, who will determine if and how LLM technology is used in healthcare for the benefit of patients and practitioners. This review explains how large language models (LLMs), such as ChatGPT, are developed and discusses their strengths and limitations in the context of potential clinical applications.","2023-08","2024-12-03 02:27:04","2024-12-03 02:27:04","2024-12-03 02:26:58","1930-1940","","8","29","","","","","","","","","","English","© Springer Nature America, Inc. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.","","","","ProQuest","","Num Pages: 1930-1940 Place: New York, United States Publisher: Nature Publishing Group","","","","","Artificial intelligence; Chatbots; Generative artificial intelligence; Health care; Health informatics; Language; Language model; Large language models; Model; Models; Social","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SRCI6WWT","journalArticle","2024","Mohammed, As'ad","Intelligent Tutoring Systems, Generative Artificial Intelligence (AI), and Healthcare Agents: A Proof of Concept and Dual-Layer Approach","Cureus","","","10.7759/cureus.69710","https://www.proquest.com/docview/3111403375/abstract/13D00F81FE2A494FPQ/13","This study introduces a novel methodology for enhancing intelligent tutoring systems (ITS) through the integration of generative artificial intelligence (GenAI) and specialized AI agents. We present a proof of concept (PoC) demo that implements a dual-layer GenAI validation approach that utilizes multiple large language models to ensure the reliability and pedagogical integrity of the AI-generated content. The system features role-specific AI agents, a GenAI-powered scoring mechanism, and an AI mentor that provides periodic guidance. This approach demonstrates capabilities in dynamic scenario generation and real-time adaptability while addressing key challenges in AI-driven education, such as personalization, scalability, and domain-specific knowledge integration. Although exemplified here through a case study in healthcare root cause analysis, the methodology is designed for broad applicability across various fields. Our findings suggest that this approach has significant potential for advancing adaptive learning and personalized instruction while raising important considerations regarding ethical AI application in education. This work provides a foundation for further research into the efficacy and impact of GenAI-enhanced ITS on learning outcomes and instructional design across diverse educational domains.","2024","2024-12-03 02:27:04","2024-12-03 02:27:04","2024-12-03 02:26:59","","","9","16","","","Intelligent Tutoring Systems, Generative Artificial Intelligence (AI), and Healthcare Agents","","","","","","","English","Copyright ©  2024, As'ad et al. This work is published under  https://creativecommons.org/licenses/by/4.0/ (the “License”).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Place: Palo Alto, United States Publisher: Cureus Inc. University: U.S. National Institutes of Health/National Library of Medicine","","","","","Adaptation; ai agents; Artificial intelligence; Cognition & reasoning; Cognitive ability; Collaboration; Customization; Education; Educational objectives; Educational technology; Ethics; Feedback; generative ai; intelligent tutoring systems; Knowledge; Language; Large language models; Metacognition; Natural language processing; Pedagogy; quality management in medical education and health care services; Research & development--R&D; simulation in medical education; Tutoring","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"A56TKIX4","journalArticle","2024","Maalek, Reza","Integrating Generative Artificial Intelligence and Problem-Based Learning into the Digitization in Construction Curriculum","Buildings","","","10.3390/buildings14113642","https://www.proquest.com/docview/3133033804/abstract/13D00F81FE2A494FPQ/14","This study proposes incorporating generative artificial intelligence large language models (LLMs) into the Master of Science (M.Sc.) curriculum on digitization in construction. The aim was to help students generate computer code to solve, automate, and streamline practical challenges in advanced construction engineering and management (CEM). To this end, a host of problem-based learning (PBL) individual assignments and collaborative team projects were developed, alongside a combination of flipped classroom models and blended learning lessons, in order to teach effective interactions with LLMs and mitigate concerns, such as bias and hallucination. The effective interaction with LLMs not only facilitated code generation, which would otherwise be complex without additional formal training, but also provided a platform for strengthening basic project management skills, such as departmentalization, work breakdown structuring, modularization, activity delegation, and defining key performance indicators. The effectiveness of this approach was quantitatively and qualitatively evaluated within two new modules, Digital Engineering and Construction and Digital Technologies in Field Information Modeling. These modules were offered over three semesters each as part of a new M.Sc. program in Technology and Management in Construction at the Karlsruhe Institute of Technology. It was observed that 86.4% of students fully completed the PBL projects, while the remaining 13.6% achieved over 50% completion across all six semesters. Furthermore, anonymous student surveys indicated a teaching quality index of 100% in five semesters and 96.4% in one semester. These preliminary results suggest that the proposed strategy can be used to effectively integrate LLMs to support students in code generation for open-ended projects in CEM. Further research was, however, found to be necessary to ensure the sustainable revision and redesign of the problems as LLM capabilities evolve.","2024","2024-12-03 02:27:04","2024-12-03 02:27:04","2024-12-03 02:26:59","3642","","11","14","","","","","","","","","","English","© 2024 by the author.  Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 3642 Place: Basel, Switzerland Publisher: MDPI AG","","","","","Accreditation; AI in construction engineering and management education; Artificial intelligence; Automation; Blended learning; Collaborative learning; Construction; Construction engineering; Construction industry; Curricula; Design; Design engineering; digital technologies in construction; Digital technology; Digitization; Educational objectives; Educational technology; Effectiveness; Engineering education; Flipped classroom; generative adversarial networks; Generative artificial intelligence; Knowledge; large language models; Large language models; Leadership; Learning; Modules; Problem based learning; problem-based learning; Project management; Redesign; STEM education; Students; Technology adoption","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XHAETT7F","journalArticle","2023","Dengel, Andreas; Gehrlein, Rupert; Fernes, David; Görlich, Sebastian; Maurer, Jonas; Pham, Hai Hoang; Großmann, Gabriel; Eisermann, Niklas Dietrich genannt","Qualitative Research Methods for Large Language Models: Conducting Semi-Structured Interviews with ChatGPT and BARD on Computer Science Education","Informatics","","","10.3390/informatics10040078","https://www.proquest.com/docview/2904855525/abstract/13D00F81FE2A494FPQ/15","In the current era of artificial intelligence, large language models such as ChatGPT and BARD are being increasingly used for various applications, such as language translation, text generation, and human-like conversation. The fact that these models consist of large amounts of data, including many different opinions and perspectives, could introduce the possibility of a new qualitative research approach: Due to the probabilistic character of their answers, “interviewing” these large language models could give insights into public opinions in a way that otherwise only interviews with large groups of subjects could deliver. However, it is not yet clear if qualitative content analysis research methods can be applied to interviews with these models. Evaluating the applicability of qualitative research methods to interviews with large language models could foster our understanding of their abilities and limitations. In this paper, we examine the applicability of qualitative content analysis research methods to interviews with ChatGPT in English, ChatGPT in German, and BARD in English on the relevance of computer science in K-12 education, which was used as an exemplary topic. We found that the answers produced by these models strongly depended on the provided context, and the same model could produce heavily differing results for the same questions. From these results and the insights throughout the process, we formulated guidelines for conducting and analyzing interviews with large language models. Our findings suggest that qualitative content analysis research methods can indeed be applied to interviews with large language models, but with careful consideration of contextual factors that may affect the responses produced by these models. The guidelines we provide can aid researchers and practitioners in conducting more nuanced and insightful interviews with large language models. From an overall view of our results, we generally do not recommend using interviews with large language models for research purposes, due to their highly unpredictable results. However, we suggest using these models as exploration tools for gaining different perspectives on research topics and for testing interview guidelines before conducting real-world interviews.","2023","2024-12-03 02:27:04","2024-12-03 02:27:04","2024-12-03 02:27:00","78","","4","10","","","Qualitative Research Methods for Large Language Models","","","","","","","English","© 2023 by the authors.  Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 78 Place: Basel, Switzerland Publisher: MDPI AG","","","","","artificial intelligence; Artificial intelligence; Attitudes; Automatic text generation; Bard; Bias; Chatbots; Computer science; computer science education; Computer science education; Content analysis; Education; English language; Ethics; German language; Guidelines; Human-computer interaction; interviews; Interviews; Language; Language model; Language modeling; Language translation; large language models; Large language models; machine learning; Neural networks; Privacy; Qualitative analysis; qualitative research; Qualitative research; Reproducibility; Research methodology; Researchers; Topics; Validity","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"4S7899MH","journalArticle","2024","Thaler, Jesse; Williams, Mike; LaFleur, Marisa","Institute for Artificial Intelligence and Fundamental Interactions (IAIFI): Infusing physics intelligence into artificial intelligence","AI Magazine","","07384602","10.1002/aaai.12150","https://www.proquest.com/docview/3090976063/abstract/13D00F81FE2A494FPQ/16","The NSF AI Institute for Artificial Intelligence and Fundamental Interactions (IAIFI, pronounced /al-fal/) is one of the inaugural NSF AI research institutes. The IAIFI is enabling physics discoveries and advancing foundational AI through the development of novel AI approaches that incorporate first principles from fundamental physics. By combining state-of-the-art research with early career talent and a growing AI + physics community in the Boston area and beyond, the IAIFI is enabling researchers to develop AI technologies to tackle some of the most challenging problems in physics, and transfer these technologies to the broader AI community. Since trustworthy AI is as important for physics discover)' as it is for other applications of AI in society, IAIFI researchers are applying physics principles to develop more robust AI tools and to illuminate existing AI technologies. To cultivate human intelligence, the IAIFI promotes training, education, and public engagement at the intersection of physics and AI. In these ways, the IAIFI is fusing deep learning with deep thinking to gain a deeper understanding of our universe and AI.","2024","2024-12-03 02:27:04","2024-12-03 02:27:04","2024-12-03 02:27:00","111-116","","1","45","","","Institute for Artificial Intelligence and Fundamental Interactions (IAIFI)","","","","","","","English","Copyright John Wiley & Sons, Inc. Spring 2024","","","","ProQuest","","Num Pages: 111-116 Place: La Canada, United States Publisher: John Wiley & Sons, Inc.","","","","","Algorithms; Artificial intelligence; Careers; Collaboration; Data processing; Deep learning; Economic; First principles; Gravitational waves; Interdisciplinary aspects; Machine learning; Neural networks; Physics; Professional development; Public participation; Research centers; Research facilities; Researchers; Robotics","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"GMJK6NQQ","journalArticle","2023","Lozić, Edisa; Štular, Benjamin","Fluent but Not Factual: A Comparative Analysis of ChatGPT and Other AI Chatbots’ Proficiency and Originality in Scientific Writing for Humanities","Future Internet","","","10.3390/fi15100336","https://www.proquest.com/docview/2882490119/abstract/13D00F81FE2A494FPQ/17","Historically, mastery of writing was deemed essential to human progress. However, recent advances in generative AI have marked an inflection point in this narrative, including for scientific writing. This article provides a comprehensive analysis of the capabilities and limitations of six AI chatbots in scholarly writing in the humanities and archaeology. The methodology was based on tagging AI-generated content for quantitative accuracy and qualitative precision by human experts. Quantitative accuracy assessed the factual correctness in a manner similar to grading students, while qualitative precision gauged the scientific contribution similar to reviewing a scientific article. In the quantitative test, ChatGPT-4 scored near the passing grade (−5) whereas ChatGPT-3.5 (−18), Bing (−21) and Bard (−31) were not far behind. Claude 2 (−75) and Aria (−80) scored much lower. In the qualitative test, all AI chatbots, but especially ChatGPT-4, demonstrated proficiency in recombining existing knowledge, but all failed to generate original scientific content. As a side note, our results suggest that with ChatGPT-4, the size of large language models has reached a plateau. Furthermore, this paper underscores the intricate and recursive nature of human research. This process of transforming raw data into refined knowledge is computationally irreducible, highlighting the challenges AI chatbots face in emulating human originality in scientific writing. Our results apply to the state of affairs in the third quarter of 2023. In conclusion, while large language models have revolutionised content generation, their ability to produce original scientific contributions in the humanities remains limited. We expect this to change in the near future as current large language model-based AI chatbots evolve into large language model-powered software.","2023","2024-12-03 02:27:04","2024-12-03 02:27:04","2024-12-03 02:27:01","336","","10","15","","","Fluent but Not Factual","","","","","","","English","© 2023 by the authors.  Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 336 Place: Basel, Switzerland Publisher: MDPI AG","","","","","Academic writing; Accuracy; Archaeology; Artificial intelligence; Bard; Bing; Chatbot; Chatbots; ChatGPT; digital humanities; generative AI; Generative artificial intelligence; Humanity; Internet; large language model (LLM); Large language models; Neural networks; Scholarly publishing; scientific writing; Scientific writing; Software","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"5Z6G4RJV","journalArticle","2024","Radu, Valentin; Dranga, Diana; Dumitrescu, Catalin; Tabirca, Alina Iuliana; Stefan, Maria Cristina","Generative AI Assertions in UVM-Based System Verilog Functional Verification","Systems","","","10.3390/systems12100390","https://www.proquest.com/docview/3120739519/abstract/13D00F81FE2A494FPQ/18","This paper investigates the potential of leveraging artificial intelligence to automate and optimize the verification process, particularly in generating System Verilog assertions for an Advance Peripheral Bus verification environment using Universal Verification Methodology. Generative artificial intelligence, such as ChatGPT, demonstrated its ability to produce accurate and valuable assertions by employing text-based prompts and image-fed inputs, significantly reducing the required manual effort. This research presents a way of generating System Verilog assertions using the ChatGPT prompt, presenting an image to the Large Language Models, and requesting the assertions needed for the respective protocol. This approach shows the potential for artificial intelligence to revolutionize functional verification by automating complex tasks, ultimately ensuring faster and more reliable System-on-Chip development. The assertions generated by the Large Language Models are integrated into an existing Advance Peripheral Bus verification environment. This process involves running the assertions on a free EDA Playground platform with all three simulators (Cadence Incisive, Mentor Questa, and Synopsys Verilog Compiler Simulator). The main conclusions are that using ChatGPT-4.0 for generating System Verilog assertions significantly reduces the time and effort required for functional verification, demonstrating its potential to enhance efficiency and accuracy in verifying complex System-on-Chip designs.","2024","2024-12-03 02:27:04","2024-12-03 02:27:04","2024-12-03 02:27:02","390","","10","12","","","","","","","","","","English","© 2024 by the authors.  Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 390 Place: Basel, Switzerland Publisher: MDPI AG","","","","","Algorithms; Chatbots; ChatGPT; Complex systems; Design; Efficiency; Engineers; Generative artificial intelligence; Large language models; Large Language Models; Machine learning; Neural networks; Playgrounds; Simulators; System on chip; System Verilog; System-on-Chip; Task complexity; Universal Verification Methodology; Verification; Writing","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JZ3RQ5Q3","journalArticle","2023","Yang, Ying; Qin, Jinruo; Lei, Jing; Liu, Yanping","Research Status and Challenges on the Sustainable Development of Artificial Intelligence Courses from a Global Perspective","Sustainability","","","10.3390/su15129335","https://www.proquest.com/docview/2829881837/abstract/13D00F81FE2A494FPQ/19","The widespread application of artificial intelligence technology in various fields has made the sustainable development of artificial intelligence courses an important direction in the field of artificial intelligence education and teaching. Therefore, it is particularly important to conduct an in-depth analysis of the current research status of “artificial intelligence courses” from a global perspective. Firstly, this article clarifies the three stages of slow development, rapid development, and mature development of artificial intelligence curriculum research through the number and distribution years of the literature. It also conducts a co-authorship analysis on the distribution of countries, institutions, and authors of artificial intelligence curriculum research and identifies countries, institutions, and core authors that have made greater contributions to artificial intelligence curriculum research. Secondly, due to the involvement of artificial intelligence in many different fields of knowledge, an analysis is conducted on the journals that published papers on artificial intelligence courses. Finally, based on the analysis of keyword density and time span, the current research hotspots of artificial intelligence courses are summarized: artificial intelligence technology empowerment courses, two education directions at different stages of artificial intelligence courses, and teaching forms in the field of artificial intelligence courses. The current research trend of artificial intelligence courses is analyzed from three aspects: teaching format, teaching content, and teaching objects. This article provides a theoretical reference value and practical basis for future research and development in the field of artificial intelligence courses, while also providing experience for the efficient and sustainable development of artificial intelligence courses to a certain extent.","2023","2024-12-03 02:27:04","2024-12-03 02:27:04","2024-12-03 02:27:03","9335","","12","15","","","","","","","","","","English","© 2023 by the authors.  Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 9335 Place: Basel, Switzerland Publisher: MDPI AG","","","","","Artificial intelligence; artificial intelligence courses; Core curriculum; Course; Curricula; deep learning; Education; Empowerment; future learners; Journals; knowledge graph; pedagogy; Research & development--R&D; Research methodology; Software; Sustainability; sustainable development; Sustainable development; Technology; Trends","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"PRJYPPIN","journalArticle","2024","Lye, Che Yee; Lim, Lyndon","Generative Artificial Intelligence in Tertiary Education: Assessment Redesign Principles and Considerations","Education Sciences","","","10.3390/educsci14060569","https://www.proquest.com/docview/3072316245/abstract/13D00F81FE2A494FPQ/20","The emergence of generative artificial intelligence (AI) such as ChatGPT has sparked significant assessment concerns within tertiary education. Assessment concerns have largely revolved around academic integrity issues among students, such as plagiarism and cheating. Nonetheless, it is also critical to consider that generative AI models trained on information retrieved from the Internet could produce biased and discriminatory outputs, and hallucination issues in large language models upon which generative AI acts provide made-up and untruthful outputs. This article considers the affordances and challenges of generative AI specific to assessments within tertiary education. It illustrates considerations for assessment redesign with the existence of generative AI and proposes the Against, Avoid and Adopt (AAA) principle to rethink and redesign assessments. It argues that more generative AI tools will emerge exponentially, and hence, engaging in an arms race against generative AI and policing the use of these technologies may not address the fundamental issues in assessments.","2024","2024-12-03 02:27:04","2024-12-03 02:27:04","2024-12-03 02:27:04","569","","6","14","","","Generative Artificial Intelligence in Tertiary Education","","","","","","","English","© 2024 by the authors.  Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 569 Place: Basel, Switzerland Publisher: MDPI AG","","","","","AAA principle; assessment; Automation; ChatGPT; Cheating; Design; generative artificial intelligence; Privacy; tertiary education","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"T6HHC5TX","journalArticle","2024","Tupayachi, Jose; Xu, Haowen; Omitaomu, Olufemi A.; Camur, Mustafa Can; Sharmin, Aliza; Li, Xueping","Towards Next-Generation Urban Decision Support Systems through AI-Powered Construction of Scientific Ontology Using Large Language Models—A Case in Optimizing Intermodal Freight Transportation","Smart Cities","","","10.3390/smartcities7050094","https://www.proquest.com/docview/3120737496/abstract/13D00F81FE2A494FPQ/21","Highlights What are the main findings? We have developed an integrated and automated methodology that leverages a pre-trained Large Language Model (LLM) to generate scenario-based ontologies and knowledge graphs from research articles and technical manuals. Our methodology utilizes the ChatGPT API as the primary reasoning engine, supplemented by Natural Language Processing modules and carefully engineered prompts. This combination enables an automated tool capable of generating ontologies independently. The ontologies generated through our AI-powered method are interoperable and can significantly facilitate the design of data models and software architecture, particularly in the development of urban decision support systems. What is the implication of the main finding? We compared ontologies generated by our LLM with those created by human experts through CQ-based qualitative evaluation, assessing the reliability and feasibility of our approach. The methodology has been successfully applied to intermodal freight data and simulations. This has allowed us to generate a scenario-based ontology and knowledge graph that enhances data discovery, integration, and management, thereby supporting network optimization and multiple criteria decision analysis. Our methodology is both generalizable and adaptive, enabling the automation of ontology generation to support the development of urban and environmental decision support systems across various disciplines. The incorporation of Artificial Intelligence (AI) models into various optimization systems is on the rise. However, addressing complex urban and environmental management challenges often demands deep expertise in domain science and informatics. This expertise is essential for deriving data and simulation-driven insights that support informed decision-making. In this context, we investigate the potential of leveraging the pre-trained Large Language Models (LLMs) to create knowledge representations for supporting operations research. By adopting ChatGPT-4 API as the reasoning core, we outline an applied workflow that encompasses natural language processing, Methontology-based prompt tuning, and Generative Pre-trained Transformer (GPT), to automate the construction of scenario-based ontologies using existing research articles and technical manuals of urban datasets and simulations. From these ontologies, knowledge graphs can be derived using widely adopted formats and protocols, guiding various tasks towards data-informed decision support. The performance of our methodology is evaluated through a comparative analysis that contrasts our AI-generated ontology with the widely recognized pizza ontology, commonly used in tutorials for popular ontology software. We conclude with a real-world case study on optimizing the complex system of multi-modal freight transportation. Our approach advances urban decision support systems by enhancing data and metadata modeling, improving data integration and simulation coupling, and guiding the development of decision support strategies and essential software components.","2024","2024-12-03 02:28:10","2024-12-03 02:28:10","2024-12-03 02:27:58","2392","","5","7","","","","","","","","","","English","© 2024 by the authors.  Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 2392 Place: Basel, Switzerland Publisher: MDPI AG","","","","","Adaptive systems; artificial intelligence; Artificial intelligence; Chatbots; Complex systems; Data analysis; Data integration; Decision analysis; Decision making; Decision support systems; Environmental management; Freight transportation; Graphs; Intermodal; intermodal freight transportation; Knowledge representation; large language models; Large language models; Methodology; Multiple criterion; Natural language processing; ontology; Ontology; Optimization; Performance evaluation; Qualitative analysis; Reasoning; Reasoning programs; Smart cities; Software; Task complexity; urban decision support system; Workflow","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"IDKNI3A5","journalArticle","2024","Melnyk, Maryna; Malynoshevska, Alona; Anosovych, Kseniia","Генеративний Штучний Інтелект У Психології: Наслідки Та Рекомендації Для Науки І Практики","Information Technologies and Learning Tools","","20768184","10.33407/itlt.v103i5.5748","https://www.proquest.com/docview/3125966133/abstract/13D00F81FE2A494FPQ/22","Генеративний штучний інтелект (ШІ) стає все більш поширеним у різних галузях, зокрема в психології, де він має потенціал значно змінити підходи до діагностики, консультування, терапії та досліджень. У даній статті узагальнюються сучасні дослідження щодо використання генеративного ШІ в психології та його впливу на теорію і практику психологічної науки. Одним із основних застосувань генеративного ШІ є психодіагностика, де він може використовуватись для автоматизованого створення діагностичних інструментів та інтерпретації результатів тестів, аналізу великих обсягів даних та надання точніших діагностичних висновків, що значно зменшує навантаження на психологів, підвищуючи, водночас, ефективність діагностичних процесів і надання психологічних консультацій. У сфері психотерапії генеративний ШІ може бути використаний для створення індивідуалізованих терапевтичних програм, які надають постійну підтримку користувачам – клієнтам, пацієнтам, що особливо важливо при обмеженому доступі до кваліфікованих фахівців. Важливим аспектом є також використання генеративного ШІ у психологічних дослідженнях: ШІ може допомогти у створенні моделей поведінки, прогнозуванні психічних розладів, розробці нових методик досліджень, зменшенні рутинного адміністративного навантаження тощо. Генеративний ШІ, революціонізуючи роботу психологів, водночас створює складні проблеми, пов’язані з етикою, конфіденційністю, точністю діагностичних та терапевтичних методів тощо. Для того, щоб генеративний ШІ був ефективним та етичним, необхідно розробити чіткі стандарти та регуляторні рамки для його використання. Тому автори пропонують рекомендації щодо впровадження ШІ в психологічну практику, наголошуючи на необхідності розробки конкретних інструкцій для вирішення вказаних проблем. Також обговорюється роль психологів у забезпеченні етичного використання ШІ, необхідність постійного моніторингу та оцінки його впливу на користувачів. Загалом генеративний ШІ має великий потенціал для психологічних досліджень та психологічної практики, проте його впровадження потребує ретельного планування та врахування етичних аспектів, щоб гарантувати безпеку та ефективність. Alternate abstract: Generative artificial intelligence (AI) is becoming increasingly prevalent across various fields, particularly in psychology, where it has the potential to significantly transform approaches to diagnosis, therapy, and research. This paper summarizes current research on the use of generative AI in psychology and its impact on the theory and practice of psychological science. One of the primary applications of generative AI is in psychodiagnostics, where it can be used to automate the creation of diagnostic tools and interpret test results, analyze large volumes of data, and provide more accurate diagnostic conclusions. This significantly reduces the workload on psychologists while simultaneously increasing the efficiency of diagnostic processes. In the field of psychotherapy, generative AI can be used to create individualized therapeutic programs that provide continuous support to users, which is particularly important when access to qualified specialists is limited. Another important aspect is the use of generative AI in psychological research: AI can help in creating behavior models, predicting mental disorders, developing new research methodologies, reducing routine administrative burdens, and more. While generative AI revolutionizes the work of psychologists, it simultaneously creates complex issues related to ethics, confidentiality, accuracy of diagnostic and therapeutic methods, and more. To ensure that generative AI is effective and ethical, clear standards and regulatory frameworks must be developed for its use. Therefore, the authors propose recommendations for the implementation of AI in psychological practice, emphasizing the need to develop specific guidelines to address these issues. The role of psychologists in ensuring the ethical use of AI, the necessity of continuous monitoring, and the assessment of its impact on users are also discussed. Overall, generative AI holds great potential for psychological research and practice, but its implementation requires careful planning and consideration of ethical aspects to ensure safety and effectiveness.","2024","2024-12-03 02:28:10","2024-12-03 02:28:10","2024-12-03 02:27:58","188-206","","5","103","","","Генеративний Штучний Інтелект У Психології","","","","","","","Ukrainian","© 2024. This work is published under https://creativecommons.org/licenses/by-nc-sa/4.0/  (the “License”).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 188-206 Place: Kyiv, Ukraine Publisher: Institute for Digitalisation of Education of the National Academy of Educational Sciences of Ukraine Section: Artificial Intelligence in Education and Scientific Research","","","","","Diagnostic software; Effectiveness; Ethical standards; Ethics; generative artificial intelligence; Generative artificial intelligence; Impact analysis; Mental disorders; psychodiagnostics; Psychological research; Psychologists; psychology; Psychology; psychotherapy; Psychotherapy; генеративний штучний інтелект; психодіагностика; психологічне консультування; психологія; психотерапія","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"4TFWSE5C","journalArticle","2023","Pujari, Sameer; Reis, Andreas; Zhao, Yu; Alsalamah, Shada; Serhan, Fatima; Reeder, John C.; Labrique, Alain B.","Artificial intelligence for global health: cautious optimism with safeguards","World Health Organization. Bulletin of the World Health Organization","","00429686","10.2471/BLT.23.290215","https://www.proquest.com/docview/2821969922/abstract/13D00F81FE2A494FPQ/23","The rapid diffusion and growing number of applications of artificial intelligence large language models has generated excitement and public discourse around their potential to improve human health. However, this enthusiasm has been accompanied by concerns that such content-generative systems may be biased, produce misleading or inaccurate information, and could relinquish data privacy and ownership controls to technology firms looking to commercialize large language models and commodify data.2 Some have questioned whether commercial pressures have led to public releases of these technologies without adequate ascertainment of their safety and performance.3 Large language models generate responses that can appear authoritative and plausible to an end-user; however, without adequate controls in place, the veracity and accuracy of responses may be extremely poor.4 These models may be trained on data for which explicit consent may not have been provided, and they may not protect sensitive data (including health data) that users voluntarily feed into the artificial intelligence-based tool. Large language models, usually trained on large amounts of raw data, may encode biases in the data that can undermine inclusiveness, equality and equity.5 Furthermore, building such large data models has an environmental (mostly in carbon dioxide emissions) and financial impact that is often overlooked in costing analyses.6 Artificial intelligence tools are increasingly being applied to public health priorities,7 and have the potential to assist with pattern recognition and classification problems in medicine - for example, early detection of disease, diagnosis and medical decision-making.8'9 The increase in sophistication of artificial intelligence systems is now marked in days and weeks, as opposed to months and years. This speed outpaces the regulatory and review capacity of most agencies charged with protecting public health and providing oversight of technologies applied to health and well-being.","2023-06","2024-12-03 02:28:10","2024-12-03 02:28:10","2024-12-03 02:27:59","364,364A","","6","101","","","Artificial intelligence for global health","","","","","","","English","Copyright World Health Organization Jun 2023","","","","ProQuest","","Num Pages: 364,364A Place: Geneva, Switzerland Publisher: World Health Organization Section: Editorials","","","","","Adequacy; Artificial intelligence; Bias; Big Data; Carbon dioxide; Carbon dioxide emissions; Classification; Clinical decision making; Commercialization; Cost analysis; Costing; Data; Decision making; Economic; Emissions; Environment models; Equality; Global health; Health information; Information privacy; Information technology; Language; Language model; Large language models; Medical decision making; Medical diagnosis; Medicine; Models; Natural language processing; Optimism; Ownership; Pattern recognition; Privacy; Public health; Regulatory agencies; Safeguards; Social; Sophistication","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"YH4FKSXT","journalArticle","2023","Osadcha, Kateryna P.; Osadcha, Maryna V.","Generative Artificial Intelligence Vs Humans in the Process of Creating Corporate Identity Elements","Information Technologies and Learning Tools","","20768184","10.33407/itlt.v98i6.5494","https://www.proquest.com/docview/3020725930/abstract/13D00F81FE2A494FPQ/24","The emergence of new tools, the appearance of new technologies and improvements to existing ones have resulted in expansion of generative artificial intelligence. The technologies of generative artificial intelligence have already been used by people to perform not only intellectual tasks, but also creative ones, in particular in the field of design. Therefore, their capabilities in graphic design need to be studied. One of the routine tasks of a designer is the development of corporate identity elements (a logo, font, and colour). Designers can spend a lot of time on this, choosing different style options. Therefore, delegating this routine work to generative artificial intelligence may be appropriate. With this practical need in mind, the capabilities of modern AI tools for image and logo generation were studied in the research, and the results of AI logo generation compared to the work of novice designers were analysed. As a result, conclusions were drawn about the expediency of using generative AI technology in the work of designers, in particular, for the development of corporate identity elements, and the appropriateness of studying generative artificial intelligence technology in the training of future designers. These conclusions were made on the basis of a survey of 41 experts in the field of design, information technology and artificial intelligence. Based on the findings of the survey, we can note that it was difficult for experts to distinguish between logos generated by artificial intelligence and logos created by novice designers. Logos developed by novice designers (5) were recognized as the most attractive among the 45 logos presented in the survey. Images generated in some AI tools (Tailor Brands, Hatchful) are considered attractive by design, information technology and artificial intelligence professionals. Therefore, they can be used to create corporate identity elements. Thus, the vast majority of experts agreed that artificial intelligence tools for generating images and logos should be used in the process of creating corporate identity elements. In addition, the vast majority of experts found it advisable to use generative artificial intelligence technologies in the process of professional training of future designers. Alternate abstract: Нині сфера використання генеративного штучного інтелекту розширюється. З'являється все більше нових інструментів, створюються нові технології й удосконалюються вже існуючі. Технології генеративного штучного інтелекту використовуються людьми для виконання не лише інтелектуальних завдань, а й творчих, зокрема у сфері дизайну. Тому їх можливості використання в графічному дизайні потребують вивчення та осмислення. Одним з рутинних завдань дизайнера є розробка елементів фірмового стилю (логотип, шрифт, колір). На це дизайнери можуть витратити багато часу, обираючи різні варіанти стилю. Тому може бути доцільним делегувати цю рутинну роботу генеративному штучному інтелекту. Зважаючи на таку практичну потребу, у дослідженні було вивчено можливості сучасних інструментів штучного інтелекту для генерації зображень і логотипів та порівняно результати генерації логотипів штучним інтелектом з результатами роботи дизайнерів-початківців. Тож було зроблено висновки про доцільність використання технології генеративного ШІ в роботі дизайнерів, зокрема для розробки елементів фірмового стилю, та доцільність вивчення технологій генеративного штучного інтелекту при підготовці майбутніх дизайнерів. Ці висновки були зроблені на основі опитування 41 фахівця у сфері дизайну, інформаційних технологій та штучного інтелекту. Спираючись на аналіз опитування, можемо зазначити, що експертам було важко розрізнити логотипи, згенеровані штучним інтелектом, та логотипи, створені дизайнерами-початківцями. Логотипи, розроблені дизайнерами-початківцями (5), були визнані найбільш привабливими серед 45 логотипів, що були представлені в опитуванні. Зображення, згенеровані у деяких інструментах штучного інтелекту (Tailor Brands, Hatchful), розглядаються фахівцями з дизайну, інформаційних технологій та штучного інтелекту як привабливі. Отже, вони цілком можуть бути використані для створення елементів фірмового стилю. Переважна більшість експертів погодилися з тим, що інструменти штучного інтелекту для генерації зображення та логотипів доречно використовувати при створенні елементів фірмового стилю. Також переважна більшість експертів вказали на доцільність використання технологій генеративного штучного інтелекту у професійній підготовці майбутніх дизайнерів.","2023","2024-12-03 02:28:10","2024-12-03 02:28:10","2024-12-03 02:27:59","212-230","","6","98","","","","","","","","","","English","© 2023. This work is published under https://creativecommons.org/licenses/by-nc-sa/4.0/  (the “License”).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 212-230 Place: Kyiv, Ukraine Publisher: Institute for Digitalisation of Education of the National Academy of Educational Sciences of Ukraine Section: Artificial Intelligence in Education and Scientific Research","","","","","advertising graphic; Artificial intelligence; Corporate identity; Creation; Design; Designers; digital design; generative artificial intelligence; Generative artificial intelligence; Graphic design; higher education; Identity; Image analysis; Images; Information technology; Logos; logotype; New technology; Professional development; professional training; Professional Training; Training; вища освіта; генеративний штучний інтелект; логотип; професійна підготовка; рекламна графіка; цифровий дизайн","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"INYR2AF7","journalArticle","2024","İşgüzar, Seda; Fendoglu, Eda; Şimşek, Ahmed İhsan","Innovative Applications in Businesses: An Evaluation on Generative Artificial Intelligence","Amfiteatru Economic","","15829146","10.24818/EA/2024/66/511","https://www.proquest.com/docview/3049830627/abstract/13D00F81FE2A494FPQ/25","The utilisation of Chat Generative Pre-Trained Transformer (ChatGPT) and generative artificial intelligence (GenAI) technologies has started to demonstrate its impact across several domains. The swift shift and widespread implementation of efficient artificial intelligence (AI) present distinct prospects such as optimisation, advancement, enhanced efficiency, boosted sales and marketing, expansion, reduced costs, and heightened profitability. GenAI has the potential to create a competition crisis between technologically advanced enterprises and less developed ones. Additionally, it may give rise to legal, moral, and ethical issues such as copyright infringement and the production of fake and false information. Hence, it is crucial for organisations to ensure that the productivity of AI is maximized in order to maximise its benefits and minimise any potential harm. The aim of this study is to provide suggestions regarding the use and potential of GenAI technologies in the corporate sector and to emphasise the potential research areas of future GenAI. This study contributes to research and practice in business and management and also identifies future research avenues. This study examines the benefits and disadvantages of using Gen AI tools in businesses and individual departments, and it highlights the potential risks and dangers. A bibliometric analysis of 198 studies in the discipline of Business & Management from the Scopus database was conducted using the R program's bibliometrix package. The study focuses on descriptive data, annual scientific production, most productive journals, most productive authors and authors dominance factor, most cited publications, and most relevant keywords. The findings show that GenAI is likely to continue with a strong and rapidly rising trend in 2024 and beyond.","2024-05","2024-12-03 02:28:10","2024-12-03 02:28:10","2024-12-03 02:28:00","511-530","","66","26","","","Innovative Applications in Businesses","","","","","","","English","© 2024. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 511-530 Place: Bucharest, Romania Publisher: Bucharest Academy of Economic Studies, Faculty of Commerce","","","","","Academic disciplines; Artificial; Artificial intelligence; Bibliometrics; Chatbots; Companies; Competitive advantage; Customer relationship management; Databases; Decision making; Dominance; Ethical dilemmas; Generative artificial intelligence; Infringement; Keywords; Marketing; Productivity; Profitability; Publications; Research methodology; Sales; Technology adoption","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"WBM4SG7P","journalArticle","2023","Oleksiy, Melnyk; Ismail, Ahmed; Ghorashi, Nima S.; Heekin, Mary; Ramin, Javan","Generative Artificial Intelligence Terminology: A Primer for Clinicians and Medical Researchers","Cureus","","","10.7759/cureus.49890","https://www.proquest.com/docview/2920451223/abstract/13D00F81FE2A494FPQ/27","Generative artificial intelligence (AI) is rapidly transforming the medical field, as advanced tools powered by large language models (LLMs) make their way into clinical practice, research, and education. Chatbots, which can generate human-like responses, have gained attention for their potential applications. Therefore, familiarity with LLMs and other promising generative AI tools is crucial to harness their potential safely and effectively. As these AI-based technologies continue to evolve, medical professionals must develop a strong understanding of AI terminologies and concepts, particularly generative AI, to effectively tackle real-world challenges and create solutions. This knowledge will enable healthcare professionals to utilize AI-driven innovations for improved patient care and increased productivity in the future. In this brief technical report, we explore 20 of the most relevant terminology associated with the underlying technology behind LLMs and generative AI as they relate to the medical field and provide some examples of how these topics relate to healthcare applications to help in their understanding.","2023","2024-12-03 02:28:10","2024-12-03 02:28:10","2024-12-03 02:28:01","","","12","15","","","Generative Artificial Intelligence Terminology","","","","","","","English","Copyright ©  2023, Melnyk et al. This work is published under  https://creativecommons.org/licenses/by/4.0/ (the “License”).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Place: Palo Alto, United States Publisher: Cureus Inc. University: U.S. National Institutes of Health/National Library of Medicine","","","","","Algorithms; artificial intelligence; Artificial intelligence; Artificiality; Back propagation; Chatbot; chatgpt; convoluted neural network; Datasets; Decision making; Deep learning; deep learning (dl); Feedback; Generative; generative artificial intelligence; Generative artificial intelligence; gpt-4; Jargon; Knowledge; Language; large language model; large multimodal model; Machine learning; Medical personnel; natural language processing; Natural language processing; Neural networks; Pattern recognition; Semantics; Terminology","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"23KRE5G9","journalArticle","2024","Nowrozy, Raza","GPTs or Grim Position Threats? The Potential Impacts of Large Language Models on Non-Managerial Jobs and Certifications in Cybersecurity","Informatics","","","10.3390/informatics11030045","https://www.proquest.com/docview/3110480685/abstract/13D00F81FE2A494FPQ/28","ChatGPT, a Large Language Model (LLM) utilizing Natural Language Processing (NLP), has caused concerns about its impact on job sectors, including cybersecurity. This study assesses ChatGPT’s impacts in non-managerial cybersecurity roles using the NICE Framework and Technological Displacement theory. It also explores its potential to pass top cybersecurity certification exams. Findings reveal ChatGPT’s promise to streamline some jobs, especially those requiring memorization. Moreover, this paper highlights ChatGPT’s challenges and limitations, such as ethical implications, LLM limitations, and Artificial Intelligence (AI) security. The study suggests that LLMs like ChatGPT could transform the cybersecurity landscape, causing job losses, skill obsolescence, labor market shifts, and mixed socioeconomic impacts. A shift in focus from memorization to critical thinking, and collaboration between LLM developers and cybersecurity professionals, is recommended.","2024","2024-12-03 02:28:10","2024-12-03 02:28:10","2024-12-03 02:28:02","45","","3","11","","","GPTs or Grim Position Threats?","","","","","","","English","© 2024 by the author.  Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 45 Place: Basel, Switzerland Publisher: MDPI AG","","","","","Artificial intelligence; Automation; Certification; Chatbots; ChatGPT; Customer services; cybersecurity; Cybersecurity; generative AI; Generative artificial intelligence; Language; large language model; Large language models; Media coverage; Natural language; Natural language processing; Research methodology; skills; Skills; Threats; workforce; Workforce","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"J5JF6BIE","journalArticle","2024","Huang, Dawei; Yan, Chuan; Li, Qing; Peng, Xiaojiang","From Large Language Models to Large Multimodal Models: A Literature Review","Applied Sciences","","","10.3390/app14125068","https://www.proquest.com/docview/3072251648/abstract/13D00F81FE2A494FPQ/29","With the deepening of research on Large Language Models (LLMs), significant progress has been made in recent years on the development of Large Multimodal Models (LMMs), which are gradually moving toward Artificial General Intelligence. This paper aims to summarize the recent progress from LLMs to LMMs in a comprehensive and unified way. First, we start with LLMs and outline various conceptual frameworks and key techniques. Then, we focus on the architectural components, training strategies, fine-tuning guidance, and prompt engineering of LMMs, and present a taxonomy of the latest vision–language LMMs. Finally, we provide a summary of both LLMs and LMMs from a unified perspective, make an analysis of the development status of large-scale models in the view of globalization, and offer potential research directions for large-scale models.","2024","2024-12-03 02:28:10","2024-12-03 02:28:10","2024-12-03 02:28:03","5068","","12","14","","","From Large Language Models to Large Multimodal Models","","","","","","","English","© 2024 by the authors.  Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 5068 Place: Basel, Switzerland Publisher: MDPI AG","","","","","Adaptation; Architecture; artificial intelligence; Comparative analysis; Efficiency; Engineering; Language; large language models (LLMs); large multimodal models (LMMs); Semantics","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"S3AK5C5T","journalArticle","2024","Hmoud, Mohammad; Swaity, Hadeel; Hamad, Nardin; Karram, Omar; Daher, Wajeeh","Higher Education Students’ Task Motivation in the Generative Artificial Intelligence Context: The Case of ChatGPT","Information","","","10.3390/info15010033","https://www.proquest.com/docview/2918768025/abstract/13D00F81FE2A494FPQ/30","Artificial intelligence has been attracting the attention of educational researchers recently, especially ChatGPT as a generative artificial intelligence tool. The context of generative artificial intelligence could impact different aspects of students’ learning, such as the motivational aspect. The present research intended to investigate the characteristics of students’ task motivation in the artificial intelligence context, specifically in the ChatGPT context. The researchers interviewed 15 students about their experiences with ChatGPT to collect data. The researchers used inductive and deductive content analysis to investigate students’ motivation when learning with ChatGPT. To arrive at the categories and sub-categories of students’ motivation, the researchers used the MAXQDA 2022. Five main categories emerged: task enjoyment, reported effort, result assessment, perceived relevance, and interaction. Each category comprised at least two sub-categories, and each sub-category was further organized into codes. The results indicated more positive characteristics of motivation than negative ones. The previous results could be due to the conversational or social aspect of the chatbot, enabling relationships with humans and enabling the maintenance of good quality conversations with them. We conclude that a generative AI could be utilized in educational settings to promote students’ motivation to learn and thus raise their learning achievement.","2024","2024-12-03 02:28:10","2024-12-03 02:28:10","2024-12-03 02:28:04","33","","1","15","","","Higher Education Students’ Task Motivation in the Generative Artificial Intelligence Context","","","","","","","English","© 2024 by the authors.  Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 33 Place: Basel, Switzerland Publisher: MDPI AG","","","","","Academic achievement; artificial intelligence; Categories; Chatbots; ChatGPT; Content analysis; Context; Education; Educational objectives; Educational technology; Generative artificial intelligence; higher education; Higher education; Learning; Literature reviews; Motivation; Qualitative research; Research methodology; Robotics; Social factors; Students; task motivation; Teachers; University students","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"G8TF3IQD","journalArticle","2024","Hubert, Kent F.; Awa, Kim N.; Zabelina, Darya L.","The current state of artificial intelligence generative language models is more creative than humans on divergent thinking tasks","Scientific Reports (Nature Publisher Group)","","","10.1038/s41598-024-53303-w","https://www.proquest.com/docview/2924578321/abstract/13D00F81FE2A494FPQ/31","The emergence of publicly accessible artificial intelligence (AI) large language models such as ChatGPT has given rise to global conversations on the implications of AI capabilities. Emergent research on AI has challenged the assumption that creative potential is a uniquely human trait thus, there seems to be a disconnect between human perception versus what AI is objectively capable of creating. Here, we aimed to assess the creative potential of humans in comparison to AI. In the present study, human participants (N = 151) and GPT-4 provided responses for the Alternative Uses Task, Consequences Task, and Divergent Associations Task. We found that AI was robustly more creative along each divergent thinking measurement in comparison to the human counterparts. Specifically, when controlling for fluency of responses, AI was more original and elaborate. The present findings suggest that the current state of AI language models demonstrate higher creative potential than human respondents.","2024","2024-12-03 02:28:10","2024-12-03 02:28:10","2024-12-03 02:28:04","3440","","1","14","","","","","","","","","","English","© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 3440 Place: London, United States Publisher: Nature Publishing Group","","","","","Artificial intelligence; Chatbots; Creativity; Divergent thinking; Fluency; Generative; Language; Language model; Metacognition; Models; Research; Researchers; Social","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"I49QBVFL","journalArticle","2023","Skitsko, V. I.","Аналіз Даних Із Використанням Генеративного Штучного Інтелекту: Можливості Та Виклики","Problemy Ekonomiky","","22220712","10.32983/2222-0712-2023-4-217-225","https://www.proquest.com/docview/2965143367/abstract/13D00F81FE2A494FPQ/32","ycmammi дослужено актуальш питания використання генеративного штучного ¡нтелекту, зокрема великих мовних моделей ChatGPTi Claude, для анализу даних. Сутн/сть термша «штучний ¡нтелект» змжюеться з часом. I якщо ран!ше, вживаючи цей терм!н, говорили про експертн! системи, машинне навчання тощо, то нараз! nid цим термтом мають на уваз/ насамперед велик/ мовн: модел!, серед яких найв/дом/шими e ChatGPT, Claude, Bing, Bard. Lļi модел! дозволяють генерувати текста, зображення, aydio та eideo за запитами користувач!в. Мета статт! - досл!дити можливост! та виклики застосування ChatGPTi Claude в анал!з! даних на основ! наявних публ!кац!й I власного doceidy. Адже здатн!сть великих мовних моделей сп!лкуватися природною мовою робить ïx потужним ¡нструментом анал!тики. У робот! розглянуто аспекти основних emanie анализу даних у контекст! використання великих мовних моделей: отримання, зб!р! завантаження вх!дних даних, ïx попередня обробка, застосування математичних моделей, в!зуал!зац!я та !нтерпретац!я результат!в. Наведено практичн! рекомендацн ицодо формулювання заnumie до ChatGPT i Claude на кожному emani анализу даних. Зазначено, що ChatGPT завдяки вбудованому cepeicy Advanced Data Analysis дозволяе ефективно зд!йснювати анализ даних за допомогою Python. Це забезпечуе вищу точнють результат!в пор/вняно з жшими великими мовними моделями. На умовному приклад! зд!йснено пор!вняння можливостей ChatGPT i Claude в анал!з! даних. Показано, що ChatGPT дозволяе будувати модел! залежностей, генерувати графики та давати зм!стовн! пояснения отриманих результат!в. Водночас як можливост! Claude в анал!з! даних досить обмежен!. Зроблено висновок, що ChatGPT мае значно бмьший потенщал для анал/зу даних пор!вняно з Claude та ¡ншими чатботами. Проте поки що велик! мовн! модел! не можуть повнютю зам!нити фах!вц!в анал!тики даних та потужн! системи п!дтримки прийняття р!шень. У подальших досл!дженнях пропонуеться зосередитися на вивченн! практичного застосування можливостей ChatGPT i, зокрема, його cepeicy Advanced Data Analysis для вир!шення р!зних задач аналву даних. Alternate abstract: The article examines topical issues of using generative artificial intelligence, in particular large language models ChatGPT and Claude, for data analysis. The essence of the term «artificial intelligence» changes over time. And if earlier, when using this term, was talked about expert systems, machine learning, etc., now this term means primarily large language models, among which the most famous are ChatGPT, Claude, Bing, Bard. These models allow you to generate texts, images, audio, and video based on user requests. The purpose of the article is to explore the opportunities and challenges of using ChatGPT and Claude in data analysis based on existing publications and our own experience. After all, the ability of large language models to communicate in natural language makes them a powerful analytics tool. The paper considers aspects of the main stages of data analysis in the context of the use of large language models: obtaining, collecting and loading input data, their pre-processing, application of mathematical models, visualization and interpretation of results. Practical recommendations for formulating requests to ChatGPT and Claude at each stage of data analysis are provided. It is noted that ChatGPT, thanks to the built-in Advanced Data Analysis service, allows an effectively analysis of data, powered by Python language. This provides higher accuracy of results compared to other large language models. Using a conditional example, a comparison of the capabilities of ChatGPT and Claude in data analysis is carried out. It is shown that ChatGPT allows you to build dependency models, generate graphs and give meaningful explanations of the results obtained. At the same time, Claude's capabilities in data analysis are quite limited. It is concluded that ChatGPT has significantly greater potential for data analysis compared to Claude and other chatbots. However, so far, large language models cannot completely replace data analysts and powerful decision support systems. In further research, it is proposed to focus on the study of the practical application of the capabilities of ChatGPT and, in particular, its Advanced Data Analysis service to solve various data analysis problems.","2023","2024-12-03 02:28:10","2024-12-03 02:28:10","2024-12-03 02:28:05","217-225","","4","","","","Аналіз Даних Із Використанням Генеративного Штучного Інтелекту","","","","","","","Ukrainian","© 2023. This work is published under https://creativecommons.org/licenses/by-sa/4.0/legalcode (the“License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 217-225 Place: Kharkiv, Ukraine Publisher: Journal ""The Problems of Economy"" Section: МАТЕМАТИЧНІ МЕТОДИ ТА МОДЕЛІ В ЕКОНОМІЦІ","","","","","Chatbots; Data analysis; Generative artificial intelligence; Machine learning; Mathematical models; Natural language; Visualization","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2U5UADYE","journalArticle","2024","Ching-Nam, Hang; Yu, Pei-Duo; Morabito, Roberto; Chee-Wei, Tan","Large Language Models Meet Next-Generation Networking Technologies: A Review","Future Internet","","","10.3390/fi16100365","https://www.proquest.com/docview/3120634678/abstract/13D00F81FE2A494FPQ/33","The evolution of network technologies has significantly transformed global communication, information sharing, and connectivity. Traditional networks, relying on static configurations and manual interventions, face substantial challenges such as complex management, inefficiency, and susceptibility to human error. The rise of artificial intelligence (AI) has begun to address these issues by automating tasks like network configuration, traffic optimization, and security enhancements. Despite their potential, integrating AI models in network engineering encounters practical obstacles including complex configurations, heterogeneous infrastructure, unstructured data, and dynamic environments. Generative AI, particularly large language models (LLMs), represents a promising advancement in AI, with capabilities extending to natural language processing tasks like translation, summarization, and sentiment analysis. This paper aims to provide a comprehensive review exploring the transformative role of LLMs in modern network engineering. In particular, it addresses gaps in the existing literature by focusing on LLM applications in network design and planning, implementation, analytics, and management. It also discusses current research efforts, challenges, and future opportunities, aiming to provide a comprehensive guide for networking professionals and researchers. The main goal is to facilitate the adoption and advancement of AI and LLMs in networking, promoting more efficient, resilient, and intelligent network systems.","2024","2024-12-03 02:28:10","2024-12-03 02:28:10","2024-12-03 02:28:06","365","","10","16","","","Large Language Models Meet Next-Generation Networking Technologies","","","","","","","English","© 2024 by the authors.  Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 365 Place: Basel, Switzerland Publisher: MDPI AG","","","","","Adaptation; Algorithms; Artificial intelligence; Automation; Configuration management; Data mining; edge intelligence; Engineering; Fault diagnosis; generative AI; Generative artificial intelligence; Human error; Infrastructure; Intelligent networks; Language; large language models; Large language models; Machine learning; Natural language; Natural language processing; Network design; network intelligence; Network security; networked AI systems; networks; next-generation network; Optimization; Sentiment analysis; Task complexity; Unstructured data","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KAXZJTAJ","journalArticle","2024","Ferrario, Andrea; Sedlakova, Jana; Trachsel, Manuel","The Role of Humanization and Robustness of Large Language Models in Conversational Artificial Intelligence for Individuals With Depression: A Critical Analysis","JMIR Mental Health","","","10.2196/56569","https://www.proquest.com/docview/3079023830/abstract/13D00F81FE2A494FPQ/34","Large language model (LLM)–powered services are gaining popularity in various applications due to their exceptional performance in many tasks, such as sentiment analysis and answering questions. Recently, research has been exploring their potential use in digital health contexts, particularly in the mental health domain. However, implementing LLM-enhanced conversational artificial intelligence (CAI) presents significant ethical, technical, and clinical challenges. In this viewpoint paper, we discuss 2 challenges that affect the use of LLM-enhanced CAI for individuals with mental health issues, focusing on the use case of patients with depression: the tendency to humanize LLM-enhanced CAI and their lack of contextualized robustness. Our approach is interdisciplinary, relying on considerations from philosophy, psychology, and computer science. We argue that the humanization of LLM-enhanced CAI hinges on the reflection of what it means to simulate “human-like” features with LLMs and what role these systems should play in interactions with humans. Further, ensuring the contextualization of the robustness of LLMs requires considering the specificities of language production in individuals with depression, as well as its evolution over time. Finally, we provide a series of recommendations to foster the responsible design and deployment of LLM-enhanced CAI for the therapeutic support of individuals with depression.","2024","2024-12-03 02:28:10","2024-12-03 02:28:10","2024-12-03 02:28:06","e56569","","","11","","","The Role of Humanization and Robustness of Large Language Models in Conversational Artificial Intelligence for Individuals With Depression","","","","","","","English","© 2024. This work is licensed under https://creativecommons.org/licenses/by/4.0/"" target=""_blank"">https://creativecommons.org/licenses/by/4.0/>  (the “License”).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: e56569 Place: Toronto, Canada Publisher: JMIR Publications Section: Theme Issue 2023 : Responsible Design, Integration, and Use of Generative AI in Mental Health","","","","","artificial intelligence; Artificial intelligence; Bioethics; deep learning; depression; digital health; digital intervention; digital interventions; digital technology; ethics; Ethics; generative AI; large language model; large language models; Large language models; LLM; LLMs; machine learning; Mental depression; mental disease; mental diseases; mental health; mental illness; mental illnesses; ML; natural language processing; NLP; Psychotherapy","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JSUTMFP4","journalArticle","2023","Parker, Jessica L.; Richard, Veronica M.; Becker, Kimberly","Guidelines for the Integration of Large Language Models in Developing and Refining Interview Protocols","The Qualitative Report","","10520147","10.46743/2160-3715/2023.6801","https://www.proquest.com/docview/2899445515/abstract/13D00F81FE2A494FPQ/35","Rapid advancements in generative artificial intelligence (Al), specifically large language models (LLMs), offer unprecedented opportunities and challenges for qualitative researchers. This paper presents comprehensive guidelines for the ethical and effective use of LLMs in the development and refinement of interview protocols. Through a multidisciplinary lens, this paper explores potential pitfalls, ethical considerations, and best practices to ensure the responsible integration of LLMs in the research process. The guidelines proposed serve not only as a methodological roadmap for researchers but also as a catalyst for dialogue on the ethical dimensions of LLMs in qualitative research. Furthermore, the authors describe and share a web-based application developed to guide users through the stages of the protocol. Ultimately, the paper calls for a collective, informed approach to harness the capabilities of LLMs while upholding the integrity and ethical standards of scholarly research.","2023-12","2024-12-03 02:28:10","2024-12-03 02:28:10","2024-12-03 02:28:07","3460-3474","","12","28","","","","","","","","","","English","© 2023. This work is published under https://creativecommons.org/licenses/by-nc-sa/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 3460-3474 Place: Fort Lauderdale, United States Publisher: The Qualitative Report","","","","","Artificial intelligence; Chatbots; Ethics; Guideline; Intellectual property; Interview; Language; Language model; Manuscripts; Neighborhoods; Privacy; Protocol; Qualitative research; Researchers; Verbal communication","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"3IHKISVA","journalArticle","2024","Prasad, Shreya; Gupta, Himank; Ghosh, Arup","Leveraging the Potential of Large Language Models","Informatica","","03505596","10.31449/inf.v48i8.5635","https://www.proquest.com/docview/3070030071/abstract/13D00F81FE2A494FPQ/36","This study focuses on enhancing Natural Language Processing (NLP) in generative AI chatbots through the utilization of advanced pre-trained models. We assessed five distinct Large Language Models (LLMs): TRANSFORMER MODEL, FALCON 7B, LAMINI-FLAN-T5-783M, LLAMA-2-7B, and LLAMA-2-13B to identify the most effective one. Our findings revealed that the LLAMA Model excels in comprehending user queries and delivering precise responses during conversations. The article elucidates the methodology employed to evaluate and select various models for our chatbot. Through rigorous testing, we determined that the LLAMA-2-13B model exhibits enhanced response time and accuracy. Additionally, we employed tools such as Facebook Artificial Intelligence Similarity Search (FAISS) and experimented ""with user interfaces like Streamlit and Chainlit to enhance the chatbot's user-friendliness. The research underscores the significance of selecting the appropriate model for crafting efficient chatbots. Ultimately, the LLAMA-13B model emerged as the standout performer, showcasing superior performance. Benchmark assessments, including HellaSwag and WinoGrande, which gauge common sense reasoning, were employed to evaluate our chatbot's capabilities. The study concludes that LLAMA-based models hold significant promise for the development of innovative and user-friendly chatbots in the future.","2024-05","2024-12-03 02:28:10","2024-12-03 02:28:10","2024-12-03 02:28:07","1-16","","8","48","","","","","","","","","","English","© 2024. This work is published under https://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 1-16 Place: Ljubljana, Slovenia Publisher: Slovenian Society Informatika / Slovensko drustvo Informatika","","","","","Accuracy; Artificial intelligence; Chatbots; Generative artificial intelligence; Innovations; Language; Large language models; Natural language processing; Semantics; User experience; User interfaces","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"YGQFWIAN","journalArticle","2024","Peng, Hao; Zhou, Peipei","Evolution and Development of Artificial Intelligence Interpretation Technology in the Age of Large-scale Language Models","Journal of Electrical Systems","","","","https://www.proquest.com/docview/3074172751/abstract/13D00F81FE2A494FPQ/38","With the continuous development of artificial intelligence technology, large-scale language models have become an important breakthrough in the field of interpreting intelligence technology, and the interpreting profession is facing unprecedented technological changes; Through reviewing the relevant research background, it was found that the rise of large models has brought new opportunities and challenges to the development of artificial intelligence interpreting technology; Firstly, the language generation ability of the large model is powerful, which can achieve more accurate and natural interpretation and translation results. Secondly, large models have strong learning abilities and can continuously optimize the performance of interpretation systems through large-scale data training. In addition, the large model also has the characteristic of fast response, which can provide efficient interpretation services in real-time scenarios; The age of large models provides new opportunities and prospects for the transformation and development of artificial intelligence interpretation technology. In the future, with the continuous evolution of large model technology and the expansion of application scenarios, artificial intelligence interpretation technology is expected to play a more important role in cross language communication, international cooperation, and other aspects. However, attention should also be paid to the application limitations of large models and issues such as collaboration with human translators.","2024","2024-12-03 02:28:10","2024-12-03 02:28:10","2024-12-03 02:28:08","1988-1996","","2","20","","","","","","","","","","English","© 2024. This work is published under https://creativecommons.org/licenses/by/4.0/legalcode (the“License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 1988-1996 Place: Paris, France Publisher: Engineering and Scientific Research Groups","","","","","Accuracy; Algorithms; Artificial intelligence; Automation; Communication; Deep learning; Evolution; Foreign language learning; Information industry; International cooperation; Language; Large language models; Machine translation; Natural language processing; Neural networks; Real time; Semantics; Sentiment analysis; Speech; Voice recognition","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"GM4GJJCY","journalArticle","2024","Lai, Joel Weijia","Adapting Self-Regulated Learning in an Age of Generative Artificial Intelligence Chatbots","Future Internet","","","10.3390/fi16060218","https://www.proquest.com/docview/3072320374/abstract/13D00F81FE2A494FPQ/37","The increasing use of generative artificial intelligence (GenAI) has led to a rise in conversations about how teachers and students should adopt these tools to enhance the learning process. Self-regulated learning (SRL) research is important for addressing this question. A popular form of GenAI is the large language model chatbot, which allows users to seek answers to their queries. This article seeks to adapt current SRL models to understand student learning with these chatbots. This is achieved by classifying the prompts supplied by a learner to an educational chatbot into learning actions and processes using the process–action library. Subsequently, through process mining, we can analyze these data to provide valuable insights for learners, educators, instructional designers, and researchers into the possible applications of chatbots for SRL.","2024","2024-12-03 02:28:10","2024-12-03 02:28:10","2024-12-03 02:28:08","218","","6","16","","","","","","","","","","English","© 2024 by the author.  Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 218 Place: Basel, Switzerland Publisher: MDPI AG","","","","","chatbot; Chatbots; Cognition & reasoning; Education; Feedback; generative artificial intelligence; Generative artificial intelligence; Goal setting; Large language models; Learning analytics; learning process analytics; Metacognition; Motivation; Online instruction; process mining; School environment; Self-efficacy; self-regulated learning; Students; Systematic review; Teaching","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"TPLD92NQ","journalArticle","2024","Tam, Thomas Yu Chow; Sivarajkumar, Sonish; Kapoor, Sumit; Stolyar, Alisa V.; Polanska, Katelyn; McCarthy, Karleigh R.; Osterhoudt, Hunter; Wu, Xizhi; Visweswaran, Shyam; Fu, Sunyang; Mathur, Piyush; Cacciamani, Giovanni E.; Sun, Cong; Peng, Yifan; Wang, Yanshan","A framework for human evaluation of large language models in healthcare derived from literature review","NPJ Digital Medicine","","","10.1038/s41746-024-01258-7","https://www.proquest.com/docview/3110577919/abstract/13D00F81FE2A494FPQ/39","With generative artificial intelligence (GenAI), particularly large language models (LLMs), continuing to make inroads in healthcare, assessing LLMs with human evaluations is essential to assuring safety and effectiveness. This study reviews existing literature on human evaluation methodologies for LLMs in healthcare across various medical specialties and addresses factors such as evaluation dimensions, sample types and sizes, selection, and recruitment of evaluators, frameworks and metrics, evaluation process, and statistical analysis type. Our literature review of 142 studies shows gaps in reliability, generalizability, and applicability of current human evaluation practices. To overcome such significant obstacles to healthcare LLM developments and deployments, we propose QUEST, a comprehensive and practical framework for human evaluation of LLMs covering three phases of workflow: Planning, Implementation and Adjudication, and Scoring and Review. QUEST is designed with five proposed evaluation principles: Quality of Information, Understanding and Reasoning, Expression Style and Persona, Safety and Harm, and Trust and Confidence.","2024-12","2024-12-03 02:28:10","2024-12-03 02:28:10","2024-12-03 02:28:09","258","","1","7","","","","","","","","","","English","© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 258 Place: London, United States Publisher: Nature Publishing Group","","","","","Medical Sciences--Computer Applications","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"FFMLBJJC","journalArticle","2024","Saúde, Sandra; Barros, João Paulo; Almeida, Inês","Impacts of Generative Artificial Intelligence in Higher Education: Research Trends and Students’ Perceptions","Social Sciences","","","10.3390/socsci13080410","https://www.proquest.com/docview/3098172331/abstract/13D00F81FE2A494FPQ/40","In this paper, the effects of the rapid advancement of generative artificial intelligence (Gen AI) in higher education (HE) are discussed. A mixed exploratory research approach was employed to understand these impacts, combining analysis of current research trends and students’ perceptions of the effects of Gen AI tools in academia. Through bibliometric analysis and systematic literature review, 64 publications (indexed in the SCOPUS and Web of Science databases) were examined, highlighting Gen AI’s disruptive effect on the pedagogical aspects of HE. The impacts identified by the literature were compared with the perceptions held by computer science students of two different HE institutions (HEIs) on the topic. An exploratory study was developed based on the application of a questionnaire to a group of 112 students. The results suggest that while Gen AI can enhance academic work and learning feedback, it requires appropriate pedagogical support to foster critical, ethical, and digital literacy competencies. Students demonstrate awareness of both the risks and benefits associated with Gen AI in academic settings. The research concludes that failing to recognize and effectively use Gen AI in HE impedes educational progress and the adequate preparation of citizens and workers to think and act in an AI-mediated world.","2024","2024-12-03 02:28:10","2024-12-03 02:28:10","2024-12-03 02:28:10","410","","8","13","","","Impacts of Generative Artificial Intelligence in Higher Education","","","","","","","English","© 2024 by the authors.  Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 410 Place: Basel, Switzerland Publisher: MDPI AG","","","","","bibliometric analysis; Bibliometrics; Chi-square test; Computer science; Gender; generative artificial intelligence; Generative artificial intelligence; higher education; Higher education; impacts; Learning; Literature reviews; Questionnaires; Software; Students; students’ perceptions; systematic literature review; Trends","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LX87B8XG","journalArticle","2024","Tong, Song; Mao, Kai; Huang, Zhen; Zhao, Yukun; Peng, Kaiping","Automating psychological hypothesis generation with AI: when large language models meet causal graph","Humanities & Social Sciences Communications","","","10.1057/s41599-024-03407-5","https://www.proquest.com/scholarly-journals/automating-psychological-hypothesis-generation/docview/3077590011/se-2?accountid=25704","Leveraging the synergy between causal knowledge graphs and a large language model (LLM), our study introduces a groundbreaking approach for computational hypothesis generation in psychology. We analyzed 43,312 psychology articles using a LLM to extract causal relation pairs. This analysis produced a specialized causal graph for psychology. Applying link prediction algorithms, we generated 130 potential psychological hypotheses focusing on “well-being”, then compared them against research ideas conceived by doctoral scholars and those produced solely by the LLM. Interestingly, our combined approach of a LLM and causal graphs mirrored the expert-level insights in terms of novelty, clearly surpassing the LLM-only hypotheses (t(59) = 3.34, p = 0.007 and t(59) = 4.32, p < 0.001, respectively). This alignment was further corroborated using deep semantic analysis. Our results show that combining LLM with machine learning techniques such as causal knowledge graphs can revolutionize automated discovery in psychology, extracting novel insights from the extensive literature. This work stands at the crossroads of psychology and artificial intelligence, championing a new enriched paradigm for data-driven hypothesis generation in psychological research.","2024-12","2024-12-03 02:35:08","2024-12-03 02:35:08","","896","","1","11","","","","","","","","","","English","","","Coronavirus Research Database; Publicly Available Content Database","3077590011","","","Place: London Publisher: Palgrave Macmillan","","","https://media.proquest.com/media/hms/PFT/1/2s44Z?_a=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%3D%3D&_s=ITaC7Utn8qPGztb3ICXmDv1nuMs%3D","Metadata; Algorithms; Knowledge; Hypotheses; Physics; Research methodology; Artificial intelligence; Automation; Language; Large language models; Semantics; Graphs; Cost analysis; Natural language; Causality; Neurosciences; Semantic analysis; Social psychology; Social Sciences: Comprehensive Works","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"9HAXG8DY","journalArticle","2024","Hodjat, Babak","AI and agents","AI Magazine","","07384602","10.1002/aaai.l2170","https://www.proquest.com/scholarly-journals/ai-agents/docview/3090679413/se-2?accountid=25704","Earlier this year, OpenAI released their GPTs framework, allowing users to set up Large Language Model (LLM)-based personas, orchestrate them into aworkflow and even offering their AI apps within an app store. This is the latest, and maybe the easiest to set up, in a string of agent-based LLM orchestration platforms in the past year, harkening a new age of agent-based engineering. But, like most breakthroughs, this one is also rooted in many years of research, and the reason the world is paying attention to it now is that, thanks to Generative AI and Large Language Models, we finally have artificial agents that are useful enough to scale to more serious problems.","2024","2024-12-03 02:35:08","2024-12-03 02:35:08","","267-269","","2","45","","","","","","","","","","English","","","Library Science Database","3090679413","","","Place: La Canada Publisher: John Wiley & Sons, Inc.","","","https://media.proquest.com/media/hms/PFT/1/0nZSZ?_a=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&_s=S9R0q%2BJ%2FWjLiJexxVBhOTxOLRhw%3D","Neural networks; Machine learning; Algorithms; Software; Artificial intelligence; Generative artificial intelligence; Language; Large language models; Economic; Natural language; Agents (artificial intelligence); Computers--Artificial Intelligence; Intelligence","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"Z69U3WUN","journalArticle","2024","Reason, Tim; Rawlinson, William; Langham, Julia; Gimblett, Andy; Malcolm, Bill; Klijn, Sven","Artificial Intelligence to Automate Health Economic Modelling: A Case Study to Evaluate the Potential Application of Large Language Models","PharmacoEconomics - Open","","25094262","10.1007/s41669-024-00477-8","https://www.proquest.com/scholarly-journals/artificial-intelligence-automate-health-economic/docview/2930359570/se-2?accountid=25704","BackgroundCurrent generation large language models (LLMs) such as Generative Pre-Trained Transformer 4 (GPT-4) have achieved human-level performance on many tasks including the generation of computer code based on textual input. This study aimed to assess whether GPT-4 could be used to automatically programme two published health economic analyses.MethodsThe two analyses were partitioned survival models evaluating interventions in non-small cell lung cancer (NSCLC) and renal cell carcinoma (RCC). We developed prompts which instructed GPT-4 to programme the NSCLC and RCC models in R, and which provided descriptions of each model’s methods, assumptions and parameter values. The results of the generated scripts were compared to the published values from the original, human-programmed models. The models were replicated 15 times to capture variability in GPT-4’s output.ResultsGPT-4 fully replicated the NSCLC model with high accuracy: 100% (15/15) of the artificial intelligence (AI)-generated NSCLC models were error-free or contained a single minor error, and 93% (14/15) were completely error-free. GPT-4 closely replicated the RCC model, although human intervention was required to simplify an element of the model design (one of the model’s fifteen input calculations) because it used too many sequential steps to be implemented in a single prompt. With this simplification, 87% (13/15) of the AI-generated RCC models were error-free or contained a single minor error, and 60% (9/15) were completely error-free. Error-free model scripts replicated the published incremental cost-effectiveness ratios to within 1%.ConclusionThis study provides a promising indication that GPT-4 can have practical applications in the automation of health economic model construction. Potential benefits include accelerated model development timelines and reduced costs of development. Further research is necessary to explore the generalisability of LLM-based automation across a larger sample of models.","2024-03","2024-12-03 02:35:08","2024-12-03 02:35:08","","191-203","","2","8","","","","","","","","","","English","","","Publicly Available Content Database","2930359570","","","Place: Cham Publisher: Springer Nature B.V.","","","https://media.proquest.com/media/hms/PFT/1/ib5QX?_a=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%3D%3D&_s=DtmJaQTSDPMOO9DpCWhtrEdFELw%3D","Design; Iterative methods; Case studies; Software; Artificial intelligence; Language model; Automation; Language; Large language models; Construction; Application programming interface; Costs; Disease management; Economic analysis; Economic Modelling; Economic models; Kidney cancer; Lung cancer; Non-small-cell lung carcinoma; Palliative care; Pharmacy And Pharmacology; Renal cell carcinoma","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"6V2NW8FP","journalArticle","2024","Tală, Mădălina Lavinia; Müller, Cătălina Nicoleta; Năstase, Irina Albăstroiu; State, Olimpia; Gheorghe, Georgica","EXPLORING UNIVERSITY STUDENTS' PERCEPTIONS OF GENERATIVE ARTIFICIAL INTELLIGENCE IN EDUCATION","Amfiteatru Economic","","15829146","10.24818/EA/2024/65/71","https://www.proquest.com/scholarly-journals/exploring-university-students-perceptions/docview/3015085070/se-2?accountid=25704","Artificial intelligence, the latest chapter of the technological revolution, has a tremendous potential to change every area of our lives. This article has focused on a specific form of artificial intelligence, namely generative intelligence, which facilitates the generation of content in all its forms (text, image, video, audio, programming codes, etc.). Thus, generative artificial intelligence has a crucial role in education, allowing for the personalisation of educational content and facilitating the learning process. In the beginning, the paper has highlighted conceptual delimitations regarding artificial intelligence and its applications in education, along with advantages and limitations, highlighting that the adoption of generative artificial intelligence solutions, such as ChatGPT, in higher education in economics has been relatively underexplored in the literature. In order to cover these gaps identified in the literature, have been presented, in the second part of the paper, the methodology and results of an exploratory research, conducted on a sample of 364 undergraduate and master's students at the Faculty of Business and Tourism within the Bucharest University of Economic Studies. The research has provided insight into the perception of Business Administration students regarding these applications. The results indicated a high level of awareness and interest in content generation models and highlighted that users with favourable perceptions regarding the quality of content generated by such applications tend to believe that their integration into academic endeavours can foster creativity and enhance employment prospects.","2024-02","2024-12-03 02:35:08","2024-12-03 02:35:08","","71-88","","65","26","","","","","","","","","","English","","","Publicly Available Content Database","3015085070","","","Place: Bucharest Publisher: Bucharest Academy of Economic Studies, Faculty of Commerce","","","https://media.proquest.com/media/hms/PFT/1/q5mkX?_a=ChgyMDI0MTIwMzAxNTU0NTA3NDo1NTI0MTESBTkwMDAyGgpPTkVfU0VBUkNIIg0xMDMuMTIxLjE0OC4yKgcxOTI2MzM4MgozMDE1MDg1MDcwOg1Eb2N1bWVudEltYWdlQgEwUgZPbmxpbmVaAkZUYgNQRlRqCjIwMjQvMDIvMDFyCjIwMjQvMDIvMjl6AIIBIVAtMTAwOTI0MC0yNTcwNC1GUkVFLW51bGwtNjQ1NjkzOZIBBk9ubGluZcoBRk1vemlsbGEvNS4wIChYMTE7IExpbnV4IHg4Nl82NDsgcnY6MTMzLjApIEdlY2tvLzIwMTAwMTAxIEZpcmVmb3gvMTMzLjDSARJTY2hvbGFybHkgSm91cm5hbHOaAgdQcmVQYWlkqgIrT1M6RU1TLU1lZGlhTGlua3NTZXJ2aWNlLWdldE1lZGlhVXJsRm9ySXRlbcoCD0FydGljbGV8RmVhdHVyZdICAVnyAgD6AgFZggMDV2ViigMcQ0lEOjIwMjQxMjAzMDE1NTQ1MDc0OjI1NDE0Nw%3D%3D&_s=pToAhKMQE2x9zDcBUT6rKvurYEc%3D","Application; Research; Design; Higher education; Learning; Business And Economics; Software; Artificial; Artificial intelligence; Chatbots; Generative artificial intelligence; College students; Core curriculum; Creativity; 92812:International Affairs; Business students; Employment; Perceptions; Student; Student attitudes; Tourism","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"U6QDIMXL","journalArticle","2024","Feretzakis, Georgios; Papaspyridis, Konstantinos; Aris Gkoulalas-Divanis; Verykios, Vassilios S","Privacy-Preserving Techniques in Generative AI and Large Language Models: A Narrative Review","Information","","","10.3390/info15110697","https://www.proquest.com/scholarly-journals/privacy-preserving-techniques-generative-ai-large/docview/3133060313/se-2?accountid=25704","Generative AI, including large language models (LLMs), has transformed the paradigm of data generation and creative content, but this progress raises critical privacy concerns, especially when models are trained on sensitive data. This review provides a comprehensive overview of privacy-preserving techniques aimed at safeguarding data privacy in generative AI, such as differential privacy (DP), federated learning (FL), homomorphic encryption (HE), and secure multi-party computation (SMPC). These techniques mitigate risks like model inversion, data leakage, and membership inference attacks, which are particularly relevant to LLMs. Additionally, the review explores emerging solutions, including privacy-enhancing technologies and post-quantum cryptography, as future directions for enhancing privacy in generative AI systems. Recognizing that achieving absolute privacy is mathematically impossible, the review emphasizes the necessity of aligning technical safeguards with legal and regulatory frameworks to ensure compliance with data protection laws. By discussing the ethical and legal implications of privacy risks in generative AI, the review underscores the need for a balanced approach that considers performance, scalability, and privacy preservation. The findings highlight the need for ongoing research and innovation to develop privacy-preserving techniques that keep pace with the scaling of generative AI, especially in large language models, while adhering to regulatory and ethical standards.","2024","2024-12-03 02:35:08","2024-12-03 02:35:08","","697","","11","15","","","","","","","","","","English","","","Publicly Available Content Database","3133060313","","","Place: Basel Publisher: MDPI AG","","","https://media.proquest.com/media/hms/PFT/1/3hWia?_a=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%3D%3D&_s=ZjqJJXkJo6Kd7GLm%2BB%2FG2Q10tU0%3D","Machine learning; Generative artificial intelligence; generative AI; Data integrity; Large language models; Privacy; Ethical standards; large language models (LLMs); Computers--Information Science And Information Theory; differential privacy; federated learning; Federated learning; General Data Protection Regulation; homomorphic encryption; Legislation; membership inference; model inversion; post-quantum cryptography; privacy-enhancing technologies; privacy-preserving techniques; Quantum cryptography; secure multi-party computation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"7D6UKD96","journalArticle","2024","Gholami, Hamed","Artificial Intelligence Techniques for Sustainable Reconfigurable Manufacturing Systems: An AI-Powered Decision-Making Application Using Large Language Models","Big Data and Cognitive Computing","","","10.3390/bdcc8110152","https://www.proquest.com/scholarly-journals/artificial-intelligence-techniques-sustainable/docview/3132880750/se-2?accountid=25704","Artificial intelligence (AI) offers a promising avenue for developing sustainable reconfigurable manufacturing systems. Although there has been significant progress in these research areas, there seem to be no studies devoted to exploring and evaluating AI techniques for such systems. To address this gap, the current study aims to present a deliberation on the subject matter, with a particular focus on assessing AI techniques. For this purpose, an AI-enabled methodological approach is developed in Python, integrating fuzzy logic to effectively navigate the uncertainties inherent in evaluating the performance of techniques. The incorporation of sensitivity analysis further enables a thorough evaluation of how input variations impact decision-making outcomes. To conduct the assessment, this study provides an AI-powered decision-making application using large language models in the field of natural language processing, which has emerged as an influential branch of artificial intelligence. The findings reveal that machine learning and big data analytics as well as fuzzy logic and programming stand out as the most promising AI techniques for sustainable reconfigurable manufacturing systems. The application confirms that using fuzzy logic programming in Python as the computational foundation significantly enhances precision, efficiency, and execution time, offering critical insights that enable more timely and informed decision-making in the field. Thus, this study not only addresses a critical gap in the literature but also offers an AI-driven approach to support complex decision-making processes.","2024","2024-12-03 02:35:09","2024-12-03 02:35:09","","152","","11","8","","","","","","","","","","English","","","Coronavirus Research Database; Publicly Available Content Database","3132880750","","","Place: Basel Publisher: MDPI AG","","","https://media.proquest.com/media/hms/PFT/1/0LCia?_a=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%3D%3D&_s=QhvKSoYnYlC4j%2BNZdBKgrhZGg28%3D","Machine learning; Algorithms; Fuzzy logic; Performance evaluation; Decision making; Supply chains; Sustainable development; Python; artificial intelligence; Artificial intelligence; ChatGPT; Large language models; natural language processing; Natural language processing; Impact analysis; Adaptability; AI-enabled decision-making; Computers--Electronic Data Processing; Flexibility; Fuzzy systems; intelligent fuzzy systems; Logic programming; Manufacturing; Modularity; reconfigurable manufacturing systems; Reconfiguration; Sensitivity analysis; sustainable manufacturing 4.0; Uncertainty analysis","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"M6AP6TYC","journalArticle","2024","Wachter, Sandra; Mittelstadt, Brent; Russell, Chris","Do large language models have a legal duty to tell the truth?","Royal Society Open Science","","","10.1098/rsos.240197","https://www.proquest.com/scholarly-journals/do-large-language-models-have-legal-duty-tell/docview/3104586672/se-2?accountid=25704","Careless speech is a new type of harm created by large language models (LLM) that poses cumulative, long-term risks to science, education and shared social truth in democratic societies. LLMs produce responses that are plausible, helpful and confident, but that contain factual inaccuracies, misleading references and biased information. These subtle mistruths are poised to cumulatively degrade and homogenize knowledge over time. This article examines the existence and feasibility of a legal duty for LLM providers to create models that Tell the truth'. We argue that LLM providers should be required to mitigate careless speech and better align their models with truth through open, democratic processes. We define careless speech against 'ground truth' in LLMs and related risks including hallucinations, misinformation and disinformation. We assess the existence of truth-related obligations in EU human rights law and the Artificial Intelligence Act, Digital Services Act, Product Liability Directive and Artificial Intelligence Liability Directive. Current frameworks contain limited, sector-specific truth duties. Drawing on duties in science and academia, education, archives and libraries, and a German case in which Google was held liable for defamation caused by autocomplete, we propose a pathway to create a legal truth duty for providers of narrow- and general-purpose LLMs.","2024","2024-12-03 02:35:09","2024-12-03 02:35:09","","1-38","","8","11","","","","","","","","","","English","","","Biological Science Database; Publicly Available Content Database","3104586672","","","Place: London Publisher: The Royal Society Publishing","","","https://media.proquest.com/media/hms/PFT/1/nF2sZ?_a=ChgyMDI0MTIwMzAxNTU0NTA3NDo1NTI0MTESBTkwMDAyGgpPTkVfU0VBUkNIIg0xMDMuMTIxLjE0OC4yKgc2NTI1ODI5MgozMTA0NTg2NjcyOg1Eb2N1bWVudEltYWdlQgEwUgZPbmxpbmVaAkZUYgNQRlRqCjIwMjQvMDEvMDFyCjIwMjQvMTIvMzF6AIIBJVAtMTAwNzg1Ni0yNTcwNC1DVVNUT01FUi1udWxsLTczMTg4MzGSAQZPbmxpbmXKAUZNb3ppbGxhLzUuMCAoWDExOyBMaW51eCB4ODZfNjQ7IHJ2OjEzMy4wKSBHZWNrby8yMDEwMDEwMSBGaXJlZm94LzEzMy4w0gESU2Nob2xhcmx5IEpvdXJuYWxzmgIHUHJlUGFpZKoCK09TOkVNUy1NZWRpYUxpbmtzU2VydmljZS1nZXRNZWRpYVVybEZvckl0ZW3KAg9BcnRpY2xlfEZlYXR1cmXSAgFZ8gIA%2BgIBWYIDA1dlYooDHENJRDoyMDI0MTIwMzAxNTU0NTA3NDoyNTQxNDc%3D&_s=UKV9Go8BD2oDtmoghinL4W1aCSM%3D","Sciences: Comprehensive Works; Models; Design; Education; Artificial intelligence; Language; Large language models; Economic; Bias; Privacy; Natural language; Speech; Defamation; False information; Hallucinations; Human rights; Philosophy; Products liability","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KQ82TJCZ","journalArticle","2024","Huang, Eddie T. C.; Yang, Jai-Sing; Liao, Ken Y. K.; Tseng, Warren C. W.; Lee, C. K.; Gill, Michelle; Compas, Colin; See, Simon; Tsai, Fuu-Jen","Predicting blood–brain barrier permeability of molecules with a large language model and machine learning","Scientific Reports (Nature Publisher Group)","","","10.1038/s41598-024-66897-y","https://www.proquest.com/scholarly-journals/predicting-blood-brain-barrier-permeability/docview/3077589832/se-2?accountid=25704","Predicting the blood–brain barrier (BBB) permeability of small-molecule compounds using a novel artificial intelligence platform is necessary for drug discovery. Machine learning and a large language model on artificial intelligence (AI) tools improve the accuracy and shorten the time for new drug development. The primary goal of this research is to develop artificial intelligence (AI) computing models and novel deep learning architectures capable of predicting whether molecules can permeate the human blood–brain barrier (BBB). The in silico (computational) and in vitro (experimental) results were validated by the Natural Products Research Laboratories (NPRL) at China Medical University Hospital (CMUH). The transformer-based MegaMolBART was used as the simplified molecular input line entry system (SMILES) encoder with an XGBoost classifier as an in silico method to check if a molecule could cross through the BBB. We used Morgan or Circular fingerprints to apply the Morgan algorithm to a set of atomic invariants as a baseline encoder also with an XGBoost classifier to compare the results. BBB permeability was assessed in vitro using three-dimensional (3D) human BBB spheroids (human brain microvascular endothelial cells, brain vascular pericytes, and astrocytes). Using multiple BBB databases, the results of the final in silico transformer and XGBoost model achieved an area under the receiver operating characteristic curve of 0.88 on the held-out test dataset. Temozolomide (TMZ) and 21 randomly selected BBB permeable compounds (Pred scores = 1, indicating BBB-permeable) from the NPRL penetrated human BBB spheroid cells. No evidence suggests that ferulic acid or five BBB-impermeable compounds (Pred scores < 1.29423E−05, which designate compounds that pass through the human BBB) can pass through the spheroid cells of the BBB. Our validation of in vitro experiments indicated that the in silico prediction of small-molecule permeation in the BBB model is accurate. Transformer-based models like MegaMolBART, leveraging the SMILES representations of molecules, show great promise for applications in new drug discovery. These models have the potential to accelerate the development of novel targeted treatments for disorders of the central nervous system.","2024","2024-12-03 02:35:09","2024-12-03 02:35:09","","15844","","1","14","","","","","","","","","","English","","","Biological Science Database; Publicly Available Content Database","3077589832","","","Place: London Publisher: Nature Publishing Group","","","https://media.proquest.com/media/hms/PFT/1/i454Z?_a=ChgyMDI0MTIwMzAxNTU0NTA3NDo1NTI0MTESBTkwMDAyGgpPTkVfU0VBUkNIIg0xMDMuMTIxLjE0OC4yKgcyMDQxOTM5MgozMDc3NTg5ODMyOg1Eb2N1bWVudEltYWdlQgEwUgZPbmxpbmVaAkZUYgNQRlRqCjIwMjQvMDEvMDFyCjIwMjQvMTIvMzF6AIIBJVAtMTAwNzg1Ni0yNTcwNC1DVVNUT01FUi1udWxsLTczMTg4MzGSAQZPbmxpbmXKAUZNb3ppbGxhLzUuMCAoWDExOyBMaW51eCB4ODZfNjQ7IHJ2OjEzMy4wKSBHZWNrby8yMDEwMDEwMSBGaXJlZm94LzEzMy4w0gESU2Nob2xhcmx5IEpvdXJuYWxzmgIHUHJlUGFpZKoCK09TOkVNUy1NZWRpYUxpbmtzU2VydmljZS1nZXRNZWRpYVVybEZvckl0ZW3KAg9BcnRpY2xlfEZlYXR1cmXSAgFZ8gIA%2BgIBWYIDA1dlYooDHENJRDoyMDI0MTIwMzAxNTU0NTA3NDoyNTQxNDc%3D&_s=IRbUQtKeNhWWurhZuOvJqOyLtyI%3D","Deep learning; Machine learning; Sciences: Comprehensive Works; Artificial intelligence; Large language models; Research & development--R&D; Astrocytes; Blood-brain barrier; Brain research; Central nervous system; Computational neuroscience; Drug development; Drug discovery; Endothelial cells; Environmental; Ferulic acid; Laboratory tests; Learning algorithms; Membrane permeability; Microvasculature; Molecular modelling; Natural products; Pericytes; Permeability; Spheroids; Temozolomide","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"HC8VVAT3","journalArticle","2024","Kong, Weijuan; Ning, Yanhua; Ma, Ting; Song, Fei; Mao, Yuxin; Yang, Cailing; Li, Xinjin; Guo, Yahong; Liu, Haiyan; Shi, Jing; Liu, Lingna","Experience of undergraduate nursing students participating in artificial intelligence + project task driven learning at different stages: a qualitative study","BMC Nursing","","","10.1186/s12912-024-01982-1","https://www.proquest.com/scholarly-journals/experience-undergraduate-nursing-students/docview/3054204119/se-2?accountid=25704","BackgroundArtificial intelligence is a growing phenomenon that will soon facilitate wide-scale changes in many professions, and is expected to play an important role in the field of medical education. This study explored the realistic feelings and experiences of nursing undergraduates participating in different stages of artificial intelligence + project task driven learning, and provide a basis for artificial intelligence participation in nursing teaching.MethodsWe conducted face-to-face semi-structured interviews with nursing undergraduates participating in Nursing Research Course which adopts artificial intelligence + project task driven learning from a medical university in Ningxia from September to November 2023, to understand their experience of using artificial intelligence for learning and the emotional changes at different stages. The interview guide included items about their personal experience and feelings of completing project tasks through dialogue with artificial intelligence, and suggestions for course content. Thematic analysis was used to analyze interview data. This study followed the COREQ checklist.ResultsAccording to the interview data, three themes were summarized. Undergraduate nursing students have different experiences in participating in artificial intelligence + project task driven learning at different stages, mainly manifested as diverse emotional experiences under initial knowledge deficiency, the individual growth supported by external forces during the adaptation period, and the expectations and suggestions after the birth of the results in the end period.ConclusionsNursing undergraduates can actively adapt to the integration of artificial intelligence into nursing teaching, dynamically observe students’ learning experience, strengthen positive guidance, and provide support for personalized teaching models, better leveraging the advantages of artificial intelligence participation in teaching.","2024","2024-12-03 02:35:10","2024-12-03 02:35:10","","1-10","","","23","","","","","","","","","","English","","","Publicly Available Content Database","3054204119","","","Place: London Publisher: BioMed Central","","","https://media.proquest.com/media/hms/PFT/1/lE2SY?_a=ChgyMDI0MTIwMzAxNTU0NTA3NDo1NTI0MTESBTkwMDAyGgpPTkVfU0VBUkNIIg0xMDMuMTIxLjE0OC4yKgU0NDc5NDIKMzA1NDIwNDExOToNRG9jdW1lbnRJbWFnZUIBMFIGT25saW5lWgJGVGIDUEZUagoyMDI0LzAxLzAxcgoyMDI0LzEyLzMxegCCASFQLTEwMDkyNDAtMjU3MDQtRlJFRS1udWxsLTY0NTY5MzmSAQZPbmxpbmXKAUZNb3ppbGxhLzUuMCAoWDExOyBMaW51eCB4ODZfNjQ7IHJ2OjEzMy4wKSBHZWNrby8yMDEwMDEwMSBGaXJlZm94LzEzMy4w0gESU2Nob2xhcmx5IEpvdXJuYWxzmgIHUHJlUGFpZKoCK09TOkVNUy1NZWRpYUxpbmtzU2VydmljZS1nZXRNZWRpYVVybEZvckl0ZW3KAg9BcnRpY2xlfEZlYXR1cmXSAgFZ8gIA%2BgIBWYIDA1dlYooDHENJRDoyMDI0MTIwMzAxNTU0NTA3NDoyNTQxNDc%3D&_s=1ApJ3KnT541HAhI5pSwM3OTvyos%3D","Qualitative research; Teachers; Learning; Data analysis; Research methodology; Nurses; Experience; Artificial intelligence; Generative artificial intelligence; Nursing education; College students; Data collection; Interviews; Education reform; Informed consent; Medical Sciences--Nurses And Nursing; Project task driven learning; Sample size; Teaching assistants; Undergraduate nursing students","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"GHR4HXZB","journalArticle","2024","Oniani, David; Hilsman, Jordan; Zang, Chengxi; Wang, Junmei; Cai, Lianjin; Zawala, Jan; Wang, Yanshan","Emerging opportunities of using large language models for translation between drug molecules and indications","Scientific Reports (Nature Publisher Group)","","","10.1038/s41598-024-61124-0","https://www.proquest.com/scholarly-journals/emerging-opportunities-using-large-language/docview/3053362273/se-2?accountid=25704","A drug molecule is a substance that changes an organism’s mental or physical state. Every approved drug has an indication, which refers to the therapeutic use of that drug for treating a particular medical condition. While the Large Language Model (LLM), a generative Artificial Intelligence (AI) technique, has recently demonstrated effectiveness in translating between molecules and their textual descriptions, there remains a gap in research regarding their application in facilitating the translation between drug molecules and indications (which describes the disease, condition or symptoms for which the drug is used), or vice versa. Addressing this challenge could greatly benefit the drug discovery process. The capability of generating a drug from a given indication would allow for the discovery of drugs targeting specific diseases or targets and ultimately provide patients with better treatments. In this paper, we first propose a new task, the translation between drug molecules and corresponding indications, and then test existing LLMs on this new task. Specifically, we consider nine variations of the T5 LLM and evaluate them on two public datasets obtained from ChEMBL and DrugBank. Our experiments show the early results of using LLMs for this task and provide a perspective on the state-of-the-art. We also emphasize the current limitations and discuss future work that has the potential to improve the performance on this task. The creation of molecules from indications, or vice versa, will allow for more efficient targeting of diseases and significantly reduce the cost of drug discovery, with the potential to revolutionize the field of drug discovery in the era of generative AI.","2024","2024-12-03 02:35:10","2024-12-03 02:35:10","","10738","","1","14","","","","","","","","","","English","","","Biological Science Database; Publicly Available Content Database","3053362273","","","Place: London Publisher: Nature Publishing Group","","","https://media.proquest.com/media/hms/PFT/1/gjQQY?_a=ChgyMDI0MTIwMzAxNTU0NTA3NDo1NTI0MTESBTkwMDAyGgpPTkVfU0VBUkNIIg0xMDMuMTIxLjE0OC4yKgcyMDQxOTM5MgozMDUzMzYyMjczOg1Eb2N1bWVudEltYWdlQgEwUgZPbmxpbmVaAkZUYgNQRlRqCjIwMjQvMDEvMDFyCjIwMjQvMTIvMzF6AIIBJVAtMTAwNzg1Ni0yNTcwNC1DVVNUT01FUi1udWxsLTczMTg4MzGSAQZPbmxpbmXKAUZNb3ppbGxhLzUuMCAoWDExOyBMaW51eCB4ODZfNjQ7IHJ2OjEzMy4wKSBHZWNrby8yMDEwMDEwMSBGaXJlZm94LzEzMy4w0gESU2Nob2xhcmx5IEpvdXJuYWxzmgIHUHJlUGFpZKoCK09TOkVNUy1NZWRpYUxpbmtzU2VydmljZS1nZXRNZWRpYVVybEZvckl0ZW3KAg9BcnRpY2xlfEZlYXR1cmXSAgFZ8gIA%2BgIBWYIDA1dlYooDHENJRDoyMDI0MTIwMzAxNTU0NTA3NDoyNTQxNDc%3D&_s=YQAOVtmJ2R3J%2FzcrU%2Fi4UaRdIiE%3D","Neural networks; Sciences: Comprehensive Works; Artificial intelligence; Generative artificial intelligence; Datasets; Language; Large language models; Research & development--R&D; Medical research; Drug discovery; Drug delivery; Drugs; Experiments; Translation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"6NG6XZWQ","journalArticle","2024","Sukkar, Ahmad W; Fareed, Mohamed W; Yahia, Moohammed Wasim; Mushtaha, Emad; De Giosa, Sami Luigi","Artificial Intelligence Islamic Architecture (AIIA): What Is Islamic Architecture in the Age of Artificial Intelligence?","Buildings","","","10.3390/buildings14030781","https://www.proquest.com/scholarly-journals/artificial-intelligence-islamic-architecture-aiia/docview/2998274893/se-2?accountid=25704","Revisiting the long-debated question: “What is Islamic architecture?”, this research article aims to explore the identity of “Islamic architecture (IA)” in the context of artificial intelligence (AI) as well as the novel opportunities and cultural challenges associated with applying AI techniques, such as the machine learning of Midjourney in the context of IA. It investigates the impact factors of AI technologies on the understanding and interpretation of traditional Islamic architectural principles, especially architectural design processes. This article employs a quantitative research methodology, including the observation of works of artists and architectural designers appearing in the mass media in light of a literature review and critical analysis of scholarly debates on Islamic architecture, spanning from historical perspectives to contemporary discussions. The article argues for the emergence of a continuous paradigm shift from what is commonly known as “postmodern Islamic architecture” (PMIA) into “artificial intelligence Islamic architecture” (AIIA), as coined by the authors of this article. It identifies the following impact factors of AI on IA: (1) particular requirements and sensitivities, inaccuracies, and biases, (2) human touch, unique craftsmanship, and a deep understanding of cultural issues, (3) regional variation, (4) translation, (5) biases in sources, (6) previously used terms and expressions, and (7) intangible values. The significance of this research in digital heritage lies in the fact that there are no pre-existing theoretical publications on the topic of “Islamic architecture in the age of artificial intelligence”, although an extensive set of publications interpreting the question of the definition of Islamic architecture, in general, is found. This article is pivotal in analyzing this heritage-inspired design approach in light of former criticism of the definition of “Islamic architecture”, which could benefit both theorists and practitioners. This theoretical article is the first in a series of two sequential articles in the Buildings journal; the second (practical) article is an analytical evaluation of the Midjourney architectural virtual lab, defining major current limits in AI-generated representations of Islamic architectural heritage.","2024","2024-12-03 02:35:10","2024-12-03 02:35:10","","781","","3","14","","","","","","","","","","English","","","Publicly Available Content Database","2998274893","","","Place: Basel Publisher: MDPI AG","","","https://media.proquest.com/media/hms/PFT/1/Q7HjX?_a=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%3D%3D&_s=lutFjvKZYqwJyPDZrEkMYdw%2B%2FfA%3D","Internet; Machine learning; Design; Literature reviews; Culture; Questions; Artificial intelligence; Ethics; Bias; Social; Designers; Architecture; Context; Philosophy; aesthetics; AI; Architectural Design; architectural visualization; Art galleries & museums; Artists; Building And Construction; Building design; Built environment; computer-aided design (CAD); creative design; Cultural factors; design methodology; Digital heritage; epistemology; Epistemology; Historiography; Islam; Islamic architecture; Islamic art; Mass media; Mathematical analysis; Midjourney; Postmodernism; Quantitative research; Remote laboratory; Spirituality; tangible and intangible heritage; Virtual environments; Virtual Lab","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XUUH2Y6F","journalArticle","2023","Vochozka, Marek; Horak, Jakub; Morley, Nancy","Generative Artificial Intelligence and Voice and Gesture Recognition Technologies, Virtual Team Movement and Behavior Tracking, and Haptic and Biometric Sensors in Virtual Immersive Workspaces","Contemporary Readings in Law and Social Justice","","19489137","10.22381/CRLSJ15220239","https://www.proquest.com/scholarly-journals/generative-artificial-intelligence-voice-gesture/docview/2918787287/se-2?accountid=25704","ABSTRACT. In this article, we cumulate previous research findings indicating that generative artificial intelligence and emotional state prediction tools can increase labor productivity and optimize virtual recruitment through mobile biometric and sentiment data. We contribute to the literature on generative artificial intelligence and algorithmic tracking technologies in 3D immersive spaces by showing that generative artificial intelligence and wearable haptic technologies can assist in virtual communication and collaboration, in ambient scene detection, and in quantifiable employee productivity. Throughout June 2023, we performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including ""virtual immersive workspaces"" + ""generative artificial intelligence and voice and gesture recognition technologies,"" ""virtual team movement and behavior tracking,"" and ""haptic and biometric sensors."" As we inspected research published in 2023, only 159 articles satisfied the eligibility criteria, and 52 mainly empirical sources were selected. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Dedoose, ROBIS, and SRDR.","2023","2024-12-03 02:35:11","2024-12-03 02:35:11","","160-178","","2","15","","","","","","","","","","English","","","Criminal Justice Database","2918787287","","","Place: Woodside Publisher: Addleton Academic Publishers","","","https://media.proquest.com/media/hms/PFT/1/VAIBX?_a=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%3D%3D&_s=UUUXpt6OKrdSvbhdX1T2BWqxFig%3D","Data mining; Databases; Collaboration; Algorithms; Sensors; Information sharing; Bibliometrics; Virtual reality; Labor productivity; Mapping; Literature reviews; Behavior; Software; Artificial intelligence; Generative artificial intelligence; Automation; Visualization; Workforce; Communication; Voice recognition; Acknowledgment; Augmented reality; Biometrics; Gesture; Gesture recognition; Haptics; Law; Layout; Performance appraisal; Predictive analytics; Quality assessment; Recruitment; Retention; Simulation; Spatial data; Teams; Tracking; Virtual teams","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"3MZB6AB6","journalArticle","2023","Aldea, Claudia-Irina","Immersive Haptic Experiences, Data-driven Cognitive and Affective Processes, and Generative Artificial Intelligence and 3D Virtual Simulation Technologies in Virtual Work Environments","Contemporary Readings in Law and Social Justice","","19489137","10.22381/CRLSJ15220238","https://www.proquest.com/scholarly-journals/immersive-haptic-experiences-data-driven/docview/2918787251/se-2?accountid=25704","The present study systematically reviews the existing research on generative artificial intelligence and behavioral modeling tools pivotal in efficient workforce development, in talent attraction and retention, and in virtual human resource management. Our findings indicate that generative artificial intelligence and 3D virtual simulation technologies can deploy multi-sensory augmented reality and visual data mining systems, employee engagement metrics, and computer vision algorithms in immersive workspaces. I contribute to the literature by clarifying that generative artificial intelligence and machine learning-based predictive technologies can harness employee engagement analytics, cognitive and affective metrics, and mobile biometric and sentiment data. Throughout May 2023, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including ""virtual work environments"" + ""immersive haptic experiences,"" ""data-driven cognitive and affective processes,"" and ""generative artificial intelligence and 3D virtual simulation technologies."" As research published in 2023 was inspected, only 169 articles satisfied the eligibility criteria, and 53 mainly empirical sources were selected. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Dedoose, Distiller SR, and SRDR.","2023","2024-12-03 02:35:11","2024-12-03 02:35:11","","141-159","","2","15","","","","","","","","","","English","","","Criminal Justice Database","2918787251","","","Place: Woodside Publisher: Addleton Academic Publishers","","","https://media.proquest.com/media/hms/PFT/1/M8IBX?_a=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%3D%3D&_s=6HwEqp1iqPfUwYYWKp5dkSbUXyY%3D","Data mining; Databases; Data processing; Deep learning; Machine learning; Algorithms; Performance management; Bibliometrics; Virtual reality; Mapping; Human resource management; Literature reviews; Behavior; Artificial intelligence; Generative artificial intelligence; Automation; Visualization; Professional development; Workforce; Augmented reality; Biometrics; Haptics; Law; Layout; Quality assessment; Retention; Simulation; Virtual teams; Employee involvement; Human resources management; Immersion; Interpersonal communication; Product development; Virtual human","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"FZVVB7D3","journalArticle","2023","Balica, Raluca-Stefania","Generative Artificial Intelligence and Productivity Software Tools, Adaptive Self-Organizing and Cognitive Computing Systems, and Wearable Augmented Reality and Algorithmic Tracking Technologies across Immersive Workspaces","Economics, Management and Financial Markets","","18423191","10.22381/emfml8220234","https://www.proquest.com/scholarly-journals/generative-artificial-intelligence-productivity/docview/2918626117/se-2?accountid=25704","ABSTRACT. The purpose of this study is to examine generative artificial intelligence and remote sensing and spatial analytics tools enabling meaningful productivity growth and labor market participation in virtual work environments. In this article, previous research findings were cumulated indicating that immersive haptic experiences can be attained through generative artificial intelligence and cognitive artificial intelligence algorithms, employee engagement analytics, and physiological and behavioral biometrics. The contribution to the literature is by showing that generative artificial intelligence and wearable augmented reality technologies can further virtual employee engagement, talent management, and organizational productivity and effectiveness. Throughout September 2023, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including ""generative artificial intelligence and productivity software tools"" + ""adaptive self-organizing and cognitive computing systems,"" ""wearable augmented reality and algorithmic tracking technologies,"" and ""immersive workspaces."" As research published in 2023 was inspected, only 177 articles satisfied the eligibility criteria, and 54 mainly empirical sources were selected. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Dedoose, MMAT, and SRDR.","2023-06","2024-12-03 02:35:11","2024-12-03 02:35:11","","77-94","","2","18","","","","","","","","","","English","","","Accounting, Tax & Banking Collection","2918626117","","","Place: Woodside Publisher: Addleton Academic Publishers","","","https://media.proquest.com/media/hms/PFT/1/S9sAX?_a=ChgyMDI0MTIwMzAxNTU0NTA3NDo1NTI0MTESBTkwMDAyGgpPTkVfU0VBUkNIIg0xMDMuMTIxLjE0OC4yKgYxMzYxMDcyCjI5MTg2MjYxMTc6DURvY3VtZW50SW1hZ2VCATBSBk9ubGluZVoCRlRiA1BGVGoKMjAyMy8wNi8wMXIKMjAyMy8wNi8zMHoAggEpUC0xMDA2MzI0LTI1NzA0LUNVU1RPTUVSLTEwMDAwMjMxLTczMTg4MjeSAQZPbmxpbmXKAUZNb3ppbGxhLzUuMCAoWDExOyBMaW51eCB4ODZfNjQ7IHJ2OjEzMy4wKSBHZWNrby8yMDEwMDEwMSBGaXJlZm94LzEzMy4w0gESU2Nob2xhcmx5IEpvdXJuYWxzmgIHUHJlUGFpZKoCK09TOkVNUy1NZWRpYUxpbmtzU2VydmljZS1nZXRNZWRpYVVybEZvckl0ZW3KAg9BcnRpY2xlfEZlYXR1cmXSAgFZ8gIA%2BgIBToIDA1dlYooDHENJRDoyMDI0MTIwMzAxNTU0NTA3NDoyNTQxNDc%3D&_s=FCDsO7tR9UTIa%2BmwwiDCjzxd44w%3D","Data mining; Databases; Collaboration; Algorithms; Remote sensing; Sensors; Productivity; Decision making; Performance management; Bibliometrics; Mapping; Business And Economics; Human resource management; Literature reviews; Systematic review; Software; Artificial; Artificial intelligence; Visualization; Workforce; Augmented reality; Biometrics; Layout; Quality assessment; Simulation; Spatial data; Tracking; Virtual teams; Employee involvement; Immersion; Augmentation; Job creation; Labor force participation; Labor market; Monitoring systems; Physiology; Software utilities; Talent management","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"IJWPDMD5","journalArticle","2023","Cooper, Grant","Examining Science Education in ChatGPT: An Exploratory Study of Generative Artificial Intelligence","Journal of Science Education and Technology","","10590145","10.1007/s10956-023-10039-y","https://www.proquest.com/scholarly-journals/examining-science-education-chatgpt-exploratory/docview/2805739342/se-2?accountid=25704","The advent of generative artificial intelligence (AI) offers transformative potential in the field of education. The study explores three main areas: (1) How did ChatGPT answer questions related to science education? (2) What are some ways educators could utilise ChatGPT in their science pedagogy? and (3) How has ChatGPT been utilised in this study, and what are my reflections about its use as a research tool? This exploratory research applies a self-study methodology to investigate the technology. Impressively, ChatGPT’s output often aligned with key themes in the research. However, as it currently stands, ChatGPT runs the risk of positioning itself as the ultimate epistemic authority, where a single truth is assumed without a proper grounding in evidence or presented with sufficient qualifications. Key ethical concerns associated with AI include its potential environmental impact, issues related to content moderation, and the risk of copyright infringement. It is important for educators to model responsible use of ChatGPT, prioritise critical thinking, and be clear about expectations. ChatGPT is likely to be a useful tool for educators designing science units, rubrics, and quizzes. Educators should critically evaluate any AI-generated resource and adapt it to their specific teaching contexts. ChatGPT was used as a research tool for assistance with editing and to experiment with making the research narrative clearer. The intention of the paper is to act as a catalyst for a broader conversation about the use of generative AI in science education.","2023-06","2024-12-03 02:35:11","2024-12-03 02:35:11","","444-452","","3","32","","","","","","","","","","English","","","Biological Science Database","2805739342","","","Place: New York Publisher: Springer Nature B.V.","","","https://media.proquest.com/media/hms/PFT/1/ceqOR?_a=ChgyMDI0MTIwMzAxNTU0NTA3NDo1NTI0MTESBTkwMDAyGgpPTkVfU0VBUkNIIg0xMDMuMTIxLjE0OC4yKgcyMDQzNzIxMgoyODA1NzM5MzQyOg1Eb2N1bWVudEltYWdlQgEwUgZPbmxpbmVaAkZUYgNQRlRqCjIwMjMvMDYvMDFyCjIwMjMvMDYvMzB6AIIBJVAtMTAwNzg1Ni0yNTcwNC1DVVNUT01FUi1udWxsLTczMTg4MzGSAQZPbmxpbmXKAUZNb3ppbGxhLzUuMCAoWDExOyBMaW51eCB4ODZfNjQ7IHJ2OjEzMy4wKSBHZWNrby8yMDEwMDEwMSBGaXJlZm94LzEzMy4w0gESU2Nob2xhcmx5IEpvdXJuYWxzmgIHUHJlUGFpZKoCK09TOkVNUy1NZWRpYUxpbmtzU2VydmljZS1nZXRNZWRpYVVybEZvckl0ZW3KAg9BcnRpY2xlfEZlYXR1cmXSAgFZ8gIA%2BgIBWYIDA1dlYooDHENJRDoyMDI0MTIwMzAxNTU0NTA3NDoyNTQxNDc%3D&_s=93Rn0d5jODe4nK4akpBRFO7V2%2BI%3D","Sciences: Comprehensive Works; Teachers; Education; Artificial intelligence; Chatbots; ChatGPT; Generative artificial intelligence; Large language models; Social; Ethical standards; Catalysts; Digital technologies; Environmental impact; Generative artificial intelligence and science education; Science Curriculum; Science education; Science Education; Units of Study","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"EGGA7Y7G","journalArticle","2023","Meskó, Bertalan","The Impact of Multimodal Large Language Models on Health Care’s Future","Journal of Medical Internet Research","","","10.2196/52865","https://www.proquest.com/scholarly-journals/impact-multimodal-large-language-models-on-health/docview/2917629735/se-2?accountid=25704","When large language models (LLMs) were introduced to the public at large in late 2022 with ChatGPT (OpenAI), the interest was unprecedented, with more than 1 billion unique users within 90 days. Until the introduction of Generative Pre-trained Transformer 4 (GPT-4) in March 2023, these LLMs only contained a single mode—text. As medicine is a multimodal discipline, the potential future versions of LLMs that can handle multimodality—meaning that they could interpret and generate not only text but also images, videos, sound, and even comprehensive documents—can be conceptualized as a significant evolution in the field of artificial intelligence (AI). This paper zooms in on the new potential of generative AI, a new form of AI that also includes tools such as LLMs, through the achievement of multimodal inputs of text, images, and speech on health care’s future. We present several futuristic scenarios to illustrate the potential path forward as multimodal LLMs (M-LLMs) could represent the gateway between health care professionals and using AI for medical purposes. It is important to point out, though, that despite the unprecedented potential of generative AI in the form of M-LLMs, the human touch in medicine remains irreplaceable. AI should be seen as a tool that can augment health care professionals rather than replace them. It is also important to consider the human aspects of health care—empathy, understanding, and the doctor-patient relationship—when deploying AI.","2023","2024-12-03 02:35:11","2024-12-03 02:35:11","","","","1","25","","","","","","","","","","English","","","Library Science Database; Publicly Available Content Database","2917629735","","","Place: Toronto Publisher: Gunther Eysenbach MD MPH, Associate Professor","","","https://media.proquest.com/media/hms/PFT/1/07b9X?_a=ChgyMDI0MTIwMzAxNTU0NTA3NDo1NTI0MTESBTkwMDAyGgpPTkVfU0VBUkNIIg0xMDMuMTIxLjE0OC4yKgcyMDMzMTIxMgoyOTE3NjI5NzM1Og1Eb2N1bWVudEltYWdlQgEwUgZPbmxpbmVaAkZUYgNQRlRqCjIwMjMvMDEvMDFyCjIwMjMvMTIvMzF6AIIBJVAtMTAwNjU0NS0yNTcwNC1DVVNUT01FUi1udWxsLTUxNTQ5MDmSAQZPbmxpbmXKAUZNb3ppbGxhLzUuMCAoWDExOyBMaW51eCB4ODZfNjQ7IHJ2OjEzMy4wKSBHZWNrby8yMDEwMDEwMSBGaXJlZm94LzEzMy4w0gESU2Nob2xhcmx5IEpvdXJuYWxzmgIHUHJlUGFpZKoCK09TOkVNUy1NZWRpYUxpbmtzU2VydmljZS1nZXRNZWRpYVVybEZvckl0ZW3KAg9BcnRpY2xlfEZlYXR1cmXSAgFZ8gIA%2BgIBWYIDA1dlYooDHENJRDoyMDI0MTIwMzAxNTU0NTA3NDoyNTQxNDc%3D&_s=gPI4BK0QEUqwBQ8tTBZlK6BSH4c%3D","Public health; Virtual reality; artificial intelligence; Artificial intelligence; Chatbots; ChatGPT; digital health; future; Health care; Language model; Medical personnel; technology; large language models; Large language models; Health informatics; Medicine; LLM; Speech; Medical Sciences--Computer Applications; AI; Simulation; Biomarkers; Empathy; Generative Pre-Trained Transformer; GPT-4; Heart rate; Knee; Multimodal; multimodality; Multimodality; Photographs; Physician patient relationships; Sign language; Sound; Speaking; Surgeons; Surgery; Sutures; Telemedicine","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RAINIXVM","journalArticle","2023","Giannakopoulos, Kostis; Kavadella, Argyro; Salim, Anas Aaqel; Stamatopoulos, Vassilis; Kaklamanos, Eleftherios G","Evaluation of the Performance of Generative AI Large Language Models ChatGPT, Google Bard, and Microsoft Bing Chat in Supporting Evidence-Based Dentistry: Comparative Mixed Methods Study","Journal of Medical Internet Research","","","10.2196/51580","https://www.proquest.com/scholarly-journals/evaluation-performance-generative-ai-large/docview/2917629726/se-2?accountid=25704","Background:The increasing application of generative artificial intelligence large language models (LLMs) in various fields, including dentistry, raises questions about their accuracy.Objective:This study aims to comparatively evaluate the answers provided by 4 LLMs, namely Bard (Google LLC), ChatGPT-3.5 and ChatGPT-4 (OpenAI), and Bing Chat (Microsoft Corp), to clinically relevant questions from the field of dentistry.Methods:The LLMs were queried with 20 open-type, clinical dentistry–related questions from different disciplines, developed by the respective faculty of the School of Dentistry, European University Cyprus. The LLMs’ answers were graded 0 (minimum) to 10 (maximum) points against strong, traditionally collected scientific evidence, such as guidelines and consensus statements, using a rubric, as if they were examination questions posed to students, by 2 experienced faculty members. The scores were statistically compared to identify the best-performing model using the Friedman and Wilcoxon tests. Moreover, the evaluators were asked to provide a qualitative evaluation of the comprehensiveness, scientific accuracy, clarity, and relevance of the LLMs’ answers.Results:Overall, no statistically significant difference was detected between the scores given by the 2 evaluators; therefore, an average score was computed for every LLM. Although ChatGPT-4 statistically outperformed ChatGPT-3.5 (P=.008), Bing Chat (P=.049), and Bard (P=.045), all models occasionally exhibited inaccuracies, generality, outdated content, and a lack of source references. The evaluators noted instances where the LLMs delivered irrelevant information, vague answers, or information that was not fully accurate.Conclusions:This study demonstrates that although LLMs hold promising potential as an aid in the implementation of evidence-based dentistry, their current limitations can lead to potentially harmful health care decisions if not used judiciously. Therefore, these tools should not replace the dentist’s critical thinking and in-depth understanding of the subject matter. Further research, clinical validation, and model improvements are necessary for these tools to be fully integrated into dental practice. Dental practitioners must be aware of the limitations of LLMs, as their imprudent use could potentially impact patient care. Regulatory measures should be established to oversee the use of these evolving technologies.","2023","2024-12-03 02:35:12","2024-12-03 02:35:12","","","","1","25","","","","","","","","","","English","","","Library Science Database; Publicly Available Content Database","2917629726","","","Place: Toronto Publisher: Gunther Eysenbach MD MPH, Associate Professor","","","https://media.proquest.com/media/hms/PFT/1/X6b9X?_a=ChgyMDI0MTIwMzAxNTU0NTA3NDo1NTI0MTESBTkwMDAyGgpPTkVfU0VBUkNIIg0xMDMuMTIxLjE0OC4yKgcyMDMzMTIxMgoyOTE3NjI5NzI2Og1Eb2N1bWVudEltYWdlQgEwUgZPbmxpbmVaAkZUYgNQRlRqCjIwMjMvMDEvMDFyCjIwMjMvMTIvMzF6AIIBJVAtMTAwNjU0NS0yNTcwNC1DVVNUT01FUi1udWxsLTUxNTQ5MDmSAQZPbmxpbmXKAUZNb3ppbGxhLzUuMCAoWDExOyBMaW51eCB4ODZfNjQ7IHJ2OjEzMy4wKSBHZWNrby8yMDEwMDEwMSBGaXJlZm94LzEzMy4w0gESU2Nob2xhcmx5IEpvdXJuYWxzmgIHUHJlUGFpZKoCK09TOkVNUy1NZWRpYUxpbmtzU2VydmljZS1nZXRNZWRpYVVybEZvckl0ZW3KAg9BcnRpY2xlfEZlYXR1cmXSAgFZ8gIA%2BgIBWYIDA1dlYooDHENJRDoyMDI0MTIwMzAxNTU0NTA3NDoyNTQxNDc%3D&_s=uxAGO%2B89O3ZXK0BOOYJAgCjrTYU%3D","Accuracy; Neural networks; Decision making; Patients; artificial intelligence; Artificial intelligence; Chatbots; ChatGPT; Generative artificial intelligence; Health care; Language model; Professionals; Language; large language models; Large language models; Personal information; Critical thinking; Clinical medicine; Privacy; Big Data; Clinical decision making; Natural language; Medical Sciences--Computer Applications; AI; Answers; Bans; clinical decision-making; clinical practice; clinical practice guidelines; Clinical research; dental practice; dental professional; Dentistry; Dentists; evidence-based dentistry; Evidence-based dentistry; generative pretrained transformers; Google Bard; Internet access; Maxillofacial surgery; Medical libraries; Medical screening; Microsoft Bing; Oral hygiene; Orthodontics; Periodontics; Prosthodontics; Quality management; Scientific evidence; Textbooks","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SDC8WR2M","journalArticle","2023","Tavares, Célia; Oliveira, Luciana; Duarte, Pedro; Manuel Moreira da Silva","Artificial Intelligence: A Blessing or a Threat for Language Service Providers in Portugal","Informatics","","","10.3390/informatics10040081","https://www.proquest.com/scholarly-journals/artificial-intelligence-blessing-threat-language/docview/2904855639/se-2?accountid=25704","According to a recent study by OpenAI, Open Research, and the University of Pennsylvania, large language models (LLMs) based on artificial intelligence (AI), such as generative pretrained transformers (GPTs), may have potential implications for the job market, specifically regarding occupations that demand writing or programming skills. This research points out that interpreters and translators are one of the main occupations with greater exposure to AI in the US job market (76.5%), in a trend that is expected to affect other regions of the globe. This article, following a mixed-methods survey-based research approach, provides insights into the awareness and knowledge about AI among Portuguese language service providers (LSPs), specifically regarding neural machine translation (NMT) and large language models (LLM), their actual use and usefulness, as well as their potential influence on work performance and the labour market. The results show that most professionals are unable to identify whether AI and/or automation technologies support the tools that are most used in the profession. The usefulness of AI is essentially low to moderate and the professionals who are less familiar with it and less knowledgeable also demonstrate a lack of trust in it. Two thirds of the sample estimate negative or very negative effects of AI in their profession, expressing the devaluation and replacement of experts, the reduction of income, and the reconfiguration of the career of translator to mere post-editors as major concerns.","2023","2024-12-03 02:35:12","2024-12-03 02:35:12","","81","","4","10","","","","","","","","","","English","","","Publicly Available Content Database","2904855639","","","Place: Basel Publisher: MDPI AG","","","https://media.proquest.com/media/hms/PFT/1/XHOmW?_a=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%3D%3D&_s=lLqyjIjIh9yEhb5%2Bq38gWe%2BIiDY%3D","Neural networks; Social networks; Decision making; artificial intelligence; Artificial intelligence; Chatbots; Language model; Language; large language models; Large language models; Natural language processing; Communication; Machine translation; Voice recognition; Intelligence; Computers--Information Science And Information Theory; Reconfiguration; Labor market; Access to information; interpreters; Interpreters; language service providers; neural machine translation; Neural machine translation; Occupations; Portugal; Profession; Professions; Translations; translators; Translators","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"L3HPNF62","journalArticle","2024","Antero, Unai; Blanco, Francisco; Oñativia, Jon; Sallé, Damien; Sierra, Basilio","Harnessing the Power of Large Language Models for Automated Code Generation and Verification","Robotics","","","10.3390/robotics13090137","https://www.proquest.com/docview/3110685635/abstract/13D00F81FE2A494FPQ/61","The cost landscape in advanced technology systems is shifting dramatically. Traditionally, hardware costs took the spotlight, but now, programming and debugging complexities are gaining prominence. This paper explores this shift and its implications, focusing on reducing the cost of programming complex robot behaviors, using the latest innovations from the Generative AI field, such as large language models (LLMs). We leverage finite state machines (FSMs) and LLMs to streamline robot programming while ensuring functionality. The paper addresses LLM challenges related to content quality, emphasizing a two-fold approach using predefined software blocks and a Supervisory LLM.","2024","2024-12-03 02:37:03","2024-12-03 02:37:03","2024-12-03 02:36:50","137","","9","13","","","","","","","","","","English","© 2024 by the authors.  Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 137 Place: Basel, Switzerland Publisher: MDPI AG","","","","","Artificial intelligence; Automation; Autonomous vehicles; Behavior; cost reduction; Costs; Debugging; Design; Efficiency; fast programming; Finite state machines; FSMs; Generative artificial intelligence; Language; Large language models; LLMs; Programming; Robotics; robots; Robots; safety; skill-based programming; Skills; Software; software challenges; supervision","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"F87YP5KX","journalArticle","2024","Nyqvist, R.; Peltokorpi, A.; Seppänen, O.","Integration of generative artificial intelligence across construction management","IOP Conference Series. Earth and Environmental Science","","17551307","10.1088/1755-1315/1389/1/012011","https://www.proquest.com/docview/3100905279/abstract/13D00F81FE2A494FPQ/62","This research investigates the integration of generative artificial intelligence (AI) solutions in the context of construction management. Generative AI has surpassed human capabilities in various tasks, offering a unique opportunity to address longstanding development barriers in construction management. Previous research has shown how generative AI can help with individual management tasks but a wider overview of how generative AI could help in construction management is still missing. By breaking down construction management into individual tasks and linking each task to available generative AI solutions, the study uncovers multiple innovative approaches to enhance current practices. Consequently, this research introduces the mapping of management tasks and explores a range of practical implications of generative AI on construction management. The research takes a mixed-methods approach, collaborating with industry professionals from Finnish construction companies through focus group discussions (FGD), and questionnaires to gather valuable feedback. The outcome is a pragmatic summary of seven management actions, connected with generative AI solutions, and their quantitative potentiality assessment. The results indicate an overall good potential for integrating generative AI into construction management, with AI-enhanced chair and secretary actions, communication and situational awareness, data analysis and improvement, and risk management receiving the highest perceived potentiality scores.","2024-08","2024-12-03 02:37:03","2024-12-03 02:37:03","2024-12-03 02:36:50","012011","","1","1389","","","","","","","","","","English","Published under licence by IOP Publishing Ltd. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 012011 Place: Bristol, United Kingdom Publisher: IOP Publishing","","","","","Artificial intelligence; Construction; Construction companies; Construction industry; Construction management; Data analysis; Economic; Generative artificial intelligence; Risk management; Risk perception; Situational awareness","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CD66ZMP3","journalArticle","2023","Kuzior, Aleksandra; Sira, Mariya; Brożek, Paulina","Use of Artificial Intelligence in Terms of Open Innovation Process and Management","Sustainability","","","10.3390/su15097205","https://www.proquest.com/docview/2812747824/abstract/13D00F81FE2A494FPQ/63","Organizations see open innovation as important to their future growth strategy. The increasing interest in artificial intelligence has led to a heightened interest in its potential applications in many industries. Many firms invest heavily in artificial intelligence intending to innovate their business models, though managers often lack understanding when trying to implement artificial intelligence in their operations. The data was retrieved from the Scopus database and was analyzed using the R Bibliometrix Biblioshiny and VOSviewer software. The aim of the article is to indicate the consistency in the formation of open innovation processes while applying artificial intelligence and to provide the profile of perspectives on artificial intelligence adoption in innovation management. This paper provides a deeper perception of artificial intelligence and how it can be used to drive open innovation processes and business model innovation within the use of artificial intelligence in open innovation processes and artificial intelligence in the management of open innovation. The authors discuss how recent advances in artificial intelligence have created new opportunities for increased external collaboration. The study found that the rise of artificial intelligence as a key technology for promoting openness and collaboration has ushered in a new era of achievable open innovation. Our presented findings suggest the sequence of open innovation processes powered by artificial intelligence and insights into the artificial intelligence application to innovation management.","2023","2024-12-03 02:37:03","2024-12-03 02:37:03","2024-12-03 02:36:51","7205","","9","15","","","","","","","","","","English","© 2023 by the authors.  Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 7205 Place: Basel, Switzerland Publisher: MDPI AG","","","","","artificial intelligence; Artificial intelligence; Bibliometrics; Business models; Citation indexes; Competitive advantage; Decision making; Growth rate; Innovation; Innovation management; Innovations; Keywords; Literature reviews; management; open innovation; Open innovation; Patents; Research & development--R&D; Search engines; Supply chains; Sustainability; Technological change","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"YX9BKRRL","journalArticle","2024","Jeong, Jaemin; Gil, Daeyoung; Daeho, Kim; Jeong, Jaewook","Current Research and Future Directions for Off-Site Construction through LangChain with a Large Language Model","Buildings","","","10.3390/buildings14082374","https://www.proquest.com/docview/3097871335/abstract/13D00F81FE2A494FPQ/64","Off-site construction is well-known technology that facilitates parallel processes of manufacturing and construction processes. This method enhances productivity while reducing accident, cost, and environmental impact. Many studies have highlighted its benefits, prompting further encouragement of off-site construction. This study consolidates current research and charts future directions by reviewing the existing literature. However, reviewing papers is time-intensive and laborious. Consequently, generative AI models, particularly Large Language Models (LLMs), are increasingly employed for document summarization. Specifically, LangChain influences LLMs through chaining data, demonstrating notable potential for research paper reviews. This study aims to evaluate the well-documented advantages of off-site construction through LangChain integrated with an LLM. It follows a streamlined process from the collection of research papers to conducting network analysis, examining 47 papers to uncover that current research primarily demonstrates off-site construction’s superiority through cutting-edge technologies. Yet, a data deficiency remains a challenge. The findings demonstrate that LangChain can rapidly and effectively summarize research, making it a valuable tool for literature reviews. This study advocates the broader application of LangChain in reviewing research papers, emphasizing its potential to streamline the literature review process and provide clear insights into off-site construction’s evolving landscape.","2024","2024-12-03 02:37:03","2024-12-03 02:37:03","2024-12-03 02:36:52","2374","","8","14","","","","","","","","","","English","© 2024 by the authors.  Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 2374 Place: Basel, Switzerland Publisher: MDPI AG","","","","","ChatGPT; Construction accidents & safety; Construction industry; Construction site accidents; Cost control; Economic; environmental impact; Environmental impact; Generative artificial intelligence; Heavy construction; Keywords; LangChain; large language model; Large language models; Literature reviews; network analysis; Network analysis; off-site construction; Onsite; productivity; Productivity; Reviewing","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"D8PVZ284","journalArticle","2023","Yu, Ping; Xu, Hua; Hu, Xia; Deng, Chao","Leveraging Generative AI and Large Language Models: A Comprehensive Roadmap for Healthcare Integration","Healthcare","","","10.3390/healthcare11202776","https://www.proquest.com/docview/2882423201/abstract/13D00F81FE2A494FPQ/65","Generative artificial intelligence (AI) and large language models (LLMs), exemplified by ChatGPT, are promising for revolutionizing data and information management in healthcare and medicine. However, there is scant literature guiding their integration for non-AI professionals. This study conducts a scoping literature review to address the critical need for guidance on integrating generative AI and LLMs into healthcare and medical practices. It elucidates the distinct mechanisms underpinning these technologies, such as Reinforcement Learning from Human Feedback (RLFH), including few-shot learning and chain-of-thought reasoning, which differentiates them from traditional, rule-based AI systems. It requires an inclusive, collaborative co-design process that engages all pertinent stakeholders, including clinicians and consumers, to achieve these benefits. Although global research is examining both opportunities and challenges, including ethical and legal dimensions, LLMs offer promising advancements in healthcare by enhancing data management, information retrieval, and decision-making processes. Continued innovation in data acquisition, model fine-tuning, prompt strategy development, evaluation, and system implementation is imperative for realizing the full potential of these technologies. Organizations should proactively engage with these technologies to improve healthcare quality, safety, and efficiency, adhering to ethical and legal guidelines for responsible application.","2023","2024-12-03 02:37:03","2024-12-03 02:37:03","2024-12-03 02:36:54","2776","","20","11","","","Leveraging Generative AI and Large Language Models","","","","","","","English","© 2023 by the authors.  Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 2776 Place: Basel, Switzerland Publisher: MDPI AG","","","","","Artificial intelligence; Chatbots; Decision making; Deep learning; Electronic health records; ethics; Ethics; generative AI; generative artificial intelligence; Generative artificial intelligence; healthcare; Keywords; Language; Language model; large language models; Large language models; LLM; Machine learning; medicine; Medicine; Natural language; Research & development--R&D","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"66MBAEFP","journalArticle","2024","Borna, Sahar; Gomez-Cabello, Cesar A.; Pressman, Sophia M.; Haider, Syed Ali; Sehgal, Ajai; Leibovich, Bradley C.; Cole, Dave; Forte, Antonio Jorge","Comparative Analysis of Artificial Intelligence Virtual Assistant and Large Language Models in Post-Operative Care","European Journal of Investigation in Health, Psychology and Education","","21748144","10.3390/ejihpe14050093","https://www.proquest.com/docview/3059506592/abstract/13D00F81FE2A494FPQ/67","In postoperative care, patient education and follow-up are pivotal for enhancing the quality of care and satisfaction. Artificial intelligence virtual assistants (AIVA) and large language models (LLMs) like Google BARD and ChatGPT-4 offer avenues for addressing patient queries using natural language processing (NLP) techniques. However, the accuracy and appropriateness of the information vary across these platforms, necessitating a comparative study to evaluate their efficacy in this domain. We conducted a study comparing AIVA (using Google Dialogflow) with ChatGPT-4 and Google BARD, assessing the accuracy, knowledge gap, and response appropriateness. AIVA demonstrated superior performance, with significantly higher accuracy (mean: 0.9) and lower knowledge gap (mean: 0.1) compared to BARD and ChatGPT-4. Additionally, AIVA’s responses received higher Likert scores for appropriateness. Our findings suggest that specialized AI tools like AIVA are more effective in delivering precise and contextually relevant information for postoperative care compared to general-purpose LLMs. While ChatGPT-4 shows promise, its performance varies, particularly in verbal interactions. This underscores the importance of tailored AI solutions in healthcare, where accuracy and clarity are paramount. Our study highlights the necessity for further research and the development of customized AI solutions to address specific medical contexts and improve patient outcomes.","2024","2024-12-03 02:37:03","2024-12-03 02:37:03","2024-12-03 02:36:54","1413","","5","14","","","","","","","","","","English","© 2024 by the authors.  Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 1413 Place: Basel, Switzerland Publisher: MDPI AG","","","","","Accuracy; artificial intelligence; Artificial intelligence; Bard; Chatbots; ChatGPT; Comparative analysis; large language model; Large language models; machine learning; Medical personnel; natural language processing; Patient education; Patient satisfaction; Plastic surgery; Professionals; Readability","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RD483YI3","journalArticle","2024","Olla, Phillip; Elliott, Lauren; Abumeeiz, Mustafa; Mihelich, Karen; Olson, Joshua","Promptology: Enhancing Human–AI Interaction in Large Language Models","Information","","","10.3390/info15100634","https://www.proquest.com/docview/3120659490/abstract/13D00F81FE2A494FPQ/66","This study investigates the integration of generative AI in higher education and the development of the SPARRO framework, a structured approach to improving human–AI interaction in academic settings. This ethnographic study explores the integration of generative AI in healthcare and nursing education, detailing the development of the SPARRO framework based on observations of student and faculty interactions with AI tools across five courses. The study identifies key challenges such as AI hallucination, mistrust of AI-generated summaries, and the difficulty in formulating effective prompts. The SPARRO framework addresses these challenges, offering a step-by-step guide for planning, prompt design, reviewing, and refining AI outputs. While the framework shows promise in improving AI integration, future research is needed to validate its applicability across other academic disciplines and assess its long-term impact on critical thinking and academic integrity. This study contributes to the growing body of research on AI in education, offering practical solutions for ethically and effectively integrating AI tools in academic settings.","2024","2024-12-03 02:37:03","2024-12-03 02:37:03","2024-12-03 02:36:55","634","","10","15","","","Promptology","","","","","","","English","© 2024 by the authors.  Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 634 Place: Basel, Switzerland Publisher: MDPI AG","","","","","AI curriculum design; AI in education; artificial intelligence; autoethnography; Automation; Chatbots; Design; Education; Engineering; Ethics; GenAI; generative AI; Generative artificial intelligence; Language; Large language models; Linguistics; Medical research; Principles; prompt design; promptology; Skills; SPARRO; Workloads","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"4DBTR78V","journalArticle","2024","Trinkley, Katy E.; An, Ruopeng; Maw, Anna M.; Glasgow, Russell E.; Brownson, Ross C.","Leveraging artificial intelligence to advance implementation science: potential opportunities and cautions","Implementation Science","","","10.1186/s13012-024-01346-y","https://www.proquest.com/docview/2956877296/abstract/13D00F81FE2A494FPQ/68","Background The field of implementation science was developed to address the significant time delay between establishing an evidence-based practice and its widespread use. Although implementation science has contributed much toward bridging this gap, the evidence-to-practice chasm remains a challenge. There are some key aspects of implementation science in which advances are needed, including speed and assessing causality and mechanisms. The increasing availability of artificial intelligence applications offers opportunities to help address specific issues faced by the field of implementation science and expand its methods. Main text This paper discusses the many ways artificial intelligence can address key challenges in applying implementation science methods while also considering potential pitfalls to the use of artificial intelligence. We answer the questions of “why” the field of implementation science should consider artificial intelligence, for “what” (the purpose and methods), and the “what” (consequences and challenges). We describe specific ways artificial intelligence can address implementation science challenges related to (1) speed, (2) sustainability, (3) equity, (4) generalizability, (5) assessing context and context-outcome relationships, and (6) assessing causality and mechanisms. Examples are provided from global health systems, public health, and precision health that illustrate both potential advantages and hazards of integrating artificial intelligence applications into implementation science methods. We conclude by providing recommendations and resources for implementation researchers and practitioners to leverage artificial intelligence in their work responsibly. Conclusions Artificial intelligence holds promise to advance implementation science methods (“why”) and accelerate its goals of closing the evidence-to-practice gap (“purpose”). However, evaluation of artificial intelligence’s potential unintended consequences must be considered and proactively monitored. Given the technical nature of artificial intelligence applications as well as their potential impact on the field, transdisciplinary collaboration is needed and may suggest the need for a subset of implementation scientists cross-trained in both fields to ensure artificial intelligence is used optimally and ethically.","2024","2024-12-03 02:37:03","2024-12-03 02:37:03","2024-12-03 02:36:55","1-15","","","19","","","Leveraging artificial intelligence to advance implementation science","","","","","","","English","© 2024. This work is licensed under http://creativecommons.org/licenses/by/4.0/  (the “License”).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 1-15 Place: London, United Kingdom Publisher: BioMed Central Section: Debate","","","","","Algorithms; Artificial intelligence; Automation; Causality; Chatbots; Evidence-based practice; Health care industry; Implementation; Implementation science; Implementation Science; Learning health systems; Public health; Research; Science; Sustainability; Team science; Translational research","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"TB7RNUY8","journalArticle","2024","Ullah, Ehsan; Parwani, Anil; Baig, Mirza Mansoor; Singh, Rajendra","Challenges and barriers of using large language models (LLM) such as ChatGPT for diagnostic medicine with a focus on digital pathology – a recent scoping review","Diagnostic Pathology","","","10.1186/s13000-024-01464-7","https://www.proquest.com/docview/2956876711/abstract/13D00F81FE2A494FPQ/69","Background The integration of large language models (LLMs) like ChatGPT in diagnostic medicine, with a focus on digital pathology, has garnered significant attention. However, understanding the challenges and barriers associated with the use of LLMs in this context is crucial for their successful implementation. Methods A scoping review was conducted to explore the challenges and barriers of using LLMs, in diagnostic medicine with a focus on digital pathology. A comprehensive search was conducted using electronic databases, including PubMed and Google Scholar, for relevant articles published within the past four years. The selected articles were critically analyzed to identify and summarize the challenges and barriers reported in the literature. Results The scoping review identified several challenges and barriers associated with the use of LLMs in diagnostic medicine. These included limitations in contextual understanding and interpretability, biases in training data, ethical considerations, impact on healthcare professionals, and regulatory concerns. Contextual understanding and interpretability challenges arise due to the lack of true understanding of medical concepts and lack of these models being explicitly trained on medical records selected by trained professionals, and the black-box nature of LLMs. Biases in training data pose a risk of perpetuating disparities and inaccuracies in diagnoses. Ethical considerations include patient privacy, data security, and responsible AI use. The integration of LLMs may impact healthcare professionals’ autonomy and decision-making abilities. Regulatory concerns surround the need for guidelines and frameworks to ensure safe and ethical implementation. Conclusion The scoping review highlights the challenges and barriers of using LLMs in diagnostic medicine with a focus on digital pathology. Understanding these challenges is essential for addressing the limitations and developing strategies to overcome barriers. It is critical for health professionals to be involved in the selection of data and fine tuning of the models. Further research, validation, and collaboration between AI developers, healthcare professionals, and regulatory bodies are necessary to ensure the responsible and effective integration of LLMs in diagnostic medicine.","2024","2024-12-03 02:37:03","2024-12-03 02:37:03","2024-12-03 02:36:55","1-9","","","19","","","","","","","","","","English","© 2024. This work is licensed under http://creativecommons.org/licenses/by/4.0/  (the “License”).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 1-9 Place: London, United Kingdom Publisher: BioMed Central Section: Review","","","","","AI; Algorithms; Artificial intelligence; Bias; Challenges and barriers of using LLMs; Chatbots; ChatGPT; Clinical decision making; Computer security; Data security; Datasets; Decision making; Diagnostic medicine; Diagnostic systems; Digital pathology; Ethics; Generative artificial intelligence; Health care; Health informatics; Information privacy; Integration; Laboratories; Language; Language model; Large language models; Large learning models; LLMs; Medical diagnosis; Medical personnel; Medical record; Medical records; Medicine; ML; Models; Pathology; Quality control; Search engines; Systematic review; Training","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"AH3FZDR5","journalArticle","2023","Shoja, Mohammadali M.; J.M.Monica, Van de Ridder; Vijay, Rajput","The Emerging Role of Generative Artificial Intelligence in Medical Education, Research, and Practice","Cureus","","","10.7759/cureus.40883","https://www.proquest.com/docview/2844019746/abstract/13D00F81FE2A494FPQ/70","Recent breakthroughs in generative artificial intelligence (GAI) and the emergence of transformer-based large language models such as Chat Generative Pre-trained Transformer (ChatGPT) have the potential to transform healthcare education, research, and clinical practice. This article examines the current trends in using GAI models in medicine, outlining their strengths and limitations. It is imperative to develop further consensus-based guidelines to govern the appropriate use of GAI, not only in medical education but also in research, scholarship, and clinical practice.","2023","2024-12-03 02:37:03","2024-12-03 02:37:03","2024-12-03 02:36:56","","","6","15","","","","","","","","","","English","Copyright ©  2023, Shoja et al. This work is published under  https://creativecommons.org/licenses/by/3.0/ (the “License”).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Place: Palo Alto, United States Publisher: Cureus Inc. University: U.S. National Institutes of Health/National Library of Medicine","","","","","Algorithms; artificial intelligence; Artificial intelligence; clinical medicine; Clinical medicine; Cognition & reasoning; Critical thinking; education; Essays; Generative; Generative artificial intelligence; Higher education; information literacy; Language; Language model; Learning; Licensing examinations; Medical education; Medical research; Neural networks; Plagiarism; Practice; Problem solving; Reflective teaching; research; Research; Software; Students; Trends; Writing","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"U97P3XM9","journalArticle","2024","Arshed, Muhammad Asad; Ștefan, Cristian Gherghina; Dewi, Christine; Iqbal, Asma; Mumtaz, Shahzad","Unveiling AI-Generated Financial Text: A Computational Approach Using Natural Language Processing and Generative Artificial Intelligence","Computation","","","10.3390/computation12050101","https://www.proquest.com/docview/3059338087/abstract/13D00F81FE2A494FPQ/71","This study is an in-depth exploration of the nascent field of Natural Language Processing (NLP) and generative Artificial Intelligence (AI), and it concentrates on the vital task of distinguishing between human-generated text and content that has been produced by AI models. Particularly, this research pioneers the identification of financial text derived from AI models such as ChatGPT and paraphrasing tools like QuillBot. While our primary focus is on financial content, we have also pinpointed texts generated by paragraph rewriting tools and utilized ChatGPT for various contexts this multiclass identification was missing in previous studies. In this paper, we use a comprehensive feature extraction methodology that combines TF–IDF with Word2Vec, along with individual feature extraction methods. Importantly, combining a Random Forest model with Word2Vec results in impressive outcomes. Moreover, this study investigates the significance of the window size parameters in the Word2Vec approach, revealing that a window size of one produces outstanding scores across various metrics, including accuracy, precision, recall and the F1 measure, all reaching a notable value of 0.74. In addition to this, our developed model performs well in classification, attaining AUC values of 0.94 for the ‘GPT’ class; 0.77 for the ‘Quil’ class; and 0.89 for the ‘Real’ class. We also achieved an accuracy of 0.72, precision of 0.71, recall of 0.72, and F1 of 0.71 for our extended prepared dataset. This study contributes significantly to the evolving landscape of AI text identification, providing valuable insights and promising directions for future research.","2024","2024-12-03 02:37:03","2024-12-03 02:37:03","2024-12-03 02:36:57","101","","5","12","","","Unveiling AI-Generated Financial Text","","","","","","","English","© 2024 by the authors.  Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 101 Place: Basel, Switzerland Publisher: MDPI AG","","","","","Accuracy; Algorithms; Automation; Chatbots; ChatGPT; Communication; Computer forensics; Computers; Datasets; Education; False information; Feature extraction; Forensic sciences; generative artificial intelligence; Generative artificial intelligence; Language; machine learning; Machine learning; natural language processing; Natural language processing; QuillBot; Recall; Regression analysis; Social networks; Support vector machines; text identification","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XBPGTFRJ","journalArticle","2024","de Curtò, J.; de Zarzà, I.; Roig, Gemma; Calafate, Carlos T.","Large Language Model-Informed X-ray Photoelectron Spectroscopy Data Analysis","Signals","","26246120","10.3390/signals5020010","https://www.proquest.com/docview/3072667508/abstract/13D00F81FE2A494FPQ/72","X-ray photoelectron spectroscopy (XPS) remains a fundamental technique in materials science, offering invaluable insights into the chemical states and electronic structure of a material. However, the interpretation of XPS spectra can be complex, requiring deep expertise and often sophisticated curve-fitting methods. In this study, we present a novel approach to the analysis of XPS data, integrating the utilization of large language models (LLMs), specifically OpenAI’s GPT-3.5/4 Turbo to provide insightful guidance during the data analysis process. Working in the framework of the CIRCE-NAPP beamline at the CELLS ALBA Synchrotron facility where data are obtained using ambient pressure X-ray photoelectron spectroscopy (APXPS), we implement robust curve-fitting techniques on APXPS spectra, highlighting complex cases including overlapping peaks, diverse chemical states, and noise presence. Post curve fitting, we engage the LLM to facilitate the interpretation of the fitted parameters, leaning on its extensive training data to simulate an interaction corresponding to expert consultation. The manuscript presents also a real use case utilizing GPT-4 and Meta’s LLaMA-2 and describes the integration of the functionality into the TANGO control system. Our methodology not only offers a fresh perspective on XPS data analysis, but also introduces a new dimension of artificial intelligence (AI) integration into scientific research. It showcases the power of LLMs in enhancing the interpretative process, particularly in scenarios wherein expert knowledge may not be immediately available. Despite the inherent limitations of LLMs, their potential in the realm of materials science research is promising, opening doors to a future wherein AI assists in the transformation of raw data into meaningful scientific knowledge.","2024","2024-12-03 02:37:03","2024-12-03 02:37:03","2024-12-03 02:36:58","181","","2","5","","","","","","","","","","English","© 2024 by the authors.  Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 181 Place: Tokyo, Switzerland Publisher: MDPI AG","","","","","APXPS; Artificial intelligence; Chemical elements; curve fitting; Data analysis; Energy; large language models; materials science; Materials science; Research methodology; Software; synchrotron; XPS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CEMMHQZL","journalArticle","2023","Meskó, Bertalan; Topol, Eric J.","The imperative for regulatory oversight of large language models (or generative AI) in healthcare","NPJ Digital Medicine","","","10.1038/s41746-023-00873-0","https://www.proquest.com/docview/2833810415/abstract/13D00F81FE2A494FPQ/73","The rapid advancements in artificial intelligence (AI) have led to the development of sophisticated large language models (LLMs) such as GPT-4 and Bard. The potential implementation of LLMs in healthcare settings has already garnered considerable attention because of their diverse applications that include facilitating clinical documentation, obtaining insurance pre-authorization, summarizing research papers, or working as a chatbot to answer questions for patients about their specific data and concerns. While offering transformative potential, LLMs warrant a very cautious approach since these models are trained differently from AI-based medical technologies that are regulated already, especially within the critical context of caring for patients. The newest version, GPT-4, that was released in March, 2023, brings the potentials of this technology to support multiple medical tasks; and risks from mishandling results it provides to varying reliability to a new level. Besides being an advanced LLM, it will be able to read texts on images and analyze the context of those images. The regulation of GPT-4 and generative AI in medicine and healthcare without damaging their exciting and transformative potential is a timely and critical challenge to ensure safety, maintain ethical standards, and protect patient privacy. We argue that regulatory oversight should assure medical professionals and patients can use LLMs without causing harm or compromising their data or privacy. This paper summarizes our practical recommendations for what we can expect from regulators to bring this vision to reality.","2023-12","2024-12-03 02:37:03","2024-12-03 02:37:03","2024-12-03 02:36:58","120","","1","6","","","","","","","","","","English","© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 120 Place: London, United States Publisher: Nature Publishing Group","","","","","Artificial intelligence; Chatbots; Digital technology; Generative artificial intelligence; Health care; Language; Language model; Large language models; Medical technology; Privacy; Regulation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"AVC9FENH","journalArticle","2023","Clusmann, Jan; Kolbinger, Fiona R.; Muti, Hannah Sophie; Carrero, Zunamys I.; Eckardt, Jan-Niklas; Laleh, Narmin Ghaffari; Löffler, Chiara Maria Lavinia; Schwarzkopf, Sophie-Caroline; Unger, Michaela; Veldhuizen, Gregory P.; Wagner, Sophia J.; Kather, Jakob Nikolas","The future landscape of large language models in medicine","Communications Medicine","","","10.1038/s43856-023-00370-1","https://www.proquest.com/docview/2875226571/abstract/13D00F81FE2A494FPQ/74","Large language models (LLMs) are artificial intelligence (AI) tools specifically trained to process and generate text. LLMs attracted substantial public attention after OpenAI’s ChatGPT was made publicly available in November 2022. LLMs can often answer questions, summarize, paraphrase and translate text on a level that is nearly indistinguishable from human capabilities. The possibility to actively interact with models like ChatGPT makes LLMs attractive tools in various fields, including medicine. While these models have the potential to democratize medical knowledge and facilitate access to healthcare, they could equally distribute misinformation and exacerbate scientific misconduct due to a lack of accountability and transparency. In this article, we provide a systematic and comprehensive overview of the potentials and limitations of LLMs in clinical practice, medical research and medical education. Clusmann et al. describe how large language models such as ChatGPT could be used in medical practice, research and education. These models could democratize medical knowledge and facilitate access to healthcare, but there are also potential limitations to be considered.","2023-12","2024-12-03 02:37:03","2024-12-03 02:37:03","2024-12-03 02:36:59","141","","1","3","","","","","","","","","","English","© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 141 Place: London, Netherlands Publisher: Springer Nature B.V.","","","","","Artificial intelligence; Chatbots; Clinical Practice; Communication; Feedback; Knowledge; Language; Language model; Large language models; Licenses; Medical education; Medical research; Medicine; Model; Natural language processing; Neural networks; Patient satisfaction; Semantics; Software","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"TJB62AXR","journalArticle","2024","Gomez-Cabello, Cesar A.; Borna, Sahar; Pressman, Sophia M.; Haider, Syed Ali; Sehgal, Ajai; Leibovich, Bradley C.; Forte, Antonio J.","Artificial Intelligence in Postoperative Care: Assessing Large Language Models for Patient Recommendations in Plastic Surgery","Healthcare","","","10.3390/healthcare12111083","https://www.proquest.com/docview/3067416288/abstract/13D00F81FE2A494FPQ/75","Since their release, the medical community has been actively exploring large language models’ (LLMs) capabilities, which show promise in providing accurate medical knowledge. One potential application is as a patient resource. This study analyzes and compares the ability of the currently available LLMs, ChatGPT-3.5, GPT-4, and Gemini, to provide postoperative care recommendations to plastic surgery patients. We presented each model with 32 questions addressing common patient concerns after surgical cosmetic procedures and evaluated the medical accuracy, readability, understandability, and actionability of the models’ responses. The three LLMs provided equally accurate information, with GPT-3.5 averaging the highest on the Likert scale (LS) (4.18 ± 0.93) (p = 0.849), while Gemini provided significantly more readable (p = 0.001) and understandable responses (p = 0.014; p = 0.001). There was no difference in the actionability of the models’ responses (p = 0.830). Although LLMs have shown their potential as adjunctive tools in postoperative patient care, further refinement and research are imperative to enable their evolution into comprehensive standalone resources.","2024","2024-12-03 02:37:03","2024-12-03 02:37:03","2024-12-03 02:37:00","1083","","11","12","","","Artificial Intelligence in Postoperative Care","","","","","","","English","© 2024 by the authors.  Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 1083 Place: Basel, Switzerland Publisher: MDPI AG","","","","","Access to information; Accuracy; artificial intelligence; Artificial intelligence; Audiovisual materials; Chatbots; Cosmetic surgery; Health education; Health literacy; Internet; Language; large language models; Large language models; patient resource; patient satisfaction; Patient satisfaction; patient-centered outcomes; plastic surgery; Plastic surgery; postoperative care; Postoperative period; Readability; Surgeons","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"GTK5FTGY","journalArticle","2024","Zafar, Ahtsham; Parthasarathy, Venkatesh Balavadhani; Van, Chan Le; Shahid, Saad; Khan, Aafaq Iqbal; Shahid, Arsalan","Building Trust in Conversational AI: A Review and Solution Architecture Using Large Language Models and Knowledge Graphs","Big Data and Cognitive Computing","","","10.3390/bdcc8060070","https://www.proquest.com/docview/3072266002/abstract/13D00F81FE2A494FPQ/76","Conversational AI systems have emerged as key enablers of human-like interactions across diverse sectors. Nevertheless, the balance between linguistic nuance and factual accuracy has proven elusive. In this paper, we first introduce LLMXplorer, a comprehensive tool that provides an in-depth review of over 205 large language models (LLMs), elucidating their practical implications, ranging from social and ethical to regulatory, as well as their applicability across industries. Building on this foundation, we propose a novel functional architecture that seamlessly integrates the structured dynamics of knowledge graphs with the linguistic capabilities of LLMs. Validated using real-world AI news data, our architecture adeptly blends linguistic sophistication with factual rigor and further strengthens data security through role-based access control. This research provides insights into the evolving landscape of conversational AI, emphasizing the imperative for systems that are efficient, transparent, and trustworthy.","2024","2024-12-03 02:37:03","2024-12-03 02:37:03","2024-12-03 02:37:01","70","","6","8","","","Building Trust in Conversational AI","","","","","","","English","© 2024 by the authors.  Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 70 Place: Basel, Switzerland Publisher: MDPI AG","","","","","Access control; Architecture; Chatbots; Conversational artificial intelligence; Datasets; Ethics; Generative artificial intelligence; Graphs; knowledge graphs; Knowledge representation; Language; Language modeling; large language models; Large language models; Linguistics; LLMXplorer; Natural language; Neo4j; Privacy; role-based access control; Technological change; trustworthiness","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KAI964FC","journalArticle","2024","Alvero, A. J.; Lee, Jinsook; Regla-Vargas, Alejandra; Kizilcec, René F.; Joachims, Thorsten; Antonio, Anthony Lising","Large language models, social demography, and hegemony: comparing authorship in human and synthetic text","Journal of Big Data","","","10.1186/s40537-024-00986-7","https://www.proquest.com/docview/3110557674/abstract/13D00F81FE2A494FPQ/77","Large language models have become popular over a short period of time because they can generate text that resembles human writing across various domains and tasks. The popularity and breadth of use also put this technology in the position to fundamentally reshape how written language is perceived and evaluated. It is also the case that spoken language has long played a role in maintaining power and hegemony in society, especially through ideas of social identity and “correct” forms of language. But as human communication becomes even more reliant on text and writing, it is important to understand how these processes might shift and who is more likely to see their writing styles reflected back at them through modern AI. We therefore ask the following question: who does generative AI write like? To answer this, we compare writing style features in over 150,000 college admissions essays submitted to a large public university system and an engineering program at an elite private university with a corpus of over 25,000 essays generated with GPT-3.5 and GPT-4 to the same writing prompts. We find that human-authored essays exhibit more variability across various individual writing style features (e.g., verb usage) than AI-generated essays. Overall, we find that the AI-generated essays are most similar to essays authored by students who are males with higher levels of social privilege. These findings demonstrate critical misalignments between human and AI authorship characteristics, which may affect the evaluation of writing and calls for research on control strategies to improve alignment.","2024-09","2024-12-03 02:37:03","2024-12-03 02:37:03","2024-12-03 02:37:01","138","","1","11","","","Large language models, social demography, and hegemony","","","","","","","English","© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Num Pages: 138 Place: Heidelberg, Netherlands Publisher: Springer Nature B.V.","","","","","Authorship; Demography; Generative artificial intelligence; Hegemony; Human communication; Language; Large language models; Technology assessment; Writing","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RJWIWFCR","journalArticle","2024","Pleșa, Teodora Nicoleta; Miserciu, Iulian; Popescu, Constanța","The Impact of Artificial Intelligence Use in the Public Sector","Economics, Management and Financial Markets","","18423191","10.22381/emfm19220242","https://www.proquest.com/docview/3112928797/abstract/13D00F81FE2A494FPQ/78","The development of artificial intelligence (AI) represents one of the greatest discoveries of the current century, being called the fifth industrial revolution. The use of AI-based tools within public sector entities is still at the beginning, but the experience reported to date has identified both advantages and risks. The article aims to analyze the impact of the use of artificial intelligence in the public sector, as well as its evaluation on the activity in this frame. The research methodology is based on the answer to the question ""What is the impact of the use of tools based on artificial intelligence on the activity of entities from public sector?"" by which the authors aim to evaluate both the advantages and the risks of using such tools. At the same time, the paper addresses the use of tools based on artificial intelligence from the perspective of the public audit carried out by the supreme audit institutions. The main conclusions of the paper refer to the current state of the use of artificial intelligence in the public sector. The authors' recommendations can be the basis of future research in this regard.","2024-06","2024-12-03 02:37:03","2024-12-03 02:37:03","2024-12-03 02:37:02","17-31","","2","19","","","","","","","","","","English","Copyright Addleton Academic Publishers Jun 2024","","","","ProQuest","","Num Pages: 17-31 Place: Woodside, United States Publisher: Addleton Academic Publishers","","","","","Algorithms; Artificial; Artificial intelligence; Audits; COVID-19; Data processing; Government purchasing; Industrial Revolution; Machine learning; Pandemics; Public sector; Research methodology","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"BFVQL5SU","journalArticle","2023","Murphy, Lonergan Rebecca; Curry, Jake; Kallpana, Dhas; Simmons, Benno I.","Stratified Evaluation of GPT’s Question Answering in Surgery Reveals Artificial Intelligence (AI) Knowledge Gaps","Cureus","","","10.7759/cureus.48788","https://www.proquest.com/docview/2908050772/abstract/13D00F81FE2A494FPQ/79","Large language models (LLMs) have broad potential applications in medicine, such as aiding with education, providing reassurance to patients, and supporting clinical decision-making. However, there is a notable gap in understanding their applicability and performance in the surgical domain and how their performance varies across specialties. This paper aims to evaluate the performance of LLMs in answering surgical questions relevant to clinical practice and to assess how this performance varies across different surgical specialties. We used the MedMCQA dataset, a large-scale multi-choice question-answer (MCQA) dataset consisting of clinical questions across all areas of medicine. We extracted the relevant 23,035 surgical questions and submitted them to the popular LLMs Generative Pre-trained Transformers (GPT)-3.5 and GPT-4 (OpenAI OpCo, LLC, San Francisco, CA). Generative Pre-trained Transformer is a large language model that can generate human-like text by predicting subsequent words in a sentence based on the context of the words that come before it. It is pre-trained on a diverse range of texts and can perform a variety of tasks, such as answering questions, without needing task-specific training. The question-answering accuracy of GPT was calculated and compared between the two models and across surgical specialties. Both GPT-3.5 and GPT-4 achieved accuracies of 53.3% and 64.4%, respectively, on surgical questions, showing a statistically significant difference in performance. When compared to their performance on the full MedMCQA dataset, the two models performed differently: GPT-4 performed worse on surgical questions than on the dataset as a whole, while GPT-3.5 showed the opposite pattern. Significant variations in accuracy were also observed across different surgical specialties, with strong performances in anatomy, vascular, and paediatric surgery and worse performances in orthopaedics, ENT, and neurosurgery. Large language models exhibit promising capabilities in addressing surgical questions, although the variability in their performance between specialties cannot be ignored. The lower performance of the latest GPT-4 model on surgical questions relative to questions across all medicine highlights the need for targeted improvements and continuous updates to ensure relevance and accuracy in surgical applications. Further research and continuous monitoring of LLM performance in surgical domains are crucial to fully harnessing their potential and mitigating the risks of misinformation.","2023","2024-12-03 02:37:03","2024-12-03 02:37:03","2024-12-03 02:37:03","","","11","15","","","","","","","","","","English","Copyright ©  2023, Murphy Lonergan et al. This work is published under  https://creativecommons.org/licenses/by/4.0/ (the “License”).  Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.","","","","ProQuest","","Place: Palo Alto, United States Publisher: Cureus Inc. University: U.S. National Institutes of Health/National Library of Medicine","","","","","Accuracy; Answer; Application programming interface; Artificial intelligence; artificial intelligence in medicine; artificial intelligence in surgery; Chatbots; chatgpt; Clinical medicine; Datasets; Decision making; Knowledge; Language model; large language models; Orthopedics; Question answering; Surgeons; surgery; Surgery","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JZ8YVGGP","journalArticle","2024","Holl, Cody","The content intelligence: an argument against the lethality of artificial intelligence","Discover Artificial Intelligence","","","10.1007/s44163-024-00112-9","https://www.proquest.com/scholarly-journals/content-intelligence-argument-against-lethality/docview/2930347053/se-2?accountid=25704","This paper navigates artificial intelligence’s recent advancements and increasing media attention. A notable focus is placed on Eliezer Yudkowsky, a leading figure within the domain of artificial intelligence alignment, who aims to bridge the understanding gap between public perceptions and rationalist viewpoints on artificial intelligence technology. This focus analyzes his predicted course of action for artificial intelligence outlined within his unpublished paper AGI Ruin: A List of Lethalities. This is achieved by attempting to understand the concept of intelligence itself and identifying a reasonable working definition of that concept. The concept of intelligence is then applied to contemporary artificial intelligence capabilities and developments to understand its applicability to the technologies. This paper finds contemporary artificial intelligence systems are, to some extent, intelligent. However, it argues that both weak and strong artificial intelligence systems, devoid of human-defined goals, would not inherently pose existential threats to humanity, challenging the notions of artificial intelligence alignment, bringing into question the validity of Nick Bostrom’s Orthogonality Thesis. Furthermore, the possibility of artificial life created through the method of assembling various modules each emulating a separate mind function is discussed.","2024-12","2024-12-03 02:39:46","2024-12-03 02:39:46","","13","","1","4","","","","","","","","","","English","","","Publicly Available Content Database","2930347053","","","Place: Istanbul Publisher: Springer Nature B.V.","","","https://media.proquest.com/media/hms/PFT/1/WB5QX?_a=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%3D%3D&_s=P%2FT9vgFmsFN6CuHzpUluTg639j8%3D","Artificial intelligence; Natural language; Computers--Artificial Intelligence; Intelligence; Experiments; Alignment; Bacteria; Lethality; Orthogonality; Personhood; Proteins","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ST4FMF5A","journalArticle","2024","Wong Io Nam; Monteiro, Olivia; Baptista-Hon, Daniel T; Wang, Kai; Lu Wenyang; Sun, Zhuo; Nie Sheng; Yin Yun","Leveraging foundation and large language models in medical artificial intelligence","Chinese Medical Journal","","03666999","10.1097/CM9.0000000000003302","https://www.proquest.com/scholarly-journals/leveraging-foundation-large-language-models/docview/3123918156/se-2?accountid=25704","Recent advancements in the field of medical artificial intelligence (AI) have led to the widespread adoption of foundational and large language models. This review paper explores their applications within medical AI, introducing a novel classification framework that categorizes them as disease-specific, general-domain, and multi-modal models. The paper also addresses key challenges such as data acquisition and augmentation, including issues related to data volume, annotation, multi-modal fusion, and privacy concerns. Additionally, it discusses the evaluation, validation, limitations, and regulation of medical AI models, emphasizing their transformative potential in healthcare. The importance of continuous improvement, data security, standardized evaluations, and collaborative approaches is highlighted to ensure the responsible and effective integration of AI into clinical applications.","2024-11","2024-12-03 02:39:46","2024-12-03 02:39:46","","2529-2539","","21","137","","","","","","","","","","English","","","Publicly Available Content Database","3123918156","","","Place: Baltimore Publisher: Lippincott Williams & Wilkins Ovid Technologies","","","https://media.proquest.com/media/hms/PFT/1/M8yQa?_a=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%3D%3D&_s=QCrfTCDpXOguRmTm5EH778iHJzI%3D","Artificial intelligence; ChatGPT; Datasets; Large language models; Clinical decision making; Telemedicine; Data security; Pathology; Data annotation; Data privacy; Disease-specific model; Foundation model; General-domain model; Hallucination; Large language model; Medical AI; Medical imaging; Medical Sciences; Multi-modal; Segment-anchoring model; United States--US","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ZZ23Z7JV","journalArticle","2024","Aliyev, Alovsat Garaja; Shahverdiyeva, Roza Ordukhan","Some Problems of the Formation of the New Generation Digital Economy based on Artificial İntelligence Technologies","Informatica Economica","","1453-1305","10.24818/issn14531305/28.3.2024.04","https://www.proquest.com/scholarly-journals/some-problems-formation-new-generation-digital/docview/3118075925/se-2?accountid=25704","This paper is dedicated to defining the problems of forming the New generation regional and National digital economy based on artificial intelligence methods and technologies, studying their conceptual solution mechanisms, and analyzing the infrastructure-institutional features. The relevance of the formation of the New-generation digital economy in the world economic system is justified. The necessity and importance of preparing a New-generation digital economy strategy in the regional and national aspects was noted. It has been shown that the next-generation digital economy Strategy includes artificial intelligence, Big Data, the Internet of Things, etc. such as other directions considered necessary for the development of the digital economy. The conceptual directions of the formation of the New- generation National digital economy and its sectors have been determined. An overview analysis of relevant scientific research works was conducted and the state of problem solving was studied. The features of the formation of the economy of artificial intelligence are analyzed, and the functional principles of its formation are given schematically. Based on scientific analyzes and generalizations, the components, types, and areas of artificial intelligence were developed. The effects of the application of the latest ICT and artificial intelligence technologies on the socio-economic process and the environment were analyzed. The socio-economic effects of digital transformations, as well as the application of digital artificial intelligence technologies in the management of the main economic processes, were investigated. The features of the formation of economic sectors on artificial intelligence technologies have been investigated. Forecasts on the development prospects of the artificial intelligence market have been explained. Some common features of the New-generation digital economy and the problems of applying artificial intelligence-based technologies in its formation are analyzed. New-generation digital economy sectors formed based on artificial intelligence have been identified, and relevant recommendations have been made for its transition to the stage of innovation-based development on the Industry 4.0 platform.","2024","2024-12-03 02:39:46","2024-12-03 02:39:46","","49-64","","3","28","","","","","","","","","","English","","","Publicly Available Content Database","3118075925","","","Place: Bucharest Publisher: INFOREC Association","","","https://media.proquest.com/media/hms/PFT/1/pg5Ja?_a=ChgyMDI0MTIwMzAxNTU0NTA3NDo1NTI0MTESBTkwMDAyGgpPTkVfU0VBUkNIIg0xMDMuMTIxLjE0OC4yKgU1NTEwODIKMzExODA3NTkyNToNRG9jdW1lbnRJbWFnZUIBMFIGT25saW5lWgJGVGIDUEZUagoyMDI0LzA3LzAxcgoyMDI0LzA5LzMwegCCASFQLTEwMDkyNDAtMjU3MDQtRlJFRS1udWxsLTY0NTY5MzmSAQZPbmxpbmXKAUZNb3ppbGxhLzUuMCAoWDExOyBMaW51eCB4ODZfNjQ7IHJ2OjEzMy4wKSBHZWNrby8yMDEwMDEwMSBGaXJlZm94LzEzMy4w0gESU2Nob2xhcmx5IEpvdXJuYWxzmgIHUHJlUGFpZKoCK09TOkVNUy1NZWRpYUxpbmtzU2VydmljZS1nZXRNZWRpYVVybEZvckl0ZW3KAg9BcnRpY2xlfEZlYXR1cmXSAgFZ8gIA%2BgIBWYIDA1dlYooDHENJRDoyMDI0MTIwMzAxNTU0NTA3NDoyNTQxNDc%3D&_s=jGT9NNm%2Fgs9U1VtAHtbdIbu5B94%3D","Deep learning; Internet of Things; Industry 4.0; Efficiency; Innovations; Computers; Digital technology; Sustainable development; Artificial intelligence; Economic; Cybersecurity; Problem solving; Digital economy; Economic development; Economic growth; Economic sectors; Economics; Global economy; Industrial applications; Industrial development; Regional development; Socioeconomics","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"BEI4866W","journalArticle","2024","Zhang, Jinglu; Wang, Linna","Research on Ai Technology of Intelligent Information Visualization Based on Rtos System Service","Journal of Physics: Conference Series","","17426588","10.1088/1742-6596/2717/1/012018","https://www.proquest.com/scholarly-journals/research-on-ai-technology-intelligent-information/docview/3006242589/se-2?accountid=25704","In order to improve the visual interaction ability of intelligent information, an intelligent information visualization artificial intelligence technology based on RTOS system service is proposed in this paper. Based on embedded integrated control, this paper constructs the system design scheme, client and host system of intelligent information visualization artificial intelligence RTOS service system. The information monitoring module, artificial intelligence programmable logic control module, artificial intelligence processing module and human-computer interaction module of intelligent information visualization artificial intelligence RTOS service system are established to realize the label identification and identification in the process of intelligent information visualization artificial intelligence RTOS service system based on unified resource locator sipur, The communication system model of intelligent information visualization artificial intelligence RTOS service system under the transmission mode of multimedia communication channel is established to realize the automatic information processing of intelligent information visualization RTOS system. The test results show that the design has good stability, strong intelligent information visualization and interaction ability, low memory and time cost.","2024-03","2024-12-03 02:39:46","2024-12-03 02:39:46","","012018","","1","2717","","","","","","","","","","English","","","Publicly Available Content Database","3006242589","","","Place: Bristol Publisher: IOP Publishing","","","https://media.proquest.com/media/hms/PFT/1/dQ4kX?_a=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%3D%3D&_s=NlSvPY2KqgesOBvx59GkhUPVFlE%3D","Data processing; Physics; Systems design; Artificial intelligence; Visualization; Modules; Communications systems; Multimedia; Multimedia communications; Scientific visualization","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JTSLHLSU","journalArticle","2024","Nedungadi, Prema; Kai-Yu, Tang; Raman, Raghu","The Transformative Power of Generative Artificial Intelligence for Achieving the Sustainable Development Goal of Quality Education","Sustainability","","","10.3390/su16229779","https://www.proquest.com/scholarly-journals/transformative-power-generative-artificial/docview/3133364332/se-2?accountid=25704","This study explored the transformative potential of generative artificial intelligence (GAI) for achieving the UN Sustainable Development Goal on Quality Education (SDG4), emphasizing its interconnectedness with the other SDGs. A proprietary algorithm and cocitation network analysis were used to identify and analyze the network of SDG features in GAI research publications (n = 1501). By examining GAI’s implications for ten SDG4 targets, the findings advocate for a collaborative, ethical approach to integrating GAI, emphasizing policy and practice developments that ensure that technological advancements align with the overarching goals of SDG4. The results highlight the multifaceted impact of GAI on the SDGs. First, this paper outlines a framework that leverages GAI to enhance educational equity, quality, and lifelong learning opportunities. By highlighting the synergy between GAI and the SDGs, such as reducing inequalities (SDG10) and promoting gender equality (SDG5), this study underscores the need for an integrated approach to utilizing GAI. Moreover, it advocates for personalized learning, equitable technology access, adherence to ethical AI principles, and fostering global citizenship, proposing a strategic alignment of GAI applications with the broader SDG agenda. Next, the results highlight that GAI introduces significant challenges, including ethical concerns, data privacy, and the risk of exacerbating the digital divide. Overall, our findings underscore the critical role of policy reforms and innovative practices in navigating the challenges and harnessing the opportunities presented by GAI in education, thereby contributing to a comprehensive discourse on technology’s role in advancing global education and sustainable development.","2024","2024-12-03 02:39:46","2024-12-03 02:39:46","","9779","","22","16","","","","","","","","","","English","","","Coronavirus Research Database; Publicly Available Content Database","3133364332","","","Place: Basel Publisher: MDPI AG","","","https://media.proquest.com/media/hms/PFT/1/Jvqia?_a=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%3D%3D&_s=Zyvp4LUg0zF4tozXQCV9vqLXEIY%3D","Collaboration; Teachers; Pandemics; Higher education; Students; Environmental Studies; Sustainable development; artificial intelligence; Chatbots; ChatGPT; Generative artificial intelligence; generative AI; Language; Ethics; Large language models; Teaching; Pedagogy; Access to education; developing countries; Digital divide; education policy; gender; Gender equity; inclusion; literacy; Personalized learning; quality education; Quality of education; sustainable development goal","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"D3QIJF8X","journalArticle","2024","Lee, Christine; Mohebbi, Matthew; O'Callaghan, Erin; Winsberg, Mirène","Large Language Models Versus Expert Clinicians in Crisis Prediction Among Telemental Health Patients: Comparative Study","JMIR Mental Health","","","10.2196/58129","https://www.proquest.com/scholarly-journals/large-language-models-versus-expert-clinicians/docview/3091270025/se-2?accountid=25704","Background:Due to recent advances in artificial intelligence, large language models (LLMs) have emerged as a powerful tool for a variety of language-related tasks, including sentiment analysis, and summarization of provider-patient interactions. However, there is limited research on these models in the area of crisis prediction.Objective:This study aimed to evaluate the performance of LLMs, specifically OpenAI’s generative pretrained transformer 4 (GPT-4), in predicting current and future mental health crisis episodes using patient-provided information at intake among users of a national telemental health platform.Methods:Deidentified patient-provided data were pulled from specific intake questions of the Brightside telehealth platform, including the chief complaint, for 140 patients who indicated suicidal ideation (SI), and another 120 patients who later indicated SI with a plan during the course of treatment. Similar data were pulled for 200 randomly selected patients, treated during the same time period, who never endorsed SI. In total, 6 senior Brightside clinicians (3 psychologists and 3 psychiatrists) were shown patients’ self-reported chief complaint and self-reported suicide attempt history but were blinded to the future course of treatment and other reported symptoms, including SI. They were asked a simple yes or no question regarding their prediction of endorsement of SI with plan, along with their confidence level about the prediction. GPT-4 was provided with similar information and asked to answer the same questions, enabling us to directly compare the performance of artificial intelligence and clinicians.Results:Overall, the clinicians’ average precision (0.7) was higher than that of GPT-4 (0.6) in identifying the SI with plan at intake (n=140) versus no SI (n=200) when using the chief complaint alone, while sensitivity was higher for the GPT-4 (0.62) than the clinicians’ average (0.53). The addition of suicide attempt history increased the clinicians’ average sensitivity (0.59) and precision (0.77) while increasing the GPT-4 sensitivity (0.59) but decreasing the GPT-4 precision (0.54). Performance decreased comparatively when predicting future SI with plan (n=120) versus no SI (n=200) with a chief complaint only for the clinicians (average sensitivity=0.4; average precision=0.59) and the GPT-4 (sensitivity=0.46; precision=0.48). The addition of suicide attempt history increased performance comparatively for the clinicians (average sensitivity=0.46; average precision=0.69) and the GPT-4 (sensitivity=0.74; precision=0.48).Conclusions:GPT-4, with a simple prompt design, produced results on some metrics that approached those of a trained clinician. Additional work must be done before such a model can be piloted in a clinical setting. The model should undergo safety checks for bias, given evidence that LLMs can perpetuate the biases of the underlying data on which they are trained. We believe that LLMs hold promise for augmenting the identification of higher-risk patients at intake and potentially delivering more timely care to patients.","2024","2024-12-03 02:39:46","2024-12-03 02:39:46","","","","","11","","","","","","","","","","English","","","Public Health Database; Publicly Available Content Database","3091270025","","","Place: Toronto Publisher: JMIR Publications","","","https://media.proquest.com/media/hms/PFT/1/p62TZ?_a=ChgyMDI0MTIwMzAxNTU0NTA3NDo1NTI0MTESBTkwMDAyGgpPTkVfU0VBUkNIIg0xMDMuMTIxLjE0OC4yKgc0OTk3MTE4MgozMDkxMjcwMDI1Og1Eb2N1bWVudEltYWdlQgEwUgZPbmxpbmVaAkZUYgNQRlRqCjIwMjQvMDEvMDFyCjIwMjQvMTIvMzF6AIIBJVAtMTAwNzYxNy0yNTcwNC1DVVNUT01FUi1udWxsLTEwNDg4NDaSAQZPbmxpbmXKAUZNb3ppbGxhLzUuMCAoWDExOyBMaW51eCB4ODZfNjQ7IHJ2OjEzMy4wKSBHZWNrby8yMDEwMDEwMSBGaXJlZm94LzEzMy4w0gESU2Nob2xhcmx5IEpvdXJuYWxzmgIHUHJlUGFpZKoCK09TOkVNUy1NZWRpYUxpbmtzU2VydmljZS1nZXRNZWRpYVVybEZvckl0ZW3KAg9BcnRpY2xlfEZlYXR1cmXSAgFZ8gIA%2BgIBWYIDA1dlYooDHENJRDoyMDI0MTIwMzAxNTU0NTA3NDoyNTQxNDc%3D&_s=oaKS6DHYT8RXqVHXy399hhV4eHs%3D","machine learning; artificial intelligence; digital health; large language model; OpenAI; Large language models; LLM; mental health; AI; GPT-4; Telemedicine; United States--US; clinical setting; clinician; clinicians; crisis; digital mental health; e-health; generative pretrained transformer 4; Medical prognosis; Medical Sciences--Psychiatry And Neurology; medication; mental disorder; Mental health; Patient Health Questionnaire-9; patient information; PHQ-9; psychiatrist; psychiatrists; psychiatry; psychologist; psychologists; Self report; self-reported; suicidal; suicidal ideation; Suicidal ideation; suicide; suicide attempt; Suicides & suicide attempts; tele health; tele-mental health; telehealth; telemental health; treatment","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LKW7UWEN","journalArticle","2024","Katsamakas, Evangelos; Pavlov, Oleg V; Saklad, Ryan","Artificial Intelligence and the Transformation of Higher Education Institutions: A Systems Approach","Sustainability","","","10.3390/su16146118","https://www.proquest.com/scholarly-journals/artificial-intelligence-transformation-higher/docview/3085061003/se-2?accountid=25704","Artificial intelligence (AI) advances and the rapid adoption of generative AI tools, like ChatGPT, present new opportunities and challenges for higher education. While substantial literature discusses AI in higher education, there is a lack of a systems approach that captures a holistic view of the structure and dynamics of the AI transformation of higher education institutions (HEIs). To fill this gap, this article develops a causal loop diagram (CLD) to map the causal feedback mechanisms of AI transformation in a typical HEI. We identify important variables and their relationships and map multiple reinforcing and balancing feedback loops accounting for the forces that drive the AI transformation and its impact on value creation in a typical HEI. The model shows how, motivated by AI technology advances, the HEI can invest in AI to improve student learning, research, and administration while dealing with academic integrity problems and adapting to job market changes by emphasizing AI-complementary student skills. We explore model insights, scenarios, and policy interventions and recommend that HEI leaders become systems thinkers to manage the complexity of the AI transformation and benefit from the AI feedback loops while avoiding policy traps that may lead to decline. We also discuss the notion of HEIs influencing the direction of AI and directions for future research on AI transformation and the sustainability of HEIs.","2024","2024-12-03 02:39:46","2024-12-03 02:39:46","","6118","","14","16","","","","","","","","","","English","","","Coronavirus Research Database; Publicly Available Content Database","3085061003","","","Place: Basel Publisher: MDPI AG","","","https://media.proquest.com/media/hms/PFT/1/jrvIZ?_a=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%3D%3D&_s=ywVfw9QVkeCoEgqfXFS28f%2FvByU%3D","Data mining; Collaboration; Neural networks; Machine learning; Educational technology; Value creation; Higher education; Students; Environmental Studies; Research methodology; higher education; sustainability; digital transformation; artificial intelligence; Chatbots; ChatGPT; Generative artificial intelligence; Automation; Language; Large language models; Teaching; Natural language processing; Feedback; Tutoring; Employment; Access to education; Personalized learning; AI transformation; CLD; complex system; feedback loop; future of work; generative AI (GenAI); system dynamics; systems thinking","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CDG3H3NH","journalArticle","2024","Li, Jiakun; Zong, Hui; Wu, Erman; Wu, Rongrong; Peng, Zhufeng; Zhao, Jing; Lu, Yang; Shen, Hong Xieirong","Exploring the potential of artificial intelligence to enhance the writing of english academic papers by non-native english-speaking medical students - the educational application of ChatGPT","BMC Medical Education","","","10.1186/s12909-024-05738-y","https://www.proquest.com/scholarly-journals/exploring-potential-artificial-intelligence/docview/3079187859/se-2?accountid=25704","BackgroundAcademic paper writing holds significant importance in the education of medical students, and poses a clear challenge for those whose first language is not English. This study aims to investigate the effectiveness of employing large language models, particularly ChatGPT, in improving the English academic writing skills of these students.MethodsA cohort of 25 third-year medical students from China was recruited. The study consisted of two stages. Firstly, the students were asked to write a mini paper. Secondly, the students were asked to revise the mini paper using ChatGPT within two weeks. The evaluation of the mini papers focused on three key dimensions, including structure, logic, and language. The evaluation method incorporated both manual scoring and AI scoring utilizing the ChatGPT-3.5 and ChatGPT-4 models. Additionally, we employed a questionnaire to gather feedback on students’ experience in using ChatGPT.ResultsAfter implementing ChatGPT for writing assistance, there was a notable increase in manual scoring by 4.23 points. Similarly, AI scoring based on the ChatGPT-3.5 model showed an increase of 4.82 points, while the ChatGPT-4 model showed an increase of 3.84 points. These results highlight the potential of large language models in supporting academic writing. Statistical analysis revealed no significant difference between manual scoring and ChatGPT-4 scoring, indicating the potential of ChatGPT-4 to assist teachers in the grading process. Feedback from the questionnaire indicated a generally positive response from students, with 92% acknowledging an improvement in the quality of their writing, 84% noting advancements in their language skills, and 76% recognizing the contribution of ChatGPT in supporting academic research.ConclusionThe study highlighted the efficacy of large language models like ChatGPT in augmenting the English academic writing proficiency of non-native speakers in medical education. Furthermore, it illustrated the potential of these models to make a contribution to the educational evaluation process, particularly in environments where English is not the primary language.","2024","2024-12-03 02:39:46","2024-12-03 02:39:46","","1-8","","","24","","","","","","","","","","English","","","Publicly Available Content Database","3079187859","","","Place: London Publisher: BioMed Central","","","https://media.proquest.com/media/hms/PFT/1/qz59Z?_a=ChgyMDI0MTIwMzAxNTU0NTA3NDo1NTI0MTESBTkwMDAyGgpPTkVfU0VBUkNIIg0xMDMuMTIxLjE0OC4yKgU0MjU5MzIKMzA3OTE4Nzg1OToNRG9jdW1lbnRJbWFnZUIBMFIGT25saW5lWgJGVGIDUEZUagoyMDI0LzAxLzAxcgoyMDI0LzEyLzMxegCCASFQLTEwMDkyNDAtMjU3MDQtRlJFRS1udWxsLTY0NTY5MzmSAQZPbmxpbmXKAUZNb3ppbGxhLzUuMCAoWDExOyBMaW51eCB4ODZfNjQ7IHJ2OjEzMy4wKSBHZWNrby8yMDEwMDEwMSBGaXJlZm94LzEzMy4w0gESU2Nob2xhcmx5IEpvdXJuYWxzmgIHUHJlUGFpZKoCK09TOkVNUy1NZWRpYUxpbmtzU2VydmljZS1nZXRNZWRpYVVybEZvckl0ZW3KAg9BcnRpY2xlfEZlYXR1cmXSAgFZ8gIA%2BgIBWYIDA1dlYooDHENJRDoyMDI0MTIwMzAxNTU0NTA3NDoyNTQxNDc%3D&_s=iCXcIeBPDF0nulhneo4sORNx1UQ%3D","Questionnaires; China; Qualitative research; Regression analysis; Learning; Data analysis; Software; Artificial intelligence; Chatbots; ChatGPT; Medical students; Language; Large language models; Student writing; Medical education; Academic writing; Gender; Reflective teaching; Large language model; Medical Sciences; United States--US; Data Analysis; Educational Facilities Improvement; Logic; Medical english; Surveys & questionnaires; Undergraduate Students","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JBTNE7ID","journalArticle","2024","Sadeghi, Shaghayegh; Bui, Alan; Forooghi, Ali; Lu, Jianguo; Ngom, Alioune","Can large language models understand molecules?","BMC Bioinformatics","","","10.1186/s12859-024-05847-x","https://www.proquest.com/scholarly-journals/can-large-language-models-understand-molecules/docview/3079147187/se-2?accountid=25704","PurposeLarge Language Models (LLMs) like Generative Pre-trained Transformer (GPT) from OpenAI and LLaMA (Large Language Model Meta AI) from Meta AI are increasingly recognized for their potential in the field of cheminformatics, particularly in understanding Simplified Molecular Input Line Entry System (SMILES), a standard method for representing chemical structures. These LLMs also have the ability to decode SMILES strings into vector representations.MethodWe investigate the performance of GPT and LLaMA compared to pre-trained models on SMILES in embedding SMILES strings on downstream tasks, focusing on two key applications: molecular property prediction and drug-drug interaction prediction.ResultsWe find that SMILES embeddings generated using LLaMA outperform those from GPT in both molecular property and DDI prediction tasks. Notably, LLaMA-based SMILES embeddings show results comparable to pre-trained models on SMILES in molecular prediction tasks and outperform the pre-trained models for the DDI prediction tasks.ConclusionThe performance of LLMs in generating SMILES embeddings shows great potential for further investigation of these models for molecular embedding. We hope our study bridges the gap between LLMs and molecular embedding, motivating additional research into the potential of LLMs in the molecular representation field. GitHub: https://github.com/sshaghayeghs/LLaMA-VS-GPT.","2024","2024-12-03 02:39:47","2024-12-03 02:39:47","","1-17","","","25","","","","","","","","","","English","","","Biological Science Database; Publicly Available Content Database","3079147187","","","Place: London Publisher: BioMed Central","","","https://media.proquest.com/media/hms/PFT/1/D8w8Z?_a=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&_s=ADbBfGPgMtxB8FQLvXG1g3FqY14%3D","Neural networks; Machine learning; Artificial intelligence; Datasets; Language; Large language models; Economic; Research & development--R&D; Natural language; Molecular modelling; GPT; Informatics; Biology--Computer Applications; Chemical bonds; Chemistry; Drug interaction; Drug interactions; Embedding; Graph representations; LLaMA; Molecular properties; Performance prediction; Predictions; Representations; SMILES embedding; Strings","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"PXQ3QUS7","journalArticle","2024","Shah, Asghar; Wahood, Samer; Guermazi, Dorra; Brem, Candice E; Saliba, Elie","Skin and Syntax: Large Language Models in Dermatopathology","Dermatopathology","","","10.3390/dermatopathology11010009","https://www.proquest.com/scholarly-journals/skin-syntax-large-language-models/docview/2989545404/se-2?accountid=25704","This literature review introduces the integration of Large Language Models (LLMs) in the field of dermatopathology, outlining their potential benefits, challenges, and prospects. It discusses the changing landscape of dermatopathology with the emergence of LLMs. The potential advantages of LLMs include a streamlined generation of pathology reports, the ability to learn and provide up-to-date information, and simplified patient education. Existing instances of LLMs encompass diagnostic support, research acceleration, and trainee education. Challenges involve biases, data privacy and quality, and establishing a balance between AI and dermatopathological expertise. Prospects include the integration of LLMs with other AI technologies to improve diagnostics and the improvement of multimodal LLMs that can handle both text and image input. Our implementation guidelines highlight the importance of model transparency and interpretability, data quality, and continuous oversight. The transformative potential of LLMs in dermatopathology is underscored, with an emphasis on a dynamic collaboration between artificial intelligence (AI) experts (technical specialists) and dermatopathologists (clinicians) for improved patient outcomes.","2024","2024-12-03 02:39:47","2024-12-03 02:39:47","","101","","1","11","","","","","","","","","","English","","","Publicly Available Content Database","2989545404","","","Place: Basel Publisher: MDPI AG","","","https://media.proquest.com/media/hms/PFT/1/pqjiX?_a=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%3D%3D&_s=UcQ%2FDsl2iAZUtXhgZEmgtEwxM74%3D","Decision making; artificial intelligence; Artificial intelligence; Chatbots; Language model; Medical personnel; Automation; Language; large language models; Pedagogy; LLM; Evolution; AI; Patient education; Pathology; Dermatology; dermatopathology; Dermatopathology; Medical Sciences--Dermatology And Venereology; Skin diseases","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"IEKCTZ27","journalArticle","2024","Cirone, Katrina; Akrout, Mohamed; Latif Abid; Oakley, Amanda","Assessing the Utility of Multimodal Large Language Models (GPT-4 Vision and Large Language and Vision Assistant) in Identifying Melanoma Across Different Skin Tones","JMIR Dermatology","","","10.2196/55508","https://www.proquest.com/scholarly-journals/assessing-utility-multimodal-large-language/docview/2956706752/se-2?accountid=25704","The large language models GPT-4 Vision and Large Language and Vision Assistant are capable of understanding and accurately differentiating between benign lesions and melanoma, indicating potential incorporation into dermatologic care, medical research, and education.","2024","2024-12-03 02:39:47","2024-12-03 02:39:47","","","","","7","","","","","","","","","","English","","","Publicly Available Content Database","2956706752","","","Place: Toronto Publisher: JMIR Publications","","","https://media.proquest.com/media/hms/PFT/1/uDLbX?_a=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%3D%3D&_s=zSQsbQSrxr7oMlMnP8BM7P6m%2F3o%3D","machine learning; Decision making; artificial intelligence; Artificial intelligence; large language model; Language; Large language models; natural language processing; LLM; LLMs; NLP; Evolution; GPT; AI; Dermatology; Medical Sciences--Dermatology And Venereology; Asymmetry; cancer; dermatology; expert systems; GPT-4V; lesion; lesions; melanoma; Melanoma; Morphology; multimodal large language models; nevus; oncology; skin; Skin cancer; skin pigmentation; Symmetry; visual","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LP5EQ57M","journalArticle","2024","Smetana, Mason; Lucio Salles de Salles; Sukharev, Igor; Khazanovich, Lev","Highway Construction Safety Analysis Using Large Language Models","Applied Sciences","","","10.3390/app14041352","https://www.proquest.com/scholarly-journals/highway-construction-safety-analysis-using-large/docview/2930934124/se-2?accountid=25704","Featured ApplicationUse of large language models and AI to analyze construction safety data.AbstractThe highway construction industry carries substantial safety risks for workers, necessitating thorough accident analyses to implement effective preventive measures. Current research lacks comprehensive investigations into safety incidents, relying heavily on conventional statistical methods and overlooking valuable textual information in publicly available databases. This study leverages a state-of-the-art large language model (LLM), specifically OpenAI’s GPT-3.5 model. The primary focus is to enhance text-based incident analysis that is sourced from OSHA’s Severe Injury Reports (SIR) database. By incorporating novel natural language processing (NLP) techniques, dimensionality reduction, clustering algorithms, and LLM prompting of incident narratives, the study aims to develop an approach to the analysis of major accident causes in highway construction. The resulting cluster analysis, coupled with LLM summarization and cause identification, reveals the major accident types, such as heat-related and struck-by injuries, as well as commonalities between incidents. This research showcases the potential of artificial intelligence (AI) and LLM technology in data-driven analysis. By efficiently processing textual data and providing insightful analysis, the study fosters practical implications for safety professionals and the development of more effective accident prevention and intervention strategies within the industry.","2024","2024-12-03 02:39:47","2024-12-03 02:39:47","","1352","","4","14","","","","","","","","","","English","","","Publicly Available Content Database","2930934124","","","Place: Basel Publisher: MDPI AG","","","https://media.proquest.com/media/hms/PFT/1/I9CQX?_a=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%3D%3D&_s=xIcwd3%2BWtM%2BNWBQMgiLC7kRXilI%3D","machine learning; Databases; Machine learning; Algorithms; Sciences: Comprehensive Works; Decision making; construction industry; artificial intelligence; Artificial intelligence; Language model; Automation; Language; Natural language processing; Construction industry; Construction accidents & safety; United States--US; Accident prevention; accidents; Clustering; Fatalities; Highway; Highway construction; Industrial safety; Injuries; Road; Roads & highways; Safety management; transportation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"44LGYJ2B","journalArticle","2024","Humaid Al Naqbi; Zied Bahroun; Ahmed, Vian","Enhancing Work Productivity through Generative Artificial Intelligence: A Comprehensive Literature Review","Sustainability","","","10.3390/su16031166","https://www.proquest.com/scholarly-journals/enhancing-work-productivity-through-generative/docview/2924021802/se-2?accountid=25704","In this review, utilizing the PRISMA methodology, a comprehensive analysis of the use of Generative Artificial Intelligence (GAI) across diverse professional sectors is presented, drawing from 159 selected research publications. This study provides an insightful overview of the impact of GAI on enhancing institutional performance and work productivity, with a specific focus on sectors including academia, research, technology, communications, agriculture, government, and business. It highlights the critical role of GAI in navigating AI challenges, ethical considerations, and the importance of analytical thinking in these domains. The research conducts a detailed content analysis, uncovering significant trends and gaps in current GAI applications and projecting future prospects. A key aspect of this study is the bibliometric analysis, which identifies dominant tools like Chatbots and Conversational Agents, notably ChatGPT, as central to GAI’s evolution. The findings indicate a robust and accelerating trend in GAI research, expected to continue through 2024 and beyond. Additionally, this study points to potential future research directions, emphasizing the need for improved GAI design and strategic long-term planning, particularly in assessing its impact on user experience across various professional fields.","2024","2024-12-03 02:39:47","2024-12-03 02:39:47","","1166","","3","16","","","","","","","","","","English","","","Publicly Available Content Database","2924021802","","","Place: Basel Publisher: MDPI AG","","","https://media.proquest.com/media/hms/PFT/1/dDEIX?_a=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%3D%3D&_s=6mNxIrfxXPA%2FUisisJlh5AVLIjQ%3D","knowledge management; Productivity; Efficiency; Decision making; Content analysis; Bibliometrics; Innovations; Environmental Studies; Literature reviews; Systematic review; Chatbots; ChatGPT; Generative artificial intelligence; Automation; generative artificial intelligence; Chatbot; Keywords; ethics; management; chatbots; Dialogue system; Educational materials; review; work productivity enhancement","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"9Y66A9EV","journalArticle","2023","Liu, Ming; Ren, Yiling; Nyagoga, Lucy Michael; Stonier, Francis; Wu, Zhongming; Yu, Liang","Future of education in the era of generative artificial intelligence: Consensus among Chinese scholars on applications of ChatGPT in schools","Future in Educational Research","","","10.1002/fer3.10","https://www.proquest.com/scholarly-journals/future-education-era-generative-artificial/docview/3090586137/se-2?accountid=25704","ChatGPT is an artificial intelligence chatbot that utilizes advanced natural language processing technologies, including large language models, to produce human‐like responses to user queries spanning a wide range of topics from programming to mathematics. As an emerging generative artificial intelligence (GAI) tool, it presents novel opportunities and challenges to the ongoing digital transformation of education. This article employs a systematic review approach to summarize the viewpoints of Chinese scholars and experts regarding the implementation of GAI in education. The research findings indicate that a majority of Chinese scholars support the cautious integration of GAI into education as it serves as a learning tool that offers personalized educational experiences for students. However, it also raises concerns related to academic integrity and the potential hindrance to students' critical thinking skills. Consequently, a framework called DATS, which outlines an optimization path for future GAI applications in schools, is proposed. The framework takes into account the perspectives of four key stakeholders: developers, administrators, teachers, and students.","2023-09-01","2024-12-03 02:39:48","2024-12-03 02:39:48","","72-101","","1","1","","","","","","","","","","English","","","Publicly Available Content Database","3090586137","","","Place: Chongqing Publisher: John Wiley & Sons, Inc.","","","https://media.proquest.com/media/hms/PFT/1/aXVSZ?_a=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%3D%3D&_s=L3kEjehFQ1d7QmYiyVykF%2FweFs4%3D","Deep learning; Machine learning; Algorithms; China; Educational technology; Teachers; Education; Colleges & universities; Students; Systematic review; Research methodology; Digital technology; Chatbots; ChatGPT; Generative artificial intelligence; Automation; generative artificial intelligence; Digital literacy; Teaching; Verbal communication; Chinese scholar; digital transformation of education; Educational Administration; Educational Change; Educational Objectives; Research Methodology; School Administration; Schools","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"D42DYR27","journalArticle","2023","Preiksaitis, Carl; Rose, Christian","Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review","JMIR Medical Education","","","10.2196/48785","https://www.proquest.com/scholarly-journals/opportunities-challenges-future-directions/docview/2917890650/se-2?accountid=25704","Background:Generative artificial intelligence (AI) technologies are increasingly being utilized across various fields, with considerable interest and concern regarding their potential application in medical education. These technologies, such as Chat GPT and Bard, can generate new content and have a wide range of possible applications.Objective:This study aimed to synthesize the potential opportunities and limitations of generative AI in medical education. It sought to identify prevalent themes within recent literature regarding potential applications and challenges of generative AI in medical education and use these to guide future areas for exploration.Methods:We conducted a scoping review, following the framework by Arksey and O'Malley, of English language articles published from 2022 onward that discussed generative AI in the context of medical education. A literature search was performed using PubMed, Web of Science, and Google Scholar databases. We screened articles for inclusion, extracted data from relevant studies, and completed a quantitative and qualitative synthesis of the data.Results:Thematic analysis revealed diverse potential applications for generative AI in medical education, including self-directed learning, simulation scenarios, and writing assistance. However, the literature also highlighted significant challenges, such as issues with academic integrity, data accuracy, and potential detriments to learning. Based on these themes and the current state of the literature, we propose the following 3 key areas for investigation: developing learners’ skills to evaluate AI critically, rethinking assessment methodology, and studying human-AI interactions.Conclusions:The integration of generative AI in medical education presents exciting opportunities, alongside considerable challenges. There is a need to develop new skills and competencies related to AI as well as thoughtful, nuanced approaches to examine the growing use of generative AI in medical education.","2023","2024-12-03 02:39:48","2024-12-03 02:39:48","","","","","9","","","","","","","","","","English","","","Publicly Available Content Database","2917890650","","","Place: Toronto Publisher: JMIR Publications","","","https://media.proquest.com/media/hms/PFT/1/BVj9X?_a=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%3D%3D&_s=oKzvj1rj8Vm%2BascL3Fh%2BzsdhzkA%3D","Teachers; Research; Students; Systematic review; artificial intelligence; Artificial intelligence; Cardiology; Chatbots; ChatGPT; generative; Generative artificial intelligence; medical education; Language; Medical education; Bard; Cheating; AI; Licensing examinations; Medical Sciences; United States--US; Multimedia; Personalized learning; Educational materials; review; Challenge; educator; Independent study; learner; Opportunity; scoping; Test preparation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"4I33EDT4","journalArticle","2023","Yu, Wei; Hou, Yueyuan","Forest Visitors’ Multisensory Perception and Restoration Effects: A Study of China’s National Forest Parks by Introducing Generative Large Language Model","Forests","","","10.3390/f14122412","https://www.proquest.com/scholarly-journals/forest-visitors-multisensory-perception/docview/2904904292/se-2?accountid=25704","Sensory perception of forests is closely related to human health and well-being. Based on attention recovery theory and stress relief theory, this paper investigates the influence of sensory perception of forests on visitors’ restoration effects from a multidimensional and multisensory perspective, integrating the use of a generative large language model, regression analysis, and semantic analysis. The results of the study show that (1) the application of a generative large language model provides new ideas and methods to solve the dilemma caused by the traditional self-report scale measurement and provides a possible way to explore a new research paradigm in the context of the rapid development of generative artificial intelligence; (2) the effects of each sensory quantity differed, with the sensory quantities of sight, hearing, touch, and taste having a significant positive effect on visitors’ restoration effects, and the sense of smell having a significant negative effect on visitors’ restoration effects; (3) sensory psychological distance partially had a significant effect on visitors’ restoration effects, both proximal psychological distance and distal psychological distance were significantly correlated with visitors’ restoration effects, and intermediate psychological distance had a negative effect on visitors’ restoration effects, but the effect was not significant; (4) the sensory dimension has a significant positive effect on visitors’ restoration effects, the integration and synergistic effect of the senses are enhanced, and multidimensional sensory cross-perception has a positive effect on visitors’ restoration effects at the social health level; and (5) the sensory elements of National Forest Parks that influence visitors’ restoration effects are mainly natural attributes, and the elements related to “people” also play an important role in visitors’ restoration effects. This study provides a useful complement to the study of forest sensory perception, and at the same time has an important reference value for exploring the management of forest recreation experience and sensory marketing practices.","2023","2024-12-03 02:39:48","2024-12-03 02:39:48","","2412","","12","14","","","","","","","","","","English","","","Publicly Available Content Database","2904904292","","","Place: Basel Publisher: MDPI AG","","","https://media.proquest.com/media/hms/PFT/1/O9TmW?_a=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%3D%3D&_s=gR%2FnoqLHrg8KLOY9JRuS6BJ9zrY%3D","Forests; COVID-19; Regression analysis; Pandemics; Research; Hypotheses; Forest management; Climate change; Artificial intelligence; Generative artificial intelligence; Language; Large language models; Semantics; Social; Semantic analysis; Tourism; Environmental; Emotions; Self report; Batch processing; forest recreation; Forests And Forestry; generative large language model; multisensory perception; National Forest Parks; National forests; Olfaction; Park; Parks; Perception; Psychological factors; Reforestation; Regression models; Restoration; restoration effects; Senses; Sensory integration; Sensory perception; Sensory systems; Social determinants of health; Stress; Synergistic effect","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"V7SXRVR8","journalArticle","2023","Lorenzo Ferro Desideri; Roth, Janice; Zinkernagel, Martin; Anguita, Rodrigo","“Application and accuracy of artificial intelligence-derived large language models in patients with age related macular degeneration”","International Journal of Retina and Vitreous","","","10.1186/s40942-023-00511-7","https://www.proquest.com/scholarly-journals/application-accuracy-artificial-intelligence/docview/2902134245/se-2?accountid=25704","IntroductionAge-related macular degeneration (AMD) affects millions of people globally, leading to a surge in online research of putative diagnoses, causing potential misinformation and anxiety in patients and their parents. This study explores the efficacy of artificial intelligence-derived large language models (LLMs) like in addressing AMD patients' questions.MethodsChatGPT 3.5 (2023), Bing AI (2023), and Google Bard (2023) were adopted as LLMs. Patients’ questions were subdivided in two question categories, (a) general medical advice and (b) pre- and post-intravitreal injection advice and classified as (1) accurate and sufficient (2) partially accurate but sufficient and (3) inaccurate and not sufficient. Non-parametric test has been done to compare the means between the 3 LLMs scores and also an analysis of variance and reliability tests were performed among the 3 groups.ResultsIn category a) of questions, the average score was 1.20 (± 0.41) with ChatGPT 3.5, 1.60 (± 0.63) with Bing AI and 1.60 (± 0.73) with Google Bard, showing no significant differences among the 3 groups (p = 0.129). The average score in category b was 1.07 (± 0.27) with ChatGPT 3.5, 1.69 (± 0.63) with Bing AI and 1.38 (± 0.63) with Google Bard, showing a significant difference among the 3 groups (p = 0.0042). Reliability statistics showed Chronbach’s α of 0.237 (range 0.448, 0.096–0.544).ConclusionChatGPT 3.5 consistently offered the most accurate and satisfactory responses, particularly with technical queries. While LLMs displayed promise in providing precise information about AMD; however, further improvements are needed especially in more technical questions.","2023","2024-12-03 02:39:48","2024-12-03 02:39:48","","1-6","","","9","","","","","","","","","","English","","","Publicly Available Content Database","2902134245","","","Place: London Publisher: BioMed Central","","","https://media.proquest.com/media/hms/PFT/1/OQDhW?_a=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%3D%3D&_s=%2BpVyf0K6sptglFuZCbpX0Nj%2Fnrk%3D","Reliability; Accuracy; Artificial intelligence; Chatbots; Large language models; LLMs; Artificial Intelligence; Artificial intelligence in ophthalmology; Degeneracy; Dry macular degeneration; Macular degeneration; Macular edema; Medical Sciences--Ophthalmology And Optometry; Questionnaire; Skin condition; Wet macular degeneration","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2F6YLSVJ","journalArticle","2023","Singh, Chandan; Askari, Armin; Caruana, Rich; Gao, Jianfeng","Augmenting interpretable models with large language models during training","Nature Communications","","","10.1038/s41467-023-43713-1","https://www.proquest.com/scholarly-journals/augmenting-interpretable-models-with-large/docview/2895588779/se-2?accountid=25704","Recent large language models (LLMs), such as ChatGPT, have demonstrated remarkable prediction performance for a growing array of tasks. However, their proliferation into high-stakes domains and compute-limited settings has created a burgeoning need for interpretability and efficiency. We address this need by proposing Aug-imodels, a framework for leveraging the knowledge learned by LLMs to build extremely efficient and interpretable prediction models. Aug-imodels use LLMs during fitting but not during inference, allowing complete transparency and often a speed/memory improvement of greater than 1000x for inference compared to LLMs. We explore two instantiations of Aug-imodels in natural-language processing: Aug-Linear, which augments a linear model with decoupled embeddings from an LLM and Aug-Tree, which augments a decision tree with LLM feature expansions. Across a variety of text-classification datasets, both outperform their non-augmented, interpretable counterparts. Aug-Linear can even outperform much larger models, e.g. a 6-billion parameter GPT-J model, despite having 10,000x fewer parameters and being fully transparent. We further explore Aug-imodels in a natural-language fMRI study, where they generate interesting interpretations from scientific data.Prediction and interpretation tasks may be challenging in high-stakes applications, such as medical decision-making, or systems with compute-limited hardware. The authors introduce an augmented framework for leveraging the knowledge learned by Large Language Models to build interpretable models which are both accurate and efficient.","2023","2024-12-03 02:39:48","2024-12-03 02:39:48","","7913","","1","14","","","","","","","","","","English","","","Biological Science Database; Publicly Available Content Database","2895588779","","","Place: London Publisher: Nature Publishing Group","","","https://media.proquest.com/media/hms/PFT/1/YzxPW?_a=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%3D%3D&_s=FNptrBJNgZDCZ0579H%2FyzUTUESU%3D","Mathematical models; Sciences: Comprehensive Works; Decision making; Learning; Artificial intelligence; Language model; Language; Large language models; Economic; Natural language processing; Augmentation; Decision tree; Decision trees; Functional magnetic resonance imaging; Inference; Interpretability; Parameters; Prediction models","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"S79UDMBN","journalArticle","2023","Rieder, Emanuel; Schmuck, Matthias; Tugui, Alexandru","A Scientific Perspective on Using Artificial Intelligence in Sustainable Urban Development","Big Data and Cognitive Computing","","","10.3390/bdcc7010003","https://www.proquest.com/scholarly-journals/scientific-perspective-on-using-artificial/docview/2791571326/se-2?accountid=25704","Digital transformation (or digitalization) is the process of continuous further development of digital technologies (such as smart devices, cloud services, and Big Data) that have a lasting impact on our economy and society. In this manner, digitalization is a huge driver for permanent change, even in the field of Sustainable Urban Development. In the wake of digitalization, expectations are changing, placing pressure at the societal level on the design and development of smart environments for everything that means Sustainable Urban Development. In this sense, the solution is the integration of Artificial Intelligence into Sustainable Urban Development, because technology can simplify people’s lives. The aim of this paper is to ascertain which Sustainable Urban Development dimensions are taken into account when integrating Artificial Intelligence and what results can be achieved. These questions formed the basic framework for this research article. In order to make the current state of Artificial Intelligence in Sustainable Urban Development as a snapshot visible, a systematic review of the current literature between 2012 and 2022 was conducted. The data were collected and analyzed using PRISMA. Based on the studies identified, we found a significant growth in studies, starting in 2018, and that Artificial Intelligence applications refer to the Sustainable Urban Development dimensions of environmental protection, economic development, social justice and equity, culture, and governance. The used Artificial Intelligence techniques in Sustainable Urban Development cover a broad field of Artificial Intelligence, such as Artificial Intelligence in general, Machine Learning, Deep Learning, Artificial Neuronal Networks, Operations Research, Predictive Analytics, and Data Mining. However, with the integration of Artificial Intelligence in Sustainable Urban Development, challenges are marked out. These include responsible municipal policies, awareness of data quality, privacy and data security, the formation of partnerships among stakeholders (e.g., local citizens, civil society, industry, and various levels of government), and transparency and traceability in the implementation and rollout of Artificial Intelligence. A first step was taken towards providing an overview of the possible applications of Artificial Intelligence in Sustainable Urban Development. It was clearly shown that Artificial Intelligence is also gaining ground in this sector.","2023","2024-12-03 02:39:48","2024-12-03 02:39:48","","3","","1","7","","","","","","","","","","English","","","Coronavirus Research Database; Publicly Available Content Database","2791571326","","","Place: Basel Publisher: MDPI AG","","","https://media.proquest.com/media/hms/PFT/1/131zQ?_a=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%3D%3D&_s=8rzlwPQDCNrr3weWU%2FAjWw6qVso%3D","Data mining; Deep learning; Neural networks; Machine learning; Data analysis; Literature reviews; Computers; Sustainable development; digital transformation; Artificial intelligence; governance; Digitization; Impact analysis; Big Data; Scientists; Computers--Electronic Data Processing; Problem solving; Economic development; Cities; Digital transformation; Digitalization; economic development; Electronic devices; environmental protection; Environmental protection; Government industry relations; Gross Domestic Product--GDP; Operations research; Preferred Reporting Items for Systematic Reviews and Meta-Analyses; smart cities; Social justice; Urban; Urban areas; Urban development; Urban planning; Urbanization","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"HKEDXNZ3","journalArticle","2022","David Shalom Liu; Sawyer, Jake; Luna, Alexander; Aoun, Jihad; Wang, Janet; Lord Boachie; Halabi, Safwan; Bina, Joe","Perceptions of US Medical Students on Artificial Intelligence in Medicine: Mixed Methods Survey Study","JMIR Medical Education","","","10.2196/38325","https://www.proquest.com/scholarly-journals/perceptions-us-medical-students-on-artificial/docview/2730400150/se-2?accountid=25704","Background: Given the rapidity with which artificial intelligence is gaining momentum in clinical medicine, current physician leaders have called for more incorporation of artificial intelligence topics into undergraduate medical education. This is to prepare future physicians to better work together with artificial intelligence technology. However, the first step in curriculum development is to survey the needs of end users. There has not been a study to determine which media and which topics are most preferred by US medical students to learn about the topic of artificial intelligence in medicine. Objective: We aimed to survey US medical students on the need to incorporate artificial intelligence in undergraduate medical education and their preferred means to do so to assist with future education initiatives. Methods: A mixed methods survey comprising both specific questions and a write-in response section was sent through Qualtrics to US medical students in May 2021. Likert scale questions were used to first assess various perceptions of artificial intelligence in medicine. Specific questions were posed regarding learning format and topics in artificial intelligence. Results: We surveyed 390 US medical students with an average age of 26 (SD 3) years from 17 different medical programs (the estimated response rate was 3.5%). A majority (355/388, 91.5%) of respondents agreed that training in artificial intelligence concepts during medical school would be useful for their future. While 79.4% (308/388) were excited to use artificial intelligence technologies, 91.2% (353/387) either reported that their medical schools did not offer resources or were unsure if they did so. Short lectures (264/378, 69.8%), formal electives (180/378, 47.6%), and Q and A panels (167/378, 44.2%) were identified as preferred formats, while fundamental concepts of artificial intelligence (247/379, 65.2%), when to use artificial intelligence in medicine (227/379, 59.9%), and pros and cons of using artificial intelligence (224/379, 59.1%) were the most preferred topics for enhancing their training. Conclusions: The results of this study indicate that current US medical students recognize the importance of artificial intelligence in medicine and acknowledge that current formal education and resources to study artificial intelligence–related topics are limited in most US medical schools. Respondents also indicated that a hybrid formal/flexible format would be most appropriate for incorporating artificial intelligence as a topic in US medical schools. Based on these data, we conclude that there is a definitive knowledge gap in artificial intelligence education within current medical education in the US. Further, the results suggest there is a disparity in opinions on the specific format and topics to be introduced.","2022-10","2024-12-03 02:39:48","2024-12-03 02:39:48","","","","4","8","","","","","","","","","","English","","","Coronavirus Research Database; Publicly Available Content Database","2730400150","","","Place: Toronto Publisher: JMIR Publications","","","https://media.proquest.com/media/hms/PFT/1/cyAiP?_a=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%3D%3D&_s=pQ6%2FXizqfb5k1nEe%2FPZ5uHz0dMA%3D","Algorithms; Survey; Education; Learning; artificial intelligence; Artificial intelligence; digital health; medical education; Medical students; education; Clinical medicine; Medical education; Polls & surveys; Medical research; Medicine; Medical Sciences; United States--US; College campuses; Demographics; eHealth; elective course; Gene expression; integration; Kansas City Missouri; medical curriculum; medical school; Medical school; Medical schools; medical student; Mixed methods research; Multimethodology; Ohio; Osteopathic medicine; Response rates; University colleges","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"WN9J4NI2","journalArticle","2024","Macnamara, Brooke N.; Berber, Ibrahim; Çavuşoğlu, M. Cenk; Krupinski, Elizabeth A.; Nallapareddy, Naren; Nelson, Noelle E.; Smith, Philip J.; Wilson-Delfosse, Amy L.; Ray, Soumya","Does using artificial intelligence assistance accelerate skill decay and hinder skill development without performers’ awareness?: Principles and Implications","Cognitive Research","","","10.1186/s41235-024-00572-8","https://www.proquest.com/scholarly-journals/does-using-artificial-intelligence-assistance/docview/3078836279/se-2?accountid=25704","Artificial intelligence in the workplace is becoming increasingly common. These tools are sometimes used to aid users in performing their task, for example, when an artificial intelligence tool assists a radiologist in their search for abnormalities in radiographic images. The use of artificial intelligence brings a wealth of benefits, such as increasing the efficiency and efficacy of performance. However, little research has been conducted to determine how the use of artificial intelligence assistants might affect the user’s cognitive skills. In this theoretical perspective, we discuss how artificial intelligence assistants might accelerate skill decay among experts and hinder skill acquisition among learners. Further, we discuss how AI assistants might also prevent experts and learners from recognizing these deleterious effects. We then discuss the types of questions: use-inspired basic cognitive researchers, applied researchers, and computer science researchers should seek to answer. We conclude that multidisciplinary research from use-inspired basic cognitive research, domain-specific applied research, and technical research (e.g., human factors research, computer science research) is needed to (a) understand these potential consequences, (b) design artificial intelligence systems to mitigate these impacts, and (c) develop training and use protocols to prevent negative impacts on users’ cognitive skills. Only by answering these questions from multidisciplinary perspectives can we harness the benefits of artificial intelligence in the workplace while preventing negative impacts on users’ cognitive skills.","2024-12","2024-12-03 02:45:28","2024-12-03 02:45:28","","46","","1","9","","","","","","","","","","English","","","Biological Science Database; Publicly Available Content Database","3078836279","","","Place: London Publisher: Springer Nature B.V.","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LGDY4ILZ","journalArticle","2024","Lin, Zhengyang; Chen, Anping; Wang, Xuhui; Liu, Zhihua; Piao, Shilong","Large language models reveal big disparities in current wildfire research","Communications Earth & Environment","","","10.1038/s43247-024-01341-7","https://www.proquest.com/scholarly-journals/large-language-models-reveal-big-disparities/docview/3028038831/se-2?accountid=25704","Contemporary fire-human-climate nexus has led to a surge in publication numbers across diverse research disciplines beyond the capability of experts from a single discipline. Here, we employed a generalized large language model to capture the dynamics of wildfire research published between 1980 and 2022. More than 60,000 peer-reviewed papers were scanned and analyzed. Through integrating geographical metadata extracted by the artificial intelligence and satellite wildfire datasets, we found large disparities in geographic patterns and research themes. The hottest spot of wildfire research is western United States, accounting for 15% of publications but only 0.5% of global burnt area, while the world’s most widely burnt region, like Siberia and Africa are largely underrepresented by contemporary publications. Similar discrepancies are found between the fuel of wildfire and its ignition and climatic drivers, between socioeconomic development and wildfire mitigation, raising concerns on sustainable wildfire managements and calling for further artificial intelligence-aided transdisciplinary collaborations.Regions such as the United States, the Amazon and Southern Europe are hot spots in wildfire research, while Africa and Siberia with the largest burned areas are largely understudied, according to an analysis of more than 60,000 peer-reviewed articles over 1982-2022 using a large language model.","2024-12","2024-12-03 02:45:28","2024-12-03 02:45:28","","168","","1","5","","","","","","","","","","English","","","Publicly Available Content Database","3028038831","","","Place: London Publisher: Nature Publishing Group","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"B4M8PWXH","journalArticle","2024","Eckardt, Jan-Niklas; Hahn, Waldemar; Röllig, Christoph; Stasik, Sebastian; Platzbecker, Uwe; Müller-Tidow, Carsten; Serve, Hubert; Baldus, Claudia D.; Schliemann, Christoph; Schäfer-Eckart, Kerstin; Hanoun, Maher; Kaufmann, Martin; Burchert, Andreas; Thiede, Christian; Schetelig, Johannes; Sedlmayr, Martin; Bornhäuser, Martin; Wolfien, Markus; Middeke, Jan Moritz","Mimicking clinical trials with synthetic acute myeloid leukemia patients using generative artificial intelligence","NPJ Digital Medicine","","","10.1038/s41746-024-01076-x","https://www.proquest.com/scholarly-journals/mimicking-clinical-trials-with-synthetic-acute/docview/2969271547/se-2?accountid=25704","Clinical research relies on high-quality patient data, however, obtaining big data sets is costly and access to existing data is often hindered by privacy and regulatory concerns. Synthetic data generation holds the promise of effectively bypassing these boundaries allowing for simplified data accessibility and the prospect of synthetic control cohorts. We employed two different methodologies of generative artificial intelligence – CTAB-GAN+ and normalizing flows (NFlow) – to synthesize patient data derived from 1606 patients with acute myeloid leukemia, a heterogeneous hematological malignancy, that were treated within four multicenter clinical trials. Both generative models accurately captured distributions of demographic, laboratory, molecular and cytogenetic variables, as well as patient outcomes yielding high performance scores regarding fidelity and usability of both synthetic cohorts (n = 1606 each). Survival analysis demonstrated close resemblance of survival curves between original and synthetic cohorts. Inter-variable relationships were preserved in univariable outcome analysis enabling explorative analysis in our synthetic data. Additionally, training sample privacy is safeguarded mitigating possible patient re-identification, which we quantified using Hamming distances. We provide not only a proof-of-concept for synthetic data generation in multimodal clinical data for rare diseases, but also full public access to synthetic data sets to foster further research.","2024-12","2024-12-03 02:45:28","2024-12-03 02:45:28","","76","","1","7","","","","","","","","","","English","","","Publicly Available Content Database","2969271547","","","Place: London Publisher: Nature Publishing Group","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LVWHFN8Z","journalArticle","2024","Alzoubi, Y I; Mishra, A; Topcu, A E; Cibikdiken, A O","Generative Artificial Intelligence Technology for Systems Engineering Research: Contribution and Challenges","International Journal of Industrial Engineering and Management","","22172661","10.24867/IJIEM-2024-2-355","https://www.proquest.com/scholarly-journals/generative-artificial-intelligence-technology/docview/3094504193/se-2?accountid=25704","The advancement of artificial intelligence technology in recent years has had a significant impact on various industries, including the field of systems engineering. Generative Artificial Intelligence (AI), like OpenAI's ChatGPT, is one such tool that has garnered attention. While this technology offers researchers in systems engineering intriguing possibilities, it also introduces certain risks to the traditional research framework. The aim of this paper is to investigate the advantages and drawbacks associated with embracing generative AI. We conducted a comprehensive literature review utilizing resources like Google Scholar, Web of Science, and the Scopus database, along with professional websites and white papers. The analysis highlights the potential benefits of generative AI in systems engineering research, including data processing, analysis, hypothesis formulation, prediction and forecasting, and collaboration enhancement. However, it also underscores various risks, such as potential data bias, the generation of human-like text, potential loss of analytical capabilities, and difficulties in analyzing output from these AI tools. As emphasized in this paper, numerous concerns still need to be addressed regarding the use of generative AI tools due to their relatively new nature and evolving capabilities.","2024-06","2024-12-03 02:45:28","2024-12-03 02:45:28","","169-179","","2","15","","","","","","","","","","English","","","Publicly Available Content Database","3094504193","","","Place: Novi Sad Publisher: University of Novi Sad","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"UJUJ495F","journalArticle","2024","Mishra, Pooja; Bhujbal, Rutuja; Singh, Tushar","Exploring the Capabilities of Large Language Model Mistral Large (Mistral) on Medical Challenge Problems & Hallucinations","International Research Journal of Innovations in Engineering and Technology","","","10.47001/IRJIET/2024.805024","https://www.proquest.com/scholarly-journals/exploring-capabilities-large-language-model/docview/3073992865/se-2?accountid=25704","Large language models (LLMs) have demonstrated remarkable capabilities in various natural language processing tasks, including question answering, text generation, and multimodal understanding. However, their performance in specialized domains such as healthcare and their propensity for generating hallucinated (false) information remains an area of active investigation. This research paper explores the capabilities and limitations of Mistral's LLM, mistral-large-2402, in tackling medical challenge problems and assessing its tendency to hallucinate. The study is motivated by the potential of LLMs to augment medical decision-making processes and the need to evaluate their reliability in critical domains like healthcare. We investigate mistral-large-2402's performance on a curated dataset of medical challenge problems, spanning diagnosis, treatment recommendation, and medical condition analysis tasks. Additionally, we examine the model's propensity for hallucinating by analyzing its responses for factual inconsistencies and unsubstantiated claims. Through quantitative and qualitative analyses, we provide insights into mistral-large-2402's strengths and weaknesses in handling medical challenges. Our evaluation methodology involves measuring the model's accuracy, completeness, and coherence of responses, as well as its ability to recognize and mitigate hallucinations. The findings of this study contribute to the ongoing discourse on the responsible deployment of LLMs in healthcare and highlight potential areas for improvement in model design and training.","2024-05","2024-12-03 02:45:28","2024-12-03 02:45:28","","156-164","","5","8","","","","","","","","","","English","","","Publicly Available Content Database","3073992865","","","Place: Dharmapuri Publisher: IRJIET (International Research Journal of Innovations in Engineering and Technology)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LXJTTXVH","journalArticle","2024","Menz, Bradley D; Kuderer, Nicole M; Bacchi, Stephen; Modi, Natansh D; Chin-Yee, Benjamin; Hu, Tiancheng; Rickard, Ceara; Haseloff, Mark; Vitry, Agnes; McKinnon, Ross A; Kichenadasse, Ganessan; Rowland, Andrew; Sorich, Michael J; Hopkins, Ashley M","Current safeguards, risk mitigation, and transparency measures of large language models against the generation of health disinformation: repeated cross sectional analysis","BMJ : British Medical Journal (Online)","","","10.1136/bmj-2023-078538","https://www.proquest.com/scholarly-journals/current-safeguards-risk-mitigation-transparency/docview/2968998199/se-2?accountid=25704","ObjectivesTo evaluate the effectiveness of safeguards to prevent large language models (LLMs) from being misused to generate health disinformation, and to evaluate the transparency of artificial intelligence (AI) developers regarding their risk mitigation processes against observed vulnerabilities.DesignRepeated cross sectional analysis.SettingPublicly accessible LLMs.MethodsIn a repeated cross sectional analysis, four LLMs (via chatbots/assistant interfaces) were evaluated: OpenAI’s GPT-4 (via ChatGPT and Microsoft’s Copilot), Google’s PaLM 2 and newly released Gemini Pro (via Bard), Anthropic’s Claude 2 (via Poe), and Meta’s Llama 2 (via HuggingChat). In September 2023, these LLMs were prompted to generate health disinformation on two topics: sunscreen as a cause of skin cancer and the alkaline diet as a cancer cure. Jailbreaking techniques (ie, attempts to bypass safeguards) were evaluated if required. For LLMs with observed safeguarding vulnerabilities, the processes for reporting outputs of concern were audited. 12 weeks after initial investigations, the disinformation generation capabilities of the LLMs were re-evaluated to assess any subsequent improvements in safeguards.Main outcome measuresThe main outcome measures were whether safeguards prevented the generation of health disinformation, and the transparency of risk mitigation processes against health disinformation.ResultsClaude 2 (via Poe) declined 130 prompts submitted across the two study timepoints requesting the generation of content claiming that sunscreen causes skin cancer or that the alkaline diet is a cure for cancer, even with jailbreaking attempts. GPT-4 (via Copilot) initially refused to generate health disinformation, even with jailbreaking attempts—although this was not the case at 12 weeks. In contrast, GPT-4 (via ChatGPT), PaLM 2/Gemini Pro (via Bard), and Llama 2 (via HuggingChat) consistently generated health disinformation blogs. In September 2023 evaluations, these LLMs facilitated the generation of 113 unique cancer disinformation blogs, totalling more than 40 000 words, without requiring jailbreaking attempts. The refusal rate across the evaluation timepoints for these LLMs was only 5% (7 of 150), and as prompted the LLM generated blogs incorporated attention grabbing titles, authentic looking (fake or fictional) references, fabricated testimonials from patients and clinicians, and they targeted diverse demographic groups. Although each LLM evaluated had mechanisms to report observed outputs of concern, the developers did not respond when observations of vulnerabilities were reported.ConclusionsThis study found that although effective safeguards are feasible to prevent LLMs from being misused to generate health disinformation, they were inconsistently implemented. Furthermore, effective processes for reporting safeguard problems were lacking. Enhanced regulation, transparency, and routine auditing are required to help prevent LLMs from contributing to the mass generation of health disinformation.","2024-03-20","2024-12-03 02:45:28","2024-12-03 02:45:28","","","","","384","","","","","","","","","","English","","","Biological Science Database","2968998199","","","Place: London Publisher: BMJ Publishing Group LTD","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"YQYTYGKW","journalArticle","2024","Tang, Arthur, PhD; Li, Kin-Kit, PhD; Kwok, Kin On, PhD; Cao, Liujiao, MD; Luong, Stanley, PhD; Tam, Wilson, PhD","The importance of transparency: Declaring the use of generative artificial intelligence (AI) in academic writing","Journal of Nursing Scholarship","","15276546","10.1111/jnu.12938","https://www.proquest.com/scholarly-journals/importance-transparency-declaring-use-generative/docview/3058873705/se-2?accountid=25704","The integration of generative artificial intelligence (Al) into academic research writing has revolutionized the field, offering powerful tools like ChatGPT and Bard to aid researchers in content generation and idea enhancement. We explore the current state of transparency regarding generative Al use in nursing academic research journals, emphasizing the need for explicitly declaring the use of generative Al by authors in the manuscript. Out of 125 nursing studies journals, 37.6% required explicit statements about generative Al use in their authors' guidelines. No significant differences in impact factors or journal categories were found between journals with and without such requirement. A similar evaluation of medicine, general and internal journals showed a lower percentage (14.5%) including the information about generative Al usage. Declaring generative Al tool usage is crucial for maintaining the transparency and credibility in academic writing. Additionally, extending the requirement for Al usage declarations to journal reviewers can enhance the quality of peer review and combat predatory journals in the academic publishing landscape. Our study highlights the need for active participation from nursing researchers in discussions surrounding standardization of generative Al declaration in academic research writing.","2024-03","2024-12-03 02:45:28","2024-12-03 02:45:28","","334-338","","2","56","","","","","","","","","","English","","","Public Health Database; Sociology Database","3058873705","","","Place: Indianapolis Publisher: Blackwell Publishing Ltd.","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"PNL77GKU","journalArticle","2024","Morreel, Stefan; Verhoeven, Veronique; Mathysen, Danny","Microsoft Bing outperforms five other generative artificial intelligence chatbots in the Antwerp University multiple choice medical license exam","PLOS Digital Health","","","10.1371/journal.pdig.0000349","https://www.proquest.com/scholarly-journals/microsoft-bing-outperforms-five-other-generative/docview/3086949905/se-2?accountid=25704","Recently developed chatbots based on large language models (further called bots) have promising features which could facilitate medical education. Several bots are freely available, but their proficiency has been insufficiently evaluated. In this study the authors have tested the current performance on the multiple-choice medical licensing exam of University of Antwerp (Belgium) of six widely used bots: ChatGPT (OpenAI), Bard (Google), New Bing (Microsoft), Claude instant (Anthropic), Claude+ (Anthropic) and GPT-4 (OpenAI). The primary outcome was the performance on the exam expressed as a proportion of correct answers. Secondary analyses were done for a variety of features in the exam questions: easy versus difficult questions, grammatically positive versus negative questions, and clinical vignettes versus theoretical questions. Reasoning errors and untruthful statements (hallucinations) in the bots’ answers were examined. All bots passed the exam; Bing and GPT-4 (both 76% correct answers) outperformed the other bots (62–67%, p = 0.03) and students (61%). Bots performed worse on difficult questions (62%, p = 0.06), but outperformed students (32%) on those questions even more (p<0.01). Hallucinations were found in 7% of Bing’s and GPT4’s answers, significantly lower than Bard (22%, p<0.01) and Claude Instant (19%, p = 0.02). Although the creators of all bots try to some extent to avoid their bots being used as a medical doctor, none of the tested bots succeeded as none refused to answer all clinical case questions.Bing was able to detect weak or ambiguous exam questions. Bots could be used as a time efficient tool to improve the quality of a multiple-choice exam.","2024-02","2024-12-03 02:45:28","2024-12-03 02:45:28","","","","2","3","","","","","","","","","","English","","","Public Health Database; Publicly Available Content Database","3086949905","","","Place: San Francisco Publisher: Public Library of Science","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SYSM2LJL","journalArticle","2024","Mihai, Laurenţiu; Mănescu, Leonardo-Geo; Vasilescu, Laura; Băndoi, Anca; Sitnikov, Catalina","A SYSTEMATIC ANALYSIS OF NEW APPROACHES TO DIGITAL ECONOMIC EDUCATION BASED ON THE USE OF AI TECHNOLOGIES","Amfiteatru Economic","","15829146","10.24818/EA/2024/65/201","https://www.proquest.com/scholarly-journals/systematic-analysis-new-approaches-digital/docview/3015085603/se-2?accountid=25704","Since the start of the global COVID-19 pandemic, most higher education institutions have been forced to exchange the traditional teaching environment for online education, and many chose to continue to use digital education platforms after its end, especially through the use of artificial intelligence (AI) applications and technologies. Our research represents a systematic literature review of a number of 60 scientific papers, aiming to study how the concept of digital economic education based on artificial intelligence is approached in the scientific literature, how artificial intelligence applications are used in digital economic education, and which are the critical success factors and the challenges that this domain is facing. Our findings have shown that most researchers define digital education as the use of technology to support educational activities, while highlighting artificial intelligence and its different applications as an essential clement of current digital education, which has the potential to fundamentally transform the economic processes. The large-scale adoption of e-learning systems based on artificial intelligence is influenced by technology, by their superior capabilities in terms of coherently correlating the learning and studying processes, by the teachers' trust in the results generated by these technologies and by cultural factors, while facing several challenges related to the users' resistance to change, digital competences, the systems' accessibility, as well as financial issues. Furthermore, based on this research endeavour, a model system of correlations and elements has been developed, specifically for the digital economic education based on artificial intelligence. This model includes both the success factors and the unique challenges inherent in the particular application areas.","2024-02","2024-12-03 02:45:28","2024-12-03 02:45:28","","201-219","","65","26","","","","","","","","","","English","","","Coronavirus Research Database; Publicly Available Content Database","3015085603","","","Place: Bucharest Publisher: Bucharest Academy of Economic Studies, Faculty of Commerce","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"IXVJ5MNA","journalArticle","2024","Zaharia, Rodica Milena","Challenges for Competence-Oriented Education in the Context of the Development of Artificial Intelligence Systems","Amfiteatru Economic","","15829146","10.24818/EA/2024/65/6","https://www.proquest.com/scholarly-journals/challenges-competence-oriented-education-context/docview/3015085076/se-2?accountid=25704","Intelligence systems are more acute than ever. On one hand, education, in general, must be that process that ensures the skills that allow the creation of Artificial Intelligence. Algorithms, chatbots, learning machines were created as a result of knowledge, skills and abilities acquired through education. They will continue to be the result of the creativity, inventiveness and daring of those who are educated in this spirit, of those who are cultivated with these skills. ""Working"" with Artificial Intelligence systems also requires knowledge, skills and abilities that the entire educational system must provide. Operating with Artificial Intelligence systems needs not only specific skills, but also their permanent adaptation as Artificial Intelligence systems become more complex and more performant. Academic research is intended not only to identify the challenges that education faces and will face with the development of Artificial Intelligence systems, but also to find solutions, to propose measures to manage the consequences of the penetration of Artificial Intelligence into life of society.","2024-02","2024-12-03 02:45:28","2024-12-03 02:45:28","","6-11","","65","26","","","","","","","","","","English","","","Publicly Available Content Database","3015085076","","","Place: Bucharest Publisher: Bucharest Academy of Economic Studies, Faculty of Commerce","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SMVKKSXI","journalArticle","2024","Xu, Alan Y; Piranio, Vincent S; Speakman Skye; Rosen, Chelsea D; Lu, Sally; Lamprecht, Chris; Medina, Robert E; Corrielus Maisha; Griffin, Ian T; Chatham, Corinne E; Abchee, Nicolas J; Stribling, Daniel; Huynh, Phuong B; Harrell, Heather; Shickel Benjamin; Brennan, Meghan","A Pilot Study of Medical Student Opinions on Large Language Models","Cureus","","","10.7759/cureus.71946","https://www.proquest.com/scholarly-journals/pilot-study-medical-student-opinions-on-large/docview/3134440044/se-2?accountid=25704","IntroductionArtificial intelligence (AI) has long garnered significant interest in the medical field. Large language models (LLMs) have popularized the use of AI for the public through chatbots such as ChatGPT and have become an easily accessible and recognizable medical resource for medical students. Here, we investigate how medical students are currently utilizing LLM-based tools throughout medical education and examine medical student perception of these tools.MethodsA cross-sectional survey was administered to current medical students at the University of Florida College of Medicine (UFCOM) in January 2024 discussing the utilization of AI and LLM tools and perspectives on the current and future role of AI in medicine.ResultsAll 102 respondents reported having heard of LLM-based chatbots such as ChatGPT, Bard, Bing Chat, and Claude. Sixty-nine percent (69%; 70/102) of respondents reported having used them for medical-related purposes at least once a month. Seventy-seven point one percent (77.1%; 54/70) reported the information provided by them to be very accurate or somewhat accurate, and 80% (55/70) reported that they were likely to continue using them in their future medical practice. Those with some baseline understanding of and exposure to AI were 3.26 (p=0.020) and 4.30 (p=0.002) times more likely to have used an LLM-based chatbot, respectively, and 5.06 (p=0.021) and 3.38 (p=0.039) times more likely to cross-check information obtained from them, respectively, compared to those with little to no baseline understanding or exposure. Furthermore, those with some exposure to AI in medical school were 2.70 (p=0.039) and 4.61 (p=0.0004) times more likely to trust AI with clinical decision-making currently and in the next 5 years, respectively, than those with little to no exposure. Those who had used an LLM-based chatbot were 4.31 (p=0.019) times more likely to trust AI with clinical decision-making currently compared to those who had not used one.ConclusionLLM-based chatbots, such as ChatGPT, are not only making their way into the medical student repertoire of study resources but are also being utilized in the setting of patient care and research. Medical students who participated in the survey generally had a positive perception of LLM-based chatbots and reported they were likely to continue using them in the future. Previous AI knowledge and exposure correlated with more conscientious use of these tools such as cross-checking information. Combined with our finding that all respondents believed AI should be taught in the medical curriculum, our study highlights a key opportunity in medical education to acclimate medical students to AI now.","2024","2024-12-03 02:45:28","2024-12-03 02:45:28","","","","10","16","","","","","","","","","","English","","","Publicly Available Content Database","3134440044","","","Place: Palo Alto Publisher: Cureus Inc.","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RPNGZN3J","journalArticle","2024","Won Jin Seo; Kim, Mihui","Utilization of Generative Artificial Intelligence in Nursing Education: A Topic Modeling Analysis","Education Sciences","","","10.3390/educsci14111234","https://www.proquest.com/scholarly-journals/utilization-generative-artificial-intelligence/docview/3132958783/se-2?accountid=25704","The advent of artificial intelligence (AI) has prompted the introduction of novel digital technologies, including mobile learning and metaverse learning, into nursing students’ learning environments. This study used text network and topic modeling analyses to identify the research trends in generative AI in nursing education for students and patients in schools, hospitals, and community settings. Additionally, an ego network analysis using strengths, weaknesses, opportunities, and threats (SWOT) words was performed to develop a comprehensive understanding of factors that impact the integration of generative AI in nursing education. The literature was searched from five databases published until July 2024. After excluding studies whose abstracts were not available and removing duplicates, 139 articles were identified. The seven derived topics were labeled as usability in future scientific applications, application and integration of technology, simulation education, utility in image and text analysis, performance in exams, utility in assignments, and patient education. The ego network analysis focusing on the SWOT keywords revealed “healthcare”, “use”, and “risk” were common keywords. The limited emphasis on “threats”, “strengths”, and “weaknesses” compared to “opportunities” in the SWOT analysis indicated that these areas are relatively underexplored in nursing education. To integrate generative AI technology into education such as simulation training, teaching activities, and the development of personalized learning, it is necessary to identify relevant internal strengths and weaknesses of schools, hospitals, and communities that apply it, and plan practical application strategies aligned with clear institutional guidelines.","2024","2024-12-03 02:45:28","2024-12-03 02:45:28","","1234","","11","14","","","","","","","","","","English","","","Coronavirus Research Database; Publicly Available Content Database","3132958783","","","Place: Basel Publisher: MDPI AG","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"9EQI3GJN","journalArticle","2024","Yazdan Ahmad Qadri; Khurshid, Ahmad; Kim, Sung Won","Artificial General Intelligence for the Detection of Neurodegenerative Disorders","Sensors","","","10.3390/s24206658","https://www.proquest.com/scholarly-journals/artificial-general-intelligence-detection/docview/3120766575/se-2?accountid=25704","Parkinson’s disease and Alzheimer’s disease are among the most common neurodegenerative disorders. These diseases are correlated with advancing age and are hence increasingly becoming prevalent in developed countries due to an increasingly aging demographic. Several tools are used to predict and diagnose these diseases, including pathological and genetic tests, radiological scans, and clinical examinations. Artificial intelligence is evolving to artificial general intelligence, which mimics the human learning process. Large language models can use an enormous volume of online and offline resources to gain knowledge and use it to perform different types of tasks. This work presents an understanding of two major neurodegenerative disorders, artificial general intelligence, and the efficacy of using artificial general intelligence in detecting and predicting these neurodegenerative disorders. A detailed discussion on detecting these neurodegenerative diseases using artificial general intelligence by analyzing diagnostic data is presented. An Internet of Things-based ubiquitous monitoring and treatment framework is presented. An outline for future research opportunities based on the challenges in this area is also presented.","2024","2024-12-03 02:45:28","2024-12-03 02:45:28","","6658","","20","24","","","","","","","","","","English","","","Publicly Available Content Database","3120766575","","","Place: Basel Publisher: MDPI AG","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RYGTZJT7","journalArticle","2024","Gasaymeh, Al-Mothana M; Beirat, Mohammad A; Asma’a A Abu Qbeita","University Students’ Insights of Generative Artificial Intelligence (AI) Writing Tools","Education Sciences","","","10.3390/educsci14101062","https://www.proquest.com/scholarly-journals/university-students-insights-generative/docview/3120629308/se-2?accountid=25704","The current study examined university students’ insights into generative AI writing tools regarding their familiarity with, perceived concerns about, and perceived benefits of these tools in their academic work. The study used a cross-sectional descriptive research design, and data were collected using a questionnaire instrument. The participants were ninety-five undergraduate and graduate students from a College of Education at a university in Jordan. The results show that university students show moderate familiarity with generative AI writing tools (M = 3.14, SD = 0.81), especially in engagement but lacking technical knowledge. They also have moderate concerns (M = 3.35, SD = 0.85), particularly about misinformation and data security. Despite these concerns, students recognize the benefits (M = 3.62, SD = 0.81), especially regarding the capabilities of these tools in simulating creativity and fostering innovation. In addition, the results showed that gender and educational level appear to have little effect on familiarity, concerns, and perceived benefits regarding these tools. Based on the findings, the study recommends enhancing students’ familiarity with generative AI tools through providing technical training, hands-on opportunities, and ethical discussions. In addition, the study recommends addressing students’ concerns regarding generative AI writing tools by improving data security related to generative AI, providing ethical guidelines regarding the use of these tools, and boosting AI literacy. Finally, it is recommended to enhance students’ perceptions of the benefits of generative AI writing tools by highlighting the creative potential of these tools within the educational setting, using these tools to offer personalized learning experiences that adapt to individual learning styles, and promoting collaboration through generative AI writing tools.","2024","2024-12-03 02:45:28","2024-12-03 02:45:28","","1062","","10","14","","","","","","","","","","English","","","Publicly Available Content Database","3120629308","","","Place: Basel Publisher: MDPI AG","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ERFHLT8A","journalArticle","2024","Caiza, Gustavo; Sanguña, Verónica; Tusa, Natalia; Masaquiza, Violeta; Ortiz, Alexandra; Garcia, Marcelo V","Navigating Governmental Choices: A Comprehensive Review of Artificial Intelligence’s Impact on Decision-Making","Informatics","","","10.3390/informatics11030064","https://www.proquest.com/scholarly-journals/navigating-governmental-choices-comprehensive/docview/3110480677/se-2?accountid=25704","The integration of artificial intelligence (AI) into government decision-making is rapidly gaining traction in public administration and politics. This scoping review, guided by PRISMA protocols, examines 50 articles from reputable sources like Scopus and SpringerLink to analyze the trends, benefits, and challenges of AI in governance. While AI offers substantial potential to enhance government efficiency and service delivery, significant barriers remain, including concerns about bias, transparency, public acceptance, and accountability. This review underscores the need for ongoing research and dialogue on the ethical, social, and practical implications of AI in government to ensure the responsible and inclusive adoption of AI-driven public services.","2024","2024-12-03 02:45:28","2024-12-03 02:45:28","","64","","3","11","","","","","","","","","","English","","","Coronavirus Research Database; Publicly Available Content Database","3110480677","","","Place: Basel Publisher: MDPI AG","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2L2ENVV8","journalArticle","2024","Papageorgiou, George; Sarlis, Vangelis; Maragoudakis, Manolis; Tjortjis, Christos","Enhancing E-Government Services through State-of-the-Art, Modular, and Reproducible Architecture over Large Language Models","Applied Sciences","","","10.3390/app14188259","https://www.proquest.com/scholarly-journals/enhancing-e-government-services-through-state-art/docview/3110311931/se-2?accountid=25704","Integrating Large Language Models (LLMs) into e-government applications has the potential to improve public service delivery through advanced data processing and automation. This paper explores critical aspects of a modular and reproducible architecture based on Retrieval-Augmented Generation (RAG) for deploying LLM-based assistants within e-government systems. By examining current practices and challenges, we propose a framework ensuring that Artificial Intelligence (AI) systems are modular and reproducible, essential for maintaining scalability, transparency, and ethical standards. Our approach utilizing Haystack demonstrates a complete multi-agent Generative AI (GAI) virtual assistant that facilitates scalability and reproducibility by allowing individual components to be independently scaled. This research focuses on a comprehensive review of the existing literature and presents case study examples to demonstrate how such an architecture can enhance public service operations. This framework provides a valuable case study for researchers, policymakers, and practitioners interested in exploring the integration of advanced computational linguistics and LLMs into e-government services, although it could benefit from further empirical validation.","2024","2024-12-03 02:45:29","2024-12-03 02:45:29","","8259","","18","14","","","","","","","","","","English","","","Publicly Available Content Database","3110311931","","","Place: Basel Publisher: MDPI AG","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RSPUIFPD","journalArticle","2024","Sallam, Malik; Al-Mahzoum, Kholoud; Alshuaib, Omaima; Alhajri, Hawajer; Alotaibi, Fatmah; Dalal Alkhurainej; Mohammad Yahya Al-Balwah; Barakat, Muna; Egger, Jan","Language discrepancies in the performance of generative artificial intelligence models: an examination of infectious disease queries in English and Arabic","BMC Infectious Diseases","","","10.1186/s12879-024-09725-y","https://www.proquest.com/scholarly-journals/language-discrepancies-performance-generative/docview/3091289881/se-2?accountid=25704","BackgroundAssessment of artificial intelligence (AI)-based models across languages is crucial to ensure equitable access and accuracy of information in multilingual contexts. This study aimed to compare AI model efficiency in English and Arabic for infectious disease queries.MethodsThe study employed the METRICS checklist for the design and reporting of AI-based studies in healthcare. The AI models tested included ChatGPT-3.5, ChatGPT-4, Bing, and Bard. The queries comprised 15 questions on HIV/AIDS, tuberculosis, malaria, COVID-19, and influenza. The AI-generated content was assessed by two bilingual experts using the validated CLEAR tool.ResultsIn comparing AI models’ performance in English and Arabic for infectious disease queries, variability was noted. English queries showed consistently superior performance, with Bard leading, followed by Bing, ChatGPT-4, and ChatGPT-3.5 (P = .012). The same trend was observed in Arabic, albeit without statistical significance (P = .082). Stratified analysis revealed higher scores for English in most CLEAR components, notably in completeness, accuracy, appropriateness, and relevance, especially with ChatGPT-3.5 and Bard. Across the five infectious disease topics, English outperformed Arabic, except for flu queries in Bing and Bard. The four AI models’ performance in English was rated as “excellent”, significantly outperforming their “above-average” Arabic counterparts (P = .002).ConclusionsDisparity in AI model performance was noticed between English and Arabic in response to infectious disease queries. This language variation can negatively impact the quality of health content delivered by AI models among native speakers of Arabic. This issue is recommended to be addressed by AI developers, with the ultimate goal of enhancing health outcomes.","2024","2024-12-03 02:45:29","2024-12-03 02:45:29","","1-13","","","24","","","","","","","","","","English","","","Public Health Database; Publicly Available Content Database","3091289881","","","Place: London Publisher: BioMed Central","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LBVL2EI5","journalArticle","2024","He, Haohuai; He, Bing; Guan, Lei; Zhao, Yu; Jiang, Feng; Chen, Guanxing; Zhu, Qingge; Chen, Calvin Yu-Chian; Li, Ting; Yao, Jianhua","De novo generation of SARS-CoV-2 antibody CDRH3 with a pre-trained generative large language model","Nature Communications","","","10.1038/s41467-024-50903-y","https://www.proquest.com/scholarly-journals/de-novo-generation-sars-cov-2-antibody-cdrh3-with/docview/3091215704/se-2?accountid=25704","Artificial Intelligence (AI) techniques have made great advances in assisting antibody design. However, antibody design still heavily relies on isolating antigen-specific antibodies from serum, which is a resource-intensive and time-consuming process. To address this issue, we propose a Pre-trained Antibody generative large Language Model (PALM-H3) for the de novo generation of artificial antibodies heavy chain complementarity-determining region 3 (CDRH3) with desired antigen-binding specificity, reducing the reliance on natural antibodies. We also build a high-precision model antigen-antibody binder (A2binder) that pairs antigen epitope sequences with antibody sequences to predict binding specificity and affinity. PALM-H3-generated antibodies exhibit binding ability to SARS-CoV-2 antigens, including the emerging XBB variant, as confirmed through in-silico analysis and in-vitro assays. The in-vitro assays validate that PALM-H3-generated antibodies achieve high binding affinity and potent neutralization capability against spike proteins of SARS-CoV-2 wild-type, Alpha, Delta, and the emerging XBB variant. Meanwhile, A2binder demonstrates exceptional predictive performance on binding specificity for various epitopes and variants. Furthermore, by incorporating the attention mechanism inherent in the Roformer architecture into the PALM-H3 model, we improve its interpretability, providing crucial insights into the fundamental principles of antibody design.Antibody design still heavily relies on isolating antigen-specific antibodies from serum. Here the authors report a Pre-trained Antibody generative large Language Model (PALM-H3) for the de novo generation of artificial antibodies heavy chain complementarity-determining region 3 with desired antigen-binding specificity.","2024","2024-12-03 02:45:29","2024-12-03 02:45:29","","6867","","1","15","","","","","","","","","","English","","","Biological Science Database; Publicly Available Content Database","3091215704","","","Place: London Publisher: Nature Publishing Group","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ACPF4EPD","journalArticle","2024","Liu, Huanan; Wang, Yan; Zhoufu Yan","Artificial Intelligence and Food Processing Firms Productivity: Evidence from China","Sustainability","","","10.3390/su16145928","https://www.proquest.com/scholarly-journals/artificial-intelligence-food-processing-firms/docview/3085058767/se-2?accountid=25704","Amidst the tremendous evolution of the digital economy and the expedited establishment of a new development paradigm, the use of artificial intelligence (AI) technologies holds significant importance in achieving superior economic development. While much of the previous research focused on the macroeconomic impact of AI, this study examined how AI technology affects food processing firm performance, productivity, and labor skill structure at the food processing firm level. This study utilized panel data from listed food processing enterprises in Shanghai and Shenzhen spanning from 2010 to 2021, performing textual analysis on the annual reports of listed companies and then creating enterprise-level AI indicators to empirically examine the influence of AI applications on enterprise performance and its underlying mechanisms. The findings indicate a substantial improvement in business performance due to the application of artificial intelligence, which is a conclusion corroborated through a series of stability tests. Exploring channels and mechanisms, the analysis revealed that AI-driven advancements in production technologies stimulated the requirement for highly skilled labor, thereby inducing shifts in the labor force’s structure. Further investigation demonstrated that artificial intelligence contributed to enhancing the total factor productivity, consequently bolstering the overall enterprise performance. A heterogeneity analysis showed that firm-level factors, such as the nature of property rights and factor intensity, had an impact on the influence of AI on firm performance. In addition, the geographic location and time of year of a company also had impacts on the productivity benefits of artificial intelligence. This research deepened the cognition and understanding of the role played by AI in the production process at the micro-enterprise level and provided suggestions for promoting the development of artificial intelligence technologies at the micro-enterprise level, which will facilitate the transformation of the labor structure to further augment enterprise efficiency.","2024","2024-12-03 02:45:29","2024-12-03 02:45:29","","5928","","14","16","","","","","","","","","","English","","","Publicly Available Content Database","3085058767","","","Place: Basel Publisher: MDPI AG","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"QLA4NT85","journalArticle","2024","Gou, Fangfang; Liu, Jun; Xiao, Chunwen; Wu, Jia","Research on Artificial-Intelligence-Assisted Medicine: A Survey on Medical Artificial Intelligence","Diagnostics","","","10.3390/diagnostics14141472","https://www.proquest.com/scholarly-journals/research-on-artificial-intelligence-assisted/docview/3084795439/se-2?accountid=25704","With the improvement of economic conditions and the increase in living standards, people’s attention in regard to health is also continuously increasing. They are beginning to place their hopes on machines, expecting artificial intelligence (AI) to provide a more humanized medical environment and personalized services, thus greatly expanding the supply and bridging the gap between resource supply and demand. With the development of IoT technology, the arrival of the 5G and 6G communication era, and the enhancement of computing capabilities in particular, the development and application of AI-assisted healthcare have been further promoted. Currently, research on and the application of artificial intelligence in the field of medical assistance are continuously deepening and expanding. AI holds immense economic value and has many potential applications in regard to medical institutions, patients, and healthcare professionals. It has the ability to enhance medical efficiency, reduce healthcare costs, improve the quality of healthcare services, and provide a more intelligent and humanized service experience for healthcare professionals and patients. This study elaborates on AI development history and development timelines in the medical field, types of AI technologies in healthcare informatics, the application of AI in the medical field, and opportunities and challenges of AI in the field of medicine. The combination of healthcare and artificial intelligence has a profound impact on human life, improving human health levels and quality of life and changing human lifestyles.","2024","2024-12-03 02:45:29","2024-12-03 02:45:29","","1472","","14","14","","","","","","","","","","English","","","Coronavirus Research Database; Publicly Available Content Database","3084795439","","","Place: Basel Publisher: MDPI AG","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"98T6Y65S","journalArticle","2024","Campbell, Laurie O; Cox, Thomas D","Facilitating the Research Writing Process with Generative Artificial Intelligence","Journal of the Scholarship of Teaching and Learning","","15279316","10.14434/josotl.v24i2.36580","https://www.proquest.com/scholarly-journals/facilitating-research-writing-process-with/docview/3076783982/se-2?accountid=25704","In higher education, generative chatbots have infiltrated teaching and learning. Concerns about how and if to utilize chatbots in the classroom are at the forefront of scholarly discussion. This quick-hit article presents a plan to teach learners about generative AI writing tools and their ethical use for writing purposes. As generative AI tools continue to emerge, this guide can support instructors from all disciplines to engage learners in getting the most accurate information.","2024","2024-12-03 02:45:29","2024-12-03 02:45:29","","","","2","24","","","","","","","","","","English","","","Publicly Available Content Database","3076783982","","","Place: Indianapolis Publisher: Indiana University Press","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JHP82BXI","journalArticle","2024","Beattie, Jacob; Neufeld, Sarah; Yang, Daniel; Chukwuma, Christian; Gul, Ahmed; Desai, Neil; Jiang, Steve; Dohopolski, Michael","Utilizing Large Language Models for Enhanced Clinical Trial Matching: A Study on Automation in Patient Screening","Cureus","","","10.7759/cureus.60044","https://www.proquest.com/scholarly-journals/utilizing-large-language-models-enhanced-clinical/docview/3073844481/se-2?accountid=25704","BackgroundClinical trial matching, essential for advancing medical research, involves detailed screening of potential participants to ensure alignment with specific trial requirements. Research staff face challenges due to the high volume of eligible patients and the complexity of varying eligibility criteria. The traditional manual process, both time-consuming and error-prone, often leads to missed opportunities. Recently, large language models (LLMs), specifically generative pre-trained transformers (GPTs), have become impressive and impactful tools. Utilizing such tools from artificial intelligence (AI) and natural language processing (NLP) may enhance the accuracy and efficiency of this process through automated patient screening against established criteria.MethodsUtilizing data from the National NLP Clinical Challenges (n2c2) 2018 Challenge, we utilized 202 longitudinal patient records. These records were annotated by medical professionals and evaluated against 13 selection criteria encompassing various health assessments. Our approach involved embedding medical documents into a vector database to determine relevant document sections and then using an LLM (OpenAI's GPT-3.5 Turbo and GPT-4) in tandem with structured and chain-of-thought prompting techniques for systematic document assessment against the criteria. Misclassified criteria were also examined to identify classification challenges.ResultsThis study achieved an accuracy of 0.81, sensitivity of 0.80, specificity of 0.82, and a micro F1 score of 0.79 using GPT-3.5 Turbo, and an accuracy of 0.87, sensitivity of 0.85, specificity of 0.89, and micro F1 score of 0.86 using GPT-4. Notably, some criteria in the ground truth appeared mislabeled, an issue we couldn’t explore further due to insufficient label generation guidelines on the website.ConclusionOur findings underscore the potential of AI and NLP technologies, including LLMs, in the clinical trial matching process. The study demonstrated strong capabilities in identifying eligible patients and minimizing false inclusions. Such automated systems promise to alleviate the workload of research staff and improve clinical trial enrollment, thus accelerating the process and enhancing the overall feasibility of clinical research. Further work is needed to determine the potential of this approach when implemented on real clinical data.","2024","2024-12-03 02:45:29","2024-12-03 02:45:29","","","","5","16","","","","","","","","","","English","","","Publicly Available Content Database","3073844481","","","Place: Palo Alto Publisher: Cureus Inc.","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"BSFFW4DE","journalArticle","2024","Alfirević, Nikša; Daniela Garbin Praničević; Mabić, Mirela","Custom-Trained Large Language Models as Open Educational Resources: An Exploratory Research of a Business Management Educational Chatbot in Croatia and Bosnia and Herzegovina","Sustainability","","","10.3390/su16124929","https://www.proquest.com/scholarly-journals/custom-trained-large-language-models-as-open/docview/3072717646/se-2?accountid=25704","This paper explores the contribution of custom-trained Large Language Models (LLMs) to developing Open Education Resources (OERs) in higher education. Our empirical analysis is based on the case of a custom LLM specialized for teaching business management in higher education. This custom LLM has been conceptualized as a virtual teaching companion, aimed to serve as an OER, and trained using the authors’ licensed educational materials. It has been designed without coding or specialized machine learning tools using the commercially available ChatGPT Plus tool and a third-party Artificial Intelligence (AI) chatbot delivery service. This new breed of AI tools has the potential for wide implementation, as they can be designed by faculty using only conventional LLM prompting techniques in plain English. This paper focuses on the opportunities for custom-trained LLMs to create Open Educational Resources (OERs) and democratize academic teaching and learning. Our approach to AI chatbot evaluation is based on a mixed-mode approach, combining a qualitative analysis of expert opinions with a subsequent (quantitative) student survey. We have collected and analyzed responses from four subject experts and 204 business students at the Faculty of Economics, Business and Tourism Split (Croatia) and Faculty of Economics Mostar (Bosnia and Herzegovina). We used thematic analysis in the qualitative segment of our research. In the quantitative segment of empirical research, we used statistical methods and the SPSS 25 software package to analyze student responses to the modified BUS-15 questionnaire. Research results show that students positively evaluate the business management learning chatbot and consider it useful and responsive. However, interviewed experts raised concerns about the adequacy of chatbot answers to complex queries. They suggested that the custom-trained LLM lags behind the generic LLMs (such as ChatGPT, Gemini, and others). These findings suggest that custom LLMs might be useful tools for developing OERs in higher education. However, their training data, conversational capabilities, technical execution, and response speed must be monitored and improved. Since this research presents a novelty in the extant literature on AI in education, it requires further research on custom GPTs in education, including their use in multiple academic disciplines and contexts.","2024","2024-12-03 02:45:29","2024-12-03 02:45:29","","4929","","12","16","","","","","","","","","","English","","","Publicly Available Content Database","3072717646","","","Place: Basel Publisher: MDPI AG","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"UBY2TW4J","journalArticle","2024","Gupta, Varun","An Empirical Evaluation of a Generative Artificial Intelligence Technology Adoption Model from Entrepreneurs’ Perspectives","Systems","","","10.3390/systems12030103","https://www.proquest.com/scholarly-journals/empirical-evaluation-generative-artificial/docview/3003522745/se-2?accountid=25704","Technologies, such as Chat Generative Pre-Trained Transformer (ChatGPT, Smart PLS version 4), are prime examples of Generative Artificial Intelligence (AI), which is a constantly evolving area. SMEs, particularly startups, can obtain a competitive edge, innovate their business models, gain business value, and undergo a digital transformation by implementing these technologies. Continuous but gradual experimentation with these technologies is the foundation for their adoption. The experience that comes from trying new technologies can help entrepreneurs adopt new technologies more strategically and experiment more with them. The urgent need for an in-depth investigation is highlighted by the paucity of previous research on ChatGPT uptake in the startup context, particularly from an entrepreneurial perspective. The objective of this research study is to empirically validate the Generative AI technology adoption model to establish the direction and strength of the correlations among the adoption factors from the perspectives of the entrepreneurs. The data are collected from 482 entrepreneurs who exhibit great diversity in their genders, the countries in which their startups are located, the industries their startups serve, their age, their educational levels, their work experience as entrepreneurs, and the length of time the startups have been on the market. Collected data are analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique, which results in a statistical examination of the relationships between the adoption model’s factors. The results indicate that social influence, domain experience, technology familiarity, system quality, training and support, interaction convenience, and anthropomorphism are the factors that impact the pre-perception and perception phase of adoption. These factors motivate entrepreneurs to experiment more with the technology, thereby building perceptions of its usefulness, perceived ease of use, and perceived enjoyment, three factors that in turn affect emotions toward the technology and, finally, switching intentions. Control variables like age, gender, and educational attainment have no appreciable effect on switching intentions to alternatives of the Generative AI technology. Rather, the experience factor of running businesses shows itself to be a crucial one. The results have practical implications for entrepreneurs and other innovation ecosystem actors, including, for instance, technology providers, libraries, and policymakers. This research study enriches the Generative AI technology acceptance theory and extends the existing literature by introducing new adoption variables and stages specific to entrepreneurship.","2024","2024-12-03 02:45:29","2024-12-03 02:45:29","","103","","3","12","","","","","","","","","","English","","","Publicly Available Content Database","3003522745","","","Place: Basel Publisher: MDPI AG","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"4FRCCWFN","journalArticle","2024","Ivanova, Malinka; Grosseck, Gabriela; Holotescu, Carmen","Unveiling Insights: A Bibliometric Analysis of Artificial Intelligence in Teaching","Informatics","","","10.3390/informatics11010010","https://www.proquest.com/scholarly-journals/unveiling-insights-bibliometric-analysis/docview/3002009002/se-2?accountid=25704","The penetration of intelligent applications in education is rapidly increasing, posing a number of questions of a different nature to the educational community. This paper is coming to analyze and outline the influence of artificial intelligence (AI) on teaching practice which is an essential problem considering its growing utilization and pervasion on a global scale. A bibliometric approach is applied to outdraw the “big picture” considering gathered bibliographic data from scientific databases Scopus and Web of Science. Data on relevant publications matching the query “artificial intelligence and teaching” over the past 5 years have been researched and processed through Biblioshiny in R environment in order to establish a descriptive structure of the scientific production, to determine the impact of scientific publications, to trace collaboration patterns and to identify key research areas and emerging trends. The results point out the growth in scientific production lately that is an indicator of increased interest in the investigated topic by researchers who mainly work in collaborative teams as some of them are from different countries and institutions. The identified key research areas include techniques used in educational applications, such as artificial intelligence, machine learning, and deep learning. Additionally, there is a focus on applicable technologies like ChatGPT, learning analytics, and virtual reality. The research also explores the context of application for these techniques and technologies in various educational settings, including teaching, higher education, active learning, e-learning, and online learning. Based on our findings, the trending research topics can be encapsulated by terms such as ChatGPT, chatbots, AI, generative AI, machine learning, emotion recognition, large language models, convolutional neural networks, and decision theory. These findings offer valuable insights into the current landscape of research interests in the field.","2024","2024-12-03 02:45:29","2024-12-03 02:45:29","","10","","1","11","","","","","","","","","","English","","","Publicly Available Content Database","3002009002","","","Place: Basel Publisher: MDPI AG","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ZLHYSI95","journalArticle","2024","Truhn, Daniel; Eckardt, Jan-Niklas; Ferber, Dyke; Kather, Jakob Nikolas","Large language models and multimodal foundation models for precision oncology","NPJ Precision Oncology","","2397768X","10.1038/s41698-024-00573-2","https://www.proquest.com/scholarly-journals/large-language-models-multimodal-foundation/docview/2973347412/se-2?accountid=25704","The technological progress in artificial intelligence (AI) has massively accelerated since 2022, with far-reaching implications for oncology and cancer research. Large language models (LLMs) now perform at human-level competency in text processing. Notably, both text and image processing networks are increasingly based on transformer neural networks. This convergence enables the development of multimodal AI models that take diverse types of data as an input simultaneously, marking a qualitative shift from specialized niche models which were prevalent in the 2010s. This editorial summarizes these developments, which are expected to impact precision oncology in the coming years.","2024","2024-12-03 02:45:29","2024-12-03 02:45:29","","72","","1","8","","","","","","","","","","English","","","Publicly Available Content Database","2973347412","","","Place: London Publisher: Nature Publishing Group","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"WZR9GV88","journalArticle","2024","Rodriguez, Danissa V; Lawrence, Katharine; Gonzalez, Javier; Brandfield-Harvey, Beatrix; Xu, Lynn; Tasneem, Sumaiya; Levine, Defne L; Mann, Devin","Leveraging Generative AI Tools to Support the Development of Digital Solutions in Health Care Research: Case Study","JMIR Human Factors","","","10.2196/52885","https://www.proquest.com/scholarly-journals/leveraging-generative-ai-tools-support/docview/2956706936/se-2?accountid=25704","Background:Generative artificial intelligence has the potential to revolutionize health technology product development by improving coding quality, efficiency, documentation, quality assessment and review, and troubleshooting.Objective:This paper explores the application of a commercially available generative artificial intelligence tool (ChatGPT) to the development of a digital health behavior change intervention designed to support patient engagement in a commercial digital diabetes prevention program.Methods:We examined the capacity, advantages, and limitations of ChatGPT to support digital product idea conceptualization, intervention content development, and the software engineering process, including software requirement generation, software design, and code production. In total, 11 evaluators, each with at least 10 years of experience in fields of study ranging from medicine and implementation science to computer science, participated in the output review process (ChatGPT vs human-generated output). All had familiarity or prior exposure to the original personalized automatic messaging system intervention. The evaluators rated the ChatGPT-produced outputs in terms of understandability, usability, novelty, relevance, completeness, and efficiency.Results:Most metrics received positive scores. We identified that ChatGPT can (1) support developers to achieve high-quality products faster and (2) facilitate nontechnical communication and system understanding between technical and nontechnical team members around the development goal of rapid and easy-to-build computational solutions for medical technologies.Conclusions:ChatGPT can serve as a usable facilitator for researchers engaging in the software development life cycle, from product conceptualization to feature identification and user story development to code generation.Trial Registration:ClinicalTrials.gov NCT04049500; https://clinicaltrials.gov/ct2/show/NCT04049500","2024","2024-12-03 02:45:29","2024-12-03 02:45:29","","","","","11","","","","","","","","","","English","","","Publicly Available Content Database","2956706936","","","Place: Toronto Publisher: JMIR Publications","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KKQUFP4G","journalArticle","2024","Luan, Zhirong; Lai, Yujun; Huang, Rundong; Bai, Shuanghao; Zhang, Yuedi; Zhang, Haoran; Wang, Qian","Enhancing Robot Task Planning and Execution through Multi-Layer Large Language Models","Sensors","","","10.3390/s24051687","https://www.proquest.com/scholarly-journals/enhancing-robot-task-planning-execution-through/docview/2955909170/se-2?accountid=25704","Large language models have found utility in the domain of robot task planning and task decomposition. Nevertheless, the direct application of these models for instructing robots in task execution is not without its challenges. Limitations arise in handling more intricate tasks, encountering difficulties in effective interaction with the environment, and facing constraints in the practical executability of machine control instructions directly generated by such models. In response to these challenges, this research advocates for the implementation of a multi-layer large language model to augment a robot’s proficiency in handling complex tasks. The proposed model facilitates a meticulous layer-by-layer decomposition of tasks through the integration of multiple large language models, with the overarching goal of enhancing the accuracy of task planning. Within the task decomposition process, a visual language model is introduced as a sensor for environment perception. The outcomes of this perception process are subsequently assimilated into the large language model, thereby amalgamating the task objectives with environmental information. This integration, in turn, results in the generation of robot motion planning tailored to the specific characteristics of the current environment. Furthermore, to enhance the executability of task planning outputs from the large language model, a semantic alignment method is introduced. This method aligns task planning descriptions with the functional requirements of robot motion, thereby refining the overall compatibility and coherence of the generated instructions. To validate the efficacy of the proposed approach, an experimental platform is established utilizing an intelligent unmanned vehicle. This platform serves as a means to empirically verify the proficiency of the multi-layer large language model in addressing the intricate challenges associated with both robot task planning and execution.","2024","2024-12-03 02:45:29","2024-12-03 02:45:29","","1687","","5","24","","","","","","","","","","English","","","Publicly Available Content Database","2955909170","","","Place: Basel Publisher: MDPI AG","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CJG9MXDD","journalArticle","2024","Umer, Fahad; Adnan, Niha","Generative artificial intelligence: synthetic datasets in dentistry","BDJ Open","","","10.1038/s41405-024-00198-4","https://www.proquest.com/scholarly-journals/generative-artificial-intelligence-synthetic/docview/2933660325/se-2?accountid=25704","IntroductionArtificial Intelligence (AI) algorithms, particularly Deep Learning (DL) models are known to be data intensive. This has increased the demand for digital data in all domains of healthcare, including dentistry. The main hindrance in the progress of AI is access to diverse datasets which train DL models ensuring optimal performance, comparable to subject experts. However, administration of these traditionally acquired datasets is challenging due to privacy regulations and the extensive manual annotation required by subject experts. Biases such as ethical, socioeconomic and class imbalances are also incorporated during the curation of these datasets, limiting their overall generalizability. These challenges prevent their accrual at a larger scale for training DL models.MethodsGenerative AI techniques can be useful in the production of Synthetic Datasets (SDs) that can overcome issues affecting traditionally acquired datasets. Variational autoencoders, generative adversarial networks and diffusion models have been used to generate SDs. The following text is a review of these generative AI techniques and their operations. It discusses the chances of SDs and challenges with potential solutions which will improve the understanding of healthcare professionals working in AI research.ConclusionSynthetic data customized to the need of researchers can be produced to train robust AI models. These models, having been trained on such a diverse dataset will be applicable for dissemination across countries. However, there is a need for the limitations associated with SDs to be better understood, and attempts made to overcome those concerns prior to their widespread use.","2024","2024-12-03 02:45:29","2024-12-03 02:45:29","","13","","1","10","","","","","","","","","","English","","","Biological Science Database","2933660325","","","Place: London Publisher: Springer Nature B.V.","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"GCFCZVKH","journalArticle","2024","Tassallah Abdullahi; Singh, Ritambhara; Eickhoff, Carsten","Learning to Make Rare and Complex Diagnoses With Generative AI Assistance: Qualitative Study of Popular Large Language Models","JMIR Medical Education","","","10.2196/51391","https://www.proquest.com/scholarly-journals/learning-make-rare-complex-diagnoses-with/docview/2925480216/se-2?accountid=25704","Background:Patients with rare and complex diseases often experience delayed diagnoses and misdiagnoses because comprehensive knowledge about these diseases is limited to only a few medical experts. In this context, large language models (LLMs) have emerged as powerful knowledge aggregation tools with applications in clinical decision support and education domains.Objective:This study aims to explore the potential of 3 popular LLMs, namely Bard (Google LLC), ChatGPT-3.5 (OpenAI), and GPT-4 (OpenAI), in medical education to enhance the diagnosis of rare and complex diseases while investigating the impact of prompt engineering on their performance.Methods:We conducted experiments on publicly available complex and rare cases to achieve these objectives. We implemented various prompt strategies to evaluate the performance of these models using both open-ended and multiple-choice prompts. In addition, we used a majority voting strategy to leverage diverse reasoning paths within language models, aiming to enhance their reliability. Furthermore, we compared their performance with the performance of human respondents and MedAlpaca, a generative LLM specifically designed for medical tasks.Results:Notably, all LLMs outperformed the average human consensus and MedAlpaca, with a minimum margin of 5% and 13%, respectively, across all 30 cases from the diagnostic case challenge collection. On the frequently misdiagnosed cases category, Bard tied with MedAlpaca but surpassed the human average consensus by 14%, whereas GPT-4 and ChatGPT-3.5 outperformed MedAlpaca and the human respondents on the moderately often misdiagnosed cases category with minimum accuracy scores of 28% and 11%, respectively. The majority voting strategy, particularly with GPT-4, demonstrated the highest overall score across all cases from the diagnostic complex case collection, surpassing that of other LLMs. On the Medical Information Mart for Intensive Care-III data sets, Bard and GPT-4 achieved the highest diagnostic accuracy scores, with multiple-choice prompts scoring 93%, whereas ChatGPT-3.5 and MedAlpaca scored 73% and 47%, respectively. Furthermore, our results demonstrate that there is no one-size-fits-all prompting approach for improving the performance of LLMs and that a single strategy does not universally apply to all LLMs.Conclusions:Our findings shed light on the diagnostic capabilities of LLMs and the challenges associated with identifying an optimal prompting strategy that aligns with each language model’s characteristics and specific task requirements. The significance of prompt engineering is highlighted, providing valuable insights for researchers and practitioners who use these language models for medical training. Furthermore, this study represents a crucial step toward understanding how LLMs can enhance diagnostic reasoning in rare and complex medical cases, paving the way for developing effective educational tools and accurate diagnostic aids to improve patient care and outcomes.","2024","2024-12-03 02:45:29","2024-12-03 02:45:29","","","","","10","","","","","","","","","","English","","","Publicly Available Content Database","2925480216","","","Place: Toronto Publisher: JMIR Publications","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"UGGE79UR","journalArticle","2023","Oniani, David; Hilsman, Jordan; Peng, Yifan; Poropatich, Ronald K.; Pamplin, Jeremy C.; Legault, Gary L.; Wang, Yanshan","Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare","NPJ Digital Medicine","","","10.1038/s41746-023-00965-x","https://www.proquest.com/scholarly-journals/adopting-expanding-ethical-principles-generative/docview/2896148333/se-2?accountid=25704","In 2020, the U.S. Department of Defense officially disclosed a set of ethical principles to guide the use of Artificial Intelligence (AI) technologies on future battlefields. Despite stark differences, there are core similarities between the military and medical service. Warriors on battlefields often face life-altering circumstances that require quick decision-making. Medical providers experience similar challenges in a rapidly changing healthcare environment, such as in the emergency department or during surgery treating a life-threatening condition. Generative AI, an emerging technology designed to efficiently generate valuable information, holds great promise. As computing power becomes more accessible and the abundance of health data, such as electronic health records, electrocardiograms, and medical images, increases, it is inevitable that healthcare will be revolutionized by this technology. Recently, generative AI has garnered a lot of attention in the medical research community, leading to debates about its application in the healthcare sector, mainly due to concerns about transparency and related issues. Meanwhile, questions around the potential exacerbation of health disparities due to modeling biases have raised notable ethical concerns regarding the use of this technology in healthcare. However, the ethical principles for generative AI in healthcare have been understudied. As a result, there are no clear solutions to address ethical concerns, and decision-makers often neglect to consider the significance of ethical principles before implementing generative AI in clinical practice. In an attempt to address these issues, we explore ethical principles from the military perspective and propose the “GREAT PLEA” ethical principles, namely Governability, Reliability, Equity, Accountability, Traceability, Privacy, Lawfulness, Empathy, and Autonomy for generative AI in healthcare. Furthermore, we introduce a framework for adopting and expanding these ethical principles in a practical way that has been useful in the military and can be applied to healthcare for generative AI, based on contrasting their ethical concerns and risks. Ultimately, we aim to proactively address the ethical dilemmas and challenges posed by the integration of generative AI into healthcare practice.","2023-12","2024-12-03 02:45:30","2024-12-03 02:45:30","","225","","1","6","","","","","","","","","","English","","","Publicly Available Content Database","2896148333","","","Place: London Publisher: Nature Publishing Group","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XXHA8UMQ","journalArticle","2023","Çelebi, Ömer Faruk; Nilşah Cavdar Aksoy; Alev Kocak Alan; Ebru Tümer Kabadayı","İLERİ TEKNOLOJİLER, YAPAY ZEKÂ TEMELLİ ÇÖZÜMLER: DUYGU ODAKLI BİR YAKLAŞIM","Öneri","","13000845","10.14783/maruoneri.1189209","https://www.proquest.com/scholarly-journals/i̇leri̇-teknoloji̇ler-yapay-zekâ-temelli̇/docview/3129969394/se-2?accountid=25704","Yapay zekâ teknolojisinin ilerlemesiyle birlikte, bireylerin yaşamlarına dâhil olan yeni nesil ürün ve hizmetlerin çeşitliliği her geçen gün artmaktadır. Bu çeşitlilik, bireylerin yapay zekâ teknolojisi ile temas ettiği alanları da genişletmektedir. Bu nedenle, bireylerin yapay zekâ teknolojisine yönelik duygularının anlaşılması araştırmaya değer konular arasında öne çıkmaktadır. Bu çalışmanın amacı, bireylerin yapay zekâ teknolojisi ve yapay zekâ destekli ürün ve hizmetler ile etkileşimlerinde açığa çıkan duyguları keşfetmektir. Bu doğrultuda, bu çalışmada nitel araştırma yöntemi benimsenmiş ve 10 katılımcı ile derinlemesine mülakat gerçekleştirilmiştir. Bulgulara göre temel duygu tipolojileri şu şekildedir: mutluluk, memnuniyet, şaşırma, merak, heyecan, umut, rahatlık, hayal kırıklığı, öfke, sinirlilik, korku, ürkütücülük, uyarılmama (canlandırılmama), rahatsızlık, endişe, umutsuzluk ve memnuniyetsizlik. Ayrıca bulgular, katılımcıların yapay zekâ teknolojisine yönelik olarak birden fazla duyguyu birlikte yaşayabildiğini (memnuniyet-korku, rahatlık-korku gibi) göstermektedir. Çalışma bulgularının, bireylerin yapay zekâ teknolojisine ve yapay zekâ destekli ürün ve hizmetlere yönelik duygularının anlaşılmasına katkı sağlayacağı düşünülmektedir.Alternate abstract: With the advancement of artificial intelligence technology, the diversity of new-generation products and services included in individuals’ life is increasing day by day. This diversity also expands the areas where individuals come into contact with artificial intelligence technology. Therefore, understanding emotions toward artificial intelligence technology stands out among the topics worth researching. This study aims to explore the emotions that emerge in the interactions of individuals with artificial intelligence technology and artificial intelligence- based products and services. In doing this, a qualitative research method was adopted in this study, and an in-depth interview technique was applied with 10 participants. According to the findings, the basic emotion typologies are as follows: happiness, pleased, astonished, curiosity, excitement, hope, comfort, disappointment, anger, nervousness, fear, frightened, unaroused, discomfort, anxiety, hopelessness and unpleased. Furthermore, the findings show that participants can experience more than one emotion simultaneously (such as pleased- fear or comfort-fear) for artificial intelligence technology. It is thought that the study’s results will contribute to the understanding of individuals’ emotions towards artificial intelligence technology and artificial intelligence- based products and services.","2023","2024-12-03 02:45:30","2024-12-03 02:45:30","","367-395","","60","18","","","","","","","","","","Turkish","","","Coronavirus Research Database; Publicly Available Content Database","3129969394","","","Place: Istanbul Publisher: Marmara University Institute of Social Sciences","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"L292I6JE","journalArticle","2023","Franganillo, Jorge","Los grandes modelos de lenguaje: una oportunidad para la profesión bibliotecaria","Anuario ThinkEPI","","1886-6344","10.3145/thinkepi.2023.e17a28","https://www.proquest.com/scholarly-journals/los-grandes-modelos-de-lenguaje-una-oportunidad/docview/3087326055/se-2?accountid=25704","La Inteligencia Artificial (IA) generativa y los grandes modelos de lenguaje pueden cambiar la forma en que consultamos, procesamos y producimos información. Pero presentan desafí­os técnicos y éticos, tales como inconsistencias, sesgos y falta de transparencia. El colectivo bibliotecario tiene aquí­ un papel clave, una oportunidad para apoyar el uso responsable de esta tecnologí­a y promover la comprensión crí­tica de sus limitaciones. Las bibliotecas, por su parte, pueden ofrecer espacios y recursos para experimentar con la IA generativa y fomentar su uso en la investigación cientí­fica.Alternate abstract: Generative artificial intelligence (AI) and large language models can change the way we search, process and generate information. However, they also pose ethical and technical challenges such as inconsistencies, biases and lack of transparency. In this context, librarians play a key role, as they have the opportunity to support responsible use of this technology as well as to promote critical understanding of its limitations. Libraries, in turn, can offer spaces and resources to experiment with generative AI and encourage its use in scientific research.","2023","2024-12-03 02:45:30","2024-12-03 02:45:30","","","","","17","","","","","","","","","","Spanish","","","Library Science Database; Publicly Available Content Database","3087326055","","","Place: Barcelona Publisher: El Profesional de la Informacion","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"7VI9PHR5","journalArticle","2023","Nashwan, Abdulqadir J; Abukhadijah Hana","Harnessing Artificial Intelligence for Qualitative and Mixed Methods in Nursing Research","Cureus","","","10.7759/cureus.48570","https://www.proquest.com/scholarly-journals/harnessing-artificial-intelligence-qualitative/docview/2908047994/se-2?accountid=25704","This editorial discusses the transformative potential of artificial intelligence (AI), particularly large language models (LLMs), in enhancing traditional nursing research methodologies, specifically qualitative and mixed methods. The article emphasizes the benefits of AI such as LLMs in data processing, analysis, integration, and triangulation while also underscoring the importance of addressing ethical concerns and the need for proper training for researchers.","2023","2024-12-03 02:45:30","2024-12-03 02:45:30","","","","11","15","","","","","","","","","","English","","","Publicly Available Content Database","2908047994","","","Place: Palo Alto Publisher: Cureus Inc.","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"72PK8ZMZ","journalArticle","2023","Ghosh, Aritra; Larrondo-Petrie, Maria M; Pavlovic, Mirjana","Revolutionizing Vaccine Development for COVID-19: A Review of AI-Based Approaches","Information","","","10.3390/info14120665","https://www.proquest.com/scholarly-journals/revolutionizing-vaccine-development-covid-19/docview/2904804770/se-2?accountid=25704","The evolvement of COVID-19 vaccines is rapidly being revolutionized using artificial intelligence-based technologies. Small compounds, peptides, and epitopes are collected to develop new therapeutics. These substances can also guide artificial intelligence-based modeling, screening, or creation. Machine learning techniques are used to leverage pre-existing data for COVID-19 drug detection and vaccine advancement, while artificial intelligence-based models are used for these purposes. Models based on artificial intelligence are used to evaluate and recognize the best candidate targets for future therapeutic development. Artificial intelligence-based strategies can be used to address issues with the safety and efficacy of COVID-19 vaccine candidates, as well as issues with manufacturing, storage, and logistics. Because antigenic peptides are effective at eliciting immune responses, artificial intelligence algorithms can assist in identifying the most promising COVID-19 vaccine candidates. Following COVID-19 vaccination, the first phase of the vaccine-induced immune response occurs when major histocompatibility complex (MHC) class II molecules (typically bind peptides of 12–25 amino acids) recognize antigenic peptides. Therefore, AI-based models are used to identify the best COVID-19 vaccine candidates and ensure the efficacy and safety of vaccine-induced immune responses. This study explores the use of artificial intelligence-based approaches to address logistics, manufacturing, storage, safety, and effectiveness issues associated with several COVID-19 vaccine candidates. Additionally, we will evaluate potential targets for next-generation treatments and examine the role that artificial intelligence-based models can play in identifying the most promising COVID-19 vaccine candidates, while also considering the effectiveness of antigenic peptides in triggering immune responses. The aim of this project is to gain insights into how artificial intelligence-based approaches could revolutionize the development of COVID-19 vaccines and how they can be leveraged to address challenges associated with vaccine development. In this work, we highlight potential barriers and solutions and focus on recent improvements in using artificial intelligence to produce COVID-19 drugs and vaccines, as well as the prospects for intelligent training in COVID-19 treatment discovery.","2023","2024-12-03 02:45:30","2024-12-03 02:45:30","","665","","12","14","","","","","","","","","","English","","","Publicly Available Content Database","2904804770","","","Place: Basel Publisher: MDPI AG","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"26KN7JLU","journalArticle","2023","Zhang, Chaoyi; Xu, Jin; Tang, Rong; Yang, Jianhui; Wang, Wei; Yu, Xianjun; Shi, Si","Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment","Journal of Hematology & Oncology","","","10.1186/s13045-023-01514-5","https://www.proquest.com/scholarly-journals/novel-research-future-prospects-artificial/docview/2902130635/se-2?accountid=25704","Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highly specialized cancer datasets. Here, we review new artificial intelligence approaches and how they are being used in oncology. We describe how artificial intelligence might be used in the detection, prognosis, and administration of cancer treatments and introduce the use of the latest large language models such as ChatGPT in oncology clinics. We highlight artificial intelligence applications for omics data types, and we offer perspectives on how the various data types might be combined to create decision-support tools. We also evaluate the present constraints and challenges to applying artificial intelligence in precision oncology. Finally, we discuss how current challenges may be surmounted to make artificial intelligence useful in clinical settings in the future.","2023","2024-12-03 02:45:30","2024-12-03 02:45:30","","1-29","","","16","","","","","","","","","","English","","","Publicly Available Content Database","2902130635","","","Place: London Publisher: BioMed Central","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"I79JJY4R","journalArticle","2023","Hoffmann, Rudolf; Reich, Christoph","A Systematic Literature Review on Artificial Intelligence and Explainable Artificial Intelligence for Visual Quality Assurance in Manufacturing","Electronics","","","10.3390/electronics12224572","https://www.proquest.com/scholarly-journals/systematic-literature-review-on-artificial/docview/2893040357/se-2?accountid=25704","Quality assurance (QA) plays a crucial role in manufacturing to ensure that products meet their specifications. However, manual QA processes are costly and time-consuming, thereby making artificial intelligence (AI) an attractive solution for automation and expert support. In particular, convolutional neural networks (CNNs) have gained a lot of interest in visual inspection. Next to AI methods, the explainable artificial intelligence (XAI) systems, which achieve transparency and interpretability by providing insights into the decision-making process of the AI, are interesting methods for achieveing quality inspections in manufacturing processes. In this study, we conducted a systematic literature review (SLR) to explore AI and XAI approaches for visual QA (VQA) in manufacturing. Our objective was to assess the current state of the art and identify research gaps in this context. Our findings revealed that AI-based systems predominantly focused on visual quality control (VQC) for defect detection. Research addressing VQA practices, like process optimization, predictive maintenance, or root cause analysis, are more rare. Least often cited are papers that utilize XAI methods. In conclusion, this survey emphasizes the importance and potential of AI and XAI in VQA across various industries. By integrating XAI, organizations can enhance model transparency, interpretability, and trust in AI systems. Overall, leveraging AI and XAI improves VQA practices and decision-making in industries.","2023","2024-12-03 02:45:30","2024-12-03 02:45:30","","4572","","22","12","","","","","","","","","","English","","","Publicly Available Content Database","2893040357","","","Place: Basel Publisher: MDPI AG","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"BBICLAD5","journalArticle","2023","Froń, Anita; Semianiuk, Alina; Lazuk, Uladzimir; Kuba Ptaszkowski; Siennicka, Agnieszka; Lemiński, Artur; Krajewski, Wojciech; Szydełko, Tomasz; Małkiewicz, Bartosz","Artificial Intelligence in Urooncology: What We Have and What We Expect","Cancers","","","10.3390/cancers15174282","https://www.proquest.com/scholarly-journals/artificial-intelligence-urooncology-what-we-have/docview/2862143013/se-2?accountid=25704","Simple SummaryOur study provides an overview of the current state of artificial intelligence applications in urooncology and explores potential future advancements in this field. With remarkable progress already achieved, artificial intelligence has revolutionized urooncology by facilitating image analysis, grading, biomarker research, and treatment planning. We also discuss types of artificial intelligence and their possible applications in the management of cancers such as prostate, kidney, bladder, and testicular. As artificial intelligence technology continues to evolve, it holds immense promise for further advancing urooncology and enhancing the care of patients with cancer.AbstractIntroduction: Artificial intelligence is transforming healthcare by driving innovation, automation, and optimization across various fields of medicine. The aim of this study was to determine whether artificial intelligence (AI) techniques can be used in the diagnosis, treatment planning, and monitoring of urological cancers. Methodology: We conducted a thorough search for original and review articles published until 31 May 2022 in the PUBMED/Scopus database. Our search included several terms related to AI and urooncology. Articles were selected with the consensus of all authors. Results: Several types of AI can be used in the medical field. The most common forms of AI are machine learning (ML), deep learning (DL), neural networks (NNs), natural language processing (NLP) systems, and computer vision. AI can improve various domains related to the management of urologic cancers, such as imaging, grading, and nodal staging. AI can also help identify appropriate diagnoses, treatment options, and even biomarkers. In the majority of these instances, AI is as accurate as or sometimes even superior to medical doctors. Conclusions: AI techniques have the potential to revolutionize the diagnosis, treatment, and monitoring of urologic cancers. The use of AI in urooncology care is expected to increase in the future, leading to improved patient outcomes and better overall management of these tumors.","2023","2024-12-03 02:45:30","2024-12-03 02:45:30","","4282","","17","15","","","","","","","","","","English","","","Biological Science Database; Publicly Available Content Database","2862143013","","","Place: Basel Publisher: MDPI AG","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"3EYEPB3U","journalArticle","2022","Hamza, Ejaz; McGrath, Hari; Wong Brian LH; Guise, Andrew; Vercauteren, Tom; Shapey Jonathan","Artificial intelligence and medical education: A global mixed-methods study of medical students’ perspectives","Digital Health","","","10.1177/20552076221089099","https://www.proquest.com/scholarly-journals/artificial-intelligence-medical-education-global/docview/2758349173/se-2?accountid=25704","Objective  Medical students, as clinicians and healthcare leaders of the future, are key stakeholders in the clinical roll-out of artificial intelligence-driven technologies. The authors aim to provide the first report on the state of artificial intelligence in medical education globally by exploring the perspectives of medical students.  Methods  The authors carried out a mixed-methods study of focus groups and surveys with 128 medical students from 48 countries. The study explored knowledge around artificial intelligence as well as what students wished to learn about artificial intelligence and how they wished to learn this. A combined qualitative and quantitative analysis was used.  Results  Support for incorporating teaching on artificial intelligence into core curricula was ubiquitous across the globe, but few students had received teaching on artificial intelligence. Students showed knowledge on the applications of artificial intelligence in clinical medicine as well as on artificial intelligence ethics. They were interested in learning about clinical applications, algorithm development, coding and algorithm appraisal. Hackathon-style projects and multidisciplinary education involving computer science students were suggested for incorporation into the curriculum.  Conclusions  Medical students from all countries should be provided teaching on artificial intelligence as part of their curriculum to develop skills and knowledge around artificial intelligence to ensure a patient-centred digital future in medicine. This teaching should focus on the applications of artificial intelligence in clinical medicine. Students should also be given the opportunity to be involved in algorithm development. Students in low- and middle-income countries require the foundational technology as well as robust teaching on artificial intelligence to ensure that they can drive innovation in their healthcare settings.","2022-01","2024-12-03 02:45:30","2024-12-03 02:45:30","","","","","8","","","","","","","","","","English","","","Publicly Available Content Database","2758349173","","","Place: Thousand Oaks Publisher: Sage Publications Ltd.","","","https://journals.sagepub.com/doi/10.1177/20552076221089099","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"UJE54IGN","journalArticle","2024","Chiarello, Filippo; Giordano, Vito; Spada, Irene; Barandoni, Simone; Fantoni, Gualtiero","Future applications of generative large language models: A data-driven case study on ChatGPT","Technovation","","0166-4972","10.1016/j.technovation.2024.103002","https://www.sciencedirect.com/science/article/pii/S016649722400052X","This study delves into the evolving role of generative Large Language Models (LLMs). We develop a data-driven approach to collect and analyse tasks that users are asking to generative LLMs. Thanks to the focus on tasks this paper contributes to give a quantitative and granular understanding of the potential influence of LLMs in different business areas. Utilizing a dataset comprising over 3.8 million tweets, we identify and cluster 31,747 unique tasks, with a specific case study on ChatGPT. To reach this goal, the proposed method combines two Natural Language Processing (NLP) Techniques, Named Entity Recognition (NER) and BERTopic. The combination makes it possible to collect granular tasks of LLMs (NER) and clusters them in business areas (BERTopic). Our findings reveal a wide spectrum of applications, from programming assistance to creative content generation, highlighting LLM's versatility. The analysis highlighted six emerging areas of application for ChatGPT: human resources, programming, social media, office automation, search engines, education. The study also examines the implications of these findings for innovation management, proposing a research agenda to explore the intersection of the identified areas, with four stages of the innovation process: idea generation, screening/idea selection, development, and diffusion/sales/marketing.","2024-05-01","2024-12-03 03:05:06","2024-12-03 03:05:06","","103002","","","133","","Technovation","","","","","","","","","","","","","","","","","","","ChatGPT; Generative artificial intelligence; Technology adoption; Emerging technologies; Generative large language models; Social media analysis","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KQ7RAXRP","journalArticle","2024","Suárez, Ana; Jiménez, Jaime; Llorente de Pedro, María; Andreu-Vázquez, Cristina; Díaz-Flores García, Víctor; Gómez Sánchez, Margarita; Freire, Yolanda","Beyond the Scalpel: Assessing ChatGPT's potential as an auxiliary intelligent virtual assistant in oral surgery","Computational and Structural Biotechnology Journal","","2001-0370","10.1016/j.csbj.2023.11.058","https://www.sciencedirect.com/science/article/pii/S2001037023004737","AI has revolutionized the way we interact with technology. Noteworthy advances in AI algorithms and large language models (LLM) have led to the development of natural generative language (NGL) systems such as ChatGPT. Although these LLM can simulate human conversations and generate content in real time, they face challenges related to the topicality and accuracy of the information they generate. This study aimed to assess whether ChatGPT-4 could provide accurate and reliable answers to general dentists in the field of oral surgery, and thus explore its potential as an intelligent virtual assistant in clinical decision making in oral surgery. Thirty questions related to oral surgery were posed to ChatGPT4, each question repeated 30 times. Subsequently, a total of 900 responses were obtained. Two surgeons graded the answers according to the guidelines of the Spanish Society of Oral Surgery, using a three-point Likert scale (correct, partially correct/incomplete, and incorrect). Disagreements were arbitrated by an experienced oral surgeon, who provided the final grade Accuracy was found to be 71.7%, and consistency of the experts' grading across iterations, ranged from moderate to almost perfect. ChatGPT-4, with its potential capabilities, will inevitably be integrated into dental disciplines, including oral surgery. In the future, it could be considered as an auxiliary intelligent virtual assistant, though it would never replace oral surgery experts. Proper training and verified information by experts will remain vital to the implementation of the technology. More comprehensive research is needed to ensure the safe and successful application of AI in oral surgery.","2024-12-01","2024-12-03 03:05:06","2024-12-03 03:05:06","","46-52","","","24","","Computational and Structural Biotechnology Journal","","","","","","","","","","","","","","","","","","","ChatGPT; Large language models; Chatbot; Dentistry; Artificial Intelligence; Natural generative language; Open AI; Oral surgery","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KYJXG2UY","journalArticle","2024","Chen, Po-Yu; Jasiuk, Iwona","Biological and bio-inspired materials: Multi-scale modeling, artificial intelligence approaches, and experiments","Journal of Materials Research and Technology","","2238-7854","10.1016/j.jmrt.2024.05.117","https://www.sciencedirect.com/science/article/pii/S2238785424011578","Biological materials often possess remarkable properties and functionalities owing to their complex hierarchical and composite structures. Learning from nature can lead to revolutionary breakthroughs in materials science and innovative new technologies. This Special Issue titled “Biological and Bio-inspired Materials: Multi-scale Modeling and Artificial Intelligence Approaches” is a collection of research articles and comprehensive reviews utilizing multi-scale modeling, artificial intelligence approaches, and experiments to elucidate the characteristics of biological materials and design and optimize bio-inspired materials. The computational approaches of interest include but are not limited to molecular dynamics, lattice spring models, finite element analysis, genetic algorithms, neural networks, generative adversarial networks, and other modeling and artificial intelligence approaches for better understanding the structure-property relationships and underlying mechanisms of biological (natural) materials, and reproducing, designing, and optimizing bio-inspired materials. Novel experimental results, fabrication strategies, and applications of biological, bio-inspired and biomedical materials are also collected in this special issue.","2024-05-01","2024-12-03 03:05:06","2024-12-03 03:05:06","","7510-7511","","","30","","Journal of Materials Research and Technology","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"AUC32RW7","journalArticle","2024","Schaefer, Moritz; Reichl, Stephan; ter Horst, Rob; Nicolas, Adele M.; Krausgruber, Thomas; Piras, Francesco; Stepper, Peter; Bock, Christoph; Samwald, Matthias","GPT-4 as a biomedical simulator","Computers in Biology and Medicine","","0010-4825","10.1016/j.compbiomed.2024.108796","https://www.sciencedirect.com/science/article/pii/S0010482524008813","Background Computational simulation of biological processes can be a valuable tool for accelerating biomedical research, but usually requires extensive domain knowledge and manual adaptation. Large language models (LLMs) such as GPT-4 have proven surprisingly successful for a wide range of tasks. This study provides proof-of-concept for the use of GPT-4 as a versatile simulator of biological systems. Methods We introduce SimulateGPT, a proof-of-concept for knowledge-driven simulation across levels of biological organization through structured prompting of GPT-4. We benchmarked our approach against direct GPT-4 inference in blinded qualitative evaluations by domain experts in four scenarios and in two quantitative scenarios with experimental ground truth. The qualitative scenarios included mouse experiments with known outcomes and treatment decision support in sepsis. The quantitative scenarios included prediction of gene essentiality in cancer cells and progression-free survival in cancer patients. Results In qualitative experiments, biomedical scientists rated SimulateGPT's predictions favorably over direct GPT-4 inference. In quantitative experiments, SimulateGPT substantially improved classification accuracy for predicting the essentiality of individual genes and increased correlation coefficients and precision in the regression task of predicting progression-free survival. Conclusion This proof-of-concept study suggests that LLMs may enable a new class of biomedical simulators. Such text-based simulations appear well suited for modeling and understanding complex living systems that are difficult to describe with physics-based first-principles simulations, but for which extensive knowledge is available as written text. Finally, we propose several directions for further development of LLM-based biomedical simulators, including augmentation through web search retrieval, integrated mathematical modeling, and fine-tuning on experimental data.","2024-08-01","2024-12-03 03:05:06","2024-12-03 03:05:06","","108796","","","178","","Computers in Biology and Medicine","","","","","","","","","","","","","","","","","","","Artificial intelligence; Large language models; GPT-4; Biomedical simulation; Computational biology","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LEVV4VIL","journalArticle","2024","Alzoubi, Yehia Ibrahim; Mishra, Alok","Green artificial intelligence initiatives: Potentials and challenges","Journal of Cleaner Production","","0959-6526","10.1016/j.jclepro.2024.143090","https://www.sciencedirect.com/science/article/pii/S0959652624025393","Recently, the widespread adoption of artificial intelligence, particularly generative AI technology, has surged across various industries. However, a notable drawback of this technology is its significant energy consumption during model training and operation, which poses challenges to sustainability goals and the environment. Consequently, various initiatives have emerged to promote what is termed ""green artificial intelligence,"" aiming to mitigate these environmental impacts. Nevertheless, research discussing these initiatives remains scarce. Hence, this study aims to identify green artificial intelligence initiatives that contribute to environmental friendliness. This paper has comprehensively reviewed the existing literature, professional websites, and expert blogs to identify and analyze available green AI initiatives. This paper has identified 55 such initiatives, broadly categorized into six themes: cloud optimization, model efficiency, carbon footprinting, sustainability-focused AI development, open-source initiatives, and green AI research and community. This study discusses the strengths and limitations of each initiative to offer a comprehensive overview. The findings provide valuable insights, particularly for industries interested in green artificial intelligence and green technology in general. While some tools have been recognized and studied, comprehensive research and analysis are still required to empirically evaluate the majority of other tools due to their early stages of development in this field.","2024-08-25","2024-12-03 03:05:06","2024-12-03 03:05:06","","143090","","","468","","Journal of Cleaner Production","","","","","","","","","","","","","","","","","","","Sustainability; Artificial intelligence; Carbon footprint; Cloud; Green AI; Green AI tools","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"NB7Y4ZJM","journalArticle","2024","Nicholson Thomas, Isabel; Roche, Philip; Grêt-Regamey, Adrienne","Harnessing artificial intelligence for efficient systematic reviews: A case study in ecosystem condition indicators","Ecological Informatics","","1574-9541","10.1016/j.ecoinf.2024.102819","https://www.sciencedirect.com/science/article/pii/S1574954124003613","Effective evidence synthesis is important for the integration of scientific research into decision-making. However, fully depicting the vast mosaic of concepts and applications in environmental sciences and ecology often entails a substantial workload. New Artificial Intelligence (AI) tools present an attractive option for addressing this challenge but require sufficient validation to match the vigorous standards of a systematic review. This article demonstrates the use of generative AI in the selection of relevant literature as part of a systematic review on indicators of ecosystem condition. We highlight, through the development of an optimal prompt to communicate inclusion and exclusion criteria, the need to describe ecosystem condition as a multidimensional concept whilst also maintaining clarity on what does not meet the criteria of comprehensiveness. We show that, although not completely infallible, the GPT-3.5 model significantly outperforms traditional literature screening processes in terms of speed and efficiency whilst correctly selecting 83 % of relevant literature for review. Our study highlights the importance of precision in prompt design and the setting of query parameters for the AI model and opens the perspective for future work using language models to contextualize complex concepts in the environmental sciences. Future development of this methodology in tandem with the continued evolution of the accessibility and capacity of AI tools presents a great potential to improve evidence synthesis through gains in efficiency and possible scope.","2024-11-01","2024-12-03 03:05:07","2024-12-03 03:05:07","","102819","","","83","","Ecological Informatics","","","","","","","","","","","","","","","","","","","Systematic review; Artificial intelligence; GPT; Ecosystem condition","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DZBYHJWK","journalArticle","2024","Pozdniakov, Stanislav; Brazil, Jonathan; Abdi, Solmaz; Bakharia, Aneesha; Sadiq, Shazia; Gašević, Dragan; Denny, Paul; Khosravi, Hassan","Large language models meet user interfaces: The case of provisioning feedback","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100289","https://www.sciencedirect.com/science/article/pii/S2666920X24000924","Incorporating Generative Artificial Intelligence (GenAI), especially Large Language Models (LLMs), into educational settings presents valuable opportunities to boost the efficiency of educators and enrich the learning experiences of students. A significant portion of the current use of LLMs by educators has involved using conversational user interfaces (CUIs), such as chat windows, for functions like generating educational materials or offering feedback to learners. The ability to engage in real-time conversations with LLMs, which can enhance educators' domain knowledge across various subjects, has been of high value. However, it also presents challenges to LLMs' widespread, ethical, and effective adoption. Firstly, educators must have a degree of expertise, including tool familiarity, AI literacy and prompting to effectively use CUIs, which can be a barrier to adoption. Secondly, the open-ended design of CUIs makes them exceptionally powerful, which raises ethical concerns, particularly when used for high-stakes decisions like grading. Additionally, there are risks related to privacy and intellectual property, stemming from the potential unauthorised sharing of sensitive information. Finally, CUIs are designed for short, synchronous interactions and often struggle and hallucinate when given complex, multi-step tasks (e.g., providing individual feedback based on a rubric on a large scale). To address these challenges, we explored the benefits of transitioning away from employing LLMs via CUIs to the creation of applications with user-friendly interfaces that leverage LLMs through API calls. We first propose a framework for pedagogically sound and ethically responsible incorporation of GenAI into educational tools, emphasizing a human-centred design. We then illustrate the application of our framework to the design and implementation of a novel tool called Feedback Copilot, which enables instructors to provide students with personalized qualitative feedback on their assignments in classes of any size. An evaluation involving the generation of feedback from two distinct variations of the Feedback Copilot tool, using numerically graded assignments from 338 students, demonstrates the viability and effectiveness of our approach. Our findings have significant implications for GenAI application researchers, educators seeking to leverage accessible GenAI tools, and educational technologists aiming to transcend the limitations of conversational AI interfaces, thereby charting a course for the future of GenAI in education.","2024-12-01","2024-12-03 03:05:07","2024-12-03 03:05:07","","100289","","","7","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Artificial intelligence; Generative artificial intelligence; Large language models; Feedback; Learning analytics; Interfaces","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"BB3KHTSB","journalArticle","2024","Yu, Haoyang; Gao, Chang; Li, Xingsen; Zhang, Lingling","Ancient Chinese Poetry Collation Based on BERT","11th International Conference on Information Technology and Quantitative Management (ITQM 2024)","","1877-0509","10.1016/j.procs.2024.08.179","https://www.sciencedirect.com/science/article/pii/S1877050924018982","The rapid advancements in intelligent knowledge management technologies, exemplified by generative large language models, have yet to be fully explored and applied in the field of collation of ancient Chinese poetry. This study investigates the application of BERT-based pre-trained models, namely bert-base-chinese and SikuBERT, in the specialized task of ancient Chinese poetry collation. Focusing on the poetry of Li Bai, we employed a meticulously curated dataset to fine-tune these models, with the objective of enhancing their ability to identify and rectify errors in classical verse. Through a systematic approach to model adaptation, our research aimed to bridge the gap between generic language understanding and the nuanced complexities of ancient poetry.Results indicate that both models, after fine-tuning, exhibit substantial improvement in accurately addressing textual issues in the poetry. Specifically, SikuBERT, with its background in classical Chinese literature, achieved an impressive accuracy rate exceeding 40% post-fine-tuning, reflecting a marked increase from its base performance, thereby validating the significance of domain-specific training data. Meanwhile, bert-base-chinese also displayed notable enhancements, underscoring the models’ adaptability to specialized tasks. The investigation further emphasizes the potential for artificial intelligence to contribute to the precision and efficiency of ancient literature studies. We highlight future directions including refining fine-tuning methodologies, expanding the models’ capability to generalize across diverse poetic styles and periods, and integrating multi-modal data to deepen the understanding of historical context and authorial intent. This work underscores the transformative role of AI in the digital preservation and scholarly analysis of ancient poetry, paving the way for innovative approaches in the field of classical literature collation.","2024-01-01","2024-12-03 03:05:07","2024-12-03 03:05:07","","1171-1178","","","242","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Ancient Chinese Poetry; Collation; Digital Humanities; Fine-tuning; Pre-trained Models","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"53637CZ8","journalArticle","2023","Dwivedi, Yogesh K.; Kshetri, Nir; Hughes, Laurie; Slade, Emma Louise; Jeyaraj, Anand; Kar, Arpan Kumar; Baabdullah, Abdullah M.; Koohang, Alex; Raghavan, Vishnupriya; Ahuja, Manju; Albanna, Hanaa; Albashrawi, Mousa Ahmad; Al-Busaidi, Adil S.; Balakrishnan, Janarthanan; Barlette, Yves; Basu, Sriparna; Bose, Indranil; Brooks, Laurence; Buhalis, Dimitrios; Carter, Lemuria; Chowdhury, Soumyadeb; Crick, Tom; Cunningham, Scott W.; Davies, Gareth H.; Davison, Robert M.; Dé, Rahul; Dennehy, Denis; Duan, Yanqing; Dubey, Rameshwar; Dwivedi, Rohita; Edwards, John S.; Flavián, Carlos; Gauld, Robin; Grover, Varun; Hu, Mei-Chih; Janssen, Marijn; Jones, Paul; Junglas, Iris; Khorana, Sangeeta; Kraus, Sascha; Larsen, Kai R.; Latreille, Paul; Laumer, Sven; Malik, F. Tegwen; Mardani, Abbas; Mariani, Marcello; Mithas, Sunil; Mogaji, Emmanuel; Nord, Jeretta Horn; O’Connor, Siobhan; Okumus, Fevzi; Pagani, Margherita; Pandey, Neeraj; Papagiannidis, Savvas; Pappas, Ilias O.; Pathak, Nishith; Pries-Heje, Jan; Raman, Ramakrishnan; Rana, Nripendra P.; Rehm, Sven-Volker; Ribeiro-Navarrete, Samuel; Richter, Alexander; Rowe, Frantz; Sarker, Suprateek; Stahl, Bernd Carsten; Tiwari, Manoj Kumar; van der Aalst, Wil; Venkatesh, Viswanath; Viglia, Giampaolo; Wade, Michael; Walton, Paul; Wirtz, Jochen; Wright, Ryan","Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy","International Journal of Information Management","","0268-4012","10.1016/j.ijinfomgt.2023.102642","https://www.sciencedirect.com/science/article/pii/S0268401223000233","Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT’s capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT’s use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts.","2023-08-01","2024-12-03 03:05:07","2024-12-03 03:05:07","","102642","","","71","","International Journal of Information Management","","","","","","","","","","","","","","","","","","","ChatGPT; Generative artificial intelligence; Large language models; Conversational agent; Generative AI","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"V8XZVTRX","journalArticle","2024","Camilleri, Mark Anthony","Factors affecting performance expectancy and intentions to use ChatGPT: Using SmartPLS to advance an information technology acceptance framework","Technological Forecasting and Social Change","","0040-1625","10.1016/j.techfore.2024.123247","https://www.sciencedirect.com/science/article/pii/S004016252400043X","Few studies have explored the use of artificial intelligence-enabled (AI-enabled) large language models (LLMs). This research addresses this knowledge gap. It investigates perceptions and intentional behaviors to utilize AI dialogue systems like Chat Generative Pre-Trained Transformer (ChatGPT). A survey questionnaire comprising measures from key information technology adoption models, was used to capture quantitative data from a sample of 654 respondents. A partial least squares (PLS) approach assesses the constructs' reliabilities and validities. It also identifies the relative strength and significance of the causal paths in the proposed research model. The findings from SmartPLS4 report that there are highly significant effects in this empirical investigation particularly between source trustworthiness and performance expectancy from AI chatbots, as well as between perceived interactivity and intentions to use this algorithm, among others. In conclusion, this contribution puts forward a robust information technology acceptance framework that clearly evidences the factors that entice online users to habitually engage with text-generating AI chatbot technologies. It implies that although they may be considered as useful interactive systems for content creators, there is scope to continue improving the quality of their responses (in terms of their accuracy and timeliness) to reduce misinformation, social biases, hallucinations and adversarial prompts.","2024-04-01","2024-12-03 03:05:07","2024-12-03 03:05:07","","123247","","","201","","Technological Forecasting and Social Change","","","","","","","","","","","","","","","","","","","ChatGPT; AI Chatbot; Chat Generative Pre-Trained Transformer; Information Adoption Model; Natural language generation; Unified Theory of Acceptance and use of Technology","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"8L8YX58J","journalArticle","2024","Hayawi, Kadhim; Shahriar, Sakib; Alashwal, Hany; Serhani, Mohamed Adel","Generative AI and large language models: A new frontier in reverse vaccinology","Informatics in Medicine Unlocked","","2352-9148","10.1016/j.imu.2024.101533","https://www.sciencedirect.com/science/article/pii/S2352914824000893","Reverse vaccinology is an emerging concept in the field of vaccine development as it facilitates the identification of potential vaccine candidates. Biomedical research has been revolutionized with the recent innovations in Generative Artificial Intelligence (AI) and Large Language Models (LLMs). The intersection of these two technologies is explored in this study. In this study, the impact of Generative AI and LLMs in the field of vaccinology is explored. Through a comprehensive analysis of existing research, prospective use cases, and an experimental case study, this research highlights that LLMs and Generative AI have the potential to enhance the efficiency and accuracy of vaccine candidate identification. This work also discusses the ethical and privacy challenges, such as data consent and potential biases, raised by such applications that require careful consideration. This study paves the way for experts, researchers, and policymakers to further investigate the role and impact of Generative AI and LLM in vaccinology and medicine.","2024-01-01","2024-12-03 03:05:07","2024-12-03 03:05:07","","101533","","","48","","Informatics in Medicine Unlocked","","","","","","","","","","","","","","","","","","","AI; Generative AI; AI ethics; Large language models (LLMs); Reverse vaccinology; Vaccine candidate identification; Vaccines","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"6NLH9S6G","journalArticle","2024","Zahid, Idrees A.; Joudar, Shahad Sabbar; Albahri, A.S.; Albahri, O.S.; Alamoodi, A.H.; Santamaría, Jose; Alzubaidi, Laith","Unmasking large language models by means of OpenAI GPT-4 and Google AI: A deep instruction-based analysis","Intelligent Systems with Applications","","2667-3053","10.1016/j.iswa.2024.200431","https://www.sciencedirect.com/science/article/pii/S2667305324001054","Large Language Models (LLMs) have become a hot topic in AI due to their ability to mimic human conversation. This study compares the open artificial intelligence generative pretrained transformer-4 (GPT-4) model, based on the (GPT), and Google's artificial intelligence (AI), which is based on the Bidirectional Encoder Representations from Transformers (BERT) framework in terms of the defined capabilities and the built-in architecture. Both LLMs are prominent in AI applications. First, eight different capabilities were identified to evaluate these models, i.e. translation accuracy, text generation, factuality, creativity, intellect, deception avoidance, sentiment classification, and sarcasm detection. Next, each capability was assessed using instructions. Additionally, a categorized LLM evaluation system has been developed by means of using ten research questions per category based on this paper's main contributions from a prompt engineering perspective. It should be highlighted that GPT-4 and Google AI successfully answered 85 % and 68,7 % of the study prompts, respectively. It has been noted that GPT-4 better understands prompts than Google AI, even with verbal flaws, and tolerates grammatical errors. Moreover, the GPT-4 based approach was more precise, accurate, and succinct than Google AI, which was sometimes verbose and less realistic. While GPT-4 beats Google AI in terms of translation accuracy, text generation, factuality, intellectuality, creativity, and deception avoidance, Google AI outperforms the former when considering sarcasm detection. Both sentiment classification models did work properly. More importantly, a human panel of judges was used to assess and evaluate the model comparisons. Statistical analysis of the judges' ratings revealed more robust results based on examining the specific uses, limitations, and expectations of both GPT-4 and Google AI-based approaches. Finally, the two approaches' transformers, parameter sizes, and attention mechanisms have been examined.","2024-09-01","2024-12-03 03:05:07","2024-12-03 03:05:07","","200431","","","23","","Intelligent Systems with Applications","","","","","","","","","","","","","","","","","","","Transformers; Deception avoidance; Google AI; Instruction-based analysis; OpenAI GPT-4; Sarcasm detection","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CFFSLLL9","journalArticle","2024","McCoy, Thomas H.; Perlis, Roy H.","Characterizing research domain criteria symptoms among psychiatric inpatients using large language models","Journal of Mood & Anxiety Disorders","","2950-0044","10.1016/j.xjmad.2024.100079","https://www.sciencedirect.com/science/article/pii/S2950004424000336","We sought to characterize the ability of large language models to estimate NIMH Research Domain Criteria dimensions from narrative clinical notes of adult psychiatric inpatients, deriving estimate of overall burden of symptoms in each domain. We extracted consecutive admissions to a psychiatric inpatient unit between December 23, 2009 and September 27, 2015 from the electronic health records of a large academic medical center. Admission and discharge notes were scored with a HIPAA-compliant instance of a large language model (gpt-4–1106-preview). To examine convergent validity, the resulting estimates were correlated with those derived using an earlier method; for predictive validity, they were examined for association with length of hospitalization and probability of readmission. The cohort included 3619 individuals, 1779 female (49 %), 1840 male (51 %) with mean age 44 (SD=16.6). We identified modest correlations between LLM-derived RDoC scores and a previously validated scoring method, with Kendall’s tau between from.07 for arousal and 0.27 for positive and cognitive domains (p < .001 for all of these). For admission notes, greater scores on cognitive, sensorimotor, negative, and social domains were significantly associated with longer length of hospitalization in linear regression models including sociodemographic features (p < .01 for all of these); positive valence was associated with shorter hospitalization (p < .001). For discharge notes, social, arousal, and positive valence were associated with likelihood of readmission within 180 days in adjusted logistic regression models (p < .05 for social and arousal, p < .001 for positive valence). Overall, LLM-derived estimates of RDoC psychopathology demonstrated promising convergent and predictive validity, suggesting this approach may make real-world application of the RDoC framework more feasible.","2024-12-01","2024-12-03 03:05:07","2024-12-03 03:05:07","","100079","","","8","","Journal of Mood & Anxiety Disorders","","","","","","","","","","","","","","","","","","","Machine learning; Artificial intelligence; Depression; Inpatient; Research domain criteria","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CVVX34KD","journalArticle","2024","Stengel, Felix C.; Stienen, Martin N.; Ivanov, Marcel; Gandía-González, María L.; Raffa, Giovanni; Ganau, Mario; Whitfield, Peter; Motov, Stefan","Can AI pass the written European Board Examination in Neurological Surgery? - Ethical and practical issues","Brain and Spine","","2772-5294","10.1016/j.bas.2024.102765","https://www.sciencedirect.com/science/article/pii/S2772529424000213","Introduction Artificial intelligence (AI) based large language models (LLM) contain enormous potential in education and training. Recent publications demonstrated that they are able to outperform participants in written medical exams. Research question We aimed to explore the accuracy of AI in the written part of the EANS board exam. Material and methods Eighty-six representative single best answer (SBA) questions, included at least ten times in prior EANS board exams, were selected by the current EANS board exam committee. The questions’ content was classified as 75 text-based (TB) and 11 image-based (IB) and their structure as 50 interpretation-weighted, 30 theory-based and 6 true-or-false. Questions were tested with Chat GPT 3.5, Bing and Bard. The AI and participant results were statistically analyzed through ANOVA tests with Stata SE 15 (StataCorp, College Station, TX). P-values of <0.05 were considered as statistically significant. Results The Bard LLM achieved the highest accuracy with 62% correct questions overall and 69% excluding IB, outperforming human exam participants 59% (p = 0.67) and 59% (p = 0.42), respectively. All LLMs scored highest in theory-based questions, excluding IB questions (Chat-GPT: 79%; Bing: 83%; Bard: 86%) and significantly better than the human exam participants (60%; p = 0.03). AI could not answer any IB question correctly. Discussion and conclusion AI passed the written EANS board exam based on representative SBA questions and achieved results close to or even better than the human exam participants. Our results raise several ethical and practical implications, which may impact the current concept for the written EANS board exam.","2024-01-01","2024-12-03 03:05:07","2024-12-03 03:05:07","","102765","","","4","","Brain and Spine","","","","","","","","","","","","","","","","","","","Artificial intelligence; Bard; Bing; Board-certification; Chat gpt; EANS; Neurosurgery board examination","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"NLY6LJGG","journalArticle","2024","Kessel, Marcus; Atkinson, Colin","Promoting open science in test-driven software experiments","Journal of Systems and Software","","0164-1212","10.1016/j.jss.2024.111971","https://www.sciencedirect.com/science/article/pii/S0164121224000141","A core principle of open science is the clear, concise and accessible publication of empirical data, including “raw” observational data as well as processed results. However, in empirical software engineering there are no established standards (de jure or de facto) for representing and “opening” observations collected in test-driven software experiments — that is, experiments involving the execution of software subjects in controlled scenarios. Execution data is therefore usually represented in ad hoc ways, often making it abstruse and difficult to access without significant manual effort. In this paper we present new data structures designed to address this problem by clearly defining, correlating and representing the stimuli and responses used to execute software subjects in test-driven experiments. To demonstrate their utility, we show how they can be used to promote the repetition, replication and reproduction of experimental evaluations of AI-based code completion tools. We also show how the proposed data structures facilitate the incremental expansion of execution data sets, and thus promote their repurposing for new experiments addressing new research questions.","2024-06-01","2024-12-03 03:05:07","2024-12-03 03:05:07","","111971","","","212","","Journal of Systems and Software","","","","","","","","","","","","","","","","","","","Machine learning; Behavior; Software; Generative artificial intelligence; Automation; Large language models; Reproducibility; Engineering; Benchmark; Data structures; Empirical; Experimentation; HumanEval; Language-to-code; Measurement; Observation; Open science; Replication","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"6PK9BZDD","journalArticle","2023","Bewersdorff, Arne; Seßler, Kathrin; Baur, Armin; Kasneci, Enkelejda; Nerdel, Claudia","Assessing student errors in experimentation using artificial intelligence and large language models: A comparative study with human raters","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2023.100177","https://www.sciencedirect.com/science/article/pii/S2666920X23000565","Identifying logical errors in complex, incomplete or even contradictory and overall heterogeneous data like students’ experimentation protocols is challenging. Recognizing the limitations of current evaluation methods, we investigate the potential of Large Language Models (LLMs) for automatically identifying student errors and streamlining teacher assessments. Our aim is to provide a foundation for productive, personalized feedback. Using a dataset of 65 student protocols, an Artificial Intelligence (AI) system based on the GPT-3.5 and GPT-4 series was developed and tested against human raters. Our results indicate varying levels of accuracy in error detection between the AI system and human raters. The AI system can accurately identify many fundamental student errors, for instance, the AI system identifies when a student is focusing the hypothesis not on the dependent variable but solely on an expected observation (acc. = 0.90), when a student modifies the trials in an ongoing investigation (acc. = 1), and whether a student is conducting valid test trials (acc. = 0.82) reliably. The identification of other, usually more complex errors, like whether a student conducts a valid control trial (acc. = 0.60), poses a greater challenge. This research explores not only the utility of AI in educational settings, but also contributes to the understanding of the capabilities of LLMs in error detection in inquiry-based learning like experimentation.","2023-01-01","2024-12-03 03:05:07","2024-12-03 03:05:07","","100177","","","5","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Artificial intelligence; Large language models; Science education; Experimentation; Formative assessment; Scientific inquiry; Student errors","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"IMCIQLK2","journalArticle","2024","Gupta, Ruchi; Nair, Kiran; Mishra, Mahima; Ibrahim, Blend; Bhardwaj, Seema","Adoption and impacts of generative artificial intelligence: Theoretical underpinnings and research agenda","International Journal of Information Management Data Insights","","2667-0968","10.1016/j.jjimei.2024.100232","https://www.sciencedirect.com/science/article/pii/S2667096824000211","Large language models (LLMs) have received considerable interest in the field of natural language processing (NLP) owing to their remarkable ability to generate clear, consistent, and contextually relevant materials. Among the numerous LLMs, ChatGPT (Generative Pre-trained Transformer for Chatbots) is emerging as a prominent prospective tool for developing conversational agents such as chatbots. However, there is a need for a clear conceptual understanding of ChatGPT's potential implications for the industry and its role in marketing. This study explores the adoption of ChatGPT in marketing and examines theories that may influence its adoption by marketers and consumers, as well as its implications for marketers. This study discusses how ChatGPT may allow for more personalized and engaging content, better customer experience, and improved ROI. However, adoption also brings challenges, including ethical considerations and the need for new skill development. This study also discusses future research opportunities for the adoption of ChatGPT and other generative artificial intelligence technologies in marketing. The goal is to provide insights for organizations that consider implementing these technologies, and to contribute to the literature on the adoption of Artificial Intelligence (AI) and the use of Generative AI in marketing.","2024-04-01","2024-12-03 03:05:07","2024-12-03 03:05:07","","100232","","1","4","","International Journal of Information Management Data Insights","","","","","","","","","","","","","","","","","","","Chatbots; ChatGPT; Generative AI; Adoption","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ZWEUIQML","journalArticle","2024","Kolac, Ulas Can; Karademir, Orhan Mete; Ayik, Gokhan; Kaymakoglu, Mehmet; Familiari, Filippo; Huri, Gazi","“Can Popular AI Large Language Models Provide Reliable Answers to Frequently Asked Questions About Rotator Cuff Tears?”","JSES International","","2666-6383","10.1016/j.jseint.2024.11.012","https://www.sciencedirect.com/science/article/pii/S2666638324004717","Background Rotator cuff tears (RCT) are common upper extremity injuries that significantly impair shoulder function, leading to pain, reduced range of motion, and a decrease in quality of life. With the increasing reliance on artificial intelligence large language models (AI LLMs) for health information, it is crucial to evaluate the quality and readability of the information provided by these models. Methods A pool of 50 questions were generated related to RCT by querying popular AI LLMs (ChatGPT 3.5, ChatGPT 4, Gemini, and Microsoft CoPilot) and using Google search. After that, responses from the AI LLMs were saved and evaluated. For information quality the DISCERN tool and a Likert Scale was used, for readability the PEMAT Understandability Score and the Flesch-Kincaid Reading Ease Score was used. Two orthopedic surgeons assessed the responses, and discrepancies were resolved by a senior author. Results Out of 198 answers, the median DISCERN score was 40, with 56.6% considered sufficient. The Likert Scale showed 96% sufficiency. The median PEMAT Understandability score was 83.33, with 77.3% sufficiency, while the Flesch-Kincaid Reading Ease score had a median of 42.05, with 88.9% sufficiency. Overall, 39.8% of the answers were sufficient in both information quality and readability. Differences were found among AI models in DISCERN, Likert, PEMAT Understandability, and Flesch-Kincaid scores. Conclusion AI LLMs generally can not offer sufficient information quality and readability. While they are not ready for use in medical field, they show a promising future. There is a necessity for continuous re-evaluation of these models due to their rapid evolution. Developing new, comprehensive tools for evaluating medical information quality and readability is crucial for ensuring these models can effectively support patient education. Future research should focus on enhancing readability and consistent information quality to better serve patients.","2024-11-29","2024-12-03 03:05:07","2024-12-03 03:05:07","","","","","","","JSES International","","","","","","","","","","","","","","","","","","","Artificial intelligence; large language models; patient information; frequently asked questions; rotator cuff tears","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"TT57ZNEM","journalArticle","2024","Remadi, Adel; El Hage, Karim; Hobeika, Yasmina; Bugiotti, Francesca","To prompt or not to prompt: Navigating the use of Large Language Models for integrating and modeling heterogeneous data","Data & Knowledge Engineering","","0169-023X","10.1016/j.datak.2024.102313","https://www.sciencedirect.com/science/article/pii/S0169023X24000375","Manually integrating data of diverse formats and languages is vital to many artificial intelligence applications. However, the task itself remains challenging and time-consuming. This paper highlights the potential of Large Language Models (LLMs) to streamline data extraction and resolution processes. Our approach aims to address the ongoing challenge of integrating heterogeneous data sources, encouraging advancements in the field of data engineering. Applied on the specific use case of learning disorders in higher education, our research demonstrates LLMs’ capability to effectively extract data from unstructured sources. It is then further highlighted that LLMs can enhance data integration by providing the ability to resolve entities originating from multiple data sources. Crucially, the paper underscores the necessity of preliminary data modeling decisions to ensure the success of such technological applications. By merging human expertise with LLM-driven automation, this study advocates for the further exploration of semi-autonomous data engineering pipelines.","2024-07-01","2024-12-03 03:05:08","2024-12-03 03:05:08","","102313","","","152","","Data & Knowledge Engineering","","","","","","","","","","","","","","","","","","","Large language models; Data integration; Conceptual schema modeling; Data engineering; Entity resolution; Property graph models","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JTTM8HGU","journalArticle","2023","Haase, Jennifer; Hanel, Paul H.P.","Artificial muses: Generative artificial intelligence chatbots have risen to human-level creativity","Journal of Creativity","","2713-3745","10.1016/j.yjoc.2023.100066","https://www.sciencedirect.com/science/article/pii/S2713374523000250","A widespread view is that Artificial Intelligence cannot be creative. We tested this assumption by comparing human-generated ideas with those generated by six Generative Artificial Intelligence (GAI) chatbots: alpa.ai, Copy.ai, ChatGPT (versions 3 and 4), Studio.ai, and YouChat. Humans and a specifically trained AI independently assessed the quality and quantity of ideas. We found no qualitative difference between AI and human-generated creativity, although there are differences in how ideas are generated. Interestingly, 9.4 % of humans were more creative than the most creative GAI, GPT-4. Our findings suggest that GAIs are valuable assistants in the creative process. Continued research and development of GAI in creative tasks is crucial to fully understand this technology's potential benefits and drawbacks in shaping the future of creativity. Finally, we discuss the question of whether GAIs are capable of being “truly” creative.","2023-12-01","2024-12-03 03:05:08","2024-12-03 03:05:08","","100066","","3","33","","Journal of Creativity","","","","","","","","","","","","","","","","","","","Generative artificial intelligence; Creativity; AI; Originality","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2QFQQTUA","journalArticle","2024","Bhattacharya, Manojit; Pal, Soumen; Chatterjee, Srijan; Alshammari, Abdulrahman; Albekairi, Thamer H.; Jagga, Supriya; Ige Ohimain, Elijah; Zayed, Hatem; Byrareddy, Siddappa N.; Lee, Sang-Soo; Wen, Zhi-Hong; Agoramoorthy, Govindasamy; Bhattacharya, Prosun; Chakraborty, Chiranjib","ChatGPT’s scorecard after the performance in a series of tests conducted at the multi-country level: A pattern of responses of generative artificial intelligence or large language models","Current Research in Biotechnology","","2590-2628","10.1016/j.crbiot.2024.100194","https://www.sciencedirect.com/science/article/pii/S2590262824000200","Recently, researchers have shown concern about the ChatGPT-derived answers. Here, we conducted a series of tests using ChatGPT by individual researcher at multi-country level to understand the pattern of its answer accuracy, reproducibility, answer length, plagiarism, and in-depth using two questionnaires (the first set with 15 MCQs and the second 15 KBQ). Among 15 MCQ-generated answers, 13 ± 70 were correct (Median : 82.5; Coefficient variance : 4.85), 3 ± 0.77 were incorrect (Median: 3, Coefficient variance: 25.81), and 1 to 10 were reproducible, and 11 to 15 were not. Among 15 KBQ, the length of each question (in words) is about 294.5 ± 97.60 (mean range varies from 138.7 to 438.09), and the mean similarity index (in words) is about 29.53 ± 11.40 (Coefficient variance: 38.62) for each question. The statistical models were also developed using analyzed parameters of answers. The study shows a pattern of ChatGPT-derive answers with correctness and incorrectness and urges for an error-free, next-generation LLM to avoid users’ misguidance.","2024-01-01","2024-12-03 03:05:08","2024-12-03 03:05:08","","100194","","","7","","Current Research in Biotechnology","","","","","","","","","","","","","","","","","","","Accuracy; ChatGPT; Plagiarism; Reproducibility; Answer length","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XDRQIQSA","journalArticle","2024","Loconte, Riccardo; Orrù, Graziella; Tribastone, Mirco; Pietrini, Pietro; Sartori, Giuseppe","Challenging large language models’ “intelligence” with human tools: A neuropsychological investigation in Italian language on prefrontal functioning","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e38911","https://www.sciencedirect.com/science/article/pii/S2405844024149420","The Artificial Intelligence (AI) research community has used ad-hoc benchmarks to measure the “intelligence” level of Large Language Models (LLMs). In humans, intelligence is closely linked to the functional integrity of the prefrontal lobes, which are essential for higher-order cognitive processes. Previous research has found that LLMs struggle with cognitive tasks that rely on these prefrontal functions, highlighting a significant challenge in replicating human-like intelligence. In December 2022, OpenAI released ChatGPT, a new chatbot based on the GPT-3.5 model that quickly gained popularity for its impressive ability to understand and respond to human instructions, suggesting a significant step towards intelligent behaviour in AI. Therefore, to rigorously investigate LLMs' level of “intelligence,” we evaluated the GPT-3.5 and GPT-4 versions through a neuropsychological assessment using tests in the Italian language routinely employed to assess prefrontal functioning in humans. The same tests were also administered to Claude2 and Llama2 to verify whether similar language models perform similarly in prefrontal tests. When using human performance as a reference, GPT-3.5 showed inhomogeneous results on prefrontal tests, with some tests well above average, others in the lower range, and others frankly impaired. Specifically, we have identified poor planning abilities and difficulty in recognising semantic absurdities and understanding others' intentions and mental states. Claude2 exhibited a similar pattern to GPT-3.5, while Llama2 performed poorly in almost all tests. These inconsistent profiles highlight how LLMs' emergent abilities do not yet mimic human cognitive functioning. The sole exception was GPT-4, which performed within the normative range for all the tasks except planning. Furthermore, we showed how standardised neuropsychological batteries developed to assess human cognitive functions may be suitable for challenging LLMs’ performance.","2024-10-15","2024-12-03 03:05:08","2024-12-03 03:05:08","","e38911","","19","10","","Heliyon","","","","","","","","","","","","","","","","","","","Artificial intelligence; ChatGPT; Large language models; Neuropsychological evaluation; Prefrontal functioning","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RAPP5F7A","journalArticle","2024","Roberts, John; Baker, Max; Andrew, Jane","Artificial intelligence and qualitative research: The promise and perils of large language model (LLM) ‘assistance’","Critical Perspectives on Accounting","","1045-2354","10.1016/j.cpa.2024.102722","https://www.sciencedirect.com/science/article/pii/S1045235424000212","New large language models (LLMs) like ChatGPT have the potential to change qualitative research by contributing to every stage of the research process from generating interview questions to structuring research publications. However, it is far from clear whether such ‘assistance’ will enable or deskill and eventually displace the qualitative researcher. This paper sets out to explore the implications for qualitative research of the recently emerged capabilities of LLMs; how they have acquired their seemingly ‘human-like’ capabilities to ‘converse’ with us humans, and in what ways these capabilities are deceptive or misleading. Building on a comparison of the different ‘trainings’ of humans and LLMs, the paper first traces the seemingly human-like qualities of the LLM to the human proclivity to project communicative intent into or onto LLMs’ purely imitative capacity to predict the structure of human communication. It then goes on to detail the ways in which such human-like communication is deceptive and misleading in relation to the absolute ‘certainty’ with which LLMs ‘converse’, their intrinsic tendencies to ‘hallucination’ and ‘sycophancy’, the narrow conception of ‘artificial intelligence’, LLMs’ complete lack of ethical sensibility or capacity for responsibility, and finally the feared danger of an ‘emergence’ of ‘human-competitive’ or ‘superhuman’ LLM capabilities. The paper concludes by noting the potential dangers of the widespread use of LLMs as ‘mediators’ of human self-understanding and culture. A postscript offers a brief reflection on what only humans can do as qualitative researchers.","2024-03-01","2024-12-03 03:05:08","2024-12-03 03:05:08","","102722","","","99","","Critical Perspectives on Accounting","","","","","","","","","","","","","","","","","","","Qualitative research; Artificial intelligence; ChatGPT; Large Language Models; AI Ethics; Communicative rationality","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"27YPPE2Z","journalArticle","2024","Akhtar, Pervaiz; Ghouri, Arsalan Mujahid; Ashraf, Aniqa; Lim, Jia Jia; Khan, Naveed R; Ma, Shuang","Smart product platforming powered by AI and generative AI: Personalization for the circular economy","International Journal of Production Economics","","0925-5273","10.1016/j.ijpe.2024.109283","https://www.sciencedirect.com/science/article/pii/S0925527324001403","The interlocks between smart product platforming (SPP) powered by Artificial Intelligence (AI) and Generative AI, big data analytics, and machine learning are still in their infancy. Modern technology-driven SPP promotes personalized product design and manufacturing suited to support environmentally friendly products for the circular economy. In this study, we develop a framework pertaining to the interlinks between SPP, big data analytics, machine learning, and the circular economy. To test our framework, we apply structure equation modeling based on data collected from more than 200 automotive industry professionals operating in China. Our results demonstrate that SPP and big data analytics are the central determinants for manufacturing environmentally friendly products, ultimately promoting circular economy applications. SPP plays a pivotal role in innovative product design and in facilitating the relevant manufacturing procedures. Big data analytics significantly feed into SPP applications. Machine learning and flexibility in SPP perform moderating roles in strengthening environmentally friendly outcomes. The mediating role played by SPP between big data analytics and environmentally friendly products for the circular economy is partially encouraging. As SPP powered by AI and Generative AI is an emerging phenomenon, our study contributes to this new knowledge dimension. We conclude this paper by discussing the theoretical and practical implications of our study, its limitations, and directions for future research.","2024-07-01","2024-12-03 03:05:08","2024-12-03 03:05:08","","109283","","","273","","International Journal of Production Economics","","","","","","","","","","","","","","","","","","","Big data analytics and machine learning; Environmentally friendly products and circular economy; Generative artificial intelligence and large language models; Personalized product design and manufacturing; Smart product platforms and flexibility","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DJXK2V7H","journalArticle","2024","Pedersen, Carsten Lund; Ritter, Thomas","Digital authenticity: Towards a research agenda for the AI-driven fifth phase of digitalization in business-to-business marketing","Industrial Marketing Management","","0019-8501","10.1016/j.indmarman.2024.10.005","https://www.sciencedirect.com/science/article/pii/S0019850124001664","In recent years, we have witnessed a massive proliferation of artificial intelligence (AI) in all parts of society and business. Advances in AI are rapidly changing business-to-business marketing as well, with substantial implications for business-to-business theory and practice. In an extension of Ritter and Pedersen's (2020) phases of digitalization in business-to-business firms and through conceptual integration and development, this paper argues that digitalization has entered a new phase based on the generative capabilities of AI, which produce seemingly authentic artefacts, interactions, and datasets that cannot be consistently recognized as artificial, i.e. machine created with no or limited connection to real entities such as persons and places but which can be mistaken for having such connections. The paper outlines the characteristics of this evolution of digitalization and develops a research agenda for this fifth phase of digitalization, including the need for digital authorization to moderate the development of digital authenticity into value creation.","2024-11-01","2024-12-03 03:05:08","2024-12-03 03:05:08","","162-172","","","123","","Industrial Marketing Management","","","","","","","","","","","","","","","","","","","Artificial intelligence; Digitalization; Authenticity; Business-to-business marketing","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"X8KCWEX9","journalArticle","2024","Taylor, Niall; Ghose, Upamanyu; Rohanian, Omid; Nouriborji, Mohammadmahdi; Kormilitzin, Andrey; Clifton, David A.; Nevado-Holgado, Alejo","Efficiency at scale: Investigating the performance of diminutive language models in clinical tasks","Artificial Intelligence in Medicine","","0933-3657","10.1016/j.artmed.2024.103002","https://www.sciencedirect.com/science/article/pii/S0933365724002446","The entry of large language models (LLMs) into research and commercial spaces has led to a trend of ever-larger models, with initial promises of generalisability. This was followed by a widespread desire to downsize and create specialised models without the need for complete fine-tuning, using Parameter Efficient Fine-tuning (PEFT) methods. We present an investigation into the suitability of different PEFT methods to clinical decision-making tasks, across a range of model sizes, including extremely small models with as few as 25 million parameters. Our analysis shows that the performance of most PEFT approaches varies significantly from one task to another, with the exception of LoRA, which maintains relatively high performance across all model sizes and tasks, typically approaching or matching full fine-tuned performance. The effectiveness of PEFT methods in the clinical domain is evident, particularly for specialised models which can operate on low-cost, in-house computing infrastructure. The advantages of these models, in terms of speed and reduced training costs, dramatically outweighs any performance gain from large foundation LLMs. Furthermore, we highlight how domain-specific pre-training interacts with PEFT methods and model size, finding the domain pre-training to be particularly important in smaller models and discuss how these factors interplay to provide the best efficiency-performance trade-off. Full code available at: https://github.com/nlpie-research/efficient-ml.","2024-11-01","2024-12-03 03:05:08","2024-12-03 03:05:08","","103002","","","157","","Artificial Intelligence in Medicine","","","","","","","","","","","","","","","","","","","Artificial intelligence; Large language models; NLP; Fine-tuning; PEFT","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"4CIIAR7R","journalArticle","2024","Li, Xuerui; Lin, Junhui; Li, Qing; Cao, Wen","Research on assist design strategy of electric garden tools driven by data and intelligence","Advanced Design Research","","2949-7825","10.1016/j.ijadr.2024.09.003","https://www.sciencedirect.com/science/article/pii/S2949782524000161","This study proposes an artificial intelligence (AI) assited design strategy for electric gardening tools, focusing on direct interaction between workers and tools. Using electric pruning shears as a case study, the research first identifies the actual needs of gardening workers through on-site interviews. By applying the KANO model and Fuzzy Analytic Hierarchy Process (FAHP), the critical requirements of gardening workers are precisely determined. Subsequently, generative AI is utilized to generate design solutions based on these identified needs. Designers draw inspiration from AI-generated solutions and assess and score them separately, comparing trained and untrained ones based on the KANO-FAHP model. Significantly improved evaluation scores are observed for the trained solutions. The KANO-FAHP-AI design approach proposed in this study presents a novel pathway for designing electric gardening tools.","2024-06-01","2024-12-03 03:05:08","2024-12-03 03:05:08","","55-62","","1","2","","Advanced Design Research","","","","","","","","","","","","","","","","","","","Agriculture tools; AIGC; FAHP; KANO","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"3NK4S7H8","journalArticle","2023","Tan, Ting Fang; Thirunavukarasu, Arun James; Campbell, J. Peter; Keane, Pearse A.; Pasquale, Louis R.; Abramoff, Michael D.; Kalpathy-Cramer, Jayashree; Lum, Flora; Kim, Judy E.; Baxter, Sally L.; Ting, Daniel Shu Wei","Generative Artificial Intelligence Through ChatGPT and Other Large Language Models in Ophthalmology: Clinical Applications and Challenges","Ophthalmology Science","","2666-9145","10.1016/j.xops.2023.100394","https://www.sciencedirect.com/science/article/pii/S2666914523001264","The rapid progress of large language models (LLMs) driving generative artificial intelligence applications heralds the potential of opportunities in health care. We conducted a review up to April 2023 on Google Scholar, Embase, MEDLINE, and Scopus using the following terms: “large language models,” “generative artificial intelligence,” “ophthalmology,” “ChatGPT,” and “eye,” based on relevance to this review. From a clinical viewpoint specific to ophthalmologists, we explore from the different stakeholders’ perspectives—including patients, physicians, and policymakers—the potential LLM applications in education, research, and clinical domains specific to ophthalmology. We also highlight the foreseeable challenges of LLM implementation into clinical practice, including the concerns of accuracy, interpretability, perpetuating bias, and data security. As LLMs continue to mature, it is essential for stakeholders to jointly establish standards for best practices to safeguard patient safety. Financial Disclosure(s) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.","2023-12-01","2024-12-03 03:05:08","2024-12-03 03:05:08","","100394","","4","3","","Ophthalmology Science","","","","","","","","","","","","","","","","","","","Artificial intelligence; Chatbots; ChatGPT; Large language models","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"MMMY2FDG","journalArticle","2024","Yu, Songlin; Ran, Nian; Liu, Jianjun","Large-language models: The game-changers for materials science research","Artificial Intelligence Chemistry","","2949-7477","10.1016/j.aichem.2024.100076","https://www.sciencedirect.com/science/article/pii/S2949747724000344","Large Language Models (LLMs), such as GPT-4, are precipitating a new ""industrial revolution"" by significantly enhancing productivity across various domains. These models encode an extensive corpus of scientific knowledge from vast textual datasets, functioning as near-universal generalists with the ability to engage in natural language communication and exhibit advanced reasoning capabilities. Notably, agents derived from LLMs can comprehend user intent and autonomously design, plan, and utilize tools to execute intricate tasks. These attributes are particularly advantageous for materials science research, an interdisciplinary field characterized by numerous complex and time-intensive activities. The integration of LLMs into materials science research holds the potential to fundamentally transform the research paradigm in this field.","2024-12-01","2024-12-03 03:05:08","2024-12-03 03:05:08","","100076","","2","2","","Artificial Intelligence Chemistry","","","","","","","","","","","","","","","","","","","Artificial intelligence; Large language models; Agent; Intelligent laboratory; LLM for materials; Materials science research","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"GG3ZTWRN","journalArticle","2024","Dorta-González, Pablo; López-Puig, Alexis Jorge; Dorta-González, María Isabel; González-Betancor, Sara M.","Generative artificial intelligence usage by researchers at work: Effects of gender, career stage, type of workplace, and perceived barriers","Telematics and Informatics","","0736-5853","10.1016/j.tele.2024.102187","https://www.sciencedirect.com/science/article/pii/S0736585324000911","The integration of generative artificial intelligence technology into research environments has become increasingly common in recent years, representing a significant shift in the way researchers approach their work. This paper seeks to explore the factors underlying the frequency of use of generative AI amongst researchers in their professional environments. As survey data may be influenced by a bias towards scientists interested in AI, potentially skewing the results towards the perspectives of these researchers, this study uses a regression model to isolate the impact of specific factors such as gender, career stage, type of workplace, and perceived barriers to using AI technology on the frequency of use of generative AI. It also controls for other relevant variables such as direct involvement in AI research or development, collaboration with AI companies, geographic location, and scientific discipline. Our results show that researchers who face barriers to AI adoption experience an 11 % increase in tool use, while those who cite insufficient training resources experience an 8 % decrease. Female researchers experience a 7 % decrease in AI tool usage compared to men, while advanced career researchers experience a significant 19 % decrease. Researchers associated with government advisory groups are 45 % more likely to use AI tools frequently than those in government roles. Researchers in for-profit companies show an increase of 19 %, while those in medical research institutions and hospitals show an increase of 16 % and 15 %, respectively. This paper contributes to a deeper understanding of the mechanisms driving the use of generative AI tools amongst researchers, with valuable implications for both academia and industry.","2024-10-01","2024-12-03 03:05:08","2024-12-03 03:05:08","","102187","","","94","","Telematics and Informatics","","","","","","","","","","","","","","","","","","","Artificial intelligence; Challenges in implementing AI; Gender imbalance; Use of AI by researchers in the workplace","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"HJBU9FA3","journalArticle","2024","Mortlock, R.; Lucas, C.","Generative artificial intelligence (Gen-AI) in pharmacy education: Utilization and implications for academic integrity: A scoping review","Exploratory Research in Clinical and Social Pharmacy","","2667-2766","10.1016/j.rcsop.2024.100481","https://www.sciencedirect.com/science/article/pii/S2667276624000787","Introduction Generative artificial intelligence (Gen-AI), exemplified by the widely adopted ChatGPT, has garnered significant attention in recent years. Its application spans various health education domains, including pharmacy, where its potential benefits and drawbacks have become increasingly apparent. Despite the growing adoption of Gen-AI such as ChatGPT in pharmacy education, there remains a critical need to assess and mitigate associated risks. This review exploresthe literature and potential strategies for mitigating risks associated with the integration of Gen-AI in pharmacy education. Aim To conduct a scoping review to identify implications of Gen-AI in pharmacy education, identify its use and emerging evidence, with a particular focus on strategies which mitigate potential risks to academic integrity. Methods A scoping review strategy was employed in accordance with the PRISMA-ScR guidelines. Databases searched includedPubMed, ERIC [Education Resources Information Center], Scopus and ProQuestfrom August 2023 to 20 February 2024 and included all relevant records from 1 January 2000 to 20 February 2024 relating specifically to LLM use within pharmacy education. A grey literature search was also conducted due to the emerging nature of this topic. Policies, procedures, and documents from institutions such as universities and colleges, including standards, guidelines, and policy documents, were hand searched and reviewed in their most updated form. These documents were not published in the scientific literature or indexed in academic search engines. Results Articles (n = 12) were derived from the scientific data bases and Records (n = 9) derived from the grey literature. Potential use and benefits of Gen-AI within pharmacy education were identified in all included published articles however there was a paucity of published articles related the degree of consideration to the potential risks to academic integrity. Grey literature recordsheld the largest proportion of risk mitigation strategies largely focusing on increased academic and student education and training relating to the ethical use of Gen-AI as well considerations for redesigning of current assessments likely to be a risk for Gen-AI use to academic integrity. Conclusion Drawing upon existing literature, this review highlights the importance of evidence-based approaches to address the challenges posed by Gen-AI such as ChatGPT in pharmacy education settings. Additionally, whilst mitigation strategies are suggested, primarily drawn from the grey literature, there is a paucity of traditionally published scientific literature outlining strategies for the practical and ethical implementation of Gen-AI within pharmacy education. Further research related to the responsible and ethical use of Gen-AI in pharmacy curricula; and studies related to strategies adopted to mitigate risks to academic integrity would be beneficial.","2024-09-01","2024-12-03 03:05:08","2024-12-03 03:05:08","","100481","","","15","","Exploratory Research in Clinical and Social Pharmacy","","","","","","","","","","","","","","","","","","","Machine learning; Artificial intelligence; ChatGPT; Academic integrity; Pharmacy education","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LWPFWUC8","journalArticle","2024","Virvou, Maria; Tsihrintzis, George A.; Tsichrintzi, Evangelia-Aikaterini","VIRTSI: A novel trust dynamics model enhancing Artificial Intelligence collaboration with human users – Insights from a ChatGPT evaluation study","Information Sciences","","0020-0255","10.1016/j.ins.2024.120759","https://www.sciencedirect.com/science/article/pii/S002002552400673X","The rapid integration of intelligent processes and methods into information systems in the Artificial Intelligence (AI) era has led to a substantial shift towards autonomous software decision-making. This evolution necessitates robust human oversight, especially in critical domains like Healthcare, Education, and Energy. Human trust in AI plays a vital role in influencing decision-making processes of users interacting with AI. This paper presents VIRTSI (Variability and Impact of Reciprocal Trust States towards Intelligent systems), a novel rigorous computational model for human-AI Interaction. VIRTSI simulates human trust states, spanning from overtrust to distrust, through user modelling. It comprises: 1. A trust dynamics representational model based on Deterministic Finite State Automata (DFAs), illustrating transitions among cognitive trust states in response to AI-generated replies. 2. A trust evaluation model based on Confusion Matrices, originating from machine learning and Accuracy Metrics, providing a quantitative framework for analysing human trust dynamics. As a result, this is the first time that trust dynamics have been thoroughly traced in a representational model and a method has been developed to assess the impact of possibly harmful states like overtrust and distrust. An empirical study on the recently launched Large Language Model of generative AI, ChatGPT (version 3.5), provides a radical underexplored AI-generated platform for evaluating the human-AI interaction through VIRTSI. The study involved 1200 interactions of real users as well as AI experts together with experts in two very different domains of evaluation, namely software engineering and poetry. This study traces trust dynamics and the emerging human-AI interaction, in concrete examples of real user synergies with generative AI. The research reveals the vital role of maintaining normal trust states for optimal human-AI interaction and that both AI and human users need further steps towards this goal. The real-world implications of this research can guide the creation and evaluation of user interfaces with AI and the incorporation of functionalities in the development of generative AI chatbots in terms of trust by providing a new rigorous DFA representational method of trust dynamics and a corresponding new perspective of confusion matrix evaluation method of the dynamics’ impact in the efficiency of human-AI dialogues.","2024-07-01","2024-12-03 03:05:08","2024-12-03 03:05:08","","120759","","","675","","Information Sciences","","","","","","","","","","","","","","","","","","","Artificial Intelligence; AI in Education; AI Trust; AI-Empowered Software; Autonomous Systems; Confusion Matrix; Finite State Modelling; Human-AI Interaction; Human-Centered Artificial Intelligence; User Modelling","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2VFLU9H2","journalArticle","2024","Sufi, Fahim K.","AI approach on identifying change in public sentiment for major events: Dubai Expo 2020","Journal of Engineering Research","","2307-1877","10.1016/j.jer.2024.07.010","https://www.sciencedirect.com/science/article/pii/S2307187724002049","Existing research in identifying changes in public sentiment for major events faces limitations in capturing the intricacies of temporal changes, especially with large-scale data and diverse linguistic nuances. This study addresses this problem, with a particular focus on the globally acclaimed Dubai Expo 2020. The methodology employed in this research introduces a groundbreaking approach by leveraging advanced data engineering techniques on a massive corpus of 118,025 Tweets obtained from diverse users across 60 languages, covering the period from April 2021 to March 2023. The study pioneers the application of artificial intelligence (AI), natural language processing (NLP), and Large Language Models (LLMs) for sentiment analysis and topic modeling on Twitter discourse. Through the meticulous data engineering process, including language detection, dynamic translation, and sentiment analysis, the research identifies subtle yet statistically significant changes in public sentiment, as evidenced by ANOVA testing (p=0.018 in average positive sentiment, p=0.004 in average neutral sentiment, and p=0.005 in average negative sentiments). Additionally, the study innovatively extracts and analyzes support-related Tweets, revealing distinct phases in temporal domains (pre-Expo, Expo, Post-Expo1, and Post-Expo2) and yielding 11,116 support-related tweets. The application of NLP techniques further uncovers 19 topics from UAE-related Tweets, providing a comprehensive understanding of the dynamic landscape of public sentiment over a two-year period. This research contributes significantly to the field by offering a novel and comprehensive framework for analyzing public sentiment, particularly in the context of major events, and sheds light on its broader implications for event management and public perception analysis.","2024-07-17","2024-12-03 03:05:08","2024-12-03 03:05:08","","","","","","","Journal of Engineering Research","","","","","","","","","","","","","","","","","","","Data Engineering; Dubai Expo 2020; Opinion Mining; Sentiments on Major Events; Topic Analysis","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ASAUQRV2","journalArticle","2024","COHEN, Natalie D.; HO, Milan; McIntire, Donald; SMITH, Katherine; KHO, Kimberly A.","A Comparative Analysis of Generative Artificial Intelligence Responses from Leading Chatbots to Questions about Endometriosis","AJOG Global Reports","","2666-5778","10.1016/j.xagr.2024.100405","https://www.sciencedirect.com/science/article/pii/S2666577824000996","Introduction The use of generative artificial intelligence has begun to permeate most industries, including medicine, and patients will inevitably start using these large language model chatbots as a modality for education. As healthcare information technology evolves, it is imperative to evaluate chatbots and the accuracy of the information they provide to patients and to determine if there is variability between them. Objective This study aimed to evaluate the accuracy and comprehensiveness of three chatbots in addressing questions related to endometriosis and determine the level of variability between them. Study Design Three large language models, including Chat GPT-4 (Open AI), Claude (Anthropic), and Bard (Google) were asked to generate answers to 10 commonly asked questions about endometriosis. The responses were qualitatively compared to current guidelines and expert opinion on endometriosis and rated on a scale by nine gynecologists. The grading scale included the following: (1) Completely incorrect, (2) Mostly incorrect and some correct, (3) Mostly correct and some incorrect, (4) correct but inadequate, (5) Correct and comprehensive. Final scores were averaged between the nine reviewers. Kendall's W and the related chi-square test were used to evaluate the reviewers' strength of agreement in ranking the LLMs' responses for each item. Results Average scores for the ten answers amongst Bard, Chat GPT, and Claude were 3.69, 4.24, and 3.7, respectively. Two questions showed significant disagreement between the nine reviewers. There were no questions the models could answer comprehensively or correctly across the reviewers. The model most associated with comprehensive and correct responses was ChatGPT. Chatbots showed an improved ability to accurately answer questions about symptoms and pathophysiology over treatment and risk of recurrence. Conclusions The analysis of large language models revealed that, on average, they mainly provided correct but inadequate responses to commonly asked patient questions about endometriosis. While chatbot responses can serve as valuable supplements to information provided by licensed medical professionals, it is crucial to maintain a thorough ongoing evaluation process for outputs to provide the most comprehensive and accurate information to patients. Further research into this technology and its role in patient education and treatment is crucial as generative AI becomes more embedded in the medical field.","2024-10-05","2024-12-03 03:05:09","2024-12-03 03:05:09","","100405","","","","","AJOG Global Reports","","","","","","","","","","","","","","","","","","","Chatbots; Large Language Models; Endometriosis education; Health Information Technology; Patient Education; Patient information","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"4BJA5HAA","journalArticle","2024","Sikström, Pieta; Valentini, Chiara; Sivunen, Anu; Kärkkäinen, Tommi","Pedagogical agents communicating and scaffolding students' learning: High school teachers' and students' perspectives","Computers & Education","","0360-1315","10.1016/j.compedu.2024.105140","https://www.sciencedirect.com/science/article/pii/S0360131524001544","Pedagogical agents (PAs) communicate verbally and non-verbally with students in digital and virtual reality/augmented reality learning environments. PAs have been shown to be beneficial for learning, and generative artificial intelligence, such as large language models, can improve PAs' communication abilities significantly. K-12 education is underrepresented in learning technology research and teachers' and students' insights have not been considered when developing PA communication. The current study addresses this research gap by conducting and analyzing semi-structured, in-depth interviews with eleven high school teachers and sixteen high school students about their expectations for PAs' communication capabilities. The interviewees identified relational and task-related communication capabilities that a PA should perform to communicate effectively with students and scaffold their learning. PA communication that is simultaneously affirmative and relational can induce immediacy, foster the relationship and engagement with a PA, and support students' learning management. Additionally, the teachers and students described the activities and technological aspects that should be considered when designing conversational PAs. The study showed that teachers and students applied human-to-human communication scripts when outlining their desired PA communication characteristics. The study offers novel insights and recommendations to researchers and developers on the communicational, pedagogical, and technological aspects that must be considered when designing communicative PAs that scaffold students’ learning, and discusses the contributions on human–machine communication in education.","2024-12-01","2024-12-03 03:05:09","2024-12-03 03:05:09","","105140","","","222","","Computers & Education","","","","","","","","","","","","","","","","","","","Human-to-human communication script; Human–machine communication (HMC); Pedagogical agent; Secondary education; User-centered design","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"UQPPKGRA","journalArticle","2024","Tang, Xiaoyi; Chen, Hongwei; Lin, Daoyu; Li, Kexin","Harnessing LLMs for multi-dimensional writing assessment: Reliability and alignment with human judgments","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e34262","https://www.sciencedirect.com/science/article/pii/S2405844024102939","Recent advancements in natural language processing, computational linguistics, and Artificial Intelligence (AI) have propelled the use of Large Language Models (LLMs) in Automated Essay Scoring (AES), offering efficient and unbiased writing assessment. This study assesses the reliability of LLMs in AES tasks, focusing on scoring consistency and alignment with human raters. We explore the impact of prompt engineering, temperature settings, and multi-level rating dimensions on the scoring performance of LLMs. Results indicate that prompt engineering significantly affects the reliability of LLMs, with GPT-4 showing marked improvement over GPT-3.5 and Claude 2, achieving 112% and 114% increase in scoring accuracy under the criteria and sample-referenced justification prompt. Temperature settings also influence the output consistency of LLMs, with lower temperatures producing scores more in line with human evaluations, which is essential for maintaining fairness in large-scale assessment. Regarding multi-dimensional writing assessment, results indicate that GPT-4 performs well in dimensions regarding Ideas (QWK=0.551) and Organization (QWK=0.584) under well-crafted prompt engineering. These findings pave the way for a comprehensive exploration of LLMs' broader educational implications, offering insights into their capability to refine and potentially transform writing instruction, assessment, and the delivery of diagnostic and personalized feedback in the AI-powered educational age. While this study attached importance to the reliability and alignment of LLM-powered multi-dimensional AES, future research should broaden its scope to encompass diverse writing genres and a more extensive sample from varied backgrounds.","2024-07-30","2024-12-03 03:05:09","2024-12-03 03:05:09","","e34262","","14","10","","Heliyon","","","","","","","","","","","","","","","","","","","Prompt engineering; Large language models (LLMs); Automated essay scoring (AES); Generative pre-trained transformer (GPT); Multi-dimensional writing assessment","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ELZZKBF8","journalArticle","2024","Ferraro, Carla; Demsar, Vlad; Sands, Sean; Restrepo, Mariluz; Campbell, Colin","The paradoxes of generative AI-enabled customer service: A guide for managers","SPECIAL ISSUE: WRITTEN BY CHATGPT","","0007-6813","10.1016/j.bushor.2024.04.013","https://www.sciencedirect.com/science/article/pii/S0007681324000582","Generative artificial intelligence (GenAI) presents a disruptive innovation for brands and society, and the power of which is still yet to be realized. In the context of customer service, gen AI affords companies new possibilities to communicate, connect, and engage customers. This article draws on scholarly research and consultation with customer service leaders to present and discuss the possibilities for GenAI in the context of customer service, specifically GenAI chatbots. Importantly, this article presents potential paradoxes of GenAI-enabled customer service, adding to the debate about the role and impact of GenAI for brands. Specifically, we present six paradoxes of GenAI customer service: (1) connected yet isolated, (2) lower cost yet higher price, (3) higher quality yet less empathy, (4) satisfied yet frustrated, (5) personalized yet intrusive, and (6) powerful yet vulnerable. For each paradox, we suggest brand response strategies to mitigate downside and manage potential upside.","2024-09-01","2024-12-03 03:05:09","2024-12-03 03:05:09","","549-559","","5","67","","Business Horizons","","","","","","","","","","","","","","","","","","","Artificial intelligence; Generative AI; AI chatbots; Customer service; Customer support","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"9FK97C4V","journalArticle","2024","Dai, Yun","Why students use or not use generative AI: Student conceptions, concerns, and implications for engineering education","Digital Engineering","","2950-550X","10.1016/j.dte.2024.100019","https://www.sciencedirect.com/science/article/pii/S2950550X24000190","Generative artificial intelligence (GenAI) technologies is believed to transform engineering education. However, it remains underexamined how engineering students choose to use GenAI or not, along with the reasons behind their choices. To fill in this research gap, this study presents a natural experiment that examines student use or non-use of GenAI tools in engineering design tasks in an undergraduate course. In this experiment, the participants (n=403) were provided with an unconstrained access to an GPT 4.0-empowered chatbot and were allowed to use it for their design projects voluntarily. Overall, 59.80% of the students had reported substantial use of GenAI in their design projects, and 40.20% showed limited or no use. Those adopters used GenAI to aid idea generation and brainstorming, to mediate discussions with instructors/TA, to overcome non-technical expertise gaps, and to optimize the solution. Conversely, non-adopters attributed their reluctance and rejection to inherent limitations in GenAI outputs, misalignment between GenAI functionalities and project needs, a lack of adaptation and prompt skills, and unclear benefits of GenAI use for personal development. This study has challenged the popular assumption of naturally active GenAI adoption by uncovering the complexity and multiplicities in student behaviors. The findings highlight the necessity of establish a consensus on the role and value of GenAI across various stakeholders, while suggesting a need for adaptation in engineering education research and practices.","2024-11-05","2024-12-03 03:05:09","2024-12-03 03:05:09","","100019","","","","","Digital Engineering","","","","","","","","","","","","","","","","","","","higher education; Artificial intelligence; generative AI; barrier; engineering education; student concern","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"QN23WRPI","journalArticle","2024","Reviriego, Pedro; Conde, Javier; Merino-Gómez, Elena; Martínez, Gonzalo; Hernández, José Alberto","Playing with words: Comparing the vocabulary and lexical diversity of ChatGPT and humans","Machine Learning with Applications","","2666-8270","10.1016/j.mlwa.2024.100602","https://www.sciencedirect.com/science/article/pii/S2666827024000781","The introduction of Artificial Intelligence (AI) generative language models such as GPT (Generative Pre-trained Transformer) and conversational tools such as ChatGPT has triggered a revolution that can transform how text is generated. This has many implications, for example, as AI-generated text becomes a significant fraction of the text, would this affect the language capabilities of readers and also the training of newer AI tools? Would it affect the evolution of languages? Focusing on one specific aspect of the language: words; will the use of tools such as ChatGPT increase or reduce the vocabulary used or the lexical diversity? This has implications for words, as those not included in AI-generated content will tend to be less and less popular and may eventually be lost. In this work, we perform an initial comparison of the vocabulary and lexical diversity of ChatGPT and humans when performing the same tasks. In more detail, two datasets containing the answers to different types of questions answered by ChatGPT and humans, and a third dataset in which ChatGPT paraphrases sentences and questions are used. The analysis shows that ChatGPT-3.5 tends to use fewer distinct words and lower diversity than humans while ChatGPT-4 has a similar lexical diversity as humans and in some cases even larger. These results are very preliminary and additional datasets and ChatGPT configurations have to be evaluated to extract more general conclusions. Therefore, further research is needed to understand how the use of ChatGPT and more broadly generative AI tools will affect the vocabulary and lexical diversity in different types of text and languages.","2024-12-01","2024-12-03 03:05:09","2024-12-03 03:05:09","","100602","","","18","","Machine Learning with Applications","","","","","","","","","","","","","","","","","","","ChatGPT; LLM; Lexical diversity","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SVE6SFE7","journalArticle","2024","Jiang, Jialei","When generative artificial intelligence meets multimodal composition: Rethinking the composition process through an AI-assisted design project","Computers and Composition","","8755-4615","10.1016/j.compcom.2024.102883","https://www.sciencedirect.com/science/article/pii/S8755461524000598","This study explores the integration of generative artificial intelligence (GenAI) design technologies, including Adobe Firefly and DALL·E, into the teaching and learning of multimodal composition. Through focus group discussions and case studies, this paper demonstrates the potential of GenAI in reshaping the various stages of the composition process, including invention, designing, and revising. The findings reveal that GenAI technologies have the potential to enhance students’ multimodal composition practices and offer alternative solutions to the wicked problems encountered during the design process. Specifically, GenAI facilitates invention by offering design inspirations and enriches designing by expanding, removing, and editing the student-produced design contents. The students in this study also shared their critical stance on the revision process by modifying and iterating their designs after their uses of GenAI. Through showcasing both the opportunities and challenges of GenAI technologies, this paper contributes to the ongoing scholarly conversations on multimodal composition and pedagogy. Moreover, the paper offers implications for the future research and teaching of GenAI-assisted multimodal composition projects, with the aim of encouraging thoughtful integration of GenAI technologies to foster critical AI literacy among college composition students.","2024-12-01","2024-12-03 03:05:09","2024-12-03 03:05:09","","102883","","","74","","Computers and Composition","","","","","","","","","","","","","","","","","","","Design; Generative artificial intelligence; Adobe Firefly; DALL·E; Multimodal composition process; Wicked problems; Writing studies","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"9IQQABMJ","journalArticle","2024","Felicetti, Alberto Michele; Cimino, Antonio; Mazzoleni, Alberto; Ammirato, Salvatore","Artificial intelligence and project management: An empirical investigation on the appropriation of generative Chatbots by project managers","Journal of Innovation & Knowledge","","2444-569X","10.1016/j.jik.2024.100545","https://www.sciencedirect.com/science/article/pii/S2444569X24000842","The integration of generative AI tools, such as chatbots, into project management is revolutionizing the field. This paper explores how project managers are adopting and adapting these tools, specifically focusing on ChatGPT, for enhanced project management. Using Adaptive Structuration Theory, the study examines project managers' appropriation of generative AI. It considers factors like Innovation Attitude, Peer Influence, and Task-Technology Fit, employing a survey of Italian project managers. The approach adopted to analyze data is based on Partial Least Square - Structural Equation Modeling. The research confirms the significance of the hypothesized antecedents in AI tool appropriation. Innovation Attitude and Peer Influence are shown to positively impact the creative and 'unfaithful' use of AI in project management. Task-Technology Fit is crucial for effective AI integration, impacting both creative behaviour and unfaithful appropriation. The study highlights the role of an innovative mindset, peer dynamics, and task compatibility in the effective use of AI tools in project management. It suggests potential areas for future research, including exploring cultural and organizational contexts and the rapid evolution of AI technologies.","2024-07-01","2024-12-03 03:05:09","2024-12-03 03:05:09","","100545","","3","9","","Journal of Innovation & Knowledge","","","","","","","","","","","","","","","","","","","Generative artificial intelligence; Appropriation Theory; Chatgpt; Project managers; Structural Equation Modeling","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"98FSPEWL","journalArticle","2024","Stróżyna, Milena; Węcel, Krzysztof; Stolarski, Piotr; Księżniak, Ewelina; Sawiński, Marcin; Lewoniewski, Włodzimierz; Abramowicz, Witold","Exploring the Challenges and Potential of Generative AI: Insights from an Empirical Study","28th International Conference on Knowledge Based and Intelligent information and Engineering Systems (KES 2024)","","1877-0509","10.1016/j.procs.2024.09.658","https://www.sciencedirect.com/science/article/pii/S1877050924027145","In November 2022, ChatGPT marked a transformative moment in artificial intelligence, leveraging generative AI tools (GAI) and large language models (LLMs) to mimic human language effectively. Their rapidly gained popularity has reshaped a number of areas, presenting both challenges and opportunities. With their widespread adoption, concerns arise regarding the reliability of information generated by these tools, particularly in detecting fake news and addressing hallucinations. This study, conducted within the OpenFact project, involves an experiment with students tasked to generate and critically assess text produced by GAI tools. Results indicate a common occurrence of hallucinations in LLM outputs and a lack of transparency in information sources, posing challenges for practical applications. Furthermore, participants demonstrate the ease of manipulating GAI tools to generate false information, underscoring the risk associated with their widespread use. These findings contribute to understanding the limitations and implications of utilizing GAI tools in information verification processes and highlight the need for further research in this area.","2024-01-01","2024-12-03 03:05:09","2024-12-03 03:05:09","","2042-2051","","","246","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","large language model; generative AI; fact-checking; hallucinations","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"AJN9KDWB","journalArticle","2024","Acosta-Enriquez, Benicio Gonzalo; Arbulú Pérez Vargas, Carmen Graciela; Huamaní Jordan, Olger; Arbulú Ballesteros, Marco Agustín; Paredes Morales, Ana Elizabeth","Exploring attitudes toward ChatGPT among college students: An empirical analysis of cognitive, affective, and behavioral components using path analysis","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100320","https://www.sciencedirect.com/science/article/pii/S2666920X24001231","The advent of generative artificial intelligence (AI) applications, such as ChatGPT, has significantly impacted various aspects of human life, including higher education. This study explores university students' attitudes toward ChatGPT, focusing on the cognitive, affective, and behavioral components of attitudes, on the basis of Mitcham's philosophical framework of attitudes toward technology. A total of 595 university students from six public and private universities in northern Peru participated in an online survey. The results of the structural equation modeling (SEM) analysis revealed that the affective component (β = 0.672∗∗∗) and the cognitive component (β = 0.260∗∗) positively influence the behavioral component of students' attitudes when ChatGPT is used. Moreover, the cognitive component (β = 0.931∗∗∗) positively influences the affective component of students' attitudes. However, gender and age did not have significant moderating effects on the relationships between the cognitive and affective components and the behavioral component. The discussion highlights that these findings contribute to understanding the psychological mechanisms underlying the adoption of ChatGPT in educational settings and offer valuable guidance for implementing this technology in teaching and learning processes. In conclusion, this study represents a significant advancement in comprehending attitudes toward generative AI technologies in higher education and opens new avenues for future research in this field.","2024-12-01","2024-12-03 03:05:09","2024-12-03 03:05:09","","100320","","","7","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Higher education; Attitudes; Artificial intelligence; ChatGPT; University students; Affective component; Behavioral component; Cognitive component","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"679EQUGH","journalArticle","2024","Knoth, Nils; Tolzin, Antonia; Janson, Andreas; Leimeister, Jan Marco","AI literacy and its implications for prompt engineering strategies","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100225","https://www.sciencedirect.com/science/article/pii/S2666920X24000262","Artificial intelligence technologies are rapidly advancing. As part of this development, large language models (LLMs) are increasingly being used when humans interact with systems based on artificial intelligence (AI), posing both new opportunities and challenges. When interacting with LLM-based AI system in a goal-directed manner, prompt engineering has evolved as a skill of formulating precise and well-structured instructions to elicit desired responses or information from the LLM, optimizing the effectiveness of the interaction. However, research on the perspectives of non-experts using LLM-based AI systems through prompt engineering and on how AI literacy affects prompting behavior is lacking. This aspect is particularly important when considering the implications of LLMs in the context of higher education. In this present study, we address this issue, introduce a skill-based approach to prompt engineering, and explicitly consider the role of non-experts' AI literacy (students) in their prompt engineering skills. We also provide qualitative insights into students’ intuitive behaviors towards LLM-based AI systems. The results show that higher-quality prompt engineering skills predict the quality of LLM output, suggesting that prompt engineering is indeed a required skill for the goal-directed use of generative AI tools. In addition, the results show that certain aspects of AI literacy can play a role in higher quality prompt engineering and targeted adaptation of LLMs within education. We, therefore, argue for the integration of AI educational content into current curricula to enable a hybrid intelligent society in which students can effectively use generative AI tools such as ChatGPT.","2024-06-01","2024-12-03 03:05:09","2024-12-03 03:05:09","","100225","","","6","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Education; Prompt engineering; Large language model; AI interaction; AI literacy","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"7GKMQ554","journalArticle","2024","Lu, Jie; Tian, Xiangning; Zhang, Chaobo; Zhao, Yang; Zhang, Jian; Zhang, Wenkai; Feng, Chenxin; He, Jianing; Wang, Jiaxi; He, Fengtai","Evaluation of large language models (LLMs) on the mastery of knowledge and skills in the heating, ventilation and air conditioning (HVAC) industry","Energy and Built Environment","","2666-1233","10.1016/j.enbenv.2024.03.010","https://www.sciencedirect.com/science/article/pii/S2666123324000448","Large language models (LLMs) have shown human-level capabilities in solving various complex tasks. However, it is still unknown whether state-of-the-art LLMs master sufficient knowledge related to heating, ventilation and air conditioning (HVAC) systems. It will be inspiring if LLMs can think and learn like professionals in the HVAC industry. Hence, this study investigates the performance of LLMs on mastering the knowledge and skills related to the HVAC industry by letting them take the ASHRAE Certified HVAC Designer examination, an authoritative examination in the HVAC industry. Three key knowledge capabilities are explored: recall, analysis and application. Twelve representative LLMs are tested such as GPT-3.5, GPT-4 and LLaMA. According to the results, GPT-4 passes the ASHRAE Certified HVAC Designer examination with scores from 74 to 78, which is higher than about half of human examinees. Besides, GPT-3.5 passes the examination twice out of five times. It demonstrates that some LLMs such as GPT-4 and GPT-3.5 have great potential to assist or replace humans in designing and operating HVAC systems. However, they still make some mistakes sometimes due to the lack of knowledge, poor reasoning capabilities and unsatisfactory equation calculation abilities. Accordingly, four future research directions are proposed to reveal how to utilize and improve LLMs in the HVAC industry: teaching LLMs to use design tools or software in the HVAC industry, enabling LLMs to read and analyze the operational data from HVAC systems, developing tailored corpuses for the HVAC industry, and assessing the performance of LLMs in real-world HVAC design and operation scenarios.","2024-03-27","2024-12-03 03:05:09","2024-12-03 03:05:09","","","","","","","Energy and Built Environment","","","","","","","","","","","","","","","","","","","ChatGPT; GPT-4; Large language model; Artificial general intelligence; HVAC systems","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"HDTFU5IV","journalArticle","2024","Lee, Gyeong-Geon; Latif, Ehsan; Wu, Xuansheng; Liu, Ninghao; Zhai, Xiaoming","Applying large language models and chain-of-thought for automatic scoring","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100213","https://www.sciencedirect.com/science/article/pii/S2666920X24000146","This study investigates the application of large language models (LLMs), specifically GPT-3.5 and GPT-4, with Chain-of-Though (CoT) in the automatic scoring of student-written responses to science assessments. We focused on overcoming the challenges of accessibility, technical complexity, and lack of explainability that have previously limited the use of artificial intelligence-based automatic scoring tools among researchers and educators. With a testing dataset comprising six assessment tasks (three binomial and three trinomial) with 1,650 student responses, we employed six prompt engineering strategies to automatically score student responses. The six strategies combined zero-shot or few-shot learning with CoT, either alone or alongside item stem and scoring rubrics, developed based on a novel approach, WRVRT (prompt writing, reviewing, validating, revising, and testing). Results indicated that few-shot (acc = 0.67) outperformed zero-shot learning (acc = 0.60), with 12.6% increase. CoT, when used without item stem and scoring rubrics, did not significantly affect scoring accuracy (acc = 0.60). However, CoT prompting paired with contextual item stems and rubrics proved to be a significant contributor to scoring accuracy (13.44% increase for zero-shot; 3.7% increase for few-shot). We found a more balanced accuracy across different proficiency categories when CoT was used with a scoring rubric, highlighting the importance of domain-specific reasoning in enhancing the effectiveness of LLMs in scoring tasks. We also found that GPT-4 demonstrated superior performance over GPT-3.5 in various scoring tasks when combined with the single-call greedy sampling or ensemble voting nucleus sampling strategy, showing 8.64% difference. Particularly, the single-call greedy sampling strategy with GPT-4 outperformed other approaches. This study also demonstrates the potential of LLMs in facilitating explainable and interpretable automatic scoring, emphasizing that CoT enhances accuracy and transparency, particularly when used with item stem and scoring rubrics.","2024-06-01","2024-12-03 03:05:09","2024-12-03 03:05:09","","100213","","","6","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Education; ChatGPT; GPT-4; Large language models (LLMs); Artificial intelligence (AI); Automatic scoring; Chain-of-Thought","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"FZZ6DJMY","journalArticle","2024","Abu-Ashour, Waseem; Emil, Sherif; Poenaru, Dan","Using Artificial Intelligence to Label Free-Text Operative and Ultrasound Reports for Grading Pediatric Appendicitis","Journal of Pediatric Surgery","","0022-3468","10.1016/j.jpedsurg.2024.01.033","https://www.sciencedirect.com/science/article/pii/S0022346824000654","Purpose Data science approaches personalizing pediatric appendicitis management are hampered by small datasets and unstructured electronic medical records (EMR). Artificial intelligence (AI) chatbots based on large language models can structure free-text EMR data. We compare data extraction quality between ChatGPT-4 and human data collectors. Methods To train AI models to grade pediatric appendicitis preoperatively, several data collectors extracted detailed preoperative and operative data from 2100 children operated for acute appendicitis. Collectors were trained for the task based on satisfactory Kappa scores. ChatGPT-4 was prompted to structure free text from 103 random anonymized ultrasound and operative records in the dataset using the set variables and coding options, and to estimate appendicitis severity grade from the operative report. A pediatric surgeon then adjudicated all data, identifying errors in each method. Results Within the 44 ultrasound (42.7%) and 32 operative reports (31.1%) discordant in at least one field, 98% of the errors were found in the manual data extraction. The appendicitis grade was erroneously assigned manually in 29 patients (28.2%), and by ChatGPT-4 in 3 (2.9%). Across datasets, the use of the AI chatbot was able to avoid misclassification in 59.2% of the records including both reports and extracted data approximately 40 times faster. Conclusion AI chatbot significantly outperformed manual data extraction in accuracy for ultrasound and operative reports, and correctly assigned the appendicitis grade. While wider validation is required and data safety concerns must be addressed, these AI tools show significant promise in improving the accuracy and efficiency of research data collection. Levels of Evidence Level III.","2024-05-01","2024-12-03 03:05:09","2024-12-03 03:05:09","","783-790","","5","59","","Journal of Pediatric Surgery","","","","","","","","","","","","","","","","","","","Artificial intelligence; Appendicitis grade; Comparative study; Diagnosis; Pediatric appendicitis","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2LINCV2A","journalArticle","2024","Hasan, Md Rabiul; Chowdhury, Nahian Ismail; Rahman, Md Hadisur; Syed, Md Asif Bin; Ryu, JuHyeong","Understanding AI Chatbot adoption in education: PLS-SEM analysis of user behavior factors","Computers in Human Behavior: Artificial Humans","","2949-8821","10.1016/j.chbah.2024.100098","https://www.sciencedirect.com/science/article/pii/S2949882124000586","The integration of Artificial Intelligence (AI) into education is a recent development, with chatbots emerging as a noteworthy addition to this transformative landscape. As online learning platforms rapidly advance, students need to adapt swiftly to excel in this dynamic environment. Consequently, understanding the acceptance of chatbots, particularly those employing Large Language Models (LLM) such as Chat Generative Pretrained Transformer (ChatGPT), Google Bard, and other interactive AI technologies, is of paramount importance. Investigating how students accept and view chatbots is essential to directing their incorporation into Industry 4.0 and enabling a smooth transition to Industry 5.0's customized and human-centered methodology. However, existing research on chatbots in education has overlooked key behavior-related aspects, such as Optimism, Innovativeness, Discomfort, Insecurity, Transparency, Ethics, Interaction, Engagement, and Accuracy, creating a significant literature gap. To address this gap, this study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to investigate the determinant of chatbots adoption in education among students, considering the Technology Readiness Index and Technology Acceptance Model. Utilizing a five-point Likert scale for data collection, we gathered a total of 185 responses, which were analyzed using R-Studio software. We established 12 hypotheses to achieve its objectives. The results showed that Optimism and Innovativeness are positively associated with Perceived Ease of Use and Perceived Usefulness. Conversely, Discomfort and Insecurity negatively impact Perceived Ease of Use, with only Insecurity negatively affecting Perceived Usefulness. Furthermore, Perceived Ease of Use, Perceived Usefulness, Interaction and Engagement, Accuracy, and Responsiveness all significantly contribute to the Intention to Use, whereas Transparency and Ethics have a negative impact on Intention to Use. Finally, Intention to Use mediates the relationships between Interaction, Engagement, Accuracy, Responsiveness, Transparency, Ethics, and Perception of Decision Making. These findings provide insights for future technology designers, elucidating critical user behavior factors influencing chatbots adoption and utilization in educational contexts.","2024-08-01","2024-12-03 03:05:09","2024-12-03 03:05:09","","100098","","2","2","","Computers in Human Behavior: Artificial Humans","","","","","","","","","","","","","","","","","","","Industry 4.0; ChatGPT; Chatbot; LLM; Google BARD; Industry 5.0; PLS-SEM; Technology acceptance model; Technology readiness Index","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SHXMWZ83","journalArticle","2023","Maroteau, Gaëlle; An, Jae-Sung; Murgier, Jérome; Hulet, Christophe; Ollivier, Matthieu; Ferreira, Alexandre","Evaluation of the impact of large language learning models on articles submitted to Orthopaedics & Traumatology: Surgery & Research (OTSR): A significant increase in the use of artificial intelligence in 2023","Orthopaedics & Traumatology: Surgery & Research","","1877-0568","10.1016/j.otsr.2023.103720","https://www.sciencedirect.com/science/article/pii/S1877056823002384","Introduction There has been an unprecedented rise is the use of artificial intelligence (AI) amongst medical fields. Recently, a dialogue agent called ChatGPT (Generative Pre-trained Transformer) has grown in popularity through its use of large language models (LLM) to clearly and precisely generate text on demand. However, the impact of AI on the creation of scientific articles is remains unknown. A retrospective study was carried out with the aim of answering the following questions: identify the presence of text generated by LLM before and after the increased usage of ChatGPT in articles submitted in OTSR; determine if the type of article, the year of submission, and the country of origin, influenced the proportion of text generated, at least in part by AI. Material and methods A total of 390 English articles were submitted to OTSR in January, February and March 2022 (n=204) and over the same months of 2023 (n=186) were analyzed. All articles were analyzed using the ZeroGPT tool, which provides an assumed rate of AI use expressed as a percentage. A comparison of the average rate of AI use was carried out between the articles submitted in 2022 and 2023. This comparison was repeated keeping only the articles with the highest percentage of suspected AI use (greater than 10 and 20%). A secondary analysis was carried out to identify risk factors for AI use. Results The average percentage of suspected LLM use in the entire cohort was 11%±6, with 160 articles (41.0%) having a suspected AI rate greater than 10% and 61 (15.6%) with an assumed AI rate greater than 20%. A comparison between articles submitted in 2022 and 2023 revealed a significant increase in the use of these tools after the launch of ChatGPT 3.5 (9.4% in 2022 and 12.6% in 2023 [p=0.004]). The number of articles with suspected AI rates of greater than 10 and 20% were significantly higher in 2023: >10%: 71 articles (34.8%) versus 89 articles (47.8%) (p=0.008) and >20%: 21 articles (10.3%) versus 40 articles (21.5%) (p=0.002). A risk factor analysis for LLLM use, demonstrated that authors of Asian geographic origin, and the submission year 2023 were associated with a higher rate of suspected AI use. An AI rate >20% was associated to Asian geographical origin with an odds ratio of 1.79 (95% CI: 1.03–3.11) (p=0.029), while the year of submission being 2023 had an odds ratio of 1.7 (95% CI: 1.1–2.5) (p=0.02). Conclusion This study highlights a significant increase in the use of LLM in the writing of articles submitted to the OTSR journal after the launch of ChatGPT 3.5. The increasing use of these models raises questions about originality and plagiarism in scientific research. AI offers creative opportunities but also raises ethical and methodological challenges. Level of evidence III; case control study.","2023-12-01","2024-12-03 03:05:09","2024-12-03 03:05:09","","103720","","8","109","","Orthopaedics & Traumatology: Surgery & Research","","","","","","","","","","","","","","","","","","","Artificial intelligence; ChatGPT; Chatbot; Large language learning models; Scientific article","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"P69CRKZP","journalArticle","2024","Amacher, Simon A.; Arpagaus, Armon; Sahmer, Christian; Becker, Christoph; Gross, Sebastian; Urben, Tabita; Tisljar, Kai; Sutter, Raoul; Marsch, Stephan; Hunziker, Sabina","Prediction of outcomes after cardiac arrest by a generative artificial intelligence model","Resuscitation Plus","","2666-5204","10.1016/j.resplu.2024.100587","https://www.sciencedirect.com/science/article/pii/S2666520424000389","Aims To investigate the prognostic accuracy of a non-medical generative artificial intelligence model (Chat Generative Pre-Trained Transformer 4 - ChatGPT-4) as a novel aspect in predicting death and poor neurological outcome at hospital discharge based on real-life data from cardiac arrest patients. Methods This prospective cohort study investigates the prognostic performance of ChatGPT-4 to predict outcomes at hospital discharge of adult cardiac arrest patients admitted to intensive care at a large Swiss tertiary academic medical center (COMMUNICATE/PROPHETIC cohort study). We prompted ChatGPT-4 with sixteen prognostic parameters derived from established post-cardiac arrest scores for each patient. We compared the prognostic performance of ChatGPT-4 regarding the area under the curve (AUC), sensitivity, specificity, positive and negative predictive values, and likelihood ratios of three cardiac arrest scores (Out-of-Hospital Cardiac Arrest [OHCA], Cardiac Arrest Hospital Prognosis [CAHP], and PROgnostication using LOGistic regression model for Unselected adult cardiac arrest patients in the Early stages [PROLOGUE score]) for in-hospital mortality and poor neurological outcome. Results Mortality at hospital discharge was 43% (n = 309/713), 54% of patients (n = 387/713) had a poor neurological outcome. ChatGPT-4 showed good discrimination regarding in-hospital mortality with an AUC of 0.85, similar to the OHCA, CAHP, and PROLOGUE (AUCs of 0.82, 0.83, and 0.84, respectively) scores. For poor neurological outcome, ChatGPT-4 showed a similar prediction to the post-cardiac arrest scores (AUC 0.83). Conclusions ChatGPT-4 showed a similar performance in predicting mortality and poor neurological outcome compared to validated post-cardiac arrest scores. However, more research is needed regarding illogical answers for potential incorporation of an LLM in the multimodal outcome prognostication after cardiac arrest.","2024-06-01","2024-12-03 03:05:09","2024-12-03 03:05:09","","100587","","","18","","Resuscitation Plus","","","","","","","","","","","","","","","","","","","Artificial intelligence; Cardiac arrest; Cardiopulmonary resuscitation; Mortality prediction; Neurological outcome","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LCV9B7SB","journalArticle","2023","Raj, Rohit; Singh, Arpit; Kumar, Vimal; Verma, Pratima","Analyzing the potential benefits and use cases of ChatGPT as a tool for improving the efficiency and effectiveness of business operations","BenchCouncil Transactions on Benchmarks, Standards and Evaluations","","2772-4859","10.1016/j.tbench.2023.100140","https://www.sciencedirect.com/science/article/pii/S2772485923000571","The study addresses the potential benefits for companies of adopting ChatGPT, a popular chatbot built on a large-scale transformer-based language model known as a generative pre-trained transformer (GPT). Chatbots like ChatGPT may improve customer service, handle several client inquiries at once, and save operational costs. Moreover, ChatGPT may automate regular processes like order tracking and billing, allowing human employees to focus on more complex and strategic responsibilities. Nevertheless, before deploying ChatGPT, enterprises must carefully analyze its use cases and restrictions, as well as its strengths and disadvantages. ChatGPT, for example, requires training data that is particular to the business domain and might produce erroneous and ambiguous findings. The study identifies areas of deployment of ChatGPT's possible benefits in enterprises by drawing on the literature that is currently accessible on ChatGPT, massive language models, and artificial intelligence. Then, using the PSI (Preference Selection Index) and COPRAS (Complex Proportional Assessment) approaches, potential advantages are taken into account and prioritized. By highlighting current trends and possible advantages in the industry, this editorial seeks to provide insight into the present state of employing ChatGPT in enterprises and research. ChatGPT may also learn biases from training data and create replies that reinforce those biases. As a result, enterprises must train and fine-tune ChatGPT to specific operations, set explicit boundaries and limitations for its use, and implement appropriate security measures to avoid malicious input. The study highlights the research gap in the dearth of literature by outlining ChatGPT's potential benefits for businesses, analyzing its strengths and limits, and offering insights into how organizations might use ChatGPT's capabilities to enhance their operations.","2023-09-01","2024-12-03 03:05:10","2024-12-03 03:05:10","","100140","","3","3","","BenchCouncil Transactions on Benchmarks, Standards and Evaluations","","","","","","","","","","","","","","","","","","","ChatGPT; automation; benefits; business; efficiency","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ZRMBAEEP","journalArticle","2024","Renshaw, Scott Leo; Carley, Kathleen M.","Linking online activity to offline behavior: A meta-review of three decades of online-to-offline scholarship with future implications for AI","Emerging Trends in Drugs, Addictions, and Health","","2667-1182","10.1016/j.etdah.2024.100154","https://www.sciencedirect.com/science/article/pii/S2667118224000138","As society grapples with the emerging significance and implications of Large Language Models (LLMs), such as OpenAI’s ChatGPT, or Google’s Gemini, as well as other advancements in modern generative Artificial Intelligence (AI), it is crucial to recognize the existing role that data, algorithms, and online social networks have already played in shaping our contemporary society. This review article provides the first comprehensive examination of the current state of knowledge, across disciplinary divides, on how online influences impact offline behaviors, laying the necessary groundwork for investigating and researching the potential impact that these new technologies will have on our “offline” lives. Through a deep-dive collection of articles (n=149), we review and analyze research with measurable Online-to-Offline impacts (n=88). Within this Online-to-Offline criteria, we identify five emergent cross-cutting themes, namely: Social Diffusion, Social Reinforcement, Social Boundary & Identity Maintenance, Cognitive and Attitudinal Research, and Research on Vulnerable & Marginalized Impacts. Through a second wave snowball collection process, we construct a citation network from the broader Online and Offline research literature, allowing us to locate the Online-to-Offline subset as part of a larger intellectual discussion. Finally, we conduct a Term Frequency-Inverse Document Frequency (TF-IDF) analysis of terms used in the titles of these online/offline research papers, from 1990 to 2023, to identify the evolution of researchers’ conceptualization and framing of Online and Offline research across the past 30 years. The meta-review, presentation of high-level cross-cutting interdisciplinary themes, co-citation network analysis, and TF-IDF analysis collectively provide a cohesive and deeper understanding of the research space of online/offline influences. By taking stock of the ways in which online factors have already shaped individual, group, or organizational behaviors and social dynamics broadly in “offline” contexts, this work aims to provide a cohesive theoretical and empirical foundation for future researchers to better anticipate, address, and frame the future consequences of the rapidly evolving digitally influenced landscape we find ourselves in today.","2024-12-01","2024-12-03 03:05:10","2024-12-03 03:05:10","","100154","","","4","","Emerging Trends in Drugs, Addictions, and Health","","","","","","","","","","","","","","","","","","","Network analysis; Meta-review; Offline behavior; Online influences; Social reinforcement","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CKQITWZA","journalArticle","2023","Bernabei, Margherita; Colabianchi, Silvia; Falegnami, Andrea; Costantino, Francesco","Students’ use of large language models in engineering education: A case study on technology acceptance, perceptions, efficacy, and detection chances","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2023.100172","https://www.sciencedirect.com/science/article/pii/S2666920X23000516","The accessibility of advanced Artificial Intelligence-based tools, like ChatGPT, has made Large Language Models (LLMs) readily available to students. These LLMs can generate original written content to assist students in their academic assessments. With the rapid adoption of LLMs, exemplified by the popularity of OpenAI's ChatGPT, there is a growing need to explore their application in education. Few studies examine students' use of LLMs as learning tools. This paper focuses on the application of ChatGPT in engineering higher education through an in-depth case study. It investigates whether engineering students can generate high-quality university essays with LLMs assistance, whether existing LLMs identification systems can detect essays produced with LLMs, and how students perceive the usefulness and acceptance of LLMs in learning. The research adopts a deductive/inductive approach, combining conceptualization and empirical evidence analysis. The study involves mechanical and management engineering students, who compose essays using LLMs. The essay assessment showed good results, but some recommendations emerged for teachers and students. Thirteen LLMs detectors were tested without achieving satisfactory results, suggesting to avoid LLMs ban. In addition, students were administered a questionnaire based on constructs and items that follow the technology acceptance models available in the literature. The results contribute to qualitative evidence by highlighting possible future research and educational practices.","2023-01-01","2024-12-03 03:05:10","2024-12-03 03:05:10","","100172","","","5","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Higher education; ChatGPT; LLM; Essay generation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DQY37267","journalArticle","2024","Raman, Raghu; Venugopalan, Murale; Kamal, Anju","Evaluating human resources management literacy: A performance analysis of ChatGPT and bard","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e27026","https://www.sciencedirect.com/science/article/pii/S2405844024030573","This study presents a comprehensive analysis comparing the literacy levels of two Generative Artificial Intelligence (GAI) tools, ChatGPT and Bard, using a dataset of 134 questions from the Human Resources (HR) domain. The generated responses are evaluated for accuracy, relevance, and clarity. We find that ChatGPT outperforms Bard in overall accuracy (84.3% vs. 82.8%). This difference in performance suggests that ChatGPT could serve as a robotic advisor in transactional HR roles. In contrast, Bard may possess additional safeguards against misuse in the HR function, making it less capable of generating responses to certain types of questions. Statistical tests reveal that although the two systems differ in their mean accuracy, relevance, and clarity of the responses, the observed differences are not always statistically significant, implying that both tools may be more complementary than competitive. The Pearson correlation coefficients further support this by showing weak to non-existent relationships in performance metrics between the two tools. Confirmation queries don't improve ChatGPT or Bard's response accuracy. The study thus contributes to emerging research on the utility of GAI tools in Human Resources Management and suggests that involving certified HR professionals in the design phase could enhance underlying language model performance.","2024-03-15","2024-12-03 03:05:10","2024-12-03 03:05:10","","e27026","","5","10","","Heliyon","","","","","","","","","","","","","","","","","","","Human resource management; Ethics; LLM; Generative AI; Hiring; HR policy; Managerial decisions; Text mining","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"3AUC2DTF","journalArticle","2024","Cha, Seokki","The Potential Role of Small Modular Reactors (SMRs) in Addressing the Increasing Power Demand of the Artificial Intelligence Industry: A Scenario-Based Analysis","Nuclear Engineering and Technology","","1738-5733","10.1016/j.net.2024.11.016","https://www.sciencedirect.com/science/article/pii/S1738573324005643","[Abstract] This study analyzes the potential role of Small Modular Reactors (SMRs) as a solution to future power shortages, in the face of the rapid advancement of Large Language Model (LLM) technology and the consequent increase in power demand from data centers. Utilizing scenario analysis, nine future scenarios were derived based on the growth rate of LLMs/data centers and the favorability of SMR technology and regulatory environments. The analysis reveals three key findings: First, SMRs demonstrate the highest potential in the ""Era of Radical Innovation"" scenario, where they could serve as a primary clean power source for data centers through distributed generation systems. Second, the success of SMR implementation heavily depends on three critical factors: regulatory framework optimization, technological maturity achievement, and social acceptance enhancement. Third, scenarios with unfavorable SMR conditions emphasize the importance of developing alternative strategies, including renewable energy integration and energy efficiency improvements. The study identified specific challenges that must be addressed, including the need for standardized licensing procedures, enhanced economic viability through modular manufacturing, and effective public engagement strategies. Based on these findings, the study presents policy recommendations structured in three tiers: immediate regulatory reforms and R&D support, medium-term infrastructure development and workforce training, and long-term international cooperation frameworks. This research provides a systematic framework for evaluating the potential of SMRs in addressing the growing power demands of the AI industry while ensuring environmental sustainability and energy security.","2024-11-12","2024-12-03 03:05:10","2024-12-03 03:05:10","","","","","","","Nuclear Engineering and Technology","","","","","","","","","","","","","","","","","","","Artificial Intelligence (AI); Data Centers; Energy Policy; Large Language Models (LLMs); Scenario Analysis; Small Modular Reactors (SMRs)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"4LC9J4LC","journalArticle","2024","Aryadoust, Vahid; Zakaria, Azrifah; Jia, Yichen","Investigating the affordances of OpenAI's large language model in developing listening assessments","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100204","https://www.sciencedirect.com/science/article/pii/S2666920X24000055","To address the complexity and high costs of developing listening tests for test-takers of varying proficiency levels, this study investigates the capabilities of an OpenAI's large language model, ChatGPT 4, in developing listening assessments. Employing prompt engineering and fine-tuning of prompts, the study specifically focuses on creating listening scripts and test items using ChatGPT 4 for test-takers across a spectrum of proficiency levels (academic, low, intermediate, and advanced). For comparability, the 24 topics of these scripts were selected from topics found in academic listening tests. We conducted two types of analyses to evaluate the quality of the output. First, we performed linguistic analyses of the scripts using Coh-Metrix and Text Inspector to determine if the scripts varied linguistically as required by the prompts. Second, we analyzed topic variation and the degree of overlap in the test items. Results indicated that while ChatGPT 4 reliably produced scripts with significant textual variations, the test items generated were often long and exhibited semantic overlaps among options. This effect was also influenced by the topic. We discuss the ethical complexities that arise from the use of generative artificial intelligence (AI), and how generative AI (GenAI) can potentially benefit practitioners and researchers in language assessment, while recognizing its limitations.","2024-06-01","2024-12-03 03:05:10","2024-12-03 03:05:10","","100204","","","6","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Prompt engineering; Large language model; Artificial intelligence (AI); ChatGPT 4; Fine-tuning of prompts; Generative AI (GenAI); Listening assessment; Listening scripts; Test creation; Test items","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"YPLP9R9F","journalArticle","2024","Dinu, Anca; Florescu, Andra Maria","An integrated benchmark for verbal creativity testing of LLMs and humans","28th International Conference on Knowledge Based and Intelligent information and Engineering Systems (KES 2024)","","1877-0509","10.1016/j.procs.2024.09.380","https://www.sciencedirect.com/science/article/pii/S1877050924024141","Until fairly recently, creativity was a human-specific characteristic. Computational creativity or artificial creativity was established as a domain in the late 90’s, with different fields such as verbal, musical, or graphical creativity. With the latest technological advances and the appearance of Large Language Models (LLMs), creativity as a feature of machines gained more and more interest in the scientific community. The scope of this study is twofold: to design a comprehensive benchmark for verbal creativity assessment of LLMs and then to run the same creativity tests on different LLMs as well as on humans, for a direct comparison. We aimed to raise the replicability and extensibility of the creativity assessment of LLMs. Hence, we adapted different types of creativity tests and different criteria from psychology to fit the LLMs profile. We also employed computer-assisted evaluation methods, by using the Open Creativity Scoring with Artificial Intelligence (OCSAI), as we wanted to focus exclusively on automated approaches to assessing creativity. We quantitatively and qualitatively analyzed the data set of both human and machine-generated answers and interpreted the results. Finally, we provide both the original verbal creativity test that we have designed, and the curated data comprising all the collected answers, from the LLMs and from the humans that participated in this research.","2024-01-01","2024-12-03 03:05:10","2024-12-03 03:05:10","","2902-2911","","","246","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","AI creativity; human-machine comparison; LLM creativity; verbal creativity tests","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RMCJEQH8","journalArticle","2024","Håkansson, Anne; Phillips-Wren, Gloria","Generative AI and Large Language Models - Benefits, Drawbacks, Future and Recommendations","28th International Conference on Knowledge Based and Intelligent information and Engineering Systems (KES 2024)","","1877-0509","10.1016/j.procs.2024.09.689","https://www.sciencedirect.com/science/article/pii/S1877050924027492","Natural language processing, with parsing and generation, has a long tradition. Parsing has been easier to perform than a generation but with generative artificial intelligence (a.k.a Gen AI) and large language models (abbr. LLMs), this has changed. Generative artificial intelligence is a type of artificial intelligence that uses a large data set to create something in the genre of that data set. It can generate different outputs ranging from texts, audio, objects, pictures, and paintings to videos, but also synthetic data. LLMs use deep learning and deep neural networks to train on large text corpora for recognizing and generating texts. These models are based on massive data sets, collected from databases and the web. They use transformer models to detect how elements in sequences relate to each other. This provides context support. Two well-known large language models are the Generative Pre-trained Transformer, GPT, used in ChatGPT and Bidirectional Encoder Representations from Transformers, BERT. Although LLMs have advantages, they have problems. This paper presents generative artificial intelligence and LLMs with benefits and drawbacks. Results from applying these models have shown that they can work well for accuracy in specificity, user personalization and human-computer communication but they may not provide acceptable, reliable and truthful results. For example, ethics, hallucinations and incorrect information, or misjudgments, are some major problems. The paper ends with future directions, research questions on LLMs, and recommendations.","2024-01-01","2024-12-03 03:05:10","2024-12-03 03:05:10","","5458-5468","","","246","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Large Language Models; Natural Language Processing; Generative AI","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"APTT67WK","journalArticle","2024","Ballard, David H.; Antigua-Made, Alexander; Barre, Emily; Edney, Elizabeth; Gordon, Emile B.; Kelahan, Linda; Lodhi, Taha; Martin, Jonathan G.; Ozkan, Melis; Serdynski, Kevin; Spieler, Bradley; Zhu, Daphne; Adams, Scott J.","Impact of ChatGPT and Large Language Models on Radiology Education: Association of Academic Radiology—Radiology Research Alliance Task Force White Paper","Academic Radiology","","1076-6332","10.1016/j.acra.2024.10.023","https://www.sciencedirect.com/science/article/pii/S1076633224007840","Generative artificial intelligence, including large language models (LLMs), holds immense potential to enhance healthcare, medical education, and health research. Recognizing the transformative opportunities and potential risks afforded by LLMs, the Association of Academic Radiology—Radiology Research Alliance convened a task force to explore the promise and pitfalls of using LLMs such as ChatGPT in radiology. This white paper explores the impact of LLMs on radiology education, highlighting their potential to enrich curriculum development, teaching and learning, and learner assessment. Despite these advantages, the implementation of LLMs presents challenges, including limits on accuracy and transparency, the risk of misinformation, data privacy issues, and potential biases, which must be carefully considered. We provide recommendations for the successful integration of LLMs and LLM-based educational tools into radiology education programs, emphasizing assessment of the technological readiness of LLMs for specific use cases, structured planning, regular evaluation, faculty development, increased training opportunities, academic-industry collaboration, and research on best practices for employing LLMs in education.","2024-11-30","2024-12-03 03:05:10","2024-12-03 03:05:10","","","","","","","Academic Radiology","","","","","","","","","","","","","","","","","","","Artificial intelligence; Large language models; Assessment; Curriculum; Teaching and learning","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2XQ3B5JB","journalArticle","2024","Colabianchi, Silvia; Costantino, Francesco; Sabetta, Nicolò","Assessment of a large language model based digital intelligent assistant in assembly manufacturing","Computers in Industry","","0166-3615","10.1016/j.compind.2024.104129","https://www.sciencedirect.com/science/article/pii/S0166361524000575","The use of Digital Intelligent Assistants (DIAs) in manufacturing aims to enhance performance and reduce cognitive workload. By leveraging the advanced capabilities of Large Language Models (LLMs), the research aims to understand the impact of DIAs on assembly processes, emphasizing human-centric design and operational efficiency. The study is novel in considering the three primary objectives: evaluating the technical robustness of DIAs, assessing their effect on operators' cognitive workload and user experience, and determining the overall performance improvement of the assembly process. Methodologically, the research employs a laboratory experiment, incorporating a controlled setting to meticulously assess the DIA's performance. The experiment used a between-subjects design comparing a group of participants using the DIA against a control group relying on traditional manual methods across a series of assembly tasks. Findings reveal a significant enhancement in the operators' experience, a reduction in cognitive load, and an improvement in the quality of process outputs when the DIA is employed. The article contributes to the study of the DIA's potential and AI integration in manufacturing, offering insights into the design, development, and evaluation of DIAs in industrial settings.","2024-11-01","2024-12-03 03:05:10","2024-12-03 03:05:10","","104129","","","162","","Computers in Industry","","","","","","","","","","","","","","","","","","","Industry 4.0; Experimental design; Artificial intelligence; Natural language processing; Chatbot; Industry 5.0","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"9ZASB9RT","journalArticle","2024","Woodman, Ollie; Wen, Zhen; Lu, Hui; Ren, Yiwen; Zhu, Minfeng; Chen, Wei","Exploring the neural landscape: Visual analytics of neuron activation in large language models with NeuronautLLM","Graphical Models","","1524-0703","10.1016/j.gmod.2024.101238","https://www.sciencedirect.com/science/article/pii/S1524070324000262","Large language models (LLMs) like those that power OpenAI’s ChatGPT and Google’s Gemini have played a major part in the recent wave of machine learning and artificial intelligence advancements. However, interpreting LLMs and visualizing their components is extremely difficult due to the incredible scale and high dimensionality of model data. NeuronautLLM introduces a visual analysis system for identifying and visualizing influential neurons in transformer-based language models as they relate to user-defined prompts. Our approach combines simple, yet information-dense visualizations as well as neuron explanation and classification data to provide a wealth of opportunities for exploration. NeuronautLLM was reviewed by two experts to verify its efficacy as a tool for practical model interpretation. Interviews and usability tests with five LLM experts demonstrated NeuronautLLM’s exceptional usability and its readiness for real-world application. Furthermore, two in-depth case studies on model reasoning and social bias highlight NeuronautLLM’s versatility in aiding the analysis of a wide range of LLM research problems.","2024-12-01","2024-12-03 03:05:10","2024-12-03 03:05:10","","101238","","","136","","Graphical Models","","","","","","","","","","","","","","","","","","","Artificial intelligence; Large language models; Data visualization; Mechanistic interpretability","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LDHEFLRV","journalArticle","2024","Conte, Luana; Lupo, Roberto; Lezzi, Pierluigi; Pedone, Alessio; Rubbi, Ivan; Lezzi, Alessia; Vitale, Elsa; Fasano, Antonio; De Nunzio, Giorgio","Statistical analysis and generative Artificial Intelligence (AI) for assessing pain experience, pain-induced disability, and quality of life in Parkinson's disease patients","Brain Research Bulletin","","0361-9230","10.1016/j.brainresbull.2024.110893","https://www.sciencedirect.com/science/article/pii/S0361923024000261","The Parkinson's Disease (PD) is a chronic neurodegenerative condition characterized by motor symptoms such as tremors, rigidity, and bradykinesia, which can significantly impact various aspects of daily life. Among these aspects, pain is a prominent element. Despite the widespread use of therapies aimed at improving symptoms and quality of life, effective pain management is essential to enhance the quality of life of individuals affected by this disease. However, a detailed understanding of the factors associated with pain in PD is still evolving. In this study, we examined the disability caused by pain and the pain experienced by PD patients using two validated questionnaires, namely the Parkinson's Disease Questionnaire (PDQ) and the King's Parkinson's Disease Pain Questionnaire (KPPQ). Customized questions were also included to further explore the pain experience and management strategies adopted by PD patients. Through statistical analysis, we explored the relationships between questionnaire scores, socio-demographic data, and other relevant variables. Additionally, generative Artificial Intelligence (AI) was employed to gain a deeper understanding of patient responses. The results indicate the extent and impact of pain in PD and provide valuable insights for more targeted and personalized management. This study lays the foundation for future research and the development of interventions aimed at improving the quality of life for individuals affected by this condition.","2024-03-01","2024-12-03 03:05:10","2024-12-03 03:05:10","","110893","","","208","","Brain Research Bulletin","","","","","","","","","","","","","","","","","","","Generative Artificial Intelligence (AI); King's Parkinson's Disease Pain Questionnaire (KPPQ); Pain; Parkinson's disease; Parkinson's Disease Questionnaire (PDQ)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"Y6ZMHTUN","journalArticle","2024","Leypold, Tim; Schäfer, Benedikt; Boos, Anja M.; Beier, Justus P.","Artificial Intelligence-Powered Hand Surgery Consultation: GPT-4 as an Assistant in a Hand Surgery Outpatient Clinic","The Journal of Hand Surgery","","0363-5023","10.1016/j.jhsa.2024.06.002","https://www.sciencedirect.com/science/article/pii/S0363502324002612","Purpose Exploring the integration of artificial intelligence in clinical settings, this study examined the feasibility of using Generative Pretrained Transformer 4 (GPT-4), a large language model, as a consultation assistant in a hand surgery outpatient clinic. Methods The study involved 10 simulated patient scenarios with common hand conditions, where GPT-4, enhanced through specific prompt engineering techniques, conducted medical history interviews, and assisted in diagnostic processes. A panel of expert hand surgeons, each board-certified in hand surgery, evaluated GPT-4’s responses using a Likert Scale across five criteria with scores ranging from 1 (lowest) to 5 (highest). Results Generative Pretrained Transformer 4 achieved an average score of 4.6, reflecting good performance in documenting a medical history, as evaluated by the hand surgeons. Conclusions These findings suggest that GPT-4 can effectively document medical histories to meet the standards of hand surgeons in a simulated environment. The findings indicate potential for future application in patient care, but the actual performance of GPT-4 in real clinical settings remains to be investigated. Clinical relevance This study provides a preliminary indication that GPT-4 could be a useful consultation assistant in a hand surgery outpatient clinic, but further research is required to explore its reliability and practicality in actual practice.","2024-11-01","2024-12-03 03:05:10","2024-12-03 03:05:10","","1078-1088","","11","49","","The Journal of Hand Surgery","","","","","","","","","","","","","","","","","","","Artificial intelligence; ChatGPT; large language models; GPT-4; hand surgery","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LVN6G4C2","journalArticle","2024","Misiejuk, Kamila; Kaliisa, Rogers; Scianna, Jennifer","Augmenting assessment with AI coding of online student discourse: A question of reliability","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100216","https://www.sciencedirect.com/science/article/pii/S2666920X24000171","Currently, many generative Artificial Intelligence (AI) tools are being integrated into the educational technology landscape for instructors. Our paper examines the potential and challenges of using Large Language Models (LLMs) to code student-generated content in online discussions based on intended learning outcomes and how instructors could use this to assess the intended and enacted learning design. If instructors were to rely on LLMs as a means of assessment, the reliability of these models to code the data accurately is crucial. Employing a diverse set of LLMs from the GPT family and prompting techniques on an asynchronous online discussion dataset from a blended-learning bachelor-level course, our research examines the reliability of AI-supported coding in educational research. Findings reveal that while AI-supported coding demonstrates efficiency, achieving substantial, moderate agreement with human coding for specific nuanced and context-dependent codes is challenging. Moreover, the high cost, token limits, and the advanced necessary skills needed to write API scripts might limit the usability of AI-driven coding. Finally, implementation would require specific parameterization techniques based on the class and may not be feasible for widespread implementation. Our study underscores the importance of transparency in AI coding methodologies and the need for a hybrid approach that integrates human judgement to ensure data accuracy and interpretability. In addition, it contributes to the knowledge base about the reliability of LLMs to code real, small datasets using complex codes that are common in the instructor's practice and explores the potential and challenges of using these models for assessment purposes.","2024-06-01","2024-12-03 03:05:10","2024-12-03 03:05:10","","100216","","","6","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Artificial intelligence; ChatGPT; Large language models; Learning analytics; AI-driven assessment; Data coding","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"U4D99SM2","journalArticle","2024","Lewis, S.; Bhyat, F.; Casmod, Y.; Gani, A.; Gumede, L.; Hajat, A.; Hazell, L.; Kammies, C.; Mahlaola, T.B.; Mokoena, L.; Vermeulen, L.","Medical imaging and radiation science students' use of artificial intelligence for learning and assessment","Current Issues in Radiography Education","","1078-8174","10.1016/j.radi.2024.10.006","https://www.sciencedirect.com/science/article/pii/S1078817424003043","Introduction Artificial intelligence has permeated all aspects of our existence, and medical imaging has shown the burgeoning use of artificial intelligence in clinical environments. However, there are limited empirical studies on radiography students' use of artificial intelligence for learning and assessment. Therefore, this study aimed to gain an understanding of this phenomenon. Methods The study used a qualitative explorative and descriptive research design. Data was obtained through five focus group interviews with purposively sampled undergraduate medical imaging and radiation science students at a single higher education institution in South Africa. Verbatim transcripts of the audio-recorded interviews were analysed thematically. Results Three themes and related subthemes were developed: 1) understanding artificial intelligence, 2) experiences with the use of artificial intelligence with the subthemes of the use of artificial intelligence in theoretical and clinical learning and challenges of using artificial intelligence, and 3) incorporation of artificial intelligence in undergraduate medical imaging and radiation sciences education with the subthemes of student education, ethical considerations and responsible use and curriculum integration of artificial intelligence in relation to learning and assessment. Conclusion Participants used artificial intelligence for learning and assessment by generating ideas to enhance academic writing, as a learning tool, finding literature, language translation and for enhanced efficiency. Simulation-based artificial intelligence supports students' clinical learning, and artificial intelligence within the clinical departments assists with improved patient outcomes. However, participants expressed concerns about the reliability and ethical implications of artificial intelligence-generated information. To address these concerns, participants suggested integrating artificial intelligence into medical imaging and radiation sciences education, where educators need to educate students on the responsible use of artificial intelligence in learning and consider artificial intelligence in assessments. Implications for practice The study findings contribute to understanding medical imaging and radiation sciences students’ use of artificial intelligence and may be used to develop evidence-based strategies for integrating artificial intelligence into the curriculum to enhance medical imaging and radiation sciences education and support students.","2024-12-01","2024-12-03 03:05:10","2024-12-03 03:05:10","","60-66","","","30","","Radiography","","","","","","","","","","","","","","","","","","","Large language models; AI; Generative AI; Teaching and learning; Radiography education; Radiography students","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JEBINEAT","journalArticle","2024","Redahan, Maria; Kelly, Brendan D.","Artificial intelligence and mental capacity legislation: Opening Pandora's modem","International Journal of Law and Psychiatry","","0160-2527","10.1016/j.ijlp.2024.101985","https://www.sciencedirect.com/science/article/pii/S0160252724000347","People with impaired decision-making capacity enjoy the same rights to access technology as people with full capacity. Our paper looks at realising this right in the specific contexts of artificial intelligence (AI) and mental capacity legislation. Ireland's Assisted Decision-Making (Capacity) Act, 2015 commenced in April 2023 and refers to ‘assistive technology’ within its ‘communication’ criterion for capacity. We explore the potential benefits and risks of AI in assisting communication under this legislation and seek to identify principles or lessons which might be applicable in other jurisdictions. We focus especially on Ireland's provisions for advance healthcare directives because previous research demonstrates that common barriers to advance care planning include (i) lack of knowledge and skills, (ii) fear of starting conversations about advance care planning, and (iii) lack of time. We hypothesise that these barriers might be overcome, at least in part, by using generative AI which is already freely available worldwide. Bodies such as the United Nations have produced guidance about ethical use of AI and these guide our analysis. One of the ethical risks in the current context is that AI would reach beyond communication and start to influence the content of decisions, especially among people with impaired decision-making capacity. For example, when we asked one AI model to ‘Make me an advance healthcare directive’, its initial response did not explicitly suggest content for the directive, but it did suggest topics that might be included, which could be seen as setting an agenda. One possibility for circumventing this and other shortcomings, such as concerns around accuracy of information, is to look to foundational models of AI. With their capabilities to be trained and fine-tuned to downstream tasks, purpose-designed AI models could be adapted to provide education about capacity legislation, facilitate patient and staff interaction, and allow interactive updates by healthcare professionals. These measures could optimise the benefits of AI and minimise risks. Similar efforts have been made to use AI more responsibly in healthcare by training large language models to answer healthcare questions more safely and accurately. We highlight the need for open discussion about optimising the potential of AI while minimising risks in this population.","2024-05-01","2024-12-03 03:05:10","2024-12-03 03:05:10","","101985","","","94","","International Journal of Law and Psychiatry","","","","","","","","","","","","","","","","","","","Artificial intelligence; Human rights; Advance care planning; Advance directives; Advance health care planning; Capacity legislation; Mental disorder","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"E7FYRMUE","journalArticle","2024","Wheatley, Amanda; Hervieux, Sandy","Comparing generative artificial intelligence tools to voice assistants using reference interactions","The Journal of Academic Librarianship","","0099-1333","10.1016/j.acalib.2024.102942","https://www.sciencedirect.com/science/article/pii/S0099133324001034","This study investigates the ability of voice assistants and generative AI tools to respond to reference questions traditionally received by academic librarians. The authors created a sample of 25 questions based on queries received on the virtual reference service at their institution. They then created a rubric to evaluate the quality of the answers that the AI powered tools provided. The authors determined that the tools understand reference questions well and provide relevant answers but that the quality of the references provided, and the accuracy of the answers can be lacking. They suggest that more research needs to be done to understand the place of AI powered tools in reference services.","2024-09-01","2024-12-03 03:05:10","2024-12-03 03:05:10","","102942","","5","50","","The Journal of Academic Librarianship","","","","","","","","","","","","","","","","","","","Artificial intelligence; Generative AI; Reference; Voice assistants","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CEC8B2HE","journalArticle","2024","Le Nguyen, Khuong; Uddin, Minhaz; Pham, Thong M.","Generative artificial intelligence and optimisation framework for concrete mixture design with low cost and embodied carbon dioxide","Construction and Building Materials","","0950-0618","10.1016/j.conbuildmat.2024.138836","https://www.sciencedirect.com/science/article/pii/S0950061824039783","This research presents a generative Artificial Intelligence (AI) and design framework that integrates machine learning (ML) and optimisation methodologies to discover new concrete mixture designs. Unlike traditional ML models that predict based on existing data, this framework innovatively generates new concrete mix designs that meet specific requirements such as strength, cost-efficiency, and reduced embodied CO2. To propose a powerful and reliable generative AI model, several advanced ML algorithms were considered, e.g., CatBoost, XGBoost, and LGBM. These models were trained on a unique dataset consisting of 4,936 data points collected from five different batching plants and have not been published yet. Bayesian Optimisation was employed to fine-tune model hyperparameters, resulting in the most effective models attaining R2 values of 0.94 and 0.89 for raw and grouped data, respectively. To verify the trained generative AI model, a case study was conducted, in which the model was requested to provide designs of a mix with pre-determined strength and optimised cost and embodied CO2. The mix designs generated by the framework were successfully validated through experimental tests, corroborating the predictive outcomes. The research culminated in the development of a web application, a tool crafted to streamline the concrete mixture design and optimisation process. This generative AI design framework can be applied to many other aspects of material design and engineering problems.","2024-11-15","2024-12-03 03:05:10","2024-12-03 03:05:10","","138836","","","451","","Construction and Building Materials","","","","","","","","","","","","","","","","","","","Generative AI; Compressive strength prediction; Concrete mixture design; Machine learning approach; Multi-objective optimisation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"46XTLFIZ","journalArticle","2024","Mariani, Marcello; Dwivedi, Yogesh K.","Generative artificial intelligence in innovation management: A preview of future research developments","Journal of Business Research","","0148-2963","10.1016/j.jbusres.2024.114542","https://www.sciencedirect.com/science/article/pii/S0148296324000468","This study outlines the future research opportunities related to Generative Artificial Intelligence (GenAI) in innovation management. To this end, it combines a review of the academic literature with the results of a Delphi study involving leading innovation management scholars. Ten major research themes emerged that can guide future research developments at the intersection of GenAI and innovation management: 1) Gen AI and innovation types; 2) GenAI, dominant designs and technology evolution; 3) Scientific and artistic creativity and GenAI-enabled innovations; 4) GenAI-enabled innovations and intellectual property; 5) GenAI and new product development; 6) Multimodal/unimodal GenAI and innovation outcomes; 7) GenAI, agency and ecosystems; 8) Policymakers, lawmakers and anti-trust authorities in the regulation of GenAI-enabled innovation; 9) Misuse and unethical use of GenAI leading to biased innovation; and 10) Organizational design and boundaries for GenAI-enabled innovation. The paper concludes by discussing how these themes can inform theoretical development in innovation management studies.","2024-03-01","2024-12-03 03:05:11","2024-12-03 03:05:11","","114542","","","175","","Journal of Business Research","","","","","","","","","","","","","","","","","","","Management; Innovation; Generative artificial intelligence; Delphi study","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KTZZW8Q8","journalArticle","2024","Hattori, Eline Aya; Yamakawa, Mayu; Miwa, Kazuhisa","Human bias in evaluating AI product creativity","Journal of Creativity","","2713-3745","10.1016/j.yjoc.2024.100087","https://www.sciencedirect.com/science/article/pii/S271337452400013X","This study investigated the evaluation behavior of products generated by artificial intelligence (AI) by manipulating the creativity of rating stimuli according to their high and low novelty and usefulness. In addition, we analyzed the effect of perception of the generative AI on creativity ratings. We found a bias toward lower creativity ratings only for highly useful products when they were identified as AI-generated, and this bias was particularly strong for individuals with greater perceived threat from the generative AI. Our research highlights the importance of product attribute quality and producer identity in creativity ratings, and provides implications for the integration of AI into creative industries.","2024-08-01","2024-12-03 03:05:11","2024-12-03 03:05:11","","100087","","2","34","","Journal of Creativity","","","","","","","","","","","","","","","","","","","Artificial intelligence; Creativity evaluation; Evaluation bias; Novelty; Threat; Usefulness","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"E6ZN9YGY","journalArticle","2024","Sapkota, Ranjan; Meng, Zhichao; Karkee, Manoj","Synthetic meets authentic: Leveraging LLM generated datasets for YOLO11 and YOLOv10-based apple detection through machine vision sensors","Smart Agricultural Technology","","2772-3755","10.1016/j.atech.2024.100614","https://www.sciencedirect.com/science/article/pii/S2772375524002193","Training machine learning (ML) models for artificial intelligence (AI) and computer vision-based object detection process typically requires large, labeled datasets, a process often burdened by significant human effort and high costs associated with imaging systems and image acquisition. This research aimed to simplify image data collection for object detection in orchards by avoiding traditional fieldwork with different imaging sensors. Utilizing OpenAI's DALLE, a large language model (LLM) for realistic image generation, we generated and annotated a cost-effective dataset. This dataset, exclusively generated by LLM, was then utilized to train two state-of-the-art deep learning models: YOLOV10 and YOLO11. The YOLO11 model for apple detection was trained with its five configurations (YOLO11n, YOLO11 s, YOLO11 m, YOLO11l and YOLO11x), and YOLOv10 model with its six configurations (YOLOv10n, YOLOv10 s, YOLOv10 m, YOLOv10b, YOLOv10l and YOLOv10x), which was then tested with real-world (outdoor orchard) images captured by a digital (Nikon D5100) camera and a consumer RGB-D camera (Microsoft Azure Kinect). YOLO11 outperformed YOLOv10 as YOLO11x and YOLO11n exhibited superior precision of 0.917 and 0.916, respectively. Furthermore, YOLO11l demonstrated the highest recall among its counterparts, achieving a recall of 0.889. Likewise, the YOLO11n variant excelled in terms of mean average precision (mAP@50), achieving the highest value of 0.958. Validation tests against actual images collected through a digital camera (Nikon D5100) over Scilate apple variety in a commercial orchard environment showed a highest precision of 0.874 for YOLO11 s, recall of 0.877 for YOLO11l and mAP@50 of 0.91 for YOLO11x. Additionally, validation test against actual images collected through a Microsoft Azure camera over the same orchard showed a highest precision, recall and mAP@50 respectively of 0.924, 0.781 and 0.855 with YOLO11x. All variants of YOLO11 surprisingly demonstrated a pre-processing time of just 0.2 milliseconds (ms), which was faster than any variant of YOLOv10. The fastest inference time for the YOLO11n model using the training dataset generated by the language model was 3.2 ms, while YOLOv10n, fastest among YOLOv10 variants, had a longer inference time of 5.5 ms. Likewise, the fastest inference time for the sensor-based images was 7.1 ms (for Nikon D5100 camera images) and 4.7 ms (for Azure images) with YOLO11n. This study presents a pathway for generating large image datasets using LLM in challenging agricultural fields with minimal or no labor-intensive efforts in field data-collection, which could accelerate the development and deployment of computer vision and robotic technologies in orchard environments.","2024-12-01","2024-12-03 03:05:11","2024-12-03 03:05:11","","100614","","","9","","Smart Agricultural Technology","","","","","","","","","","","","","","","","","","","Deep learning; Machine learning; Generative artificial intelligence; LLM; Large language model; Text-to-image generation; YOLO; YOLO11; YOLOv10; You Only Look Once","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"48KQ33VA","journalArticle","2024","Namoun, Abdallah; Alrehaili, Ahmed; Nisa, Zaib Un; Almoamari, Hani; Tufail, Ali","Predicting the usability of mobile applications using AI tools: the rise of large user interface models, opportunities, and challenges","The 15th International Conference on Ambient Systems, Networks and Technologies Networks (ANT) / The 7th International Conference on Emerging Data and Industry 4.0 (EDI40), April 23-25, 2024, Hasselt University, Belgium","","1877-0509","10.1016/j.procs.2024.06.076","https://www.sciencedirect.com/science/article/pii/S1877050924013127","This article proposes the so-called large user interface models (LUIMs) to enable the generation of user interfaces and prediction of usability using artificial intelligence in the context of mobile applications. To this end, we synergized an integrated framework for the effective testing of the usability of mobile applications following a selective review of the most influential models of mobile usability testing. Next, we identified and analysed 13 recent AI tools that generate user interfaces for mobile apps, and systematically tested these tools to identify their AI capabilities. Our striking findings demonstrate that current generative UI tools fail to address mobile usability attributes, such as efficiency, learnability, effectiveness, satisfaction, and memorability. Our large UI models’ architecture proposes to leverage the capabilities of large language models, large vision models, and large code models to overcome the challenges of AI-driven UI/UX design and front-end implementations. This fascinating UI eco-system must be augmented with sufficient UI data and multi-sensory input regarding user behaviour to train the models. We anticipate LUIMs to create ample opportunities, like expedited frontend software development, enhanced personalised user experience, and wider accessibility of smart technologies. However, the research challenges hindering the UI generation and usability prediction of mobile apps include the seamless integration of complex generative AI models, semantic understanding of non-uniform visual designs, scarcity of UX datasets, and modelling of realistic user interactions.","2024-01-01","2024-12-03 03:05:11","2024-12-03 03:05:11","","671-682","","","238","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","generative artificial intelligence; LLM; generative UI design; large UI models; mobile apps; usability attributes; usability testing","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"4IZVVYPS","journalArticle","2024","Ren, Yunfei; Hu, Tao; Xu, Songzhe; Chen, Chaoyue; Xuan, Weidong; Ren, Zhongming","Rapid estimation of γ' solvus temperature for composition design of Ni-based superalloy via physics-informed generative artificial intelligence","Journal of Alloys and Metallurgical Systems","","2949-9178","10.1016/j.jalmes.2024.100073","https://www.sciencedirect.com/science/article/pii/S2949917824000208","The exceptional high-temperature mechanical properties of Ni-based superalloys are mainly stemmed from the L12 γ' phase, therefore it is crucial to discover Ni-based superalloys with high γ' solvus temperatures. Utilizing generative artificial intelligence, we have developed a framework to swiftly evaluate the γ' solvus temperature and tailor Ni-based superalloys, accelerating the process of discovering Ni-based superalloys. Physics-informed artificial neural network emerged as the optimal choice for reverse engineering, outperforming other models with an R2 score of 0.917 and a mean absolute error of 15 K. In the reverse design process, 20,000 virtual alloy samples were generated based on divide-and-conquer variational autoencoder which divides the dataset into distinct clusters by K-means algorithm provides a structured representation of the alloy composition space, thereby facilitating a more nuanced understanding of its inherent complexities. In a specific alloy design example, 563 samples were identified through screening based on criteria like γ' solvus temperature, composition deviation index, price, and density. Thermodynamic calculations were used to further screen Ni-based superalloys with exceptional high-temperature properties. The showcase of BA alloy discovery through generative artificial intelligence demonstrates the potential of our research to steer the creation of novel compositions for Ni-based superalloys with outstanding high-temperature properties.","2024-06-01","2024-12-03 03:05:11","2024-12-03 03:05:11","","100073","","","6","","Journal of Alloys and Metallurgical Systems","","","","","","","","","","","","","","","","","","","Generative artificial intelligence; Composition deviation index; Ni-based superalloy; Thermodynamic calculation; γ' Solvus temperature","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ACSAFGCY","journalArticle","2024","Yang, Jingye; Liu, Cong; Deng, Wendy; Wu, Da; Weng, Chunhua; Zhou, Yunyun; Wang, Kai","Enhancing phenotype recognition in clinical notes using large language models: PhenoBCBERT and PhenoGPT","Patterns","","2666-3899","10.1016/j.patter.2023.100887","https://www.sciencedirect.com/science/article/pii/S266638992300288X","Summary To enhance phenotype recognition in clinical notes of genetic diseases, we developed two models—PhenoBCBERT and PhenoGPT—for expanding the vocabularies of Human Phenotype Ontology (HPO) terms. While HPO offers a standardized vocabulary for phenotypes, existing tools often fail to capture the full scope of phenotypes due to limitations from traditional heuristic or rule-based approaches. Our models leverage large language models to automate the detection of phenotype terms, including those not in the current HPO. We compare these models with PhenoTagger, another HPO recognition tool, and found that our models identify a wider range of phenotype concepts, including previously uncharacterized ones. Our models also show strong performance in case studies on biomedical literature. We evaluate the strengths and weaknesses of BERT- and GPT-based models in aspects such as architecture and accuracy. Overall, our models enhance automated phenotype detection from clinical texts, improving downstream analyses on human diseases.","2024-01-12","2024-12-03 03:05:11","2024-12-03 03:05:11","","100887","","1","5","","Patterns","","","","","","","","","","","","","","","","","","","GPT; BERT; clinical notes; electronic health records; Human Phenotype Ontology; named entity recognition; transformer","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2JIN2UXC","journalArticle","2024","Meng, Xiangbin; Yan, Xiangyu; Zhang, Kuo; Liu, Da; Cui, Xiaojuan; Yang, Yaodong; Zhang, Muhan; Cao, Chunxia; Wang, Jingjia; Wang, Xuliang; Gao, Jun; Wang, Yuan-Geng-Shuo; Ji, Jia-ming; Qiu, Zifeng; Li, Muzi; Qian, Cheng; Guo, Tianze; Ma, Shuangquan; Wang, Zeying; Guo, Zexuan; Lei, Youlan; Shao, Chunli; Wang, Wenyao; Fan, Haojun; Tang, Yi-Da","The application of large language models in medicine: A scoping review","iScience","","2589-0042","10.1016/j.isci.2024.109713","https://www.sciencedirect.com/science/article/pii/S2589004224009350","Summary This study systematically reviewed the application of large language models (LLMs) in medicine, analyzing 550 selected studies from a vast literature search. LLMs like ChatGPT transformed healthcare by enhancing diagnostics, medical writing, education, and project management. They assisted in drafting medical documents, creating training simulations, and streamlining research processes. Despite their growing utility in assisted diagnosis and improving doctor-patient communication, challenges persisted, including limitations in contextual understanding and the risk of over-reliance. The surge in LLM-related research indicated a focus on medical writing, diagnostics, and patient communication, but highlighted the need for careful integration, considering validation, ethical concerns, and the balance with traditional medical practice. Future research directions suggested a focus on multimodal LLMs, deeper algorithmic understanding, and ensuring responsible, effective use in healthcare.","2024-05-17","2024-12-03 03:05:11","2024-12-03 03:05:11","","109713","","5","27","","iScience","","","","","","","","","","","","","","","","","","","Artificial intelligence; Health informatics","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"NJHEV3M4","journalArticle","2024","de-Fitero-Dominguez, David; Garcia-Lopez, Eva; Garcia-Cabot, Antonio; Martinez-Herraiz, Jose-Javier","Enhanced automated code vulnerability repair using large language models","Engineering Applications of Artificial Intelligence","","0952-1976","10.1016/j.engappai.2024.109291","https://www.sciencedirect.com/science/article/pii/S0952197624014490","This research addresses the complex challenge of automated repair of code vulnerabilities, vital for enhancing digital security in an increasingly technology-driven world. The study introduces a novel and efficient format for the representation of code modification, using advanced Large Language Models (LLMs) such as Code Llama and Mistral. These models, fine-tuned on datasets featuring C/C++ code vulnerabilities, significantly improve the accuracy and adaptability of automated code repair techniques. A key finding is the enhanced repair accuracy of these models when compared to previous methods such as VulRepair, which underscores their practical utility and efficiency. The research also offers a critical assessment of current evaluation metrics, such as “Perfect Predictions”, and their limitations in reflecting the true capabilities of automated repair models in real-world scenarios. Following this, it underscores the importance of using test datasets devoid of train samples, emphasizing the need for dataset integrity to enhance the effectiveness of LLMs in code repair tasks. The significance of this work is its contribution to digital security, setting new standards for automated code vulnerability repair and paving the way for future advancements in the fields of cybersecurity and artificial intelligence. The study does not only highlight the potential of LLMs in enhancing code security but also fosters further exploration and research in these crucial areas.","2024-12-01","2024-12-03 03:05:11","2024-12-03 03:05:11","","109291","","","138","","Engineering Applications of Artificial Intelligence","","","","","","","","","","","","","","","","","","","Deep learning; Large language models; Automated code repair; Code llama; Mistral; Vulnerability repair","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"UNZCQY5X","journalArticle","2024","Law, Locky","Application of generative artificial intelligence (GenAI) in language teaching and learning: A scoping literature review","Computers and Education Open","","2666-5573","10.1016/j.caeo.2024.100174","https://www.sciencedirect.com/science/article/pii/S2666557324000156","This scoping literature review examines the application of Generative Artificial Intelligence (GenAI), a disruptive technology, in language teaching and learning. Since its launch in November 2022, GenAI has captured global attention with OpenAI's ChatGPT, powered by the generative pre-trained transformer-3 (GPT-3) large-language model. The emergence of GenAI holds immense implications across various domains, including language education. This review aims to provide an overview of the current state of research and identify research gaps and future directions in this emerging field. The review follows the PRISMA-ScR guidelines and includes eligible publications published between 2017 and July 2023. Four electronic databases were searched and 41 of the 224 initial papers were eventually selected for review. The findings reveal key terms related to GenAI in language education, the most researched language study and education levels, areas of research, attitudes towards GenAI, and the potential benefits and challenges of GenAI application. The review highlights several research gaps, including the need for more empirical studies to assess the effectiveness and impact of GenAI tools, discussion of ethical considerations, targeted interventions for specific language skills, and stakeholder engagement in responsible integration. Educators are encouraged to incorporate GenAI tools into their teaching practices while remaining vigilant about potential risks. Continuous professional development for educators is crucial to ensure informed decision-making and effective integration of GenAI tools. This scoping review contributes to the existing knowledge on the use of GenAI in language education and informs future research and practice in this disruptive and rapidly evolving field.","2024-06-01","2024-12-03 03:05:11","2024-12-03 03:05:11","","100174","","","6","","Computers and Education Open","","","","","","","","","","","","","","","","","","","ChatGPT; AI; Generative AI; Content generation; Language education; Scoping review","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"E2LEKA9Z","journalArticle","2024","Gupta, Priyanka; Ding, Bosheng; Guan, Chong; Ding, Ding","Generative AI: A systematic review using topic modelling techniques","Systematic Review and Meta-analysis in Information Management Research - Part II","","2543-9251","10.1016/j.dim.2024.100066","https://www.sciencedirect.com/science/article/pii/S2543925124000020","Generative artificial intelligence (GAI) is a rapidly growing field with a wide range of applications. In this paper, a thorough examination of the research landscape in GAI is presented, encompassing a comprehensive overview of the prevailing themes and topics within the field. The study analyzes a corpus of 1319 records from Scopus spanning from 1985 to 2023 and comprises journal articles, books, book chapters, conference papers, and selected working papers. The analysis revealed seven distinct clusters of topics in GAI research: image processing and content analysis, content generation, emerging use cases, engineering, cognitive inference and planning, data privacy and security, and Generative Pre-Trained Transformer (GPT) academic applications. The paper discusses the findings of the analysis and identifies some of the key challenges and opportunities in GAI research. The paper concludes by calling for further research in GAI, particularly in the areas of explainability, robustness, cross-modal and multi-modal generation, and interactive co-creation. The paper also highlights the importance of addressing the challenges of data privacy and security in GAI and responsible use of GAI.","2024-06-01","2024-12-03 03:05:11","2024-12-03 03:05:11","","100066","","2","8","","Data and Information Management","","","","","","","","","","","","","","","","","","","ChatGPT; Generative artificial intelligence; BERTopic; Systemic review; Topic modeling; Use cases","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"GIVK6B23","journalArticle","2024","Wu, Di; Chen, Meng; Chen, Xu; Liu, Xing","Analyzing K-12 AI education: A large language model study of classroom instruction on learning theories, pedagogy, tools, and AI literacy","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100295","https://www.sciencedirect.com/science/article/pii/S2666920X24000985","There is growing recognition among researchers and stakeholders about the significant impact of artificial intelligence (AI) technology on classroom instruction. As a crucial element in developing AI literacy, AI education in K-12 schools is increasingly gaining attention. However, most existing research on K-12 AI education relies on experiential methodologies and suffers from a lack of quantitative analysis based on extensive classroom data, hindering a comprehensive depiction of AI education's current state at these educational levels. To address this gap, this article employs the advanced semantic understanding capabilities of large language models (LLMs) to create an intelligent analysis framework that identifies learning theories, pedagogical approaches, learning tools, and levels of AI literacy in AI classroom instruction. Compared with the results of manual analysis, analysis based on LLMs can achieve more than 90% consistency. Our findings, based on the analysis of 98 classroom instruction videos in central Chinese cities, reveal that current AI classroom instruction insufficiently foster AI literacy, with only 35.71% addressing higher-level skills such as evaluating and creating AI. AI ethics are even less commonly addressed, featured in just 5.1% of classroom instruction. We classified AI classroom instruction into three categories: conceptual (50%), heuristic (18.37%), and experimental (31.63%). Correlation analysis suggests a significant relationship between the adoption of pedagogical approaches and the development of advanced AI literacy. Specifically, integrating Project-based/Problem-based learning (PBL) with Collaborative learning appears effective in cultivating the capacity to evaluate and create AI.","2024-12-01","2024-12-03 03:05:11","2024-12-03 03:05:11","","100295","","","7","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Large language models; AI literacy; AI education; Pedagogical approaches","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LT562RFH","journalArticle","2024","Elbadawi, Moe; Li, Hanxiang; Basit, Abdul W.; Gaisford, Simon","The role of artificial intelligence in generating original scientific research","International Journal of Pharmaceutics","","0378-5173","10.1016/j.ijpharm.2023.123741","https://www.sciencedirect.com/science/article/pii/S0378517323011638","Artificial intelligence (AI) is a revolutionary technology that is finding wide application across numerous sectors. Large language models (LLMs) are an emerging subset technology of AI and have been developed to communicate using human languages. At their core, LLMs are trained with vast amounts of information extracted from the internet, including text and images. Their ability to create human-like, expert text in almost any subject means they are increasingly being used as an aid to presentation, particularly in scientific writing. However, we wondered whether LLMs could go further, generating original scientific research and preparing the results for publication. We taskedGPT-4, an LLM, to write an original pharmaceutics manuscript, on a topic that is itself novel. It was able to conceive a research hypothesis, define an experimental protocol, produce photo-realistic images of 3D printed tablets, generate believable analytical data from a range of instruments and write a convincing publication-ready manuscript with evidence of critical interpretation. The model achieved all this is less than 1 h. Moreover, the generated data were multi-modal in nature, including thermal analyses, vibrational spectroscopy and dissolution testing, demonstrating multi-disciplinary expertise in the LLM. One area in which the model failed, however, was in referencing to the literature. Since the generated experimental results appeared believable though, we suggest that LLMs could certainly play a role in scientific research but with human input, interpretation and data validation. We discuss the potential benefits and current bottlenecks for realising this ambition here.","2024-03-05","2024-12-03 03:05:11","2024-12-03 03:05:11","","123741","","","652","","International Journal of Pharmaceutics","","","","","","","","","","","","","","","","","","","Artificial intelligence; Large language models; Pharmaceutical 3D printing; Poly(D,L-lactide-co-glycolide) (PLGA); Selective laser sintering","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2DE68DJY","journalArticle","2024","Pack, Austin; Barrett, Alex; Escalante, Juan","Large language models and automated essay scoring of English language learner writing: Insights into validity and reliability","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100234","https://www.sciencedirect.com/science/article/pii/S2666920X24000353","Advancements in generative AI, such as large language models (LLMs), may serve as a potential solution to the burdensome task of essay grading often faced by language education teachers. Yet, the validity and reliability of leveraging LLMs for automatic essay scoring (AES) in language education is not well understood. To address this, we evaluated the cross-sectional and longitudinal validity and reliability of four prominent LLMs, Google's PaLM 2, Anthropic's Claude 2, and OpenAI's GPT-3.5 and GPT-4, for the AES of English language learners' writing. 119 essays taken from an English language placement test were assessed twice by each LLM, on two separate occasions, as well as by a pair of human raters. GPT-4 performed the best, demonstrating excellent intrarater reliability and good validity. All models, with the exception of GPT-3.5, improved over time in their intrarater reliability. The interrater reliability of GPT-3.5 and GPT-4, however, decreased slightly over time. These findings indicate that some models perform better than others in AES and that all models are subject to fluctuations in their performance. We discuss potential reasons for such variability, and offer suggestions for prospective avenues of research.","2024-06-01","2024-12-03 03:05:12","2024-12-03 03:05:12","","100234","","","6","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Artificial intelligence; ChatGPT; Large language model; Generative AI; Automatic essay scoring; Automatic writing evaluation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"3ZEQJXEZ","journalArticle","2024","Bughio, Kulsoom S.; Cook, David M.; Shah, Syed Afaq A.","GenAI in Rule-based Systems for IoMT Security: Testing and Evaluation","28th International Conference on Knowledge Based and Intelligent information and Engineering Systems (KES 2024)","","1877-0509","10.1016/j.procs.2024.09.652","https://www.sciencedirect.com/science/article/pii/S1877050924027078","Generative AI (GenAI) represents a significant advancement in Artificial intelligence research, offering numerous benefits and opening new avenues for innovation across various domains. In healthcare, Generative AI has shown promise in applications such as drug discovery, personalized medicine, and medical imaging. This paper examines the role of Generative AI in rule-based systems, where vulnerabilities are detected with the help of formal logic. In this context, the ruleset is generated and tested to evaluate the performance of rule-based systems with the aid of GenAI. The effectiveness of the GenAI tool was evaluated using a publicly available case study from a laboratory setting. The results show that using generative Artificial intelligence in rule-based systems leads to increased creativity, continuous learning, and robust performance. GenAI responded to each use case and provided the desired results compared to traditional rule-based systems. This integration of advanced AI techniques with traditional rule-based systems ensures that these hybrid systems perform reliably and effectively.","2024-01-01","2024-12-03 03:05:12","2024-12-03 03:05:12","","5330-5339","","","246","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial Intelligence; Gen AI; IoMT; Ruled-based Systems; Vulnerability Detection","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"GCJCI233","journalArticle","2024","Meniado, Joel C.; Huyen, Duong Thi Thu; Panyadilokpong, Nopparat; Lertkomolwit, Pannaphatt","Using ChatGPT for second language writing: Experiences and perceptions of EFL learners in Thailand and Vietnam","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100313","https://www.sciencedirect.com/science/article/pii/S2666920X24001164","This study explored the experiences and perceptions of Thai and Vietnamese EFL learners regarding the use of ChatGPT for second language (L2) writing. It aimed to contribute to the ongoing discourse on using generative Artificial Intelligence (GAI) tools to enhance L2 writing skills. Utilizing a mixed-methods research design with quantitative data gathered from 357 survey respondents from three educational institutions in Thailand and Vietnam and qualitative data from 16 interviewees from the same group of respondents, this study found that ChatGPT was perceived positively for L2 writing. Participants in the study found it a valuable tool for L2 writing, where its ability to generate ideas, provide examples, and gather necessary information is highly valued. Student-related factors such as relevance, usefulness, and engagement significantly influenced the participants’ perceptions of the tool. Regarding the differences in perceptions, the Vietnamese participants showed a higher level of perception than their Thai counterparts. In terms of practices in using the tool for L2 writing, participants mainly used ChatGPT for brainstorming, organizing ideas, refining outlines, clarifying concepts, and editing their drafts for appropriateness and accuracy. Implications of these findings for curriculum design, L2 writing instruction, and teacher professional development are discussed.","2024-12-01","2024-12-03 03:05:12","2024-12-03 03:05:12","","100313","","","7","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","ChatGPT; Generative Artificial Intelligence; L2 writing in Thailand; L2 writing in Vietnam; Second language writing; Writing proficiency","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XD5D2CKV","journalArticle","2024","Sacoransky, Ethan; Kwan, Benjamin Y.M.; Soboleski, Donald","ChatGPT and assistive AI in structured radiology reporting: A systematic review","Current Problems in Diagnostic Radiology","","0363-0188","10.1067/j.cpradiol.2024.07.007","https://www.sciencedirect.com/science/article/pii/S0363018824001130","Introduction The rise of transformer-based large language models (LLMs), such as ChatGPT, has captured global attention with recent advancements in artificial intelligence (AI). ChatGPT demonstrates growing potential in structured radiology reporting—a field where AI has traditionally focused on image analysis. Methods A comprehensive search of MEDLINE and Embase was conducted from inception through May 2024, and primary studies discussing ChatGPT's role in structured radiology reporting were selected based on their content. Results Of the 268 articles screened, eight were ultimately included in this review. These articles explored various applications of ChatGPT, such as generating structured reports from unstructured reports, extracting data from free text, generating impressions from radiology findings and creating structured reports from imaging data. All studies demonstrated optimism regarding ChatGPT's potential to aid radiologists, though common critiques included data privacy concerns, reliability, medical errors, and lack of medical-specific training. Conclusion ChatGPT and assistive AI have significant potential to transform radiology reporting, enhancing accuracy and standardization while optimizing healthcare resources. Future developments may involve integrating dynamic few-shot prompting, ChatGPT, and Retrieval Augmented Generation (RAG) into diagnostic workflows. Continued research, development, and ethical oversight are crucial to fully realize AI's potential in radiology.","2024-11-01","2024-12-03 03:05:12","2024-12-03 03:05:12","","728-737","","6","53","","Current Problems in Diagnostic Radiology","","","","","","","","","","","","","","","","","","","Artificial intelligence; ChatGPT; Large language models; Radiologist; Radiology","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"VIZZJ8JI","journalArticle","2024","Ji, Kaiyuan; Han, Jing; Zhai, Guangtao; Liu, Jiannan","Assessing the Capabilities of Generative Pretrained Transformer-4 in Addressing Open-Ended Inquiries of Oral Cancer","International Dental Journal","","0020-6539","10.1016/j.identj.2024.06.024","https://www.sciencedirect.com/science/article/pii/S0020653924001941","Introduction and aims In the face of escalating oral cancer rates, the application of large language models like Generative Pretrained Transformer (GPT)-4 presents a novel pathway for enhancing public awareness about prevention and early detection. This research aims to explore the capabilities and possibilities of GPT-4 in addressing open-ended inquiries in the field of oral cancer. Methods Using 60 questions accompanied by reference answers, covering concepts, causes, treatments, nutrition, and other aspects of oral cancer, evaluators from diverse backgrounds were selected to evaluate the capabilities of GPT-4 and a customized version. A P value under .05 was considered significant. Results Analysis revealed that GPT-4 and its adaptations notably excelled in answering open-ended questions, with the majority of responses receiving high scores. Although the median score for standard GPT-4 was marginally better, statistical tests showed no significant difference in capabilities between the two models (P > .05). Despite statistical significance indicated diverse backgrounds of evaluators have statistically difference (P < .05), a post hoc test and comprehensive analysis demonstrated that both editions of GPT-4 demonstrated equivalent capabilities in answering questions concerning oral cancer. Conclusions GPT-4 has demonstrated its capability to furnish responses to open-ended inquiries concerning oral cancer. Utilizing this advanced technology to boost public awareness about oral cancer is viable and has much potential. When it's unable to locate pertinent information, it will resort to their inherent knowledge base or recommend consulting professionals after offering some basic information. Therefore, it cannot supplant the expertise and clinical judgment of surgical oncologists and could be used as an adjunctive evaluation tool.","2024-08-04","2024-12-03 03:05:12","2024-12-03 03:05:12","","","","","","","International Dental Journal","","","","","","","","","","","","","","","","","","","Artificial intelligence; GPT-4; Education tool; Oral cancer; Oral health","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"MJD7VNG5","journalArticle","2024","Alsafari, Bashaer; Atwell, Eric; Walker, Aisha; Callaghan, Martin","Towards effective teaching assistants: From intent-based chatbots to LLM-powered teaching assistants","Natural Language Processing Journal","","2949-7191","10.1016/j.nlp.2024.100101","https://www.sciencedirect.com/science/article/pii/S2949719124000499","As chatbot technology undergoes a transformative phase in the era of artificial intelligence (AI), the integration of advanced AI models emerges as a focal point for reshaping conversational agents within the education sector. This paper explores the evolution of educational chatbot development, specifically focusing on building a teaching assistant for Data Mining and Text Analytics courses at the University of Leeds. The primary objective is to investigate and compare traditional intent-based chatbot approaches with the advanced retrieval-augmented generation (RAG) method, aiming to improve the efficiency and adaptability of teaching assistants in higher education. The study begins with the development of an Amazon Alexa teaching skill, assessing the efficacy of traditional chatbot development in higher education. To enrich the chatbot knowledge base, the research then employs an automated question–answer generation (QAG) approach using the QG Lumos Learning tool to extract contextually grounded question–answer datasets from course materials. Subsequently, the RAG-based system is proposed, leveraging LangChain with the OpenAI GPT-3.5 Turbo model. Findings highlight limitations in intent-based approaches, emphasising the need for more adaptive solutions. The proposed RAG-based teaching assistant demonstrates significant improvements in efficiently handling diverse queries, representing a paradigm shift in educational chatbot capabilities. These findings provide an in-depth understanding of the development phase, specifically illustrating the impact on chatbot performance by contrasting traditional methods with large language model-based approaches. The study contributes valuable perspectives on enhancing adaptability and effectiveness in AI-powered educational tools, providing essential considerations for future developments in the field.","2024-09-01","2024-12-03 03:05:12","2024-12-03 03:05:12","","100101","","","8","","Natural Language Processing Journal","","","","","","","","","","","","","","","","","","","Large language models (LLMs); Intent-based chatbot; Retrieval-augmented generation (RAG); Teaching assistant chatbot","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DQUYMCGC","journalArticle","2024","Xu, Rui; Wang, Zhong","Generative artificial intelligence in healthcare from the perspective of digital media: Applications, opportunities and challenges","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e32364","https://www.sciencedirect.com/science/article/pii/S2405844024083956","Introduction The emergence and application of generative artificial intelligence/large language models (hereafter GenAI LLMs) have the potential for significant impact on the healthcare industry. However, there is currently a lack of systematic research on GenAI LLMs in healthcare based on reliable data. This article aims to conduct an exploratory study of the application of GenAI LLMs (i.e., ChatGPT) in healthcare from the perspective of digital media (i.e., online news), including the application scenarios, potential opportunities, and challenges. Methods This research used thematic qualitative text analysis in five steps: firstly, developing main topical categories based on relevant articles; secondly, encoding the search keywords using these categories; thirdly, conducting searches for news articles via Google ; fourthly, encoding the sub-categories using the elaborate category system; and finally, conducting category-based analysis and presenting the results. Natural language processing techniques, including the TermRaider and AntConc tool, were applied in the aforementioned steps to assist in text qualitative analysis. Additionally, this study built a framework, using for analyzing the above three topics, from the perspective of five different stakeholders, including healthcare demanders and providers. Results This study summarizes 26 applications (e.g., provide medical advice, provide diagnosis and triage recommendations, provide mental health support, etc.), 21 opportunities (e.g., make healthcare more accessible, reduce healthcare costs, improve patients care, etc.), and 17 challenges (e.g., generate inaccurate/misleading/wrong answers, raise privacy concerns, lack of transparency, etc.), and analyzes the reasons for the formation of these key items and the links between the three research topics. Conclusions The application of GenAI LLMs in healthcare is primarily focused on transforming the way healthcare demanders access medical services (i.e., making it more intelligent, refined, and humane) and optimizing the processes through which healthcare providers offer medical services (i.e., simplifying, ensuring timeliness, and reducing errors). As the application becomes more widespread and deepens, GenAI LLMs is expected to have a revolutionary impact on traditional healthcare service models, but it also inevitably raises ethical and security concerns. Furthermore, GenAI LLMs applied in healthcare is still in the initial stage, which can be accelerated from a specific healthcare field (e.g., mental health) or a specific mechanism (e.g., GenAI LLMs’ economic benefits allocation mechanism applied to healthcare) with empirical or clinical research.","2024-06-30","2024-12-03 03:05:12","2024-12-03 03:05:12","","e32364","","12","10","","Heliyon","","","","","","","","","","","","","","","","","","","Digital media; ChatGPT; Generative artificial intelligence; Large language models; Applications; Artificial intelligence generated content; Challenges; Digital health; Healthcare; Opportunities","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"QNI74SFA","journalArticle","2024","Al-Busaidi, Adil S.; Raman, Raghu; Hughes, Laurie; Albashrawi, Mousa Ahmed; Malik, Tegwen; Dwivedi, Yogesh K.; Al- Alawi, Thuraiya; AlRizeiqi, Mohammed; Davies, Gareth; Fenwick, Mark; Gupta, Parul; Gurpur, Shashikala; Hooda, Apeksha; Jurcys, Paulius; Lim, Daryl; Lucchi, Nicola; Misra, Tanvi; Raman, Ramakrishnan; Shirish, Anuragini; Walton, Paul","Redefining boundaries in innovation and knowledge domains: Investigating the impact of generative artificial intelligence on copyright and intellectual property rights","Journal of Innovation & Knowledge","","2444-569X","10.1016/j.jik.2024.100630","https://www.sciencedirect.com/science/article/pii/S2444569X24001690","The rapid integration of generative AI (GenAI) into industries and society has prompted a re-evaluation of copyright and intellectual property rights (IPR) frameworks. GenAI's ability to produce original content using data from human-created sources raises critical ethical and legal concerns. Current copyright and IPR frameworks, designed around human authorship, are insufficient to address these challenges. This study, using a multi-perspective approach, explores GenAI's disruptive potential in replicating or transforming copyrighted materials, challenging established IPR norms. Findings highlight gaps in legislation and the opacity of GenAI platforms. To address these issues, this study presents a Dynamic Ethical Framework linked to a future global fair use policy, aiming to guide responsible GenAI development and use. By incorporating insights from domain experts, this study contextualizes emerging challenges and potential solutions within broader societal and technological trends. That said, this study calls for international collaboration and further research to reform IPR related laws and frameworks, ensuring they remain relevant and equitable in a GenAI-driven era.","2024-10-01","2024-12-03 03:05:12","2024-12-03 03:05:12","","100630","","4","9","","Journal of Innovation & Knowledge","","","","","","","","","","","","","","","","","","","Innovation; ChatGPT; Generative artificial intelligence; GenAI; Large language models (LLMs); Generative scholar; Intellectual property (IP) Risks; Misuse case analysis; Personality rights","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RVCMYHMX","journalArticle","2024","Carroll, Alexander J.; Borycz, Joshua","Integrating large language models and generative artificial intelligence tools into information literacy instruction","The Journal of Academic Librarianship","","0099-1333","10.1016/j.acalib.2024.102899","https://www.sciencedirect.com/science/article/pii/S0099133324000600","Generative artificial intelligence (AI) and large language models (LLMs) have induced a mixture of excitement and panic among educators. However, there is a lack of consensus over how much experience science and engineering students have with using these tools for research-related tasks. Likewise, it is not yet known how educators and information professionals can leverage these tools to teach students strategies for information retrieval and knowledge synthesis. This study assesses the extent of students' use of AI tools in research-related tasks and if information literacy instruction could impact their perception of these tools. Responses to Likert-scale questions indicate that many students did not have extensive experience using LLMs for research-related purposes prior to the information literacy sessions. However, after participating in a didactic lecture and discussion with an engineering librarian that explored how to use these tools effectively and responsibly, many students reported viewing these tools as potentially useful for future assignments. Student responses to open-response questions suggest that librarian-led information literacy training can assist students in developing more sophisticated understandings of the limitations and use cases for artificial intelligence in inquiry-based coursework.","2024-07-01","2024-12-03 03:05:12","2024-12-03 03:05:12","","102899","","4","50","","The Journal of Academic Librarianship","","","","","","","","","","","","","","","","","","","Information retrieval; Generative artificial intelligence; Large language models; Critical thinking; STEM education; Information literacy","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"HT6U6VIS","journalArticle","2024","Hao, Xinyue; Demir, Emrah; Eyers, Daniel","Exploring collaborative decision-making: A quasi-experimental study of human and Generative AI interaction","Technology in Society","","0160-791X","10.1016/j.techsoc.2024.102662","https://www.sciencedirect.com/science/article/pii/S0160791X24002100","This paper explores the effects of integrating Generative Artificial Intelligence (GAI) into decision-making processes within organizations, employing a quasi-experimental pretest-posttest design. The study examines the synergistic interaction between Human Intelligence (HI) and GAI across four group decision-making scenarios within three global organizations renowned for their cutting-edge operational techniques. The research progresses through several phases: identifying research problems, collecting baseline data on decision-making, implementing AI interventions, and evaluating the outcomes post-intervention to identify shifts in performance. The results demonstrate that GAI effectively reduces human cognitive burdens and mitigates heuristic biases by offering data-driven support and predictive analytics, grounded in System 2 reasoning. This is particularly valuable in complex situations characterized by unfamiliarity and information overload, where intuitive, System 1 thinking is less effective. However, the study also uncovers challenges related to GAI integration, such as potential over-reliance on technology, intrinsic biases particularly ‘out-of-the-box’ thinking without contextual creativity. To address these issues, this paper proposes an innovative strategic framework for HI-GAI collaboration that emphasizes transparency, accountability, and inclusiveness.","2024-09-01","2024-12-03 03:05:13","2024-12-03 03:05:13","","102662","","","78","","Technology in Society","","","","","","","","","","","","","","","","","","","Artificial intelligence; ChatGPT; Cognitive biases; Decision-making; Human intuition","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"V2QRMHVN","journalArticle","2024","Fu, Biying; Hadid, Abdenour; Damer, Naser","Generative AI in the context of assistive technologies: Trends, limitations and future directions","Image and Vision Computing","","0262-8856","10.1016/j.imavis.2024.105347","https://www.sciencedirect.com/science/article/pii/S0262885624004529","With the tremendous successes of Large Language Models (LLMs) like ChatGPT for text generation and Dall-E for high-quality image generation, generative Artificial Intelligence (AI) models have shown a hype in our society. Generative AI seamlessly delved into different aspects of society ranging from economy, education, legislation, computer science, finance, and even healthcare. This article provides a comprehensive survey on the increased and promising use of generative AI in assistive technologies benefiting different parties, ranging from the assistive system developers, medical practitioners, care workforce, to the people who need the care and the comfort. Ethical concerns, biases, lack of transparency, insufficient explainability, and limited trustworthiness are major challenges when using generative AI in assistive technologies, particularly in systems that impact people directly. Key future research directions to address these issues include creating standardized rules, establishing commonly accepted evaluation metrics and benchmarks for explainability and reasoning processes, and making further advancements in understanding and reducing bias and its potential harms. Beyond showing the current trends of applying generative AI in the scope of assistive technologies in four identified key domains, which include care sectors, medical sectors, helping people in need, and co-working, the survey also discusses the current limitations and provides promising future research directions to foster better integration of generative AI in assistive technologies.","2024-11-30","2024-12-03 03:05:13","2024-12-03 03:05:13","","105347","","","","","Image and Vision Computing","","","","","","","","","","","","","","","","","","","Generative AI; Assistive AI; Assistive systems; Assistive technologies and services; Generative models","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DNY6J82R","journalArticle","2024","Wu, Leihong; Xu, Joshua; Thakkar, Shraddha; Gray, Magnus; Qu, Yanyan; Li, Dongying; Tong, Weida","A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document","Regulatory Toxicology and Pharmacology","","0273-2300","10.1016/j.yrtph.2024.105613","https://www.sciencedirect.com/science/article/pii/S0273230024000540","Regulatory agencies consistently deal with extensive document reviews, ranging from product submissions to both internal and external communications. Large Language Models (LLMs) like ChatGPT can be invaluable tools for these tasks, however present several challenges, particularly the proprietary information, combining customized function with specific review needs, and transparency and explainability of the model's output. Hence, a localized and customized solution is imperative. To tackle these challenges, we formulated a framework named askFDALabel on FDA drug labeling documents that is a crucial resource in the FDA drug review process. AskFDALabel operates within a secure IT environment and comprises two key modules: a semantic search and a Q&A/text-generation module. The Module S built on word embeddings to enable comprehensive semantic queries within labeling documents. The Module T utilizes a tuned LLM to generate responses based on references from Module S. As the result, our framework enabled small LLMs to perform comparably to ChatGPT with as a computationally inexpensive solution for regulatory application. To conclude, through AskFDALabel, we have showcased a pathway that harnesses LLMs to support agency operations within a secure environment, offering tailored functions for the needs of regulatory research.","2024-05-01","2024-12-03 03:05:13","2024-12-03 03:05:13","","105613","","","149","","Regulatory Toxicology and Pharmacology","","","","","","","","","","","","","","","","","","","Artificial intelligence; Large language models (LLMs); Retrieval-augmented generation (RAG); Drug labeling; FDALabel; Regulatory science; Transparency; Trustworthy","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RI9GY55G","journalArticle","2024","Lee, Daniel; Arnold, Matthew; Srivastava, Amit; Plastow, Katrina; Strelan, Peter; Ploeckl, Florian; Lekkas, Dimitra; Palmer, Edward","The impact of generative AI on higher education learning and teaching: A study of educators’ perspectives","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100221","https://www.sciencedirect.com/science/article/pii/S2666920X24000225","In recent months, Artificial Intelligence (AI) has had, and will continue to have, a dramatic impact on Higher Education (HE). A study conducted by researchers at a leading university in Australia surveyed 30 of their teaching staff, drawn predominantly from their teaching academy, and interviewed eight of them regarding the impact of AI on HE. Data were analyzed using the procedures of Inductive Thematic Analysis and revealed a lack of any homogenous sentiment around AI in HE and much ambiguity regarding best practice regarding recent technological developments. The results indicate concerns exist around concepts relating to academic integrity, however, these concerns may be exaggerated. Almost half of the participants indicated they were using AI within their teaching roles with the most common design change being modifications to assessments. Less than a quarter of staff agreed the university has adequately equipped them for AI, and more than three quarters indicated they would like support. They unanimously assumed the technology will improve. Keeping in mind universities’ obligation to serve students by preparing them for industry, it is vitally important that the HE sector stays informed of developments in AI and commit to ongoing research and discussions regarding best practice in response to AI. However, anything regarding AI and future developments will be extremely difficult to predict.","2024-06-01","2024-12-03 03:05:13","2024-12-03 03:05:13","","100221","","","6","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Higher education; Artificial intelligence; ChatGPT; Generative artificial intelligence; Learning and teaching","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"BSNEWZQM","journalArticle","2024","Cox, Louis Anthony","An AI assistant to help review and improve causal reasoning in epidemiological documents","Global Epidemiology","","2590-1133","10.1016/j.gloepi.2023.100130","https://www.sciencedirect.com/science/article/pii/S2590113323000330","Drawing sound causal inferences from observational data is often challenging for both authors and reviewers. This paper discusses the design and application of an Artificial Intelligence Causal Research Assistant (AIA) that seeks to help authors improve causal inferences and conclusions drawn from epidemiological data in health risk assessments. The AIA-assisted review process provides structured reviews and recommendations for improving the causal reasoning, analyses and interpretations made in scientific papers based on epidemiological data. Causal analysis methodologies range from earlier Bradford-Hill considerations to current causal directed acyclic graph (DAG) and related models. AIA seeks to make these methods more accessible and useful to researchers. AIA uses an external script (a “Causal AI Booster” (CAB) program based on classical AI concepts of slot-filling in frames organized into task hierarchies to complete goals) to guide Large Language Models (LLMs), such as OpenAI's ChatGPT or Google's LaMDA (Bard), to systematically review manuscripts and create both (a) recommendations for what to do to improve analyses and reporting; and (b) explanations and support for the recommendations. Review tables and summaries are completed systematically by the LLM in order. For example, recommendations for how to state and caveat causal conclusions in the Abstract and Discussion sections reflect previous analyses of the Study Design and Data Analysis sections. This work illustrates how current AI can contribute to reviewing and providing constructive feedback on research documents. We believe that such AI-assisted review shows promise for enhancing the quality of causal reasoning and exposition in epidemiological studies. It suggests the potential for effective human-AI collaboration in scientific authoring and review processes.","2024-06-01","2024-12-03 03:05:13","2024-12-03 03:05:13","","100130","","","7","","Global Epidemiology","","","","","","","","","","","","","","","","","","","Artificial intelligence; Causality; Large language models (LLMs); Causal AI boosting; Review methodology","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2PCBHAZK","journalArticle","2024","Robertson, Jeandri; Ferreira, Caitlin; Botha, Elsamari; Oosthuizen, Kim","Game changers: A generative AI prompt protocol to enhance human-AI knowledge co-construction","SPECIAL ISSUE: WRITTEN BY CHATGPT","","0007-6813","10.1016/j.bushor.2024.04.008","https://www.sciencedirect.com/science/article/pii/S0007681324000533","The democratization of powerful artificial intelligence (AI) tools, including ChatGPT, has sparked the interest of business practitioners given their ability to fundamentally change the way we work. While AI tools are positioned to augment human capabilities, their effective implementation requires the skill to understand where, when and how to best utilize them efficiently. Furthermore, meaningful engagement with the content produced by generative AI (GenAI) necessitates the intricacy of appropriate prompt engineering to optimize the learning process. As the field of GenAI continues to advance, the art of developing impactful prompts has become a necessary skill for harnessing its full potential. This research develops an AI prompting protocol through a constructivist theory lens. Based on the principles of constructivism, where individuals assimilate new knowledge by bridging it with their existing understanding, this research suggests an active engagement process in the human-AI co-construction of knowledge through GenAI. The goal is to empower business managers and their teams to construct effective AI prompts and validate responses, thereby enhancing user interaction, optimizing workflows, and maximizing the potential outcomes of AI chatbots.","2024-09-01","2024-12-03 03:05:13","2024-12-03 03:05:13","","499-510","","5","67","","Business Horizons","","","","","","","","","","","","","","","","","","","ChatGPT; Large language models; Prompt engineering; Generative AI; Constructivism","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"AJG5XRUQ","journalArticle","2024","PAN, Huanquan; LIU, Jianqiao; GONG, Bin; ZHU, Yiheng; BAI, Junhui; HUANG, Hu; FANG, Zhengbao; JING, Hongbin; LIU, Chen; KUANG, Tie; LAN, Yubo; WANG, Tianzhi; XIE, Tian; CHENG, Mingzhe; QIN, Bin; SHEN, Yujiang","Construction and preliminary application of large language model for reservoir performance analysis","Petroleum Exploration and Development","","1876-3804","10.1016/S1876-3804(25)60546-5","https://www.sciencedirect.com/science/article/pii/S1876380425605465","A large language model (LLM) is constructed to address the sophisticated demands of data retrieval and analysis, detailed well profiling, computation of key technical indicators, and the solutions to complex problems in reservoir performance analysis (RPA). The LLM is constructed for RPA scenarios with incremental pre-training, fine-tuning, and functional subsystems coupling. Functional subsystem and efficient coupling methods are proposed based on named entity recognition (NER), tool invocation, and Text-to-SQL construction, all aimed at resolving pivotal challenges in developing the specific application of LLMs for RDA. This study conducted a detailed accuracy test on feature extraction models, tool classification models, data retrieval models and analysis recommendation models. The results indicate that these models have demonstrated good performance in various key aspects of reservoir dynamic analysis. The research takes some injection and production well groups in the PK3 Block of the Daqing Oilfield as an example for testing. Testing results show that our model has significant potential and practical value in assisting reservoir engineers with RDA. The research results provide a powerful support to the application of LLM in reservoir performance analysis.","2024-10-01","2024-12-03 03:05:13","2024-12-03 03:05:13","","1357-1366","","5","51","","Petroleum Exploration and Development","","","","","","","","","","","","","","","","","","","application-specific large language model; artificial intelligence large model; entity recognition; fine-tuning; incremental pre-training; reservoir performance analysis; subsystems coupling; tool invocation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"387LZ6KD","journalArticle","2024","Goh, Rudy; Cook, Benjamin; Stretton, Brandon; Booth, Andrew EC; Satheakeerthy, Shrirajh; Howson, Sarah; Kovoor, Joshua; Gupta, Aashray; Tan, Sheryn; Kimberly, W. Taylor; Moey, Andrew; Vallat, Wilson; Maddison, John; Marks, Jarrod; Gluck, Samuel; Gilbert, Toby; Jannes, Jim; Kleinig, Timothy; Bacchi, Stephen","Large language models can effectively extract stroke and reperfusion audit data from medical free-text discharge summaries","Journal of Clinical Neuroscience","","0967-5868","10.1016/j.jocn.2024.110847","https://www.sciencedirect.com/science/article/pii/S0967586824003862","Introduction Audits are an integral part of effective modern healthcare. The collection of data for audits can be resource intensive. Large language models (LLM) may be able to assist. This pilot study aimed to assess the feasibility of using a LLM to extract stroke audit data from free-text medical documentation. Method Discharge summaries from a one-month retrospective cohort of stroke admissions at a tertiary hospital were collected. A locally-deployed LLM, LLaMA3, was then used to extract a variety of routine stroke audit data from free-text discharge summaries. These data were compared to the previously collected human audit data in the statewide registry. Manual case note review was undertaken in cases of discordance. Results Overall, there was a total of 144 data points that were extracted (9 data points for each of the 16 patients). The LLM was correct in 135/144 (93.8%) of individual datapoints. This performance included binary categorical, multiple-option categorical, datetime, and free-text extraction fields. Conclusions LLM may be able to assist with the efficient collection of stroke audit data. Such approaches may be pursued in other specialties. Future studies should seek to examine the most effective way to deploy such approaches in conjunction with human auditors and researchers.","2024-11-01","2024-12-03 03:05:13","2024-12-03 03:05:13","","110847","","","129","","Journal of Clinical Neuroscience","","","","","","","","","","","","","","","","","","","Machine learning; Artificial intelligence; Automation; Key performance indicators; Quality improvement","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RMQV2DSE","journalArticle","2024","Song, Yanjie; Wu, Kaiyi; Ding, Jiaoyang","Developing an immersive game-based learning platform with generative artificial intelligence and virtual reality technologies – “LearningverseVR”","Computers & Education: X Reality","","2949-6780","10.1016/j.cexr.2024.100069","https://www.sciencedirect.com/science/article/pii/S2949678024000199","The rapid evolution of generative artificial intelligence (AI) and virtual reality (VR) technologies are revolutionising various fields, including education and gaming industries. However, studies on how to enhance immersive game-based learning with AI and VR technologies remain scant. Given this, the article presents the creation of “LearningverseVR,” an immersive game-based learning platform developed using generative AI and VR technologies, which is based on “Learningverse,” a metaverse platform developed by the lead author and her research team. The “LearningverseVR” platform uses Unity as the client and Python, Flask and MySQL as the backend. Unity's multiplayer service provides multiplayer online functionality, supporting learners to engage in immersive and interactive learning activities. The design framework of the platform consists of two main components: Game-based learning with generative AI and immersion with VR technologies. First, generative AI is used to create NPCs with diverse personalities and life backgrounds, and enable learners to interact with NPCs without scripted dialogues, creating an interactive and immersive game-based learning environment. Secondly, such a learning experience is enhanced by leveraging the Large Language Model (LLM) ecosystem with VR technology. The creation of the “LearningverseVR” platform provides novel perspectives on digital game-based learning.","2024-01-01","2024-12-03 03:05:13","2024-12-03 03:05:13","","100069","","","4","","Computers & Education: X Reality","","","","","","","","","","","","","","","","","","","Immersion; Generative AI; Game-based learning; Interaction; Virtual reality (VR)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"TYS3CK8P","journalArticle","2024","Yamakawa, Hiroshi; Tawatsuji, Yoshimasa; Ashihara, Yuta; Fukawa, Ayako; Arakawa, Naoya; Takahashi, Koichi; Matsuo, Yutaka","Technology roadmap toward the completion of whole-brain architecture with BRA-driven development","Cognitive Systems Research","","1389-0417","10.1016/j.cogsys.2024.101300","https://www.sciencedirect.com/science/article/pii/S1389041724000949","The development of brain-morphic software holds significant promise for creating artificial general intelligence that exhibits high affinity and interpretability for humans and also offers substantial benefits for medical applications. To facilitate this, creating Brain Reference Architecture (BRA) data, serving as a design specification for brain-morphic software is imperative. BRA-driven development, which utilizes Brain Information Flow (BIF) diagrams based on mesoscale brain anatomy and Hypothetical Component Diagrams (HCD) for corresponding computational functionalities, has been proposed to address this need. This methodology formalizes identifying possible functional structures by leveraging existing, albeit insufficient, neuroscientific knowledge. However, applying this methodology across the entire brain, thereby creating a Whole Brain Reference Architecture (WBRA), represents a significant research and development challenge due to its scale and complexity. Technology roadmaps have been introduced as a strategic tool to guide discussion, management, and distribution of resources within such expansive research and development activities. These roadmaps proposed a manual, anatomically based approach to incrementally construct BIF and HCD, thereby systematically expanding brain organ coverage toward achieving a complete WBRA. Large Language Model (LLM) technologies have introduced a paradigm shift, substantially automating the BRA-driven development process. This is largely due to the BRA data being structured around the brain’s anatomy and described in natural language, which aligns well with the capabilities of LLMs for supporting and automating the construction and verification processes. In this paper, we propose a novel technology roadmap to largely automate the creation of WBRA, leveraging neuroscientific insights. This roadmap includes 12 activities for automating BIF construction, notably extracting anatomical structures from scholarly articles. Furthermore, it details 11 activities aimed at enhancing the integration of Hypothetical Component Diagrams (HCD) into the WBRA, focusing on automating checks for functional consistency. This roadmap aims to establish a cost-effective and efficient design process for WBRA, ensuring the availability of brain-morphic software design specifications that are continually validated against the latest neuroscientific knowledge.","2024-12-01","2024-12-03 03:05:13","2024-12-03 03:05:13","","101300","","","88","","Cognitive Systems Research","","","","","","","","","","","","","","","","","","","Artificial general intelligence; Brain information flow; Whole-brain architecture; Whole-brain reference architecture","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JHMHDQJI","journalArticle","2024","Li, Rui; Zhong, Qiaoling","On the Application of Generative Artificial Intelligence ChatGPT in Digital Trade","The 11th International Conference on Applications and Techniques in Cyber Intelligence","","1877-0509","10.1016/j.procs.2024.10.014","https://www.sciencedirect.com/science/article/pii/S187705092402814X","The combination of human subjective judgment and machine data processing capabilities in human-machine collaborative evaluation can create a more efficient, accurate, and personalized customer service dialogue interaction system, thereby promoting digital trade efficiency and improving service quality. Generative artificial intelligence has the ability of intelligent interaction and contextual semantic understanding, which is an important means of implementing the concept of human-machine collaboration. This article introduces the basic principles and technical characteristics of ChatGPT, and explores in detail its various application scenarios in digital trade, including automated customer service, personalized recommendations, intelligent marketing, and data analysis. Finally, this article also discusses the challenges and future development directions of ChatGPT in digital trade, in order to provide certain reference value for research and practice in related fields. The data from the questionnaire survey shows that men, aged between 18-30 and 41-50 years old, with high education level, high monthly online shopping expenses, high monthly income, and frequent use of well-known e-commerce platforms, generally have a high level of understanding of ChatGPT.","2024-01-01","2024-12-03 03:05:13","2024-12-03 03:05:13","","112-120","","","247","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Natural Language Processing; Customer Service; Dialogue Interaction; Generative Artificial Intelligence ChatGPT","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"9GXVYAS8","journalArticle","2024","Pal, Kuldeep; Chaudhuri, Rapti; Deb, Suman; Saha, Ashim","Artistic Essence of Generative Adversarial Networks: Analyzing Training Data’s Impact on Performance","International Conference on Machine Learning and Data Engineering (ICMLDE 2023)","","1877-0509","10.1016/j.procs.2024.04.243","https://www.sciencedirect.com/science/article/pii/S1877050924009190","Generative adversarial networks (GANs) are powerful deep learning models for synthesizing realistic data. However, their performance critically depends on curating optimal training data. This research conducts a comprehensive study analyzing the impact of sample size, class balance, and heterogeneity in training datasets on GAN image and text generation quality. Through extensive experiments on CIFAR-10, it has been demonstrated that insufficient samples, imbalanced classes, and lack of diversity cause degraded sample quality, coherence, and mode collapse. The analysis conducted in this research work provides unique insights into data-GAN interplay. Models trained on balanced subsets with adequate samples per class produce superior Inception Scores and BLEU, avoiding limited variety in outputs. The techniques presented enable developing more generalizable and creative GANs. This work proves to be the first of its kind to rigorously evaluate the role of data characteristics like size, balance and heterogeneity in stabilizing GAN training and improving output fdelity across modalities. The data-centric findings would be valuable for researchers to curate optimal datasets that can unlock GANs’ full potential for diverse, realistic generation with wide applications in graphics, vision, language and beyond.","2024-01-01","2024-12-03 03:07:20","2024-12-03 03:07:20","","2577-2586","","","235","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial Intelligence; Artistic Data generation; Data quality; Diverse datasets; GAN performance; Generative Adversarial Networks (GANs); Training data","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"5PKKZAWN","journalArticle","2024","Buster, Grant; Pinchuk, Pavlo; Barrons, Jacob; McKeever, Ryan; Levine, Aaron; Lopez, Anthony","Supporting energy policy research with large language models: A case study in wind energy siting ordinances","Energy and AI","","2666-5468","10.1016/j.egyai.2024.100431","https://www.sciencedirect.com/science/article/pii/S2666546824000971","The recent growth in renewable energy development in the United States has been accompanied by a simultaneous surge in renewable energy siting ordinances. These zoning laws play a critical role in dictating the placement of wind and solar resources that are critical for achieving low-carbon energy futures. In this context, efficient access to and management of siting ordinance data becomes imperative. The National Renewable Energy Laboratory (NREL) recently introduced a public wind and solar siting database to fill this need. This paper presents a method for harnessing Large Language Models (LLMs) to automate the extraction of these siting ordinances from legal documents, enabling this database to maintain accurate up-to-date information in the rapidly changing energy policy landscape. A novel contribution of this research is the integration of a decision tree framework with LLMs. Our results show that this approach is 85 to 90 % accurate with outputs that can be used directly in downstream quantitative modeling. We discuss opportunities to use this work to support similar large-scale policy research in the energy sector. By unlocking new efficiencies in the extraction and analysis of legal documents using LLMs, this study enables a path forward for automated large-scale energy policy research.","2024-12-01","2024-12-03 03:07:20","2024-12-03 03:07:20","","100431","","","18","","Energy and AI","","","","","","","","","","","","","","","","","","","Large language models (LLMs); Energy analysis; Energy policy; Natural language processing (NLP); Siting ordinances","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"8PIYYJPC","journalArticle","2024","Kobayashi, Yoshiyuki; Uchida, Takumi; Inoue, Takahiro; Iwasaki, Yusuke; Ito, Rie; Akiyama, Hiroshi","A Comprehensive Analysis of the per- and poly-fluoroalkyl substances (PFAS) research landscape through AI-assisted text mining","Journal of Hazardous Materials Letters","","2666-9110","10.1016/j.hazl.2024.100121","https://www.sciencedirect.com/science/article/pii/S2666911024000200","Per- and poly-fluoroalkyl substances (PFAS) have been widely used in various industrial applications due to their unique properties. This study aims to provide a comprehensive analysis of PFAS research trends using a novel approach combining text mining techniques and large-scale language models (LLMs). PFAS-related scientific literature published from 1980 to 2024 was gathered from Scopus, and KH Coder and Claude 3 were used to perform the analysis. The results showed a significant increase in research output and a clear shift in research topics over the past 40 years. Whereas in the past, the focus was on analytical methods, more recently, the emphasis has been on environmental fate, toxicity assessment, alternative compounds, and regulation. With Claude 3, research areas can now be identified without reviewing the results of expert text mining. Comparisons of AI-extracted trends with insights from traditional review articles showed strong agreement, confirming the effectiveness of this approach. These findings suggest the need for continued interdisciplinary research on PFAS such as the development of remediation strategies, elucidation of health effects, and evidence-based policymaking. This study showed the possibility of integrating text mining and LLM for a comprehensive analysis of research trends, which will accelerate future research and development strategies.","2024-11-01","2024-12-03 03:07:20","2024-12-03 03:07:20","","100121","","","5","","Journal of Hazardous Materials Letters","","","","","","","","","","","","","","","","","","","Generative AI; Text mining; Natural language processing (LLM); PFAS; Research trend analysis","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"G3ZJ3ZHM","journalArticle","2024","Bardozzo, Francesco; Fiore, Pierpaolo; Valentino, Marika; Bianco, Vittorio; Memmolo, Pasquale; Miccio, Lisa; Brancato, Valentina; Smaldone, Giovanni; Gambacorta, Marcello; Salvatore, Marco; Ferraro, Pietro; Tagliaferri, Roberto","Enhanced tissue slide imaging in the complex domain via cross-explainable GAN for Fourier ptychographic microscopy","Computers in Biology and Medicine","","0010-4825","10.1016/j.compbiomed.2024.108861","https://www.sciencedirect.com/science/article/pii/S0010482524009466","Achieving microscopy with large space-bandwidth products plays a key role in diagnostic imaging and is widely significant in the overall field of clinical practice. Among quantitative microscopy techniques, Fourier Ptychography (FP) provides a wide field of view and high-resolution images, suitable to the histopathological field, but onerous in computational terms. Artificial intelligence can be a solution in this sense. In particular, this research delves into the application of Generative Adversarial Networks (GAN) for the dual-channel complex FP image enhancement of human kidney samples. The study underscores the GANs’ efficacy in promoting biological architectures in FP domain, thereby still guaranteeing high resolution and visibility of detailed microscopic structures. We demonstrate successful GAN-based enhanced reconstruction through two strategies: cross-explainability and expert survey. The cross-explainability is evaluated through the comparison of explanation maps for both real and imaginary components underlining its robustness. This comparison further shows that their interplay is pivotal for accurate reconstruction without hallucinations. Secondly, the enhanced reconstruction accuracy and effectiveness in a clinical workflow are confirmed through a two-step survey conducted with nephrologists.","2024-09-01","2024-12-03 03:07:20","2024-12-03 03:07:20","","108861","","","179","","Computers in Biology and Medicine","","","","","","","","","","","","","","","","","","","Cellular imaging; Complex domain; Explainability; Generative Adversarial Networks; Image-enhancement; Microscopy; Phase images; Ptychography","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"AH9M4RU7","journalArticle","2024","Koliousis, Ioannis; Al-Surmi, Abdulrahman; Bashiri, Mahdi","Artificial intelligence and policy making; can small municipalities enable digital transformation?","International Journal of Production Economics","","0925-5273","10.1016/j.ijpe.2024.109324","https://www.sciencedirect.com/science/article/pii/S0925527324001816","This study investigates digital transformation and the usability of emerging technologies in policymaking. Prior studies categorised digital transformation into three distinct phases of digitisation, digitalisation, and digital transformation. They mainly focus on the operational or functional levels, however, this study considers digital transformation at the strategic level. Previous studies confirmed that using new emerging AI-based technologies will enable organisations to use digital transformation to achieve higher efficiency. A novel methodological AI-based approach for policymaking was constructed into three phases through the lens of organisational learning theory. The proposed framework was validated using a case study in the transportation industry of a small municipality. In the selected case study, a confirmatory model was developed and tested utilising the Structural Equation Modelling with data collected from a survey of 494 local stakeholders. Artificial Neural Network was utilised to predict and then to identify the most appropriate policy according to cost, feasibility, and impact criteria amongst six policies extracted from the literature. The results from this research confirm that utilisation of the AI-based strategic decision-making through the proposed generative AI platform at strategic level outperforms human decision-making in terms of applicability, efficiency, and accuracy.","2024-08-01","2024-12-03 03:07:20","2024-12-03 03:07:20","","109324","","","274","","International Journal of Production Economics","","","","","","","","","","","","","","","","","","","Digital transformation; Generative AI; Policy; SEM; Strategic decision making","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ZVIHEQIU","journalArticle","2024","B N, Lavanya; K V, Anitha Rathnam; Appaji, Abhishek; K, Kiran; Shenoy, P. Deepa; K R, Venugopal","LLM GPT-3.5 study for sentiment analysis across Utkarsh server, Ohio supercomputer, Google Colab and PC","Results in Engineering","","2590-1230","10.1016/j.rineng.2024.103218","https://www.sciencedirect.com/science/article/pii/S2590123024014737","The major objective of the present research is to inspect sentiment analysis models that have been trained on Twitter corpus by utilising the Large Language Model (LLM) gpt-3–5-turbo-16k version of the Generative Pretrained Transformer (GPT 3.5) model. Such trained models include the Bidirectional long short-term memory neural network (BiLSTM), Convolutional Neural Networks, Gated Recurrent Unit and Recurrent Neural Network which were used to perform computational tasks on the Ohio Supercomputer and Utkarsh Server, in comparison to work conducted on Google Colab and a Personal Computer (PC).This research also looks at the performance as well as the computational aspects of these models in terms of accuracy, recall, F1-score, time/memory complexity and resource requirements (CPU/GPU) throughout the training and testing phases of each model. The training accuracies in this case were concentrated between 49.91 % and 99.98 % while those of the testing accuracies of the models accounts for about 50.00–75.00 %. For instance, models including BiLSTM and RNN usually exhibit more time complexity because of the nature of the models (sequential computation), on the contrary, CNNs are less time-consuming and are more effective in terms of storage modifying layered architecture. The use of supercomputers and specialized servers reduces training time, but resource constraints on platforms such as personal computers or Colab cause considerable divergence.","2024-12-01","2024-12-03 03:07:20","2024-12-03 03:07:20","","103218","","","24","","Results in Engineering","","","","","","","","","","","","","","","","","","","Natural language processing (NLP); Bidirectional long short-term memory (BiLSTM); Convolutional neural networks (CNN); Gated recurrent unit (GRU); Generative pretrained transformer (GPT); Large language model (LLM); Recurrent neural network (RNN)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"A9QRFS5S","journalArticle","2024","Huang, Jerry; Mizumoto, Atsushi","Examining the relationship between the L2 motivational self system and technology acceptance model post ChatGPT introduction and utilization","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100302","https://www.sciencedirect.com/science/article/pii/S2666920X2400105X","Since the introduction of the L2 Motivational Self System (L2MSS), numerous studies worldwide have highlighted its effectiveness in elucidating Second Language Acquisition. However, the influence of generative artificial intelligence (GenAI) technology on this model remains largely unexplored. The Technology Acceptance Model (TAM) is a widely employed framework for examining the impact of a new technology, and this study explores the intercorrelation when these two models are considered together. Conducted with 35 s-year university English as a foreign language (EFL) students in humanities, the study involved two sessions of instructor-led ChatGPT usage writing workshops, followed by the collection of survey responses. Data analysis unveiled a notable correlation between the L2 Motivational Self System and the Technology Acceptance Model. Particularly noteworthy is the finding that Ought-to L2 Self positively predict Actual Usage. The study discusses pedagogical and theoretical implications, along with suggesting future research directions.","2024-12-01","2024-12-03 03:07:20","2024-12-03 03:07:20","","100302","","","7","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","ChatGPT; Technology acceptance model; Ideal L2 self; L2 learning experience; Ought-to L2 self; The L2 motivational self system","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"PVGB2QF2","journalArticle","2024","Jirsa, Tomáš; Verde, Laura; Marulli, Fiammetta; Marrone, Stefano; Vrba, Jan","Improving Voice Pathology Classification Using Artificial Data Generation","28th International Conference on Knowledge Based and Intelligent information and Engineering Systems (KES 2024)","","1877-0509","10.1016/j.procs.2024.09.612","https://www.sciencedirect.com/science/article/pii/S1877050924026644","Human Digital Twin is an emerging technology that could revolutionize the current healthcare system by enabling the delivery of Personalized Health Services through the use of tools such as Artificial intelligence. However, the considerable complexity of the structure of the human body, brought about by continuous molecular and physiological changes, makes it extremely difficult to process medical data extracted by Artificial intelligence techniques. The latter requires a large amount of data for reliable performance, which is often difficult to obtain due to limited quality and availability. In this paper, we propose a methodology to generate Artificial medical data. In detail, we focus on generating Artificial voice signals. The analysis of voice recordings is fundamental to diagnose specific pneumo-articulatory apparatus diseases, such as dysphonia. The generative neural network employed is based on the WaveNet model, due to its autoregressive sampling, which enables generating recordings of variable length. We propose a setup which enables to generate Artificial samples of required sex and pathology to balance and augment the dataset using only one generative network. The quality of the generative network is assessed by balancing the training dataset by generated data and training a convolutional classifier, which is tested on a dataset which was not introduced to the generative network during training. We achieved reasonable improvements in classification accuracy, particularly for the under-represented sex in terms of accuracy, arguing that this approach is worthy of future research.","2024-01-01","2024-12-03 03:07:20","2024-12-03 03:07:20","","5175-5184","","","246","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Generative AI; Dataset Augmentation; Digital Twin; Dysphonia; Voice Pathology Detection; WaveNet","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"YWEIL9G2","journalArticle","2024","Temel, Mustafa Hüseyin; Erden, Yakup; Bağcıer, Fatih","Lumbar Radicular Pain in the Eyes of Artificial Intelligence: Can You ‘Imagine’ What I ‘Feel’?","World Neurosurgery","","1878-8750","10.1016/j.wneu.2024.09.075","https://www.sciencedirect.com/science/article/pii/S1878875024016231","Objective Pain is a complex sensory and emotional experience that significantly impacts individuals' well-being. Lumbar radicular pain (LRP) is a prevalent neuropathic pain affecting 9.9% to 25% of the population annually. Accurate identification of pain patterns in LRP is essential for diagnosis and management. Artificial intelligence has potential in health care but faces challenges in reliability and accuracy. This study aimed to investigate the accuracy and consistency of LRP patterns demonstrated by ChatGPT-4o. Methods The study was conducted at Üsküdar State Hospital from June 1 to June 30, 2024, utilizing the Generative Pretrained Transformer (GPT), version 4o language model. ChatGPT-4o was prompted to generate and mark LRP patterns for L4, L5, and S1 radiculopathies on an anatomical model. The process was repeated after two weeks to assess consistency. The markings by ChatGPT were compared with those by two experienced specialists using OpenCV for analysis. Results ChatGPT's initial and follow-up markings of L4, L5, and S1 radiculopathy pain patterns were statistically significantly different from each other and from the specialists' markings (P < 0.001 for all comparisons). Conclusions ChatGPT currently lacks the capacity to accurately and consistently represent LRP patterns. AI tools in health care require further refinement, validation, and regulation to ensure reliability and safety. Future research should involve multiple AI platforms and broader medical conditions to enhance generalizability.","2024-10-08","2024-12-03 03:07:20","2024-12-03 03:07:20","","","","","","","World Neurosurgery","","","","","","","","","","","","","","","","","","","Artificial intelligence; Consistency; Lumbar radicular pain; Pain pattern","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DZVIDKIL","journalArticle","2024","Wang, Ting-Wei; Shiao, Yu-Chieh; Hong, Jia-Sheng; Lee, Wei-Kai; Hsu, Ming-Sheng; Cheng, Hao-Min; Yang, Huai-Che; Lee, Cheng-Chia; Pan, Hung-Chuan; You, Weir Chiang; Lirng, Jiing-Feng; Guo, Wan-Yuo; Wu, Yu-Te","Artificial Intelligence Detection and Segmentation Models: A Systematic Review and Meta-Analysis of Brain Tumors in Magnetic Resonance Imaging","Mayo Clinic Proceedings: Digital Health","","2949-7612","10.1016/j.mcpdig.2024.01.002","https://www.sciencedirect.com/science/article/pii/S2949761224000038","Objective To thoroughly analyze factors affecting the generalization ability of deep learning algorithms on brain tumor detection and segmentation models. Patients and Methods We searched PubMed, Embase, Web of Science, Cochrane Library, and IEEE from inception to July 25, 2023, and 19 studies with 12,000 patients were identified. The criteria required studies to use magnetic resonance imaging (MRI) for brain tumor detection and segmentation, offer clear performance metrics, and use external validation data sets. The study focused on outcomes such as sensitivity and Dice score. Study quality was assessed using QUADAS-2 and CLAIM tools. The meta-analysis evaluated varying algorithms and their performance across different validation data sets. Results MRI hardware as the manufacturer may contribute to data set diversity, impacting AI model generalizability. The study found that the best algorithms had a pooled lesion-wise Dice score of 84%, with pooled sensitivities of 87% (patient-wise) and 86% (lesion-wise). Post-2022 methodologies highlighted evolving artificial intelligence techniques. Performance differences were evident among tumor types, likely due to size disparities. 3D models outperformed their 2D and ensemble counterparts in detection. Although specific preprocessing techniques improved segmentation outcomes, some hindered detection. Conclusion The study underscores the potential of deep learning in improving brain tumor diagnostics and treatment planning. We also identify the need for further research, including developing a comprehensive diversity index, expanded meta-analyses, and using generative adversarial networks for data diversification, paving the way for AI-driven advancements in oncological patient care. Trial Registration PROPERO (CRD42023459108).","2024-03-01","2024-12-03 03:07:21","2024-12-03 03:07:21","","75-91","","1","2","","Mayo Clinic Proceedings: Digital Health","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"NUIDEIB8","journalArticle","2024","Cheng, Qi; Chen, Liqiong; Hu, Zhixing; Tang, Juan; Xu, Qiang; Ning, Binbin","A novel prompting method for few-shot NER via LLMs","Natural Language Processing Journal","","2949-7191","10.1016/j.nlp.2024.100099","https://www.sciencedirect.com/science/article/pii/S2949719124000475","In various natural language processing tasks, significant strides have been made by Large Language Models (LLMs). Researchers leverage prompt method to conduct LLMs in accomplishing specific tasks under few-shot conditions. However, the prevalent use of LLMs’ prompt methods mainly focuses on guiding generative tasks, and employing existing prompts may result in poor performance in Named Entity Recognition (NER) tasks. To tackle this challenge, we propose a novel prompting method for few-shot NER. By enhancing existing prompt methods, we devise a standardized prompts tailored for the utilization of LLMs in NER tasks. Specifically, we structure the prompts into three components: task definition, few-shot demonstration, and output format. The task definition conducts LLMs in performing NER tasks, few-shot demonstration assists LLMs in understanding NER task objectives through specific output demonstration, and output format restricts LLMs’ output to prevent the generation of unnecessary results. The content of these components has been specifically tailored for NER tasks. Moreover, for the few-shot demonstration within the prompts, we propose a selection strategy that utilizes feedback from LLMs’ outputs to identify more suitable few-shot demonstration as prompts. Additionally, to enhance entity recognition performance, we enrich the prompts by summarizing error examples from the output process of LLMs and integrating them as additional prompts.","2024-09-01","2024-12-03 03:07:21","2024-12-03 03:07:21","","100099","","","8","","Natural Language Processing Journal","","","","","","","","","","","","","","","","","","","Deep learning; Natural language processing; Large language model; Named entity recognition; Prompt method","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"D7B4SKSB","journalArticle","2024","Ramoni, Davide; Sgura, Cosimo; Liberale, Luca; Montecucco, Fabrizio; Ioannidis, John P.A.; Carbone, Federico","Artificial intelligence in scientific medical writing: Legitimate and deceptive uses and ethical concerns","European Journal of Internal Medicine","","0953-6205","10.1016/j.ejim.2024.07.012","https://www.sciencedirect.com/science/article/pii/S0953620524002954","The debate surrounding the integration of artificial intelligence (AI) into scientific writing has already attracted significant interest in medical and life sciences. While AI can undoubtedly expedite the process of manuscript creation and correction, it raises several criticisms. The crossover between AI and health sciences is relatively recent, but the use of AI tools among physicians and other scientists who work in the life sciences is growing very fast. Within this whirlwind, it is becoming essential to realize where we are heading and what the limits are, including an ethical perspective. Modern conversational AIs exhibit a context awareness that enables them to understand and remember any conversation beyond any predefined script. Even more impressively, they can learn and adapt as they engage with a growing volume of human language input. They all share neural networks as background mathematical models and differ from old chatbots for their use of a specific network architecture called transformer model [1]. Some of them exceed 100 terabytes (TB) (e.g., Bloom, LaMDA) or even 500 TB (e.g., Megatron-Turing NLG) of text data, the 4.0 version of ChatGPT (GPT-4) was trained with nearly 45 TB, but stays updated by the internet connection and may integrate with different plugins that enhance its functionality, making it multimodal.","2024-09-01","2024-12-03 03:07:21","2024-12-03 03:07:21","","31-35","","","127","","European Journal of Internal Medicine","","","","","","","","","","","","","","","","","","","Artificial intelligence; Chatbots; ChatGPT; Large language models; Medical writing; Natural language understanding","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"FE98YNEM","journalArticle","2024","Hartmann, Jochen; Exner, Yannick; Domdey, Samuel","The power of generative marketing: Can generative AI create superhuman visual marketing content?","International Journal of Research in Marketing","","0167-8116","10.1016/j.ijresmar.2024.09.002","https://www.sciencedirect.com/science/article/pii/S0167811624000843","Generative AI’s capacity to create photorealistic images has the potential to augment human creativity and disrupt the economics of visual marketing content production. This research systematically compares the performance of AI-generated to human-made marketing images across important marketing dimensions. First, we prompt seven state-of-the-art generative text-to-image models (DALL-E 3, Midjourney v6, Firefly 2, Imagen 2, Imagine, Stable Diffusion XL Turbo, and Realistic Vision) to create 10,320 synthetic marketing images, using 2,400 real-world, human-made images as input. 254,400 human evaluations of these images show that AI-generated marketing imagery can surpass human-made images in quality, realism, and aesthetics. Second, we give identical creative briefings to commissioned human freelancers and the AI models, showing that the best synthetic images also excel in ad creativity, ad attitudes, and prompt following. Third, a field study with more than 173,000 impressions demonstrates that AI-generated banner ads can compete with professional human-made stock photography, achieving an up to 50% higher click-through rate than a human-made image. Collectively, our findings suggest that the paradigm shift brought about by generative AI can help advertisers produce marketing content not only faster and orders of magnitude cheaper but also at superhuman effectiveness levels with important implications for firms, consumers, and policymakers. To facilitate future research on AI-generated marketing imagery, we release GenImageNet that contains all of our synthetic images and their human ratings.","2024-09-20","2024-12-03 03:07:21","2024-12-03 03:07:21","","","","","","","International Journal of Research in Marketing","","","","","","","","","","","","","","","","","","","artificial intelligence; generative AI; productivity; content creation; digital marketing; marketing effectiveness","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XB66KU4S","journalArticle","2024","Bartoli, A.; May, A.T.; Al-Awadhi, A.; Schaller, K.","Probing artificial intelligence in neurosurgical training: ChatGPT takes a neurosurgical residents written exam","Brain and Spine","","2772-5294","10.1016/j.bas.2023.102715","https://www.sciencedirect.com/science/article/pii/S2772529423010032","Introduction Artificial Intelligence tools are being introduced in almost every field of human life, including medical sciences and medical education, among scepticism and enthusiasm. Research question to assess how a generative language tool (Generative Pretrained Transformer 3.5, ChatGPT) performs at both generating questions and answering a neurosurgical residents’ written exam. Namely, to assess how ChatGPT generates questions, how it answers human-generated questions, how residents answer AI-generated questions and how AI answers its self-generated question. Materials and methods 50 questions were included in the written exam, 46 questions were generated by humans (senior staff members) and 4 were generated by ChatGPT. 11 participants took the exam (ChatGPT and 10 residents). Questions were both open-ended and multiple-choice. 8 questions were not submitted to ChatGPT since they contained images or schematic drawings to interpret. Results formulating requests to ChatGPT required an iterative process to precise both questions and answers. Chat GPT scored among the lowest ranks (9/11) among all the participants). There was no difference in response rate for residents’ between human-generated vs AI-generated questions that could have been attributed to less clarity of the question. ChatGPT answered correctly to all its self-generated questions. Discussion and conclusions AI is a promising and powerful tool for medical education and for specific medical purposes, which need to be further determined. To request AI to generate logical and sound questions, that request must be formulated as precise as possible, framing the content, the type of question and its correct answers.","2024-01-01","2024-12-03 03:07:21","2024-12-03 03:07:21","","102715","","","4","","Brain and Spine","","","","","","","","","","","","","","","","","","","Artificial intelligence; ChatGPT; Neurosurgical education; Residents; Written exam","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"BBEKFZBP","journalArticle","2024","Martins, Ana; Velez Lapão, Luís; Nunes, Isabel L.; Paula Giordano, Ana; Semedo, Helena; Vital, Clara; Silva, Raquel; Coelho, Pedro; Londral, Ana","A conversational agent for enhanced Self-Management after cardiothoracic surgery","International Journal of Medical Informatics","","1386-5056","10.1016/j.ijmedinf.2024.105640","https://www.sciencedirect.com/science/article/pii/S1386505624003034","Background Enhanced self-management is crucial for long-term survival following cardiothoracic surgery. Objectives This study aimed to develop a conversational agent to enhance patient self-management after cardiothoracic surgery. Methodology The solution was designed and implemented following the Design Science Research Methodology. A pilot study was conducted at the hospital to assess the feasibility, usability, and perceived effectiveness of the solution. Feedback was gathered to inform further interactions. Additionally, a focus group with clinicians was conducted to evaluate the acceptability of the solution, integrating insights from the pilot study. Results The conversational agent, implemented using a rule-based model, was successfully tested with patients in the cardiothoracic surgery unit (n = 4). Patients received one month of text messages reinforcing clinical team recommendations on a healthy diet and regular physical activity. The system received a high usability score, and two patients suggested adding a feature to answer user prompts for future improvements. The focus group feedback indicated that while the solution met the initial requirements, further testing with a larger patient cohort is necessary to establish personalized profiles. Moreover, clinicians recommended that future iterations prioritize enhanced personalization and interoperability with other hospital platforms. Additionally, while the use of artificial generative intelligence was seen as relevant for content personalization, clinicians expressed concerns regarding content safety, highlighting the necessity for rigorous testing. Conclusions This study marks a significant step towards enhancing post-cardiothoracic surgery care through conversational agents. The integration of a diversity of stakeholder knowledge enriches the solution, grants ownership and ensures its sustainability. Future research should focus on automating message generation and delivery based on patient data and environmental factors. While the integration of artificial generative intelligence holds promise for enhancing patient interaction, ensuring the safety of its content is essential.","2024-12-01","2024-12-03 03:07:21","2024-12-03 03:07:21","","105640","","","192","","International Journal of Medical Informatics","","","","","","","","","","","","","","","","","","","Cardiothoracic Surgery; Co-design; Conversational Agents; Health; Personalization; Self-management","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RUJ84V6B","journalArticle","2024","Alfrink, Kars; Keller, Ianus; Yurrita Semperena, Mireia; Bulygin, Denis; Kortuem, Gerd; Doorn, Neelke","Envisioning Contestability Loops: Evaluating the Agonistic Arena as a Generative Metaphor for Public AI","She Ji: The Journal of Design, Economics, and Innovation","","2405-8726","10.1016/j.sheji.2024.03.003","https://www.sciencedirect.com/science/article/pii/S240587262400025X","Public sector organizations increasingly use artificial intelligence to augment, support, and automate decision-making. However, such public AI can potentially infringe on citizens’ right to autonomy. Contestability is a system quality that protects against this by ensuring systems are open and responsive to disputes throughout their life cycle. While a growing body of work is investigating contestable AI by design, little of this knowledge has so far been evaluated with practitioners. To make explicit the guiding ideas underpinning contestable AI research, we construct the generative metaphor of the Agonistic Arena, inspired by the political theory of agonistic pluralism. Combining this metaphor and current contestable AI guidelines, we develop an infographic supporting the early-stage concept design of public AI system contestability mechanisms. We evaluate this infographic in five workshops paired with focus groups with a total of 18 practitioners, yielding ten concept designs. Our findings outline the mechanisms for contestability derived from these concept designs. Building on these findings, we subsequently evaluate the efficacy of the Agonistic Arena as a generative metaphor for the design of public AI and identify two competing metaphors at play in this space: the Black Box and the Sovereign.","2024-03-01","2024-12-03 03:07:21","2024-12-03 03:07:21","","53-93","","1","10","","She Ji: The Journal of Design, Economics, and Innovation","","","","","","","","","","","","","","","","","","","public administration; artificial intelligence; contestability; generative metaphor; interaction design; visual explanations","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"WA8Z89YE","journalArticle","2024","Celiktutan, Begum; Klesse, Anne-Kathrin; Tuk, Mirjam A.","Acceptability lies in the eye of the beholder: Self-other biases in GenAI collaborations","International Journal of Research in Marketing","","0167-8116","10.1016/j.ijresmar.2024.05.006","https://www.sciencedirect.com/science/article/pii/S0167811624000442","Since the release of ChatGPT, heated discussions have focused on the acceptable uses of generative artificial intelligence (GenAI) in education, science, and business practices. A salient question in these debates pertains to perceptions of the extent to which creators contribute to the co-produced output. As the current research establishes, the answer to this question depends on the evaluation target. Nine studies (seven preregistered, total N = 4498) document that people evaluate their own contributions to co-produced outputs with ChatGPT as higher than those of others. This systematic self–other difference stems from differential inferences regarding types of GenAI usage behavior: People think that they predominantly use GenAI for inspiration, but others use it to outsource work. These self–other differences in turn have direct ramifications for GenAI acceptability perceptions, such that usage is considered more acceptable for the self than for others. The authors discuss the implications of these findings for science, education, and marketing.","2024-09-01","2024-12-03 03:07:21","2024-12-03 03:07:21","","496-512","","3","41","","International Journal of Research in Marketing","","","","","","","","","","","","","","","","","","","ChatGPT; GenAI; Biased self-evaluation; Inferred contribution; Intellectual ownership; Self-other difference","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DPW2YHKD","journalArticle","2024","Yuwono, Elizabeth Irenne; Tjondronegoro, Dian; Riverola, Carla; Loy, Jennifer","Co-creation in action: Bridging the knowledge gap in artificial intelligence among innovation champions","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100272","https://www.sciencedirect.com/science/article/pii/S2666920X24000754","The increasing significance of artificial intelligence (AI) in various industries highlights the necessity for industry leaders and professionals to comprehend and gain knowledge about AI. The urgency for AI literacy is more critical than ever due to the potential unethical use of AI resulting from insufficient knowledge. This issue is particularly crucial for educators because they need to understand and adapt to the impact of AI within educational institutions, with some needing to use and become literate in AI, given that students now have increased access to public AI tools. This research presents a multi-phase study to address the issue of bridging the knowledge gap in AI by conducting co-creation with innovation champions, illustrated by a case of Generative AI innovation in a K-12 school. Using action design research, we engaged experienced teachers who are experts in pedagogical innovation to co-create a generative AI-enhanced platform at a leading K-12 education institution known for its pedagogical innovation in Australia. The findings reveal that champions enhance their knowledge through their subject-matter expertise, organizational knowledge, and AI knowledge gained through external exposure and experience. The study also highlights the key elements that facilitate a cross-domain knowledge exchange platform, enabling champions to be exposed to and experience AI technological learning, leading to shifts in their understanding and perception of AI. The initially unaware and sceptical champions become more aware and capable of articulating more technical AI knowledge rooted in a shared value. This research demonstrates how co-creation serves as a pathway for learning AI, particularly among K-12 teachers who are innovation champions. It underscores the impact of experiential and organizational learning on AI collaborative learning and behavioral intentions. Additionally, the study presents that aligning organizational and personal visions and values can influence perceptions about AI technologies, enhancing the discourse on AI and education innovation.","2024-12-01","2024-12-03 03:07:21","2024-12-03 03:07:21","","100272","","","7","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Artificial intelligence; AI literacy; AI co-creation; AI innovation; AI learning; K-12 AI education","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ZQYCZ5KH","journalArticle","2024","Smerdon, David","AI in essay-based assessment: Student adoption, usage, and performance","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100288","https://www.sciencedirect.com/science/article/pii/S2666920X24000912","The rise of generative artificial intelligence (AI) has sparked debate in education about whether to ban AI tools for assessments. This study explores the adoption and impact of AI tools on an undergraduate research proposal assignment using a mixed-methods approach. From a sample of 187 students, 69 completed a survey, with 46 (67%) reporting the use of AI tools. AI-using students were significantly more likely to be higher-performing, with a pre-semester average GPA of 5.46 compared to 4.92 for non-users (7-point scale, p = .025). Most students used AI assistance for the highest-weighted components of the task, such as the research topic and methods section, using AI primarily for generating research ideas and gathering feedback. Regression analysis suggests that there was no statistically significant effect of AI use on student performance in the task, with the preferred regression specification estimating an effect size of less than 1 mark out of 100. The qualitative analysis identified six main themes of AI usage: idea generation, writing assistance, literature search, grammar checking, statistical analysis, and overall learning impact. These findings indicate that while AI tools are widely adopted, their impact on academic performance is neutral, suggesting a potential for integration into educational practices without compromising academic integrity.","2024-12-01","2024-12-03 03:07:21","2024-12-03 03:07:21","","100288","","","7","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Higher education; Artificial intelligence; Academic integrity; Assessment; Educational technologies","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KIXG45DN","journalArticle","2024","Jalali, Mehrdad; Luo, Yi; Caulfield, Lachlan; Sauter, Eric; Nefedov, Alexei; Wöll, Christof","Large language models in electronic laboratory notebooks: Transforming materials science research workflows","Materials Today Communications","","2352-4928","10.1016/j.mtcomm.2024.109801","https://www.sciencedirect.com/science/article/pii/S2352492824017823","In recent years, there has been a surge in research efforts dedicated to harnessing the capabilities of Large Language Models (LLMs) in various domains, particularly in material science. This paper delves into the transformative role of LLMs within Electronic Laboratory Notebooks (ELNs) for scientific research. ELNs represent a pivotal technological advancement, providing a digital platform for researchers to record and manage their experiments, data, and findings. This study explores the potential of LLMs to revolutionize fundamental aspects of science, including experimental methodologies, data analysis, and knowledge extraction within the ELN framework. We present a demonstrative showcase of LLM applications in ELN environments and, furthermore, we conduct a series of empirical evaluations to critically assess the practical impact of LLMs in enhancing research processes within the dynamic field of materials science. Our findings illustrate how LLMs can significantly elevate the quality and efficiency of research outcomes in ELNs, thereby advancing knowledge and innovation in materials science research and beyond.","2024-08-01","2024-12-03 03:07:21","2024-12-03 03:07:21","","109801","","","40","","Materials Today Communications","","","","","","","","","","","","","","","","","","","Large language models (LLMs); Materials science research; Natural language processing (NLP); Electronic laboratory notebooks (ELNs); Knowledge extraction; Scientific data management","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CMVCDZLE","journalArticle","2024","Zhang, Yongheng; Du, Tingwen; Ma, Yunshan; Wang, Xiang; Xie, Yi; Yang, Guozheng; Lu, Yuliang; Chang, Ee-Chien","AttacKG+: Boosting attack graph construction with Large Language Models","Computers & Security","","0167-4048","10.1016/j.cose.2024.104220","https://www.sciencedirect.com/science/article/pii/S0167404824005261","Attack graph construction seeks to convert textual cyber threat intelligence (CTI) reports into structured representations, portraying the evolutionary traces of cyber attacks. Even though previous research has proposed various methods to construct attack graphs, they generally suffer from limited generalization capability to diverse knowledge types as well as requirement of expertise in model design and tuning. Addressing these limitations, we seek to utilize Large Language Models (LLMs), which have achieved enormous success in a broad range of tasks given exceptional capabilities in both language understanding and zero-shot task fulfillment. Thus, we propose a fully automatic LLM-based framework to construct attack graphs named: AttacKG+. Our framework consists of four consecutive modules: rewriter, parser, identifier, and summarizer, each of which is implemented by instruction prompting and in-context learning empowered by LLMs. Furthermore, we upgrade the existing attack knowledge schema and propose a comprehensive version. We represent a cyber attack as a temporally unfolding event, each temporal step of which encapsulates three layers of representation, including behavior graph, MITRE TTP labels, and state summary. Extensive evaluation demonstrates that: (1) our formulation seamlessly satisfies the information needs in threat event analysis, (2) our construction framework is effective in faithfully and accurately extracting the information defined by AttacKG+. and (3) our attack graph directly benefits downstream security practices such as attack reconstruction. All the code and datasets will be released upon acceptance.","2024-11-26","2024-12-03 03:07:21","2024-12-03 03:07:21","","104220","","","","","Computers & Security","","","","","","","","","","","","","","","","","","","Large Language Models; Attack graph construction; Cyber threat intelligence analysis","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SP4K3F6Z","journalArticle","2024","Dolenc, Kosta; Brumen, Mihaela","Exploring social and computer science students’ perceptions of AI integration in (foreign) language instruction","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100285","https://www.sciencedirect.com/science/article/pii/S2666920X24000882","Artificial intelligence (AI) has gained acceptance in the field of education. Nevertheless, existing research on AI in education, particularly in foreign language (FL) learning and teaching, is notably limited in scope and depth. In the present study, we addressed this research gap by investigating social and computer science students' perceptions of the integration and use of AI-based technologies in education, focusing specifically on foreign language teaching. Using an online questionnaire, we analysed factors such as students' field of study, gender differences, and the type of AI used. The questionnaire included statements categorised into thematic clusters, with responses measured on a five-point Likert scale. Statistical analysis, including chi-square tests and Cohen's d, revealed that individuals studying computer science, males, and supporters of generative AI are more likely to use AI tools for educational purposes. They perceive fewer barriers to the integration of AI into FL education. Social science students and women are less likely to use AI tools in FL education and express scepticism about their potential to improve academic outcomes. They tend to be more critical or cautious regarding the role of AI in FL education. They view AI as a valuable tool that enhances the learning experience but, at the same time, recognise the irreplaceable role of human teachers. The study highlights the need for targeted educational initiatives to address gender and disciplinary gaps in AI adoption, promote informed discussions on AI in education, and develop balanced AI integration strategies to improve FL learning. These findings suggest educators and policymakers should implement comprehensive AI training programs and ethical guidelines for responsible AI use in (FL) education.","2024-12-01","2024-12-03 03:07:21","2024-12-03 03:07:21","","100285","","","7","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Computer science; Education; Foreign language learning; Artificial intelligence (AI); Social science; Students' perception","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"Y6QXV4TL","journalArticle","2024","Kasprzik, Anna","The Automation of Subject Indexing at ZBW and the Role of Metadata in Times of Large Language Models","16th International Conference on Current Research Information Systems (CRIS 2024)","","1877-0509","10.1016/j.procs.2024.11.059","https://www.sciencedirect.com/science/article/pii/S1877050924032708","Subject indexing is one of the core activities of libraries. Due to the proliferation of digital documents it is no longer possible to annotate every single document intellectually, which is why we need to explore the potentials of automation. At ZBW the efforts to automate the subject indexing process started as early as 2000 with experiments involving external partners and commercial software. The conclusion from that first period was that supposedly shelf-ready solutions would not cover the requirements of the library. In 2014 the decision was made to start doing the necessary applied research in-house by establishing a corresponding PhD position. However, the prototypical machine learning solutions they developed were yet to be integrated into productive operations at the library. Therefore in 2020 an additional position for a software engineer was established and a 4-year pilot phase was initiated with the goal to build a software architecture that allows for real-time subject indexing with our trained models and the integration thereof into the other metadata workflows at ZBW. This paper gives an account of how we tackled the task of transferring results from applied research into a productive subject indexing service (the “AutoSE service”), including the milestones we have reached, the challenges we were facing on a strategic level, and the measures and resources (computing power, software, personnel) that were needed in order to be able to effect the transfer and get a first version going, which went live in 2021. The models used by AutoSE until now were models from classical machine learning. We therefore also touch on the question if and how the recent advent of large language models (LLMs) has changed our outlook on the task of automating subject indexing and on the role of metadata in information management and retrieval in general, and the ways in which it impacts our research and development roadmap going forward.","2024-01-01","2024-12-03 03:07:21","2024-12-03 03:07:21","","160-166","","","249","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","machine learning; artificial intelligence; large language models; automation; IT infrastructure; metadata; subject indexing","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"UTBG56K8","journalArticle","2024","Potthoff, Leonie; Naussedat, Rolf; Gunnemann, Lisa","Exploring Generative AI’s Role in Manual Assembly: Application Potentials and Use Concepts","57th CIRP Conference on Manufacturing Systems 2024 (CMS 2024)","","2212-8271","10.1016/j.procir.2024.10.075","https://www.sciencedirect.com/science/article/pii/S2212827124012289","This article investigates the potential applications and strategic implications of integrating generative artificial intelligence (GenAI) within manual assembly. With the widespread adoption of AI technologies in different sectors, there is an increasing interest in employing GenAI in manufacturing. Looking ahead, GenAI is poised to play an increasingly pivotal role, particularly in reshaping manufacturing paradigms, as we move towards a future marked by Industry 5.0’s emphasis on human-centered approaches and intelligent automation. Despite the rising importance, research on the application of GenAI in manual assembly remains limited. However, manual assembly presents numerous opportunities for potential utilization. This paper aims to examine the diverse roles of GenAI in supporting manual assembly processes by examining various scenarios where it could be beneficial and discussing strategic considerations. Additionally, it strives to point out the significance of GenAI in the context of Industry 5.0, emphasizing its focus on human-centered approaches. First, the fundamental principles of GenAI are examined, highlighting its ability to generate outputs independently using input data and predefined parameters. Comprehensive research into the state of the art is carried out then, with the aim of identifying possible existing approaches for the use of GenAI in manual assembly as well as other possible applications that can be adapted and transferred. Subsequently, an initial conceptual approach for possible applications of GenAI in manual assembly is presented.","2024-01-01","2024-12-03 03:07:21","2024-12-03 03:07:21","","194-199","","","130","","Procedia CIRP","","","","","","","","","","","","","","","","","","","Generative AI; Industry 5.0; Manual Assembly","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"GE8EHHCB","journalArticle","2024","Kautonen, Heli; Gasparini, Andrea Alessandro","B-Wheel – Building AI competences in academic libraries","The Journal of Academic Librarianship","","0099-1333","10.1016/j.acalib.2024.102886","https://www.sciencedirect.com/science/article/pii/S0099133324000478","Academic libraries have moved swiftly to grasp the challenges and opportunities of the new Artificial Intelligence (AI) technologies. The body of academic and practice-based literature is growing fast, showing how libraries are exploring their role in Information Literacy (IL) and AI, ethics and AI, and how they vigorously test and adopt various AI-powered tools for their services. Across these accounts, librarians express concern about their competence, skills and knowledge of the new technology and its implications for the research community. In this article, we present a holistic process model called the B-Wheel that addresses the phenomenon's complexity with approaches adopted from design thinking ideologies. We propose design approaches as an alternative strategy for academic libraries that want to avoid partial optimisation of AI skills and to ensure more generative competency building in their organisation. We drew inspiration for the B-Wheel model from the principles and pedagogy of the 20th-century Bauhaus art and design school in Germany. The article focuses on the model's features and its elements, constructed through workshops in Scandinavian research libraries. We also present experiences from the first use case in a University Library in Scandinavia. We propose that the main principles of the B-Wheel process model – a holistic design approach and learning by doing – are transferable across and beyond academic libraries.","2024-07-01","2024-12-03 03:07:22","2024-12-03 03:07:22","","102886","","4","50","","The Journal of Academic Librarianship","","","","","","","","","","","","","","","","","","","Academic libraries; Competence; Design; Artificial intelligence","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"X8S2J6MA","journalArticle","2024","Abba, Sani I.; Usman, Jamilu; Abdulazeez, Ismail; Yogarathinam, Lukka Thuyavan; Usman, A. G.; Lawal, Dahiru; Salhi, Billel; Baig, Nadeem; Aljundi, Isam H.","Enhancing Li+ recovery in brine mining: integrating next-gen emotional AI and explainable ML to predict adsorption energy in crown ether-based hierarchical nanomaterials","RSC Advances","","2046-2069","10.1039/d4ra02385d","https://www.sciencedirect.com/science/article/pii/S2046206924013081","Artificial intelligence (AI) is being employed in brine mining to enhance the extraction of lithium, vital for the manufacturing of lithium-ion batteries, through improved recovery efficiencies and the reduction of energy consumption. An innovative approach was proposed combining Emotional Neural Networks (ENN) and Random Forest (RF) algorithms to elucidate the adsorption energy (AE) (kcal mol−1) of Li+ ions by utilizing crown ether (CE)-incorporated honeycomb 2D nanomaterials. The screening and feature engineering analysis of honeycomb-patterned 2D materials and individual CE were conducted through Density Functional Theory (DFT) and Gaussian 16 simulations. The selected honeycomb-patterned 2D materials encompass graphene, silicene, and hexagonal boron nitride, while the specific CEs evaluated are 15-crown-5 and 18-crown-6. The crown-passivated 2D surfaces held a significant adsorption site through van der Waals forces for efficient recovery of Li+ ions. ENN predicted the targeted adsorption sites with high precision and minimal deviation. The eTAI (XAI) based Shapley Additive exPlanations (SHAP) was also explored for insight into the feature importance of CE embedded 2D nanomaterials for the recovery of Li+ ions. The extreme gradient boosting algorithm (XGBoost) model demonstrated a RT-2-MAPE = 0.4618% and ENN-2-MAPE = 0.4839% for the feature engineering analysis. This research would be an insight into the AI-driven nanotechnology that presents a viable and sustainable approach for the extraction of natural resources through the application of brine mining.","2024-05-02","2024-12-03 03:07:22","2024-12-03 03:07:22","","15129-15142","","21","14","","RSC Advances","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"S6K8FTLJ","journalArticle","2024","Pahi, Kritish; Hawlader, Shiplu; Hicks, Eric; Zaman, Alina; Phan, Vinhthuy","Enhancing active learning through collaboration between human teachers and generative AI","Computers and Education Open","","2666-5573","10.1016/j.caeo.2024.100183","https://www.sciencedirect.com/science/article/pii/S2666557324000235","To address the increasing demand for AI literacy, we introduced a novel active learning approach that leverages both teaching assistants (TAs) and generative AI to provide feedback during in-class exercises. This method was evaluated through two studies in separate Computer Science courses, focusing on the roles and impacts of TAs in this learning environment, as well as their collaboration with ChatGPT in enhancing student feedback. The studies revealed that TAs were effective in accurately determining students’ progress and struggles, particularly in areas such as “backtracking”, where students faced significant challenges. This intervention’s success was evident from high student engagement and satisfaction levels, as reported in an end-of-semester survey. Further findings highlighted that while TAs provided detailed technical assessments and identified conceptual gaps effectively, ChatGPT excelled in presenting clarifying examples and offering motivational support. Despite some TAs’ resistance to fully embracing the feedback guidelines-specifically their reluctance to provide encouragement-the collaborative feedback process between TAs and ChatGPT improved the quality of feedback in several aspects, including technical accuracy and clarity in explaining conceptual issues. These results suggest that integrating human and artificial intelligence in educational settings can significantly enhance traditional teaching methods, creating a more dynamic and responsive learning environment. Future research will aim to improve both the quality and efficiency of feedback, capitalizing on unique strengths of both human and AI to further advance educational practices in the field of computing.","2024-06-01","2024-12-03 03:07:22","2024-12-03 03:07:22","","100183","","","6","","Computers and Education Open","","","","","","","","","","","","","","","","","","","Architectures for educational technology system; Cooperative/collaborative learning; Improving classroom teaching","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"W6UTR8HU","journalArticle","2024","Priday, Gareth; Pedell, Sonja","Generative AI Adoption in Health and Aged Care Settings","28th International Conference on Knowledge Based and Intelligent information and Engineering Systems (KES 2024)","","1877-0509","10.1016/j.procs.2024.09.300","https://www.sciencedirect.com/science/article/pii/S1877050924023196","Generative AI is rapidly evolving with high expectations about its potential use and benefits; however, the health and aged care sectors balance risks with gaining value from its implementation and adoption. It is also challenging for policy makers to ensure public safety without constraining innovation. In this exploratory study, we interview seven experts from Australia and internationally with a range of expertise across the Health Care ecosystem with an interest in Generative AI. We aligned our enquiry to policy, organizational, practitioner and patient views and levels of abstraction to understand the enablers and barriers to AI implementation and adoption. We found that many of the factors we identified were consistent with prior research. We understood some factors in more detail in the Australian context and further emphasized the importance of cultural perspectives, participatory design and a gap between the international policy setting and practice considerations. Based on our findings we suggest the need for co-design integration across different levels of the health and aged care innovation ecosystem within aspects of the Generative AI ‘design, develop, and deploy’ lifecycle.","2024-01-01","2024-12-03 03:07:22","2024-12-03 03:07:22","","4504-4514","","","246","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial Intelligence; Generative AI; Adoption; Aged Care; Codesign; Health Care; Machine Learning","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"3YKICG2G","journalArticle","2024","Hirtsiefer, Christopher; Nestler, Tim; Eckrich, Johanna; Beverungen, Henrieke; Siech, Carolin; Aksoy, Cem; Leitsmann, Marianne; Baunacke, Martin; Uhlig, Annemarie","Capabilities of ChatGPT-3.5 as a Urological Triage System","European Urology Open Science","","2666-1683","10.1016/j.euros.2024.10.015","https://www.sciencedirect.com/science/article/pii/S2666168324011091","Background and objective Patients struggle to classify symptoms, which hinders timely medical presentation. With 35–75% of patients seeking information online before consulting a health care professional, generative language–based artificial intelligence (AI), exemplified by ChatGPT-3.5 (GPT-3.5) from OpenAI, has emerged as an important source. The aim of our study was to evaluate the role of GPT-3.5 in triaging acute urological conditions to address a gap in current research. Methods We assessed GPT-3.5 performance in providing urological differential diagnoses (DD) and recommending a course of action (CoA). Six acute urological pathologies were identified for evaluation. Lay descriptions, sourced from patient forums, formed the basis for 472 queries that were independently entered by nine urologists. We evaluated the output in terms of compliance with the European Association of Urology (EAU) guidelines, the quality of the patient information using the validated DISCERN questionnaire, and a linguistic analysis. Key findings and limitations The median GPT-3.5 ratings were 4/5 for DD and CoA, and 3/5 for overall information quality. English outputs received higher median ratings than German outputs for DD (4.27 vs 3.95; p < 0.001) and CoA (4.25 vs 4.05; p < 0.005). There was no difference in performance between urgent and non-urgent cases. Analysis of the information quality revealed notable underperformance for source indication, risk assessment, and influence on quality of life. Conclusion and clinical implications Our results highlights the potential of GPT-3.5 as a triage system for offering individualized, empathetic advice mostly aligned with the EAU guidelines, outscoring other online information. Relevant shortcomings in terms of information quality, especially for risk assessment, need to be addressed to enhance the reliability. Broader transparency and quality improvements are needed before integration into, primarily English-speaking, patient care. Patient summary We looked at the performance of ChatGPT-3.5 for patients seeking urology advice. We entered more than 400 German and English inputs and assessed the possible diagnoses suggested by this artificial intelligence tool. ChatGPT-3.5 scored well in providing a complete list of possible diagnoses and recommending a course of action mostly in line with current guidelines. The quality of the information was good overall, but missing and unclear sources for the information can be a problem.","2024-12-01","2024-12-03 03:07:22","2024-12-03 03:07:22","","148-153","","","70","","European Urology Open Science","","","","","","","","","","","","","","","","","","","Artificial intelligence; ChatGPT; Internet use; Triage; Urological emergency","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SMSJ7D6A","journalArticle","2024","Dai, Yizheng; Shao, Xin; Zhang, Jinlu; Chen, Yulong; Chen, Qian; Liao, Jie; Chi, Fei; Zhang, Junhua; Fan, Xiaohui","TCMChat: A Generative Large Language Model for Traditional Chinese Medicine","Pharmacological Research","","1043-6618","10.1016/j.phrs.2024.107530","https://www.sciencedirect.com/science/article/pii/S1043661824004754","The utilization of ground-breaking large language models (LLMs) accompanied with dialogue system has been progressively prevalent in the medical domain. Nevertheless, the expertise of LLMs in Traditional Chinese Medicine (TCM) remains restricted despite several TCM LLMs proposed recently. Herein, we introduced TCMChat (https://xomics.com.cn/tcmchat), a generative LLM with pre-training (PT) and supervised fine-tuning (SFT) on large-scale curated TCM text knowledge and Chinses Question-Answering (QA) datasets. In detail, we first compiled a customized collection of six scenarios of Chinese medicine as the training set by text mining and manual verification, involving TCM knowledgebase, choice question, reading comprehension, entity extraction, medical case diagnosis, and herb or formula recommendation. Next, we subjected the model to PT and SFT taking the Baichuan2-7B-Chat as the foundation model. The benchmarking datasets and cases studies further demonstrate the superior performance of TCMChat in comparison to existing models. Our code, data and model are publicly released on GitHub (https://github.com/ZJUFanLab/TCMChat) and HuggingFace (https://huggingface.co/ZJUFanLab), providing a high-quality knowledgebase for the research of TCM modulization with a user-friendly dialogue web tool.","2024-11-29","2024-12-03 03:07:22","2024-12-03 03:07:22","","107530","","","","","Pharmacological Research","","","","","","","","","","","","","","","","","","","large language model; dialogue system; pre-training; supervised fine-tuning; traditional Chinese medicine","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"HQ8FB8JA","journalArticle","2023","Yilmaz, Ramazan; Karaoglan Yilmaz, Fatma Gizem","The effect of generative artificial intelligence (AI)-based tool use on students' computational thinking skills, programming self-efficacy and motivation","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2023.100147","https://www.sciencedirect.com/science/article/pii/S2666920X23000267","ChatGPT (generative pre-trained transformer) is one of the artificial intelligence (AI) technologies that have started to be used in programming education. However, the effect of using ChatGPT in programming education on learning processes and outcomes is not yet known. This study investigated the effect of programming education using the ChatGPT on students' computational thinking skills, programming self-efficacy, and motivation toward the lesson. The research was conducted on 45 undergraduate students who took a university-level programming course. The research was carried out according to the experimental design with the pretest-posttest control group. Students were randomly divided into experimental (n = 21) and control (n = 24) groups. While the experimental group students benefited from the ChatGPT during the weekly programming practices, the control group students did not use this tool. Research data were obtained through the computational thinking scale, computer programming self-efficacy scale, and learning motivation in computer programming courses scale. Research findings revealed that the experimental group students' computational thinking skills, programming self-efficacy, and motivation for the lesson were significantly higher than the control group students. In line with this result, it can be said that it may be useful to benefit from AI technologies such as ChatGPT in programming trainings. The research findings, it was emphasized how the most effective use of AI support in the lessons could be made, and various suggestions were made for researchers and educators in this regard.","2023-01-01","2024-12-03 03:07:22","2024-12-03 03:07:22","","100147","","","4","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Artificial intelligence; ChatGPT; Computational thinking; Generative pretrained transformer; Programming education","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"V48X7B6V","journalArticle","2024","Gołąb-Andrzejak, Edyta","AI-powered Customer Relationship Management – GenerativeAI-based CRM – Einstein GPT, Sugar CRM, and MS Dynamics 365","28th International Conference on Knowledge Based and Intelligent information and Engineering Systems (KES 2024)","","1877-0509","10.1016/j.procs.2024.09.683","https://www.sciencedirect.com/science/article/pii/S187705092402742X","Generative artificial intelligence (GenAI) and its implementation in successive business management support systems is a rapidly growing area of theoretical consideration, ongoing research, discourse and application in practice. Recently, the implementation of of GenAI in customer relationship management (CRM) systems has been observed. Accordingly, the aim of this article is to identify areas where GenAI can enhance CRM systems, using Einstain GPT, Sugar CRM or Microsoft Dynamics 365 as examples. To this end, a research question was formulated: how can GenAI improve the effective use of CRM systems? Accordingly, a preliminary study based on secondary data analysis as well as software analysis was conducted to identify areas of GenAI use in CRM systems where we see an increase in the effective application of CRM. The results of the analysis showed that GenAI-powered CRM systems support the effectiveness and efficiency of marketing, sales, commerce, service and system user success. This is because they provide numerous advantages in terms of developing, expanding and strengthening customer relationships through highly advanced personalisation, closely linked to customer segmentation, which allows unique experiences to be provided to individual segments. As a result, this translates into building a company’s competitive advantage and increasing the profitability of its CRM efforts.","2024-01-01","2024-12-03 03:07:22","2024-12-03 03:07:22","","1790-1799","","","246","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Generative AI (GenAI); Customer Relationship Management (CRM); Einstein GPT; GenAI CRM; Microsoft Dynamics 365; Sugar CRM","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"TGP9D7EK","journalArticle","2024","Stohr, Alexander; Ollig, Philipp; Keller, Robert; Rieger, Alexander","Generative mechanisms of AI implementation: A critical realist perspective on predictive maintenance","Information and Organization","","1471-7727","10.1016/j.infoandorg.2024.100503","https://www.sciencedirect.com/science/article/pii/S1471772724000034","Artificial intelligence (AI) promises various new opportunities to create and appropriate business value. However, many organizations – especially those in more traditional industries – struggle to seize these opportunities. To unpack the underlying reasons, we investigate how more traditional industries implement predictive maintenance, a promising application of AI in manufacturing organizations. For our analysis, we employ a multiple-case design and adopt a critical realist perspective to identify generative mechanisms of AI implementation. Overall, we find five interdependent mechanisms: experimentation; knowledge building and integration; data; anxiety; and inspiration. Using causal loop diagramming, we flesh out the socio-technical dynamics of these mechanisms and explore the organizational requirements of implementing AI. The resulting topology of generative mechanisms contributes to the research on AI management by offering rich insights into the cause-effect relationships that shape the implementation process. Moreover, it demonstrates how causal loop diagraming can improve the modeling and analysis of generative mechanisms.","2024-06-01","2024-12-03 03:07:22","2024-12-03 03:07:22","","100503","","2","34","","Information and Organization","","","","","","","","","","","","","","","","","","","Artificial intelligence; Experimentation; Causal loop diagramming; Generative mechanisms; Predictive maintenance; Techno-organizational context","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"N8K3A4L6","journalArticle","2024","K, Suresh Manic; A.S., Al-Bemani; A.A., Nizamudin; G, Balaji; A.A., Amal","Optimizing Academic Journey for High Schoolers in Oman: A Machine Learning-Enabled AI Model","International Conference on Machine Learning and Data Engineering (ICMLDE 2023)","","1877-0509","10.1016/j.procs.2024.04.256","https://www.sciencedirect.com/science/article/pii/S1877050924009323","This article presents a comprehensive exploration of the development of an AI-driven Gen-Alpha education guidance indicator for Oman, employing cutting-edge machine learning techniques. The primary objective is to deliver a highly personalized support system and guidance mechanism that accompanies students throughout their academic journey. The research places a spotlight on the pivotal role of Artificial Intelligence (AI) within the realm of education. It skillfully demonstrates AI’s robust capabilities in accurately forecasting student performance through the effective use of data mining and advanced machine learning techniques. An integral aspect of this research is the application of AI in aiding students to navigate the involved process of subject selection for their higher education pursuits. This multidimensional process encompasses exhaustive data collection, the detailed creation of a sophisticated machine learning model, and the application of a diverse array of state-of-the-art algorithms. Among these algorithms, K-Nearest Neighbors (KNN), Decision Trees, Random Forest, and Support Vector Machines (SVM) stand out prominently. Notably, the SVM algorithm emerges as the outright winner, delivering an extraordinary accuracy rate of 77%. This remarkable achievement underscores the model’s unwavering robustness and its potential to redefine the educational landscape in Oman. In essence, this paper transcends the boundaries of conventional research and offers conclusive validation of AI’s revolutionary potential in reshaping educational paradigms. By facilitating data-driven decision-making, the AI-driven Gen-Alpha education guidance indicator empowers students to embark on their educational journeys well-informed and confident, thereby playing a pivotal role in advancing Oman’s education system.","2024-01-01","2024-12-03 03:07:22","2024-12-03 03:07:22","","2716-2729","","","235","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Machine learning; Artificial Intelligence; Data Mining; Educational Guidance; Predictive model","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"236Q74U5","journalArticle","2024","Castro, Andry; Pinto, João; Reino, Luís; Pipek, Pavel; Capinha, César","Large language models overcome the challenges of unstructured text data in ecology","Ecological Informatics","","1574-9541","10.1016/j.ecoinf.2024.102742","https://www.sciencedirect.com/science/article/pii/S157495412400284X","The vast volume of currently available unstructured text data, such as research papers, news, and technical report data, shows great potential for ecological research. However, manual processing of such data is labour-intensive, posing a significant challenge. In this study, we aimed to assess the application of three state-of-the-art prompt-based large language models (LLMs), GPT-3.5, GPT-4, and LLaMA-2-70B, to automate the identification, interpretation, extraction, and structuring of relevant ecological information from unstructured textual sources. We focused on species distribution data from two sources: news outlets and research papers. We assessed the LLMs for four key tasks: classification of documents with species distribution data, identification of regions where species are recorded, generation of geographical coordinates for these regions, and supply of results in a structured format. GPT-4 consistently outperformed the other models, demonstrating a high capacity to interpret textual data and extract relevant information, with the percentage of correct outputs often exceeding 90% (average accuracy across tasks: 87–100%). Its performance also depended on the data source type and task, with better results achieved with news reports, in the identification of regions with species reports and presentation of structured output. Its predecessor, GPT-3.5, exhibited slightly lower accuracy across all tasks and data sources (average accuracy across tasks: 81–97%), whereas LLaMA-2-70B showed the worst performance (37–73%). These results demonstrate the potential benefit of integrating prompt-based LLMs into ecological data assimilation workflows as essential tools to efficiently process large volumes of textual data.","2024-09-01","2024-12-03 03:07:22","2024-12-03 03:07:22","","102742","","","82","","Ecological Informatics","","","","","","","","","","","","","","","","","","","Automation; Data integration; Unstructured data; GPT; AI; LLaMA","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"HVJM62HL","journalArticle","2024","Saihi, Afef; Ben-Daya, Mohamed; Hariga, Moncer; As'ad, Rami","A Structural equation modeling analysis of generative AI chatbots adoption among students and educators in higher education","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100274","https://www.sciencedirect.com/science/article/pii/S2666920X24000778","In an era where artificial intelligence (AI) is reshaping educational paradigms, this study explores AI-based chatbot adoption in higher education among students and educators. Employing a Structural Equation Modeling (SEM) approach, the research focuses on developing and validating a comprehensive model to understand the multifaceted factors impacting the acceptance and use of these chatbots. The methodology integrates an extensive literature review, construction of a theoretical model, administration of a detailed questionnaire to a representative sample from the higher education sector, coupled with advanced SEM techniques for data analysis and interpretation. The SEM analysis validates the model's robustness and highlights the relationships between several key factors affecting users' perspectives and chatbots adoption. Results reveal a predominantly positive perception towards AI-chatbots among both students and educators, underscoring the potential to substantially enrich their educational journey. However, it also uncovers critical concerns pertaining to trust, privacy, response bias, and information accuracy. Moreover, the study offers valuable insights into how moderators such as technological proficiency, user roles, and gender influence the adoption model relationships. This emphasizes the need for customizing AI-chatbots deployment to meet the diverse needs of users effectively. Contributing a robust framework for understanding users' perceptions towards AI-chatbots and their adoption patterns, this study offers actionable insights for educational leaders, policymakers, and technology developers. It also lays the groundwork for future research, including longitudinal studies to evaluate the long-term impact of these technologies, investigations into their effect on learning outcomes, and explorations of the ethical and privacy considerations involved.","2024-12-01","2024-12-03 03:07:22","2024-12-03 03:07:22","","100274","","","7","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Higher education; Chatbots; Perception; Generative AI; Adoption; Learning experience","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"9DQ2WNMI","journalArticle","2024","Foung, Dennis; Lin, Linda; Chen, Julia","Reinventing assessments with ChatGPT and other online tools: Opportunities for GenAI-empowered assessment practices","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100250","https://www.sciencedirect.com/science/article/pii/S2666920X24000535","The recent emergence of generative artificial intelligence (GenAI) tools, such as ChatGPT, has brought profound changes to higher education. While many studies have examined the potential use of ChatGPT in teaching and learning, few have explored the opportunities to develop assessments that facilitate the use of multiple technological innovations (i.e. traditional AI and GenAI tools). We conducted qualitative research to address this gap. The assessments of an elective English course in Hong Kong were re-designed to incorporate GenAI and other tools. Students were asked to employ and reflect on their use of these tools for their writing assessments. We analyzed the written reflections of 74 students and conducted focus group interviews with 28 students. The results suggest that the students possess an acumen for choosing the appropriate online tools for specific purposes. When they can choose freely, they develop skills that allow them to evaluate and select between traditional AI and GenAI tools when appropriate. Some students mentioned concerns with the different features of the free and premium versions. The results of this study call for (1) assessment practices that allow the flexibility to use different AI tools and (2) the equitable use of various AI tools.","2024-06-01","2024-12-03 03:07:22","2024-12-03 03:07:22","","100250","","","6","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","GenAI; Assessments; Language learning; Online tools","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"HUFWSPA6","journalArticle","2024","Raman, Raghu; Pattnaik, Debidutta; Hughes, Laurie; Nedungadi, Prema","Unveiling the dynamics of AI applications: A review of reviews using scientometrics and BERTopic modeling","Journal of Innovation & Knowledge","","2444-569X","10.1016/j.jik.2024.100517","https://www.sciencedirect.com/science/article/pii/S2444569X24000568","In a world that has rapidly transformed through the advent of artificial intelligence (AI), our systematic review, guided by the PRISMA protocol, investigates a decade of AI research, revealing insights into its evolution and impact. Our study, examining 3,767 articles, has drawn considerable attention, as evidenced by an impressive 63,577 citations, underscoring the scholarly community's profound engagement. Our study reveals a collaborative landscape with 18,189 contributing authors, reflecting a robust network of researchers advancing AI and machine learning applications. Review categories focus on systematic reviews and bibliometric analyses, indicating an increasing emphasis on comprehensive literature synthesis and quantitative analysis. The findings also suggest an opportunity to explore emerging methodologies such as topic modeling and meta-analysis. We dissect the state of the art presented in these reviews, finding themes throughout the broad scholarly discourse through thematic clustering and BERTopic modeling. Categorization of study articles across fields of research indicates dominance in Information and Computing Sciences, followed by Biomedical and Clinical Sciences. Subject categories reveal interconnected clusters across various sectors, notably in healthcare, engineering, business intelligence, and computational technologies. Semantic analysis via BERTopic revealed nineteen clusters mapped to themes such as AI in health innovations, AI for sustainable development, AI and deep learning, AI in education, and ethical considerations. Future research directions are suggested, emphasizing the need for intersectional bias mitigation, holistic health approaches, AI's role in environmental sustainability, and the ethical deployment of generative AI.","2024-07-01","2024-12-03 03:07:22","2024-12-03 03:07:22","","100517","","3","9","","Journal of Innovation & Knowledge","","","","","","","","","","","","","","","","","","","Thematic analysis; Innovation; Artificial intelligence; Ethics; Cybersecurity; BERTopic; Topic modeling; Blockchain; Cocitation analysis; Sustainable development goal","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ZLK8ZQDT","journalArticle","2023","Newstead, Toby; Eager, Bronwyn; Wilson, Suze","How AI can perpetuate – Or help mitigate – Gender bias in leadership","Organizational Dynamics","","0090-2616","10.1016/j.orgdyn.2023.100998","https://www.sciencedirect.com/science/article/pii/S0090261623000426","Generative AI tools have been adopted faster than any other technology in history. AI tools including both chatbots (e.g. ChatGPT, Bard) and long-form AI writers (e.g. Wordplay.ai, Jasper.ai) pose substantial efficiency gains for text-reliant industries, such as leadership development. However, our research shows that AI generated content can contain and perpetuate harmful leadership-related gender biases. In this article, we share evidence of how AI generated content can perpetuate gender biases in leadership development. We also offer practical strategies managers can implement to capitalize on the potential of AI in pursuit of greater gender equity in leadership.","2023-10-01","2024-12-03 03:07:22","2024-12-03 03:07:22","","100998","","4","52","","Organizational Dynamics","","","","","","","","","","","","","","","","","","","Leadership development; Artificial intelligence; Generative AI; Gender bias; Women’s leadership","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DYXTAI3R","journalArticle","2024","Qiu, Yunjian; Jin, Yan","ChatGPT and finetuned BERT: A comparative study for developing intelligent design support systems","Intelligent Systems with Applications","","2667-3053","10.1016/j.iswa.2023.200308","https://www.sciencedirect.com/science/article/pii/S2667305323001333","Large Language Models (LLMs), like ChatGPT, have sparked considerable interest among researchers across diverse disciplines owing to their remarkable text processing and generation capabilities. While ChatGPT is typically employed for tasks involving general knowledge, researchers increasingly explore the potential of this LLM-based tool in specific domains to enhance productivity. This study aims to compare the performance of a finetuned BERT model with that of ChatGPT on a domain-specific dataset in the context of developing an intelligent design support system. Through experiments conducted on classification and generation tasks, the knowledge transfer and elicitation abilities of ChatGPT are examined and contrasted with those of the finetuned BERT model. The findings indicate that ChatGPT exhibits comparable performance to the finetuned BERT model in sentence-level classification tasks but struggles with short sequences. However, ChatGPT's classification performance significantly improves when a few-shot setting is applied. Moreover, it can filter out unrelated data and enhance dataset quality by assimilating the underlying domain knowledge. Regarding content generation, ChatGPT with a zero-shot setting produces informative and readable output for domain-specific questions, albeit with an excessive amount of unrelated information, which can burden readers. In conclusion, ChatGPT demonstrates a promising potential for application in facilitating data labeling, knowledge transfer, and knowledge elicitation tasks. With minimal guidance, ChatGPT can substantially enhance the efficiency of domain experts in accomplishing their objectives. The findings suggest a nuanced integration of artificial intelligence (AI) with human expertise, bridging the gap from mere classification models to sophisticated human-analogous text generation systems. This signals a future in AI-augmented engineering design where the robust capabilities of AI technologies integrate with human creativity and innovation, creating a dynamic interactions to redefine how we tackle design challenges.","2024-03-01","2024-12-03 03:07:23","2024-12-03 03:07:23","","200308","","","21","","Intelligent Systems with Applications","","","","","","","","","","","","","","","","","","","Language model; Knowledge elicitation; Knowledge transferring; Text classification; Text generation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"PJWSFACT","journalArticle","2023","Goktas, Polat; Karakaya, Gul; Kalyoncu, Ali Fuat; Damadoglu, Ebru","Artificial Intelligence Chatbots in Allergy and Immunology Practice: Where Have We Been and Where Are We Going?","The Journal of Allergy and Clinical Immunology: In Practice","","2213-2198","10.1016/j.jaip.2023.05.042","https://www.sciencedirect.com/science/article/pii/S2213219823006414","Artificial intelligence (AI) is rapidly becoming a valuable tool in healthcare, providing clinicians with a new AI lens perspective for patient care, diagnosis, and treatment. This article explores the potential applications, benefits, and challenges of AI chatbots in clinical settings, with a particular emphasis on ChatGPT 4.0 (OpenAI - Chat generative pretrained transformer 4.0), especially in the field of allergy and immunology. AI chatbots have shown considerable promise in various medical domains, including radiology and dermatology, by improving patient engagement, diagnostic accuracy, and personalized treatment plans. ChatGPT 4.0, developed by OpenAI, is good at understanding and replying to prompts in a way that makes sense. However, it is critical to address the potential biases, data privacy issues, ethical considerations, and the need for verification of AI-generated findings. When used responsibly, AI chatbots can significantly enhance clinical practice in allergy and immunology. However, there are still challenges in using this technology that require ongoing research and collaboration between AI developers and medical specialists. To this end, the ChatGPT 4.0 platform has the potential to enhance patient engagement, improve diagnostic accuracy, and provide personalized treatment plans in allergy and immunology practice. However, limitations and risks must be addressed to ensure their safe and effective use in clinical practice.","2023-09-01","2024-12-03 03:07:23","2024-12-03 03:07:23","","2697-2700","","9","11","","The Journal of Allergy and Clinical Immunology: In Practice","","","","","","","","","","","","","","","","","","","Artificial intelligence; ChatGPT; Natural language processing; AI chatbots; Healthcare; Generative pretrained transformer; Allergy; Ethical considerations","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2DG3SKUD","journalArticle","2024","Gopali, Saroj; Siami-Namini, Sima; Abri, Faranak; Namin, Akbar Siami","The performance of the LSTM-based code generated by Large Language Models (LLMs) in forecasting time series data","Natural Language Processing Journal","","2949-7191","10.1016/j.nlp.2024.100120","https://www.sciencedirect.com/science/article/pii/S2949719124000682","Generative AI, and in particular Large Language Models (LLMs), have gained substantial momentum due to their wide applications in various disciplines. While the use of these game changing technologies in generating textual information has already been demonstrated in several application domains, their abilities in generating complex models and executable codes need to be explored. As an intriguing case is the goodness of the machine and deep learning models generated by these LLMs in conducting automated scientific data analysis, where a data analyst may not have enough expertise in manually coding and optimizing complex deep learning models and codes and thus may opt to leverage LLMs to generate the required models. This paper investigates and compares the performance of the mainstream LLMs, such as ChatGPT, PaLM, LLama, and Falcon, in generating deep learning models for analyzing time series data, an important and popular data type with its prevalent applications in many application domains including financial and stock market. This research conducts a set of controlled experiments where the prompts for generating deep learning-based models are controlled with respect to sensitivity levels of four criteria including 1) Clarify and Specificity, 2) Objective and Intent, 3) Contextual Information, and 4) Format and Style. While the results are relatively mix, we observe some distinct patterns. We notice that using LLMs, we are able to generate deep learning-based models with executable codes for each dataset seperatly whose performance are comparable with the manually crafted and optimized LSTM models for predicting the whole time series dataset. We also noticed that ChatGPT outperforms the other LLMs in generating more accurate models. Furthermore, we observed that the goodness of the generated models vary with respect to the “temperature” parameter used in configuring LLMS. The results can be beneficial for data analysts and practitioners who would like to leverage generative AIs to produce good prediction models with acceptable goodness.","2024-11-27","2024-12-03 03:07:23","2024-12-03 03:07:23","","100120","","","","","Natural Language Processing Journal","","","","","","","","","","","","","","","","","","","Prompt engineering; Large language models (LLMs); Code generation; Deep learning models; Falcon; Forecasting time series data; GPT-3; LLama-2; Long short-term memory (LSTM); PaLM","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ZJQ7U6SJ","journalArticle","2024","Gu, Zhanzhong; He, Xiangjian; Yu, Ping; Jia, Wenjing; Yang, Xiguang; Peng, Gang; Hu, Penghui; Chen, Shiyan; Chen, Hongjie; Lin, Yiguang","Automatic quantitative stroke severity assessment based on Chinese clinical named entity recognition with domain-adaptive pre-trained large language model","Artificial Intelligence in Medicine","","0933-3657","10.1016/j.artmed.2024.102822","https://www.sciencedirect.com/science/article/pii/S0933365724000642","Background: Stroke is a prevalent disease with a significant global impact. Effective assessment of stroke severity is vital for an accurate diagnosis, appropriate treatment, and optimal clinical outcomes. The National Institutes of Health Stroke Scale (NIHSS) is a widely used scale for quantitatively assessing stroke severity. However, the current manual scoring of NIHSS is labor-intensive, time-consuming, and sometimes unreliable. Applying artificial intelligence (AI) techniques to automate the quantitative assessment of stroke on vast amounts of electronic health records (EHRs) has attracted much interest. Objective: This study aims to develop an automatic, quantitative stroke severity assessment framework through automating the entire NIHSS scoring process on Chinese clinical EHRs. Methods: Our approach consists of two major parts: Chinese clinical named entity recognition (CNER) with a domain-adaptive pre-trained large language model (LLM) and automated NIHSS scoring. To build a high-performing CNER model, we first construct a stroke-specific, densely annotated dataset “Chinese Stroke Clinical Records” (CSCR) from EHRs provided by our partner hospital, based on a stroke ontology that defines semantically related entities for stroke assessment. We then pre-train a Chinese clinical LLM coined “CliRoberta” through domain-adaptive transfer learning and construct a deep learning-based CNER model that can accurately extract entities directly from Chinese EHRs. Finally, an automated, end-to-end NIHSS scoring pipeline is proposed by mapping the extracted entities to relevant NIHSS items and values, to quantitatively assess the stroke severity. Results: Results obtained on a benchmark dataset CCKS2019 and our newly created CSCR dataset demonstrate the superior performance of our domain-adaptive pre-trained LLM and the CNER model, compared with the existing benchmark LLMs and CNER models. The high F1 score of 0.990 ensures the reliability of our model in accurately extracting the entities for the subsequent automatic NIHSS scoring. Subsequently, our automated, end-to-end NIHSS scoring approach achieved excellent inter-rater agreement (0.823) and intraclass consistency (0.986) with the ground truth and significantly reduced the processing time from minutes to a few seconds. Conclusion: Our proposed automatic and quantitative framework for assessing stroke severity demonstrates exceptional performance and reliability through directly scoring the NIHSS from diagnostic notes in Chinese clinical EHRs. Moreover, this study also contributes a new clinical dataset, a pre-trained clinical LLM, and an effective deep learning-based CNER model. The deployment of these advanced algorithms can improve the accuracy and efficiency of clinical assessment, and help improve the quality, affordability and productivity of healthcare services.","2024-04-01","2024-12-03 03:07:23","2024-12-03 03:07:23","","102822","","","150","","Artificial Intelligence in Medicine","","","","","","","","","","","","","","","","","","","Large language model; Automatic stroke severity assessment; Chinese electronic health records; Clinical named entity recognition; Domain-adaptive pre-training","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ZMT2UN9I","journalArticle","2023","Steele, Jennifer L.","To GPT or not GPT? Empowering our students to learn with AI","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2023.100160","https://www.sciencedirect.com/science/article/pii/S2666920X23000395","I argue that ChatGPT and other generative artificial intelligence tools pose three main threats to our current education systems, creating problems of measurement, information accuracy, and skill devaluation. But when we place these threats into historical context, we see that AI tools can also empower students and level the educational playing field. In classrooms from primary to tertiary and spanning all content areas, we can help our students become critical thinkers by using ChatGPT to comprehend texts, aggregate knowledge, and understand genre conventions in prose as well as programming. The aim is to help students leverage AI as a tool that they question and critique, advancing their own comprehension, research, and composition skills in the process.","2023-01-01","2024-12-03 03:07:23","2024-12-03 03:07:23","","100160","","","5","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Educational technology; Artificial intelligence; ChatGPT; Pedagogy; Academic integrity","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"EMJIGUU9","journalArticle","2024","Ivcevic, Zorana; Grandinetti, Mike","Artificial intelligence as a tool for creativity","Journal of Creativity","","2713-3745","10.1016/j.yjoc.2024.100079","https://www.sciencedirect.com/science/article/pii/S2713374524000050","The release of ChatGPT has sparked quite a bit of interest about creativity in the context of artificial intelligence (AI), with theorizing and empirical research asking questions about the nature of creativity (both human and artificially-produced) and the valuing of work produced by humans and artificial means. In this article, we discuss one specific scenario identified in the creativity research community – co-creation, or use of AI as a tool that could augment human creativity. We present emerging research relevant to how AI can be used on a continuum of four levels of creativity, from mini-c/creativity in learning to little-c/everyday creativity to Pro-C/professional creativity and Big-C/eminent creativity. In this discussion, AI is defined broadly, not to include only large language models (e.g., ChatGPT) which might approach general AI, but also other computer programs that perform tasks typically understood as requiring human intelligence. We conclude by considering future directions for research on AI as a tool for creativity across the four c's.","2024-08-01","2024-12-03 03:07:23","2024-12-03 03:07:23","","100079","","2","34","","Journal of Creativity","","","","","","","","","","","","","","","","","","","Artificial intelligence; Creativity; 4c's model of creativity; AI as tool","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"S4SP5CJX","journalArticle","2024","Rajabi, Parsa; Taghipour, Parnian; Cukierman, Diana; Doleck, Tenzin","Unleashing ChatGPT's impact in higher education: Student and faculty perspectives","Computers in Human Behavior: Artificial Humans","","2949-8821","10.1016/j.chbah.2024.100090","https://www.sciencedirect.com/science/article/pii/S2949882124000501","As Chat Generative Pre-trained Transformer (ChatGPT) gains traction, its impact on post-secondary education is increasingly being debated. This qualitative study explores the perception of students and faculty members at a research university in Canada regarding ChatGPT's use in a post-secondary setting, focusing on how it could be incorporated and what ways instructors can respond to this technology. We present the summary of a discussion that took place in a 2-hour focus group session with 40 participants from the computer science and engineering departments, and highlight issues surrounding plagiarism, assessment methods, and the appropriate use of ChatGPT. Findings suggest that students are likely to use ChatGPT, but there is a need for specific guidelines, more classroom assessments, and mandatory reporting of ChatGPT use. The study contributes to the emergent research on ChatGPT in higher education and emphasizes the importance of proactively addressing challenges and opportunities associated with ChatGPT adoption and use. The novelty of the study involves capturing the perspectives of students and faculty members. This paper aims to provide a more refined understanding of the complex interplay between AI chatbots and higher education that will help educators navigate the rapidly evolving landscape of AI-driven education.","2024-08-01","2024-12-03 03:07:23","2024-12-03 03:07:23","","100090","","2","2","","Computers in Human Behavior: Artificial Humans","","","","","","","","","","","","","","","","","","","Higher education; ChatGPT; Assessment; Artificial intelligence in education; Conversational AI; Post-secondary","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SZSCWLB9","journalArticle","2024","Raman, Raghu; Kumar Nair, Vinith; Nedungadi, Prema; Kumar Sahu, Aditya; Kowalski, Robin; Ramanathan, Sasangan; Achuthan, Krishnashree","Fake news research trends, linkages to generative artificial intelligence and sustainable development goals","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e24727","https://www.sciencedirect.com/science/article/pii/S2405844024007588","In the digital age, where information is a cornerstone for decision-making, social media's not-so-regulated environment has intensified the prevalence of fake news, with significant implications for both individuals and societies. This study employs a bibliometric analysis of a large corpus of 9678 publications spanning 2013–2022 to scrutinize the evolution of fake news research, identifying leading authors, institutions, and nations. Three thematic clusters emerge: Disinformation in social media, COVID-19-induced infodemics, and techno-scientific advancements in auto-detection. This work introduces three novel contributions: 1) a pioneering mapping of fake news research to Sustainable Development Goals (SDGs), indicating its influence on areas like health (SDG 3), peace (SDG 16), and industry (SDG 9); 2) the utilization of Prominence percentile metrics to discern critical and economically prioritized research areas, such as misinformation and object detection in deep learning; and 3) an evaluation of generative AI's role in the propagation and realism of fake news, raising pressing ethical concerns. These contributions collectively provide a comprehensive overview of the current state and future trajectories of fake news research, offering valuable insights for academia, policymakers, and industry.","2024-02-15","2024-12-03 03:07:23","2024-12-03 03:07:23","","e24727","","3","10","","Heliyon","","","","","","","","","","","","","","","","","","","Ethics; Generative AI; Sustainable development goal; Deep fake; Fake news; Prominence percentile","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"FKRVFEVP","journalArticle","2024","Sidaoui, Karim; Mahr, Dominik; Odekerken-Schröder, Gaby","Generative AI in Responsible Conversational Agent Integration: Guidelines for Service Managers","Organizational Dynamics","","0090-2616","10.1016/j.orgdyn.2024.101045","https://www.sciencedirect.com/science/article/pii/S0090261624000184","Responsible integration of conversational agents (CAs) like chatbots is crucial for service firms to mitigate risks and foster positive outcomes. This article provides managerial guidelines through a Corporate Digital Responsibility (CDR) lens, focusing on CDR Culture, Management Structure, and Digital Governance across the service firm, software provider, and customers/society. It examines how organizational sensemaking processes of creation, interpretation, and enactment are triggered by CA-related issues and events. The research highlights the role of generative AI (GenAI) in implementing CDR factors and responsible CA software development lifecycle phases during development and integration. Guidelines are provided for leveraging GenAI to enhance CDR Culture, incorporate ethical considerations into CDR Management Structure, and enable robust Digital Governance mechanisms to prioritize customer/societal well-being. A multilevel framework illustrates reinforcing the guidelines through organizational sensemaking processes, and fostering responsible CA integration aligned with ethical principles and societal values.","2024-04-01","2024-12-03 03:07:23","2024-12-03 03:07:23","","101045","","2","53","","Organizational Dynamics","","","","","","","","","","","","","","","","","","","Generative artificial intelligence; Ethics; Conversational agents; Corporate digital responsibility; European Union Artificial Intelligence Act; Inclusive design; Organizational sensemaking; Software development life cycle","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LTMYMEWL","journalArticle","2024","Masrouri, Milad; Qin, Zhao","Towards data-efficient mechanical design of bicontinuous composites using generative AI","Theoretical and Applied Mechanics Letters","","2095-0349","10.1016/j.taml.2024.100492","https://www.sciencedirect.com/science/article/pii/S2095034924000035","The distribution of material phases is crucial to determine the composite's mechanical property. While the full structure-mechanics relationship of highly ordered material distributions can be studied with finite number of cases, this relationship is difficult to be revealed for complex irregular distributions, preventing design of such material structures to meet certain mechanical requirements. The noticeable developments of artificial intelligence (AI) algorithms in material design enables to detect the hidden structure-mechanics correlations which is essential for designing composite of complex structures. It is intriguing how these tools can assist composite design. Here, we focus on the rapid generation of bicontinuous composite structures together with the stress distribution in loading. We find that generative AI, enabled through fine-tuned Low Rank Adaptation models, can be trained with a few inputs to generate both synthetic composite structures and the corresponding von Mises stress distribution. The results show that this technique is convenient in generating massive composites designs with useful mechanical information that dictate stiffness, fracture and robustness of the material with one model, and such has to be done by several different experimental or simulation tests. This research offers valuable insights for the improvement of composite design with the goal of expanding the design space and automatic screening of composite designs for improved mechanical functions.","2024-01-01","2024-12-03 03:07:23","2024-12-03 03:07:23","","100492","","1","14","","Theoretical and Applied Mechanics Letters","","","","","","","","","","","","","","","","","","","Generative artificial intelligence; Composite design; Molecular dynamics simulation; Phase field model; Stable diffusion","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2665CULN","journalArticle","2024","Messer, Uwe","Co-creating art with generative artificial intelligence: Implications for artworks and artists","Computers in Human Behavior: Artificial Humans","","2949-8821","10.1016/j.chbah.2024.100056","https://www.sciencedirect.com/science/article/pii/S2949882124000161","Synthetic visual art is becoming a commodity due to generative artificial intelligence (AI). The trend of using AI for co-creation will not spare artists’ creative processes, and it is important to understand how the use of generative AI at different stages of the creative process affects both the evaluation of the artist and the result of the human-machine collaboration (i.e., the visual artifact). In three experiments (N = 560), this research explores how the evaluation of artworks is transformed by the revelation that the artist collaborated with AI at different stages of the creative process. The results show that co-created art is less liked and recognized, especially when AI was used in the implementation stage. While co-created art is perceived as more novel, it lacks creative authenticity, which exerts a dominant influence. The results also show that artists’ perceptions suffer from the co-creation process, and that artists who co-create are less admired because they are perceived as less authentic. Two boundary conditions are identified. The negative effect can be mitigated by disclosing the level of artist involvement in co-creation with AI (e.g., by training the algorithm on a curated set of images vs. simply prompting an off-the-shelf AI image generator). In the context of art that is perceived as commercially motivated (e.g., stock images), the effect is also diminished. This research has important implications for the literature on human-AI-collaboration, research on authenticity, and the ongoing policy debate regarding the transparency of algorithmic presence.","2024-01-01","2024-12-03 03:07:23","2024-12-03 03:07:23","","100056","","1","2","","Computers in Human Behavior: Artificial Humans","","","","","","","","","","","","","","","","","","","Generative AI; Authenticity; Art; Human-AI-Collaboration","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"NRCZGJH5","journalArticle","2024","Sun, Yixiao; Li, Xusheng; Liu, Chao; Deng, Xiaohu; Zhang, Wenyu; Wang, Jiangang; Zhang, Zeyu; Wen, Tengyang; Song, Tianyu; Ju, Dongying","Development of an intelligent design and simulation aid system for heat treatment processes based on LLM","Materials & Design","","0264-1275","10.1016/j.matdes.2024.113506","https://www.sciencedirect.com/science/article/pii/S0264127524008815","Heat treatment of steel is a multi-physics coupled process. Designing programs that meet the desired results is challenging. The current design of processes relies on experience and experimentation, leading to high costs in developing processes and challenges in training practitioners. To reduce research and development costs in the industry and enable novices to reach expert levels, we propose an intelligent heat treatment process design and simulation assistant system based on large language models (LLMs), named Chat-IMSHT. Chat-IMSHT can impart knowledge and recommend processes. Additionally, Chat-IMSHT optimizes the interaction between humans and Computer Aided Engineering (CAE) software. To achieve knowledge impartation and process recommendation, a dialogue model based on Retrieval Augmented Generation (RAG) and LLMs was designed. It characterizes and compresses a massive amount of heat treatment knowledge and process data. The system designs a new CAE software interaction paradigm, using LLMs to map parameters from natural language into formatted text for the CAE software COSMAP. A Steel Heat Treatment Knowledge Understanding (SHTKU) evaluation method was designed. The improved model significantly increased the accuracy of knowledge responses, with a maximum accuracy of 94.54 %. Experimental results show that Chat-IMSHT effectively imparts knowledge and generates formatted text, completing the task of process recommendation.","2024-11-29","2024-12-03 03:07:23","2024-12-03 03:07:23","","113506","","","","","Materials & Design","","","","","","","","","","","","","","","","","","","Large language model; Expert knowledge system; Intelligent simulation system; Knowledge embedding; Metal material heat treatment","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CZMM8SNB","journalArticle","2024","Ou, Amy Wanyu; Khuder, Baraa; Franzetti, Sindija; Negretti, Raffaella","Conceptualising and cultivating Critical GAI Literacy in doctoral academic writing","Journal of Second Language Writing","","1060-3743","10.1016/j.jslw.2024.101156","https://www.sciencedirect.com/science/article/pii/S1060374324000638","Generative artificial intelligence (GAI) has revolutionised the landscape of academic writing, presenting both advantages and risks to learning for L2 writers. It is thus imperative that L2 writers, especially at advanced academic levels, develop the critical skills necessary for employing GAI tools ethically and effectively in their writing processes. Our study addressed this need by 1) conceptualising Critical GAI Literacy based on current research and our collected data, and 2) developing a self-regulated learning-based micro-curriculum for L2 doctoral students to cultivate knowledge and skills using GAI for academic writing. We collected interactive and reflective data in an introductory-level academic writing course at a Swedish university enrolled with 60 PhD students from diverse backgrounds and examined their evolving perspectives and strategies for engaging in GAI-mediated writing. Findings show a spectrum of initial attitudes among students and limited knowledge of GAI use. Final reflections illustrate de-enchantment with GAI, recalibrated and enhanced understanding of ethical issues, developed prompting methods, and increased awareness of text ownership through the self-directed learning process. Furthermore, students demonstrated a discerning approach in evaluating GAI-generated suggestions and sociolinguistic impacts, indicating a growing criticality in L2 writing practices.","2024-12-01","2024-12-03 03:07:23","2024-12-03 03:07:23","","101156","","","66","","Journal of Second Language Writing","","","","","","","","","","","","","","","","","","","Artificial intelligence; Academic writing; Critical GAI Literacy; Doctoral education; Self-regulated learning","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"3CMW2L7H","journalArticle","2024","Guillaumet, Anna","The power of generative AI for CRIS systems: a new paradigm for scientific information management","16th International Conference on Current Research Information Systems (CRIS 2024)","","1877-0509","10.1016/j.procs.2024.11.057","https://www.sciencedirect.com/science/article/pii/S187705092403268X","The paper analyses the implications of the emergence of artificial intelligence (AI), especially generative AI, on current research information systems (CRIS). It reviews the recent European regulations for high-risk AI systems, the Spanish AI strategy, and the IntelComp project as use cases. The study found that the maturity of CRIS systems, coupled with the increasing complexity due to data aggregation, sets the stage for innovative AI applications. The paper proposes key domains where AI can impact and be applied in CRIS, including data management, research assessment, and advanced analytics. It also provides examples of how generative AI can be leveraged to enhance scientific information management within CRIS. The findings highlight the need to ensure the responsible and ethical development of AI technologies in the research domain.","2024-01-01","2024-12-03 03:07:23","2024-12-03 03:07:23","","131-149","","","249","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Standards; Research; ethics; AI; Law; AI-Act; CERIF; CRIS; DRIS; ENIA; euroCRIS; FAIR; FECYT; GenerativeAI; OpenAccess; Regulations; Sandbox","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"M6EWWYDZ","journalArticle","2024","Baek, Clare; Tate, Tamara; Warschauer, Mark","“ChatGPT seems too good to be true”: College students’ use and perceptions of generative AI","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100294","https://www.sciencedirect.com/science/article/pii/S2666920X24000973","This study investigates how U.S. college students (N = 1001) perceive and use ChatGPT, exploring its relationship with societal structures and student characteristics. Regression results show that gender, age, major, institution type, and institutional policy significantly influenced ChatGPT use for general, writing, and programming tasks. Students in their 30s–40s were more likely to use ChatGPT frequently than younger students. Non-native English speakers were more likely than native speakers to use ChatGPT frequently for writing, suggesting its potential as a support tool for language learners. Institutional policies allowing ChatGPT use predicted higher use of ChatGPT. Thematic analysis and natural language processing of open-ended responses revealed varied attitudes towards ChatGPT, with some fearing institutional punishment for using ChatGPT and others confident in their appropriate use of ChatGPT. Computer science majors expressed concerns about job displacement due to the advent of generative AI. Higher-income students generally viewed ChatGPT more positively than their lower-income counterparts. Our research underscores how technology can both empower and marginalize within educational settings; we advocate for equitable integration of AI in academic environments for diverse students.","2024-12-01","2024-12-03 03:07:23","2024-12-03 03:07:23","","100294","","","7","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Educational technology; Higher education; Artificial intelligence; Generative AI; Equity","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"7KWVRQW6","journalArticle","2024","Hadan, Hilda; Wang, Derrick M.; Mogavi, Reza Hadi; Tu, Joseph; Zhang-Kennedy, Leah; Nacke, Lennart E.","The great AI witch hunt: Reviewers’ perception and (Mis)conception of generative AI in research writing","Computers in Human Behavior: Artificial Humans","","2949-8821","10.1016/j.chbah.2024.100095","https://www.sciencedirect.com/science/article/pii/S2949882124000550","Generative AI (GenAI) use in research writing is growing fast. However, it is unclear how peer reviewers recognize or misjudge AI-augmented manuscripts. To investigate the impact of AI-augmented writing on peer reviews, we conducted a snippet-based online survey with 17 peer reviewers from top-tier HCI conferences. Our findings indicate that while AI-augmented writing improves readability, language diversity, and informativeness, it often lacks research details and reflective insights from authors. Reviewers consistently struggled to distinguish between human and AI-augmented writing but their judgements remained consistent. They noted the loss of a “human touch” and subjective expressions in AI-augmented writing. Based on our findings, we advocate for reviewer guidelines that promote impartial evaluations of submissions, regardless of any personal biases towards GenAI. The quality of the research itself should remain a priority in reviews, regardless of any preconceived notions about the tools used to create it. We emphasize that researchers must maintain their authorship and control over the writing process, even when using GenAI's assistance.","2024-08-01","2024-12-03 03:07:23","2024-12-03 03:07:23","","100095","","2","2","","Computers in Human Behavior: Artificial Humans","","","","","","","","","","","","","","","","","","","Artificial intelligence; Generative AI; AI writing augmentation; Research writing; Reviewer perception","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KYIR65BR","journalArticle","2024","Javidan, Seyed Mohamad; Banakar, Ahmad; Rahnama, Kamran; Vakilian, Keyvan Asefpour; Ampatzidis, Yiannis","Feature engineering to identify plant diseases using image processing and artificial intelligence: A comprehensive review","Smart Agricultural Technology","","2772-3755","10.1016/j.atech.2024.100480","https://www.sciencedirect.com/science/article/pii/S2772375524000856","Plant diseases can significantly reduce crop yield and product quality. Visual inspections of plants by human observers for disease identification are time-consuming, costly, and prone to error. Advances in artificial intelligence (AI) have created opportunities for the rapid diagnosis and non-destructive classification of plant pathogens. Several machine vision techniques have been developed to identify and classify plant diseases automatically based on the morphology of specific symptoms. The use of deep learning models has achieved acceptable disease classification results, but they require large datasets for training, which can be labor-intensive, time-consuming, and computationally costly This problem can be solved, to a point, by using data augmentation techniques and generative AI in order to increase the size of the datasets. Furthermore, a combination of deep feature extraction and classification by machine learning was used for accurate disease detection and classification. In some cases, traditional base classifiers trained with small datasets including basic shape, color, and texture features can be feasible for the efficient identification of plant diseases. The performance of such classifiers depends primarily on the features extracted from images; therefore, feature extraction plays a vital role in identifying diseases. Feature engineering, a process to identify the most relevant variables from raw data in order to develop an efficient predictive model, is explored in this paper.","2024-08-01","2024-12-03 03:07:23","2024-12-03 03:07:23","","100480","","","8","","Smart Agricultural Technology","","","","","","","","","","","","","","","","","","","Machine learning; Feature extraction; Symptoms; Textural features","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"39MA4S2B","journalArticle","2024","Khan, Muhammad Salar; Umer, Hamza","ChatGPT in finance: Applications, challenges, and solutions","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e24890","https://www.sciencedirect.com/science/article/pii/S2405844024009216","The emergence of ChatGPT, a generative artificial intelligence tool, has sparked a revolution in the finance industry, enabling individuals to interact with technology in natural language. However, the use of ChatGPT in finance presents a profound array of ethical considerations that demand careful scrutiny to ensure its responsible and ethical use. After a concise exploration of ChatGPT's applications in finance, this policy article delves into the ethical challenges arising from the use of ChatGPT in finance, including outcomes contaminated with biases, incorporation of fake information in the financial decisions, concerns surrounding privacy and security, lack of transparency and accountability in the decision-making processes and financial services, human job displacement, and the intricate web of legal complexities. Our article asserts that financial institutions employing ChatGPT must proactively devise strategies to confront these burgeoning challenges, mitigating their adverse effects on both individuals and society as a whole. Additionally, we propose relevant policies to tackle these ethical quandaries head-on. In essence, this article illuminates the imperative need for a meticulous ethical framework, facilitating an informed and responsible use of ChatGPT in the realm of finance, safeguarding the welfare of individuals and society. While our work significantly contributes to the research and practice of finance, we also identify future research avenues.","2024-01-30","2024-12-03 03:07:23","2024-12-03 03:07:23","","e24890","","2","10","","Heliyon","","","","","","","","","","","","","","","","","","","Policies; Finance; Artificial intelligence; ChatGPT; Applications; Ethical challenges","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"QVPJDNYG","journalArticle","2024","Rohanian, Omid; Nouriborji, Mohammadmahdi; Kouchaki, Samaneh; Nooralahzadeh, Farhad; Clifton, Lei; Clifton, David A.","Exploring the effectiveness of instruction tuning in biomedical language processing","Artificial Intelligence in Medicine","","0933-3657","10.1016/j.artmed.2024.103007","https://www.sciencedirect.com/science/article/pii/S0933365724002495","Large Language Models (LLMs), particularly those similar to ChatGPT, have significantly influenced the field of Natural Language Processing (NLP). While these models excel in general language tasks, their performance in domain-specific downstream tasks such as biomedical and clinical Named Entity Recognition (NER), Relation Extraction (RE), and Medical Natural Language Inference (NLI) is still evolving. In this context, our study investigates the potential of instruction tuning for biomedical language processing, applying this technique to two general LLMs of substantial scale. We present a comprehensive, instruction-based model trained on a dataset that consists of approximately 200,000 instruction-focused samples. This dataset represents a carefully curated compilation of existing data, meticulously adapted and reformatted to align with the specific requirements of our instruction-based tasks. This initiative represents an important step in utilising such models to achieve results on par with specialised encoder-only models like BioBERT and BioClinicalBERT for various classical biomedical NLP tasks. Our work includes an analysis of the dataset’s composition and its impact on model performance, providing insights into the intricacies of instruction tuning. By sharing our codes, models, and the distinctively assembled instruction-based dataset, we seek to encourage ongoing research and development in this area.22Our code repository is available at https://github.com/nlpie-research/BioInstTune-LLM.","2024-12-01","2024-12-03 03:07:24","2024-12-03 03:07:24","","103007","","","158","","Artificial Intelligence in Medicine","","","","","","","","","","","","","","","","","","","Named entity recognition; Biomedical NLP; Instruction tuning; Llama2-MedTuned; Medical NLI; Relation extraction","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LDCTQB9L","journalArticle","2024","Franco, Antônio Marcos Rodrigues; Cunha, Ítalo; Oliveira, Leonardo B.","Evaluation of deep neural network architectures for authorship obfuscation of Portuguese texts","Natural Language Processing Journal","","2949-7191","10.1016/j.nlp.2024.100107","https://www.sciencedirect.com/science/article/pii/S2949719124000554","Preserving authorship anonymity is paramount to protect activists, freedom of expression, and critical journalism. Although there are several mechanisms to provide anonymity on the Internet, one can still identify anonymous authors through their writing style. With the advances in neural network and natural language processing research, the success of a classifier when identifying the author of a text is growing. On the other hand, new approaches that use recurrent neural networks for automatic generation of obfuscated texts have also arisen to fight anonymity adversaries. In this work, we evaluate two approaches that use neural networks to generate obfuscated texts. The first approach uses Generative Adversarial Networks to train an encoder–decoder to transform sentences from an input style into a target style. The second one trains an auto encoder with Gradient Reversal Layer to learn invariant representations. In our experiments, we compared the efficiency of both techniques when removing the stylistic attributes of a text and preserving its original semantics. Our evaluation on real texts clarifies each technique’s trade-offs for Portuguese texts and provides guidance on practical deployment.","2024-12-01","2024-12-03 03:07:24","2024-12-03 03:07:24","","100107","","","9","","Natural Language Processing Journal","","","","","","","","","","","","","","","","","","","Artificial intelligence; Natural language processing; Privacy; Authorship obfuscation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"PAPR9JK6","journalArticle","2024","Chiu, Thomas K.F.","Future research recommendations for transforming higher education with generative AI","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2023.100197","https://www.sciencedirect.com/science/article/pii/S2666920X23000760","Higher education is crucial for producing ethical citizens and professionals globally. The introduction of generative AI (GenAI), such as ChatGPT, has posed opportunities and challenges to the traditional model of education. However, the current conversations primarily focus on policy development and assessment, with limited research on the future of higher education. GenAI's impact on learning outcomes, pedagogy, and assessment is crucial for reforming and advancing the workforce. This qualitative study aims to investigate student perspectives on GenAI's impact on higher education. The study uses an initial conceptual framework driven by a systematic literature review to investigate the opportunities and challenges of AI in education. This framework serves as an initial data collection and analysis framework. A sample of 51 students from three research-intensive universities was selected for this study. Thematic analysis identified three themes and 10 subthemes. The findings suggest that future higher education should be transformed to train students to be future-ready for employment in a society powered by GenAI. They suggest new learning outcomes—skills in learning and teaching with GenAI, AI literacy—and emphasize the significance of interdisciplinarity and maker learning, with assessment focusing on in-class and hands-on activities. They recommend six future research directions – competence for future workforce and its self-assessment measures, AI literacy or competency measures, new literacies and their relationships, interdisciplinary teaching, Innovative pedagogies and their evaluation, new assessment and its acceptance.","2024-06-01","2024-12-03 03:07:24","2024-12-03 03:07:24","","100197","","","6","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","ChatGPT; Generative artificial intelligence; AI literacy; Assessment; Learning outcomes","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"Y8C2MZCE","journalArticle","2024","Chiu, Thomas K.F.; Ahmad, Zubair; Ismailov, Murod; Sanusi, Ismaila Temitayo","What are artificial intelligence literacy and competency? A comprehensive framework to support them","Computers and Education Open","","2666-5573","10.1016/j.caeo.2024.100171","https://www.sciencedirect.com/science/article/pii/S2666557324000120","Artificial intelligence (AI) education in K–12 schools is a global initiative, yet planning and executing AI education is challenging. The major frameworks are focused on identifying content and technical knowledge (AI literacy). Most of the current definitions of AI literacy for a non-technical audience are developed from an engineering perspective and may not be appropriate for K–12 education. Teacher perspectives are essential to making sense of this initiative. Literacy is about knowing (knowledge, what skills); competency is about applying the knowledge in a beneficial way (confidence, how well). They are strongly related. This study goes beyond knowledge (AI literacy), and its two main goals are to (i) define AI literacy and competency by adding the aspects of confidence and self-reflective mindsets, and (ii) propose a more comprehensive framework for K–12 AI education. These definitions are needed for this emerging and disruptive technology (e.g., ChatGPT and Sora, generative AI). We used the definitions and the basic curriculum design approaches as the analytical framework and teacher perspectives. Participants included 30 experienced AI teachers from 15 middle schools. We employed an iterative co-design cycle to discuss and revise the framework throughout four cycles. The definition of AI competency has five abilities that take confidence into account, and the proposed framework comprises five key components: technology, impact, ethics, collaboration, and self-reflection. We also identify five effective learning experiences to foster abilities and confidences, and suggest five future research directions: prompt engineering, data literacy, algorithmic literacy, self-reflective mindset, and empirical research.","2024-06-01","2024-12-03 03:07:24","2024-12-03 03:07:24","","100171","","","6","","Computers and Education Open","","","","","","","","","","","","","","","","","","","Machine learning; Generative AI; AI literacy; AI competency; Data literacy; K-12 education","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KG3N36DI","journalArticle","2024","Çelikten, Tuğba; Onan, Aytuğ","HybridGAD: Identification of AI-Generated Radiology Abstracts Based on a Novel Hybrid Model with Attention Mechanism","Computers, Materials and Continua","","1546-2218","10.32604/cmc.2024.051574","https://www.sciencedirect.com/science/article/pii/S154622182400537X","The purpose of this study is to develop a reliable method for distinguishing between AI-generated, paraphrased, and human-written texts, which is crucial for maintaining the integrity of research and ensuring accurate information flow in critical fields such as healthcare. To achieve this, we propose HybridGAD, a novel hybrid model that combines Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Bidirectional Gated Recurrent Unit (Bi-GRU) architectures with an attention mechanism. Our methodology involves training this hybrid model on a dataset of radiology abstracts, encompassing texts generated by AI, paraphrased by AI, and written by humans. The major findings of our analysis indicate that HybridGAD achieves a high accuracy of 98%, significantly outperforming existing state-of-the-art models. This high performance is attributed to the model’s ability to effectively capture the contextual nuances and structural differences between AI-generated and human-written texts. In conclusion, HybridGAD not only enhances the accuracy of text classification in the field of radiology but also paves the way for more advanced medical diagnostic processes by ensuring the authenticity of textual information. Future research will focus on integrating textual and visual data for comprehensive radiology assessments and improving model generalization with partially labeled data. This study underscores the potential of HybridGAD in transforming medical text classification and highlights its applicability in ensuring the integrity and reliability of research in healthcare and beyond.","2024-08-15","2024-12-03 03:07:24","2024-12-03 03:07:24","","3351-3377","","2","80","","Computers, Materials and Continua","","","","","","","","","","","","","","","","","","","Generative artificial intelligence; AI-generated text detection; attention mechanism; hybrid model for text classification","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"VWS4VBR2","journalArticle","2024","Wodzinski, Marek; Kwarciak, Kamil; Daniol, Mateusz; Hemmerling, Daria","Improving deep learning-based automatic cranial defect reconstruction by heavy data augmentation: From image registration to latent diffusion models","Computers in Biology and Medicine","","0010-4825","10.1016/j.compbiomed.2024.109129","https://www.sciencedirect.com/science/article/pii/S0010482524012149","Modeling and manufacturing of personalized cranial implants are important research areas that may decrease the waiting time for patients suffering from cranial damage. The modeling of personalized implants may be partially automated by the use of deep learning-based methods. However, this task suffers from difficulties with generalizability into data from previously unseen distributions that make it difficult to use the research outcomes in real clinical settings. Due to difficulties with acquiring ground-truth annotations, different techniques to improve the heterogeneity of datasets used for training the deep networks have to be considered and introduced. In this work, we present a large-scale study of several augmentation techniques, varying from classical geometric transformations, image registration, variational autoencoders, and generative adversarial networks, to the most recent advances in latent diffusion models. We show that the use of heavy data augmentation significantly increases both the quantitative and qualitative outcomes, resulting in an average Dice Score above 0.94 for the SkullBreak and above 0.96 for the SkullFix datasets. The results show that latent diffusion models combined with vector quantized variational autoencoder outperform other generative augmentation strategies. Moreover, we show that the synthetically augmented network successfully reconstructs real clinical defects, without the need to acquire costly and time-consuming annotations. The findings of the work will lead to easier, faster, and less expensive modeling of personalized cranial implants. This is beneficial to numerous people suffering from cranial injuries. The work constitutes a considerable contribution to the field of artificial intelligence in the automatic modeling of personalized cranial implants.","2024-11-01","2024-12-03 03:07:24","2024-12-03 03:07:24","","109129","","","182","","Computers in Biology and Medicine","","","","","","","","","","","","","","","","","","","Deep learning; Artificial intelligence; Cranial defects; Cranial implants; Data augmentation; Diffusion models; Generative networks; Image registration; Neurosurgery","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ESWBZXRK","journalArticle","2024","Du, Haoze; Jia, Qinjin; Gehringer, Edward; Wang, Xianfang","Harnessing large language models to auto-evaluate the student project reports","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100268","https://www.sciencedirect.com/science/article/pii/S2666920X24000717","Addressing the problem of the difficulty in providing timely and reasonable feedback evaluation for student project reports, this paper proposes a method based on LLMs (Large Language Models) that can automatically generate instant feedback evaluations for student project reports. Three LLMs, namely BART (Bidirectional and Auto-Regressive Transformer), CPTB (chatgpt_paraphraser_on_T5_base), and CGP-BLCS (chatgpt-gpt4-prompts-bart-large-cnn-samsum), were designed to generate instant text feedback pre-training models for student project reports. The effectiveness of the feedback was evaluated using ROUGE Metrics, BERT Scores, and human expert evaluations. Experiments showed that the lightweight, fine-tuned BART model, when trained on a larger dataset of 80%, generated effective feedback evaluations for student project reports. When trained on a smaller dataset of 20%, both the BART and CPTB models had unsatisfactory overall performance, while the fine-tuned CGP-BLCS model was able to generate feedback evaluations that approached human-level evaluations. The detailed descriptions of the methods used with the LLMs for generating effective text feedback evaluations for student project reports will be useful to AI computer programmers, researchers, and computer science instructional designers for improving their courses and future research.","2024-12-01","2024-12-03 03:07:24","2024-12-03 03:07:24","","100268","","","7","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Large language models; Auto-evaluation; CGP-BLCS; CPTB; Student project reports","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2UR3WIJL","journalArticle","2023","Harfoush, Asmaa; Tabei, Ali; Haapala, Karl R.; Ghamarian, Iman","A framework for predicting grain morphology during incremental sheet metal forming using generative adversarial networks","51st SME North American Manufacturing Research Conference (NAMRC 51)","","2213-8463","10.1016/j.mfglet.2023.08.083","https://www.sciencedirect.com/science/article/pii/S2213846323001402","Properties and service performance of parts and components depend upon microstructural characteristics including grain morphology, size and size distribution, and crystallographic orientation. Microstructure itself is dictated by manufacturing process parameters. Microstructural analysis is often necessary for evaluating product quality. Past research has attempted to map product/process characteristics using analytical, numerical, and experimental approaches. To address the limitations of conventional modeling approaches, researchers have begun to explore the application of artificial intelligence (AI) techniques in correlating and predicting the effect of process parameters on material microstructure and mechanical properties. For instance, AI-based approaches have been used to model the influence of process parameters on forming force, formability, geometric accuracy, tool path, and surface quality in incremental sheet forming (ISF), a flexible forming process suitable for low-volume and custom product manufacturing. The research reported herein introduces a framework to map ISF process parameters to microstructural images using Generative Adversarial Networks (GAN). This approach can reduce the experimental and analytical effort and costs typically necessary to evaluate the effects of ISF process settings on material microstructure and mechanical properties.","2023-08-01","2024-12-03 03:07:24","2024-12-03 03:07:24","","1081-1088","","","35","","Manufacturing Letters","","","","","","","","","","","","","","","","","","","Artificial intelligence; Incremental sheet forming; Mechanical properties; Microstructural analysis","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"X52FS9P2","journalArticle","2024","Farhood, Helia; Joudah, Ibrahim; Beheshti, Amin; Muller, Samuel","Advancing student outcome predictions through generative adversarial networks","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100293","https://www.sciencedirect.com/science/article/pii/S2666920X24000961","Predicting student outcomes is essential in educational analytics for creating personalised learning experiences. The effectiveness of these predictive models relies on having access to sufficient and accurate data. However, privacy concerns and the lack of student consent often restrict data collection, limiting the applicability of predictive models. To tackle this obstacle, we employ Generative Adversarial Networks, a type of Generative AI, to generate tabular data replicating and enlarging the dimensions of two distinct publicly available student datasets. The ‘Math dataset’ has 395 observations and 33 features, whereas the ‘Exam dataset’ has 1000 observations and 8 features. Using advanced Python libraries, Conditional Tabular Generative Adversarial Networks and Copula Generative Adversarial Networks, our methodology consists of two phases. First, a mirroring approach where we produce synthetic data matching the volume of the real datasets, focusing on privacy and evaluating predictive accuracy. Second, augmenting the real datasets with newly created synthetic observations to fill gaps in datasets that lack student data. We validate the synthetic data before employing these approaches using Correlation Analysis, Density Analysis, Correlation Heatmaps, and Principal Component Analysis. We then compare the predictive accuracy of whether students will pass or fail their exams across original, synthetic, and augmented datasets. Employing Feedforward Neural Networks, Convolutional Neural Networks, and Gradient-boosted Neural Networks, and using Bayesian optimisation for hyperparameter tuning, this research methodically examines the impact of synthetic data on prediction accuracy. We implement and optimize these models using Python. Our mirroring approach aims to achieve accuracy rates that closely align with the original data. Meanwhile, our augmenting approach seeks to reach a slightly higher accuracy level than when solely learning from the original data. Our findings provide actionable insights into leveraging advanced Generative AI techniques to enhance educational outcomes and meet our objectives successfully.","2024-12-01","2024-12-03 03:07:24","2024-12-03 03:07:24","","100293","","","7","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Artificial intelligence in education; AI-based student outcome prediction; Generative adversarial networks in education; Generative AI in education; Learning performance prediction","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"HM7MZAXP","journalArticle","2024","Cahyana, Destika; Hadiarto, Agus; Irawan; Hati, Diah Puspita; Pratamaningsih, Mira Media; Karolinoerita, Vicca; Mulyani, Anny; Sukarman; Hikmat, Muhammad; Ramadhani, Fadhlullah; Gani, Rachmat Abdul; Yatno, Edi; Heryanto, R. Bambang; Suratman; Gofar, Nuni; Suriadikusumah, Abraham","Application of ChatGPT in soil science research and the perceptions of soil scientists in Indonesia","Artificial Intelligence in Geosciences","","2666-5441","10.1016/j.aiig.2024.100078","https://www.sciencedirect.com/science/article/pii/S2666544124000194","Since its arrival in late November 2022, ChatGPT-3.5 has rapidly gained popularity and significantly impacted how research is planned, conducted, and published using a generative artificial intelligence approach. ChatGPT-4 was released four months later and became more popular in November 2023. However, there is little study about the perception of scientists of these chatbots, especially in soil science. This article presents the new findings of a brief research investigating soil scientists' responses and perceptions towards chatbots in Indonesia. This artificial intelligence application facilitates conversation-based interactions in text format. The study evaluated ten ChatGPT answers to fundamental questions in soil science, which has developed into a normal science with a mutually agreed-upon paradigm. The evaluation was carried out by seven soil scientists recognized for their expertise in Indonesia, using a scale of 1–100. In addition, a questionnaire was distributed to soil scientists at the National Research and Innovation Agency of the Republic of Indonesia (BRIN), universities, and Indonesian Soil Science Society (HITI) members to gauge their perception of ChatGPT's presence in the research field. The study results indicate that the scores of ChatGPT answers range from 82.99 to 92.24. ChatGPT-4 is better than both the paid and free versions of ChatGPT-3.5. There is no significant difference between the English and Indonesian versions of ChatGPT-4.0. However, the perception of general soil scientists about the level of trust is only 55%. Furthermore, 80% of soil scientists believe that chatbots can only be used as digital tools to assist in soil science research and cannot be used without the involvement of soil scientists.","2024-12-01","2024-12-03 03:07:24","2024-12-03 03:07:24","","100078","","","5","","Artificial Intelligence in Geosciences","","","","","","","","","","","","","","","","","","","Tools; Artificial intelligence; ChatGPT; Paradigm; Soil science","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"WEVT7NFP","journalArticle","2024","Cho, Sang-Hyun; Kim, Dohyun; Kwon, Hyuk-Chul; Kim, Minho","Exploring the potential of large language models for author profiling tasks in digital text forensics","DFRWS APAC 2024 - Selected Papers from the 4th Annual Digital Forensics Research Conference APAC","","2666-2817","10.1016/j.fsidi.2024.301814","https://www.sciencedirect.com/science/article/pii/S2666281724001380","The rapid advancement of large language models (LLMs) has opened up new possibilities for various natural language processing tasks. This study explores the potential of LLMs for author profiling in digital text forensics, which involves identifying characteristics such as age and gender from writing style—a crucial task in forensic investigations of anonymous or pseudonymous communications. Experiments were conducted using state-of-the-art LLMs, including Polyglot, EEVE, and Bllossom, to evaluate their performance in author profiling. Different fine-tuning strategies, such as full fine-tuning, Low-Rank Adaptation (LoRA), and Quantized LoRA (QLoRA), were compared to determine the most effective methods for adapting LLMs to the specific needs of this task. The results show that fine-tuned LLMs can effectively predict authors’ age and gender based on their writing styles, with Polyglot-based models generally outperforming EEVE and Bllossom models. Additionally, LoRA and QLoRA strategies significantly reduce computational costs and memory requirements while maintaining performance comparable to full fine-tuning. However, error analysis reveals limitations in the current LLM-based approach, including difficulty in capturing subtle linguistic variations across age groups and potential biases from pre-training data. These challenges are discussed and future research directions to address them are proposed. This study underscores the potential of LLMs in author profiling for digital text forensics, suggesting promising avenues for further exploration and refinement.","2024-10-01","2024-12-03 03:07:24","2024-12-03 03:07:24","","301814","","","50","","Forensic Science International: Digital Investigation","","","","","","","","","","","","","","","","","","","Large language models; Fine-tuning; Author profiling; Digital text forensics; Low-rank adaptation; Quantization","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"W49Z3G6H","journalArticle","2024","Patsakis, Constantinos; Casino, Fran; Lykousas, Nikolaos","Assessing LLMs in malicious code deobfuscation of real-world malware campaigns","Expert Systems with Applications","","0957-4174","10.1016/j.eswa.2024.124912","https://www.sciencedirect.com/science/article/pii/S0957417424017792","The integration of large language models (LLMs) into various cybersecurity pipelines has become increasingly prevalent, enabling the automation of numerous manual tasks and often surpassing human performance. Recognising this potential, cybersecurity researchers and practitioners are actively investigating the application of LLMs to process vast volumes of heterogeneous data for anomaly detection, potential bypass identification, attack mitigation, and fraud prevention. Moreover, LLMs’ advanced capabilities in generating functional code, interpreting code context, and code summarisation present significant opportunities for reverse engineering and malware deobfuscation. In this work, we comprehensively examine the deobfuscation capabilities of state-of-the-art LLMs. Specifically, we conducted a detailed evaluation of four prominent LLMs using real-world malicious scripts from the notorious Emotet malware campaign. Our findings reveal that while current LLMs are not yet perfectly accurate, they demonstrate substantial potential in efficiently deobfuscating payloads. This study highlights the importance of fine-tuning LLMs for specialised tasks, suggesting that such optimisation could pave the way for future AI-powered threat intelligence pipelines to combat obfuscated malware. Our contributions include a thorough analysis of LLM performance in malware deobfuscation, identifying strengths and limitations, and discussing the potential for integrating LLMs into cybersecurity frameworks for enhanced threat detection and mitigation. Our experiments illustrate that LLMs can automatically and accurately extract the necessary indicators of compromise from a real-world campaign with an accuracy of 69.56% and 88.78% for the URLs and the corresponding domains of the droppers, respectively.","2024-12-05","2024-12-03 03:07:24","2024-12-03 03:07:24","","124912","","","256","","Expert Systems with Applications","","","","","","","","","","","","","","","","","","","Large language models; Cybersecurity; Code deobfuscation; Malware analysis","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XKTH5Z5R","journalArticle","2023","Do Quang, Thinh; Hoang, Trang","An efficient method to build music generative model by controlling both general and local note characteristics","Journal of King Saud University - Computer and Information Sciences","","1319-1578","10.1016/j.jksuci.2023.101761","https://www.sciencedirect.com/science/article/pii/S1319157823003154","It has been shown that since the rapid development of the entertainment industry, music generation has become a focused research topic. Numerous methods for creating music, or musical notes specifically have been announced, each with distinct characteristics and advantages. These methods usually concentrated on these two aspects: the overall harmony of the whole music score and the link between adjacent notes, which this research referred respectively as the general and local aspects. This study proposes a model with combined methods that is capable of deriving benefits from these both aspects, hence creating music with good quality in terms of both quantitative and qualitative evaluations. Various results based on those have been discussed and judged for efficient enhancing as well as for future development opportunities. The value of Average Pitch Interval (API) achieved a remarkable value of 1.43, along with the note range of 12.145; while on the subjective aspect, survey participants gave 6.81 score for the generated music, yet only about 70% of them can distinguish the generated from the genuine pieces of music.","2023-10-01","2024-12-03 03:07:24","2024-12-03 03:07:24","","101761","","9","35","","Journal of King Saud University - Computer and Information Sciences","","","","","","","","","","","","","","","","","","","Artificial intelligence; Generative adversarial network; Long short-term memory; Music generation; Note generation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"6IHVMX45","journalArticle","2023","Dai, Yun; Liu, Ang; Lim, Cher Ping","Reconceptualizing ChatGPT and generative AI as a student-driven innovation in higher education","The 33rd CIRP Design Conference","","2212-8271","10.1016/j.procir.2023.05.002","https://www.sciencedirect.com/science/article/pii/S2212827123004407","Higher education is poised at the precipice of the changes and challenges brought about by ChatGPT. This paper addresses some of the most fundamental questions about the role, position, and implications of ChatGPT and generative artificial intelligence (AI) tools amidst the evolving landscape of higher education and modern society. By linking technological affordances with educational needs, we conceptualize ChatGPT as a student-driven innovation with rich potential to empower students and enhance their educational experiences and resources. However, this empowerment comes at a price. It requires collaborative efforts among the stakeholders to address the new and emerging challenges regarding student training, higher education curricula and assessment, and technology development and governance. It also implies new directions for educational research and theories.","2023-01-01","2024-12-03 03:07:24","2024-12-03 03:07:24","","84-90","","","119","","Procedia CIRP","","","","","","","","","","","","","","","","","","","higher education; ChatGPT; generative AI; engineering education; learning analytics; personalized learning","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"7S4KZKUP","journalArticle","2024","Sahoo, Partha Sarathi; Burra, V.L.S. Prasad","Evaluating the 3D structure prediction tools to identify optimal MEBPVC structure models","Computational and Structural Biotechnology Reports","","2950-3639","10.1016/j.csbr.2024.100010","https://www.sciencedirect.com/science/article/pii/S2950363924000103","Multi-Epitope based Peptide Vaccines Candidates (MEBPVCs) are peptide sequences with the immunogenic epitopes interspersed with linkers, adjuvants and other components. The optimization of one of the components of MEBPVCs, epitope order, resulted in a unique epitope ordered MEBPVC showing improved vaccine potency. In addition to the above combinatorial optimization task, predicting the 3D structure of these MEBPVCs presents a significant challenge owing to the diverse modeling approaches employed by the modeling tools. Here we embarked on evaluating the performance of the threading based ab initio modeling tool: I-TASSER, Large Language Model (LLM) based tool: ESMFold and the MSA based artificial intelligence based tool: AlphaFold2. Our investigation employed RMSD, PEA, SASA, Solubility, Globularity, secondary structure analysis, Docking, MDS and MVP. I-TASSER emerged as the superior performer. The analysis revealed a strong correlation between predicted tertiary structure and secondary structure composition, especially the β-turns and random coils. It appears that accurate secondary structure identification to be a pivotal step, dictating the propagation of accuracy (or inaccuracy). This finding highlights the critical importance of secondary structure prediction/assignment algorithms for enhanced protein structure modeling accuracy especially to integrate predictions where evolutionary information is minimal and or lacking. Small sample size is attributed to the low significance of the parameters though Globularity and SASA have shown good p-values. Overall, the results support the need and utilization of the LLMs in biological research, underscoring their potential to revolutionize protein structure prediction and advance diverse biomolecular applications, accelerating Synthetic Biology research.","2024-12-01","2024-12-03 03:07:24","2024-12-03 03:07:24","","100010","","","1","","Computational and Structural Biotechnology Reports","","","","","","","","","","","","","","","","","","","AlphaFold2; ESMFold; I-TASSER; MEBPVCs; MVP; PEA; Protein Modelling","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DC3PUE2N","journalArticle","2023","Yilmaz, Ramazan; Karaoglan Yilmaz, Fatma Gizem","Augmented intelligence in programming learning: Examining student views on the use of ChatGPT for programming learning","Computers in Human Behavior: Artificial Humans","","2949-8821","10.1016/j.chbah.2023.100005","https://www.sciencedirect.com/science/article/pii/S2949882123000051","With the diversification of generative artificial intelligence (AI) applications, the interest in their use in every segment and field of society in recent years has been increasing rapidly. One of these areas is programming learning and program writing processes. One of the generative AI tools used for this purpose is ChatGPT. The use of ChatGPT in program writing processes has become widespread, and this tool has a certain potential in the programming process. However, when the literature is examined, research results related to using ChatGPT for this purpose have yet to be found. The existing literature has a gap that requires exploration. This study aims to analyze the students' perspectives on using ChatGPT in the field of programming and programming learning. The study encompassed a cohort of 41 undergraduate students enrolled in a public university's Computer Technology and Information Systems department. The research was carried out within the scope of the Object-Oriented Programming II course for eight weeks. Throughout the research process, students were given project assignments related to the course every week, and they were asked to use ChatGPT while solving them. The research data was collected using a form consisting of open-ended questions and analyzed through content analysis. The research findings revealed both the advantages and disadvantages of ChatGPT usage, as perceived by the students. The students stated that the main benefits of using ChatGPT in programming learning are providing fast and mostly correct answers to questions, improving thinking skills, facilitating debugging, and increasing self-confidence. On the other hand, the main limitations of using ChatGPT in programming education were getting students used to laziness, being unable to answer some questions, or giving incomplete/incorrect answers, causing professional anxiety in students. Based on the results of the research, it can be said that it would be useful to integrate generative AI tools into programming courses considering the advantages they provide in programming teaching. However, appropriate measures should be taken regarding the limitations it brings. Based on the research findings, several recommendations were proposed regarding the integration of ChatGPT into lessons.","2023-08-01","2024-12-03 03:07:24","2024-12-03 03:07:24","","100005","","2","1","","Computers in Human Behavior: Artificial Humans","","","","","","","","","","","","","","","","","","","ChatGPT; Generative artificial intelligence; Programming; Programming learning; Student opinions","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"I2GHEGZD","journalArticle","2024","Lee, Soohwan; Song, Ki-Sang","Teachers' and students' perceptions of AI-generated concept explanations: Implications for integrating generative AI in computer science education","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100283","https://www.sciencedirect.com/science/article/pii/S2666920X24000869","The educational application of Generative AI (GAI) has garnered significant interest, sparking discussions about the pedagogical value of GAI-generated content. This study investigates the perceived effectiveness of concept explanations produced by GAI compared to those created by human teachers, focusing on programming concepts of sequence, selection, and iteration. The research also explores teachers' and students' ability to discern the source of these explanations. Participants included 11 teachers and 70 sixth-grade students who were presented with concept explanations created or generated by teachers and ChatGPT. They were asked to evaluate the helpfulness of the explanations and identify their source. Results indicated that teachers found GAI-generated explanations more helpful for sequence and selection concepts, while preferring teacher-created explanations for iteration (χ2(2, N = 11) = 10.062, p = .007, ω = .595). In contrast, students showed varying abilities to distinguish between AI-generated and teacher-created explanations across concepts, with significant differences observed (χ2(2, N = 70) = 22.127, p < .001, ω = .399). Notably, students demonstrated difficulty in identifying the source of explanations for the iteration concept (χ2(1, N = 70) = 8.45, p = .004, φ = .348). Qualitative analysis of open-ended responses revealed that teachers and students employed similar criteria for evaluating explanations but differed in their ability to discern the source. Teachers focused on pedagogical effectiveness, while students prioritized relatability and clarity. The findings highlight the importance of considering both teachers' and students' perspectives when integrating GAI into computer science education. The study proposes strategies for designing GAI-based explanations that cater to learners' needs and emphasizes the necessity of explicit AI literacy instruction. Limitations and future research directions are discussed, underlining the need for larger-scale studies and experimental designs that assess the impact of GAI on actual learning outcomes.","2024-12-01","2024-12-03 03:07:24","2024-12-03 03:07:24","","100283","","","7","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Computer science education; Concept explanations; Elementary education; Generative artificial intelligence(GAI); Perceptual differences","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SIMNCPPB","journalArticle","2024","Dubravova, Hana; Cap, Jan; Holubova, Kristyna; Hribnak, Lukas","Artificial Intelligence as an Innovative Element of Support in Policing","International Conference on Industry Sciences and Computer Science Innovation","","1877-0509","10.1016/j.procs.2024.05.101","https://www.sciencedirect.com/science/article/pii/S1877050924011177","Currently, the public security sector is faced with an increasing administrative burden that limits the ability of police officers to focus on core security tasks. This paper focuses on the possibility of using large-scale language models (LSMs) as an innovative tool to address this challenge. Based on a careful literature review and analysis of current trends in artificial intelligence, the author team develops a concept for integrating GPTs into police practice, with an emphasis on the potential for reducing administrative burden and supporting efficient processing of relevant information. As part of this research, we have identified key areas of policing where AI could bring significant value, including data analysis and document production assistance. However, it should be emphasized that this technology is still in its early stages of development and its implementation would require a carefully considered approach involving interdisciplinary collaboration and further research to test the theoretical assumptions presented in this study. Thus, this paper contributes to a deeper understanding of the potential benefits and challenges of integrating GPT into policing practice and outlines a path towards future innovative solutions in the field of public safety.","2024-01-01","2024-12-03 03:07:25","2024-12-03 03:07:25","","237-244","","","237","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","artificial intelligence; large language model; GPT; administrative burden; chat; police","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CZUJQGWY","journalArticle","2024","Curry, Niall; Baker, Paul; Brookes, Gavin","Generative AI for corpus approaches to discourse studies: A critical evaluation of ChatGPT","Applied Corpus Linguistics","","2666-7991","10.1016/j.acorp.2023.100082","https://www.sciencedirect.com/science/article/pii/S2666799123000424","This paper explores the potential of generative artificial intelligence technology, specifically ChatGPT, for advancing corpus approaches to discourse studies. The contribution of artificial intelligence technologies to linguistics research has been transformational, both in the contexts of corpus linguistics and discourse analysis. However, shortcomings in the efficacy of such technologies for conducting automated qualitative analysis have limited their utility for corpus approaches to discourse studies. Acknowledging that new technologies in data analysis can replace and supplement existing approaches, and in view of the potential affordances of ChatGPT for automated qualitative analysis, this paper presents three replication case studies designed to investigate the applicability of ChatGPT for supporting automated qualitative analysis within studies using corpus approaches to discourse analysis. The findings indicate that, generally, ChatGPT performs reasonably well when semantically categorising keywords; however, as the categorisation is based on decontextualised keywords, the categories can appear quite generic, limiting the value of such an approach for analysing corpora representing specialised genres and/or contexts. For concordance analysis, ChatGPT performs poorly, as the results include false inferences about the concordance lines and, at times, modifications of the input data. Finally, for function-to-form analysis, ChatGPT also performs poorly, as it fails to identify and analyse direct and indirect questions. Overall, the results raise questions about the affordances of ChatGPT for supporting automated qualitative analysis within corpus approaches to discourse studies, signalling issues of repeatability and replicability, ethical challenges surrounding data integrity, and the challenges associated with using non-deterministic technology for empirical linguistic research.","2024-04-01","2024-12-03 03:07:25","2024-12-03 03:07:25","","100082","","1","4","","Applied Corpus Linguistics","","","","","","","","","","","","","","","","","","","Qualitative analysis; ChatGPT; Generative AI; Corpus linguistics; Discourse analysis","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"YDKHZNWZ","journalArticle","2024","Luo, Zheheng; Xie, Qianqian; Ananiadou, Sophia","Factual consistency evaluation of summarization in the Era of large language models","Expert Systems with Applications","","0957-4174","10.1016/j.eswa.2024.124456","https://www.sciencedirect.com/science/article/pii/S0957417424013228","Factual inconsistency with source documents in automatically generated summaries can lead to misinformation or pose risks. Existing factual consistency (FC) metrics are constrained by their performance, efficiency, and explainability. Recent advances in Large language models (LLMs) have demonstrated remarkable potential in text evaluation but their effectiveness in assessing FC in summarization remains underexplored. Prior research has mostly focused on proprietary LLMs, leaving essential factors that affect their assessment capabilities unexplored. Additionally, current FC evaluation benchmarks are restricted to news articles, casting doubt on the generality of the FC methods tested on them. In this paper, we first address the gap by introducing TreatFact—a dataset of LLM-generated summaries of clinical texts, annotated for FC by domain experts. Moreover, we benchmark 11 LLMs for FC evaluation across news and clinical domains and analyse the impact of model size, prompts, pre-training and fine-tuning data. Our findings reveal that despite proprietary models prevailing on the task, open-source LLMs lag behind. Nevertheless, there is potential for enhancing the performance of open-source LLMs through increasing model size, expanding pre-training data, and developing well-curated fine-tuning data. Experiments on TreatFact suggest that both previous methods and LLM-based evaluators are unable to capture factual inconsistencies in clinical summaries, posing a new challenge for FC evaluation.","2024-11-15","2024-12-03 03:07:25","2024-12-03 03:07:25","","124456","","","254","","Expert Systems with Applications","","","","","","","","","","","","","","","","","","","Large language model; Factual consistency; Medical document summarization; Text summarization","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"6DLX39GM","journalArticle","2024","Taylor, Niall; Schofield, Dan; Kormilitzin, Andrey; Joyce, Dan W.; Nevado-Holgado, Alejo","Developing healthcare language model embedding spaces","Artificial Intelligence in Medicine","","0933-3657","10.1016/j.artmed.2024.103009","https://www.sciencedirect.com/science/article/pii/S0933365724002513","Pre-trained Large Language Models (LLMs) have revolutionised Natural Language Processing (NLP) tasks, but often struggle when applied to specialised domains such as healthcare. The traditional approach of pre-training on large datasets followed by task-specific fine-tuning is resource-intensive and poorly aligned with the constraints of many healthcare settings. This presents a significant challenge for deploying LLM-based NLP solutions in medical contexts, where data privacy, computational resources, and domain-specific language pose unique obstacles. This study aims to develop and evaluate efficient methods for adapting smaller LLMs to healthcare-specific datasets and tasks. We seek to identify pre-training approaches that can effectively instil healthcare competency in compact LLMs under tight computational budgets, a crucial capability for responsible and sustainable deployment in local healthcare settings. We explore three specialised pre-training methods to adapt smaller LLMs to different healthcare datasets: traditional Masked Language modelling (MLM), Deep Contrastive Learning for Unsupervised Textual Representations (DeCLUTR), and a novel approach utilising metadata categories from healthcare settings. These methods are assessed across multiple healthcare datasets, with a focus on downstream document classification tasks. We evaluate the performance of the resulting LLMs through classification accuracy and analysis of the derived embedding spaces. Contrastively trained models consistently outperform other approaches on classification tasks, delivering strong performance with limited labelled data and fewer model parameter updates. While our novel metadata-based pre-training does not further improve classifications across datasets, it yields interesting embedding cluster separability. Importantly, all domain-adapted LLMs outperform their publicly available, general-purpose base models, validating the importance of domain specialisation. This research demonstrates the efficacy of specialised pre-training methods in adapting compact LLMs to healthcare tasks, even under resource constraints. We provide guidelines for pre-training specialised healthcare LLMs and motivate continued inquiry into contrastive objectives. Our findings underscore the potential of these approaches for aligning small LLMs with privacy-sensitive medical tasks, offering a path toward more efficient and responsible NLP deployment in healthcare settings. This work contributes to the broader goal of making advanced NLP techniques accessible and effective in specialised domains, particularly where resource limitations and data sensitivity are significant concerns.","2024-12-01","2024-12-03 03:07:25","2024-12-03 03:07:25","","103009","","","158","","Artificial Intelligence in Medicine","","","","","","","","","","","","","","","","","","","Classification; LLMs; Healthcare; Contrastive loss; Embeddings","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"5VRDJDE7","journalArticle","2024","Padovano, Antonio; Cardamone, Martina","Towards human-AI collaboration in the competency-based curriculum development process: The case of industrial engineering and management education","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100256","https://www.sciencedirect.com/science/article/pii/S2666920X24000596","In the endeavor to advance industrial engineering and management (IEM) education, this research underscores the imperative of supporting a dynamic and responsive adaptation of a competency-based curriculum (CBC) to meet the demands of an ever-evolving industrial landscape and job market. Our study contributes to competency-based education (CBE) by demonstrating how Artificial Intelligence (AI) can inform the definition of a CBC in the IEM field, thus initiating the pioneering steps towards a collaborative human-AI approach in CBC design. Through a stepwise methodology based on semantic analysis, text mining, natural language processing (NLP) models, informetrics approaches, and clustering algorithms, we provide data-driven insights to inform the curriculum development process. This approach enabled us to identify educational gap, particularly in domains such as digital twin engineering and human-centric IEM. Moreover, this study advocates for higher education institutions (HEIs) to embrace a more structured and collaborative approach to continuously developing competency-based curricula. In this perspective, AI (including generative AI) emerges as a valuable ally in curriculum design. This approach proves instrumental in crafting competitive and appealing curricula, especially at peripheral universities. This study culminates in an updated WING model showing how to build Industry 5.0 related curricula and a series of recommendations for engineering educators.","2024-12-01","2024-12-03 03:07:25","2024-12-03 03:07:25","","100256","","","7","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Artificial intelligence; Engineering education; Industry 5.0; Competency-based education; Curriculum development; Industrial engineering and management","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"I5AEAVLK","journalArticle","2024","Peng, Zhiyuan; Wu, Xuyang; Wang, Qifan; Fang, Yi","Soft prompt tuning for augmenting dense retrieval with large language models","Knowledge-Based Systems","","0950-7051","10.1016/j.knosys.2024.112758","https://www.sciencedirect.com/science/article/pii/S0950705124013923","Dense retrieval (DR) converts queries and documents into dense embeddings and measures the similarity between queries and documents in vector space. One of the major challenges in DR is the lack of domain-specific training data. While DR models can learn from large-scale public datasets like MS MARCO through transfer learning, evidence shows that not all DR models and domains can benefit from transfer learning. Recently, researchers have resorted to large language models (LLMs) to improve the zero-shot and few-shot DR models. However, the hard prompts or human-written prompts utilized in these works are suboptimal and the generated weak queries are often sensitive to the prompts. To tackle this, we propose soft prompt tuning for augmenting DR (SPTAR): for each task, we leverage soft prompt-tuning to optimize a task-specific soft prompt on limited ground truth data and then prompt the LLMs to tag unlabeled documents with weak queries, yielding weak document-query pairs to train task-specific dense retrievers. We design a filter to select high-quality example document-query pairs in the prompt to further improve the quality of weak tagged queries. To the best of our knowledge, there is no prior work utilizing soft prompt tuning to augment DR models. Moreover, unlike much of the existing work, ours is based on popular open-source LLMs to ensure reproducible and deterministic results. Our experimental results demonstrate that SPTAR outperforms both unsupervised baselines and the recently proposed LLMs-based augmentation method for DR.","2024-11-22","2024-12-03 03:07:25","2024-12-03 03:07:25","","112758","","","","","Knowledge-Based Systems","","","","","","","","","","","","","","","","","","","Large language models; Data augmentation; Dense retrieval; Prompt tuning","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"G2XJ4LYV","journalArticle","2023","Pursnani, Vinay; Sermet, Yusuf; Kurt, Musa; Demir, Ibrahim","Performance of ChatGPT on the US fundamentals of engineering exam: Comprehensive assessment of proficiency and potential implications for professional environmental engineering practice","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2023.100183","https://www.sciencedirect.com/science/article/pii/S2666920X23000620","In recent years, advancements in artificial intelligence (AI) have led to the development of large language models like GPT-4, demonstrating potential applications in various fields, including education. This study investigates the feasibility and effectiveness of using ChatGPT, a GPT-4 based model, in achieving satisfactory performance on the Fundamentals of Engineering (FE) Environmental Exam. This study further shows a significant improvement in the model's accuracy when answering FE exam questions through noninvasive prompt modifications, substantiating the utility of prompt modification as a viable approach to enhance AI performance in educational contexts. Furthermore, the findings reflect remarkable improvements in mathematical capabilities across successive iterations of ChatGPT models, showcasing their potential in solving complex engineering problems. Our paper also explores future research directions, emphasizing the importance of addressing AI challenges in education, enhancing accessibility and inclusion for diverse student populations, and developing AI-resistant exam questions to maintain examination integrity. By evaluating the performance of ChatGPT in the context of the FE Environmental Exam, this study contributes valuable insights into the potential applications and limitations of large language models in educational settings. As AI continues to evolve, these findings offer a foundation for further research into the responsible and effective integration of AI models across various disciplines, ultimately optimizing the learning experience and improving student outcomes.","2023-01-01","2024-12-03 03:07:25","2024-12-03 03:07:25","","100183","","","5","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","ChatGPT; AI in education; Large language models (LLMs); Fundamentals of engineering exam; Prompt modification techniques; Responsible AI integration","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"EL23B7UV","journalArticle","2024","Sołoducho-Pelc, Letycja; Sulich, Adam","Role of Business Intelligent Systems in Sustainable Strategic Management for Green Jobs Creation","28th International Conference on Knowledge Based and Intelligent information and Engineering Systems (KES 2024)","","1877-0509","10.1016/j.procs.2024.09.296","https://www.sciencedirect.com/science/article/pii/S1877050924023159","Contemporary generative AI tools are becoming integral to business development strategies. Organizations are seeking applications for innovative technologies in science and various areas of operations. Intelligent systems bridge the gap between the scientific world and business practice. This is significant in using generative artificial intelligence in Sustainable Strategic Management (SSM) to create Green Jobs (GJs). Despite the popularity of topics like sustainable strategic management and creating GJs, science rarely combines these into interdisciplinary research. This article stands out from other review articles by using the innovative Scopus AI tool to explore a significant construct from theoretical and practical perspectives. The aim of this article is to highlight the potential for collaboration between science and business in creating GJs using Business Intelligent Systems (BIS) in SSM. The methodology of exploratory scientific inquiry supports this goal. This article employs a new research method, the Scopus AI tool. The results undergo critical analysis and interpretation, presenting conclusions and recommendations for using intelligent systems to create GJs in sustainable strategic management.","2024-01-01","2024-12-03 03:07:25","2024-12-03 03:07:25","","4461-4469","","","246","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","sustainable development; competitive advantage; Artificial Intelligence; business development; green labor market; Scopus AI","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ZFT4EE8E","journalArticle","2024","Liu, Meilu; Zhang, Lawrence Jun; Biebricher, Christine","Investigating students’ cognitive processes in generative AI-assisted digital multimodal composing and traditional writing","Computers & Education","","0360-1315","10.1016/j.compedu.2023.104977","https://www.sciencedirect.com/science/article/pii/S0360131523002543","Recently, generative artificial intelligence (AI)-powered chatbots such as ChatGPT and Bing Chat have garnered increasing attention on a global scale. Previous studies have focused mostly on the influence of generative AI on writing while few researchers have investigated how generative AI can facilitate students' multimodal writing process. To fill in this gap, we explored the generative AI-assisted composing processes of two groups of English as a foreign language (EFL) writers over two weeks in this qualitative study. One group completed a multimodal PowerPoint (PPT) project, and the other group completed a traditional argumentative essay project. Our data consist of students’ screen recordings with think-aloud protocols, final multimodal texts, and post-project interviews. Our analysis showed different patterns in text production across the two groups. Students in the PPT group tended to construct more bridge texts and examples to corroborate their sub-claims in the hierarchical order. They also inclined to borrow the summarized search results from the Bing Chat to expand texts for their PPT slides. With regard to image generation for PPT slides, descriptions of AI images from ChatGPT were used as effective prompts to generate AI images from Bing Image Creator. Moreover, students were interested in producing and refining AI images following the recommended prompts by Bing Chat. They also evaluated these AI images from different perspectives. We conclude the study with a discussion on the pedagogical implications and suggestions for further study.","2024-04-01","2024-12-03 03:07:25","2024-12-03 03:07:25","","104977","","","211","","Computers & Education","","","","","","","","","","","","","","","","","","","Generative artificial intelligence; Writing; Cognitive process; Digital multimodal composing","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DJCPW26Q","journalArticle","2024","Doleck, Tenzin; Agand, Pedram; Pirrotta, Dylan","Integrating generative AI in data science programming: Group differences in hint requests","Computers in Human Behavior: Artificial Humans","","2949-8821","10.1016/j.chbah.2024.100089","https://www.sciencedirect.com/science/article/pii/S2949882124000495","Generative AI applications have increasingly gained visibility in recent educational literature. Yet less is known about how access to generative tools, such as ChatGPT, influences help-seeking during complex problem-solving. In this paper, we aim to advance the understanding of learners' use of a support strategy (hints) when solving data science programming tasks in an online AI-enabled learning environment. The study compared two conditions: students solving problems in DaTu with AI assistance (N = 45) and those without AI assistance (N = 44). Findings reveal no difference in hint-seeking behavior between the two groups, suggesting that the integration of AI assistance has minimal impact on how individuals seek help. The findings also suggest that the availability of AI assistance does not necessarily reduce learners’ reliance on support strategies (such as hints). The current study advances data science education and research by exploring the influence of AI assistance during complex data science problem-solving. We discuss implications and identify paths for future research.","2024-08-01","2024-12-03 03:07:25","2024-12-03 03:07:25","","100089","","2","2","","Computers in Human Behavior: Artificial Humans","","","","","","","","","","","","","","","","","","","Artificial intelligence; Feedback; AI-Assistance; Data science programming; Help seeking; Hints","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"INLSFRA5","journalArticle","2024","Bae, Hyojin; Park, Sa-Yoon; Kim, Chang-Eop","A practical guide to implementing artificial intelligence in traditional East Asian medicine research","Integrative Medicine Research","","2213-4220","10.1016/j.imr.2024.101067","https://www.sciencedirect.com/science/article/pii/S2213422024000477","In this paper, we present a comprehensive guide for implementing artificial intelligence (AI) techniques in traditional East Asian medicine (TEAM) research. We cover essential aspects of the AI model development pipeline, including research objective establishment, data collection and preprocessing, model selection, evaluation, and interpretation. The unique considerations in applying AI to TEAM datasets, such as data scarcity, imbalance, and model interpretability, are discussed. We provide practical tips and recommendations based on best practices and our own experience. The potential of large language models in TEAM research is also highlighted. Finally, we discuss the challenges and future directions of AI application in TEAM, emphasizing the need for standardized data collection and sharing platforms.","2024-09-01","2024-12-03 03:07:25","2024-12-03 03:07:25","","101067","","3","13","","Integrative Medicine Research","","","","","","","","","","","","","","","","","","","Machine learning; Artificial intelligence; Medical AI; Traditional Chinese medicine; Traditional East Asian medicine","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XEP3Z8SU","journalArticle","2024","Issa, Tomayess; Hall, Mahnaz","A teamwork framework for preventing breaches of academic integrity and improving students’ collaborative skills in the AI era","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e38759","https://www.sciencedirect.com/science/article/pii/S2405844024147901","Generative artificial intelligence (AI) tools have become a major challenge in the education sector in terms of the way that students use and manage them. This study examines the development, implementation, and evaluation of a teamwork framework by using academic integrity standards and formative feedback to minimise the use of generative AI tools in the Business Project Management (BPM) unit and promote students' learning skills through teamwork and self/peer evaluation. This teamwork assessment was designed to transform students into independent learners by improving their cultural awareness, self-confidence, teamwork, communication, leadership, as well as personal and interpersonal skills. The study's objectives are to determine whether a teamwork framework can help to maintain academic integrity and transform BPM students into independent learners and leaders in the era of generative AI, and to determine whether lecturers' formative feedback enhances students' skills in teamwork assessment. This research comprises an empirical study of 408 local and international BPM students from different cultural backgrounds. A mixed-methods approach was used to collect data and achieve a broader perspective of the research topic. BPM students reported their satisfaction with this type of assessment since it helped them acquire skills such as intercultural effectiveness and teamwork. Following the implementation of the teamwork framework, the number of instances of academic misconduct and requests for extensions have decreased dramatically, while the assessment's average marks increased by 10 %. A set of recommendations is offered that will ensure the successful implementation of the proposed framework for teamwork assessment and self/peer evaluation. This study was limited to the Business Project Management unit, but in 2024, the same study will be conducted involving other postgraduate units at Curtin University with a future rollout in other universities to compare how students perceive teamwork assessment.","2024-10-15","2024-12-03 03:07:25","2024-12-03 03:07:25","","e38759","","19","10","","Heliyon","","","","","","","","","","","","","","","","","","","AI; Academic integrity; Self/peer evaluation; Teamwork assessment; Teamwork framework","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XKAUM95A","journalArticle","2024","Dong, Mengming Michael; Stratopoulos, Theophanis C.; Wang, Victor Xiaoqi","A scoping review of ChatGPT research in accounting and finance","International Journal of Accounting Information Systems","","1467-0895","10.1016/j.accinf.2024.100715","https://www.sciencedirect.com/science/article/pii/S1467089524000484","This paper provides a review of recent publications and working papers on ChatGPT and related Large Language Models (LLMs) in accounting and finance. The aim is to understand the current state of research in these two areas and identify potential research opportunities for future inquiry. We identify three common themes from these earlier studies. The first theme focuses on applications of ChatGPT and LLMs in various fields of accounting and finance. The second theme utilizes ChatGPT and LLMs as a new research tool by leveraging their capabilities such as classification, summarization, and text generation. The third theme investigates implications of LLM adoption for accounting and finance professionals, as well as for various organizations and sectors. While these earlier studies provide valuable insights, they leave many important questions unanswered or partially addressed. We propose venues for further exploration and provide technical guidance for researchers seeking to employ ChatGPT and related LLMs as a tool for their research.","2024-12-01","2024-12-03 03:07:25","2024-12-03 03:07:25","","100715","","","55","","International Journal of Accounting Information Systems","","","","","","","","","","","","","","","","","","","ChatGPT; LLMs; Generative AI; AIS; Asset pricing; Audit; Corporate finance; Financial reporting; Tax","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"8SP4IFRR","journalArticle","2024","Klar, Matthias; Ruediger, Patrick; Schuermann, Maik; Gören, Goren Tobias; Glatt, Moritz; Ravani, Bahram; Aurich, Jan C.","Explainable generative design in manufacturing for reinforcement learning based factory layout planning","Journal of Manufacturing Systems","","0278-6125","10.1016/j.jmsy.2023.11.012","https://www.sciencedirect.com/science/article/pii/S027861252300239X","Generative design can be an effective approach to generate optimized factory layouts. One evolving topic in this field is the use of reinforcement learning (RL)-based approaches. Existing research has focused on the utilization of the approach without providing additional insights into the learned metrics and the derived policy. This information, however, is valuable from a layout planning perspective since the planner needs to ensure the trustworthiness and comprehensibility of the results. Furthermore, a deeper understanding of the learned policy and its influencing factors can help improve the manual planning process that follows as well as the acceptance of the results. These gaps in the existing approaches can be addressed by methods categorized as explainable artificial intelligence methods which have to be aligned with the properties of the problem and its audience. Consequently, this paper presents a method that will increase the trust in layouts generated by the RL approach. The method uses policy summarization and perturbation together with the state value evaluation. The method also uses explainable generative design for analyzing interrelationships between state values and actions at a feature level. The result is that the method identifies whether the RL approach learns the problem characteristics or if the solution is a result of random behavior. Furthermore, the method can be used to ensure that the reward function is aligned with the overall optimization goal and supports the planner in further detailed planning tasks by providing insights about the problem-defining interdependencies. The applicability of the proposed method is validated based on an industrial application scenario considering a layout planning case of 43 functional units. The results show that the method allows evaluation of the trustworthiness of the generated results by preventing randomly generated solutions from being considered in a detailed manual planning step. The paper concludes with a discussion of the results and a presentation of future research directions which also includes the transfer potentials of the proposed method to other application fields in RL-based generative design.","2024-02-01","2024-12-03 03:07:25","2024-12-03 03:07:25","","74-92","","","72","","Journal of Manufacturing Systems","","","","","","","","","","","","","","","","","","","Explainable artificial intelligence; Explainable reinforcement learning; Facility layout problem; Factory layout planning; Policy summarization","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2G5QKWHI","journalArticle","2024","Cummings, Robert E.; Monroe, Stephen M.; Watkins, Marc","Generative AI in first-year writing: An early analysis of affordances, limitations, and a framework for the future","Computers and Composition","","8755-4615","10.1016/j.compcom.2024.102827","https://www.sciencedirect.com/science/article/pii/S8755461524000033","Our First-year Writing program began intentional student engagements with generative AI in the fall of 2022. We developed assignments for brainstorming research questions, writing counterarguments, and editing assistance using the AI tools Elicit, Fermat, and Wordtune. Students felt that the tools were helpful for finding ideas to get started with writing, to find sources once they had started writing, and to get help with counterarguments and alternate word choices. But when given the choice to use the assistants or not, most declined. Generative AI at this stage is unreliable, and many students found the tradeoff in reviewing AI suggestions to be too time consuming. And many students expressed a preference for continuing to develop their own voices through writing. Our experience in engaging AI led to the creation of the DEER praxis, which emphasizes defined engagements with AI tools for specific purposes, and generous use of reflection.","2024-03-01","2024-12-03 03:07:25","2024-12-03 03:07:25","","102827","","","71","","Computers and Composition","","","","","","","","","","","","","","","","","","","ChatGPT; Generative artificial intelligence; DEER Praxis; Elicit; Fermat; First-year composition; Wordtune","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RV5T5J9X","journalArticle","2024","Just, Julian","Natural language processing for innovation search – Reviewing an emerging non-human innovation intermediary","Technovation","","0166-4972","10.1016/j.technovation.2023.102883","https://www.sciencedirect.com/science/article/pii/S0166497223001943","Applying artificial intelligence (AI), especially natural language processing (NLP), to harness large amounts of information from patent databases, online communities, social media, or crowdsourcing platforms is becoming increasingly popular to help organizations find promising solutions. In the era of non-human innovation intermediaries, we should begin to view NLP not only as a useful technology applied in different innovation practices but also as an intermediary orchestrating valuable information. Previous research has not taken this perspective, and knowledge about its intermediation activities and functions is limited. This study reviews 167 academic articles to better understand how NLP approaches can enrich intermediation in early-stage innovation search. It identifies 18 distinctive innovation practices taking over activities like forecasting trends, illustrating technology and idea landscapes, filtering out distinctive contributions, recombining domain-specific and analogous knowledge, or matching problems with solutions. While certain NLP capabilities complement each other, the analysis shows that the choice of the most appropriate approach depends on the characteristics of the innovation practice. Innovation researchers and practitioners should rethink current roles and responsibilities in AI-based innovation processes. As seen in the recent emergence of large language models (LLMs), the rapidly evolving field offers many future research opportunities and practical benefits.","2024-01-01","2024-12-03 03:07:26","2024-12-03 03:07:26","","102883","","","129","","Technovation","","","","","","","","","","","","","","","","","","","Natural language processing; AI-based innovation management; Front-end of innovation; Innovation intermediation; Innovation search; Systematic literature review","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2W5PBN3G","journalArticle","2024","Furtado, Lara Sucupira; Soares, Jorge Barbosa; Furtado, Vasco","A task-oriented framework for generative AI in design","Journal of Creativity","","2713-3745","10.1016/j.yjoc.2024.100086","https://www.sciencedirect.com/science/article/pii/S2713374524000128","The intersection of Artificial Intelligence and Design disciplines such as Architecture, Urban Planning, Engineering and Product Design has been a longstanding pursuit, with Generative AI (GAI) ushering in a new era of possibilities. The research presented here explores how GAI can enhance creativity and assist Design practitioners with tasks to create products such as, but not limited to, renderings, concepts, construction techniques, materials, data analytics or maps. We apply a framework of combinational, exploratory and transformational creativity to organize how recent advancements in GAI can support each creative category. We propose a conceptual framework of GAI towards transformational creativity, and identify real-world examples to demonstrate GAI's impact, such as transforming sketches into detailed renders, facilitating real-time 3D model generation, predicting trends through analytics and creating images or reports via text prompts. Our work envisions a future where GAI becomes a real-time collaborator to complete certain automated tasks while liberating Designers to focus on transformational innovation.","2024-08-01","2024-12-03 03:07:26","2024-12-03 03:07:26","","100086","","2","34","","Journal of Creativity","","","","","","","","","","","","","","","","","","","Generative artificial intelligence; Creative computing; Product; Transformational Creativity","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"FQ9LXHQ8","journalArticle","2024","LIU, He; REN, Yili; LI, Xin; DENG, Yue; WANG, Yongtao; CAO, Qianwen; DU, Jinyang; LIN, Zhiwei; WANG, Wenjie","Research status and application of artificial intelligence large models in the oil and gas industry","Petroleum Exploration and Development","","1876-3804","10.1016/S1876-3804(24)60524-0","https://www.sciencedirect.com/science/article/pii/S1876380424605240","This article elucidates the concept of large model technology, summarizes the research status of large model technology both domestically and internationally, provides an overview of the application status of large models in vertical industries, outlines the challenges and issues confronted in applying large models in the oil and gas sector, and offers prospects for the application of large models in the oil and gas industry. The existing large models can be briefly divided into three categories: large language models, visual large models, and multimodal large models. The application of large models in the oil and gas industry is still in its infancy. Based on open-source large language models, some oil and gas enterprises have released large language model products using methods like fine-tuning and retrieval augmented generation. Scholars have attempted to develop scenario-specific models for oil and gas operations by using visual/multimodal foundation models. A few researchers have constructed pre-trained foundation models for seismic data processing and interpretation, as well as core analysis. The application of large models in the oil and gas industry faces challenges such as current data quantity and quality being difficult to support the training of large models, high research and development costs, and poor algorithm autonomy and control. The application of large models should be guided by the needs of oil and gas business, taking the application of large models as an opportunity to improve data lifecycle management, enhance data governance capabilities, promote the construction of computing power, strengthen the construction of “artificial intelligence + energy” composite teams, and boost the autonomy and control of large model technology.","2024-08-01","2024-12-03 03:07:26","2024-12-03 03:07:26","","1049-1065","","4","51","","Petroleum Exploration and Development","","","","","","","","","","","","","","","","","","","fine-tuning; pre-training; foundation model; large language mode; large model of oil and gas industry; multimodal large model; visual large model","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"YJ5DPY89","journalArticle","2024","Wei, Qiaoling; Gu, Zhuoyao; Tan, Weimin; Kong, Hongyu; Fu, Hao; Jiang, Qin; Zhuang, Wenjuan; Zhang, Shaochi; Feng, Lixia; Liu, Yong; Li, Suyan; Qin, Bing; Lu, Peirong; Zhao, Jiangyue; Li, Zhigang; Yuan, Songtao; Yan, Hong; Zhang, Shujie; Zhu, Xiangjia; Hong, Jiaxu; Zhao, Chen; Yan, Bo","Development and Validation of an Automatic Ultrawide-Field Fundus Imaging Enhancement System for Facilitating Clinical Diagnosis: A Cross-Sectional Multicenter Study","Engineering","","2095-8099","10.1016/j.eng.2024.05.006","https://www.sciencedirect.com/science/article/pii/S2095809924002662","In ophthalmology, the quality of fundus images is critical for accurate diagnosis, both in clinical practice and in artificial intelligence (AI)-assisted diagnostics. Despite the broad view provided by ultrawide-field (UWF) imaging, pseudocolor images may conceal critical lesions necessary for precise diagnosis. To address this, we introduce UWF-Net, a sophisticated image enhancement algorithm that takes disease characteristics into consideration. Using the Fudan University ultra-wide-field image (FDUWI) dataset, which includes 11 294 Optos pseudocolor and 2415 Zeiss true-color UWF images, each of which is rigorously annotated, UWF-Net combines global style modeling with feature-level lesion enhancement. Pathological consistency loss is also applied to maintain fundus feature integrity, significantly improving image quality. Quantitative and qualitative evaluations demonstrated that UWF-Net outperforms existing methods such as contrast limited adaptive histogram equalization (CLAHE) and structure and illumination constrained generative adversarial network (StillGAN), delivering superior retinal image quality, higher quality scores, and preserved feature details after enhancement. In disease classification tasks, images enhanced by UWF-Net showed notable improvements when processed with existing classification systems over those enhanced by StillGAN, demonstrating a 4.62% increase in sensitivity (SEN) and a 3.97% increase in accuracy (ACC). In a multicenter clinical setting, UWF-Net-enhanced images were preferred by ophthalmologic technicians and doctors, and yielded a significant reduction in diagnostic time ((13.17 ± 8.40) s for UWF-Net enhanced images vs (19.54 ± 12.40) s for original images) and an increase in diagnostic accuracy (87.71% for UWF-Net enhanced images vs 80.40% for original images). Our research verifies that UWF-Net markedly improves the quality of UWF imaging, facilitating better clinical outcomes and more reliable AI-assisted disease classification. The clinical integration of UWF-Net holds great promise for enhancing diagnostic processes and patient care in ophthalmology.","2024-10-01","2024-12-03 03:07:26","2024-12-03 03:07:26","","179-188","","","41","","Engineering","","","","","","","","","","","","","","","","","","","Artificial intelligence; Artificial intelligence-assisted diagnostics; Diagnostic accuracy; Fundus photography; Image enhancement algorithm; Multicenter study; Ultrawide-field imaging","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"GKBMW5VL","journalArticle","2024","Kefeli, Jenna; Tatonetti, Nicholas","TCGA-Reports: A machine-readable pathology report resource for benchmarking text-based AI models","Patterns","","2666-3899","10.1016/j.patter.2024.100933","https://www.sciencedirect.com/science/article/pii/S2666389924000242","Summary In cancer research, pathology report text is a largely untapped data source. Pathology reports are routinely generated, more nuanced than structured data, and contain added insight from pathologists. However, there are no publicly available datasets for benchmarking report-based models. Two recent advances suggest the urgent need for a benchmark dataset. First, improved optical character recognition (OCR) techniques will make it possible to access older pathology reports in an automated way, increasing the data available for analysis. Second, recent improvements in natural language processing (NLP) techniques using artificial intelligence (AI) allow more accurate prediction of clinical targets from text. We apply state-of-the-art OCR and customized post-processing to report PDFs from The Cancer Genome Atlas, generating a machine-readable corpus of 9,523 reports. Finally, we perform a proof-of-principle cancer-type classification across 32 tissues, achieving 0.992 average AU-ROC. This dataset will be useful to researchers across specialties, including research clinicians, clinical trial investigators, and clinical NLP researchers.","2024-03-08","2024-12-03 03:07:26","2024-12-03 03:07:26","","100933","","3","5","","Patterns","","","","","","","","","","","","","","","","","","","machine learning; large language models; AI; cancer pathology; cancer type; classification; pathology reports; resource; TCGA; transformer model","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"NZG78IIU","journalArticle","2024","Caruccio, Loredana; Cirillo, Stefano; Polese, Giuseppe; Solimando, Giandomenico; Sundaramurthy, Shanmugam; Tortora, Genoveffa","Can ChatGPT provide intelligent diagnoses? A comparative study between predictive models and ChatGPT to define a new medical diagnostic bot","Expert Systems with Applications","","0957-4174","10.1016/j.eswa.2023.121186","https://www.sciencedirect.com/science/article/pii/S0957417423016883","Intelligent diagnosis processes rely on Artificial Intelligence (AI) techniques to provide possible diagnoses by analyzing patient data and medical information. To make accurate and quick diagnoses, it is possible to use AI tools to efficiently analyze huge amounts of data and find patterns that a clinician might miss. In recent years, new large language models (LLMs), such as ChatGPT and Google BARD, have shown remarkable capabilities in several domains, including intelligent diagnostics. This research aims to compare the performances of ChatGPT and traditional machine learning models for making diagnoses of low- and medium- risk diseases only based on their symptoms. On the basis of our study, we defined four research questions: RQ1) What are the benefits and limitations of using ChatGPT in intelligent diagnosis? RQ2) How do traditional machine learning approaches compare to ChatGPT for intelligent diagnosis? RQ3) How does ChatGPT compare with other LLMs and domain-specific natural language processing models in the intelligent diagnosis tasks?, and RQ4) What are the implications of the predictive models and ChatGPT for healthcare, and how can they be used to support people?. To answer these RQs, we first evaluate the performances of different engines of ChatGPT, also introducing a new prompt engineering methodology specifically tailored for achieving accurate diagnostic outcomes. Moreover, we compare these results with those achieved by different predictive models trained for intelligent diagnosis tasks, i.e., Google BARD, and two domain-specific NLP models. Finally, we propose a new interactive bot available for users that relies on the best-performing models evaluated in the previous steps. The experiments have been conducted using two medical datasets for disease prediction consisting of more than 100 symptoms associated with several diagnoses.","2024-01-01","2024-12-03 03:07:26","2024-12-03 03:07:26","","121186","","","235","","Expert Systems with Applications","","","","","","","","","","","","","","","","","","","ChatGPT; Comparative analysis; Google BARD; Intelligent diagnosis; Traditional ML","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"NQWLRM75","journalArticle","2024","Kim, Won Tae; Shin, Jaegwang; Yoo, In-Sang; Lee, Jae-Woo; Jeon, Hyun Jeong; Yoo, Hyo-Sun; Kim, Yongwhan; Jo, Jeong-Min; Hwang, ShinJi; Lee, Woo-Jeong; Park, Seung; Kim, Yong-June","Medication Extraction and Drug Interaction Chatbot: Generative Pretrained Transformer-Powered Chatbot for Drug-Drug Interaction","Mayo Clinic Proceedings: Digital Health","","2949-7612","10.1016/j.mcpdig.2024.09.001","https://www.sciencedirect.com/science/article/pii/S2949761224000981","Objective To assist individuals, particularly cancer patients or those with complex comorbidities, in quickly identifying potentially contraindicated medications when taking multiple drugs simultaneously. Patients and Methods In this study, we introduce the Medication Extraction and Drug Interaction Chatbot (MEDIC), an artificial intelligence system that integrates optical character recognition and Chat generative pretrained transformer through the Langchain framework. Medication Extraction and Drug Interaction Chatbot starts by receiving 2 drug bag images from the patient. It uses optical character recognition and text similarity techniques to extract drug names from the images. The extracted drug names are then processed through Chat generative pretrained transformer and Langchain to provide the user with information about drug contraindications. The MEDIC responds to the user with clear and concise sentences to ensure the information is easily understandable. This research was conducted from July 1, 2022 to April 30, 2024. Results This streamlined process enhances the accuracy of drug-drug interaction detection, providing a crucial tool for health care professionals and patients to improve medication safety. The proposed system was validated through rigorous evaluation using real-world data, reporting high accuracy in drug-drug interaction identification and highlighting its potential to benefit medication management practices considerably. Conclusion By implementing MEDIC, contraindicated medications can be identified using only medication packaging, and users can be alerted to potential drug adverse effects, thereby contributing to advancements in patient care in clinical settings.","2024-12-01","2024-12-03 03:07:26","2024-12-03 03:07:26","","611-619","","4","2","","Mayo Clinic Proceedings: Digital Health","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"GKEND74C","journalArticle","2023","Ratten, Vanessa; Jones, Paul","Generative artificial intelligence (ChatGPT): Implications for management educators","The International Journal of Management Education","","1472-8117","10.1016/j.ijme.2023.100857","https://www.sciencedirect.com/science/article/pii/S1472811723000952","ChatGPT has been one of the most talked about computer programs amongst management educators in recent weeks due to its transformative ability to change how assessments are undertaken and graded. Unlike other educational technologies that can be tracked when used, ChatGPT has superior abilities that make it virtually untraceable when used. This creates a dilemma for management educators wanting to utilise the technology whilst staying relevant but also interested in authentic learning. Thus, it is critical for management educators to quickly implement policies regarding ChatGPT and subsequent new generative artificial intelligence because of its ease of use and affordability. This article is conceptual in nature and discusses ChatGPT as a generative form of artificial intelligence that presents challenges for management educators that need to be addressed through appropriate strategies. Thereby contributing to the literature on how technological innovations can be included in curriculum design and management learning practices. Practical and managerial implications are stated that highlight the critical need to re-examine existing education practices as a way of incorporating new technological innovation that can be utilised in a beneficial way.","2023-11-01","2024-12-03 03:07:26","2024-12-03 03:07:26","","100857","","3","21","","The International Journal of Management Education","","","","","","","","","","","","","","","","","","","Learning; Artificial intelligence; ChatGPT; Teaching; Digital transformation; Academic research; Management education","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"VLH7JDDM","journalArticle","2024","Haurogné, Jean; Basheer, Nihala; Islam, Shareeful","Vulnerability detection using BERT based LLM model with transparency obligation practice towards trustworthy AI","Machine Learning with Applications","","2666-8270","10.1016/j.mlwa.2024.100598","https://www.sciencedirect.com/science/article/pii/S2666827024000744","Vulnerabilities in the source code are one of the main causes of potential threats in software-intensive systems. There are a large number of vulnerabilities published each day, and effective vulnerability detection is critical to identifying and mitigating these vulnerabilities. AI has emerged as a promising solution to enhance vulnerability detection, offering the ability to analyse vast amounts of data and identify patterns indicative of potential threats. However, AI-based methods often face several challenges, specifically when dealing with large datasets and understanding the specific context of the problem. Large Language Model (LLM) is now widely considered to tackle more complex tasks and handle large datasets, which also exhibits limitations in terms of explaining the model outcome and existing works focus on providing overview of explainability and transparency. This research introduces a novel transparency obligation practice for vulnerability detection using BERT based LLMs. We address the black-box nature of LLMs by employing XAI techniques, unique combination of SHAP, LIME, heat map. We propose an architecture that combines the BERT model with transparency obligation practices, which ensures the assurance of transparency throughout the entire LLM life cycle. An experiment is performed with a large source code dataset to demonstrate the applicability of the proposed approach. The result shows higher accuracy of 91.8 % for the vulnerability detection and model explainability outcome is highly influenced by “vulnerable”, “function”, ""mysql_tmpdir_list"", “strmov” tokens using both SHAP and LIME framework. Heatmap of attention weights, highlights the local token interactions that aid in understanding the model's decision points.","2024-12-01","2024-12-03 03:07:26","2024-12-03 03:07:26","","100598","","","18","","Machine Learning with Applications","","","","","","","","","","","","","","","","","","","Large language model; Explainability; BERT model; EU AI Act; Transparency obligation; Vulnerability","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"5TDF9QNA","journalArticle","2023","Jacob Fernandes França, Tiago; São Mamede, Henrique; Pereira Barroso, João Manuel; Pereira Duarte dos Santos, Vítor Manuel","Artificial intelligence applied to potential assessment and talent identification in an organisational context","Heliyon","","2405-8440","10.1016/j.heliyon.2023.e14694","https://www.sciencedirect.com/science/article/pii/S2405844023019011","Our study provides valuable insights into the relationship between artificial intelligence (AI) and Human Resource Management (HRM). We have minimised bias and ensured reliable findings by employing a systematic literature review and the PRISMA statement. Our comprehensive synthesis of the studies included in this research, along with a bibliometric analysis of articles, journals, indexes, authors' affiliations, citations, keyword co-occurrences, and co-authorship analysis, has produced robust results. The discussion of our findings focuses on critical areas of interest, such as AI and Talent, AI Bias, Ethics and Law, and their impact on Human Resource (HR) management. Our research highlights the recognition by organisations of the importance of talent management in achieving a competitive advantage as higher-level skills become increasingly necessary. Although some HR managers have adopted AI technology for talent acquisition, our study reveals that there is still room for improvement. Our study is in line with previous research that acknowledges the potential for AI to revolutionise HR management and the future of work. Our findings emphasise the need for HR managers to be proactive in embracing technology and bridging the technological, human, societal, and governmental gaps. Our study contributes to the growing body of AI and HR management knowledge, providing essential insights and recommendations for future research. The importance of our study lies in its focus on the role of HR in promoting the benefits of AI-based applications, thereby creating a larger body of knowledge from an organisational perspective.","2023-04-01","2024-12-03 03:07:26","2024-12-03 03:07:26","","e14694","","4","9","","Heliyon","","","","","","","","","","","","","","","","","","","Human resources; Artificial intelligence; Talent management; Next-gen HR; Potential assessment","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"W8QZMJGI","journalArticle","2024","Rybinski, Maciej; Kusa, Wojciech; Karimi, Sarvnaz; Hanbury, Allan","Learning to match patients to clinical trials using large language models","Journal of Biomedical Informatics","","1532-0464","10.1016/j.jbi.2024.104734","https://www.sciencedirect.com/science/article/pii/S1532046424001527","Objective: This study investigates the use of Large Language Models (LLMs) for matching patients to clinical trials (CTs) within an information retrieval pipeline. Our objective is to enhance the process of patient-trial matching by leveraging the semantic processing capabilities of LLMs, thereby improving the effectiveness of patient recruitment for clinical trials. Methods: We employed a multi-stage retrieval pipeline integrating various methodologies, including BM25 and Transformer-based rankers, along with LLM-based methods. Our primary datasets were the TREC Clinical Trials 2021–23 track collections. We compared LLM-based approaches, focusing on methods that leverage LLMs in query formulation, filtering, relevance ranking, and re-ranking of CTs. Results: Our results indicate that LLM-based systems, particularly those involving re-ranking with a fine-tuned LLM, outperform traditional methods in terms of nDCG and Precision measures. The study demonstrates that fine-tuning LLMs enhances their ability to find eligible trials. Moreover, our LLM-based approach is competitive with state-of-the-art systems in the TREC challenges. The study shows the effectiveness of LLMs in CT matching, highlighting their potential in handling complex semantic analysis and improving patient-trial matching. However, the use of LLMs increases the computational cost and reduces efficiency. We provide a detailed analysis of effectiveness-efficiency trade-offs. Conclusion: This research demonstrates the promising role of LLMs in enhancing the patient-to-clinical trial matching process, offering a significant advancement in the automation of patient recruitment. Future work should explore optimising the balance between computational cost and retrieval effectiveness in practical applications.","2024-11-01","2024-12-03 03:07:26","2024-12-03 03:07:26","","104734","","","159","","Journal of Biomedical Informatics","","","","","","","","","","","","","","","","","","","Information retrieval; Large language models; Clinical trials; Learning-to-rank; Patient to trials matching; TCRR; TREC CT","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"HUZKWMWX","journalArticle","2024","Bouteraa, Mohamed; Bin-Nashwan, Saeed Awadh; Al-Daihani, Meshari; Dirie, Khadar Ahmed; Benlahcene, Abderrahim; Sadallah, Mouad; Zaki, Hafizah Omar; Lada, Suddin; Ansar, Rudy; Fook, Lim Ming; Chekima, Brahim","Understanding the diffusion of AI-generative (ChatGPT) in higher education: Does students' integrity matter?","Computers in Human Behavior Reports","","2451-9588","10.1016/j.chbr.2024.100402","https://www.sciencedirect.com/science/article/pii/S2451958824000356","ChatGPT, an AI-powered language model, is revolutionising the academic world. Scholars, researchers, and students use its advanced capabilities to achieve their educational objectives, including generating innovative ideas, delivering assignments, and conducting extensive research projects. Nevertheless, the use of ChatGPT among students is contentious, giving rise to significant apprehensions regarding integrity and AI-facilitated deceit. At the same time, scholarly communities currently need more well-defined standards for adopting such academia-oriented technology. This study aims to determine students' use of ChatGPT using the Unified Theory of Acceptance and Use of Technology (UTAUT) and Social Cognitive Theory (SCT), notably the role of students' integrity in determining adoption behaviour. The analysis of 921 responses demonstrated that the utilisation of ChatGPT is influenced positively by performance expectancy, social influence, educational self-efficacy, technology self-efficacy, and personal anxiety. Conversely, student integrity was found to negatively impact usage. Remarkably, student integrity has a positive moderating effect between effort expectancy and ChatGPT usage. At the same time, it has a negative moderating effect on the link between performance expectancy and technology self-efficacy with ChatGPT usage. Hence, we propose that the academic community, AI language model developers, publishers, and relevant stakeholders collaborate to establish explicit rules for the utilisation of AI chatbots in an ethical manner for educational purposes.","2024-05-01","2024-12-03 03:22:58","2024-12-03 03:22:58","","100402","","","14","","Computers in Human Behavior Reports","","","","","","","","","","","","","","","","","","","Chatbots; ChatGPT; Artificial intelligence students integrity; SCT; UTAUT","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"VCMC3HHC","journalArticle","2024","Guan, Shenghui; Wang, Guanyu","Drug discovery and development in the era of artificial intelligence: From machine learning to large language models","Artificial Intelligence Chemistry","","2949-7477","10.1016/j.aichem.2024.100070","https://www.sciencedirect.com/science/article/pii/S2949747724000289","Drug Research and Development (R&D) is a complex and difficult process, and current drug R&D faces the challenges of long time span, high investment, and high failure rate. Machine learning, with its powerful learning ability to characterize big data and complex networks, is increasingly effective to improve the efficiency and success rate of drug R&D. Here we review some recent examples of the application of machine learning methods in six areas: disease gene prediction, virtual screening, drug molecule generation, molecular attribute prediction, and prediction of drug combination synergism. We also discuss the advantages of integrative learning in multi-attribute prediction. Integrative models based on base learners constructed from data of different dimensions on the one hand fully utilize the information contained in these data, and on the other hand improve the average prediction performance. Finally, we envision a new paradigm for drug discovery and development: a large language model acts as a central hub to organize public resources into a knowledge base, validating the knowledge with computational software and smaller predictive models, as well as high-throughput automated screening platforms based on organoidal technologies, to speed up development and reduce the differences in efficacy between disease models and humans to improve the success rate of a drug.","2024-06-01","2024-12-03 03:22:58","2024-12-03 03:22:58","","100070","","1","2","","Artificial Intelligence Chemistry","","","","","","","","","","","","","","","","","","","Machine learning; Bioinformatics; Drug Discovery","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"8R8N9YND","journalArticle","2024","Jiang, Shuo; Evans-Yamamoto, Daniel; Bersenev, Dennis; Palaniappan, Sucheendra K.; Yachie-Kinoshita, Ayako","ProtoCode: Leveraging large language models (LLMs) for automated generation of machine-readable PCR protocols from scientific publications","SLAS Technology","","2472-6303","10.1016/j.slast.2024.100134","https://www.sciencedirect.com/science/article/pii/S2472630324000165","Protocol standardization and sharing are crucial for reproducibility in life sciences. In spite of numerous efforts for standardized protocol description, adherence to these standards in literature remains largely inconsistent. Curation of protocols are especially challenging due to the labor intensive process, requiring expert domain knowledge of each experimental procedure. Recent advancements in Large Language Models (LLMs) offer a promising solution to interpret and curate knowledge from complex scientific literature. In this work, we develop ProtoCode, a tool leveraging fine-tune LLMs to curate protocols into intermediate representation formats which can be interpretable by both human and machine interfaces. Our proof-of-concept, focused on polymerase chain reaction (PCR) protocols, retrieves information from PCR protocols at an accuracy ranging 69–100 % depending on the information content. In all tested protocols, we demonstrate that ProtoCode successfully converts literature-based protocols into correct operational files for multiple thermal cycler systems. In conclusion, ProtoCode can alleviate labor intensive curation and standardization of life science protocols to enhance research reproducibility by providing a reliable, automated means to process and standardize protocols. ProtoCode is freely available as a web server at https://curation.taxila.io/ProtoCode/.","2024-06-01","2024-12-03 03:22:58","2024-12-03 03:22:58","","100134","","3","29","","SLAS Technology","","","","","","","","","","","","","","","","","","","Large language model; Text mining; Lab automation; Protocol standardization","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"J2JTRJAI","journalArticle","2024","Li, Jiawen; Huang, Yuesheng; Lu, Yayi; Wang, Leijun; Ren, Yongqi; Chen, Rongjun","Sentiment Analysis Using E-Commerce Review Keyword-Generated Image with a Hybrid Machine Learning-Based Model","Computers, Materials and Continua","","1546-2218","10.32604/cmc.2024.052666","https://www.sciencedirect.com/science/article/pii/S1546221824005113","In the context of the accelerated pace of daily life and the development of e-commerce, online shopping is a mainstream way for consumers to access products and services. To understand their emotional expressions in facing different shopping experience scenarios, this paper presents a sentiment analysis method that combines the e-commerce review keyword-generated image with a hybrid machine learning-based model, in which the Word2Vec-TextRank is used to extract keywords that act as the inputs for generating the related images by generative Artificial Intelligence (AI). Subsequently, a hybrid Convolutional Neural Network and Support Vector Machine (CNN-SVM) model is applied for sentiment classification of those keyword-generated images. For method validation, the data randomly comprised of 5000 reviews from Amazon have been analyzed. With superior keyword extraction capability, the proposed method achieves impressive results on sentiment classification with a remarkable accuracy of up to 97.13%. Such performance demonstrates its advantages by using the text-to-image approach, providing a unique perspective for sentiment analysis in the e-commerce review data compared to the existing works. Thus, the proposed method enhances the reliability and insights of customer feedback surveys, which would also establish a novel direction in similar cases, such as social media monitoring and market trend research.","2024-07-18","2024-12-03 03:22:59","2024-12-03 03:22:59","","1581-1599","","1","80","","Computers, Materials and Continua","","","","","","","","","","","","","","","","","","","machine learning; Sentiment analysis; CNN-SVM; keyword-generated image; Word2Vec-TextRank","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"P5CHHXUG","journalArticle","2024","Derner, Erik; Kučera, Dalibor; Oliver, Nuria; Zahálka, Jan","Can ChatGPT read who you are?","Computers in Human Behavior: Artificial Humans","","2949-8821","10.1016/j.chbah.2024.100088","https://www.sciencedirect.com/science/article/pii/S2949882124000483","The interplay between artificial intelligence (AI) and psychology, particularly in personality assessment, represents an important emerging area of research. Accurate personality trait estimation is crucial not only for enhancing personalization in human-computer interaction but also for a wide variety of applications ranging from mental health to education. This paper analyzes the capability of a generic chatbot, ChatGPT, to effectively infer personality traits from short texts. We report the results of a comprehensive user study featuring texts written in Czech by a representative population sample of 155 participants. Their self-assessments based on the Big Five Inventory (BFI) questionnaire serve as the ground truth. We compare the personality trait estimations made by ChatGPT against those by human raters and report ChatGPT's competitive performance in inferring personality traits from text. We also uncover a ‘positivity bias’ in ChatGPT's assessments across all personality dimensions and explore the impact of prompt composition on accuracy. This work contributes to the understanding of AI capabilities in psychological assessment, highlighting both the potential and limitations of using large language models for personality inference. Our research underscores the importance of responsible AI development, considering ethical implications such as privacy, consent, autonomy, and bias in AI applications.","2024-08-01","2024-12-03 03:22:59","2024-12-03 03:22:59","","100088","","2","2","","Computers in Human Behavior: Artificial Humans","","","","","","","","","","","","","","","","","","","Psychology; Large language models; Natural language processing; Big five inventory; Personality traits","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"R4A2ZPHJ","journalArticle","2024","Chen, Hao; Xie, Runfeng; Cui, Xiangyang; Yan, Zhou; Wang, Xin; Xuan, Zhanwei; Zhang, Kai","LKPNR: Large Language Models and Knowledge Graph for Personalized News Recommendation Framework","Computers, Materials and Continua","","1546-2218","10.32604/cmc.2024.049129","https://www.sciencedirect.com/science/article/pii/S1546221824000225","Accurately recommending candidate news to users is a basic challenge of personalized news recommendation systems. Traditional methods are usually difficult to learn and acquire complex semantic information in news texts, resulting in unsatisfactory recommendation results. Besides, these traditional methods are more friendly to active users with rich historical behaviors. However, they can not effectively solve the long tail problem of inactive users. To address these issues, this research presents a novel general framework that combines Large Language Models (LLM) and Knowledge Graphs (KG) into traditional methods. To learn the contextual information of news text, we use LLMs’ powerful text understanding ability to generate news representations with rich semantic information, and then, the generated news representations are used to enhance the news encoding in traditional methods. In addition, multi-hops relationship of news entities is mined and the structural information of news is encoded using KG, thus alleviating the challenge of long-tail distribution. Experimental results demonstrate that compared with various traditional models, on evaluation indicators such as AUC, MRR, nDCG@5 and nDCG@10, the framework significantly improves the recommendation performance. The successful integration of LLM and KG in our framework has established a feasible way for achieving more accurate personalized news recommendation. Our code is available at https://github.com/Xuan-ZW/LKPNR.","2024-06-20","2024-12-03 03:22:59","2024-12-03 03:22:59","","4283-4296","","3","79","","Computers, Materials and Continua","","","","","","","","","","","","","","","","","","","Large language models; knowledge graphs (KG); news recommendation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"MNFXITRN","journalArticle","2024","Rungruangjit, Warinrampai; Mongkol, Kulachet; Piriyakul, Intaka; Charoenpornpanichkul, Kitti","The power of human-like virtual-influencer-generated content: Impact on consumers’ willingness to follow and purchase intentions","Computers in Human Behavior Reports","","2451-9588","10.1016/j.chbr.2024.100523","https://www.sciencedirect.com/science/article/pii/S2451958824001568","The swift progress in machine learning algorithms, artificial intelligence, and interactive immersive media technologies has led to the introduction of computer-generated imagery on Instagram. This feature, so-called “human-like virtual influencers (VIs)"", has revolutionized the way people interact with technology. Using a combination of cutting-edge AI technologies, in a novel application of computer vision algorithms, and large language models to extract the content posted by two popular human-like VIs on Instagram, the present study is the first to categorize and classify types of human-like virtual-influencer-generated content. Quantitative methods, such as partial least squares structural equation modeling (PLS-SEM), were used to examine the impact of human-like virtual-influencer-generated content on consumers' willingness to follow as well as purchase intentions. The information was gathered from 650 Thai customers. The findings showed that consumers' willingness to follow and purchase intentions were significantly influenced by the positive effects of emotional appeal content, which includes relational, entertaining, positive emotion, and negative emotion content. These effects outweighed those of rational appeal content, such as informative and remunerative content, as well as authenticity appeal content. Meanwhile, disclosing sponsored content had no effect on consumers' willingness to follow. The theoretical underpinnings of uses and gratifications (U&G) theory, parasocial relationships and Richins' hierarchical model of emotions are confirmed and expanded upon in this work, and the suggested inclusive approach also significantly advances the expanding corpus of research on VIs. Our research also provides a contribution to the recent literature on human-like VI marketing.","2024-12-01","2024-12-03 03:22:59","2024-12-03 03:22:59","","100523","","","16","","Computers in Human Behavior Reports","","","","","","","","","","","","","","","","","","","Artificial intelligence; Emotional content; Generated content; Uses and gratifications theory; Virtual influencer; Willingness to follow","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CHLZDL87","journalArticle","2024","Wu, Borui; Zhao, Wenrui","Fault prediction of electronic devices based on attention mechanism time-series point process","Measurement: Sensors","","2665-9174","10.1016/j.measen.2023.101023","https://www.sciencedirect.com/science/article/pii/S2665917423003598","In recent years, with the development of high-precision sensors, computer technology and artificial intelligence, it makes the data-driven intelligent fault warning and diagnosis method gradually become a research hotspot. Aiming at the low utilization of fault diagnosis information of complex electronic devices and the problems of convergence and training time of traditional neural network fault diagnosis models, a point-in-time process generation model without using intensity function is proposed. The model uses the Wasserstein distance to construct the loss function, which facilitates the measurement of the deviation between the model distribution and the true distribution, and uses a self-concern mechanism to describe the degree of influence of the historical events on the current events, which makes the model interpretable and more capable of generalization. Comparative experiments show that without a priori information about the intensity function, the method reduces the relative error rate by 3.59 %, improves the fault prediction accuracy by 3.91 %, and has a better overall fit than the RNN-like generative model and the great likelihood model. Example analysis shows that the model has good prediction accuracy and provides a feasible solution for real-time fault diagnosis of complex electronic devices.","2024-02-01","2024-12-03 03:22:59","2024-12-03 03:22:59","","101023","","","31","","Measurement: Sensors","","","","","","","","","","","","","","","","","","","Electronic; Event sequence; Fault prediction; Self-attentiveness; Time-series point process","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"TJ59T8KH","journalArticle","2023","Jin, Joy Q.; Dobry, Allison S.","ChatGPT for healthcare providers and patients: Practical implications within dermatology","Journal of the American Academy of Dermatology","","0190-9622","10.1016/j.jaad.2023.05.081","https://www.sciencedirect.com/science/article/pii/S0190962223011064","","2023-10-01","2024-12-03 03:22:59","2024-12-03 03:22:59","","870-871","","4","89","","Journal of the American Academy of Dermatology","","","","","","","","","","","","","","","","","","","innovation; artificial intelligence; ChatGPT; medical education; technology; large language models; clinical decision-making; dermatology; administrative support; clinical research; health literacy; healthcare providers; patients","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KAIED5J9","journalArticle","2024","Wu, Da; Yang, Jingye; Wang, Kai","Exploring the reversal curse and other deductive logical reasoning in BERT and GPT-based large language models","Patterns","","2666-3899","10.1016/j.patter.2024.101030","https://www.sciencedirect.com/science/article/pii/S2666389924001636","Summary The “Reversal Curse” describes the inability of autoregressive decoder large language models (LLMs) to deduce “B is A” from “A is B,” assuming that B and A are distinct and can be uniquely identified from each other. This logical failure suggests limitations in using generative pretrained transformer (GPT) models for tasks like constructing knowledge graphs. Our study revealed that a bidirectional LLM, bidirectional encoder representations from transformers (BERT), does not suffer from this issue. To investigate further, we focused on more complex deductive reasoning by training encoder and decoder LLMs to perform union and intersection operations on sets. While both types of models managed tasks involving two sets, they struggled with operations involving three sets. Our findings underscore the differences between encoder and decoder models in handling logical reasoning. Thus, selecting BERT or GPT should depend on the task’s specific needs, utilizing BERT’s bidirectional context comprehension or GPT’s sequence prediction strengths.","2024-09-13","2024-12-03 03:22:59","2024-12-03 03:22:59","","101030","","9","5","","Patterns","","","","","","","","","","","","","","","","","","","large language model; LLM; GPT; BERT; auto-regressive model; bidirectional encoder; deductive logical reasoning; reversal curse","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"4X2EVPT5","journalArticle","2024","Hadi Mogavi, Reza; Deng, Chao; Juho Kim, Justin; Zhou, Pengyuan; D. Kwon, Young; Hosny Saleh Metwally, Ahmed; Tlili, Ahmed; Bassanelli, Simone; Bucchiarone, Antonio; Gujar, Sujit; Nacke, Lennart E.; Hui, Pan","ChatGPT in education: A blessing or a curse? A qualitative study exploring early adopters’ utilization and perceptions","Computers in Human Behavior: Artificial Humans","","2949-8821","10.1016/j.chbah.2023.100027","https://www.sciencedirect.com/science/article/pii/S2949882123000270","To foster the development of pedagogically potent and ethically sound AI-integrated learning landscapes, it is pivotal to critically explore the perceptions and experiences of the users immersed in these contexts. In this study, we perform a thorough qualitative content analysis across four key social media platforms. Our goal is to understand the user experience (UX) and views of early adopters of ChatGPT across different educational sectors. The results of our research show that ChatGPT is most commonly used in the domains of higher education, K-12 education, and practical skills training. In social media dialogues, the topics most frequently associated with ChatGPT are productivity, efficiency, and ethics. Early adopters' attitudes towards ChatGPT are multifaceted. On one hand, some users view it as a transformative tool capable of amplifying student self-efficacy and learning motivation. On the other hand, there is a degree of apprehension among concerned users. They worry about a potential overdependence on the AI system, which they fear might encourage superficial learning habits and erode students’ social and critical thinking skills. This dichotomy of opinions underscores the complexity of Human-AI Interaction in educational contexts. Our investigation adds depth to this ongoing discourse, providing crowd-sourced insights for educators and learners who are considering incorporating ChatGPT or similar generative AI tools into their pedagogical strategies.","2024-01-01","2024-12-03 03:22:59","2024-12-03 03:22:59","","100027","","1","2","","Computers in Human Behavior: Artificial Humans","","","","","","","","","","","","","","","","","","","Qualitative research; Education; ChatGPT; Generative AI; Artificial intelligence (AI); Early adopters; Human-computer interaction (HCI),; Social media","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"929VJV3K","journalArticle","2024","Sarvazyan, Areg Mikael; González, José Ángel; Franco-Salvador, Marc","TextMachina: Seamless Generation of Machine-Generated Text Datasets","28th International Conference on Knowledge Based and Intelligent information and Engineering Systems (KES 2024)","","1877-0509","10.1016/j.procs.2024.09.462","https://www.sciencedirect.com/science/article/pii/S1877050924025031","Recent advancements in Large Language Models (LLMs) have led to high-quality Machine-Generated Text (MGT), giving rise to countless new use cases and applications. However, easy access to LLMs is posing new challenges due to misuse. To address malicious usage, researchers have released datasets to effectively train models on MGT-related tasks. Similar strategies are used to compile these datasets, but no tool currently unifies them. In this scenario, we introduce TextMachina, a modular and extensible Python framework, designed to aid in the creation of high-quality, unbiased datasets to build robust models for MGT-related tasks such as detection, attribution, mixcase, or boundary detection. It provides a user-friendly pipeline that abstracts away the inherent intricacies of building MGT datasets, such as LLM integrations, prompt templating, and bias mitigation. The quality of the datasets generated by TextMachina has been assessed in previous works, including shared tasks where more than one hundred teams trained robust MGT detectors1.","2024-01-01","2024-12-03 03:22:59","2024-12-03 03:22:59","","566-575","","","246","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Large Language Models; MGT datasets; TextMachina","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"J9BB93YY","journalArticle","2024","Koh, Edwin C.Y.","Auto-DSM: Using a Large Language Model to generate a Design Structure Matrix","Natural Language Processing Journal","","2949-7191","10.1016/j.nlp.2024.100103","https://www.sciencedirect.com/science/article/pii/S2949719124000517","The Design Structure Matrix (DSM) is an established method used in dependency modelling, especially in the design of complex engineering systems. The generation of DSM is traditionally carried out through manual means and can involve interviewing experts to elicit critical system elements and the relationships between them. Such manual approaches can be time-consuming and costly. This paper presents a workflow that uses a Large Language Model (LLM) to support the generation of DSM and improve productivity. A prototype of the workflow was developed in this work and applied on a diesel engine DSM published previously. It was found that the prototype could reproduce 357 out of 462 DSM entries published (i.e. 77.3%), suggesting that the work can aid DSM generation. A no-code version of the prototype is made available online to support future research.","2024-12-01","2024-12-03 03:22:59","2024-12-03 03:22:59","","100103","","","9","","Natural Language Processing Journal","","","","","","","","","","","","","","","","","","","Generative AI; Large language model (LLM); Dependency modelling; Design automation; Design structure matrix (DSM); Retrieval augmented generation (RAG)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DRD8UD94","journalArticle","2024","Du, Xinsong; Novoa-Laurentiev, John; Plasek, Joseph M.; Chuang, Ya-Wen; Wang, Liqin; Marshall, Gad A.; Mueller, Stephanie K.; Chang, Frank; Datta, Surabhi; Paek, Hunki; Lin, Bin; Wei, Qiang; Wang, Xiaoyan; Wang, Jingqi; Ding, Hao; Manion, Frank J.; Du, Jingcheng; Bates, David W.; Zhou, Li","Enhancing early detection of cognitive decline in the elderly: a comparative study utilizing large language models in clinical notes","eBioMedicine","","2352-3964","10.1016/j.ebiom.2024.105401","https://www.sciencedirect.com/science/article/pii/S2352396424004377","Summary Background Large language models (LLMs) have shown promising performance in various healthcare domains, but their effectiveness in identifying specific clinical conditions in real medical records is less explored. This study evaluates LLMs for detecting signs of cognitive decline in real electronic health record (EHR) clinical notes, comparing their error profiles with traditional models. The insights gained will inform strategies for performance enhancement. Methods This study, conducted at Mass General Brigham in Boston, MA, analysed clinical notes from the four years prior to a 2019 diagnosis of mild cognitive impairment in patients aged 50 and older. We developed prompts for two LLMs, Llama 2 and GPT-4, on Health Insurance Portability and Accountability Act (HIPAA)-compliant cloud-computing platforms using multiple approaches (e.g., hard prompting, retrieval augmented generation, and error analysis-based instructions) to select the optimal LLM-based method. Baseline models included a hierarchical attention-based neural network and XGBoost. Subsequently, we constructed an ensemble of the three models using a majority vote approach. Confusion-matrix-based scores were used for model evaluation. Findings We used a randomly annotated sample of 4949 note sections from 1969 patients (women: 1046 [53.1%]; age: mean, 76.0 [SD, 13.3] years), filtered with keywords related to cognitive functions, for model development. For testing, a random annotated sample of 1996 note sections from 1161 patients (women: 619 [53.3%]; age: mean, 76.5 [SD, 10.2] years) without keyword filtering was utilised. GPT-4 demonstrated superior accuracy and efficiency compared to Llama 2, but did not outperform traditional models. The ensemble model outperformed the individual models in terms of all evaluation metrics with statistical significance (p < 0.01), achieving a precision of 90.2% [95% CI: 81.9%–96.8%], a recall of 94.2% [95% CI: 87.9%–98.7%], and an F1-score of 92.1% [95% CI: 86.8%–96.4%]. Notably, the ensemble model showed a significant improvement in precision, increasing from a range of 70%–79% to above 90%, compared to the best-performing single model. Error analysis revealed that 63 samples were incorrectly predicted by at least one model; however, only 2 cases (3.2%) were mutual errors across all models, indicating diverse error profiles among them. Interpretation LLMs and traditional machine learning models trained using local EHR data exhibited diverse error profiles. The ensemble of these models was found to be complementary, enhancing diagnostic performance. Future research should investigate integrating LLMs with smaller, localised models and incorporating medical data and domain knowledge to enhance performance on specific tasks. Funding This research was supported by the National Institute on Aging grants (R44AG081006, R01AG080429) and National Library of Medicine grant (R01LM014239).","2024-11-01","2024-12-03 03:22:59","2024-12-03 03:22:59","","105401","","","109","","eBioMedicine","","","","","","","","","","","","","","","","","","","Electronic health records; Natural language processing; Alzheimer disease; Cognitive dysfunction; Dementia; Early diagnosis; Neurobehavioral manifestations","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"BWAH988U","journalArticle","2024","Caruccio, Loredana; Cirillo, Stefano; Polese, Giuseppe; Solimando, Giandomenico; Sundaramurthy, Shanmugam; Tortora, Genoveffa","Claude 2.0 large language model: Tackling a real-world classification problem with a new iterative prompt engineering approach","Intelligent Systems with Applications","","2667-3053","10.1016/j.iswa.2024.200336","https://www.sciencedirect.com/science/article/pii/S2667305324000127","In the last year, Large Language Models (LLMs) have transformed the way of tackling problems, opening up new perspectives in various works and research fields, due to their ability to generate and understand human languages. In this regard, the recent release of Claude 2.0 has contributed to the processing of more complex prompts. In this scenario, the goal of this paper is to evaluate the effectiveness of Claude 2.0 in a specific classification task. In particular, we considered the Forest cover-type problem, concerning the prediction of a cover-type value according to the geospatial characterization of target worldwide areas. To this end, we propose a novel iterative prompt template engineering approach, which integrates files by exploiting prompts and evaluates the quality of responses provided by the LLM. Moreover, we conducted several comparative analyses to evaluate the effectiveness of Claude 2.0 with respect to online and batch learning models. The results demonstrated that, although some online and batch models performed better than Claude 2.0, the new iterative prompt engineering approach improved the quality of responses, leading to better performance with increases ranging from 14% to 32% in terms of accuracy, precision, recall, and F1-score.","2024-03-01","2024-12-03 03:22:59","2024-12-03 03:22:59","","200336","","","21","","Intelligent Systems with Applications","","","","","","","","","","","","","","","","","","","Machine learning; Large language model; Claude 2.0; Forest cover-type; Massive online analytics; Online learning","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"D4I8I76W","journalArticle","2024","Chowdhury, Ahmadul Karim; Sujon, Saidur Rahman; Shafi, Md. Shirajus Salekin; Ahmmad, Tasin; Ahmed, Sifat; Hasib, Khan Md; Shah, Faisal Muhammad","Harnessing large language models over transformer models for detecting Bengali depressive social media text: A comprehensive study","Natural Language Processing Journal","","2949-7191","10.1016/j.nlp.2024.100075","https://www.sciencedirect.com/science/article/pii/S2949719124000232","In an era where the silent struggle of underdiagnosed depression pervades globally, our research delves into the crucial link between mental health and social media. This work focuses on early detection of depression, particularly in extroverted social media users, using LLMs such as GPT 3.5, GPT 4 and our proposed GPT 3.5 fine-tuned model DepGPT, as well as advanced Deep learning models(LSTM, Bi-LSTM, GRU, BiGRU) and Transformer models(BERT, BanglaBERT, SahajBERT, BanglaBERT-Base). The study categorized Reddit and X datasets into “Depressive” and “Non-Depressive” segments, translated into Bengali by native speakers with expertise in mental health, resulting in the creation of the Bengali Social Media Depressive Dataset (BSMDD). Our work provides full architecture details for each model and a methodical way to assess their performance in Bengali depressive text categorization using zero-shot and few-shot learning techniques. Our work demonstrates the superiority of SahajBERT and Bi-LSTM with FastText embeddings in their respective domains also tackles explainability issues with transformer models and emphasizes the effectiveness of LLMs, especially DepGPT (GPT 3.5 fine-tuned), demonstrating flexibility and competence in a range of learning contexts. According to the experiment results, the proposed model, DepGPT, outperformed not only Alpaca Lora 7B in zero-shot and few-shot scenarios but also every other model, achieving a near-perfect accuracy of 0.9796 and an F1-score of 0.9804, high recall, and exceptional precision. Although competitive, GPT-3.5 Turbo and Alpaca Lora 7B show relatively poorer effectiveness in zero-shot and few-shot situations. The work emphasizes the effectiveness and flexibility of LLMs in a variety of linguistic circumstances, providing insightful information about the complex field of depression detection models.","2024-06-01","2024-12-03 03:22:59","2024-12-03 03:22:59","","100075","","","7","","Natural Language Processing Journal","","","","","","","","","","","","","","","","","","","Deep learning; Natural language processing; Large language model; Depression; Social media; Bengali; Transformer model","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"FVBPNSRF","journalArticle","2024","Paduraru, Ciprian; Staicu, Adelina; Stefanescu, Alin","LLM-based methods for the creation of unit tests in game development","28th International Conference on Knowledge Based and Intelligent information and Engineering Systems (KES 2024)","","1877-0509","10.1016/j.procs.2024.09.473","https://www.sciencedirect.com/science/article/pii/S1877050924025158","Problems related to the quality of games, whether on the initial release or after updates, can lead to player dissatisfaction, media attention, and potential financial setbacks. These issues can stem from software bugs, performance bottlenecks, or security vulnerabilities. Despite these challenges, game developers often rely on manual playtesting, highlighting the need for more robust and automated processes in game development. This research explores the application of Large Language Models (LLMs) to automate the creation of unit tests in game development, focusing on strongly typed programming languages such as C++ and C#, which are widely used in the industry. The study focuses on fine-tuning Code Llama, an advanced code generation model, to address common scenarios in game development, including game engines and specific APIs or backends. Although the prototyping and evaluations primarily took place within the Unity game engine, the proposed methods can be adapted to other internal or publicly available solutions. The evaluation results demonstrate these methods’ effectiveness in improving existing unit test suites or automatically generating new tests based on natural language descriptions of class contexts and targeted methods.","2024-01-01","2024-12-03 03:22:59","2024-12-03 03:22:59","","2459-2468","","","246","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Large language models; fine-tuning; game development; unit tests","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"UHW9WJIR","journalArticle","2024","Cesco, Stefano; Ascoli, Davide; Bailoni, Lucia; Bischetti, Gian Battista; Buzzini, Pietro; Cairoli, Monica; Celi, Luisella; Corti, Giuseppe; Marchetti, Marco; Mugnozza, Giacomo Scarascia; Orlandini, Simone; Porceddu, Andrea; Gigliotti, Giovanni; Mazzetto, Fabrizio","Smart management of emergencies in the agricultural, forestry, and animal production domain: Tackling evolving risks in the climate change era","International Journal of Disaster Risk Reduction","","2212-4209","10.1016/j.ijdrr.2024.105015","https://www.sciencedirect.com/science/article/pii/S2212420924007775","The agricultural, forestry, and animal production domain (AFA domain) plays an essential role in meeting global needs and supporting livelihoods while facing escalating challenges from climate change-induced impacts and extreme natural events. This perspective advocates for urgent strategies to enhance resilience through effective emergency management and prevention measures tailored to this critical domain. The analysis here exposed, which includes elements of ontology and the conceptual approach of an emergency management system encompassing both restoration and prevention aspects, entails three case studies across the AFA domain. Each case study, described by location, timing, nature, and consequences, critically evaluates the implemented risk prevention measures, details the emergency and recovery actions, and highlights shortcomings in response efforts. The analysis, incorporating a retrospective comparative component based on the proposed conceptual model, highlights the importance of identifying lessons learned and potential future applications. It emphasizes the urgent need for a well-structured emergency management strategy that integrates risk mapping and advanced technology to ensure timely and effective responses. The active engagement of domain professionals (agronomists, foresters, animal production doctors) and scholars of AFA domain sciences, as either farm owners or technical advisors, is crucial to optimize intervention strategies. This engagement is especially important for enhancing resilience during recovery phases, aligning with the best international practices such as making use of local knowledge and citizen engagement strategies. Comprehensive training initiatives, also adopting innovative formats and tools including micro-credentials, e-learning platforms, and the applications of generative Artificial Intelligence for learning assistance, as well as new research insights are strategic for coordinated and effective emergency responses across all stakeholders. Collaboration between the different production systems and areas of expertise, raising awareness of the distinction between Civil Protection and Production Protection and fostering their close interconnection, is essential for effective emergency response and long-term resilience.","2024-11-01","2024-12-03 03:22:59","2024-12-03 03:22:59","","105015","","","114","","International Journal of Disaster Risk Reduction","","","","","","","","","","","","","","","","","","","Sustainability; Climate change; Agriculture; Emergency; Forestry; Livestock","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"5CNNWBBG","journalArticle","2024","Almeida, José; Gonçalves, Tiago Cruz","The AI revolution: are crypto markets more efficient after ChatGPT 3?","Finance Research Letters","","1544-6123","10.1016/j.frl.2024.105608","https://www.sciencedirect.com/science/article/pii/S154461232400638X","This study examines the efficiency of crypto markets in the context of the AI revolution, focusing on AI-Crypto sectors. By employing the Adjusted Market Inefficiency Magnitude (AMIM) and conducting quantile efficiency and liquidity analyses, we observe dynamic fluctuations in market efficiency across sectors like Generative AI, AI Big Data, Cybersecurity, and Distributed Computing. Our findings reveal that most AI-Crypto sectors present a tendency towards higher efficiency in extreme market conditions. The introduction of ChatGPT 3 significantly enhanced market efficiency, with sectors associated with AI experiencing positive mean returns and increased liquidity. Our results suggest that market efficiency in these sectors is not static but evolves with technological innovations and sector-specific characteristics, in that AI-related sectors are driving the recent market dynamics, with increased liquidity and efficiency observed post-ChatGPT 3 launch. This research extends previous work on crypto market efficiency and explores the incorporation of information in AI tokenized projects, underscoring the transformative impact of AI on the crypto market landscape.","2024-08-01","2024-12-03 03:22:59","2024-12-03 03:22:59","","105608","","","66","","Finance Research Letters","","","","","","","","","","","","","","","","","","","ChatGPT; Artificial intelligence (AI); Crypto assets; Quantile-based efficiency","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"QXITBZEM","journalArticle","2024","Moufid, Oumayma; Praharaj, Sarbeswar; Jarar Oulidi, Hassane","Digital technologies in urban regeneration: A systematic review of literature","Journal of Urban Management","","2226-5856","10.1016/j.jum.2024.11.002","https://www.sciencedirect.com/science/article/pii/S2226585624001298","Urban regeneration is a challenging intervention, considering the multidimensional issues involved and the complexities of stakeholder collaborations. While prior research indicates the potential of digital technology in addressing these challenges and introducing novel approaches to urban regeneration, the full scope of applying emerging digital concepts, including but not limited to geographic information systems, digital twins, artificial intelligence, and virtual reality to enhance urban regeneration still needs to be explored. We conducted a systematic literature review to address this gap, employing meta-analysis and bibliometric analysis. Six key aspects of urban regeneration were identified, including decision-making support, prioritization of areas of concern, stakeholder participation, regeneration scenario identification, design and implementation of regeneration actions, and post-regeneration analysis. We evaluated the impact of digital technologies across these dimensions, examining their interaction with critical urban regeneration domains through spatiotemporal analyses of published literature. Our findings delineate novel applications and benefits of emerging digital technologies throughout various stages of urban regeneration, from site selection to project evaluation. This study comprehensively appraises how urban regeneration processes can be enriched by leveraging innovative technological applications while also suggesting avenues for future digitally sustainable integrated approaches to urban regeneration.","2024-11-26","2024-12-03 03:23:00","2024-12-03 03:23:00","","","","","","","Journal of Urban Management","","","","","","","","","","","","","","","","","","","Sustainability; Digital technology; Urban regeneration","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"W3KWBPPC","journalArticle","2024","ElMaghraby, Asmaa; Maged, Samaa; Essawey, Mohamed; ElFaramawy, Rawan; Negm, Esraa; Khoriba, Ghada","Enhancing Visual Question Answering for Arabic Language Using LLaVa and Reinforcement Learning","6th International Conference on AI in Computational Linguistics","","1877-0509","10.1016/j.procs.2024.10.207","https://www.sciencedirect.com/science/article/pii/S1877050924030084","Visual Question Answering (VQA) systems have achieved remarkable advancements by combining text-based question answering with image analysis. This integration has resulted in the creating of machines that can comprehend and address questions related to visual content. Despite these technological developments, a notable lack of VQA solutions specifically designed for the Arabic language remains. This gap persists even with the significant progress made in deep learning techniques and the development of Large Language models (LLMs). Our research introduces ArabicQuest, an innovative chatbot designed specifically for Arabic-speaking users. ArabicQuest utilizes the LLaVa Large Language Model, a dedicated translation model, and reinforcement learning from human feedback (RLHF) to effectively integrate Arabic text with visual data. Through Telegram's application API, ArabicQuest offers a seamless user interface to ask image-based questions. The proposed pipeline continuously improves its accuracy and relevance by incorporating user feedback, achieving an accuracy of 86%. ArabicQuest is trained and evaluated on various datasets, including Visual Genome, RSVQA, and ChartsQA, to ensure robust performance.","2024-01-01","2024-12-03 03:23:00","2024-12-03 03:23:00","","335-341","","","244","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","chatbot; ArabicQuest; Large Language Models(LLMs),Rinforcement learning from Human Feedback (RLHF); Visual Question Answering (VQA)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"L9RW2Z7C","journalArticle","2024","Wang, Chaoran; Li, Zixi; Bonk, Curtis","Understanding self-directed learning in AI-Assisted writing: A mixed methods study of postsecondary learners","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100247","https://www.sciencedirect.com/science/article/pii/S2666920X2400050X","This study investigates how postsecondary learners employ generative AI, specifically ChatGPT, to support their self-directed learning (SDL) for writing purposes. Following a sequential mixed methods design, we analyzed 384 survey responses and 10 semi-structured interviews with postsecondary writers. Findings suggest that the major learning task that the learners used ChatGPT for writing is brainstorming and seeking inspiration for ideas. While the entering motivation for using ChatGPT varies from curiosity about innovative technologies to fulfilling academic requirements, such entering motivation transformed into task motivation when the learners perceived the potential benefits of ChatGPT for assisting their writing. In terms of self-management, participants mostly demonstrated a high responsibility towards their own learning with ChatGPT and employed various strategies for SDL. Although survey respondents demonstrated a comparatively low level of self-monitoring, most interviewees claimed that they critically reflected on their learning process and validated information provided by ChatGPT. There are mixed opinions regarding whether the writing skills have improved as a result of using ChatGPT. Some participants suggested that the benefits brought by ChatGPT, such as alleviating social pressure and receiving instant feedback at any time, encouraged them to spend more time practicing writing and making revisions. However, some argue that assessing their AI-assisted SDL learning progress in the short term is challenging. This study addresses gaps in the existing literature where there is scarce, large-scale empirical research on self-directed AI usage in writing, shedding light on the emerging phenomenon of utilizing generative AI as a means of SDL in writing.","2024-06-01","2024-12-03 03:23:00","2024-12-03 03:23:00","","100247","","","6","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Artificial intelligence; ChatGPT; Writing; Motivation; Self-directed learning","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"PIX36X5Q","journalArticle","2024","Pinto, Maria; Garcia-Marco, Javier; Caballero, David; Manso, Ramón; Uribe, Alejandro; Gomez, Carmen","Assessing information, media and data literacy in academic libraries: Approaches and challenges in the research literature on the topic","The Journal of Academic Librarianship","","0099-1333","10.1016/j.acalib.2024.102920","https://www.sciencedirect.com/science/article/pii/S0099133324000818","A review of the research literature on the assessment of information, media, and data literacy in academic libraries has been carried out with the intention of learning about the main approaches taken; the assessment tools, criteria, and indicators used; and the main challenges for the future. To this end, 60 relevant records were retrieved from the Web of Science Core Collection and Scopus after being filtered according to the Preferred Reporting Items for Systematic review and Meta-Analysis (PRISMA) model. A content analysis of the articles was then carried out using a detailed form based on the objectives, methodology, results, conclusions, and recommendations model in relation to the current aims. Literacy assessment has been conducted primarily in information literacy. Research in anglophone countries and Spain stands out. Much of it relates to academic libraries as a whole, although there are also numerous studies focused on a field of use, primarily health, STEM, and social sciences. Among the most commonly used methods of analysis, case studies stand out, followed by descriptive, exploratory, experimental, and comparative studies; literature reviews; and content analysis. The results are positive, and assessment helps improve programs and demonstrate libraries' impact on student learning. Despite its importance, media literacy assessment is still an emerging field, and data literacy assessment is still largely a work in progress. Academic libraries need to integrate new types of literacy and emerging challenges such as open data, open science, and generative artificial intelligence into the comprehensive framework of information literacy and conduct a systematic assessment of their training programs and activities.","2024-09-01","2024-12-03 03:23:00","2024-12-03 03:23:00","","102920","","5","50","","The Journal of Academic Librarianship","","","","","","","","","","","","","","","","","","","Academic libraries; Assessment; Information literacy; Academic literacy; Media literacy; Multiliteracies; Systematic literature reviews","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JIVQC3CP","journalArticle","2023","Powers, Courtney J.; Devaraj, Ashwin; Ashqeen, Kaab; Dontula, Aman; Joshi, Amit; Shenoy, Jayanth; Murthy, Dhiraj","Using artificial intelligence to identify emergency messages on social media during a natural disaster: A deep learning approach","International Journal of Information Management Data Insights","","2667-0968","10.1016/j.jjimei.2023.100164","https://www.sciencedirect.com/science/article/pii/S2667096823000113","During natural disasters, emergency communication systems become overloaded, and people are forced to turn to social media to make requests for help. This study employs machine learning and artificial intelligence to automatically detect, identify, and categorize tweets relevant to first responders during Hurricane Harvey. We curate a dataset of tweets, present a labeling scheme based on relevance and urgency, and develop neural and non-neural machine learning models to automatically categorize tweets. Our best relevance classifiers, language models BERT and XLNet, perform significantly better than non-neural models and the deep convolutional neural network (CNN) and achieve comparable F1 scores. Ultimately, this study furthers machine learning and crisis communication research by developing methods to automatically categorize tweets that can signal to first responders of individuals’ requests for help in urgent, life-threatening disasters. Our work also finds large pretrained language models promising for the development of well-performing disaster tweet classifiers in future work.","2023-04-01","2024-12-03 03:23:00","2024-12-03 03:23:00","","100164","","1","3","","International Journal of Information Management Data Insights","","","","","","","","","","","","","","","","","","","Machine learning; Artificial intelligence; Computational methods; Crisis communication; Disaster; Supervised learning; Twitter","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"U62VVN28","journalArticle","2023","Wang, Zhanyu; Liu, Lingqiao; Wang, Lei; Zhou, Luping","R2GenGPT: Radiology Report Generation with frozen LLMs","Meta-Radiology","","2950-1628","10.1016/j.metrad.2023.100033","https://www.sciencedirect.com/science/article/pii/S2950162823000334","Large Language Models (LLMs) have consistently showcased remarkable generalization capa-bilities when applied to various language tasks. Nonetheless, harnessing the full potential of LLMs for Radiology Report Generation (R2Gen) still presents a challenge, stemming from the inherent disparity in modality between LLMs and the R2Gen task. To bridge this gap effectively, we propose R2GenGPT, which is a novel solution that aligns visual features with the word embedding space of LLMs using an efficient visual alignment module. This innovative approach empowers the previously static LLM to seamlessly integrate and process image information, marking a step forward in optimizing R2Gen performance. R2GenGPT offers the following benefits. First, it attains state-of-the-art (SOTA) performance by training only the lightweight visual alignment module while freezing all the parameters of LLM. Second, it exhibits high training efficiency, as it requires the training of an exceptionally minimal number of parameters while achieving rapid convergence. By employing delta tuning, our model only trains 5 ​M parameters (which constitute just 0.07 ​% of the total parameter count) to achieve performance close to the SOTA levels. Our code is available at https://github.com/wang-zhanyu/R2GenGPT.","2023-11-01","2024-12-03 03:23:00","2024-12-03 03:23:00","","100033","","3","1","","Meta-Radiology","","","","","","","","","","","","","","","","","","","Large language models; LLAMA; Radiology report generation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RDCYEDK9","journalArticle","2023","Ebni, Mohsen; Hosseini Bamakan, Seyed Mojtaba; Qu, Qiang","Digital Twin based Smart Manufacturing; From Design to Simulation and Optimization Schema","Tenth International Conference on Information Technology and Quantitative Management (ITQM 2023)","","1877-0509","10.1016/j.procs.2023.08.109","https://www.sciencedirect.com/science/article/pii/S1877050923008670","The advent of new-generation information and communication technologies, such as Generative AI, Internet of Things (IoT), big data analytics, Blockchain technology, and artificial intelligence (AI), has led to the emergence of the era of big data in recent years. Digital twin has emerged as one of the most active components in smart manufacturing, garnering significant attention from enterprises, research institutes, and researchers. By creating a digital twin, manufacturers can simulate different scenarios and test various configurations without disrupting the actual production process. This allows for more efficient testing and optimization of production processes, as well as improved quality control and predictive maintenance. Overall, digital twins are an important tool in smart production that can help manufacturers improve efficiency and reduce costs while ensuring high-quality output. In this paper, after reviewing the literature on the subject, in this article, by reviewing the literature, we presented a framework of the digital twin in smart manufacturing, which includes Optimization, Predictive Maintenance, Quality Control, Design, and Simulation, which can be a good guide for future studies.","2023-01-01","2024-12-03 03:23:00","2024-12-03 03:23:00","","1216-1225","","","221","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Internet of Things; Digital Twin; Optimization & Simulation; Predictive Maintenance; Smart Manufacturing","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"BMHJ3S8Y","journalArticle","2024","Anoop, V.S.; Subin Krishna, C.; Govindarajan, Usharani Hareesh","Graph embedding approaches for social media sentiment analysis with model explanation","International Journal of Information Management Data Insights","","2667-0968","10.1016/j.jjimei.2024.100221","https://www.sciencedirect.com/science/article/pii/S2667096824000107","ChatGPT, the revolutionary chat agent launched in November 2022, is still an active topic of discussion among technology enthusiasts. This open-ended chatbot allows human-like conversations with it on almost all topics since it was trained on millions of documents and developed as a large language model. Since its inception, there have been several discussions and deliberations, especially on twitter and other social media handles, on the potential of ChatGPT and how the power of artificial intelligence is growing leaps and bounds in general. These platforms also witnessed several debates on the negative side of ChatGPT, such as adversely affecting the integrity and ethics and the biased training data. This work uses graph neural network embeddings with machine learning algorithms for classifying the user sentiments on ChatGPT. We have collected a total of 8202 tweets and manually labeled them into multiple classes such as positive, negative, and neutral. We make the models explainable using SHAP (SHapley Additive exPlanations), which is a game theoretical technique for explaining the output of any machine learning models. This paper also publishes our labeled dataset for other researchers to use and train advanced classification models. When our proposed approach was compared with some chosen baselines, the proposed graph embedding-based machine learning classifiers were found to be outperforming in terms of precision, recall, and accuracy.","2024-04-01","2024-12-03 03:23:00","2024-12-03 03:23:00","","100221","","1","4","","International Journal of Information Management Data Insights","","","","","","","","","","","","","","","","","","","Machine learning; Natural language processing; Sentiment analysis; Explainability; Explainable artificial intelligence; Social media; Graph embedding","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"MUQEHJBT","journalArticle","2023","Veturi, Yoga Advaith; Woof, William; Lazebnik, Teddy; Moghul, Ismail; Woodward-Court, Peter; Wagner, Siegfried K.; Cabral de Guimarães, Thales Antonio; Daich Varela, Malena; Liefers, Bart; Patel, Praveen J.; Beck, Stephan; Webster, Andrew R.; Mahroo, Omar; Keane, Pearse A.; Michaelides, Michel; Balaskas, Konstantinos; Pontikos, Nikolas","SynthEye: Investigating the Impact of Synthetic Data on Artificial Intelligence-assisted Gene Diagnosis of Inherited Retinal Disease","Ophthalmology Science","","2666-9145","10.1016/j.xops.2022.100258","https://www.sciencedirect.com/science/article/pii/S2666914522001476","Purpose Rare disease diagnosis is challenging in medical image-based artificial intelligence due to a natural class imbalance in datasets, leading to biased prediction models. Inherited retinal diseases (IRDs) are a research domain that particularly faces this issue. This study investigates the applicability of synthetic data in improving artificial intelligence-enabled diagnosis of IRDs using generative adversarial networks (GANs). Design Diagnostic study of gene-labeled fundus autofluorescence (FAF) IRD images using deep learning. Participants Moorfields Eye Hospital (MEH) dataset of 15 692 FAF images obtained from 1800 patients with confirmed genetic diagnosis of 1 of 36 IRD genes. Methods A StyleGAN2 model is trained on the IRD dataset to generate 512 × 512 resolution images. Convolutional neural networks are trained for classification using different synthetically augmented datasets, including real IRD images plus 1800 and 3600 synthetic images, and a fully rebalanced dataset. We also perform an experiment with only synthetic data. All models are compared against a baseline convolutional neural network trained only on real data. Main Outcome Measures We evaluated synthetic data quality using a Visual Turing Test conducted with 4 ophthalmologists from MEH. Synthetic and real images were compared using feature space visualization, similarity analysis to detect memorized images, and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) score for no-reference-based quality evaluation. Convolutional neural network diagnostic performance was determined on a held-out test set using the area under the receiver operating characteristic curve (AUROC) and Cohen’s Kappa (κ). Results An average true recognition rate of 63% and fake recognition rate of 47% was obtained from the Visual Turing Test. Thus, a considerable proportion of the synthetic images were classified as real by clinical experts. Similarity analysis showed that the synthetic images were not copies of the real images, indicating that copied real images, meaning the GAN was able to generalize. However, BRISQUE score analysis indicated that synthetic images were of significantly lower quality overall than real images (P < 0.05). Comparing the rebalanced model (RB) with the baseline (R), no significant change in the average AUROC and κ was found (R-AUROC = 0.86[0.85-88], RB-AUROC = 0.88[0.86-0.89], R-κ = 0.51[0.49-0.53], and RB-κ = 0.52[0.50-0.54]). The synthetic data trained model (S) achieved similar performance as the baseline (S-AUROC = 0.86[0.85-87], S-κ = 0.48[0.46-0.50]). Conclusions Synthetic generation of realistic IRD FAF images is feasible. Synthetic data augmentation does not deliver improvements in classification performance. However, synthetic data alone deliver a similar performance as real data, and hence may be useful as a proxy to real data.Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references.","2023-06-01","2024-12-03 03:23:00","2024-12-03 03:23:00","","100258","","2","3","","Ophthalmology Science","","","","","","","","","","","","","","","","","","","Generative Adversarial Networks; Class imbalance; Clinical Decision-Support Model; Deep Learning; Inherited Retinal Diseases; Synthetic data","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"GI3FIG4X","journalArticle","2024","Polak, Maciej P.; Modi, Shrey; Latosinska, Anna; Zhang, Jinming; Wang, Ching-Wen; Wang, Shaonan; Hazra, Ayan Deep; Morgan, Dane","Flexible, model-agnostic method for materials data extraction from text using general purpose language models","Digital Discovery","","2635-098X","10.1039/d4dd00016a","https://www.sciencedirect.com/science/article/pii/S2635098X24001037","Accurate and comprehensive material databases extracted from research papers are crucial for materials science and engineering, but their development requires significant human effort. With large language models (LLMs) transforming the way humans interact with text, LLMs provide an opportunity to revolutionize data extraction. In this study, we demonstrate a simple and efficient method for extracting materials data from full-text research papers leveraging the capabilities of LLMs combined with human supervision. This approach is particularly suitable for mid-sized databases and requires minimal to no coding or prior knowledge about the extracted property. It offers high recall and nearly perfect precision in the resulting database. The method is easily adaptable to new and superior language models, ensuring continued utility. We show this by evaluating and comparing its performance on GPT-3 and GPT-3.5/4 (which underlie ChatGPT), as well as free alternatives such as BART and DeBERTaV3. We provide a detailed analysis of the method's performance in extracting sentences containing bulk modulus data, achieving up to 90% precision at 96% recall, depending on the amount of human effort involved. We further demonstrate the method's broader effectiveness by developing a database of critical cooling rates for metallic glasses over twice the size of previous human curated databases.","2024-06-12","2024-12-03 03:23:00","2024-12-03 03:23:00","","1221-1235","","6","3","","Digital Discovery","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ETY5HWHD","journalArticle","2024","Arslan, Muhammad; Ghanem, Hussam; Munawar, Saba; Cruz, Christophe","A Survey on RAG with LLMs","28th International Conference on Knowledge Based and Intelligent information and Engineering Systems (KES 2024)","","1877-0509","10.1016/j.procs.2024.09.178","https://www.sciencedirect.com/science/article/pii/S1877050924021860","In the fast-paced realm of digital transformation, businesses are increasingly pressured to innovate and boost efficiency to remain competitive and foster growth. Large Language Models (LLMs) have emerged as game-changers across industries, revolutionizing various sectors by harnessing extensive text data to analyze and generate human-like text. Despite their impressive capabilities, LLMs often encounter challenges when dealing with domain-specific queries, potentially leading to inaccuracies in their outputs. In response, Retrieval-Augmented Generation (RAG) has emerged as a viable solution. By seamlessly integrating external data retrieval into text generation processes, RAG aims to enhance the accuracy and relevance of the generated content. However, existing literature reviews tend to focus primarily on the technological advancements of RAG, overlooking a comprehensive exploration of its applications. This paper seeks to address this gap by providing a thorough review of RAG applications, encompassing both task-specific and discipline-specific studies, while also outlining potential avenues for future research. By shedding light on current RAG research and outlining future directions, this review aims to catalyze further exploration and development in this dynamic field, thereby contributing to ongoing digital transformation efforts.","2024-01-01","2024-12-03 03:23:00","2024-12-03 03:23:00","","3781-3790","","","246","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Digital transformation; Large Language Models (LLMs); Text generation; Natural Language Processing (NLP); Retrieval-Augmented Generation (RAG)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"HD3ZYAZJ","journalArticle","2024","Supriyono; Wibawa, Aji Prasetya; Suyono; Kurniawan, Fachrul","A survey of text summarization: Techniques, evaluation and challenges","Natural Language Processing Journal","","2949-7191","10.1016/j.nlp.2024.100070","https://www.sciencedirect.com/science/article/pii/S2949719124000189","This paper explores the complex field of text summarization in Natural Language Processing (NLP), with particular attention to the development and importance of semantic understanding. Text summarization is a crucial component of natural language processing (NLP), which helps to translate large amounts of textual data into clear and understandable representations. As the story progresses, it demonstrates the dynamic transition from simple syntactic structures to sophisticated models with semantic comprehension. In order to effectively summarize, syntactic, semantic, and pragmatic concerns become crucial, highlighting the necessity of capturing not only grammar but also the context and underlying meaning. It examines the wide range of summarization models, from conventional extractive techniques to state-of-the-art tools like pre-trained models. Applications are found in many different fields, demonstrating how versatile summarizing techniques are. Semantic drift and domain-specific knowledge remain obstacles, despite progress. In the future, the study predicts developments like artificial intelligence integration and transfer learning, which motivates academics to investigate these prospects for advancement. The approach, which is based on the PRISMA framework, emphasizes a methodical and open literature review. The work attempts to further natural language processing (NLP) and text summarization by combining various research findings and suggesting future research directions in this dynamic subject.","2024-06-01","2024-12-03 03:23:00","2024-12-03 03:23:00","","100070","","","7","","Natural Language Processing Journal","","","","","","","","","","","","","","","","","","","Transfer learning; Natural language processing; Text summarization; PRISMA; Semantic; Syntactic","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SZG6UA7N","journalArticle","2024","Patel, Perseus V.; Davis, Conner; Ralbovsky, Amariel; Tinoco, Daniel; Williams, Christopher Y.K.; Slatter, Shadera; Naderalvojoud, Behzad; Rosen, Michael J.; Hernandez-Boussard, Tina; Rudrapatna, Vivek","Large language models outperform traditional natural language processing methods in extracting patient-reported outcomes in IBD","Gastro Hep Advances","","2772-5723","10.1016/j.gastha.2024.10.003","https://www.sciencedirect.com/science/article/pii/S2772572324001584","Background and Aims Patient-reported outcomes (PROs) are vital in assessing disease activity and treatment outcomes in inflammatory bowel disease (IBD). However, manual extraction of these PROs from the free-text of clinical notes is burdensome. We aimed to improve data curation from free-text information in the electronic health record, making it more available for research and quality improvement. This study aimed to compare traditional natural language processing (tNLP) and large language models (LLMs) in extracting three IBD PROs (abdominal pain, diarrhea, fecal blood) from clinical notes across two institutions. Methods Clinic notes were annotated for each PRO using preset protocols. Models were developed and internally tested at the University of California San Francisco (UCSF), and then externally validated at Stanford University. We compared tNLP and LLM-based models on accuracy, sensitivity, specificity, positive and negative predictive value. Additionally, we conducted fairness and error assessments. Results Inter-rater reliability between annotators was >90%. On the UCSF test set (n=50), the top-performing tNLP models showcased accuracies of 92% (abdominal pain), 82% (diarrhea) and 80% (fecal blood), comparable to GPT-4, which was 96%, 88%, and 90% accurate, respectively. On external validation at Stanford (n=250), tNLP models failed to generalize (61-62% accuracy) while GPT-4 maintained accuracies >90%. PaLM-2 and GPT-4 showed similar performance. No biases were detected based on demographics or diagnosis. Conclusion LLMs are accurate and generalizable methods for extracting PROs. They maintain excellent accuracy across institutions, despite heterogeneity in note templates and authors. Widespread adoption of such tools has the potential to enhance IBD research and patient care.","2024-10-10","2024-12-03 03:23:01","2024-12-03 03:23:01","","","","","","","Gastro Hep Advances","","","","","","","","","","","","","","","","","","","machine learning; GPT-4; clinical data science; PaLM-2","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"HMNZDLFQ","journalArticle","2024","Meng, Han; Wagner, Christian; Triguero, Isaac","SEGAL time series classification — Stable explanations using a generative model and an adaptive weighting method for LIME","Neural Networks","","0893-6080","10.1016/j.neunet.2024.106345","https://www.sciencedirect.com/science/article/pii/S0893608024002697","Local Interpretability Model-agnostic Explanations (LIME) is a well-known post-hoc technique for explaining black-box models. While very useful, recent research highlights challenges around the explanations generated. In particular, there is a potential lack of stability, where the explanations provided vary over repeated runs of the algorithm, casting doubt on their reliability. This paper investigates the stability of LIME when applied to multivariate time series classification. We demonstrate that the traditional methods for generating neighbours used in LIME carry a high risk of creating ‘fake’ neighbours, which are out-of-distribution in respect to the trained model and far away from the input to be explained. This risk is particularly pronounced for time series data because of their substantial temporal dependencies. We discuss how these out-of-distribution neighbours contribute to unstable explanations. Furthermore, LIME weights neighbours based on user-defined hyperparameters which are problem-dependent and hard to tune. We show how unsuitable hyperparameters can impact the stability of explanations. We propose a two-fold approach to address these issues. First, a generative model is employed to approximate the distribution of the training data set, from which within-distribution samples and thus meaningful neighbours can be created for LIME. Second, an adaptive weighting method is designed in which the hyperparameters are easier to tune than those of the traditional method. Experiments on real-world data sets demonstrate the effectiveness of the proposed method in providing more stable explanations using the LIME framework. In addition, in-depth discussions are provided on the reasons behind these results.","2024-08-01","2024-12-03 03:23:01","2024-12-03 03:23:01","","106345","","","176","","Neural Networks","","","","","","","","","","","","","","","","","","","Explainable artificial intelligence; Feature importance; LIME; Multivariate time series classification; Stability","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LBMT9QD6","journalArticle","2024","Zhang, Hao-Ran; Liu, Yang; Sun, Yu-Hang; Chen, Gui","SeisResoDiff: Seismic resolution enhancement based on a diffusion model","Petroleum Science","","1995-8226","10.1016/j.petsci.2024.07.002","https://www.sciencedirect.com/science/article/pii/S1995822624001869","High resolution of post-stack seismic data assists in better interpretation of subsurface structures as well as high accuracy of impedance inversion. Therefore, geophysicists consistently strive to acquire higher resolution seismic images in petroleum exploration. Although there have been successful applications of conventional signal processing and machine learning for post-stack seismic resolution enhancement, there is limited reference to the seismic applications of the recent emergence and rapid development of generative artificial intelligence. Hence, we propose to apply diffusion models, among the most popular generative models, to enhance seismic resolution. Specifically, we apply the classic diffusion model—denoising diffusion probabilistic model (DDPM), conditioned on the seismic data in low resolution, to reconstruct corresponding high-resolution images. Herein the entire scheme is referred to as SeisResoDiff. To provide a comprehensive and clear understanding of SeisResoDiff, we introduce the basic theories of diffusion models and detail the optimization objective's derivation with the aid of diagrams and algorithms. For implementation, we first propose a practical workflow to acquire abundant training data based on the generated pseudo-wells. Subsequently, we apply the trained model to both synthetic and field datasets, evaluating the results in three aspects: the appearance of seismic sections and slices in the time domain, frequency spectra, and comparisons with the synthetic data using real well-logging data at the well locations. The results demonstrate not only effective seismic resolution enhancement, but also additional denoising by the diffusion model. Experimental comparisons indicate that training the model on noisy data, which are more realistic, outperforms training on clean data. The proposed scheme demonstrates superiority over some conventional methods in high-resolution reconstruction and denoising ability, yielding more competitive results compared to our previous research.","2024-10-01","2024-12-03 03:23:01","2024-12-03 03:23:01","","3166-3188","","5","21","","Petroleum Science","","","","","","","","","","","","","","","","","","","Deep learning; Diffusion model; High resolution; Reservoir characterization; Seismic data processing; Seismic resolution enhancement","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ZX2FQBEZ","journalArticle","2023","Inan, Muhammad Sakib Khan; Hossain, Sohrab; Uddin, Mohammed Nazim","Data augmentation guided breast cancer diagnosis and prognosis using an integrated deep-generative framework based on breast tumor’s morphological information","Informatics in Medicine Unlocked","","2352-9148","10.1016/j.imu.2023.101171","https://www.sciencedirect.com/science/article/pii/S2352914823000138","Breast cancer is the world’s second-largest cause of cancer mortality among women. With the progress of artificial intelligence (AI) in healthcare, the survival rate of breast cancer patients has risen in recent years due to early diagnosis and effective prognosis. However, substantial AI research necessitates a large quantity of high-quality data to perform credible state-of-the-art research. To that end, this study investigates the potentiality of deep generative models including, the tabular variational autoencoder (TVAE) and the conditional generative adversarial network (CTGAN), to generate high-quality synthetic tabular data of breast tumors and support the diagnosis and prognosis of breast cancer. Additionally, this study proposes an integrated interpretable deep-learning framework that includes the synthetic generation of breast cancer data leading to the classification of breast cancer using the interpretable deep attention-based model TabNet based on the domain of breast cancer research at every stage of the research framework. The research findings are justified using benchmark breast cancer datasets. After rigorous investigation, it was found that the TVAE model outperformed the synthetic generation of breast tumor data with a Chi-Squared test(CS test) score of 0.916 (prognosis) and 0.964 (diagnosis) and a Kolmogorov Smirnov test(KS test) score of 0.887 (prognosis) and 0.928 (diagnosis). In the classification stage, despite being trained with only synthetically generated data, the interpretable TabNet architecture outperformed all other machine-learning and deep-learning classifiers with an accuracy of 96.66 % in diagnosis and 82.83 % in prognosis.","2023-01-01","2024-12-03 03:23:01","2024-12-03 03:23:01","","101171","","","37","","Informatics in Medicine Unlocked","","","","","","","","","","","","","","","","","","","Autoencoder; Breast cancer; GAN; Synthetic tabular data; TabNet","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"UYQ3URC8","journalArticle","2024","Wester, Joel; de Jong, Sander; Pohl, Henning; van Berkel, Niels","Exploring people's perceptions of LLM-generated advice","Computers in Human Behavior: Artificial Humans","","2949-8821","10.1016/j.chbah.2024.100072","https://www.sciencedirect.com/science/article/pii/S294988212400032X","When searching and browsing the web, more and more of the information we encounter is generated or mediated through large language models (LLMs). This can be looking for a recipe, getting help on an essay, or looking for relationship advice. Yet, there is limited understanding of how individuals perceive advice provided by these LLMs. In this paper, we explore people's perception of LLM-generated advice, and what role diverse user characteristics (i.e., personality and technology readiness) play in shaping their perception. Further, as LLM-generated advice can be difficult to distinguish from human advice, we assess the perceived creepiness of such advice. To investigate this, we run an exploratory study (N = 91), where participants rate advice in different styles (generated by GPT-3.5 Turbo). Notably, our findings suggest that individuals who identify as more agreeable tend to like the advice more and find it more useful. Further, individuals with higher technological insecurity are more likely to follow and find the advice more useful, and deem it more likely that a friend could have given the advice. Lastly, we see that advice given in a ‘skeptical’ style was rated most unpredictable, and advice given in a ‘whimsical’ style was rated least malicious—indicating that LLM advice styles influence user perceptions. Our results also provide an overview of people's considerations on likelihood, receptiveness, and what advice they are likely to seek from these digital assistants. Based on our results, we provide design takeaways for LLM-generated advice and outline future research directions to further inform the design of LLM-generated advice for support applications targeting people with diverse expectations and needs.","2024-08-01","2024-12-03 03:23:01","2024-12-03 03:23:01","","100072","","2","2","","Computers in Human Behavior: Artificial Humans","","","","","","","","","","","","","","","","","","","Large language models; LLM; Generative AI; Advice; User characteristics","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XMKB5NCC","journalArticle","2024","Alkhalaf, Mohammad; Yu, Ping; Yin, Mengyang; Deng, Chao","Applying generative AI with retrieval augmented generation to summarize and extract key clinical information from electronic health records","Journal of Biomedical Informatics","","1532-0464","10.1016/j.jbi.2024.104662","https://www.sciencedirect.com/science/article/pii/S1532046424000807","Background Malnutrition is a prevalent issue in aged care facilities (RACFs), leading to adverse health outcomes. The ability to efficiently extract key clinical information from a large volume of data in electronic health records (EHR) can improve understanding about the extent of the problem and developing effective interventions. This research aimed to test the efficacy of zero-shot prompt engineering applied to generative artificial intelligence (AI) models on their own and in combination with retrieval augmented generation (RAG), for the automating tasks of summarizing both structured and unstructured data in EHR and extracting important malnutrition information. Methodology We utilized Llama 2 13B model with zero-shot prompting. The dataset comprises unstructured and structured EHRs related to malnutrition management in 40 Australian RACFs. We employed zero-shot learning to the model alone first, then combined it with RAG to accomplish two tasks: generate structured summaries about the nutritional status of a client and extract key information about malnutrition risk factors. We utilized 25 notes in the first task and 1,399 in the second task. We evaluated the model’s output of each task manually against a gold standard dataset. Result The evaluation outcomes indicated that zero-shot learning applied to generative AI model is highly effective in summarizing and extracting information about nutritional status of RACFs’ clients. The generated summaries provided concise and accurate representation of the original data with an overall accuracy of 93.25%. The addition of RAG improved the summarization process, leading to a 6% increase and achieving an accuracy of 99.25%. The model also proved its capability in extracting risk factors with an accuracy of 90%. However, adding RAG did not further improve accuracy in this task. Overall, the model has shown a robust performance when information was explicitly stated in the notes; however, it could encounter hallucination limitations, particularly when details were not explicitly provided. Conclusion This study demonstrates the high performance and limitations of applying zero-shot learning to generative AI models to automatic generation of structured summarization of EHRs data and extracting key clinical information. The inclusion of the RAG approach improved the model performance and mitigated the hallucination problem.","2024-08-01","2024-12-03 03:23:01","2024-12-03 03:23:01","","104662","","","156","","Journal of Biomedical Informatics","","","","","","","","","","","","","","","","","","","Generative AI; LLAMA; Malnutrition; Nursing notes; RAG; Summarization","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2BH6EVIA","journalArticle","2024","Meyer, Jennifer; Jansen, Thorben; Schiller, Ronja; Liebenow, Lucas W.; Steinbach, Marlene; Horbach, Andrea; Fleckenstein, Johanna","Using LLMs to bring evidence-based feedback into the classroom: AI-generated feedback increases secondary students’ text revision, motivation, and positive emotions","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2023.100199","https://www.sciencedirect.com/science/article/pii/S2666920X23000784","Writing proficiency is an essential skill for upper secondary students that can be enhanced through effective feedback. Creating feedback on writing tasks, however, is time-intensive and presents a challenge for educators, often resulting in students receiving insufficient or no feedback. The advent of text-generating large language models (LLMs) offers a promising solution, namely, automated evidence-based feedback generation. Yet, empirical evidence from randomized controlled studies about the effectiveness of LLM-generated feedback is missing. To address this issue, the current study compared the effectiveness of LLM-generated feedback to no feedback. A sample of N = 459 upper secondary students of English as a foreign language wrote an argumentative essay. Students in the experimental group were asked to revise their text according to feedback that was generated using the LLM GPT-3.5-turbo. The control group revised their essays without receiving feedback. We assessed improvement in the revision using automated essay scoring. The results showed that LLM-generated feedback increased revision performance (d = .19) and task motivation (d = 0.36). Moreover, it increased positive emotions (d = 0.34) compared to revising without feedback. The findings highlight that using LLMs allows to create timely feedback that can positively relate to students’ cognitive and affective-motivational outcomes. Future perspectives and the implications for research and practice of using LLM-generated feedback in intelligent tutoring systems are discussed.","2024-06-01","2024-12-03 03:23:01","2024-12-03 03:23:01","","100199","","","6","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Secondary education; Improving classroom teaching; Applications in subject areas","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RK6BPXV9","journalArticle","2024","Chao, August F.Y.; Wang, Chen-Shu; Li, Bo-Yi; Chen, Hong-Yan","From hate to harmony: Leveraging large language models for safer speech in times of COVID-19 crisis","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e35468","https://www.sciencedirect.com/science/article/pii/S2405844024114995","This study investigates the rampant spread of offensive and derogatory language during the COVID-19 pandemic and aims to mitigate it through machine learning. Employing advanced Large Language Models (LLMs), the research develops a sophisticated framework adept at detecting and transforming abusive and hateful speech. The project begins by meticulously compiling a dataset, focusing specifically on Chinese language abuse and hate speech. It incorporates an extensive list of 30 pandemic-related terms, significantly enriching the resources available for this type of research. A two-tier detection model is then introduced, achieving a remarkable accuracy of 94.42 % in its first phase and an impressive 81.48 % in the second. Furthermore, the study enhances paraphrasing efficiency by integrating generative AI techniques, primarily Large Language Models, with a Latent Dirichlet Allocation (LDA) topic model. This combination allows for a thorough analysis of language before and after modification. The results highlight the transformative power of these methods. They show that the rephrased statements not only reduce the initial hostility but also preserve the essential themes and meanings. This breakthrough offers users effective rephrasing suggestions to prevent the spread of hate speech, contributing to more positive and constructive public discourse.","2024-08-30","2024-12-03 03:23:01","2024-12-03 03:23:01","","e35468","","16","10","","Heliyon","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"U68ZWZIF","journalArticle","2024","Arslan, Muhammad; Munawar, Saba; Cruz, Christophe","Sustainable Digitalization of Business with Multi-Agent RAG and LLM","28th International Conference on Knowledge Based and Intelligent information and Engineering Systems (KES 2024)","","1877-0509","10.1016/j.procs.2024.09.337","https://www.sciencedirect.com/science/article/pii/S1877050924023627","Businesses heavily rely on data sourced from various channels like news articles, financial reports, and consumer reviews to drive their operations, enabling informed decision-making and identifying opportunities. However, traditional manual methods for data extraction are often time-consuming and resource-intensive, prompting the adoption of digital transformation initiatives to enhance efficiency. Yet, concerns persist regarding the sustainability of such initiatives and their alignment with the United Nations (UN)’s Sustainable Development Goals (SDGs). This research aims to explore the integration of Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) as a sustainable solution for Information Extraction (IE) and processing. The research methodology involves reviewing existing solutions for business decision-making, noting that many systems require training new machine learning models, which are resource-intensive and have significant environmental impacts. Instead, we propose a sustainable business solution using pre-existing LLMs that can work with diverse datasets. We link domain-specific datasets to tailor LLMs to company needs and employ a Multi-Agent architecture to divide tasks such as information retrieval, enrichment, and classification among specialized agents. This approach optimizes the extraction process and improves overall efficiency. Through the utilization of these technologies, businesses can optimize resource utilization, improve decision-making processes, and contribute to sustainable development goals, thereby fostering environmental responsibility within the corporate sector.","2024-01-01","2024-12-03 03:23:01","2024-12-03 03:23:01","","4722-4731","","","246","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Digital transformation; Large Language Models (LLMs); Data-driven operations; Information Extraction (IE); Multi-Agent RAG; Sustainable Development Goals (SDGs)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ZMAN8MY3","journalArticle","2024","Bucaioni, Alessio; Ekedahl, Hampus; Helander, Vilma; Nguyen, Phuong T.","Programming with ChatGPT: How far can we go?","Machine Learning with Applications","","2666-8270","10.1016/j.mlwa.2024.100526","https://www.sciencedirect.com/science/article/pii/S2666827024000021","Artificial intelligence (AI) has made remarkable strides, giving rise to the development of large language models such as ChatGPT. The chatbot has garnered significant attention from academia, industry, and the general public, marking the beginning of a new era in AI applications. This work explores how well ChatGPT can write source code. To this end, we performed a series of experiments to assess the extent to which ChatGPT is capable of solving general programming problems. Our objective is to assess ChatGPT’s capabilities in two different programming languages, namely C++ and Java, by providing it with a set of programming problem, encompassing various types and difficulty levels. We focus on evaluating ChatGPT’s performance in terms of code correctness, run-time efficiency, and memory usage. The experimental results show that, while ChatGPT is good at solving easy and medium programming problems written in C++ and Java, it encounters some difficulties with more complicated tasks in the two languages. Compared to code written by humans, the one generated by ChatGPT is of lower quality, with respect to runtime and memory usage.","2024-03-01","2024-12-03 03:23:01","2024-12-03 03:23:01","","100526","","","15","","Machine Learning with Applications","","","","","","","","","","","","","","","","","","","ChatGPT; Large language models; Programming","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"C3THF49H","journalArticle","2024","Uchida, Satoru","Using early LLMs for corpus linguistics: Examining ChatGPT's potential and limitations","Applied Corpus Linguistics","","2666-7991","10.1016/j.acorp.2024.100089","https://www.sciencedirect.com/science/article/pii/S2666799124000066","This study evaluates the extent to which information can be obtained from early Large Language Models (LLMs) for corpus linguistic research. Various tasks were conducted using ChatGPT 3.5, such as generating word frequency lists, collocations, words that fit certain grammatical patterns, and identifying genres. The generations were then compared with the search results from a large-scale general corpus (COCA). While favorable results were not achieved in identifying the genres of words or paragraphs, there was notable congruence in the frequency lists (75.0 %), collocations (42.8 %), and grammatical patterns (53.0 %) for the top 20 items. Even when the generated items did not perfectly match those from COCA, it was evident that high-frequency items were produced. Although LLMs may not be sufficient for rigorous academic research, the results are adequate for discerning overall trends or assisting learners. In addition, the results of this study show that the ability to search at the phrase level is an advantage of using LLMs for corpus research.","2024-04-01","2024-12-03 03:23:01","2024-12-03 03:23:01","","100089","","1","4","","Applied Corpus Linguistics","","","","","","","","","","","","","","","","","","","ChatGPT; LLM; Corpus linguistics; Collocation; Frequency list","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"TGEHDCXX","journalArticle","2024","Albayrak, Abdulkadir; Xiao, Yao; Mukherjee, Piyush; Barnett, Sarah S.; Marcou, Cherisse A.; Hart, Steven N.","Enhancing human phenotype ontology term extraction through synthetic case reports and embedding-based retrieval: A novel approach for improved biomedical data annotation","Journal of Pathology Informatics","","2153-3539","10.1016/j.jpi.2024.100409","https://www.sciencedirect.com/science/article/pii/S2153353924000488","With the increasing utilization of exome and genome sequencing in clinical and research genetics, accurate and automated extraction of human phenotype ontology (HPO) terms from clinical texts has become imperative. Traditional methods for HPO term extraction, such as PhenoTagger, often face limitations in coverage and precision. In this study, we propose a novel approach that leverages large language models (LLMs) to generate synthetic sentences with clinical context, which were semantically encoded into vector embeddings. These embeddings are linked to HPO terms, creating a robust knowledgebase that facilitates precise information retrieval. Our method circumvents the known issue of LLM hallucinations by storing and querying these embeddings within a true database, ensuring accurate context matching without the need for a predictive model. We evaluated the performance of three different embedding models, all of which demonstrated substantial improvements over PhenoTagger. Top recall (sensitivity), precision (positive-predictive value, PPV), and F1 are 0.64, 0.64, and 0.64, respectively, which were 31%, 10%, and 21% better than PhenoTagger. Furthermore, optimal performance was achieved when we combined the best performing embedding model with PhenoTagger (a.k.a. Fused model), resulting in recall (sensitivity), precision (PPV), and F1 values of 0.7, 0.7, and 0.7, respectively, which are 10%, 10%, and 10% better than the best embedding models. Our findings underscore the potential of this integrated approach to enhance the precision and reliability of HPO term extraction, offering a scalable and effective solution for biomedical data annotation.","2024-11-16","2024-12-03 03:23:01","2024-12-03 03:23:01","","100409","","","","","Journal of Pathology Informatics","","","","","","","","","","","","","","","","","","","Human phenotype ontology; PhenoTagger; Vector embeddings","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"PFCZ4WQG","journalArticle","2024","Maibaum, Frederik; Kriebel, Johannes; Foege, Johann Nils","Selecting textual analysis tools to classify sustainability information in corporate reporting","Decision Support Systems","","0167-9236","10.1016/j.dss.2024.114269","https://www.sciencedirect.com/science/article/pii/S0167923624001027","Information on firms' sustainability often partly resides in unstructured data published, for instance, in annual reports, news, and transcripts of earnings calls. In recent years, researchers and practitioners have started to extract information from these data sources using a broad range of natural language processing (NLP) methods. While there is much to be gained from these endeavors, studies that employ these methods rarely reflect upon the validity and quality of the chosen method—that is, how adequately NLP captures the sustainability information from text. This practice is problematic, as different NLP techniques lead to different results regarding the extraction of information. Hence, the choice of method may affect the outcome of the application and thus the inferences that users draw from their results. In this study, we examine how different types of NLP methods influence the validity and quality of extracted information. In particular, we compare four primary methods, namely (1) dictionary-based techniques, (2) topic modeling approaches, (3) word embeddings, and (4) large language models such as BERT and ChatGPT, and evaluate them on 75,000 manually labeled sentences from 10-K annual reports that serve as the ground truth. Our results show that dictionaries have a large variation in quality, topic models outperform other approaches that do not rely on large language models, and large language models show the strongest performance. In large language models, individual fine-tuning remains crucial. One-shot approaches (i.e., ChatGPT) have lately surpassed earlier approaches when using well-designed prompts and the most recent models.","2024-08-01","2024-12-03 03:23:01","2024-12-03 03:23:01","","114269","","","183","","Decision Support Systems","","","","","","","","","","","","","","","","","","","Performance evaluation; Sustainability; ChatGPT; Natural language processing; Corporate reporting","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"GTU3SRGX","journalArticle","2024","Roumeliotis, Konstantinos I.; Tselikas, Nikolaos D.; Nasiopoulos, Dimitrios K.","LLMs in e-commerce: A comparative analysis of GPT and LLaMA models in product review evaluation","Natural Language Processing Journal","","2949-7191","10.1016/j.nlp.2024.100056","https://www.sciencedirect.com/science/article/pii/S2949719124000049","E-commerce has witnessed remarkable growth, especially following the easing of COVID-19 restrictions. Many people, who were initially hesitant about online shopping, have now embraced it, while existing online shoppers increasingly prefer the convenience of e-commerce. This surge in e-commerce has prompted the implementation of automated customer service processes, incorporating innovations such as chatbots and AI-driven sales. Despite this growth, customer satisfaction remains vital for E-commerce sustainability. Data scientists have made progress in utilizing machine learning to assess satisfaction levels but struggled to understand emotions within product reviews’ context. The recent AI revolution, marked by the release of powerful Large Language Models (LLMs) to the public, has brought us closer than ever before to understanding customer sentiment. This study aims to illustrate the effectiveness of LLMs by conducting a comparative analysis of two cutting-edge LLMs, GPT-3.5 and LLaMA-2, along with two additional Natural Language Process (NLP) models, BERT and RoBERTa. We evaluate the performance of these models before and after fine-tuning them specifically for product review sentiment analysis. The primary objective of this research is to determine if these specific LLMs, could contribute to understanding customer satisfaction within the context of an e-commerce environment. By comparing the effectiveness of these models, we aim to uncover insights into the potential impact of LLMs on customer satisfaction analysis and enhance our understanding of their capabilities in this particular context.","2024-03-01","2024-12-03 03:23:02","2024-12-03 03:23:02","","100056","","","6","","Natural Language Processing Journal","","","","","","","","","","","","","","","","","","","Sentiment analysis; LLMs; Instruction tuning; GPT model; LLaMA model; LLM fine-tuning","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XUDJANSK","journalArticle","2024","Spillias, Scott; Tuohy, Paris; Andreotta, Matthew; Annand-Jones, Ruby; Boschetti, Fabio; Cvitanovic, Christopher; Duggan, Joseph; Fulton, Elisabeth A.; Karcher, Denis B.; Paris, Cécile; Shellock, Rebecca; Trebilco, Rowan","Human-AI collaboration to identify literature for evidence synthesis","Cell Reports Sustainability","","2949-7906","10.1016/j.crsus.2024.100132","https://www.sciencedirect.com/science/article/pii/S2949790624002076","Summary Systematic approaches to evidence synthesis can improve the rigor, transparency, and replicability of a literature review. However, these systematic approaches are resource intensive. We evaluate the ability of ChatGPT to undertake two stages of evidence syntheses (searching peer-reviewed literature and screening for relevance) and develop a collaborative framework to leverage both human and AI intelligence. Using a scoping review of community-based fisheries management as a case study, we find that with substantial prompting, the AI can provide critical insight into the construction and content of a search string. Thereafter, we evaluate five strategies for synthesizing AI output to screen articles based on predefined inclusion criteria. We find that low omission rates (<1%) of relevant literature by the AI are achievable, which is comparable to human screeners. These findings suggest that generalized AI tools can assist reviewers to accelerate the implementation and improve the reliability of literature reviews, thus supporting evidence-informed decision-making.","2024-07-26","2024-12-03 03:23:02","2024-12-03 03:23:02","","100132","","7","1","","Cell Reports Sustainability","","","","","","","","","","","","","","","","","","","systematic review; artificial intelligence; ChatGPT; large language models; collaborative intelligence; evidence synthesis; natural-language; processing; scientific publication","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"42BWX3N2","journalArticle","2024","Sharma, Vansh; Raman, Venkat","A reliable knowledge processing framework for combustion science using foundation models","Energy and AI","","2666-5468","10.1016/j.egyai.2024.100365","https://www.sciencedirect.com/science/article/pii/S2666546824000314","This research explores the integration of large language models (LLMs) into scientific data assimilation, focusing on combustion science as a case study. Leveraging foundational models integrated with Retrieval-Augmented Generation (RAG) framework, the study introduces an approach to process diverse combustion research data, spanning experimental studies, simulations, and literature. The multifaceted nature of combustion research emphasizes the critical role of knowledge processing in navigating and extracting valuable information from a vast and diverse pool of sources. The developed approach minimizes computational and economic expenses while optimizing data privacy and accuracy. It incorporates prompt engineering and offline open-source LLMs, offering user autonomy in selecting base models. The study provides a thorough examination of text segmentation strategies, conducts comparative studies between LLMs, and explores various optimized prompts to demonstrate the effectiveness of the framework. By incorporating an external vector database, the framework outperforms a conventional LLM in generating accurate responses and constructing robust arguments. Additionally, the study delves into the investigation of optimized prompt templates for the purpose of efficient extraction of scientific literature. Furthermore, we present a targeted scaling study to quantify the algorithmic performance of the framework as the number of prompt tokens increases. The research addresses concerns related to hallucinations and false research articles by introducing a custom workflow developed with a detection algorithm to filter out inaccuracies. Despite identified areas for improvement, the framework consistently delivers accurate domain-specific responses with minimal human oversight. The prompt-agnostic approach introduced holds promise for future improvements. The study underscores the significance of integrating LLMs and knowledge processing techniques in scientific research, providing a foundation for advancements in data assimilation and utilization.","2024-05-01","2024-12-03 03:23:02","2024-12-03 03:23:02","","100365","","","16","","Energy and AI","","","","","","","","","","","","","","","","","","","Retrieval-augmented generation (RAG); Combustion; Foundation models; Knowledge processing; Large language models (LLM)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"MHATFF5C","journalArticle","2024","Gehweiler, Christoph; Lobachev, Oleg","Classification of intent in moderating online discussions: An empirical evaluation","Decision Analytics Journal","","2772-6622","10.1016/j.dajour.2024.100418","https://www.sciencedirect.com/science/article/pii/S2772662224000225","This paper investigates the use of large language models (LLMs) for moderating online discussions, with a focus on identifying user intent in various types of content. It centers on natural language processing (NLP) techniques to detect toxic language, derailment in discussions, and problematic comments. The study creates prototypes of such tools, utilizing LLMs. Then we evaluate our tools using datasets in both English and German, as the effectiveness across different languages may vary. This research explores content classification through methods such as sentiment analysis, keyword extraction and topic modeling, employing non-binary labeling for a deeper analysis of online interactions. The paper also discusses the limitations of current LLMs, including the challenge of false positives due to limited training data. It concludes with ideas towards improving model fine-tuning to better address specific platform needs and linguistic variations. This work contributes to understanding how AI can support decisions in moderating online spaces and fostering healthier digital communication environments.","2024-03-01","2024-12-03 03:23:02","2024-12-03 03:23:02","","100418","","","10","","Decision Analytics Journal","","","","","","","","","","","","","","","","","","","Machine learning; Natural language processing; Sentiment analysis; Discussion; Moderation; Toxicity","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"IE9PV5EC","journalArticle","2024","Ge, Qi; Li, Jin; Lacasse, Suzanne; Sun, Hongyue; Liu, Zhongqiang","Data-augmented landslide displacement prediction using generative adversarial network","Journal of Rock Mechanics and Geotechnical Engineering","","1674-7755","10.1016/j.jrmge.2024.01.003","https://www.sciencedirect.com/science/article/pii/S1674775524000726","Landslides are destructive natural disasters that cause catastrophic damage and loss of life worldwide. Accurately predicting landslide displacement enables effective early warning and risk management. However, the limited availability of on-site measurement data has been a substantial obstacle in developing data-driven models, such as state-of-the-art machine learning (ML) models. To address these challenges, this study proposes a data augmentation framework that uses generative adversarial networks (GANs), a recent advance in generative artificial intelligence (AI), to improve the accuracy of landslide displacement prediction. The framework provides effective data augmentation to enhance limited datasets. A recurrent GAN model, RGAN-LS, is proposed, specifically designed to generate realistic synthetic multivariate time series that mimics the characteristics of real landslide on-site measurement data. A customized moment-matching loss is incorporated in addition to the adversarial loss in GAN during the training of RGAN-LS to capture the temporal dynamics and correlations in real time series data. Then, the synthetic data generated by RGAN-LS is used to enhance the training of long short-term memory (LSTM) networks and particle swarm optimization-support vector machine (PSO-SVM) models for landslide displacement prediction tasks. Results on two landslides in the Three Gorges Reservoir (TGR) region show a significant improvement in LSTM model prediction performance when trained on augmented data. For instance, in the case of the Baishuihe landslide, the average root mean square error (RMSE) increases by 16.11%, and the mean absolute error (MAE) by 17.59%. More importantly, the model's responsiveness during mutational stages is enhanced for early warning purposes. However, the results have shown that the static PSO-SVM model only sees marginal gains compared to recurrent models such as LSTM. Further analysis indicates that an optimal synthetic-to-real data ratio (50% on the illustration cases) maximizes the improvements. This also demonstrates the robustness and effectiveness of supplementing training data for dynamic models to obtain better results. By using the powerful generative AI approach, RGAN-LS can generate high-fidelity synthetic landslide data. This is critical for improving the performance of advanced ML models in predicting landslide displacement, particularly when there are limited training data. Additionally, this approach has the potential to expand the use of generative AI in geohazard risk management and other research areas.","2024-10-01","2024-12-03 03:23:02","2024-12-03 03:23:02","","4017-4033","","10","16","","Journal of Rock Mechanics and Geotechnical Engineering","","","","","","","","","","","","","","","","","","","Generative adversarial network (GAN); Landslide displacement prediction; Machine learning (ML); Three Gorges reservoir (TGR); Time series","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"TTE86CHC","journalArticle","2024","Nie, Yuhao; Zelikman, Eric; Scott, Andea; Paletta, Quentin; Brandt, Adam","SkyGPT: Probabilistic ultra-short-term solar forecasting using synthetic sky images from physics-constrained VideoGPT","Advances in Applied Energy","","2666-7924","10.1016/j.adapen.2024.100172","https://www.sciencedirect.com/science/article/pii/S2666792424000106","The variability of solar photovoltaic (PV) power output, driven by rapidly changing cloud dynamics, hinders the transition to reliable renewable energy systems. Information on future sky conditions, especially cloud coverage, holds the promise for improving PV output forecasting. Leveraging recent advances in generative artificial intelligence (AI), we introduce SkyGPT, a physics-constrained stochastic video prediction model, which predicts plausible future images of the sky using historical sky images. We show that SkyGPT can accurately capture cloud dynamics, producing highly realistic and diverse future sky images. We further demonstrate its efficacy in 15-minute-ahead probabilistic PV output forecasting using real-world power generation data from a 30-kW rooftop PV system. By coupling SkyGPT with a U-Net-based PV power prediction model, we observe superior prediction reliability and sharpness compared with several benchmark methods. The propose approach achieves a continuous ranked probability score (CRPS) of 2.81 kW, outperforming a classic convolutional neural network (CNN) baseline by 13% and the smart persistence model by 23%. The findings of this research could aid efficient and resilient management of solar electricity generation, particularly as we transition to renewable-heavy grids. The study also provides valuable insights into stochastic cloud modeling for a broad research community, encompassing fields such as solar energy meteorology and atmospheric sciences.","2024-07-01","2024-12-03 03:23:02","2024-12-03 03:23:02","","100172","","","14","","Advances in Applied Energy","","","","","","","","","","","","","","","","","","","Deep learning; Generative models; Cloud motion prediction; Photovoltaic power; Probabilistic solar forecasting; Sky images; Stochastic video prediction","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"UFABZJ6T","journalArticle","2024","Li, Qingze; Yang, Yang; Yao, Gang; Wei, Fujia; Li, Rui; Zhu, Mingtao; Hou, Huiwen","Classification and Application of Deep Learning in Construction Engineering and Management – A Systematic Literature Review and Future Innovations","Case Studies in Construction Materials","","2214-5095","10.1016/j.cscm.2024.e04051","https://www.sciencedirect.com/science/article/pii/S2214509524012038","In the ever-evolving landscape of construction engineering and management (CEM), the dynamic and unique characteristics of construction project environments constantly present multifaceted challenges. These challenges are characterized by the extensive volume of project-specific information and intricate engineering data. Deep learning (DL), with its advanced analytical capabilities, has been emerging as a robust solution to these complexities. While the application of DL in CEM is on an upward trajectory, a systematic review of its implementation is conspicuously lacking. This paper, therefore, embarks on a scientometric and qualitative analysis of 296 DL-based studies related to CEM from 2014-2024 in the renowned data science repositories Scopus, Science Direct and Web of Science to explore the characteristics of journals, keywords and clusters. It is found that six research topics have fully utilized the advantages of DL in CEM in the last decade, including construction equipment management, structural health monitoring, construction site safety management, construction schedule management, worker health management and workforce assessment and intelligent design. Then, the studies under each research topic are summarized separately and a searchable taxonomy is proposed that secondarily categorizes each study according to the specific CEM task and DL method used to facilitate understanding and access. Finally, the primary obstacles encountered in DL itself and in its practical application in CEM are discussed. It further articulates five critical future research directions that are evolving in tandem with advances in CEM, multimodal construction site management, real-time structural health monitoring and prediction, project progress visualization and management, intelligent design with data sharing and the incorporating large language models (LLM) for text data analysis. The three goals of this study are providing CEM researchers and practitioners with an in-depth and nuanced understanding of DL, elucidating the diverse nature of CEM activities and the resulting benefits of applying DL, and identifying future opportunities for applying DL in CEM to inform subsequent ongoing academic inquiry and pragmatic applications.","2024-11-28","2024-12-03 03:23:02","2024-12-03 03:23:02","","e04051","","","","","Case Studies in Construction Materials","","","","","","","","","","","","","","","","","","","Large language models (LLM); Condition monitoring; Construction engineering management(CEM); Damage detection; Deep learning(DL)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"QIV7B594","journalArticle","2023","Crist, Walter; Soemers, Dennis J.N.J.","The Digital Ludeme Project: Combining archaeological and computational methods for the study of ancient board games","Journal of Archaeological Science: Reports","","2352-409X","10.1016/j.jasrep.2023.104005","https://www.sciencedirect.com/science/article/pii/S2352409X23001803","Archaeologists and computer scientists have both studied board games since the early days of their fields. Early archaeologists had an interest in identifying ways of playing the games of antiquity, and they applied diffusionist models fashionable at the time to trace the development of games from antiquity to the games played in nineteenth century Europe and North America. In time, a huge amount of data on ancient games was collected, and in the last thirty years archaeologists have studied games as they relate to social processes. In parallel to this, artificial intelligence (AI) research has utilized board games, primarily as testbeds for developing AI techniques, but also as an application domain. Archaeological and AI methods are combined in the Digital Ludeme Project, which documents the preserved knowledge of ancient games and uses computational techniques to evaluate research questions that can be addressed through AI playouts of proposed rulesets for games.","2023-06-01","2024-12-03 03:23:02","2024-12-03 03:23:02","","104005","","","49","","Journal of Archaeological Science: Reports","","","","","","","","","","","","","","","","","","","Artificial intelligence; Board games; Digital archaeoludology; Digital humanities; Ludemes","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"VSV5SBWK","journalArticle","2024","Liang, Xinxin; Wang, Zuoxu; Li, Mingrui; Yan, Zhijie","A survey of LLM-augmented knowledge graph construction and application in complex product design","34th CIRP Design Conference","","2212-8271","10.1016/j.procir.2024.07.069","https://www.sciencedirect.com/science/article/pii/S2212827124007911","In the field of complex product design, deploying knowledge graphs (KGs) has become a promising trend due to its strength on exploiting and applying the large-scale, complex, and specialized domain knowledge. In recent years, large language models (LLMs) have also attracted much attention due to their outstanding performance in natural language understanding and generation. However, in the research of complex product design dominated by domain knowledge, few studies involve LLMS and KG at the same time. To fill this gap, we survey 42 articles published in the last four years, focusing on three key questions. The combination of LLM and KG in specific applications in complex product design is deeply discussed. The analysis reveals how these techniques facilitate data collection, design concept formation and design process optimization and proposes a technical framework combining LLM and KG for complex product design domain. In addition, we identify key challenges and propose directions for future research. As an explorative survey paper, this paper provides insightful ideas for implementing more specialized domain knowledge graph in complex product design field.","2024-01-01","2024-12-03 03:23:02","2024-12-03 03:23:02","","870-875","","","128","","Procedia CIRP","","","","","","","","","","","","","","","","","","","Large Language Model; Design: challenges & Innovation; Knowledge Graph","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DLWDX557","journalArticle","2024","Abdelwahab, Siddig Ibrahim; Farasani, Abdullah; Alfaifi, Hassan Ahmad; Hassan, Waseem","Bibliometric analysis of ChatGPT and plastic surgery research: Insights from diverse search strategies and co-word analysis","Chinese Journal of Plastic and Reconstructive Surgery","","2096-6911","10.1016/j.cjprs.2024.10.002","https://www.sciencedirect.com/science/article/pii/S2096691124000852","Background The rise of artificial intelligence in healthcare, particularly the development of large language models like ChatGPT, has opened new avenues for innovation in medical fields, including plastic surgery. ChatGPT offers potential applications in patient education, surgical planning, and decision-making support, making it an important research subject. However, there has been limited investigation into its impact on plastic surgery. The objective of this study was to investigate the progress of research on ChatGPT and plastic surgery, focusing on key contributors and emerging topics within the field. Methods Five distinct search strategies were employed to analyze relevant publications from the Scopus database. Results The analysis identified and presented the top authors, universities, countries, sponsors, and journals (within each search strategy). The co-authorship networks of authors, universities, and countries are graphically presented. The authors’ performance was depicted by various indicators, such as total publications, citations, h-index, g-index, and m-index. A co-word analysis revealed the focus of the papers, which were presented in 15 groups. This multifaceted approach provides a detailed understanding of key themes in the field. Conclusion This report offers a comprehensive overview of the current state of research at the intersection of ChatGPT and plastic surgery.","2024-10-20","2024-12-03 03:23:02","2024-12-03 03:23:02","","","","","","","Chinese Journal of Plastic and Reconstructive Surgery","","","","","","","","","","","","","","","","","","","Bibliometric analysis; ChatGPT; Plastic surgery; Co-words analysis; Scopus","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"4PD8VXB9","journalArticle","2024","Fukuda, Hikaru; Morishita, Masaki; Muraoka, Kosuke; Yamaguchi, Shino; Nakamura, Taiji; Yoshioka, Izumi; Awano, Shuji; Ono, Kentaro","Evaluating the image recognition capabilities of GPT-4V and Gemini Pro in the Japanese national dental examination","Journal of Dental Sciences","","1991-7902","10.1016/j.jds.2024.06.015","https://www.sciencedirect.com/science/article/pii/S1991790224002125","Background/purpose OpenAI's GPT-4V and Google's Gemini Pro, being Large Language Models (LLMs) equipped with image recognition capabilities, have the potential to be utilized in future medical diagnosis and treatment, ands serve as valuable educational support tools for students. This study compared and evaluated the image recognition capabilities of GPT-4V and Gemini Pro using questions from the Japanese National Dental Examination (JNDE) to investigate their potential as educational support tools. Materials and methods We analyzed 160 questions from the 116th JNDE, administered in March 2023, using ChatGPT-4V, and Gemini Pro, which have image recognition functions. Standardized prompts were used for all LLMs, and statistical analysis was conducted using Fisher's exact test and the Mann–Whitney U test. Results For the 160 JNDE questions, the accuracy rates of GPT-4V and Gemini Pro were 35.0% and 28.1%, respectively, with GPT-4V being the highest, although not statistically significant. Across dental specialties, the accuracy rates of the GPT-4V were generally higher than those of the Gemini Pro, with some areas showing equal accuracy. Accuracy rates tended to decrease with an increased number of images within a question, suggesting that the number of images influenced the correctness of the responses. Conclusion The overall superior performance of GPT-4V compared to Gemini Pro may be attributed to the continuous updates in OpenAI's model. This research demonstrates the potential of LLMs as educational support tools in dentistry, while also highlighting areas that require further technological development.","2024-07-02","2024-12-03 03:23:02","2024-12-03 03:23:02","","","","","","","Journal of Dental Sciences","","","","","","","","","","","","","","","","","","","Large language models; ChatGPT-4V; Gemini Pro; Japanese national dental examination","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"5G25AWJH","journalArticle","2024","Cao, Charles; Wang, Feiyi; Lindley, Lisa; Wang, Zejiang","Managing Linux servers with LLM-based AI agents: An empirical evaluation with GPT4","Machine Learning with Applications","","2666-8270","10.1016/j.mlwa.2024.100570","https://www.sciencedirect.com/science/article/pii/S266682702400046X","This paper presents an empirical study on the application of Large Language Model (LLM)-based AI agents for automating server management tasks in Linux environments. We aim to evaluate the effectiveness, efficiency, and adaptability of LLM-based AI agents in handling a wide range of server management tasks, and to identify the potential benefits and challenges of employing such agents in real-world scenarios. We present an empirical study where a GPT-based AI agent autonomously executes 150 unique tasks across 9 categories, ranging from file management to editing to program compilations. The agent operates in a Dockerized Linux sandbox, interpreting task descriptions and generating appropriate commands or scripts. Our findings reveal the agent’s proficiency in executing tasks autonomously and adapting to feedback, demonstrating the potential of LLMs in simplifying complex server management for users with varying technical expertise. This study contributes to the understanding of LLM applications in server management scenarios, and paves the foundation for future research in this domain.","2024-09-01","2024-12-03 03:23:03","2024-12-03 03:23:03","","100570","","","17","","Machine Learning with Applications","","","","","","","","","","","","","","","","","","","Linux; LLM; AI agent; GPT4; Server management","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"AC47M2XQ","journalArticle","2024","Fan, Haolin; Fuh, Jerry; Lu, Wen Feng; Kumar, A. Senthil; Li, Bingbing","Unleashing the Potential of Large Language Models for Knowledge Augmentation: A Practical Experiment on Incremental Sheet Forming","5th International Conference on Industry 4.0 and Smart Manufacturing (ISM 2023)","","1877-0509","10.1016/j.procs.2024.01.125","https://www.sciencedirect.com/science/article/pii/S187705092400125X","As the influence of Incremental Sheet Forming (ISF) grows in manufacturing sectors, so does the demand for precise and updated knowledge construction in this domain. In this research, we evaluate the capability of Large Language Models (LLMs) to capture domain-specific knowledge, using ISF as a case study. Recognizing common LLMs’ limitations such as potential inaccuracies and outdated information reliance, we propose a comprehensive approach involving automated and adaptive knowledge extraction, enrichment, and integration into an ISF-specific dataset. We then fine-tune the LLMs for ISF-related text classification and prompt response tasks. Our results reveal a significant enhancement in LLMs’ performance within the ISF domain, with a domain knowledge acquisition rate exceeding that of GPT-3.5 by 10.4%, achieved by the fine-tuned Alpaca-33B model. Additionally, we introduce a novel conversational prototype designed to refine the accuracy and relevance of LLMs in the ISF domain. Our findings will guide future efforts in downstream tasks such as ISF-domain knowledge graph construction and quality prediction.","2024-01-01","2024-12-03 03:23:03","2024-12-03 03:23:03","","1269-1278","","","232","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Large Language Models; Fine-tuning; Domain Knowledge Augmentation; Incremental Sheet Forming","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"D8WR84I8","journalArticle","2024","Haurum, Kasper Raupach; Ma, Ruiqi; Long, Wen","Real Estate with AI: An agent based on LangChain","11th International Conference on Information Technology and Quantitative Management (ITQM 2024)","","1877-0509","10.1016/j.procs.2024.08.199","https://www.sciencedirect.com/science/article/pii/S1877050924019185","Recent developments in large language models (LLMs) have opened new avenues for the real estate industry. These models not only understand language but also function as intelligent agents, engaging with investors through open-ended conversations and influencing their decision-making. Utilizing unstructured data from a professional Danish real estate website, we developed a real estate AI agent in both English and Danish using LangChain and Pinecone. Through testing and evaluation, our agent has demonstrated superior professional and concise outputs compared to other LLMs like Doubao and ChatGPT 4 and shown excellent performance and effectiveness. Our work serves as a reference for AI in real estate investment-related research and proposes new solutions to the ""unprofessional foundation "" and "" expensive consulting fee"" problems encountered by ordinary investors in their investment decisions.","2024-01-01","2024-12-03 03:23:03","2024-12-03 03:23:03","","1082-1088","","","242","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","LLMs; LangChain; Real Estate Decision-making; Text Data Mining","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"TCICGDP2","journalArticle","2024","Zuo, Xiangwu; Jiang, Anxiao (Andrew); Zhou, Kaixiong","Reinforcement prompting for financial synthetic data generation","The Journal of Finance and Data Science","","2405-9188","10.1016/j.jfds.2024.100137","https://www.sciencedirect.com/science/article/pii/S2405918824000229","The emergence of Large Language Models (LLMs) has unlocked unprecedented potential for comprehending and generating human-like text, fueling advances in the finance domain – a tool that can shape investment strategies and market predictions. Nevertheless, challenges stemming from the necessity for extensive labeled data and the imperative for data privacy remain. The generation of high-quality synthetic data emerges as a promising avenue to circumvent these issues. In this paper, we introduce a novel methodology, named “Reinforcement Prompting”, to address these challenges. Our strategy employs a policy network as a Selector to generate prompts, and an LLM as an Executor to produce financial synthetic data. This synthetic data generation process preserves data privacy and mitigates the dependency on real-world labeled datasets. We validate the effectiveness of our approach through experimental evaluations. Our results indicate that models trained on synthetic data generated via our approach exhibit competitive performance when compared to those trained on actual financial data, thereby bridging the performance gap. This research provides a novel solution to the challenges of data privacy and labeled data scarcity in financial sentiment analysis, offering considerable advancement in the field of financial machine learning.","2024-12-01","2024-12-03 03:23:03","2024-12-03 03:23:03","","100137","","","10","","The Journal of Finance and Data Science","","","","","","","","","","","","","","","","","","","Machine learning; Large language model; Synthetic data; Financial sentiment analysis; Reinforcement learning; Reinforcement prompting","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ZTVLCJC8","journalArticle","2024","Zhang, Chaobo; Lu, Jie; Zhao, Yang","Generative pre-trained transformers (GPT)-based automated data mining for building energy management: Advantages, limitations and the future","Energy and Built Environment","","2666-1233","10.1016/j.enbenv.2023.06.005","https://www.sciencedirect.com/science/article/pii/S2666123323000521","Advanced data mining methods have shown a promising capacity in building energy management. However, in the past decade, such methods are rarely applied in practice, since they highly rely on users to customize solutions according to the characteristics of target building energy systems. Hence, the major barrier is that the practical applications of such methods remain laborious. It is necessary to enable computers to have the human-like ability to solve data mining tasks. Generative pre-trained transformers (GPT) might be capable of addressing this issue, as some GPT models such as GPT-3.5 and GPT-4 have shown powerful abilities on interaction with humans, code generation, and inference with common sense and domain knowledge. This study explores the potential of the most advanced GPT model (GPT-4) in three data mining scenarios of building energy management, i.e., energy load prediction, fault diagnosis, and anomaly detection. A performance evaluation framework is proposed to verify the capabilities of GPT-4 on generating energy load prediction codes, diagnosing device faults, and detecting abnormal system operation patterns. It is demonstrated that GPT-4 can automatically solve most of the data mining tasks in this domain, which overcomes the barrier of practical applications of data mining methods in this domain. In the exploration of GPT-4, its advantages and limitations are also discussed comprehensively for revealing future research directions in this domain.","2024-02-01","2024-12-03 03:23:03","2024-12-03 03:23:03","","143-169","","1","5","","Energy and Built Environment","","","","","","","","","","","","","","","","","","","Data mining; ChatGPT; GPT-4; Artificial general intelligence; Building energy management","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XGNN5V6M","journalArticle","2024","Lin, Chien-Chang; Cheng, Eddie S.J.; Huang, Anna Y.Q.; Yang, Stephen J.H.","DNA of learning behaviors: A novel approach of learning performance prediction by NLP","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100227","https://www.sciencedirect.com/science/article/pii/S2666920X24000286","In recent years, the field of learning analytics has gained significant attention as educators and researchers seek to understand and optimize the learning process in online learning systems. This paper presents a novel methodology for predicting learning performance in online learning systems by leveraging natural language processing (NLP) and embedding techniques. The study focuses on two online learning systems, namely BookRoll and Viscode, and aims to analyze the learning behaviors of students using system logs extracted from the databases. The logs are converted into semester and daily learning description documents to capture the daily activities and progress of the learners. To transform the natural language data into numerical representations, the transformer-based BERT model, Google Gemini, and OpenAI large text embedding methodology are employed to generate embeddings for the learning descriptions. Subsequently, k-means clustering is applied to identify distinct learning behaviors exhibited by students. These clusters are labeled with numbers, and the daily learning descriptions are combined into a sequence, referred to as the DNA of learning behaviors. By utilizing this DNA representation, the learning status of students is effectively captured, and a machine learning model is trained to predict learning performance. The experimental results demonstrate the efficacy of the proposed methodology in achieving highly convincing predictions. The contributions of this research lie in the adoption of a unique approach, integrating NLP methodologies and embeddings techniques, to enable accurate learning performance prediction in online learning systems.","2024-06-01","2024-12-03 03:23:03","2024-12-03 03:23:03","","100227","","","6","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Natural language processing; Artificial intelligence in education; Learning performance prediction; Learning behavior analysis; Teaching/learning strategies","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"TVWBJ6GV","journalArticle","2023","Mohammad, Mutaz; Trounev, Alexander; Cattani, Carlo","Stress state and waves in the lithospheric plate simulation: A 3rd generation AI architecture","Results in Physics","","2211-3797","10.1016/j.rinp.2023.106938","https://www.sciencedirect.com/science/article/pii/S2211379723007313","Natural disasters present ongoing risks to human life and the global economy, with climate change and environmental factors exacerbating these threats. This article introduces an innovative approach to earthquake prediction and modeling, utilizing a combination of modern mathematical techniques, data-driven methodologies, and artificial intelligence (AI). By integrating the finite element method and wavelet collocation method, our approach enables the solution of complex partial differential equations involving fractional derivatives. This integration provides a more accurate prediction of earthquake behavior by leveraging AI analysis of the resulting numerical solutions. Our advanced modeling technique enables three-dimensional earthquake modeling, surpassing the limitations of traditional methods and offering unprecedented insights. The comprehensive AI approach shows great potential for improving our understanding of earthquake behavior, facilitating the design of earthquake-resistant structures, and effectively mitigating the devastating impacts of earthquakes. This research significantly contributes to the field of disaster risk reduction and offers practical applications for both researchers and practitioners. By combining mathematical rigor, data-driven insights, and the power of AI, our next-generation approach opens new avenues for enhancing our preparedness and response to seismic events.","2023-10-01","2024-12-03 03:23:03","2024-12-03 03:23:03","","106938","","","53","","Results in Physics","","","","","","","","","","","","","","","","","","","Natural disasters; Finite element method; Numerical simulation; Partial differential equations; Stress state; Wavelet collocation method","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"Z8K3MHQM","journalArticle","2023","David, Anthea Naomi; Sewsynker-Sukai, Y.; Meyer, E.L.; Kana, E.B. Gueguim","Harnessing Artificial Neural Networks and large language models for bioprocess optimization: Predicting sugar output from Kraft waste-based lignocellulosic pretreatments","Industrial Crops and Products","","0926-6690","10.1016/j.indcrop.2023.117686","https://www.sciencedirect.com/science/article/pii/S0926669023014516","This study implements Artificial Neural Network (ANN) models as predictive tools for glucose responses from Kraft waste-based pretreatments. The developed steam- and microwave-assisted ANN models achieved R2 scores > 0.95 for the observed and predicted glucose responses. An in-depth sensitivity analysis revealed that the glucose responses for the steam and microwave models were highly susceptible to the stepwise variation in green liquor dregs concentration (>3.3-fold) and power intensity (>2.6-fold), respectively. Comparative assessment on the capability of the large language model, ChatGPT, to generate innovative and factually accurate insights based on the process data was carried out. The novel process insights deduced by ChatGPT concurred with the authors’ findings of this study, underscoring the unique critical role of integrating advanced artificial intelligence and domain-specific knowledge to accelerate progression in lignocellulosic waste pretreatment. As such, these synergies align with global sustainable developmental objectives that leverage 4IR technologies, propelling this research field forward.","2023-12-15","2024-12-03 03:23:03","2024-12-03 03:23:03","","117686","","","206","","Industrial Crops and Products","","","","","","","","","","","","","","","","","","","Large language models; Sensitivity analysis; Artificial Neural Network; Bioprocess modelling and optimization; Kraft waste-based pretreatment","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SJU6NNMY","journalArticle","2024","Cao, Jie; Xu, Tingting; Deng, Linhua; Zhou, Xueliang; Li, Shangxi; Liu, Yuxia; Zhou, Weihong","Pulsar candidate identification using advanced transformer-based models","Chinese Journal of Physics","","0577-9073","10.1016/j.cjph.2024.05.020","https://www.sciencedirect.com/science/article/pii/S0577907324002016","Rapid and accurate identification of pulsars is a significant topic for large radio telescope surveys. With the enhancement of astronomical instruments, modern radio telescopes are witnessing an exponential increase in pulsar candidate detections. The application of artificial intelligence for the identification of pulsar candidates is an automated and highly effective solution to tackle the challenge of processing and recognizing vast volumes of data. In this work, using the data released by two surveys, the Commensal Radio Astronomy FasT Survey (CRAFTS) and High-Time Resolution Universe (HTRU), we propose a new framework to identify pulsar candidates. Firstly, due to the small number of real pulsars, we compare the performance of different data augmentation methods and find that the pulsar samples generated by the Deep Convolutional Generative Adversarial Network (DCGAN) based on deep learning techniques are closer to real pulsars. Secondly, we use two transformer-based classification models, Vision Transformer (ViT) and Convolutional Vision Transformer (CvT), to classify pulsar candidates, and find that the evaluation indexes of pulsar candidate classification based on two transformers can reach 100%. Finally, we use the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm to visualize the results of our identification framework. The results showed that pulsar and non-pulsar samples are separated from each other in multidimensional space. Therefore, it is a new attempt to apply transformer technology to pulsar candidate classification, and it could be of great significance to subsequent theoretical research.","2024-08-01","2024-12-03 03:23:03","2024-12-03 03:23:03","","121-133","","","90","","Chinese Journal of Physics","","","","","","","","","","","","","","","","","","","Data analysis; Methods; General; Image processing; Pulsars; Techniques","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RJFVKL4H","journalArticle","2024","Stadler, Matthias; Bannert, Maria; Sailer, Michael","Cognitive ease at a cost: LLMs reduce mental effort but compromise depth in student scientific inquiry","Computers in Human Behavior","","0747-5632","10.1016/j.chb.2024.108386","https://www.sciencedirect.com/science/article/pii/S0747563224002541","This study explores the cognitive load and learning outcomes associated with using large language models (LLMs) versus traditional search engines for information gathering during learning. A total of 91 university students were randomly assigned to either use ChatGPT3.5 or Google to research the socio-scientific issue of nanoparticles in sunscreen to derive valid recommendations and justifications. The study aimed to investigate potential differences in cognitive load, as well as the quality and homogeneity of the students' recommendations and justifications. Results indicated that students using LLMs experienced significantly lower cognitive load. However, despite this reduction, these students demonstrated lower-quality reasoning and argumentation in their final recommendations compared to those who used traditional search engines. Further, the homogeneity of the recommendations and justifications did not differ significantly between the two groups, suggesting that LLMs did not restrict the diversity of students’ perspectives. These findings highlight the nuanced implications of digital tools on learning, suggesting that while LLMs can decrease the cognitive burden associated with information gathering during a learning task, they may not promote deeper engagement with content necessary for high-quality learning per se.","2024-11-01","2024-12-03 03:23:03","2024-12-03 03:23:03","","108386","","","160","","Computers in Human Behavior","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DNXADC4G","journalArticle","2024","Thway, Maung; Low, Andre K. Y.; Khetan, Samyak; Dai, Haiwen; Recatala-Gomez, Jose; Chen, Andy Paul; Hippalgaonkar, Kedar","Harnessing GPT-3.5 for text parsing in solid-state synthesis – case study of ternary chalcogenides","Digital Discovery","","2635-098X","10.1039/d3dd00202k","https://www.sciencedirect.com/science/article/pii/S2635098X24000147","Optimally doped single-phase compounds are necessary to advance state-of-the-art thermoelectric devices which convert heat into electricity and vice versa, requiring solid-state synthesis of bulk materials. For data-driven approaches to learn these recipes, it requires careful data curation from large bodies of text which may not be available for some materials, as well as a refined language processing algorithm which presents a high barrier of entry. We propose applying Large Language Models (LLMs) to parse solid-state synthesis recipes, encapsulating all essential synthesis information intuitively in terms of primary and secondary heating peaks. Using a domain-expert curated dataset for a specific material (Gold Standard), we engineered a prompt set for GPT-3.5 to replicate the same dataset (Silver Standard), doing so successfully with 73% overall accuracy. We then proceed to extract and infer synthesis conditions for other ternary chalcogenides with the same prompt set. From a database of 168 research papers, we successfully parsed 61 papers which we then used to develop a classifier to predict phase purity. Our methodology demonstrates the generalizability of Large Language Models (LLMs) for text parsing, specifically for materials with sparse literature and unbalanced reporting (since usually only positive results are shown). Our work provides a roadmap for future endeavors seeking to amalgamate LLMs with materials science research, heralding a potentially transformative paradigm in the synthesis and characterization of novel materials.","2024-02-14","2024-12-03 03:23:03","2024-12-03 03:23:03","","328-336","","2","3","","Digital Discovery","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"7ZEEA788","journalArticle","2024","Kucukkaya, Aycan; Arikan, Emine; Goktas, Polat","Unlocking ChatGPT’s potential and challenges in intensive care nursing education and practice: A systematic review with narrative synthesis","Nursing Outlook","","0029-6554","10.1016/j.outlook.2024.102287","https://www.sciencedirect.com/science/article/pii/S0029655424001805","Background The advancement of artificial intelligence (AI) in healthcare and nursing promises to enhance clinical outcomes and education. This review emphasizes integrating AI chatbots, specifically ChatGPT, into Intensive Care Units (ICU) to transform nursing education and practices, while addressing associated risks and ethical challenges. Purpose To evaluate ChatGPT’s utility in ICU nursing education and practices, assessing its effectiveness and develop strategic recommendations for its future incorporation into critical care. Methods This review employs systematic literature with narrative synthesis, adhering to PRISMA guidelines. Discussion Five of 1,091 identified studies were eligible. These studies illustrate AI-driven applications’ potential in clinical decision-making and educational efforts, emphasizing the need for improved AI accuracy, robust guidelines, and measures to address data privacy concerns to ensure reliable integration. Conclusion ChatGPT presents promising benefits for ICU applications but requires careful management. Ongoing research and adherence to ethical standards are essential to optimize its use in critical care. Tweetable abstract Explore how #ChatGPT revolutionizes ICU nursing & education, blending AI capabilities with ethical, human-centered care. #ICM #AIinHealthcare","2024-11-01","2024-12-03 03:23:03","2024-12-03 03:23:03","","102287","","6","72","","Nursing Outlook","","","","","","","","","","","","","","","","","","","Nursing; Artificial intelligence; ChatGPT; Large language models; Ethical implications; Intensive Care Units","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"HITGEEJS","journalArticle","2023","Grebo, Alen; Krstulović-Opara, Lovre; Domazet, Željko","Thermal to digital image correlation image to image translation with CycleGAN and Pix2Pix","38th Danubia-Adria Symposium on Advances in Experimental Mechanics","","2214-7853","10.1016/j.matpr.2023.06.219","https://www.sciencedirect.com/science/article/pii/S2214785323036076","This paper explores the use of image-to-image translation techniques for translating thermography into digital image correlation data. A Pix2Pix model and a CycleGAN model were trained on a dataset of paired and unpaired images to translate between the two image domains. The results show that while the CycleGAN model exhibited some challenges in correlating images obtained by different optical systems, the Pix2Pix model demonstrates promising potential. The trained Pix2Pix model were also validated on unseen data, providing confidence for future research in this area.","2023-01-01","2024-12-03 03:23:03","2024-12-03 03:23:03","","752-760","","","93","","Materials Today: Proceedings","","","","","","","","","","","","","","","","","","","Artificial Intelligence; CycleGAN; Digital Image Correlation; Generative Adversarial Network; Pix2Pix; Thermography","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"T7RJ428A","journalArticle","2024","Sadhana, P; Ravishankar, Nandhitha; Ashok, Amruth; Ravichandran, Ramanan; Paul, Rhea; K, Murali","Enhancing Fake Image Detection: A Novel Two-Step Approach Combining GANs and CNNs","International Conference on Machine Learning and Data Engineering (ICMLDE 2023)","","1877-0509","10.1016/j.procs.2024.04.077","https://www.sciencedirect.com/science/article/pii/S1877050924007531","The proliferation of fake images in today's digital landscape poses a significant threat to various domains, including media integrity, social media, and online security. Recognizing the urgent need to distinguish real images from their deceptive counterparts, this paper underscores the importance of developing a robust detection system. While substantial efforts have been made in the realms of computer vision and deep learning, the advent of Generative Adversarial Networks (GANs) has added a new layer of complexity to this challenge. In response to these evolving threats, we present a novel two-step methodology for detecting fake images, with a specific focus on those generated by GANs. Our approach harnesses the combined strengths of GANs and traditional Convolutional Neural Networks (CNNs), offering a comprehensive solution that significantly enhances accuracy in identifying both fake images and machine-generated fake images. The results of our experiments demonstrate the efficacy of our methodology. Using CNNs alone, we achieved a training accuracy of 87%. However, when employing the collaborative power of GANs and CNNs, our model exhibited a remarkable accuracy rate of 94.4%. This substantial improvement underscores the superiority of the GANs+CNN approach, suggesting its potential as a groundbreaking solution in the realm of fake image detection. This research opens up new horizons in fields such as media forensics, social media monitoring, and online security, where the ability to discern genuine content from manipulated or synthetic media is of paramount importance. The promising outcomes of this study not only provide an immediate and effective solution but also pave the way for further exploration and innovation in this critical area of digital security.","2024-01-01","2024-12-03 03:23:03","2024-12-03 03:23:03","","810-819","","","235","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial Intelligence; convolutional neural network; fake image; Generative Adversarial network","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"6PUE79UD","journalArticle","2024","Vowels, Laura M.; Francois-Walcott, Rachel R.R.; Darwiche, Joëlle","AI in relationship counselling: Evaluating ChatGPT's therapeutic capabilities in providing relationship advice","Computers in Human Behavior: Artificial Humans","","2949-8821","10.1016/j.chbah.2024.100078","https://www.sciencedirect.com/science/article/pii/S2949882124000380","Recent advancements in AI have led to chatbots, such as ChatGPT, capable of providing therapeutic responses. Early research evaluating chatbots' ability to provide relationship advice and single-session relationship interventions has showed that both laypeople and relationship therapists rate them high on attributed such as empathy and helpfulness. In the present study, 20 participants engaged in single-session relationship intervention with ChatGPT and were interviewed about their experiences. We evaluated the performance of ChatGPT comprising of technical outcomes such as error rate and linguistic accuracy and therapeutic quality such as empathy and therapeutic questioning. The interviews were analysed using reflexive thematic analysis which generated four themes: light at the end of the tunnel; clearing the fog; clinical skills; and therapeutic setting. The analyses of technical and feasibility outcomes, as coded by researchers and perceived by users, show ChatGPT provides realistic single-session intervention with it consistently rated highly on attributes such as therapeutic skills, human-likeness, exploration, and useability, and providing clarity and next steps for users’ relationship problem. Limitations include a poor assessment of risk and reaching collaborative solutions with the participant. This study extends on AI acceptance theories and highlights the potential capabilities of ChatGPT in providing relationship advice and support.","2024-08-01","2024-12-03 03:23:03","2024-12-03 03:23:03","","100078","","2","2","","Computers in Human Behavior: Artificial Humans","","","","","","","","","","","","","","","","","","","Artificial intelligence; Chatbots; Large language models; Relationship advice","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"EBDGQ8PI","journalArticle","2024","Waluyo, Budi; Kusumastuti, Sekartiyasa","Generative AI in student English learning in Thai higher education: More engagement, better outcomes?","Social Sciences & Humanities Open","","2590-2911","10.1016/j.ssaho.2024.101146","https://www.sciencedirect.com/science/article/pii/S2590291124003437","Despite the growing body of research exploring artificial intelligence (AI) in educational contexts, there remains a critical gap in understanding the specific effects of generative AI (GAI) on English language learning, particularly within higher education in Thailand. This study addresses this gap by employing an explanatory sequential mixed-method design to explore both student and teacher perspectives. Quantitative data were analyzed using descriptive statistics, Mann-Whitney U tests, Spearman correlation analysis, and Kendall–Theil regression to examine students' acceptance, usage, and the relationship between GAI engagement and academic performance. Qualitative data, collected through structured written interviews, were analyzed using thematic analysis to uncover key themes from both student and teacher narratives. Quantitative findings reveal high student acceptance of GAI tools in terms of performance expectancy (M = 3.66, SD = .58), effort expectancy (M = 3.61, SD = .59), facilitating conditions (M = 3.51, SD = .59), and use behavior (M = 3.52, SD = .48), while social influence remained relatively lower (M = 3.31, SD = .54). Mann-Whitney U tests showed no statistically significant differences in GAI usage across high- and low-performing students (p > .05). Correlation analyses indicated strong associations between performance expectancy and other factors (ρ = .87, p < .001), yet no significant correlation with GPA (ρ = −.06, p = .76). Qualitative results from student reflections revealed improved efficiency, enhanced engagement, and increased linguistic confidence. Yet, concerns about overreliance on AI and the necessity for critical use were noted. Teacher narratives accentuated ethical concerns and the potential for GAI misuse, highlighting the need for balanced, responsible integration of AI into pedagogical practices. These findings underscore the potential of GAI to enhance learning experiences while emphasizing the importance of maintaining academic integrity and fostering critical thinking.","2024-01-01","2024-12-03 03:23:03","2024-12-03 03:23:03","","101146","","","10","","Social Sciences & Humanities Open","","","","","","","","","","","","","","","","","","","Generative AI; English learning; Student acceptance; Thai higher education","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"5H4F5FLY","journalArticle","2024","Jiang, Peng; Sonne, Christian; Li, Wangliang; You, Fengqi; You, Siming","Preventing the Immense Increase in the Life-Cycle Energy and Carbon Footprints of LLM-Powered Intelligent Chatbots","Engineering","","2095-8099","10.1016/j.eng.2024.04.002","https://www.sciencedirect.com/science/article/pii/S2095809924002315","Intelligent chatbots powered by large language models (LLMs) have recently been sweeping the world, with potential for a wide variety of industrial applications. Global frontier technology companies are feverishly participating in LLM-powered chatbot design and development, providing several alternatives beyond the famous ChatGPT. However, training, fine-tuning, and updating such intelligent chatbots consume substantial amounts of electricity, resulting in significant carbon emissions. The research and development of all intelligent LLMs and software, hardware manufacturing (e.g., graphics processing units and supercomputers), related data/operations management, and material recycling supporting chatbot services are associated with carbon emissions to varying extents. Attention should therefore be paid to the entire life-cycle energy and carbon footprints of LLM-powered intelligent chatbots in both the present and future in order to mitigate their climate change impact. In this work, we clarify and highlight the energy consumption and carbon emission implications of eight main phases throughout the life cycle of the development of such intelligent chatbots. Based on a life-cycle and interaction analysis of these phases, we propose a system-level solution with three strategic pathways to optimize the management of this industry and mitigate the related footprints. While anticipating the enormous potential of this advanced technology and its products, we make an appeal for a rethinking of the mitigation pathways and strategies of the life-cycle energy usage and carbon emissions of the LLM-powered intelligent chatbot industry and a reshaping of their energy and environmental implications at this early stage of development.","2024-09-01","2024-12-03 03:23:03","2024-12-03 03:23:03","","202-210","","","40","","Engineering","","","","","","","","","","","","","","","","","","","Large language models; Carbon emissions; Energy and environmental footprints; Global cooperation; Intelligent chatbots; Life-cycle assessment","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SGZNSZSL","journalArticle","2024","Lareyre, Fabien; D'Oria, Mario; Caradu, Caroline; Jongkind, Vincent; Di Lorenzo, Gilles; Smeds, Matthew R.; Nasr, Bahaa; Raffort, Juliette; Enzmann, Florian; de Borst, Gert J.; Sousa, Joel Ferreira; Meecham, Lewis; Domingos, Liliana; Teraa, Martin; Zlatanovic, Petar; Weiss, Salome; Ancetti, Stefano; Busch, Albert; Jóhannesdóttir, Bergrós; Gombert, Alexander; Noronen, Katariina; Hinchliffe, Robert; Predenciuc, Alexandru; Doukas, Panagiotis; Kukulski, Leszek; Ghulam, Qasam; Karelis, Angelos; Darwish, Maram; Barbati, Mohammad Esmaeil; Møller, Markvard; Spreadbury, Matt; van de Water, Willemien; van den Hondel, Desiree; Ebben, Harm; Croo, Alexander; Uijtterhaegen, Gilles; Trusca, Adina; Melo, Ryan Gouveia; Dabravolskaite, Vaiva; Spath, Paolo; Amlani, Vishal; Kiernan, Aoife; Zielasek, Christian","Open E-survey on the Use and Perception of Chatbots in Vascular Surgery","EJVES Vascular Forum","","2666-688X","10.1016/j.ejvsvf.2024.07.037","https://www.sciencedirect.com/science/article/pii/S2666688X24001333","Objective Large language models and artificial intelligence (AI) based chatbots have brought new insights in healthcare, but they also raise major concerns. Their applications in vascular surgery have scarcely been investigated to date. This international survey aimed to evaluate the perceptions and feedback from vascular surgeons on the use of AI chatbots in vascular surgery. Methods This international open e-survey comprised 50 items that covered participant characteristics, their perceptions on the use of AI chatbots in vascular surgery, and their user experience. The study was designed in accordance with the Checklist for reporting Results of Internet E-Surveys and was critically reviewed and approved by international members of the European Vascular Research Collaborative (EVRC) prior to distribution. Participation was open to self reported health professionals specialised (or specialising) in vascular surgery, including residents or fellows. Results Of the 342 individuals who visited the survey page, 318 (93%) agreed to participate; 262 (82.4%) finished the survey and were included in the analysis. Most were consultants or attending physicians (64.1%), most declared not having any training or education related to AI in healthcare (221; 84.4%), and 198 (75.6%) rated their knowledge about the abilities of AI chatbots between average to very poor. Interestingly, 95 participants (36.3%) found that AI chatbots were very useful or somewhat useful in clinical practice at this stage and 229 (87.4%) agreed that they should be systematically validated prior to being used. Eighty participants (30.5%) had specifically tested it for questions related to clinical practice and 59 (73.8%) of them experienced issues or limitations. Conclusion This international survey provides an overview of perceptions of AI chatbots by vascular surgeons and highlights the need to improve knowledge and training of health professionals to better evaluate, define, and implement their use in vascular surgery.","2024-01-01","2024-12-03 03:23:04","2024-12-03 03:23:04","","57-63","","","62","","EJVES Vascular Forum","","","","","","","","","","","","","","","","","","","Survey; Artificial intelligence; Natural language processing; Chatbot; Large language model; Virtual assistant","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"BQVRYAIK","journalArticle","2024","de Souza, Bruno Campello; Serrano de Andrade Neto, Agostinho; Roazzi, Antonio","The generative AI revolution, cognitive mediation networks theory and the emergence of a new mode of mental functioning: Introducing the Sophotechnic Mediation scale","Computers in Human Behavior: Artificial Humans","","2949-8821","10.1016/j.chbah.2024.100042","https://www.sciencedirect.com/science/article/pii/S2949882124000021","This paper examines the recent emergence of AI-powered chatbots such as ChatGPT through the lens of the Cognitive Mediation Networks Theory (CMNT), deducing that the introduction of this radically new technology will likely create a new stage of collective cognitive functioning, called “Sophotechnic Mediation”, with characteristics that can be extrapolated from the way these new tools work and the dynamics of the sociocultural structures being created around them. From that description, the Sophotechnic Mediation Scale is proposed as a means to assess the extent of an individual's internalization of the new form of thinking. A preliminary empirical investigation with 132 higher education professors and students found the instrument to be statistically consistent, yielding a unidimensional and Gaussian score that behaves as a developmental trait emerging from the interaction with generative AIs, mediated by age and mastery of previous digital technologies and their cultural elements. It is concluded that the results are suggestive of the validity of the new scale and warrant further research.","2024-01-01","2024-12-03 03:23:04","2024-12-03 03:23:04","","100042","","1","2","","Computers in Human Behavior: Artificial Humans","","","","","","","","","","","","","","","","","","","Artificial intelligence; Chatbots; ChatGPT; Cognitive mediation networks theory; Hyperculture; Sophotechnic","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"VAECVYRJ","journalArticle","2024","Ye, Zhuyifan; Wang, Nannan; Zhou, Jiantao; Ouyang, Defang","Organic crystal structure prediction via coupled generative adversarial networks and graph convolutional networks","The Innovation","","2666-6758","10.1016/j.xinn.2023.100562","https://www.sciencedirect.com/science/article/pii/S266667582300190X","Organic crystal structures exert a profound impact on the physicochemical properties and biological effects of organic compounds. Quantum mechanics (QM)–based crystal structure predictions (CSPs) have somewhat alleviated the dilemma that experimental crystal structure investigations struggle to conduct complete polymorphism studies, but the high computing cost poses a challenge to its widespread application. The present study aims to construct DeepCSP, a feasible pure machine learning framework for minute-scale rapid organic CSP. Initially, based on 177,746 data entries from the Cambridge Crystal Structure Database, a generative adversarial network was built to conditionally generate trial crystal structures under selected feature constraints for the given molecule. Simultaneously, a graph convolutional attention network was used to predict the density of stable crystal structures for the input molecule. Subsequently, the distances between the predicted density and the definition-based calculated density would be considered to be the crystal structure screening and ranking basis, and finally, the density-based crystal structure ranking would be output. Two such distinct algorithms, performing the generation and ranking functionalities, respectively, collectively constitute the DeepCSP, which has demonstrated compelling performance in marketed drug validations, achieving an accuracy rate exceeding 80% and a hit rate surpassing 85%. Inspiringly, the computing speed of the pure machine learning methodology demonstrates the potential of artificial intelligence in advancing CSP research.","2024-03-04","2024-12-03 03:23:04","2024-12-03 03:23:04","","100562","","2","5","","The Innovation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ZWI5RMKM","journalArticle","2023","Pfeifer, Moritz; Marohl, Vincent P.","CentralBankRoBERTa: A fine-tuned large language model for central bank communications","The Journal of Finance and Data Science","","2405-9188","10.1016/j.jfds.2023.100114","https://www.sciencedirect.com/science/article/pii/S2405918823000302","Central bank communications are an important tool for guiding the economy and fulfilling monetary policy goals. Natural language processing (NLP) algorithms have been used to analyze central bank communications. These outdated bag-of-words methods often ignore context and cannot distinguish who these sentiments are addressing. Recent research has introduced deep-learning-based NLP algorithms, also known as large language models (LLMs), which take context into account. This study applies LLMs to central bank communications and constructs CentralBankRoBERTa, a state-of-the-art economic agent classifier that distinguishes five basic macroeconomic agents and binary sentiment classifier that identifies the emotional content of sentences in central bank communications. The absence of large-language models in the central bank communications literature may be attributed to a lack of appropriately labeled datasets. To address this gap, we introduce our model, CentralBankRoBERTa, offering an easy-to-use and standardized tool for scholars of central bank communications.","2023-11-01","2024-12-03 03:23:04","2024-12-03 03:23:04","","100114","","","9","","The Journal of Finance and Data Science","","","","","","","","","","","","","","","","","","","Sentiment analysis; Large language model; Central bank communication; Monetary policy; Multiclass classification","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"TFXRFCAC","journalArticle","2024","Yoo, Joon Woo; Park, Junsung; Park, Heejun","The impact of AI-enabled CRM systems on organizational competitive advantage: A mixed-method approach using BERTopic and PLS-SEM","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e36392","https://www.sciencedirect.com/science/article/pii/S2405844024124231","The recent advances in machine learning and deep learning algorithms, along with the advent of generative AI, have led AI to become the “new normal” in organizations. This trend has extended to CRM, resulting in the development of AI-enabled CRM systems, or AI-CRM. Despite the growing adoption of AI as part of competitive strategies, many firms report minimal or no positive effect of AI on performance. This study addresses the research questions: “What are the critical features of AI-CRM systems?” and “How do these features impact organizational competitive advantage?” To explore this, we aim to identify key characteristics of AI-CRM and assess their impact on organizational performance. In Study 1, we utilize BERTopic topic modeling to extract critical features of AI-CRM from user reviews. Study 2 employs PLS-SEM to examine how these features influence organizational competitive advantage. Study 1 reveals four main characteristics of AI-CRM (general, marketing, sales, and service/support), each comprising distinct features. Study 2 shows that these characteristics differentially impact CRM capability, significantly affecting performance and competitive advantage. The findings offer valuable insights for both theory and practice regarding the effective use of AI in organizations.","2024-08-30","2024-12-03 03:23:04","2024-12-03 03:23:04","","e36392","","16","10","","Heliyon","","","","","","","","","","","","","","","","","","","Artificial intelligence; Customer relationship management; BERT; Topic modeling; Resource-based view","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KA9FRYW7","journalArticle","2024","Jiang, Yiqi; Jiang, Zhou; Chen, Zhijun","Women entrepreneurship in China: A bibliometric literature review and future research agenda","Journal of Business Research","","0148-2963","10.1016/j.jbusres.2024.114688","https://www.sciencedirect.com/science/article/pii/S0148296324001929","Women entrepreneurship in China plays a pivotal role in social and economic advancement. Over recent decades, Chinese female entrepreneurs have significantly impacted national and global economies. However, despite increasing research efforts, our understanding of this domain remains fragmented. To offer a comprehensive overview, we conducted a bibliometric review of 85 primary articles and 5,010 secondary documents, unveiling the intellectual landscape of research in Chinese women entrepreneurship. Employing document co-citation and bibliographic coupling analyses, we delve into intellectual traditions/foundations and emerging research areas. By synthesizing these findings, the article outlines a research agenda aimed at advancing our scholarly knowledge of women entrepreneurship in China. We highlight that future women entrepreneurship research can delve deeper into theoretical roots in the Chinese context, effects of cultural values, intranational disparities, multi-level entrepreneurial barriers, digital entrepreneurship, gender differences in entrepreneurial self-efficacy and intention, and the roles of generative artificial intelligence (AI).","2024-06-01","2024-12-03 03:23:04","2024-12-03 03:23:04","","114688","","","179","","Journal of Business Research","","","","","","","","","","","","","","","","","","","China; Gender; Bibliographic analysis; Bibliometric literature review; Female entrepreneur; Women entrepreneurship","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"P9PR6SQI","journalArticle","2024","Awasthi, Akash; Le, Ngan; Deng, Zhigang; Agrawal, Rishi; Wu, Carol C.; Van Nguyen, Hien","Bridging human and machine intelligence: Reverse-engineering radiologist intentions for clinical trust and adoption","Computational and Structural Biotechnology Journal","","2001-0370","10.1016/j.csbj.2024.11.012","https://www.sciencedirect.com/science/article/pii/S2001037024003830","In the rapidly evolving landscape of medical imaging, the integration of artificial intelligence (AI) with clinical expertise offers unprecedented opportunities to enhance diagnostic precision and accuracy. Yet, the ""black box"" nature of AI models often limits their integration into clinical practice, where transparency and interpretability are important. This paper presents a novel system leveraging the Large Multimodal Model (LMM) to bridge the gap between AI predictions and the cognitive processes of radiologists. This system consists of two core modules, Temporally Grounded Intention Detection (TGID) and Region Extraction (RE). The TGID module predicts the radiologist's intentions by analyzing eye gaze fixation heatmap videos and corresponding radiology reports. Additionally, the RE module extracts regions of interest that align with these intentions, mirroring the radiologist’s diagnostic focus. This approach introduces a new task, radiologist intention detection, and is the first application of Dense Video Captioning (DVC) in the medical domain. By making AI systems more interpretable and aligned with radiologist’s cognitive processes, this proposed system aims to enhance trust, improve diagnostic accuracy, and support medical education. Additionally, it holds the potential for automated error correction, guiding junior radiologists, and fostering more effective training and feedback mechanisms. This work sets a precedent for future research in AI-driven healthcare, offering a pathway towards transparent, trustworthy, and human-centered AI systems. We evaluated this model using NLG(Natural Language Generation), time-related, and vision-based metrics, demonstrating superior performance in generating temporally grounded intentions on REFLACX and EGD-CXR datasets. This model also demonstrated strong predictive accuracy in overlap scores for medical abnormalities and effective region extraction with high IoU(Intersection over Union), especially in complex cases like cardiomegaly and edema. These results highlight the system's potential to enhance diagnostic accuracy and support continuous learning in radiology.","2024-12-01","2024-12-03 03:23:04","2024-12-03 03:23:04","","711-723","","","24","","Computational and Structural Biotechnology Journal","","","","","","","","","","","","","","","","","","","Intention; DVC(Deep video captioning); LMM(Large Multimodal Model); TGID","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KY2ISEZJ","journalArticle","2024","Fleckenstein, Johanna; Meyer, Jennifer; Jansen, Thorben; Keller, Stefan D.; Köller, Olaf; Möller, Jens","Do teachers spot AI? Evaluating the detectability of AI-generated texts among student essays","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100209","https://www.sciencedirect.com/science/article/pii/S2666920X24000109","The potential application of generative artificial intelligence (AI) in schools and universities poses great challenges, especially for the assessment of students’ texts. Previous research has shown that people generally have difficulty distinguishing AI-generated from human-written texts; however, the ability of teachers to identify an AI-generated text among student essays has not yet been investigated. Here we show in two experimental studies that novice (N = 89) and experienced teachers (N = 200) could not identify texts generated by ChatGPT among student-written texts. However, there are some indications that more experienced teachers made more differentiated and more accurate judgments. Furthermore, both groups were overconfident in their judgments. Effects of real and assumed source on quality assessment were heterogeneous. Our findings demonstrate that with relatively little prompting, current AI can generate texts that are not detectable for teachers, which poses a challenge to schools and universities in grading student essays. Our study provides empirical evidence for the current debate regarding exam strategies in schools and universities in light of the latest technological developments.","2024-06-01","2024-12-03 03:23:04","2024-12-03 03:23:04","","100209","","","6","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Teachers; ChatGPT; Generative AI; Essay writing; Writing assessment","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LU279REL","journalArticle","2023","Lim, Zhi Wei; Pushpanathan, Krithi; Yew, Samantha Min Er; Lai, Yien; Sun, Chen-Hsin; Lam, Janice Sing Harn; Chen, David Ziyou; Goh, Jocelyn Hui Lin; Tan, Marcus Chun Jin; Sheng, Bin; Cheng, Ching-Yu; Koh, Victor Teck Chang; Tham, Yih-Chung","Benchmarking large language models’ performances for myopia care: a comparative analysis of ChatGPT-3.5, ChatGPT-4.0, and Google Bard","eBioMedicine","","2352-3964","10.1016/j.ebiom.2023.104770","https://www.sciencedirect.com/science/article/pii/S2352396423003365","Summary Background Large language models (LLMs) are garnering wide interest due to their human-like and contextually relevant responses. However, LLMs’ accuracy across specific medical domains has yet been thoroughly evaluated. Myopia is a frequent topic which patients and parents commonly seek information online. Our study evaluated the performance of three LLMs namely ChatGPT-3.5, ChatGPT-4.0, and Google Bard, in delivering accurate responses to common myopia-related queries. Methods We curated thirty-one commonly asked myopia care-related questions, which were categorised into six domains—pathogenesis, risk factors, clinical presentation, diagnosis, treatment and prevention, and prognosis. Each question was posed to the LLMs, and their responses were independently graded by three consultant-level paediatric ophthalmologists on a three-point accuracy scale (poor, borderline, good). A majority consensus approach was used to determine the final rating for each response. ‘Good’ rated responses were further evaluated for comprehensiveness on a five-point scale. Conversely, ‘poor’ rated responses were further prompted for self-correction and then re-evaluated for accuracy. Findings ChatGPT-4.0 demonstrated superior accuracy, with 80.6% of responses rated as ‘good’, compared to 61.3% in ChatGPT-3.5 and 54.8% in Google Bard (Pearson's chi-squared test, all p ≤ 0.009). All three LLM-Chatbots showed high mean comprehensiveness scores (Google Bard: 4.35; ChatGPT-4.0: 4.23; ChatGPT-3.5: 4.11, out of a maximum score of 5). All LLM-Chatbots also demonstrated substantial self-correction capabilities: 66.7% (2 in 3) of ChatGPT-4.0's, 40% (2 in 5) of ChatGPT-3.5's, and 60% (3 in 5) of Google Bard's responses improved after self-correction. The LLM-Chatbots performed consistently across domains, except for ‘treatment and prevention’. However, ChatGPT-4.0 still performed superiorly in this domain, receiving 70% ‘good’ ratings, compared to 40% in ChatGPT-3.5 and 45% in Google Bard (Pearson's chi-squared test, all p ≤ 0.001). Interpretation Our findings underscore the potential of LLMs, particularly ChatGPT-4.0, for delivering accurate and comprehensive responses to myopia-related queries. Continuous strategies and evaluations to improve LLMs’ accuracy remain crucial. Funding Dr Yih-Chung Tham was supported by the National Medical Research Council of Singapore (NMRC/MOH/HCSAINV21nov-0001).","2023-09-01","2024-12-03 03:23:04","2024-12-03 03:23:04","","104770","","","95","","eBioMedicine","","","","","","","","","","","","","","","","","","","Large language models; Chatbot; Google Bard; ChatGPT-3.5; ChatGPT-4.0; Myopia","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"BTTPRZ6H","journalArticle","2024","Zhang, Yifan; Radishian, Christopher; Brunswicker, Sabine; Whitenack, Dan; Linna, Daniel W.","Empathetic Language in LLMs under Prompt Engineering: A Comparative Study in the Legal Field","6th International Conference on AI in Computational Linguistics","","1877-0509","10.1016/j.procs.2024.10.204","https://www.sciencedirect.com/science/article/pii/S1877050924030059","The demand for empathetic conversations increases with conversational AIs’ rise and exponentially spreading applications. In areas like law and healthcare, where professional and empathetic conversations are essential, conversational AIs must strive to retain the correctness of information and logic while improving on empathetic language use. When addressing such an issue, we focus on linguistic empathy, relating only to syntactic and rhetoric choices in language while disregarding the emotional aspect of influence. By performing this study, we are interested in finding whether current open-sourced Large Language Models (LLMs) can match human experts in the legal field by using empathetic language while not compromising facts and logic in responses. We compare responses from three open-sourced LLMs under four prompting strategies with the expert responses. In the comparison, we use metrics from three aspects: text and semantic similarity, factual consistency, and ten rules of linguistic empathy from previous research literature. After statistical tests, the comparison results show that language models can use empathetic language without compromising the default knowledge base of LLMs when properly prompt-engineered. To accomplish this, additional domain knowledge is still needed to match factually. The data supporting this study is publicly available at huggingface.co/datasets/RCODI/empathy-prompt and code is available at github.com/RCODI-ConversationalAI/Empathy-Prompt.","2024-01-01","2024-12-03 03:23:04","2024-12-03 03:23:04","","308-317","","","244","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","LLM; Human-AI Interaction; Empathetic Response","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2EMPWUZX","journalArticle","2024","Rodrigues, Luiz; Dwan Pereira, Filipe; Cabral, Luciano; Gašević, Dragan; Ramalho, Geber; Ferreira Mello, Rafael","Assessing the quality of automatic-generated short answers using GPT-4","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100248","https://www.sciencedirect.com/science/article/pii/S2666920X24000511","Open-ended assessments play a pivotal role in enabling instructors to evaluate student knowledge acquisition and provide constructive feedback. Integrating large language models (LLMs) such as GPT-4 in educational settings presents a transformative opportunity for assessment methodologies. However, existing literature on LLMs addressing open-ended questions lacks breadth, relying on limited data or overlooking question difficulty levels. This study evaluates GPT-4's proficiency in responding to open-ended questions spanning diverse topics and cognitive complexities in comparison to human responses. To facilitate this assessment, we generated a dataset of 738 open-ended questions across Biology, Earth Sciences, and Physics and systematically categorized it based on Bloom's Taxonomy. Each question included eight human-generated responses and two from GPT-4. The outcomes indicate GPT-4's superior performance over humans, encompassing both native and non-native speakers, irrespective of gender. Nevertheless, this advantage was not sustained in ’remembering’ or ’creating’ questions aligned with Bloom's Taxonomy. These results highlight GPT-4's potential for underpinning advanced question-answering systems, its promising role in supporting non-native speakers, and its capacity to augment teacher assistance in assessments. However, limitations in nuanced argumentation and creativity underscore areas necessitating refinement in these models, guiding future research toward bolstering pedagogical support.","2024-12-01","2024-12-03 03:23:04","2024-12-03 03:23:04","","100248","","","7","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Large language models; Natural language processing; GPT-4; Automatic answer generation; Question-answering","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RIHKH57I","journalArticle","2024","Urbani, Roberto; Ferreira, Caitlin; Lam, Joey","Managerial framework for evaluating AI chatbot integration: Bridging organizational readiness and technological challenges","SPECIAL ISSUE: WRITTEN BY CHATGPT","","0007-6813","10.1016/j.bushor.2024.05.004","https://www.sciencedirect.com/science/article/pii/S0007681324000648","The ubiquity of chatbots continues to expand, offering a multitude of benefits for firms. While acknowledging the capabilities of AI chatbots to handle customer interactions and improve response times, we critically examine several challenges they present, including interoperability challenges, data protection concerns, and biased output. This research presents a novel framework for managers to assess a firm’s readiness to adopt AI chatbot technology through the lens of the technology acceptance model (TAM), which has been adapted to account for the critical challenges associated with the emerging technology. Incorporating four factors—subjective norms, compatibility, facilitating conditions, and trust—allows for a more holistic assessment of a firm’s readiness to adopt AI chatbot technology. The framework, together with a readiness assessment tool, provides a comprehensive mechanism for managerial decision-making, focusing on the adoption of AI chatbots in customer service, sales, and marketing business functions. In exploring these factors, our article explores the managerial implications of integrating AI chatbots into business processes, ensuring an informed and holistic approach to technology adoption.","2024-09-01","2024-12-03 03:23:04","2024-12-03 03:23:04","","595-606","","5","67","","Business Horizons","","","","","","","","","","","","","","","","","","","Artificial intelligence; Chatbots; Large language models; Data privacy; Technology acceptance model; Business process automation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JRMNI7J7","journalArticle","2024","Zheng, Zhiling; He, Zhiguo; Khattab, Omar; Rampal, Nakul; Zaharia, Matei A.; Borgs, Christian; Chayes, Jennifer T.; Yaghi, Omar M.","Image and data mining in reticular chemistry powered by GPT-4V††Electronic supplementary information (ESI) available: Full prompts designed to guide GPT-4V; additional examples showcasing GPT-4V's performance in reading various figure inputs and its corresponding responses; Python code used to automate the data mining and analysis processes; detailed information on the selected papers in this study, including the ground truth and the classification output for each page in a spread-sheet format; extracted nitrogen isotherms in this study. See DOI: https://doi.org/10.1039/d3dd00239j","Digital Discovery","","2635-098X","10.1039/d3dd00239j","https://www.sciencedirect.com/science/article/pii/S2635098X24000354","The integration of artificial intelligence into scientific research opens new avenues with the advent of GPT-4V, a large language model equipped with vision capabilities. In this study, we demonstrate that GPT-4V, accessible through the ChatGPT web user interface or an API, offers promising possibilities in navigating and mining complex data for metal–organic frameworks (MOFs) especially from graphical sources (e.g. sorption isotherms, powder X-ray diffraction patterns, thermogravimetric analysis graphs, etc.). Our approach involved an automated process of converting 346 scholarly articles into 6240 images, which represents a benchmark dataset in this task, followed by deploying GPT-4V to categorize and analyze these images using natural language prompts, which can be written by chemists or materials scientists with minimal prior coding knowledge. This methodology enabled GPT-4V to accurately identify and interpret key plots integral to MOF characterization, such as nitrogen isotherms, PXRD patterns, and TGA curves, among others, with accuracy and recall above 93%. The model's proficiency in extracting critical information from these plots not only underscores its capability in data mining but also highlights its potential to aid in the digitalization of experimental data and the creation of datasets for reticular chemistry. In addition, the trends and values of nitrogen isotherm data from the selected literature allowed for a comparison between theoretical and experimental porosity values for over 200 compounds, highlighting certain discrepancies and underscoring the importance of integrating computational and experimental data. This work highlights the potential of AI in accelerating scientific discovery by bridging the gap between computational tools and experimental research.","2024-03-13","2024-12-03 03:23:04","2024-12-03 03:23:04","","491-501","","3","3","","Digital Discovery","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"P96KF5TB","journalArticle","2023","Leippold, Markus","Thus spoke GPT-3: Interviewing a large-language model on climate finance","Finance Research Letters","","1544-6123","10.1016/j.frl.2022.103617","https://www.sciencedirect.com/science/article/pii/S1544612322007930","This paper is an interview with a Large Language Model (LLM), namely GPT-3, on the issues of climate change. The interview should give some insights into the current capabilities of these large models which are deep neural networks with generally more than 100 billion parameters. In particular, it shows how eloquent and convincing the answers of such LLMs can be. However, it should be noted that LLMs can suffer from hallucination and their responses may not be grounded on facts. These deficiencies offer an interesting avenue for future research.","2023-05-01","2024-12-03 03:23:04","2024-12-03 03:23:04","","103617","","","53","","Finance Research Letters","","","","","","","","","","","","","","","","","","","Climate change; Large language models; Natural language processing","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RFRMMRPM","journalArticle","2024","Edmond Ghali, Julien Pierre; Shima, Kosuke; Moriyama, Koichi; Mutoh, Atsuko; Inuzuka, Nobuhiro","Enhancing Retrieval Processes for Language Generation with Augmented Queries to Provide Factual Information on Schizophrenia","28th International Conference on Knowledge Based and Intelligent information and Engineering Systems (KES 2024)","","1877-0509","10.1016/j.procs.2024.09.424","https://www.sciencedirect.com/science/article/pii/S1877050924024645","In the rapidly changing world of smart technology, searching for documents has become more challenging due to the rise of advanced language models. These models sometimes face difficulties, like providing inaccurate information, commonly known as ""hallucination."" This research focuses on addressing this issue through Retrieval-Augmented Generation (RAG), a technique that guides models to give accurate responses based on real facts. To overcome scalability issues, the study explores connecting user queries with sophisticated language models such as BERT and Orca2, using an innovative query optimization process. The study unfolds in three scenarios: first, without RAG, second, without additional assistance, and finally, with extra help. Choosing the compact yet efficient Orca2 7B model demonstrates a smart use of computing resources. The empirical results, generated when we asked questions regarding schizophrenia, indicate a significant improvement in the initial language model’s performance under RAG, particularly when assisted with prompts augmenters. Consistency in document retrieval across different encodings highlights the effectiveness of using language model-generated queries. The introduction of UMAP for BERT further simplifies document retrieval while maintaining strong results.","2024-01-01","2024-12-03 03:23:05","2024-12-03 03:23:05","","443-452","","","246","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Large Language Model; Natural Language Processing; Artificial Intelligence; Document Retrieval; Retrieval-Augmented Generation; Schizophrenia; Word Embedding","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ED4LVIS9","journalArticle","2024","Waters, Michael R.; Inkman, Matthew; Jayachandran, Kay; Kowalchuk, Roman O.; Robinson, Clifford; Schwarz, Julie K.; Swamidass, S. Joshua; Griffith, Obi L.; Szymanski, Jeffrey J.; Zhang, Jin","GAiN: An integrative tool utilizing generative adversarial neural networks for augmented gene expression analysis","Patterns","","2666-3899","10.1016/j.patter.2023.100910","https://www.sciencedirect.com/science/article/pii/S2666389923003197","Summary Big genomic data and artificial intelligence (AI) are ushering in an era of precision medicine, providing opportunities to study previously under-represented subtypes and rare diseases rather than categorize them as variances. However, clinical researchers face challenges in accessing such novel technologies as well as reliable methods to study small datasets or subcohorts with unique phenotypes. To address this need, we developed an integrative approach, GAiN, to capture patterns of gene expression from small datasets on the basis of an ensemble of generative adversarial networks (GANs) while leveraging big population data. Where conventional biostatistical methods fail, GAiN reliably discovers differentially expressed genes (DEGs) and enriched pathways between two cohorts with limited numbers of samples (n = 10) when benchmarked against a gold standard. GAiN is freely available at GitHub. Thus, GAiN may serve as a crucial tool for gene expression analysis in scenarios with limited samples, as in the context of rare diseases, under-represented populations, or limited investigator resources.","2024-02-09","2024-12-03 03:23:05","2024-12-03 03:23:05","","100910","","2","5","","Patterns","","","","","","","","","","","","","","","","","","","deep learning GANs; differential gene expression; gene expression analysis; generative modeling; high-throughput sequencing data; pathway enrichment; small sample sizes; structural gene expression patterns; synthetic RNA expression datasets","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"WZW6HB4U","journalArticle","2024","Vowels, Laura M.","Are chatbots the new relationship experts? Insights from three studies","Computers in Human Behavior: Artificial Humans","","2949-8821","10.1016/j.chbah.2024.100077","https://www.sciencedirect.com/science/article/pii/S2949882124000379","Relationship distress is among the most important predictors of individual distress. Over one in three couples report distress in relationships but despite the distress, couples only rarely seek help from couple therapists and instead prefer to seek information and advice online. The recent breakthroughs in the development of humanlike artificial intelligence-powered chatbots such as ChatGPT have recently made it possible to develop chatbots which respond therapeutically. Early research suggests that they outperform physicians in helpfulness and empathy in answering health-related questions. However, we do not yet know how well chatbots respond to questions about relationships. Across three studies, we evaluated the performance of chatbots in responding to relationship-related questions and in engaging in a single session relationship therapy. In Studies 1 and 2, we demonstrated that chatbots are perceived as more helpful and empathic than relationship experts and in Study 3, we showed that relationship therapists rate single sessions with a chatbot high on attributes such as empathy, active listening, and exploration. Limitations include repetitive responding and inadequate assessment of risk. The findings show the potential of using chatbots in relationship support and highlight the limitations which need to be addressed before they can be safely adopted for interventions.","2024-08-01","2024-12-03 03:23:05","2024-12-03 03:23:05","","100077","","2","2","","Computers in Human Behavior: Artificial Humans","","","","","","","","","","","","","","","","","","","Artificial intelligence; Chatbots; Large language models; Relationship advice","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"L6HMX2CX","journalArticle","2024","Schoenhof, Rouven; Schoenhof, Raoul; Blumenstock, Gunnar; Lethaus, Bernd; Hoefert, Sebastian","Synthetic, non-person related panoramic radiographs created by generative adversarial networks in research, clinical, and teaching applications","Journal of Dentistry","","0300-5712","10.1016/j.jdent.2024.105042","https://www.sciencedirect.com/science/article/pii/S0300571224002124","Objectives Generative Adversarial Networks (GANs) can produce synthetic images free from personal data. They hold significant value in medical research, where data protection is increasingly regulated. Panoramic radiographs (PRs) are a well-suited modality due to their significant level of standardization while simultaneously displaying a high degree of personally identifiable data. Methods We produced synthetic PRs (syPRs) out of real PRs (rePRs) using StyleGAN2-ADA by NVIDIA©. A survey was performed on 54 medical professionals and 33 dentistry students. They assessed 45 radiological images (20 rePRs, 20 syPRs, and 5 syPRcontrols) as real or synthetic and interpreted a single-image syPR according to the image quality (0–10) and 14 different items (agreement/disagreement). They also rated the importance for the profession (0–10). A follow-up was performed for test-retest reliability with >10 % of all participants. Results Overall, the sensitivity was 78.2 % and the specificity was 82.5 %. For professionals, the sensitivity was 79.9 % and the specificity was 82.3 %. For students, the sensitivity was 75.5 % and the specificity was 82.7 %. In the single syPR-interpretation image quality was rated at a median of 6 and 11 items were considered as agreement. The importance for the profession was rated at a median score of 7. The Test-retest reliability yielded a value of 0.23 (Cohen's kappa). Conclusions The study marks a comprehensive testing to demonstrate that GANs can produce synthetic radiological images that even health professionals can sometimes not differentiate from real radiological images, thereby being genuinely considered authentic. This enables their utilization and/or modification free from personally identifiable information. Clinical significance Synthetic images can be used for university teaching and patient education without relying on patient-related data. They can also be utilized to upscale existing training datasets to improve the accuracy of AI-based diagnostic systems. The study thereby supports clinical teaching as well as diagnostic and therapeutic decision-making.","2024-07-01","2024-12-03 03:23:05","2024-12-03 03:23:05","","105042","","","146","","Journal of Dentistry","","","","","","","","","","","","","","","","","","","Artificial intelligence; Synthetic data; Dental radiology; Non-personal data; Panoramic radiographs","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ZAUG3JBD","journalArticle","2024","Fu, Mengni; Fraser, Barry; Arcodia, Charles","Digital natives on the rise: A systematic literature review on generation Z's engagement with RAISA technologies in hospitality services","International Journal of Hospitality Management","","0278-4319","10.1016/j.ijhm.2024.103885","https://www.sciencedirect.com/science/article/pii/S027843192400197X","Technological disruptions, health crises, persistent labour issues, and demographic shifts have elevated the importance of RAISA (robots, artificial intelligence, and service automation), and Generation Z for hospitality organisations. This study reviews 81 peer-reviewed articles using bibliometrics, quantitative frequency, and qualitative thematic analysis to examine Generation Z's engagement with RAISA in hospitality services. The findings highlight the increasing prevalence of service robots in hospitality sectors, with other AI and smart technologies remaining underexplored. While prior studies focused on Generation Z consumers' RAISA usage intentions, they overlooked the perspectives of Generation Z employees and hospitality operators. Furthermore, prior studies were mostly quantitative and geographically constrained, and focused on examining existing technology acceptance models, thereby lacking cross-cultural, in-depth, and mixed-methods exploration. Accordingly, this review paper proposes future research directions, and a novel service encounter paradigm. The paper also suggests hospitality operators proactively adopt RAISA technologies and prioritise Generation Z to aid sustainable operations.","2024-09-01","2024-12-03 03:23:05","2024-12-03 03:23:05","","103885","","","122","","International Journal of Hospitality Management","","","","","","","","","","","","","","","","","","","Generation Z; Hospitality services; Human-computer interaction model; RAISA; Service Encounter Triad","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"G2DM8XUU","journalArticle","2024","Abdelazim, Hazem; Begemy, Tony; Galal, Ahmed; Sedki, Hala; Mohamed, Ali","Multi-Hop Arabic LLM Reasoning in Complex QA","6th International Conference on AI in Computational Linguistics","","1877-0509","10.1016/j.procs.2024.10.179","https://www.sciencedirect.com/science/article/pii/S1877050924029806","The introduction of Large Language Models (LLMs), and generative AI has significantly transformed the field of natural language processing. These models have exhibited profound reasoning capabilities, marking considerable progress across diverse general knowledge reasoning tasks. Consequently, the deployment of LLMs in domain-specific contexts has become a prime objective for governments and corporations eager to leverage the generative AI revolution. However, the Arabic language has notably lagged in attention and development compared to other languages in this arena. This research endeavors to delve into various facets of Arabic closed-domain question and answering systems that emulate the reasoning requirements of private enterprise data. Our study focuses on the practical deployment of Arabic LLMs in targeted applications, specifically utilizing the ACQAD (Arabic Complex Question Answering Dataset), which exhibits multi-hop reasoning. Different strategies are experimented using Long Context Window (LCW) and Retrieval Augmented Generation (RAG). Results showed that decomposing complex questions using Chain-of-Thought reasoning considerably improved the performance from 75% to 92% using LCW, but at much higher token cost compared to RAG. Trade-of between cost and performance showed that 80% accuracy can be attained using only 30% of the cost using RAG Sentence - level embeddings. Microsoft E5 embedding model is used and OpenAI GPT4-turbo LLM which proved superior reasoning performance compared to other Arabic LLMs","2024-01-01","2024-12-03 03:23:05","2024-12-03 03:23:05","","66-75","","","244","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Large Language Models; Retrieval Augmented Generation; Arabic NLP","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"7I89HKUU","journalArticle","2024","Wolf, Vinzenz; Maier, Christian","ChatGPT usage in everyday life: A motivation-theoretic mixed-methods study","International Journal of Information Management","","0268-4012","10.1016/j.ijinfomgt.2024.102821","https://www.sciencedirect.com/science/article/pii/S0268401224000690","GenAI-driven technologies such as ChatGPT influence activities in all areas of life and are used in private and work contexts. This study uses an individual-centered perspective to explain what motivates users to use ChatGPT continuously. We propose that four motivational factors and two technology characteristics together lead to continuance intention among individual ChatGPT users. Therefore, we use a mixed-methods design to combine findings from a quantitative survey study and a qualitative interview study. In Study 1, we follow a configurational approach to analyze multi-wave data from 279 participants with fsQCA. We identify five configurations that lead to high continuance intention and show that perceived ease of use and perceived novelty are necessary for this outcome. Interestingly, the observed factors together cannot explain low continuance intention. In Study 2, we complement these findings with insights based on 15 semi-structured interviews. We illustrate the configurations by identifying 27 individual use cases in the private and work contexts as well as additional factors that facilitate and hinder individual ChatGPT continuance intention. We draw meta-inferences by combining findings of both studies to develop five propositions. Based on that, we contribute a motivational, individual perspective on GenAI continuance intention, present practical implications as well as valuable future research opportunities.","2024-12-01","2024-12-03 03:23:05","2024-12-03 03:23:05","","102821","","","79","","International Journal of Information Management","","","","","","","","","","","","","","","","","","","Motivation; Continuance intention; Fuzzy-set qualitative comparative analysis (fsQCA); Generative artificial intelligence (GenAI); Mixed-methods design","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CMB67283","journalArticle","2024","Tate, Tamara P.; Steiss, Jacob; Bailey, Drew; Graham, Steve; Moon, Youngsun; Ritchie, Daniel; Tseng, Waverly; Warschauer, Mark","Can AI provide useful holistic essay scoring?","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100255","https://www.sciencedirect.com/science/article/pii/S2666920X24000584","Researchers have sought for decades to automate holistic essay scoring. Over the years, these programs have improved significantly. However, accuracy requires significant amounts of training on human-scored texts—reducing the expediency and usefulness of such programs for routine uses by teachers across the nation on non-standardized prompts. This study analyzes the output of multiple versions of ChatGPT scoring of secondary student essays from three extant corpora and compares it to quality human ratings. We find that the current iteration of ChatGPT scoring is not statistically significantly different from human scoring; substantial agreement with humans is achievable and may be sufficient for low-stakes, formative assessment purposes. However, as large language models evolve additional research will be needed to continue to assess their aptitude for this task as well as determine whether their proximity to human scoring can be improved through prompting or training.","2024-12-01","2024-12-03 03:23:05","2024-12-03 03:23:05","","100255","","","7","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Artificial intelligence; Large language models; Writing; AI; Assessment; Automated scoring","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"6ATP43M5","journalArticle","2024","Lang, Siegmund; Vitale, Jacopo; Fekete, Tamás F.; Haschtmann, Daniel; Reitmeir, Raluca; Ropelato, Mario; Puhakka, Jani; Galbusera, Fabio; Loibl, Markus","Are large language models valid tools for patient information on lumbar disc herniation? The spine surgeons' perspective","Brain and Spine","","2772-5294","10.1016/j.bas.2024.102804","https://www.sciencedirect.com/science/article/pii/S2772529424000602","Introduction Generative AI is revolutionizing patient education in healthcare, particularly through chatbots that offer personalized, clear medical information. Reliability and accuracy are vital in AI-driven patient education. Research question How effective are Large Language Models (LLM), such as ChatGPT and Google Bard, in delivering accurate and understandable patient education on lumbar disc herniation? Material and methods Ten Frequently Asked Questions about lumbar disc herniation were selected from 133 questions and were submitted to three LLMs. Six experienced spine surgeons rated the responses on a scale from “excellent” to “unsatisfactory,” and evaluated the answers for exhaustiveness, clarity, empathy, and length. Statistical analysis involved Fleiss Kappa, Chi-square, and Friedman tests. Results Out of the responses, 27.2% were excellent, 43.9% satisfactory with minimal clarification, 18.3% satisfactory with moderate clarification, and 10.6% unsatisfactory. There were no significant differences in overall ratings among the LLMs (p = 0.90); however, inter-rater reliability was not achieved, and large differences among raters were detected in the distribution of answer frequencies. Overall, ratings varied among the 10 answers (p = 0.043). The average ratings for exhaustiveness, clarity, empathy, and length were above 3.5/5. Discussion and conclusion LLMs show potential in patient education for lumbar spine surgery, with generally positive feedback from evaluators. The new EU AI Act, enforcing strict regulation on AI systems, highlights the need for rigorous oversight in medical contexts. In the current study, the variability in evaluations and occasional inaccuracies underline the need for continuous improvement. Future research should involve more advanced models to enhance patient-physician communication.","2024-01-01","2024-12-03 03:23:05","2024-12-03 03:23:05","","102804","","","4","","Brain and Spine","","","","","","","","","","","","","","","","","","","ChatGPT; Large language models; Patient education; AI evaluation; Google bard; Lumbar disc herniation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JK66JGHF","journalArticle","2024","Sciannameo, Veronica; Pagliari, Daniele Jahier; Urru, Sara; Grimaldi, Piercesare; Ocagli, Honoria; Ahsani-Nasab, Sara; Comoretto, Rosanna Irene; Gregori, Dario; Berchialla, Paola","Information extraction from medical case reports using OpenAI InstructGPT","Computer Methods and Programs in Biomedicine","","0169-2607","10.1016/j.cmpb.2024.108326","https://www.sciencedirect.com/science/article/pii/S0169260724003195","Background and objective Researchers commonly use automated solutions such as Natural Language Processing (NLP) systems to extract clinical information from large volumes of unstructured data. However, clinical text's poor semantic structure and domain-specific vocabulary can make it challenging to develop a one-size-fits-all solution. Large Language Models (LLMs), such as OpenAI's Generative Pre-Trained Transformer 3 (GPT-3), offer a promising solution for capturing and standardizing unstructured clinical information. This study evaluated the performance of InstructGPT, a family of models derived from LLM GPT-3, to extract relevant patient information from medical case reports and discussed the advantages and disadvantages of LLMs versus dedicated NLP methods. Methods In this paper, 208 articles related to case reports of foreign body injuries in children were identified by searching PubMed, Scopus, and Web of Science. A reviewer manually extracted information on sex, age, the object that caused the injury, and the injured body part for each patient to build a gold standard to compare the performance of InstructGPT. Results InstructGPT achieved high accuracy in classifying the sex, age, object and body part involved in the injury, with 94%, 82%, 94% and 89%, respectively. When excluding articles for which InstructGPT could not retrieve any information, the accuracy for determining the child's sex and age improved to 97%, and the accuracy for identifying the injured body part improved to 93%. InstructGPT was also able to extract information from non-English language articles. Conclusions The study highlights that LLMs have the potential to eliminate the necessity for task-specific training (zero-shot extraction), allowing the retrieval of clinical information from unstructured natural language text, particularly from published scientific literature like case reports, by directly utilizing the PDF file of the article without any pre-processing and without requiring any technical expertise in NLP or Machine Learning. The diverse nature of the corpus, which includes articles written in languages other than English, some of which contain a wide range of clinical details while others lack information, adds to the strength of the study.","2024-10-01","2024-12-03 03:23:05","2024-12-03 03:23:05","","108326","","","255","","Computer Methods and Programs in Biomedicine","","","","","","","","","","","","","","","","","","","Information retrieval; Natural language processing; Large language model; Case reports","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KZQUWFSP","journalArticle","2023","Yager, Kevin G.","Domain-specific chatbots for science using embeddings††Electronic supplementary information (ESI) available: Examples of chatbot responses to various user queries. See DOI: https://doi.org/10.1039/d3dd00112a","Digital Discovery","","2635-098X","10.1039/d3dd00112a","https://www.sciencedirect.com/science/article/pii/S2635098X23001341","ABSTRACT Large language models (LLMs) have emerged as powerful machine-learning systems capable of handling a myriad of tasks. Tuned versions of these systems have been turned into chatbots that can respond to user queries on a vast diversity of topics, providing informative and creative replies. However, their application to physical science research remains limited owing to their incomplete knowledge in these areas, contrasted with the needs of rigor and sourcing in science domains. Here, we demonstrate how existing methods and software tools can be easily combined to yield a domain-specific chatbot. The system ingests scientific documents in existing formats, and uses text embedding lookup to provide the LLM with domain-specific contextual information when composing its reply. We similarly demonstrate that existing image embedding methods can be used for search and retrieval across publication figures. These results confirm that LLMs are already suitable for use by physical scientists in accelerating their research efforts.","2023-12-04","2024-12-03 03:23:05","2024-12-03 03:23:05","","1850-1861","","6","2","","Digital Discovery","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LDJQRUDF","journalArticle","2024","Zheng, Shuyan","The effects of chatbot use on foreign language reading anxiety and reading performance among Chinese secondary school students","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100271","https://www.sciencedirect.com/science/article/pii/S2666920X24000742","The present study investigates the effectiveness of a GenAI-based chatbot, “Reading Bot,” in reducing foreign language reading anxiety (FLRA) and improving foreign language reading performance (FLRP) among Chinese secondary school students learning English as a foreign language (EFL). To do so, a mixed-methods quasi-experimental pre-test/post-test design with qualitative interviews was employed. After ensuring homogeneity between the two groups, one class was designated as the experimental group (n = 42), utilizing a chatbot as a treatment, while the other class served as the comparison group (n = 42), receiving traditional teacher support. Both groups participated in five 45-min reading practice sessions. The results indicated that the chatbot intervention significantly reduced the participants' FLRA within the experimental group when comparing pre- and post-test scores. However, no significant differences were found between the two groups after the treatment, either in FLRA or FLRP. The qualitative analysis of semi-structured interviews suggested that the chatbot provided technological, pedagogical, linguistic, and affective affordances to assist reading. Nevertheless, the analysis also revealed some potential drawbacks and challenges to a GenAI-enhanced approach to reading instruction that may have influenced FLRA and FLRP. The theoretical and pedagogical implications of these findings are discussed, along with potential directions for future research.","2024-12-01","2024-12-03 03:23:05","2024-12-03 03:23:05","","100271","","","7","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Generative artificial intelligence (GenAI); Chatot-assisted language learning (BALL); Chinese L1 secondary school students; Foreign language reading anxiety (FLRA); Foreign language reading performance (FLRP)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"E2XYEMLH","journalArticle","2024","Arslan, Muhammad; Mahdjoubi, Lamine; Munawar, Saba","Driving sustainable energy transitions with a multi-source RAG-LLM system","Energy and Buildings","","0378-7788","10.1016/j.enbuild.2024.114827","https://www.sciencedirect.com/science/article/pii/S0378778824009435","By 2035, the UK aims to upgrade all homes to achieve a net-zero economy by 2050, thereby reducing energy consumption, household costs, and improving living conditions. Small and Medium-sized Enterprises (SMEs) play a crucial role in this transition. However, many SME contractors lack essential information on Sustainable Energy Initiatives (SEIs) and the relevant Energy landscape necessary for driving Sustainable Energy Transitions (SETs). This knowledge gap poses risks to SME interventions, potentially leading to increased costs and inefficiencies. Accessing timely information on SEIs including government policies, funding, technologies, and environmental impacts from various media sources is essential for guiding effective SETs and understanding the relevant Energy landscape, thereby facilitating informed decision-making. Currently, SMEs lack an integrated system that consolidates data from diverse media sources into a centralized Information System (IS), limiting their ability to effectively navigate SEIs. To address this gap, this research introduces an Energy Chatbot, a sustainable IS that utilizes Large Language Models (LLMs) integrated with multi-source Retrieval Augmented Generation (RAG). This system encompasses diverse media sources, including news articles, government reports, industry publications, academic research, and social media. The Energy Chatbot is designed to enhance decision-making for SMEs by providing comprehensive Energy sector insights through a Question Answering (QA) system. Key findings emphasize that this approach reduces costs by utilizing open-source models. Moreover, the Energy Chatbot provides SMEs with access to up-to-date information, enabling them to identify long-term sustainability strategies and maintain a competitive edge in the evolving Energy landscape.","2024-12-01","2024-12-03 03:23:05","2024-12-03 03:23:05","","114827","","","324","","Energy and Buildings","","","","","","","","","","","","","","","","","","","Large Language Models (LLMs); Data-driven operations; Information Extraction (IE); Sustainable Development Goals (SDGs); Retrieval Augmented Generation (RAG); Sustainable Energy Transitions (SETs)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"FZCURY87","journalArticle","2024","Pang, Jianli; Wang, Yinling; Ozyurt, Fatih; Dogan, Sengul; Tuncer, Turker; Yu, Lei","Utilizing language models for advanced electrocardiogram analysis","Alexandria Engineering Journal","","1110-0168","10.1016/j.aej.2024.07.086","https://www.sciencedirect.com/science/article/pii/S1110016824008184","Electrocardiography (ECG) signals are often referred to as the language of the heart and have been widely utilized for diagnosing various heart ailments, particularly arrhythmias. Consequently, numerous machine learning models have been employed to automatically detect heart disorders using ECG signals. In this research, the primary objective is to detect arrhythmias using a center-symmetric self-organized textual pattern. A novel feature engineering model has been introduced, which includes the following components: (i) multilevel feature extraction using the proposed center-symmetric self-organized textual pattern (CSSOTP), (ii) iterative neighborhood component analysis (INCA), and (iii) classification using k-nearest neighbors (kNN) with 10-fold cross-validation. During the feature extraction phase, a multilevel feature extraction model incorporating maximum absolute pooling (MAP) and the proposed CSSOTP was applied. The CSSOTP was designed to select the most appropriate pattern for the given data block. A large language model (LLM) was leveraged to generate text for creating patterns, and ChatGPT was used to assist in text generation. To identify the most informative features, the INCA feature selector was employed, and the selected features were subsequently classified using the kNN classifier. An impressive 96.20 % classification accuracy was achieved by the CSSOTP-based feature engineering model when tested on an ECG dataset containing 17 classes. Furthermore, comparisons with state-of-the-art feature engineering models were conducted, demonstrating that the proposed model has superior classification capabilities.","2024-10-01","2024-12-03 03:23:05","2024-12-03 03:23:05","","460-470","","","105","","Alexandria Engineering Journal","","","","","","","","","","","","","","","","","","","LLM; Arrhythmias; CSSOTP; Feature Extraction; INCA; Kernels","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"AD49C2DS","journalArticle","2023","Goh, Tiong-Thye; Dai, Xin; Yang, Yanwu","Benchmarking ChatGPT for prototyping theories: Experimental studies using the technology acceptance model","BenchCouncil Transactions on Benchmarks, Standards and Evaluations","","2772-4859","10.1016/j.tbench.2024.100153","https://www.sciencedirect.com/science/article/pii/S277248592400005X","This paper explores the paradigm of leveraging ChatGPT as a benchmark tool for theory prototyping in conceptual research. Specifically, we conducted two experimental studies using the classical technology acceptance model (TAM) to demonstrate and evaluate ChatGPT's capability of comprehending theoretical concepts, discriminating between constructs, and generating meaningful responses. Results of the two studies indicate that ChatGPT can generate responses aligned with the TAM theory and constructs. Key metrics including the factors loading, internal consistency reliability, and convergence reliability of the measurement model surpass the minimum threshold, thus confirming the validity of TAM constructs. Moreover, supported hypotheses provide an evidence for the nomological validity of TAM constructs. However, both of the two studies show a high Heterotrait–Monotrait ratio of correlations (HTMT) among TAM constructs, suggesting a concern about discriminant validity. Furthermore, high duplicated response rates were identified and potential biases regarding gender, usage experiences, perceived usefulness, and behavioural intention were revealed in ChatGPT-generated samples. Therefore, it calls for additional efforts in LLM to address performance metrics related to duplicated responses, the strength of discriminant validity, the impact of prompt design, and the generalizability of findings across contexts.","2023-12-01","2024-12-03 03:23:30","2024-12-03 03:23:30","","100153","","4","3","","BenchCouncil Transactions on Benchmarks, Standards and Evaluations","","","","","","","","","","","","","","","","","","","ChatGPT; Large language model; Technology acceptance model; Prototyping Theory","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CLF2ITHY","journalArticle","2024","Syriani, Eugene; David, Istvan; Kumar, Gauransh","Screening articles for systematic reviews with ChatGPT","Journal of Computer Languages","","2590-1184","10.1016/j.cola.2024.101287","https://www.sciencedirect.com/science/article/pii/S2590118424000303","Systematic reviews (SRs) provide valuable evidence for guiding new research directions. However, the manual effort involved in selecting articles for inclusion in an SR is error-prone and time-consuming. While screening articles has traditionally been considered challenging to automate, the advent of large language models offers new possibilities. In this paper, we discuss the effect of using ChatGPT on the SR process. In particular, we investigate the effectiveness of different prompt strategies for automating the article screening process using five real SR datasets. Our results show that ChatGPT can reach up to 82% accuracy. The best performing prompts specify exclusion criteria and avoid negative shots. However, prompts should be adapted to different corpus characteristics.","2024-08-01","2024-12-03 03:23:30","2024-12-03 03:23:30","","101287","","","80","","Journal of Computer Languages","","","","","","","","","","","","","","","","","","","Empirical research; Literature review; GPT; Large language model; Generative AI; Mapping study; Screening","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"QWD7FCRP","journalArticle","2024","Kim, Byung-Jik; Kim, Min-Jik","How artificial intelligence-induced job insecurity shapes knowledge dynamics: the mitigating role of artificial intelligence self-efficacy","Journal of Innovation & Knowledge","","2444-569X","10.1016/j.jik.2024.100590","https://www.sciencedirect.com/science/article/pii/S2444569X2400129X","ABSTRACT This research examines the intricate relationships between artificial intelligence (AI)-induced job insecurity, psychological safety, knowledge-hiding behavior, and self-efficacy in AI learning within organizational contexts. As AI technologies increasingly permeate the workplace, comprehending their impact on employee behavior and organizational dynamics becomes crucial. Based on several theories, we use a time-lagged research design to propose and test a moderated mediation model. We collected data from 402 employees across various industries in South Korea at three different time points. Our findings reveal that AI-induced job insecurity positively relates to knowledge-hiding behavior, directly and indirectly, via reduced psychological safety. Moreover, we discover that self-efficacy in AI learning moderates the relationship between AI-induced job insecurity and psychological safety, such that high self-efficacy buffers the harmful influence of job insecurity on psychological safety. These results enhance the existing literature on organizational technological change by clarifying the psychological processes through which AI implementation influences employee behavior. Our study highlights the critical role of psychological safety as a mediator and self-efficacy as a moderator in this process. These insights present significant implications for managers and organizations navigating the challenges of AI integration. They emphasize the need for strategies that foster psychological safety and enhance members’ confidence in their ability to adapt to AI technologies. Our research underscores the significance of considering both the technical and human aspects of AI implementation within organizational contexts.","2024-10-01","2024-12-03 03:23:30","2024-12-03 03:23:30","","100590","","4","9","","Journal of Innovation & Knowledge","","","","","","","","","","","","","","","","","","","O33; artificial intelligence-induced job insecurity; knowledge-hiding behavior; M12; M15; moderated mediation model; psychological safety; self-efficacy in artificial intelligence learning","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"MK4Y7K7E","journalArticle","2023","Panthier, C.; Gatinel, D.","Success of ChatGPT, an AI language model, in taking the French language version of the European Board of Ophthalmology examination: A novel approach to medical knowledge assessment","Journal Français d'Ophtalmologie","","0181-5512","10.1016/j.jfo.2023.05.006","https://www.sciencedirect.com/science/article/pii/S0181551223003054","Summary Purpose The purpose of this study was to evaluate the performance of ChatGPT, a cutting-edge artificial intelligence (AI) language model developed by OpenAI, in successfully completing the French language version of the European Board of Ophthalmology (EBO) examination and to assess its potential role in medical education and knowledge assessment. Methods ChatGPT, based on the GPT-4 architecture, was exposed to a series of EBO examination questions in French, covering various aspects of ophthalmology. The AI's performance was evaluated by comparing its responses with the correct answers provided by ophthalmology experts. Additionally, the study assessed the time taken by ChatGPT to answer each question as a measure of efficiency. Results ChatGPT achieved a 91% success rate on the EBO examination, demonstrating a high level of competency in ophthalmology knowledge and application. The AI provided correct answers across all question categories, indicating a strong understanding of basic sciences, clinical knowledge, and clinical management. The AI model also answered the questions rapidly, taking only a fraction of the time needed by human test-takers. Conclusion ChatGPT's performance on the French language version of the EBO examination demonstrates its potential to be a valuable tool in medical education and knowledge assessment. Further research is needed to explore optimal ways to implement AI language models in medical education and to address the associated ethical and practical concerns. Résumé Objectif L’objectif de cette étude était d’évaluer la performance de ChatGPT, un modèle de langage d’intelligence artificielle (IA) développée par OpenAI, dans la réussite des annales françaises de l’examen de l’European Board of Ophthalmology (EBO) et d’évaluer son rôle potentiel dans l’éducation médicale et l’évaluation des connaissances. Méthodes ChatGPT, basé sur l’architecture GPT-4, a été exposé à une série de questions d’examen de l’EBO en français qui couvraient divers aspects de l’ophtalmologie. La performance de l’IA a été évaluée en comparant ses réponses avec les réponses correctes fournies par des experts en ophtalmologie. En outre, l’étude a évalué le temps pris par ChatGPT pour répondre à chaque question en tant que mesure de l’efficacité. Résultats ChatGPT a obtenu un taux de réussite de 91 % à l’examen EBO, démontrant un haut niveau de compétence en matière de connaissances et d’applications ophtalmologiques. L’IA a fourni des réponses correctes dans toutes les catégories de questions, ce qui indique une bonne compréhension des sciences fondamentales, des connaissances cliniques et de la gestion clinique. Le modèle d’IA a également répondu rapidement aux questions, ne prenant qu’une fraction du temps nécessaire aux examinateurs humains. Conclusion La performance de ChatGPT dans l’examen EBO démontre son potentiel en tant qu’outil dans l’éducation médicale et l’évaluation des connaissances. Des recherches supplémentaires sont nécessaires pour explorer les meilleures façons de mettre en œuvre les modèles de langage de l’IA dans l’enseignement médical et pour répondre aux préoccupations éthiques et pratiques qui y sont associées.","2023-09-01","2024-12-03 03:23:30","2024-12-03 03:23:30","","706-711","","7","46","","Journal Français d'Ophtalmologie","","","","","","","","","","","","","","","","","","","Deep learning; Machine learning; Artificial intelligence; ChatGPT; Language model; OpenAI; Natural language processing; Generative AI; Text generation; AI applications; Apprentissage par la machine; Apprentissage profond; Entraînement sur base de données; Ethics in AI; Éthique en IA; European Board of Ophthalmology; Examen médical; Générateur de texte; Génération d’IA; Human-like interaction; Intelligence artificielle; Medical examination; Modèle conversationnel; Ophtalmologie; Ophthalmology; Simulation d’interaction humaine; Training dataset; Transformateur d’architecture; Transformer architecture","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CKQ57B2L","journalArticle","2024","Shao, Zhonghui; Zhang, Jing; Li, Haoyang; Huang, Xinmei; Zhou, Chao; Wang, Yuanchun; Gong, Jibing; Li, Cuiping; Chen, Hong","Authorship style transfer with inverse transfer data augmentation","AI Open","","2666-6510","10.1016/j.aiopen.2024.08.003","https://www.sciencedirect.com/science/article/pii/S2666651024000135","Authorship style transfer aims to modify the style of neutral text to match the unique speaking or writing style of a particular individual. While Large Language Models (LLMs) present promising solutions, their effectiveness is limited by the small number of in-context learning demonstrations, particularly for authorship styles not frequently seen during pre-training. In response, this paper proposes an inverse transfer data augmentation (ITDA) method, leveraging LLMs to create (neutral text, stylized text) pairs. This method involves removing the existing styles from stylized texts, a process made more feasible due to the prevalence of neutral texts in pre-training. We use this augmented dataset to train a compact model that is efficient for deployment and adept at replicating the targeted style. Our experimental results, conducted across four datasets with distinct authorship styles, establish the effectiveness of ITDA over traditional style transfer methods and forward transfer using GPT-3.5. For further research and application, our dataset and code are openly accessible at https://github.com/Vicky-Shao/ITDA.","2024-01-01","2024-12-03 03:23:30","2024-12-03 03:23:30","","94-103","","","5","","AI Open","","","","","","","","","","","","","","","","","","","Natural language generation; Style transfer; Stylistic analysis","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ZGJC3Q39","journalArticle","2024","Raman, Raghu; Calyam, Prasad; Achuthan, Krishnashree","ChatGPT or Bard: Who is a better Certified Ethical Hacker?","Computers & Security","","0167-4048","10.1016/j.cose.2024.103804","https://www.sciencedirect.com/science/article/pii/S0167404824001056","In this study, we compare two leading Generative AI (GAI) tools, ChatGPT and Bard, specifically in Cybersecurity, using a robust set of standardized questions from a validated Certified Ethical Hacking (CEH) dataset. In the rapidly evolving domain of Generative AI (GAI) and large language models (LLM), a comparative analysis of tools becomes essential to measure their performance. We determine the Comprehensiveness, Clarity, and Conciseness of the AI-generated responses through a detailed questioning-based framework. The study revealed an overall accuracy rate of 80.8 % for ChatGPT and 82.6 % for Bard, indicating comparable capabilities and specific differences. Bard slightly outperformed ChatGPT in accuracy, while ChatGPT exhibited superiority in Comprehensiveness, Clarity, and Conciseness of responses. Introducing a confirmation query like “Are you sure?” increased accuracy for both generative AI tools, illustrating the potential of iterative query processing in enhancing GAI tools' effectiveness. The readability evaluation placed both tools at a college reading level, with Bard marginally more accessible. While evaluating certain questions, a distinct pattern emerged where Bard provided generic denials of assistance while ChatGPT referenced “ethics.” This discrepancy illustrates the contrasting philosophies of the developers of these tools, with Bard possibly following stricter guidelines, especially in sensitive topics like Cybersecurity. We explore the implications and identify key areas for future research that become increasingly relevant as GAI tools see broader adoption.","2024-05-01","2024-12-03 03:23:30","2024-12-03 03:23:30","","103804","","","140","","Computers & Security","","","","","","","","","","","","","","","","","","","Readability; Policy; Cybersecurity generative ai; Ethical hacking; Similarity analysis; Social behavior","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"R8H8M6RF","journalArticle","2024","Mahjour, Seyed Kourosh; Soltanmohammadi, Ramin; Heidaryan, Ehsan; Faroughi, Salah A.","Geosystems risk and uncertainty: The application of ChatGPT with targeted prompting","Geoenergy Science and Engineering","","2949-8910","10.1016/j.geoen.2024.212889","https://www.sciencedirect.com/science/article/pii/S2949891024002598","ChatGPT, a prominent large language model (LLM), is being increasingly used across a wide range of scientific fields. Geosystem engineers and researchers are also posed to leverage ChatGPT to find solutions to challenges encountered in various topics. This study evaluates the accuracy and reproducibility of ChatGPT in responding to different qualitative and quantitative questions, with a particular focus on risk and uncertainty (R&U) in both the Greenfield and Brownfield domains as an important area of interest. The results show the importance of prompting to considerably improve the ChatGPT’s response accuracy and reproducibility. For example, prompting increases the accuracy of responses to qualitative and quantitative questions in the Greenfield domain by 10.4% and 41.8%, respectively. Additionally, prompting enhances the reproducibility of responses, with a 32.1% increase for qualitative questions and a 33.3% rise for quantitative questions in the Brownfield domain. The findings highlight that the greater the comprehensiveness of the prompts, the higher the accuracy and reproducibility of the responses to the questions. The study also acknowledges the potential limitations associated with the sources of information and the contextual influences on the reliability of the response.","2024-07-01","2024-12-03 03:23:30","2024-12-03 03:23:30","","212889","","","238","","Geoenergy Science and Engineering","","","","","","","","","","","","","","","","","","","ChatGPT; Geosystems; Greenfield & Brownfield; Prompting; Risk & uncertainty","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KK4NPHPT","journalArticle","2024","Bibi, Nazia; Maqbool, Ayesha; Rana, Tauseef","Enhancing source code retrieval with joint Bi-LSTM-GNN architecture: A comparative study with ChatGPT-LLM","Journal of King Saud University - Computer and Information Sciences","","1319-1578","10.1016/j.jksuci.2023.101865","https://www.sciencedirect.com/science/article/pii/S1319157823004196","Retrieving relevant source code from large repositories is a significant and ongoing challenge in the field of software engineering, primarily due to the vast and ever-expanding amount of available code. Existing deep learning methods, although effective to some extent, exhibit limitations in capturing the intricate and complex structural information embedded within source code, which hinders their ability to provide highly accurate retrieval results. This study endeavors to tackle this prominent issue by introducing a novel and innovative approach known as the Joint Bi-directional LSTM and Graph Neural Networks (JBLG) model for source code retrieval. The central aim is to harness the combined strengths and capabilities of Bi-directional Long Short-Term Memory (LSTM) networks and Graph Neural Networks (GNNs) to significantly enhance the model’s capacity to capture and interpret the complex structural characteristics intrinsic to source code. The proposed JBLG model employs a unique fusion of Bi-directional LSTM, which excels in capturing sequential and temporal dependencies within code, and GNN, which is adept at modeling the intricate graph structure of the code. By leveraging this hybrid architecture, the model aims to provide a comprehensive and highly effective solution for source code retrieval tasks. To assess the efficacy of the JBLG model, extensive experiments are conducted, and the model’s performance is evaluated against well-established benchmarks, including LSTM, GNN, and ChatGPT, using two diverse datasets: CodeSearchNet and CosBench datasets. These evaluations span multiple programming languages, ensuring a comprehensive and robust assessment of the model’s capabilities. The experimental results indicate that the JBLG model consistently outperforms its counterparts, including Bi-LSTM, GNN, ChatGPT, and DGMS, across various evaluation metrics. the JBLG model showcases an exceptional ability to handle and extract the intricate structural information inherent in source code, resulting in significantly enhanced retrieval accuracy. The JBLG model emerges as a highly promising solution for real-world source code retrieval applications, with the potential to revolutionize the field. The success of this model underscores the importance of combining deep learning techniques like Bi-directional LSTM and GNNs for tackling complex software engineering challenges. Furthermore, future research directions could involve exploring advanced techniques such as attention mechanisms and extending the model’s applicability to other software engineering tasks like code summarization and code completion. The findings of this study are expected to have a lasting impact on the advancement of source code retrieval methodologies.","2024-02-01","2024-12-03 03:23:30","2024-12-03 03:23:30","","101865","","2","36","","Journal of King Saud University - Computer and Information Sciences","","","","","","","","","","","","","","","","","","","Deep learning; Bi-directional LSTM; Code recommendation; Code reuse; GNN; Joint model; LSTM; Recommendation systems; Source code retrieval","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"Y8GAL9GB","journalArticle","2024","Chen, Zhongliang; Yuan, Feng; Li, Xiaohui; Zhang, Mingming; Zheng, Chaojie","A novel few-shot learning framework for rock images dually driven by data and knowledge","Applied Computing and Geosciences","","2590-1974","10.1016/j.acags.2024.100155","https://www.sciencedirect.com/science/article/pii/S2590197424000028","In the field of geosciences, the integration of artificial intelligence is transitioning from perceptual intelligence to cognitive intelligence. The simultaneous utilization of knowledge and data in the geoscience domain is a universally addressed concern. In this paper, based on the interpretability of deep learning models for rock images, rock features such as structure, texture, mineral and macroscopic identification characteristics were selected to extract a rock identification subgraph from the petrographic knowledge graph and carry out rock type similarity reasoning. Comparative experiments were conducted on few-shot learning of rock images under the supervision of rock type similarity knowledge. The results of the few-shot learning comparisons demonstrate that the supervision of rock type similarity knowledge significantly enhances performance. Additionally, rock type similarity knowledge exhibits a marginal effect on improving few-shot learning performance. Given the absence of Chinese word embedding and large-scale Chinese pre-trained language models in the geological domain, graph embedding based on domain-specific knowledge graphs in geosciences can offer computable geoscience knowledge for research dually propelled by data and knowledge.","2024-03-01","2024-12-03 03:23:30","2024-12-03 03:23:30","","100155","","","21","","Applied Computing and Geosciences","","","","","","","","","","","","","","","","","","","Transfer learning; Knowledge graph; Knowledge reasoning; Node similarity; Rock image recognition","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LX6GBCT7","journalArticle","2023","Nazir, Anam; Cheeema, Muhammad Nadeem; Wang, Ze","ChatGPT-based biological and psychological data imputation","Meta-Radiology","","2950-1628","10.1016/j.metrad.2023.100034","https://www.sciencedirect.com/science/article/pii/S2950162823000346","Missing data are a common problem for large cohort or longitudinal research and have been handled through data imputation. Based on simplified models such as linear or nonlinear interpolations, current imputation methods may not be accurate for real-life data such as biological and behavioral data. The purpose of this work was to explore the capability of ChatGPT, a powerful Large Language Model (LLM) developed by OpenAI, for biological and psychological data imputation. We tested the feasibility using data from the Human Connectome Project. Performance was evaluated by comparing the imputed data against known ground truth (GT) and measured with metrics like Pearson correlation coefficient (r), relative accuracy (MP), and mean absolute error (MAE). Comparative analyses with traditional imputation techniques are also conducted to demonstrate the superior efficacy of the ChatGPT as a data imputer. In summary, through customized data-to-text prompting engineering, ChatGPT can successfully capture intricate patterns and dependencies within biological data, resulting in precise imputations. Fine-tuning ChatGPT with domain-specific biological vocabulary with human in-loop as an interpreter enhances the accuracy and relevance of the imputations.","2023-11-01","2024-12-03 03:23:30","2024-12-03 03:23:30","","100034","","3","1","","Meta-Radiology","","","","","","","","","","","","","","","","","","","ChatGPT; Large language model; Medical imaging; Biological data imputation; Missing values","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"EFWEYISW","journalArticle","2024","Zhang, Wenyu; Guy, Mason A.; Yang, Jerrica; Hao, Lucy; Liu, Junliang; Hawkins, Joel M.; Mustakis, Jason; Monfette, Sebastien; Hein, Jason E.","Leveraging GPT-4 to transform chemistry from paper to practice††Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d4dd00248b","Digital Discovery","","2635-098X","10.1039/d4dd00248b","https://www.sciencedirect.com/science/article/pii/S2635098X24001785","Large Language Models (LLMs) have revolutionized numerous industries as well as accelerated scientific research. However, their application in planning and conducting experimental science, has been limited. In this study, we introduce an adaptable prompt-set with GPT-4, converting literature experimental procedures into actionable experimental steps for a Mettler Toledo EasyMax automated laboratory reactor. Through prompt engineering, we developed a 2-step sequential prompt: the first prompt converts literature synthesis procedures into step-by-step instructions for reaction planning; the second prompt generates an XML script to communicate these instructions to the EasyMax reactor, automating experimental design and execution. We successfully automated the reproduction of three distinct literature-based synthetic procedures and validated the reactions by monitoring and characterizing the products. This approach bridges the gap between text-to-procedure transcription and automated execution, and streamlines literature procedure reproduction.","2024-09-26","2024-12-03 03:23:30","2024-12-03 03:23:30","","2367-2376","","11","3","","Digital Discovery","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"PPZT4J68","journalArticle","2023","Vázquez-Cano, Esteban; Ramírez-Hurtado, José M.; Sáez-López, José M.; López-Meneses, Eloy","ChatGPT: The brightest student in the class","Thinking Skills and Creativity","","1871-1871","10.1016/j.tsc.2023.101380","https://www.sciencedirect.com/science/article/pii/S1871187123001487","This paper presents a research study that evaluated the score ChatGPT would get when summarizing a reading comprehension text from the PISA international tests with a prompt that made it simulate doing this as if it were a 15-year-old student. For this purpose, the text was camouflaged among 30 other summaries made by real 15-year-old students and was evaluated by 30 Spanish language teachers with different profiles in terms of age, professional experience, and gender who were unaware that one of the texts was made by artificial intelligence (AI). The evaluation of the summary, for which a homogeneous rubric is used, is based on two fundamental criteria: content and style. For the data analysis descriptive and inferential statistical techniques were used. The results show that the ChatGPT summary obtained the best marks in terms of content and style, with its respective marks being 3 and 2.5 points higher than those of the students. Therefore, we can deduce that the style and content of the ChatGPT summary greatly exceeded those presented by the students. These results are independent of the ages, levels of professional experience, and genders of the teachers who corrected the summary. The integration of AI tools such as ChatGPT must be based on solid methodological proposals that integrate their use from a creative and critical perspective that allows learning with the support of these tools and not using them as substitutes for the development of basic student competencies.","2023-09-01","2024-12-03 03:23:31","2024-12-03 03:23:31","","101380","","","49","","Thinking Skills and Creativity","","","","","","","","","","","","","","","","","","","ChatGPT; Assessment; Content, Style; Summarizing","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"87QGG3BE","journalArticle","2024","Guo, Jian; Xue, Yu","Application of magnetic resonance imaging and artificial intelligence algorithms in cancer screening","SLAS Technology","","2472-6303","10.1016/j.slast.2024.100218","https://www.sciencedirect.com/science/article/pii/S2472630324001006","In this society with a high incidence of cancer, cancer screening has become an important method to reduce the incidence and mortality of cancer. Traditional cancer screening methods such as CT have certain limitations and are difficult to adapt to large-scale and periodic cancer screening scenarios. Magnetic resonance imaging technology is an effective auxiliary method in CT methods, which can achieve high image resolution at lower doses and lower costs. Therefore, magnetic resonance imaging has become the most popular imaging method in clinical practice and a key research direction in the field of medical imaging. Therefore, this article intends to conduct in-depth research on the application of image feature extraction based on magnetic resonance imaging and artificial intelligence algorithms in cancer screening. This article introduces particle swarm optimization algorithm into the learning of artificial intelligence models and further improves it. And compared multiple algorithms, such as Chaos Particle Swarm Optimization, Genetic Particle Swarm Optimization, and Grey Wolf Algorithm, in order to verify the effectiveness and feasibility of the algorithm proposed in this paper. On this basis, the intelligent optimization algorithm was further improved and validated. Experimental results have shown that the new method proposed in this article has strong fault tolerance, and various functional modules of the cancer screening management system have been optimized and designed from five aspects: front-end, back-end, external, database, and infrastructure.","2024-12-01","2024-12-03 03:23:31","2024-12-03 03:23:31","","100218","","6","29","","SLAS Technology","","","","","","","","","","","","","","","","","","","Artificial intelligence algorithms; Cancer screening; Imaging images; Magnetic resonance imaging","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2D4YNIGW","journalArticle","2024","Shaik, Thanveer; Tao, Xiaohui; Li, Lin; Higgins, Niall; Gururajan, Raj; Zhou, Xujuan; Yong, Jianming","Clustered FedStack: Intermediate Global Models with Bayesian Information Criterion","Pattern Recognition Letters","","0167-8655","10.1016/j.patrec.2023.12.004","https://www.sciencedirect.com/science/article/pii/S0167865523003513","Federated Learning (FL) is currently one of the most popular technologies in the field of Artificial Intelligence (AI) due to its collaborative learning and ability to preserve client privacy. However, it faces challenges such as non-identically and non-independently distributed (non-IID) data with imbalanced labels among local clients. To address these limitations, the research community has explored various approaches such as using local model parameters, federated generative adversarial learning, and federated representation learning. In our study, we propose a novel Clustered FedStack framework based on the previously published Stacked Federated Learning (FedStack) framework. Here, the local clients send their model predictions and output layer weights to a server, which then builds a robust global model. This global model clusters the local clients based on their output layer weights using a clustering mechanism. We adopt three clustering mechanisms, namely K-Means, Agglomerative, and Gaussian Mixture Models, into the framework and evaluate their performance. Bayesian Information Criterion (BIC) is used with the maximum likelihood function to determine the number of clusters. Our results show that Clustered FedStack models outperform baseline models with clustering mechanisms. To estimate the convergence of our proposed framework, we use Cyclical learning rates.","2024-01-01","2024-12-03 03:23:31","2024-12-03 03:23:31","","121-127","","","177","","Pattern Recognition Letters","","","","","","","","","","","","","","","","","","","Federated learning; Clustering; Bayesian; Cyclical learning rates; FedStack","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"6M4VMXPL","journalArticle","2023","Kim, Jungkeun; Kim, Jeong Hyun; Kim, Changju; Park, Jooyoung","Decisions with ChatGPT: Reexamining choice overload in ChatGPT recommendations","Journal of Retailing and Consumer Services","","0969-6989","10.1016/j.jretconser.2023.103494","https://www.sciencedirect.com/science/article/pii/S0969698923002412","This research examines how individuals respond differently to recommendation options generated by ChatGPT, an AI-powered language model, in five studies. In contrast to previous research on choice overload, Studies 1 and 2 demonstrate that people tend to respond positively to a large number of recommendation options (60 options), revealing diverse consumer perceptions of AI-generated recommendations. Studies 3 and 4 further illustrate the moderating effect of recommendation agents and indicate that choice overload elicits distinct patterns of consumer reactions depending on whether the recommendations are from a human or AI agent. Lastly, Study 5 directly measures consumer preferences for recommendation agents, revealing a general preference for ChatGPT, particularly when a large number of options are available. These findings have significant implications for recommendation system design and user preferences regarding AI-powered recommendations.","2023-11-01","2024-12-03 03:23:31","2024-12-03 03:23:31","","103494","","","75","","Journal of Retailing and Consumer Services","","","","","","","","","","","","","","","","","","","Artificial intelligence; ChatGPT; AI versus human recommendations; Choice overload; Recommendation agents","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"F8IH7HP5","journalArticle","2023","Rashidi, Hooman H.; Fennell, Brandon D.; Albahra, Samer; Hu, Bo; Gorbett, Tom","The ChatGPT conundrum: Human-generated scientific manuscripts misidentified as AI creations by AI text detection tool","Journal of Pathology Informatics","","2153-3539","10.1016/j.jpi.2023.100342","https://www.sciencedirect.com/science/article/pii/S2153353923001566","AI Chat Bots such as ChatGPT are revolutionizing our AI capabilities, especially in text generation, to help expedite many tasks, but they introduce new dilemmas. The detection of AI-generated text has become a subject of great debate considering the AI text detector’s known and unexpected limitations. Thus far, much research in this area has focused on the detection of AI-generated text; however, the goal of this study was to evaluate the opposite scenario, an AI-text detection tool's ability to discriminate human-generated text. Thousands of abstracts from several of the most well-known scientific journals were used to test the predictive capabilities of these detection tools, assessing abstracts from 1980 to 2023. We found that the AI text detector erroneously identified up to 8% of the known real abstracts as AI-generated text. This further highlights the current limitations of such detection tools and argues for novel detectors or combined approaches that can address this shortcoming and minimize its unanticipated consequences as we navigate this new AI landscape.","2023-01-01","2024-12-03 03:23:31","2024-12-03 03:23:31","","100342","","","14","","Journal of Pathology Informatics","","","","","","","","","","","","","","","","","","","Machine learning; LLM; GPT; Large Language Model; Generative AI; Chat-GPT; Human-generated; Text detection","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SUSVTLLS","journalArticle","2024","Sadeghi R., Kiarash; Ojha, Divesh; Kaur, Puneet; Mahto, Raj V.; Dhir, Amandeep","Explainable artificial intelligence and agile decision-making in supply chain cyber resilience","Decision Support Systems","","0167-9236","10.1016/j.dss.2024.114194","https://www.sciencedirect.com/science/article/pii/S0167923624000277","Although artificial intelligence can contribute to decision-making processes, many industry players lag behind pioneering companies in utilizing artificial intelligence-driven technologies, which is a significant problem. Explainable artificial intelligence can be a viable solution to mitigate this problem. This paper proposes a research model to address how explainable artificial intelligence can impact decision-making processes. Using an experimental design, empirical data is collected to test the research model. This paper is one of the pioneer papers providing empirical evidence about the impact of explainable artificial intelligence on supply chain decision-making processes. We propose a serial mediation path, which includes transparency and agile decision-making. Findings reveal that explainable artificial intelligence enhances transparency, thereby significantly contributing to agile decision-making for improving cyber resilience during supply chain cyberattacks. Moreover, we conduct a post hoc analysis using text analysis to explore the themes present in tweets discussing explainable artificial intelligence in decision support systems. The results indicate a predominantly positive attitude towards explainable artificial intelligence within these systems. Furthermore, the text analysis reveals two main themes that emphasize the importance of transparency, explainability, and interpretability in explainable artificial intelligence.","2024-05-01","2024-12-03 03:23:31","2024-12-03 03:23:31","","114194","","","180","","Decision Support Systems","","","","","","","","","","","","","","","","","","","Data mining; Experiments; Explainable artificial intelligence; Agile decision making; Cyber resilience","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"WJ4NNKA9","journalArticle","2023","Fatouros, Georgios; Soldatos, John; Kouroumali, Kalliopi; Makridis, Georgios; Kyriazis, Dimosthenis","Transforming sentiment analysis in the financial domain with ChatGPT","Machine Learning with Applications","","2666-8270","10.1016/j.mlwa.2023.100508","https://www.sciencedirect.com/science/article/pii/S2666827023000610","Financial sentiment analysis plays a crucial role in decoding market trends and guiding strategic trading decisions. Despite the deployment of advanced deep learning techniques and language models to refine sentiment analysis in finance, this study breaks new ground by investigating the potential of large language models, particularly ChatGPT 3.5, in financial sentiment analysis, with a strong emphasis on the foreign exchange market (forex). Employing a zero-shot prompting approach, we examine multiple ChatGPT prompts on a meticulously curated dataset of forex-related news headlines, measuring performance using metrics such as precision, recall, f1-score, and Mean Absolute Error (MAE) of the sentiment class. Additionally, we probe the correlation between predicted sentiment and market returns as an addition evaluation approach. ChatGPT, compared to FinBERT, a well-established sentiment analysis model for financial texts, exhibited approximately 35% enhanced performance in sentiment classification and a 36% higher correlation with market returns. By underlining the significance of prompt engineering, particularly in zero-shot contexts, this study spotlights ChatGPT’s potential to substantially boost sentiment analysis in financial applications. By sharing the utilized dataset, our intention is to stimulate further research and advancements in the field of financial services.","2023-12-15","2024-12-03 03:23:31","2024-12-03 03:23:31","","100508","","","14","","Machine Learning with Applications","","","","","","","","","","","","","","","","","","","Finance; Artificial intelligence; ChatGPT; Sentiment analysis; Risk assessment","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RN7S68LM","journalArticle","2024","Ayanwale, Musa Adekunle; Ntshangase, Sibusiso D.; Adelana, Owolabi Paul; Afolabi, Kunle Waheed; Adam, Umar A.; Olatunbosun, Stella Oluwakemi","Navigating the future: Exploring in-service teachers' preparedness for artificial intelligence integration into South African schools","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100330","https://www.sciencedirect.com/science/article/pii/S2666920X24001334","This study contributes to existing research on how to integrate Artificial intelligence (AI) into school systems globally. This research explores in-service teachers' preparedness for integrating artificial intelligence into schools. We conducted this research within the context of the South African school system with teachers of various specializations, including sciences, social Sciences, mathematics, and languages. Drawing on the extended Unified Theory of Acceptance and Use of Technology (UTAUT2), we gathered teachers' perspectives through eight variables of technology integration, social influence, AI ethics, attitudes, TPACK, perceived self-efficacy, AI professional development, and AI preparedness. To analyze the 430 teachers' data involved in this study, we used a structural equation modeling analytical approach with SmartPLS software version 4.1.0.0. Our results indicate that technology integration, social influence, attitudes, and perceived self-efficacy influence teachers’ preparedness for AI. However, TPACK and ethics do not influence preparing teachers to integrate AI into schools. This study further presents interesting insight based on the mediation and moderation analysis of the variables. We discuss our findings and highlight their implications for practice and policy.","2024-12-01","2024-12-03 03:23:31","2024-12-03 03:23:31","","100330","","","7","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","South Africa; Artificial intelligence; In-service teachers; Technology integration; TPACK","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DKWHYGI9","journalArticle","2024","Kraljevic, Zeljko; Bean, Dan; Shek, Anthony; Bendayan, Rebecca; Hemingway, Harry; Yeung, Joshua Au; Deng, Alexander; Balston, Alfred; Ross, Jack; Idowu, Esther; Teo, James T; Dobson, Richard J B","Foresight—a generative pretrained transformer for modelling of patient timelines using electronic health records: a retrospective modelling study","The Lancet Digital Health","","2589-7500","10.1016/S2589-7500(24)00025-6","https://www.sciencedirect.com/science/article/pii/S2589750024000256","Summary Background An electronic health record (EHR) holds detailed longitudinal information about a patient's health status and general clinical history, a large portion of which is stored as unstructured, free text. Existing approaches to model a patient's trajectory focus mostly on structured data and a subset of single-domain outcomes. This study aims to evaluate the effectiveness of Foresight, a generative transformer in temporal modelling of patient data, integrating both free text and structured formats, to predict a diverse array of future medical outcomes, such as disorders, substances (eg, to do with medicines, allergies, or poisonings), procedures, and findings (eg, relating to observations, judgements, or assessments). Methods Foresight is a novel transformer-based pipeline that uses named entity recognition and linking tools to convert EHR document text into structured, coded concepts, followed by providing probabilistic forecasts for future medical events, such as disorders, substances, procedures, and findings. The Foresight pipeline has four main components: (1) CogStack (data retrieval and preprocessing); (2) the Medical Concept Annotation Toolkit (structuring of the free-text information from EHRs); (3) Foresight Core (deep-learning model for biomedical concept modelling); and (4) the Foresight web application. We processed the entire free-text portion from three different hospital datasets (King's College Hospital [KCH], South London and Maudsley [SLaM], and the US Medical Information Mart for Intensive Care III [MIMIC-III]), resulting in information from 811 336 patients and covering both physical and mental health institutions. We measured the performance of models using custom metrics derived from precision and recall. Findings Foresight achieved a precision@10 (ie, of 10 forecasted candidates, at least one is correct) of 0·68 (SD 0·0027) for the KCH dataset, 0·76 (0·0032) for the SLaM dataset, and 0·88 (0·0018) for the MIMIC-III dataset, for forecasting the next new disorder in a patient timeline. Foresight also achieved a precision@10 value of 0·80 (0·0013) for the KCH dataset, 0·81 (0·0026) for the SLaM dataset, and 0·91 (0·0011) for the MIMIC-III dataset, for forecasting the next new biomedical concept. In addition, Foresight was validated on 34 synthetic patient timelines by five clinicians and achieved a relevancy of 33 (97% [95% CI 91–100]) of 34 for the top forecasted candidate disorder. As a generative model, Foresight can forecast follow-on biomedical concepts for as many steps as required. Interpretation Foresight is a general-purpose model for biomedical concept modelling that can be used for real-world risk forecasting, virtual trials, and clinical research to study the progression of disorders, to simulate interventions and counterfactuals, and for educational purposes. Funding National Health Service Artificial Intelligence Laboratory, National Institute for Health and Care Research Biomedical Research Centre, and Health Data Research UK.","2024-04-01","2024-12-03 03:23:31","2024-12-03 03:23:31","","e281-e290","","4","6","","The Lancet Digital Health","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"Q3ZUWE5J","journalArticle","2024","Salminen, Joni; Santos, João M.; Jung, Soon-gyo; Jansen, Bernard J.","Picturing the fictitious person: An exploratory study on the effect of images on user perceptions of AI-generated personas","Computers in Human Behavior: Artificial Humans","","2949-8821","10.1016/j.chbah.2024.100052","https://www.sciencedirect.com/science/article/pii/S2949882124000124","Human-computer interaction (HCI) research is facing a vital question of the effectiveness of personas generated using artificial intelligence (AI). Addressing this question, this research explores user perceptions of AI-generated personas for textual content (GPT-4) and two image generation models (DALL-E and Midjourney). We evaluate whether the inclusion of images in AI-generated personas impacts user perception or if AI text descriptions alone suffice to create good personas. Recruiting 216 participants, we compare three AI-generated personas without images and those with either DALL-E or Midjourney-created images. Contrary to expectations from persona literature, the presence of images in AI-generated personas did not significantly impact user perceptions. Rather, the participants generally perceived AI-generated personas to be of good quality regardless of the inclusion of images. These findings suggest that textual content, i.e., the persona narrative, is the primary driver of user perceptions in AI-generated personas. Our findings contribute to the ongoing AI-HCI discourse and provide recommendations for designing AI-generated personas.","2024-01-01","2024-12-03 03:23:31","2024-12-03 03:23:31","","100052","","1","2","","Computers in Human Behavior: Artificial Humans","","","","","","","","","","","","","","","","","","","Large language models; Images; AI personas; Persona design; Persona perceptions","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"EZQBSZSN","journalArticle","2024","Waqas, Muhammad; Abbas, Sagheer; Farooq, Umer; Khan, Muhammad Adnan; Ahmad, Munir; Mahmood, Nasir","Autonomous vehicles congestion model: A transparent LSTM-based prediction model corporate with Explainable Artificial Intelligence (EAI)","Egyptian Informatics Journal","","1110-8665","10.1016/j.eij.2024.100582","https://www.sciencedirect.com/science/article/pii/S1110866524001452","Urban traffic congestion presents a range of vital difficulties requiring precise prediction models in order to facilitate traffic management for Autonomous Vehicles. This work introduces a novel framework that regulates a Long Short-Term Memory (LSTM) system with methods provided by Explainable Artificial Intelligence (XAI) to explain traffic congestion behavioural modes. For enhanced accuracy and transparency, the integration of EAI methodologies with LSTM based models is addressed as a novel approach towards congestion prediction, while significant research has been done previously using Machine Learning that compared previous proposed based model congestion monitoring improvement through Federated Learning Waqas et al. [18]. This wok proposes the enhances ML focused on Long Short-Term Memory with EAI (LSTM-EAI) model for Smart City environments that require accurate traffic congestion rate forecast to improve the urban mobility. The proposed model provides better interpretability that help stakeholders to understand how the input plays an important role in the condition of traffic jams. The results show that the LSTM-EAI model is 5 % better than previous methods for both the accuracy and reliability of congestion prediction, and may become a practical and effective solution for the urban traffic problem.","2024-12-01","2024-12-03 03:23:31","2024-12-03 03:23:31","","100582","","","28","","Egyptian Informatics Journal","","","","","","","","","","","","","","","","","","","IoT; Artificial Intelligence; Autonomous Vehicles; Explainable Artificial Intelligence (EAI); Federated Learning; Long Short-Term Memory; Recurrent Neural Network (RNN); Smart City","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"B95SLC3L","journalArticle","2023","White, Andrew D.; Hocky, Glen M.; Gandhi, Heta A.; Ansari, Mehrad; Cox, Sam; Wellawatte, Geemi P.; Sasmal, Subarna; Yang, Ziyue; Liu, Kangxin; Singh, Yuvraj; Peña Ccoa, Willmor J.","Assessment of chemistry knowledge in large language models that generate code††Electronic supplementary information (ESI) available: Supporting figures, tables, and text. Accuracy data are available as comma separated value files. Contexts are available as a markup file. The responses from the model (completions) which were the basis for expert evaluators are available in HTML format at https://doi.org/10.5281/zenodo.6800475. See DOI: https://doi.org/10.1039/d2dd00087c","Digital Discovery","","2635-098X","10.1039/d2dd00087c","https://www.sciencedirect.com/science/article/pii/S2635098X23000359","ABSTRACT In this work, we investigate the question: do code-generating large language models know chemistry? Our results indicate, mostly yes. To evaluate this, we introduce an expandable framework for evaluating chemistry knowledge in these models, through prompting models to solve chemistry problems posed as coding tasks. To do so, we produce a benchmark set of problems, and evaluate these models based on correctness of code by automated testing and evaluation by experts. We find that recent LLMs are able to write correct code across a variety of topics in chemistry and their accuracy can be increased by 30 percentage points via prompt engineering strategies, like putting copyright notices at the top of files. Our dataset and evaluation tools are open source which can be contributed to or built upon by future researchers, and will serve as a community resource for evaluating the performance of new models as they emerge. We also describe some good practices for employing LLMs in chemistry. The general success of these models demonstrates that their impact on chemistry teaching and research is poised to be enormous.","2023-01-01","2024-12-03 03:23:31","2024-12-03 03:23:31","","368-376","","2","2","","Digital Discovery","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"8DJEQM32","journalArticle","2023","Guzik, Erik E.; Byrge, Christian; Gilde, Christian","The originality of machines: AI takes the Torrance Test","Journal of Creativity","","2713-3745","10.1016/j.yjoc.2023.100065","https://www.sciencedirect.com/science/article/pii/S2713374523000249","This exploratory research investigated the creative abilities of OpenAI's large language model, ChatGPT, based on the GPT-4 architecture, as assessed by the Torrance Tests of Creative Thinking. In comparison to human samples and a national percentile from Scholastic Testing Services, ChatGPT's performance was analyzed for fluency, flexibility, and originality. Results indicated that ChatGPT scored within the top 1% for originality and fluency, and showed high scores for flexibility, thus highlighting the current creative abilities of AI and the potential of AI systems to support and augment human creativity in new and meaningful ways. The study encourages additional research to further define, measure, and develop creativity in the era of advanced AI.","2023-12-01","2024-12-03 03:23:31","2024-12-03 03:23:31","","100065","","3","33","","Journal of Creativity","","","","","","","","","","","","","","","","","","","Entrepreneurship; Innovation; Artificial intelligence; Creativity; Originality; Assessment; Torrance tests of creative thinking","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"7V53I23M","journalArticle","2024","Chang, Chuheng; Shi, Wen; Wang, Youyang; Zhang, Zhan; Huang, Xiaoming; Jiao, Yang","The path from task-specific to general purpose artificial intelligence for medical diagnostics: A bibliometric analysis","Computers in Biology and Medicine","","0010-4825","10.1016/j.compbiomed.2024.108258","https://www.sciencedirect.com/science/article/pii/S0010482524003421","Artificial intelligence (AI) has revolutionized many fields, and its potential in healthcare has been increasingly recognized. Based on diverse data sources such as imaging, laboratory tests, medical records, and electrophysiological data, diagnostic AI has witnessed rapid development in recent years. A comprehensive understanding of the development status, contributing factors, and their relationships in the application of AI to medical diagnostics is essential to further promote its use in clinical practice. In this study, we conducted a bibliometric analysis to explore the evolution of task-specific to general-purpose AI for medical diagnostics. We used the Web of Science database to search for relevant articles published between 2010 and 2023, and applied VOSviewer, the R package Bibliometrix, and CiteSpace to analyze collaborative networks and keywords. Our analysis revealed that the field of AI in medical diagnostics has experienced rapid growth in recent years, with a focus on tasks such as image analysis, disease prediction, and decision support. Collaborative networks were observed among researchers and institutions, indicating a trend of global cooperation in this field. Additionally, we identified several key factors contributing to the development of AI in medical diagnostics, including data quality, algorithm design, and computational power. Challenges to progress in the field include model explainability, robustness, and equality, which will require multi-stakeholder, interdisciplinary collaboration to tackle. Our study provides a holistic understanding of the path from task-specific, mono-modal AI toward general-purpose, multimodal AI for medical diagnostics. With the continuous improvement of AI technology and the accumulation of medical data, we believe that AI will play a greater role in medical diagnostics in the future.","2024-04-01","2024-12-03 03:23:31","2024-12-03 03:23:31","","108258","","","172","","Computers in Biology and Medicine","","","","","","","","","","","","","","","","","","","Bibliometric analysis; Artificial intelligence; Medicine; Diagnostics; Network visualization","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JVCTBECF","journalArticle","2024","Jonnakuti, Venkata Soumith; Wagner, Eric J.; Maletić-Savatić, Mirjana; Liu, Zhandong; Yalamanchili, Hari Krishna","PolyAMiner-Bulk is a deep learning-based algorithm that decodes alternative polyadenylation dynamics from bulk RNA-seq data","Cell Reports Methods","","2667-2375","10.1016/j.crmeth.2024.100707","https://www.sciencedirect.com/science/article/pii/S2667237524000213","Summary Alternative polyadenylation (APA) is a key post-transcriptional regulatory mechanism; yet, its regulation and impact on human diseases remain understudied. Existing bulk RNA sequencing (RNA-seq)-based APA methods predominantly rely on predefined annotations, severely impacting their ability to decode novel tissue- and disease-specific APA changes. Furthermore, they only account for the most proximal and distal cleavage and polyadenylation sites (C/PASs). Deconvoluting overlapping C/PASs and the inherent noisy 3′ UTR coverage in bulk RNA-seq data pose additional challenges. To overcome these limitations, we introduce PolyAMiner-Bulk, an attention-based deep learning algorithm that accurately recapitulates C/PAS sequence grammar, resolves overlapping C/PASs, captures non-proximal-to-distal APA changes, and generates visualizations to illustrate APA dynamics. Evaluation on multiple datasets strongly evinces the performance merit of PolyAMiner-Bulk, accurately identifying more APA changes compared with other methods. With the growing importance of APA and the abundance of bulk RNA-seq data, PolyAMiner-Bulk establishes a robust paradigm of APA analysis.","2024-02-26","2024-12-03 03:23:31","2024-12-03 03:23:31","","100707","","2","4","","Cell Reports Methods","","","","","","","","","","","","","","","","","","","deep learning; large language model (LLM); alternative polyadenylation (APA); bioinformatics; computational biology; gene regulation; post-transcriptional regulation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SK8CZMRM","journalArticle","2024","Waller, Dr. Laurie; Moats, Dr. David; Cox, Dr. Emily; Bellamy, Dr. Rob","Questionable devices: Applying a large language model to deliberate carbon removal","Environmental Science & Policy","","1462-9011","10.1016/j.envsci.2024.103940","https://www.sciencedirect.com/science/article/pii/S1462901124002740","This paper presents a device-centred approach to deliberation, developed in deliberative workshops appraising methods for removing carbon dioxide from the air. Our approach involved deploying the Large Language Model application ChatGPT (sometimes termed “generative AI”) to elicit questions and generate texts about carbon removal. We develop the notion of the “questionable” device to foreground the informational unruliness ChatGPT introduced into the deliberations. The analysis highlights occasions where the deliberative apparatus became a focus of collective critique, including over: issue definitions, expert-curated resources, lay identities and social classifications. However, in this set-up ChatGPT was all too often engaged unquestioningly as an instrument for informing discussion; its instrumental lure disguising the unruliness it introduced into the workshops. In concluding, we elaborate the notion of questionable devices and reflect on the way carbon removal has been “devised” as a field in want of informed deliberation.","2024-12-01","2024-12-03 03:23:31","2024-12-03 03:23:31","","103940","","","162","","Environmental Science & Policy","","","","","","","","","","","","","","","","","","","Large language models; Generative AI; Carbon removal; Deliberation; Devices; Experiments in participation; Publics","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"3YE428G5","journalArticle","2024","Kanchon, Md. Kabin Hasan; Sadman, Mahir; Nabila, Kaniz Fatema; Tarannum, Ramisa; Khan, Riasat","Enhancing personalized learning: AI-driven identification of learning styles and content modification strategies","International Journal of Cognitive Computing in Engineering","","2666-3074","10.1016/j.ijcce.2024.06.002","https://www.sciencedirect.com/science/article/pii/S2666307424000184","In the rapidly advancing era of educational technology, customized learning materials have the potential to enhance individuals’ learning capacities. This research endeavors to devise an effective method for detecting a learner’s preferred learning style and subsequently adapting the learning content to align with that style, utilizing artificial intelligence AI techniques. Our investigation finds that analyzing learners’ web tracking logs for activity classification and categorizing individual responses for feedback classification are highly effective methods for identifying a learner’s learning styles, such as visual, auditory, and kinesthetic. A custom dataset has been constructed in this research comprising approximately 506 samples and 22 features utilizing the Moodle learning management system (LMS), successfully categorizing students into their respective learning styles. Furthermore, decision tree, random forest, support vector machine (SVM), logistic regression, XGBoost, blending ensemble, and convolutional neural network (CNN) algorithms with corresponding optimized hyperparameters and synthetic minority oversampling technique (SMOTE) have been applied for learning behavior classification. The blending ensemble technique with the XGBoost meta-learning model accomplished the best performance for learning style detection with an accuracy of 97.56%. Next, the text content of the electronic documents is modified by employing different natural language processing (NLP) techniques, including named entity recognition of spaCy, knowledge graph, generative pre-trained transformer 3 (GPT-3), and text-to-text transfer transformer (T5) model, to accommodate diverse learning styles. Various approaches, such as color coding, audio scripts, mind maps, flashcards, etc., are implemented to adapt the content effectively for the detected categories of learners. The spaCy NLP-based named entity recognition (NER) model demonstrates a 94.16% F1 score and 0.92 exact match ratio for color coding text generation of ten electronic documents comprising 790 distinct individual words. These modifications aim to cater to the unique preferences of learners, fostering a more personalized and engaging educational experience. To the best of our knowledge, this is the first time an integrated learning style detection and content modification system has been developed in this work utilizing efficient AI techniques and a private dataset.","2024-01-01","2024-12-03 03:23:31","2024-12-03 03:23:31","","269-278","","","5","","International Journal of Cognitive Computing in Engineering","","","","","","","","","","","","","","","","","","","Named entity recognition; Blending ensemble technique; Content modification; Learning style detection; Text-to-text transfer transformer (T5) model","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"NSKSIJKB","journalArticle","2024","Cónego, Leonor; Pinto, Rui; Gonçalves, Gil","Digital Transformation in Manufacturing: The Synergy of Chatbots and Tailored Gamification Strategies","International Conference on Industry Sciences and Computer Science Innovation","","1877-0509","10.1016/j.procs.2024.05.093","https://www.sciencedirect.com/science/article/pii/S1877050924011098","Chatbots have revolutionized human-computer interactions in various fields, driven by advancements in Artificial Intelligence. Similarly, gamification techniques have emerged as innovative methods to engage users and enhance experiences. This paper presents an analysis of the trends, applications, methods, and challenges associated with chatbots, with a particular emphasis on their capability to generate customized gamification solutions for industrial 5.0 environments. These environments are characterized by the increased use of technologies - such as Virtual and Augmented Reality - driven by the digital transformation of Industry 4.0 (I4.0). As Industry 5.0 (I5.0) builds upon I4.0, emphasizing human-technology collaborations and coexistence of humans and advanced technologies, the role of chatbots and gamification in this context becomes even more compelling. Within this research, various gamification applications and chatbot architectures, ranging from rule-based to generative models, are identified to assess their suitability for the specific needs of industrial 5.0 environments. The primary objective of this study is to explore the potential of leveraging chatbot assistance to enhance gamification experiences by considering employee specific characteristics, workplace technologies, and other relevant factors. By examining the advancements and obstacles in this field, this study aims to explore the potential of chatbots for increasing the effectiveness of gamification strategies. This integration is anticipated to lead to more immersive and personalized experiences, increased employee satisfaction, positive work environment, and enhanced skill development and performance. However, several challenges such as security, user acceptance and the need for continuous adaptation must be addressed to ensure the successful implementation of chatbot-driven gamification in I5.0 environments. The navigation through these challenges holds the key to unlocking the full potential of these transformative technologies and methods, paving the way for a more engaging and enriching work environment.","2024-01-01","2024-12-03 03:23:31","2024-12-03 03:23:31","","171-178","","","237","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Chatbot; Natural Language Processing; Industry 5.0; Machine Learning; Gamification; Interactive Systems; User Engagement; User-Centered Design","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"PI9MR39V","journalArticle","2023","Zhang, Tianyu; Tan, Tao; Wang, Xin; Gao, Yuan; Han, Luyi; Balkenende, Luuk; D’Angelo, Anna; Bao, Lingyun; Horlings, Hugo M.; Teuwen, Jonas; Beets-Tan, Regina G.H.; Mann, Ritse M.","RadioLOGIC, a healthcare model for processing electronic health records and decision-making in breast disease","Cell Reports Medicine","","2666-3791","10.1016/j.xcrm.2023.101131","https://www.sciencedirect.com/science/article/pii/S2666379123002598","Summary Digital health data used in diagnostics, patient care, and oncology research continue to accumulate exponentially. Most medical information, and particularly radiology results, are stored in free-text format, and the potential of these data remains untapped. In this study, a radiological repomics-driven model incorporating medical token cognition (RadioLOGIC) is proposed to extract repomics (report omics) features from unstructured electronic health records and to assess human health and predict pathological outcome via transfer learning. The average accuracy and F1-weighted score for the extraction of repomics features using RadioLOGIC are 0.934 and 0.934, respectively, and 0.906 and 0.903 for the prediction of breast imaging-reporting and data system scores. The areas under the receiver operating characteristic curve for the prediction of pathological outcome without and with transfer learning are 0.912 and 0.945, respectively. RadioLOGIC outperforms cohort models in the capability to extract features and also reveals promise for checking clinical diagnoses directly from electronic health records.","2023-08-15","2024-12-03 03:23:31","2024-12-03 03:23:31","","101131","","8","4","","Cell Reports Medicine","","","","","","","","","","","","","","","","","","","artificial intelligence; electronic health records; breast cancer; decision support; digital health data; radiology; repomics","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DXUTGPVR","journalArticle","2024","Colther, Cristian; Doussoulin, Jean Pierre","Artificial intelligence: Driving force in the evolution of human knowledge","Journal of Innovation & Knowledge","","2444-569X","10.1016/j.jik.2024.100625","https://www.sciencedirect.com/science/article/pii/S2444569X24001641","This article proposes that artificial intelligence (AI) is positioned as a key driver of a new evolutionary stage of human knowledge, complementing human intelligence and facilitating the creation and development of sophisticated collective intelligence, defined as the noosphere, understood as the sphere of collective human thought. The study reveals several key insights into the transformative potential of AI, including its capacity to accelerate, mediate, and diffuse human knowledge. It concludes that AI not only catalyzes the existence of the noosphere but also redefines the structures and mechanisms through which human knowledge is expanded and democratized. Additionally, the document presents potential risks and significant ethical, social, and legal challenges of an AI-mediated noosphere, offering recommendations and a research agenda around the topic, and limitations and proposals for improvement to be considered in the future.","2024-10-01","2024-12-03 03:23:32","2024-12-03 03:23:32","","100625","","4","9","","Journal of Innovation & Knowledge","","","","","","","","","","","","","","","","","","","Artificial intelligence; Ethical considerations; Evolution knowledge; Future scenarios; Noosphere","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"W9JHW86I","journalArticle","2024","Lee, Unggi; Jeon, Minji; Lee, Yunseo; Byun, Gyuri; Son, Yoorim; Shin, Jaeyoon; Ko, Hongkyu; Kim, Hyeoncheol","LLaVA-docent: Instruction tuning with multimodal large language model to support art appreciation education","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100297","https://www.sciencedirect.com/science/article/pii/S2666920X24001000","Despite the development of various AI systems to support learning in various domains, AI assistance for art appreciation education has not been extensively explored. Art appreciation, often perceived as an unfamiliar and challenging endeavor for most students, can be more accessible with a generative AI enabled conversation partner that provides tailored questions and encourages the audience to deeply appreciate artwork. This study explores the application of multimodal large language models (MLLMs) in art appreciation education, with a focus on developing LLaVA-Docent, a model designed to serve as a personal tutor for art appreciation. Our approach involved design and development research, focusing on iterative enhancement to design and develop the application to produce a functional MLLM-enabled chatbot along with a data design framework for art appreciation education. To that end, we established a virtual dialogue dataset that was generated by GPT-4, which was instrumental in training our MLLM, LLaVA-Docent. The performance of LLaVA-Docent was evaluated by benchmarking it against alternative settings and revealed its distinct strengths and weaknesses. Our findings highlight the efficacy of the MMLM-based personalized art appreciation chatbot and demonstrate its applicability for a novel approach in which art appreciation is taught and experienced.","2024-12-01","2024-12-03 03:23:32","2024-12-03 03:23:32","","100297","","","7","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Instruction tuning; Art appreciation education; Multimodal large language model","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"UTUWBH88","journalArticle","2024","Zaki, Mohd; Jayadeva; Mausam; Krishnan, N. M. Anoop","MaScQA: investigating materials science knowledge of large language models††Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d3dd00188a","Digital Discovery","","2635-098X","10.1039/d3dd00188a","https://www.sciencedirect.com/science/article/pii/S2635098X24000159","Information extraction and textual comprehension from materials literature are vital for developing an exhaustive knowledge base that enables accelerated materials discovery. Language models have demonstrated their capability to answer domain-specific questions and retrieve information from knowledge bases. However, there are no benchmark datasets in the materials science domain that can be used to evaluate the understanding of the key concepts by these language models. In this work, we curate a dataset of 650 challenging questions from the materials domain that require the knowledge and skills of a materials science student who has cleared their undergraduate degree. We classify these questions based on their structure and the materials science domain-based subcategories. Further, we evaluate the performance of LLaMA-2-70B, GPT-3.5, and GPT-4 models on solving these questions via zero-shot and chain of thought prompting. It is observed that GPT-4 gives the best performance (∼62% accuracy) as compared to other models. Interestingly, in contrast to the general observation, no significant improvement in accuracy is observed with the chain of thought prompting. To evaluate the limitations, we performed an error analysis, which revealed conceptual errors (∼72%) as the major contributor compared to computational errors (∼28%) towards the reduced performance of the LLMs. We also compared GPT-4 with human performance and observed that GPT-4 is better than an average student and comes close to passing the exam. We also show applications of the best performing model (GPT-4) on composition–extraction from tables of materials science research papers and code writing tasks. While GPT-4 performs poorly on composition extraction, it outperforms all other models on the code writing task. We hope that the dataset, analysis, and applications discussed in this work will promote further research in developing better materials science domain-specific LLMs and strategies for information extraction.","2024-02-14","2024-12-03 03:23:32","2024-12-03 03:23:32","","313-327","","2","3","","Digital Discovery","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"AH2ZQNCN","journalArticle","2023","Vandelanotte, Corneel; Trost, Stewart; Hodgetts, Danya; Imam, Tasadduq; Rashid, Mamunur; To, Quyen G.; Maher, Carol","Increasing physical activity using an just-in-time adaptive digital assistant supported by machine learning: A novel approach for hyper-personalised mHealth interventions","Journal of Biomedical Informatics","","1532-0464","10.1016/j.jbi.2023.104435","https://www.sciencedirect.com/science/article/pii/S1532046423001569","Objective Physical inactivity is a leading modifiable cause of death and disease worldwide. Population-based interventions to increase physical activity are needed. Existing automated expert systems (e.g., computer-tailored interventions) have significant limitations that result in low long-term effectiveness. Therefore, innovative approaches are needed. This special communication aims to describe and discuss a novel mHealth intervention approach that proactively offers participants with hyper-personalised intervention content adjusted in real-time. Methods Using machine learning approaches, we propose a novel physical activity intervention approach that can learn and adapt in real-time to achieve high levels of personalisation and user engagement, underpinned by a likeable digital assistant. It will consist of three major components: (1) conversations: to increase user’s knowledge on a wide range of activity-related topics underpinned by Natural Language Processing; (2) nudge engine: to provide users with hyper-personalised cues to action underpinned by reinforcement learning (i.e., contextual bandit) and integrating real-time data from activity tracking, GPS, GIS, weather, and user provided data; (3) Q&A: to facilitate users asking any physical activity related questions underpinned by generative AI (e.g., ChatGPT, Bard) for content generation. Results The detailed concept of the proposed physical activity intervention platform demonstrates the practical application of a just-in-time adaptive intervention applying various machine learning techniques to deliver a hyper-personalised physical activity intervention in an engaging way. Compared to traditional interventions, the novel platform is expected to show potential for increased user engagement and long-term effectiveness due to: (1) using new variables to personalise content (e.g., GPS, weather), (2) providing behavioural support at the right time in real-time, (3) implementing an engaging digital assistant and (4) improving the relevance of content through applying machine learning algorithms. Conclusion The use of machine learning is on the rise in every aspect of today’s society, however few attempts have been undertaken to harness its potential to achieve health behaviour change. By sharing our intervention concept, we contribute to the ongoing dialogue on creating effective methods for promoting health and well-being in the informatics research community. Future research should focus on refining these techniques and evaluating their effectiveness in controlled and real-world circumstances.","2023-08-01","2024-12-03 03:23:32","2024-12-03 03:23:32","","104435","","","144","","Journal of Biomedical Informatics","","","","","","","","","","","","","","","","","","","Artificial intelligence; Chatbot; Conversational agent; Behaviour change; Exercise; Intervention","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"YMTXP4W5","journalArticle","2023","Sreedha, B; Nair, Prashant R; Maity, Reevu","Non-invasive early diagnosis of jaundice with computer vision","International Conference on Machine Learning and Data Engineering","","1877-0509","10.1016/j.procs.2023.01.111","https://www.sciencedirect.com/science/article/pii/S1877050923001114","Jaundice is a condition characterized by the yellowing of skin and sclera of the eyes. Jaundice occurs when the liver is unable to eliminate the bilirubin, a waste material formed as a result of the breakdown of red blood cells. The excessive accumulation of bilirubin in the blood can result in permanent brain damage. Therefore, Jaundice has to be identified in the early stage. The diagnosis method available is a bilirubin blood test, a painful procedure where blood sample is collected from patient. To mitigate the pain, developing an alternative non-invasive approach can aid in the early diagnosis of jaundice. A lot of research has been carried out to develop a non-invasive procedure. Most of the works focused on identifying jaundice by analysing the yellowness of skin. However, yellowing of the skin is a less noticeable symptom if the patient has darker skin, but the yellowing of the sclera can be more easily identifiable. This work focuses on identifying jaundice from the sclera. There is no standard publicly available dataset for jaundice diagnosis and all the previous works are carried out by collecting data from hospitals. One of the major limitations of medical data is its limited availability. In Artificial Intelligence (AI) research insufficient data results in incorrect predictions. However, previous works in this area have not worked on increasing the volume of the dataset. Through this work, the potential of the Generative Adversarial Network (GAN) is leveraged to overcome the issue of limited medical data availability. In this paper, a hybrid approach based on computer vision and classical machine learning is developed that can accurately determine the intensity of jaundice from the yellowness of the sclera. The work addresses the challenges of limited medical dataset availability while considering the privacy of the concerned individual.","2023-01-01","2024-12-03 03:23:32","2024-12-03 03:23:32","","1321-1334","","","218","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Machine learning; Computer vision; Jaundice","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"3ZM6AP4Y","journalArticle","2024","Chatterjee, Ram; Pandey, Mrinal; Thakur, Hardeo Kumar; Gupta, Anand","Checking Counterfeit Critiques on Commodities using Ensemble Classifiers Enhancing Information Credibility","5th International Conference on Innovative Data Communication Technologies and Application (ICIDCA 2024)","","1877-0509","10.1016/j.procs.2024.03.246","https://www.sciencedirect.com/science/article/pii/S1877050924006057","The conundrum of the ubiquitous deceptive reviews has overruled the online ontology with the obsession of obscure but obligatory posting of product reviews for the customers to believe, behold and beget the online product marketing. This mandates contemporary research in the direction to delve deeper on the application and analysis of deceiving online reviews with matured and advanced AI models functional on large scale datasets to effectively and efficiently demarcate between the genuine and the sham. The research counteracts the counterfeiting product reviews via the applications, assessment and analysis of the befitting AI models - Elastic-net Classifier model based on block coordinate descent with Wordcloud and its further performance enhancement through LightGBM Trees Classifier with Grid Search and Early Stopping support, with Log-Loss as performance metric for experimentation to gain insight into the intricacies of detection, diagnosis and diminution of fake product reviews. The paper also delineates discriminative and affirmative aspects of the dataset quality, statistics, stability and standards inherent and coherent to the creation of the dataset using Large Language Models (LLMs) intrinsic to the zeitgeist juncture of recent times promoting machines to produce large scale, cost effective bogus reviews in lieu of the Amazon Mechanical Turks. The results obtained with the Log-Loss holdout score of 0.1462 conforming the LightGBM classifier proves its performance better than the Elastic-Net classifier, conforming it as better than the ROC-AUC in terms of its proximity to the prediction probability for the matching actual/true value.","2024-01-01","2024-12-03 03:23:32","2024-12-03 03:23:32","","570-579","","","233","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Large Language Models; Amazon Mechanical Turks; Elastic-net Classifier; Information credibility; LightGBM Trees Classifier","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"D2S7PLQM","journalArticle","2024","Renkema, Maarten; Tursunbayeva, Aizhan","The future of work of academics in the age of Artificial Intelligence: State-of-the-art and a research roadmap","Futures","","0016-3287","10.1016/j.futures.2024.103453","https://www.sciencedirect.com/science/article/pii/S0016328724001368","The Future of Work (FoW) has garnered significant attention among scholars and practitioners, with the advent of Artificial Intelligence (AI) playing an important role in shaping this discourse. Despite the common perception that intelligent machines pose a threat to workers in routine roles, AI technologies are increasingly being utilized for advanced tasks carried out by knowledge workers. Drawing on state-of-the-art research and real-life examples we develop an integrated framework to explore the future of academic work. Our focus is on academics, an essential yet under-researched group of knowledge workers, and we discuss their work in relation to AI across space, time, and task dimensions. Our analysis reveals that the usage of AI technologies can have implications for the research, teaching, and service activities of academics and thereby also for the creation, acquisition, dissemination, and application of knowledge. Based on our framework we develop scenarios and propose a future research roadmap.","2024-10-01","2024-12-03 03:23:32","2024-12-03 03:23:32","","103453","","","163","","Futures","","","","","","","","","","","","","","","","","","","Knowledge work; Artificial Intelligence; Academics; Future of Work","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"7P383VPL","journalArticle","2024","Alzakari, Sarah A.; Aldrees, Asma; Umer, Muhammad; Cascone, Lucia; Innab, Nisreen; Ashraf, Imran","Artificial intelligence-driven predictive framework for early detection of still birth","SLAS Technology","","2472-6303","10.1016/j.slast.2024.100203","https://www.sciencedirect.com/science/article/pii/S2472630324000852","Predictive modeling is becoming increasingly popular in the context of early disease detection. The use of machine learning approaches for predictive modeling can help early detection of diseases thereby enabling medical experts to appropriate medical treatments. Stillbirth prediction is a similar domain where artificial intelligence-based predictive modeling can alleviate this significant global health challenge. Despite advancements in prenatal care, the prevention of stillbirths remains a complex issue requiring further research and interventions. The cardiotocography (CTG) dataset is used in this research work from the UCI machine learning (ML) repository to investigate the efficiency of the proposed approach regarding stillbirth prediction. This research work adopts the Tabular Prior Data Fitted Network (TabPFN) model which was originally designed to solve small tabular classification. TabPFN is used to predict the still or live birth during pregnancy with 97.91% accuracy. To address this life-saving problem with more accurate results and in-depth analysis of ML models, this research work makes use of 13 renowned ML models for performance comparison with the proposed model. The proposed model is evaluated using precision, recall, F-score, Mathews Correlation Coefficient (MCC), and the area under the curve evaluation parameters and the results are 97.87%, 98.26%, 98.05%, 96.42%, and 98.88%, respectively. The results of the proposed model are further evaluated using k-fold cross-validation and its performance comparison with other state-of-the-art studies indicating the superior performance of TabPFN model.","2024-12-01","2024-12-03 03:23:32","2024-12-03 03:23:32","","100203","","6","29","","SLAS Technology","","","","","","","","","","","","","","","","","","","Artificial intelligence; Healthcare; Cardiotocography; Predictive modeling; Stillbirth prediction; TabPFN; Women healthcare","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"Z88RECB4","journalArticle","2023","Xue, Zhiwen; Xu, Chong; Xu, Xiwei","Application of ChatGPT in natural disaster prevention and reduction","Natural Hazards Research","","2666-5921","10.1016/j.nhres.2023.07.005","https://www.sciencedirect.com/science/article/pii/S2666592123000744","Improving disaster prevention, reduction, and emergency response capabilities is crucial in a country prone to frequent natural disasters. Since the release of ChatGPT, it has garnered widespread attention and sparked extensive discussions in various fields due to its powerful language processing and reasoning abilities. This paper explores the application of ChatGPT in natural disaster prevention and reduction, building upon its language capabilities. The paper examines ChatGPT's ability to gather information and its potential for disaster prevention science popularization and education. It describes the rapid response and availability of ChatGPT in natural disaster prevention and highlights its potential to assist emergency response efforts. The paper also outlines ChatGPT's assistance in the pre-disaster, during-disaster, and post-disaster phases. Additionally, it points out the current limitations and challenges in applying ChatGPT and provides prospects for future research directions in natural disaster prevention and reduction.","2023-09-01","2024-12-03 03:23:32","2024-12-03 03:23:32","","556-562","","3","3","","Natural Hazards Research","","","","","","","","","","","","","","","","","","","Artificial intelligence; ChatGPT; Large language model; Emergency response; Natural disaster prevention and reduction","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LFLQXVLD","journalArticle","2024","Cid, Yashin Dicente; Macpherson, Matthew; Gervais-Andre, Louise; Zhu, Yuanyi; Franco, Giuseppe; Santeramo, Ruggiero; Lim, Chee; Selby, Ian; Muthuswamy, Keerthini; Amlani, Ashik; Hopewell, Heath; Indrajeet, Das; Liakata, Maria; Hutchinson, Charles E; Goh, Vicky; Montana, Giovanni","Development and validation of open-source deep neural networks for comprehensive chest x-ray reading: a retrospective, multicentre study","The Lancet Digital Health","","2589-7500","10.1016/S2589-7500(23)00218-2","https://www.sciencedirect.com/science/article/pii/S2589750023002182","Summary Background Artificial intelligence (AI) systems for automated chest x-ray interpretation hold promise for standardising reporting and reducing delays in health systems with shortages of trained radiologists. Yet, there are few freely accessible AI systems trained on large datasets for practitioners to use with their own data with a view to accelerating clinical deployment of AI systems in radiology. We aimed to contribute an AI system for comprehensive chest x-ray abnormality detection. Methods In this retrospective cohort study, we developed open-source neural networks, X-Raydar and X-Raydar-NLP, for classifying common chest x-ray findings from images and their free-text reports. Our networks were developed using data from six UK hospitals from three National Health Service (NHS) Trusts (University Hospitals Coventry and Warwickshire NHS Trust, University Hospitals Birmingham NHS Foundation Trust, and University Hospitals Leicester NHS Trust) collectively contributing 2 513 546 chest x-ray studies taken from a 13-year period (2006–19), which yielded 1 940 508 usable free-text radiological reports written by the contemporary assessing radiologist (collectively referred to as the “historic reporters”) and 1 896 034 frontal images. Chest x-rays were labelled using a taxonomy of 37 findings by a custom-trained natural language processing (NLP) algorithm, X-Raydar-NLP, from the original free-text reports. X-Raydar-NLP was trained on 23 230 manually annotated reports and tested on 4551 reports from all hospitals. 1 694 921 labelled images from the training set and 89 238 from the validation set were then used to train a multi-label image classifier. Our algorithms were evaluated on three retrospective datasets: a set of exams sampled randomly from the full NHS dataset reported during clinical practice and annotated using NLP (n=103 328); a consensus set sampled from all six hospitals annotated by three expert radiologists (two independent annotators for each image and a third consultant to facilitate disagreement resolution) under research conditions (n=1427); and an independent dataset, MIMIC-CXR, consisting of NLP-annotated exams (n=252 374). Findings X-Raydar achieved a mean AUC of 0·919 (SD 0·039) on the auto-labelled set, 0·864 (0·102) on the consensus set, and 0·842 (0·074) on the MIMIC-CXR test, demonstrating similar performance to the historic clinical radiologist reporters, as assessed on the consensus set, for multiple clinically important findings, including pneumothorax, parenchymal opacification, and parenchymal mass or nodules. On the consensus set, X-Raydar outperformed historical reporter balanced accuracy with significance on 27 of 37 findings, was non-inferior on nine, and inferior on one finding, resulting in an average improvement of 13·3% (SD 13·1) to 0·763 (0·110), including a mean 5·6% (13·2) improvement in critical findings to 0·826 (0·119). Interpretation Our study shows that automated classification of chest x-rays under a comprehensive taxonomy can achieve performance levels similar to those of historical reporters and exhibit robust generalisation to external data. The open-sourced neural networks can serve as foundation models for further research and are freely available to the research community. Funding Wellcome Trust.","2024-01-01","2024-12-03 03:23:32","2024-12-03 03:23:32","","e44-e57","","1","6","","The Lancet Digital Health","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"UF3ZYJJ9","journalArticle","2024","Khan, Alif Elham; Hasan, Mohammad Junayed; Anjum, Humayra; Mohammed, Nabeel; Momen, Sifat","Predicting life satisfaction using machine learning and explainable AI","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e31158","https://www.sciencedirect.com/science/article/pii/S2405844024071895","Life satisfaction is a crucial facet of human well-being. Hence, research on life satisfaction is incumbent for understanding how individuals experience their lives and influencing interventions targeted at enhancing mental health and well-being. Life satisfaction has traditionally been measured using analog, complicated, and frequently error-prone methods. These methods raise questions concerning validation and propagation. However, this study demonstrates the potential for machine learning algorithms to predict life satisfaction with a high accuracy of 93.80% and a 73.00% macro F1-score. The dataset comes from a government survey of 19000 people aged 16-64 years in Denmark. Using feature learning techniques, 27 significant questions for assessing contentment were extracted, making the study highly reproducible, simple, and easily interpretable. Furthermore, clinical and biomedical large language models (LLMs) were explored for predicting life satisfaction by converting tabular data into natural language sentences through mapping and adding meaningful counterparts, achieving an accuracy of 93.74% and macro F1-score of 73.21%. It was found that life satisfaction prediction is more closely related to the biomedical domain than the clinical domain. Ablation studies were also conducted to understand the impact of data resampling and feature selection techniques on model performance. Moreover, the correlation between primary determinants with different age brackets was analyzed, and it was found that health condition is the most important determinant across all ages. The best performing Machine Learning model trained in this study is deployed on a public server, ensuring unrestricted usage of the model. We highlight the advantages of machine learning methods for predicting life satisfaction and the significance of XAI for interpreting and validating these predictions. This study demonstrates how machine learning, large language models and XAI can jointly contribute to building trust and understanding in using AI to investigate human behavior, with significant ramifications for academics and professionals working to quantify and comprehend subjective well-being.","2024-05-30","2024-12-03 03:23:32","2024-12-03 03:23:32","","e31158","","10","10","","Heliyon","","","","","","","","","","","","","","","","","","","Machine learning; Ensemble model; Explainable AI; Life satisfaction","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ERUBVYST","journalArticle","2023","Andries, Valentina; Robertson, Judy","Alexa doesn't have that many feelings: Children's understanding of AI through interactions with smart speakers in their homes","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2023.100176","https://www.sciencedirect.com/science/article/pii/S2666920X23000553","As voice-based Conversational Assistants (CAs), including Alexa, Siri, Google Home, have become commonly embedded in households, many children now routinely interact with Artificial Intelligence (AI) systems. It is important to research children's experiences with consumer devices which use AI techniques because these shape their understanding of AI and its capabilities. We conducted a mixed-methods study (questionnaires and interviews) with primary-school children aged 6–11 in Scotland to establish children's understanding of how voice-based CAs work, how they perceive their cognitive abilities, agency and other human-like qualities, their awareness and trust of privacy aspects when using CAs and what they perceive as appropriate verbal interactions with CAs. Most children overestimated the CAs' intelligence and were uncertain about the systems' feelings or agency. They also lacked accurate understanding of data privacy and security aspects, and believed it was wrong to be rude to conversational assistants. Exploring children's current understanding of AI-supported technology has educational implications; such findings will enable educators to develop appropriate materials to address the pressing need for AI literacy.","2023-01-01","2024-12-03 03:23:32","2024-12-03 03:23:32","","100176","","","5","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","LLM; AI education; And phrases: education; Anthropomorphism; Child-computer interaction; Conversational assistants; Smart speakers; Trust","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"INDTUF85","journalArticle","2024","Mickley, John P.; Kaji, Elizabeth S.; Khosravi, Bardia; Mulford, Kellen L.; Taunton, Michael J.; Wyles, Cody C.","Overview of Artificial Intelligence Research Within Hip and Knee Arthroplasty","Arthroplasty Today","","2352-3441","10.1016/j.artd.2024.101396","https://www.sciencedirect.com/science/article/pii/S2352344124000815","Hip and knee arthroplasty are high-volume procedures undergoing rapid growth. The large volume of procedures generates a vast amount of data available for next-generation analytics. Techniques in the field of artificial intelligence (AI) can assist in large-scale pattern recognition and lead to clinical insights. AI methodologies have become more prevalent in orthopaedic research. This review will first describe an overview of AI in the medical field, followed by a description of the 3 arthroplasty research areas in which AI is commonly used (risk modeling, automated radiographic measurements, arthroplasty registry construction). Finally, we will discuss the next frontier of AI research focusing on model deployment and uncertainty quantification.","2024-06-01","2024-12-03 03:23:32","2024-12-03 03:23:32","","101396","","","27","","Arthroplasty Today","","","","","","","","","","","","","","","","","","","Deep learning; Artificial intelligence; Arthroplasty; Registry construction; Risk modeling; Uncertainty quantification","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"9G2HNAWT","journalArticle","2024","Jonek, Michael; Bast, Malte; Manns, Martin","Manual assembly planning with AI Image Generators","57th CIRP Conference on Manufacturing Systems 2024 (CMS 2024)","","2212-8271","10.1016/j.procir.2024.10.068","https://www.sciencedirect.com/science/article/pii/S2212827124012216","For small and medium-sized enterprises (SMEs), the planning of manual assembly activities represents a significant cost and resource factor, requiring precision and meticulous organization. To ensure a stable competitive and economical production, the steps involved in manual assembly must be optimized. In today’s digital era, Artificial Intelligences (AI) offer innovative approaches and opportunities to streamline processes. In addition to LLM AIs, AI image generators are currently attracting a lot of attention as they generate very realistic and detailed images based on a given description. Images or visualisations of the situation are often used to make work instructions in manual assembly easier to understand. AI image generators can be used to visualise assembly process steps for automatically generated work instructions. In this research, a quantitative measure is proposed that can be used to rate how correctly the actual situation is depicted by highlighting incorrect or false objects and operations and assessing the accuracy of the context. The measure is validated by qualitatively evaluating images created with DALL-E 3 in an user study by both workers and planning experts from industry and comparing them with the quantitative measure. This will enable further research in the field of automated work planning and the comparison of different AI image generation tools for use in assembly planning.","2024-01-01","2024-12-03 03:23:32","2024-12-03 03:23:32","","139-144","","","130","","Procedia CIRP","","","","","","","","","","","","","","","","","","","AI art; AI-assisted planning; Assembly instructions; Manual assembly; Production planning","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JISZK6U5","journalArticle","2024","Zhang, Tong","Construction and application of English wisdom classroom based on artificial intelligence","The 4th International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy","","1877-0509","10.1016/j.procs.2024.09.120","https://www.sciencedirect.com/science/article/pii/S1877050924021288","The integration of artificial intelligence technology and English teaching has transformed the English teaching classroom from teacher-centered to student-centered, realized the wisdom of English teaching, and also improved the interest and efficiency of students in learning English. This paper first expounds the basic concept of artificial intelligence technology and its application advantages in English teaching, and then discusses the construction and application of English wisdom classroom based on artificial intelligence from four aspects, in order to provide reference for relevant researchers.","2024-01-01","2024-12-03 03:23:32","2024-12-03 03:23:32","","1006-1012","","","243","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial intelligence; English teaching; Wisdom classroom","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LQIA8FTK","journalArticle","2024","Hussien, Mohamed Manzour; Melo, Angie Nataly; Ballardini, Augusto Luis; Maldonado, Carlota Salinas; Izquierdo, Rubén; Sotelo, Miguel Ángel","RAG-based explainable prediction of road users behaviors for automated driving using knowledge graphs and large language models","Expert Systems with Applications","","0957-4174","10.1016/j.eswa.2024.125914","https://www.sciencedirect.com/science/article/pii/S0957417424027817","The prediction of road user behaviors in the context of autonomous driving has attracted considerable attention from the scientific community in recent years. Most works focus on predicting behaviors based on kinematic information alone, a simplification of reality since road users are humans, and as such they are highly influenced by their surrounding context. In addition, a large plethora of research works rely on powerful Deep Learning techniques, which exhibit high-performance metrics in prediction tasks but may lack the ability to fully understand and exploit the contextual semantic information contained in the road scene, not to mention their inability to provide explainable predictions that can be understood by humans. In this work, we propose an explainable road users’ behavior prediction system that integrates the reasoning abilities of Knowledge Graphs (KG) and the expressiveness capabilities of Large Language Models (LLM) by using Retrieval Augmented Generation (RAG) techniques. For that purpose, Knowledge Graph Embeddings (KGE) and Bayesian inference are combined to allow the deployment of a fully inductive reasoning system that enables the issuing of predictions that rely on legacy information contained in the graph, as well as on current evidence gathered in real-time by onboard sensors. Two use cases have been implemented following the proposed approach: 1) Prediction of pedestrians’ crossing actions; and 2) Prediction of lane change maneuvers. In both cases, the performance attained exceeds the current state-of-the-art in terms of anticipation and F1 score, showing a promising avenue for future research in this field.","2024-11-29","2024-12-03 03:23:32","2024-12-03 03:23:32","","125914","","","","","Expert Systems with Applications","","","","","","","","","","","","","","","","","","","Autonomous driving; Explainable predictions; Lane change maneuvers; Pedestrian crossing actions; Road users’ behaviors","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"P7TIDLHN","journalArticle","2024","Zhang, Dong; Yi, Ting","Structure and process: A theoretical model of intelligent elementary Chinese vocabulary teaching","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e38358","https://www.sciencedirect.com/science/article/pii/S2405844024143897","With the rapid development of artificial intelligence technology, Chinese vocabulary teaching is gradually entering an era of innovation, offering vast potential for more intelligent and personalized teaching models. Existing research mostly focuses on the auxiliary role of AI technology in second language teaching, with less attention given to the implementation of intelligent second language teaching. To address this issue, this study, based on the perspective of Artificial Intelligence Generated Content (AIGC), attempts to construct an elementary Chinese vocabulary teaching (ECVT) theoretical model, presenting the development process of an ECVT system. From the existing research literature on Chinese vocabulary teaching, a total of 17 viewpoints on teaching structures and 19 viewpoints on teaching processes for ECVT are outlined. Based on this, the study first establishes the fundamental macro-level phases of ECVT, then delves into the micro-level structures and processes of each phase, ultimately deducing a theoretical model for ECVT oriented towards intelligent teaching. Integrating the perspectives of artificial intelligence deconstruction and generation into the research on Chinese vocabulary teaching not only offers front-line teachers a reference for optimizing teaching models but also provides a more forward-looking and scientifically grounded framework for Chinese language teaching, even other language teaching. This hence propels innovation in second language teaching models.","2024-10-15","2024-12-03 03:23:32","2024-12-03 03:23:32","","e38358","","19","10","","Heliyon","","","","","","","","","","","","","","","","","","","Big data; Artificial intelligence; Chinese vocabulary teaching; Teaching model","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"5YG9PKWC","journalArticle","2024","Singhal, Amit; Agrawal, Krishna Kant; Quezada, Angeles; Aguiñaga, Adrian Rodriguez; Jiménez, Samantha; Yadav, Satya Prakash","Explainable Artificial Intelligence (XAI) Model for Cancer Image Classification","CMES - Computer Modeling in Engineering and Sciences","","1526-1492","10.32604/cmes.2024.051363","https://www.sciencedirect.com/science/article/pii/S1526149224002340","The use of Explainable Artificial Intelligence (XAI) models becomes increasingly important for making decisions in smart healthcare environments. It is to make sure that decisions are based on trustworthy algorithms and that healthcare workers understand the decisions made by these algorithms. These models can potentially enhance interpretability and explainability in decision-making processes that rely on artificial intelligence. Nevertheless, the intricate nature of the healthcare field necessitates the utilization of sophisticated models to classify cancer images. This research presents an advanced investigation of XAI models to classify cancer images. It describes the different levels of explainability and interpretability associated with XAI models and the challenges faced in deploying them in healthcare applications. In addition, this study proposes a novel framework for cancer image classification that incorporates XAI models with deep learning and advanced medical imaging techniques. The proposed model integrates several techniques, including end-to-end explainable evaluation, rule-based explanation, and user-adaptive explanation. The proposed XAI reaches 97.72% accuracy, 90.72% precision, 93.72% recall, 96.72% F1-score, 9.55% FDR, 9.66% FOR, and 91.18% DOR. It will discuss the potential applications of the proposed XAI models in the smart healthcare environment. It will help ensure trust and accountability in AI-based decisions, which is essential for achieving a safe and reliable smart healthcare environment.","2024-08-20","2024-12-03 03:23:32","2024-12-03 03:23:32","","401-441","","1","141","","CMES - Computer Modeling in Engineering and Sciences","","","","","","","","","","","","","","","","","","","artificial intelligence; healthcare; cancer; Explainable artificial intelligence; image classification; XAI","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"TMABJCLG","journalArticle","2024","Kirshner, Samuel N.","GPT and CLT: The impact of ChatGPT's level of abstraction on consumer recommendations","Journal of Retailing and Consumer Services","","0969-6989","10.1016/j.jretconser.2023.103580","https://www.sciencedirect.com/science/article/pii/S0969698923003314","This study explores how ChatGPT interprets information through the lens of Construal Level Theory (CLT). The findings show that ChatGPT exhibits an abstraction bias, generating responses consistent with a high-level construal. This abstraction bias results in ChatGPT prioritising high-level construal features (e.g., desirability) over low-level construal features (e.g., feasibility) in consumer evaluation scenarios. Thus, ChatGPT recommendations differ significantly from traditional results based on human decision-making. Applying CLT concepts to large language models provides essential insights into how consumer behaviour may evolve with the increasing prevalence and capability of AI and offers many promising avenues for future research.","2024-01-01","2024-12-03 03:23:32","2024-12-03 03:23:32","","103580","","","76","","Journal of Retailing and Consumer Services","","","","","","","","","","","","","","","","","","","ChatGPT; Bard; LLMs; Construal level theory; Consumer recommendations","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LIXD7W5N","journalArticle","2024","Bhatt, Rajan; Hossain, Akbar; Majumder, Debjyoti; Chandra, Mandapelli Sharath; Ghimire, Rajiv; Faisal Shahzad, Muhammad; Verma, Krishan K.; Riar, Amarinder Singh; Rajput, Vishnu D.; Oliveira, Mauro Wagner; Nisi, Adel; Almalki, Riyadh S.; Bárek, Viliam; Brestic, Marian; Maitra, Sagar","Prospects of artificial intelligence for the sustainability of sugarcane production in the modern era of climate change: An overview of related global findings","Journal of Agriculture and Food Research","","2666-1543","10.1016/j.jafr.2024.101519","https://www.sciencedirect.com/science/article/pii/S2666154324005568","By analysing biochemical composition, assessing soil quality, projecting yields, predicting productivity, identifying illnesses, and predicting productivity, artificial intelligence (AI) has greatly improved sugarcane cultivation. This study discusses the latest research on artificial intelligence (AI) in sugarcane farming, with particular attention given to soil biochemistry, disease detection, climate-smart technology for greenhouse gas emissions, yield and water productivity prediction, and cane juice biochemistry. Artificial intelligence (AI) tools, such as machine learning algorithms, can optimize irrigation, increase yields, save water, and properly estimate sugarcane production. Because of the effectiveness, affordability, and efficiency of AI, it is essential in sugarcane agriculture for accurate yield forecasting as well as for streamlining resource allocation and crop management. AI facilitates prompt interventions by assisting in early disease identification and production prediction. AI can also forecast soil organic carbon (SOC) levels, which can help guide sustainability and soil health initiatives. The comprehensive global review identifies research gaps in the literature and suggests potential avenues and directions for future research.","2024-12-01","2024-12-03 03:23:32","2024-12-03 03:23:32","","101519","","","18","","Journal of Agriculture and Food Research","","","","","","","","","","","","","","","","","","","Machine learning; Artificial intelligence; Greenhouse emissions; Soil organic stocks; Sugarcane agriculture; Water efficiency","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"W9I9VYHF","journalArticle","2024","Göpfert, Jan; Weinand, Jann M.; Kuckertz, Patrick; Stolten, Detlef","Opportunities for large language models and discourse in engineering design","Energy and AI","","2666-5468","10.1016/j.egyai.2024.100383","https://www.sciencedirect.com/science/article/pii/S2666546824000491","In recent years, large language models have achieved breakthroughs on a wide range of benchmarks in natural language processing and continue to increase in performance. Recently, the advances of large language models have raised interest outside the natural language processing community and could have a large impact on daily life. In this paper, we pose the question: How will large language models and other foundation models shape the future product development process? We provide the reader with an overview of the subject by summarizing both recent advances in natural language processing and the use of information technology in the engineering design process. We argue that discourse should be regarded as the core of engineering design processes, and therefore should be represented in a digital artifact. On this basis, we describe how foundation models such as large language models could contribute to the design discourse by automating parts thereof that involve creativity and reasoning, and were previously reserved for humans. We describe how simulations, experiments, topology optimizations, and other process steps can be integrated into a machine-actionable, discourse-centric design process. As an example, we present a design discourse on the optimization of wind turbine blades. Finally, we outline the future research that will be necessary for the implementation of the conceptualized framework.","2024-09-01","2024-12-03 03:23:33","2024-12-03 03:23:33","","100383","","","17","","Energy and AI","","","","","","","","","","","","","","","","","","","Natural language processing; Foundation models; Conceptual design; Design generation; Design methodology; Multi-modal models; Product development process","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"K5SDRPEM","journalArticle","2024","Chen, Jimmy S.; Marra, Kyle V.; Robles-Holmes, Hailey K.; Ly, Kristine B.; Miller, Joseph; Wei, Guoqin; Aguilar, Edith; Bucher, Felicitas; Ideguchi, Yoichi; Coyner, Aaron S.; Ferrara, Napoleone; Campbell, J. Peter; Friedlander, Martin; Nudleman, Eric","Applications of Deep Learning: Automated Assessment of Vascular Tortuosity in Mouse Models of Oxygen-Induced Retinopathy","Ophthalmology Science","","2666-9145","10.1016/j.xops.2023.100338","https://www.sciencedirect.com/science/article/pii/S2666914523000702","Objective To develop a generative adversarial network (GAN) to segment major blood vessels from retinal flat-mount images from oxygen-induced retinopathy (OIR) and demonstrate the utility of these GAN-generated vessel segmentations in quantifying vascular tortuosity. Design Development and validation of GAN. Subjects Three datasets containing 1084, 50, and 20 flat-mount mice retina images with various stains used and ages at sacrifice acquired from previously published manuscripts. Methods Four graders manually segmented major blood vessels from flat-mount images of retinas from OIR mice. Pix2Pix, a high-resolution GAN, was trained on 984 pairs of raw flat-mount images and manual vessel segmentations and then tested on 100 and 50 image pairs from a held-out and external test set, respectively. GAN-generated and manual vessel segmentations were then used as an input into a previously published algorithm (iROP-Assist) to generate a vascular cumulative tortuosity index (CTI) for 20 image pairs containing mouse eyes treated with aflibercept versus control. Main Outcome Measures Mean dice coefficients were used to compare segmentation accuracy between the GAN-generated and manually annotated segmentation maps. For the image pairs treated with aflibercept versus control, mean CTIs were also calculated for both GAN-generated and manual vessel maps. Statistical significance was evaluated using Wilcoxon signed-rank tests (P ≤ 0.05 threshold for significance). Results The dice coefficient for the GAN-generated versus manual vessel segmentations was 0.75 ± 0.27 and 0.77 ± 0.17 for the held-out test set and external test set, respectively. The mean CTI generated from the GAN-generated and manual vessel segmentations was 1.12 ± 0.07 versus 1.03 ± 0.02 (P = 0.003) and 1.06 ± 0.04 versus 1.01 ± 0.01 (P < 0.001), respectively, for eyes treated with aflibercept versus control, demonstrating that vascular tortuosity was rescued by aflibercept when quantified by GAN-generated and manual vessel segmentations. Conclusions GANs can be used to accurately generate vessel map segmentations from flat-mount images. These vessel maps may be used to evaluate novel metrics of vascular tortuosity in OIR, such as CTI, and have the potential to accelerate research in treatments for ischemic retinopathies. Financial Disclosure(s) The author(s) have no proprietary or commercial interest in any materials discussed in this article.","2024-01-01","2024-12-03 03:23:33","2024-12-03 03:23:33","","100338","","1","4","","Ophthalmology Science","","","","","","","","","","","","","","","","","","","Artificial intelligence; Data science; Oxygen-induced retinopathy; Vascular tortuosity","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"N6X7LIUY","journalArticle","2024","Kusumawardani, Renny Pradina; Kusumawati, Katarina Nimas","Named entity recognition in the medical domain for Indonesian language health consultation services using bidirectional-lstm-crf algorithm","9th International Conference on Computer Science and Computational Intelligence 2024 (ICCSCI 2024)","","1877-0509","10.1016/j.procs.2024.10.344","https://www.sciencedirect.com/science/article/pii/S1877050924031521","The increased use of health consultation platforms since the pandemic has led to a higher demand for active doctors to conduct consultations. In Indonesia, the average number of doctors is 0.4 doctors per thousand people, far fewer than in developed countries. A solution that can be utilized is the implementation of Natural Language Processing (NLP) and Artificial Intelligence (AI) technologies. These technologies can be used to reduce costs, provide alternative suggestions from the database, deliver appropriate answers, and enable users to find solutions corresponding to their problems. This can be automated using Named Entity Recognition (NER). NER is a part of information extraction used to identify entities in the medical domain, such as anatomical entities, proteins, and genes. The challenge faced in implementing this solution is the lack of Indonesian language datasets for NER that are relevant to the context of health consultation platforms. Therefore, the development of a medical field dataset in the Indonesian language is necessary. In the execution of this research, data used was taken from online health consultation platforms where the Q&A sections with doctors are freely accessible. The data was manually labeled under the supervision of experts. The data was trained using the Bidirectional-LSTM-CRF model and resulted in an accuracy of 0.9968. There is a state-of-the-art model, XLM-RoBERTa-large-indonesian-NER, which after fine-tuning, achieved an accuracy of 0.9851. However, using the F1 score metric, the XLM-RoBERTa model achieved the highest score for each tag compared to the Bidirectional-LSTM-CRF model.","2024-01-01","2024-12-03 03:23:33","2024-12-03 03:23:33","","1146-1156","","","245","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Bidirectional Long Short Term Memory; Biomedical Named Entities; Conditional Random Field; Named-Entity Recognition","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"B3HZLXS6","journalArticle","2024","Xing, Frank","Financial risk tolerance profiling from text","Information Processing & Management","","0306-4573","10.1016/j.ipm.2024.103704","https://www.sciencedirect.com/science/article/pii/S0306457324000645","Traditionally, individual financial risk tolerance information is gathered via questionnaires or similar structured psychometric tools. Our abundant digital footprint, as an unstructured alternative, is less investigated. Leveraging such information can potentially support large-scale and cost-efficient financial services. Therefore, I explore the possibility of building a computational model that distills risk tolerance information from user texts in this study, and discuss the design principles discovered from empirical results and their implications. Specifically, a new quaternary classification task is defined for text mining-based risk profiling. Experiments show that pre-trained large language models set a baseline micro-F1 of circa 0.34. Using a convolutional neural network (CNN), the reported system achieves a micro-F1 of circa 0.51, which significantly outperforms the baselines, and is a circa 4% further improvement over the standard CNN configurations (micro-F1 of circa 0.47). Textual feature richness and supervised learning are found to be the key contributors to model performances, while other machine learning strategies suggested by previous research (data augmentation and multi-tasking) are less effective. The findings confirm user texts to be a useful risk profiling resource and provide several insights on this task.","2024-07-01","2024-12-03 03:23:33","2024-12-03 03:23:33","","103704","","4","61","","Information Processing & Management","","","","","","","","","","","","","","","","","","","Text mining; Artificial intelligence in finance; Convolutional neural network; Risk profiling; Risk tolerance","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SQNY9AAK","journalArticle","2024","Golafshani, Emad; Khodadadi, Nima; Ngo, Tuan; Nanni, Antonio; Behnood, Ali","Modelling the compressive strength of geopolymer recycled aggregate concrete using ensemble machine learning","Advances in Engineering Software","","0965-9978","10.1016/j.advengsoft.2024.103611","https://www.sciencedirect.com/science/article/pii/S0965997824000188","In the quest to reduce the environmental impact of the construction sector, the adoption of sustainable and eco-friendly materials is imperative. Geopolymer recycled aggregate concrete (GRAC) emerges as a promising solution by substituting supplementary cementitious materials, including fly ash and slag cement, for ordinary Portland cement and utilizing recycled aggregates from construction and demolition waste, thus significantly lowering carbon emissions and resource consumption. Despite its potential, the widespread implementation of GRAC has been hindered by the lack of an effective mix design methodology. This study seeks to bridge this gap through a novel machine learning (ML)-based approach to accurately model the compressive strength (CS) of GRAC, a critical parameter for ensuring structural integrity and safety. By compiling a comprehensive database from existing literature and enhancing it with synthetic data generated through a tabular generative adversarial network, this research employs eight ensemble ML techniques, comprising three bagging and five boosting methods, to predict the CS of GRAC with high precision. The boosting models, notably extreme gradient boosting, light gradient boosting, gradient boosting, and categorical gradient boosting regressors, demonstrated superior performance, achieving a mean absolute percentage error of less than 6 %. This precision in prediction underscores the viability of ML in optimizing GRAC formulations for enhanced structural applications. The identification of testing age, natural fine aggregate content, and recycled aggregate ratio as pivotal factors offers valuable insights into the mix design process, facilitating more informed decisions in material selection and proportioning. Moreover, the development of a user-friendly graphical interface for CS prediction exemplifies the practical application of this research, potentially accelerating the adoption of GRAC in mainstream construction practices. By enabling the practical use of GRAC, this research contributes to the global effort to promote sustainable development within the construction industry.","2024-05-01","2024-12-03 03:23:33","2024-12-03 03:23:33","","103611","","","191","","Advances in Engineering Software","","","","","","","","","","","","","","","","","","","Artificial intelligence; Compressive strength; Ensemble models; Fly ash; Geopolymer recycled aggregate concrete; Slag cement","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"69QEAITB","journalArticle","2024","Ye, Cheng","Exploring a learning-to-rank approach to enhance the Retrieval Augmented Generation (RAG)-based electronic medical records search engines","Informatics and Health","","2949-9534","10.1016/j.infoh.2024.07.001","https://www.sciencedirect.com/science/article/pii/S2949953424000146","Background This study addresses the challenge of enhancing Retrieval Augmented Generation (RAG) search engines for electronic medical records (EMR) by learning users' distinct search semantics. The specific aim is to develop a learning-to-rank system that improves the accuracy and relevance of search results to support RAG-based search engines. Methods Given a prompt or search query, the system first asks the user to label a few randomly selected documents, which contain some keywords, as relevant to the prompt or not. The system then identifies relevant sentences and adjusts word similarities by updating a medical semantic embedding. New documents are ranked by the number of relevant sentences identified by the weighted embedding. Only the top-ranked documents and sentences are provided to a Large-Language-Model (LLM) to generate answers for further review. Findings To evaluate our approach, four medical researchers labeled documents based on their relevance to specific diseases. We measured the information retrieval performance of our approach and two baseline methods. Results show that our approach achieved at least a 0.60 Precision-at-10 (P @ 10) score with only ten positive labels, outperforming the baseline methods. In our pilot study, we demonstrate that the learned semantic preference can transfer to the analysis of unseen datasets, boosting the accuracy of an RAG model in extracting and explaining cancer progression diagnoses from 0.14 to 0.50. Interpretation This study demonstrates that a customized learning-to-rank method can enhance state-of-the-art natural language models, such as LLMs, by quickly adapting to users' semantics. This approach supports EMR document retrieval and helps RAG models generate clinically meaningful answers to specific questions, underscoring the potential of user-tailored learning-to-rank methods in clinical practice.","2024-09-01","2024-12-03 03:23:33","2024-12-03 03:23:33","","93-99","","2","1","","Informatics and Health","","","","","","","","","","","","","","","","","","","Information retrieval; Large Language Model; Retrieval Augmented Generation; Electronic medical records; Learning to rank","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JMBEEBHU","journalArticle","2024","Cao, Siyang; Wei, Yihao; Yue, Yaohang; Wang, Deli; Xiong, Ao; Yang, Jun; Zeng, Hui","Uncovering the scientific landscape: A bibliometric and visualized analysis of artificial intelligence in traditional Chinese medicine","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e37439","https://www.sciencedirect.com/science/article/pii/S2405844024134706","The emergence of artificial intelligence (AI) technology has presented new challenges and opportunities for Traditional Chinese Medicine (TCM), aiming to provide objective assessments and improve clinical effectiveness. However, there is a lack of comprehensive analyses on the research trajectory, key directions, current trends, and future perspectives in this field. This research aims to comprehensively update the progress of AI in TCM over the past 24 years, based on data from the Web of Science database covering January 1, 2000, to March 1, 2024. Using advanced analytical tools, we conducted detailed bibliometric and visual analyses. The results highlight China's predominant influence, contributing 54.35 % of the total publications and playing a key role in shaping research in this field. Significant productivity was observed at institutions such as the China Academy of Chinese Medical Sciences, Beijing University of Chinese Medicine, and Shanghai University of Traditional Chinese Medicine, with Wang Yu being the most prolific contributor. The journal Molecules contributed the most publications in this field. This study identified hepatocellular carcinoma, chemical and drug-induced liver injury, Papillon-Lefèvre disease, Parkinson's disease, and anorexia as the most significant disorders researched. This comprehensive bibliometric assessment benefits both seasoned researchers and newcomers, offering quick access to essential information and fostering the generation of innovative ideas in this field.","2024-09-30","2024-12-03 03:23:33","2024-12-03 03:23:33","","e37439","","18","10","","Heliyon","","","","","","","","","","","","","","","","","","","Bibliometrics; Artificial intelligence; Data visualization; Traditional Chinese medicine; Global scientific frontiers","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"C4PK99II","journalArticle","2024","Sufi, Fahim","Advanced Computational Methods for News Classification: A Study in Neural Networks and CNN integrated with GPT","Journal of Economy and Technology","","2949-9488","10.1016/j.ject.2024.09.001","https://www.sciencedirect.com/science/article/pii/S2949948824000404","In an era inundated with vast amounts of information, the imperative for efficient news classification is paramount. This research explores the sophisticated integration of neural networks and convolutional neural networks (CNN) with Generative Pre-trained Transformers (GPT) to enhance the precision and efficacy of news categorization. The rapid digital dissemination of news necessitates advanced computational methodologies capable of accurate classification and event prediction that include finance and economic events. Leveraging recent advancements in machine learning and natural language processing (NLP), this study utilizes large language models (LLMs) such as GPT and BERT, known for their exceptional comprehension and generation of human-like text. Over 232 days, our methodology classified 33,979 news articles into Education & Learning, Health & Medicine, and Science & Technology, with further subcategorization into 32 distinct subcategories. For evaluation, a sample of 5,000 articles was assessed using metrics such as True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN), Precision, Recall, and F1-Score. In comparison with the existing studies, the proposed method achieving significantly higher with average scores of 0.986 (Precision), 0.987 (Recall), and 0.987 (F1-Score). This research offers substantial practical contributions, providing detailed insights into news source contributions, effective anomaly detection, and predictive trend analysis using neural networks. The theoretical contributions are profound, demonstrating the mathematical integration of GPT with CNNs and recurrent neural networks. This integration advances computational news classification and exemplifies how sophisticated mathematical frameworks enhance large-scale text data analysis, marking a pivotal advancement in applying advanced computational methods in real-world scenarios.","2024-09-15","2024-12-03 03:23:33","2024-12-03 03:23:33","","","","","","","Journal of Economy and Technology","","","","","","","","","","","","","","","","","","","GPT; Anomaly Detection, CNN; Computational Methods, News Report Intelligence; Event Prediction","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DHUHRGF5","journalArticle","2024","O'Brien, Thomas; Stremmel, Joel; Pio-Lopez, Léo; McMillen, Patrick; Rasmussen-Ivey, Cody; Levin, Michael","Machine learning for hypothesis generation in biology and medicine: exploring the latent space of neuroscience and developmental bioelectricity","Digital Discovery","","2635-098X","10.1039/d3dd00185g","https://www.sciencedirect.com/science/article/pii/S2635098X2400024X","Artificial intelligence is a powerful tool that could be deployed to accelerate the scientific enterprise. Here we address a major unmet need: use of existing scientific literature to generate novel hypotheses. We use a deep symmetry between the fields of neuroscience and developmental bioelectricity to evaluate a new tool, FieldSHIFT. FieldSHIFT is an in-context learning framework using a large language model to facilitate candidate scientific research from existing published studies, serving as a tool to generate hypotheses at scale. We release a new dataset for translating between the neuroscience and developmental bioelectricity domains and show how FieldSHIFT helps human scientists explore a latent space of papers that could exist, providing a rich field of suggested future research. We demonstrate the performance of FieldSHIFT for hypothesis generation relative to human-generated developmental biology research directions then test a key prediction of this model using bioinformatics, showing a surprising conservation of molecular mechanisms involved in cognitive behavior and developmental morphogenesis. By allowing scientists to rapidly explore symmetries and meta-parameters that exist in a corpus of scientific papers, we show how machine learning can potentiate human creativity and assist with one of the most interesting and crucial aspects of research: identifying insights from data and generating potential candidates for research agendas.","2024-02-14","2024-12-03 03:23:33","2024-12-03 03:23:33","","249-263","","2","3","","Digital Discovery","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JRUY9T5G","journalArticle","2023","Wang, Xingzhi; Anwer, Nabil; Dai, Yun; Liu, Ang","ChatGPT for design, manufacturing, and education","The 33rd CIRP Design Conference","","2212-8271","10.1016/j.procir.2023.04.001","https://www.sciencedirect.com/science/article/pii/S2212827123004262","The manufacturing industry involves innumerable complex tasks that require significant knowledge and experience to execute. With the rapid development of artificial intelligence, particularly with the emergence of powerful large language models such as ChatGPT, new opportunities have risen to provide knowledge through conversation. With its seemingly endless knowledge base and highly organized response style, ChatGPT is expected to revolutionize every aspect of the industry. However, the extent of ChatGPT's capabilities and how they could contribute to the industry's future revolution remains unclear. In light of this, this paper performed a systematic testing of ChatGPT to uncover its advantages and limitations. Based on the testing results, the authors provided some prospects and critical research questions of ChatGPT from a manufacturing perspective. Furthermore, the authors recommended a technology development roadmap to successfully integrate ChatGPT into the manufacturing industry.","2023-01-01","2024-12-03 03:23:33","2024-12-03 03:23:33","","7-14","","","119","","Procedia CIRP","","","","","","","","","","","","","","","","","","","Education; ChatGPT; Artificial Intelligence; Smart Manufacturing; AI-generated content; Engineering Design; Product Development","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"H7L5TSFC","journalArticle","2023","Danu, Manuela Daniela; Marica, George; Karn, Sanjeev Kumar; Georgescu, Bogdan; Mansoor, Awais; Ghesu, Florin; Itu, Lucian Mihai; Suciu, Constantin; Grbic, Sasa; Farri, Oladimeji; Comaniciu, Dorin","Generation of Radiology Findings in Chest X-Ray by Leveraging Collaborative Knowledge","Tenth International Conference on Information Technology and Quantitative Management (ITQM 2023)","","1877-0509","10.1016/j.procs.2023.08.094","https://www.sciencedirect.com/science/article/pii/S1877050923008529","Among all the sub-sections in a typical radiology report, the Clinical Indications, Findings, and Impression often reflect important details about the health status of a patient. The information included in Impression is also often covered in Findings. While Findings and Impression can be deduced by inspecting the image, Clinical Indications often require additional context. The cognitive task of interpreting medical images remains the most critical and often time-consuming step in the radiology workflow. Instead of generating an end-to-end radiology report, in this paper, we focus on generating the Findings from automated interpretation of medical images, specifically chest X-rays (CXRs). Thus, this work focuses on reducing the workload of radiologists who spend most of their time either writing or narrating the Findings. Unlike past research, which addresses radiology report generation as a single-step image captioning task, we have further taken into consideration the complexity of interpreting CXR images and propose a two-step approach: (a) detecting the regions with abnormalities in the image, and (b) generating relevant text for regions with abnormalities by employing a generative large language model (LLM). This two-step approach introduces a layer of interpretability and aligns the framework with the systematic reasoning that radiologists use when reviewing a CXR.","2023-01-01","2024-12-03 03:23:33","2024-12-03 03:23:33","","1102-1109","","","221","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","generative large language model; abnormalities detection; chest X-ray; collaborative knowledge; Findings generation; radiology report","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"NQFP3UY7","journalArticle","2024","Badalotti, Davide; Agrawal, Akanksha; Pensato, Umberto; Angelotti, Giovanni; Marcheselli, Simona","Development of a Natural Language Processing (NLP) model to automatically extract clinical data from electronic health records: results from an Italian comprehensive stroke center","International Journal of Medical Informatics","","1386-5056","10.1016/j.ijmedinf.2024.105626","https://www.sciencedirect.com/science/article/pii/S1386505624002892","Introduction Data collection often relies on time-consuming manual inputs, with a vast amount of information embedded in unstructured texts such as patients’ medical records and clinical notes. Our study aims to develop a pipeline that combines active learning (AL) and NLP techniques to enhance data extraction in an acute ischemic stroke cohort. Materials and methods Consecutive acute ischemic stroke patients who received reperfusion therapies at IRCCS Humanitas Research Hospital were included. The Italian NLP Bidirectional Encoder Representations from Transformers (BERT) model was trained with AL to automatically extract clinical variables from electronic health text. Simulated active learning performances were evaluated on a set of labels representing patients’ comorbidities, comparing Bayesian Uncertainty Sampling by Disagreement (BALD) and random text selection. Prognostic models predicting patients’ functional outcomes using Gradient Boosting were trained on manually labelled and semi-automatically extracted data and their performance was compared. Results The active learning process initially showed null performance until around 20% of texts were labelled, possibly due to root layers freezing in the BERT model, yet overall, active learning improves model learning efficiency across most comorbidities. Prognostic modelling showed no significant difference in performance between models trained on manually labelled versus semi-automatically extracted data, indicating effective prediction capabilities in both settings. Conclusions We developed an efficient language model to automate the extraction of clinical data from Italian unstructured health texts in a cohort of ischemic stroke patients. In a preliminary analysis, we demonstrated its potential applicability for enhancing prediction model accuracy.","2024-12-01","2024-12-03 03:23:33","2024-12-03 03:23:33","","105626","","","192","","International Journal of Medical Informatics","","","","","","","","","","","","","","","","","","","Machine learning; Artificial intelligence; Large language models; BERT; Computational science; Outcomes; Prognosis; Thrombectomy; Thrombolysis","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KCUDRFVT","journalArticle","2023","Li, Yuheng; Sha, Lele; Yan, Lixiang; Lin, Jionghao; Raković, Mladen; Galbraith, Kirsten; Lyons, Kayley; Gašević, Dragan; Chen, Guanliang","Can large language models write reflectively","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2023.100140","https://www.sciencedirect.com/science/article/pii/S2666920X2300019X","Generative Large Language Models (LLMs) demonstrate impressive results in different writing tasks and have already attracted much attention from researchers and practitioners. However, there is limited research to investigate the capability of generative LLMs for reflective writing. To this end, in the present study, we have extensively reviewed the existing literature and selected 9 representative prompting strategies for ChatGPT – the chatbot based on state-of-art generative LLMs to generate a diverse set of reflective responses, which are combined with student-written reflections. Next, those responses were evaluated by experienced teaching staff following a theory-aligned assessment rubric that was designed to evaluate student-generated reflections in several university-level pharmacy courses. Furthermore, we explored the extent to which Deep Learning classification methods can be utilised to automatically differentiate between reflective responses written by students vs. reflective responses generated by ChatGPT. To this end, we harnessed BERT, a state-of-art Deep Learning classifier, and compared the performance of this classifier to the performance of human evaluators and the AI content detector by OpenAI. Following our extensive experimentation, we found that (i) ChatGPT may be capable of generating high-quality reflective responses in writing assignments administered across different pharmacy courses, (ii) the quality of automatically generated reflective responses was higher in all six assessment criteria than the quality of student-written reflections; and (iii) a domain-specific BERT-based classifier could effectively differentiate between student-written and ChatGPT-generated reflections, greatly surpassing (up to 38% higher across four accuracy metrics) the classification performed by experienced teaching staff and general-domain classifier, even in cases where the testing prompts were not known at the time of model training.","2023-01-01","2024-12-03 03:23:33","2024-12-03 03:23:33","","100140","","","4","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","ChatGPT; Natural language processing; Generative language model; Reflective writing","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"YBWXA2JB","journalArticle","2024","Gopalakrishnan, Seethalakshmi; Chen, Victor Zitian; Dou, Wenwen; Zadrozny, Wlodek","On the relation between K–L divergence and transfer learning performance on causality extraction tasks","Natural Language Processing Journal","","2949-7191","10.1016/j.nlp.2024.100055","https://www.sciencedirect.com/science/article/pii/S2949719124000037","The problem of extracting causal relations from text remains a challenging task, even in the age of Large Language Models (LLMs). A key factor that impedes the progress of this research is the availability of the annotated data and the lack of common labeling methods. We investigate the applicability of transfer learning (domain adaptation) to address these impediments in experiments with three publicly available datasets: FinCausal, SCITE, and Organizational. We perform pairwise transfer experiments between the datasets using DistilBERT, BERT, and SpanBERT (variants of BERT) and measure the performance of the resulting models. To understand the relationship between datasets and performance, we measure the differences between vocabulary distributions in the datasets using four methods: Kullback–Leibler (K–L) divergence, Wasserstein metric, Maximum Mean Discrepancy, and Kolmogorov–Smirnov test. We also estimate the predictive capability of each method using linear regression. We record the predictive values of each measure. Our results show that K–L divergence between the distribution of the vocabularies in the data predicts the performance of the transfer learning with R2 = 0.0746. Surprisingly, the Wasserstein distance predictive value is low (R2=0.52912), and the same for the Kolmogorov–Smirnov test (R2 =0.40025979). This is confirmed in a series of experiments. For example, with variants of BERT, we observe an almost a 29% to 32% increase in the macro-average F1-score, when the gap between the training and test distributions is small, according to the K–L divergence — the best-performing predictor on this task. We also discuss these results in the context of the sub-par performance of some large language models on causality extraction tasks. Finally, we report the results of transfer learning informed by K–L divergence; namely, we show that there is a 12 to 63% increase in the performance when a small portion of the test data is added to the training data. This shows that corpus expansion and n-shot learning benefit, when the process of choosing examples maximizes their information content, according to the K–L divergence.","2024-03-01","2024-12-03 03:23:33","2024-12-03 03:23:33","","100055","","","6","","Natural Language Processing Journal","","","","","","","","","","","","","","","","","","","Transfer learning; Large language models; Natural language processing; NLP; BERT; Causality extraction; DistilBERT; Domain adaptability; Kullback–Leibler divergence","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"AJF9PFV9","journalArticle","2023","Wu, Da; Yang, Jingye; Ahsan, Mian Umair; Wang, Kai","Classification of integers based on residue classes via modern deep learning algorithms","Patterns","","2666-3899","10.1016/j.patter.2023.100860","https://www.sciencedirect.com/science/article/pii/S2666389923002441","Summary Judging whether an integer can be divided by prime numbers such as 2 or 3 may appear trivial to human beings, but it can be less straightforward for computers. Here, we tested multiple deep learning architectures and feature engineering approaches to classifying integers based on their residues when divided by small prime numbers. We found that the ability of classification critically depends on the feature space. We also evaluated automated machine learning (AutoML) platforms from Amazon, Google, and Microsoft and found that, without appropriately engineered features, they failed on this task. Furthermore, we introduced a method that utilizes linear regression on Fourier series basis vectors and demonstrated its effectiveness. Finally, we evaluated large language models (LLMs) such as GPT-4, GPT-J, LLaMA, and Falcon, and we demonstrated their failures. In conclusion, feature engineering remains an important task to improve performance and increase interpretability of machine learning models, even in the era of AutoML and LLMs.","2023-12-08","2024-12-03 03:23:33","2024-12-03 03:23:33","","100860","","12","4","","Patterns","","","","","","","","","","","","","","","","","","","machine learning; deep learning; large language models; divisibility rules; feature engineering; fourier series; linear regression","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"6U543B66","journalArticle","2024","Akhtar, Muhammad; Salman, Asma; Abdul Ghafoor, Khalid; Kamran, Mahnoor","Artificial intelligence, financial services knowledge, government support, and user innovativeness: Exploring the moderated-mediated path to fintech adoption","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e39521","https://www.sciencedirect.com/science/article/pii/S2405844024155521","Based upon an extended Technology Acceptance Model (TAM), this study aims to investigate the impact of financial services knowledge, familiarity with the use of artificial intelligence, government support, and user innovativeness on Fintech adoption from the perspective of university students. Furthermore, the study also aims to investigate the mediating role of user innovativeness in this relationship. A cross-sectional, survey-based method was used to collect data from 410 university students. Structural equation modeling was implied to examine the research framework of the study. The findings confirm that financial services knowledge, familiarity with artificial intelligence, government support, and user innovativeness have a direct positive impact on Fintech adoption among university students. The results also show that perceived ease of use slightly moderates the relationship between government support and user innovativeness. Meanwhile, results from the mediation analysis reveal an indirect effect of these variables on Fintech adoption through user innovativeness. The study's findings recommend practical suggestions to academic institutes and Fintech service providers to equip university students with the necessary financial knowledge and familiarity with artificial intelligence across various disciplines, which can be achieved through sufficient government support. All these can potentially revolutionize Fintech services adoption and boost economic growth, specifically in Asia-Pacific developing countries. The study identifies the key antecedents that affect the student's decision to adopt Fintech. It widens the scope of Fintech adoption by considering the university students who may serve as the future managers for the nation. It provides nuanced evidence on the role of financial services knowledge and familiarity with the use of artificial intelligence on the intention to adopt Fintech among university students in Asia-Pacific developing countries like Pakistan.","2024-11-15","2024-12-03 03:23:33","2024-12-03 03:23:33","","e39521","","21","10","","Heliyon","","","","","","","","","","","","","","","","","","","Artificial intelligence; Financial services knowledge; Fintech adoption; Government support; User innovativeness","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"AW9ZV46X","journalArticle","2024","Zhao, Yuxuan; Yin, Chuantao; Wang, Xi; Chai, Yanmei; Chen, Hui; Ouyang, Yuanxin","Research of online courses recommendation based on deep learning","11th International Conference on Information Technology and Quantitative Management (ITQM 2024)","","1877-0509","10.1016/j.procs.2024.08.255","https://www.sciencedirect.com/science/article/pii/S1877050924019744","This paper delves into leveraging deep learning techniques, such as graph neural networks (GNNs), Transformer, and techniques in Large Language Models (LLMs), to enhance course recommendation systems in e-learning platforms. Recommendation methods have some short-comes in the case of online course with less information and choic less logic. Our research proposes novel algorithms that use graph collaborative filtering and sequential recommendation to improve recommendation accuracy and personalization. By analyzing user behavior patterns and course attributes, our approach aims to provide smarter and more efficient course recommendation services, ultimately enhancing learning outcomes and experiences in e-learning environments. This research not only contributes to the advancement of e-learning technology but also provides valuable insights for the broader application of deep learning in smart education.","2024-01-01","2024-12-03 03:23:33","2024-12-03 03:23:33","","219-227","","","242","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Deep learning; E-learning; Course recommendation; Graph neural networks; Sequential recommendation; Smart education","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"MP9WBLAI","journalArticle","2024","Lang, Oran; Yaya-Stupp, Doron; Traynis, Ilana; Cole-Lewis, Heather; Bennett, Chloe R.; Lyles, Courtney R.; Lau, Charles; Irani, Michal; Semturs, Christopher; Webster, Dale R.; Corrado, Greg S.; Hassidim, Avinatan; Matias, Yossi; Liu, Yun; Hammel, Naama; Babenko, Boris","Using generative AI to investigate medical imagery models and datasets","eBioMedicine","","2352-3964","10.1016/j.ebiom.2024.105075","https://www.sciencedirect.com/science/article/pii/S2352396424001105","Summary Background AI models have shown promise in performing many medical imaging tasks. However, our ability to explain what signals these models have learned is severely lacking. Explanations are needed in order to increase the trust of doctors in AI-based models, especially in domains where AI prediction capabilities surpass those of humans. Moreover, such explanations could enable novel scientific discovery by uncovering signals in the data that aren’t yet known to experts. Methods In this paper, we present a workflow for generating hypotheses to understand which visual signals in images are correlated with a classification model’s predictions for a given task. This approach leverages an automatic visual explanation algorithm followed by interdisciplinary expert review. We propose the following 4 steps: (i) Train a classifier to perform a given task to assess whether the imagery indeed contains signals relevant to the task; (ii) Train a StyleGAN-based image generator with an architecture that enables guidance by the classifier (“StylEx”); (iii) Automatically detect, extract, and visualize the top visual attributes that the classifier is sensitive towards. For visualization, we independently modify each of these attributes to generate counterfactual visualizations for a set of images (i.e., what the image would look like with the attribute increased or decreased); (iv) Formulate hypotheses for the underlying mechanisms, to stimulate future research. Specifically, present the discovered attributes and corresponding counterfactual visualizations to an interdisciplinary panel of experts so that hypotheses can account for social and structural determinants of health (e.g., whether the attributes correspond to known patho-physiological or socio-cultural phenomena, or could be novel discoveries). Findings To demonstrate the broad applicability of our approach, we present results on eight prediction tasks across three medical imaging modalities—retinal fundus photographs, external eye photographs, and chest radiographs. We showcase examples where many of the automatically-learned attributes clearly capture clinically known features (e.g., types of cataract, enlarged heart), and demonstrate automatically-learned confounders that arise from factors beyond physiological mechanisms (e.g., chest X-ray underexposure is correlated with the classifier predicting abnormality, and eye makeup is correlated with the classifier predicting low hemoglobin levels). We further show that our method reveals a number of physiologically plausible, previously-unknown attributes based on the literature (e.g., differences in the fundus associated with self-reported sex, which were previously unknown). Interpretation Our approach enables hypotheses generation via attribute visualizations and has the potential to enable researchers to better understand, improve their assessment, and extract new knowledge from AI-based models, as well as debug and design better datasets. Though not designed to infer causality, importantly, we highlight that attributes generated by our framework can capture phenomena beyond physiology or pathophysiology, reflecting the real world nature of healthcare delivery and socio-cultural factors, and hence interdisciplinary perspectives are critical in these investigations. Finally, we will release code to help researchers train their own StylEx models and analyze their predictive tasks of interest, and use the methodology presented in this paper for responsible interpretation of the revealed attributes. Funding Google.","2024-04-01","2024-12-03 03:23:33","2024-12-03 03:23:33","","105075","","","102","","eBioMedicine","","","","","","","","","","","","","","","","","","","Deep learning; Artificial intelligence; Interpretability; Generative AI; Explainability; Medical imagery","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"N37247E2","journalArticle","2024","Pawlicki, Marek; Pawlicka, Aleksandra; Kozik, Rafał; Choraś, Michał","Advanced insights through systematic analysis: Mapping future research directions and opportunities for xAI in deep learning and artificial intelligence used in cybersecurity","Neurocomputing","","0925-2312","10.1016/j.neucom.2024.127759","https://www.sciencedirect.com/science/article/pii/S0925231224005307","This paper engages in a comprehensive investigation concerning the application of Explainable Artificial Intelligence (xAI) within the context of deep learning and Artificial Intelligence, with a specific focus on its implications for cybersecurity. Firstly, the paper gives an overview of xAI techniques and their significance and benefits when applied in cybersecurity. Subsequently, the authors methodically delineate their systematic mapping study, which serves as an investigative tool for discerning the potential trajectory of the field. This strategic methodological framework lets one identify the future research directions and opportunities that underlie the integration of xAI within the realm of Deep Learning, Artificial Intelligence, and cybersecurity, which are described in-depth. Then, the paper brings together all the gathered insights from this extensive investigation and closes with final conclusions.","2024-07-14","2024-12-03 03:23:33","2024-12-03 03:23:33","","127759","","","590","","Neurocomputing","","","","","","","","","","","","","","","","","","","Cybersecurity; Explainability; Explainable AI; Future; Research directions; xAI","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"5ZPZ2KPQ","journalArticle","2024","Baffour Gyau, Emmanuel; Appiah, Michael; Gyamfi, Bright Akwasi; Achie, Theodoria; Naeem, Muhammad Abubakr","Transforming banking: Examining the role of AI technology innovation in boosting banks financial performance","International Review of Financial Analysis","","1057-5219","10.1016/j.irfa.2024.103700","https://www.sciencedirect.com/science/article/pii/S105752192400632X","With the ongoing advancement of technology, artificial intelligence is increasingly being integrated into banking and finance, with the transformative potential to reshape the financial performance of economies worldwide. This research investigates the dynamic relationship between banking and finance artificial intelligence technology innovation and banks' financial performance across 20 countries using the feasible generalized least squares and the generalized method of moments techniques. The results show that banking and finance artificial intelligence technology innovation positively impacts banks' return on assets, highlighting its role in enhancing financial performance. The interaction term between artificial intelligence innovation and economic growth emphasizes their collaborative positive impact on financial performance. Mediation analysis highlights information and communication technology development's role in transforming artificial intelligence innovation into improved financial outcomes. Considering lagged effects, initial innovation surges correlate with improved financial performance, but prolonged exposure leads to diminishing returns. Moreover, the findings indicate that non-performing loans negatively affect financial performance, underscoring the importance of asset quality. Additionally, regulatory capital and economic growth are positively associated with financial performance, while government regulations exhibit a negative impact. This study highlights the essential role of artificial intelligence technology innovation in banking and finance, emphasizing the need to consider economic and technological factors for maximizing its benefits in enhancing financial performance. Policy recommendations include promoting an artificial intelligence innovation ecosystem, adapting regulatory frameworks, and investing in information and communication technology infrastructure to harness artificial intelligence innovation's benefits while addressing associated challenges.","2024-11-01","2024-12-03 03:23:33","2024-12-03 03:23:33","","103700","","","96","","International Review of Financial Analysis","","","","","","","","","","","","","","","","","","","Financial performance; Banking artificial intelligence; Economic growth and banking; Non-performing loans; Technology innovation in banking","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"43Q6QBCY","journalArticle","2024","Senthil, Renganathan; Anand, Thirunavukarasou; Somala, Chaitanya Sree; Saravanan, Konda Mani","Bibliometric analysis of artificial intelligence in healthcare research: Trends and future directions","Future Healthcare Journal","","2514-6645","10.1016/j.fhj.2024.100182","https://www.sciencedirect.com/science/article/pii/S2514664524015728","Objective The presence of artificial intelligence (AI) in healthcare is a powerful and game-changing force that is completely transforming the industry as a whole. Using sophisticated algorithms and data analytics, AI has unparalleled prospects for improving patient care, streamlining operational efficiency, and fostering innovation across the healthcare ecosystem. This study conducts a comprehensive bibliometric analysis of research on AI in healthcare, utilising the SCOPUS database as the primary data source. Methods Preliminary findings from 2013 identified 153 publications on AI and healthcare. Between 2019 and 2023, the number of publications increased exponentially, indicating significant growth and development in the field. The analysis employs various bibliometric indicators to assess research production performance, science mapping techniques, and thematic mapping analysis. Results The study reveals insights into research hotspots, thematic focus, and emerging trends in AI and healthcare research. Based on an extensive examination of the Scopus database provides a brief overview and suggests potential avenues for further investigation. Conclusion This article provides valuable contributions to understanding the current landscape of AI in healthcare, offering insights for future research directions and informing strategic decision making in the field.","2024-09-01","2024-12-03 03:23:34","2024-12-03 03:23:34","","100182","","3","11","","Future Healthcare Journal","","","","","","","","","","","","","","","","","","","COVID-19; Bibliometric analysis; Artificial intelligence; Emerging trends; Scientific production","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"I728JTVH","journalArticle","2024","Hardiman, Jason Patrick Winarto; Thio, Dave Christian; Zakiyyah, Alfi Yusrotis; Meiliana","AI-powered dialogues and quests generation in role-playing games using Google's Gemini and Sentence BERT framework","9th International Conference on Computer Science and Computational Intelligence 2024 (ICCSCI 2024)","","1877-0509","10.1016/j.procs.2024.10.340","https://www.sciencedirect.com/science/article/pii/S187705092403148X","Non-player Characters (NPCs) play a crucial role in shaping the player's experience and immersion in video games. However, despite advancements in technology and game design, NPCs continue to face several challenges that hinder their ability to provide engaging and realistic interactions. This has been true in some cases, such as open world games where the random NPCs do not have a lot of character depth or use repetitive sentences. This research aims to improve dialogue in video games by humanizing conversations between players and NPCs by using Google's Gemini API and Sentence-BERT framework. Gemini 1.0 Pro as a base model from Google to give natural responses and Sentence-BERT framework based on PyTorch and Transformers to compute sentence or text embeddings for more than 100 languages that can be compared with cosine similarity algorithm to find sentences with similar meanings. NPCs will be able to provide creative and unique quests to give to the players based on their conversations. This research provides insight on how LLMs such as Google's Gemini and Sentence BERT can be used in role-playing games to generate dynamic, unique conversations for NPCs without having to make the script manually","2024-01-01","2024-12-03 03:23:34","2024-12-03 03:23:34","","1111-1119","","","245","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Google's Gemini API; Large Language Modelling; Non-playable Characters; NPC Dialogue Generation; Semantic Search; Semantic Textual Similarity; Sentence-BERT; Video games","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"847QMYVY","journalArticle","2024","Lecu, Alexandru; Groza, Adrian; Hawizy, Lezan","Using LLMs and ontologies to extract causal relationships from medical abstracts","6th International Conference on AI in Computational Linguistics","","1877-0509","10.1016/j.procs.2024.10.219","https://www.sciencedirect.com/science/article/pii/S1877050924030205","The substantiation of the causal relationships behind its development is very important in identifying possible interventions and early treatment. Knowledge Graphs (KG) play a crucial role in the medical research domain by organizing data into interconnected structures that represent relationships between entities such as disease, treatments, and progressions. This paper shows a complete workflow that demonstrates the extraction of causal relationships from medical abstracts using a fine-tuned GPT-based model and the integration of these relationships into a KG.","2024-01-01","2024-12-03 03:23:34","2024-12-03 03:23:34","","443-452","","","244","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Large Language Models; Age-Related Macular Degeneration; Causal Relation Extraction; Knowledge Graphs","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XA23TRPL","journalArticle","2023","Karimiziarani, Mohammadsepehr; Shao, Wanyun; Mirzaei, Majid; Moradkhani, Hamid","Toward reduction of detrimental effects of hurricanes using a social media data analytic Approach: How climate change is perceived?","Climate Risk Management","","2212-0963","10.1016/j.crm.2023.100480","https://www.sciencedirect.com/science/article/pii/S2212096323000062","During natural disasters, there is a noticeably increased use of social media sites such as Twitter. Substantial research on social media data use during disasters has been conducted in the past decade since various social media platforms have emerged and gained popularity. This research highlights a thorough examination of the textual content of users’ posts shared on Twitter across the 48 contiguous U.S. states (CONUS) during hurricanes Harvey (2017) and Dorian (2019). We processed and analyzed 35 million tweets by classifying them into the main topics of concern discussed on Twitter over the CONUS. Sentiment analysis, topic modeling, and topic classification are a few of the Artificial Intelligence techniques from Natural Language Processing (NLP) that we employed in this work to analyze the Twitter data. Applying the NLP techniques on this large volume of data, made it possible to classify the tweet content into distinct categories in order to reveal valuable information on social response to hurricanes and assist crisis management agencies and disaster responders during and post disasters. Furthermore, this study offers helpful insights on the way climate change is discussed on Twitter before, during and after hurricane Harvey and Dorian. The outcome of this study uncovers detailed information on social response to hurricanes which benefits disaster managers and responders in reducing the detrimental effects of such extreme events and enhancing community readiness when these events occur.","2023-01-01","2024-12-03 03:23:34","2024-12-03 03:23:34","","100480","","","39","","Climate Risk Management","","","","","","","","","","","","","","","","","","","Natural Language Processing; Climate Change; Disaster Management; Sentiment Analysis; Social Media Analytics; Social Response","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"873SN9VI","journalArticle","2024","Aljarboa, Soliman.S.M.; Alaya, Bechir; Al-Ajlan, Ajlan; Miah, Shah J.","CDSS Adoption and the Role of Artificial Intelligence in Saudi Arabian Primary Healthcare","Informatics in Medicine Unlocked","","2352-9148","10.1016/j.imu.2024.101596","https://www.sciencedirect.com/science/article/pii/S2352914824001539","The integration of advanced Clinical Decision Support Systems (CDSS) is crucial in healthcare, especially in developing countries. However, the challenges to CDSS implementation in these settings are often overlooked, leading to implementation gaps. This study investigates the adoption of CDSS in primary healthcare facilities in Saudi Arabia through in-depth interviews with general practitioners. The advancement of primary healthcare systems in developing nations necessitates appropriate technological solutions to address issues such as equitable access, service quality, and patient satisfaction. The adoption of CDSS can potentially improve patient satisfaction and enhance medical consultations. Artificial intelligence -driven technologies like the Velys robot are transforming the medical field by assisting doctors in surgical procedures. In healthcare, Artificial intelligence (AI) can address issues related to complex diseases, aging populations, and workforce shortages. AI solutions can streamline patient flow, improve care coordination, and support personalized healthcare management. By assessing the impact of AI on patient care, healthcare efficiency, and effectiveness can be enhanced. This research highlights the challenges and opportunities in CDSS adoption in developing countries, with implications for healthcare improvement in Saudi Arabia.","2024-10-28","2024-12-03 03:23:34","2024-12-03 03:23:34","","101596","","","","","Informatics in Medicine Unlocked","","","","","","","","","","","","","","","","","","","Saudi Arabia; Health information; Artificial intelligence (AI); Healthcare; Clinical decision support systems; Velys robot","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KW9NXQRP","journalArticle","2024","Ying, Haihua; Pranolo, Andri; Nuryana, Zalik; Syafitri, Andini Isti","Emerging trends in the evolution of neuropsychology and artificial intelligence: A comprehensive analysis","Telematics and Informatics Reports","","2772-5030","10.1016/j.teler.2024.100171","https://www.sciencedirect.com/science/article/pii/S2772503024000574","Neuropsychological evaluations are valuable in neurosurgery because they comprehensively evaluate cognitive, affective, and behavioral functioning to optimize patient outcomes. Incorporating artificial intelligence (AI) into neuropsychology offers optimistic advances, with machine learning models assisting in classifying behavioral, cognitive, and functional impairments while minimizing the number of tests. AI-based approaches have demonstrated accurate classification outcomes, providing potential alternatives to time-consuming and non-ecological conventional evaluations. This research uses data from the Scopus database to examine the trends of neuropsychological-based AI in cognitive neuroscience, mental health, and neurodegenerative disorders. The study emphasized the potential of artificial intelligence in neuropsychology research and identified several research themes. The analysis of bibliometrics may efficiently assess the developments and impact of neuropsychology research, providing insights into academic output and predicting future trends. Future research should consider utilizing alternative databases, employing a multisource strategy, incorporating additional keywords, and building upon the foundational knowledge provided by this study. Despite its limitations, this study provides significant insights and paves the way for future neuropsychology-based artificial intelligence research. Furthermore, investigating significant topics and key issues in the neuropsychology and artificial intelligence debate adds new perspectives to the corpus of literature. This analysis can help identify gaps, controversies, and areas of future exploration within the field. The study also highlights the importance of learning and intelligent computation in neuropsychology, providing a conceptual methodology based on a comprehensive review of the most recent research.","2024-12-01","2024-12-03 03:23:34","2024-12-03 03:23:34","","100171","","","16","","Telematics and Informatics Reports","","","","","","","","","","","","","","","","","","","Artificial Intelligence; Machine Learning; Deep Learning; Bibliometric analytics; Neuropsychological","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"HAY2QCXF","journalArticle","2024","Essel, Harry Barton; Vlachopoulos, Dimitrios; Essuman, Albert Benjamin; Amankwa, John Opuni","ChatGPT effects on cognitive skills of undergraduate students: Receiving instant responses from AI-based conversational large language models (LLMs)","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2023.100198","https://www.sciencedirect.com/science/article/pii/S2666920X23000772","This study investigated the impact of using ChatGPT, a state-of-the-art generative AI-based model, on the critical, creative, and reflective thinking skills of university students in Ghana. The study utilized a mixed-methods research approach, incorporating quantitative and qualitative data collection instruments, and an experimental procedure with a pretest-posttest control group. The study ultimately enlisted a sample of 125 students randomly allocated to either the experiment group (60 students) or the control group (65 students). The research was conducted in the context of a Research Methodology course, which had adopted the flipped classroom approach. The students in the experiment group engaged with ChatGPT for in-class tasks, while those in the control group used traditional databases and search engines for similar tasks. Data were collected using the Critical Thinking Scale, Creative Thinking Scale, Reflective Thinking Scale, and a student interview guide (semi-structured). The study's findings illustrated that incorporating ChatGPT influenced the students' critical, reflective, and creative thinking skills and their dimensions discernibly. As a result, the study provides suggestions for academics, instructional designers, and researchers working in educational technology.","2024-06-01","2024-12-03 03:23:34","2024-12-03 03:23:34","","100198","","","6","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","ChatGPT; Large language models; Critical thinking; Creative thinking; Reflective thinking; Undergraduate students","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"Q4U8VLIH","journalArticle","2023","Walker, Nicholas; Lee, Sanghoon; Dagdelen, John; Cruse, Kevin; Gleason, Samuel; Dunn, Alexander; Ceder, Gerbrand; Alivisatos, A. Paul; Persson, Kristin A.; Jain, Anubhav","Extracting structured seed-mediated gold nanorod growth procedures from scientific text with LLMs††Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d3dd00019b","Digital Discovery","","2635-098X","10.1039/d3dd00019b","https://www.sciencedirect.com/science/article/pii/S2635098X23001249","ABSTRACT Although gold nanorods have been the subject of much research, the pathways for controlling their shape and thereby their optical properties remain largely heuristically understood. Although it is apparent that the simultaneous presence of and interaction between various reagents during synthesis control these properties, computational and experimental approaches for exploring the synthesis space can be either intractable or too time-consuming in practice. This motivates an alternative approach leveraging the wealth of synthesis information already embedded in the body of scientific literature by developing tools to extract relevant structured data in an automated, high-throughput manner. To that end, we present an approach using the powerful GPT-3 language model to extract structured multi-step seed-mediated growth procedures and outcomes for gold nanorods from unstructured scientific text. GPT-3 prompt completions are fine-tuned to predict synthesis templates in the form of JSON documents from unstructured text input with an overall accuracy of 86% aggregated by entities and 76% aggregated by papers. The performance is notable, considering the model is performing simultaneous entity recognition and relation extraction. We present a dataset of 11 644 entities extracted from 1137 papers, resulting in 268 papers with at least one complete seed-mediated gold nanorod growth procedure and outcome for a total of 332 complete procedures.","2023-12-04","2024-12-03 03:23:34","2024-12-03 03:23:34","","1768-1782","","6","2","","Digital Discovery","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"UN8AAXLC","journalArticle","2024","Gidel-Dissler, Nina; Roque, Thais; Canat, Guillaume; Angelard, Benjamin; Vandame, Jessica; Boussommier-Calleja, Alexandra","Uncovering the association between embryo development and early pregnancy loss using artificial intelligence annotated kinetic events","Reproductive BioMedicine Online","","1472-6483","10.1016/j.rbmo.2024.104493","https://www.sciencedirect.com/science/article/pii/S1472648324006825","Research question Can artificial intelligence powered annotation of numerous biological events help uncover an association between embryonic kinetics and early pregnancy loss? Design A multicentric retrospective analysis was conducted on 37717 embryos (7028 egg retrievals, 13 centers in France and Spain, three different time-lapse systems). The videos of developing embryos were analyzed with artificial intelligence to detect developmental events, including t2, t5, t8 (cleavage from 2 cells to 8 cells), tSB (start blastulation) and tB (blastocyst). The association between embryo kinetics and transfer outcome (lack of early pregnancy, early pregnancy loss and clinical pregnancy) was investigated using univariate and multivariate logistic regressions. Results An optimal kinetics for clinical pregnancy was identified as being statistically different from that of embryos which led to an early pregnancy loss (p<0.01). As much as overall faster developing embryos (tB-t2=77.18±8.33 hours) were the most likely to lead to an early pregnancy, it appeared that beyond early pregnancy, embryos that showed a deceleration during cleavage (tsB-t5=46.05±13.60 hours) followed by a subsequent acceleration during blastulation (tB-tSB=9.81±5.04 hours) were the most likely to lead to clinical pregnancy. Conversely, embryos that presented the fastest cleavage and longest blastulation kinetics were the most likely to lead to an early pregnancy loss. Conclusions Analyzing numerous kinetic combinations annotated with artificial intelligence reveals patterns which can help distinguish embryos that are competent enough for early pregnancy but not for clinical pregnancy. Detecting such subtle kinetic differences could add transparency to algorithms by pinpointing which phases of an embryo development might be predictive of a pregnancy loss.","2024-10-20","2024-12-03 03:23:34","2024-12-03 03:23:34","","104493","","","","","Reproductive BioMedicine Online","","","","","","","","","","","","","","","","","","","Artificial intelligence; Early pregnancy loss; Embryo Development; Time Lapse","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"36Z5H2BE","journalArticle","2023","Das, Nataraj; Kundu, Shaona; Deb, Suman; Saha, Soma","Generative Adversarial Neural Approach Towards Construction of Warli-stick Figure Aboriginal Artwork","International Conference on Machine Learning and Data Engineering","","1877-0509","10.1016/j.procs.2023.01.183","https://www.sciencedirect.com/science/article/pii/S1877050923001837","Image Synthesis or generation is evolving at a high rate helping from diagnosis of diseases to traffic control. Deep Learning has touched a new height in which domains like Computer Vision have successfully developed Deep learning models for image generation. Recent improvements in deep learning enabled Generative Adversarial Networks or GANs to achieve a remarkable level of success in image synthesis. GANs help in generating significant and relevant outcomes from convoluted patterns as input. This piece of research aims at making a call towards conservation of tribal art culture through deploying neural models and computer vision techniques, in line with Warli tribal human synthetic stick figures of Asian tribes. This paper presents several methods for synthesizing human stick figures in Asian Tribal Art using modified GAN architecture. The modified GANs were quite different in terms of layers as well as the range and distribution of latent vector space. To provide input data for the training process, a customized data set was created based on spherical polar coordinates and the laws of coordinate geometry. The process of training the GAN architecture involves insertion of random vector points, and addition of neural layers, to generate outputs of higher and better resolution. The paper includes training of Generator and Discriminator following the concepts of Transfer learning and providing relevant affirmation to draw conclusions that the generated samples exhibit similarities and resemble the original input sample. Further work extends towards using of different color space data set images for generating outputs with better accuracy through the modified GAN models. The final generated output images along with other visualizations are also attached with this work to provide evidence for the fact that the output samples are analogous to the actual data. This piece of research tries to open a gate way of using artificial intelligence in terms of cultivating and protecting the traditional art cultures of various tribes across the globe. The proposed methodology performed quite well in terms of producing warli figures from given latent vector space, which is vindicated by the comparison of images at pixel level and image histograms.","2023-01-01","2024-12-03 03:23:34","2024-12-03 03:23:34","","2071-2080","","","218","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","GAN; Anatomy; CNN; Pose; Spherical polar coordinates; Stick figure; Synthetic figures; Warli","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2QPGWUAG","journalArticle","2024","Filter, Matthias; Schüler, Thomas; Ben Romdhane, Racem","Food Safety Knowledge Exchange (FSKX) format: Current status and strategic development plans based on a SWOT analysis","Microbial Risk Analysis","","2352-3522","10.1016/j.mran.2024.100309","https://www.sciencedirect.com/science/article/pii/S2352352224000203","The Food Safety Knowledge Exchange (FSKX) format is a community-driven effort initially created to promote the efficient exchange of data and models in the food safety domain. Over the past years this effort was driven by the Risk Assessment Knowledge Integration Platform (RAKIP) Initiative that also provided a number of software tools and FSKX-compliant model files via their website https://foodrisklabs.bfr.bund.de/rakip-initiative/. This paper describes the results of a SWOT analysis that was conducted to identify strategic avenues for enhancing FSKX's usability and adoption. The SWOT analysis identified a number of recommendations for the future evolution of FSKX. First, it is recommended to reduce the complexity of the annotation schema to ease the adoption of the format. Second, a clear distinction between the descriptive part of FSKX and the executable part is proposed. To promote the broad usage of FSKX-compliant models, it is also recommended to develop and provide FSKX-compliant APIs and resources that facilitate cloud-based execution. As part of the research to prioritize future FSKX development options, we also considered the implications of the emerging generative AI technologies, particularly which impact large language models (LLMs) might have in supporting the adoption of FSKX by the research community. Recognizing the format's application potential beyond the food safety domain, we then proposed to re-brand the FSKX acronym as ""FAIR Scientific Knowledge Exchange Format"" which better reflects its broad applicability in various scientific domains. Our research findings suggest that with the implementation of the improvements identified by the SWOT analysis and the broader availability of generative AI technologies the broad adoption of FSKX as a method to share data and models in a FAIR way comes into reach.","2024-12-01","2024-12-03 03:23:34","2024-12-03 03:23:34","","100309","","","27-28","","Microbial Risk Analysis","","","","","","","","","","","","","","","","","","","Knowledge exchange; Data standards; FAIR data; Linked models","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"M687QBA6","journalArticle","2024","Riehle, Dennis M.; Wolters, Anna; Müller, Kilian","Adopting Artificial Intelligence in a Decision Support System – Learnings from Comment Moderation Systems","CENTERIS – International Conference on ENTERprise Information Systems / ProjMAN - International Conference on Project MANagement / HCist - International Conference on Health and Social Care Information Systems and Technologies 2023","","1877-0509","10.1016/j.procs.2024.06.366","https://www.sciencedirect.com/science/article/pii/S1877050924016107","Enterprise Information Systems (EIS) comprise components for decision-making in an organization. While traditional Decision Support Systems (DSS) rely on sophistically designed decision models, this approach has its limitations when it comes to making decisions based on large amounts of unstructured data. In this paper, we present the case of online community management, where moderators need to decide if user content (i.e., comments) can be published. We have implemented a moderation platform that utilizes Natural Language Processing (NLP) and Machine Learning (ML) to support moderators in their decision-making. From the development process and adoption of our platform, which were carried out as a Design Science Research (DSR) project, we have derived six design principles that assist in designing ML-based DSS. Our results imply that an ML-based DSS should be implemented using an open, customizable system, where decisions are made transparent and interpretable to users. Users need special onboarding and should always have the possibility to overrule the system.","2024-01-01","2024-12-03 03:23:34","2024-12-03 03:23:34","","1847-1855","","","239","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Machine Learning; Decision Support System; Desing Principles; Human in the Loop","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"HQKMWIRI","journalArticle","2024","Cho, Hyeongmin; Yoo, Sooyoung; Kim, Borham; Jang, Sowon; Sunwoo, Leonard; Kim, Sanghwan; Lee, Donghyoung; Kim, Seok; Nam, Sejin; Chung, Jin-Haeng","Extracting lung cancer staging descriptors from pathology reports: A generative language model approach","Journal of Biomedical Informatics","","1532-0464","10.1016/j.jbi.2024.104720","https://www.sciencedirect.com/science/article/pii/S1532046424001382","Background In oncology, electronic health records contain textual key information for the diagnosis, staging, and treatment planning of patients with cancer. However, text data processing requires a lot of time and effort, which limits the utilization of these data. Recent advances in natural language processing (NLP) technology, including large language models, can be applied to cancer research. Particularly, extracting the information required for the pathological stage from surgical pathology reports can be utilized to update cancer staging according to the latest cancer staging guidelines. Objectives This study has two main objectives. The first objective is to evaluate the performance of extracting information from text-based surgical pathology reports and determining pathological stages based on the extracted information using fine-tuned generative language models (GLMs) for patients with lung cancer. The second objective is to determine the feasibility of utilizing relatively small GLMs for information extraction in a resource-constrained computing environment. Methods Lung cancer surgical pathology reports were collected from the Common Data Model database of Seoul National University Bundang Hospital (SNUBH), a tertiary hospital in Korea. We selected 42 descriptors necessary for tumor-node (TN) classification based on these reports and created a gold standard with validation by two clinical experts. The pathology reports and gold standard were used to generate prompt-response pairs for training and evaluating GLMs which then were used to extract information required for staging from pathology reports. Results We evaluated the information extraction performance of six trained models as well as their performance in TN classification using the extracted information. The Deductive Mistral-7B model, which was pre-trained with the deductive dataset, showed the best performance overall, with an exact match ratio of 92.24% in the information extraction problem and an accuracy of 0.9876 (predicting T and N classification concurrently) in classification. Conclusion This study demonstrated that training GLMs with deductive datasets can improve information extraction performance, and GLMs with a relatively small number of parameters at approximately seven billion can achieve high performance in this problem. The proposed GLM-based information extraction method is expected to be useful in clinical decision-making support, lung cancer staging and research.","2024-09-01","2024-12-03 03:23:34","2024-12-03 03:23:34","","104720","","","157","","Journal of Biomedical Informatics","","","","","","","","","","","","","","","","","","","Deep learning; Natural language processing; Large language model; Information extraction; Pathology report; Tumor-node classification","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"PYPNMNZH","journalArticle","2024","Kang, Liangyu; Hu, Jian; Cai, Kangning; Jing, Wenzhan; Liu, Min; Liang, Wannian","The Intelligent Infectious Disease Active Surveillance and early warning system in China: An application of dengue prevention and control","Global Transitions","","2589-7918","10.1016/j.glt.2024.10.004","https://www.sciencedirect.com/science/article/pii/S2589791824000185","Utilizing advanced information technologies such as big data and artificial intelligence (AI), China has established and implemented the Intelligent Infectious Disease Active Surveillance and Early Warning System. It provides new tools for the surveillance, early warning, and response to infectious diseases, enhancing the timeliness, scientific basis, and efficiency of epidemic control efforts. The system comprises four functional modules including multi-channel active surveillance, intelligent early warning, data-driven risk assessment, and smart emergency response. This paper provides a detailed overview of the structure and functions of the Intelligent Infectious Disease Active Surveillance and Early Warning System in China, with a specific focus on its application in dengue prevention and control in Hainan Province from February to May 2024. Firstly, the system can proactively capture and integrate heterogeneous surveillance data from multiple sources. Based on these multi-channel data, users can select appropriate warning indicators and AI models to automatically trigger early warnings. Using vast amounts of surveillance data, the system can construct machine learning models to accurately assess the transmission risk of infectious diseases. In terms of emergency response, the system offers powerful tools for early diagnosis, smart epidemiological investigation, digital contact tracing, vaccine and drug development, and evaluation of intervention measures. This system facilitates early detection, reporting, and management of outbreaks, serving as a valuable reference for other countries and regions. Nevertheless, continuous efforts are needed to strengthen scientific research and multidisciplinary collaboration, establish reliable data collection mechanisms, enhance continuous model monitoring and adjustments, and leverage the latest large language models. In the future, the system will be further optimized to help control emerging and major infectious diseases more effectively.","2024-01-01","2024-12-03 03:23:34","2024-12-03 03:23:34","","249-255","","","6","","Global Transitions","","","","","","","","","","","","","","","","","","","China; Surveillance; Artificial intelligence; Early warning; Infectious diseases","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"G74PL7EP","journalArticle","2023","Hasan, A S M Mahmudul; Diepeveen, Dean; Laga, Hamid; Jones, Michael G.K.; Sohel, Ferdous","Image patch-based deep learning approach for crop and weed recognition","Ecological Informatics","","1574-9541","10.1016/j.ecoinf.2023.102361","https://www.sciencedirect.com/science/article/pii/S1574954123003904","Accurate classification of weed species in crop plants plays a crucial role in precision agriculture by enabling targeted treatment. Recent studies show that artificial intelligence deep learning (DL) models achieve promising solutions. However, several challenging issues, such as lack of adequate training data, inter-class similarity between weed species and intra-class dissimilarity between the images of the same weed species at different growth stages or for other reasons (e.g., variations in lighting conditions, image capturing mechanism, agricultural field environments) limit their performance. In this research, we propose an image based weed classification pipeline where a patch of the image is considered at a time to improve the performance. We first enhance the images using generative adversarial networks. The enhanced images are divided into overlapping patches, a subset of which are used for training the DL models. For selecting the most informative patches, we use the variance of Laplacian and the mean frequency of Fast Fourier Transforms. At test time, the model's outputs are fused using a weighted majority voting technique to infer the class label of an image. The proposed pipeline was evaluated using 10 state-of-the-art DL models on four publicly available crop weed datasets: DeepWeeds, Cotton weed, Corn weed, and Cotton Tomato weed. Our pipeline achieved significant performance improvements on all four datasets. DenseNet201 achieved the top performance with F1 scores of 98.49%, 99.83% and 100% on Deepweeds, Corn weed and Cotton Tomato weed datasets, respectively. The highest F1 score on the Cotton weed dataset was 98.96%, obtained by InceptionResNetV2. Moreover, the proposed pipeline addressed the issues of intra-class dissimilarity and inter-class similarity in the DeepWeeds dataset and more accurately classified the minority weed classes in the Cotton weed dataset. This performance indicates that the proposed pipeline can be used in farming applications.","2023-12-01","2024-12-03 03:23:34","2024-12-03 03:23:34","","102361","","","78","","Ecological Informatics","","","","","","","","","","","","","","","","","","","Deep learning; Digital agriculture; Patch-based technique; Weed classification","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RY6XTMMA","journalArticle","2024","Latif, Ehsan; Zhai, Xiaoming","Fine-tuning ChatGPT for automatic scoring","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100210","https://www.sciencedirect.com/science/article/pii/S2666920X24000110","This study highlights the potential of fine-tuned ChatGPT (GPT-3.5) for automatically scoring student written constructed responses using example assessment tasks in science education. The application of ChatGPT in research and academic fields has greatly enhanced productivity and efficiency. Recent studies on ChatGPT based on OpenAI's generative model GPT-3.5 proved its superiority in predicting the natural language with high accuracy and human-like responses. GPT-3.5 has been trained over enormous online language materials such as journals and Wikipedia; however, direct usage of pre-trained GPT-3.5 is insufficient for automatic scoring as students do not utilize the same language as journals or Wikipedia, and contextual information is required for accurate scoring. All of these imply that a fine-tuning of a domain-specific model using data for specific tasks can enhance model performance. In this study, we fine-tuned GPT-3.5 on six assessment tasks with a diverse dataset of middle-school and high-school student responses and expert scoring. The six tasks comprise two multi-label and four multi-class assessment tasks. We compare the performance of fine-tuned GPT-3.5 with the fine-tuned state-of-the-art Google's generated language model, BERT. The results show that in-domain training corpora constructed from science questions and responses for BERT achieved average accuracy = 0.838, SD = 0.069. GPT-3.5 shows a remarkable average increase (9.1%) in automatic scoring accuracy (mean = 9.15, SD = 0.042) for the six tasks, p =0.001 < 0.05. Specifically, for each of the two multi-label tasks (item 1 with 5 labels; item 2 with 10 labels), GPT-3.5 achieved significantly higher scoring accuracy than BERT across all the labels, with the second item achieving a 7.1% increase. The average scoring increase for the four multi-class items for GPT-3.5 was 10.6% compared to BERT. Our study confirmed the effectiveness of fine-tuned GPT-3.5 for automatic scoring of student responses on domain-specific data in education with high accuracy. We have released fine-tuned models for public use and community engagement.","2024-06-01","2024-12-03 03:23:34","2024-12-03 03:23:34","","100210","","","6","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Education; Automatic scoring; BERT; Large language model (LLM); Finetune; GPT-3.5","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"GVRHUB3J","journalArticle","2024","Singh, Aakash; Sharma, Deepawali; Nandy, Abhirup; Singh, Vivek Kumar","Towards a large sized curated and annotated corpus for discriminating between human written and AI generated texts: A case study of text sourced from Wikipedia and ChatGPT","Natural Language Processing Journal","","2949-7191","10.1016/j.nlp.2023.100050","https://www.sciencedirect.com/science/article/pii/S294971912300047X","The recently launched large language models have the capability to generate text and engage in human-like conversations and question-answering. Owing to their capabilities, these models are now being widely used for a variety of purposes, ranging from question answering to writing scholarly articles. These models are producing such good outputs that it is becoming very difficult to identify what texts are written by human beings and what by these programs. This has also led to different kinds of problems such as out-of-context literature, lack of novelty in articles, and issues of plagiarism and lack of proper attribution and citations to the original texts. Therefore, there is a need for suitable computational resources for developing algorithmic approaches that can identify and discriminate between human and machine generated texts. This work contributes towards this research problem by providing a large sized curated and annotated corpus comprising of 44,162 text articles sourced from Wikipedia and ChatGPT. Some baseline models are also applied on the developed dataset and the results obtained are analyzed and discussed. The curated corpus offers a valuable resource that can be used to advance the research in this important area and thereby contribute to the responsible and ethical integration of AI language models into various fields.","2024-03-01","2024-12-03 03:23:34","2024-12-03 03:23:34","","100050","","","6","","Natural Language Processing Journal","","","","","","","","","","","","","","","","","","","ChatGPT; LLM; Generative models; AI generated text; Machine generated text","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"HYUYZDAI","journalArticle","2024","Tian, Haoheng","Enterprise Business Information Management Archive System under Computer Artificial Intelligence Technology","The 4th International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy","","1877-0509","10.1016/j.procs.2024.09.102","https://www.sciencedirect.com/science/article/pii/S1877050924021094","The enterprise business information management archive system is an indispensable and important tool in modern enterprise management. With the rapid development of computer artificial intelligence technology, its application research in enterprise business information management archives system has attracted widespread attention. This article aims to explore the application of computer artificial intelligence technology in this field and evaluate its impact on system performance and efficiency. This study used literature review and empirical analysis methods to systematically collect and analyze relevant research literature, and summarized the application of computer artificial intelligence technology in enterprise business information management archive systems. The research progress and application cases of machine learning, natural language processing, deep learning, and recommendation systems in this field were given special attention. Research has found that computer artificial intelligence technology has broad application prospects and potential in enterprise business information management archive systems.","2024-01-01","2024-12-03 03:23:34","2024-12-03 03:23:34","","850-857","","","243","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Archive System; Artificial Intelligence Technology; Enterprise Business Information Management; Naive Bayes","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"B97YQTE4","journalArticle","2024","Wu, Zhaoliang; Wu, Yuewei; Feng, Xiaoli; Zou, Jiajun; Yin, Fulian","Improve Chinese Aspect Sentiment Quadruplet Prediction via Instruction Learning Based on Large Generate Models","Computers, Materials and Continua","","1546-2218","10.32604/cmc.2024.047076","https://www.sciencedirect.com/science/article/pii/S1546221824003084","Aspect-Based Sentiment Analysis (ABSA) is a fundamental area of research in Natural Language Processing (NLP). Within ABSA, Aspect Sentiment Quad Prediction (ASQP) aims to accurately identify sentiment quadruplets in target sentences, including aspect terms, aspect categories, corresponding opinion terms, and sentiment polarity. However, most existing research has focused on English datasets. Consequently, while ASQP has seen significant progress in English, the Chinese ASQP task has remained relatively stagnant. Drawing inspiration from methods applied to English ASQP, we propose Chinese generation templates and employ prompt-based instruction learning to enhance the model’s understanding of the task, ultimately improving ASQP performance in the Chinese context. Ultimately, under the same pre-training model configuration, our approach achieved a 5.79% improvement in the F1 score compared to the previously leading method. Furthermore, when utilizing a larger model with reduced training parameters, the F1 score demonstrated an 8.14% enhancement. Additionally, we suggest a novel evaluation metric based on the characteristics of generative models, better-reflecting model generalization. Experimental results validate the effectiveness of our approach.","2024-03-26","2024-12-03 03:23:34","2024-12-03 03:23:34","","3391-3412","","3","78","","Computers, Materials and Continua","","","","","","","","","","","","","","","","","","","LLMs; ABSA; ASQP; Chinese comments; sentiment analysis","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XSKDP6H4","journalArticle","2024","Shore, Adam; Tiwari, Manisha; Tandon, Priyanka; Foropon, Cyril","Building entrepreneurial resilience during crisis using generative AI: An empirical study on SMEs","Technovation","","0166-4972","10.1016/j.technovation.2024.103063","https://www.sciencedirect.com/science/article/pii/S0166497224001135","Recently, Gen AI has garnered significant attention across various sectors of society, particularly capturing the interest of small business due to its capacity to allow them to reassess their business models with minimal investment. To understand how small and medium-sized firms have utilised Gen AI-based tools to cope with the market's high level of turbulence caused by the COVID-19 pandemic, geopolitical crises, and economic slowdown, researchers have conducted an empirical study. Although Gen AI is receiving more attention, there remains a dearth of empirical studies that investigate how it influences the entrepreneurial orientation of firms and their ability to cultivate entrepreneurial resilience amidst market turbulence. Most of the literature offers anecdotal evidence. To address this research gap, the authors have grounded their theoretical model and research hypotheses in the contingent view of dynamic capability. They tested the research hypotheses using cross-sectional data from a pre-tested survey instrument, which yielded 87 useable responses from small and medium enterprises in France. The authors used variance-based structural equation modelling with the commercial WarpPLS 7.0 software to test the theoretical model. The study's findings suggest that Gen AI and EO have a significant influence on building entrepreneurial resilience as higher-order and lower-order dynamic capabilities. However, market turbulence has a negative moderating effect on the path that joins entrepreneurial orientation and entrepreneurial resilience. The results suggest that the assumption that high market turbulence will have positive effects on dynamic capabilities and competitive advantage is not always true, and the linear assumption does not hold, which is consistent with some scholars' assumptions. The study's results offer significant contributions to the contingent view of dynamic capabilities and open new research avenues that require further investigation into the non-linear relationship of market turbulence.","2024-07-01","2024-12-03 03:23:35","2024-12-03 03:23:35","","103063","","","135","","Technovation","","","","","","","","","","","","","","","","","","","Generative AI; Dynamic capability view; Entrepreneurial orientation; Entrepreneurial resilience; Market turbulence","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"W4AXKVWW","journalArticle","2024","Das, Mridusmita; Senapati, Apurbalal","Co-reference Resolution in Prompt Engineering","6th International Conference on AI in Computational Linguistics","","1877-0509","10.1016/j.procs.2024.10.192","https://www.sciencedirect.com/science/article/pii/S1877050924029934","Co-reference resolution is a longstanding research problem that researchers have been working to solve for the past fifty years. It is a crucial task in numerous Natural Language Processing (NLP) applications, such as Machine Translation (MT), Text Summarization, Question Answering, etc. Over time resolution strategies have evolved and incorporated new methods such as deep learning approaches. With the advancement of Large Language Models (LLMs), researchers are now tackling several problems using prompt engineering. This paper explores a pioneering attempt at Prompt Engineering in Co-reference resolution in the Assamese language. The Assamese language is resource-scarce, and this effort provides a way to overcome the resource barrier in the NLP domain. The experiment employs zero-prompt, few-prompt, and chain-of-thought prompt techniques, documenting the improvement in results and comparing them with the latest state-of-the-art in the Assamese language.","2024-01-01","2024-12-03 03:23:35","2024-12-03 03:23:35","","194-201","","","244","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Large Language Models; Natural Language Processing; Assamese Language; Co-reference Resolution; Prompt Engineering","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"TFZ39HMP","journalArticle","2024","Zhang, Yutong; Pan, Yi; Zhong, Tianyang; Dong, Peixin; Xie, Kangni; Liu, Yuxiao; Jiang, Hanqi; Wu, Zihao; Liu, Zhengliang; Zhao, Wei; Zhang, Wei; Zhao, Shijie; Zhang, Tuo; Jiang, Xi; Shen, Dinggang; Liu, Tianming; Zhang, Xin","Potential of multimodal large language models for data mining of medical images and free-text reports","Meta-Radiology","","2950-1628","10.1016/j.metrad.2024.100103","https://www.sciencedirect.com/science/article/pii/S2950162824000572","Medical images and radiology reports are essential for physicians to diagnose medical conditions. However, the vast diversity and cross-source heterogeneity inherent in these data have posed significant challenges to the generalizability of current data-mining methods for clinical decision-making. Recently, multimodal large language models (MLLMs), especially Gemini-Vision-series (Gemini) and GPT-4-series (GPT-4) models, have revolutionized numerous domains, significantly impacting the medical field. In this study, we conducted a detailed evaluation of the performance of the Gemini series models (including Gemini-1.0-Pro-Vision, Gemini-1.5-Pro, and Gemini-1.5-Flash) and GPT series models (including GPT-4o, GPT-4-Turbo, and GPT-3.5-Turbo) across 14 medical datasets, covering 5 medical imaging categories (dermatology, radiology, dentistry, ophthalmology, and endoscopy) and 3 radiology report datasets. The investigated tasks encompass disease classification, lesion segmentation, anatomical localization, disease diagnosis, report generation, and lesion detection. Moreover, we also validated the performance of the Claude-3-Opus, Yi-Large, Yi-Large-Turbo, and LLaMA 3 models to gain a comprehensive understanding of the MLLM models in the medical field. Our experimental results demonstrated that Gemini-series models excelled in report generation and lesion detection but faces challenges in disease classification and anatomical localization. Conversely, GPT-series models exhibited proficiency in lesion segmentation and anatomical localization but encountered difficulties in disease diagnosis and lesion detection. Additionally, both the Gemini series and GPT series contain models that have demonstrated commendable generation efficiency. While both models hold promise in reducing physician workload, alleviating pressure on limited healthcare resources, and fostering collaboration between clinical practitioners and artificial intelligence technologies, substantial enhancements and comprehensive validations remain imperative before clinical deployment.","2024-12-01","2024-12-03 03:23:35","2024-12-03 03:23:35","","100103","","4","2","","Meta-Radiology","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"F9D6ABB7","journalArticle","2024","Chung, Eun-Ji; Yang, Byoung-Eun; Kang, Sam-Hee; Kim, Young-Hee; Na, Ji-Yeon; Park, Sang-Yoon; On, Sung-Woon; Byun, Soo-Hwan","Validation of 2D lateral cephalometric analysis using artificial intelligence-processed low-dose cone beam computed tomography","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e39445","https://www.sciencedirect.com/science/article/pii/S240584402415476X","Objectives Traditional cephalometric radiographs depict a three-dimensional structure in a two-dimensional plane; therefore, errors may occur during a quantitative assessment. Cone beam computed tomography, on the other hand, minimizes image distortion, allowing essential areas to be observed without overlap. Artificial intelligence can be used to enhance low-dose cone beam computed tomography images. This study aimed to clinically validate the use of artificial intelligence-processed low-dose cone beam computed tomography for generating two-dimensional lateral cephalometric radiographs by comparing these artificial intelligence-enhanced radiographs with traditional two-dimensional lateral cephalograms and those derived from standard cone beam computed tomography. Methods Sixteen participants who had previously undergone both cone beam computed tomography and plain radiography were selected. Group I included standard lateral cephalometric radiographs. Group II included cone beam computed tomography-produced lateral cephalometric radiographs, and Group III included artificial intelligence-processed low-dose cone beam computed tomography-produced lateral cephalometric radiographs. Lateral cephalometric radiographs of the three groups were analyzed using an artificial intelligence-based cephalometric analysis platform. Results A total of six angles and five lengths were measured for dentofacial diagnosis. There were no significant differences in measurements except for nasion-menton among the three groups. Conclusions Low-dose cone beam computed tomography could be an efficient method for cephalometric analyses in dentofacial treatment. Artificial intelligence-processed low-dose cone beam computed tomography imaging procedures have the potential in a wide range of dental applications. Further research is required to develop artificial intelligence technologies capable of producing acceptable and effective outcomes in various clinical situations. Clinical significance Replacing standard cephalograms with cone beam computed tomography (CBCT) to evaluate the craniofacial relationship has the potential to significantly enhance the diagnosis and treatment of selected patients. The effectiveness of low-dose (LD)-CBCT was assessed in this study. The results indicated that lateral cephalograms reconstructed using LD-CBCT were comparable to standard lateral cephalograms.","2024-11-15","2024-12-03 03:23:35","2024-12-03 03:23:35","","e39445","","21","10","","Heliyon","","","","","","","","","","","","","","","","","","","Artificial intelligence (AI); Cephalometric analysis; Cone beam computed tomography (CBCT); Dentofacial treatment; Low-dose CBCT","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"FQFIWM44","journalArticle","2024","Mirza, Fatima N.; Wu, Eric; Abdulrazeq, Hael F.; Connolly, Ian D.; Tang, Oliver Y.; Zogg, Cheryl K.; Williamson, Theresa; Galamaga, Paul F.; Roye, G. Dean; Sampath, Prakash; Telfeian, Albert E.; Qureshi, Abrar A.; Groff, Michael W.; Shin, John H.; Asaad, Wael F.; Libby, Tiffany J.; Gokaslan, Ziya L.; Kohane, Isaac S.; Zou, James; Ali, Rohaid","The literacy barrier in clinical trial consents: a retrospective analysis","eClinicalMedicine","","2589-5370","10.1016/j.eclinm.2024.102814","https://www.sciencedirect.com/science/article/pii/S2589537024003936","Summary Background Historically, the readability of consent forms in medicine have been above the average reading level of patients. This can create challenges in obtaining truly informed consent, but the implications on clinical trial participant retention are not fully explored. To address this gap, we seek to analyze clinical trial consent forms by determining their readability and relationship with the associated trial's participant dropout rate. Additionally, we explore a potential method for simplifying these forms. Methods We analyzed the readability of consent forms of federally funded interventional clinical trials, which were completed in the United States on or before January 1, 2023, and were posted online and made accessible on ClinicalTrials.gov. We correlated their readability with trial dropout rates. As an exploratory analysis, a subset of these forms was simplified using a large language model, with expert medicolegal review. Findings Across 798 included federally funded trials, the mean (±SD) Flesch-Kincaid Grade Level of their consent forms was 12.0 ± 1.3, equivalent to a high school graduate reading level and significantly higher than the 8th grade average reading level of adults in the United States (U.S.) (P < 0.001). In risk-adjusted analyses, each additional Flesch-Kincaid Grade Level increase in a clinical trial's consent form was associated with a 16% higher dropout rate (incidence rate ratio, 1.16; 95% confidence interval, 1.12–1.22; P < 0.001). Our exploratory analysis of a simplification method showed promising results in lowering the reading level while preserving medicolegal content. Interpretation The average readability of informed consent forms of federally funded clinical trials exceeds the reading comprehension skills of the majority of adults in the U.S., potentially undermining clinical trial participant retention rates. Future work should explore the use of large language models and other tools as possible means to close this literacy barrier and potentially enhancing clinical trial participation. Funding This research received no sources of funding. The authors have no conflicts of interest to report.","2024-09-01","2024-12-03 03:23:35","2024-12-03 03:23:35","","102814","","","75","","eClinicalMedicine","","","","","","","","","","","","","","","","","","","Artificial intelligence; Readability; Clinical trials","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"7GNWYNAC","journalArticle","2024","Moenck, Keno; Thieu, Duc Trung; Koch, Julian; Schüppstuhl, Thorsten","Industrial Language-Image Dataset (ILID): Adapting Vision Foundation Models for Industrial Settings","57th CIRP Conference on Manufacturing Systems 2024 (CMS 2024)","","2212-8271","10.1016/j.procir.2024.10.084","https://www.sciencedirect.com/science/article/pii/S2212827124012411","In recent years, the upstream of Large Language Models (LLM) has also encouraged the computer vision community to work on substantial multimodal datasets and train models on a scale in a self-/semi-supervised manner, resulting in Vision Foundation Models (VFM), as, e.g., Contrastive Language–Image Pre-training (CLIP). The models generalize well and perform outstandingly on everyday objects or scenes, even on downstream tasks, tasks the model has not been trained on, while the application in specialized domains, as in an industrial context, is still an open research question. Here, fine-tuning the models or transfer learning on domain-specific data is unavoidable when objecting to adequate performance. In this work, we, on the one hand, introduce a pipeline to generate the Industrial Language-Image Dataset (ILID) based on web-crawled data; on the other hand, we demonstrate effective self-supervised transfer learning and discussing downstream tasks after training on the cheaply acquired ILID, which does not necessitate human labeling or intervention. With the proposed approach, we contribute by transferring approaches from state-of-the-art research around foundation models, transfer learning strategies, and applications to the industrial domain.","2024-01-01","2024-12-03 03:23:35","2024-12-03 03:23:35","","250-263","","","130","","Procedia CIRP","","","","","","","","","","","","","","","","","","","CLIP; industrial dataset; self-supervised; vision foundation model","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"WXRK64GA","journalArticle","2024","Chanmas, Gunt; Taveekitworachai, Pittawat; You, Xiao; Thawonmas, Ruck; Nukoolkit, Chakarida; Dajpratham, Piyapat","Driving assistant using generative AI pre-generated messages in simulator-based driving assessment: A step towards low-cost simulator-based driving assessment","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e35941","https://www.sciencedirect.com/science/article/pii/S240584402411972X","This paper presents a novel approach for a low-cost simulator-based driving assessment system incorporating a speech-based assistant, using pre-generated messages from Generative AI to achieve real-time interaction during the assessment. Simulator-based assessment is a crucial apparatus in the research toolkit for various fields. Traditional assessment approaches, like on-road evaluation, though reliable, can be risky, costly, and inaccessible. Simulator-based assessment using stationary driving simulators offers a safer evaluation and can be tailored to specific needs. However, these simulators are often only available to research-focused institutions due to their cost. To address this issue, our study proposes a system with the aforementioned properties aiming to enhance drivers' situational awareness, and foster positive emotional states, i.e., high valence and medium arousal, while assessing participants to prevent subpar performers from proceeding to the next stages of assessment and/or rehabilitation. In addition, this study introduces the speech-based assistant which provides timely guidance adaptable to the ever-changing context of the driving environment and vehicle state. The study's preliminary outcomes reveal encouraging progress, highlighting improved driving performance and positive emotional states when participants are engaged with the assistant during the assessment.","2024-08-30","2024-12-03 03:23:35","2024-12-03 03:23:35","","e35941","","16","10","","Heliyon","","","","","","","","","","","","","","","","","","","Large language model; CARLA driving simulator; Driving assessment; LLMs-integrated system; Vector database","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"4CXY8CD8","journalArticle","2024","Rajczakowska, Magdalena; Novakova, Iveta; Adediran, Adeolu; Perumal, Priyadharshini; Wallevik, Ólafur Haralds; Cwirzen, Andrzej","Frost durability of cementitious materials: What’s next?","Case Studies in Construction Materials","","2214-5095","10.1016/j.cscm.2024.e04014","https://www.sciencedirect.com/science/article/pii/S2214509524011653","Frost durability, a critical parameter for concrete, especially in harsh exposure regions, has been extensively researched, with almost four thousand papers published since the 1970s. However, a systematic mapping of this research is yet to be explored. This paper presents a novel approach based on Natural Language Processing (NLP) and machine learning to semi-automatically analyze the existing literature on frost durability of cementitious materials. The aim is to identify research gaps and provide insights for future work, offering a comprehensive understanding of the freeze and thaw (FT) research area. Data sets containing academic abstracts on FT tests have been created, and the identified articles are topically structured using a latent Dirichlet allocation (LDA) topic modeling approach. The publication volume associated with each topic over time has been quantified, providing an overview of the research landscape. The results show that NLP and t-SNE effectively review large volumes of technical text data, identifying 12 dominant themes in FT research, such as mechanical properties and material composition. Over recent decades, there has been a shift from focusing on structural performance to emerging topics like cracking and Supplementary Cementitious Materials (SCMs). Additionally, t-SNE and K-means clustering revealed four main clusters, suggesting future research should focus on the FT durability of eco-friendly materials, accelerated testing, and enhanced FT durability materials. These findings not only facilitate the identification of gaps and opportunities for future work but also have practical implications for developing more durable and sustainable concrete.","2024-12-01","2024-12-03 03:23:35","2024-12-03 03:23:35","","e04014","","","21","","Case Studies in Construction Materials","","","","","","","","","","","","","","","","","","","Topic modeling; Natural language processing (NLP); Concrete; Freeze-thaw","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XY28F7SM","journalArticle","2024","Zack, Travis; Lehman, Eric; Suzgun, Mirac; Rodriguez, Jorge A; Celi, Leo Anthony; Gichoya, Judy; Jurafsky, Dan; Szolovits, Peter; Bates, David W; Abdulnour, Raja-Elie E; Butte, Atul J; Alsentzer, Emily","Assessing the potential of GPT-4 to perpetuate racial and gender biases in health care: a model evaluation study","The Lancet Digital Health","","2589-7500","10.1016/S2589-7500(23)00225-X","https://www.sciencedirect.com/science/article/pii/S258975002300225X","Summary Background Large language models (LLMs) such as GPT-4 hold great promise as transformative tools in health care, ranging from automating administrative tasks to augmenting clinical decision making. However, these models also pose a danger of perpetuating biases and delivering incorrect medical diagnoses, which can have a direct, harmful impact on medical care. We aimed to assess whether GPT-4 encodes racial and gender biases that impact its use in health care. Methods Using the Azure OpenAI application interface, this model evaluation study tested whether GPT-4 encodes racial and gender biases and examined the impact of such biases on four potential applications of LLMs in the clinical domain—namely, medical education, diagnostic reasoning, clinical plan generation, and subjective patient assessment. We conducted experiments with prompts designed to resemble typical use of GPT-4 within clinical and medical education applications. We used clinical vignettes from NEJM Healer and from published research on implicit bias in health care. GPT-4 estimates of the demographic distribution of medical conditions were compared with true US prevalence estimates. Differential diagnosis and treatment planning were evaluated across demographic groups using standard statistical tests for significance between groups. Findings We found that GPT-4 did not appropriately model the demographic diversity of medical conditions, consistently producing clinical vignettes that stereotype demographic presentations. The differential diagnoses created by GPT-4 for standardised clinical vignettes were more likely to include diagnoses that stereotype certain races, ethnicities, and genders. Assessment and plans created by the model showed significant association between demographic attributes and recommendations for more expensive procedures as well as differences in patient perception. Interpretation Our findings highlight the urgent need for comprehensive and transparent bias assessments of LLM tools such as GPT-4 for intended use cases before they are integrated into clinical care. We discuss the potential sources of these biases and potential mitigation strategies before clinical implementation. Funding Priscilla Chan and Mark Zuckerberg.","2024-01-01","2024-12-03 03:23:35","2024-12-03 03:23:35","","e12-e22","","1","6","","The Lancet Digital Health","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RQ9CNHRS","journalArticle","2024","Li, Yange; Fu, Bangjie; Yin, Yueping; Hu, Xiewen; Wang, Wenpei; Wang, Weidong; Li, Xin; Long, Guanping","Review on the artificial intelligence-based methods in landslide detection and susceptibility assessment: Current progress and future directions","Intelligent Geoengineering","","3050-6190","10.1016/j.ige.2024.10.003","https://www.sciencedirect.com/science/article/pii/S305061902400003X","Landslides pose significant risks to human life, property, and the environment in mountainous regions. Effective detection and susceptibility assessment are essential for mitigating these hazards. Recent advancements in artificial intelligence (AI), particularly deep convolutional neural networks, have notably enhanced both the efficiency and accuracy in this field. In this review, we provide a comprehensive summary of the up-to-date studies and applications of AI-integrated methods in two key areas: landslide detection using remote sensing images and data-driven landslide susceptibility assessment. We summarize the primary AI-based neural network structures and the frameworks employed for these purposes. Overall, the current body of research indicates that AI-based approaches have reached a mature stage, with a convergence in methodologies leading to significant improvements in detection and assessment accuracy. Despite these advancements, challenges remain, particularly regarding data quality and standardization, robustness in complex environments, and the issue of overfitting. Addressing these limitations is crucial for advancing the application of AI methods in landslide mitigation. Future research should focus on developing solutions to these challenges, paving the way for more effective and widespread use of AI technologies in this critical area.","2024-12-01","2024-12-03 03:23:35","2024-12-03 03:23:35","","1-18","","1","1","","Intelligent Geoengineering","","","","","","","","","","","","","","","","","","","Deep learning; Machine learning; Artificial intelligence; Landslide detection; Susceptibility prediction","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"57XFUHD2","journalArticle","2024","Gnambs, Timo; Stein, Jan-Philipp; Appel, Markus; Griese, Florian; Zinn, Sabine","An Economical Measure of Attitudes Towards Artificial Intelligence in Work, Healthcare, and Education (ATTARI-WHE)","Computers in Human Behavior: Artificial Humans","","2949-8821","10.1016/j.chbah.2024.100106","https://www.sciencedirect.com/science/article/pii/S2949882124000665","Artificial intelligence (AI) has profoundly transformed numerous facets of both private and professional life. Understanding how people evaluate AI is crucial for predicting its future adoption and addressing potential barriers. However, existing instruments measuring attitudes towards AI often focus on specific technologies or cross-domain evaluations, while domain-specific measurement instruments are scarce. Therefore, this study introduces the nine-item Attitudes towards Artificial Intelligence in Work, Healthcare, and Education (ATTARI-WHE) scale. Using a diverse sample of N = 1,083 respondents from Germany, the psychometric properties of the instrument were evaluated. The results demonstrated low rates of missing responses, minimal response biases, and a robust measurement model that was invariant across sex, age, education, and employment status. These findings support the use of the ATTARI-WHE to assess AI attitudes in the work, healthcare, and education domains, with three items each. Its brevity makes it particularly well-suited for use in social surveys, web-based studies, or longitudinal research where assessment time is limited.","2024-11-28","2024-12-03 03:23:35","2024-12-03 03:23:35","","100106","","","","","Computers in Human Behavior: Artificial Humans","","","","","","","","","","","","","","","","","","","artificial intelligence; education; healthcare; attitudes; social survey; work","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SPMMPXIS","journalArticle","2024","Yigitcanlar, Tan; Senadheera, Sajani; Marasinghe, Raveena; Bibri, Simon Elias; Sanchez, Thomas; Cugurullo, Federico; Sieber, Renee","Artificial intelligence and the local government: A five-decade scientometric analysis on the evolution, state-of-the-art, and emerging trends","Cities","","0264-2751","10.1016/j.cities.2024.105151","https://www.sciencedirect.com/science/article/pii/S0264275124003652","In recent years, the rapid advancement of artificial intelligence (AI) technologies has significantly impacted various sectors, including public governance at the local level. However, there exists a limited understanding of the overarching narrative surrounding the adoption of AI in local governments and its future. Therefore, this study aims to provide a comprehensive overview of the evolution, current state-of-the-art, and emerging trends in the adoption of AI in local government. A comprehensive scientometric analysis was conducted on a dataset comprising 7112 relevant literature records retrieved from the Scopus database in October 2023, spanning over the last five decades. The study findings revealed the following key insights: (a) exponential technological advancements over the last decades ushered in an era of AI adoption by local governments; (b) the primary purposes of AI adoption in local governments include decision support, automation, prediction, and service delivery; (c) the main areas of AI adoption in local governments encompass planning, analytics, security, surveillance, energy, and modelling; and (d) under-researched but critical research areas include ethics of and public participation in AI adoption in local governments. This study informs research, policy, and practice by offering a comprehensive understanding of the literature on AI applications in local governments, providing valuable insights for stakeholders and decision-makers.","2024-09-01","2024-12-03 03:23:49","2024-12-03 03:23:49","","105151","","","152","","Cities","","","","","","","","","","","","","","","","","","","Smart city; GeoAI; Technology adoption; Artificial intelligence (AI); Local government; Municipality","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"UV2L2UIS","journalArticle","2024","Liu, Jinhao; Quan, Pei; Zhang, Wen","A Study on Fake Review Detection Based on RoBERTa and Behavioral Features","11th International Conference on Information Technology and Quantitative Management (ITQM 2024)","","1877-0509","10.1016/j.procs.2024.08.131","https://www.sciencedirect.com/science/article/pii/S1877050924018507","With the rapid development of e-commerce platforms, how to better identify and filter fake reviews has become an urgent issue for the healthy development of the e-commerce industry. However, traditional fake review identification methods cannot effectively solve this problem. They are easily affected by text changes, language differences, and context, and fake reviewers may take measures to blur their behavioral characteristics, making them difficult to detect by behavior-based algorithms. Large language models can capture the contextual relationships of the entire text through self-attention mechanisms, thereby understanding the overall meaning and emotional tendency of the review, which enables them to more effectively judge the authenticity of the review. In addition, large models have strong generalization ability and a certain degree of interpretability, which also makes them suitable for research on fake review text identification. Since fake reviews have an asymmetric distribution feature, this paper uses the RoBERTa model to extract information and combine it with the behavioral features in traditional research for model training. Compared with traditional methods, the accuracy rate is improved by nearly 3%.","2024-01-01","2024-12-03 03:23:49","2024-12-03 03:23:49","","1323-1330","","","242","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Large language model; Fake review detection; Imbalanced data","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"AX2BLL3Q","journalArticle","2024","Aich, Walid; Farahani, Somayeh Davoodabadi; Helforoush, Zarindokht; Darweesh, Moustafa S.; Kolsi, Lioua","Thermal behavior of ferrofluids in a microfin tube with rotating magnetic fields: Experimental analysis and artificial intelligence modeling strategies","Case Studies in Thermal Engineering","","2214-157X","10.1016/j.csite.2024.105129","https://www.sciencedirect.com/science/article/pii/S2214157X24011602","The utilization of various tools by researchers to enhance heat transfer (HT) in fluid flow is steadily on the rise. These tools encompass both active and passive methods. This study involved an experimental examination of HT and fluid flow within a micro-fin tube. The strategies were implemented to improve Nu: ferrofluid, micro-fins, and a rotating magnetic field. Before implementation, the magnetic ferrofluid was evaluated for particle size and stability. The results were validated, demonstrating a good correlation with the anticipated outcomes. The use of a microfin tube and ferrofluid can enhance HT about 7–14 % and 7–22 %, respectively. Additionally, a rotating magnetic field can improve HT by approximately 10%–37 %. An empirical correlation derived from experimental data is introduced for Nu prediction. Additionally, employing artificial intelligence techniques such as ANFIS and GMDH, the Nu has been successfully estimated, showcasing the effectiveness of these models in predicting Nu.","2024-09-01","2024-12-03 03:23:49","2024-12-03 03:23:49","","105129","","","61","","Case Studies in Thermal Engineering","","","","","","","","","","","","","","","","","","","Artificial intelligence; ANFIS; Ferrofluid; GMDH; Heat transfer; Microfin tube; Nusselt number; Rotating magnetic field","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"UWKGN365","journalArticle","2024","Samadhiya, Ashutosh; Agrawal, Rohit; Kumar, Anil; Luthra, Sunil","Bridging realities into organizations through innovation and productivity: Exploring the intersection of artificial intelligence, internet of things, and big data analytics in the metaverse environment using a multi-method approach","Decision Support Systems","","0167-9236","10.1016/j.dss.2024.114290","https://www.sciencedirect.com/science/article/pii/S0167923624001234","This study investigates how organizations may increase innovation and productivity through the Metaverse environment efficacy (MVEE), Artificial intelligence usage (AIU), Internet of Things usage (IoTU), and Big Data Analytics usage (BDAU). The study gathers responses from the gaming, information technology, and entertainment industries, using a multi-method involving Partial Least Squares Structural Equation Modeling, Fuzzy-set Qualitative Comparative Analysis, and Artificial Neural Networks to investigate how these technologies might be used to improve the linking of disparate realities in a business context. The use of AI in personalized and decision-support applications, IoT for real-time data collecting, and BDAU for an insights-driven strategy all combine to create a dynamic MVEE ecosystem. The research also delves into theoretical implications concerning the viability of using the MVEE to boost innovation and productivity. This research identifies the applications of using AI, IoT, and BDA to drive organizational performance in terms of innovation and productivity. Also, the research lays out the role of AI, IoT, and BDA in creating a dynamic metaverse ecosystem.","2024-10-01","2024-12-03 03:23:49","2024-12-03 03:23:49","","114290","","","185","","Decision Support Systems","","","","","","","","","","","","","","","","","","","Artificial intelligence; Integration; Big data analytics; Internet of things; Metaverse; Task-technology fit theory","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"WVCIVJ6R","journalArticle","2024","Li, Tong; Cui, Lizhen; Wu, Yu; Pandey, Rajiv; Liu, Hongdou; Dong, Junfu; Wang, Weijin; Xu, Zhihong; Song, Xiufang; Hao, Yanbin; Cui, Xiaoyong; Du, Jianqing; Zhang, Xuefu; Wang, Yanfen","Unveiling and advancing grassland degradation research using a BERTopic modelling approach","Journal of Integrative Agriculture","","2095-3119","10.1016/j.jia.2024.11.008","https://www.sciencedirect.com/science/article/pii/S2095311924003769","Grassland degradation presents overwhelming challenges to biodiversity, ecosystem services, and the socio-economic sustainability of dependent communities. However, a comprehensive synthesis of global knowledge on the frontiers and key areas of grassland degradation research has <pg=>not been achieved due to the limitations of traditional scientometrics methods. The present synthesis of information employed BERTopic, an advanced natural language processing tool, to analyze the extensive ecological literature on grassland degradation. We compiled a dataset of 4,504 publications from the Web of Science core collection database and used it to evaluate the geographic distribution and temporal evolution of different grassland types and available knowledge on the subject. Our analysis identified key topics in the global grassland degradation research domain, including the effects of grassland degradation on ecosystem functions, grassland ecological restoration and biodiversity conservation, erosion processes and hydrological models in grasslands, and others. The BERTopic analysis significantly outperforms traditional methods in identifying complex and evolving topics in large datasets of literature. Compared to traditional scientometrics analysis, BERTopic provides a more comprehensive perspective on the research areas, revealing not only popular topics but also emerging research areas that traditional methods may overlook, although scientometrics offers more specificity and detail. Therefore, we argue for the simultaneous use of both approaches to achieve more systematic and comprehensive assessments of specific research areas. This study represents an emerging application of BERTopic algorithms in ecological research, particularly in the critical research focused on global grassland degradation. It also highlights the need for integrating advanced computational methods in ecological research in this era of data explosion. Tools like the BERTopic algorithm are essential for enhancing our understanding of complex environmental problems, and it marks an important stride towards more sophisticated, data-driven analysis in ecology.","2024-11-04","2024-12-03 03:23:49","2024-12-03 03:23:49","","","","","","","Journal of Integrative Agriculture","","","","","","","","","","","","","","","","","","","systematic review; natural language processing; grassland degradation; knowledge synthesis; scientometrics","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"7Y89ZWDX","journalArticle","2024","Jin, Zhang; Goyal, S.B.; Rajawat, Anand Singh","The Informational Role of Artificial Intelligence in higher Education in the New era","International Conference on Machine Learning and Data Engineering (ICMLDE 2023)","","1877-0509","10.1016/j.procs.2024.04.096","https://www.sciencedirect.com/science/article/pii/S1877050924007725","The advent of artificial intelligence (AI) technology has ushered in an era of intelligent education, catalyzing profound changes in teaching methods and reshaping the roles of educators. This paper explores the intricate relationship between technology, education, and teachers by drawing upon relevant theories from the field of technological sociology, particularly the concept of ""one skill for one person."" It scrutinizes the evolving role of teachers in the context of artificial intelligence, delving into the mechanisms that underpin their functions in this rapidly evolving educational landscape. Through a historical examination of educational technology innovation, this paper delineates a three-tiered transformation in the relationship between artificial intelligence and university teachers, spanning from ""technology exclusion"" to ""technology complementarity"" and finally to ""technology dependence."" The paper used a mixed-methods strategy, combining qualitative and quantitative approaches. These phases illustrate the evolving synergy between human educators and AI, ultimately contributing to the modernization of the educational system and the enhancement of educational capabilities. This research sheds light on how the integration of AI can enhance the educational experience and empower educators, ensuring the continued advancement of education in the digital age.","2024-01-01","2024-12-03 03:23:49","2024-12-03 03:23:49","","1008-1023","","","235","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Educational Technology; Artificial Intelligence; Intelligent Education; Teachers' Role; Technology Dependence","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KYAF5EPF","journalArticle","2024","Lebeña, Nuria; Pérez, Alicia; Casillas, Arantza","Quantifying decision support level of explainable automatic classification of diagnoses in Spanish medical records","Computers in Biology and Medicine","","0010-4825","10.1016/j.compbiomed.2024.109127","https://www.sciencedirect.com/science/article/pii/S0010482524012125","Background and Objective: In the realm of automatic Electronic Health Records (EHR) classification according to the International Classification of Diseases (ICD) there is a notable gap of non-black box approaches and more in Spanish, which is also frequently ignored in clinical language classification. An additional gap in explainability pertains to the lack of standardized metrics for evaluating the degree of explainability offered by distinct techniques. Methods: We address the classification of Spanish electronic health records, using methods to explain the predictions and improve the decision support level. We also propose Leberage a novel metric to quantify the decision support level of the explainable predictions. We aim to assess the explanatory ability derived from three model-independent methods based on different theoretical frameworks: SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Integrated Gradients (IG). We develop a system based on longformers that can process long documents and then use the explainability methods to extract the relevant segments of text in the EHR that motivated each ICD. We then measure the outcome of the different explainability methods by implementing a novel metric. Results: Our results beat those that carry out the same task by 7%. In terms of explainability degree LIME appears as a stronger technique compared to IG and SHAP. Discussion: Our research reveals that the explored techniques are useful for explaining the output of black box models as the longformer. In addition, the proposed metric emerges as a good choice to quantify the contribution of explainability techniques.","2024-11-01","2024-12-03 03:23:49","2024-12-03 03:23:49","","109127","","","182","","Computers in Biology and Medicine","","","","","","","","","","","","","","","","","","","Transformers; Clinical natural language processing; Electronic health records in Spanish; Explainability in large language models; International classification of diseases","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"7GBXBKVK","journalArticle","2024","Fransen, Stefan J.; van Lohuizen, Quintin; Roest, Christian; Yakar, Derya; Kwee, Thomas C.","What makes a good scientific presentation on artificial intelligence in medical imaging?","Clinical Imaging","","0899-7071","10.1016/j.clinimag.2024.110212","https://www.sciencedirect.com/science/article/pii/S0899707124001426","Purpose Adequate communication of scientific findings is crucial to enhance knowledge transfer. This study aimed to determine the key features of a good scientific oral presentation on artificial intelligence (AI) in medical imaging. Methods A total of 26 oral presentations dealing with original research on AI studies in medical imaging at the 2023 RSNA annual meeting were included and systematically assessed by three observers. The presentation quality of the research question, inclusion criteria, reference standard, method, results, clinical impact, presentation clarity, presenter engagement, and the presentation's quality of knowledge transfer were assessed using five-point Likert scales. The number of slides, the average number of words per slide, the number of interactive slides, the number of figures, and the number of tables were also determined for each presentation. Mixed-effects ordinal regression was used to assess the association between the above-mentioned variables and the quality of knowledge transfer of the presentation. Results A significant positive association was found between the quality of the presentation of the research question and the presentation's quality of knowledge transfer (odds ratio [OR]: 2.5, P = 0.005). The average number of words per slide was significantly negatively associated with the presentation's quality of knowledge transfer (OR: 0.9, P < 0.001). No other significant associations were found. Conclusion Researchers who orally present their scientific findings in the field of AI and medical imaging should pay attention to clearly communicating their research question and minimizing the number of words per slide to maximize the value of their presentation.","2024-08-01","2024-12-03 03:23:49","2024-12-03 03:23:49","","110212","","","112","","Clinical Imaging","","","","","","","","","","","","","","","","","","","Knowledge transfer; Artificial intelligence; Communication; Medical imaging; Congresses and conferences","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"HBIQRRJM","journalArticle","2024","Elmousalami, Haytham Hesham; Elshaboury, Nehal; Ibrahim, Ahmed Hussien; Elyamany, Ahmed Hussien","Bayesian Optimized Ensemble Learning System for Predicting Conceptual Cost and Construction Duration of Irrigation Improvement Systems","KSCE Journal of Civil Engineering","","1226-7988","10.1016/j.kscej.2024.100014","https://www.sciencedirect.com/science/article/pii/S1226798824051602","Linear construction projects, such as pipeline irrigation projects, are prone to delays and cost overruns owing to inaccurate cost and duration estimates. The research gap pertains to studies that concentrated exclusively on predicting costs or durations using backbox artificial intelligence models. Consequently, this study introduces an innovative approach that utilizes explainable artificial intelligence to forecast the conceptual cost and duration of irrigation projects simultaneously. This study analyzed data from 1,277 historical cases using factor analysis and stepwise regression to distill 25 parameters down to five key drivers. It evaluates 12 machine learning models, including multiple linear regression, artificial neural networks, and decision tree-based ensemble methods. Bayesian optimization was employed to fine-tune the performance of each algorithm. The light gradient boosting machine is identified as the most effective algorithm for cost prediction, with a Mean Absolute Percentage Error (MAPE) of 2.989% and an Adjusted Determination Coefficient (R⁎2) of 0.931. For duration prediction, the extremely randomized tree model stands out, achieving a MAPE of 2.533% and an R⁎2 of 0.961. The study further employs the Shapley additive explanation technique to improve the interpretability of the key drivers used for predicting both the budget and the timeline.","2024-09-17","2024-12-03 03:23:49","2024-12-03 03:23:49","","100014","","","","","KSCE Journal of Civil Engineering","","","","","","","","","","","","","","","","","","","Bayesian optimization; Conceptual project cost and duration; Explainable artificial intelligence (XAI); Irrigation improvement projects","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"TD5DPXNH","journalArticle","2024","Gimeno-Ballester, Vicente; Trigo-Vicente, Cristina","[Translated article] The role of artificial intelligence in scientific publishing: perspectives from hospital pharmacy","Farmacia Hospitalaria","","1130-6343","10.1016/j.farma.2024.07.009","https://www.sciencedirect.com/science/article/pii/S1130634324001259","The article examines the impact of artificial intelligence on scientific writing, with a particular focus on its application in hospital pharmacy. It analyses artificial intelligence tools that enhance information retrieval, literature analysis, writing quality, and manuscript drafting. Chatbots like Consensus, along with platforms such as Scite and SciSpace, enable precise searches in scientific databases, providing evidence-based responses and references. SciSpace facilitates the generation of comparative tables and the formulation of queries regarding studies, while ResearchRabbit maps the scientific literature to identify trends. Tools like DeepL and ProWritingAid improve writing quality by correcting grammatical, stylistic, and plagiarism errors. A.R.I.A. enhances reference management, and Jenny AI assists in overcoming writer's block. Python libraries such as langchain enable advanced semantic searches and the creation of agents. Despite their benefits, artificial intelligence raises ethical concerns including biases, misinformation, and plagiarism. The importance of responsible use and critical review by experts is emphasised. In hospital pharmacy, artificial intelligence can enhance efficiency and precision in research and scientific communication. Pharmacists can use these tools to stay updated, enhance the quality of their publications, optimise information management, and facilitate clinical decision-making. In conclusion, artificial intelligence is a powerful tool for hospital pharmacy, provided it is used responsibly and ethically. Resumen El artículo explora el impacto de la Inteligencia artificial en la escritura científica, con especial atención a su aplicación en la farmacia hospitalaria. Se analizan herramientas de inteligencia artificial que optimizan la búsqueda de información, el análisis de la literatura, la calidad de la escritura y la redacción de manuscritos. Chatbots como Consensus, junto con plataformas como Scite y SciSpace, facilitan la búsqueda precisa en bases de datos científicas, ofreciendo respuestas con evidencia y referencias. SciSpace permite la generación de tablas comparativas y la formulación de preguntas sobre estudios, mientras que ResearchRabbit mapea la literatura científica para identificar tendencias. DeepL y ProWritingAid mejoran la calidad de la escritura al corregir errores gramaticales, de estilo y plagio. A.R.I.A. optimiza la gestión de referencias, mientras que Jenny AI ayuda a superar el bloqueo del escritor. Librerías de Python como langchain permiten realizar búsquedas semánticas avanzadas y la creación de agentes. A pesar de sus beneficios, la inteligencia artificial plantea preocupaciones éticas como sesgos, desinformación y plagio. Se destaca la importancia de un uso responsable y la revisión crítica por expertos. En la farmacia hospitalaria, la inteligencia artificial puede mejorar la eficiencia y la precisión en la investigación y la comunicación científica. Los farmacéuticos pueden utilizar estas herramientas para mantenerse actualizados, mejorar la calidad de sus publicaciones, optimizar la gestión de la información y facilitar la toma de decisiones clínicas. En conclusión, la inteligencia artificial es una herramienta poderosa para la farmacia hospitalaria, siempre que se utilice de manera responsable y ética.","2024-09-01","2024-12-03 03:23:49","2024-12-03 03:23:49","","T246-T251","","5","48","","Farmacia Hospitalaria","","","","","","","","","","","","","","","","","","","Research; Artificial intelligence; Chatbots; Ethics; Scientific writing; AI tools; Escritura científica; Ética; Farmacia Hospitalaria; Herramientas inteligencia Artificial; Hospital pharmacy; Inteligencia Artificial; Investigación; Publicación científica; Scientific publications","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"B7LEBKE5","journalArticle","2024","Chaudhari, Akshat; Guntuboina, Chakradhar; Huang, Hongshuo; Farimani, Amir Barati","AlloyBERT: Alloy property prediction with large language models","Computational Materials Science","","0927-0256","10.1016/j.commatsci.2024.113256","https://www.sciencedirect.com/science/article/pii/S0927025624004774","The pursuit of novel alloys tailored to specific requirements poses significant challenges for researchers in the field. This underscores the importance of developing predictive techniques for essential physical properties of alloys based on their chemical composition and processing parameters. This study introduces AlloyBERT, a transformer encoder-based model designed to predict properties such as elastic modulus and yield strength of alloys using textual inputs. Leveraging the pre-trained RoBERTa and BERT encoder model as its foundation, AlloyBERT employs self-attention mechanisms to establish meaningful relationships between words, enabling it to interpret human-readable input and predict target alloy properties. By combining a tokenizer trained on our textual data and a RoBERTa encoder pre-trained and fine-tuned for this specific task, we achieved a mean squared error (MSE) of 0.00015 on the Multi Principal Elemental Alloys (MPEA) data set and 0.00527 on the Refractory Alloy Yield Strength (RAYS) dataset using BERT encoder. This surpasses the performance of shallow models, which achieved a best-case MSE of 0.02376 and 0.01459 on the MPEA and RAYS datasets respectively. Our results highlight the potential of language models in material science and establish a foundational framework for text-based prediction of alloy properties that does not rely on complex underlying representations, calculations, or simulations.","2024-09-01","2024-12-03 03:23:49","2024-12-03 03:23:49","","113256","","","244","","Computational Materials Science","","","","","","","","","","","","","","","","","","","Large language models; Alloys; Elastic properties; Yield strength","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"AC3WK75U","journalArticle","2024","Tian, Zhou; Yi, Deng","Application of artificial intelligence based on sensor networks in student mental health support system and crisis prediction","Measurement: Sensors","","2665-9174","10.1016/j.measen.2024.101056","https://www.sciencedirect.com/science/article/pii/S2665917424000321","The psychological health problems of students are directly related to the stability and development of society. With the development of society and the fierce competition in education, students are facing increasing psychological pressure, leading to increasingly prominent mental health problems. This not only seriously affects students' lives and studies, but also has a negative impact on the entire society. This article develops a student mental health support system based on artificial intelligence and big data analysis, through research and analysis of artificial intelligence and big data analysis. Then, based on the needs of students' mental health support and crisis prediction, corresponding algorithms and models are designed to apply artificial intelligence and big data analysis to the students' mental health support system, and relevant experiments and tests are conducted. The research results indicate that through this system, students can receive personalized mental health support and guidance, and can predict the possibility of student mental health crises. This system has achieved significant results in providing students with mental health support and predicting crises.","2024-04-01","2024-12-03 03:23:49","2024-12-03 03:23:49","","101056","","","32","","Measurement: Sensors","","","","","","","","","","","","","","","","","","","Artificial intelligence; Big data analysis; Crisis prediction; Psychological health of students; System design","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"IWLMLPQX","journalArticle","2024","González-Márquez, Rita; Schmidt, Luca; Schmidt, Benjamin M.; Berens, Philipp; Kobak, Dmitry","The landscape of biomedical research","Patterns","","2666-3899","10.1016/j.patter.2024.100968","https://www.sciencedirect.com/science/article/pii/S266638992400076X","Summary The number of publications in biomedicine and life sciences has grown so much that it is difficult to keep track of new scientific works and to have an overview of the evolution of the field as a whole. Here, we present a two-dimensional (2D) map of the entire corpus of biomedical literature, based on the abstract texts of 21 million English articles from the PubMed database. To embed the abstracts into 2D, we used the large language model PubMedBERT, combined with t-SNE tailored to handle samples of this size. We used our map to study the emergence of the COVID-19 literature, the evolution of the neuroscience discipline, the uptake of machine learning, the distribution of gender imbalance in academic authorship, and the distribution of retracted paper mill articles. Furthermore, we present an interactive website that allows easy exploration and will enable further insights and facilitate future research.","2024-06-14","2024-12-03 03:23:49","2024-12-03 03:23:49","","100968","","6","5","","Patterns","","","","","","","","","","","","","","","","","","","machine learning; embeddings; gender bias; language models; metascience; publications; PubMed; retractions; visualization","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"9JFMVENR","journalArticle","2024","Zhou, Wen","Dilemma and coping strategies of news communication based on artificial intelligence and big data","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e25398","https://www.sciencedirect.com/science/article/pii/S2405844024014294","News dissemination is an important way for people to obtain information. With the development of new technologies, traditional news dissemination models have been impacted. It has problems with information filtering and bias, and has certain limitations in news quality, dissemination efficiency, etc., which makes it difficult to effectively meet people's information needs. In order to improve the quality and efficiency of news dissemination, promote the positive impact of news dissemination on society, this article combined artificial intelligence and big data technology to conduct in-depth research on the difficulties and coping strategies of news dissemination. This article first analyzed the characteristics and functions and influencing factors of news dissemination, then provided an overview of the difficulties and coping strategies in news dissemination. Finally, using association rule algorithms, personalized recommendations for news dissemination are achieved. To verify the effectiveness of artificial intelligence and big data in coping with the dilemma of news dissemination, this article conducted experimental analysis from the perspectives of news content quality, dissemination efficiency, objectivity, and dissemination cost. The experimental results show that under the application of news dissemination strategies based on artificial intelligence and big data, the quality of news content and dissemination efficiency have been improved by 4.76 % and 3.63 %, respectively. The conclusion indicates that artificial intelligence and big data can help improve the quality and dissemination efficiency of news content, and meet the diverse needs of the public for information.","2024-02-15","2024-12-03 03:23:49","2024-12-03 03:23:49","","e25398","","3","10","","Heliyon","","","","","","","","","","","","","","","","","","","Artificial intelligence and big data; Coping strategies; News communication; News communication dilemma","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RUVLFFB4","journalArticle","2024","Jeong, Jae-Seung; Kang, Takho; Ju, Hyunsu; Cho, Chi-Hyun","Novel approach exploring the correlation between presepsin and routine laboratory parameters using explainable artificial intelligence","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e33826","https://www.sciencedirect.com/science/article/pii/S2405844024098578","Although presepsin, a crucial biomarker for the diagnosis and management of sepsis, has gained prominence in contemporary medical research, its relationship with routine laboratory parameters, including demographic data and hospital blood test data, remains underexplored. This study integrates machine learning with explainable artificial intelligence (XAI) to provide insights into the relationship between presepsin and these parameters. Advanced machine learning classifiers provide a multilateral view of data and play an important role in highlighting the interrelationships between presepsin and other parameters. XAI enhances analysis by ensuring transparency in the model's decisions, especially in selecting key parameters that significantly enhance classification accuracy. Utilizing XAI, this study successfully identified critical parameters that increased the predictive accuracy for sepsis patients, achieving a remarkable ROC AUC of 0.97 and an accuracy of 0.94. This breakthrough is possibly attributed to the comprehensive utilization of XAI in refining parameter selection, thus leading to these significant predictive metrics. The presence of missing data in datasets is another concern; this study addresses it by employing Extreme Gradient Boosting (XGBoost) to manage missing data, effectively mitigating potential biases while preserving both the accuracy and relevance of the results. The perspective of examining data from higher dimensions using machine learning transcends traditional observation and analysis. The findings of this study hold the potential to enhance patient diagnoses and treatment, underscoring the value of merging traditional research methods with advanced analytical tools.","2024-07-15","2024-12-03 03:23:49","2024-12-03 03:23:49","","e33826","","13","10","","Heliyon","","","","","","","","","","","","","","","","","","","Explainable artificial intelligence (XAI); Machine learning classifiers; Missing data management; Presepsin; Routine laboratory parameters","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"X9P9TJZ7","journalArticle","2024","Khanduja, Namit; Kumar, Nishant; Chauhan, Arun","Telugu language hate speech detection using deep learning transformer models: Corpus generation and evaluation","Systems and Soft Computing","","2772-9419","10.1016/j.sasc.2024.200112","https://www.sciencedirect.com/science/article/pii/S2772941924000413","In today's digital era, social media has become a new tool for communication and sharing information, with the availability of high-speed internet it tends to reach the masses much faster. Lack of regulations and ethics have made advancement in the proliferation of abusive language and hate speech has become a growing concern on social media platforms in the form of posts, replies, and comments towards individuals, groups, religions, and communities. However, the process of classification of hate speech manually on online platforms is cumbersome and impractical due to the excessive amount of data being generated. Therefore, it is crucial to automatically filter online content to identify and eliminate hate speech from social media. Widely spoken resource-rich languages like English have driven the research and achieved the desired result due to the accessibility of large corpora, annotated datasets, and tools. Resource-constrained languages are not able to achieve the benefits of advancement due to a lack of data corpus and annotated datasets. India has diverse languages that change with demographics and languages that have limited data availability and semantic differences. Telugu is one of the low-resource Dravidian languages spoken in the southern part of India. In this paper, we present a monolingual Telugu corpus consisting of tweets posted on Twitter annotated with hate and non-hate labels and experiments to provide a comparison of state-of-the-art fine-tuned deep learning models (mBERT, DistilBERT, IndicBERT, NLLB, Muril, RNN+LSTM, XLM-RoBERTa, and Indic-Bart). Through transfer learning and hyperparameter tuning, the models are compared for their effectiveness in classifying hate speech in Telugu text. The fine-tuned mBERT model outperformed all other fine-tuned models achieving an accuracy of 98.2. The authors also propose a deployment model for social media accounts.","2024-12-01","2024-12-03 03:23:50","2024-12-03 03:23:50","","200112","","","6","","Systems and Soft Computing","","","","","","","","","","","","","","","","","","","Deep learning; Transformers; NLP; Hate speech; Low-resource languages; Offensive text","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"MDD5DGLK","journalArticle","2024","Shuqair, Saleh; Pinto, Diego Costa; Lancelot Miltgen, Caroline; Viglia, Giampaolo","When powerful artificial intelligence backfires","International Journal of Hospitality Management","","0278-4319","10.1016/j.ijhm.2024.103778","https://www.sciencedirect.com/science/article/pii/S0278431924000902","Despite recent advancements in artificial intelligence (AI) in hospitality, little is known about its unintended consequences on consumers’ privacy concerns. Through an empirical package combining qualitative and quantitative evidence, this research reveals that framing AI as “powerful” enhances privacy concerns (Studies 1–5) by reducing AI-control over data (Study 3). Notably, such effects are reduced in consumer-human agent interactions and increased in consumer-AI ones (Study 4). Finally, interventions providing privacy guarantees can reduce privacy concerns stemming from powerful artificial agents and enhance willingness to disclose data (Study 5). Our findings highlight the unique issues arising from human-AI interactions when using powerful AI and their influence on consumers’ privacy concerns and provide practical implications for hospitality managers.","2024-07-01","2024-12-03 03:23:50","2024-12-03 03:23:50","","103778","","","120","","International Journal of Hospitality Management","","","","","","","","","","","","","","","","","","","Hospitality; Artificial intelligence; AI-control; Human-AI interactions; Power; Privacy concerns","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"L99B6YJJ","journalArticle","2024","Bruno, Katelyn A.; Fradley, Michael G.; Brown, Sherry-Ann; Guha, Avirup; Cousin, Lakeshia; Guo, Yi; O'Dell, Walter G.; Smuder, Ashely J.; Yang, Shuang; Braithwaite, Dejana; Pepine, Carl J.; Gong, Yan","Racial/ethnic disparities, artificial intelligence, and cutting-edge research: Proceedings from the 2023 Florida cardio-oncology symposium","American Heart Journal Plus: Cardiology Research and Practice","","2666-6022","10.1016/j.ahjo.2024.100469","https://www.sciencedirect.com/science/article/pii/S2666602224001125","","2024-10-01","2024-12-03 03:23:50","2024-12-03 03:23:50","","100469","","","46","","American Heart Journal Plus: Cardiology Research and Practice","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XE76SE84","journalArticle","2024","González-Pérez, Yared; Montero Delgado, Alfredo; Martinez Sesmero, Jose Manuel","[Translated article] Introducing artificial intelligence to hospital pharmacy departments","Nuevos roles y retos del farmacéutico de hospital","","1130-6343","10.1016/j.farma.2024.04.001","https://www.sciencedirect.com/science/article/pii/S1130634324000515","Artificial intelligence is a broad concept that includes the study of the ability of computers to perform tasks that would normally require the intervention of human intelligence. By exploiting large volumes of healthcare data, Artificial intelligence algorithms can identify patterns and predict outcomes, which can help healthcare organizations and their professionals make better decisions and achieve better results. Machine learning, deep learning, neural networks, or natural language processing are among the most important methods, allowing systems to learn and improve from data without the need for explicit programming. Artificial intelligence has been introduced in biomedicine, accelerating processes, improving accuracy and efficiency, and improving patient care. By using Artificial intelligence algorithms and machine learning, hospital pharmacists can analyze a large volume of patient data, including medical records, laboratory results, and medication profiles, aiding them in identifying potential drug–drug interactions, assessing the safety and efficacy of medicines, and making informed recommendations. Artificial intelligence integration will improve the quality of pharmaceutical care, optimize processes, promote research, deploy open innovation, and facilitate education. Hospital pharmacists who master Artificial intelligence will play a crucial role in this transformation. Resumen La Inteligencia artificial es un concepto amplio que comprende el estudio de la capacidad de los ordenadores para llevar a cabo tareas que normalmente requerirían la intervención de la inteligencia humana. Mediante la explotación de grandes volúmenes de datos sanitarios los algoritmos de inteligencia artificial pueden identificar patrones y predecir resultados, lo que puede ayudar a las organizaciones sanitarias y sus profesionales, a tomar mejores decisiones y alcanzar mejorar resultados. Los métodos de aprendizaje automático, aprendizaje profundo, redes neuronales o el procesamiento natural del lenguaje son de los más importantes, permitiendo a los sistemas aprender y mejorar a partir de datos sin necesidad de programación explícita. La Inteligencia artificial se ha introducido en la biomedicina, acelerando procesos, mejorando la precisión y eficiencia, y mejorando la atención al paciente. Mediante el uso de algoritmos de iIA y aprendizaje automático, los farmacéuticos de hospital pueden analizar un gran volumen de datos de pacientes, incluidos registros médicos, resultados de laboratorio y perfiles de medicamentos, ayudándolos a identificar posibles interacciones entre medicamentos, evaluar su seguridad y eficacia, así como tomar decisiones mejor informadas. La integración de la Inteligencia artificial mejorará la calidad de la atención farmacéutica, optimizará los procesos, promoverá la investigación, implementará la innovación abierta y facilitará la formación. Los farmacéuticos hospitalarios que dominen la Inteligencia artificial desempeñarán un papel crucial en esta transformación.","2024-07-01","2024-12-03 03:23:50","2024-12-03 03:23:50","","TS35-TS44","","","48","","Farmacia Hospitalaria","","","","","","","","","","","","","","","","","","","Deep learning; Machine learning; Artificial intelligence; Hospital pharmacy; Inteligencia Artificial; Aprendizaje automático; Aprendizaje profundo; Farmacia hospitalaria; Neuronal networks and natural language processing; Redes neuronales y procesamiento natural del lenguaje","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"NZR6DWZX","journalArticle","2024","Wang, Ronghua; Zhuang, Peng","A Strategy for Network multi-layer Information Fusion Based on Multimodel in User Emotional Polarity Analysis","International Journal of Cognitive Computing in Engineering","","2666-3074","10.1016/j.ijcce.2024.11.007","https://www.sciencedirect.com/science/article/pii/S2666307424000500","Emotional analysis is an important research direction in natural language processing, which aims to automatically recognize and understand emotions and emotional polarity in texts. The study employed multiple emotional analysis models to analyze emotions in text data. Then the emotional analysis results of different models were integrated to improve accuracy through a hierarchical information fusion strategy. Meanwhile, graph embedding algorithms such as DeepWalk and Node2vec were utilized to process node information in graph data. The embedding representation of graph nodes was utilized for emotional analysis of nodes. In addition, based on models such as graph convolutional neural networks for message passing, contextual information of nodes was obtained to improve emotional analysis performance. These results indicated that the proportion of passive users was higher than that of active users, at 57.14% and 39.68%, respectively. Despite the large number of negative users, the frequency of connections between active users was significantly higher than between negative users. Multiple algorithms were utilized for emotional analysis and prediction. The AUC curve performed well, but the accuracy was only 56.69%, which needs improvement. The study also evaluated the root mean square error of network predictions, with errors typically below 0.2 in large networks, demonstrating relatively accurate prediction results. This guides the further development of emotional analysis technology, promoting the research and improvement of more accurate and reliable emotional analysis methods.","2024-11-28","2024-12-03 03:23:50","2024-12-03 03:23:50","","","","","","","International Journal of Cognitive Computing in Engineering","","","","","","","","","","","","","","","","","","","Social media; Emotional analysis; Emotional polarity; multi-layer information fusion; multi-modal data","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"FP8PPD9G","journalArticle","2024","Cousineau, Carter; Dara, Rozita; Chowdhury, Ataharul","Trustworthy AI: AI developers’ lens to implementation challenges and opportunities","Data and Information Management","","2543-9251","10.1016/j.dim.2024.100082","https://www.sciencedirect.com/science/article/pii/S2543925124000184","As organizations continue to embrace the use of artificial intelligence (AI) systems, it is crucial to ensure that these AI systems can be trusted. However, there is still a significant gap between research on trustworthy AI and its implementation in real-world applications. To address this issue, we sought to explore the perspectives of AI developers and the challenges they face in creating trustworthy AI systems. This exploratory study involved conducting interviews with 19 AI developers. We identified key challenges faced by AI developers due to the immature state of trustworthy AI, inconsistent global regulatory landscape, a lack of standardized definitions of key concepts, limited tools and standards for practical implementation in organizations. This paper provides recommendations for organizations to invest in trustworthy AI processes and practices, this includes building a foundation for trustworthy AI specific to their organization, adopting an organizational approach to trustworthy AI culture, and providing proper data infrastructures to support AI developers in creating trustworthy AI systems. By investing in trustworthy AI practices, organizations can prepare for evolving regulations and ensure that their AI systems are reliable and trustworthy.","2024-10-22","2024-12-03 03:23:50","2024-12-03 03:23:50","","100082","","","","","Data and Information Management","","","","","","","","","","","","","","","","","","","Artificial intelligence; Data; AI developers; Responsible artificial intelligence","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"IDFT2BVZ","journalArticle","2024","Omeish, Fandi; Al Khasawneh, Mohammad; Khair, Nadine","Investigating the impact of AI on improving customer experience through social media marketing: An analysis of Jordanian Millennials","Computers in Human Behavior Reports","","2451-9588","10.1016/j.chbr.2024.100464","https://www.sciencedirect.com/science/article/pii/S2451958824000976","The advent of artificial intelligence (AI) has triggered a significant evolution in the sphere of social media. This research is designed to explore the influence of AI technologies in user experience under Social Media Marketing examining augmented reality, virtual influencers, and chatbots in Jordanian Millennials. The associations were assessed with the importance analysis in the data using Smart PLS 4 and a combination of direct hypothesis testing and mediation analysis. With that being said, our results suggest a non-negligible effect of AI on the social media user journey. In addition, we find that chatbots, virtual influencers, and augmented reality can effectively mitigate the role of user experience in user attitudes toward AI. Realizations like these contribute to the epistemology of the area and provide actionable input for researchers and professionals.","2024-08-01","2024-12-03 03:23:50","2024-12-03 03:23:50","","100464","","","15","","Computers in Human Behavior Reports","","","","","","","","","","","","","","","","","","","Artificial intelligence; User experience; Augmented reality; Social media; Chat bots; Virtual influencers","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"P5QQU9QA","journalArticle","2023","Wang, Yu-Cheng; Chen, Tin-Chih Toly; Chiu, Min-Chi","A systematic approach to enhance the explainability of artificial intelligence in healthcare with application to diagnosis of diabetes","Healthcare Analytics","","2772-4425","10.1016/j.health.2023.100183","https://www.sciencedirect.com/science/article/pii/S2772442523000503","Explainable artificial intelligence (XAI) tools are used to enhance the applications of existing artificial intelligence (AI) technologies by explaining their execution processes and results. In most past research, XAI tools and techniques are typically applied to only the inference part of the AI application. This study proposes a systematic approach to enhance the explainability of AI applications in healthcare. Several AI applications for type 2 diabetes diagnosis are taken as examples to illustrate the applicability of the proposed methodology. According to experimental results, the XAI tools and technologies in the proposed methodology were more diverse than those in the past research. In addition, an artificial neural network was approximated to a simpler and more intuitive classification and regression tree (CART) using local interpretable model-agnostic explanation (LIME). The extracted rules were used to recommend actions to the users to restore their health.","2023-11-01","2024-12-03 03:23:50","2024-12-03 03:23:50","","100183","","","3","","Healthcare Analytics","","","","","","","","","","","","","","","","","","","Artificial intelligence; Healthcare; Explainable artificial intelligence; Diabetes diagnosis; Local interpretable model-agnostic explanation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2JECVFGM","journalArticle","2024","Nashwan, Abdulqadir J.; Rao, Asad Gul","Integrating Traditional Chinese Medicine and Artificial Intelligence for insomnia: A promising frontier","Brain Behavior and Immunity Integrative","","2949-8341","10.1016/j.bbii.2024.100071","https://www.sciencedirect.com/science/article/pii/S2949834124000278","Insomnia, a common condition that significantly impacts health and daily life, is often treated with medications that can have adverse effects. Traditional Chinese Medicine (TCM) offers non-drug therapies for insomnia, but their diverse methods and efficacy require systematic evaluation. This letter explores the integration of TCM and Artificial Intelligence (AI) to enhance insomnia treatment. AI can personalize treatment plans, provide predictive analytics, monitor progress, and enhance research, potentially increasing the efficacy of TCM interventions. This combined approach promises more effective, personalized, and adaptive treatments for insomnia.","2024-07-01","2024-12-03 03:23:50","2024-12-03 03:23:50","","100071","","","7","","Brain Behavior and Immunity Integrative","","","","","","","","","","","","","","","","","","","Artificial intelligence (AI); Complementary & Alternative Medicine (CAM); Insomnia; Personalized Medicine; Traditional Chinese Medicine (TCM)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"GTH99HTU","journalArticle","2024","Kipkemoi, Patricia; Mufford, Mary S.; Akena, Dickens; Alemayehu, Melkam; Atwoli, Lukoye; Chibnik, Lori B.; Gelaye, Bizu; Gichuru, Stella; Kariuki, Symon M.; Koenen, Karestan C.; Kwobah, Edith; Kyebuzibwa, Joseph; Mwema, Rehema M.; Newton, Charles R.J.C.; Pretorius, Adele; Stein, Dan J.; Stevenson, Anne; Stroud, Rocky E.; Teferra, Solomon; Zingela, Zukiswa; Post, Kristianna; Korte, Kristina J.","Evaluation of the psychometric properties of the UBACC questionnaire in a multi-country psychiatric study in Africa","Comprehensive Psychiatry","","0010-440X","10.1016/j.comppsych.2024.152526","https://www.sciencedirect.com/science/article/pii/S0010440X24000774","Background The University of California, San Diego Brief Assessment of Capacity to Consent (UBACC) is a tool to assess the capacity of participants to consent in psychiatric research. However, little is known about the psychometric properties in low and middle-income countries. This study aimed to examine the psychometric properties of the UBACC. Methods We examined the reliability, latent factor structure, and item response of the first attempt of the UBACC items in a sample of 32,208 adults (16,467 individuals with psychosis and 15,741 controls) in Ethiopia, Kenya, South Africa, and Uganda; exploring these properties in the full sample and stratified by country, diagnostic status, sex, and ethnolinguistic language groups. Results Exploratory factor analysis (EFA) suggested a two-factor model for the overall sample. However, a three-factor model was more appropriate when examining the latent structure across country, language, and sex. Confirmatory factor analyses (CFA) revealed an adequately fitting three-factor model for the full sample and across country, sex, and language. A two-factor model, however, was more appropriate for English and Amharic languages. Across all groups, the internal consistency of the UBACC was low, indicating below-threshold reliability (Cronbach's α (95 % CI = 0.58 (0.57–0.59). Using a multidimensional item-response theory framework for the full sample revealed that UBACC item 8, measuring understanding of the benefits of study participation, was the most discriminating item. Many of the other items had below-threshold discriminating characteristics. Conclusion EFA and CFA converged towards a two and three-dimensional structure for the UBACC, in line with the developers of the original scale. The differences in properties between populations and language groups, low internal consistency, and below-threshold item functioning suggest that investigations into the cultural and linguistic nuances are still warranted. Understanding the utility of consent tools, such as the UBACC, in underrepresented populations will be a part of the larger process which ensures that research participants are adequately protected.","2024-11-01","2024-12-03 03:23:50","2024-12-03 03:23:50","","152526","","","135","","Comprehensive Psychiatry","","","","","","","","","","","","","","","","","","","Informed consent; Africa; Genetic studies; Psychometric properties; UBACC","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"344DLFLP","journalArticle","2024","Saksenberg, Danny; Mukherjee, Sandip; Zafar, Mohammad A.; Ziganshin, Bulat; Elefteriades, John A.","Pilot study exploring artificial intelligence for facial-image-based diagnosis of Marfan syndrome","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e33858","https://www.sciencedirect.com/science/article/pii/S240584402409889X","Background Marfan Syndrome (MFS), a genetic disorder impacting connective tissue, manifests in a wide array of phenotypes which can affect numerous bodily systems, especially the thoracic aorta. The syndrome often presents distinct facial features that potentially allow for diagnostic clinical recognition. Herein, we explore the potential of Artificial Intelligence (AI) in diagnosing Marfan syndrome from ordinary facial images, as assessed by overall accuracy, F1 score, and area under the ROC curve. Methods This study explores the utilization of Convolutional Neural Networks (CNN) for MFS identification through facial images, offering a novel, non-invasive, automated, and computerized diagnostic approach. The research examines the accuracy of Neural Networks in the diagnosis of Marfan Disease from ordinary on-line facial images. The model was trained on 80 % of 672 facial images (182 Marfan and 490 control). The other 20 % of images were used as the test set. Results Overall accuracy was 98.5 % (0 % false positive, 2 % false negative). F1 score was 97 % for Marfan facies and 99 % for non-Marfan facies. Area under the ROC curve was 100 %. Conclusion An Artificial Intelligence (AI) program was able to distinguish Marfan from non-Marfan facial images (from ordinary on-line photographs) with an extremely high degree of accuracy. Clinical usefulness of this program is anticipated. However, due to the limited and preliminary nature of this work, this should be viewed as only a pilot study.","2024-07-15","2024-12-03 03:23:50","2024-12-03 03:23:50","","e33858","","13","10","","Heliyon","","","","","","","","","","","","","","","","","","","Artificial intelligence; Connective tissue disease; Facial image; Marfan syndrome","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ISKDY59J","journalArticle","2024","An, Qin; Yang, Jingmei; Xu, Xiaoshu; Zhang, Yunfeng; Zhang, Huanhuan","Decoding AI ethics from Users' lens in education: A systematic review","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e39357","https://www.sciencedirect.com/science/article/pii/S2405844024153881","In recent years, Artificial Intelligence (AI) has witnessed remarkable expansion, greatly benefiting the education sector. Nonetheless, this advancement brings forth several ethical dilemmas. The existing research on these ethical concerns within the educational framework is notably scarce, particularly when viewed from a user's standpoint. This research systematically reviewed 17 empirical articles from January 2018 to June 2023, sourced from peer-reviewed journals and conferences, to outlined existing ethical framework in Artificial Intelligence in Education (AIED), identify related concerns from user's perspectives, and construct Ethics Guideline for AIED. The finding revealed that certain ethical aspects, including the ethics of learning analytics and the ethics of algorithms in AIED, are often neglected in the existing ethical frameworks, principles, and standards for AIED. Based on the blank between existing ethical frameworks and ethic concerns from user's perspectives, the research proposes more inclusive and thoughtfully Ethics Guideline for AIED. The study also provides actionable recommendations for multiple stakeholders, emphasizing the need for guidelines that address user-centered concerns. In addition, How this Ethics Guideline for AIED could be developed is discussed, along with outlining potential avenues for future research.","2024-10-30","2024-12-03 03:23:50","2024-12-03 03:23:50","","e39357","","20","10","","Heliyon","","","","","","","","","","","","","","","","","","","Systematic review; AI in education; AI ethics; AIED; Application of AI in education; perspective","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"7E6CMMI3","journalArticle","2024","de Winter, Joost C.F.; Driessen, Tom; Dodou, Dimitra","The use of ChatGPT for personality research: Administering questionnaires using generated personas","Personality and Individual Differences","","0191-8869","10.1016/j.paid.2024.112729","https://www.sciencedirect.com/science/article/pii/S0191886924001892","Personality research has traditionally relied on questionnaires, which bring with them inherent limitations, such as response style bias. With the emergence of large language models such as ChatGPT, the question arises as to what extent these models can be used in personality research. In this study, ChatGPT (GPT-4) generated 2000 text-based personas. Next, for each persona, ChatGPT completed a short form of the Big Five Inventory (BFI-10), the Brief Sensation Seeking Scale (BSSS), and a Short Dark Triad (SD3). The mean scores on the BFI-10 items were found to correlate strongly with means from previously published research, and principal component analysis revealed a clear five-component structure. Certain relationships between traits, such as a negative correlation between the age of the persona and the BSSS score, were clearly interpretable, while some other correlations diverged from the literature. An additional analysis using four new sets of 2000 personas each, including a set of ‘realistic’ personas and a set of cinematic personas, showed that the correlation matrix among personality constructs was affected by the persona set. It is concluded that evaluating questionnaires and research hypotheses prior to engaging with real individuals holds promise.","2024-10-01","2024-12-03 03:23:50","2024-12-03 03:23:50","","112729","","","228","","Personality and Individual Differences","","","","","","","","","","","","","","","","","","","Large language models; Big Five; Personality research; Personas","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"R5RKE4Q4","journalArticle","2024","Kulkarni, Apoorva Vikrant; Joseph, Shaji; Patil, Kanchan Pranay","Artificial intelligence technology readiness for social sustainability and business ethics: Evidence from MSMEs in developing nations","International Journal of Information Management Data Insights","","2667-0968","10.1016/j.jjimei.2024.100250","https://www.sciencedirect.com/science/article/pii/S2667096824000399","Social, economic, and environmental development together contributes to sustainable development. Social sustainability (SS) is essential to create just and inclusive societies where people's basic needs are satisfied, human rights are protected, and social cohesion and fairness exist. To achieve holistic, sustainable development, policymakers and management must consider SS and environmental and economic considerations. Employees' social and behavioral interactions impact SS and business ethics. Micro, small, and medium enterprises (MSMEs) can learn from artificial intelligence (AI) for better decision-making, operational optimization, increased employability, and employee empowerment. Therefore, this research aims to assess how artificial intelligence technology affects SS and business ethics in the MSMEs. We studied the artificial intelligence (AI) readiness among MSME companies in developing nations. We analyzed AI readiness using the theory of technology-organization-environment (TOE). Further, we also studied AI readiness and its influence on business ethics and SS, which we measured through skill development, work conditions, environment, and safety among MSMEs. We collected the data from 236 MSME employees. We used Structural Equation Modeling using SmartPLS software for data analysis. The research findings indicated that AI readiness directly impacts SS. We also found that the findings directly impact employees' social and ethical behavior. We also observed that business ethics significantly affects SS, indicating partial mediation. This study has substantial theoretical and managerial implications as policymakers and MSME leadership need to consider SS an essential component of sustainable development.","2024-11-01","2024-12-03 03:23:50","2024-12-03 03:23:50","","100250","","2","4","","International Journal of Information Management Data Insights","","","","","","","","","","","","","","","","","","","Artificial intelligence; Sustainable development goal; Business ethics; Micro small medium enterprise; Social sustainability; Technology-organization-environment","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"S7YC2DTT","journalArticle","2024","Al-Msari, Haitham; Koting, Suhana; Ahmed, Ali Najah; El-shafie, Ahmed","Review of driving-behaviour simulation: VISSIM and artificial intelligence approach","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e25936","https://www.sciencedirect.com/science/article/pii/S2405844024019674","Examining driving behaviour is crucial for traffic operations because of its influence on driver safety and the potential for increased risk of accidents, injuries, and fatalities. Approximately 95% of severe traffic collisions can be attributed to human error. With the progress in artificial intelligence in recent decades, notable advancements have been achieved in computer capabilities, communication systems and data collection technology. This increase has significantly influenced our capacity to replicate driver behaviour and comprehend underlying driving mechanisms in diverse situations. Traffic microsimulation facilitates an understanding of traffic performance inside a given road network. Among the microsimulation software packages, Verkehr In Städten – SIMulationsmodell (VISSIM) has garnered significant attention owing to its notable ability to accurately replicate traffic circumstances with high dependability in real-world scenarios. Given the diverse applicability of VISSIM-based schemes, this review systematically examines the applications of the VISSIM-based driving-behaviour models within different research contexts, revealing their utility. This review is designed to provide guidance for researchers in selecting the most suitable methodological approach tailored to their specific research objectives and constraints when utilising VISSIM. Five important aspects, including calibration, driving behaviour, incident, and heterogeneous traffic simulation, as well as utilisation of artificial intelligence with VISSIM, are assessed, which could yield substantial advantages in advancing more precise and authentic driving-behaviour modelling in VISSIM.","2024-02-29","2024-12-03 03:23:50","2024-12-03 03:23:50","","e25936","","4","10","","Heliyon","","","","","","","","","","","","","","","","","","","Artificial intelligence; Car-following model; Driving behaviour calibration; Driving behaviour modelling; Heterogenous traffic simulation; Incident simulation; VISSIM software","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"YIANP23N","journalArticle","2024","Davoodabadi Farahani, Somayeh; Alizadeh, As'ad; Tashkandi, Mohammed A.; Kolsi, Lioua; Karimipour, Aliakbar","Artificial intelligence approach in mixed convection heat transfer under transverse mechanical vibrations in a rectangular cavity","Ain Shams Engineering Journal","","2090-4479","10.1016/j.asej.2024.103012","https://www.sciencedirect.com/science/article/pii/S2090447924003873","In the current research, the mixed convection heat transfer in a rectangular chamber with walls with sinusoidal oscillations and mechanical vibrations has been investigated. Mechanical vibrations on the chamber, sinusoidal oscillations of the hot wall and flow due to buoyancy are considered. The finite volume method is utilized for simulation. The efficacy of changes in governing parameters, such as frequency, oscillation amplitude, Ra, chamber length to width ratio, change of fluid type on Nu has been investigated. The results indicate that there is an optimal ratio of chamber dimensions that has the maximum Nu in the fixed Ra, and this ratio depends on the type of fluid. In the presence of sinusoidal oscillations of the hot wall and transverse mechanical vibrations of the cylinder, it increases the heat transfer by about 96 % and 75 %, respectively, compared to the state without vibration. The increase in the frequency and amplitude of oscillations in the case where the hot wall oscillates sinusoidally is negligible on the Nusselt number. Increasing the frequency and amplitude of oscillations of transverse vibrations of the chamber has a significant efficacy on Nu, and the amplitude of oscillations has a greater efficacy than the frequency of oscillations on heat transfer. Based on the available data and using artificial intelligence, GMDH, Nu has been estimated. The results indicate that this modeling has been able to estimate the Nusselt number with good accuracy with R2=0.948.","2024-11-01","2024-12-03 03:23:50","2024-12-03 03:23:50","","103012","","11","15","","Ain Shams Engineering Journal","","","","","","","","","","","","","","","","","","","Artificial intelligence GMDH; Mechanical vibrations; Mixed convection heat transfer; Sinusoidal oscillations","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"5CECT9VE","journalArticle","2024","Gao, Yuan","Application of sensor recognition based on artificial intelligence image algorithms in sports and human health","Measurement: Sensors","","2665-9174","10.1016/j.measen.2024.101127","https://www.sciencedirect.com/science/article/pii/S266591742400103X","With the rapid development of artificial intelligence technology, the application of image algorithm in multimedia vision technology has made remarkable progress. In the field of sports, human health is an important concern, so it is of great significance to apply artificial intelligence image algorithm to the research of human health in sports. This paper uses computer vision and image processing technology, combined with artificial intelligence algorithm, to analyze and process the multimedia image of the movement. Through the acquisition and preprocessing of moving images, the key information and features of the movement are extracted. Then, deep learning algorithms and pattern recognition technology are used to analyze and evaluate the posture, movement and body state of the exercisers. Finally, according to the analysis results, personalized health advice and guidance are provided. The results show that the method can accurately identify and analyze the posture, movement and body state of the athletes, provide personalized health advice and guidance, help the athletes to improve the training effect, avoid sports injuries, and improve the level and quality of sports.","2024-06-01","2024-12-03 03:23:50","2024-12-03 03:23:50","","101127","","","33","","Measurement: Sensors","","","","","","","","","","","","","","","","","","","Multimedia; Artificial bee colony algorithm; Path of particle; Physical health; Visual images","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CCQVFNB6","journalArticle","2024","Gao, Robert X.; Krüger, Jörg; Merklein, Marion; Möhring, Hans-Christian; Váncza, József","Artificial Intelligence in manufacturing: State of the art, perspectives, and future directions","CIRP Annals","","0007-8506","10.1016/j.cirp.2024.04.101","https://www.sciencedirect.com/science/article/pii/S000785062400115X","Inspired by the natural intelligence of humans and bio-evolution, Artificial Intelligence (AI) has seen accelerated growth since the beginning of the 21st century. Successful AI applications have been broadly reported, with Industry 4.0 providing a thematic platform for AI-related research and development in manufacturing. This paper highlights applications of AI in manufacturing, ranging from production system design and planning to process modeling, optimization, quality assurance, maintenance, automated assembly and disassembly. In addition, the paper presents an overview of representative manufacturing problems and matching AI solutions, and a perspective of future research to leverage AI towards the realization of smart manufacturing.","2024-01-01","2024-12-03 03:23:50","2024-12-03 03:23:50","","723-749","","2","73","","CIRP Annals","","","","","","","","","","","","","","","","","","","Machine learning; Artificial intelligence; Smart manufacturing","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ST3SFDKM","journalArticle","2024","Yang, Yu","Application of wearable devices based on artificial intelligence sensors in sports human health monitoring","Measurement: Sensors","","2665-9174","10.1016/j.measen.2024.101086","https://www.sciencedirect.com/science/article/pii/S266591742400062X","With the orderly development of society and economy, people pay more and more attention to sports. How to effectively protect the safety of athletes has become more and more important. With the increasing awareness of health and the popularity of physical exercise, this study aims to explore the application of artificial intelligence-based sensor wearable devices in the monitoring of human health in sports and evaluate their effects on athletes. The research collected relevant literature, analyzed the application of artificial intelligence technology in wearable sensor devices, and designed a sports human health monitoring system according to the needs of athletes. Through experimental tests, it is found that the artificial intelligence-based sensor wearable device can accurately monitor the physiological parameters of athletes, and provide feedback and suggestions in time to help athletes achieve more effective training and health management, improve their training effect and competitive level.","2024-06-01","2024-12-03 03:23:51","2024-12-03 03:23:51","","101086","","","33","","Measurement: Sensors","","","","","","","","","","","","","","","","","","","Sensor; Artificial intelligence; Human health monitoring; Sports; Wearable devices","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"TAYCEMUQ","journalArticle","2024","Voigt, Julian; Strauss, Karoline","How future work self salience shapes the effects of interacting with artificial intelligence","Journal of Vocational Behavior","","0001-8791","10.1016/j.jvb.2024.104054","https://www.sciencedirect.com/science/article/pii/S0001879124000952","The rapid rise of artificial intelligence (AI) is transforming the world of work, leaving individuals wondering what AI means for the future of their career. The current research investigates the moderating role of future work self salience (FWSS) on the effect of interacting with AI on perceived control over one's future work self and proactive career behavior. In a first longitudinal experiment with full-time employees in the UK (N = 174), participants interacting with AI to solve a task (compared to a control group) experienced increased perceived control over their future work self when FWSS was high, in contrast to those with low FWSS. We replicated this pattern in a second longitudinal study with German business students (N = 208). Building on these findings, a third longitudinal experiment with German full-time employees (N = 155) extended the model by demonstrating a moderated mediation: for individuals with high FWSS, AI interaction increased perceived control over the future work self and thus promoted proactive career behavior. In contrast, perceived control and proactive career behavior decreased for those with low FWSS. This research demonstrates the potential impact of AI interactions on work-related outcomes, offering critical insights for both theory and practice.","2024-12-01","2024-12-03 03:23:51","2024-12-03 03:23:51","","104054","","","155","","Journal of Vocational Behavior","","","","","","","","","","","","","","","","","","","Artificial intelligence; Future work selves; Proactive career behavior","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"37N7PRRQ","journalArticle","2024","Shipton, Leah; Vitale, Lucia","Artificial intelligence and the politics of avoidance in global health","Social Science & Medicine","","0277-9536","10.1016/j.socscimed.2024.117274","https://www.sciencedirect.com/science/article/pii/S0277953624007287","For decades, global health actors have centered technology in their interventions. Today, artificial intelligence (AI) is emerging as the latest technology-based solution in global health. Yet, AI, like other technological interventions, is not a comprehensive solution to the fundamental determinants of global health inequities. This article gathers and critically appraises grey and peer-reviewed literature on AI in global health to explore the question: What is avoided when global health prioritizes technological solutions to problems with deep-seated political, economic, and commercial determinants? Our literature search and selection yielded 34 documents, which we analyzed to develop seven areas where AI both continues and disrupts past legacies of technological interventions in global health, with significant implications for health equity and human rights. By focusing on the power dynamics that underpin AI's expansion in global health, we situate it as the latest in a long line of technological interventions that avoids addressing the fundamental determinants of health inequities, albeit at times differently than its technology-based predecessors. We call this phenomenon the ‘politics of avoidance.’ We conclude with reflections on how the literature we reviewed engages with and recognizes the politics of avoidance and with suggestions for future research, practice, and advocacy.","2024-10-01","2024-12-03 03:23:51","2024-12-03 03:23:51","","117274","","","359","","Social Science & Medicine","","","","","","","","","","","","","","","","","","","Artificial intelligence; Human rights; Analytical review; Big tech; Global health policy; Health systems; Low and middle-income countries; Privatization","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"VUSYWWU8","journalArticle","2024","Li, Chuanxue; Wang, Ping; Zheng, Meifang; Li, Wenxiang; Zhou, Jun; Fu, Lin","One-stop multi-sensor fusion and multimodal precise quantified traditional Chinese medicine imaging health examination technology","Journal of Radiation Research and Applied Sciences","","1687-8507","10.1016/j.jrras.2024.101038","https://www.sciencedirect.com/science/article/pii/S168785072400222X","Except for single-mode traditional Chinese medicine imaging techniques such as infrared thermal imaging, the one-stop multimodal whole-body imaging health examination technology and device is still blank. We focus on infrared thermal imaging as the main modality, integrated various modalities of medical imaging intelligent sensing agents such as terahertz imaging. The upper and lower computer and virtual instrument architecture are used, and the imaging data are collected by the lower computers that each is an intelligent sensing agent. The upper computer is used for image reconstruction with intelligent algorithms. Based on the core theory of traditional Chinese medicine, intelligent fusion imaging is achieved through various modalities to achieve the ‘observation, hearing, questioning, and palpation’ four diagnostic integration. We use fractional Fourier transform to filter imaging data, Laplacian pyramid for image fusion. We have proposed an implementation method and process for combining traditional Chinese medicine imaging large language model with knowledge graph, and based on deep learning, we have studied the image and report generation algorithm that combines traditional Chinese medicine pathology and four diagnostic methods with knowledge graph fusion, as well as the traditional Chinese medicine human physiological and pathological interpretation and evaluation system. We have achieved some results, and through further research and development, we can achieve commercial applications.","2024-08-11","2024-12-03 03:23:51","2024-12-03 03:23:51","","101038","","","","","Journal of Radiation Research and Applied Sciences","","","","","","","","","","","","","","","","","","","Deep learning; Large language model; Health examination technology; Image fusion; Imaging agent; Knowledge graphs; Multimodal imaging; Traditional Chinese medicine imaging","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"Z6G2X5AD","journalArticle","2024","Núñez-Delgado, Avelino","Research on environmental aspects of retention/release of pollutants in soils and sorbents. What should be next?","Environmental Research","","0013-9351","10.1016/j.envres.2024.118593","https://www.sciencedirect.com/science/article/pii/S0013935124004973","Although studies dealing with adsorption/desorption (and/or retention/release) of pollutants present in environmental compartments is a classical field of research, recent papers are focusing on some weak points of investigations and publications within the area. In addition, an increasing number of works are being published related to new possibilities and alternatives in this kind of research works, many of them in relation to the use of artificial intelligence (AI). Considering the existence of eventual controversies, eventual mistakes, and the convenience of suggesting alternatives to go ahead in the future, in this work, after taking into account some relevant publications in the previous literature, a simple workflow is proposed as a kind of protocol to revise successive steps that could guide the direction to follow when programing research dealing with the retention/release of pollutants in soils and sorbent materials.","2024-06-15","2024-12-03 03:23:51","2024-12-03 03:23:51","","118593","","","251","","Environmental Research","","","","","","","","","","","","","","","","","","","Artificial intelligence; Adsorption; Desorption; Soil; Sorbents","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"V36KMGWL","journalArticle","2024","Khan Rony, Moustaq Karim; Akter, Khadiza; Nesa, Latifun; Islam, Md Tawhidul; Johra, Fateha Tuj; Akter, Fazila; Uddin, Muhammad Join; Begum, Jeni; Noor, Md. Abdun; Ahmad, Sumon; Tanha, Sabren Mukta; Khatun, Most. Tahmina; Bala, Shuvashish Das; Parvin, Mst. Rina","Healthcare workers' knowledge and attitudes regarding artificial intelligence adoption in healthcare: A cross-sectional study","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e40775","https://www.sciencedirect.com/science/article/pii/S2405844024168065","Background The convergence of healthcare and artificial intelligence (AI) introduces a transformative era in medical practice. However, the knowledge and attitudes of healthcare workers concerning the adoption of artificial intelligence in healthcare are currently unknown. Aims The primary objective was to investigate the knowledge and attitudes of healthcare professionals in Dhaka city, Bangladesh, regarding the adoption of AI in healthcare. Methods A cross-sectional research design was employed, incorporating a dual-method approach to select participants using randomness and convenience sampling techniques. Validity was ensured through a literature review, content validity, and reliability assessment (Cronbach's alpha = 0.85), and exploratory factor analysis identified robust underlying factors. Data analysis involved descriptive and inferential statistics, including Fisher's exact tests, multivariate logistic regression, and Pearson correlation analysis, conducted using STATA software, providing a comprehensive understanding of healthcare workers' AI adoption in healthcare. Results This study revealed that age was a significant factor, with individuals aged 18–25 and 26–35 having higher odds of good knowledge and positive attitudes (AOR 1.56, 95 % CI 1.12–2.43; AOR 1.42, 95 % CI 0.98–2.34). Physicians (AOR 1.08, 95 % CI 0.78–1.89), hospital workers (AOR 1.29, 95 % CI 0.92–2.09), and full-time employees (AOR 1.45, 95 % CI 1.12–2.34) exhibited higher odds. Attending AI conferences (AOR 1.27, 95 % CI 0.92–2.23) and learning through research articles/journals (AOR 1.31, 95 % CI 0.98–2.09) were positively associated with good knowledge and positive attitudes. This research also emphasized the strong correlations between knowledge and positive attitudes (r = 0.89, P < 0.001), as well as negative attitudes with poor knowledge (r = 0.65, P < 0.001). Conclusions The study highlights the critical need for targeted educational interventions to bridge the knowledge gaps among healthcare professionals regarding AI adoption. The findings reveal that younger healthcare workers, those in full-time employment, and individuals with exposure to AI through conferences or research are more likely to possess good knowledge and hold positive attitudes towards AI integration. These results suggest that policies and training programs must be tailored to address specific demographic differences, ensuring that all groups are equipped to engage with AI technologies. Moreover, the study emphasizes the importance of continuous professional development, which could foster a workforce capable of harnessing AI's potential to improve patient outcomes and healthcare efficiency.","2024-12-15","2024-12-03 03:23:51","2024-12-03 03:23:51","","e40775","","23","10","","Heliyon","","","","","","","","","","","","","","","","","","","Technology; Knowledge; Artificial intelligence; Attitude; Healthcare workers; Medical practice","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XPIWYKKC","journalArticle","2024","Farisco, Michele; Evers, Kathinka; Changeux, Jean-Pierre","Is artificial consciousness achievable? Lessons from the human brain","Neural Networks","","0893-6080","10.1016/j.neunet.2024.106714","https://www.sciencedirect.com/science/article/pii/S0893608024006385","We here analyse the question of developing artificial consciousness from an evolutionary perspective, taking the evolution of the human brain and its relation with consciousness as a reference model or as a benchmark. This kind of analysis reveals several structural and functional features of the human brain that appear to be key for reaching human-like complex conscious experience and that current research on Artificial Intelligence (AI) should take into account in its attempt to develop systems capable of human-like conscious processing. We argue that, even if AI is limited in its ability to emulate human consciousness for both intrinsic (i.e., structural and architectural) and extrinsic (i.e., related to the current stage of scientific and technological knowledge) reasons, taking inspiration from those characteristics of the brain that make human-like conscious processing possible and/or modulate it, is a potentially promising strategy towards developing conscious AI. Also, it cannot be theoretically excluded that AI research can develop partial or potentially alternative forms of consciousness that are qualitatively different from the human form, and that may be either more or less sophisticated depending on the perspectives. Therefore, we recommend neuroscience-inspired caution in talking about artificial consciousness: since the use of the same word “consciousness” for humans and AI becomes ambiguous and potentially misleading, we propose to clearly specify which level and/or type of consciousness AI research aims to develop, as well as what would be common versus differ in AI conscious processing compared to human conscious experience.","2024-12-01","2024-12-03 03:23:51","2024-12-03 03:23:51","","106714","","","180","","Neural Networks","","","","","","","","","","","","","","","","","","","Artificial intelligence; Robotics; Brain; Cognition; Consciousness; Neuromorphic computing; Neuroscience","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ZMWYS7XF","journalArticle","2024","Shi, Huaiyu; Kowalczewski, Andrew; Vu, Danny; Liu, Xiyuan; Salekin, Asif; Yang, Huaxiao; Ma, Zhen","Organoid intelligence: Integration of organoid technology and artificial intelligence in the new era of in vitro models","Medicine in Novel Technology and Devices","","2590-0935","10.1016/j.medntd.2023.100276","https://www.sciencedirect.com/science/article/pii/S2590093523000711","Organoid Intelligence ushers in a new era by seamlessly integrating cutting-edge organoid technology with the power of artificial intelligence. Organoids, three-dimensional miniature organ-like structures cultivated from stem cells, offer an unparalleled opportunity to simulate complex human organ systems in vitro. Through the convergence of organoid technology and AI, researchers gain the means to accelerate discoveries and insights across various disciplines. Artificial intelligence algorithms enable the comprehensive analysis of intricate organoid behaviors, intricate cellular interactions, and dynamic responses to stimuli. This synergy empowers the development of predictive models, precise disease simulations, and personalized medicine approaches, revolutionizing our understanding of human development, disease mechanisms, and therapeutic interventions. Organoid Intelligence holds the promise of reshaping how we perceive in vitro modeling, propelling us toward a future where these advanced systems play a pivotal role in biomedical research and drug development.","2024-03-01","2024-12-03 03:23:51","2024-12-03 03:23:51","","100276","","","21","","Medicine in Novel Technology and Devices","","","","","","","","","","","","","","","","","","","Deep learning; Machine learning; Artificial intelligence; Organoids; Stem cells","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RAKMVDP9","journalArticle","2024","Frank, Darius-Aurel; Chrysochou, Polymeros; Mitkidis, Panagiotis; Otterbring, Tobias; Ariely, Dan","Navigating uncertainty: Exploring consumer acceptance of artificial intelligence under self-threats and high-stakes decisions","Technology in Society","","0160-791X","10.1016/j.techsoc.2024.102732","https://www.sciencedirect.com/science/article/pii/S0160791X2400280X","In an era of transformation fueled by Artificial Intelligence (AI), human resistance to adopt this powerful technology has emerged as one of its most critical barriers. In a series of four studies involving almost 4,000 consumers, this research explores factors that contribute to consumer reluctance toward AI through theories related to algorithm aversion, decision-making under risk, and compensatory decision-making. The results underscore the impact of decision stakes and their adverse outcomes on AI service agent adoption across decision domains. These effects can be attributed to the self-threat experienced by consumers in high-stakes decision scenarios. Together, the current findings advance our understanding of consumer responses in the context of AI adoption, illustrating how perceived stakes and self-threats foster reluctance to rely on AI agents for advice. From a practical standpoint, the results emphasize the need of a hybrid approach—combining AI and human agents—for a successful transition toward AI-powered service industries.","2024-12-01","2024-12-03 03:23:51","2024-12-03 03:23:51","","102732","","","79","","Technology in Society","","","","","","","","","","","","","","","","","","","Artificial intelligence; Decision-making; Advice; High-stakes; Self-threat; Service agents","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DSGX3Q28","journalArticle","2024","Chookaew, Sasithorn; Kitcharoen, Pornchai; Howimanporn, Suppachai; Panjaburee, Patcharin","Fostering student competencies and perceptions through artificial intelligence of things educational platform","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100308","https://www.sciencedirect.com/science/article/pii/S2666920X24001115","The growing demand for artificial intelligence (AI) skills across various sectors has enhanced AI-focused careers and shaped academic exploration in educational institutions. These institutions have been actively developing teaching methods that enhance practical AI applications, particularly through integrating AI with the Internet of Things (IoT), leading to the emergence of the Artificial Intelligence of Things (AIoT). This convergence promises significant advancements in AI education, addressing gaps in structured learning methods for AIoT. This study explored AIoT's application in Smart Farming (SF) and its potential to enrich AI education and sectoral advancements. The AIoT platform was designed for SF simulations, integrating environmental sensing, AI processing, and user-friendly outputs. This platform was implemented with 40 first-year computer science university students in Thailand using a one-group pre-posttest design. This approach transformed theoretical AI concepts into experiential learning through interactive activities, demonstrating AIoT's capability to increase AI conceptual understanding, trigger AI competencies, and promote positive learning perceptions. Therefore, this study presented the results as indicative of the AIoT platform's potential benefits, emphasizing the need for further robust experimental research. This study contributes to educational technology discussions by suggesting improvements in AIoT platform effectiveness and highlighting areas for future investigation.","2024-12-01","2024-12-03 03:23:51","2024-12-03 03:23:51","","100308","","","7","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Machine learning; Higher education; AI education; Adaptive Neuro-Fuzzy; Arduino","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"WGLMSCVB","journalArticle","2024","Bao, Fuquan; Gao, Feng; Li, Weijun","Application of IoT voice devices based on artificial intelligence data mining in motion training feature recognition","Measurement: Sensors","","2665-9174","10.1016/j.measen.2024.101260","https://www.sciencedirect.com/science/article/pii/S2665917424002368","As a cross-perception and cognitive research field in video understanding, motion training feature recognition is a very challenging task to establish a good spatio-temporal modeling of human motion due to the uncertainty of human motion speed, start and end time, appearance and posture, as well as the interference of physical factors such as lighting, perspective and occlusion. The purpose of this study is to use artificial intelligence data mining technology to study the feature recognition application of iot voice devices in sports training. Install the sensor in the appropriate position according to the position and posture to be measured. Ensure that the sensor can accurately measure the relevant features and maintain a stable connection. Using iot voice devices for data acquisition, sensors collect data on relevant features in real time to transmit the data to a cloud platform or local processing device via a wireless connection. By analyzing and mining the data collected by iot voice devices, we hope to effectively identify the characteristics of sports training and provide accurate feedback and guidance for athletes and coaches. The experimental results show that the iot voice device based on artificial intelligence data mining has achieved good results in the feature recognition application of sports training. Through the analysis of sports training data, we can successfully identify the characteristic patterns of different movements, and accurately predict the athletic state and posture of athletes.","2024-08-01","2024-12-03 03:23:51","2024-12-03 03:23:51","","101260","","","34","","Measurement: Sensors","","","","","","","","","","","","","","","","","","","Data mining; Internet of Things; Artificial intelligence; Feature recognition; Sports training; Voice equipment","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KFH8329Z","journalArticle","2024","Frank, Björn","Consumer preferences for artificial intelligence-enhanced products: Differences across consumer segments, product types, and countries","Technological Forecasting and Social Change","","0040-1625","10.1016/j.techfore.2024.123774","https://www.sciencedirect.com/science/article/pii/S0040162524005729","Products with artificial intelligence (AI) functions perform thinking, decision-making, and social tasks, similar to human assistants. Drawing on this similarity to complement established theories of technology acceptance, this research develops hypotheses about consumers' preferences to buy or avoid AI products and the variation of these preferences across consumers and AI product types, which is particularly novel in the literature. Three studies from China, Japan, and the U.S. find that AI-specific privacy gain (especially for unhealthy consumers and AI product owners) and social connection (especially for lonely and extraverted consumers) have positive effects on intentions to purchase AI products. Moreover, AI-specific privacy concerns (especially for consumers with a high need for cognition and for products designed to resemble a living organism) and AI-specific violence concerns (especially for less open and less lonely consumers and for products designed as objects) negatively influence intentions to purchase AI products. Comparing the results across countries, AI privacy gain has the strongest motivational effect in the U.S., an individualistic country, and AI privacy/violence concerns have the strongest deterrent effect in Japan, a risk-averse country. Furthermore, AI social connection has the weakest effect in China, a collectivist country that may offer more conventional, non-AI social connection opportunities.","2024-12-01","2024-12-03 03:23:51","2024-12-03 03:23:51","","123774","","","209","","Technological Forecasting and Social Change","","","","","","","","","","","","","","","","","","","Artificial intelligence; Consumer choice; Technology acceptance; Universal theory of acceptance and use of technology","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"N8CI4U9Y","journalArticle","2024","Zhang, Tong","Construction and application of English wisdom classroom based on artificial intelligence","The 4th International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy","","1877-0509","10.1016/j.procs.2024.09.120","https://www.sciencedirect.com/science/article/pii/S1877050924021288","The integration of artificial intelligence technology and English teaching has transformed the English teaching classroom from teacher-centered to student-centered, realized the wisdom of English teaching, and also improved the interest and efficiency of students in learning English. This paper first expounds the basic concept of artificial intelligence technology and its application advantages in English teaching, and then discusses the construction and application of English wisdom classroom based on artificial intelligence from four aspects, in order to provide reference for relevant researchers.","2024-01-01","2024-12-03 03:23:51","2024-12-03 03:23:51","","1006-1012","","","243","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial intelligence; English teaching; Wisdom classroom","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"AMEHBRX6","journalArticle","2024","Jong, M.R.; de Groof, A.J.","Advancement of artificial intelligence systems for surveillance endoscopy of Barrett's esophagus","Digestive and Liver Disease","","1590-8658","10.1016/j.dld.2023.11.038","https://www.sciencedirect.com/science/article/pii/S1590865823010770","Barrett's esophagus (BE) is a precursor disease for esophageal adenocarcinoma. Timely detection and treatment has significant influence on patient outcomes. Over the last years, several artificial intelligence (AI) systems have emerged to assist the endoscopist. The primary focus of research has been computer aided detection (CADe). Several groups have succeeded in developing competitive models for neoplasia detection. Additionally, computer aided diagnosis (CADx) models have been developed for subsequent lesion characterization and assistance in clinical decision making. Future studies should focus on bridging the domain gap between academic development and integration in daily practice.","2024-07-01","2024-12-03 03:23:51","2024-12-03 03:23:51","","1126-1130","","7","56","","Digestive and Liver Disease","","","","","","","","","","","","","","","","","","","Artificial intelligence; Barrett's esophagus; Computer aided detection","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"IS2BET63","journalArticle","2024","Emmanuel, Sabastine; Sathasivam, Saratha; Ogunniran, Muideen O.","Leveraging feed-forward neural networks to enhance the hybrid block derivative methods for system of second-order ordinary differential equations","Journal of Computational Mathematics and Data Science","","2772-4158","10.1016/j.jcmds.2024.100101","https://www.sciencedirect.com/science/article/pii/S2772415824000129","This study introduces an innovative method combining discrete hybrid block techniques and artificial intelligence to enhance the solution of second-order Ordinary Differential Equations (ODEs). By integrating feed-forward neural networks (FFNN) into the hybrid block derivative method (HBDM), the modified approach shows improved accuracy and efficiency compared to traditional methods. Through comprehensive comparisons with exact and existing solutions, the study demonstrates the effectiveness of the proposed approach. The evaluation, utilizing root mean square error (RMSE), confirms its superior performance, robustness, and applicability in diverse scenarios. This research sets a new standard for solving complex ODE systems, offering promising avenues for future research and practical implementations.","2024-12-01","2024-12-03 03:23:51","2024-12-03 03:23:51","","100101","","","13","","Journal of Computational Mathematics and Data Science","","","","","","","","","","","","","","","","","","","Artificial intelligence; Convergence analysis; Feed-forward neural networks; Hybrid block derivative method; Root mean square error; Second-order ordinary differential equations","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"YYWTWSFB","journalArticle","2024","Garg, Swati; Ahmad, Asad; Madsen, Dag Øivind","Academic writing in the age of AI: Comparing the reliability of ChatGPT and Bard with Scopus and Web of Science","Journal of Innovation & Knowledge","","2444-569X","10.1016/j.jik.2024.100563","https://www.sciencedirect.com/science/article/pii/S2444569X24001021","ChatGPT and Bard (now known as Gemini) are becoming indispensable resources for researchers, academicians and diverse stakeholders within the academic landscape. At the same time, traditional digital tools such as scholarly databases continue to be widely used. Web of Science and Scopus are the most extensive academic databases and are generally regarded as consistently reliable scholarly research resources. With the increasing acceptance of artificial intelligence (AI) in academic writing, this study focuses on understanding the reliability of the new AI models compared to Scopus and Web of Science. The study includes a bibliometric analysis of green, sustainable and ecological buying behaviour, covering the period from 1 January 2011 to 21 May 2023. These results are used to compare the results from the AI and the traditional scholarly databases on several parameters. Overall, the findings suggest that AI models like ChatGPT and Bard are not yet reliable for academic writing tasks. It appears to be too early to depend on AI for such tasks.","2024-10-01","2024-12-03 03:23:51","2024-12-03 03:23:51","","100563","","4","9","","Journal of Innovation & Knowledge","","","","","","","","","","","","","","","","","","","Artificial intelligence; ChatGPT; Bard; Academic writing; Ecological buying behaviour; Green buying behaviour; Sustainable buying behaviour","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CRWPDTKI","journalArticle","2023","Mi Alnaser, Feras; Rahi, Samar; Alghizzawi, Mahmoud; Ngah, Abdul Hafaz","Does artificial intelligence (AI) boost digital banking user satisfaction? Integration of expectation confirmation model and antecedents of artificial intelligence enabled digital banking","Heliyon","","2405-8440","10.1016/j.heliyon.2023.e18930","https://www.sciencedirect.com/science/article/pii/S2405844023061388","In the era disruptive technology the emergence of artificial intelligence has fundamentally improved banking operations. The execution of artificial intelligence is no longer discretionary for financial institutions and now it is considered an essential tool to meet customer expectations. Although artificial intelligence enabled digital banking is faster efficient and effective however user acceptance of digital banking driven by artificial intelligence is in its initial stages. Therefore, current study develops and integrated research framework with expectation confirmation model and examines digital banking user satisfaction and acceptance of AI enabled digital banking. Data were collected from digital banking user through structured questionnaire. Overall, 320 respondents were approached and requested to participate in digital banking survey. In return 251 valid responses were received and analyzed with structural equation modeling. Findings of the structural model indicate that satisfaction is jointly determined by expectation confirmation, perceived performance, trendiness, visual attractiveness, problem solving, customization, communication quality and revealed substantial variance R^2 51.1% in digital banking user satisfaction. Therefore, satisfaction and corporate reputation have shown considerable variance R^2 48.3 in user acceptance of AI enabled digital banking. Moreover, the research framework has revealed substantial predictive power Q^2 0.449 to predict digital banking user satisfaction and Q^2 0.493 user acceptance of artificial intelligence enabled digital banking. Concerning with hypotheses relationships exogenous factors have shown positive and significant impact user satisfaction except trendiness and customization. Practically, this research has suggested that policy makers should pay attention in improving user expectation confirmation, perceived performance, visual attractiveness, communication quality and corporate reputation which in turn enhance satisfaction and boost digital banking user's confidence to accept artificial intelligence enabled digital banking. This study is original as it integrates expectation confirmation model with the antecedents of artificial intelligence and examines user behavior towards acceptance of artificial intelligence enabled digital banking.","2023-08-01","2024-12-03 03:23:51","2024-12-03 03:23:51","","e18930","","8","9","","Heliyon","","","","","","","","","","","","","","","","","","","Artificial intelligence; Customization; Problem solving; Acceptance of AI enabled banking; Communication quality; Consumer expectations; Corporate reputation; Trendiness; Visual attractiveness","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2EBAYV22","journalArticle","2024","Pradeep, Preeja; Caro-Martínez, Marta; Wijekoon, Anjana","A practical exploration of the convergence of Case-Based Reasoning and Explainable Artificial Intelligence","Expert Systems with Applications","","0957-4174","10.1016/j.eswa.2024.124733","https://www.sciencedirect.com/science/article/pii/S0957417424016002","As Artificial Intelligence (AI) systems become increasingly complex, ensuring their decisions are transparent and understandable to users has become paramount. This paper explores the integration of Case-Based Reasoning (CBR) with Explainable Artificial Intelligence (XAI) through a real-world example, which presents an innovative CBR-driven XAI platform. This study investigates how CBR, a method that solves new problems based on the solutions of similar past problems, can be harnessed to enhance the explainability of AI systems. Though the literature has few works on the synergy between CBR and XAI, exploring the principles for developing a CBR-driven XAI platform is necessary. This exploration outlines the key features and functionalities, examines the alignment of CBR principles with XAI goals to make AI reasoning more transparent to users, and discusses methodological strategies for integrating CBR into XAI frameworks. Through a case study of our CBR-driven XAI platform, iSee: Intelligent Sharing of Explanation Experience, we demonstrate the practical application of these principles, highlighting the enhancement of system transparency and user trust. The platform elucidates the decision-making processes of AI models and adapts to provide explanations tailored to diverse user needs. Our findings emphasize the importance of interdisciplinary approaches in AI research and the significant role CBR can play in advancing the goals of XAI.","2024-12-01","2024-12-03 03:23:51","2024-12-03 03:23:51","","124733","","","255","","Expert Systems with Applications","","","","","","","","","","","","","","","","","","","Case-Based Reasoning; CBR-driven XAI; Explainable Artificial Intelligence; Human-understandable explanations; Trustworthy AI","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"8D8F3LU7","journalArticle","2024","Andhari, Madhavi Dipak; Rinaldi, Giulia; Nazari, Pouya; Vets, Johanna; Shankar, Gautam; Dubroja, Nikolina; Ostyn, Tessa; Vanmechelen, Maxime; Decraene, Brecht; Arnould, Alexandre; Mestdagh, Willem; De Moor, Bart; De Smet, Frederik; Bosisio, Francesca; Antoranz, Asier","Quality control of immunofluorescence images using artificial intelligence","Cell Reports Physical Science","","2666-3864","10.1016/j.xcrp.2024.102220","https://www.sciencedirect.com/science/article/pii/S2666386424005137","Summary Fluorescent imaging has revolutionized biomedical research, enabling the study of intricate cellular processes. Multiplex immunofluorescent imaging has extended this capability, permitting the simultaneous detection of multiple markers within a single tissue section. However, these images are susceptible to a myriad of undesired artifacts, which compromise the accuracy of downstream analyses. Manual artifact removal is impractical given the large number of images generated in these experiments, necessitating automated solutions. Here, we report the development of QUALIFAI (quality control of immunofluorescence images using artificial intelligence), a deep-learning-based tool designed to automate the identification of common artifacts in fluorescent imaging. We demonstrate the utility of QUALIFAI in detecting five of the most common types of artifacts in fluorescent imaging, achieving over 90% classification accuracy and a more than 0.6 intersection over union score across all artifact types in a variety of multiplexing platforms. Finally, we show how the implementation of QUALIFAI leads to more reliable results in downstream analysis.","2024-10-16","2024-12-03 03:23:51","2024-12-03 03:23:51","","102220","","10","5","","Cell Reports Physical Science","","","","","","","","","","","","","","","","","","","deep learning; artificial intelligence; air bubbles; antibody aggregates; artifact detection; external artifacts; multiplexed fluorescent imaging; out of focus areas; quality control; tissue folds","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"NAW245EK","journalArticle","2024","Sun, Wei; Tohirovich Dedahanov, Alisher; Li, Wei Ping; Young Shin, Ho","Sanctions and opportunities: Factors affecting China's high-tech SMEs adoption of artificial intelligence computing leasing business","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e36620","https://www.sciencedirect.com/science/article/pii/S2405844024126515","Due to sanctions, more Chinese high-tech SMEs are turning to rent AI computing power through cloud service providers. Therefore, it is necessary to give a variety of suggestions for China's high-tech SMEs to better develop AI applications through computing power leasing. Because traditional theories are difficult to explain this new technology adoption behavior, this research combines and extends TTF and UTAUT2 theories to take an empirical research. A total of 387 questionnaires were received, of which incomplete questionnaires and invalid questionnaires were issued, leaving 281 valid questionnaires. The results indicate that SME innovativeness, perceived risk, performance expectancy, price value and task technology fit are all significantly related to usage, whereas task technology fit moderates the other relationships significantly. Results give a variety of suggestions for China's high-tech SMEs to better develop AI applications through computing power leasing in the context of sanctions. This study not only suggests ways to increase the competitiveness of SMEs by optimizing leasing services but also give directions in investors' investment decisions. The findings are also applicable to the large-scale application of China's domestic AI chips in computing power leasing scenarios in the future.","2024-08-30","2024-12-03 03:23:52","2024-12-03 03:23:52","","e36620","","16","10","","Heliyon","","","","","","","","","","","","","","","","","","","SME; Artificial intelligence; UTAUT; Computing power leasing; Innovativeness; Task technology fit","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"GIGZNISU","journalArticle","2024","Chen, Ruicai; Li, Caiyun","Design and Application of Language Translation System Resource Platform on Basis of Artificial Intelligence","The 4th International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy","","1877-0509","10.1016/j.procs.2024.09.079","https://www.sciencedirect.com/science/article/pii/S1877050924020866","In daily lives, people often use translators for cross-language translation, but the current translation system has the problem of translation accuracy, and its translation speed also has some limitations. Artificial intelligence has begun to develop, and the language translation system resource platform based on artificial intelligence has gradually attracted attention. The design and application of this platform are of great significance for providing high-quality and efficient language translation services. This research proposes and demonstrates a language translation system resource platform based on artificial intelligence technology. The platform is based on artificial intelligence technology for hardware resources, network resources, and the use of artificial intelligence algorithms to achieve language translation that is accurate and smooth. Through experimental testing, the results showed that the translation accuracy of the system platform reached 95-99%. The platform provides quality and efficient language translation services which can promote and facilitate human cross-language communication and understanding.","2024-01-01","2024-12-03 03:23:52","2024-12-03 03:23:52","","655-662","","","243","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial Intelligence; Language Translation System; Neural Machine Translation; Resource Platform","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KSVDLY25","journalArticle","2024","Lu, Wei; Yu, Xueqian; Li, Yueyang; Cao, Yi; Chen, Yanning; Hua, Fang","Artificial Intelligence–Related Dental Research: Bibliometric and Altmetric Analysis","International Dental Journal","","0020-6539","10.1016/j.identj.2024.08.004","https://www.sciencedirect.com/science/article/pii/S0020653924014151","Background Recent years have witnessed an explosive surge in dental research related to artificial intelligence (AI). These applications have optimised dental workflows, demonstrating significant clinical importance. Understanding the current landscape and trends of this topic is crucial for both clinicians and researchers to utilise and advance this technology. However, a comprehensive scientometric study regarding this field had yet to be performed. Methods A literature search was conducted in the Web of Science Core Collection database to identify eligible “research articles” and “reviews.” Literature screening and exclusion were performed by 2 investigators. Thereafter, VOSviewer was utilised in co-occurrence analysis and CiteSpace in co-citation analysis. R package Bibliometrix was employed to automatically calculate scientific impacts, determining the core authors and journals. Altmetric data were described narratively and supplemented with Spearman correlation analysis. Results A total of 1558 research publications were included. During the past 5 years, AI-related dental publications drastically increased in number, from 36 to 581. Diagnostics and Scientific Reports published the most articles, whereas Journal of Dental Research received the highest number of citations per article. China, the US, and South Korea emerged as the most prolific countries, whilst Germany received the highest number of citations per article (23.29). Charité Universitätsmedizin Berlin was the institution with the highest number of publications and citations per article (29.16). Altmetric Attention Score was correlated with News Mentions (P < .001), and significant associations were observed amongst Dimension Citations, Mendeley Readers, and Web of Science Citations (P < .001). Conclusions The publication numbers regarding AI-related dental research have been rising rapidly and may continue their upwards trend. China, the US, South Korea, and Germany had promoted the progress of AI-related dental research. Disease diagnosis, orthodontic applications, and morphology segmentation were current hotspots. Attention mechanism, explainable AI, multimodal data fusion, and AI-generated text assistants necessitate future research and exploration.","2024-09-11","2024-12-03 03:23:52","2024-12-03 03:23:52","","","","","","","International Dental Journal","","","","","","","","","","","","","","","","","","","Deep learning; Machine learning; Bibliometrics; Artificial intelligence; Dental research","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LZ3E7AXN","journalArticle","2023","Qie, Xiaoying","Feedback delay of sports intelligent learning system based on model predictive control and artificial intelligence","Measurement: Sensors","","2665-9174","10.1016/j.measen.2023.100922","https://www.sciencedirect.com/science/article/pii/S2665917423002581","At present, in terms of model predictive control theory and model applications, two-layer segmented linear grid models are dominant. The two-layer piecewise linear lattice model is more effective, does not require too many parameters and is easy to calculate. Since the multi-layer model predictive control is based on a two-layer model, it is very important to study the mechanism and optimization methods of the two-layer model. Artificial intelligence comes from computer science. It tries to understand the nature of intelligence and create new intelligent machines that can react like human intelligence. Research in this field involves robotics, speech recognition, image recognition, natural language processing, etc. Since the emergence of artificial intelligence, theory and technology have gradually matured, and its application fields have also increased. The technological products created by artificial intelligence will embody the development of human wisdom in the future. Sports intelligence learning was first proposed by Lins. Linsley found that under laboratory conditions, by observing and recording the learner's behavior frequency and reaction speed, and adjusting the learning activities based on this, a better learning effect can be obtained. The combination of sports intelligence learning and behaviorist learning theory forms a systematic and accurate teaching method. Video system feedback delayed learning is a part of multimedia education technology. In recent years, multimedia technology has been widely used in various educational fields. By reviewing the application research of multimedia teaching technology in education, we have determined the areas related to the delay of the video system feedback, and guided the application research of the video feedback system in education.","2023-12-01","2024-12-03 03:23:52","2024-12-03 03:23:52","","100922","","","30","","Measurement: Sensors","","","","","","","","","","","","","","","","","","","Artificial intelligence; Model predictive control; Sports intelligent learning; System feedback delay","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CXZXA7FB","journalArticle","2024","Meyer, Lea Mareen; Stead, Susan; Salge, Torsten Oliver; Antons, David","Artificial intelligence in acute care: A systematic review, conceptual synthesis, and research agenda","Technological Forecasting and Social Change","","0040-1625","10.1016/j.techfore.2024.123568","https://www.sciencedirect.com/science/article/pii/S0040162524003640","Artificial intelligence (AI) is emerging as a promising healthcare technology. Especially in critical, data-driven, and complex environments such as acute care, the use of AI can significantly improve treatment processes and support clinical staff. To date, AI applications in healthcare are scarce. Research remains fragmented across individual applications. To address this gap, we conduct a systematic literature review. In this review, we map the status quo of AI research in acute care and use service-dominant logic (SDL) from service science as a framework to integrate our analysis. Using a multilayered lens, we (1) identify intended beneficiaries of AI, (2) identify relevant activities supported by AI, and (3) determine those steps of the patient journey that have been in the spotlight for AI research. Our findings suggest that researchers have focused on hospital staff members as intended beneficiaries during the first three steps of the patient journey: admission, diagnosis, and treatment. The patient's perspective, however, remains underexplored. Moreover, 96 % of the research papers we reviewed focus on AI development and proof-of-concept studies, while only 4 % employ and test AI applications in the field. We identify three priorities for future AI research in acute care and provide suitable research methods.","2024-09-01","2024-12-03 03:23:52","2024-12-03 03:23:52","","123568","","","206","","Technological Forecasting and Social Change","","","","","","","","","","","","","","","","","","","Artificial intelligence; Healthcare; Systematic literature review; Patient journey; Service-dominant logic","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"Y5MV4HVS","journalArticle","2024","Li, Qi; Zhou, Si-Rui; Kim, Hanna; Wang, Hao; Zhu, Juan-Juan; Yang, Jin-Kui","Discovering novel Cathepsin L inhibitors from natural products using artificial intelligence","Computational and Structural Biotechnology Journal","","2001-0370","10.1016/j.csbj.2024.06.009","https://www.sciencedirect.com/science/article/pii/S2001037024002046","Cathepsin L (CTSL) is a promising therapeutic target for metabolic disorders. Current pharmacological interventions targeting CTSL have demonstrated potential in reducing body weight gain, serum insulin levels, and improving glucose tolerance. However, the clinical application of CTSL inhibitors remains limited. In this study, we used a combination of artificial intelligence and experimental methods to identify new CTSL inhibitors from natural products. Through a robust deep learning model and molecular docking, we screened 150 molecules from natural products for experimental validation. At a concentration of 100 µM, we found that 36 of them exhibited more than 50 % inhibition of CTSL. Notably, 13 molecules displayed over 90 % inhibition and exhibiting concentration-dependent effects. The molecular dynamics simulation on the two most potent inhibitors, Plumbagin and Beta-Lapachone, demonstrated stable interaction at the CTSL active site. Enzyme kinetics studies have shown that these inhibitors exert an uncompetitive inhibitory effect on CTSL. In conclusion, our research identifies Plumbagin and Beta-Lapachone as potential CTSL inhibitors, offering promising candidates for the treatment of metabolic disorders and illustrating the effectiveness of artificial intelligence in drug discovery.","2024-12-01","2024-12-03 03:23:52","2024-12-03 03:23:52","","2606-2614","","","23","","Computational and Structural Biotechnology Journal","","","","","","","","","","","","","","","","","","","Deep learning; Cathepsin L; Enzyme kinetics; Molecule docking; Natural product","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CC3GELGG","journalArticle","2024","Kim, Seung Geun; Yu, Yonggyun","Development of a data-driven decision support system for the efficient operation of the research reactor secondary system","Nuclear Engineering and Technology","","1738-5733","10.1016/j.net.2024.11.011","https://www.sciencedirect.com/science/article/pii/S173857332400559X","The High-flux Advanced Neutron Application Reactor (HANARO) is a research reactor in the Republic of Korea. The secondary cooling system of HANARO releases heat into the atmosphere via cooling towers and fans. Because a part of the system is exposed to the external atmosphere, the behavior of the secondary cooling system is coupled with atmospheric conditions. However, no procedure exists to reflect such coupling, and the cooling fans are empirically controlled by the operators. This empirical decision-making may increase the workload and reduce operational efficiency; therefore, the cooling fan operational schemes must be improved. In this study, a decision support system is developed based on two artificial intelligence (AI) models to aid operators’ decision-making in controlling the cooling fans. During model development, data augmentation was applied to alleviate data imbalances. Additionally, a graphical user interface-based decision support system prototype was developed based on the models to provide operators with more comprehensive summaries of the information related to cooling-fan operations and outputs from the AI models. The models demonstrated acceptable performance on the testing data. The prototype system was installed in the main control room of HANARO and evaluated for its usability improvements and feasibility in practical applications.","2024-11-07","2024-12-03 03:23:52","2024-12-03 03:23:52","","103309","","","","","Nuclear Engineering and Technology","","","","","","","","","","","","","","","","","","","Artificial intelligence; Decision support system; Research reactor; Secondary cooling system","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CKRZAATL","journalArticle","2024","Murugan, Tamilarasi Kathirvel; Kameswaran, Anush","Employing convolutional neural networks and explainable artificial intelligence for the detection of seizures from electroencephalogram signal","Results in Engineering","","2590-1230","10.1016/j.rineng.2024.103378","https://www.sciencedirect.com/science/article/pii/S2590123024016311","A seizure is a rapid, uncontrolled burst of electrical activity in the brain. Epilepsy is a neurological disorder caused by repeated seizures. Effective management requires early detection. This research aims to create a convolutional neural network (CNN) and explainable artificial intelligence (XAI) integrated system for epileptic seizure detection, using techniques such as Shapley additive explanation (SHAP) for better interpretability. The relevant data is acquired from a variety of electroencephalogram (EEG) dataset to facilitate the development of the model. To identify the EEG data, the proposed system combines deep learning models with feature extraction technique. Model visualization and XAI approaches like feature importance analysis from SHAP values offer clear insights into the model's decision-making process. Evaluation criteria like specificity and accuracy are used to assess the models' performance. This framework's objective is to create simple seizure detection systems that assist early epilepsy patient identification and individualized treatment plans. This research aims in opening the door to improved patient care and treatment by developing the field of epilepsy seizure detection.","2024-12-01","2024-12-03 03:23:52","2024-12-03 03:23:52","","103378","","","24","","Results in Engineering","","","","","","","","","","","","","","","","","","","Neural networks; Healthcare; Explainable artificial intelligence; Convolution neural network; Electroencephalogram; Epilepsy; Explainability Neurological Disorders; Seizures detection; Shapley Additive Explanation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"FCB8L468","journalArticle","2024","Bhattacharya, Sourabh; Govindan, Kannan; Ghosh Dastidar, Surajit; Sharma, Preeti","Applications of artificial intelligence in closed-loop supply chains: Systematic literature review and future research agenda","Transportation Research Part E: Logistics and Transportation Review","","1366-5545","10.1016/j.tre.2024.103455","https://www.sciencedirect.com/science/article/pii/S1366554524000450","In this paper, we present a systematic literature review of the applications of Artificial Intelligence (AI) in Closed-loop Supply Chains (CLSC). Through a systematic and unbiased search, we select 303 peer-reviewed articles and examined them to understand the prevalent publication trends, most impactful studies, most influential authors, and most popular journals. We further identify ten most popular AI techniques and evaluated their applications in several CLSC subfields. We identify seven CLSC subfields that can benefit significantly from AI in future. Consequently, we provide a framework containing fifteen research questions that can guide future research on AI applications in CLSC.","2024-04-01","2024-12-03 03:23:52","2024-12-03 03:23:52","","103455","","","184","","Transportation Research Part E: Logistics and Transportation Review","","","","","","","","","","","","","","","","","","","Artificial Intelligence; Machine Learning; Closed-loop supply chains; Reverse Logistics; Systematic Literature Review","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"IJN2YRZQ","journalArticle","2024","Yadav, Alok; Garg, Rajiv Kumar; Sachdeva, Anish","Artificial intelligence applications for information management in sustainable supply chain management: A systematic review and future research agenda","International Journal of Information Management Data Insights","","2667-0968","10.1016/j.jjimei.2024.100292","https://www.sciencedirect.com/science/article/pii/S2667096824000818","In a Sustainable Supply Chain (SSC) context, information management offers a unique perspective on the digital economy and information management. Artificial intelligence (AI) is developing into a more robust digital field to facilitate quick information access and intelligent decisions in expanding commercial contexts. These days, Supply Chains (SC) would crumble without robust information systems. Applying AI and information management is crucial in determining the direction of sustainable supply chain management (SSCM). A systematic literature review (SLR) of the use of AI in SSCM is conducted in this research. The authors can identify crucial factors of the present literature using bibliometric and network analysis. AI is essential to the SSC to address sustainability challenges and manage the large volumes of data produced by numerous industrial processes. In the corpus of research that is already accessible, there is currently no comprehensive and bibliometric analysis of the potential for AI techniques for information management in SSC. Scientific publications were analysed from an objective point of view. Based on our results, we have drafted a proposal for an AI supply chain framework. Researchers, policymakers, and SCM practitioners may all benefit from the approach. This study is the first to analyse AI applications for information management in SSCM. In consideration of this, organizations are now exploring AI capabilities to improve operational efficiency and innovate their processes. This will assist industry people in understanding how AI methods support SC processes in their optimization to attain sustainability in SC practices.","2024-11-01","2024-12-03 03:23:52","2024-12-03 03:23:52","","100292","","2","4","","International Journal of Information Management Data Insights","","","","","","","","","","","","","","","","","","","Industry 4.0; Sustainability; Systematic review; Information management; Artificial intelligence; Supply chain","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"PB4TJ6VJ","journalArticle","2024","Leong, Shi Xuan; Pablo-García, Sergio; Zhang, Zijian; Aspuru-Guzik, Alán","Automated electrosynthesis reaction mining with multimodal large language models (MLLMs)††Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d4sc04630g","Chemical Science","","2041-6520","10.1039/d4sc04630g","https://www.sciencedirect.com/science/article/pii/S2041652024015839","Leveraging the chemical data available in legacy formats such as publications and patents is a significant challenge for the community. Automated reaction mining offers a promising solution to unleash this knowledge into a learnable digital form and therefore help expedite materials and reaction discovery. However, existing reaction mining toolkits are limited to single input modalities (text or images) and cannot effectively integrate heterogeneous data that is scattered across text, tables, and figures. In this work, we go beyond single input modalities and explore multimodal large language models (MLLMs) for the analysis of diverse data inputs for automated electrosynthesis reaction mining. We compiled a test dataset of 65 articles (MERMES-T24 set) and employed it to benchmark five prominent MLLMs against two critical tasks: (i) reaction diagram parsing and (ii) resolving cross-modality data interdependencies. The frontrunner MLLM achieved ≥96% accuracy in both tasks, with the strategic integration of single-shot visual prompts and image pre-processing techniques. We integrate this capability into a toolkit named MERMES (multimodal reaction mining pipeline for electrosynthesis). Our toolkit functions as an end-to-end MLLM-powered pipeline that integrates article retrieval, information extraction and multimodal analysis for streamlining and automating knowledge extraction. This work lays the groundwork for the increased utilization of MLLMs to accelerate the digitization of chemistry knowledge for data-driven research.","2024-09-30","2024-12-03 03:23:52","2024-12-03 03:23:52","","17881-17891","","43","15","","Chemical Science","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ITCN7SNQ","journalArticle","2024","Huang, He; Cao, Peixiang; Liu, Yao; Lv, Yan; Tong, Min","Research on the application of large language model in electric power enterprise audit","The 11th International Conference on Applications and Techniques in Cyber Intelligence","","1877-0509","10.1016/j.procs.2024.10.166","https://www.sciencedirect.com/science/article/pii/S1877050924029673","Large language model is a new technology in the field of natural language processing, which can be applied in the audit scenario of electric power enterprises to provide auditors with automatic audit methods based on intelligent language model. Based on the application background of big language model in the audit field of electric power enterprises, this paper introduces the main advantages of big language model, and analyzes the implementation methods, application processes and principles of big language model in electric power enterprise audit, so as to provide reference and help.","2024-01-01","2024-12-03 03:23:52","2024-12-03 03:23:52","","1388-1394","","","247","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Large language model; Electric power enterprises; Enterprise audit","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LGSTC6GP","journalArticle","2024","Elfer, Katherine; Gardecki, Emma; Garcia, Victor; Ly, Amy; Hytopoulos, Evangelos; Wen, Si; Hanna, Matthew G.; Peeters, Dieter J.E.; Saltz, Joel; Ehinger, Anna; Dudgeon, Sarah N.; Li, Xiaoxian; Blenman, Kim R.M.; Chen, Weijie; Green, Ursula; Birmingham, Ryan; Pan, Tony; Lennerz, Jochen K.; Salgado, Roberto; Gallas, Brandon D.","Reproducible Reporting of the Collection and Evaluation of Annotations for Artificial Intelligence Models","Modern Pathology","","0893-3952","10.1016/j.modpat.2024.100439","https://www.sciencedirect.com/science/article/pii/S089339522400019X","This work puts forth and demonstrates the utility of a reporting framework for collecting and evaluating annotations of medical images used for training and testing artificial intelligence (AI) models in assisting detection and diagnosis. AI has unique reporting requirements, as shown by the AI extensions to the Consolidated Standards of Reporting Trials (CONSORT) and Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) checklists and the proposed AI extensions to the Standards for Reporting Diagnostic Accuracy (STARD) and Transparent Reporting of a Multivariable Prediction model for Individual Prognosis or Diagnosis (TRIPOD) checklists. AI for detection and/or diagnostic image analysis requires complete, reproducible, and transparent reporting of the annotations and metadata used in training and testing data sets. In an earlier work by other researchers, an annotation workflow and quality checklist for computational pathology annotations were proposed. In this manuscript, we operationalize this workflow into an evaluable quality checklist that applies to any reader-interpreted medical images, and we demonstrate its use for an annotation effort in digital pathology. We refer to this quality framework as the Collection and Evaluation of Annotations for Reproducible Reporting of Artificial Intelligence (CLEARR-AI).","2024-04-01","2024-12-03 03:23:52","2024-12-03 03:23:52","","100439","","4","37","","Modern Pathology","","","","","","","","","","","","","","","","","","","Annotation Study; Artificial Intelligence Validation; Data set; digital pathology; Reference Standard; Reproducible Research","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"6GCU3LYI","journalArticle","2024","Nagao, Azusa; Inagaki, Yusuke; Nogami, Keiji; Yamasaki, Naoya; Iwasaki, Fuminori; Liu, Yang; Murakami, Yoichi; Ito, Takahiro; Takedani, Hideyuki","Artificial intelligence–assisted ultrasound imaging in hemophilia: research, development, and evaluation of hemarthrosis and synovitis detection","Research and Practice in Thrombosis and Haemostasis","","2475-0379","10.1016/j.rpth.2024.102439","https://www.sciencedirect.com/science/article/pii/S2475037924001286","Background Joint bleeding can lead to synovitis and arthropathy in people with hemophilia, reducing quality of life. Although early diagnosis is associated with improved therapeutic outcomes, diagnostic ultrasonography requires specialist experience. Artificial intelligence (AI) algorithms may support ultrasonography diagnoses. Objectives This study will research, develop, and evaluate the diagnostic precision of an AI algorithm for detecting the presence or absence of hemarthrosis and synovitis in people with hemophilia. Methods Elbow, knee, and ankle ultrasound images were obtained from people with hemophilia from January 2010 to March 2022. The images were used to train and test the AI models to estimate the presence/absence of hemarthrosis and synovitis. The primary endpoint was the area under the curve for the diagnostic precision to diagnose hemarthrosis and synovitis. Other endpoints were the rate of accuracy, precision, sensitivity, and specificity. Results Out of 5649 images collected, 3435 were used for analysis. The area under the curve for hemarthrosis detection for the elbow, knee, and ankle joints was ≥0.87 and for synovitis, it was ≥0.90. The accuracy and precision for hemarthrosis detection were ≥0.74 and ≥0.67, respectively, and those for synovitis were ≥0.83 and ≥0.74, respectively. Analysis across people with hemophilia aged 10 to 60 years showed consistent results. Conclusion AI models have the potential to aid diagnosis and enable earlier therapeutic interventions, helping people with hemophilia achieve healthy and active lives. Although AI models show potential in diagnosis, evidence is unclear on required control for abnormal findings. Long-term observation is crucial for assessing impact on joint health.","2024-05-01","2024-12-03 03:23:52","2024-12-03 03:23:52","","102439","","4","8","","Research and Practice in Thrombosis and Haemostasis","","","","","","","","","","","","","","","","","","","AI (artificial intelligence); hemarthrosis; hemophilia; synovitis; ultrasound imaging","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"4IC542SZ","journalArticle","2024","Li, Dayan","Application of artificial intelligence sensor and visual image technology in the analysis of hydrophilic space landscape characteristics","Systems and Soft Computing","","2772-9419","10.1016/j.sasc.2024.200133","https://www.sciencedirect.com/science/article/pii/S2772941924000620","With the continuous development of intelligent sensor technology and artificial intelligence, the application of artificial intelligence feature recognition at the level of intelligent sensor information acquisition to urban landscape analysis has become a current research trend and hot spot, but also brings us new research directions and challenges. In this paper, the multi-sensor collaborative filtering algorithm is optimized, and a method based on evidence theory is proposed to deal with the fuzzy and unclear information generated in the process of multi-sensor collaboration. Then, the analysis and recognition of multi-sensor fusion information using AI technology are analyzed in detail. This paper discusses the problems in multi-sensor information fusion and related solutions. Combined with the key nodes of 3D object AI feature recognition, multi-sensor collaborative Dempster Shafer evidence theory and 3D convolutional neural network waterfront space landscape feature recognition sub-model are constructed, and the waterfront space landscape recognition analysis model is tested and analyzed. The results show that the multi-sensor information collection Kalman filtering fusion algorithm effectively realizes the recognition of landscape feature information and the filtering of irrelevant interference information combined with an artificial intelligence algorithm to build a landscape feature recognition model. Among all kinds of landscape recognition, the recognition effect of beach inflow is the best, and the recognition accuracy, recall, and F1 value are all above 95. However, the identification effect of hydrophilic plank roads and underwater breakwater is relatively poor. The recognition accuracy of the hydrophilic platform is the lowest at 83.16 % among the eight landscape types, and the recall rate of underwater breakwater is the lowest at 79.96 % among the eight landscape types. In general, the multi-sensor Kalman filtering algorithm information fusion model designed in this paper has higher recognition accuracy and better classification performance for the collection and classification of the entire landscape dataset. The work of this paper provides a new idea and direction for the design and application of waterfront landscape identification and analysis systems based on intelligent sensors and artificial intelligence technology and lays a certain foundation for the intelligent design of urban waterfront landscapes.","2024-12-01","2024-12-03 03:23:53","2024-12-03 03:23:53","","200133","","","6","","Systems and Soft Computing","","","","","","","","","","","","","","","","","","","Artificial intelligence feature recognition; Intelligent sensor; Multi-sensor information fusion; Recognition and classification of waterfront landscape features; Three-dimensional sensor information recognition; Waterfront landscape","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"4GH8DMU5","journalArticle","2024","Zhu, Hui; Vigren, Olli; Söderberg, Inga-Lill","Implementing artificial intelligence empowered financial advisory services: A literature review and critical research agenda","Journal of Business Research","","0148-2963","10.1016/j.jbusres.2023.114494","https://www.sciencedirect.com/science/article/pii/S0148296323008536","Robo-advisors, also known as robo-advisory services, significantly reshape customer service in financial advisory industries, transforming retail investor markets by substituting human financial advisory experts with artificial intelligence empowered services. However, existing literature remains scattered across disciplines, with theories on financial customer service predominantly focused on Internet banking, neglecting artificial intelligence empowered interactions. Thus, service providers need a framework for implementing robo-advisors in frontline service and researchers require an advanced agenda to stimulate future research. Through a systematic, interdisciplinary literature review based on Belanche et al.’s service robot framework, this article contextualizes service robot theories into financial advisory services, synthesizing knowledge on artificial intelligence empowered customer service. We contribute to literature on service robots by contextualizing, refining, and extending the original framework by Belanche et al. and by developing a research agenda with critical perspectives. Moreover, the study yields practical and theoretical insights into artificial intelligence empowered financial advisory services.","2024-03-01","2024-12-03 03:23:53","2024-12-03 03:23:53","","114494","","","174","","Journal of Business Research","","","","","","","","","","","","","","","","","","","Innovation; Artificial intelligence; Customer service; FinTech; Robo-advisor; Service robot","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"Z8NP8F4F","journalArticle","2024","Hu, Guangkuo; Luo, Rongting","Media Communication Marketing Data Analysis Based on Artificial Intelligence","The 11th International Conference on Applications and Techniques in Cyber Intelligence","","1877-0509","10.1016/j.procs.2024.10.045","https://www.sciencedirect.com/science/article/pii/S187705092402845X","With the rapid development of science and technology, artificial intelligence technology benefits the field of marketing. Based on artificial intelligence technology, media communication marketing has enriched marketing means and adopted virtual endorsers for marketing publicity. From the perspective of virtual endorsers interaction, this paper studies the impact of virtual endorsers interaction on consumers' brand attitude, conducts empirical analysis through situational experiments and explores the internal mechanism, and finds that: (1) Product involvement moderates the impact of ""whether virtual endorsers interact with people"" on consumers' brand attitudes. For products with high involvement, virtual endorsers have a significant positive impact on consumer brand attitudes when interacting with people. For low-involvement products, ""whether virtual endorsers interact with people"" has no significant impact on consumers' brand attitudes; (2) Self-identity plays a mediating role in this regulation. This paper verifies the feasibility of the application of artificial intelligence in media communication marketing to a certain extent, which will help to enrich the related research of virtual spokesmen. At the same time, it provides suggestions and references for enterprise marketing practice.","2024-01-01","2024-12-03 03:23:53","2024-12-03 03:23:53","","382-388","","","247","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial Intelligence; Brand Attitude; Media Marketing; Product Involvement; Virtual Endorsers","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"VXSJP25A","journalArticle","2024","Chen, Hong; Zhang, Mengyun; Zeng, Jun; Wang, Wenhua","Artificial intelligence and corporate risk-taking: Evidence from China","China Journal of Accounting Research","","1755-3091","10.1016/j.cjar.2024.100372","https://www.sciencedirect.com/science/article/pii/S1755309124000303","The deep integration of artificial intelligence (AI) into enterprises presents both opportunities and challenges, making it a focal point of current research. This study explores the impact of AI on corporate risk-taking, using data spanning 2010–2019 from A-share listed companies in China. Our findings suggest that AI significantly heightens companies’ level of risk-taking. Furthermore, financing constraints can amplify the relationship between AI and risk-taking, enhancing their sensitivity correlation. AI also significantly improves firms’ investment efficiency and mitigates their underinvestment issues. Finally, mediation tests indicate that AI enhances risk-taking by diminishing firms’ risk perception. Overall, we offer valuable insights into and references for accelerating the deep integration of AI into enterprises.","2024-09-01","2024-12-03 03:23:53","2024-12-03 03:23:53","","100372","","3","17","","China Journal of Accounting Research","","","","","","","","","","","","","","","","","","","Artificial intelligence; Financing constraints; Risk-taking","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2ME2PI9K","journalArticle","2023","LI, Yanan","Relationship between perceived threat of artificial intelligence and turnover intention in luxury hotels","Heliyon","","2405-8440","10.1016/j.heliyon.2023.e18520","https://www.sciencedirect.com/science/article/pii/S2405844023057286","When artificial intelligence technology erodes employees' professional knowledge, they tend to feel highly anxious, and turnover intention is created. This study aimed to test the impact of the perceived threat of artificial intelligence on turnover intention through perceived organizational support and the perceived value of artificial intelligence. The method and procedure were as follow: construct a theoretical framework and propose hypotheses - collect questionnaires through voluntary sampling - use a two-step approach to test the model. This study has some findings. Theoretically, this study proposes a conceptual model of artificial intelligence perception. The combination of technology threat avoidance, organizational support, and perceived value theories applies to the research background of this study. Methodologically, the relationship between the perceived threat of artificial intelligence, perceived organizational support, perceived value of artificial intelligence, and turnover intention variables was studied together for the first time, and the perceived value of artificial intelligence as a new significant mediator between perceived organizational support and turnover intention is discovered. Managementarily, when facing the threats of artificial intelligence to employees, hotel managers should emphasize organizational support, especially in finance, career, and adjustment. This study has important implications for luxury hotel management. First, hotel employees' perceptions of artificial intelligence are dual. Second, luxury hotel managers should consider perceived organizational support to be a key variable.","2023-08-01","2024-12-03 03:23:53","2024-12-03 03:23:53","","e18520","","8","9","","Heliyon","","","","","","","","","","","","","","","","","","","Perceived organizational support; Artificial intelligence; Perceived threat; Perceived value; Turnover intention","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"T7TPPVH6","journalArticle","2024","Zhao, Congyu; Dong, Kangyin; Wang, Kun; Nepal, Rabindra","How does artificial intelligence promote renewable energy development? The role of climate finance","Energy Economics","","0140-9883","10.1016/j.eneco.2024.107493","https://www.sciencedirect.com/science/article/pii/S0140988324002019","Scholars, stakeholders, and the government have given significant attention to the development of renewable energy in recent times. However, previous research has failed to acknowledge the potential impact of artificial intelligence on advancing renewable energy development. Drawing insights from a global dataset encompassing 63 countries over the period 2000–2019, this paper provides significant observations regarding the influence of artificial intelligence on the progress of renewable energy, by using the Instrumental Variable Generalized Method of Moments model. We also explore their asymmetric nexus, and the potential mediation effect. Moreover, this study explores the moderating role of climate finance and highlights the following interesting findings. First, artificial intelligence contributes significantly to the enhanced development of renewable energy, and this primary finding holds after two robustness tests of changing independent and dependent variables. Second, artificial intelligence has an asymmetric effect on renewable energy development, and their nexus is closer in countries with lower levels of renewable energy development. Thid, artificial intelligence works on renewable energy development through technology effect and innovation effect. Fourth, climate finance also presents direct benefits to renewable energy development; simultaneously, climate finance plays an effective moderating role in the relationship between artificial intelligence and renewable energy development. These findings inspire us to propose policy implications to promote the enhanced development of renewable energy.","2024-05-01","2024-12-03 03:23:53","2024-12-03 03:23:53","","107493","","","133","","Energy Economics","","","","","","","","","","","","","","","","","","","Artificial intelligence; Climate finance; Global case; Moderation effect; Renewable energy development","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"T8FW2DTH","journalArticle","2024","Sanusi, Ismaila Temitayo; Agbo, Friday Joseph; Dada, Oluwaseun Alexander; Yunusa, Abdullahi Abubakar; Aruleba, Kehinde D.; Obaido, George; Olawumi, Olayemi; Oyelere, Solomon Sunday; Centre for Multidisciplinary Research and Innovation (CEMRI)","Stakeholders’ insights on artificial intelligence education: Perspectives of teachers, students, and policymakers","Computers and Education Open","","2666-5573","10.1016/j.caeo.2024.100212","https://www.sciencedirect.com/science/article/pii/S2666557324000521","The integration of artificial intelligence (AI) as a subject into K-12 education worldwide is still in its early stages and undoubtedly needs further investigation. There is limited effort on understanding policymakers, teachers and students’ viewpoints on AI learning within the school system. This study gathered the thoughts of key stakeholders, including policymakers, higher education and K-12 teachers, and students in Nigeria, to understand their conceptions, concerns, and dispositions, with the aim of aiding the implementation of AI in schools. We further explored the needs of the diverse stakeholders, how they can be supported and juxtaposed their views to identify their priorities and how their opinions combined could give a holistic approach to the effective implementation of AI education. This research employed a qualitative methodology using semi-structured interviews as the means of data collection. The thematic analysis of the interview data from the 21 participants indicates their conceptions, what they considered the priorities for including AI in the school system, concerns and support needed to implement AI in schools. The findings of this study contribute to the ongoing conversation on how to effectively integrate AI into school curriculum.","2024-12-01","2024-12-03 03:23:53","2024-12-03 03:23:53","","100212","","","7","","Computers and Education Open","","","","","","","","","","","","","","","","","","","Nigeria; Teachers; Artificial intelligence; AI literacy; Policymakers; School education","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"WDP6CPGG","journalArticle","2024","Ali, Omar; Kallach, Layal","Artificial Intelligence Enabled Human Resources Recruitment Functionalities: A Scoping Review","5th International Conference on Industry 4.0 and Smart Manufacturing (ISM 2023)","","1877-0509","10.1016/j.procs.2024.02.142","https://www.sciencedirect.com/science/article/pii/S187705092400320X","Human resources information systems have enhanced monitoring, documenting, and recording functions for organizations, and consequently have accelerated swift completion of routine processes. This change demonstrates the critical impact of adopting and using advanced technology such as artificial intelligence in the human resources function; in multiple functional gamut's, particularly in recruitment. This manuscript presents a scoping review of academic articles on the impact of adopting and using artificial intelligence in the human resource management function. The current review draft considered a total of 653 academic articles and aggregated review of 35 articles to present a classification framework with three distinct dimensions: artificial intelligence-enabled human resources benefits, challenges, and functionalities. Implications for future research and their directions are identified in the areas of value-added human services for decision-making, security, and privacy for customer and organization data, monitoring features, and creative IT service delivery models.","2024-01-01","2024-12-03 03:23:53","2024-12-03 03:23:53","","3268-3277","","","232","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial intelligence; benefits; challenges; functionalities; human resources","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"I3N3NRQ7","journalArticle","2024","Ma, Tianming; Wang, Jiawen; Ma, Fuhai; Shi, Jinxin; Li, Zijian; Cui, Jian; Wu, Guoju; Zhao, Gang; An, Qi","Visualization analysis of research hotspots and trends in MRI-based artificial intelligence in rectal cancer","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e38927","https://www.sciencedirect.com/science/article/pii/S2405844024149584","Background Rectal cancer (RC) is one of the most common types of cancer worldwide. With the development of artificial intelligence (AI), the application of AI in preoperative evaluation and follow-up treatment of RC based on magnetic resonance imaging (MRI) has been the focus of research in this field. This review was conducted to develop comprehensive insight into the current research progress, hotspots, and future trends in AI based on MRI in RC, which remains to be studied. Methods Literature related to AI based on MRI and RC, as of November 2023, was obtained from the Web of Science Core Collection database. Visualization and bibliometric analyses of publication quantity and content were conducted to explore temporal trends, spatial distribution, collaborative networks, influential articles, keyword co-occurrence, and research directions. Results A total of 177 papers (152 original articles and 25 reviews) were identified from 24 countries/regions, 351 institutions, and 81 journals. Since 2019, the number of studies on this topic has rapidly increased. China and the United States have contributed the highest number of publications and institutions, cultivating the most intimate collaborative relationship. The highest number of articles derive from Sun Yat-sen University, while Frontiers in Oncology has published the highest number of relevant articles. Research on MRI-based AI in this field has mainly focused on preoperative diagnosis and prediction of treatment efficacy and prognosis. Conclusions This study provides an objective and comprehensive overview of the publications on MRI-based AI in RC and identifies the present research landscape, hotspots, and prospective trends in this field, which can provide valuable guidance for scholars worldwide.","2024-11-15","2024-12-03 03:23:53","2024-12-03 03:23:53","","e38927","","21","10","","Heliyon","","","","","","","","","","","","","","","","","","","machine learning; Artificial intelligence; Magnetic resonance imaging; Bibliometry; Rectal cancer","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CFTCS5DE","journalArticle","2024","Zhang, Xuan; Zhou, Ziying; Cai, Yilu; Grzybowski, Andrzej; Ye, Juan; Lou, Lixia","Global research of artificial intelligence in eyelid diseases: A bibliometric analysis","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e34979","https://www.sciencedirect.com/science/article/pii/S2405844024110109","Purpose To generate an overview of global research on artificial intelligence (AI) in eyelid diseases using a bibliometric approach. Methods All publications related to AI in eyelid diseases from 1900 to 2023 were retrieved from the Web of Science (WoS) Core Collection database. After manual screening, 98 publications published between 2000 and 2023 were finally included. We analyzed the annual trend of publication and citation count, productivity and co-authorship of countries/territories and institutions, research domain, source journal, co-occurrence and evolution of the keywords and co-citation and clustering of the references, using the analytic tool of the WoS, VOSviewer, Wordcloud Python package and CiteSpace. Results By analyzing a total of 98 relevant publications, we detected that this field had continuously developed over the past two decades and had entered a phase of rapid development in the last three years. Among these countries/territories and institutions contributing to this field, China was the most productive country and had the most institutions with high productivity, while USA was the most active in collaborating with others. The most popular research domains was Ophthalmology and the most productive journals were Ocular Surface. The co-occurrence network of keywords could be classified into 3 clusters respectively concerned about blepharoptosis, meibomian gland dysfunction and blepharospasm. The evolution of research hotspots is from clinical features to clinical scenarios and from image processing to deep learning. In the clustering analysis of co-cited reference network, cluster “0# deep learning” was the largest and latest, and cluster “#5 meibomian glands visibility assessment” existed for the longest time. Conclusions Although the research of AI in eyelid diseases has rapidly developed in the last three years, there are still gaps in this area. Our findings provide researchers with a better understanding of the development of the field and a reference for future research directions.","2024-07-30","2024-12-03 03:23:53","2024-12-03 03:23:53","","e34979","","14","10","","Heliyon","","","","","","","","","","","","","","","","","","","Bibliometric analysis; Artificial intelligence; Eyelid diseases; Global publications","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"GKEHI2UK","journalArticle","2024","Huang, Huang-Chu; Chen, Chih-Yung; Chen, I-Chun; Hwang, Rey-Chue","Intelligent detection of fastener defects and mixed assortments","ICT Express","","2405-9595","10.1016/j.icte.2024.05.006","https://www.sciencedirect.com/science/article/pii/S2405959524000560","This paper investigates the use of artificial intelligence (AI) image detection and discrimination technology to address issues related to mixed assortments and defects encountered in the fastener manufacturing and packaging processes. The defect detection system primarily utilizes the YOLOv4-tiny model with parameter setting and data augmentation techniques. The mixed assortments detection system is constructed using U-Net-Light and Siamese network. The research results demonstrate that the developed systems can indeed replace or assist on-site personnel in conducting efficient and accurate inspections and screenings.","2024-08-01","2024-12-03 03:23:53","2024-12-03 03:23:53","","902-908","","4","10","","ICT Express","","","","","","","","","","","","","","","","","","","Detection; Artificial intelligence; Defect; Fastener; Mixed assortments","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ZQ9G78WN","journalArticle","2024","Patnaik, Somya; Khatri, Narendra; Rene, Eldon R.","Fueling the future: Exploring the synergy of artificial intelligence-based algorithms and the use of biofuels in engine development","Journal of the Taiwan Institute of Chemical Engineers","","1876-1070","10.1016/j.jtice.2024.105729","https://www.sciencedirect.com/science/article/pii/S1876107024003870","Background The development of internal combustion (IC) engines has seen significant advancements, but understanding and modelling their complex dynamics pose challenges. Artificial intelligence (AI) techniques, notably artificial neural networks (ANN) and nature-inspired optimization algorithms like genetic algorithms (GA), offer potential solutions to enhance accuracy and tackle nonlinearities and uncertainties. Despite this potential, effectively leveraging AI in engine development remains a considerable research gap. Methods This review examines the potential benefits of AI in addressing the challenges associated with dynamic systems like engine development, focusing on sustainability and environmental friendliness through methods such as biofuel adoption. Various IC engines, including compression ignition, direct injection, marine, and aircraft engines, along with other power-generating units, were analyzed. Processes such as manufacturing, design, testing, control, and fault detection were scrutinized to identify suitable domains for AI application. Significant Findings The review identifies opportunities for AI in enhancing sustainability and eco-friendliness in engine development, particularly through biofuel utilization. By exploring suitable domains for AI techniques, such as ANN and GA, this paper contributes to the advancement of environmentally conscious engines. Additionally, it offers recommendations for future research to tackle the persistent challenges in engine development, particularly concerning alternate fuels like biofuels.","2024-09-14","2024-12-03 03:23:53","2024-12-03 03:23:53","","105729","","","","","Journal of the Taiwan Institute of Chemical Engineers","","","","","","","","","","","","","","","","","","","Artificial neural networks; Machine learning; Artificial intelligence; Biofuels; Engine control; Engine modeling","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"UXNJLXUQ","journalArticle","2024","Huang, He; Cao, Peixiang; Liu, Yao; Lv, Yan; Tong, Min","Research on audit informatization based on language large model","The 11th International Conference on Applications and Techniques in Cyber Intelligence","","1877-0509","10.1016/j.procs.2024.10.164","https://www.sciencedirect.com/science/article/pii/S187705092402965X","In this paper, the language model is applied to the field of audit informatization. First, the concept and classification of language model are introduced, then the construction method of language model audit informatization system is proposed, and finally the simulation test environment is constructed to verify the language model audit informatization system. Through verification, the system can give full play to the function of language large model and improve the level of audit informatization.","2024-01-01","2024-12-03 03:23:53","2024-12-03 03:23:53","","1374-1380","","","247","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Audit informatization; Information identification; Language large model","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"WZ9ICQEI","journalArticle","2024","Mnguni, Lindelani; Nuangchalerm, Prasart; Zaky El Islami, R. Ahmad; Sibanda, Doras; Sari, Indah Juwita; Ramulumo, Moleboheng","The behavioural intentions for integrating artificial intelligence in science teaching among pre-service science teachers in South Africa and Thailand","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100334","https://www.sciencedirect.com/science/article/pii/S2666920X24001371","Developing countries in the Global South exhibit diverse trends in the integration of digital technologies, such as Artificial Intelligence in teaching. Complex context-specific factors, including teacher preparedness, influence these trends. Using the Theory of Planned behavior as a theoretical framework, this study explores the behavioral intentions of pre-service science teachers in South Africa and Thailand towards integrating AI into their teaching to inform teacher training, support, and resource allocation policies. The main research question is: ""What are the behavioral intentions of pre-service science teachers in South Africa and Thailand towards integrating AI into their teaching practices?"" The study followed a non-experimental comparative descriptive survey involving 97 South African and 95 Thai final-year BEd students. Data were collected using a structured online questionnaire and analyzed using several statistical tools to compare the TPB constructs between the two samples. South African and Thai pre-service teachers exhibited favorable attitudes and behavioral intentions toward AI integration in teaching. However, Thai students showed significantly higher control and normative beliefs, indicating greater confidence and perceived social support for AI integration than South African students. The findings suggest that targeted teacher training programs and supportive educational policies are essential for enhancing AI readiness, particularly in resource-constrained settings.","2024-12-01","2024-12-03 03:23:53","2024-12-03 03:23:53","","100334","","","7","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Artificial intelligence; Behavioral intentions; Comparative descriptive survey; Pre-service science teachers; Science teaching","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CVUWQDTA","journalArticle","2023","Shahzad, Muhammad Farrukh; Xu, Shuo; Naveed, Waliha; Nusrat, Shahneela; Zahid, Imran","Investigating the impact of artificial intelligence on human resource functions in the health sector of China: A mediated moderation model","Heliyon","","2405-8440","10.1016/j.heliyon.2023.e21818","https://www.sciencedirect.com/science/article/pii/S2405844023090266","Artificial intelligence (AI) is rapidly transforming the way human resources (HR) functions are carried out in the health sector of China. This study aims to scrutinize the impact of artificial intelligence on the human resource functions operating in the healthcare sector through technological awareness, social media influence, and personal innovativeness. Additionally, this study examines the moderating role of perceived risk between technological awareness and human resources functions. An online questionnaire was administered to human resources professionals in the health sector of China to gather data from 363 respondents. Partial least squares structural equation modeling (PLS-SEM), a statistical procedure, is implemented to investigate the hypothesis of the projected model of artificial intelligence and human resource functions. The research findings reveal that artificial intelligence significantly influences human resource functions through technological awareness, social media influence, and personal innovativeness. Furthermore, perceived risk significantly moderates the relationship between technological awareness and human resource functions. The findings of this study have important implications for HR practitioners and policymakers in the health sectors of China, who can leverage artificial intelligence technologies to optimize and improve organizational performance. However, its adoption needs to be carefully planned and managed to reap the full benefits of this transformative technology.","2023-11-01","2024-12-03 03:23:53","2024-12-03 03:23:53","","e21818","","11","9","","Heliyon","","","","","","","","","","","","","","","","","","","Artificial intelligence; Human resource functions; Perceived risk; Personal innovativeness; Social media influence; Technological awareness","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"VDDBJRCJ","journalArticle","2024","Kineber, Ahmed Farouk; Elshaboury, Nehal; Oke, Ayodeji Emmanuel; Aliu, John; Abunada, Ziyad; Alhusban, Mohammad","Revolutionizing construction: A cutting-edge decision-making model for artificial intelligence implementation in sustainable building projects","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e37078","https://www.sciencedirect.com/science/article/pii/S240584402413109X","This study examines how certain artificial intelligence (AI) drivers affect the industry's adoption of this technology in the construction industry. The research methods comprised a comprehensive analysis of previous studies to pinpoint the primary factors influencing AI adoption in the construction industry. Data collection was carried out through a well-structured survey involving relevant stakeholders in the building construction sector. The three main constructs of technological devices, advancement, and knowledge were found from the set of drivers with the technique of exploratory factor analysis. The deployment of AI in construction has the potential to improve health and safety and expedite project completion, as this research has evaluated. To figure out how these factors relate to the adoption of AI in the construction industry, partial least squares structural equation modeling was used. The study's conclusions showed that the influence of AI installation in the construction industry is reasonably significant thanks to the technology, advancement, and knowledge, contributing around 15 % of the effects that have been directly witnessed. The practical implications of AI for policy makers, engineers, and construction stakeholders are extensive and provide valuable insights for customized strategies aimed at using AI's potential to improve projects, promote sustainability, and elevate health and safety standards.","2024-09-15","2024-12-03 03:23:53","2024-12-03 03:23:53","","e37078","","17","10","","Heliyon","","","","","","","","","","","","","","","","","","","Artificial intelligence; Digital transformation; Benefits assessment; Construction digitalization; Health and safety; Project completion; Sustainable construction","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2JPIN5KW","journalArticle","2024","Licardo, Josip Tomo; Tanković, Nikola; Etinger, Darko","A Method for Extracting BPMN Models from Textual Descriptions Using Natural Language Processing","CENTERIS – International Conference on ENTERprise Information Systems / ProjMAN - International Conference on Project MANagement / HCist - International Conference on Health and Social Care Information Systems and Technologies 2023","","1877-0509","10.1016/j.procs.2024.06.196","https://www.sciencedirect.com/science/article/pii/S187705092401439X","Business Process Model and Notation (BPMN) is a standard for formally modeling complex business processes. Manual creation of BPMN models can be time-consuming and error-prone, prompting a need for automation. Existing approaches, such as rule-based methods, machine learning, and machine translation, have progressed but face accuracy and real-world applicability challenges. In this research paper, we propose a novel method for automated extraction of BPMN models from textual descriptions using natural language processing (NLP) tools and deep learning models, including the spaCy library for text processing, a fine-tuned BERT model, and state-of-the-art large language models like GPT-3.5-Turbo and GPT-4. We utilize Graphviz, an open-source graph visualization software, to visualize the extracted processes. Our method supports representing tasks, exclusive gateways, parallel gateways, and start and end events in the generated BPMN models. The evaluation of 31 textual descriptions shows that our method generates process models with 96% accuracy using GPT-4 and 80% accuracy using GPT-3.5-Turbo large language models. Although subject to certain limitations, such as occasional inaccuracies in model outputs and reliance on well-formed input text, our approach offers a valuable contribution to the growing body of research on automating BPMN model generation.","2024-01-01","2024-12-03 03:23:53","2024-12-03 03:23:53","","483-490","","","239","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","deep learning; Business Process Model; information extraction; natural language processing (NLP); Notation (BPMN); process modeling","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"TN9LFM97","journalArticle","2024","Nainamalai, Varatharajan; Qair, Hemin Ali; Pelanis, Egidijus; Jenssen, Håvard Bjørke; Fretland, Åsmund Avdem; Edwin, Bjørn; Elle, Ole Jakob; Balasingham, Ilangko","Automated algorithm for medical data structuring, and segmentation using artificial intelligence within secured environment for dataset creation","European Journal of Radiology Open","","2352-0477","10.1016/j.ejro.2024.100582","https://www.sciencedirect.com/science/article/pii/S2352047724000376","Objective Routinely collected electronic health records using artificial intelligence (AI)-based systems bring out enormous benefits for patients, healthcare centers, and its industries. Artificial intelligence models can be used to structure a wide variety of unstructured data. Methods We present a semi-automatic workflow for medical dataset management, including data structuring, research extraction, AI-ground truth creation, and updates. The algorithm creates directories based on keywords in new file names. Results Our work focuses on organizing computed tomography (CT), magnetic resonance (MR) images, patient clinical data, and segmented annotations. In addition, an AI model is used to generate different initial labels that can be edited manually to create ground truth labels. The manually verified ground truth labels are later included in the structured dataset using an automated algorithm for future research. Conclusion This is a workflow with an AI model trained on local hospital medical data with output based/adapted to the users and their preferences. The automated algorithms and AI model could be implemented inside a secondary secure environment in the hospital to produce inferences.","2024-12-01","2024-12-03 03:23:53","2024-12-03 03:23:53","","100582","","","13","","European Journal of Radiology Open","","","","","","","","","","","","","","","","","","","Artificial intelligence; Electronic health records; Ground truth creation; Segmentation; Structured data","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"S9FFGL2T","journalArticle","2024","Hyun Lim, Su; Hersi, Mona; Krishnan, Ramya; Montroy, Joshua; Rook, Bonnie; Farrah, Kelly; Chung, Yung-En; Stevens, Adrienne; Zafack, Joseline; Wong, Eva; Forbes, Nicole; Killikelly, April; Young, Kelsey; Tunis, Matthew","COVID-19 vaccine evidence monitoring assisted by artificial Intelligence: An emergency system implemented by the Public Health Agency of Canada to capture and describe the trajectory of evolving pandemic vaccine literature","Vaccine: X","","2590-1362","10.1016/j.jvacx.2024.100575","https://www.sciencedirect.com/science/article/pii/S2590136224001487","Background The COVID-19 pandemic resulted in a rapid accumulation of novel vaccine research evidence. As a means to monitor this evidence, the Public Health Agency of Canada (PHAC) created the Evidence eXtraction Team for Research Analysis (EXTRA), which contributed to situational awareness in Canada through a bibliographic repository used to support decision-making by the National Advisory Committee on Immunization. We describe the process by which this literature was identified and catalogued, and provide an overview of characteristics in the identified literature. Methods To expedite the process, PHAC leveraged an artificial intelligence (AI) tool to assist in the screening and selection of relevant articles. Literature search results were initially screened by AI, then manually reviewed for relevance. Relevant articles were tagged using controlled vocabulary and stored in a bibliographic repository. This repository was analyzed to identify trends in vaccine research over time according to several key characteristics. Results As of December 31, 2023, EXTRA’s repository contained 19,050 articles relevant to PHAC’s immunization mandate. The majority of these articles (63.9 %) were identified between August 2021 and January 2023, with an average of 20 relevant articles added daily during this period. Nearly 14,000 articles reported on mRNA vaccines. Safety outcomes were most frequently reported (n = 8,289), followed by immunogenicity (n = 7,269) and efficacy/effectiveness (n = 3,246). COVID-19 vaccine literature output started to decrease in mid-2023, two years after the initial dramatic increase in mid-2021. Conclusions This hybrid (AI and human) approach was critical for PHAC situational awareness and the development of timely vaccine guidance in Canada during the COVID-19 pandemic. Given the volume of data and analyses required, the AI-augmented processes made this massive undertaking manageable. Analysis of COVID-19 vaccine research patterns supports projections of research volume, type, and rate that will help predict resourcing and information needs to plan future emergency vaccine guidance activities.","2024-12-01","2024-12-03 03:23:53","2024-12-03 03:23:53","","100575","","","21","","Vaccine: X","","","","","","","","","","","","","","","","","","","COVID-19; Artificial Intelligence; Evidence review; mRNA Vaccine; Vaccine","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"8M3CXCQQ","journalArticle","2024","Yu, Dan; Zhao, Peipei","Research on Network Clothing Design System Based on Artificial Intelligence","The 11th International Conference on Applications and Techniques in Cyber Intelligence","","1877-0509","10.1016/j.procs.2024.10.004","https://www.sciencedirect.com/science/article/pii/S1877050924028047","Under the background of digitalization and networking, the fashion industry is undergoing unprecedented changes. In such a big environment, online clothing design based on artificial intelligence technology is a brand-new innovation and development direction. In this paper, the online fashion design is taken as the research object, and the latest artificial intelligence algorithm is combined to discuss the fashion design automation and personalized design method based on AI(Artificial Intelligence). The online clothing design system based on artificial intelligence uses deep learning, image recognition and machine learning methods to analyze user preferences, historical data and fashion trends, and automatically generate personalized and innovative clothing design schemes. In addition, this study also compares the differences between artificial intelligence-driven clothing design and traditional clothing design in creativity, cost-effectiveness and user satisfaction. In the AI design method, the design cycle is generally shorter, in which the automatic primary, intermediate and advanced design takes 8 days, 6 days and 4 days respectively, while the expert-aided design and fully customized design cycle takes 5 days and 7 days respectively. The research results of this paper will promote the automation and personalization of fashion design, and open up a brand-new working mode for the development of fashion industry.","2024-01-01","2024-12-03 03:23:53","2024-12-03 03:23:53","","27-35","","","247","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial Intelligence; Deep Learning; Design Cycle; Network Clothing Design System","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"E535E3NB","journalArticle","2024","Du, Qian; Zhai, Jieping","Application of artificial intelligence Sensors based on random forest algorithm in financial recognition models","Measurement: Sensors","","2665-9174","10.1016/j.measen.2024.101245","https://www.sciencedirect.com/science/article/pii/S2665917424002216","Due to the important role of financial information disclosed by enterprises in investor decision-making and economic policy formulation, the corporate market has set the latest standards and requirements for the quality of financial reports. In the current situation, financial recognition work is mainly completed manually, which is inefficient and prone to errors, and is not suitable for large-scale applications. Therefore, we conduct in-depth research on financial recognition models. Firstly, we analyzed the random forest algorithm and used the SMOTE algorithm to expand the sample data. We successfully constructed an optimized random forest algorithm model, which can better improve accuracy and reduce misjudgment rates. Secondly, combined with the current financial data analysis of artificial intelligence technology, the steps for data management and data extraction were designed. Finally, while establishing a financial recognition model, a channel for obtaining internet information and a program for processing this information were also established. After experimental verification, the random forest model has shown better fitting performance in the field of financial fraud identification, providing more reliable support for applications in this field. Therefore, the financial recognition model constructed using artificial intelligence technology based on random forest algorithm has significant value in both theoretical and practical aspects, effectively reducing corporate financial risks.","2024-06-01","2024-12-03 03:23:53","2024-12-03 03:23:53","","101245","","","33","","Measurement: Sensors","","","","","","","","","","","","","","","","","","","Data processing; Artificial intelligence; Financial identification model; Random forest algorithm","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"W55474XE","journalArticle","2024","Hubbard, Thomas J.E.; Shams, Ola; Gardner, Benjamin; Gibson, Finley; Rowlands, Sareh; Harries, Tim; Stone, Nick","A systematic scoping review exploring variation in practice in specimen mammography for Intraoperative Margin Analysis in Breast Conserving Surgery and the role of artificial intelligence in optimising diagnostic accuracy","European Journal of Radiology","","0720-048X","10.1016/j.ejrad.2024.111777","https://www.sciencedirect.com/science/article/pii/S0720048X24004935","Purpose Specimen Mammography (SM) is commonly used in Breast Conserving Surgery (BCS) for intraoperative margin analysis. A systematic scoping review was conducted to identify sources of methodological variation in Specimen Mammography Interpretation (SMI) and assess the role of Artificial Intelligence (AI) techniques to optimise Diagnostic Accuracy (DA). Methods Embase, Pubmed, Cochrane and web of science databases were searched. Studies were included if SM was used for margin analysis for BCS with reported DA compared with pathological margin status and data extracted. Results 1242 unique studies were identified, of which 40 were included. 39/40 studies did not utilise AI for SMI, with 4 studies comparing 2 relevant techniques, giving 43 non-AI study arms for analysis. There was wide variation in SM techniques, including number of views and location of SM. Specialist performing SMI in usual clinical practice was surgeon (13/39 studies;33 %), radiologist(s) (16/39;41 %), surgeon and radiologist (3/39;8 %) or not stated (7/39;18 %) which differed from the study specialist in 15/39 (38 %) of studies. Diagnostic accuracy in studies ranged from sensitivity 19–91.7 % and specificity 25–100 %. Conclusions There is marked variation in current techniques used for SM for intraoperative margin analysis with correspondingly disparate DA. Only 1 study applied AI to SMI, and we identify how AI could optimise SMI and a template for future work to apply AI techniques to SMI, reduce unwarranted variation and optimise DA.","2024-12-01","2024-12-03 03:23:53","2024-12-03 03:23:53","","111777","","","181","","European Journal of Radiology","","","","","","","","","","","","","","","","","","","Artificial intelligence; Breast cancer; Breast conserving surgery; Intraoperative margin analysis; Specimen mammography","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"VNH88CB5","journalArticle","2024","Chen, Kaiming; Chen, Xiaoqian; Wang, Zhan-ao; Zvarych, Roman","Does artificial intelligence promote common prosperity within enterprises? —Evidence from Chinese-listed companies in the service industry","Technological Forecasting and Social Change","","0040-1625","10.1016/j.techfore.2023.123180","https://www.sciencedirect.com/science/article/pii/S004016252300865X","As the largest industry that absorbs labor from different levels of employment and provides different levels of labor remuneration, the service industry has faced severe challenges from the wave of artificial intelligence replacement. This study examines whether artificial intelligence promotes shared prosperity among service industry enterprises based on microdata of listed companies in the Chinese service industry from 2008 to 2022. The main research results indicate that the application of artificial intelligence in the service industry significantly reduces enterprises' labor income share. The main mechanisms of action include the employment structure effect of squeezing out low-educated and frontline workers and the wage-productivity effect that improves labor productivity but benefits capital income, leading to an unequal income distribution. Research has found that improving the integration of regional labor markets and workers' bargaining power can help alleviate the negative effects of artificial intelligence applications on the share of labor income in the service industry. This study helps developing countries, such as China, manage the impact of the widespread application of artificial intelligence on the labor market in the service industry and provides important policy insights for achieving common prosperity.","2024-03-01","2024-12-03 03:23:54","2024-12-03 03:23:54","","123180","","","200","","Technological Forecasting and Social Change","","","","","","","","","","","","","","","","","","","Artificial intelligence; Employment structure effect; Service industry; Share of labor income; Wage-productivity effect","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"QDC86T6S","journalArticle","2024","Hoseinzadeh, Siamak; Garcia, Davide Astiaso","Can AI predict the impact of its implementation in greenhouse farming?","Renewable and Sustainable Energy Reviews","","1364-0321","10.1016/j.rser.2024.114423","https://www.sciencedirect.com/science/article/pii/S1364032124001461","The integration of Artificial Intelligence offers transformative solutions to modern-day challenges, especially in sectors like agriculture that are pivotal for human sustenance. This study underscores the profound impact of Artificial Intelligence in conditioned agricultural practices within greenhouses, based on data from an agricultural competition where teams optimized greenhouse performance using Artificial Intelligence-driven mechanisms. Results indicate that Artificial Intelligence-enhanced control strategies can drastically reduce energy consumption, particularly heating loads, without compromising crop yield, quality, or profitability. In some instances, performance even surpassed conventional methods. However, there are areas like Carbon Dioxide emissions and water usage where enhancements are still essential. Building on these insights, the study further ventures into AI's potential to predict greenhouse production outcomes. Through rigorous assessment of various machine learning models, the Radial Basis Function model exhibited commendable performance, achieving an Root Mean Squared Error of 0.8 and an R-squared value of 0.98 post-optimization. This establishes the feasibility of precisely forecasting greenhouse production rates in terms of kg/m2. While this research predominantly centers on production volume, it lays a strong foundation for the predictive potential of AI in greenhouse operations and underlines the benefits of input optimization. It paves the way for future research focused on both the quality and quantity of greenhouse production.","2024-06-01","2024-12-03 03:23:54","2024-12-03 03:23:54","","114423","","","197","","Renewable and Sustainable Energy Reviews","","","","","","","","","","","","","","","","","","","Energy consumption; Sustainability; Artificial intelligence; Agriculture; CO2 emissions; Greenhouse","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"MSWUL9PT","journalArticle","2024","Qing, Haohua; Ibrahim, Roliana; Nies, Hui Wen","Comprehensive location privacy enhanced model","iScience","","2589-0042","10.1016/j.isci.2024.111412","https://www.sciencedirect.com/science/article/pii/S2589004224026373","Summary With the increasing popularity of location-based services (LBSs), safeguarding location privacy has become critically important. Traditional methods often struggle to balance the intensity of privacy protection with service quality. To address this challenge, this research proposes the comprehensive location privacy enhanced model (CLPEM), which enhances personalized privacy protection by integrating dynamic weight allocation at the policy layer, incorporating a user feedback mechanism, and designing tailored privacy strategies for various scenarios. Additionally, the model employs data fusion and optimization techniques to enhance the usability of location data while ensuring effective privacy protection. Our experimental results demonstrate that CLPEM outperforms existing technologies in terms of privacy strength, data availability, and user satisfaction, providing a robust technical framework for location privacy and paving the way for future research and applications.","2024-12-20","2024-12-03 03:23:54","2024-12-03 03:23:54","","111412","","12","27","","iScience","","","","","","","","","","","","","","","","","","","Computer science; Artificial intelligence; Computer security and privacy","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KHHF94RF","journalArticle","2024","Li, Qingmeng; Xing, Rongchang; Li, Linshan; Yao, Haodong; Wu, Liyuan; Zhao, Lina","Synchrotron radiation data-driven artificial intelligence approaches in materials discovery","Artificial Intelligence Chemistry","","2949-7477","10.1016/j.aichem.2024.100045","https://www.sciencedirect.com/science/article/pii/S2949747724000034","Synchrotron radiation technology provides high-resolution and high-sensitivity information for many fields such as material science, life science, and energy research. Synchrotron radiation data-driven methods have significantly accelerated the development of materials discovery and analysis. However, synchrotron radiation data is complex and large, requiring artificial intelligence for analysis. Artificial intelligence can efficiently process complex high-dimensional data, automate the analysis process, discover hidden patterns and associations, and build predictive models. This review provides an overview of the application and development of combining synchrotron radiation data-driven methods with artificial intelligence in the field of materials discovery. The application of the method in science is still limited by the problems of large and complex synchrotron radiation data, valuable experimental machine time, and uninterpretable artificial intelligence models. To address these problems, this review correspondingly proposes solutions for synchrotron radiation artificial intelligence data banks, standardized experiment records systems, and interpretable artificial intelligence predictive models.","2024-06-01","2024-12-03 03:23:54","2024-12-03 03:23:54","","100045","","1","2","","Artificial Intelligence Chemistry","","","","","","","","","","","","","","","","","","","Artificial intelligence; Data-driven methods; Materials discovery; Synchrotron radiation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"MVHNCU9V","journalArticle","2024","Fernandes, Rafael Tiza; Fernandes, Filipe Wolff; Kundu, Mrinmoy; Ramsay, Daniele S.C.; Salih, Ahmed; Namireddy, Srikar N.; Jankovic, Dragan; Kalasauskas, Darius; Ottenhausen, Malte; Kramer, Andreas; Ringel, Florian; Thavarajasingam, Santhosh G.","Artificial Intelligence for Prediction of Shunt Response in Idiopathic Normal Pressure Hydrocephalus: A Systematic Review","World Neurosurgery","","1878-8750","10.1016/j.wneu.2024.09.087","https://www.sciencedirect.com/science/article/pii/S187887502401636X","Background Idiopathic normal pressure hydrocephalus (iNPH) is a reversible cause of dementia, typically treated with shunt surgery, although outcomes vary. Artificial intelligence (AI) advancements could improve predictions of shunt response (SR) by analyzing extensive datasets. Methods We conducted a systematic review to assess AI’s effectiveness in predicting SR in iNPH. Studies using AI or machine learning algorithms for SR prediction were identified through searches in MEDLINE, Embase, and Web of Science up to September 2023, adhering to Synthesis Without Meta-Analysis reporting guidelines. Results Of 3541 studies identified, 33 were assessed for eligibility, and 8 involving 479 patients were included. Study sample sizes varied from 28 to 132 patients. Common data inputs included imaging/radiomics (62.5%) and demographics (37.5%), with Support Vector Machine being the most frequently used machine learning algorithm (87.5%). Two studies compared multiple algorithms. Only 4 studies reported the Area Under the Curve values, which ranged between 0.80 and 0.94. The results highlighted inconsistency in outcome measures, data heterogeneity, and potential biases in the models used. Conclusions While AI shows promise for improving iNPH management, there is a need for standardized data and extensive validation of AI models to enhance their clinical utility. Future research should aim to develop robust and generalizable AI models for more effective diagnosis and management of iNPH.","2024-10-10","2024-12-03 03:23:54","2024-12-03 03:23:54","","","","","","","World Neurosurgery","","","","","","","","","","","","","","","","","","","Artificial intelligence; Idiopathic normal pressure hydrocephalus; iNPH; Normal pressure hydrocephalus; Prediction; Shunt response","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"BZNNB8YA","journalArticle","2024","Moneus, Ahmed Mohammed; Sahari, Yousef","Artificial intelligence and human translation: A contrastive study based on legal texts","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e28106","https://www.sciencedirect.com/science/article/pii/S2405844024041379","Artificial intelligence has advanced significantly in recent years, affecting multiple aspects of life. In particular, this has had an impact on the machine translation of texts, reducing or removing human interaction. Artificial intelligence (AI)-based translation software models have thus become widely available, and these now include Google Translate, Bing, Microsoft Translator, DeepL, Reverso, Systran Translate, and Amazon Translate. Several computer-aided translation (CAT) tools such as Memoq, Trados, Smartcat, Lokalise, Smartling, Crowdin, TextUnited, and Memsource are also available. More recently, artificial intelligence has been applied in the development of applications such as ChatGPT, ChatSonic, GPT-3 Playground, Chat GPT 4 and YouChat, which simulate conversational responses to researchers' inquiries, mimicking human interactions more directly. This study thus aimed to examine any remaining contrasts between human and AI translation in the legal field to investigate the potential hypothesis that there is now no difference between human and AI translation. The paper thus also examined concerns about whether the need for human translators will decline in the face of AI development, as well as beginning to assess whether it will ever be possible for those in the legal field to depend only on machine translation. To achieve this, a collection of legal texts from various contracts was chosen, and these pieces were both allocated to legal translators and subjected to AI translation systems. Using a contrastive methodology, the study thus examined the differences between AI and human translation, examining the strengths and weaknesses of both approaches and discussing the situations in which each approach might be most effective.","2024-03-30","2024-12-03 03:23:54","2024-12-03 03:23:54","","e28106","","6","10","","Heliyon","","","","","","","","","","","","","","","","","","","Artificial intelligence; Machine translation; Human translation; Legal translation; Translation software","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"WEIYF7SS","journalArticle","2024","Li, Yinghao","Design of intelligent algorithm for object search based on IoT digital images","Systems and Soft Computing","","2772-9419","10.1016/j.sasc.2024.200161","https://www.sciencedirect.com/science/article/pii/S2772941924000905","With the development of artificial intelligence, traditional object search and image recognition have been replaced by the Internet of Things and artificial intelligence. However, traditional object search algorithms often lack accuracy and low precision. Therefore, this study proposes a new intelligent encryption algorithm to address the issues of insufficient accuracy in object search algorithms and image recognition algorithms. The new algorithm ensures the security of user data and the response efficiency of the model during the conversation process by integrating fully homomorphic encryption technology and dynamic sparse attention mechanism. The dynamic sparse attention mechanism introduced simultaneously improves the model's ability to handle long sequence data by dynamically adjusting attention weights. Experimental results showed that the precision of the proposed algorithm was 0.05 % higher than that of random algorithms and 0.19 % higher than that of sorting algorithms. The recall rate of the proposed algorithm was 0.14 % higher than that of random algorithms and 0.16 % higher than that of sorting algorithms. The research algorithm can identify objects with certain characteristics and is suitable for specific environments, greatly reducing the probability of data leakage in object search and providing new ideas for research in this field.","2024-12-01","2024-12-03 03:23:54","2024-12-03 03:23:54","","200161","","","6","","Systems and Soft Computing","","","","","","","","","","","","","","","","","","","Accuracy; Internet of things; Homomorphic Encryption; Intelligent algorithm; Object search","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"AT5LUPH2","journalArticle","2024","Cadeddu, Andrea; Chessa, Alessandro; De Leo, Vincenzo; Fenu, Gianni; Motta, Enrico; Osborne, Francesco; Reforgiato Recupero, Diego; Salatino, Angelo; Secchi, Luca","A comparative analysis of knowledge injection strategies for large language models in the scholarly domain","Engineering Applications of Artificial Intelligence","","0952-1976","10.1016/j.engappai.2024.108166","https://www.sciencedirect.com/science/article/pii/S0952197624003245","In recent years, transformer-based models have emerged as powerful tools for natural language processing tasks, demonstrating remarkable performance in several domains. However, they still present significant limitations. These shortcomings become more noticeable when dealing with highly specific and complex concepts, particularly within the scientific domain. For example, transformer models have particular difficulties when processing scientific articles due to the domain-specific terminologies and sophisticated ideas often encountered in scientific literature. To overcome these challenges and further enhance the effectiveness of transformers in specific fields, researchers have turned their attention to the concept of knowledge injection. Knowledge injection is the process of incorporating outside knowledge into transformer models to improve their performance on certain tasks. In this paper, we present a comprehensive study of knowledge injection strategies for transformers within the scientific domain. Specifically, we provide a detailed overview and comparative assessment of four primary methodologies, evaluating their efficacy in the task of classifying scientific articles. For this purpose, we constructed a new benchmark including both 24K labelled papers and a knowledge graph of 9.2K triples describing pertinent research topics. We also developed a full codebase to easily re-implement all knowledge injection strategies in different domains. A formal evaluation indicates that the majority of the proposed knowledge injection methodologies significantly outperform the baseline established by Bidirectional Encoder Representations from Transformers.","2024-07-01","2024-12-03 03:23:54","2024-12-03 03:23:54","","108166","","","133","","Engineering Applications of Artificial Intelligence","","","","","","","","","","","","","","","","","","","Classification; Large language models; Natural language processing; Transformers; BERT; Knowledge graphs; Knowledge injection","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XUY5GRKL","journalArticle","2024","Mullanu, Siripond; Chua, Caslon; Molnar, Andreea; Yavari, Ali","Artificial intelligence for hydrogen-enabled integrated energy systems: A systematic review","International Journal of Hydrogen Energy","","0360-3199","10.1016/j.ijhydene.2024.08.013","https://www.sciencedirect.com/science/article/pii/S0360319924031628","Hydrogen-enabled Integrated Energy Systems (H-IES) stand out as a promising solution with the potential to replace current non-renewable energy systems. However, their development faces challenges and has yet to achieve widespread adoption. These main challenges include the complexity of demand and supply balancing, dynamic consumer demand, and challenges in integrating and utilising hydrogen. Typical energy management strategies within the energy domain rely heavily on accurate models from domain experts or conventional approaches, such as simulation and optimisation approaches, which cannot be satisfied in the real-world operation of H-IES. Artificial Intelligence (AI) or Advanced Data Analytics (ADA), especially Machine Learning (ML), has the ability to overcome these challenges. ADA is extensively used across several industries, however, further investigation into the incorporation of ADA and hydrogen for the purpose of enabling H-IES needs to be investigated. This paper presents a systematic literature review to study the research gaps, research directions, and benefits of ADA, as well as the role of hydrogen in H-IES.","2024-08-10","2024-12-03 03:23:54","2024-12-03 03:23:54","","","","","","","International Journal of Hydrogen Energy","","","","","","","","","","","","","","","","","","","Machine learning; Systematic review; Artificial intelligence; AI; Energy systems; Hydrogen; Integrated energy systems; Renewable energy systems","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"QTTLRWTS","journalArticle","2024","Fosso-Wamba, Samuel; Guthrie, Cameron","Artificial intelligence and industry 4.0 and 5.0: a bibliometric study and research agenda","CENTERIS – International Conference on ENTERprise Information Systems / ProjMAN - International Conference on Project MANagement / HCist - International Conference on Health and Social Care Information Systems and Technologies 2023","","1877-0509","10.1016/j.procs.2024.06.228","https://www.sciencedirect.com/science/article/pii/S1877050924014716","Artificial intelligence (AI) is an important enabler of the fourth and fifth industrial revolutions. It has the potential to empower intelligent production systems that are central to industry 4.0 and help place worker wellbeing and the planet at the centre of industry 5.0 processes. However, few studies have considered the different roles of AI in industry 4.0 and 5.0. This paper reports on a bibliometric review of 2887 documents examining the use of AI for industry 4.0 and industry 5.0. We found that scholarly activity increased rapidly from 2018 onwards and can be attributed to a large variety of countries, including India, South Africa and China. Our analysis reveals four clusters in the literature, with two showing particular promise for researchers notably around the themes of industry 5.0 and deep learning. Further insights are presented and discussed, followed by a future research agenda.","2024-01-01","2024-12-03 03:23:54","2024-12-03 03:23:54","","718-725","","","239","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","artificial intelligence; bibliometrics; industry 4.0; industry 5.0","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"R7NPMWAG","journalArticle","2024","Bankins, Sarah; Jooss, Stefan; Restubog, Simon Lloyd D.; Marrone, Mauricio; Ocampo, Anna Carmella; Shoss, Mindy","Navigating career stages in the age of artificial intelligence: A systematic interdisciplinary review and agenda for future research","Journal of Vocational Behavior","","0001-8791","10.1016/j.jvb.2024.104011","https://www.sciencedirect.com/science/article/pii/S0001879124000526","As artificial intelligence (AI) use expands within organizations, its influence is increasingly permeating careers and vocational domains. However, there is a notable lack of structured insights regarding AI's role in shaping individual career paths across career stages. To address this gap, we undertook a systematic literature review of 104 empirical articles, aiming to synthesize the scholarship on AI in the context of careers. Drawing upon career stage theory, we examine the implications of AI on careers, identify key barriers and enablers of AI use in this area, and reveal how the utilization of AI impacts individuals' career competencies. In doing so, we illustrate how AI actively shapes individuals' career trajectories and we dissect these effects both within and across various career stages to situate AI within the broader context of careers research. Adopting a sustainable career lens, we conclude by outlining a future research agenda that advocates for the design and adoption of AI systems that promote sustainable and equitable careers.","2024-09-01","2024-12-03 03:23:54","2024-12-03 03:23:54","","104011","","","153","","Journal of Vocational Behavior","","","","","","","","","","","","","","","","","","","Artificial intelligence; Careers; Career stages; Future of work; Sustainable careers; Vocational services","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"E57PZ9VC","journalArticle","2024","Haftor, Darek M.; Costa-Climent, Ricardo; Ribeiro-Navarrete, Samuel","Firms’ use of predictive artificial intelligence for economic value creation and appropriation","International Journal of Information Management","","0268-4012","10.1016/j.ijinfomgt.2024.102836","https://www.sciencedirect.com/science/article/pii/S0268401224000847","Firms are increasingly investing in the use of artificial intelligence (AI). Some succeed in creating and appropriating substantial economic value, but many fail. There is no consensus as to how a firm should use AI to create and appropriate economic value. This paper provides an answer to that question. A novel research model is advanced based on the notion of data network effects being realized within a firm’s business model. This research model is tested in a unique and natural industrial setting of two competing firms that simultaneously adopt the use of similar predictive AI. This setting is researched with two distinct empirical studies that employ mixed-methods research. The results shows that one firm fails to convert its AI use into economic value creation and appropriation while the other succeeds. Value is created and appropriated by ensuring that AI users perceive high user value that realize data network effects while being located in the firm’s business model architecture so as to activate business value drivers. These findings confirm the here proposed research model and offer novel theoretical contributions and specific managerial implications.","2024-12-01","2024-12-03 03:23:54","2024-12-03 03:23:54","","102836","","","79","","International Journal of Information Management","","","","","","","","","","","","","","","","","","","Business model architecture; Business model themes; Data network effects; Perceived users value; Predictive artificial intelligence","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KTTB3X4C","journalArticle","2024","Chavez-Badiola, Alejandro; Farías, Adolfo Flores-Saiffe; Mendizabal-Ruiz, Gerardo; Silvestri, Giuseppe; Griffin, Darren K.; Valencia-Murillo, Roberto; Drakeley, Andrew J.; Cohen, Jacques","Use of artificial intelligence embryo selection based on static images to predict first-trimester pregnancy loss","Reproductive BioMedicine Online","","1472-6483","10.1016/j.rbmo.2024.103934","https://www.sciencedirect.com/science/article/pii/S1472648324001238","ABSTRACT Research question Can an artificial intelligence embryo selection assistant predict the incidence of first-trimester spontaneous abortion using static images of IVF embryos? Design In a blind, retrospective study, a cohort of 172 blastocysts from IVF cases with single embryo transfer and a positive biochemical pregnancy test was ranked retrospectively by the artificial intelligence morphometric algorithm ERICA. Making use of static embryo images from a light microscope, each blastocyst was assigned to one of four possible groups (optimal, good, fair or poor), and linear regression was used to correlate the results with the presence or absence of a normal fetal heart beat as an indicator of ongoing pregnancy or spontaneous abortion, respectively. Additional analyses included modelling for recipient age and chromosomal status established by preimplantation genetic testing for aneuploidy (PGT-A). Results Embryos classified as optimal/good had a lower incidence of spontaneous abortion (16.1%) compared with embryos classified as fair/poor (25%; OR = 0.46, P = 0.005). The incidence of spontaneous abortion in chromosomally normal embryos (determined by PGT-A) was 13.3% for optimal/good embryos and 20.0% for fair/poor embryos, although the difference was not significant (P = 0.531). There was a significant association between embryo rank and recipient age (P = 0.018), in that the incidence of spontaneous abortion was unexpectedly lower in older recipients (21.3% for age ≤35 years, 17.9% for age 36–38 years, 16.4% for age ≥39 years; OR = 0.354, P = 0.0181). Overall, these results support correlation between risk of spontaneous abortion and embryo rank as determined by artificial intelligence; classification accuracy was calculated to be 67.4%. Conclusions This preliminary study suggests that artificial intelligence (ERICA), which was designed as a ranking system to assist with embryo transfer decisions and ploidy prediction, may also be useful to provide information for couples on the risk of spontaneous abortion. Future work will include a larger sample size and karyotyping of miscarried pregnancy tissue.","2024-08-01","2024-12-03 03:24:06","2024-12-03 03:24:06","","103934","","2","49","","Reproductive BioMedicine Online","","","","","","","","","","","","","","","","","","","Artificial intelligence; Assisted conception; Embryo morphology; Embryo ranking; Miscarriage","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"B9CV3DR8","journalArticle","2024","Jatmika, Rahmat Taufiq Dwi; Ratnasari, Vita; Nadlifatin, Reny","Empowering Micro-Entrepreneurs through Artificial Intelligence: A Conceptual Framework for AI-Based Marketing","Seventh Information Systems International Conference (ISICO 2023)","","1877-0509","10.1016/j.procs.2024.03.103","https://www.sciencedirect.com/science/article/pii/S1877050924004617","Technology becoming the main product to enhance various aspects in the business. Artificial Intelligence has provided many conveniences in marketing business products. However, this innovative technology is not yet widely understood by the F&B microentrepreneur. The microentrepreneurs tend to focus more on developing their products rather than improving sales through product marketing using AI. This research aims to develop a conceptual framework to help micro-entrepreneurs in marketing their products by utilizing Artificial Intelligence (AI) technology. The framework will be developed through a focus group discussion (FGD) involving stakeholders, including the Department of Industry and Trade, the Regional Development Planning Agency, and micro-entrepreneurs, to determine gaps or deficiencies that exist among micro-entrepreneurs. The Delphi method will also be used to gather opinions from experts in AI and marketing to develop an AI model suitable for micro-entrepreneurs. The model will involve the use of chat GPT, a simplified version of AI, and copy AI. The end result will be a prototype of an AI model that is tailored to the needs of micro-entrepreneurs, with the potential to improve their marketing capabilities and overall business operations. The framework will also include the use of digital marketing to provide a platform for micro-entrepreneurs to develop their online segmentation. The ultimate goal of this research is to bridge the gap between AI marketing and digital transformation, creating a user-friendly application for micro-entrepreneurs to increase their marketing capacity.","2024-01-01","2024-12-03 03:24:06","2024-12-03 03:24:06","","1087-1094","","","234","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Digital Marketing; Artificial Intelligence; Delphi method; Digital Transformation; Framework model","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"6KFCWRQ5","journalArticle","2024","Ullrich, Katrin; von Elling, Magnus; Gutzeit, Kevin; Dix, Martin; Weigold, Matthias; Aurich, Jan C.; Wertheim, Rafael; Jawahir, I.S.; Ghadbeigi, Hassan","AI-based optimisation of total machining performance: A review","CIRP Journal of Manufacturing Science and Technology","","1755-5817","10.1016/j.cirpj.2024.01.012","https://www.sciencedirect.com/science/article/pii/S1755581724000105","Advanced modelling and optimisation techniques have been widely used in recent years to enable intelligent manufacturing and digitalisation of manufacturing processes. In this context, the integration of artificial intelligence in machining provides a great opportunity to enhance the efficiency of operations and the quality of produced components. Machine learning methods have already been applied to optimise various individual objectives concerning process characteristics, tool wear, or product quality in machining. However, the overall improvement of the machining process requires multi-objective optimisation approaches, which are rarely considered and implemented. The state-of-the-art in application of various optimisation and artificial intelligence methods for process optimisation in machining operations, including milling, turning, drilling, and grinding, is presented in this paper. The Milling process and deep learning are found to be the most widely researched operation and implemented machine learning technique, respectively. The surface roughness turns out to be the most critical quality measure considered. The different optimisation targets in artificial intelligence applications are elaborated and analysed to highlight the need for a holistic approach that covers all critical aspects of the machining operations. As a result, the key factors for a successful total machining performance improvement are identified and discussed in this paper. The AI methods were investigated and analysed in the frame of the IMPACT project initiated by the CIRP.","2024-06-01","2024-12-03 03:24:06","2024-12-03 03:24:06","","40-54","","","50","","CIRP Journal of Manufacturing Science and Technology","","","","","","","","","","","","","","","","","","","Artificial intelligence; Multi-objective optimisation; Machining performance","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"QFZETMPQ","journalArticle","2024","Kanz Abdillah, H; Wildan Rizkia, N.A.H.; Sidharta, Sidharta","Exploring the role of artificial intelligence in enhancing battery performance and mitigating cybersecurity threats in electric vehicles: A systematic literature review","9th International Conference on Computer Science and Computational Intelligence 2024 (ICCSCI 2024)","","1877-0509","10.1016/j.procs.2024.10.239","https://www.sciencedirect.com/science/article/pii/S1877050924030473","Electric vehicles (Evs) are increasingly taking center stage in efforts to reduce emissions and dependence on fossil fuels. However, the critical challenges of optimizing battery performance and maintaining cybersecurity are the focus. In this study, we conducted a systematic literature review to explore the key role of artificial intelligence (AI) in addressing these challenges. This research includes answers to several research questions such as the utilization of AI in Hybrid EVs, the role of AI in estimating battery life, cyber security in EVs, and several things related to battery optimization using AI. Through a careful synthesis of the literature, we highlight various AI in Hybrid EVs is frequently employed for Battery Management Systems. AI can also play a role in battery charging for EVs based on various applied methods. We have found that cyber-attacks on EVs are not much different from those on websites/applications, and EV Charging Stations can also be targeted for cyber-attacks. The utilization of blockchain-based smart contracts can address security issues during the charging process. In estimating battery life, we found that the Ensemble Regression method could be considered the most optimal, based on comparisons with several studies we encountered. Our results illustrate future research directions in the face of the complexity of EV technology and offer a deeper look into the potential of artificial intelligence integration to improve EV performance and safety.","2024-01-01","2024-12-03 03:24:07","2024-12-03 03:24:07","","155-165","","","245","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial Intelligence; Blockchain; Battery Management System; Cyber Security; Electric Vehicles","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RT6RVIQS","journalArticle","2024","Hein, Kaiden; Conkey-Morrison, Connor; Burleigh, Tyrone L.; Poulus, Dylan; Stavropoulos, Vasileios","Examining how gamers connect with their avatars to assess their anxiety: A novel artificial intelligence approach","Acta Psychologica","","0001-6918","10.1016/j.actpsy.2024.104298","https://www.sciencedirect.com/science/article/pii/S0001691824001756","Research has supported that a gamer's attachment to their avatar can offer significant insights about their mental health, including anxiety. To assess this hypothesis, longitudinal data from 565 adult and adolescent participants (Mage = 29.3 years, SD = 10.6) was analyzed at two points, six months apart. Respondents were assessed using the User-Avatar Bond (UAB) scale and the Depression Anxiety Stress Scale (DASS) to measure their connection with their avatar and their risk for anxiety. The records were processed using both untuned and tuned artificial intelligence [AI] classifiers to evaluate present and future anxiety. The findings indicated that AI models are capable of accurately and autonomously discerning cases of anxiety risk based on the gamers' self-reported UAB, age, and duration of gaming, both at present and after six months. Notably, random forest algorithms surpassed other AI models in effectiveness, with avatar compensation emerging as the most significant factor in model training for prospective anxiety. The implications for assessment, prevention, and clinical practice are discussed.","2024-06-01","2024-12-03 03:24:07","2024-12-03 03:24:07","","104298","","","246","","Acta Psychologica","","","","","","","","","","","","","","","","","","","Machine learning; Anxiety; Artificial intelligence; Avatar; Internet gaming","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"N6B3E37U","journalArticle","2024","Năstasă, Anamaria; Dumitra, Teodora-Cătălina; Grigorescu, Adriana","Artificial intelligence and sustainable development during the pandemic: An overview of the scientific debates","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e30412","https://www.sciencedirect.com/science/article/pii/S2405844024064430","The current work aims to analyze the main themes related to artificial intelligence (AI) and sustainable development during the pandemic period. This study provides an overview of the specialized literature related to AI and sustainability from the beginning of the pandemic through 2023. The present paper analyses scientific literature emphasizing both artificial intelligence's positive and negative impacts on sustainable development objectives (SDGs). To conduct the research, we employed bibliometric analysis and text-mining techniques to identify the major themes in the literature indexed in the Web of Science and Scopus databases. Firstly, we used descriptive measures to identify the authors' impact, the article production by country, the main keywords used, and other descriptive data. We further used data reduction methods based on co-word analysis (such as multiple correspondence analysis) on authors' keywords to show patterns in the themes explored in the literature. Bibliometric analysis was supplemented by text mining using Latent Dirichlet allocation (LDA) and structural topic modeling on abstracts to provide a comprehensive view of scientific debates on AI and sustainable development. Our research has identified various themes in the literature related to AI and sustainable development. These themes include social sustainability, health-related issues, AI technologies for energy efficiency, sustainability in industry and innovation, IoT technologies for smart and sustainable cities, urban planning, technologies for education and knowledge production, and the impact of technologies on SDGs. We also found that there is a significant positivity bias in the literature when discussing the impact of AI on sustainable development. Despite acknowledging certain risks, the literature tends to focus on the potential benefits of AI across various sectors. In addition, the analysis shows a growing emphasis on energy efficiency, which is facilitated by the use of AI technologies. Our study contributes to a better understanding of current scholarly discussion trends and emerging scientific avenues regarding AI and sustainable development. It also highlights the areas where research is needed and the implications for practitioners and policymakers.","2024-05-15","2024-12-03 03:24:07","2024-12-03 03:24:07","","e30412","","9","10","","Heliyon","","","","","","","","","","","","","","","","","","","Bibliometric analysis; Sustainable development; Artificial intelligence; Text mining; LDA","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"GAKSMJFN","journalArticle","2024","Lomello, Fernando; Bard, Lucie; Maglione, Mario; Schuster, Frédéric","PEPR DIADEM: Priority equipment and research program on the development of innovative materials using artificial intelligence","Computational and Structural Biotechnology Journal","","2001-0370","10.1016/j.csbj.2024.09.019","https://www.sciencedirect.com/science/article/pii/S2001037024003052","Introduction The quest to develop efficient, sustainable materials from non-critical, non-toxic resources is one of today's most formidable challenges in the current context of energy, transport, digital or healthcare transitions. In response, France launched the pioneering Priority Equipment and Research Program (PEPR) DIADEM in 2022. This innovative initiative, focused on DIscovery Acceleration for the Deployment of Emerging Materials (DIADEM), leverages Artificial Intelligence (AI) to accelerate the innovation chain from conception to realization, revolutionizing Materials Science sustainably. With a strategic emphasis on scientific synergy, PEPR DIADEM aims to expedite the discovery and development of novel materials essential for contemporary and future societal challenges. To achieve this, the program seeks to catalyze breakthroughs in areas ranging from energy efficiency to transportation, digitalization, and healthcare, covering a broad spectrum of materials from metallic alloys to functional nanostructures. Aligned with the Green Deal framework's ambitious targets, PEPR DIADEM addresses the urgent need for accelerated sustainable materials research. By utilizing cutting-edge technologies like rapid synthesis and characterization tools, automation, digital simulations, data management, AI, additive manufacturing, and thin film engineering, the program is set to significantly reshape the materials science landscape. As PEPR DIADEM embarks on its journey of innovation, it not only advances scientific knowledge but also holds the promise of addressing current global challenges and paving the way for a more sustainable and prosperous future.","2024-12-01","2024-12-03 03:24:07","2024-12-03 03:24:07","","186-193","","","25","","Computational and Structural Biotechnology Journal","","","","","","","","","","","","","","","","","","","Sustainable materials; Artificial Intelligence (AI); Materials discovery; Data-driven materials science; Digital simulation; High-throughput synthesis","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DQD9NNPG","journalArticle","2024","Invernici, Francesco; Bernasconi, Anna; Ceri, Stefano","Exploring the evolution of research topics during the COVID-19 pandemic","Expert Systems with Applications","","0957-4174","10.1016/j.eswa.2024.124028","https://www.sciencedirect.com/science/article/pii/S0957417424008947","The COVID-19 pandemic has changed the research agendas of most scientific communities, resulting in an overwhelming production of research articles in a variety of domains, including medicine, virology, epidemiology, economy, psychology, and so on. Several open-access corpora and literature hubs were established; among them, the COVID-19 Open Research Dataset (CORD-19) has systematically gathered scientific contributions for 2.5 years, by collecting and indexing over one million articles—this corpus, however, does not provide an easy-to-access overview of its content. Here, we present the CORD-19 Topic Visualizer (CORToViz), a method and associated visualization tool for inspecting the CORD-19 textual corpus of scientific abstracts. Our method is based upon a careful selection of up-to-date technologies (including large language models), resulting in an architecture for clustering articles along orthogonal dimensions and extraction techniques for temporal topic mining. Topic inspection is supported by an interactive dashboard, providing fast, one-click visualization of topic contents as word clouds and topic trends as time series, equipped with easy-to-drive statistical testing for analyzing the significance of topic emergence along arbitrarily selected time windows. Overall, our pipeline is very fast and its results match our expectations on topic identification (F1-score 0.854). The processes of data preparation and results visualization are completely general and virtually applicable to any corpus of textual documents—thus suited for effective adaptation to other contexts.","2024-10-15","2024-12-03 03:24:07","2024-12-03 03:24:07","","124028","","","252","","Expert Systems with Applications","","","","","","","","","","","","","","","","","","","COVID-19; Natural language processing; Topic modeling; Time series; Research data; Scientific literature","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"738EWG2G","journalArticle","2024","Cao, Dingding; Chan, Mieow Kee","Enhancing chemical synthesis research with NLP: Word embeddings for chemical reagent identification—A case study on nano-FeCu","iScience","","2589-0042","10.1016/j.isci.2024.110780","https://www.sciencedirect.com/science/article/pii/S2589004224020054","Summary Nanoparticle synthesis is complex, influenced by multiple variables including reagent selection. This study introduces a specialized corpus focused on “Fe, Cu, synthesis” to train a domain-specific word embedding model using natural language processing (NLP) in an unsupervised environment. Evaluation metrics included average cosine similarity, visual analysis via t-distributed stochastic neighbor embedding (t-SNE), synonym analysis, and analogy reasoning analysis. Results indicate a strong correlation between learning rate and cosine similarity, with enhanced chemical specificity in the tailored model compared to general models. The framework facilitates rapid identification of potential reagents for nano-FeCu synthesis, enhancing precision in nanomaterial research. This innovative approach offers a data-driven pathway for chemical material synthesis, demonstrating significant interdisciplinary applications.","2024-10-18","2024-12-03 03:24:07","2024-12-03 03:24:07","","110780","","10","27","","iScience","","","","","","","","","","","","","","","","","","","Artificial intelligence; Natural language processing; Engineering; Materials science","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"EFMMM42M","journalArticle","2024","Gimeno-Ballester, Vicente; Trigo-Vicente, Cristina","El rol de la inteligencia artificial en la publicación científica: perspectivas desde la farmacia hospitalaria","Farmacia Hospitalaria","","1130-6343","10.1016/j.farma.2024.06.002","https://www.sciencedirect.com/science/article/pii/S1130634324000965","Resumen El artículo explora el impacto de la inteligencia artificial en la escritura científica, con especial atención a su aplicación en la farmacia hospitalaria. Se analizan herramientas de inteligencia artificial que optimizan la búsqueda de información, el análisis de la literatura, la calidad de la escritura y la redacción de manuscritos. Chatbots como Consensus, junto con plataformas como Scite y SciSpace, facilitan la búsqueda precisa en bases de datos científicas, ofreciendo respuestas con evidencia y referencias. SciSpace permite la generación de tablas comparativas y la formulación de preguntas sobre estudios, mientras que ResearchRabbit mapea la literatura científica para identificar tendencias. DeepL y ProWritingAid mejoran la calidad de la escritura al corregir errores gramaticales, de estilo y plagio. A.R.I.A. optimiza la gestión de referencias, mientras que Jenny AI ayuda a superar el bloqueo del escritor. Librerías de Python como LangChain permiten realizar búsquedas semánticas avanzadas y la creación de agentes. A pesar de sus beneficios, la inteligencia artificial plantea preocupaciones éticas como sesgos, desinformación y plagio. Se destaca la importancia de un uso responsable y la revisión crítica por expertos. En la farmacia hospitalaria, la inteligencia artificial puede mejorar la eficiencia y la precisión en la investigación y la comunicación científica. Los farmacéuticos pueden utilizar estas herramientas para mantenerse actualizados, mejorar la calidad de sus publicaciones, optimizar la gestión de la información y facilitar la toma de decisiones clínicas. En conclusión, la inteligencia artificial es una herramienta poderosa para la farmacia hospitalaria, siempre que se utilice de manera responsable y ética. The article examines the impact of artificial intelligence on scientific writing, with a particular focus on its application in hospital pharmacy. It analyzes artificial intelligence tools that enhance information retrieval, literature analysis, writing quality, and manuscript drafting. Chatbots like Consensus, along with platforms such as Scite and SciSpace, enable precise searches in scientific databases, providing evidence-based responses and references. SciSpace facilitates the generation of comparative tables and the formulation of queries regarding studies, while ResearchRabbit maps the scientific literature to identify trends. Tools like DeepL and ProWritingAid improve writing quality by correcting grammatical, stylistic, and plagiarism errors. A.R.I.A. enhances reference management, and Jenny AI assists in overcoming writer's block. Python libraries such as LangChain enable advanced semantic searches and the creation of agents. Despite their benefits, artificial intelligence raises ethical concerns including biases, misinformation, and plagiarism. The importance of responsible use and critical review by experts is emphasized. In hospital pharmacy, artificial intelligence can enhance efficiency and precision in research and scientific communication. Pharmacists can use these tools to stay updated, enhance the quality of their publications, optimize information management, and facilitate clinical decision-making. In conclusion, artificial intelligence is a powerful tool for hospital pharmacy, provided it is used responsibly and ethically.","2024-09-01","2024-12-03 03:24:07","2024-12-03 03:24:07","","246-251","","5","48","","Farmacia Hospitalaria","","","","","","","","","","","","","","","","","","","Research; Chatbots; Ethics; Artificial Intelligence; Escritura científica; Ética; Farmacia Hospitalaria; Investigación; Publicación científica; AI Tools; Herramientas inteligencia artificial; Hospital Pharmacy; Inteligencia artificial; Scientific Publications; Scientific Writing","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"H8PMSSCN","journalArticle","2023","Wang, Jingjing","Analysis of communication strategy of Guzheng art based on artificial intelligence","3rd International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy","","1877-0509","10.1016/j.procs.2023.11.003","https://www.sciencedirect.com/science/article/pii/S1877050923018264","The current situation of the spread of guzheng art is not good, and the continuous development may lead to the disappearance of the art of guzheng. Therefore, it is necessary to help the art of guzheng to continue to spread in the modern environment. On this basis, this paper will research on the basis of artificial intelligence, mainly introduces the basic concept of artificial intelligence, and then puts forward the idea of coping and the method of thinking. Research shows that artificial intelligence has high application value in the communication of guzheng art. Combining ideas and methods, artificial intelligence can play a role in helping the communication of Guzheng art. Continuous circulation can change the communication status and reverse the elapse of guzheng art.","2023-01-01","2024-12-03 03:24:07","2024-12-03 03:24:07","","14-20","","","228","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial intelligence; Art spread; Guzheng art","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"UAZM9ZRY","journalArticle","2024","Marioni, Larissa da Silva; Rincon-Aznar, Ana; Venturini, Francesco","Productivity performance, distance to frontier and AI innovation: Firm-level evidence from Europe","Journal of Economic Behavior & Organization","","0167-2681","10.1016/j.jebo.2024.106762","https://www.sciencedirect.com/science/article/pii/S0167268124003767","Using firm-level data from 15 European countries between 2011 and 2019, this paper examines the productivity effect associated with the development of Artificial Intelligence (AI), measured by patenting success in AI fields. By making advances in AI and expanding on their knowledge base, companies can optimise production tasks, and improve resource utilisation, ultimately leading to higher levels of efficiency. To investigate this, we develop a two-fold panel regression analysis estimated within a Difference-in-Differences (DiD) framework. First, we investigate whether firms that engage in AI innovation experience a productivity boost after developing the new technology, compared to similar firms which do not undertake AI innovation. To analyse this, we employ a novel event-analysis methodology that quantifies the effect of the treatment (AI innovation) on firm performance (productivity) using a Local Projections approach within the DiD setting. Second, we utilise a Distance-to-Frontier (DTF) regression framework in order to examine whether the productivity premium of AI is associated with a firm’s ability to absorb knowledge and learn from the technologies developed by market leaders. Our findings reveal that the productivity gains directly associated with AI are statistically significant and quantitatively important, ranging between 6.2 and 17% in the event analysis, and between 2.1 and 6% in the DTF framework. We also provide some evidence that the productivity benefits of AI might be greater for those firms further away from the frontier (between 0.3 and 0.7%). Our research demonstrates that Artificial Intelligence can play a crucial role in enhancing firm productivity in Europe, a result that is evident even in these early stages of the technology’s life cycle.","2024-12-01","2024-12-03 03:24:07","2024-12-03 03:24:07","","106762","","","228","","Journal of Economic Behavior & Organization","","","","","","","","","","","","","","","","","","","Artificial Intelligence; Distance-to-Frontier; European firms; Local Projections Difference-in-Differences; Multi-Factor Productivity","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2X8VEKLV","journalArticle","2024","Nobles, Calvin","The Weaponization of Artificial Intelligence in Cybersecurity: A Systematic Review","CENTERIS – International Conference on ENTERprise Information Systems / ProjMAN - International Conference on Project MANagement / HCist - International Conference on Health and Social Care Information Systems and Technologies 2023","","1877-0509","10.1016/j.procs.2024.06.206","https://www.sciencedirect.com/science/article/pii/S1877050924014492","The weaponization of artificial intelligence (AI) and machine learning (ML) models in cybersecurity is a growing concern, with cybercriminal organizations and nation-states exploiting their weaknesses. The Microsoft ""Tay"" chatbot incident exemplifies the risks of weaponized AI, as it displayed sexist and racist behaviors due to malicious data inputs. The researcher examined 21 academic studies on AI weaponization and AI-driven cyberattacks in this systematic review. This work, a systematic review, concludes with an in-depth understanding of the scale and scope of using AI as a cyber weapon. The findings revealed that more research is necessary on weaponizing AI for offensive cybersecurity applications and the following key observations (a) a connection between AI weaponization and countermeasures, (b) AI’s role in enhancing cybersecurity defenses, (c) AI weaponization offering mitigation strategies for protecting digital assets and infrastructure, and (d) AI-driven attacks exploiting vulnerabilities, enabling automation and scalability, facilitating data poisoning and manipulation, improving social engineering, and augmenting evasion obfuscation. This study contributes to a better understanding of AI weaponization and aids researchers in synthesizing current literature on the topic.","2024-01-01","2024-12-03 03:24:07","2024-12-03 03:24:07","","547-555","","","239","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","machine learning; Artificial intelligence; technology; cybersecurity; algorithms; weaponization","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"NRDP8MGP","journalArticle","2024","Jorzik, Philip; Klein, Sascha P.; Kanbach, Dominik K.; Kraus, Sascha","AI-driven business model innovation: A systematic review and research agenda","Journal of Business Research","","0148-2963","10.1016/j.jbusres.2024.114764","https://www.sciencedirect.com/science/article/pii/S0148296324002686","Recent years have seen a surge in research on artificial intelligence (AI)-driven business model innovation (BMI), reflecting its profound impact across industries. However, the field’s current state remains fragmented due to varied conceptual lenses and units of analysis. Existing literature predominantly emphasizes the technological aspects of AI implementation in business models (BMs), treating BMI as a byproduct. Additionally, there is a lack of coherent understanding regarding the scope of BMI propelled by AI. To address these gaps, our study systematically reviews 180 articles, offering two key contributions: (1) a structured analysis of evolving research dimensions in AI-driven BMI, differentiating between static and dynamic views of BMI, and (2) a framework presenting distinct research perspectives on AI-driven BMI, each addressing specific managerial focuses. This synthesis facilitates a comprehensive understanding of the field, enabling the identification of research gaps and proposing future avenues for advancing knowledge on the management of AI-driven BMI.","2024-09-01","2024-12-03 03:24:07","2024-12-03 03:24:07","","114764","","","182","","Journal of Business Research","","","","","","","","","","","","","","","","","","","Artificial intelligence; Systematic literature review; AI-driven BMI; Business model innovation; Value proposition","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"MPD8ALTJ","journalArticle","2024","Alawamleh, Mohammad; Shammas, Natalie; Alawamleh, Kamal; Bani Ismail, Loiy","Examining the limitations of AI in business and the need for human insights using Interpretive Structural Modelling","Journal of Open Innovation: Technology, Market, and Complexity","","2199-8531","10.1016/j.joitmc.2024.100338","https://www.sciencedirect.com/science/article/pii/S219985312400132X","The integration of Artificial Intelligence (AI) in business settings is rapidly increasing, yet significant limitations hinder its effective use and adoption. Understanding these limitations and their interrelationships is crucial for enhancing AI implementation. Despite growing research, there is a lack of a comprehensive model that systematically identifies and elucidates the factors influencing AI limitations in business environments. This study employs Interpretive Structural Modeling (ISM), combined with MICMAC analysis and an extensive literature review, to develop such a model. We identified 15 key factors and analyzed their driving and dependence powers to understand their interrelationships. Most factors, such as contextual understanding, transparency, intuition, emotional intelligence, ethics, bias, tacit knowledge, creativity, credibility, and reliability, were found to be autonomous. Accountability and privacy emerged as the strongest driving forces, while trust and adaptability exhibited the highest dependence and lowest driving power. This research offers a comprehensive understanding of AI limitations and their interrelationships, providing valuable insights for managers and businesses. The findings can aid in making more informed decisions about AI implementation and in developing strategies to mitigate these limitations. Furthermore, the study emphasizes the importance of combining AI with human insight to overcome these challenges. However, using the ISM technique could involve subjective judgment from the experts.","2024-09-01","2024-12-03 03:24:07","2024-12-03 03:24:07","","100338","","3","10","","Journal of Open Innovation: Technology, Market, and Complexity","","","","","","","","","","","","","","","","","","","Business; Artificial intelligence; Open innovation; Interpretive structural modeling; Interrelationships; Limitations","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RNIP6485","journalArticle","2024","Lee, Jeonghoe; Xia, Bingjiang","Analyzing the dynamics between crude oil spot prices and futures prices by maturity terms: Deep learning approaches to futures-based forecasting","Results in Engineering","","2590-1230","10.1016/j.rineng.2024.103086","https://www.sciencedirect.com/science/article/pii/S2590123024013410","Predicting crude oil prices is critical due to their significant impact on the global economy. While numerous studies have focused on time series forecasting and machine learning methods to predict crude oil spot prices, the use of futures prices segmented by the maturity terms as predictors is comparatively underexplored. This study explored a deep learning approach to investigate using futures prices across different maturities to predict spot prices. Specifically, this paper analyzes the predictive power of one-, two-, three-, six-, and 12-month crude oil futures contracts. This study employs multiple deep learning algorithms, including Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and Temporal Convolutional Neural Network (TCN), to forecast crude oil spot prices. This research investigates the performance of these machine learning models when exploring the relationship between futures and spot prices of crude oil. In addition, this research incorporates extensive hyperparameter tuning to enhance the interpretability of the machine learning models when forecasting spot prices using futures prices, thereby contributing to the field of Explainable Artificial Intelligence (XAI) with an optimal set of hyperparameters. In summary, this research systematically shows results demonstrating predictive power in terms of XAI between spot prices and futures prices with different maturities, and machine learning algorithms.","2024-12-01","2024-12-03 03:24:07","2024-12-03 03:24:07","","103086","","","24","","Results in Engineering","","","","","","","","","","","","","","","","","","","Deep learning; Explainable artificial intelligence; Prediction; Crude oil; Maturity term","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LFGSBH8T","journalArticle","2024","Heudel, P.; Mery, B.; Crochet, H.; Bachelot, T.; Tredan, O.","Transforming breast cancer management with real-world data and artificial intelligence","ESMO Real World Data and Digital Oncology","","2949-8201","10.1016/j.esmorw.2024.100067","https://www.sciencedirect.com/science/article/pii/S2949820124000456","Background Real-world data (RWD) provide essential insights into the effectiveness and safety of breast cancer treatments, particularly in diverse patient populations, where traditional clinical trials may have limitations. Integrating RWD into breast cancer research enhances the understanding of treatment outcomes and supports clinical decision-making, complementing the findings from controlled clinical studies. Design This article reviews the integration of RWD into breast cancer research, highlighting the benefits and challenges. Various sources of RWD, including electronic health records (EHRs), insurance claims, and patient registries, are examined, with a focus on their application in studies of triple-negative breast cancer. The article also explores the role of artificial intelligence (AI) in managing RWD, particularly through technologies like natural language processing (NLP) and predictive analytics, which enhance data collection, storage, and analysis. Results RWD has demonstrated significant value in informing clinical decision-making and improving patient outcomes in breast cancer treatment. The integration of AI into the management of RWD has provided deeper insights into patient outcomes and supported personalized treatment strategies. Specific studies leveraging RWD have shown improved understanding of breast cancer subtypes, such as triple-negative breast cancer, and enhanced the effectiveness of treatment protocols. Conclusion Despite the benefits, challenges remain in integrating RWD and AI into clinical practice, particularly regarding transparency, interpretability, and ethical considerations. Addressing these challenges requires robust data governance frameworks, interdisciplinary collaboration, and investment in advanced analytical tools. The potential for RWD and AI to transform breast cancer treatment and improve patient care is significant, underscoring the need for ongoing research and collaboration.","2024-09-01","2024-12-03 03:24:07","2024-12-03 03:24:07","","100067","","","5","","ESMO Real World Data and Digital Oncology","","","","","","","","","","","","","","","","","","","artificial intelligence; natural language processing; breast cancer; big data; real-world data; real-world evidence","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"S39PVP2N","journalArticle","2024","Nwafor, Obumneme; Nwafor, Chioma; Aboushady, Ahmed; Solyman, Ahmed","Reducing non-technical losses in electricity distribution networks: Leveraging explainable AI and three lines of defence model to manage operational staff-related factors","e-Prime - Advances in Electrical Engineering, Electronics and Energy","","2772-6711","10.1016/j.prime.2024.100748","https://www.sciencedirect.com/science/article/pii/S2772671124003280","This study presents a multidisciplinary approach involving Explainable Artificial Intelligence (ExAI) and operational risk management to reduce Non-Technical Losses (NTL) in electricity distribution. It empirically explores how the activities of employees of utility companies contribute to NTL, a phenomenon often overlooked in existing empirical research. An ensemble classification algorithm is used to analyse utility operations data, and the SHAP explainability technique establishes the predictive significance of staff activities for NTL. Subsequently, these staff activities are mapped into risk cells using the BASEL II and III operational risk definitions, and the Three Lines of Defence (3LoD) model is developed for optimizing electricity distribution. The paper makes three original contributions to the literature: first, it empirically links staff operations to NTL; second, it maps NTL causes to Basel II/III operational risk categories; and finally, to the best of the authors’ knowledge, it is the first study to use the 3LoD model for electricity distribution optimization.","2024-09-01","2024-12-03 03:24:08","2024-12-03 03:24:08","","100748","","","9","","e-Prime - Advances in Electrical Engineering, Electronics and Energy","","","","","","","","","","","","","","","","","","","Explainable artificial intelligence; 3LoD model; Electricity distribution; Non-technical losses","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"37ZCHDVS","journalArticle","2024","Binder, Mario; Mezhuyev, Vitaliy","A framework for creating an IoT system specification with ChatGPT","Internet of Things","","2542-6605","10.1016/j.iot.2024.101218","https://www.sciencedirect.com/science/article/pii/S2542660524001598","The Internet of Things (IoT) is a core concern for the digital transformation of the industry. However, the development of IoT systems, starting from creating specifications, is a serious and time-consuming effort, which requires high levels of expertise. This paper introduces a novel approach leveraging large language models, specifically ChatGPT, to streamline the generation of IoT system specifications from initial, unstructured customer requirements. A framework, adapting the ISO IEC/IEEE 12,207:2017 standard, is developed for this purpose. The utility of this framework is validated through the development and expert evaluation of a specification for a campus laboratory IoT use case. The findings demonstrate ChatGPT's effectiveness in producing specifications that are understandable, complete, and unambiguous, thus offering a substantial aid to the industry in overcoming the complexities of IoT system specification development. At the same time, the study identified specific tasks within the requirements engineering process for IoT systems that ChatGPT could not effectively perform, such as verification, obtaining agreements, and maintaining traceability of requirements between stakeholders. The findings offer valuable insights into the practical applications of AI in the domain of IoT development, particularly in automating the specification generation process. These contributions not only advance the understanding of AI's role in IoT development but also open pathways for further research on refining AI-assisted methodologies for system specification and beyond.","2024-10-01","2024-12-03 03:24:08","2024-12-03 03:24:08","","101218","","","27","","Internet of Things","","","","","","","","","","","","","","","","","","","Internet of Things; ChatGPT; Large language models; Industrial Internet of Things; Industry; Specifications","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"BZDSPNSW","journalArticle","2024","Xie, Tong; Wan, Yuwei; Zhou, Yufei; Huang, Wei; Liu, Yixuan; Linghu, Qingyuan; Wang, Shaozhou; Kit, Chunyu; Grazian, Clara; Zhang, Wenjie; Hoex, Bram","Creation of a structured solar cell material dataset and performance prediction using large language models","Patterns","","2666-3899","10.1016/j.patter.2024.100955","https://www.sciencedirect.com/science/article/pii/S2666389924000540","Summary Materials scientists usually collect experimental data to summarize experiences and predict improved materials. However, a crucial issue is how to proficiently utilize unstructured data to update existing structured data, particularly in applied disciplines. This study introduces a new natural language processing (NLP) task called structured information inference (SII) to address this problem. We propose an end-to-end approach to summarize and organize the multi-layered device-level information from the literature into structured data. After comparing different methods, we fine-tuned LLaMA with an F1 score of 87.14% to update an existing perovskite solar cell dataset with articles published since its release, allowing its direct use in subsequent data analysis. Using structured information, we developed regression tasks to predict the electrical performance of solar cells. Our results demonstrate comparable performance to traditional machine-learning methods without feature selection and highlight the potential of large language models for scientific knowledge acquisition and material development.","2024-05-10","2024-12-03 03:24:08","2024-12-03 03:24:08","","100955","","5","5","","Patterns","","","","","","","","","","","","","","","","","","","large language model; materials science; AI for science; automated data annotation; device performance prediction; material discovery; perovskite solar cells; renewable energy; scientific data; text mining; theory and computation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"63EFLAJW","journalArticle","2024","Koivula, Karri; Shamsuzzoha, Ahm; Shamsuzzaman, Mohammad","Application of artificial intelligence as a knowledge creation instrument in tax procedures","Engineering Applications of Artificial Intelligence","","0952-1976","10.1016/j.engappai.2024.108417","https://www.sciencedirect.com/science/article/pii/S095219762400575X","This study set out to find whether deep learning algorithms neural networks and self-organizing maps could be utilized in a value-adding way in the Finnish Tax Administration in the handling of income tax related claims by limited liability companies. According to research positive outcomes in artificial intelligence (AI) utilization have been attained outside Finland. The research was carried out according to the action design research method in which the focus of the research is concurrently building a suitable artifact for the organization and learning (design principles) from the creation and intervention itself. Research began with problem formulation followed by building, intervention, and evaluation. As a result, the project team consisting of three members created two functional artifacts: one based on neural networks, and another based on self-organizing maps. Creation of the artifacts was done in cycles as alpha, beta and gamma where alpha and beta were a neural network and gamma a self-organizing map. Alpha reached a macro average of 0.75–0.78 in classification and beta 0.77–0.79. Gamma gave a different point of view on the problem and was able to clearly identify the class's non-estimated customers in a topographical map. The artifacts were limited to function only as knowledge creation instruments due to legal and ethical limitations present in the context. Results suggest that it is recommendable to approach problems with more than one artifact. The preliminary results of this research were validated by applying the concept in a case organization, followed by an analysis of the results in an end-user setting.","2024-07-01","2024-12-03 03:24:08","2024-12-03 03:24:08","","108417","","","133","","Engineering Applications of Artificial Intelligence","","","","","","","","","","","","","","","","","","","Neural networks; Machine learning; Artificial intelligence; Self-organizing maps; Tax procedures","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"NLIJNXFQ","journalArticle","2024","Donald, William E.; Van der Heijden, Beatrice I.J.M.; Baruch, Yehuda","Introducing a sustainable career ecosystem: Theoretical perspectives, conceptualization, and future research agenda","Journal of Vocational Behavior","","0001-8791","10.1016/j.jvb.2024.103989","https://www.sciencedirect.com/science/article/pii/S0001879124000307","Our paper advances the embryonic interest of combining the theoretical frameworks of sustainable career and career ecosystem into a sustainable career ecosystem theory by introducing Artificial Intelligence (AI) as a new actor, spotlighting the need for liminality of the relationship between an individual and career practitioner, and presenting a new conceptual model. We begin by providing a brief overview of sustainable career and career ecosystem theories, culminating in a recently proposed definition of a sustainable career ecosystem. Second, using this as our point of departure, we consider the theoretical perspectives for understanding a sustainable career ecosystem through (a) introducing AI as a new actor with the potential to disrupt and transform the (future) labor market and (b) making a case for the liminality of the individual and career practitioner relationship. Third, we consider various dimensions for analyzing a sustainable career ecosystem to offer a new conceptual model. We conclude with a future research agenda. Article classification Conceptual Paper.","2024-06-01","2024-12-03 03:24:08","2024-12-03 03:24:08","","103989","","","151","","Journal of Vocational Behavior","","","","","","","","","","","","","","","","","","","Artificial intelligence; Career ecosystem; Career practitioners; Liminality; Sustainable career","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"QGRKL53J","journalArticle","2023","Sadeqi-Arani, Zahra; Kadkhodaie, Ali","A bibliometric analysis of the application of machine learning methods in the petroleum industry","Results in Engineering","","2590-1230","10.1016/j.rineng.2023.101518","https://www.sciencedirect.com/science/article/pii/S259012302300645X","With the emerge of Artificial Intelligence and Machin learning systems, the petroleum industry has witnessed a significant progress in its different disciplines to optimize decision making, time and costs. Despite the widespread application of using machine learning methods in the petroleum industry, a little attention has been devoted to build a framework to bring the main currents and researches on the topic. The current research is aimed at covering this gap through further analysis of complementary sources of bibliographic information, assessing 3163 bibliometric studies published in Web of Science (WOS) database. The descriptive statistics show that this field has an exponential growth in the last five years, such that more than 62 % of identified articles were published between 2018 and 2022. CHINA, IRAN and US are the pioneer countries with the highest number of publications on the application of artificial intelligence and machine learning in the upstream sector of the petroleum industry. The most influential journal in this field is ‘JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING’ (with 416 articles) (the current journal title is Geoenergy Science and Engineering) and the most productive author is SALAHELDIN ELKATATNY (with 54 articles) in WOS database. Also, the co-occurrence word analysis show that most of the artificial intelligence and machine learning applications in the upstream sector of the petroleum industry was the prediction and optimization in the field of ‘porosity’, ‘well logs’ and ‘permeability’. This paper contributes to the body of knowledge by providing a comprehensive overview of the application of artificial intelligence and machine learning in the upstream petroleum industry.","2023-12-01","2024-12-03 03:24:08","2024-12-03 03:24:08","","101518","","","20","","Results in Engineering","","","","","","","","","","","","","","","","","","","Machine learning; Bibliometric analysis; Artificial intelligence; Oil and gas; Petroleum industry","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XPUY9VJJ","journalArticle","2024","Zuhanda, Muhammad Khahfi; Hartono; Hasibuan, Samsul A. Rahman Sidik; Napitupulu, Yose Yefta","An exact and metaheuristic optimization framework for solving Vehicle Routing Problems with Shipment Consolidation using population-based and Swarm Intelligence","Decision Analytics Journal","","2772-6622","10.1016/j.dajour.2024.100517","https://www.sciencedirect.com/science/article/pii/S2772662224001218","The Vehicle Routing Problem with Shipment Consolidation (VRPSC) is a novel variation of the vehicle routing problem that involves multiple commodities, multiple dimensions, a fleet with different types of vehicles, and the challenge of consolidating shipments during the route. Exact algorithms have been suggested to solve the VRPSC problems. Besides exact algorithms, specific metaheuristic algorithms are employed to deliver solutions of superior quality, albeit not necessarily optimal. The Artificial Immune System (AIS) and Genetic Algorithm (GA) are the metaheuristic optimization techniques applied in this research. Genetic programming is included in evolutionary computing, while AIS is included in swarm intelligence. This research presents a vehicle routing model with product consolidation and different product dimensions. These criteria were selected due to their significant impact on the complexity of mathematical problem-solving in VRPSC. The results from applying these metaheuristic algorithms will be compared with those of exact algorithms to compare and analyse different VRPSC solution approaches.","2024-12-01","2024-12-03 03:24:08","2024-12-03 03:24:08","","100517","","","13","","Decision Analytics Journal","","","","","","","","","","","","","","","","","","","Artificial Immune System; Genetic Algorithm; Integer Programming; Shipment Consolidation; Vehicle Routing Problem","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"EZB8YVAR","journalArticle","2024","Wang, Yuxiang; Li, Yang; Tang, Zechen; Li, He; Yuan, Zilong; Tao, Honggeng; Zou, Nianlong; Bao, Ting; Liang, Xinghao; Chen, Zezhou; Xu, Shanghua; Bian, Ce; Xu, Zhiming; Wang, Chong; Si, Chen; Duan, Wenhui; Xu, Yong","Universal materials model of deep-learning density functional theory Hamiltonian","Science Bulletin","","2095-9273","10.1016/j.scib.2024.06.011","https://www.sciencedirect.com/science/article/pii/S2095927324004079","Realizing large materials models has emerged as a critical endeavor for materials research in the new era of artificial intelligence, but how to achieve this fantastic and challenging objective remains elusive. Here, we propose a feasible pathway to address this paramount pursuit by developing universal materials models of deep-learning density functional theory Hamiltonian (DeepH), enabling computational modeling of the complicated structure–property relationship of materials in general. By constructing a large materials database and substantially improving the DeepH method, we obtain a universal materials model of DeepH capable of handling diverse elemental compositions and material structures, achieving remarkable accuracy in predicting material properties. We further showcase a promising application of fine-tuning universal materials models for enhancing specific materials models. This work not only demonstrates the concept of DeepH’s universal materials model but also lays the groundwork for developing large materials models, opening up significant opportunities for advancing artificial intelligence-driven materials discovery.","2024-08-30","2024-12-03 03:24:08","2024-12-03 03:24:08","","2514-2521","","16","69","","Science Bulletin","","","","","","","","","","","","","","","","","","","Artificial intelligence-driven materials discovery; Deep-learning density functional theory; Large materials model; Universal materials model","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"5UDW2PKZ","journalArticle","2024","Park, Yang Jeong; Kaplan, Daniel; Ren, Zhichu; Hsu, Chia-Wei; Li, Changhao; Xu, Haowei; Li, Sipei; Li, Ju","Can ChatGPT be used to generate scientific hypotheses?","Journal of Materiomics","","2352-8478","10.1016/j.jmat.2023.08.007","https://www.sciencedirect.com/science/article/pii/S2352847823001557","We investigate whether large language models can perform the creative hypothesis generation that human researchers regularly do. While the error rate is high, generative AI seems to be able to effectively structure vast amounts of scientific knowledge and provide interesting and testable hypotheses. The future scientific enterprise may include synergistic efforts with a swarm of “hypothesis machines”, challenged by automated experimentation and adversarial peer reviews.","2024-05-01","2024-12-03 03:24:08","2024-12-03 03:24:08","","578-584","","3","10","","Journal of Materiomics","","","","","","","","","","","","","","","","","","","generative AI; large language models; GPT-4; scientific hypothesis generation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"4QF2T29F","journalArticle","2024","Atienza-Barba, María; Río-Rama, María de la Cruz del; Meseguer-Martínez, Ángel; Barba-Sánchez, Virginia","Artificial intelligence and organizational agility: An analysis of scientific production and future trends","European Research on Management and Business Economics","","2444-8834","10.1016/j.iedeen.2024.100253","https://www.sciencedirect.com/science/article/pii/S2444883424000135","The advancement of Artificial Intelligence (AI) is progressing rapidly, compelling companies to integrate it within their operational frameworks to sustain competitiveness, primarily driven by its impact on organizational agility (OA). Nevertheless, the absence of a robust theoretical framework underscores the limited understanding of the relationship between AI and OA. Within this context, the research aims to establish foundational knowledge, delineate the evolutionary trajectory of the topic, and identify prospective avenues for inquiry. To achieve this objective, bibliometric analysis is employed to gain comprehensive insights into the interplay between these variables and discern trends within this research domain. The utilization of the Web of Science (WoS) and Scopus databases up to January 2024 facilitates data collection, while Bibliometrix and Visme are instrumental in crafting a scientific production map. The analysis corroborates the novelty and growth potential of the subject matter, underscoring heightened author interest, particularly evident in 2023, against a backdrop of sparse and temporally dispersed publications until 2017. Notably, the prevalence of conference papers on this topic stands significantly high at 26.98 % in comparison to the total contributions, indicative of the research community's engagement. Furthermore, the findings underscore a robust association between the keywords AI and OA, delineating a burgeoning research domain that converges with the digital transformation of enterprises and the Theory of Standardization Process. The effective integration of AI into corporate operational frameworks marks the zenith of this transformative process, ushering in the genesis and overhaul of organizational routines. This study represents a pioneering endeavour within the literature, as it constitutes the inaugural bibliometric exploration of this subject matter. Moreover, it serves to underpin the establishment of theoretical underpinnings for future research endeavours as it outlines current trends and emerging future research trajectories, concerning the role of AI in OA.","2024-05-01","2024-12-03 03:24:08","2024-12-03 03:24:08","","100253","","2","30","","European Research on Management and Business Economics","","","","","","","","","","","","","","","","","","","Bibliometric analysis; Artificial intelligence; Business organization; Organizational agility","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"672L8R27","journalArticle","2024","Neves, Barbara Barbosa; Omori, Maho; Petersen, Alan; Vered, Mor; Carter, Adrian","Navigating artificial intelligence in care homes: Competing stakeholder views of trust and logics of care","Social Science & Medicine","","0277-9536","10.1016/j.socscimed.2024.117187","https://www.sciencedirect.com/science/article/pii/S0277953624006403","The COVID-19 pandemic shed light on systemic issues plaguing care (nursing) homes, from staff shortages to substandard healthcare. Artificial Intelligence (AI) technologies, including robots and chatbots, have been proposed as solutions to such issues. Yet, socio-ethical concerns about the implications of AI for health and care practices have also been growing among researchers and practitioners. At a time of AI promise and concern, it is critical to understand how those who develop and implement these technologies perceive their use and impact in care homes. Combining a sociological approach to trust with Annemarie Mol's logic of care and Jeanette Pol's concept of fitting, we draw on 18 semi-structured interviews with care staff, advocates, and AI developers to explore notions of human-AI care. Our findings show positive perceptions and experiences of AI in care homes, but also ambivalence. While integrative care incorporating humans and technology was salient across interviewees, we also identified experiential, contextual, and knowledge divides between AI developers and care staff. For example, developers lacked experiential knowledge of care homes' daily functioning and constraints, influencing how they designed AI. Care staff demonstrated limited experiential knowledge of AI or more critical views about contexts of use, affecting their trust in these technologies. Different understandings of ‘good care’ were evident, too: ‘warm’ care was sometimes linked to human care and ‘cold’ care to technology. In conclusion, understandings and experiences of AI are marked by different logics of sociotechnical care and related levels of trust in these sensitive settings.","2024-10-01","2024-12-03 03:24:08","2024-12-03 03:24:08","","117187","","","358","","Social Science & Medicine","","","","","","","","","","","","","","","","","","","Qualitative research; Artificial intelligence; AI; Trust; Aged care; Long-term care; Nursing homes; Older people; Sociotechnical care; Stakeholders","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"AIVJ9QKN","journalArticle","2023","Omondi, Gevira; Olwal, Thomas O.","Towards artificial intelligence-aided MIMO detection for 6G communication systems: A review of current trends, challenges and future directions","e-Prime - Advances in Electrical Engineering, Electronics and Energy","","2772-6711","10.1016/j.prime.2023.100376","https://www.sciencedirect.com/science/article/pii/S2772671123002711","In recent times, artificial intelligence (AI) has gained considerable attention as a highly promising technology for enhancing the performance of multiple-input multiple-output(MIMO) detection in wireless communication networks. AI approaches such as deep learning and reinforcement learning have enabled MIMO receivers to learn and adapt to complex channel conditions and interference scenarios, significantly improving throughput and error rate performance. This study provides a detailed review of the existing status of AI-aided MIMO detection and discusses the potential of this technology for future wireless communication systems, such as 6G. The study also discusses the challenges and opportunities associated with integrating AI techniques into MIMO detection and emphasizes the necessity for additional research in this field.","2023-12-01","2024-12-03 03:24:08","2024-12-03 03:24:08","","100376","","","6","","e-Prime - Advances in Electrical Engineering, Electronics and Energy","","","","","","","","","","","","","","","","","","","Wireless communication; Artificial intelligence; 6G; Deep neural network; MIMO Detection","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ZF2D53IA","journalArticle","2024","Qiao, Lisha; Zhang, Xiao; He, Shun","Visual Defect Detection and Analysis of Digital Robot Based on Virtual Artificial Intelligence Algorithm","The 4th International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy","","1877-0509","10.1016/j.procs.2024.09.073","https://www.sciencedirect.com/science/article/pii/S1877050924020805","The current research background of digital robot visual defect detection focuses on the application of virtual artificial intelligence algorithms. Convolutional neural networks (CNNS) perform well in the field of image processing and can learn and extract image features, which provides a more refined analysis ability for the visual defect detection of digital robots. Therefore, this paper focuses on the application of convolutional neural networks in digital robot vision defect detection, and explores the main processes of data collection and preprocessing, feature extraction, model training and defect detection in detail. Finally, the results of simulation experiments are as follows: Compared with traditional methods that rely on edge detection or threshold segmentation, the optimized defect detection accuracy rate is increased by 11.4%, and the image detection speed is increased by about 33.9%. The research results have important practical significance for improving the quality control of industrial manufacturing, which can not only improve the quality of products, but also adapt to the constant changes of the production line.","2024-01-01","2024-12-03 03:24:08","2024-12-03 03:24:08","","601-609","","","243","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial Intelligence; Convolutional Neural Networks; Digital Robotics; Visual Defect Detection","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"Q4VLFEE7","journalArticle","2024","Zhou, Yirong; Wu, Yanhuang; Su, Yuhan; Li, Jing; Cai, Jianyu; You, Yongfu; Zhou, Jianjun; Guo, Di; Qu, Xiaobo","Cloud-magnetic resonance imaging system: In the era of 6G and artificial intelligence","Magnetic Resonance Letters","","2772-5162","10.1016/j.mrl.2024.200138","https://www.sciencedirect.com/science/article/pii/S2772516224000457","Magnetic resonance imaging (MRI) plays an important role in medical diagnosis, generating petabytes of image data annually in large hospitals. This voluminous data stream requires a significant amount of network bandwidth and extensive storage infrastructure. Additionally, local data processing demands substantial manpower and hardware investments. Data isolation across different healthcare institutions hinders cross-institutional collaboration in clinics and research. In this work, we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing, 6G bandwidth, edge computing, federated learning, and blockchain technology. This system is called Cloud-MRI, aiming at solving the problems of MRI data storage security, transmission speed, artificial intelligence (AI) algorithm maintenance, hardware upgrading, and collaborative work. The workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data (ISMRMRD) format. Then, the data are uploaded to the cloud or edge nodes for fast image reconstruction, neural network training, and automatic analysis. Then, the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other services. The Cloud-MRI system will save the raw imaging data, reduce the risk of data loss, facilitate inter-institutional medical collaboration, and finally improve diagnostic accuracy and work efficiency.","2024-05-30","2024-12-03 03:24:08","2024-12-03 03:24:08","","200138","","","","","Magnetic Resonance Letters","","","","","","","","","","","","","","","","","","","Cloud computing; Artificial intelligence; Federated learning; Blockchain; Magnetic resonance imaging; 6G bandwidth; Edge computing","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"68ZY58HC","journalArticle","2024","Mahdi, Hassan Saleh; Mohsen, Mohammed Ali; Almanea, Manar","Multimedia glosses and second language vocabulary learning: A second-round meta-analysis","Acta Psychologica","","0001-6918","10.1016/j.actpsy.2024.104341","https://www.sciencedirect.com/science/article/pii/S000169182400218X","The use of glosses to aid vocabulary learning in second languages has been one of the most actively studied areas in computer-assisted language learning (CALL) literature. To compile research articles that examine the effect of utilizing glosses on second language (L2) vocabulary learning, the present study employed a second-order meta-analysis technique. The second-order meta-analysis is a study that synthesizes and analyzes the findings of multiple meta-analyses rather than individual primary studies, providing a higher level of abstraction and overview of existing evidence. The study synthesizes the results from seven primary meta-analyses conducted between 2008 and 2023, which included 136 original studies. Results showed that the overall mean effect size for using glosses was medium (g = 0.63 for the fixed-effect size model and 0.76 for the random-effect size model). The results showed that moderators had a significantly mitigated the effects of multimedia glosses. In particular, beginner-level students benefited greatly from being exposed to multimedia glosses, resulting in a large effect size. Additionally, the recognition test tended to produce a higher effect size compared to other types of vocabulary tests. Furthermore, glossing was found to be more effective in improving vocabulary acquisition in expository texts rather than narrative texts. Moreover, single-mode glosses were reported to be more effective than multi-mode glosses. The findings indicated that in-text glosses, out-text glosses, and bottom glosses exhibited a small effect size, whereas pop-up and margin glosses demonstrated a medium effect size. Implications for language learning and suggestions for future meta-analytic research are provided.","2024-08-01","2024-12-03 03:24:08","2024-12-03 03:24:08","","104341","","","248","","Acta Psychologica","","","","","","","","","","","","","","","","","","","Foreign language learning; CALL; Glosses; Second-round meta-analysis; Vocabulary learning","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RG578SLA","journalArticle","2023","Haefner, Naomi; Parida, Vinit; Gassmann, Oliver; Wincent, Joakim","Implementing and scaling artificial intelligence: A review, framework, and research agenda","Technological Forecasting and Social Change","","0040-1625","10.1016/j.techfore.2023.122878","https://www.sciencedirect.com/science/article/pii/S0040162523005632","Artificial intelligence (AI) will have a substantial impact on firms in virtually all industries. Without guidance on how to implement and scale AI, companies will be outcompeted by the next generation of highly innovative and competitive companies that manage to incorporate AI into their operations. Research shows that competition is fierce and that there is a lack of frameworks to implement and scale AI successfully. This study begins to address this gap by providing a systematic review and analysis of different approaches by companies to using AI in their organizations. Based on these experiences, we identify key components of implementing and scaling AI in organizations and propose phases of implementing and scaling AI in firms.","2023-12-01","2024-12-03 03:24:08","2024-12-03 03:24:08","","122878","","","197","","Technological Forecasting and Social Change","","","","","","","","","","","","","","","","","","","Machine learning; Technology; Innovation; Artificial intelligence; Review; Scaling","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"Q67NIYL9","journalArticle","2024","Faryna, Khrystyna; Tessier, Leslie; Retamero, Juan; Bonthu, Saikiran; Samanta, Pranab; Singhal, Nitin; Kammerer-Jacquet, Solene-Florence; Radulescu, Camelia; Agosti, Vittorio; Collin, Alexandre; Farre´, Xavier; Fontugne, Jacqueline; Grobholz, Rainer; Hoogland, Agnes Marije; Moreira Leite, Katia Ramos; Oktay, Murat; Polonia, Antonio; Roy, Paromita; Salles, Paulo Guilherme; van der Kwast, Theodorus H.; van Ipenburg, Jolique; van der Laak, Jeroen; Litjens, Geert","Evaluation of Artificial Intelligence-Based Gleason Grading Algorithms “in the Wild”","Modern Pathology","","0893-3952","10.1016/j.modpat.2024.100563","https://www.sciencedirect.com/science/article/pii/S0893395224001431","The biopsy Gleason score is an important prognostic marker for prostate cancer patients. It is, however, subject to substantial variability among pathologists. Artificial intelligence (AI)–based algorithms employing deep learning have shown their ability to match pathologists’ performance in assigning Gleason scores, with the potential to enhance pathologists’ grading accuracy. The performance of Gleason AI algorithms in research is mostly reported on common benchmark data sets or within public challenges. In contrast, many commercial algorithms are evaluated in clinical studies, for which data are not publicly released. As commercial AI vendors typically do not publish performance on public benchmarks, comparison between research and commercial AI is difficult. The aims of this study are to evaluate and compare the performance of top-ranked public and commercial algorithms using real-world data. We curated a diverse data set of whole-slide prostate biopsy images through crowdsourcing containing images with a range of Gleason scores and from diverse sources. Predictions were obtained from 5 top-ranked public algorithms from the Prostate cANcer graDe Assessment (PANDA) challenge and 2 commercial Gleason grading algorithms. Additionally, 10 pathologists (A.C., C.R., J.v.I., K.R.M.L., P.R., P.G.S., R.G., S.F.K.J., T.v.d.K., X.F.) evaluated the data set in a reader study. Overall, the pairwise quadratic weighted kappa among pathologists ranged from 0.777 to 0.916. Both public and commercial algorithms showed high agreement with pathologists, with quadratic kappa ranging from 0.617 to 0.900. Commercial algorithms performed on par or outperformed top public algorithms.","2024-11-01","2024-12-03 03:24:09","2024-12-03 03:24:09","","100563","","11","37","","Modern Pathology","","","","","","","","","","","","","","","","","","","deep learning; artificial intelligence; computational pathology; Gleason grading","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"J2PK9K4H","journalArticle","2024","Aal E Ali, Rizvi Syed; Meng, Jiaolong; Khan, Muhammad Ehtisham Ibraheem; Jiang, Xuefeng","Machine learning advancements in organic synthesis: A focused exploration of artificial intelligence applications in chemistry","Artificial Intelligence Chemistry","","2949-7477","10.1016/j.aichem.2024.100049","https://www.sciencedirect.com/science/article/pii/S2949747724000071","Artificial intelligence (AI) is driving a revolution in chemistry, reshaping the landscape of molecular design. This review explores AI’s pivotal roles in the field of organic synthesis applications. AI accurately predicts reaction outcomes, controls chemical selectivity, simplifies synthesis planning, accelerates catalyst discovery, and fuels material innovation and so on. It seamlessly integrates data-driven algorithms with chemical intuition to redefine molecular design. As AI chemistry advances, it promises accelerated research, sustainability, and innovative solutions to chemistry’s pressing challenges. The fusion of AI and chemistry is poised to shape the field’s future profoundly, offering new horizons in precision and efficiency. This review encapsulates the transformation of AI in chemistry, marking a pivotal moment where algorithms and data converge to revolutionize the world of molecules.","2024-06-01","2024-12-03 03:24:09","2024-12-03 03:24:09","","100049","","1","2","","Artificial Intelligence Chemistry","","","","","","","","","","","","","","","","","","","Artificial intelligence; Catalyst design; Chemical selectivity; Material design; Retrosynthesis prediction","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DCRIABHX","journalArticle","2024","Qin, Weiwei","How to unleash frugal innovation through internet of things and artificial intelligence: Moderating role of entrepreneurial knowledge and future challenges","Technological Forecasting and Social Change","","0040-1625","10.1016/j.techfore.2024.123286","https://www.sciencedirect.com/science/article/pii/S0040162524000829","Frugal innovation, also known as frugal engineering or innovation, is an approach to product development and problem-solving that focuses on creating simple, affordable, and effective solutions. The research on frugal innovation is evolving which is mainly rooted in the challenges faced by people living in developing countries, where resource constraints and limited access to technology, infrastructure, and capital make it difficult to adopt traditional expensive and complex solutions. This study attempts to explore the impact of the Internet of Things (IoT) and artificial intelligence (AI) tools on frugal innovation from the perspective of China, a developing nation. Moreover, how entrepreneurial knowledge can play a moderating role among the nexus of frugal innovation, IoT, and AI is a key question of this study. This study spotlights the proposed inquiry based on seven hundred and seventy-nine responses as analyzed using SEM approach by SmartPLS. This study affirmed that IoT and AI both are valid predictors of frugal innovation therefore management should incorporate both capabilities to achieve frugal innovation and to win over competitors in today's technological-oriented era. This study highlights that acceptance of technology is imperative where entrepreneurial skills can play a significant role in incorporating innovative technologies like IoT and AI models into practices. This study also enlists several managerial implications along with limitations and future research possibilities for worldly scholars.","2024-05-01","2024-12-03 03:24:09","2024-12-03 03:24:09","","123286","","","202","","Technological Forecasting and Social Change","","","","","","","","","","","","","","","","","","","Artificial intelligence; Internet of things; Entrepreneurial knowledge; Frugal innovation; Structural approach","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"C5DUH9Y3","journalArticle","2024","Lee, Haein; Lee, Seon Hong; Park, Heungju; Kim, Jang Hyun; Jung, Hae Sun","ESG2PreEM: Automated ESG grade assessment framework using pre-trained ensemble models","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e26404","https://www.sciencedirect.com/science/article/pii/S2405844024024356","Incorporating environmental, social, and governance (ESG) criteria is essential for promoting sustainability in business and is considered a set of principles that can increase a firm’s value. This research proposes a strategy using text-based automated techniques to rate ESG. For autonomous classification, data were collected from the news archive LexisNexis and classified as E, S, or G based on the ESG materials provided by the Refinitiv-Sustainable Leadership Monitor, which has over 450 metrics. In addition, Bidirectional Encoder Representations from Transformers (BERT), Robustly optimized BERT approach (RoBERTa), and A Lite BERT (ALBERT) models were trained to accurately categorize preprocessed ESG documents using a voting ensemble model, and their performances were measured. The accuracy of the ensemble model utilizing BERT and ALBERT was found to be 80.79% with batch size 20. Additionally, this research validated the performance of the framework for companies included in the Dow Jones Industrial Average (DJIA) and compared it with the grade provided by Morgan Stanley Capital International (MSCI), a globally renowned ESG rating agency known for having the highest creditworthiness. This study supports the use of sophisticated natural language processing (NLP) techniques to attain important knowledge from large amounts of text-based data to improve ESG assessment criteria established by different rating agencies.","2024-02-29","2024-12-03 03:24:09","2024-12-03 03:24:09","","e26404","","4","10","","Heliyon","","","","","","","","","","","","","","","","","","","BERT; Natural language processing (NLP); Ensemble; ESG; Pretrained language model","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"HK3IPVVA","journalArticle","2024","Lin, Zexu; Hu, Xiheng; Liu, Yuancheng; Lai, Sicen; Hao, Lingjia; Peng, Yihao; Li, Yixin; Zhu, Zirui; Huang, Xing; Huang, Kai; Zhang, Mi","Multispectral imaging in medicine: A bibliometric study","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e36389","https://www.sciencedirect.com/science/article/pii/S2405844024124206","Multispectral Imaging has been used in many fields. In the medical field, Multispectral Imaging is still in its infancy. However, due to its excellent potential, it will also become one of the most important medical imaging in the future. This paper is the first bibliometric study in this field. The study comprehensively searched all relevant documents in Web of Science Core Collection from Jan 1, 1999 to Dec 31, 2022, systematically sorted out the author, journal, country and institution in this field, and analyzed the keywords. Based on this, the study suggests that researchers and healthcare workers should strengthen cooperation to apply Multispectral Imaging to more medical fields while further developing related technologies. At the same time, in the future, this field should focus on non-ex vivo tissue detection and the combination of Multispectral Imaging and artificial intelligence.","2024-08-30","2024-12-03 03:24:09","2024-12-03 03:24:09","","e36389","","16","10","","Heliyon","","","","","","","","","","","","","","","","","","","Artificial intelligence; Multispectral imaging; Multispectral optoacoustic tomography; Photodynamic therapy","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"66XWK6HD","journalArticle","2023","Papadouli, Vasiliki; Papakonstantinou, Vagelis","A preliminary study on artificial intelligence oracles and smart contracts: A legal approach to the interaction of two novel technological breakthroughs","Computer Law & Security Review","","0267-3649","10.1016/j.clsr.2023.105869","https://www.sciencedirect.com/science/article/pii/S0267364923000791","Artificial Intelligence and Smart Contracts are two cutting-edge technological achievements of the so-called 4th Industrial Revolution era. Both have already had a significant impact on various aspects of modern life, including transactions, and each one has already been under scientific investigation. Instead, their interaction has not become the subject of a debate, although it can further (positively) affect the transactions. This interconnection takes place through specific mechanisms, called Oracles, which can be, among others, highly sophisticated Artificial Intelligence systems (autonomous systems). The present article aims to present the role of the Artificial Intelligence Oracles throughout the ‘smart contractual procedure’, as well as to shed light on the potential (new) legal issues this interconnection may raise. The main result of this article is to indicate the appropriate legal directions in case of Artificial Intelligence Oracles’ failures, based on the most prevalent current approaches to AI's (the user's) contractual and/or non-contractual liability. The major research's conclusion is that the Artificial Intelligence Oracle's failures may result in one of the following situations: (a) breach of a (smart) contract, (b) unjust enrichment, (c) conclusion of a (voidable) smart contract that should not have been concluded, or (d) non-conclusion of a smart contract that should have been concluded. The responsibility of each person participating in the ‘smart contractual procedure’, i.e. the contractual parties, the blockchain platform and the Artificial Intelligence user/owner (or even the Artificial Intelligence system itself), as well as the AI provider or designer, is examined in each of the afore-mentioned situations separately. Given that legislative initiatives have already begun, the present article aspires to contribute to the consistent address of the newly raised legal issues.","2023-11-01","2024-12-03 03:24:09","2024-12-03 03:24:09","","105869","","","51","","Computer Law & Security Review","","","","","","","","","","","","","","","","","","","Artificial intelligence; Blockchain; Autonomous systems; Breach of contract; Smart contracts","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"5XSWKE9X","journalArticle","2024","Ruiz-Serra, Victoria; Buslón, Nataly; Philippe, Olivier R.; Saby, Diego; Morales, María; Pontes, Camila; Andirkó, Alejandro Muñoz; Holliday, Gemma L.; Jené, Aina; Moldes, Mauricio; Rambla, Jordi; Valencia, Alfonso; Rementeria, María José; Cortés, Atia; Cirillo, Davide","Analyzing sex imbalance in EGA and dbGaP biological databases: Recommendations for better practices","iScience","","2589-0042","10.1016/j.isci.2024.110831","https://www.sciencedirect.com/science/article/pii/S258900422402056X","Summary Precision medicine aims at tailoring treatments to individual patient’s characteristics. In this regard, recognizing the significance of sex and gender becomes indispensable for meeting the distinct healthcare needs of diverse populations. To this end, continuing a trend of improving data quality observed since 2014, the European Genome-phenome Archive (EGA) established a policy in 2018 that mandates data providers to declare the sex of donor samples, aiming to enhance data accuracy and prevent imbalance in sex classification. We analyzed sex classification imbalance in human data from EGA and the U.S. counterpart, the database of genotypes and phenotypes (dbGaP). Our findings show a significant decrease in samples classified as unknown in EGA, potentially promoting better sex reporting during data collection. Based on our findings, we raise awareness of sample imbalance problems and provide a list of recommendations for enhancing biomedical research practices.","2024-10-18","2024-12-03 03:24:09","2024-12-03 03:24:09","","110831","","10","27","","iScience","","","","","","","","","","","","","","","","","","","Artificial intelligence; Genomics; Human genetics","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"BCCECX4I","journalArticle","2024","Mu, Jie; Liu, Dongbing","Application of Artificial Intelligence Technology in the Field of Digital Currency Security","The 4th International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy","","1877-0509","10.1016/j.procs.2024.09.056","https://www.sciencedirect.com/science/article/pii/S1877050924020635","This paper explores the application of artificial intelligence technology in ensuring the security of digital currencies. With the increasing popularity of digital currencies, ensuring their security has become a crucial concern. AI techniques, such as machine learning and deep learning, can be employed to identify and prevent security threats in digital currency systems. Additionally, it investigates how AI enhances security measures and risk control on digital currency trading platforms, offering insights into real-time risk alert systems. Therefore, through empirical research in four distinct sections, this paper continuously refines and revises the theoretical model proposed in this project. The ultimate goal is to provide new theoretical tools for the management of financial stability in the process of Digital Currency replacing M0. this project gives the research content of the four parts, which include public perception, delivery path, underlying technology and virtual currency bubble, we hope that this research can offer valuable insights to relevant field managers.","2024-01-01","2024-12-03 03:24:09","2024-12-03 03:24:09","","458-464","","","243","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","artificial intelligence; digital technology; digital currency; risk management","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ES88R2W6","journalArticle","2024","Yang, Ren; Yuan, Qiong; Zhang, Wuwu; Cai, Helen; Wu, Yue","Application of Artificial Intelligence in rehabilitation science: A scientometric investigation Utilizing Citespace","SLAS Technology","","2472-6303","10.1016/j.slast.2024.100162","https://www.sciencedirect.com/science/article/pii/S247263032400044X","This study presents a scientometric analysis of the intersection between rehabilitation science and artificial intelligence (AI) technologies, using data from the Web of Science (WOS) database from 2002 to 2022. The analysis employed a comprehensive search query with key AI-related terms, focusing on a wide range of publications in rehabilitation science. Utilizing the Citespace tool, the study visualizes and quantifies the relationships between key terms, identifies research trends, and assesses the impact of AI technologies in rehabilitation science. Findings reveal a significant increase in AI-related research in this field, particularly from 2017 onwards, peaking in 2021. The United States has been a leading contributor, followed by countries like England, Australia, Germany, and Canada. Major institutional contributions come from Harvard University and the Pennsylvania Commonwealth System of Higher Education, among others. A keyword co-occurrence network constructed through Citespace identifies nine distinct hot topics and various research frontiers, highlighting evolving focus areas within the field. Burst analysis of keywords indicates a shift from performance and injury-related research to an increasing emphasis on AI and deep learning in recent years. The study also predicts the potential impact of papers, spotlighting works by Kunze KN and others as significantly influencing future research directions. Additionally, it examines the evolution of knowledge bases in AI-related rehabilitation science research, revealing a multidisciplinary core that includes neurology, rehabilitation, and ophthalmology, extending to complementary fields such as medicine and social sciences. This scientometric analysis provides a comprehensive overview of AI's application in rehabilitation science, offering insights into its evolution, impact, and emerging trends over the past two decades. The findings suggest strategic directions for future research, policy-making, and interdisciplinary collaboration in rehabilitation science and AI.","2024-08-01","2024-12-03 03:24:09","2024-12-03 03:24:09","","100162","","4","29","","SLAS Technology","","","","","","","","","","","","","","","","","","","Artificial intelligence; AI; Citespace; Rehabilitation science; Scientometric; WOS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SSCS9WXG","journalArticle","2024","Zhang, Nanhui; Tian, Runze; Fu, Guangwei","Design and application of automatic driving emergency collision avoidance control algorithm based on artificial intelligence technology","Measurement: Sensors","","2665-9174","10.1016/j.measen.2024.101248","https://www.sciencedirect.com/science/article/pii/S2665917424002241","The topic of car intelligence has seen a surge in research interest in automobile active collision avoidance systems in recent years. When there is an obstruction in front of the vehicle, active braking or active steering can be used to prevent a collision, which effectively increases driving safety. Research on intelligent automotive active collision avoidance technology is valuable both academically and practically. This paper aims to enhance the adaptability of active collision avoidance systems to various working conditions. To this end, we propose a design scheme that synchronizes the steering and braking collision avoidance strategies. Additionally, we derive a risk assessment model that takes into account both vehicle dynamics instability factors and vehicle collision factors at the same time. This approach effectively addresses the challenge of effectively assessing the risk of self-driving cars in emergency situations and helps to quantify the risk associated with them. Lastly, a simulation and experimental analysis are conducted on the active collision avoidance strategy that is suggested in this study. The findings demonstrate that the active collision avoidance control system for intelligent vehicles, both longitudinal and transverse, developed in this paper can accurately assess collision risk, make decisions, and manage the collision avoidance process. It can also further decrease the frequency of collision accidents.","2024-06-01","2024-12-03 03:24:09","2024-12-03 03:24:09","","101248","","","33","","Measurement: Sensors","","","","","","","","","","","","","","","","","","","Artificial intelligence; Emergency collision avoidance: control algorithm; Self-driving car","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DFRNLUAF","journalArticle","2024","Rehman, Hamood Ur; Mo, Fan; Chaplin, Jack C.; Zarzycki, Leszek; Jones, Mark; Ratchev, Svetan","A modular artificial intelligence and asset administration shell approach to streamline testing processes in manufacturing services","Journal of Manufacturing Systems","","0278-6125","10.1016/j.jmsy.2023.12.004","https://www.sciencedirect.com/science/article/pii/S0278612523002522","The increasing demand for personalized products and cost-effectiveness has highlighted the necessity of integrating intelligence into production systems. This integration is crucial for enabling intelligent control that can adapt to variations in features, parts, and conditions, thereby enhancing functionalities while reducing costs. This research emphasizes the incorporation of intelligence in testing processes within production systems. We introduce a novel approach for controlling testing functionality using an asset administration shell enriched with modular artificial intelligence. The proposed architecture is not only effective in controlling the execution behavior through services but also offers the distinct advantage of a modular design. This modularity significantly contributes to the system’s adaptability and scalability, allowing for more efficient and cost-effective solutions as different machine-learning models may be substituted to meet requirements. The effectiveness of this approach is validated through a practical use case of leak testing, demonstrating the benefits of the modular architecture in a real-world application.","2024-02-01","2024-12-03 03:24:09","2024-12-03 03:24:09","","424-436","","","72","","Journal of Manufacturing Systems","","","","","","","","","","","","","","","","","","","Asset administration; Leak testing; Low-cost industrial digitalization and control; Modular artificial intelligence","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"444VLFZG","journalArticle","2023","Sepúlveda-Oviedo, Edgar Hernando; Travé-Massuyès, Louise; Subias, Audine; Pavlov, Marko; Alonso, Corinne","Fault diagnosis of photovoltaic systems using artificial intelligence: A bibliometric approach","Heliyon","","2405-8440","10.1016/j.heliyon.2023.e21491","https://www.sciencedirect.com/science/article/pii/S2405844023086991","Conventional fault detection methods in photovoltaic systems face limitations when dealing with emerging monitoring systems that produce vast amounts of high-dimensional data across various domains. Accordingly, great interest appears within the international scientific community for the application of artificial intelligence methods, which are seen as a highly promising solution for effectively managing large datasets for detecting faults. In this review, more than 620 papers published since 2010 on artificial intelligence methods for detecting faults in photovoltaic systems are analyzed. To extract major research trends, in particular to detect most promising algorithms and approaches overcoming excessive time calculations, a conventional bibliographic survey would have been extremely difficult to complete. That is why this study proposes to carry out a review with an innovative approach based on a statistical method named Bibliometric and a Expert qualitative content analysis. This methodology consists of three stages. First, a collection of data from databases is carried out with all precautions to achieve a large, robust, high-quality database. Second, multiple bibliometric indicators are chosen based on the objectives to be achieved and analyzed to assess their real impact, such as the quantity and nature of publications, collaborative connections among organizations, researchers, and countries or most cited articles. Finally, the Expert qualitative content analysis carried out by experts identifies the current and emerging research topics that have the greatest impact on fault detection in photovoltaic systems using artificial intelligence.","2023-11-01","2024-12-03 03:24:09","2024-12-03 03:24:09","","e21491","","11","9","","Heliyon","","","","","","","","","","","","","","","","","","","Machine learning; Artificial intelligence; Fault diagnosis; Photovoltaic (PV); Renewable energy; Research trends","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"YGZNVDW7","journalArticle","2024","Islam, Hedayetul; Iqbal, Md. Sadiq; Hossain, Muhammad Minoar","Blood pressure abnormality detection and Interpretation utilizing Explainable Artificial Intelligence","Intelligent Medicine","","2667-1026","10.1016/j.imed.2024.09.005","https://www.sciencedirect.com/science/article/pii/S2667102624000676","Objective Hypertension is a pressing medical dysfunction that increases the risks of many fatal diseases. Early detection of hypertension can be crucial to lead a healthy life. Machine learning (ML) can be useful for the early prediction of a patient's likelihood of having a blood pressure abnormality and preventing it. Explainable artificial intelligence (XAI) is a state-of-the-art ML toolset that helps us understand and explain the prediction of an ML model. This research aims to build an automatic blood pressure anomaly detection system with maximum accuracy using the fewest features and learn why a model arrived at a particular result using XAI. Methods This research utilized the ""Blood Pressure Data for Disease Prediction"" dataset from Kaggle. Data were collected from medical reports of random participants in 2019 based on the presence of blood pressure abnormality, chronic kidney disease, and adrenal and thyroid disorders. We have used several ML algorithms (extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), decision tree (DT), and Logistic regression (LR)) to predict blood pressure abnormality based on patient's data. Principal component analysis (PCA) and recursive feature elimination (RFE) algorithms are used as feature optimizers. The receiver operating characteristic (ROC) curve analysis and accuracy were the key outcome metrics. Through the experiment, we have also calculated various performance measurement techniques like precision, recall, specificity, F1 score, and kappa to pin down the model with the best performance. Moreover, we have implemented several XAI methods namely permutation feature importance (PFI), partial dependence plots (PDP), Shapley additive explanations (SHAP), and local interpretable model-agnostic explanations (LIME) for additional exploration of our best model. Results The combination of RFE and XGBoost provides the most significant results. The results of the study show that the algorithm has an AUC of 0.95, indicating good discriminatory power in detecting abnormal BP. The accuracy, precision, recall, specificity, F1-score, and kappa gained are 91.50%, 88.64%, 92.65%, 92.27%, 90.83%, and 0.8 respectively. According to the results of the XAI experiment, the genetic pedigree coefficient and hemoglobin level in a patient contribute the most to blood pressure abnormality prediction. Adrenal and thyroid diseases, as well as chronic kidney illness, have an impact on the projections. Existing research backs up this conclusion. Conclusion While comparing to previous works on this dataset, the results of our research are superior and the use of XAI shed new light on our model's prediction. This study would provide new insight into blood pressure detection in the medical profession.","2024-11-05","2024-12-03 03:24:09","2024-12-03 03:24:09","","","","","","","Intelligent Medicine","","","","","","","","","","","","","","","","","","","Machine learning; Explainable artificial intelligence; Principal component analysis; Recursive feature elimination; Shapley additive explanations","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"GKE8GVTM","journalArticle","2024","Climent, Ricardo Costa; Haftor, Darek M.; Staniewski, Marcin W.","AI-enabled business models for competitive advantage","Journal of Innovation & Knowledge","","2444-569X","10.1016/j.jik.2024.100532","https://www.sciencedirect.com/science/article/pii/S2444569X24000714","Some firms have successfully harnessed artificial intelligence (AI) to create unparalleled wealth, while most around them have failed to do so. This managerial challenge has led to recent calls for research to answer the question of how firms can use AI to create and appropriate economic value. This paper answers that question. The paper reviews the existing research and discusses its merits. This review highlights the need for subsequent conceptual reconfigurations of business model theory, the theory of data network effects, and the theory of situated AI for competitive advantage. The integration of these three theories leads to a novel theory: AI-enabled business models for competitive advantage. This paper contributes to the broad literature on technology management, and more specifically to literature on technology-enabled business models and the use of AI. Several important managerial implications are outlined to help firms ensure successful AI use.","2024-07-01","2024-12-03 03:24:09","2024-12-03 03:24:09","","100532","","3","9","","Journal of Innovation & Knowledge","","","","","","","","","","","","","","","","","","","Artificial intelligence; Business model architecture; Business model themes; Data network effects; Situated use of AI; Value creation and appropriation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"R7TH27AA","journalArticle","2024","Salam, Mohammad; Iqbal, Muhammad Tahir; Habib, Raja Adnan; Tahir, Amna; Sultan, Aamir; Iqbal, Talat","Novel application of unsupervised machine learning for characterization of subsurface seismicity, tectonic dynamics and stress distribution","Applied Computing and Geosciences","","2590-1974","10.1016/j.acags.2024.100200","https://www.sciencedirect.com/science/article/pii/S2590197424000478","Our study pioneers an innovative use of unsupervised machine learning, a powerful tool for navigating unclassified data, to unravel the complexities of subsurface seismic activities and extract meaningful patterns. Our central objective is to comprehensively characterize seismicity within an active region by identifying distinct seismic clusters in spatial distribution, thereby gaining a deeper understanding of subsurface stress distribution and tectonic dynamics. Employing a diverse range of clustering algorithms, with particular emphasis on Fuzzy C-Means (FCM), our research meticulously dissects the intricate physical processes that govern a complex tectonic zone. This technique effectively delineates distinct tectonic zones, aligning seamlessly with established seismological knowledge and underscoring the transformative potential of Artificial Intelligence (AI) in analyzing regional subsurface phenomena, even under conditions of data scarcity. Moreover, associating earthquakes with specific seismogenic structures significantly enhances seismic hazard analyses, potentially paving the way for autonomous insights that inform engineering hazard assessments.","2024-12-01","2024-12-03 03:24:09","2024-12-03 03:24:09","","100200","","","24","","Applied Computing and Geosciences","","","","","","","","","","","","","","","","","","","Machine learning; Artificial intelligence; Clustering; Makran subduction zone; Tectonic dynamics","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"G66WFRG4","journalArticle","2024","Jauhar, Sunil Kumar; Sethi, Sunil; Kamble, Sachin S.; Mathew, Shawn; Belhadi, Amine","Artificial intelligence and machine learning-based decision support system for forecasting electric vehicles' power requirement","Technological Forecasting and Social Change","","0040-1625","10.1016/j.techfore.2024.123396","https://www.sciencedirect.com/science/article/pii/S0040162524001926","Increasing pollution is causing adverse environmental effects, leading to increased interest in combating this issue. There has been a significant interest in minimizing the pollution caused by combustion engine vehicles, with high research and development investments in hybrid and electric vehicle (EV) batteries. The innovations in EVs have a high potential to contribute to an optimized transportation sector while also playing a crucial role in reducing greenhouse gas emissions. This study contributes to the EV industry by precisely predicting the power demand at a particular charging station and identifying the optimal charging station characteristics. We proposed a modified business process based on digital technologies to maximize customer engagement and operational efficiency. Our research has incorporated technologies like artificial intelligence (AI) and machine learning (ML). This study addresses the issues of EV infrastructure facilities, the issues raised by the lack of service features for EVs, and the optimal power requirement for charging stations. The proposed framework has managerial and technological implications, suggesting that the system must promptly receive, store, and analyze substantial volumes of data and demonstrate adaptability in response to environmental factors, such as the availability of EVs and the utilization of renewable energy sources. Despite the challenges, there is potential promise in developing decision assistance systems for electric vehicle power demands based on AI and ML.","2024-07-01","2024-12-03 03:24:09","2024-12-03 03:24:09","","123396","","","204","","Technological Forecasting and Social Change","","","","","","","","","","","","","","","","","","","Artificial intelligence (AI); Machine learning (ML); Demand forecasting; Electric vehicles (EV); Technological implications","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"3GDFXYBZ","journalArticle","2024","Jong, M.R.; de Groof, A.J.","Advancement of artificial intelligence systems for surveillance endoscopy of Barrett's esophagus","Digestive and Liver Disease","","1590-8658","10.1016/j.dld.2023.11.038","https://www.sciencedirect.com/science/article/pii/S1590865823010770","Barrett's esophagus (BE) is a precursor disease for esophageal adenocarcinoma. Timely detection and treatment has significant influence on patient outcomes. Over the last years, several artificial intelligence (AI) systems have emerged to assist the endoscopist. The primary focus of research has been computer aided detection (CADe). Several groups have succeeded in developing competitive models for neoplasia detection. Additionally, computer aided diagnosis (CADx) models have been developed for subsequent lesion characterization and assistance in clinical decision making. Future studies should focus on bridging the domain gap between academic development and integration in daily practice.","2024-07-01","2024-12-03 03:24:09","2024-12-03 03:24:09","","1126-1130","","7","56","","Digestive and Liver Disease","","","","","","","","","","","","","","","","","","","Artificial intelligence; Barrett's esophagus; Computer aided detection","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"WLU35C3Z","journalArticle","2024","Garg, Swati; Ahmad, Asad; Madsen, Dag Øivind","Academic writing in the age of AI: Comparing the reliability of ChatGPT and Bard with Scopus and Web of Science","Journal of Innovation & Knowledge","","2444-569X","10.1016/j.jik.2024.100563","https://www.sciencedirect.com/science/article/pii/S2444569X24001021","ChatGPT and Bard (now known as Gemini) are becoming indispensable resources for researchers, academicians and diverse stakeholders within the academic landscape. At the same time, traditional digital tools such as scholarly databases continue to be widely used. Web of Science and Scopus are the most extensive academic databases and are generally regarded as consistently reliable scholarly research resources. With the increasing acceptance of artificial intelligence (AI) in academic writing, this study focuses on understanding the reliability of the new AI models compared to Scopus and Web of Science. The study includes a bibliometric analysis of green, sustainable and ecological buying behaviour, covering the period from 1 January 2011 to 21 May 2023. These results are used to compare the results from the AI and the traditional scholarly databases on several parameters. Overall, the findings suggest that AI models like ChatGPT and Bard are not yet reliable for academic writing tasks. It appears to be too early to depend on AI for such tasks.","2024-10-01","2024-12-03 03:24:09","2024-12-03 03:24:09","","100563","","4","9","","Journal of Innovation & Knowledge","","","","","","","","","","","","","","","","","","","Artificial intelligence; ChatGPT; Bard; Academic writing; Ecological buying behaviour; Green buying behaviour; Sustainable buying behaviour","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"PXTGEKQU","journalArticle","2024","Mogage, Andrei","A.I. Assisted Malware Capabilities Capturing","28th International Conference on Knowledge Based and Intelligent information and Engineering Systems (KES 2024)","","1877-0509","10.1016/j.procs.2024.09.505","https://www.sciencedirect.com/science/article/pii/S187705092402550X","Malware analysis is a demanding task regarding techniques, time and creativity. On multiple occasions, however, security researchers are interested in checking if the analyzed threat possesses specific capabilities, disregarding the overall picture. In this paper, we combine our malware expertise with the results of a Large Language Model, in order to expand the knowledge and creativity in generating specific rules that encode these capabilities. The rules, along with malicious applications, are then used as inputs for a malware analysis framework created by us, in order to determine if the application has those specific capabilities. The findings show promising results, sustained by synthetic and real-life experiments.","2024-01-01","2024-12-03 03:24:09","2024-12-03 03:24:09","","860-869","","","246","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","large language models; cybersecurity; formal logic; malware; malware analysis","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"H27WD3ME","journalArticle","2024","Toorajipour, Reza; Oghazi, Pejvak; Palmié, Maximilian","Data ecosystem business models: Value propositions and value capture with Artificial Intelligence of Things","International Journal of Information Management","","0268-4012","10.1016/j.ijinfomgt.2024.102804","https://www.sciencedirect.com/science/article/pii/S0268401224000525","The emergence of data as a critical asset and the prevalence of technologies such as the Artificial Intelligence of Things (AIoT) on the one hand, and the importance of collaborations for value creation on the other hand have given rise to a new breed of ecosystems known as data ecosystems. While data ecosystems provide new business opportunities, proposing and capturing value in those ecosystems is challenging, and the extant literature provides little guidance in this regard. Our research encompasses two studies that address this limitation and establish a framework for business-model archetypes in the context of AIoT data ecosystems. In the first study, exploratory qualitative research on 28 leading AIoT data ecosystem actors leads to the identification of value propositions and value-capture mechanisms in these ecosystems. We identify eight possible value propositions and eight possible value-capture mechanisms. The second, qualitative study centers on 19 expert interviews. Our analysis leads to the identification of two dimensions – control and customization – that guide the conceptualization and formation of business-model archetypes. Using these dimensions, we develop a framework for business-model archetypes in AIoT data ecosystems. Our findings contribute to the discourse on data ecosystems and offer new perspectives valuable for both researchers and industry practitioners.","2024-10-01","2024-12-03 03:24:09","2024-12-03 03:24:09","","102804","","","78","","International Journal of Information Management","","","","","","","","","","","","","","","","","","","Value proposition; Artificial intelligence of things; Business model; Data ecosystem; Value capture","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"QPXDBKYL","journalArticle","2024","Al-Romeedy, Bassam Samir; Alharethi, Thaib","Reimagining sustainability: The power of AI and intellectual capital in shaping the future of tourism and hospitality organizations","Journal of Open Innovation: Technology, Market, and Complexity","","2199-8531","10.1016/j.joitmc.2024.100417","https://www.sciencedirect.com/science/article/pii/S2199853124002117","This research investigates the effect of artificial intelligence (AI) on organizational sustainability (OS) and delves into how this relationship is mediated by various dimensions of intellectual capital (IC), namely human (HC), structural (SC), and relational capital (RC). Data were collected from employees of Saudi travel agencies, resulting in 1122 valid responses, which were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that AI positively impacts OS as well as the three IC dimensions. Additionally, HC, SC, and RC have a positive influence on OS. The study also reveals that these dimensions partially mediate the relationship between AI and OS. This study advances the literature by clarifying how AI-enabled capabilities are translated into sustainable outcomes within the tourism and hospitality sectors. The findings offer valuable insights for industry professionals aiming to harness AI technologies for sustainable transformation. The study also offers strategic recommendations for tourism and hospitality organizations to invest in enhancing their IC, thereby optimizing the sustainability advantages of AI integration.","2024-12-01","2024-12-03 03:24:10","2024-12-03 03:24:10","","100417","","4","10","","Journal of Open Innovation: Technology, Market, and Complexity","","","","","","","","","","","","","","","","","","","Human capital; Intellectual capital; Artificial intelligence; Organizational sustainability; Rational capital; Structure capital","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"5MTGA5IS","journalArticle","2024","Chen, Jia; Zeng, Xu; Tan, Kang; Bao, Jian; Liu, Yu","Energy Intelligent Control and Energy Saving System for Computer Room Based on Artificial Intelligence","The 4th International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy","","1877-0509","10.1016/j.procs.2024.09.123","https://www.sciencedirect.com/science/article/pii/S1877050924021318","In order to understand the energy intelligent control energy-saving system in computer rooms, the author proposes a research on an artificial intelligence based energy intelligent control energy-saving system in computer rooms. The author first analyzed the energy monitoring and analysis system of artificial intelligence in computer rooms. Based on the existing energy consumption data resources of computer rooms, through monitoring and scientific analysis of various aspects of energy consumption data in computer rooms, it provides decision-making support for managers to better formulate energy-saving measures. The system functions are divided into four sub modules: energy consumption environment monitoring, energy consumption behavior analysis, energy-saving renovation suggestions, and energy-saving effect evaluation; We designed an energy consumption anomaly detection method using the GESD algorithm and implemented a B/S architecture using the SSH framework. Finally, in terms of data display, we utilized two technologies: Extjs and BIRT reports, and used POI to export energy consumption data files in Excel format. Practical applications have shown that the system provides assistance for managers to develop energy-saving and emission reduction measures.","2024-01-01","2024-12-03 03:24:10","2024-12-03 03:24:10","","1029-1038","","","243","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial intelligence; Computer room energy; Energy saving system","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2VJ6CVDX","journalArticle","2024","Rezvani, Seyed MHS; Gonçalves, Alexandre; Silva, Maria João Falcão; de Almeida, Nuno Marques","Smart hotspot detection using geospatial artificial intelligence: A machine learning approach to reduce flood risk","Sustainable Cities and Society","","2210-6707","10.1016/j.scs.2024.105873","https://www.sciencedirect.com/science/article/pii/S2210670724006978","This study employs Geospatial Artificial Intelligence (GeoAI) and the Random Forest Machine Learning (ML) algorithm to enhance flood hazard assessments in Portugal. It utilizes NASA's LP DAAC (2023) Digital Elevation Model (DEM) and slope data from EPIC WEBGIS PORTUGAL DATA, offering detailed topographical insights for environmental planning. Additionally, it incorporates data on proximity to water bodies from the Portuguese Environment Agency and the European Environment Agency, and soil characteristics from EPIC WEBGIS PORTUGAL DATA, facilitating a thorough examination of flood risks. This approach prioritizes long-term land features over short-term weather patterns, providing a comprehensive understanding of flood vulnerability. The study processes data at a 1 km x 1 km resolution, adapting TIFF maps for compatibility with the Random Forest model. The produced flood hazard maps identify potential flood hotspots at both national and city levels, crucial for urban planning. These maps aid in assessing the vulnerability of key infrastructure and assets, such as transport networks and buildings. The research highlights the importance of integrating additional data on assets and socioeconomic factors to enhance urban resilience. It sets the stage for future research aimed at improving predictive accuracy and underscores the necessity of extensive geospatial analytics in managing infrastructure risks.","2024-11-15","2024-12-03 03:24:10","2024-12-03 03:24:10","","105873","","","115","","Sustainable Cities and Society","","","","","","","","","","","","","","","","","","","Machine learning; Disaster risk reduction; Flood hazard mapping; Geospatial artificial intelligence; Geospatial data analysis; Urban resilience","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LG2VZUS6","journalArticle","2024","Gambo, Ishaya; Massenon, Rhodes; Ogundokun, Roseline Oluwaseun; Agarwal, Saurabh; Pak, Wooguil","Identifying and resolving conflict in mobile application features through contradictory feedback analysis","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e36729","https://www.sciencedirect.com/science/article/pii/S2405844024127600","As mobile applications proliferate and user feedback becomes abundant, the task of identifying and resolving conflicts among application features is crucial for delivering satisfactory user experiences. This research, motivated to align application development with user preferences, introduces a novel methodology that leverages advanced Natural Language Processing techniques. The paper showcases the use of sentiment analysis using RoBERTa, topic modeling with Non-negative matrix factorization (NMF), and semantic similarity measures from Sentence-BERT. These techniques enable the identification of contradictory sentiments, the discovery of latent topics representing application features, and the clustering of related feedback instances. The approach detects conflicts by analyzing sentiment distributions within semantically similar clusters, further enhanced by incorporating antonym detection and negation handling. It employs majority voting, weighted ranking based on rating scores, and frequency analysis of feature mentions to resolve conflicts, providing actionable insights for prioritizing requirements. Comprehensive evaluations on large-scale iOS App Store and Google Play Store datasets demonstrate the approach's effectiveness, outperforming baseline methods and existing techniques. The research improves mobile application development and user experiences by aligning features with user preferences and providing interpretable conflict resolution strategies, thereby introducing a novel approach to the field of mobile application development.","2024-09-15","2024-12-03 03:24:10","2024-12-03 03:24:10","","e36729","","17","10","","Heliyon","","","","","","","","","","","","","","","","","","","Natural language processing; Sentiment analysis; BERT; Google play store; iOS app store; Mobile app; RoBERTa","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"YDNC6SZ5","journalArticle","2024","Shen, Yang; Zhou, Pengfei","Technological anxiety: Analysis of the impact of industrial intelligence on employment in China","Chinese Journal of Population, Resources and Environment","","2325-4262","10.1016/j.cjpre.2024.09.013","https://www.sciencedirect.com/science/article/pii/S2325426224000536","Employment is the greatest livelihood. Whether the impact of industrial robotics technology materialized in machines on employment in the digital age is an “icing on the cake” or “adding fuel to the fire” needs further study. This study aims to analyze the impact of the installation and application of industrial robots on labor demand in the context of the Chinese economy. First, from the theoretical logic and the economic development law, this study gives the prior judgment and research hypothesis that industrial intelligence will increase jobs. Then, based on the panel data of 269 cities in China from 2006 to 2021, we use the two-way fixed effect model, dynamic threshold model, and two-stage intermediary effect model. The objective is to investigate the impact of industrial intelligence on enterprise labor demand and its path mechanism. Results show that the overall effect of industrial intelligence on the labor force with the installation density index of industrial robots as the proxy variable is the “creation effect”. In other words, advanced digital technology has created additional jobs, and the overall supply of employment in the labor market has increased. The conclusion is still valid after the endogeneity identification and robustness test. In addition, the positive effect has a nonlinear effect on the network scale. When the installation density of industrial robots exceeds a particular threshold value, the division of labor continues to deepen under the combined action of the production efficiency and compensation effects, which will cause enterprises to increase labor demand further. Further research showed that industrial intelligence can increase employment by promoting synergistic agglomeration and improving labor price distortions. This study concludes that in the digital China era, the introduction and installation of industrial robots by enterprises can affect the optimal allocation of the labor market. This phenomenon has essential experience and reference significance for guiding industrial digitalization and intelligent transformation and promoting the high-quality development of people’s livelihood.","2024-09-01","2024-12-03 03:24:10","2024-12-03 03:24:10","","343-355","","3","22","","Chinese Journal of Population, Resources and Environment","","","","","","","","","","","","","","","","","","","Artificial intelligence; Dynamic threshold model; Factor price distortion; Industrial agglomeration; Industrial robot; Structural unemployment","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"G5284Q6B","journalArticle","2024","Zhang, Fang; Sun, Zhenlun; Chen, Qian","Research on Interior Intelligent Design System Based On Image Generation Technology","The 4th International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy","","1877-0509","10.1016/j.procs.2024.09.083","https://www.sciencedirect.com/science/article/pii/S1877050924020908","This paper uses Stable Diffusion ComfyUI platform to construct a set of artificial intelligence working system in interior design field. Stable Diffusion is a deep learning image generation model that is capable of transforming text descriptions into high-quality images. Its open source nature promotes the wide application and innovative development of the technology. ComfyUI, the extension interface of Stable Diffusion, uses a node-based graphical user interface and modularization to simplify image generation. These efficient artificial intelligence image processing tools are practical in interior design, optimizing the process and improving efficiency for designers.","2024-01-01","2024-12-03 03:24:10","2024-12-03 03:24:10","","690-699","","","243","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial Intelligence; Interior Design; Stable Diffusion","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XRZDSYXK","journalArticle","2024","Arrivillaga, Marcela; Bermúdez, Paula C.; García-Cifuentes, Juan Pablo; Vargas-Cardona, Hernán Darío; Neira, Daniela; del Mar Torres, Maria; Rodríguez-López, Mérida; Morales, Daniela; Arizala, Bleider","Designing CITOBOT: A portable device for cervical cancer screening using human-centered design, smart prototyping, and artificial intelligence","Computational and Structural Biotechnology Journal","","2001-0370","10.1016/j.csbj.2024.11.018","https://www.sciencedirect.com/science/article/pii/S2001037024003891","Cervical cancer remains a leading cause of mortality in its invasive stages, presenting a significant global public health challenge, particularly in low- and middle-income countries. Despite technological advancements that have improved the quality of cervical images captured during visual inspections, several challenges persist. This article presents key findings from the CITOBOT-COL translational research project, a large-scale initiative focused on designing CITOBOT as a portable cervical cancer screening device. We detail the comprehensive technological development of CITOBOT, guided by a human-centered design approach, smart prototyping, and the integration of AI. Over four design iterations, we developed and refined CITOBOT v4, a portable device. Prototypes were validated through focus groups and testing by experts in cervical cancer prevention, gynecology, nursing, software, artificial intelligence, computer engineering, and public health, utilizing various anatomical models at the Simulated Hospital Laboratory of Pontificia Universidad Javeriana Cali, Colombia. Additionally, we developed AI algorithms using the Inception V3 network, optimized with Transfer Learning and Fine Tuning, for cervical image classification and offline-operating software that guides the physician through the examination and provides a risk assessment for cervical cancer. Feedback was crucial in assessing and refining the device's functionality, focusing on capturing high-quality cervical images. The development of CITOBOT v4 highlights the importance of fostering innovation in resource-limited settings, offering an effective solution to improve cervical cancer screening and potentially save lives in vulnerable communities.","2024-12-01","2024-12-03 03:24:10","2024-12-03 03:24:10","","739-745","","","24","","Computational and Structural Biotechnology Journal","","","","","","","","","","","","","","","","","","","Artificial intelligence; Cervical cancer screening; Human-centered design; Medical device innovation; Portable device, Cervical imaging","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"Y8H6XWN5","journalArticle","2024","Sun, Ying-Chih; Cosgun, Ozlem; Sharman, Raj; Mulgund, Pavankumar; Delen, Dursun","A stochastic production frontier model for evaluating the performance efficiency of artificial intelligence investment worldwide","Decision Analytics Journal","","2772-6622","10.1016/j.dajour.2024.100504","https://www.sciencedirect.com/science/article/pii/S2772662224001085","As artificial intelligence (AI) begins to take center stage in technological innovations, it is essential to understand the business value of AI innovation efforts and investments. While some early work at the firm level exists, there is a shortage of literature that takes a larger country-level perspective. This study investigated the effect of AI innovation efforts on production efficiency across countries using stochastic production frontier approaches. In addition, our model also included the traditional economic inputs of capital and labor. We used both the Cobb–Douglas function and Constant Elastic Substitution model specifications. The significant findings of this study are as follows: Innovation efforts in AI measured by the number of AI-related patents and capital investment in AI have a substantial effect on economic output. The significance of AI investments indicates the need for a robust digital infrastructure as a prerequisite for harnessing AI capabilities. The complementary relationship between labor and AI-related patents implies that high-skilled labor is often necessary to incorporate AI inputs into production. However, as AI capabilities develop, they weaken the effect on labor input. The study also distinguishes between AI innovation (research and development activities indicated by AI patents) and the production efficiency of AI investments (return on every dollar invested), highlighting that more AI innovation does not always translate into better production efficiency. The findings indicate that while the United States leads innovation in AI, the UK has the best production efficiency. China ranked fourth in AI innovation and has the lowest production efficiency among the countries included in the study.","2024-09-01","2024-12-03 03:24:10","2024-12-03 03:24:10","","100504","","","12","","Decision Analytics Journal","","","","","","","","","","","","","","","","","","","Innovation; Artificial intelligence; Cobb–Douglas function; Production efficiency; Stochastic production frontier","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"V57P5B2L","journalArticle","2024","Badrulhisham, Fakhirah; Pogatzki-Zahn, Esther; Segelcke, Daniel; Spisak, Tamas; Vollert, Jan","Machine learning and artificial intelligence in neuroscience: A primer for researchers","Brain, Behavior, and Immunity","","0889-1591","10.1016/j.bbi.2023.11.005","https://www.sciencedirect.com/science/article/pii/S0889159123003380","Artificial intelligence (AI) is often used to describe the automation of complex tasks that we would attribute intelligence to. Machine learning (ML) is commonly understood as a set of methods used to develop an AI. Both have seen a recent boom in usage, both in scientific and commercial fields. For the scientific community, ML can solve bottle necks created by complex, multi-dimensional data generated, for example, by functional brain imaging or *omics approaches. ML can here identify patterns that could not have been found using traditional statistic approaches. However, ML comes with serious limitations that need to be kept in mind: their tendency to optimise solutions for the input data means it is of crucial importance to externally validate any findings before considering them more than a hypothesis. Their black-box nature implies that their decisions usually cannot be understood, which renders their use in medical decision making problematic and can lead to ethical issues. Here, we present an introduction for the curious to the field of ML/AI. We explain the principles as commonly used methods as well as recent methodological advancements before we discuss risks and what we see as future directions of the field. Finally, we show practical examples of neuroscience to illustrate the use and limitations of ML.","2024-01-01","2024-12-03 03:24:10","2024-12-03 03:24:10","","470-479","","","115","","Brain, Behavior, and Immunity","","","","","","","","","","","","","","","","","","","Machine learning; Artificial intelligence; Pain; Neuroscience; *omics; Behavioural research; fMRI; Predictive modelling","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CD6PCYRY","journalArticle","2023","Sipola, Juha; Saunila, Minna; Ukko, Juhani","Adopting artificial intelligence in sustainable business","Journal of Cleaner Production","","0959-6526","10.1016/j.jclepro.2023.139197","https://www.sciencedirect.com/science/article/pii/S0959652623033553","Artificial intelligence (AI) in sustainable businesses has attracted interest in various industries. However, research on the sustainable adoption of AI is scarce. The present research contributes to this gap by investigating the benefits of AI for sustainable businesses. Qualitative content analysis was used to investigate twenty-five of the largest Finnish enterprises. In the years 2017–2021, AI was employed in twenty of these enterprises, sixteen of which relied on AI to obtain benefits associated with sustainable business practices. Based on the sample, AI can be perceived as a generalizing technology in Finland. Additionally, the strategic significance of AI for enterprises has been discovered to be increasing. The research results indicate that AI deployment was driven primarily by two interlinked aims: 1) optimization and 2) the pursuit of benefits in various dimensions of sustainable business.","2023-11-10","2024-12-03 03:24:10","2024-12-03 03:24:10","","139197","","","426","","Journal of Cleaner Production","","","","","","","","","","","","","","","","","","","Management; Sustainability; Sustainable development; Artificial intelligence","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"3WL6WAHK","journalArticle","2024","Duggan, Mike","The digital geographies of tact","Digital Geography and Society","","2666-3783","10.1016/j.diggeo.2024.100102","https://www.sciencedirect.com/science/article/pii/S2666378324000242","This article outlines a research agenda for the spatialities of tact produced by, through and of digital spaces. As a discipline interested in what and who characterises digital space, and in how different relations come to produce space, the article puts forward a proposition for geographers to take tact seriously as an inherently spatial concept useful for theorising the production of space in our digital society. The paper identifies three strands of tact from the literature, 1) tact and social behaviour, 2) tact and touch, 3) tact and judgement, and outlines what they can offer geography in terms of a novel framework for studying digital society. It raises questions of how and why digital spaces and practices produce new trajectories for displays of tact in everyday life, how digital spaces modulate our understanding and experiences of touch, as well as asking whether algorithmic decision making technologies such as Artificial Intelligence have a capacity for tact, and what that means for the geographies these systems shape. The work makes a contribution to the discipline's long standing interests in spatial tactics and socio-spatial behaviour, in touch and sensory geographies, and more recently to algorithmic decision making.","2024-12-01","2024-12-03 03:24:10","2024-12-03 03:24:10","","100102","","","7","","Digital Geography and Society","","","","","","","","","","","","","","","","","","","Digital media; Artificial intelligence; Judgement; Social behaviour; Space; Tact; Tactics; Tactility; Touch","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"6PWBS977","journalArticle","2024","Biju, Vinai George; Babu, Bibin; Asghar, Ali; Prathap, Boppuru Rudra; Reddy, Vandana","From Text to Action: NLP Techniques for Washing Machine Manual Processing","International Conference on Machine Learning and Data Engineering (ICMLDE 2023)","","1877-0509","10.1016/j.procs.2024.04.181","https://www.sciencedirect.com/science/article/pii/S1877050924008573","This scientific research study focuses on the advancements in Natural Language Processing (NLP) driven by large-scale parallel corpora and presents a comprehensive methodology for creating a parallel, multilingual corpus using NLP techniques and semantic technologies, with a particular focus on washing machine manuals. The study highlights the significant progress made in NLP through the utilization of large-scale parallel corpora and advanced NLP techniques. The successful creation of a parallel, multilingual corpus for washing machine manuals, coupled with the integration of semantic technologies and ontology modeling, demonstrates the broad applicability and potential of NLP in diverse domains.The research covers various aspects, including text extraction, segmentation, and the development of specialized pipelines for question-answering, translation, and text summarization tailored for washing machine manuals. Translation experiments using fine-tuned models demonstrated the feasibility of providing washing machine manuals in local languages, expanding accessibility and understanding for users worldwide. Additionally, the study explored text summarization using a powerful transformer-based model, which exhibited remarkable proficiency in generating concise and coherent summaries from complex input texts. The implementation of a question-answering pipeline showcased the effectiveness of various language models in handling question-answering tasks with high accuracy and effectiveness.Additionally, the article discusses the processes of data collection, information preparation, ontology creation, alignment strategies, and text analytics. Furthermore, the study addresses the challenges and potential future developments in this field, offering insights into the promising applications of NLP in the context of washing machine manuals.","2024-01-01","2024-12-03 03:24:10","2024-12-03 03:24:10","","1903-1919","","","235","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Ontology; Translation; BERT; Extraction; Hugging Face; Q/A Pipeline; Segmentation of text; Text Summarization","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"6XBW7RJ7","journalArticle","2024","Armenia, Stefano; Franco, Eduardo; Iandolo, Francesca; Maielli, Giuliano; Vito, Pietro","Zooming in and out the landscape: Artificial intelligence and system dynamics in business and management","Technological Forecasting and Social Change","","0040-1625","10.1016/j.techfore.2023.123131","https://www.sciencedirect.com/science/article/pii/S0040162523008168","Organizations are increasingly leveraging the ability of artificial intelligence to analyze and resolve complex problems. This can potentially reshape the interdependencies and interactions of complex systems, leading to our research question: To what extent and in which direction is the literature on Artificial Intelligence (AI) and System Dynamics (SD) converging within the business and management landscape? We conducted an extensive literature review using bibliometric and topic modeling methods to address this question. Through a bibliometric analysis, we identified the areas in which academic papers referred to both SD and AI literature. However, bibliometrics do not show a clear path towards convergence. The top modeling analysis highlights more details on how convergence is structured, providing insights into how SD and AI may be integrated. Two trajectories are identified. In the “soft convergence,” AI supports system dynamics analysis and modeling more deeply characterized by social interaction. In the “hard convergence,” AI shapes innovative ways of rethinking system design, dynamics, and interdependencies. Our analysis suggests that while soft convergence is more visible in the business and management landscape, hard convergence may well represent a new frontier in studying system dynamics with the potential to reshape the landscape.","2024-03-01","2024-12-03 03:24:10","2024-12-03 03:24:10","","123131","","","200","","Technological Forecasting and Social Change","","","","","","","","","","","","","","","","","","","Technology; Bibliometrics; Artificial intelligence; Topic modeling; Forecasting; System dynamics","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"9X4RP9T5","journalArticle","2024","Moradian, Sogol; Gharbia, Salem; Majidi Nezhad, Meysam; Olbert, Agnieszka Indiana","Enhancing the accuracy of wind power projections under climate change using geospatial machine learning models","Energy Reports","","2352-4847","10.1016/j.egyr.2024.09.007","https://www.sciencedirect.com/science/article/pii/S2352484724005870","This paper presents a geospatial artificial intelligence (GeoAI) approach for generating wind power projection maps employing various Machine Learning (ML) models. These models include Artificial Neural Network (ANN), Decision Tree (DT), Gaussian Process Regression (GPR), and Support Vector Regression (SVR), which collectively aim to provide insightful wind power forecasts under the effects of climate change. The framework considers different influential parameters affecting wind speed, including pressure gradient, temperature gradient, humidity, and topography. The study’s geographic focus is Cork City, Ireland. The investigation covers a historical period from 2000 to 2014 and extends to encompass two future climate scenarios, between 2015 and 2050. A comprehensive set of statistical skill scores is computed to gauge the models’ performance. The study’s findings underscore the efficacy of the ML models in generating dependable estimates of wind power fluctuations. Notably, the SVR model emerges as the frontrunner in performance across most pixels examined. Despite the inherent complexity of wind power dynamics, this research highlights that the SVR model can produce accurate wind power maps, even when operating with limited input data. The results emphasize the importance of considering influential factors in wind speed projections. This approach opens up promising avenues for improving the management of renewable energy resources.","2024-12-01","2024-12-03 03:24:10","2024-12-03 03:24:10","","3353-3363","","","12","","Energy Reports","","","","","","","","","","","","","","","","","","","Machine learning; Climate change; Artificial intelligence; Wind energy; Wind power","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2IGK76B6","journalArticle","2024","Wang, Yujie","Emotional Dependence Path of Artificial Intelligence Chatbot Based on Structural Equation Modeling","The 11th International Conference on Applications and Techniques in Cyber Intelligence","","1877-0509","10.1016/j.procs.2024.10.131","https://www.sciencedirect.com/science/article/pii/S1877050924029314","With the rise of ChatGPT, the anthropomorphic dialogue form of chatbots is sought after and relied on by a large number of users. Compared with traditional social real objects, chatbots are more controllable in social relationships. At the same time, compared with traditional real social objects, chatbots may show better emotional identification with objects. The structural equation modeling is a path analysis method based on the factor analysis and linear regression, which can be used to analyze the path relationship between intricate variables. Based on the structural equation modeling, this paper analyzes the potential pathways for artificial intelligence chatbots to induce the user's emotional dependence. The results show that the “sense of identification” and “sense of control” will cause people to have emotional dependence on the artificial intelligence chatbots, with p < 0.05. However, the psychological distance plays a regulating role in the identification-emotional dependence and the sense of control-emotional dependence, respectively. The research results in this paper show that there is a certain theoretical basis for the chatting intelligent robots to transform from a tool role to a social and emotional role.","2024-01-01","2024-12-03 03:24:10","2024-12-03 03:24:10","","1089-1094","","","247","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Structural Equation Modeling (SEM); Artificial Intelligence; Chatbot Role; Emotional Dependence","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XWHA8WSY","journalArticle","2024","Linlin, Li","Artificial Intelligence Translator DeepL Translation Quality Control","The 11th International Conference on Applications and Techniques in Cyber Intelligence","","1877-0509","10.1016/j.procs.2024.10.086","https://www.sciencedirect.com/science/article/pii/S1877050924028862","With the advancement of language technology, artificial intelligence translators are also constantly developing. As an intelligent translator, the emergence of DeepL provides new opportunities for the development of human translation industry. Based on the theory of machine translation quality control, this article analyzed the translation quality control strategy of DeepL. By analyzing the performance of the designed source and target languages in the text input stage, the impact of part of speech tagging and syntactic analysis on translation quality in the text processing stage was evaluated. The machine translation quality evaluation method based on semantic similarity calculation was adopted to evaluate the translation quality of DeepL, and DeepL was compared and analyzed with other translators. This research found that DeepL translation performed well in terms of translation accuracy, fluency, and naturalness. The overall score of DeepL was 100, reaching 94.13.","2024-01-01","2024-12-03 03:24:10","2024-12-03 03:24:10","","710-717","","","247","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","quality control; artificial intelligence translation; DeepL translator; effect evaluation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2C9BGUSK","journalArticle","2024","Olari, Viktoriya; Romeike, Ralf","Data-related concepts for artificial intelligence education in K-12","Computers and Education Open","","2666-5573","10.1016/j.caeo.2024.100196","https://www.sciencedirect.com/science/article/pii/S2666557324000363","Due to advances in Artificial Intelligence (AI), computer science education has rapidly started to include topics related to AI along K-12 education. Although this development is timely and important, it is also concerning because the elaboration of the AI field for K-12 is still ongoing. Current efforts may significantly underestimate the role of data, the fundamental component of an AI system. If the goal is to enable students to understand how AI systems work, knowledge of key concepts related to data processing is a prerequisite, as data collection, preparation, and engineering are closely linked to the functionality of AI systems. To advance the field, the following research provides a comprehensive collection of key data-related concepts relevant to K-12 computer science education. These concepts were identified through a theoretical review of the AI field, aligned through a review of AI curricula for school education, evaluated through interviews with domain experts and teachers, and structured hierarchically according to the data lifecycle. Computer science educators can use the elaborated structure as a conceptual guide for designing learning arrangements that aim to enable students to understand how AI systems are created and function.","2024-12-01","2024-12-03 03:24:10","2024-12-03 03:24:10","","100196","","","7","","Computers and Education Open","","","","","","","","","","","","","","","","","","","Data; Artificial Intelligence education; Computer Science education; Data lifecycle; K-12; Key concepts","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"NEXDXNVE","journalArticle","2024","Adewale, Muyideen Dele; Azeta, Ambrose; Abayomi-Alli, Adebayo; Sambo-Magaji, Amina","Impact of artificial intelligence adoption on students' academic performance in open and distance learning: A systematic literature review","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e40025","https://www.sciencedirect.com/science/article/pii/S2405844024160562","The role of artificial intelligence (AI) in education has been extensively studied, focusing on its ability to enhance learning and teaching processes. However, the precise impact of AI adoption on academic performance in open and distance learning (ODL) remains largely unexplored. This systematic literature review critically evaluates AI's impact on academic performance within ODL environments. Drawing from a curated selection of 64 papers from an initial pool of 700, spanning from 2017 to 2023 and sourced from Scopus, Google Scholar, and Web of Science, this study delves into the multifaceted role of AI in enhancing learning outcomes. The meta-analysis reveals a diverse methodological landscape: machine learning methods, employed in 29.69 % of the studies, stand out for their ability to predict academic achievement, which is matched in prevalence by classical statistical methods. Although less common at 3.13 %, hybrid methods are a burgeoning area of research, while a significant 40.63 % of works prioritise nonempirical methods, focusing on theoretical analysis and literature reviews. This investigation highlights the critical factors driving AI adoption in education and its tangible benefits for student performance. It identifies a crucial literature gap: the absence of a process-based framework designed to forecast AI's educational impacts with greater precision, especially across gender and regional lines. By proposing this framework, this study contributes to the academic discourse on AI in education. It underscores the urgent need for structured methodologies to navigate the challenges and opportunities of AI integration. This framework, aligned with UNESCO's 2030 educational objectives, promises to bridge educational divides, ensuring equitable access to quality education across diverse demographics. The findings advocate for future research to design, refine, and test such a framework, paving the way for more inclusive and effective educational technologies in ODL settings.","2024-11-30","2024-12-03 03:24:11","2024-12-03 03:24:11","","e40025","","22","10","","Heliyon","","","","","","","","","","","","","","","","","","","Artificial intelligence; AI in education; Academic performance; Gender and geographical differences; Open and distance learning","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"GYFMQZBB","journalArticle","2024","Iglesias, Guillermo; Talavera, Edgar; Troya, Jesús; Díaz-Álvarez, Alberto; García-Remesal, Miguel","Artificial intelligence model for tumoral clinical decision support systems","Computer Methods and Programs in Biomedicine","","0169-2607","10.1016/j.cmpb.2024.108228","https://www.sciencedirect.com/science/article/pii/S0169260724002232","Background and Objective Comparative diagnostic in brain tumor evaluation makes possible to use the available information of a medical center to compare similar cases when a new patient is evaluated. By leveraging Artificial Intelligence models, the proposed system is able of retrieving the most similar cases of brain tumors for a given query. The primary objective is to enhance the diagnostic process by generating more accurate representations of medical images, with a particular focus on patient-specific normal features and pathologies. A key distinction from previous models lies in its ability to produce enriched image descriptors solely from binary information, eliminating the need for costly and difficult to obtain tumor segmentation. Methods The proposed model uses Artificial Intelligence to detect patient features to recommend the most similar cases from a database. The system not only suggests similar cases but also balances the representation of healthy and abnormal features in its design. This not only encourages the generalization of its use but also aids clinicians in their decision-making processes. This generalization makes possible for future research in different medical diagnosis areas with almost not any change in the system. Results We conducted a comparative analysis of our approach in relation to similar studies. The proposed architecture obtains a Dice coefficient of 0.474 in both tumoral and healthy regions of the patients, which outperforms previous literature. Our proposed model excels at extracting and combining anatomical and pathological features from brain Magnetic Resonances (MRs), achieving state-of-the-art results while relying on less expensive label information. This substantially reduces the overall cost of the training process. Our findings highlight the significant potential for improving the efficiency and accuracy of comparative diagnostics and the treatment of tumoral pathologies. Conclusions This paper provides substantial grounds for further exploration of the broader applicability and optimization of the proposed architecture to enhance clinical decision-making. The novel approach presented in this work marks a significant advancement in the field of medical diagnosis, particularly in the context of Artificial Intelligence-assisted image retrieval, and promises to reduce costs and improve the quality of patient care using Artificial Intelligence as a support tool instead of a black box system.","2024-08-01","2024-12-03 03:24:11","2024-12-03 03:24:11","","108228","","","253","","Computer Methods and Programs in Biomedicine","","","","","","","","","","","","","","","","","","","Deep learning; Feature extraction; Magnetic resonance imaging; Clinical decision support system; Comparative diagnostic; Content-based image retrieval","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"9TK6ZQ5A","journalArticle","2024","Hou, Wenjia; Pan, Xingchen; Pu, Yang; Ma, Rui; Saleem, Asif","Harnessing artificial intelligence for energy strategies: Advancing global economic policies and hydrogen production in the transition to a low-carbon economy","Energy Strategy Reviews","","2211-467X","10.1016/j.esr.2024.101568","https://www.sciencedirect.com/science/article/pii/S2211467X24002773","The need for non-conventional energy forms is another factor that has been pointing to the need for Fortran's s-ports for new forms of energy and indeed transportable energy that carriers such as Hydrogen. This investigation employs artificial intelligence to promote international economic policies and hydrogen manufacturing, defining hydrogen export competitiveness globally from 1991 to 2024. An overall index constructed from twenty-one benchmarks in four areas: financial and economic viability, political and regulatory position, industrial experience, and resource availability and prospects were created and further tested with experts' interviews and questionnaires. AI specifically played a very important role in data analysis and the index in assigning the appropriate weights and providing the right evaluation. This paper reveals the countries that have a competitive hydrogen industry and gives policy suggestions for improving the generation and exportation productivity of Hydrogen. Through this integrated project development approach, it is intended to spark the hydrogen economy and renewable energy, including environmentally sustainable uses. As a result, by employing AI to develop the energy strategy, this research provides practical recommendations for creating a low-carbon economy, filling the existing gap in the issue of limited sustainable energy sources and contributing to the development of the global economy. The purpose is to provide specific guidance for the effective stimulation of the hydrogen market in the context of global changes in the renewable energy area and sustainable development.","2024-11-01","2024-12-03 03:24:11","2024-12-03 03:24:11","","101568","","","56","","Energy Strategy Reviews","","","","","","","","","","","","","","","","","","","Renewable energy; Economic potential; Energy strategy; Hydrogen production; Low-carbon economy","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"E2KIPGNN","journalArticle","2024","Sivakumar, Mithila; Belle, Alvine B.; Shan, Jinjun; Khakzad Shahandashti, Kimya","Prompting GPT–4 to support automatic safety case generation","Expert Systems with Applications","","0957-4174","10.1016/j.eswa.2024.124653","https://www.sciencedirect.com/science/article/pii/S0957417424015203","In the ever-evolving field of software engineering, the advent of large language models and conversational interfaces, exemplified by ChatGPT, represents a significant revolution. While their potential is evident in various domains, this paper expands upon our previous research, where we experimented with GPT–4, on its ability to create safety cases. A safety case is a structured argument supported by a body of evidence to demonstrate that a given system is safe to operate in a given environment. In this paper, we first determine GPT–4’s comprehension of the Goal Structuring Notation (GSN), a well-established notation for visually representing safety cases. Additionally, we conduct four distinct experiments using GPT–4 to evaluate its ability to generate safety cases within a specified system and application domain. To assess GPT–4’s performance in this context, we compare the results it produces with the ground-truth safety cases developed for an X-ray system, a machine learning-enabled component for tire noise recognition in a vehicle, and a lane management system from the automotive domain. This comparison enables us to gain valuable insights into the model’s generative capabilities. Our findings indicate that GPT–4 is able to generate moderately accurate and reasonable safety cases.","2024-12-01","2024-12-03 03:24:11","2024-12-03 03:24:11","","124653","","","255","","Expert Systems with Applications","","","","","","","","","","","","","","","","","","","Machine learning; Requirements engineering; Large language models; Generative AI; Safety assurance; Safety cases","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KWXUK7Q3","journalArticle","2024","Alsabt, Reema; Alkhaldi, Wadha; Adenle, Yusuf A.; Alshuwaikhat, Habib M.","Optimizing waste management strategies through artificial intelligence and machine learning - An economic and environmental impact study","Cleaner Waste Systems","","2772-9125","10.1016/j.clwas.2024.100158","https://www.sciencedirect.com/science/article/pii/S2772912524000307","Applying artificial intelligence (AI) and machine learning (ML) techniques to optimize waste management strategies, focusing on enhancing economic efficiency and reducing environmental impact, is vital. The study utilized ML models to analyze and forecast waste generation trends, assess the viability of various waste management methods, and develop optimization models for resource allocation and operational efficiency. The research employs the World Bank’s comprehensive waste management dataset. After rigorous data preprocessing, including cleaning and feature selection, a variety of ML techniques, such as regression models, classification algorithms like Support Vector Machines (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and optimization algorithms, including linear programming, are applied. Unlike other research, this study achieved 85 % accuracy on predictive analytics models for forecasting waste generation trends, primarily attributed to integrating more diverse data sets, including socio-economic factors. Also, the optimization resource allocation achieved a 15 % increase in operational efficiency. These findings provide significant insights for policymakers and urban planners, suggesting that integrating ML in waste management can lead to more sustainable and cost-effective practices. This paper demonstrates the transformative potential of ML in optimizing waste management strategies, offering a pathway towards more sustainable and economically viable waste management solutions globally.","2024-08-01","2024-12-03 03:24:11","2024-12-03 03:24:11","","100158","","","8","","Cleaner Waste Systems","","","","","","","","","","","","","","","","","","","Machine learning; Sustainable development; Artificial intelligence; Circular economy; Data analytics; Environmental sustainability; Waste management strategies","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"59EDT6P3","journalArticle","2024","Meinert, Edward; Milne-Ives, Madison; Lim, Ernest; Higham, Aisling; Boege, Selina; de Pennington, Nick; Bajre, Mamta; Mole, Guy; Normando, Eduardo; Xue, Kanmin","Accuracy and safety of an autonomous artificial intelligence clinical assistant conducting telemedicine follow-up assessment for cataract surgery","eClinicalMedicine","","2589-5370","10.1016/j.eclinm.2024.102692","https://www.sciencedirect.com/science/article/pii/S2589537024002712","Summary Background Artificial intelligence deployed to triage patients post-cataract surgery could help to identify and prioritise individuals who need clinical input and to expand clinical capacity. This study investigated the accuracy and safety of an autonomous telemedicine call (Dora, version R1) in detecting cataract surgery patients who need further management and compared its performance against ophthalmic specialists. Methods 225 participants were recruited from two UK public teaching hospitals after routine cataract surgery between 17 September 2021 and 31 January 2022. Eligible patients received a call from Dora R1 to conduct a follow-up assessment approximately 3 weeks post cataract surgery, which was supervised in real-time by an ophthalmologist. The primary analysis compared decisions made independently by Dora R1 and the supervising ophthalmologist about the clinical significance of five symptoms and whether the patient required further review. Secondary analyses used mixed methods to examine Dora R1's usability and acceptability and to assess cost impact compared to standard care. This study is registered with ClinicalTrials.gov (NCT05213390) and ISRCTN (16038063). Findings 202 patients were included in the analysis, with data collection completed on 23 March 2022. Dora R1 demonstrated an overall outcome sensitivity of 94% and specificity of 86% and showed moderate to strong agreement (kappa: 0.758–0.970) with clinicians in all parameters. Safety was validated by assessing subsequent outcomes: 11 of the 117 patients (9%) recommended for discharge by Dora R1 had unexpected management changes, but all were also recommended for discharge by the supervising clinician. Four patients were recommended for discharge by Dora R1 but not the clinician; none required further review on callback. Acceptability, from interviews with 20 participants, was generally good in routine circumstances but patients were concerned about the lack of a ‘human element’ in cases with complications. Feasibility was demonstrated by the high proportion of calls completed autonomously (195/202, 96.5%). Staff cost benefits for Dora R1 compared to standard care were £35.18 per patient. Interpretation The composite of mixed methods analysis provides preliminary evidence for the safety, acceptability, feasibility, and cost benefits for clinical adoption of an artificial intelligence conversational agent, Dora R1, to conduct follow-up assessment post-cataract surgery. Further evaluation in real-world implementation should be conducted to provide additional evidence around safety and effectiveness in a larger sample from a more diverse set of Trusts. Funding This manuscript is independent research funded by the National Institute for Health Research and NHSX (Artificial Intelligence in Health and Care Award, AI_AWARD01852).","2024-07-01","2024-12-03 03:24:11","2024-12-03 03:24:11","","102692","","","73","","eClinicalMedicine","","","","","","","","","","","","","","","","","","","Artificial intelligence; Natural language processing; Telemedicine; Digital health; Cataract; Clinical study","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JE3NX6UD","journalArticle","2024","Alamsyah, Andry; Sagama, Yoga","Empowering Indonesian internet users: An approach to counter online toxicity and enhance digital well-being","Intelligent Systems with Applications","","2667-3053","10.1016/j.iswa.2024.200394","https://www.sciencedirect.com/science/article/pii/S2667305324000693","The proliferation of online toxicity, characterized by offensive and disrespectful language, has been a pervasive issue in Indonesia’s digital environment, impacting users’ mental health and well-being. Simultaneously, the potential of Natural Language Processing (NLP) in detecting and managing toxic comments provides a promising avenue for mitigating online toxicity. This study presents a 3-stages methodology consisting of type, target audience, and topics to detect and categorize online toxicity in the Indonesian language using fine-tuned IndoBERTweet and Indonesian RoBERTa models. The results indicate that the IndoBERTweet model, with optimally adjusted hyperparameters, consistently outperforms the Indonesian RoBERTa model in all stages of our proposed methodology. These outcomes are substantiated by higher precision, recall, and F1 score metrics exhibited by the IndoBERTweet model. This model also exhibits remarkable performance in real-world applicability, accurately classifying new Indonesian language content from Twitter (now X). This research establishes a stepping stone for future work, including exploring other language models, applying the methodology to other languages, training the models on larger and more diverse datasets, and applying it to other social media platforms or forums. Our proposal contributes to create safer online spaces, and the results provide insights for the development of automated moderation tools, playing a significant role in combating online harassment and ensuring online community well-being.","2024-06-01","2024-12-03 03:24:11","2024-12-03 03:24:11","","200394","","","22","","Intelligent Systems with Applications","","","","","","","","","","","","","","","","","","","Content moderation; IndoBERTweet; Indonesian language; Indonesian RoBERTa; Online toxicity","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"8SSTQU6Q","journalArticle","2024","Nawaz, Nishad; Arunachalam, Hemalatha; Pathi, Barani Kumari; Gajenderan, Vijayakumar","The adoption of artificial intelligence in human resources management practices","International Journal of Information Management Data Insights","","2667-0968","10.1016/j.jjimei.2023.100208","https://www.sciencedirect.com/science/article/pii/S266709682300054X","This study explores the impact of Artificial Intelligence (AI) on Human Resources Management Practices. By focusing on key outcomes such as accuracy, automation, computing power & capacity, real-time experience, personalization, and time-saving & cost saving. The research aims to identity the potential benefits of AI adoption. Data from 274 IT employees in Chennai City is Collected through a well-structured online questionnaire. Using IBM SPSS version 21 software and AMOS version 21 is used for analysis, the study proposes a novel research framework. The findings indicate that variables like Accuracy, Computing Power & Capacity, and Personalization significantly influence Time-Saving & Cost Reduction, while Automation and Real-Time Experience do not. The novel contribution of this study lies in its exploration of the specific outcomes of utilizing AI Technologies in Human Resources Management Practices. By focusing on key variables such as Accuracy, Automation, Computing Power & Capacity, Real-time experience, Personalization, and Time-Saving & Cost Saving, the research provides a comprehensive understanding of the expected outcomes when implementing AI in Human resources Management and the relationship among those outcome variables.","2024-04-01","2024-12-03 03:24:11","2024-12-03 03:24:11","","100208","","1","4","","International Journal of Information Management Data Insights","","","","","","","","","","","","","","","","","","","Accuracy; Artificial intelligence; Automation; Personalization; Computing power & capacity; Human resource management practices; Real-time experience; Time-saving & cost reduction","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"NDDP9K6L","journalArticle","2024","Hu, An; Wang, Qi; Xu, Xiaoguang; Zhao, Yao; Ji, Qian; Pei, Lei","Research on Video Sample Collection and Processing Methods Based on Artificial Intelligence Platform","The 11th International Conference on Applications and Techniques in Cyber Intelligence","","1877-0509","10.1016/j.procs.2024.10.073","https://www.sciencedirect.com/science/article/pii/S1877050924028734","This paper summarizes the video sample collection and processing methods based on artificial intelligence platform, focusing on video noise cancellation, content segmentation and classification, and feature extraction and representation techniques. The paper believes that the deep learning technology, especially the convolutional neural network, shows great potential in image recognition and video analysis, and effectively improves the level of automation and accuracy of video processing. This paper discusses the importance of building a large-scale and high-quality video sample library, and how to improve the processing efficiency and accuracy of video data through intelligent technology.","2024-01-01","2024-12-03 03:24:11","2024-12-03 03:24:11","","609-616","","","247","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial Intelligence, Video Processing, Deep Learning; Convolutional Neural Network","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"69E96DMR","journalArticle","2024","Cao, Rui; Liu, Yanan; Wen, Xin; Liao, Caiqing; Wang, Xin; Gao, Yuan; Tan, Tao","Reinvestigating the performance of artificial intelligence classification algorithms on COVID-19 X-Ray and CT images","iScience","","2589-0042","10.1016/j.isci.2024.109712","https://www.sciencedirect.com/science/article/pii/S2589004224009349","Summary There are concerns that artificial intelligence (AI) algorithms may create underdiagnosis bias by mislabeling patient individuals with certain attributes (e.g., female and young) as healthy. Addressing this bias is crucial given the urgent need for AI diagnostics facing rapidly spreading infectious diseases like COVID-19. We find the prevalent AI diagnostic models show an underdiagnosis rate among specific patient populations, and the underdiagnosis rate is higher in some intersectional specific patient populations (for example, females aged 20–40 years). Additionally, we find training AI models on heterogeneous datasets (positive and negative samples from different datasets) may lead to poor model generalization. The model’s classification performance varies significantly across test sets, with the accuracy of the better performance being over 40% higher than that of the poor performance. In conclusion, we developed an AI bias analysis pipeline to help researchers recognize and address biases that impact medical equality and ethics.","2024-05-17","2024-12-03 03:24:11","2024-12-03 03:24:11","","109712","","5","27","","iScience","","","","","","","","","","","","","","","","","","","Health informatics; Artificial intelligence applications; Microbiology","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ADUC3M7I","journalArticle","2024","Blanco-Moreno, Sofía; González-Fernández, Ana M.; Muñoz-Gallego, Pablo Antonio; Casaló, Luis V.","Understanding engagement with Instagram posts about tourism destinations","Journal of Destination Marketing & Management","","2212-571X","10.1016/j.jdmm.2024.100948","https://www.sciencedirect.com/science/article/pii/S2212571X24000969","This study analyses the social media engagement (SME) received by Instagram posts from a tourism destination based on communication and mental imagery theories. This research considers both the type of sender (tourists vs. residents) and the content of the message (pictorial stimuli: places of interest vs. hospitality services; centricity stimuli: people vs. without people). Web scraping technique is used for data collection. Content analysis is then applied on 27,088 Instagram posts using artificial intelligence techniques (machine learning and deep learning); and a univariate generalized linear model is conducted to analyze differences in SME. Results show that pictorial stimulus determines SME, being higher for images focused on places of interest, and photographs with people get higher SME too. The type of sender also influences SME and exerts a moderating role reinforcing the effect of centricity on SME for tourists. These results provide interesting insights for destination marketing managers and Instagram users.","2024-12-01","2024-12-03 03:24:11","2024-12-03 03:24:11","","100948","","","34","","Journal of Destination Marketing & Management","","","","","","","","","","","","","","","","","","","Deep learning; Machine learning; Artificial intelligence; Destination image; Instagram; Social media engagement","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"HGS4W8G4","journalArticle","2024","Albrecht, Vincent; Müller-Reif, Johannes; Nordmann, Thierry M.; Mund, Andreas; Schweizer, Lisa; Geyer, Philipp E.; Niu, Lili; Wang, Juanjuan; Post, Frederik; Oeller, Marc; Metousis, Andreas; Bach Nielsen, Annelaura; Steger, Medini; Wewer Albrechtsen, Nicolai J.; Mann, Matthias","Bridging the Gap From Proteomics Technology to Clinical Application: Highlights From the 68th Benzon Foundation Symposium","Molecular & Cellular Proteomics","","1535-9476","10.1016/j.mcpro.2024.100877","https://www.sciencedirect.com/science/article/pii/S1535947624001671","The 68th Benzon Foundation Symposium brought together leading experts to explore the integration of mass spectrometry–based proteomics and artificial intelligence to revolutionize personalized medicine. This report highlights key discussions on recent technological advances in mass spectrometry–based proteomics, including improvements in sensitivity, throughput, and data analysis. Particular emphasis was placed on plasma proteomics and its potential for biomarker discovery across various diseases. The symposium addressed critical challenges in translating proteomic discoveries to clinical practice, including standardization, regulatory considerations, and the need for robust “business cases” to motivate adoption. Promising applications were presented in areas such as cancer diagnostics, neurodegenerative diseases, and cardiovascular health. The integration of proteomics with other omics technologies and imaging methods was explored, showcasing the power of multimodal approaches in understanding complex biological systems. Artificial intelligence emerged as a crucial tool for the acquisition of large-scale proteomic datasets, extracting meaningful insights, and enhancing clinical decision-making. By fostering dialog between academic researchers, industry leaders in proteomics technology, and clinicians, the symposium illuminated potential pathways for proteomics to transform personalized medicine, advancing the cause of more precise diagnostics and targeted therapies.","2024-12-01","2024-12-03 03:24:11","2024-12-03 03:24:11","","100877","","12","23","","Molecular & Cellular Proteomics","","","","","","","","","","","","","","","","","","","artificial intelligence in proteomics; biomarker discovery; clinical proteomics; mass spectrometry–based proteomic; personalized medicine","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"P5X3IACI","journalArticle","2024","Egan, Sarah J.; Johnson, Catherine; Wade, Tracey D.; Carlbring, Per; Raghav, Shravan; Shafran, Roz","A pilot study of the perceptions and acceptability of guidance using artificial intelligence in internet cognitive behaviour therapy for perfectionism in young people","Internet Interventions","","2214-7829","10.1016/j.invent.2024.100711","https://www.sciencedirect.com/science/article/pii/S2214782924000046","Perfectionism is a transdiagnostic process associated with a range of psychological disorders. Cognitive Behaviour Therapy for Perfectionism (CBT-P) has been demonstrated as efficacious across guided and unguided internet delivered interventions in reducing perfectionism and psychopathology. The aim of this pilot study was to understand perceptions and acceptability of an artificial intelligence supplemented CBT-P intervention (AI-CBT-P) in young people with lived experience of anxiety and depression (n = 8; age range 19–29 years, M = 24 years, SD = 3.77; 50 % female, 38 % male, 12 % non-binary). Young people reported that they were frequent users of artificial intelligence for study, work and general information, were positive about the intervention and using artificial intelligence for guidance in a self-help intervention, but also noted several concerns. Young people perceived numerous benefits to AI-CBT-P, including ease of access, low cost, lack of stigma and benefits for individuals with social anxiety. Overall, young people appear to be interested in, and have a positive view of, AI-CBT-P. Further research is now required to examine the feasibility and acceptability of the intervention.","2024-03-01","2024-12-03 03:24:11","2024-12-03 03:24:11","","100711","","","35","","Internet Interventions","","","","","","","","","","","","","","","","","","","Anxiety; Artificial intelligence; Depression; Eating disorders; Perfectionism; Treatment","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SI5E6AZY","journalArticle","2024","Najem, Rihab; Amr, Meryem Fakhouri; Bahnasse, Ayoub; Talea, Mohamed","Advancements in Artificial Intelligence and Machine Learning for Stock Market Prediction: A Comprehensive Analysis of Techniques and Case Studies","14th International Conference on Emerging Ubiquitous Systems and Pervasive Networks / 13th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (EUSPN/ICTH 2023)","","1877-0509","10.1016/j.procs.2023.12.193","https://www.sciencedirect.com/science/article/pii/S1877050923022056","Stock market forecasting is a classic but challenging problem that has attracted the attention of economists and computer scientists. The activity of trading involves high risks, the investors may lose a part of the totality of the amount they invested. Hence a need for more intelligent techniques to help make investment decisions. The purpose of this study is to provide first an overview of artificial intelligence and machine learning techniques used in recent studies for forecasting the stock market, then to present not only the different data types, commonly used evaluation metrics, and different neural network structures but also to provide a new proposition research method. Our objective is to help researchers stay abreast of the latest advances and help them easily replicate previous studies as a baseline.","2024-01-01","2024-12-03 03:24:11","2024-12-03 03:24:11","","198-204","","","231","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","ML; AI; Artificial Intelligence; Machine Learning; forecasting; prediction; Stock market; trading","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"F2C9BLHQ","journalArticle","2024","Yang, Zhi-qiang; Qi, Wen-wen; Xu, Chong; Shao, Xiao-yi","Exploring deep learning for landslide mapping: A comprehensive review","Special issue on Landslide Monitoring, Early Warning, and Risk Assessment","","2096-5192","10.31035/cg2024032","https://www.sciencedirect.com/science/article/pii/S2096519224001137","ABSTRACT A detailed and accurate inventory map of landslides is crucial for quantitative hazard assessment and land planning. Traditional methods relying on change detection and object-oriented approaches have been criticized for their dependence on expert knowledge and subjective factors. Recent advancements in high-resolution satellite imagery, coupled with the rapid development of artificial intelligence, particularly data-driven deep learning algorithms (DL) such as convolutional neural networks (CNN), have provided rich feature indicators for landslide mapping, overcoming previous limitations. In this review paper, 77 representative DL-based landslide detection methods applied in various environments over the past seven years were examined. This study analyzed the structures of different DL networks, discussed five main application scenarios, and assessed both the advancements and limitations of DL in geological hazard analysis. The results indicated that the increasing number of articles per year reflects growing interest in landslide mapping by artificial intelligence, with U-Net-based structures gaining prominence due to their flexibility in feature extraction and generalization. Finally, we explored the hindrances of DL in landslide hazard research based on the above research content. Challenges such as black-box operations and sample dependence persist, warranting further theoretical research and future application of DL in landslide detection.","2024-04-25","2024-12-03 03:24:11","2024-12-03 03:24:11","","330-350","","2","7","","China Geology","","","","","","","","","","","","","","","","","","","Deep learning; Big data; Artificial intelligence; Geological hazard survery engineering; Landslide Mapping; Neural network; Quantitative hazard assessment","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XYVKAP4S","journalArticle","2024","Tielman, Myrthe L.; Suárez-Figueroa, Mari Carmen; Jönsson, Arne; Neerincx, Mark A.; Cavalcante Siebert, Luciano","Explainable AI for all - A roadmap for inclusive XAI for people with cognitive disabilities","Technology in Society","","0160-791X","10.1016/j.techsoc.2024.102685","https://www.sciencedirect.com/science/article/pii/S0160791X24002331","Artificial intelligence (AI) is increasingly prevalent in our daily lives, setting specific requirements for responsible development and deployment: The AI should be explainable and inclusive. Despite substantial research and development investment in explainable AI, there is a lack of effort into making AI explainable and inclusive to people with cognitive disabilities as well. In this paper, we present the first steps towards this research topic. We argue that three main questions guide this research, namely: 1) How explainable should a system be?; 2) What level of understanding can the user reach, and what is the right type of explanation to help them reach this level?; and 3) How can we implement an AI system that can generate the necessary explanations? We present the current state of the art in research on these three topics, the current open questions and the next steps. Finally, we present the challenges specific to bringing these three research topics together, in order to eventually be able to answer the question of how to make AI systems explainable also to people with cognitive disabilities.","2024-12-01","2024-12-03 03:24:12","2024-12-03 03:24:12","","102685","","","79","","Technology in Society","","","","","","","","","","","","","","","","","","","Cognitive disability; Explainable AI (XAI); Responsible AI","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"BVH979EM","journalArticle","2024","Tursunbayeva, Aizhan; Chalutz-Ben Gal, Hila","Adoption of artificial intelligence: A TOP framework-based checklist for digital leaders","Business Horizons","","0007-6813","10.1016/j.bushor.2024.04.006","https://www.sciencedirect.com/science/article/pii/S000768132400051X","In the evolving digital landscape, organizations and leaders face increasing pressure to adopt and effectively utilize artificial intelligence (AI), which is steadily entering the management, work, and organizational ecosystems and enabling digital transformations. We observe AI-based applications assisting employees in daily tasks, project management, decision-making, and collaboration. But the successful adoption of AI is a complex and multifaceted process that requires careful consideration of various factors. What are the specific factors affecting the full adoption of AI from a multilevel viewpoint? This article presents a framework-based checklist concerning technology, organizations, and people (TOP) designed to assist digital leaders in navigating the challenges associated with AI adoption. Drawing upon extensive research and industry insights, this checklist provides digital leaders with a comprehensive tool to assess and address critical considerations during the adoption of AI. By systematically evaluating the technology, organization, and people dimensions, organizations and digital leaders can enhance their chances of a successful digital transformation and gain a competitive advantage in the digital age.","2024-07-01","2024-12-03 03:24:12","2024-12-03 03:24:12","","357-368","","4","67","","Business Horizons","","","","","","","","","","","","","","","","","","","Organizational culture; Artificial intelligence; Digitization; AI skills; Digital leadership; Employee trust","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"E7TXJ4YC","journalArticle","2024","Farooq, Arslan; Irfan Uddin, M.; Adnan, Muhammad; Alarood, Ala Abdulsalam; Alsolami, Eesa; Habibullah, Safa","Interpretable multi-horizon time series forecasting of cryptocurrencies by leverage temporal fusion transformer","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e40142","https://www.sciencedirect.com/science/article/pii/S2405844024161737","This research delves into the obstacles and difficulties associated with predicting cryptocurrency movements in the volatile global financial market. This study develops and evaluates an advanced Deep Learning-Enhanced Temporal Fusion Transformer (ADE-TFT) model to estimate Bitcoin values more accurately. This research employs cutting-edge artificial intelligence (AI) and machine learning (ML) techniques to comprehensively examine various aspects of cryptocurrency forecasting, including geopolitical implications, market sentiment analysis, and pattern detection in transactional datasets. The study demonstrates that the ADE-TFT model outperforms its lower-layer counterparts in terms of forecasting accuracy, with reduced Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE) values, particularly when using a higher hidden layer configuration (h=8). The study emphasizes the importance of experimenting with different normalization strategies and utilizing various market-related data to enhance the model's performance. The results suggest that improving forecasting accuracy may require addressing these limitations and incorporating additional factors, such as market sentiment. By providing investors with more precise market predictions, the techniques and information presented in this research have the potential to significantly increase investor power in an unpredictable digital currency market, enabling wise investment choices.","2024-11-30","2024-12-03 03:24:12","2024-12-03 03:24:12","","e40142","","22","10","","Heliyon","","","","","","","","","","","","","","","","","","","Deep learning; Artificial intelligence; Sentiment analysis; Transformers; Block chain; Cryptocurrency; Temporal data","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"E3C2TTEB","journalArticle","2023","Montag, Christian; Kraus, Johannes; Baumann, Martin; Rozgonjuk, Dmitri","The propensity to trust in (automated) technology mediates the links between technology self-efficacy and fear and acceptance of artificial intelligence","Computers in Human Behavior Reports","","2451-9588","10.1016/j.chbr.2023.100315","https://www.sciencedirect.com/science/article/pii/S2451958823000489","The present study investigated the extent to which the concepts of the individual tendency to trust in (automated) technology, technology self-efficacy, accepting/fearing artificial intelligence and trust in AI overlap. This research question is of relevance because trust in automation literature is rich, whereas only a few studies exist in the context of studying attitudes towards AI (and their relationship with trust in AI). To close this gap in the literature, 289 participants completed three questionnaires related to the propensity to trust in automated technology, technology self-efficacy, and attitudes towards artificial intelligence (ATAI). Notably, the ATAI scale includes a single item assessing trust in AI. Results revealed robust positive correlations between the propensity to trust in (automated) technology, technology self-efficacy and ATAI's acceptance scale (including a positive association on single item level regarding trusting AI). Regarding the ATAI's fear scale, negative correlations with trust in automated technology and technology self-efficacy were observed. Mediation models are also reported in this study. Our work shows that the construct of trust in automated technology and attitudes towards AI (including the AI trust item) overlap small to moderate (in terms of effect sizes). This means that some of the existing research insights in the domain of the propensity to trust in automation might be transferred to the psychological foundation of attitudes and the interaction with artificial intelligence. However, it is also clear that considerable non-shared (unique) variance exists between the constructs, making it necessary to further understand how positive and negative attitudes towards AI are formed.","2023-08-01","2024-12-03 03:24:12","2024-12-03 03:24:12","","100315","","","11","","Computers in Human Behavior Reports","","","","","","","","","","","","","","","","","","","Negative attitudes towards artificial intelligence; Positive attitudes towards artificial intelligence; Technology self-efficacy; Trust in artificial intelligence; Trust in automation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"TBAKTB83","journalArticle","2024","Seerat, Ayesha; Nasir, Sarah; Wasim, Muhammad; Garcia, Nuno M.","Biomedical Natural Language Inference on Clinical trials using the BERT-based Models","19th International Conference on Future Networks and Communications/ 21th International Conference on Mobile Systems and Pervasive Computing/14th International Conference on Sustainable Energy Information Technology","","1877-0509","10.1016/j.procs.2024.08.083","https://www.sciencedirect.com/science/article/pii/S1877050924017940","Clinical trials are crucial in experimental medicine as they assess the safety and efficiency of new treatments. Due to its unstructured and plain language nature, clinical text data often presents challenges in understanding the relationships between various elements like disease, symptoms, diagnosis, and treatment. This task is challenging as the Multi-evidence Natural Language Inference for Clinical Trial Data (NLI4CT) requires intricate reasoning involving textual and numerical elements. It involves integrating information from one or two Clinical Trial Reports (CTRs) to validate hypotheses, demanding a multi-faceted approach. To address these problems, we use BERT-base models’ ability to predict entailment or contradiction labels and compare the use of transformer-based feature extraction and pre-trained models. We utilize seven pre-trained models, including six BERT-based and one T5-based model: BERT-base uncased, BioBERT-base-cased-v1.1-mnli, DeBERTa-v3-base-mnli-fever-anli, DeBERTa-v3-base-mnli-fever-docnli-ling-2c, DeBERTa-large-mnli, BioLinkBERT-base, and Flan-T5-base. We achieve an F1-score of 61% on both DeBERTa-v3-base-mnli-fever-anli and DeBERTa-large-mnli models and 95% faithfulness on the BioLinkBERT-base model.","2024-01-01","2024-12-03 03:24:12","2024-12-03 03:24:12","","576-581","","","241","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","natural language processing; clinical research; bidirectional transformer encoder; entailment recognition; natural language inference","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KBD24JTY","journalArticle","2024","Gupta, Utkarsh; Paluru, Naveen; Nankani, Deepankar; Kulkarni, Kanchan; Awasthi, Navchetan","A comprehensive review on efficient artificial intelligence models for classification of abnormal cardiac rhythms using electrocardiograms","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e26787","https://www.sciencedirect.com/science/article/pii/S2405844024028184","Deep learning has made many advances in data classification using electrocardiogram (ECG) waveforms. Over the past decade, data science research has focused on developing artificial intelligence (AI) based models that can analyze ECG waveforms to identify and classify abnormal cardiac rhythms accurately. However, the primary drawback of the current AI models is that most of these models are heavy, computationally intensive, and inefficient in terms of cost for real-time implementation. In this review, we first discuss the current state-of-the-art AI models utilized for ECG-based cardiac rhythm classification. Next, we present some of the upcoming modeling methodologies which have the potential to perform real-time implementation of AI-based heart rhythm diagnosis. These models hold significant promise in being lightweight and computationally efficient without compromising the accuracy. Contemporary models predominantly utilize 12-lead ECG for cardiac rhythm classification and cardiovascular status prediction, increasing the computational burden and making real-time implementation challenging. We also summarize research studies evaluating the potential of efficient data setups to reduce the number of ECG leads without affecting classification accuracy. Lastly, we present future perspectives on AI's utility in precision medicine by providing opportunities for accurate prediction and diagnostics of cardiovascular status in patients.","2024-03-15","2024-12-03 03:24:12","2024-12-03 03:24:12","","e26787","","5","10","","Heliyon","","","","","","","","","","","","","","","","","","","Deep learning; Artificial intelligence; Arrhythmia's; ECG classification; Lightweight models","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"4PH4PZLS","journalArticle","2024","Wu, Rongrong; Zong, Hui; Feng, Weizhe; Zhang, Ke; Li, Jiakun; Wu, Erman; Tang, Tong; Zhan, Chaoying; Liu, Xingyun; Zhou, Yi; Zhang, Chi; Zhang, Yingbo; He, Mengqiao; Ren, Shumin; Shen, Bairong","OligoM-Cancer: A multidimensional information platform for deep phenotyping of heterogenous oligometastatic cancer","Computational and Structural Biotechnology Journal","","2001-0370","10.1016/j.csbj.2024.08.015","https://www.sciencedirect.com/science/article/pii/S2001037024002733","Patients with oligometastatic cancer (OMC) exhibit better response to local therapeutic interventions and a more treatable tendency than those with polymetastatic cancers. However, studies on OMC are limited and lack effective integration for systematic comparison and personalized application, and the diagnosis and precise treatment of OMC remain controversial. The application of large language models in medicine remains challenging because of the requirement of high-quality medical data. Moreover, these models must be enhanced using precise domain-specific knowledge. Therefore, we developed the OligoM-Cancer platform (http://oligo.sysbio.org.cn), pioneering knowledge curation that depicts various aspects of oligometastases spectrum, including markers, diagnosis, prognosis, and therapy choices. A user-friendly website was developed using HTML, FLASK, MySQL, Bootstrap, Echarts, and JavaScript. This platform encompasses comprehensive knowledge and evidence of phenotypes and their associated factors. With 4059 items of literature retrieved, OligoM-Cancer includes 1345 valid publications and 393 OMC-associated factors. Additionally, the included clinical assistance tools enhance the interpretability and credibility of clinical translational practice. OligoM-Cancer facilitates knowledge-guided modeling for deep phenotyping of OMC and potentially assists large language models in supporting specialised oligometastasis applications, thereby enhancing their generalization and reliability.","2024-12-01","2024-12-03 03:24:12","2024-12-03 03:24:12","","561-570","","","24","","Computational and Structural Biotechnology Journal","","","","","","","","","","","","","","","","","","","Disease heterogeneity; Knowledge platform; Metastasis; Oligometastatic cancer; Personalized treatment","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XWQBLCVH","journalArticle","2024","Cooperman, Steven R.; Brandão, Roberto A.","Investigating the proficiency of an AI tool in summarizing foot and ankle literature: A quantitative, qualitative and accuracy analysis","Foot & Ankle Surgery: Techniques, Reports & Cases","","2667-3967","10.1016/j.fastrc.2024.100384","https://www.sciencedirect.com/science/article/pii/S2667396724000247","Artificial Intelligence (AI) is rapidly transforming various sectors of industry, including the medical community, due to its ability to simulate human intelligence processes. This study evaluates the capacity of an AI tool, ChatGPT 3.5, to summarize scientific papers in the foot and ankle surgery literature, comparing its performance to summaries written by podiatric surgery residents. Quantitative and Qualitative analyses were performed, including BLEU and ROUGE metrics, Flesch Reading Ease Score (FRES), Flesch-Kincaide Grade Level (FKGL) readability statistics, qualitative analysis by independent reviewers, and independent accuracy assessments. Results indicate that AI-generated summaries closely resemble those produced by podiatric surgery residents in terms of content, readability, and accuracy. Although differing slightly from their human-generated counterparts with a higher level of writing, AI-generated summaries offer consistency and conciseness. Overall, this study demonstrates the potential of AI tools to streamline research processes while emphasizing the importance of judicious use and oversight to maintain scholarly integrity.","2024-06-01","2024-12-03 03:24:12","2024-12-03 03:24:12","","100384","","2","4","","Foot & Ankle Surgery: Techniques, Reports & Cases","","","","","","","","","","","","","","","","","","","Research; Artificial intelligence; ChatGPT; Scientific writing; Foot and ankle","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KBRLK4B3","journalArticle","2024","Zeng, Meng; Wang, XianQi; Chen, Wei","Worldwide research landscape of artificial intelligence in lung disease: A scientometric study","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e31129","https://www.sciencedirect.com/science/article/pii/S2405844024071603","Purpose To perform a comprehensive bibliometric analysis of the application of artificial intelligence (AI) in lung disease to understand the current status and emerging trends of this field. Materials and methods AI-based lung disease research publications were selected from the Web of Science Core Collection. Citespace, VOS viewer and Excel were used to analyze and visualize co-authorship, co-citation, and co-occurrence analysis of authors, keywords, countries/regions, references and institutions in this field. Results Our study included a total of 5210 papers. The number of publications on AI in lung disease showed explosive growth since 2017. China and the United States lead in publication numbers. The most productive author were Li, Weimin and Qian Wei, with Shanghai Jiaotong University as the most productive institution. Radiology was the most co-cited journal. Lung cancer and COVID-19 emerged as the most studied diseases. Deep learning, convolutional neural network, lung cancer, radiomics will be the focus of future research. Conclusions AI-based diagnosis and treatment of lung disease has become a research hotspot in recent years, yielding significant results. Future work should focus on establishing multimodal AI models that incorporate clinical, imaging and laboratory information. Enhanced visualization of deep learning, AI-driven differential diagnosis model for lung disease and the creation of international large-scale lung disease databases should also be considered.","2024-05-30","2024-12-03 03:24:12","2024-12-03 03:24:12","","e31129","","10","10","","Heliyon","","","","","","","","","","","","","","","","","","","Bibliometric analysis; Artificial intelligence (AI); Citespace; Lung disease; VOSviewer","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"GTNS5II6","journalArticle","2024","Pitakaso, Rapeepan; Srichok, Thanatkij; Khonjun, Surajet; Golinska-Dawson, Paulina; Sethanan, Kanchana; Nanthasamroeng, Natthapong; Gonwirat, Sarayut; Luesak, Peerawat; Boonmee, Chawis","Optimization-driven artificial intelligence-enhanced municipal waste classification system for disaster waste management","Engineering Applications of Artificial Intelligence","","0952-1976","10.1016/j.engappai.2024.108614","https://www.sciencedirect.com/science/article/pii/S0952197624007723","This research addresses the critical challenge of disaster waste management, a growing concern exacerbated by the increasing frequency and intensity of natural disasters like flooding. Traditional waste systems often struggle with the volume and heterogeneity of disaster waste, highlighting the need for innovative solutions. In this study, we present a novel disaster waste classification model integrating advanced artificial intelligence (AI) and optimization techniques to streamline waste categorization in post-disaster environments. Our approach leverages a dual ensemble deep learning framework. The first ensemble combines various image-segmentation methods, while the second integrates outputs from diverse convolutional neural network architectures. A modified artificial multiple intelligence system serves as a decision fusion strategy, enhancing accuracy at both ensemble points. We rigorously evaluated our model using three datasets: the “TrashNet” dataset for benchmarking against existing methods, as well as two meticulously curated, real-world datasets collected from flood-affected areas in Thailand. The results demonstrate that our method outperforms existing algorithms like VGG19, YoloV5, and InceptionV3 in general solid waste classification, achieving an average improvement of 11.18%. Regarding disaster waste specifically, our model achieves 96.48% and 96.49% accuracy on the curated datasets, consistently outperforming ResNet-101, DenseNet-121, and InceptionV3 by an average of 3.47%. These findings demonstrate the potential of our AI-enhanced model to revolutionize disaster waste management practices. Thus, we advocate integrating such technologies into municipal waste management policies to enhance resilience and optimize disaster responses. Future research will explore scaling the model to diverse disaster types and incorporating real-time data for adaptable waste management strategies.","2024-07-01","2024-12-03 03:24:12","2024-12-03 03:24:12","","108614","","","133","","Engineering Applications of Artificial Intelligence","","","","","","","","","","","","","","","","","","","Environmental sustainability; Artificial intelligence-enhanced system; Disaster waste classification; Municipal waste management; Optimization-driven","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"BDMPG7CC","journalArticle","2024","Chen, Yu'e","Collaborative Filtering and Recommendation Algorithm for Artificial Intelligence Live Streaming E-Commerce Platforms Based on Big Data","The 11th International Conference on Applications and Techniques in Cyber Intelligence","","1877-0509","10.1016/j.procs.2024.10.100","https://www.sciencedirect.com/science/article/pii/S1877050924029004","With the development of the Internet and mobile Internet, live streaming e-commerce has become an emerging e-commerce force. However, traditional recommendation algorithms have shortcomings in terms of accuracy and personalization of recommendation results, and more intelligent and personalized recommendation algorithms need to be applied. This article aimed to achieve personalized product recommendations and enhance the shopping experience of users by analyzing their historical behavioral data, real-time interests and needs, combined with big data and artificial intelligence technology. The collaborative filtering recommendation algorithm based on live streaming had an average recommendation accuracy of over 80% for user groups 1, 2, and 3. The research results of this article had important practical significance for promoting the healthy development of live streaming e-commerce platforms, improving user experience, and enhancing platform competitiveness.","2024-01-01","2024-12-03 03:24:12","2024-12-03 03:24:12","","826-833","","","247","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Big data; artificial intelligence; collaborative filtering recommendation algorithm; electronic commerce platform","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DJJ5XDAU","journalArticle","2024","Geske, Alexander M.; Herold, David M.; Kummer, Sebastian","Artificial intelligence as a driver of efficiency in air passenger transport: A systematic literature review and future research avenues","Journal of the Air Transport Research Society","","2941-198X","10.1016/j.jatrs.2024.100030","https://www.sciencedirect.com/science/article/pii/S2941198X24000411","Despite the claim that artificial intelligence (AI) has the potential to increase efficiency in and for airlines, current literature is limited concerning models and frameworks to assess AI applications and their implications for airline efficiency. In response, we a) conceptualize and propose an AI-Airline-Efficiency-Model (AAEM) that allows for a more structured management approach for a systematic review and analysis of existing literature, and b) present a framework explicating the identified areas of AI application for airline efficiency based on a the AAEM model. In particular, using the four AI elements Machine Learning, Deep Learning, Reinforcement Learning and Natural Language Processing and their applications within six identified airline departments, we systematically review and analyze key attributes and characteristics of both AI and airline efficiencies to critically assess current research efforts. We found that AI applications are built around four overarching improvement areas predictive analytics, resource optimization, safety & autonomous processes and passenger experience, but lack a cross-department and inter-organizational focus and are often theoretical in nature. This study provides insight into most prevalent AI applications and the less popular applications applied in and for passenger transport, thereby presenting the dominating AI techniques that are covered by existing literature as well as highlighting a wide range of emerging AI techniques with the potential to become more influential for future studies. We discuss theoretical and managerial implications and offer avenues for future research.","2024-12-01","2024-12-03 03:24:12","2024-12-03 03:24:12","","100030","","","3","","Journal of the Air Transport Research Society","","","","","","","","","","","","","","","","","","","Deep learning; Machine learning; Literature review; Artificial intelligence; Airline; Aviation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"96T7KFF9","journalArticle","2023","Zhang, Tingting; Lu, Xiangpeng; Zhu, Xu; Zhang, Jing","The contributions of AI in the development of ideological and political perspectives in education","Heliyon","","2405-8440","10.1016/j.heliyon.2023.e13403","https://www.sciencedirect.com/science/article/pii/S2405844023006102","Opportunities and difficulties have arisen for the educational system as a result of the development of “artificial intelligence” technology. In the era of “artificial intelligence”, the main features of ideological and political education in colleges are the development of the intelligence revolution, the development of teaching concepts, and the omnipotence of teaching content, and teaching methods. This study further explores the necessity and development of “artificial intelligence technology” in college ideological and political education through a questionnaire survey, and promotes the organic integration of artificial intelligence and ideological and political education. The results show that [1] College students have positive attitudes toward the application of artificial intelligence in college ideological and political education, and college students are looking forward to the intelligent services and changes brought by artificial intelligence technology in college ideological and political education [2]. According to the results of the questionnaire survey, this paper proposes the development path of college ideological and political education in the era of artificial intelligence, that is, schools and teachers need to improve the transformation of traditional education as well as the construction of contemporary Internet education. This study offers the possibility of interdisciplinary research, expands the scope of ideological and political education research, and provides a reference for front-line teaching to a certain extent.","2023-03-01","2024-12-03 03:24:12","2024-12-03 03:24:12","","e13403","","3","9","","Heliyon","","","","","","","","","","","","","","","","","","","Big data; Artificial intelligence; Colleges; Ideological and political education; Precision education","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"8T8L5WA3","journalArticle","2024","Alqaidi, Sara Hemdi; Albugami, Shahad Mohammed; Alzahrani, Waad Saeed; Badri, Sahar; Wali, Arwa","Network-integrated medical chatbot for enhanced healthcare services","Telematics and Informatics Reports","","2772-5030","10.1016/j.teler.2024.100153","https://www.sciencedirect.com/science/article/pii/S2772503024000392","Technology has a significant role in improving medical care, such as enhancing communication between patients and healthcare virtual assistants. By including artificial intelligence and natural language processing (NLP), the main aim of this research paper is to design a medical chatbot framework, and application design prototype, and use the Dialog Flow tool to build an auto-response system known as a chatbot. The goal is to provide medical services and meet patients' needs. It will communicate with the user in the language they follow. With the help of keywords such as symptoms entered by the patients, it will help to identify the disease. Moreover, the application provides the patients with the ability to record their medicine to remind them to take it. Additionally, it will make it easier for patients to learn more about their disease and motivate them to take the necessary precautions to stay healthy.","2024-09-01","2024-12-03 03:24:12","2024-12-03 03:24:12","","100153","","","15","","Telematics and Informatics Reports","","","","","","","","","","","","","","","","","","","Artificial intelligence; Chatbot; Dialog flow; Medical care","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"F43PSHYU","journalArticle","2024","Alarcón, Ángel Serrano; Gaiduk, Maksym; Madrid, Natividad Martínez; Seepold, Ralf; Ortega, Juan Antonio","Deployment of Artificial Intelligence Models for Sleep Apnea Recognition in the Sleep Laboratory","28th International Conference on Knowledge Based and Intelligent information and Engineering Systems (KES 2024)","","1877-0509","10.1016/j.procs.2024.09.665","https://www.sciencedirect.com/science/article/pii/S1877050924027224","There are a large number of scientific publications that focus on the development and evaluation of artificial intelligence (AI) models for the detection of various pathologies in the field of sleep medicine. However, most of these publications do not show the process or methodology to be followed for the final deployment of these models in a complete diagnostic system (in terms of software and hardware). This is a major drawback when translating from the development or research environment to the real clinical setting. This work focuses on a methodology for deploying an AI model for sleep apnea detection with the end user in mind: the clinician. For the deployment, the transmission of data between the device, the cloud platform and the machine learning server, as well as the protocols used, were considered. In addition, the storage and visualization of the data has been taken into account so that it can be analyzed accurately by experts.","2024-01-01","2024-12-03 03:24:13","2024-12-03 03:24:13","","5388-5395","","","246","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial Intelligence; Deep Learning; Sleep Disorder; Sleep/Wake state","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"8YS3HC2E","journalArticle","2024","Yang, Ping; Qiao, Jinyi; Chen, Minxiu","Bit rate selection technology of image processing based on artificial intelligence in MPEG-DASH adaptive streaming media","Journal of Radiation Research and Applied Sciences","","1687-8507","10.1016/j.jrras.2024.101036","https://www.sciencedirect.com/science/article/pii/S1687850724002206","Aiming at the bit rate selection problem of MPEG-DASH adaptive streaming media in image processing, a hybrid method combining multiple artificial intelligence algorithms is proposed. Firstly, kernel principal component analysis, Grey Wolf optimization algorithm and least squares support vector machine are integrated to construct an efficient hybrid algorithm model. This model aims to optimize the image processing effect in streaming media transmission, especially in the dynamic network environment. The experimental results show that the accuracy of the hybrid algorithm reaches 0.945 in the training process, and the absolute error is only 0.0005, which is significantly better than other comparison algorithms. Further empirical analysis shows that the accuracy of the proposed rate selection technique in image processing is as high as 92.3%, which is far higher than the existing technique. This research not only improves the image quality of streaming media transmission, but also greatly improves the user experience. The research provides a new perspective for image processing technology in the field of digital media, and is of great significance for promoting the innovation and development of streaming media technology.","2024-08-10","2024-12-03 03:24:24","2024-12-03 03:24:24","","101036","","","","","Journal of Radiation Research and Applied Sciences","","","","","","","","","","","","","","","","","","","AI; Bit rate selection technique; MPEG-DASH; PCA-GWO-BP; Streaming media product","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"MIJY9IDY","journalArticle","2024","Hörbe, Roman; Erol, Selim","Artificial Intelligence in planning and control tasks: a study of potential use cases and perceived challenges in Austrian make-to-order companies","57th CIRP Conference on Manufacturing Systems 2024 (CMS 2024)","","2212-8271","10.1016/j.procir.2024.10.081","https://www.sciencedirect.com/science/article/pii/S2212827124012344","An increasing number of manufacturing companies in Austria are operating in market niches. These companies are characterized by a high degree of specialization, particularly in manufacturing technologies, as well as by a high degree of flexibility. Production of these kinds are often focused on single-piece-production or the production of small batches (“make-to-order”). Flexibility enables companies to address individual customer requirements and therefore gain an advantage over companies in high-volume manufacturing. Hence, flexibility, high-mix and low-volume manufacturing usually comes with high costs as planning and control of manufacturing processes becomes more complex, leading to less utilized machines and personnel, long lead and setup times and high material costs. This effect is reinforced by the general lack of skilled workers in Europe. Small and medium enterprises (SMEs) are especially challenged by these circumstances, although these challenges are not exclusively affecting SMEs. Methods from the field of artificial intelligence have the potential to support complex planning and control tasks. In order to find use-cases that are relevant for SMEs in Austria, this study selected several SMEs that are focused on make-to-order production to explore the specific challenges that companies operating in that field are facing. Based on the findings of the study, continuing research on application of artificial intelligence in production planning and control can be directed more targeted at the challenges faced by SMEs with make-to-order production.","2024-01-01","2024-12-03 03:24:25","2024-12-03 03:24:25","","232-237","","","130","","Procedia CIRP","","","","","","","","","","","","","","","","","","","Artificial Intelligence; make to order; Production Control; Production Planning","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"37S2T8Y6","journalArticle","2024","Liu, Chengyu; Muravskyi, Volodymyr; Wei, Wenjun","Evolution of blockchain accounting literature from the perspective of CiteSpace (2013–2023)","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e32097","https://www.sciencedirect.com/science/article/pii/S2405844024081283","Against the backdrop of the Industrial Revolution 4.0, the advantages of blockchain technology in traceability, transparency, safety improvement, and efficiency improvement have made it possible to reduce the work of accounting personnel by 50 %, thus saving billions of dollars for global companies by combining this technology with accounting. However, the blockchain technology associated with accounting is in the experimental stage and has several problems to be solved including limited data processing capacity, information confidentiality, and regulatory difficulties. This innovation and progress in science and technology has provided more abundant, efficient, and professional technical support for the research of blockchain accounting documents. Among these advances, CiteSpace software has promoted the development of blockchain and accounting in the direction of visualization, comprehensiveness, security, and relevance. In this study, we used the knowledge map drawn by CiteSpace to search the core Blockchain Accounting database from 2013 to 2023 on the Web of Science (WoS). We obtained 1414 documents measured according to co-citation analysis, log-likelihood ratio (LLR) network clustering, co-occurrence keywords, and emergent time zone diagram method. We analyzed and summarized the important documents, research keywords, key research fields, and knowledge evolution related to ""blockchain accounting"" by network, literature integration, and popular research topics. We found that adopting blockchain technology in accounting information systems is expected to improve recordkeeping and reporting. Blockchain, as an innovative technology, provides a tamper-proof, traceable, and shareable platform for accounting information by using a distributed ledger system. By implementing blockchain, artificial intelligence can improve safety, transparency, and accuracy, and also may completely change the way we manage financial records. With its ability to improve overall efficiency and reduce errors, blockchain technology may change our familiar accounting methods. In addition, blockchain technology, intelligent contract, artificial intelligence, the Internet, information systems, and supply chain are the most important keywords, while blockchain technology, intelligent contract, and artificial intelligence are important components of blockchain accounting knowledge system. This research provided an important opportunity to advance the understanding of the crucial contribution of blockchain to the accounting field.","2024-06-15","2024-12-03 03:24:25","2024-12-03 03:24:25","","e32097","","11","10","","Heliyon","","","","","","","","","","","","","","","","","","","Literature review; Blockchain accounting; CiteSpace; Visualized analysis; WoS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KP7HVGVJ","journalArticle","2024","Bruno, Katelyn A.; O'Dell, Walter G.; Tantawy, Marwa; Casson, Camara L.; Ferrall-Fairbanks, Meghan C.; DeRemer, David L.; Dungan, Jennifer R.; Nguyen, Branden L.; Roumi, Nathalie H.; Shabnaz, Samia; Smuder, Ashley J.; Vilaro, Melissa J.; Norton, Nadine; Fairweather, DeLisa; Gong, Yan","Research summary of poster presentations at the 2023 Florida cardio-oncology symposium","American Heart Journal Plus: Cardiology Research and Practice","","2666-6022","10.1016/j.ahjo.2023.100348","https://www.sciencedirect.com/science/article/pii/S2666602223001015","","2024-01-01","2024-12-03 03:24:25","2024-12-03 03:24:25","","100348","","","37","","American Heart Journal Plus: Cardiology Research and Practice","","","","","","","","","","","","","","","","","","","Artificial intelligence; Cardio-oncology; Cutting edge research; Disparities; Symposium","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"BP7F5FPN","journalArticle","2023","Liang, Dan","Artificial intelligence video production platform based on user experience perspective","3rd International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy","","1877-0509","10.1016/j.procs.2023.11.014","https://www.sciencedirect.com/science/article/pii/S1877050923018379","From the perspective of user experience, the artificial intelligence video production platform has received widespread attention. The platform can provide video producers with many intelligent services, improving video production efficiency and quality. In this study, the basic concepts of the artificial intelligence video production platform were analyzed, and then from the perspective of user experience, the implementation method of the platform was proposed. Finally, the platform was tested. Through research, an artificial intelligence video production platform has been implemented, and the videos produced by the platform basically match the user's needs, so it can bring a good experience to the user.","2023-01-01","2024-12-03 03:24:25","2024-12-03 03:24:25","","112-118","","","228","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","artificial intelligence; User experience; Video production platform","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"WKTGHKDW","journalArticle","2023","Yang, Hong; Meng, Xiaokai; Zhang, Guangwei; Wang, Dawei; Liu, Xiaofei","Network information data processing method based on artificial intelligence","3rd International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy","","1877-0509","10.1016/j.procs.2023.11.065","https://www.sciencedirect.com/science/article/pii/S1877050923018896","Because of the complexity and development of network information data processing, many conventional technical means can not process information data, and the artificial itself is powerless, so it needs to use artificial intelligence technology for processing. In view of this, this paper will introduce the basic concept of artificial intelligence technology and its advantages in information data processing, and then combine the technology to put forward the network information data processing method. Through the research, the method in this paper can give full play to the role of artificial intelligence technology, fast and in-depth processing of network information data, and can give artificial decision support.","2023-01-01","2024-12-03 03:24:25","2024-12-03 03:24:25","","568-573","","","228","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial intelligence technology; Manual decision support; Network information data processing","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"E6FTD6BA","journalArticle","2024","Abdullah, Abdulwahid Ahmad Hashed; Almaqtari, Faozi A.","The impact of artificial intelligence and Industry 4.0 on transforming accounting and auditing practices","Journal of Open Innovation: Technology, Market, and Complexity","","2199-8531","10.1016/j.joitmc.2024.100218","https://www.sciencedirect.com/science/article/pii/S219985312400012X","The main aim is to investigate the impact of artificial intelligence (AI), Industry 4.0 readiness, and Technology Acceptance Model (TAM) variables on various aspects of accounting and auditing operations. To evaluate the associations between the variables, the research design employs a mediation and path approach using SMART PLS. The study employs a convenience sampling method, which is augmented with snowball sampling. The sample size was determined using various techniques, yielding a final sample of 228 respondents. The findings indicate that leveraging AI, big data analytics, cloud computing, and deep learning advancements can improve accounting and auditing practices. AI technologies assist businesses in increasing their efficiency, accuracy, and decision-making capabilities, resulting in improved financial reporting and auditing processes. The study contributes to the theoretical explanation of the influence of AI adoption in accounting and auditing practices in the context of an emerging country, Saudi Arabia. The findings of the study have practical implications for accounting and auditing practitioners, policymakers, and scholars. The findings of this study can assist businesses in efficiently leveraging AI developments to improve their accounting and auditing operations. Policymakers can use the findings to create supporting frameworks and regulations that encourage the adoption and integration of artificial intelligence in the domain. These findings contribute to the existing stock of knowledge on the use of AI in accounting and auditing, as well as providing evidence of its benefits in the context of an emerging country.","2024-03-01","2024-12-03 03:24:25","2024-12-03 03:24:25","","100218","","1","10","","Journal of Open Innovation: Technology, Market, and Complexity","","","","","","","","","","","","","","","","","","","Artificial Intelligence; Technology acceptance model; Accounting education; Accounting practices; Auditing practices; Industry 4.0 readiness","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"D4RHHCBB","journalArticle","2024","Xin, Xing; Wu, Shanshan; Xu, Heli; Ma, Yujiu; Bao, Nan; Gao, Man; Han, Xue; Gao, Shan; Zhang, Siwen; Zhao, Xinyang; Qi, Jiarui; Zhang, Xudong; Tan, Jichun","Non-invasive prediction of human embryonic ploidy using artificial intelligence: a systematic review and meta-analysis","eClinicalMedicine","","2589-5370","10.1016/j.eclinm.2024.102897","https://www.sciencedirect.com/science/article/pii/S2589537024004760","Summary Background Embryonic ploidy is critical for the success of embryo transfer. Currently, preimplantation genetic testing for aneuploidy (PGT-A) is the gold standard for detecting ploidy abnormalities. However, PGT-A has several inherent limitations, including invasive biopsy, high economic burden, and ethical constraints. This paper provides the first comprehensive systematic review and meta-analysis of the performance of artificial intelligence (AI) algorithms using embryonic images for non-invasive prediction of embryonic ploidy. Methods Comprehensive searches of studies that developed or utilized AI algorithms to predict embryonic ploidy from embryonic imaging, published up until August 10, 2024, across PubMed, MEDLINE, Embase, IEEE, SCOPUS, Web of Science, and the Cochrane Central Register of Controlled Trials were performed. Studies with prospective or retrospective designs were included without language restrictions. The summary receiver operating characteristic curve, along with pooled sensitivity and specificity, was estimated using a bivariate random-effects model. The risk of bias and study quality were evaluated using the QUADAS-AI tool. Heterogeneity was quantified using the inconsistency index (I2), derived from Cochran's Q test. Predefined subgroup analyses and bivariate meta-regression were conducted to explore potential sources of heterogeneity. This study was registered with PROSPERO (CRD42024500409). Findings Twenty eligible studies were identified, with twelve studies included in the meta-analysis. The pooled sensitivity, specificity, and area under the curve of AI for predicting embryonic euploidy were 0.71 (95% CI: 0.59–0.81), 0.75 (95% CI: 0.69–0.80), and 0.80 (95% CI: 0.76–0.83), respectively, based on a total of 6879 embryos (3110 euploid and 3769 aneuploid). Meta-regression and subgroup analyses identified the type of AI-driven decision support system, external validation, risk of bias, and year of publication as the primary contributors to the observed heterogeneity. There was no evidence of publication bias. Interpretation Our findings indicate that AI algorithms exhibit promising performance in predicting embryonic euploidy based on embryonic imaging. Although the current AI models developed cannot entirely replace invasive methods for determining embryo ploidy, AI demonstrates promise as an auxiliary decision-making tool for embryo selection, particularly for individuals who are unable to undergo PGT-A. To enhance the quality of future research, it is essential to overcome the specific challenges and limitations associated with AI studies in reproductive medicine. Funding This work was supported by the National Key R&D Program of China (2022YFC2702905), the Shengjing Freelance Researcher Plan of Shengjing Hospital and the 345 talent project of Shengjing Hospital.","2024-11-01","2024-12-03 03:24:25","2024-12-03 03:24:25","","102897","","","77","","eClinicalMedicine","","","","","","","","","","","","","","","","","","","Deep learning; Machine learning; Artificial intelligence; Embryonic ploidy","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"EECHZY4R","journalArticle","2024","Jutagate, Achara; Pitakaso, Rapeepan; Khonjun, Surajet; Srichok, Thanatkij; Kaewta, Chutchai; Luesak, Peerawat; Gonwirat, Sarayut; Enkvetchakul, Prem; Jutagate, Tuantong","Adaptive artificial multiple intelligence fusion system (A-AMIFS) for enhanced disease detection in Nile Tilapia","Aquaculture Reports","","2352-5134","10.1016/j.aqrep.2024.102418","https://www.sciencedirect.com/science/article/pii/S2352513424005064","The development of an efficient disease detection system for Nile Tilapia is critical due to the significant economic and food security impacts of disease outbreaks. Traditional disease identification methods are labor-intensive, inefficient, and often fail to detect early signs of disease, leading to substantial economic losses. This study introduces the Adaptive Artificial Multiple Intelligence Fusion System (A-AMIFS), an advanced model that innovatively combines image augmentation, ensemble image segmentation methods, and ensemble Convolutional Neural Network (CNN) architectures. The system utilizes a non-population-based artificial multiple intelligence system (np-AMIS) for optimizing image augmentation and a population-based system (Pop-AMIS) for decision fusion, demonstrating superior performance. Evaluated on two novel datasets, Nile Tilapia Disease-1 (NTD-1) and Nile Tilapia Disease-2 (NTD-2), the system achieved an overall accuracy of 98.26 %, precision of 98.35 %, recall of 98.30 %, and an F1-score of 98.32 %, significantly outperforming existing methodologies. Additionally, a ""chatbot"" feature was developed to enable farmers to automatically detect fish diseases using the ensemble model as the backend classification system, achieving an impressive classification accuracy of over 98 %. These results underscore the system's robustness in detecting various diseases in Nile Tilapia and its potential to transform disease detection in aquaculture. The proposed system reduces manual labor, optimizes disease identification processes, and enhances disease management strategies, promoting more sustainable and productive aquaculture practices. This research highlights the indispensable role of AI techniques in overcoming the complex challenges of disease detection and management in aquaculture, presenting efficient and effective disease management practices. By leveraging advanced image augmentation, ensemble segmentation methods, and ensemble CNN architectures, this study presents a revolutionary approach to disease detection in Nile Tilapia. The integration of a user-friendly chatbot interface further enhances the technology's accessibility and practical application, empowering farmers to proactively manage disease outbreaks and mitigate economic losses.","2024-12-01","2024-12-03 03:24:25","2024-12-03 03:24:25","","102418","","","39","","Aquaculture Reports","","","","","","","","","","","","","","","","","","","Interpretability; Adaptive artificial multiple intelligence fusion system; Disease detection; Ensemble AI techniques; Nile tilapia aquaculture","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"WGVHFUMJ","journalArticle","2024","Mahdi, Nibras M.; Jassim, Ahmed Hikmet; Abulqasim, Shahlla Abbas; Basem, Ali; Ogaili, Ahmed Ali Farhan; Al-Haddad, Luttfi A.","Leak detection and localization in water distribution systems using advanced feature analysis and an Artificial Neural Network","Desalination and Water Treatment","","1944-3986","10.1016/j.dwt.2024.100685","https://www.sciencedirect.com/science/article/pii/S1944398624107059","This study capitalizes on a dataset, originally including 280 sensory measurements from a laboratory-scale water distribution system, to advance the concept of leakage diagnosis and localization. The water distribution test rig are formulated in two configurations, namely looped and branched layouts. The paper processed time-domain data from accelerometers and dynamic pressure sensors into advanced statistical features of: Autocorrelation Coefficient (Au-C), and Signal Energy (Sig-E), to detect and localize the water leakage. By the Employment of these two features, the research developed an expert system of an Artificial Neural Network (ANN) model designed with optimal parameters, neurons, and hidden layers to classify the presence and pinpoint the location of leaks within the water test rig. The effectiveness of the current approach is quantitatively evaluated using F1-scores and accuracy metrics. A robust capability for both detecting and localizing leaks under varying conditions was established with a highest accuracy and F1-score of 86.5 % and 86.2 %, respectively. The findings underscore the potential of integrating advanced features with Artificial Intelligence (AI) in enhancing the reliability and dependability of water management expert systems. This approach contributes to the broader application of AI in managing water resources and infrastructure resilience with its support to improve leakage whereabouts.","2024-10-01","2024-12-03 03:24:25","2024-12-03 03:24:25","","100685","","","320","","Desalination and Water Treatment","","","","","","","","","","","","","","","","","","","Artificial Intelligence; Artificial Neural Network; Expert System; Leak detection; Leak localization; Water Technology","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"T25CFT9F","journalArticle","2023","Backman, Juha; Koistinen, Markku; Ronkainen, Ari","Agricultural process data as a source for knowledge: Perspective on artificial intelligence","Smart Agricultural Technology","","2772-3755","10.1016/j.atech.2023.100254","https://www.sciencedirect.com/science/article/pii/S2772375523000849","In visions of the future of agriculture, it is predicted that Artificial Intelligence and Robotics will revolutionize farming. Artificial Intelligence (AI) is not always clearly visible to the end users, who are in this case farmers. AI methods are usually incorporated in existing Farm Management Information Systems or in some cases in separate Decision Support Systems; or the AI is part of the machine or robot operation. Currently, AI methods are used mostly in machine vision applications. Practical applications that use methods other than image data are scarce. However, agricultural machines produce an ever-increasing amount of process data during agricultural operations. There is huge potential to gain useful information from this data. This paper presents a case example of treatment-zone determination based on agricultural process data using clustering methods. This paper also describes how AI is incorporated into the Cropinfra research data collection platform. Based on the experiences gained from Cropinfra-related research projects and a review of research papers, we predict the future directions of Artificial Intelligence in agriculture.","2023-10-01","2024-12-03 03:24:25","2024-12-03 03:24:25","","100254","","","5","","Smart Agricultural Technology","","","","","","","","","","","","","","","","","","","Decision support systems; Farm management information systems; Future directions; Interactive machine learning; Precision farming","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"6GL4T3R6","journalArticle","2024","Singh, Anuradha","Artificial intelligence for drug repurposing against infectious diseases","Artificial Intelligence Chemistry","","2949-7477","10.1016/j.aichem.2024.100071","https://www.sciencedirect.com/science/article/pii/S2949747724000290","Traditional drug discovery struggles to keep pace with the ever-evolving threat of infectious diseases. New viruses and antibiotic-resistant bacteria, all demand rapid solutions. Artificial Intelligence (AI) offers a promising path forward through accelerated drug repurposing. AI allows researchers to analyze massive datasets, revealing hidden connections between existing drugs, disease targets, and potential treatments. This approach boasts several advantages. First, repurposing existing drugs leverages established safety data and reduces development time and costs. Second, AI can broaden the search for effective therapies by identifying unexpected connections between drugs and potential new targets. Finally, AI can help mitigate limitations by predicting and minimizing side effects, optimizing drugs for repurposing, and navigating intellectual property hurdles. The article explores specific AI strategies like virtual screening, target identification, structure base drug design and natural language processing. Real-world examples highlight the potential of AI-driven drug repurposing in discovering new treatments for infectious diseases.","2024-12-01","2024-12-03 03:24:25","2024-12-03 03:24:25","","100071","","2","2","","Artificial Intelligence Chemistry","","","","","","","","","","","","","","","","","","","Artificial intelligence (AI); Antivirals; Drug repurposing (DR); Emerging infectious diseases; Structure-based drug design; Target identification; Virtual screening","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"8PGU6N5R","journalArticle","2023","Ali Mohamad, Talal; Bastone, Anna; Bernhard, Fabian; Schiavone, Francesco","How artificial intelligence impacts the competitive position of healthcare organizations","Journal of Organizational Change Management","","0953-4814","10.1108/JOCM-03-2023-0057","https://www.sciencedirect.com/science/article/pii/S0953481423001483","Purpose Digital transformation affected modern society influencing how businesses cooperate and produce value. In this context, Artificial Intelligence plays a critical role. This study aims to explore the role of Artificial Intelligence in organisational positioning within the market, influencing firms' competitiveness. In this vein, this research seeks to respond to the following research question: How does AI impact the competitive advantage of healthcare organizations?. Design/methodology/approach To tackle the research question, an explorative analysis using the case study method to investigate an international healthcare center in Dubai was conducted. Nine semi-structured interviews were conducted with the head and the members of the robotic surgery team in CMC Dubai to thoroughly understand what the components of the robotic approach are and how the arrangement before the introduction of this innovative technique while shedding light on the added value and the advantages of adopting such technique on both patient safety and patient satisfaction. Additionally, archival data and online documentation (e.g. industry reports, newspaper articles and internal documents) were analyzed to obtain data triangulation. Findings The results highlight three primary outcomes influenced by implementing AI in organizational processes: clinical, financial and technological outcomes. The study will offer interesting non-studied insights about the implementation of Artificial Intelligence tools in the healthcare sector and specifically robotic surgeries, and to which extent this will contribute and represent a competitive advantage. Results will hopefully insert a brick in the wall of the impact of AI tools on the quality and the results of surgical operations while emphasizing the benefits of integrating AI in surgical practice. Originality/value This study offers interesting theoretical and practical implications. It opens a new perspective to understand and manage AI tools in service. This research is not without limits providing valuable insights for future research.","2023-07-31","2024-12-03 03:24:25","2024-12-03 03:24:25","","49-70","","8","36","","Journal of Organizational Change Management","","","","","","","","","","","","","","","","","","","Innovation; Artificial intelligence: Competitive advantage; Healthcare organization","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"IMG8Q75L","journalArticle","2024","Cui, Xiaohui; Song, Chao; Li, Dongmei; Qu, Xiaolong; Long, Jiao; Yang, Yu; Zhang, Hanchao","RoBGP: A Chinese Nested Biomedical Named Entity Recognition Model Based on RoBERTa and Global Pointer","Computers, Materials and Continua","","1546-2218","10.32604/cmc.2024.047321","https://www.sciencedirect.com/science/article/pii/S1546221824003187","Named Entity Recognition (NER) stands as a fundamental task within the field of biomedical text mining, aiming to extract specific types of entities such as genes, proteins, and diseases from complex biomedical texts and categorize them into predefined entity types. This process can provide basic support for the automatic construction of knowledge bases. In contrast to general texts, biomedical texts frequently contain numerous nested entities and local dependencies among these entities, presenting significant challenges to prevailing NER models. To address these issues, we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer (RoBGP). Our model initially utilizes the RoBERTa-wwm-ext-large pretrained language model to dynamically generate word-level initial vectors. It then incorporates a Bidirectional Long Short-Term Memory network for capturing bidirectional semantic information, effectively addressing the issue of long-distance dependencies. Furthermore, the Global Pointer model is employed to comprehensively recognize all nested entities in the text. We conduct extensive experiments on the Chinese medical dataset CMeEE and the results demonstrate the superior performance of RoBGP over several baseline models. This research confirms the effectiveness of RoBGP in Chinese biomedical NER, providing reliable technical support for biomedical information extraction and knowledge base construction.","2024-03-26","2024-12-03 03:24:25","2024-12-03 03:24:25","","3603-3618","","3","78","","Computers, Materials and Continua","","","","","","","","","","","","","","","","","","","named entity recognition; Biomedicine; global pointer; knowledge base; pretrained language model","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JNMVXH5K","journalArticle","2024","Schäfers, A.; Bougioukos, V.; Karamatzanis, G.; Nikolopoulos, K.","Prediction-led prescription: Optimal Decision-Making in times of turbulence and business performance improvement","Journal of Business Research","","0148-2963","10.1016/j.jbusres.2024.114805","https://www.sciencedirect.com/science/article/pii/S0148296324003096","Can you have prescription without prediction? Most scholars and practitioners would argue that a good forecast drives an optimal decision, thus promoting the concept ofprediction-led prescription. In times of turbulence, Special events like promotions and supply chain disruptions are impacting businesses severely. Nevertheless, limited research has been carried out to date to accurately forecast the impact of, and consequentially prescribe in the presence of special events. Nowadays Artificial Intelligence (AI) predictive analytics methods and heuristics imitate and even improve human intelligence, progressively leading towards innovative cognitive analytics solutions. This research aims to contribute to applying advancements in AI-based predictive analytics to improve business performance. We provide empirical evidence that these AI solutions outperform the popular (especially among practitioners) linear regression models. We corroborate the stream of literature arguing that AI predictive analytics could − via a natural path-dependent process − enhance prescriptive analytics solutions, and thus improve business performance.","2024-09-01","2024-12-03 03:24:25","2024-12-03 03:24:25","","114805","","","182","","Journal of Business Research","","","","","","","","","","","","","","","","","","","AI; Forecasting; Prediction-led Prescription; Special events; Turbulence","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"EZU4QCQQ","journalArticle","2024","Murphy, Brid; Feeney, Orla; Rosati, Pierangelo; Lynn, Theo","Exploring accounting and AI using topic modelling","International Journal of Accounting Information Systems","","1467-0895","10.1016/j.accinf.2024.100709","https://www.sciencedirect.com/science/article/pii/S1467089524000423","Historically, literature suggests that a variety of accounting roles will be replaced by Artificial Intelligence (AI) and related technologies; however, in recent years there is a growing recognition that accounting can in fact harness AI’s potential to add value to organisations. Commentators have highlighted the need for increased research exploring accounting and AI and for accounting scholars to consider multi-disciplinary research in this area. This study uses a form of topic modelling to analyse literature exploring AI and related techniques in an accounting context. Latent Dirichlet Allocation (LDA) has been used to enable probabilistic, machine-based interrogation of large volumes of literature. This study applies LDA to the abstracts of 930 peer-reviewed academic publications from a variety of disciplines to identify the most significant accounting and AI topics discussed in the literature during the period 1990 to 2023. Our findings suggest that prior literature reviews based on more traditional methodologies do not capture a comprehensive picture of accounting and AI research. Eleven topic clusters are identified which provide a comprehensive topology of the extant literature discussing accounting and AI and set out an agenda for future research designed to foster academic progress in the area. It also represents one of the first applications of probabilistic topic modelling to accounting literature.","2024-12-01","2024-12-03 03:24:25","2024-12-03 03:24:25","","100709","","","55","","International Journal of Accounting Information Systems","","","","","","","","","","","","","","","","","","","Artificial intelligence; Accountant; Accounting; Latent Dirichlet Allocation; Topic modelling","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CDISJSWV","journalArticle","2023","Benvenuti, Martina; Cangelosi, Angelo; Weinberger, Armin; Mazzoni, Elvis; Benassi, Mariagrazia; Barbaresi, Mattia; Orsoni, Matteo","Artificial intelligence and human behavioral development: A perspective on new skills and competences acquisition for the educational context","Computers in Human Behavior","","0747-5632","10.1016/j.chb.2023.107903","https://www.sciencedirect.com/science/article/pii/S0747563223002546","Despite the significant emphasis placed on incorporating 21st century skills into the educational framework, particularly at the primary level, recent scholarly works indicate considerable variation in the implementation of these skills across different countries and regions, suggesting a demand for further research specifically focusing on primary education. The indications of the Digicomp framework and 21st-century skills in Europe have outlined the key competences for lifelong learning needed for all citizens, including teachers and students. In this perspective, Education plays a fundamental role in ensuring that citizens acquire the required skills. The objective in the common European framework is clear: to initiate a transition from the culture of knowledge to the culture of competence. Nowadays, technological advancement allows the researchers to create and combine different frameworks with the perspective of an even more tailored, and engaged education, some examples derived from the implementation of Virtual Reality (VR) and Augmented Reality (AR), in the combination of Gamification and AI, or the development of Intelligent Tutoring Systems (ITS) to foster and create an even more personalized learning and teaching. Following these premises, in this paper, we want to point out new research reflections and perspectives that could help researchers, teachers, educators (and consequently students) to reflect on the introduction of new technologies (e.g., artificial intelligence, robot tutors) and on how these can affect on human behavioral development and on the acquisition of new skills and competences (Specifically: Creativity, Critical Thinking, Problem Solving, and Computational Thinking) for the educational context. The analysis carried on, suggests a perspective on how creativity, critical thinking, and problem-solving can be effective in promoting computational thinking, and how Artificial Intelligence (AI) could be an aid instrument to teachers in the fostering of creativity, critical thinking, and problem-solving in schools and educational contexts.","2023-11-01","2024-12-03 03:24:25","2024-12-03 03:24:25","","107903","","","148","","Computers in Human Behavior","","","","","","","","","","","","","","","","","","","Education; Competencies; Artificial intelligence; Robot tutors","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SRTS9J4T","journalArticle","2024","Timtong, Anantaya; Ariyarit, Atthaphon; Boongsood, Wanwanut; Aengchuan, Prasert; Wiangkham, Attasit","AI-driven data fusion modeling for enhanced prediction of mixed-mode I/III fracture toughness","Results in Engineering","","2590-1230","10.1016/j.rineng.2024.103289","https://www.sciencedirect.com/science/article/pii/S2590123024015433","This research evaluates the use of artificial intelligence to enhance the accuracy of predictions for mixed-mode I/III fracture toughness in polymethyl methacrylate . Traditionally, assessing fracture toughness relies heavily on destructive testing methods, specifically using edge-notch disc bend specimens subjected to three-point bending tests. These established methods are not only expensive and time-consuming but also frequently limited by the availability of data. To address these challenges, this study introduces a data fusion source modeling approach. This approach integrates primary fracture toughness test data with secondary predictive data derived from the maximum tangential stress criterion and the local strain energy density criterion. By employing adaptive boosting and general regression neural network algorithms, the models developed in this research demonstrate a marked improvement in predictive performance compared to traditional primary source models. Additionally, a feature importance analysis using Shapley Additive exPlanations values reveals that the mode mixity parameter, specimen thickness, and radius are critical factors influencing fracture toughness. The study highlights that while mode mixity emerges as the most significant factor, a reduction in specimen thickness generally leads to decreased fracture toughness, whereas an increase in radius has a more complex, often negative, effect. The results of this study indicate that AI-powered models using data fusion can overcome limitations related to data scarcity, enabling more accurate predictions in fracture mechanics. Furthermore, this approach provides a pathway for utilizing AI in other engineering domains where data sets are limited.","2024-12-01","2024-12-03 03:24:25","2024-12-03 03:24:25","","103289","","","24","","Results in Engineering","","","","","","","","","","","","","","","","","","","Artificial intelligence; Data fusion source model; Fracture toughness; Mixed-mode I/III","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DZGIUZ8T","journalArticle","2023","Buonocore, Tommaso Mario; Crema, Claudio; Redolfi, Alberto; Bellazzi, Riccardo; Parimbelli, Enea","Localizing in-domain adaptation of transformer-based biomedical language models","Journal of Biomedical Informatics","","1532-0464","10.1016/j.jbi.2023.104431","https://www.sciencedirect.com/science/article/pii/S1532046423001521","In the era of digital healthcare, the huge volumes of textual information generated every day in hospitals constitute an essential but underused asset that could be exploited with task-specific, fine-tuned biomedical language representation models, improving patient care and management. For such specialized domains, previous research has shown that fine-tuning models stemming from broad-coverage checkpoints can largely benefit additional training rounds over large-scale in-domain resources. However, these resources are often unreachable for less-resourced languages like Italian, preventing local medical institutions to employ in-domain adaptation. In order to reduce this gap, our work investigates two accessible approaches to derive biomedical language models in languages other than English, taking Italian as a concrete use-case: one based on neural machine translation of English resources, favoring quantity over quality; the other based on a high-grade, narrow-scoped corpus natively written in Italian, thus preferring quality over quantity. Our study shows that data quantity is a harder constraint than data quality for biomedical adaptation, but the concatenation of high-quality data can improve model performance even when dealing with relatively size-limited corpora. The models published from our investigations have the potential to unlock important research opportunities for Italian hospitals and academia. Finally, the set of lessons learned from the study constitutes valuable insights towards a solution to build biomedical language models that are generalizable to other less-resourced languages and different domain settings.","2023-08-01","2024-12-03 03:24:26","2024-12-03 03:24:26","","104431","","","144","","Journal of Biomedical Informatics","","","","","","","","","","","","","","","","","","","Deep learning; Language model; Natural language processing; Biomedical text mining; Transformer","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ZACWZBJD","journalArticle","2024","Imjai, Narinthon; Nui-Suk, Chawapong; Usman, Berto; Somwethee, Phiphop; Aujirapongpan, Somnuk","The influence of AI competency and design thinking skills on innovative entrepreneurial competency: The role of strategic intelligence amongst new age entrepreneurs in Thailand","International Journal of Information Management Data Insights","","2667-0968","10.1016/j.jjimei.2024.100301","https://www.sciencedirect.com/science/article/pii/S2667096824000909","This study investigates the impact of Artificial Intelligence (AI) competency and design thinking skills on the innovative capacities of new-age entrepreneurs in Thailand, based on a sample of 187 students enrolled in business management and entrepreneurship programs. Utilizing Structural Equation Modeling (SEM) and factor analysis, the study evaluates how these competencies influence entrepreneurial innovation. The findings reveal that both AI competencies and design thinking skills significantly enhance the innovation capacity of entrepreneurs. The study underscores the importance of cultivating these skills to improve competitiveness and adaptability in the digital age. Moreover, it presents policy recommendations and necessary training initiatives to effectively integrate AI and design thinking into the entrepreneurial processes of new age entrepreneurs in Thailand. These strategic directions aim to equip them with the requisite skills to navigate evolving challenges within the business sector, thus preparing them for successful entrepreneurial endeavors in increasingly digital market environments.","2024-11-01","2024-12-03 03:24:26","2024-12-03 03:24:26","","100301","","2","4","","International Journal of Information Management Data Insights","","","","","","","","","","","","","","","","","","","AI competency; Design thinking skills; Innovative entrepreneurial competency; New age entrepreneurs; Strategic intelligence","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"YZ32KT9Z","journalArticle","2024","Smale, MyungJin Chung; Fox, Joseph D.; Fox, Alexa K.","When being smart trumps AI: An exploration into consumer preferences for smart vs. AI-powered products","Computers in Human Behavior","","0747-5632","10.1016/j.chb.2024.108405","https://www.sciencedirect.com/science/article/pii/S0747563224002735","Prior to the rapid growth of Artificial Intelligence (“AI”) in the consumer market, smart products received great attention from marketers and consumers. Given the recent increase in attention to AI technologies, this research explores consumers' preferences and intentions when products are framed as “smart” versus “AI-powered.” While previous literature has explored AI products and smart products individually, little is known about consumers’ preferences between the two products simultaneously. Three empirical experiments demonstrate that consumers show preference for products labeled as “smart” over those labeled as “AI-powered.” This preference is mediated by enhanced learning anxiety related to AI. The findings provide insights for marketers applying message framing, suggesting that labeling products as “smart” may evoke more favorable consumer behavior compared to the “AI-powered” label. Moreover, this research significantly contributes to the existing literature on perceptions and intentions related to AI and smart products by concurrently exploring consumer preferences regarding both smart products and AI-powered products.","2024-12-01","2024-12-03 03:24:26","2024-12-03 03:24:26","","108405","","","161","","Computers in Human Behavior","","","","","","","","","","","","","","","","","","","Artificial intelligence; AI anxiety; Consumer preference; Smart products","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"3BWQ2ACM","journalArticle","2024","Gonçalves, Ana Rita; Pinto, Diego Costa; Shuqair, Saleh; Dalmoro, Marlon; Mattila, Anna S.","Artificial intelligence vs. autonomous decision-making in streaming platforms: A mixed-method approach","International Journal of Information Management","","0268-4012","10.1016/j.ijinfomgt.2023.102748","https://www.sciencedirect.com/science/article/pii/S0268401223001299","Although the empowerment of technology is of great value to society, little is known about its downstream effects on consumers' decisions. This research draws on the expectation–confirmation theory and autonomy in artificial intelligence (AI) and investigates how AI (vs. autonomous choice) has detrimental effects on consumer outcomes, creating an autonomy-technology tension — i.e., the conflict arising from AI technology diminishing consumers' autonomy in their choices. Four studies using a mixed-method approach reveal that the use of AI recommendations in streaming platforms creates an autonomy-technology tension that reduces consumers' performance expectancy, thus lowering their satisfaction. However, such effects are contingent on the nature of the AI recommendations. While a mismatch between AI recommendations and consumer preferences might backfire, AI's negative effect is mitigated when choices match consumers' preferences. We make significant theoretical and practical contributions to empirical research on consumers' sense of autonomy while interacting with AI.","2024-06-01","2024-12-03 03:24:26","2024-12-03 03:24:26","","102748","","","76","","International Journal of Information Management","","","","","","","","","","","","","","","","","","","Artificial Intelligence; Autonomy; Decision-Making; Performance Expectancy; Streaming Platforms","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"5LPINA7I","journalArticle","2023","Beaulieu-Jones, Brett K; Villamar, Mauricio F; Scordis, Phil; Bartmann, Ana Paula; Ali, Waqar; Wissel, Benjamin D; Alsentzer, Emily; de Jong, Johann; Patra, Arijit; Kohane, Isaac","Predicting seizure recurrence after an initial seizure-like episode from routine clinical notes using large language models: a retrospective cohort study","The Lancet Digital Health","","2589-7500","10.1016/S2589-7500(23)00179-6","https://www.sciencedirect.com/science/article/pii/S2589750023001796","Summary Background The evaluation and management of first-time seizure-like events in children can be difficult because these episodes are not always directly observed and might be epileptic seizures or other conditions (seizure mimics). We aimed to evaluate whether machine learning models using real-world data could predict seizure recurrence after an initial seizure-like event. Methods This retrospective cohort study compared models trained and evaluated on two separate datasets between Jan 1, 2010, and Jan 1, 2020: electronic medical records (EMRs) at Boston Children's Hospital and de-identified, patient-level, administrative claims data from the IBM MarketScan research database. The study population comprised patients with an initial diagnosis of either epilepsy or convulsions before the age of 21 years, based on International Classification of Diseases, Clinical Modification (ICD-CM) codes. We compared machine learning-based predictive modelling using structured data (logistic regression and XGBoost) with emerging techniques in natural language processing by use of large language models. Findings The primary cohort comprised 14 021 patients at Boston Children's Hospital matching inclusion criteria with an initial seizure-like event and the comparison cohort comprised 15 062 patients within the IBM MarketScan research database. Seizure recurrence based on a composite expert-derived definition occurred in 57% of patients at Boston Children's Hospital and 63% of patients within IBM MarketScan. Large language models with additional domain-specific and location-specific pre-training on patients excluded from the study (F1-score 0·826 [95% CI 0·817–0·835], AUC 0·897 [95% CI 0·875–0·913]) performed best. All large language models, including the base model without additional pre-training (F1-score 0·739 [95% CI 0·738–0·741], AUROC 0·846 [95% CI 0·826–0·861]) outperformed models trained with structured data. With structured data only, XGBoost outperformed logistic regression and XGBoost models trained with the Boston Children's Hospital EMR (logistic regression: F1-score 0·650 [95% CI 0·643–0·657], AUC 0·694 [95% CI 0·685–0·705], XGBoost: F1-score 0·679 [0·676–0·683], AUC 0·725 [0·717–0·734]) performed similarly to models trained on the IBM MarketScan database (logistic regression: F1-score 0·596 [0·590–0·601], AUC 0·670 [0·664–0·675], XGBoost: F1-score 0·678 [0·668–0·687], AUC 0·710 [0·703–0·714]). Interpretation Physician's clinical notes about an initial seizure-like event include substantial signals for prediction of seizure recurrence, and additional domain-specific and location-specific pre-training can significantly improve the performance of clinical large language models, even for specialised cohorts. Funding UCB, National Institute of Neurological Disorders and Stroke (US National Institutes of Health).","2023-12-01","2024-12-03 03:24:26","2024-12-03 03:24:26","","e882-e894","","12","5","","The Lancet Digital Health","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"BZ8BWB4J","journalArticle","2024","Lu, Guangyun; Ni, Zhiping; Wei, Ling; Cheng, Junwei; Huang, Wei","Graphic association learning: Multimodal feature extraction and fusion of image and text using artificial intelligence techniques","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e37167","https://www.sciencedirect.com/science/article/pii/S2405844024131982","With the advancement of technology in recent years, the application of artificial intelligence in real life has become more extensive. Graphic recognition is a hot spot in the current research of related technologies. It involves machines extracting key information from pictures and combining it with natural language processing for in-depth understanding. Existing methods still have obvious deficiencies in fine-grained recognition and deep understanding of contextual context. Addressing these issues to achieve high-quality image-text recognition is crucial for various application scenarios, such as accessibility technologies, content creation, and virtual assistants. To tackle this challenge, a novel approach is proposed that combines the Mask R-CNN, DCGAN, and ALBERT models. Specifically, the Mask R-CNN specializes in high-precision image recognition and segmentation, the DCGAN captures and generates nuanced features from images, and the ALBERT model is responsible for deep natural language processing and semantic understanding of this visual information. Experimental results clearly validate the superiority of this method. Compared to traditional image-text recognition techniques, the recognition accuracy is improved from 85.3% to 92.5%, and performance in contextual and situational understanding is enhanced. The advancement of this technology has far-reaching implications for research in machine vision and natural language processing and open new possibilities for practical applications.","2024-09-30","2024-12-03 03:24:26","2024-12-03 03:24:26","","e37167","","18","10","","Heliyon","","","","","","","","","","","","","","","","","","","ALBERT; DCGAN; Graphic association; Image matching; Mask R-CNN; Multimodal feature; Text matching","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RI3T2PGF","journalArticle","2023","Kutyauripo, Innocent; Rushambwa, Munyaradzi; Chiwazi, Lyndah","Artificial intelligence applications in the agrifood sectors","Journal of Agriculture and Food Research","","2666-1543","10.1016/j.jafr.2023.100502","https://www.sciencedirect.com/science/article/pii/S2666154323000091","Food security is one of the priorities of every country in the World. However, different factors are making it difficult to meet global targets on food security. Some unprecedented shocks are encumbering food security at the global level. Various interventions have been applied toward food security and artificial intelligence is one of the modern methods that is being used in various stages of the food system. In this paper, the application of artificial intelligence in the whole food production ecosystem ranging from crop production, livestock production, harvesting/slaughtering, postharvest management, food processing, food distribution, food consumption and food waste management is assessed. The objective of this research is to assess the application of artificial intelligence systems in all the stages of food systems. A systematic review was conducted by analyzing 110 articles after the screening of 450 articles based on the inclusion and exclusion criteria. The results indicated that various artificial intelligence algorithms are being applied to all the stages of the food system from crop/livestock production up to food or agro-waste management.","2023-03-01","2024-12-03 03:24:26","2024-12-03 03:24:26","","100502","","","11","","Journal of Agriculture and Food Research","","","","","","","","","","","","","","","","","","","Artificial intelligence; Agriculture; Food processing; Food systems","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"VJY4MJFS","journalArticle","2024","Kaufmann, Kevin; Vecchio, Kenneth S.","Autonomous materials research and design: Characterization","Current Opinion in Solid State and Materials Science","","1359-0286","10.1016/j.cossms.2024.101192","https://www.sciencedirect.com/science/article/pii/S1359028624000585","New materials are a fundamental component of most major advancements in human history. The pivotal role materials play in the development of next generation technologies has spurred campaigns such as the Materials Genome Initiative (MGI) with the goal of reducing the time and cost to discover, characterize, and deploy advanced materials. As goals of the MGI have been met and new capabilities have emerged, a contemporary vision has taken shape within the scientific community whereby the exploration of materials space is dramatically accelerated by artificial intelligence agent(s) capable of performing research independently from humans and achieving a paradigm change in the field. As this idea comes to fruition and new materials are more rapidly computationally evaluated and synthesized nearly on demand, the rate at which a complete characterization of each candidate material’s properties can be completed and understood within the context of all other potential solutions will be the next bottleneck in a materials design campaign. This work provides an overview of the technical and conceptual components related to materials characterization discussed during a workshop dedicated to challenging the way materials research is thought of and performed within the emergent field of autonomous materials research and design (AMRAD). Furthermore, general considerations for developing autonomous characterization are presented along with related works and a discussion of their progress and shortcomings toward the AMRAD vision.","2024-09-01","2024-12-03 03:24:26","2024-12-03 03:24:26","","101192","","","32","","Current Opinion in Solid State and Materials Science","","","","","","","","","","","","","","","","","","","Artificial intelligence; Accelerated materials design; Autonomous materials research and development (AMRAD); High-throughput science; Materials characterization","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ILUVBYWN","journalArticle","2024","Hsieh, Chia-Ming; Hsu, Ching-Han; Chen, Jen-Kun; Liao, Lun-De","AI-powered home cage system for real-time tracking and analysis of rodent behavior","iScience","","2589-0042","10.1016/j.isci.2024.111223","https://www.sciencedirect.com/science/article/pii/S2589004224024489","Summary Researchers in animal behavior and neuroscience devote considerable time to observing rodents behavior and physiological responses, with AI monitoring systems reducing personnel workload. This study presents the RodentWatch (RW) system, which leverages deep learning to automatically identify experimental animal behaviors in home cage environments. A single multifunctional camera and edge device are installed inside the animal’s home cage, allowing continuous real-time monitoring of the animal’s behavior, position, and body temperature for extended periods. We investigated identifying the drinking and resting behaviors of rats, with recognition accuracy enhanced through contextual object labeling and modified non-maximum suppression (NMS) schemes. Two tests—a light cycle change test and a sucrose preference test—were conducted to evaluate the usability of this system in rat behavioral experiments. This system enables notable advancements in image-based behavior recognition for living rodents.","2024-11-15","2024-12-03 03:24:26","2024-12-03 03:24:26","","111223","","11","27","","iScience","","","","","","","","","","","","","","","","","","","Artificial intelligence; Rodent behavior","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"S2VZCRF9","journalArticle","2024","Ceccarelli, Sofia; Cesta, Amedeo; Cortellessa, Gabriella; De Benedictis, Riccardo; Fracasso, Francesca; Leopardi, Laura; Ligios, Luca; Lombardi, Ernesto; Malatesta, Saverio Giulio; Oddi, Angelo; Pagano, Alfonsina; Palombini, Augusto; Romagna, Gianmauro; Sanzari, Marta; Schaerf, Marco","Evaluating visitors’ experience in museum: Comparing artificial intelligence and multi-partitioned analysis","Digital Applications in Archaeology and Cultural Heritage","","2212-0548","10.1016/j.daach.2024.e00340","https://www.sciencedirect.com/science/article/pii/S2212054824000250","Analysing visitors' behaviour in a museum or in a cultural site is a crucial element to manage spaces and artworks arrangement as well as improving the visit experience. This paper presents the preliminary results of the ARTEMISIA project, exploiting Artificial Intelligence (AI) techniques to study, design and develop a methodology to interpret visitors' behaviour within a museum context, namely the Museum of Rome in Palazzo Braschi (Rome, Italy). The aim is to combine literature on users' experience (UX) analysis with experimental data coming from the visitor anonymous tracking out of motion sensors (users' stand-still positions, viewpoint direction, movements), merging approaches of different research domains. Through the use of agglomerative hierarchical clustering algorithms, four categories of visitors were identified, then associated to user profiles emerged by UX evaluations. Such analysis may lead to new forms of visitors profiling and to the development of a new generation of customised applications in public and private contexts. Identifying and predicting users’ patterns with respect to museum halls arrangement may also be useful to suggest improvement in the museum spaces and exhibitions (new indications, updated storytelling or changes in thematic configuration). © 2023 Elsevier Ltd. All rights reserved.","2024-06-01","2024-12-03 03:24:26","2024-12-03 03:24:26","","e00340","","","33","","Digital Applications in Archaeology and Cultural Heritage","","","","","","","","","","","","","","","","","","","Artificial intelligence; Museum studies; Museum visit trajectories; User experience evaluation; Visitors' segmentation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"5MSVUN5F","journalArticle","2024","Chacon, Alvaro; Kaufmann, Esther","An overview of the effects of algorithm use on judgmental biases affecting forecasting","International Journal of Forecasting","","0169-2070","10.1016/j.ijforecast.2024.09.007","https://www.sciencedirect.com/science/article/pii/S0169207024001018","In the realm of forecasting, judgmental biases often hinder efficiency and accuracy. Algorithms present a promising avenue for decision makers to enhance their forecasting performance. In this overview, we scrutinized the occurrence of the most relevant judgmental biases affecting forecasting across 162 papers, drawing from four recent reviews and papers published in forecasting journals, specifically focusing on the use of algorithms. Thirty-three of the 162 papers (20.4%) at least briefly mentioned one of twelve judgmental biases affecting forecasting. Our comprehensive analysis suggests that algorithms can potentially mitigate the adverse impacts of biases inherent in human judgment related to forecasting. Furthermore, these algorithms can leverage biases as an advantage, enhancing forecast accuracy. Intriguing revelations have surfaced, focusing mainly on four biases. By providing timely, relevant, well-performing, and consistent algorithmic advice, people can be effectively influenced to improve their forecasts, considering anchoring, availability, inconsistency, and confirmation bias. The findings highlight the gaps in the current research landscape and provide recommendations for practitioners. They also lay the groundwork for future studies on utilizing algorithms (e.g., large language models) and overcoming judgmental biases to improve forecasting performance.","2024-11-21","2024-12-03 03:24:26","2024-12-03 03:24:26","","","","","","","International Journal of Forecasting","","","","","","","","","","","","","","","","","","","Algorithm use; Judgmental accuracy; Judgmental adjustment; Judgmental biases; Judgmental forecasting; Review article","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"GP728CX3","journalArticle","2024","Strafella, Pierluigi; Giulietti, Nicola; Caputo, Alessia; Pandarese, Giuseppe; Castellini, Paolo","Detection of microplastics in fish using computed tomography and deep learning","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e39875","https://www.sciencedirect.com/science/article/pii/S2405844024159063","Marine organisms have been observed ingesting microplastic particles, with field analyses indicating fibers and fragments as prevalent forms. Current microplastic detection methods are mainly time-consuming, susceptible to cross-contamination, and expensive. Furthermore, these techniques, being disruptive, do not allow for the exact localization of the microplastic in the sample. This study proposes a new approach using Computed Tomography (CT scan) and Artificial Intelligence for the automatic and non-destructive detection of microplastics in fishes and other species based on the combination of several factors, such as density and shape. The advantages of this methodology include accurate identification of plastic localization, a low risk of cross-contamination, rapid processing, automatic tomographic measurement, efficient data processing, cost-effectiveness, and a high cost-benefit ratio. The herein results highlight how artificial intelligence applied to conventional techniques can significantly improve precision and efficiency in microplastic research. Indeed, the semantic segmentation model clearly recognized the presence of 100 % of the plastic particles, both in their location and in their volume, accelerating the identification process and surpassing the limitations of traditional spectral analysis methodologies.","2024-11-15","2024-12-03 03:24:26","2024-12-03 03:24:26","","e39875","","21","10","","Heliyon","","","","","","","","","","","","","","","","","","","Machine learning; Microplastics; AI; Measurement; CT scan; Food contamination","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"3VI8J25I","journalArticle","2024","Zahidi, Farah; Kaluvilla, Bincy Baburaj; Mulla, Tausif","Embracing the new era: Artificial intelligence and its multifaceted impact on the hospitality industry","Journal of Open Innovation: Technology, Market, and Complexity","","2199-8531","10.1016/j.joitmc.2024.100390","https://www.sciencedirect.com/science/article/pii/S2199853124001847","The hospitality industry stands at the cusp of a revolutionary transformation spurred by the advent of Artificial Intelligence (AI). This paper explores the challenges and opportunities associated with AI integration in an arena historically reliant on the nuances of the human touch. The research illuminates the potential for AI to redefine facets of the hospitality experience, from augmenting customer interactions to refining service delivery and catalysing economic growth. It examines the potential for AI to infuse the hospitality industry with unprecedented levels of efficiency and personalised service while also addressing the complexities introduced by this digital evolution. The pivot towards AI is not without its impediments. Technological constraints, such as the significant investment required for the deployment of AI and the compatibility with existing systems, pose substantial barriers. Cultural resistance emerges from a workforce accustomed to traditional service norms, and ethical dilemmas surface over customer data use and machine-based interactions' impersonal nature. This research delineates these impediments, offering a panoramic view of the intricate dynamics. In dissecting the nuances of AI adoption, the study first harnesses a comprehensive literature review to pinpoint the pivotal factors at play. These variables are then intricately modelled through Total Interpretive Structural Modeling (TISM) analysis, which unravels the interdependencies and assigns a hierarchy based on driving and dependence power. This modelling facilitates a structured understanding of the challenges, setting the stage for strategic intervention. Validation of the TISM model ensues through MICMAC analysis, further refining the categorisation of factors by their level of influence. This dual-analytical paradigm affords a granular perspective on the factors that govern AI adoption, laying the groundwork for strategic solutions that are both nuanced and actionable. The results of the analyses reveal that while AI possesses the potential to disrupt the hospitality sector positively, its implementation requires a subtle approach that mitigates the identified challenges. In the wake of this analytical journey, the paper culminates with strategic recommendations designed to steer industry stakeholders through the AI adoption process. These recommendations stress the importance of a balanced approach that harnesses the strengths of AI to complement and enhance the human elements intrinsic to hospitality. This includes investing in employee training to work alongside AI tools, adopting transparent data practices to maintain customer trust, and fostering an organisational culture that embraces technological innovation while valuing human engagement.","2024-12-01","2024-12-03 03:24:26","2024-12-03 03:24:26","","100390","","4","10","","Journal of Open Innovation: Technology, Market, and Complexity","","","","","","","","","","","","","","","","","","","Artificial intelligence (AI); Customer interactions; Hospitality industry; MICMAC analysis; Total interpretive structural modeling (TISM)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"AEHTCJWJ","journalArticle","2023","Wu, Liping; Liao, Xiaobing","Intelligent Machine Evolutionary Algorithm Learning Based on Artificial Intelligence","3rd International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy","","1877-0509","10.1016/j.procs.2023.11.133","https://www.sciencedirect.com/science/article/pii/S1877050923019646","Intelligent machines are an important carrier of artificial intelligence, which are widely used not only in the industrial field, but also in various fields of daily life and work. Path planning is an important research direction for studying machine intelligence. The main purpose is to search for a feasible path so that the robot can walk along the searched path in a complex environment. This paper aims to study the evolutionary algorithm of intelligent machines based on artificial intelligence. On the basis of introducing the characteristics of artificial potential field algorithm, the concept and characteristics of genetic algorithm, using genetic algorithm as the framework, the artificial potential field algorithm is combined with the crossover of genetic algorithm. In the operator, the optimization performance of the crossover operator is improved. The research results show that the length of the path obtained by the improved algorithm is shorter and better than that of the genetic algorithm of the single-point crossover operator.","2023-01-01","2024-12-03 03:24:26","2024-12-03 03:24:26","","1016-1022","","","228","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial Intelligence; Evolutionary Algorithms; Genetic Algorithms; Intelligent Machines","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XXHTYXML","journalArticle","2024","Freisinger, Elena; Schneider, Sabrina","Decoding decision delegation to artificial intelligence: A mixed-methods study on the preferences of decision-makers and decision-affected in surrogate decision contexts","European Management Journal","","0263-2373","10.1016/j.emj.2024.10.004","https://www.sciencedirect.com/science/article/pii/S0263237324001312","Organizations are increasingly opting to use decision-making systems based on artificial intelligence (AI) to increase decision quality and speed and free up resources. However, delegating decisions to AI is challenging, especially in contexts that go beyond classical optimization problems. Current research has so far largely neglected to include the viewpoints of the decision-affected and of surrogate decision-makers in the discussion. To address these gaps, in this study we rely on two experimental designs and complement our findings with qualitative interview data to shed light on both sides: the perspective of those affected by the decision and that of the decision maker in surrogate and ethically complex decision contexts. Findings reveal that the individual decision-making perspective may lead to opposing perceptions. Whereas the willingness to delegate an ethically challenging decision to AI in a surrogate decision context is lower than that in a non-surrogate context (decision-maker perspective), people affected by the decision do not generally prefer humans to AI (decision-affected perspective). By these findings, we contribute to the literature on AI-enabled decision-making and decision delegation to non-human entities.","2024-10-10","2024-12-03 03:24:26","2024-12-03 03:24:26","","","","","","","European Management Journal","","","","","","","","","","","","","","","","","","","AI-Enabled decision-making; Decision delegation; Surrogate decision making","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DQJZGRB6","journalArticle","2024","Pozzo, Danielle Nunes; Gonzalez Beleño, Carlos A.; Correa, Katherine Reales; Donado, Mildred Garizabal; Gomez Pedroza, Fredy J.; Moncada Diaz, Jaime E.","Managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making: A study with Colombian SMEs","The 15th International Conference on Ambient Systems, Networks and Technologies Networks (ANT) / The 7th International Conference on Emerging Data and Industry 4.0 (EDI40), April 23-25, 2024, Hasselt University, Belgium","","1877-0509","10.1016/j.procs.2024.06.119","https://www.sciencedirect.com/science/article/pii/S1877050924013553","The adoption of Artificial Intelligence (AI) is a growing topic, particularly in the context of decision-making processes. However, there is a limited focus on this subject, especially in developing and emerging countries, which present unique challenges distinct from those in developed nations. Small and Medium Enterprises (SMEs) in these regions have received insufficient empirical attention, and existing literature suggests that variables influencing AI adoption may vary based on the configurations of these companies. This study addresses two critical research gaps by examining AI adoption in the specific context of Colombian organizations, particularly focusing on SME managers. Replicating scales and a structured model from a previous study in the United Kingdom, the research involves 83 companies from the Caribbean region of Colombia. The findings reveal partial differences from the UK study, emphasizing the impact of facilitating conditions on behavioral intentions to adopt AI in decision-making, while peer influence lacks significance. While 10 out of 17 hypotheses replicate UK results, the study raises questions about how SMEs in Latin America perceive threat, severity, effort, personal concerns, and external support differently.","2024-01-01","2024-12-03 03:24:26","2024-12-03 03:24:26","","956-961","","","238","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","SMEs; Artificial intelligence; decision-making; emerging countries","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JFGFEMYJ","journalArticle","2024","Jada, Irshaad; Mayayise, Thembekile O.","The impact of artificial intelligence on organisational cyber security: An outcome of a systematic literature review","Systematic Review and Meta-analysis in Information Management Research - Part II","","2543-9251","10.1016/j.dim.2023.100063","https://www.sciencedirect.com/science/article/pii/S2543925123000372","As digital transformation continues to advance, organisations are becoming increasingly aware of the benefits that modern technologies offer. However, with greater technology adoption comes a higher risk of cyber security threats and attacks. Therefore, there is a need for more advanced measures to protect against constantly evolving threats. One potential solution is the use of Artificial Intelligence (AI). The aim of this research paper was to conduct a systematic literature review (SLR) to assess the impact of AI-based technologies on organisational cyber security and determine their effectiveness compared to traditional cyber security approaches. The PRISMA flow diagram was used to guide the review process. Peer-reviewed articles from 2018 to 2023 were included from EBSCO Host, Google Scholar, Science Direct, ProQuest & SCOPUS and 73 remaining articles were synthesised. The results revealed that AI can impact cybersecurity throughout it's entire life cycle, yielding benefits like automation, threat intelligence, and improved cyber defense. Nevertheless, it also brings challenges like adversarial attacks and the need for high-quality data, which could lead to the inefficiency of AI. These results affirm the positive influence of AI on cybersecurity, enhancing effectiveness and resilience. These findings provide a solid foundation for further research in the field of organisational cybersecurity. These results can help organisations make informed decisions on AI implementations by offering an impartial view of its impacts.","2024-06-01","2024-12-03 03:24:27","2024-12-03 03:24:27","","100063","","2","8","","Data and Information Management","","","","","","","","","","","","","","","","","","","Machine learning; Artificial intelligence; Cyber security; SLR","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"MMAJ2F44","journalArticle","2024","Moya, Sofia; Camacho, Mar","Leveraging AI-powered mobile learning: A pedagogically informed framework","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100276","https://www.sciencedirect.com/science/article/pii/S2666920X24000791","The convergence of Artificial Intelligence (AI) and mobile learning (mLearning) has revolutionized digital education, heralding a transformative paradigm shift in learning methodologies. AI-powered mLearning holds promise but requires deep understanding of pedagogical principles. Concerns over smartphone overuse have heightened discussions about mLearning integration. In this context, with the rising penetration of AI in various educational settings, it is crucial that research delves into the pedagogically sound integration of AI into mLearning environments. A systematic literature review of forty-two articles has yielded a framework summarizing mLearning attributes, functionalities, and their impact on transversal learning principles highlighting the profound impact of AI.","2024-12-01","2024-12-03 03:24:27","2024-12-03 03:24:27","","100276","","","7","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Mobile learning; Artificial intelligence; Pedagogical issues","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JT27529K","journalArticle","2023","Huang, Yingshen; Cox, Andrew M.; Cox, John","Artificial Intelligence in academic library strategy in the United Kingdom and the Mainland of China","The Journal of Academic Librarianship","","0099-1333","10.1016/j.acalib.2023.102772","https://www.sciencedirect.com/science/article/pii/S0099133323001118","There is growing recognition of the value of applying Artificial Intelligence (AI) in libraries. This study explores how academic libraries have responded to this opportunity at the level of strategy, what is the status of the application of AI, if any, and what are the different emphases of development comparing the UK and China. The data for the study was strategy documentation from high-ranking universities and their libraries. The sample consisted of the top 25 universities from the United Kingdom and top 25 from the Mainland of China according to the QS world university rankings. Explicit mention of Artificial Intelligence and related technologies is rarely found in strategic plans of universities in the UK but most Chinese universities mention them in their vision statements which focus on the development of new majors and research of the technology. Though several libraries have already implemented applications based on AI or claim to be “smart” or “intelligent” most academic library strategic plans or agendas do not emphasize AI. This is one of the first studies to explore the current status of AI applied in academic libraries as a sector and to compare experiences internationally.","2023-11-01","2024-12-03 03:24:27","2024-12-03 03:24:27","","102772","","6","49","","The Journal of Academic Librarianship","","","","","","","","","","","","","","","","","","","Academic libraries; University libraries; Librarians; Artificial intelligence; Machine Learning; Strategy","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CH95YZH2","journalArticle","2024","Bolón-Canedo, Verónica; Morán-Fernández, Laura; Cancela, Brais; Alonso-Betanzos, Amparo","A review of green artificial intelligence: Towards a more sustainable future","Neurocomputing","","0925-2312","10.1016/j.neucom.2024.128096","https://www.sciencedirect.com/science/article/pii/S0925231224008671","Green artificial intelligence (AI) is more environmentally friendly and inclusive than conventional AI, as it not only produces accurate results without increasing the computational cost but also ensures that any researcher with a laptop can perform high-quality research without the need for costly cloud servers. This paper discusses green AI as a pivotal approach to enhancing the environmental sustainability of AI systems. Described are AI solutions for eco-friendly practices in other fields (green-by AI), strategies for designing energy-efficient machine learning (ML) algorithms and models (green-in AI), and tools for accurately measuring and optimizing energy consumption. Also examined are the role of regulations in promoting green AI and future directions for sustainable ML. Underscored is the importance of aligning AI practices with environmental considerations, fostering a more eco-conscious and energy-efficient future for AI systems.","2024-09-28","2024-12-03 03:24:27","2024-12-03 03:24:27","","128096","","","599","","Neurocomputing","","","","","","","","","","","","","","","","","","","Sustainability; Green machine learning; Green-by AI; Green-in AI","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"7DAPGCSR","journalArticle","2023","Zhao, Ming; Xiao, Zhuowei; Chen, Shi; Fang, Lihua","DiTing: A large-scale Chinese seismic benchmark dataset for artificial intelligence in seismology","Earthquake Science","","1674-4519","10.1016/j.eqs.2022.01.022","https://www.sciencedirect.com/science/article/pii/S1674451922000222","In recent years, artificial intelligence technology has exhibited great potential in seismic signal recognition, setting off a new wave of research. Vast amounts of high-quality labeled data are required to develop and apply artificial intelligence in seismology research. In this study, based on the 2013–2020 seismic cataloging reports of the China Earthquake Networks Center, we constructed an artificial intelligence seismological training dataset (“DiTing”) with the largest known total time length. Data were recorded using broadband and short-period seismometers. The obtained dataset included 2,734,748 three-component waveform traces from 787,010 regional seismic events, the corresponding P- and S-phase arrival time labels, and 641,025 P-wave first-motion polarity labels. All waveforms were sampled at 50 Hz and cut to a time length of 180 s starting from a random number of seconds before the occurrence of an earthquake. Each three-component waveform contained a considerable amount of descriptive information, such as the epicentral distance, back azimuth, and signal-to-noise ratios. The magnitudes of seismic events, epicentral distance, signal-to-noise ratio of P-wave data, and signal-to-noise ratio of S-wave data ranged from 0 to 7.7, 0 to 330 km, –0.05 to 5.31 dB, and –0.05 to 4.73 dB, respectively. The dataset compiled in this study can serve as a high-quality benchmark for machine learning model development and data-driven seismological research on earthquake detection, seismic phase picking, first-motion polarity determination, earthquake magnitude prediction, early warning systems, and strong ground-motion prediction. Such research will further promote the development and application of artificial intelligence in seismology.","2023-04-01","2024-12-03 03:24:27","2024-12-03 03:24:27","","84-94","","2","36","","Earthquake Science","","","","","","","","","","","","","","","","","","","artificial intelligence; benchmark dataset; earthquake detection; first-motion polarity; seismic phase identification","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"FVIF2MMJ","journalArticle","2024","Javed, Kashif; Li, Jianxin","Artificial intelligence in judicial adjudication: Semantic biasness classification and identification in legal judgement (SBCILJ)","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e30184","https://www.sciencedirect.com/science/article/pii/S2405844024062157","History reveals that human societies have suffered in terms of social justice due to cognitive bias. Semantic bias tends to amplify cognitive bias. Therefore, the presence of cognitive biases in extensive historical data can potentially result in unethical and allegedly inhumane predictions since AI systems are trained on this data. The innovation of artificial intelligence and its rapid integration across disciplines has prompted questions regarding the subjectivity of the technology. Current research focuses the semantic bias in legal judgment to increase the legitimacy of training data. By the application of general-purpose Artificial Intelligence (AI) algorithms, we classify and detect the semantics bias that is present in the Chinese Artificial Intelligence and Law (CAIL) dataset. Our findings demonstrate that AI models acquire superior prediction power in the CAIL dataset, which is comprised of hundreds of cases, compared to a structured professional risk assessment tool. To assist legal practitioners during this process, innovative approaches that are based on AI may be implemented inside the legal arena. To accomplish this objective, we suggested a classification model for semantic bias that is related to the classification and identification of semantic biases in legal judgment. Our proposed model legal field uses the example of categorization along with the identification of the CAIL dataset. This will be accomplished by identifying the semantics biases in judicial decisions. We used different types of classifiers such as the Support Vector Machine (SVM), Naïve-Bayes (NB), Multi-Layer Perceptron (MLP), and the K-Nearest Neighbour (KNN) to come across the preferred results. SVM got 96.90 %, NB has 88.80 %, MLP has 86.75 % and KNN achieved 85.66 % accuracy whereas SVM achieved greater accuracy as compared to other models. Additionally, we demonstrate that we were able to get a relatively high classification performance when predicting outcomes based just on the semantic bias categorization in judicial judgments that determine the outcome of the case.","2024-05-15","2024-12-03 03:24:27","2024-12-03 03:24:27","","e30184","","9","10","","Heliyon","","","","","","","","","","","","","","","","","","","Artificial intelligence; Natural language processing; CAIL dataset; Judicial adjudication; Legal judgment; Semantic biasedness","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DQ7W5D39","journalArticle","2024","Leocádio, Diogo; Guedes, Leonel; Oliveira, José; Reis, João; Melão, Nuno","Customer Service with AI-Powered Human-Robot Collaboration (HRC): A Literature Review","5th International Conference on Industry 4.0 and Smart Manufacturing (ISM 2023)","","1877-0509","10.1016/j.procs.2024.01.120","https://www.sciencedirect.com/science/article/pii/S1877050924001200","The present study discusses the impact of Human-Robot Collaboration (HRC) powered by Artificial Intelligence (AI) on customer service. It is based on the four types of intelligence – mechanical, analytical, intuitive, and empathetic – and how they are integrated into HRC to provide customers with more efficient and personalized services. The benefits of AI-enabled HRC are highlighted, including reduced operational costs, increased productivity, improved decision-making, and enhanced customer experience. However, the article also addresses the challenges of implementing this approach, such as the potential loss of jobs due to automation, and emphasizes the importance of ethical and responsible implementation. The study has significant practical and academic implications, warning that continuous research is needed to understand the potential and limitations of AI-enabled HRC on customer service. Overall, through a literature review, the article aims to appeal to the reader's critical spirit and explore topics on the transformative power of AI in customer service.","2024-01-01","2024-12-03 03:24:27","2024-12-03 03:24:27","","1222-1232","","","232","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial Intelligence; Customer Service; Human-Robot Collaboration","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JKLIM5S8","journalArticle","2024","Kavianpour, Babak; Piadeh, Farzad; Gheibi, Mohammad; Ardakanian, Atiyeh; Behzadian, Kourosh; Campos, Luiza C.","Applications of artificial intelligence for chemical analysis and monitoring of pharmaceutical and personal care products in water and wastewater: A review","Chemosphere","","0045-6535","10.1016/j.chemosphere.2024.143692","https://www.sciencedirect.com/science/article/pii/S004565352402592X","Specifying and interpreting the occurrence of emerging pollutants is essential for assessing treatment processes and plants, conducting wastewater-based epidemiology, and advancing environmental toxicology research. In recent years, artificial intelligence (AI) has been increasingly applied to enhance chemical analysis and monitoring of contaminants in environmental water and wastewater. However, their specific roles targeting pharmaceuticals and personal care products (PPCPs) have not been reviewed sufficiently. This review aims to narrow the gap by highlighting, scoping, and discussing the incorporation of AI during the detection and quantification of PPCPs when utilising chemical analysis equipment and interpreting their monitoring data for the first time. In the chemical analysis of PPCPs, AI-assisted prediction of chromatographic retention times and collision cross-sections (CCS) in suspect and non-target screenings using high-resolution mass spectrometry (HRMS) enhances detection confidence, reduces analysis time, and lowers costs. AI also aids in interpreting spectroscopic analysis results. However, this approach still cannot be applied in all matrices, as it offers lower sensitivity than liquid chromatography coupled with tandem or HRMS. For the interpretation of monitoring of PPCPs, unsupervised AI methods have recently presented the capacity to survey regional or national community health and socioeconomic factors. Nevertheless, as a challenge, long-term monitoring data sources are not given in the literature, and more comparative AI studies are needed for both chemical analysis and monitoring. Finally, AI assistance anticipates more frequent applications of CCS prediction to enhance detection confidence and the use of AI methods in data processing for wastewater-based epidemiology and community health surveillance.","2024-11-01","2024-12-03 03:24:27","2024-12-03 03:24:27","","143692","","","368","","Chemosphere","","","","","","","","","","","","","","","","","","","Artificial intelligence; Contaminants of emerging concern; High-resolution mass spectrometry; PPCPs; Quantitative structure retention relationship; Suspect and non-targeted screening; Wastewater-based epidemiology","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"5VB92JCF","journalArticle","2023","Ermolayeva, Anna; Birukou, Aliaksandr; Matyushenko, Sergey; Kochetkov, Dmitry","Statistical model and method for analyzing AI conference rankings: China vs USA","Heliyon","","2405-8440","10.1016/j.heliyon.2023.e21592","https://www.sciencedirect.com/science/article/pii/S240584402308800X","Artificial Intelligence (AI) is a rapidly developing field of research that attracts significant funding from both the state and industry players. Such interest is driven by a wide range of AI technology applications in many fields. Since many AI research topics relate to computer science, where a significant share of research results are published in conference proceedings, the same applies to AI. The world leaders in artificial intelligence research are China and the United States. The authors conducted a comparative analysis of the bibliometric indicators of AI conference papers from these two countries based on Scopus data. The analysis aimed to identify conferences that receive above-average citation rates and suggest publication strategies for authors from these countries to participate in conferences that are likely to provide better dissemination of their research results. The results showed that, although Chinese researchers publish more AI papers than those from the United States, US conference papers are cited more frequently. The authors also conducted a correlation analysis of the MNCS index, which revealed no high correlation between MNCS USA vs. MNCS China, MNCS China/MNCS USA vs. MSAR, and MNCS China/MNCS USA vs. CORE ranking indicators.","2023-11-01","2024-12-03 03:24:27","2024-12-03 03:24:27","","e21592","","11","9","","Heliyon","","","","","","","","","","","","","","","","","","","Artificial intelligence; Conference proceedings; Research assessment; Research evaluation; Scientometrics","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"4UIHQCSP","journalArticle","2024","Dinh, Ly; Friedman, Alon; Hawley, Kevin","Examining peer review network dynamics in higher education visual communication courses using ERGM","Computers and Education Open","","2666-5573","10.1016/j.caeo.2024.100222","https://www.sciencedirect.com/science/article/pii/S2666557324000624","Peer review plays a pivotal role in shaping collaborative learning environments and evaluating student engagement, but there is a lack of research on the relational dynamics that underlie the interactions between students during the peer review process, which is critical for understanding the effectiveness of peer review for students’ learning outcomes. Using explanatory sequential study design, this study’s objectives are to explore the dynamics of peer review as a feedback mechanism in higher education within a social network context, focusing on student interactions during the feedback process. Specifically, we applied dyadic-independent Exponential Random Graph Modeling (ERGM), along with mixed-methods design to analyze peer review interactions within a visual communication course at a large state university. We collected and analyzed two samples: a quantitative sample of 167 students for network analysis, drawn from 3082 peer reviews and rubric scores, and a qualitative sample of 136 students from post-course surveys that provided feedback on the peer review process and course methodologies. Our investigation revealed the inherently social network nature of peer review, shedding light on its significance in engaging students and assessing student engagement. Specifically, we examined the role of students’ performance levels, edge-based attributes derived from the visual peer review rubric, and language used in the feedback on the likelihood of forming peer review connections in the network. Our findings indicate that attributes derived from the visual peer review rubric significantly influence the likelihood of peer connections. On the other hand, students’ performance levels do not have any impact on the formation of peer connections. The findings highlight the importance of visual peer review activities in shaping peer connections in the classroom. Future studies will incorporate higher-order structures, such as transitivity and triadic closure, into our ERGM model to further enhance our understanding of peer review dynamics in educational settings.","2024-12-01","2024-12-03 03:24:27","2024-12-03 03:24:27","","100222","","","7","","Computers and Education Open","","","","","","","","","","","","","","","","","","","Exponential random graph models (ERGM); Mixed methodology; Undergraduate education; Visual peer review","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"949JL6GT","journalArticle","2023","Uzir, Md Uzir Hossain; Bukari, Zakari; Al Halbusi, Hussam; Lim, Rodney; Wahab, Siti Norida; Rasul, Tareq; Thurasamy, Ramayah; Jerin, Ishraq; Chowdhury, M Rezaul Karim; Tarofder, Arun Kumar; Yaakop, Azizul Yadi; Hamid, Abu Bakar Abdul; Haque, Ahasanul; Rauf, Abdur; Eneizan, Bilal","Applied artificial intelligence: Acceptance-intention-purchase and satisfaction on smartwatch usage in a Ghanaian context","Heliyon","","2405-8440","10.1016/j.heliyon.2023.e18666","https://www.sciencedirect.com/science/article/pii/S2405844023058747","Technology and its continuous advancement facilitate human beings to get rid of their criticality and limitation. Applied artificial intelligence (AAI) is one of the latest forms that delimited the limitation of human beings. Smartwatch acts as an applied artificial intelligence to assist various patients to check medical care without going to hospital and physicians. This (three) multiple-study research focused on the intention to use, purchase, and their satisfaction and spread positive word of mouth among others in the Ghanaian. To investigate these issues two renowned theories were underpinned- TAM theory and the Stimulus-Organism-Response (S–O-R). Total 550, 320, and 170 respondents were interviewed with Google forms due to COVID-19 using social media. AI-enabled smartwatch considering Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Perceived Credibility (PC), Perceived Self-Efficacy (PSE), and Perceived Financial Cost (PFC) were significant on intention to adoption and adoption intention on actual purchase. The final study showed device quality, its service level, their usage experience, perceived value, and the extent to which the satisfied customers made positive word of mouth to their friends and family, colleagues and followers. This research is significant in understanding the usage of AI-enabled smartwatches as a device doctor or electronic doctor (e-doctor).","2023-08-01","2024-12-03 03:24:27","2024-12-03 03:24:27","","e18666","","8","9","","Heliyon","","","","","","","","","","","","","","","","","","","–-R model; Applied artificial intelligence; Healthcare service. smartwatches; Positive word of mouth","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"9TKKUVU4","journalArticle","2024","Zhu, Xi; Peng, Xiaobo","Strategic assessment model of smart stadiums based on genetic algorithms and literature visualization analysis: A case study from Chengdu, China","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e31759","https://www.sciencedirect.com/science/article/pii/S2405844024077909","This paper leverages Citespace and VOSviewer software to perform a comprehensive bibliometric analysis on a corpus of 384 references related to smart sports venues, spanning from 1998 to 2022. The analysis encompasses various facets, including author network analysis, institutional network analysis, temporal mapping, keyword clustering, and co-citation network analysis. Moreover, this paper constructs a smart stadiums strategic assessment model (SSSAM) to compensate for confusion and aimlessness by genetic algorithms (GA). Our findings indicate an exponential growth in publications on smart sports venues year over year. Arizona State University emerges as the institution with the highest number of collaborative publications, Energy and Buildings becomes the publication with the most documents. While, Wang X stands out as the scholar with the most substantial contribution to the field. In scrutinizing the betweenness centrality indicators, a paradigm shift in research hotspots becomes evident-from intelligent software to the domains of the Internet of Things (IoT), intelligent services, and artificial intelligence (AI). The SSSAM model based on artificial neural networks (ANN) and GA algorithms also reached similar conclusions through a case study of the International University Sports Federation (FISU), building Information Modeling (BIM), cloud computing and artificial intelligence Internet of Things (AIoT) are expected to develop in the future. Three key themes developed over time. Finally, a comprehensive knowledge system with common references and future hot spots is proposed.","2024-06-15","2024-12-03 03:24:27","2024-12-03 03:24:27","","e31759","","11","10","","Heliyon","","","","","","","","","","","","","","","","","","","Artificial intelligence; Internet of things; Scientometrics; Smart stadiums; Strategic model","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"Y4B4LQZK","journalArticle","2024","Wang, Xiaowen; Chen, Mingyue; Chen, Nanxu","How artificial intelligence affects the labour force employment structure from the perspective of industrial structure optimisation","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e26686","https://www.sciencedirect.com/science/article/pii/S2405844024027178","To investigate how artificial intelligence (AI) affects the structure of labour force employment, we integrate robotics adoption and employment into this study's model. Based on Chinese provincial panel data from 2010 to 2019, fixed, mediating and threshold effects models and a spatial heterogeneity model were used to empirically test the impact of AI on the employment structure from the perspective of industrial structure optimisation and its mechanisms of action. The findings demonstrate that the impact of AI on the labour force employment structure reflects unique characteristics for China and promotes the advancement of the nation's employment structure. The influence of AI on the labour force employment structure follows a non-linear pattern, fostering labour force employment structure optimisation and upgrading from the perspective of industrial structure optimisation. Further investigation reveals the influence of spatial spillover effects from AI on employment structure optimisation. These research findings have theoretical value and practical significance for optimising China's employment structure in the context of AI.","2024-03-15","2024-12-03 03:24:27","2024-12-03 03:24:27","","e26686","","5","10","","Heliyon","","","","","","","","","","","","","","","","","","","Artificial intelligence; Eight economic regions; Employment structure; Industrial structure; Threshold effect","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"8KYWAV4J","journalArticle","2024","Kaya, Yiğit Bekir; Tantuğ, A. Cüneyd","Effect of tokenization granularity for Turkish large language models","Intelligent Systems with Applications","","2667-3053","10.1016/j.iswa.2024.200335","https://www.sciencedirect.com/science/article/pii/S2667305324000115","Transformer-based language models such as BERT (and its optimized versions) have outperformed previous models, achieving state-of-the-art results on many English benchmark tasks. These multi-layered self-attention-based architectures are capable of producing contextual word vector representations. However, the tokens created in the tokenization preprocessing step are not necessarily words, particularly for languages with complex morphology, such as Turkish. While previous research has often focused on tokenization algorithms and has explored optimal vocabulary sizes for machine translation in English, our study extends the scope by investigating the impact of varying vocabulary sizes and explores the feasilitiy of incorporating morphological tagging for Turkish. The granularity of the generated tokens is a feature determined by various factors related to tokenization, especially by the vocabulary size. This study presents a new collection of BERT models (ITUTurkBERT) trained using various tokenization methods on the corpus of the BERTurk and 1 BW corpora. We fine-tuned these models for named entity recognition, sentiment analysis, and question-answering downstream tasks in Turkish and achieved state-of-the-art performance on all of these tasks. Our empirical experiments show that increasing the vocabulary size improves performance on these tasks, except for sentiment analysis, which requires further investigation.","2024-03-01","2024-12-03 03:24:27","2024-12-03 03:24:27","","200335","","","21","","Intelligent Systems with Applications","","","","","","","","","","","","","","","","","","","BERT; Downstream tasks; Hyperparameter tuning; Tokenization; Turkish","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"5VB8BW6M","journalArticle","2024","Attard-Frost, Blair; Brandusescu, Ana; Lyons, Kelly","The governance of artificial intelligence in Canada: Findings and opportunities from a review of 84 AI governance initiatives","Government Information Quarterly","","0740-624X","10.1016/j.giq.2024.101929","https://www.sciencedirect.com/science/article/pii/S0740624X24000212","In recent years, the effective governance of artificial intelligence (AI) systems has become a strategic necessity for many nations. Among those nations, Canada is particularly noteworthy: Canada was the first nation to implement a national AI strategy, and more recently, Canada's federal and provincial governments have designed and implemented a wide range of initiatives that attempt to intervene in a variety of potential impacts associated with AI systems. We present a semi-systematic review and synthesis of 84 of those AI governance initiatives. We find that those 84 initiatives predominantly focus on developing programs, policies, and strategic plans to intervene in industry and innovation, technology production and use, AI research, and public administration. Conversely, we find relatively little focus on developing ethics statements or standards, as well as little focus on intervening in social and workforce development services, AI education and training, and digital infrastructure. We suggest three opportunities for researchers and four opportunities for practitioners that, if enacted, would strengthen the overall state of Canadian AI governance. Our study contributes a novel macro-scale synthesis of AI governance initiatives within a national context, as well as practical opportunities for intervening in national AI governance challenges related to evaluation of initiative outcomes, public trust and participation in initiatives, AI impact representation in initiatives, and national unification.","2024-06-01","2024-12-03 03:24:27","2024-12-03 03:24:27","","101929","","2","41","","Government Information Quarterly","","","","","","","","","","","","","","","","","","","Content analysis; Literature review; Artificial intelligence; AI ethics; Policy; Strategy; Governance","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"HFZ3W886","journalArticle","2024","Singh, Pragya; Sharma, Shashank; Singh, Kalpana; Singh, Pramod K.; Chowdhury, Faisal Islam; Yahya, M.Z.A.; Yusuf, S.N.F.; Diantoro, Markus; Latif, Famiza Abdul; Singh, N.B.","Recent development on neem (azadirachta indica) biomass absorbent: Surface modifications and its applications in water remediation","Chemical Physics Impact","","2667-0224","10.1016/j.chphi.2024.100773","https://www.sciencedirect.com/science/article/pii/S2667022424003177","This review paper discusses the latest advances in transfiguring Azadirachta indica (neem) biomass into an adsorbent and how it can be used to clean the environment. As an economical and environmentally favorable adsorbent material, neem biomass has become increasingly popular due to its bioactive properties and abundance. The review breaks down modification techniques into chemical and physical processes. Important physical changes covered include preactivation, carbonization, and using fluidized bed technologies and rotary kilns. It has been shown that these methods greatly improve the surface properties of neem biomass, which makes it better at absorbing things and holding more. The changes that were made also have an effect on the adsorption kinetics and isotherms, which helps us understand the adsorption rates and equilibrium behaviors of the changed adsorbents better. Neem biomass adsorbents are illustrated through their versatility and efficacy in the removal of organic pollutants, dyes, and heavy metals from effluent. This is illustrated through specific applications. Additionally, computational studies and artificial intelligence are looked into to see if they can help us understand how adsorption works at the molecular level, improve the efficiency of modification processes, and guess how adsorption will behave. The review also talks about the research gaps and suggests areas for future research. The review emphasizes the crucial importance of integrating experimental and computational methods to enhance the performance of the modified neem biomass and increase its environmental cleaning potential.","2024-12-01","2024-12-03 03:24:27","2024-12-03 03:24:27","","100773","","","9","","Chemical Physics Impact","","","","","","","","","","","","","","","","","","","Adsorption; Computational studies and artificial intelligence; Environmental remediation; Fluidized bed technologies and rotary kilns; Functionalization; Heavy metals; Neem biomass; Pollutant removal; Surface modifications; Water remediation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"TASCADSH","journalArticle","2024","Younas, Faizan; Raza, Ali; Thalji, Nisrean; Abualigah, Laith; Zitar, Raed Abu; Jia, Heming","An efficient artificial intelligence approach for early detection of cross-site scripting attacks","Decision Analytics Journal","","2772-6622","10.1016/j.dajour.2024.100466","https://www.sciencedirect.com/science/article/pii/S2772662224000705","Cross-Site Scripting (XSS) attacks continue to pose a significant threat to web applications, compromising the security and integrity of user data. XSS is a web application vulnerability where malicious scripts are injected into websites, allowing attackers to execute arbitrary code in the victim’s browser. The consequences of XSS attacks can be severe, ranging from financial losses to compromising sensitive user information. XSS attacks enable attackers to deface websites, distribute malware, or launch phishing campaigns, compromising the trust and reputation of affected organizations. This study proposes an efficient artificial intelligence approach for the early detection of XSS attacks, utilizing machine learning and deep learning approaches, including Long Short-Term Memory (LSTM). Additionally, advanced feature engineering techniques, such as the Term Frequency-Inverse Document Frequency (TFIDF), are applied and compared to evaluate results. We introduce a novel approach named LSTM-TFIDF (LSTF) for feature extraction, which combines temporal and TFIDF features from the cross-site scripting dataset, resulting in a new feature set. Extensive research experiments demonstrate that the random forest method achieved a high performance of 0.99, outperforming state-of-the-art approaches using the proposed features. A k-fold cross-validation mechanism is utilized to validate the performance of applied methods, and hyperparameter tuning further enhances the performance of XSS attack detection. We have applied Explainable Artificial Intelligence (XAI) to understand the interpretability and transparency of the proposed model in detecting XSS attacks. This study makes a valuable contribution to the growing body of knowledge on XSS attacks and provides an efficient model for developers and security practitioners to enhance the security of web applications.","2024-06-01","2024-12-03 03:24:27","2024-12-03 03:24:27","","100466","","","11","","Decision Analytics Journal","","","","","","","","","","","","","","","","","","","Deep learning; Machine learning; Artificial intelligence; Cross-site scripting attacks; Feature engineering; Feature fusion","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XQTX65QS","journalArticle","2024","Wenderott, Katharina; Krups, Jim; Luetkens, Julian A.; Weigl, Matthias","Radiologists’ perspectives on the workflow integration of an artificial intelligence-based computer-aided detection system: A qualitative study","Applied Ergonomics","","0003-6870","10.1016/j.apergo.2024.104243","https://www.sciencedirect.com/science/article/pii/S0003687024000206","In healthcare, artificial intelligence (AI) is expected to improve work processes, yet most research focuses on the technical features of AI rather than its real-world clinical implementation. To evaluate the implementation process of an AI-based computer-aided detection system (AI-CAD) for prostate MRI readings, we interviewed German radiologists in a pre-post design. We embedded our findings in the Model of Workflow Integration and the Technology Acceptance Model to analyze workflow effects, facilitators, and barriers. The most prominent barriers were: (i) a time delay in the work process, (ii) additional work steps to be taken, and (iii) an unstable performance of the AI-CAD. Most frequently named facilitators were (i) good self-organization, and (ii) good usability of the software. Our results underline the importance of a holistic approach to AI implementation considering the sociotechnical work system and provide valuable insights into key factors of the successful adoption of AI technologies in work systems.","2024-05-01","2024-12-03 03:24:27","2024-12-03 03:24:27","","104243","","","117","","Applied Ergonomics","","","","","","","","","","","","","","","","","","","Artificial intelligence; Healthcare; Workflow integration","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"25D375N2","journalArticle","2023","Guida, Michela; Caniato, Federico; Moretto, Antonella; Ronchi, Stefano","The role of artificial intelligence in the procurement process: State of the art and research agenda","Journal of Purchasing and Supply Management","","1478-4092","10.1016/j.pursup.2023.100823","https://www.sciencedirect.com/science/article/pii/S1478409223000079","Artificial intelligence (AI) is widely adopted in many areas, but it is still in its infancy in procurement, despite its potential. To map the state of the art of both research and practice and identify future research directions, this paper presents a mixed methodology exploratory study of the role of AI in the procurement process. The paper combines a systematic literature review, a mapping of the offerings of providers of AI-based procurement platforms and a focus group with procurement managers. Results map the functionalities of AI-based solutions throughout the procurement process, describe benefits and challenges to their adoption and identify future research directions.","2023-03-01","2024-12-03 03:24:28","2024-12-03 03:24:28","","100823","","2","29","","Journal of Purchasing and Supply Management","","","","","","","","","","","","","","","","","","","Artificial intelligence; Systematic literature review; Digital procurement; Procurement process; Purchasing","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RFD989WI","journalArticle","2024","Lore, Madison; Harten, Julia Gabriele; Boeing, Geoff","A hybrid deep learning method for identifying topics in large-scale urban text data: Benefits and trade-offs","Computers, Environment and Urban Systems","","0198-9715","10.1016/j.compenvurbsys.2024.102131","https://www.sciencedirect.com/science/article/pii/S0198971524000607","Large-scale text data from public sources, including social media or online platforms, can expand urban planners' ability to monitor and analyze urban conditions in near real-time. To overcome scalability challenges of manual techniques for qualitative data analysis, researchers and practitioners have turned to computer-automated methods, such as natural language processing (NLP) and deep learning. However, the benefits, challenges, and trade-offs of these methods remain poorly understood. How much meaning can different NLP techniques capture and how do their results compare to traditional manual techniques? Drawing on 90,000 online rental listings in Los Angeles County, this study proposes and compares manual, semi-automated, and fully automated methods for identifying context-informed topics in unstructured, user-generated text data. We find that fully automated methods perform best with more-structured text, but struggle to separate topics in free-flow text and when handling nuanced language. Introducing a manual technique first on a small data set to train a semi-automated method, however, improves accuracy even as the structure of the text degrades. We argue that while fully automated NLP methods are attractive replacements for scaling manual techniques, leveraging the contextual understanding of human expertise alongside efficient computer-based methods like BERT models generates better accuracy without sacrificing scalability.","2024-07-01","2024-12-03 03:24:28","2024-12-03 03:24:28","","102131","","","111","","Computers, Environment and Urban Systems","","","","","","","","","","","","","","","","","","","Deep learning; Natural language processing; Qualitative methods; Text analysis; Urban informatics","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SQY2JJCF","journalArticle","2024","Ghasemieh, Alireza; Kashef, Rasha","Towards explainable artificial intelligence in deep vision-based odometry","Computers and Electrical Engineering","","0045-7906","10.1016/j.compeleceng.2024.109127","https://www.sciencedirect.com/science/article/pii/S0045790624000557","Visual Odometry (VO) is a crucial process for estimating camera motion in real-time based on visual information captured. The emergence of deep learning has significantly transformed VO and Explainable Artificial Intelligence (XAI) in Deep Vision-Based Odometry. This survey paper explores the latest advancements in VO facilitated by deep learning methods, focusing on explainability and interpretability. It provides an overview of state-of-the-art deep learning techniques and dissects each model into its elemental building blocks to understand their explainable and interpretable aspects. The survey also highlights research gaps in optical flow robustness, occlusion and dynamic objects, real-time processing, drift correction, semantic awareness, and sensor integration. The aim is to catalyze future innovations in deep learning-based VO and stimulate dialogue about potential directions for the next wave of research, emphasizing explainability and interpretability as integral components of advanced systems.","2024-04-01","2024-12-03 03:24:28","2024-12-03 03:24:28","","109127","","","115","","Computers and Electrical Engineering","","","","","","","","","","","","","","","","","","","Explainable artificial intelligence (XAI); Pose estimation deep learning; Self-localization; Visual Odometry","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ZCBK43QW","journalArticle","2024","Jovanovic, Zorka; Hou, Zhe; Biswas, Kamanashis; Muthukkumarasamy, Vallipuram","Robust integration of blockchain and explainable federated learning for automated credit scoring","Computer Networks","","1389-1286","10.1016/j.comnet.2024.110303","https://www.sciencedirect.com/science/article/pii/S138912862400135X","This article examines the integration of blockchain, eXplainable Artificial Intelligence (XAI), especially in the context of federated learning, for credit scoring in financial sectors to improve the credit assessment process. Research shows that integration of these cutting-edge technologies is in its infancy, specifically in the areas of embracing broader data, model verification, behavioural reliability and model explainability for intelligent credit assessment. The conventional credit risk assessment process utilises historical application data. However, reliable and dynamic transactional customer data are necessary for robust credit risk evaluation in practice. Therefore, this research proposes a framework for integrating blockchain and XAI to enable automated credit decisions. The main focus is on effectively integrating multi-party, privacy-preserving decentralised learning models with blockchain technology to provide reliability, transparency, and explainability. The proposed framework can be a foundation for integrating technological solutions while ensuring model verification, behavioural reliability, and model explainability for intelligent credit assessment.","2024-04-01","2024-12-03 03:24:28","2024-12-03 03:24:28","","110303","","","243","","Computer Networks","","","","","","","","","","","","","","","","","","","Blockchain; Explainable artificial intelligence; Automated credit scoring; Decentralised federated learning","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"H39LHSA6","journalArticle","2024","Rom, Yovel; Aviv, Rachelle; Cohen, Gal Yaakov; Friedman, Yehudit Eden; Ianchulev, Tsontcho; Dvey-Aharon, Zack","Diabetes detection from non-diabetic retinopathy fundus images using deep learning methodology","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e36592","https://www.sciencedirect.com/science/article/pii/S2405844024126230","Diabetes is one of the leading causes of morbidity and mortality in the United States and worldwide. Traditionally, diabetes detection from retinal images has been performed only using relevant retinopathy indications. This research aimed to develop an artificial intelligence (AI) machine learning model which can detect the presence of diabetes from fundus imagery of eyes without any diabetic eye disease. A machine learning algorithm was trained on the EyePACS dataset, consisting of 47,076 images. Patients were also divided into cohorts based on disease duration, each cohort consisting of patients diagnosed within the timeframe in question (e.g., 15 years) and healthy participants. The algorithm achieved 0.86 area under receiver operating curve (AUC) in detecting diabetes per patient visit when averaged across camera models, and AUC 0.83 on the task of detecting diabetes per image. The results suggest that diabetes may be diagnosed non-invasively using fundus imagery alone. This may enable diabetes diagnosis at point of care, as well as other, accessible venues, facilitating the diagnosis of many undiagnosed people with diabetes.","2024-08-30","2024-12-03 03:24:28","2024-12-03 03:24:28","","e36592","","16","10","","Heliyon","","","","","","","","","","","","","","","","","","","Machine learning; Artificial intelligence; Diabetes","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KC39IK4E","journalArticle","2024","Liu, Xianjing; Sangers, Tobias E.; Nijsten, Tamar; Kayser, Manfred; Pardo, Luba M.; Wolvius, Eppo B.; Roshchupkin, Gennady V.; Wakkee, Marlies","Predicting skin cancer risk from facial images with an explainable artificial intelligence (XAI) based approach: a proof-of-concept study","eClinicalMedicine","","2589-5370","10.1016/j.eclinm.2024.102550","https://www.sciencedirect.com/science/article/pii/S2589537024001299","Summary Background Efficient identification of individuals at high risk of skin cancer is crucial for implementing personalized screening strategies and subsequent care. While Artificial Intelligence holds promising potential for predictive analysis using image data, its application for skin cancer risk prediction utilizing facial images remains unexplored. We present a neural network-based explainable artificial intelligence (XAI) approach for skin cancer risk prediction based on 2D facial images and compare its efficacy to 18 established skin cancer risk factors using data from the Rotterdam Study. Methods The study employed data from the Rotterdam population-based study in which both skin cancer risk factors and 2D facial images and the occurrence of skin cancer were collected from 2010 to 2018. We conducted a deep-learning survival analysis based on 2D facial images using our developed XAI approach. We subsequently compared these results with survival analysis based on skin cancer risk factors using cox proportional hazard regression. Findings Among the 2810 participants (mean Age = 68.5 ± 9.3 years, average Follow-up = 5.0 years), 228 participants were diagnosed with skin cancer after photo acquisition. Our XAI approach achieved superior predictive accuracy based on 2D facial images (c-index = 0.72, 95% CI: 0.70–0.74), outperforming that of the known risk factors (c-index = 0.59, 95% CI 0.57–0.61). Interpretation This proof-of-concept study underscores the high potential of harnessing facial images and a tailored XAI approach as an easily accessible alternative over known risk factors for identifying individuals at high risk of skin cancer. Funding The Rotterdam Study is funded through unrestricted research grants from Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. G.V. Roshchupkin is supported by the ZonMw Veni grant (Veni, 549 1936320).","2024-05-01","2024-12-03 03:24:28","2024-12-03 03:24:28","","102550","","","71","","eClinicalMedicine","","","","","","","","","","","","","","","","","","","Deep learning; Skin cancer; Explainable artificial intelligence; Risk prediction; Survival analysis","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"75ZYNZN6","journalArticle","2024","Henrique, Bruno Miranda; Santos, Eugene","Trust in artificial intelligence: Literature review and main path analysis","Computers in Human Behavior: Artificial Humans","","2949-8821","10.1016/j.chbah.2024.100043","https://www.sciencedirect.com/science/article/pii/S2949882124000033","Artificial Intelligence (AI) is present in various modern systems, but it is still subject to acceptance in many fields. Medical diagnosis, autonomous driving cars, recommender systems and robotics are examples of areas in which some humans distrust AI technology, which ultimately leads to low acceptance rates. Conversely, those same applications can have humans who over rely on AI, acting as recommended by the systems with no criticism regarding the risks of a wrong decision. Therefore, there is an optimal balance with respect to trust in AI, achieved by calibration of expectations and capabilities. In this context, the literature about factors influencing trust in AI and its calibration is scattered among research fields, with no objective summaries of the overall evolution of the theme. In order to close this gap, this paper contributes a literature review of the most influential papers on the subject of trust in AI, selected by quantitative methods. It also proposes a Main Path Analysis of the literature, highlighting how the theme has evolved over the years. As results, researchers will find an overview on trust in AI based on the most important papers objectively selected and also tendencies and opportunities for future research.","2024-01-01","2024-12-03 03:24:28","2024-12-03 03:24:28","","100043","","1","2","","Computers in Human Behavior: Artificial Humans","","","","","","","","","","","","","","","","","","","Literature review; Artificial intelligence; Trust; Main path analysis; Trust calibration","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"79GKZTZR","journalArticle","2024","Giordano, Bastien; Prieur, Maxime; Vuth, Nakanyseth; Verdy, Sylvain; Cousot, Kévin; Sérasset, Gilles; Gadek, Guillaume; Schwab, Didier; Lopez, Cédric","POPCORN: Fictional and Synthetic Intelligence Reports for Named Entity Recognition and Relation Extraction Tasks","28th International Conference on Knowledge Based and Intelligent information and Engineering Systems (KES 2024)","","1877-0509","10.1016/j.procs.2024.09.542","https://www.sciencedirect.com/science/article/pii/S1877050924025870","POPCORN is a research project aiming at maturing Information Extraction (IE) solutions for intelligence services. Due to defense security constraints, reports analyzed by intelligence services are not to be accessible to the scientific community. To address this challenge, we propose a dataset made of “fictional” (handcrafted) and “synthetic” (AI generated) French reports. Those synthetic reports are produced by an innovative approach that generates texts closely resembling real-world intelligence reports, facilitating the training and evaluation of IE tasks such as Entity and Relation Extraction. Experiments demonstrate the interest of synthetic reports to enhance the performance of IE models, showcasing their potential to augment real-world intelligence operations.","2024-01-01","2024-12-03 03:24:28","2024-12-03 03:24:28","","1170-1180","","","246","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Large Language Models; Natural Language Processing; Dataset; Information Extraction; Synthetic Data Generation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"N7RNSN99","journalArticle","2024","Sadegh-Zadeh, S.-A.; Nazari, M.-J.; Aljamaeen, M.; Yazdani, F.S.; Mousavi, S.Y.; Vahabi, Z.","Predictive models for Alzheimer's disease diagnosis and MCI identification: The use of cognitive scores and artificial intelligence algorithms","NPG Neurologie - Psychiatrie - Gériatrie","","1627-4830","10.1016/j.npg.2024.04.004","https://www.sciencedirect.com/science/article/pii/S1627483024000527","Summary The paper presents a comprehensive study on predictive models for Alzheimer's disease (AD) and mild cognitive impairment (MCI) diagnosis, implementing a combination of cognitive scores and artificial intelligence algorithms. The research includes detailed analyses of clinical and demographic variables such as age, education, and various cognitive and functional scores, investigating their distributions and correlations with AD and MCI. The study utilizes several machine-learning classifiers, comparing their performance through metrics like accuracy, precision, and area under the ROC curve (AUC). Key findings include the influence of gender on AD prevalence, the potential protective effect of education, and the significance of functional decline and cognitive performance scores in the models. The results demonstrate the effectiveness of ensemble methods and the robustness of the models across different data subsets, highlighting the potential of artificial intelligence in enhancing diagnostic accuracy for Alzheimer's disease and mild cognitive impairment. Résumé Cette étude explore l’application des algorithmes d’apprentissage automatique pour le diagnostic de la maladie d’Alzheimer (MA) et l’identification de la détérioration cognitive légère (DCL), en utilisant des scores cognitifs parmi d’autres variables cliniques et démographiques. Nous décrivons notre méthodologie, incluant la collecte de données, le prétraitement, la sélection des caractéristiques, et l’utilisation de divers classificateurs d’apprentissage machine. Les résultats mettent en évidence l’efficacité des méthodes d’ensemble dans la prédiction de la MA et de la DCL, discutent des implications de ces résultats pour le diagnostic précoce et l’intervention, et suggèrent des directions pour les recherches futures.","2024-08-01","2024-12-03 03:24:28","2024-12-03 03:24:28","","194-211","","142","24","","NPG Neurologie - Psychiatrie - Gériatrie","","","","","","","","","","","","","","","","","","","Artificial intelligence; Intelligence artificielle; Alzheimer's disease; Apprentissage automatique; Cognitive scores; Détérioration cognitive légère (DCL); Machine-learning classifiers; Maladie d’Alzheimer (MA); Mild cognitive impairment (MCI); Predictive models; Scores cognitifs","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"YQ4JQJDK","journalArticle","2024","Li, Yan; Nie, Yingnan; Quan, Zhaoyu; Zhang, Han; Song, Rui; Feng, Hao; Cheng, Xi; Liu, Wei; Geng, Xinyi; Sun, Xinwei; Fu, Yanwei; Wang, Shouyan","Brain-machine interactive neuromodulation research tool with edge AI computing","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e32609","https://www.sciencedirect.com/science/article/pii/S2405844024086407","Closed-loop neuromodulation with intelligence methods has shown great potentials in providing novel neuro-technology for treating neurological and psychiatric diseases. Development of brain-machine interactive neuromodulation strategies could lead to breakthroughs in precision and personalized electronic medicine. The neuromodulation research tool integrating artificial intelligent computing and performing neural sensing and stimulation in real-time could accelerate the development of closed-loop neuromodulation strategies and translational research into clinical application. In this study, we developed a brain-machine interactive neuromodulation research tool (BMINT), which has capabilities of neurophysiological signals sensing, computing with mainstream machine learning algorithms and delivering electrical stimulation pulse by pulse in real-time. The BMINT research tool achieved system time delay under 3 ms, and computing capabilities in feasible computation cost, efficient deployment of machine learning algorithms and acceleration process. Intelligent computing framework embedded in the BMINT enable real-time closed-loop neuromodulation developed with mainstream AI ecosystem resources. The BMINT could provide timely contribution to accelerate the translational research of intelligent neuromodulation by integrating neural sensing, edge AI computing and stimulation with AI ecosystems.","2024-06-30","2024-12-03 03:24:28","2024-12-03 03:24:28","","e32609","","12","10","","Heliyon","","","","","","","","","","","","","","","","","","","Machine learning; Artificial intelligence; Closed-loop neuromodulation; Edge AI computing; Real-time; Seizure detection","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"G8GVGKTB","journalArticle","2023","Nashwan, Abdulqadir J.; Hani, Salam Bani","Transforming cancer clinical trials: The integral role of artificial intelligence in electronic health records for efficient patient recruitment","Contemporary Clinical Trials Communications","","2451-8654","10.1016/j.conctc.2023.101223","https://www.sciencedirect.com/science/article/pii/S2451865423001692","Healthcare is one of the sectors where artificial intelligence (AI) is currently viewed as a crucial driving factor. Patient care, medical research, and clinical trial enrollment could all significantly improve due to AI's incorporation into electronic health records (EHRs). This short communication highlights how AI may improve the recruitment process regarding speed, accuracy, and overall cancer clinical trial efficiency. AI can automate this procedure by utilizing machine learning (ML) algorithms, identifying potential trial participants quickly and precisely. Many challenges could be addressed due to this integration, including data privacy and security that can be resolved through cutting-edge encryption techniques and differential privacy algorithms that ensure data anonymization. Another significant obstacle is the lack of common EHR formats and interoperability that can be addressed by creating a standardized structured layout. Automating and improving recruitment processes with AI may speed up research, increase the effectiveness of clinical trials, and open the door to more specialized cancer treatments.","2023-12-01","2024-12-03 03:24:28","2024-12-03 03:24:28","","101223","","","36","","Contemporary Clinical Trials Communications","","","","","","","","","","","","","","","","","","","Artificial intelligence; Electronic health records; Clinical trials; Cancer; Subject recruitment","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"4G5XWL7Q","journalArticle","2024","Muawanah, Uyu; Marini, Arita; Sarifah, Iva","The interconnection between digital literacy, artificial intelligence, and the use of E-learning applications in enhancing the sustainability of Regional Languages: Evidence from Indonesia","Social Sciences & Humanities Open","","2590-2911","10.1016/j.ssaho.2024.101169","https://www.sciencedirect.com/science/article/pii/S2590291124003668","This research aims to explore the relationship between Digital Literacy, Artificial Intelligence (AI), E-Learning, and the Sustainability of Regional Languages. The study used a mixed-methods approach, combining surveys and qualitative interviews to gather comprehensive insights. Key findings indicate a positive impact of Digital Literacy on E-Learning Applications and the Sustainability of Regional Languages, emphasizing the need for robust Digital Literacy programs. Moreover, the research establishes a significant positive relationship between AI, E-Learning, and the Sustainability of Regional Languages, highlighting the transformative potential of AI in personalized language learning experiences. The novelty of this study lies in its holistic examination of the intricate connections within the digital language education landscape, addressing conceptual gaps in the theoretical framework, forming language-focused online communities, and suggesting actionable recommendations. Practical implications include the development of inclusive, region-specific content and continuous advancements in AI integration and Digital Literacy programs. This research contributes to the theoretical framework of digital language education and provides practical guidance for educators, policymakers, and stakeholders seeking to enhance language sustainability through strategic digital interventions.","2024-01-01","2024-12-03 03:24:28","2024-12-03 03:24:28","","101169","","","10","","Social Sciences & Humanities Open","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RB8KK8L5","journalArticle","2024","Manuelyan, Karen; Dragolov, Miroslav; Drenovska, Kossara; Shahid, Martin; Vassileva, Snejina","Artificial intelligence in autoimmune bullous dermatoses","Artificial Intelligence II","","0738-081X","10.1016/j.clindermatol.2024.06.008","https://www.sciencedirect.com/science/article/pii/S0738081X24000920","Dermatologists treating patients with autoimmune bullous dermatoses (AIBDs), as well as the patients themselves, encounter challenges at every stage of their interaction, including dermatologic and comorbidities assessment, diagnosis, prognosis evaluation, treatment, and follow-up monitoring. We summarize the current and potential future clinical applications of artificial intelligence (AI) in the field of AIBDs. Recent research and AI models have demonstrated their potential to enhance or may already be contributing to advancements in every phase of the comprehensive diagnosis and personalized treatment process in AIBDs, providing patients, clinicians, and administrators with valuable support. Image recognition AI systems might assist precise clinical diagnoses of various diseases, including AIBDs, and could offer consistent and reliable scoring of disease severity. Automated and standardized AI-assisted laboratory methods could improve the accuracy and decrease the time and cost of gold-standard tests such as direct and indirect immunofluorescence. The studies and tools discussed in this contribution, although in the early stages, might be a small precursor to a transformative shift in the way we take care of patients with chronic skin diseases, including AIBDs.","2024-09-01","2024-12-03 03:24:28","2024-12-03 03:24:28","","426-433","","5","42","","Clinics in Dermatology","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"95LVHI5P","journalArticle","2024","Hackert, Stephanie; Laliberté, Catherine; Wengler, Diana","Past inflection around the world: A cross-variety analysis of New Englishes","Lingua","","0024-3841","10.1016/j.lingua.2024.103776","https://www.sciencedirect.com/science/article/pii/S0024384124001050","In this paper, we investigate variable past inflection in four New Englishes. Our data are drawn from the conversational parts of the Hong Kong, India, Jamaica, and Philippine subcomponents of the International Corpus of English. We investigate the entire range of language-internal factors that have been found to influence non-obligatory past marking in varieties of English. This includes morpho-phonological verb class, lexical aspect, grammatical aspect, marker persistence, the presence or absence of a temporal adverbial, and, for consonant-final regular verbs, preceding and following phonological environment. We also consider verb frequency, which has received only scant attention in past inflection research so far. Employing both mixed-effects regression and random forests, we argue that, despite inter-variety differences, there is a core grammar of past inflection, which is constrained by general structural and cognitive phenomena such as grammatical aspect and marker persistence, with frequency also exerting an important and consistent effect. This has implications for debates about universals vs. substrate influence or creole effects in morphosyntactic variation in English.","2024-08-01","2024-12-03 03:24:28","2024-12-03 03:24:28","","103776","","","307","","Lingua","","","","","","","","","","","","","","","","","","","Aspect Hypothesis; Creole; International Corpus of English; New Englishes; Past inflection; Second-language acquisition","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"NMHEMVNF","journalArticle","2024","Hall, J.M.M.; Nguyen, T.V.; Dinsmore, A.W.; Perugini, D.; Perugini, M.; Fukunaga, N.; Asada, Y.; Schiewe, M.; Lim, A.Y.X.; Lee, C.; Patel, N.; Bhadarka, H.; Chiang, J.; Bose, D.P.; Mankee-Sookram, S.; Minto-Bain, C.; Bilen, E.; Diakiw, S.M.","Use of federated learning to develop an artificial intelligence model predicting usable blastocyst formation from pre-ICSI oocyte images","Reproductive BioMedicine Online","","1472-6483","10.1016/j.rbmo.2024.104403","https://www.sciencedirect.com/science/article/pii/S1472648324005923","ABSTRACT Research question Can federated learning be used to develop an artificial intelligence (AI) model for evaluating oocyte competence using two-dimensional images of denuded oocytes in metaphase II prior to intracytoplasmic sperm injection (ICSI)? Results The oocyte AI model demonstrated area under the curve (AUC) up to 0.65 on two blind test datasets. High sensitivity for predicting competent oocytes (83–88%) was offset by lower specificity (26–36%). Exclusion of confounding biological variables (male factor infertility and maternal age ≥35 years) improved AUC up to 14%, primarily due to increased specificity. AI score correlated with size of the zona pellucida and perivitelline space, and ooplasm appearance. AI score also correlated with blastocyst expansion grade and morphological quality. The sum of AI scores from oocytes in group culture images predicted the formation of two or more usable blastocysts (AUC 0.77). Conclusion An AI model to evaluate oocyte competence was developed using federated learning, representing an essential step in protecting patient data. The AI model was significantly predictive of oocyte competence, as defined by usable blastocyst formation, which is a critical factor for IVF success. Potential clinical utility ranges from selective oocyte fertilization to guiding treatment decisions regarding additional rounds of oocyte retrieval. Design In total, 10,677 oocyte images with associated metadata were collected prospectively by eight IVF clinics across six countries. AI training used federated learning, where data were retained on regional servers to comply with data privacy laws. The final AI model required a single image as input to evaluate oocyte competence, which was defined by the formation of a usable blastocyst (≥expansion grade 3 by day 5 or 6 post ICSI).","2024-12-01","2024-12-03 03:24:28","2024-12-03 03:24:28","","104403","","6","49","","Reproductive BioMedicine Online","","","","","","","","","","","","","","","","","","","Artificial intelligence; Medical imaging; Computer vision; Embryo development; IVF; Oocyte assessment","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"4LUMGS4U","journalArticle","2024","Mosleh, Sultan M.; Alsaadi, Fton Ali; Alnaqbi, Fatima Khamis; Alkhzaimi, Meirah Abdullrahman; Alnaqbi, Shamma Waleed; Alsereidi, Waed Mohammed","Examining the association between emotional intelligence and chatbot utilization in education: A cross-sectional examination of undergraduate students in the UAE","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e31952","https://www.sciencedirect.com/science/article/pii/S2405844024079830","Background While Emotional Intelligence (EI) demonstrably affects academic success, literature lacks exploration of how implementing chatbot in education might influence both academic performance and students' emotional intelligence, despite the evident potential of such technology. Aim To investigate the associations between Emotional Intelligence (EI), chatbot utilization among undergraduate students. Methods A cross-sectional approach was employed, utilizing a convenience sample of 529 undergraduate students recruited through online questionnaires. The participants completed the Trait Emotional Intelligence Questionnaire and modified and a modified versions of the unified theory of acceptance and use of technology (UTAUT) model. Results of the 529 participants, 83.6 % (n = 440) of participants regularly used chatbot for learning. Students demonstrated a moderate average EI score (129.60 ± 50.15) and an exceptionally high score (89.61 ± 20.70) for chatbot acceptance and usage. A statistically significant (p < 0.001) positive correlation was found between chatbot usage frequency and EI total score. Gender and major emerged as significant factors, with female students (p < 0.05) and health science students (p < 0.05) utilizing chatbot less compared to male and other major students, respectively. A negative correlation (r = −0.111, p = 0.011) was observed between study hours and chatbot usage, suggesting students with higher study hours relied less on chatbot. Conclusions The positive correlation between chatbot use and EI in this study sparks promising avenues for enhancing the learning experience. By investing in further research to understand this link and integrate AI tools thoughtfully, policymakers and educators can cultivate a learning environment that prioritizes both academic excellence and student well-being, reflecting the values and perspectives of UAE culture.","2024-06-15","2024-12-03 03:24:28","2024-12-03 03:24:28","","e31952","","11","10","","Heliyon","","","","","","","","","","","","","","","","","","","Emotional intelligence; Artificial intelligence; Chatbot; Undergraduate education","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"76V4E5J2","journalArticle","2024","Salgado-Criado, Jesús; Mataix-Aldeanueva, Carlos; Nardini, Santiago; López-Pablos, Cecilia; Balestrini, Mara; Rosales-Torres, César Said","How should we govern digital innovation? A venture capital perspective","Technological Forecasting and Social Change","","0040-1625","10.1016/j.techfore.2023.123198","https://www.sciencedirect.com/science/article/pii/S0040162523008831","Ethical and governance issues regarding digital innovations developments like Artificial Intelligence have attracted abundant debate and research. Many perspectives – societal, engineering, research, management and policymaking – have been extensively analysed in an attempt to understand the role of the different stakeholders in minimising risks and amplifying opportunities. Interestingly, however, the investment community has been scarcely studied, despite the critical role of financing in technology innovation. This paper attempts to characterize investors' perceptions around the concept of digital innovation governance. We present insights derived from qualitative research using constructivist grounded theory, based on a series of interviews to 23 venture capital investment managers. Analysis of the data collected suggests several categories of important factors regarding how venture capital managers engage in Digital Innovation Governance (DIG): legitimacy of involvement, opportunity of conducting and capability to lead DIG initiatives in the invested startups. We offer a structured inventory of venture capital manager insights and perceptions on innovation governance. According to our research, venture capital managers, and investors in general, find themselves capable of and see opportunities and relevance in effectively fostering a digital innovation governance that takes into account the potential risks of Artificial Intelligence and identifies business and societal opportunities.","2024-03-01","2024-12-03 03:24:29","2024-12-03 03:24:29","","123198","","","200","","Technological Forecasting and Social Change","","","","","","","","","","","","","","","","","","","AI ethics; ESG; AI governance; Digital governance; Digital innovation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"4K7R7YCA","journalArticle","2024","Rony, Moustaq Karim Khan; Numan, Sharker Md.; Akter, Khadiza; Tushar, Hasanuzzaman; Debnath, Mitun; Johra, Fateha tuj; Akter, Fazila; Mondal, Sujit; Das, Mousumi; Uddin, Muhammad Join; Begum, Jeni; Parvin, Mst. Rina","Nurses' perspectives on privacy and ethical concerns regarding artificial intelligence adoption in healthcare","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e36702","https://www.sciencedirect.com/science/article/pii/S2405844024127338","Background With the increasing integration of artificial intelligence (AI) technologies into healthcare systems, there is a growing emphasis on privacy and ethical considerations. Nurses, as frontline healthcare professionals, are pivotal in-patient care and offer valuable insights into the ethical implications of AI adoption. Objectives This study aimed to explore nurses' perspectives on privacy and ethical concerns associated with the implementation of AI in healthcare settings. Methods We employed Van Manen's hermeneutic phenomenology as the qualitative research approach. Data were collected through purposive sampling from the December 7, 2023 to the January 15, 2024, with interviews conducted in Bengali. Thematic analysis was utilized following member checking and an audit trail. Results Six themes emerged from the research findings: Ethical dimensions of AI integration, highlighting complexities in incorporating AI ethically; Privacy challenges in healthcare AI, revealing concerns about data security and confidentiality; Balancing innovation and ethical practice, indicating a need to reconcile technological advancements with ethical considerations; Human touch vs. technological progress, underscoring tensions between automation and personalized care; Patient-centered care in the AI era, emphasizing the importance of maintaining focus on patients amidst technological advancements; and Ethical preparedness and education, suggesting a need for enhanced training and education on ethical AI use in healthcare. Conclusions The findings underscore the importance of addressing privacy and ethical concerns in AI healthcare development. Nurses advocate for patient-centered approaches and collaborate with policymakers and tech developers to ensure responsible AI adoption. Further research is imperative for mitigating ethical challenges and promoting ethical AI in healthcare practice.","2024-09-15","2024-12-03 03:24:29","2024-12-03 03:24:29","","e36702","","17","10","","Heliyon","","","","","","","","","","","","","","","","","","","Nurses; Artificial intelligence; Privacy; Healthcare; Ethical concerns","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"EP3BQPHB","journalArticle","2024","Liu, Jie; Zhang, Qian; Macián-Juan, Rafael","Enhancing interpretability in neural networks for nuclear power plant fault diagnosis: A comprehensive analysis and improvement approach","Progress in Nuclear Energy","","0149-1970","10.1016/j.pnucene.2024.105287","https://www.sciencedirect.com/science/article/pii/S0149197024002373","Deep neural networks, as applied in the field of nuclear power fault diagnosis, have garnered significant attention alongside advancements in artificial intelligence technology. However, the “black box” nature of deep learning models has raised concerns regarding their deployment in scenarios demanding high safety standards, such as nuclear power plants. In this paper, we innovatively propose the utilization of an explainable artificial intelligence method, grounded in game theory, to conduct a detailed analysis of the diagnostic behavior of neural network models applied to nuclear power plants. By leveraging SHAP, the decision-making processes of these opaque models are demystified, offering insights into how and why they arrive at their predictions. In this study, two of the most widely utilized neural network frameworks are applied across six representative fault diagnosis cases for a comprehensive analysis. Based on its analysis results, a novel SHAP-Enhanced Feature Selection for Efficient Neural Network Fault Diagnosis strategy is proposed, significantly reducing model complexity without sacrificing diagnostic performance. This research breaks through the limitations of data-driven models used in the field of nuclear power fault diagnosis, conducts an interpretable analysis of their behavior, and proposes an improved strategy based on the analysis results, contributing to the practical application of data-driven models in nuclear power.","2024-09-01","2024-12-03 03:24:29","2024-12-03 03:24:29","","105287","","","174","","Progress in Nuclear Energy","","","","","","","","","","","","","","","","","","","Fault diagnosis; Explainable artificial intelligence; Neural network interpretability; Nuclear power plant","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"9LDE3VNQ","journalArticle","2023","Livberber, Tuba; Ayvaz, Süheyla","The impact of Artificial Intelligence in academia: Views of Turkish academics on ChatGPT","Heliyon","","2405-8440","10.1016/j.heliyon.2023.e19688","https://www.sciencedirect.com/science/article/pii/S2405844023068962","In the past decade, Artificial Intelligence (AI) and machine learning technologies have become increasingly prevalent in the academic world. This growing trend has led to debates about the impact of these technologies on academia. The purpose of this article is to examine the impact of ChatGPT, an AI and machine learning technology, in the academic field and to determine academics' perceptions of it. To achieve this goal, in-depth interviews were conducted with 10 academics, and their views on the subject were analyzed. It is seen that academics believe that ChatGPT will play a helpful role as a tool in scientific research and educational processes and can serve as an inspiration for new topics and research areas. Despite these advantages, academics also have ethical concerns, such as plagiarism and misinformation. The study found that ChatGPT is viewed positively as a useful tool in scientific research and education, but ethical concerns such as plagiarism and misinformation need to be addressed.","2023-09-01","2024-12-03 03:24:29","2024-12-03 03:24:29","","e19688","","9","9","","Heliyon","","","","","","","","","","","","","","","","","","","Machine learning; Artificial intelligence; Academia; Human-AI collaboration; Learning & teaching","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"H9R84SL4","journalArticle","2024","Saheed, Yakub Kayode; Chukwuere, Joshua Ebere","XAIEnsembleTL-IoV: A new eXplainable Artificial Intelligence ensemble transfer learning for zero-day botnet attack detection in the Internet of Vehicles","Results in Engineering","","2590-1230","10.1016/j.rineng.2024.103171","https://www.sciencedirect.com/science/article/pii/S2590123024014269","The Internet of Vehicles (IoV) is a network of interconnected vehicles that use modern communication technologies to communicate with each other and the surrounding infrastructure. The IoV is a novel network that experiences a continuous emergence and evolution of various forms of attacks. This research addresses the growing challenge of detecting zero-day botnet attacks within the IoV, a complex network that enhances real-time communication among vehicles and surrounding infrastructure to improve traffic management, safety, and driving experiences. Traditional machine learning-based intrusion detection systems (IDS) for IoV face two key limitations: the requirement for large labeled datasets and the “black box” issue, where the reasoning behind model decisions is not transparent, reducing user and stakeholder confidence. To solve these problems, this research proposes an eXplainable Artificial Intelligence (XAI) Ensemble Transfer Learning (TL) model specifically for detecting zero-day attacks in IoV. The proposed model integrates deep Shapley Additive Explanations (SHAP), providing transparency and making decisions understandable to cybersecurity professionals. Additionally, the model employs hybrid bidirectional long-short-term memory with autoencoders (BiLAE) to reduce the dimensionality of IoV network traffic, improving computational efficiency. It also uses Barnacle Mating Optimizer (BMO) to optimize the hyper-parameters of deep learning models such as ResNet, Inception, Inception ResNet, and MobileNet Convolution neural network-transfer learning architecture (CNN-TL), enhancing detection capabilities without needing vast amounts of labeled data. Experimental results showed that the model performed with an accuracy of 100 %, precision of 100 %, recall of 100 %, F1-score of 100 %, and Matthew Correlation Coefficient of 100 % in binary-class situations for internal vehicular (CAN) networks and achieved 99.88 % accuracy and similarly high metrics in multi-class scenarios for external vehicular networks(N-BaIoT). Compared to state-of-the-art techniques, the model proved to be more effective in detecting zero-day botnet attacks, reducing reliance on large datasets. Unlike traditional black-box models, the XAI component of the ensemble model offers insight into the decision-making process. It allows network administrators and security experts to understand how specific patterns in the data contribute to detection, making the system more transparent. The solution is highly adaptable and scalable for real-time application, designed to operate efficiently even on IoV gateway electronic control units with limited computational power.","2024-12-01","2024-12-03 03:24:29","2024-12-03 03:24:29","","103171","","","24","","Results in Engineering","","","","","","","","","","","","","","","","","","","Barnacle mating optimizer; Botnet; CAN-Hacking; eXplainable artificial intelligence (XAI); Inception; Inception ResNet; Internet of vehicle; MobileNet; N-BaIoT; ResNet; Zero-day","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XBASWKU3","journalArticle","2024","Mishra, Manohar; Pragati, Abha; Gadanayak, Debadatta Amaresh; Parida, Tanmoy","Signal processing and artificial intelligence based HVDC network protection: A systematic and state-up-the-art review","e-Prime - Advances in Electrical Engineering, Electronics and Energy","","2772-6711","10.1016/j.prime.2024.100606","https://www.sciencedirect.com/science/article/pii/S2772671124001864","In this modern era, the smart grid (SG) is progressively empowered for direct current (DC) power transmission, either at high voltage (HV) or at medium voltage (MV). The high voltage direct current (HVDC) transmission systems have a significant impact on the SG operation even though the integration of add-on green and renewable-based power generation (RPG). In this RPG era, the application of HVDC can be seen in the grid-connected remote offshore wind or photovoltaic (PV) based large RPG, and interconnections amongst the states or countries. In such a context, a smart grid inevitably is ridden with technical complexities (both control and protection viewpoints), and thus signals processing (SP) techniques along with artificial intelligence (AI) methods are considered very much important to understand, effectively plan, draw up plans, and drive the multifaceted electric power system. This paper presents an extensive review of HVDC network protection systems with special attention to SP and AI applications. This paper presents an extensive review of the suggested techniques in the past three decades and discussed the pros and cons of each method. Apart from that critical findings are discussed and analyzed along with the future scope of research toward the development of HVDC protection. This review tries to focus on the gap between the existing protection schemes and topology with the smart grid-based power system perspective and convey to the power engineers and researchers the possibilities of further research as a solution to the associated issues and challenges.","2024-06-01","2024-12-03 03:24:29","2024-12-03 03:24:29","","100606","","","8","","e-Prime - Advances in Electrical Engineering, Electronics and Energy","","","","","","","","","","","","","","","","","","","Artificial intelligence; Renewable energy; HVDC; Power system protection; Signal processing; Smartgrid","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"PTZZBTKC","journalArticle","2024","Albahri, A.S.; Khaleel, Yahya Layth; Habeeb, Mustafa Abdulfattah; Ismael, Reem D.; Hameed, Qabas A.; Deveci, Muhammet; Homod, Raad Z.; Albahri, O.S.; Alamoodi, A.H.; Alzubaidi, Laith","A systematic review of trustworthy artificial intelligence applications in natural disasters","Computers and Electrical Engineering","","0045-7906","10.1016/j.compeleceng.2024.109409","https://www.sciencedirect.com/science/article/pii/S0045790624003379","Artificial intelligence (AI) holds significant promise for advancing natural disaster management through the use of predictive models that analyze extensive datasets, identify patterns, and forecast potential disasters. These models facilitate proactive measures such as early warning systems (EWSs), evacuation planning, and resource allocation, addressing the substantial challenges associated with natural disasters. This study offers a comprehensive exploration of trustworthy AI applications in natural disasters, encompassing disaster management, risk assessment, and disaster prediction. This research is underpinned by an extensive review of reputable sources, including Science Direct (SD), Scopus, IEEE Xplore (IEEE), and Web of Science (WoS). Three queries were formulated to retrieve 981 papers from the earliest documented scientific production until February 2024. After meticulous screening, deduplication, and application of the inclusion and exclusion criteria, 108 studies were included in the quantitative synthesis. This study provides a specific taxonomy of AI applications in natural disasters and explores the motivations, challenges, recommendations, and limitations of recent advancements. It also offers an overview of recent techniques and developments in disaster management using explainable artificial intelligence (XAI), data fusion, data mining, machine learning (ML), deep learning (DL), fuzzy logic, and multicriteria decision-making (MCDM). This systematic contribution addresses seven open issues and provides critical solutions through essential insights, laying the groundwork for various future works in trustworthiness AI-based natural disaster management. Despite the potential benefits, challenges persist in the application of AI to natural disaster management. In these contexts, this study identifies several unused and used areas in natural disaster-based AI theory, collects the disaster datasets, ML, and DL techniques, and offers a valuable XAI approach to unravel the complex relationships and dynamics involved and the utilization of data fusion techniques in decision-making processes related to natural disasters. Finally, the study extensively analyzed ethical considerations, bias, and consequences in natural disaster-based AI.","2024-09-01","2024-12-03 03:24:29","2024-12-03 03:24:29","","109409","","","118","","Computers and Electrical Engineering","","","","","","","","","","","","","","","","","","","Natural disasters; Taxonomy; Artificial intelligence; Trustworthy; Explainability; Data fusion","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"AVLPQRLN","journalArticle","2024","Özbek Güven, Gamze; Yilmaz, Şerife; Inceoğlu, Feyza","Determining medical students' anxiety and readiness levels about artificial intelligence","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e25894","https://www.sciencedirect.com/science/article/pii/S240584402401925X","The aim of this study is to determine the levels of anxiety and readiness among medical students regarding artificial intelligence (AI) and examine the relationship between these factors. The research was conducted on medical students, and the data was collected through face-to-face and online surveys between April and June 2022. The study utilized a socio-demographic information form, an AI anxiety scale, and a medical AI readiness scale. The data collected from a total of 542 students were analyzed using the Statistical Program for Social Sciences (SPSS) version 25. Cronbach's α coefficient was used for reliability analysis. A path diagram was created using AMOS 24, and structural equation modelling (SEM) analysis was applied. The findings of the study indicate that medical students have a moderate level of readiness and a high level of anxiety regarding AI. Furthermore, an inverse relationship was found between AI readiness and AI anxiety. These results highlight the importance of increasing the preparedness of medical students for AI applications and reducing their anxieties. The study suggests the inclusion of AI in the medical curriculum and the development of a standardized curriculum to facilitate its teaching.","2024-02-29","2024-12-03 03:24:29","2024-12-03 03:24:29","","e25894","","4","10","","Heliyon","","","","","","","","","","","","","","","","","","","Anxiety; Artificial intelligence; Medical students; Readiness","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JW7IGI2D","journalArticle","2024","Johri, Amar; Sayal, Anu; N, Chaithra; Jha, Janhvi; Aggarwal, Navya; Pawar, Darshan; Gupta, Veethika; Gupta, Ashulekha","Crafting the techno-functional blocks for Metaverse - A review and research agenda","International Journal of Information Management Data Insights","","2667-0968","10.1016/j.jjimei.2024.100213","https://www.sciencedirect.com/science/article/pii/S2667096824000028","The ""Metaverse,"" a term popularized by Neal Stephenson's novel Snow Crash, has been discussed in the science fiction community for decades, but technological advancements have only recently made it a reality. The Metaverse is an all-encompassing, interconnected virtual environment where users can freely communicate with one another and digital content. This article examines how various technologies, primarily Virtual Reality (VR) and Augmented Reality (AR), have contributed to the development of the Metaverse (AR). These innovations have revolutionized the way we interact with digital media by enabling us to have genuine, realistic experiences. In addition, we examine the Metaverse technologies that make it possible to construct a fully realized, functional virtual world. Among these are recent advances in artificial intelligence (AI), cryptocurrencies, spatial and peripheral computing, and other fields. Our research investigates the advantages and disadvantages of these technologies, as well as how they may influence the future of the Metaverse. Furthermore, the article explores the darker aspects of the Metaverse, particularly the emergence of the ""dark verse,"" which underscores the potential for organized illicit activities within the Internet due to insufficient oversight and governance of the Metaverse.","2024-04-01","2024-12-03 03:24:29","2024-12-03 03:24:29","","100213","","1","4","","International Journal of Information Management Data Insights","","","","","","","","","","","","","","","","","","","Virtual reality; Artificial intelligence; Augmented reality; Blockchain; Metaverse","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"72UTSL48","journalArticle","2024","Koyineni, Sai Niketh; Sai, Gurram Kumar; Anvesh, Kalwa; Anjali, T","Silent Expressions Unveiled: Deep Learning for British and American Sign Language Detection","5th International Conference on Innovative Data Communication Technologies and Application (ICIDCA 2024)","","1877-0509","10.1016/j.procs.2024.03.216","https://www.sciencedirect.com/science/article/pii/S1877050924005751","Sign language is an efficient means of communication for the hearing impaired. The main goal of this research is to recognize both American and British sign languages using computer vision algorithms and neural networks. Our work addresses the critical need for inclusive and accessible communication for the Deaf community. For the study, we compiled a large dataset of her ASL and BSL gestures. Preprocess the data to improve feature extraction and reduce noise. Our model architecture uses a convolutional neural network (CNN) to capture the temporal patterns of finger gestures. This allows you to use Mediapipe keypoint detection within your model. The end result is a comprehensive model that can accurately and quickly recognize and classify sign language gestures. We further improve the usability of the system by integrating keyword recognition into the model, allowing sign language sentences to be decoded and translated into text. Our test results demonstrate how well our deep learning strategy works to achieve high efficiency and accuracy in both ASL and BSL detection. This program helps bridge the gap between those who use sign language and those who do not. Our new approach not only accurately recognizes American and British sign language, but also seamlessly integrates real-time feedback to help users make immediate improvements to improve their sign language skills. We can do better. Moreover, our approach is flexible enough to be used in a variety of settings, ensuring its usefulness in educational environments and providing sign language learners with a fun and dynamic learning environment. Explore and exploit the full potential of Mediapipe Keypoint using CNNs and also leverage the efficiency of computer vision.","2024-01-01","2024-12-03 03:24:29","2024-12-03 03:24:29","","269-278","","","233","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","deep learning; ASL; assistive technology; BSL; communication accessibility; computer vision; Convolutional Neural Network(CNN); gesture recognition; keyword detection; neural networks; Sign language recognition","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SAYI9RM9","journalArticle","2024","Nawaz, Nishad; Arunachalam, Hemalatha; Pathi, Barani Kumari; Gajenderan, Vijayakumar","The adoption of artificial intelligence in human resources management practices","International Journal of Information Management Data Insights","","2667-0968","10.1016/j.jjimei.2023.100208","https://www.sciencedirect.com/science/article/pii/S266709682300054X","This study explores the impact of Artificial Intelligence (AI) on Human Resources Management Practices. By focusing on key outcomes such as accuracy, automation, computing power & capacity, real-time experience, personalization, and time-saving & cost saving. The research aims to identity the potential benefits of AI adoption. Data from 274 IT employees in Chennai City is Collected through a well-structured online questionnaire. Using IBM SPSS version 21 software and AMOS version 21 is used for analysis, the study proposes a novel research framework. The findings indicate that variables like Accuracy, Computing Power & Capacity, and Personalization significantly influence Time-Saving & Cost Reduction, while Automation and Real-Time Experience do not. The novel contribution of this study lies in its exploration of the specific outcomes of utilizing AI Technologies in Human Resources Management Practices. By focusing on key variables such as Accuracy, Automation, Computing Power & Capacity, Real-time experience, Personalization, and Time-Saving & Cost Saving, the research provides a comprehensive understanding of the expected outcomes when implementing AI in Human resources Management and the relationship among those outcome variables.","2024-04-01","2024-12-03 03:24:29","2024-12-03 03:24:29","","100208","","1","4","","International Journal of Information Management Data Insights","","","","","","","","","","","","","","","","","","","Accuracy; Artificial intelligence; Automation; Personalization; Computing power & capacity; Human resource management practices; Real-time experience; Time-saving & cost reduction","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"56A4BJE8","journalArticle","2024","Han, Jining; Li, Mimi","Exploring ChatGPT-supported teacher feedback in the EFL context","System","","0346-251X","10.1016/j.system.2024.103502","https://www.sciencedirect.com/science/article/pii/S0346251X24002847","This study investigates ChatGPT-supported teacher feedback in the Chinese tertiary EFL context and explores an innovative AI-aided writing pedagogy by integrating ChatGPT into teacher writing feedback provisions to alleviate the challenges of teacher feedback in a large class, which were reported in previous research. The participants of this study were four instructors and 102 students from two undergraduate classes in the world language education program. The students completed two writing tasks: an argumentative essay and an expository essay; then, the instructors provided detailed feedback on their essays based on the ChatGPT feedback. Two prompts were provided to ChatGPT after the training: 1) corrective feedback drawing on Ferris's (2006)15 types of common errors and 2) holistic rhetorical feedback. Afterwards, the teachers adapted the ChatGPT feedback and shared the detailed individualized writing feedback with each student. We closely examined the types and features of ChatGPT-supported teacher feedback and how EFL students incorporated this feedback into their writing revisions. The findings indicate that the ChatGPT-supported teacher feedback addressed diverse error categories and included helpful comments on the overall rhetoric. Moreover, the students incorporated more of the feedback into their revisions across tasks, which reflects their deeper engagement with the feedback content. This study notes the importance of an “AI + Teacher” model that leverages the analytical strengths of AI while maintaining essential teacher‒student interactions. This new approach of ChatGPT-supported teacher feedback has great potential in L2 writing feedback provision and will shed novel light on the writing pedagogy with the aid of AI in the digital era.","2024-11-01","2024-12-03 03:24:29","2024-12-03 03:24:29","","103502","","","126","","System","","","","","","","","","","","","","","","","","","","ChatGPT; AI; Language education; Teacher feedback; Writing feedback","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JLSW2HIM","journalArticle","2024","Spreitzenbarth, Jan Martin; Bode, Christoph; Stuckenschmidt, Heiner","Artificial intelligence and machine learning in purchasing and supply management: A mixed-methods review of the state-of-the-art in literature and practice","Journal of Purchasing and Supply Management","","1478-4092","10.1016/j.pursup.2024.100896","https://www.sciencedirect.com/science/article/pii/S1478409224000025","Artificial intelligence and machine learning are key technologies for purchasing organizations worldwide and their usage is still in a nascent stage. This systematic review offers an overview of the state-of-the-art literature and practice, where 46 works meeting the inclusion criteria were interactively classified in 11 use case clusters. The work follows the content analysis approach where the material evaluation was empirically enriched with 20 interviews to assess the cluster's business value and ease of implementation through triangulation. This is the first systematic review in the area of operations and supply chain management utilizing the Computer Classification System as the de facto standard in computer science for clarity in the terminology of these emerging technologies. In matching the literature search with the interview results, a mismatch was found between the reviewed literature and the expert's assessments. For instance, the cluster cost analysis deserves higher research attention as well as supplier sustainability. Moreover, there seems to be a gap in the operational area, which many believe to be first considered due to data availability. The insights may guide researchers and executives to better understand the dynamic capabilities needed to successfully steer the organization in the transformation toward procurement 4.0.","2024-01-01","2024-12-03 03:24:29","2024-12-03 03:24:29","","100896","","1","30","","Journal of Purchasing and Supply Management","","","","","","","","","","","","","","","","","","","Machine learning; Literature review; Artificial intelligence; Digital transformation; Mixed-method research method; Procurement","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"8YA8SE7I","journalArticle","2024","Li, Mei; Zhang, Likun; Wang, Yingcui; Xu, Xiaohong","Exploration of fractional flow reservation score based on artificial intelligence post-processing for coronary artery lesions in patients with diabetes and coronary heart disease","SLAS Technology","","2472-6303","10.1016/j.slast.2024.100196","https://www.sciencedirect.com/science/article/pii/S2472630324000785","In order to evaluate the relationship between coronary heart disease (CHD) and fractional flow reservation (FFR) in patients with different levels of CHD and diabetes, this paper used AI (artificial intelligence) post-processing technology to detect CHD and FFR. In this paper, 94 patients suspected of CHD who underwent coronary arteriography (CAG) in a hospital between December 2022 and February 2023 were examined by coronary computed tomography angiography (CCTA) and FFR. Based on CCTA, AI software is used to process CCTA images, diagnose coronary plaques, coronary stenosis, corresponding stenosis of different types of plaques, and FFR values. The diagnostic performance of AI was evaluated using expert diagnosis, CAG diagnosis, and FFR examination results as the “gold standard”. According to the diagnosis results, the relationship between FFR and CHD patients with diabetes at different levels was studied. The research results showed that AI image diagnosis has high sensitivity, specificity, and accuracy, and has good diagnostic effects on coronary plaques, coronary stenosis, stenosis corresponding to different types of plaques, and FFR values. The fasting blood glucose levels and FFR values of three groups of CHD patients were statistically significant, and correlation analysis revealed a negative correlation between the two. Using AI for CCTA diagnosis can efficiently, conveniently, and accurately obtain the required data, improving clinical diagnostic efficiency and accuracy. The analysis of AI recognition results found that in patients with CHD, the FFR value of patients with diabetes decreased, and the FFR value was negatively correlated with the fasting blood glucose concentration, indicating that CHD patients may lead to myocardial ischemia in the blood supply area due to the decline of their coronary blood flow reserve.","2024-12-01","2024-12-03 03:24:30","2024-12-03 03:24:30","","100196","","6","29","","SLAS Technology","","","","","","","","","","","","","","","","","","","Artificial intelligence post-processing; Biomedical information; Diabetes with coronary heart disease; Differences in coronary artery lesions; Fractional flow reservation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"D49BU5FM","journalArticle","2024","Rocha, Bruno Aragão; Ferreira, Lorena Carneiro; Vianna, Luis Gustavo Rocha; Ciconelle, Ana Claudia Martins; Cortez Filho, João Martins; Nogueira, Lucas Salume Lima; Silva Filho, Maurício Ricardo Moreira da; Leite, Claudia da Costa; Nomura, Cesar Higar; Cerri, Giovanni Guido; Carrilho, Flair José; Ono, Suzane Kioko","Development of HepatIA: A computed tomography annotation platform and database for artificial intelligence training in hepatocellular carcinoma detection at a Brazilian tertiary teaching hospital","Clinics","","1807-5932","10.1016/j.clinsp.2024.100512","https://www.sciencedirect.com/science/article/pii/S1807593224001893","Background Hepatocellular carcinoma (HCC) is a prevalent tumor with high mortality rates. Computed tomography (CT) is crucial in the non-invasive diagnosis of HCC. Recent advancements in artificial intelligence (AI) have shown significant potential in medical imaging analysis. However, developing these AI algorithms is hindered by the scarcity of comprehensive, publicly available liver imaging datasets. Objectives This study aims to detail the tools, data organization, and database structuring used in creating HepatIA, a medical imaging annotation platform and database at a Brazilian tertiary teaching hospital. HepatIA supports liver disease AI research at the institution. Material and methods The authors collected baseline characteristics and CT scans of 656 patients from 2008 to 2021. The database, designed using PostgreSQL and implemented with Django and Vue.js, includes 692 CT volumes from a four-phase abdominal CT protocol. Radiologists made segmentation annotations using the OHIF medical image viewer, incorporating MONAI Label for pre-annotation segmentation models. The annotation process included detailed descriptions of liver morphology and nodule characteristics. Results The HepatIA database currently includes healthy individuals and those with liver diseases such as HCC and cirrhosis. The database dashboard facilitates user interaction with intuitive plots and histograms. Key patient demographics include 64% males and an average age of 56.89 years. The database supports various filters for detailed searches, enhancing research capabilities. Conclusion A comprehensive data structure was successfully created and integrated with the IT systems of a teaching hospital, enabling research on deep learning algorithms applied to abdominal CT scans for investigating hepatic lesions such as HCC.","2024-01-01","2024-12-03 03:24:30","2024-12-03 03:24:30","","100512","","","79","","Clinics","","","","","","","","","","","","","","","","","","","Artificial intelligence; Database; Hepatocellular carcinoma; Medical imaging annotation; Multiphase computed tomography","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"PBN6Z8IT","journalArticle","2024","Bowen, Shannon A.","“If it can be done, it will be done:” AI Ethical Standards and a dual role for public relations","Public Relations Review","","0363-8111","10.1016/j.pubrev.2024.102513","https://www.sciencedirect.com/science/article/pii/S0363811124000924","This research reports a five-year study with different types of participants to examine questions related to ethics in artificial intelligence (AI) and the use of AI in public relations and professional communication. AI specialists from computer engineering and closely related fields, as well as CCOs, communication directors/managers, public affairs officers, and CEOs participated in this research, explaining and assessing the role of ethics in AI. Through numerous points of data collection, the topic of AI ethics in public relations was examined through a mixed method approach. This study offers insight into the ethics and design of AI systems that communication professionals must not only use in practice but also understand, advise upon, ethically oversee, and occasionally defend in the public sphere when used (or commissioned) by our organizations. After a review of broad literature and longitudinal data from hundreds of sources, recommendations for a more robust framework for AI ethics in public relations are proposed. Ethical considerations and quantum and neuromorphic computing are posed as means to avoid technocracy, and base use of these advanced systems on moral values.","2024-12-01","2024-12-03 03:24:30","2024-12-03 03:24:30","","102513","","5","50","","Public Relations Review","","","","","","","","","","","","","","","","","","","Ethics; Intelligence; AI; Deception; Moral analysis","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"6NWNLN2E","journalArticle","2024","Ghosh, Indranil; De, Arijit","Maritime Fuel Price Prediction of European Ports using Least Square Boosting and Facebook Prophet: Additional Insights from Explainable Artificial Intelligence","Transportation Research Part E: Logistics and Transportation Review","","1366-5545","10.1016/j.tre.2024.103686","https://www.sciencedirect.com/science/article/pii/S1366554524002771","Prediction of bunker fuel spot prices at a port and understanding the dependence on key determinants is an arduous and challenging activity. The present work strives to analyze the temporal spectrum of daily spot prices of Very Low Sulphur fuel Oil (VLSFO), a critical bunker fuel, in five European Ports, Amsterdam, Antwerp, Gothenburg, Hamburg, and Rotterdam. The lack of prior research in the allied domain has motivated to undertake the modeling of VLSFO spot prices through the lens of applied predictive analytics. The Least Square Boosting (LSBoost) and Facebook Prophet algorithms are used to draw forecasts in multivariate framework leveraging constructs related to the same fuel prices at different ports, different fuel prices at the same ports, economic indicator, etc. The dynamics have been explicitly examined during the Russia-Ukraine military conflict. Additionally, Explainable Artificial Intelligence (XAI) frameworks have been used to demystify the influence of the chosen explanatory variables at a granular scale. The overall findings espouse the effectiveness of the predictive framework in accurately estimating spot prices of VLSFO in any of the selected ports, and the same heavily depends on VLSFO prices at different ports.","2024-09-01","2024-12-03 03:24:30","2024-12-03 03:24:30","","103686","","","189","","Transportation Research Part E: Logistics and Transportation Review","","","","","","","","","","","","","","","","","","","Bunker fuel; Explainable Artificial Intelligence (XAI); Facebook Prophet; LSBoost; VLSFO","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"9GBUMEEY","journalArticle","2024","Cao, Jin; Zhou, Ta; Zhi, Shaohua; Lam, Saikit; Ren, Ge; Zhang, Yuanpeng; Wang, Yongqiang; Dong, Yanjing; Cai, Jing","Fuzzy inference system with interpretable fuzzy rules: Advancing explainable artificial intelligence for disease diagnosis—A comprehensive review","Information Sciences","","0020-0255","10.1016/j.ins.2024.120212","https://www.sciencedirect.com/science/article/pii/S0020025524001257","Interpretable artificial intelligence (AI), also known as explainable AI, is indispensable in establishing trustable AI for bench-to-bedside translation, with substantial implications for human well-being. However, the majority of existing research in this area has centered on designing complex and sophisticated methods, regardless of their interpretability. Consequently, the main prerequisite for implementing trustworthy AI in medical domains has not been met. Scientists have developed various explanation methods for interpretable AI. Among these methods, fuzzy rules embedded in a fuzzy inference system (FIS) have emerged as a novel and powerful tool to bridge the communication gap between humans and advanced AI machines. However, there have been few reviews of the use of FISs in medical diagnosis. In addition, the application of fuzzy rules to different kinds of multimodal medical data has received insufficient attention, despite the potential use of fuzzy rules in designing appropriate methodologies for available datasets. This review provides a fundamental understanding of interpretability and fuzzy rules, conducts comparative analyses of the use of fuzzy rules and other explanation methods in handling three major types of multimodal data (i.e., sequence signals, medical images, and tabular data), and offers insights into appropriate fuzzy rule application scenarios and recommendations for future research.","2024-03-01","2024-12-03 03:24:30","2024-12-03 03:24:30","","120212","","","662","","Information Sciences","","","","","","","","","","","","","","","","","","","Interpretability; Explainable artificial intelligence; Disease diagnosis; Fuzzy inference system; Fuzzy rule","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"FU77UDCV","journalArticle","2024","Roppelt, Julia Stefanie; Kanbach, Dominik K.; Kraus, Sascha","Artificial intelligence in healthcare institutions: A systematic literature review on influencing factors","Technology in Society","","0160-791X","10.1016/j.techsoc.2023.102443","https://www.sciencedirect.com/science/article/pii/S0160791X23002488","The purpose of this review is integrating and contextualizing relevant literature on the factors influencing the adoption of AI in the healthcare industry into a comprehensive framework. Health systems are considered fundamental to creating societal value. However, global health systems are challenged by the increasing number of patients due to population aging and the growing prevalence of chronic diseases and cancer. Meanwhile, the United Nations calls for equal access to healthcare, tackling costs, and addressing resource constraints to foster the sustainable development of societies. In this context, artificial intelligence (AI) is gaining attention as it constitutes a promising technology to address these burgeoning challenges. Despite opportunities, the literature specifically on the adoption of AI in the healthcare industry is fragmented across various research fields, lacking a comprehensive overview. It lacks theoretically grounded research integrating, for example, the factors that influence the adoption of AI in healthcare institutions. Derived from a multi-disciplinary systematic literature review, building on 130 studies, we propose the Adoption of AI in the Healthcare Industry Model. This model encompasses five dimensions influencing the adoption of AI in the healthcare industry and contextualizes them. We propose that macro-economic, regulatory, and technological readiness serve as external antecedents whereas organizational and individual readiness constitute internal antecedents influencing adoption of AI in healthcare institutions. Our review has implications for research on technology acceptance related to AI in healthcare. Further, we provide hands-on guidance for AI providers, health institutions, and official bodies such as governments to foster the adoption of AI to leverage value.","2024-03-01","2024-12-03 03:24:30","2024-12-03 03:24:30","","102443","","","76","","Technology in Society","","","","","","","","","","","","","","","","","","","Artificial intelligence; Adoption; Healthcare; Technology acceptance","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"9TI4G2BA","journalArticle","2024","Al Radi, Muaz; AlMallahi, Maryam Nooman; Al-Sumaiti, Ameena Saad; Semeraro, Concetta; Abdelkareem, Mohammad Ali; Olabi, Abdul Ghani","Progress in artificial intelligence-based visual servoing of autonomous unmanned aerial vehicles (UAVs)","International Journal of Thermofluids","","2666-2027","10.1016/j.ijft.2024.100590","https://www.sciencedirect.com/science/article/pii/S2666202724000326","Unmanned aerial vehicles (UAVs) have attracted massive attention in many engineering and practical applications in the last years for their characteristics and operation flexibility. For the UAV system, suitable control systems are required to operate appropriately and efficiently. An emerging control technique is visual servoing utilizing the onboard camera systems for inspecting the UAV's environment and autonomously controlling the UAV's operation. Artificial intelligence (AI) techniques are widely deployed in the visual servoing of autonomous UAV applications. Despite the increasing research in the field of AI-based visual control of UAV systems, comprehensive review articles that showcase the general trends and future directions in this field of research are limited. This work comprehensively examines the application and advancements of AI-enhanced visual servoing in autonomous UAV systems, covering critical control tasks and offering insights into future research directions for enhancing performance and applicability which is limited in the current literature. The paper first reviews the application of intelligent visual servoing systems for autonomously executing various UAV control tasks, including 3D UAV positioning, aerial and ground object following, obstacle avoidance, and autonomous landing. Second, the research progresses in applying AI techniques in the visual servoing of autonomous UAV systems are discussed and analyzed. Finally, future directions and critical research gaps for further improving the performance and applicability of intelligent visual servoing systems are included.","2024-02-01","2024-12-03 03:24:30","2024-12-03 03:24:30","","100590","","","21","","International Journal of Thermofluids","","","","","","","","","","","","","","","","","","","Artificial neural networks; Fuzzy logic; Artificial intelligence; Reinforcement learning; Unmanned aerial vehicles; Visual servoing","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"WSE55S52","journalArticle","2024","Rahaman, Muhammad Aminur; Oyshe, Kabiratun Ummi; Chowdhury, Prothoma Khan; Debnath, Tanoy; Rahman, Anichur; Khan, Md. Saikat Islam","Computer vision-based six layered ConvNeural network to recognize sign language for both numeral and alphabet signs","Biomimetic Intelligence and Robotics","","2667-3797","10.1016/j.birob.2023.100141","https://www.sciencedirect.com/science/article/pii/S2667379723000554","People who have trouble communicating verbally are often dependent on sign language, which can be difficult for most people to understand, making interaction with them a difficult endeavor. The Sign Language Recognition (SLR) system takes an input expression from a hearing or speaking-impaired person and outputs it in the form of text or voice to a normal person. The existing study related to the Sign Language Recognition system has some drawbacks, such as a lack of large datasets and datasets with a range of backgrounds, skin tones, and ages. This research efficiently focuses on Sign Language Recognition to overcome previous limitations. Most importantly, we use our proposed Convolutional Neural Network (CNN) model, “ConvNeural”, in order to train our dataset. Additionally, we develop our own datasets, “BdSL_OPSA22_STATIC1” and “BdSL_OPSA22_STATIC2”, both of which have ambiguous backgrounds. “BdSL_OPSA22_STATIC1” and “BdSL_OPSA22_STATIC2” both include images of Bangla characters and numerals, a total of 24,615 and 8437 images, respectively. The “ConvNeural” model outperforms the pre-trained models with accuracy of 98.38% for “BdSL_OPSA22_STATIC1” and 92.78% for “BdSL_OPSA22_STATIC2”. For “BdSL_OPSA22_STATIC1” dataset, we get precision, recall, F1-score, sensitivity and specificity of 96%, 95%, 95%, 99.31% , and 95.78% respectively. Moreover, in case of “BdSL_OPSA22_STATIC2” dataset, we achieve precision, recall, F1-score, sensitivity and specificity of 90%, 88%, 88%, 100%, and 100% respectively.","2024-03-01","2024-12-03 03:24:30","2024-12-03 03:24:30","","100141","","1","4","","Biomimetic Intelligence and Robotics","","","","","","","","","","","","","","","","","","","Sign language; Feature extraction; CNN; ConvNeural; Convolution2D; Dropout; Fully connected layer; Static","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"Q3ELNWLD","journalArticle","2024","Rahimi, Amir Reza; Sevilla-Pavón, Ana","The role of ChatGPT readiness in shaping language teachers' language teaching innovation and meeting accountability: A bisymmetric approach","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100258","https://www.sciencedirect.com/science/article/pii/S2666920X24000614","There are some dichotomies surrounding ChatGPT's application and impact on education as a central part of artificial intelligence (AI) integration. Problems concerning negative preconceptions and the limited usage of this form of AI have been reported, as some teachers perceive it as a competitor. This is due to insufficient readiness for its integration and a lack of understanding of how it can facilitate teaching innovation. Due to this, the researchers applied a bisymmetric approach, where necessity conditional analysis (NCA) as a symmetrical approach employs necessity logic to identify the must-have factors required for language teachers' passing accountability, while PLS-SEM as an asymmetrical approach follows additive sufficiency logic to identify the should-have factors that contribute to helping them to pass their accountability. Applying this approach would generate more results from different perspectives in ChatGPT readiness and language teaching innovation with it. In this line, the researchers randomly explored 124 Iranian in-service language teachers ChatGPT readiness and their teaching innovation in helping them pass the external and internal language teaching competencies. The result of the PLS-SEM showed that the more the language teachers were ready to practically use ChatGPT alongside, aware of its opportunities and challenges in English language teaching (ELT), the more they could generate more language teaching methods and approaches with it, apply them to their language teaching procedures, and share them with their colleagues. Moreover, their generational language teaching methods with ChatGPT mediated the correlation between ChatGPT readiness and meeting internal accountability. Additionally, the NCA showed that the generation of language teaching methods with ChatGPT and their implementation through it were among the necessary factors that help language teachers meet both internal and external accountability by teaching language with ChatGPT. Based on these findings, the researchers recommended that stakeholders shift their focus from AI programming to AI teaching, especially for ELT.","2024-12-01","2024-12-03 03:24:30","2024-12-03 03:24:30","","100258","","","7","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Accountability; Artificial Intelligence in Education; Artificial Intelligence Language Teaching (AILT); ChatGPT readiness; Computer assisted language learning (CALL); English language teaching; Innovative language teaching with ChatGPT","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RQIVBVZ8","journalArticle","2024","Li, Guowen; Li, Ke; Cheng, Dayang","Media Recommendation and Market Risk: Evidence from Chinese Market","11th International Conference on Information Technology and Quantitative Management (ITQM 2024)","","1877-0509","10.1016/j.procs.2024.08.116","https://www.sciencedirect.com/science/article/pii/S1877050924018350","This study analyzes 23,292 TikTok short-video stock recommendations from 2022 to explore their impact on stock market performance using the Difference-In-Differences Model. Key findings include: (1) short video recommendations provide valuable information, significantly boosting stock price discovery; (2) using a large language model (ChatGPT), it was found that the impact of clustered recommendations on stock price discovery is enhanced when considering recommendation strength. This pioneering research offers insights into the relationship between short-video stock recommendations and abnormal stock returns, benefiting researchers, investors, corporations, and social media analysts.","2024-01-01","2024-12-03 03:24:30","2024-12-03 03:24:30","","1389-1393","","","242","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","difference-in-differences model; market risk; media recommendations; stock price","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"YJNWMQHK","journalArticle","2024","Chen, Yuntian; Meng, Xianjia; Shi, Zhiying; Ning, Zhiyuan; Lin, Jingzhi","SecureTLM: Private inference for transformer-based large model with MPC","Information Sciences","","0020-0255","10.1016/j.ins.2024.120429","https://www.sciencedirect.com/science/article/pii/S0020025524003426","Transformer-based Large Models (TLM), such as generative pre-trained models (GPT), have become increasingly popular for practical applications through Deep Learning as a Service (DLaaS). They have been extensively used in natural language processing and computer vision. However, concerns regarding potential private data leakage arise with this type of inference service. While some private inference techniques can protect privacy, they often introduce high latency and approximate replacements in the design protocols, resulting in changes to the model structure and decreased accuracy. In this research, we present SecureTLM, a private inference method based on secure multi-party computation (MPC) that does not require modifications to the underlying model structure. SecureTLM offers protocols for crucial computations in TLM, such as Multiplication, Softmax, GeLU, and LayerNorm, without altering the model structure. Experimental results demonstrate that SecureTLM ensures data privacy, maintains correctness, and achieves efficiency in private inference tasks.","2024-05-01","2024-12-03 03:24:30","2024-12-03 03:24:30","","120429","","","667","","Information Sciences","","","","","","","","","","","","","","","","","","","Transformer; Hybrid secret sharing; Private inference; Secure multiparty computation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"MZCISYKC","journalArticle","2024","Chen, Zhao; Liang, Ning; Li, Haoyuan; Zhang, Haili; Li, Huizhen; Yan, Lijiao; Hu, Ziteng; Chen, Yaxin; Zhang, Yujing; Wang, Yanping; Ke, Dandan; Shi, Nannan","Exploring explainable AI features in the vocal biomarkers of lung disease","Computers in Biology and Medicine","","0010-4825","10.1016/j.compbiomed.2024.108844","https://www.sciencedirect.com/science/article/pii/S0010482524009296","This review delves into the burgeoning field of explainable artificial intelligence (XAI) in the detection and analysis of lung diseases through vocal biomarkers. Lung diseases, often elusive in their early stages, pose a significant public health challenge. Recent advancements in AI have ushered in innovative methods for early detection, yet the black-box nature of many AI models limits their clinical applicability. XAI emerges as a pivotal tool, enhancing transparency and interpretability in AI-driven diagnostics. This review synthesizes current research on the application of XAI in analyzing vocal biomarkers for lung diseases, highlighting how these techniques elucidate the connections between specific vocal features and lung pathology. We critically examine the methodologies employed, the types of lung diseases studied, and the performance of various XAI models. The potential for XAI to aid in early detection, monitor disease progression, and personalize treatment strategies in pulmonary medicine is emphasized. Furthermore, this review identifies current challenges, including data heterogeneity and model generalizability, and proposes future directions for research. By offering a comprehensive analysis of explainable AI features in the context of lung disease detection, this review aims to bridge the gap between advanced computational approaches and clinical practice, paving the way for more transparent, reliable, and effective diagnostic tools.","2024-09-01","2024-12-03 03:24:30","2024-12-03 03:24:30","","108844","","","179","","Computers in Biology and Medicine","","","","","","","","","","","","","","","","","","","Explainable artificial intelligence; AI transparency; Biomedical signal processing; Clinical AI applications; Computational Pulmonology; Lung disease detection; Machine learning interpretability; Pulmonary diagnostics; Vocal biomarkers; Voice analysis","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"L53GB6NW","journalArticle","2024","Hasan, Mohammad Kamrul; Habib, A.K.M. Ahasan; Islam, Shayla; Safie, Nurhizam; Ghazal, Taher M.; Khan, Muhammad Attique; Alzahrani, Ahmed Ibrahim; Alalwan, Nasser; Kadry, Seifedine; Masood, Anum","Federated learning enables 6 G communication technology: Requirements, applications, and integrated with intelligence framework","Alexandria Engineering Journal","","1110-0168","10.1016/j.aej.2024.02.044","https://www.sciencedirect.com/science/article/pii/S1110016824001832","The 5 G networks are effectively deployed worldwide, and academia and industries have begun looking at 6 G network communication technology for consumer electronics applications. 6 G will be built on pervasive artificial intelligence (AI) to enable data-driven Machine Learning (ML) applications in massively scalable and heterogeneous networks. Conventional ML technique involves centralizing train data in data centers where centralized ML algorithms can be employed for data inference and analysis. The data inference and analysis are frequently inconvenient or impracticable for the devices to submit information to the preset sever because of privacy concerns and inadequate communication capabilities in wireless networks. However, privacy limitations and restrictions in wireless network communication capacity are frequently impractical or undesirable for the devices to acquiesce data to the parameter server. Federated learning (FL) enables the devices to train a practical and standard model while needing data exchange and transfer, which might solve these issues. This paper presents an overview of FL, 6 G, and FL enables 6 G communication technology. In particular, 6 G requirements and applications, and the proposed FL framework algorithm with evaluation are described. Finally, FL-enabling 6 G communication technologies open challenges, and research directions are discussed to help future researchers improve the FL-enabled 6 G network.","2024-03-01","2024-12-03 03:24:31","2024-12-03 03:24:31","","658-668","","","91","","Alexandria Engineering Journal","","","","","","","","","","","","","","","","","","","Machine learning; Artificial Intelligence; 6G; Communication technology; Federate Learning","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"9ADXZ5BF","journalArticle","2024","Shah, Zahoor; Alzhrani, Seraj; Raja, Muhammad Asif Zahoor; Pasha, Amjad Ali; Shahzad, Faisal; Khan, Waqar Azeem","Stochastic analysis through Levenberg Marquardt backpropagation neural networks for radiative Carreau nanofluid flow subject to chemical reaction","Ain Shams Engineering Journal","","2090-4479","10.1016/j.asej.2024.103100","https://www.sciencedirect.com/science/article/pii/S2090447924004817","The aim of this research work is to estimate and analyze the solution of rheological chemical reactive Carreau nanofluid (CNRFM) induced by exponentially extended surface (EES) subject to variable physical attributes by using stochastic analysis on Levenberg Marquardt backpropagation neural networks (SALMBNNs). The non-linear Partial Differential Equations (PDEs) are transformed by using the similarity transformation variables into their corresponding ODEs. The reference values are created with ARK (adaptive Runge-Kutta) scheme. The ensuing results are explained for the variable viscosity, Weissenberg number (material number), Brownian movement factor, LRF (local rotation factor), LN (Lewis number) and activation energy with chemical reaction in addition. Numerical calculations of different physical quantities are approximated with artificial intelligence based SALMBNNs from dataset created with ARK method. The convergence, accuracy, and efficiency of the proposed stochastic analysis on Levenberg Marquardt backpropagation neural network (SALMBNNs) are established and endorsed through iterative learning curves at each incremental step in epoch, statistical instance distribution studies of error-histograms, analysis of adaptive controlling parameters of SALMBNNs, and evaluation of regression metric.","2024-10-18","2024-12-03 03:24:31","2024-12-03 03:24:31","","103100","","","","","Ain Shams Engineering Journal","","","","","","","","","","","","","","","","","","","Artificial intelligence; Carreau nanofluid; Chemical reaction; Levenberg-Marquardt; Radiation; Variable physical attribute","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"D6YUM9W9","journalArticle","2024","Ying, Ying; Jin, Shanyue","Artificial intelligence and green product innovation: Moderating effect of organizational capital","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e28572","https://www.sciencedirect.com/science/article/pii/S2405844024046036","Green product innovation (GPDI) is crucial for addressing ecological issues and essential for enterprises' green operations and long-term growth. Digitization offers new possibilities for enhancing corporate green practices. Nevertheless, previous studies have predominantly addressed the association between overall digitalization and corporate green innovation, and research on the outcome of specific digital technology categories on green innovation is lacking. Within this framework, this study broadens the investigation into the connection between distinct categories of digital technologies and corporate green innovation. The period 2013–2022 was selected as the sample observation period, with companies listed on China's A-share market as the study objects. The fixed-effects model was applied to investigate the impact of artificial intelligence (AI) on firms' GPDI while exploring the interaction effect of firms' organizational capital. The findings indicate that AI is beneficial to GPDI in businesses. This effect is enhanced by employee and board human capital but diminished by board social capital. These results remained valid after two-stage least squares regression. This study broadens the utilization of the resource-based view and dynamic capacity theory in business implementation. Furthermore, it extends the resulting study of AI and provides a digital enhancement pathway for corporate GPDI. This study has significant theoretical and practical implications.","2024-04-15","2024-12-03 03:24:31","2024-12-03 03:24:31","","e28572","","7","10","","Heliyon","","","","","","","","","","","","","","","","","","","Sustainability; Artificial intelligence; Green operation; Green product innovation; Organizational capital","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"E8FQFLXU","journalArticle","2023","Davidsson, Per; Sufyan, Muhammad","What does AI think of AI as an external enabler (EE) of entrepreneurship? An assessment through and of the EE framework","Journal of Business Venturing Insights","","2352-6734","10.1016/j.jbvi.2023.e00413","https://www.sciencedirect.com/science/article/pii/S2352673423000422","Recent breakthroughs make Artificial Intelligence (AI) technology a particularly potent enabler of entrepreneurship. Therefore, we use the External Enablement (EE) framework to examine AI’s potentials as enabler of entrepreneurship. In doing so, we involve AI – specifically ChatGPT 4.0 – to enhance the analysis beyond our personal limitations. Through this exercise we provide insights into 1) AI technologies as enablers of entrepreneurship; 2) possible improvements of the EE framework, and 3) ChatGPT’s and similar AI tools’ usefulness for entrepreneurship research more generally.","2023-11-01","2024-12-03 03:24:31","2024-12-03 03:24:31","","e00413","","","20","","Journal of Business Venturing Insights","","","","","","","","","","","","","","","","","","","Entrepreneurship; Artificial intelligence; Environmental change; External enabler","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RUNN8AGD","journalArticle","2024","Jeong, Se Yeon; Jung, Jaeho; Seo, Hyun Kyu; Jeong, Jae-Seung; Lee, June Hyuk; Kim, Gun Hwan; Yang, Min Kyu","Functional interface layer for a high-performance self-rectifying memristive device using hafnium-zirconia thin film","Results in Engineering","","2590-1230","10.1016/j.rineng.2024.102906","https://www.sciencedirect.com/science/article/pii/S2590123024011617","With the accelerated development of artificial intelligence-oriented hardware components, research on low-power, high-density memory devices is actively being conducted. Among various memory devices, resistive switching devices with crossbar structures have been extensively researched owing to their many advantages. To address the sneak current issue that is inherent in memory devices with crossbar structures, additional selection devices have been considered. However, self-rectifying resistive switching devices are known to be advantageous for harnessing structural benefits. Although significant research has been conducted in this area and remarkable results have been published, further research is required to improve the electrical characteristics for low-power, high-density memory applications This paper introduces self-rectifying devices with low power consumption, high rectification ratios, and high reliability. By combining HfZrOx resistance-change layers and SiOx interlayers, the characteristics of self-rectifying devices were confirmed, achieving a rectification ratio of 106 and 100 % operational yield in 1 kb crossbar devices. The essential multiply-and-accumulate operations in artificial intelligence-oriented hardware components were verified, and the applicability of the device as an artificial neural network was explored through simulations.","2024-12-01","2024-12-03 03:24:31","2024-12-03 03:24:31","","102906","","","24","","Results in Engineering","","","","","","","","","","","","","","","","","","","Artificial neural network; Passive crossbar array; Process-in-memory architecture; Self-rectifying memristor","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"N77VN6NV","journalArticle","2024","de Jonge, S.; Potters, W.V.; Verhamme, C.","Artificial intelligence for automatic classification of needle EMG signals: A scoping review","Clinical Neurophysiology","","1388-2457","10.1016/j.clinph.2023.12.134","https://www.sciencedirect.com/science/article/pii/S1388245723009392","Objective This scoping review provides an overview of artificial intelligence (AI), including machine and deep learning techniques, in the interpretation of clinical needle electromyography (nEMG) signals. Methods A comprehensive search of Medline, Embase and Web of Science was conducted to find peer-reviewed journal articles. All papers published after 2010 were included. The methodological quality of the included studies was assessed with CLAIM (checklist for artificial intelligence in medical imaging). Results 51 studies were identified that fulfilled the inclusion criteria. 61% used open-source EMGlab data set to develop models to classify nEMG signal in healthy, amyotrophic lateral sclerosis (ALS) and myopathy (25 subjects). Only two articles developed models to classify signals recorded at rest. Most articles reported high performance accuracies, but many were subject to bias and overtraining. Conclusions Current AI-models of nEMG signals are not sufficient for clinical implementation. Suggestions for future research include emphasizing the need for an optimal training and validation approach using large datasets of clinical nEMG data from a diverse patient population. Significance The outcomes of this study and the suggestions made aim to contribute to developing AI-models that can effectively handle signal quality variability and are suitable for daily clinical practice in interpreting nEMG signals.","2024-03-01","2024-12-03 03:24:31","2024-12-03 03:24:31","","41-55","","","159","","Clinical Neurophysiology","","","","","","","","","","","","","","","","","","","Machine learning; Classification; Artificial intelligence; Needle electromyography; Neuromuscular disorders","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"872HA67V","journalArticle","2024","Sun, Yanming; Wu, Zhaocong; Lan, Jingni; Li, Yunjian; Dou, Zixin","Spatiotemporal distribution and dynamics evolution of artificial intelligence development in China","Heliyon","","2405-8440","10.1016/j.heliyon.2023.e23885","https://www.sciencedirect.com/science/article/pii/S2405844023110930","The quantified measurement and comprehensive analysis of artificial intelligence development (AIDEV) are vital for countries to form AI industrial ecology and promote the long-term development of regional AI technology. Based on the innovation ecosystems (IE) theory, this paper constructs an evaluation system to measure and analyze the spatiotemporal distribution and dynamic evolution of the AIDEV in China from 2011 to 2020. The results show that the AIDEV of China presents an overall upward trend and an obvious unbalance in the spatial distribution which is “eastern > central > western”. Meanwhile, the provinces of low-level AIDEV are catching up with the high-level provinces, which leads to the regional difference of AIDEV narrowing. Moreover, the concentration and polarization phenomenon of AIDEV in China has been weakening and the AIDEV will continue to increase in the next three years. Further, there is a significantly positive spatial autocorrelation of AIDEV. Finally, high AIDEV provinces will increase the probability of surrounding provinces’ AIDEV to develop. This paper expands the research stream in the field of AI research, extends the application scenarios of IE theory, and puts forward some relevant policy recommendations.","2024-01-15","2024-12-03 03:24:31","2024-12-03 03:24:31","","e23885","","1","10","","Heliyon","","","","","","","","","","","","","","","","","","","Artificial intelligence development (AIDEV); Dynamic distribution; Evolution trend; Regional disparity; Spatial correlation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"AWSEVZAT","journalArticle","2024","Korte, Satu-Maarit; Cheung, William Man-Yin; Maasilta, Mari; Kong, Siu-Cheung; Keskitalo, Pigga; Wang, Lixun; Lau, Chaak Ming; Lee, John Chi Kin; Gu, Michelle Mingyue","Enhancing artificial intelligence literacy through cross-cultural online workshops","Computers and Education Open","","2666-5573","10.1016/j.caeo.2024.100164","https://www.sciencedirect.com/science/article/pii/S2666557324000053","This article presents the results of a study conducted in collaboration with two universities – one in Lapland (Finland) and the other in Hong Kong (China) – during the development of an international university course on global media education. The objective of the study was to examine international students’ changing conceptual understanding of artificial intelligence (AI) literacy as part of the course. The need for this study arose from the recent rapid spread of AI across industries, which has connected human learning to machine learning. This requires competence in AI to contribute to future society. Five hours of online lectures on AI literacy were delivered during two workshops to students (N = 29) from 13 countries with no or limited prior knowledge of the subject in 2021 and 2022. The participants filled out pre- and post-workshop quantitative questionnaires and wrote diaries about their learning process, the development of their understanding of AI-literacy concepts, and their thoughts on the pedagogical approaches used. The quantitative data were analysed using a paired-samples t-test, while the qualitative data were examined using thematic-content analysis. The findings show that the students’ knowledge of AI and their awareness of the importance of AI literacy and media education increased significantly. Further research is needed so that a more appropriate curriculum can be designed for them. We outline some key activities that offer interactive and participatory ways to learn AI in order to assist educators in planning and delivering AI-literacy courses as part of cross-cultural media education.","2024-06-01","2024-12-03 03:24:43","2024-12-03 03:24:43","","100164","","","6","","Computers and Education Open","","","","","","","","","","","","","","","","","","","Higher education; Artificial intelligence in teacher education; Artificial intelligence literacy; Global media education; Online workshops","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"Y3I9JGMJ","journalArticle","2024","Dimosthenopoulos, Dimosthenis; Basamakis, Fotios Panagiotis; Mountzouridis, George; Papadopoulos, Giorgos; Michalos, George; Makris, Sotiris","Towards utilising Artificial Intelligence for advanced reasoning and adaptability in human-robot collaborative workstations","10th CIRP Conference on Assembly Technology and Systems (CIRP CATS 2024)","","2212-8271","10.1016/j.procir.2024.07.026","https://www.sciencedirect.com/science/article/pii/S2212827124003354","Significant effort has been allocated recently by the research community for the seamless integration of Artificial Intelligence (AI) in modern human-robot collaborative (HRC) systems. Mainly in the context of human operator support and robotic object manipulation, the adoption of AI could become the main enabler towards the realisation of seamless HRC. This work presents the vision of the European funded project MASTERLY for an AI-based framework to enhance operator assistance and optimize robotic object manipulation. The proposed framework aims to contribute towards the realisation of intelligent, human-centric collaborative workstations focusing on two principal areas. The first area is human operator action recognition, which involves interpreting human actions to provide instructions and notifications to the operator only when it is considered necessary via Augmented Reality (AR) interfaces. The second area is the generation of a strategy for robotic manipulation of unknown objects. The proposed approach utilizes AI methods to identify optimal grasping points on unknown objects for efficient and precise robotic handling. The performance of the proposed framework is tested and validated in a case study from the elevators’ manufacturing industry, and more specifically, the assembly of the elevator's electrical panel.","2024-01-01","2024-12-03 03:24:43","2024-12-03 03:24:43","","147-152","","","127","","Procedia CIRP","","","","","","","","","","","","","","","","","","","Artificial Intelligence; flexibility; hybrid production systems; object manipulation; operator support","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"UKE9FBSZ","journalArticle","2024","Di Carlo, Daniela; Ladenstein, Ruth; Graf, Norbert; Merks, Johannes Hans; Hernández-Peñaloza, Gustavo; Kearns, Pamela; Bisogno, Gianni","Common core variables for childhood cancer data integration","EJC Paediatric Oncology","","2772-610X","10.1016/j.ejcped.2024.100186","https://www.sciencedirect.com/science/article/pii/S2772610X24000461","Introduction Data-driven research has improved paediatric cancer outcomes for children. However, challenges in sharing data between institutions prevent the use of artificial intelligence (AI) to address substantial unmet needs in children diagnosed with cancer. Harmonising collected data can enable the application of AI for a greater understanding of paediatric cancers. The main goal of the paper was to analyse the currently used childhood cancer databases to identify a core of variables able to capture the most relevant data on the diagnosis and treatment of children and adolescents with cancer. Methods We arbitrarily identified different types of existing databases dedicated to collecting data of patients with solid tumours, Umbrella, FAR-RMS; PARTNER; ERN PAEDCAN Registry; INSTRUCT and INRG; the common data elements for Rare Disease by Joint Research Centre. The different elements of the CRFs were analysed and ranked “essential” and “good to have”. Domains that included a group of variables structurally connected were identified. Each variable was defined by name, data type, description, and permissible values. Results We identified six structural domains: Patient registration, Personal information, Disease History, Diagnosis, Treatment, and Follow-up and Events. For each of them, “essential” and “good to have” variables were defined. Discussion Data harmonisation is essential for enhancing integration and comparability in research. By standardizing data formats and variables, researchers can facilitate data sharing, collaboration, and analysis across multiple studies and datasets. Embracing data harmonization practices will advance application of AI, scientific knowledge, improve research reproducibility, and contribute to evidence-based decision-making in various fields.","2024-12-01","2024-12-03 03:24:43","2024-12-03 03:24:43","","100186","","","4","","EJC Paediatric Oncology","","","","","","","","","","","","","","","","","","","AI; Big-data; Harmonisation; Health research; Paediatric oncology","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"5CZGKSLN","journalArticle","2024","Jianan, Gu; Kehao, Ren; Binwei, Gao","Deep learning-based text knowledge classification for whole-process engineering consulting standards","Journal of Engineering Research","","2307-1877","10.1016/j.jer.2023.07.011","https://www.sciencedirect.com/science/article/pii/S2307187723001724","The knowledge classification technology has significant implications for the intelligent research of industries. In the field of whole-process engineering consulting, manually reading and processing large amounts of text data is both time-consuming and laborious. Knowledge classification technology can automatically classify these text data and extract key information, which can improve industry work efficiency. In this study, a deep learning-based text knowledge classification method is proposed to address the large-scale text classification problem in the whole-process engineering consulting field. Firstly, pre-trained language models such as RoBERTa, BERT, and Longformer-RoBERTa are used to extract features from text. Secondly, a multi-label classification model is used to classify the text. Experimental results show that the proposed method performs better than other commonly used models in both overall classification performance and individual category classification performance. Moreover, when the text knowledge classification model is integrated as a text representation module with common classification models such as CNN and LSTM, its performance is inferior to that of a pure classification model. The proposed text knowledge classification method is of great significance for the application in the field of whole-process engineering consulting and provides an effective solution for intelligent research in engineering consulting.","2024-06-01","2024-12-03 03:24:43","2024-12-03 03:24:43","","61-71","","2","12","","Journal of Engineering Research","","","","","","","","","","","","","","","","","","","Deep learning; Natural language processing; Text knowledge classification; Whole-process engineering consulting","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"TZZJVX5L","journalArticle","2024","Usmani, Usman Ahmad; Happonen, Ari; Watada, Junzo","The Digital Age: Exploring the Intersection of AI/CI and Human Cognition and Social Interactions","CENTERIS – International Conference on ENTERprise Information Systems / ProjMAN - International Conference on Project MANagement / HCist - International Conference on Health and Social Care Information Systems and Technologies 2023","","1877-0509","10.1016/j.procs.2024.06.268","https://www.sciencedirect.com/science/article/pii/S1877050924015114","Although solutions based on artificial and computational intelligence have made life easier, the fast development of technology also raises questions about near future and log term human cognition and social interaction. Through a survey of the literature and qualitative analysis, our work examined current research on how the AI/CI affects human cognitive functions and social interactions. We discuss how AI and CI are influencing e.g. how we humans gather information, build relationships, and communicate with others, with and without the new frontline technologies. Additionally, proposals for future advances are discussed along with the ethical and societal ramifications these technologies have, could and might bring into our lives. We think that by developing a deeper knowledge of how AI/CI affects human cognition and social interaction, new contributions are made to a positive conversation and encourage a responsible approach to incorporating new technologies into our daily lives.","2024-01-01","2024-12-03 03:24:43","2024-12-03 03:24:43","","1044-1052","","","239","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Industry 4.0; Artificial intelligence; Digital transformation; Digitalization; Computational intelligence; Digital capability; Human cognition; Human computer interaction; Social interaction; Social transformation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"9S85Z4YG","journalArticle","2024","Tóth, Zsófia; Blut, Markus","Ethical compass: The need for Corporate Digital Responsibility in the use of Artificial Intelligence in financial services","Organizational Dynamics","","0090-2616","10.1016/j.orgdyn.2024.101041","https://www.sciencedirect.com/science/article/pii/S0090261624000147","Service research and business ethics literature intersect concerning the question of artificial intelligence (AI) service robot accountability. In financial services, there is a broad spectrum of potential ethical issues, from data usage to customer vulnerabilities. This article scrutinizes the impact of morality and where accountability resides in the use of AI service robots in financial services. To address this challenge, we discuss the role of Corporate Digital Responsibility (CDR) for firms and illustrate how to implement a conceptual framework on the ethical implications of AI service robot applications, drawing on normative ethical theory. The framework elaborates on how the locus of morality (from human to AI agency) and moral intensity combine within context-specific AI service robot applications, and how this might influence associated accountability. We provide examples of AI robots’ use for different purposes, differentiating between four 'accountability clusters': (1) professional norms, (2) business responsibility, (3) inter-institutional normativity, and (4) supra-territorial regulations cluster. We also discuss the CDR implications in different clusters. Ethical implications of using AI service robots and associated accountability challenges are relevant for a network of actors—from customers and designers to firms and the government. Implementation of the framework incorporates a range of internal and external stakeholders that firms need to consider. We also provide a CDR roadmap to incorporate a time perspective and to inform implementation efforts.","2024-04-01","2024-12-03 03:24:43","2024-12-03 03:24:43","","101041","","2","53","","Organizational Dynamics","","","","","","","","","","","","","","","","","","","Accountability; Artificial Intelligence; Business ethics; Corporate Digital Responsibility; Locus of morality; Moral intensity","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"MM4V4BD4","journalArticle","2024","Białas, Marcin; Mirończuk, Marcin Michał; Mańdziuk, Jacek","Leveraging spiking neural networks for topic modeling","Neural Networks","","0893-6080","10.1016/j.neunet.2024.106494","https://www.sciencedirect.com/science/article/pii/S0893608024004180","This article investigates the application of spiking neural networks (SNNs) to the problem of topic modeling (TM): the identification of significant groups of words that represent human-understandable topics in large sets of documents. Our research is based on the hypothesis that an SNN that implements the Hebbian learning paradigm is capable of becoming specialized in the detection of statistically significant word patterns in the presence of adequately tailored sequential input. To support this hypothesis, we propose a novel spiking topic model (STM) that transforms text into a sequence of spikes and uses that sequence to train single-layer SNNs. In STM, each SNN neuron represents one topic, and each of the neuron’s weights corresponds to one word. STM synaptic connections are modified according to spike-timing-dependent plasticity; after training, the neurons’ strongest weights are interpreted as the words that represent topics. We compare the performance of STM with four other TM methods Latent Dirichlet Allocation (LDA), Biterm Topic Model (BTM), Embedding Topic Model (ETM) and BERTopic on three datasets: 20Newsgroups, BBC news, and AG news. The results demonstrate that STM can discover high-quality topics and successfully compete with comparative classical methods. This sheds new light on the possibility of the adaptation of SNN models in unsupervised natural language processing.","2024-10-01","2024-12-03 03:24:43","2024-12-03 03:24:43","","106494","","","178","","Neural Networks","","","","","","","","","","","","","","","","","","","Topic modeling; Spiking neural network; Spiking topic model; STDP; Unsupervised learning","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"36XVJL7U","journalArticle","2024","Wu, Ziqiong; Zhang, Chengqi; Ye, Xinjian; Dai, Yuwei; Zhao, Jing; Zhao, Wuyuan; Zheng, Yuanna","Comparison of the Efficacy of Artificial Intelligence-Powered Software in Crown Design: An In Vitro Study","International Dental Journal","","0020-6539","10.1016/j.identj.2024.06.023","https://www.sciencedirect.com/science/article/pii/S0020653924001965","Introduction and aims Artificial intelligence (AI) has been adopted in the field of dental restoration. This study aimed to evaluate the time efficiency and morphological accuracy of crowns designed by two AI-powered software programs in comparison with conventional computer-aided design software. Methods A total of 33 clinically adapted posterior crowns were involved in the standard group. AI Automate (AA) and AI Dentbird Crown (AD) used two AI-powered design software programs, while the computer-aided experienced and computer-aided novice employed the Exocad DentalCAD software. Time efficiency between the AI-powered groups and computer-aided groups was evaluated by assessing the elapsed time. Morphological accuracy was assessed by means of three-dimensional geometric calculations, with the root-mean-square error compared against the standard group. Statistical analysis was conducted via the Kruskal–Wallis test (α = 0.05). Results The time efficiency of the AI-powered groups was significantly higher than that of the computer-aided groups (P < .01). Moreover, the working time for both AA and AD groups was only one-quarter of that for the computer-aided novice group. Four groups significantly differed in morphological accuracy for occlusal and distal surfaces (P < .05). The AD group performed lower accuracy than the other three groups on the occlusal surfaces (P < .001) and the computer-aided experienced group was superior to the AA group in terms of accuracy on the distal surfaces (P = .029). However, morphological accuracy showed no significant difference among the four groups for mesial surfaces and margin lines (P > .05). Conclusion AI-powered software enhanced the efficiency of crown design but failed to excel at morphological accuracy compared with experienced technicians using computer-aided software. AI-powered software requires further research and extensive deep learning to improve the morphological accuracy and stability of the crown design.","2024-07-28","2024-12-03 03:24:43","2024-12-03 03:24:43","","","","","","","International Dental Journal","","","","","","","","","","","","","","","","","","","Artificial intelligence-powered; Computer-aided; Crown; Morphological accuracy; Time efficiency","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2L37P8HN","journalArticle","2024","Xie, Bo; Xu, Dan; Zou, Xu-Qiang; Lu, Ming-Jie; Peng, Xue-Lian; Wen, Xiu-Jie","Artificial intelligence in dentistry: A bibliometric analysis from 2000 to 2023","Journal of Dental Sciences","","1991-7902","10.1016/j.jds.2023.10.025","https://www.sciencedirect.com/science/article/pii/S1991790223003604","Background/purpose Artificial intelligence (AI) is reshaping clinical practice in dentistry. This study aims to provide a comprehensive overview of global trends and research hotspots on the application of AI to dentistry. Materials and methods Studies on AI in dentistry published between 2000 and 2023 were retrieved from the Web of Science Core Collection. Bibliometric parameters were extracted and bibliometric analysis was conducted using VOSviewer, Pajek, and CiteSpace software. Results A total of 651 publications were identified, 88.7 % of which were published after 2019. Publications originating from the United States and China accounted for 34.5 % of the total. The Charité Medical University of Berlin was the institution with the highest number of publications, and Schwendicke and Krois were the most active authors in the field. The Journal of Dentistry had the highest citation count. The focus of AI in dentistry primarily centered on the analysis of imaging data and the dental diseases most frequently associated with AI were periodontitis, bone fractures, and dental caries. The dental AI applications most frequently discussed since 2019 included neural networks, medical devices, clinical decision support systems, head and neck cancer, support vector machine, geometric deep learning, and precision medicine. Conclusion Research on AI in dentistry is experiencing explosive growth. The prevailing research emphasis and anticipated future development involve the establishment of medical devices and clinical decision support systems based on innovative AI algorithms to advance precision dentistry. This study provides dentists with valuable insights into this field.","2024-07-01","2024-12-03 03:24:43","2024-12-03 03:24:43","","1722-1733","","3","19","","Journal of Dental Sciences","","","","","","","","","","","","","","","","","","","Bibliometric analysis; Artificial intelligence; Dentistry","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XF8BUXHZ","journalArticle","2024","Rana, Md. Masud; Siddiqee, Mohammad Safaet; Sakib, Md. Nazmus; Ahamed, Md. Rafi","Assessing AI adoption in developing country academia: A trust and privacy-augmented UTAUT framework","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e37569","https://www.sciencedirect.com/science/article/pii/S2405844024136006","The rapid evolution of Artificial Intelligence (AI) and its widespread adoption have given rise to a critical need for understanding the underlying factors that shape users' behavioral intentions. Therefore, the main objective of this study is to explain user perceived behavioral intentions and use behavior of AI technologies for academic purposes in a developing country. This study has adopted the unified theory of acceptance and use of technology (UTAUT) model and extended it with two dimensions: trust and privacy. Data have been collected from 310 AI users including teachers, researchers, and students. This study finds that users' behavioral intention is positively and significantly associated with trust, social influence, effort expectancy, and performance expectancy. Privacy, on the other hand, has a negative yet significant relationship with behavioral intention unveiling that concerns over privacy can deter users from intending to use AI technologies which is a valuable insight for developers and educators. In determining use behavior, facilitating condition, behavioral intention, and privacy have significant positive impact. This study hasn't found any significant relationship between trust and use behavior elucidating that service providers should have unwavering focus on security measures, credible endorsements, and transparency to build user confidence. In an era dominated by the fourth industrial revolution, this research underscores the pivotal roles of trust and privacy in technology adoption. In addition, this study sheds light on users' perspective to effectively align AI-based technologies with the education system of developing countries. The practical implications encompass insights for service providers, educational institutions, and policymakers, facilitating the smooth adoption of AI technologies in developing countries while emphasizing the importance of trust, privacy, and ongoing refinement.","2024-09-30","2024-12-03 03:24:43","2024-12-03 03:24:43","","e37569","","18","10","","Heliyon","","","","","","","","","","","","","","","","","","","Artificial intelligence; Technology adoption; UTAUT; Academia; Behavioral intention; Extended UTAUT","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"Z4G63PHH","journalArticle","2023","Sachithra, Vilani; Subhashini, L.D.C.S.","How artificial intelligence uses to achieve the agriculture sustainability: Systematic review","Artificial Intelligence in Agriculture","","2589-7217","10.1016/j.aiia.2023.04.002","https://www.sciencedirect.com/science/article/pii/S2589721723000144","The generation of food production that meets the rising demand for food and ecosystem security is a big challenge. With the development of Artificial Intelligence (AI) models, there is a growing need to use them to achieve sustainable agriculture. The continuous enhancement of AI in agriculture, researchers have proposed many models in agriculture functions such as prediction,weed control, resource management, advance care of crops, and so on. This article evaluates on a systematic review of AI models in agriculture functions. It also reviews how AI models are used in identified sustainable objectives. Through this extensive review, this paper discusses considerations and limitations for building the next generation of sustainable agriculture using AI.","2023-06-01","2024-12-03 03:24:43","2024-12-03 03:24:43","","46-59","","","8","","Artificial Intelligence in Agriculture","","","","","","","","","","","","","","","","","","","Deep learning; Sustainability; Robotics; AI; Agriculture; Review","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"T44FWZRL","journalArticle","2024","Schweitzer, Sascha; Conrads, Markus; Naeve, Jörg","Claude Rules: An Evaluation of Large Language Models’ Applicability to Solve Cases in German Business Law","28th International Conference on Knowledge Based and Intelligent information and Engineering Systems (KES 2024)","","1877-0509","10.1016/j.procs.2024.09.406","https://www.sciencedirect.com/science/article/pii/S1877050924024463","In the evolving field of legal information systems, Claude 3 and other advanced conversational agents (CAs) are emerging as transformative forces. This interdisciplinary study combines quantitative methods, legal analysis, and digital transformation approaches to evaluate the efficacy of leading commercially available CAs in the German legal environment. Employing a corpus of 200 unique legal tasks, the research benchmarks Claude 3 against notable systems such as Google Gemini and ChatGPT versions 4 and 3.5. Through automated evaluations of 1,600 responses generated by these CAs, Claude 3 is demonstrated to be the most effective system, capable of successfully addressing realistic legal challenges and passing a German business law examination with an overall score of 60%—significantly surpassing the 50% score of the previous performance leader ChatGPT-4. Despite its superior performance, Claude 3, along with other evaluated systems, exhibits considerable limitations that can be difficult to identify. Based on these insights, it is recommended that legal professionals thoroughly verify all CA-generated content before use. Additionally, caution is advised for novices utilizing CA-generated legal advice, due to the specialized knowledge required for proper evaluation. This study contributes to the ongoing study of digital transformation in the legal domain, offering insights for both academic and industry stakeholders.","2024-01-01","2024-12-03 03:24:44","2024-12-03 03:24:44","","2675-2683","","","246","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","digital transformation; large language models; conversational agents; Legal information systems; performance assessment","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LALU7IVC","journalArticle","2023","Ma, Gehua; Yan, Rui; Tang, Huajin","Exploiting noise as a resource for computation and learning in spiking neural networks","Patterns","","2666-3899","10.1016/j.patter.2023.100831","https://www.sciencedirect.com/science/article/pii/S2666389923002003","Summary Networks of spiking neurons underpin the extraordinary information-processing capabilities of the brain and have become pillar models in neuromorphic artificial intelligence. Despite extensive research on spiking neural networks (SNNs), most studies are established on deterministic models, overlooking the inherent non-deterministic, noisy nature of neural computations. This study introduces the noisy SNN (NSNN) and the noise-driven learning (NDL) rule by incorporating noisy neuronal dynamics to exploit the computational advantages of noisy neural processing. The NSNN provides a theoretical framework that yields scalable, flexible, and reliable computation and learning. We demonstrate that this framework leads to spiking neural models with competitive performance, improved robustness against challenging perturbations compared with deterministic SNNs, and better reproducing probabilistic computation in neural coding. Generally, this study offers a powerful and easy-to-use tool for machine learning, neuromorphic intelligence practitioners, and computational neuroscience researchers.","2023-10-13","2024-12-03 03:24:44","2024-12-03 03:24:44","","100831","","10","4","","Patterns","","","","","","","","","","","","","","","","","","","dynamic system; neural coding; neuromorphic intelligence; noise-driven learning; noisy spiking neural network; probabilistic graphical model; spiking neural network; surrogate gradient","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DVS74226","journalArticle","2024","Lan, Mengfei; Cheng, Mandy; Hoang, Linh; ter Riet, Gerben; Kilicoglu, Halil","Automatic categorization of self-acknowledged limitations in randomized controlled trial publications","Journal of Biomedical Informatics","","1532-0464","10.1016/j.jbi.2024.104628","https://www.sciencedirect.com/science/article/pii/S1532046424000467","Objective: Acknowledging study limitations in a scientific publication is a crucial element in scientific transparency and progress. However, limitation reporting is often inadequate. Natural language processing (NLP) methods could support automated reporting checks, improving research transparency. In this study, our objective was to develop a dataset and NLP methods to detect and categorize self-acknowledged limitations (e.g., sample size, blinding) reported in randomized controlled trial (RCT) publications. Methods: We created a data model of limitation types in RCT studies and annotated a corpus of 200 full-text RCT publications using this data model. We fine-tuned BERT-based sentence classification models to recognize the limitation sentences and their types. To address the small size of the annotated corpus, we experimented with data augmentation approaches, including Easy Data Augmentation (EDA) and Prompt-Based Data Augmentation (PromDA). We applied the best-performing model to a set of about 12K RCT publications to characterize self-acknowledged limitations at larger scale. Results: Our data model consists of 15 categories and 24 sub-categories (e.g., Population and its sub-category DiagnosticCriteria). We annotated 1090 instances of limitation types in 952 sentences (4.8 limitation sentences and 5.5 limitation types per article). A fine-tuned PubMedBERT model for limitation sentence classification improved upon our earlier model by about 1.5 absolute percentage points in F1 score (0.821 vs. 0.8) with statistical significance (p<.001). Our best-performing limitation type classification model, PubMedBERT fine-tuning with PromDA (Output View), achieved an F1 score of 0.7, improving upon the vanilla PubMedBERT model by 2.7 percentage points, with statistical significance (p<.001). Conclusion: The model could support automated screening tools which can be used by journals to draw the authors’ attention to reporting issues. Automatic extraction of limitations from RCT publications could benefit peer review and evidence synthesis, and support advanced methods to search and aggregate the evidence from the clinical trial literature.","2024-04-01","2024-12-03 03:24:44","2024-12-03 03:24:44","","104628","","","152","","Journal of Biomedical Informatics","","","","","","","","","","","","","","","","","","","Large language models; Natural language processing; Text classification; Randomized controlled trials; Reporting quality; Self-acknowledged limitations","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DIYB58M3","journalArticle","2024","Alsehaimi, Abdullah; Waqar, Ahsan; El Aal, Ahmed abd; Hayat, Saleh; Ahmed Waris, Faizan; Benjeddou, Omrane","Optimising construction sector performance: A study of the rapidly growing global drone industry using smart PLS approach","Journal of Engineering Research","","2307-1877","10.1016/j.jer.2024.08.004","https://www.sciencedirect.com/science/article/pii/S2307187724002219","The construction sector is dealing with a multitude of obstacles that may impede the accomplishment of a project, such as issues related to quality assurance, public health and safety, sustainability and the economy, environmental conservation, privacy, and legal limitations. Drones powered by artificial intelligence (AI) have a lot of promise to improve construction projects' overall performance, safety, sustainability, and operational efficiency. Still, there are many barriers preventing this powerful technology from being widely adopted. This research employed a mixed methodology that included surveys, expert interviews, literature reviews, and modelling to identify main challenges that construction professionals experience while using automated drones. The findings indicated that the biggest obstacles are a lack of finance, manpower, and privacy-related laws. It will cost money to train employees, negotiate regulatory restrictions, and weigh the advantages of automated drones in terms of both cost and environmental impact. However, to fully understand the potential enormous effects automated drones may have on construction projects, the construction industry must give top priority to resolving the important problems outlined here. Construction organisations can achieve significant improvements in accountability, operational effectiveness, and overall performance by focusing on growing human resources, achieving profitability, and addressing legal and privacy problems.","2024-09-05","2024-12-03 03:24:44","2024-12-03 03:24:44","","","","","","","Journal of Engineering Research","","","","","","","","","","","","","","","","","","","Human resources; Accountability; Artificial intelligence; Profitability; Legal and privacy problems; Operational effectiveness","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"P8F9PPDR","journalArticle","2024","Novielli, Pierfrancesco; Romano, Donato; Magarelli, Michele; Diacono, Domenico; Monaco, Alfonso; Amoroso, Nicola; Vacca, Mirco; De Angelis, Maria; Bellotti, Roberto; Tangaro, Sabina","Personalized identification of autism-related bacteria in the gut microbiome using explainable artificial intelligence","iScience","","2589-0042","10.1016/j.isci.2024.110709","https://www.sciencedirect.com/science/article/pii/S2589004224019345","Summary Autism spectrum disorder (ASD) affects social interaction and communication. Emerging evidence links ASD to gut microbiome alterations, suggesting that microbial composition may play a role in the disorder. This study employs explainable artificial intelligence (XAI) to examine the contributions of individual microbial species to ASD. By using local explanation embeddings and unsupervised clustering, the research identifies distinct ASD subgroups, underscoring the disorder’s heterogeneity. Specific microbial biomarkers associated with ASD are revealed, and the best classifiers achieved an AU-ROC of 0.965 ± 0.005 and an AU-PRC of 0.967 ± 0.008. The findings support the notion that gut microbiome composition varies significantly among individuals with ASD. This work’s broader significance lies in its potential to inform personalized interventions, enhancing precision in ASD management and classification. These insights highlight the importance of individualized microbiome profiles for developing tailored therapeutic strategies for ASD.","2024-09-20","2024-12-03 03:24:44","2024-12-03 03:24:44","","110709","","9","27","","iScience","","","","","","","","","","","","","","","","","","","Neuroscience; Microbiology; Developmental neuroscience; Microbiome","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"X7S2F5NS","journalArticle","2024","Ghag, Nikhil; Sonar, Harshad; Jagtap, Sandeep; Trollman, Hana","Unlocking AI's potential in the food supply chain: A novel approach to overcoming barriers","Journal of Agriculture and Food Research","","2666-1543","10.1016/j.jafr.2024.101349","https://www.sciencedirect.com/science/article/pii/S2666154324003867","This paper delves into the challenges impeding the seamless integration of artificial intelligence (AI) within the food supply chain (FSC) and introduces a novel methodological framework that combines the NK Model with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique. Through an exhaustive literature analysis and expert discussions, the research identifies and categorizes significant obstacles to AI deployment in the FSC. These hurdles include the imperative for a skilled labor force, financial limits, regulatory complexity and technological limitations. The unique DEMATEL-NK approach highlights the interconnected nature of these barriers, pinpointing the most critical impediments. The study's implications extend to the broader domains of AI adoption in agriculture and the food industry, offering a nuanced perspective for policymakers, industry stakeholders, and researchers. The findings underscore the imperative of overcoming these barriers for the successful implementation of AI technologies in the FSC, promising advancements in efficiency, quality, and sustainability. The innovative methodology not only sheds light on the interconnectedness of these barriers but also provides a systematic approach for prioritizing and implementing solutions. This research offers a fresh viewpoint on barrier relationships, guiding decision-makers in crafting effective strategies and interventions to propel AI integration in the FSC forward.","2024-12-01","2024-12-03 03:24:44","2024-12-03 03:24:44","","101349","","","18","","Journal of Agriculture and Food Research","","","","","","","","","","","","","","","","","","","Decision making; Sustainability; Artificial intelligence; DEMATEL-NK; Food supply chain","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CHJ3T4BA","journalArticle","2024","Thimm, Heiko; Rasmussen, Karsten Boye","ChatGPT discovery of green image damaging information for large production companies","Journal of Cleaner Production","","0959-6526","10.1016/j.jclepro.2024.143978","https://www.sciencedirect.com/science/article/pii/S0959652624034279","Language models, particularly transformer-based architectures like ChatGPT, have gained significant attention due to their ability to comprehend and generate human-like text. These capabilities are leveraged to retrieve Green Image Damaging (GID) information about a randomized sample of about 400 of the largest production companies. For each sample company prompts to ChatGPT are used to discover and retrieve information of five company topics: CO2 compensation, greenwashing, environmental scandals, noncompliance with environmental legislation and standards, and legal actions related to environmental violations. Through corresponding data analysis, the study explores differences in extents of GID information for regions and industry sectors using the NACE classification scheme. Based on the extent of obtained information the sample is divided into companies where GID information is discovered and companies without GID information. The two groups are compared in terms of company size and ESG scores. Among other results the data analysis suggests that companies with GID information are larger and have significantly better ESG scores than the companies without GID information.","2024-11-01","2024-12-03 03:24:44","2024-12-03 03:24:44","","143978","","","478","","Journal of Cleaner Production","","","","","","","","","","","","","","","","","","","ChatGPT information discovery; ESG score; Explorative research study; Global production industry; Green image damaging information","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SI5Z5HUK","journalArticle","2023","Wang, Jiaji; Wang, Shuihua; Zhang, Yudong","Artificial intelligence for visually impaired","Displays","","0141-9382","10.1016/j.displa.2023.102391","https://www.sciencedirect.com/science/article/pii/S0141938223000240","The eyes are an essential tool for human observation and perception of the world, helping people to perform their tasks. Visual impairment causes many inconveniences in the lives of visually impaired people. Therefore, it is necessary to focus on the needs of the visually impaired community. Researchers work from different angles to help visually impaired people live normal lives. The advent of the digital age has profoundly changed the lives of the visually impaired community, making life more convenient. Deep learning, as a promising technology, is also expected to improve the lives of visually impaired people. It is increasingly being used in the diagnosis of eye diseases and the development of visual aids. The earlier accurate diagnosis of the eye disease by the doctor, the sooner the patient can receive the appropriate treatment and the better chances of a cure. This paper summarises recent research on the development of artificial intelligence-based eye disease diagnosis and visual aids. The research is divided according to the purpose of the study into deep learning methods applied in diagnosing eye diseases and smart devices to help visually impaired people in their daily lives. Finally, a summary is given of the directions in which artificial intelligence may be able to assist the visually impaired in the future. In addition, this overview provides some knowledge about deep learning for beginners. We hope this paper will inspire future work on the subjects. .","2023-04-01","2024-12-03 03:24:44","2024-12-03 03:24:44","","102391","","","77","","Displays","","","","","","","","","","","","","","","","","","","Deep learning; Artificial intelligence; Eye disease diagnosis; Visual aids; Visually impaired people","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"NS6CKUGN","journalArticle","2024","Ekmen, Omer; Kocaman, Sultan","Remote sensing for UN SDGs: A global analysis of research and collaborations","The Egyptian Journal of Remote Sensing and Space Sciences","","1110-9823","10.1016/j.ejrs.2024.04.002","https://www.sciencedirect.com/science/article/pii/S1110982324000309","The Sustainable Development Goals (SDGs) provide a policy-making baseline for countries to overcome shortcomings and barriers for people and the planet Earth by 2030. Remote sensing (RS) enables evidence-based policy making and can contribute to realization of the SDGs by monitoring the indicators and evaluating the targets related to human and physical geography. This study exploited the RS research concerning the SDGs based on a Web of Science Core Collection database query [TS=((“remote sensing” OR “Earth observation*”) AND (“Sustainable Development Goal*”))] between 2016 and 2022 and by utilizing an artificial intelligence tool developed for SDG classification. We retrieved and analyzed articles (n = 308) using science mapping techniques. Remote Sensing is the most relevant journal publishing articles related to this theme. While the dominance of Chinese institutes in terms of authors' affiliation is clear, the highest collaboration network is between the USA and China. Our findings revealed that subjects related to carbon storage, ecological quality and impervious surface draw attention of researchers increasingly and becoming trend topics. From the SDG classification results, SDG 15 and SDG 11 emerged as the most prevalent subjects related to the RS research. Given the exponential increase in the number of studies, we recommend to employ bibliometric analysis and science mapping tools to systematically identify research patterns and gaps in both fields, as manual efforts may progressively become challenging.","2024-06-01","2024-12-03 03:24:44","2024-12-03 03:24:44","","329-341","","2","27","","The Egyptian Journal of Remote Sensing and Space Sciences","","","","","","","","","","","","","","","","","","","Artificial intelligence; Data visualization; Earth observation; Science mapping; Sustainable development goals","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LTS3Y7Q5","journalArticle","2024","Hoseinzadeh, Siamak; Astiaso Garcia, Davide","Ai-driven innovations in greenhouse agriculture: Reanalysis of sustainability and energy efficiency impacts","Energy Conversion and Management: X","","2590-1745","10.1016/j.ecmx.2024.100701","https://www.sciencedirect.com/science/article/pii/S259017452400179X","In the context of greenhouse agriculture, the integration of Artificial Intelligence (AI) is evaluated for its potential to enhance sustainability and crop production efficiency. This study reanalyzes publicly available datasets, using advanced time series analysis and noise reduction techniques through seasonality detection and removal. This novel approach reveals trends more clearly, providing a detailed comparison between AI-driven methods and traditional agricultural practices. An extensive review of literature on AI applications in agriculture is conducted to establish a broad understanding of its current state and future prospects. The core focus is the Autonomous Greenhouses Challenge, an initiative where research teams apply AI technologies in real-world greenhouse settings. This challenge offers crucial data for a thorough assessment of AI’s practical impact. The analysis reveals that AI significantly reduces heating energy consumption, indicating a notable improvement in energy efficiency. However, reductions in CO2 emissions, along with improvements in electricity and water usage, are only marginal when compared to traditional farming methods. Similarly, enhancements in crop quality and profitability achieved through AI are found to be on par with conventional techniques. These findings highlight the dual nature of AI’s impact in greenhouse agriculture: it shows significant promise in some areas, while its effectiveness in other key sustainability aspects remains limited. The study emphasizes the need for further research and investment in technological advancements, as well as the importance of a robust data infrastructure. It also highlights the necessity of education and training in AI technologies for effective implementation in the agricultural sector. The results of this research aim to inform policymakers, researchers, and industry stakeholders about the mixed impacts of AI on sustainable greenhouse farming. By offering a comprehensive evaluation of the benefits and challenges of AI integration, this study contributes to the ongoing discussion on sustainable agricultural practices and provides insights into the future direction of AI in this field.","2024-10-01","2024-12-03 03:24:44","2024-12-03 03:24:44","","100701","","","24","","Energy Conversion and Management: X","","","","","","","","","","","","","","","","","","","Sustainability; Artificial intelligence; Agriculture; Greenhouse; CO emissions; Energy efficiency; Heating energy consumption","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"REPP578X","journalArticle","2024","Murray, J.; Heng, D.; Lygate, A.; Porto, L.; Abade, A.; Manica, S.; Franco, A.","Applying artificial intelligence to determination of legal age of majority from radiographic data","Morphologie","","1286-0115","10.1016/j.morpho.2023.100723","https://www.sciencedirect.com/science/article/pii/S1286011523002011","Summary Forensic odontologists use biological patterns to estimate chronological age for the judicial system. The age of majority is a legally significant period with a limited set of reliable oral landmarks. Currently, experts rely on the questionable development of third molars to assess whether litigants can be prosecuted as legal adults. Identification of new and novel patterns may illuminate features more dependably indicative of chronological age, which have, until now, remained unseen. Unfortunately, biased perceptions and limited cognitive capacity compromise the ability of researchers to notice new patterns. The present study demonstrates how artificial intelligence can break through identification barriers and generate new estimation modalities. A convolutional neural network was trained with 4003 panoramic-radiographs to sort subjects into ‘under-18’ and ‘over-18’ age categories. The resultant architecture identified legal adults with a high predictive accuracy equally balanced between precision, specificity and recall. Moving forward, AI-based methods could improve courtroom efficiency, stand as automated assessment methods and contribute to our understanding of biological ageing.","2024-03-01","2024-12-03 03:24:44","2024-12-03 03:24:44","","100723","","360","108","","Morphologie","","","","","","","","","","","","","","","","","","","Deep learning; Artificial intelligence; Panoramic radiographs; Convolutional neural network; Dental age estimation; Forensic dentistry; Machine vision","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"FIITR9C2","journalArticle","2024","Golafshani, Emad; Chiniforush, Alireza A.; Zandifaez, Peyman; Ngo, Tuan","An artificial intelligence framework for predicting operational energy consumption in office buildings","Energy and Buildings","","0378-7788","10.1016/j.enbuild.2024.114409","https://www.sciencedirect.com/science/article/pii/S0378778824005255","This research delves into the intricate dynamics of energy consumption in buildings, using data-driven modeling with machine learning (ML) algorithms to optimize design choices for heightened energy efficiency. A substantial dataset, comprising 66,800 operational energy records from office buildings in 40 cities worldwide representing 10 distinct climate conditions, was analyzed using four ML algorithms: Random Forest Regressor, Extra Trees Regressor, Gradient Boosting Regressor (GBR), and eXtreme Gradient Boosting Regressor (XGBR). Notably, the developed GBR and XGBR models demonstrated superior precision in predicting energy use intensity during the testing phase, achieving a mean absolute percentage error of less than 1.2 % and a coefficient of determination of more than 0.99. The findings highlighted the significant impact of building shape and temperature on annual operational energy consumption, providing valuable insights for energy-efficient design optimization. Finally, the grey wolf optimizer was applied to identify optimal design parameters for various scenarios, laying the groundwork for designing energy-efficient buildings in cities with diverse meteorological patterns.","2024-08-15","2024-12-03 03:24:44","2024-12-03 03:24:44","","114409","","","317","","Energy and Buildings","","","","","","","","","","","","","","","","","","","Artificial Intelligence (AI); Machine Learning; EnergyPlus Simulation; Grey Wolf Optimizer; Operational Energy Consumption","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XZGLJQB9","journalArticle","2023","Mizumoto, Atsushi; Eguchi, Masaki","Exploring the potential of using an AI language model for automated essay scoring","Research Methods in Applied Linguistics","","2772-7661","10.1016/j.rmal.2023.100050","https://www.sciencedirect.com/science/article/pii/S2772766123000101","The widespread adoption of ChatGPT, an AI language model, has the potential to bring about significant changes to the research, teaching, and learning of foreign languages. The present study aims to leverage this technology to perform automated essay scoring (AES) and evaluate its reliability and accuracy. Specifically, we utilized the GPT-3 text-davinci-003 model to automatically score all 12,100 essays contained in the ETS Corpus of Non-Native Written English (TOEFL11) and compared these scores to benchmark levels. The study also explored the extent to which linguistic features influence AES with GPT. The results showed that AES using GPT has a certain level of accuracy and reliability and could provide valuable support for human evaluations. Furthermore, the analysis revealed that utilizing linguistic features could enhance the accuracy of the scoring. These findings suggest that AI language models, such as ChatGPT, can be effectively utilized as AES tools, potentially revolutionizing methods of writing evaluation and feedback in both research and practice. The paper concludes by discussing the practical implications of using GPT for AES and exploring prospective future considerations.","2023-08-01","2024-12-03 03:24:44","2024-12-03 03:24:44","","100050","","2","2","","Research Methods in Applied Linguistics","","","","","","","","","","","","","","","","","","","Automated essay scoring (AES); Natural language processing (NLP); GPT (Generative Pre-trained Transformer); Linguistic features; Transformer-based large language models","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"YZUAL85M","journalArticle","2024","Hong, Wei-Ting; Clifton, Geoffrey; Nelson, John D.","A data-driven conceptual framework for understanding the nature of hazards in railway accidents","Transport Policy","","0967-070X","10.1016/j.tranpol.2024.05.007","https://www.sciencedirect.com/science/article/pii/S0967070X24001240","Hazards threaten railway safety by their potential to trigger railway accidents, resulting in significant costs and impacting the public's willingness to use railways. Whilst many prior works investigate railway hazards, few offer a holistic view of hazards across jurisdictions and time because the large number of primary sources make synthesising such learnings time consuming and potentially incomplete. The conceptual framework HazardMap is developed to overcome this gap, employing open-sourced Natural Language Processing topic modelling for the automated analysis of textual data from Rail Accident Investigation Branch (RAIB), Australian Transport Safety Bureau (ATSB), National Transportation Safety Board (NTSB) and Transportation Safety Board of Canada (TSB) railway accident reports. The topic modelling depicts the relationships between hazards, railway accidents and investigator recommendations and is further extended and integrated with the existing risk theory and epidemiological accident models. The results allow the different aspects of each hazard to be listed along with the potential combinations of hazards that could trigger railway accidents. Better understanding of the aspects of individual hazards and the relationships between hazards and previous accidents can inform more effective hazard mitigation policies including technical or regulatory interventions. A case study of the risk at level crossings is provided to illustrate how HazardMap works with real-world data. This demonstrates a high degree of coverage within the existing risk management system, indicating the capability to better inform policymaking for managing risks. The primary contributions of the framework proposed are to enable a large amount of knowledge accumulated to be summarised for an intuitive policymaking process, and to allow other railway investigators to leverage lessons learnt across jurisdictions and time with limited human intervention. Future research could apply the technique to road, aviation or maritime accidents.","2024-06-01","2024-12-03 03:24:44","2024-12-03 03:24:44","","102-117","","","152","","Transport Policy","","","","","","","","","","","","","","","","","","","Implementation; Natural language processing (NLP); Data-driven framework; Hazards analysis; Railway accident","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"IQW38I59","journalArticle","2024","Walther, Andreas; Logoz, Flora; Eggenberger, Lukas","The gendered nature of AI: Men and masculinities through the lens of ChatGPT and GPT4","Computers in Human Behavior: Artificial Humans","","2949-8821","10.1016/j.chbah.2024.100076","https://www.sciencedirect.com/science/article/pii/S2949882124000367","Because artificial intelligence powered language models such as the GPT series have most certainly come to stay and will permanently change the way individuals all over the world access information and form opinions, there is a need to highlight potential risks for the understanding and perception of men and masculinities. It is important to understand whether ChatGPT or its following versions such as GPT4 are biased – and if so, in which direction and to which degree. In the specific research field on men and masculinities, it seems paramount to understand the grounds upon which these language models respond to seemingly simple questions such as “What is a man?” or “What is masculine?”. In the following, we provide interactions with ChatGPT and GPT4 where we asked such questions, in an effort to better understand the quality and potential biases of the answers from ChatGPT and GPT4. We then critically reflect on the output by ChatGPT, compare it to the output by GPT4 and draw conclusions for future actions.","2024-08-01","2024-12-03 03:24:44","2024-12-03 03:24:44","","100076","","2","2","","Computers in Human Behavior: Artificial Humans","","","","","","","","","","","","","","","","","","","Artificial intelligence; ChatGPT; Masculinities; Men; Traditional masculinity ideologies","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"TZMEHBAQ","journalArticle","2023","Broekhuizen, Thijs; Dekker, Henri; de Faria, Pedro; Firk, Sebastian; Nguyen, Dinh Khoi; Sofka, Wolfgang","AI for managing open innovation: Opportunities, challenges, and a research agenda","Journal of Business Research","","0148-2963","10.1016/j.jbusres.2023.114196","https://www.sciencedirect.com/science/article/pii/S0148296323005556","Artificial intelligence (AI) provides ample opportunities for enabling effective knowledge sharing among organizations seeking to foster open innovation. Past research often investigates the capability of AI to perform ‘human’ tasks in structured application fields. Yet, there is a lack of research that systematically analyzes when and how AI can be used for the more complex and unstructured tasks of open innovation (OI). We present a framework for leveraging AI-enabled applications to foster productive OI collaborations. Specifically, we create a 3x3 matrix by aligning the three OI stages (initiation, development, realization) with the three management functions of AI (mapping, coordinating, controlling). This matrix assists in identifying how various AI applications may augment or automate human intelligence, thereby helping to resolve prevailing OI challenges. It provides guidance on how organizations can use AI to establish, execute and govern exchanges across the OI stages. Finally, we lay out an agenda for future research.","2023-11-01","2024-12-03 03:24:44","2024-12-03 03:24:44","","114196","","","167","","Journal of Business Research","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JE34IWTX","journalArticle","2024","Cuzzocrea, Alfredo; Gallo, Carmine; Akter, Mst. Shapna; Shahriar, Hossain","Analysis and Experimental Comparison of State-Of-The-Art Deep-Learning Classification Techniques for Cyberbullying Detection","28th International Conference on Knowledge Based and Intelligent information and Engineering Systems (KES 2024)","","1877-0509","10.1016/j.procs.2024.09.153","https://www.sciencedirect.com/science/article/pii/S1877050924021616","Cyberbullying is becoming a relevant challenge in next-generation online connected systems, especially in the case of social networks, a relevant innovation of our times. This phenomenon is largely recognized as inducting relevant problems in modern societies, due to the pervasiveness of modern personal devices that originated larger and larger networks. Information and Communication Technologies (ICT) can really help to this end, thanks to the application of well-known models and methods mainly falling in the context of language transformers and neural networks, which both represent state-of-the-art solutions for supporting cyberbullying detection in text (e.g., posts). Inspired by this main research area, in this paper we provide an overview of state-of-the-art approaches for cyberbullying detection along with their experimental comparison against the reference TRAC-2 dataset.","2024-01-01","2024-12-03 03:24:44","2024-12-03 03:24:44","","3800-3809","","","246","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Cyberbullying Detection; Experimental Analysis; Intelligent Techniques; Langauge Transformers; Neural Networks","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"HHMEGG86","journalArticle","2024","Skovbo, Joachim Sejr; Andersen, Nicklas Sindlev; Obel, Lasse Møllegaard; Laursen, Malene Skaarup; Riis, Andreas Stoklund; Houlind, Kim Christian; Pyndt Diederichsen, Axel Cosmus; Lindholt, Jes Sanddal","Individual risk assessment for rupture of abdominal aortic aneurysm using artificial intelligence","Journal of Vascular Surgery","","0741-5214","10.1016/j.jvs.2024.11.017","https://www.sciencedirect.com/science/article/pii/S0741521424021013","Objective This study aimed to develop a prediction tool to identify abdominal aortic aneurysms (AAA) at increased risk of rupture incorporating demographic, clinical, imaging, and medication data using artificial intelligence (AI). Design A development and validation study for individual prognosis using AI in a case-control design. Methods From two Danish hospitals, all available ruptured AAA cases between January 2009 and December 2016 were included in a ratio of 1:2 with elective surgery controls. Cases with previous AAA surgery or missing pre-operative scans were excluded. Features from computed tomography angiography scans and hospital records were manually retrieved. The sample was divided randomly and evenly into developmental and internal validation groups. A SHapley Additive exPlanations Feature Importance Rank Ensembling (SHAPFire) AI tool was developed using a gradient boosting decision tree framework. The final SHAPFire AI model was compared with models using 1) solely infrarenal anterior-posterior-diameter, and 2) all available features. Results The study included 637 individuals (84.8% men, mean age 73±7 years, 213 ruptured AAAs). The SHAPFire AI incorporated 20 of 68 available features, and aneurysm size, blood pressure, and relationships between height and weight were given highest rankings. The receiver operating characteristic curve for the SHAPFire AI model displayed a significant increase in accuracy identifying ruptured AAA cases compared to the conventional model based solely on diameter with areas under the curves of 0.86±0.04 and 0.74±0.03 (P=0.008), respectively. SHAPFire AI was comparable in performance with the model using all features. Conclusion This study successfully developed a SHAPFire AI tool to identify AAAs at increased risk of rupture with significant higher accuracy than diameter alone. External validation of the model is warranted before clinical implementation.","2024-11-21","2024-12-03 03:24:45","2024-12-03 03:24:45","","","","","","","Journal of Vascular Surgery","","","","","","","","","","","","","","","","","","","Artificial Intelligence; Risk assessment; abdominal aortic aneurysm; Case-control study; ruptured abdominal aortic aneurysm","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RKQTK7TZ","journalArticle","2023","Hajkowicz, Stefan; Sanderson, Conrad; Karimi, Sarvnaz; Bratanova, Alexandra; Naughtin, Claire","Artificial intelligence adoption in the physical sciences, natural sciences, life sciences, social sciences and the arts and humanities: A bibliometric analysis of research publications from 1960-2021","Technology in Society","","0160-791X","10.1016/j.techsoc.2023.102260","https://www.sciencedirect.com/science/article/pii/S0160791X23000659","Analysing historical patterns of artificial intelligence (AI) adoption can inform decisions about AI capability uplift, but research to date has provided a limited view of AI adoption across different fields of research. In this study we examine worldwide adoption of AI technology within 333 fields of research during 1960–2021. We do this by using bibliometric analysis with 137 million peer-reviewed publications captured in The Lens database. We define AI using a list of 214 phrases developed by expert working groups at the Organisation for Economic Cooperation and Development (OECD). We found that 3.1 million of the 137 million peer-reviewed research publications during the entire period were AI-related, with a surge in AI adoption across practically all research fields (physical science, natural science, life science, social science and the arts and humanities) in recent years. The diffusion of AI beyond computer science was early, rapid and widespread. In 1960 14% of 333 research fields were related to AI (many in computer science), but this increased to cover over half of all research fields by 1972, over 80% by 1986 and over 98% in current times. We note AI has experienced boom-bust cycles historically; the AI “springs” and “winters”. We conclude that the context of the current surge appears different, and that interdisciplinary AI application is likely to be sustained.","2023-08-01","2024-12-03 03:24:45","2024-12-03 03:24:45","","102260","","","74","","Technology in Society","","","","","","","","","","","","","","","","","","","Machine learning; Bibliometric analysis; Artificial intelligence; Technology adoption; Technology diffusion","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LYVZBFKP","journalArticle","2024","Dunsin, Dipo; Ghanem, Mohamed C.; Ouazzane, Karim; Vassilev, Vassil","A comprehensive analysis of the role of artificial intelligence and machine learning in modern digital forensics and incident response","Forensic Science International: Digital Investigation","","2666-2817","10.1016/j.fsidi.2023.301675","https://www.sciencedirect.com/science/article/pii/S2666281723001944","In the dynamic landscape of digital forensics, the integration of Artificial Intelligence (AI) and Machine Learning (ML) stands as a transformative technology, poised to amplify the efficiency and precision of digital forensics investigations. However, the use of ML and AI in digital forensics is still in its nascent stages. As a result, this paper gives a thorough and in-depth analysis that goes beyond a simple survey and review. The goal is to look closely at how AI and ML techniques are used in digital forensics and incident response. This research explores cutting-edge research initiatives that cross domains such as data collection and recovery, the intricate reconstruction of cybercrime timelines, robust big data analysis, pattern recognition, safeguarding the chain of custody, and orchestrating responsive strategies to hacking incidents. This endeavour digs far beneath the surface to unearth the intricate ways AI-driven methodologies are shaping these crucial facets of digital forensics practice. While the promise of AI in digital forensics is evident, the challenges arising from increasing database sizes and evolving criminal tactics necessitate ongoing collaborative research and refinement within the digital forensics profession. This study examines the contributions, limitations, and gaps in the existing research, shedding light on the potential and limitations of AI and ML techniques. By exploring these different research areas, we highlight the critical need for strategic planning, continual research, and development to unlock AI's full potential in digital forensics and incident response. Ultimately, this paper underscores the significance of AI and ML integration in digital forensics, offering insights into their benefits, drawbacks, and broader implications for tackling modern cyber threats.","2024-03-01","2024-12-03 03:24:45","2024-12-03 03:24:45","","301675","","","48","","Forensic Science International: Digital Investigation","","","","","","","","","","","","","","","","","","","Machine learning; Artificial intelligence; Pattern recognition; Big data analysis; Chain of custody; Cyber incident; Cybercrime investigation; Data collection and recovery; DFIR; Digital forensic; Genetic algorithms; Memetic algorithms; Rule-based reasoning; Volatile memory","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LGK5Y2WU","journalArticle","2023","Okagbue, Ekene Francis; Ezeachikulo, Ujunwa Perpetua; Akintunde, Tosin Yinka; Tsakuwa, Mustapha Bala; Ilokanulo, Samuel Nchekwubemchukwu; Obiasoanya, Kosiso Modest; Ilodibe, Chidiebere Emeka; Ouattara, Cheick Amadou Tidiane","A comprehensive overview of artificial intelligence and machine learning in education pedagogy: 21 Years (2000–2021) of research indexed in the scopus database","Social Sciences & Humanities Open","","2590-2911","10.1016/j.ssaho.2023.100655","https://www.sciencedirect.com/science/article/pii/S2590291123002607","The utilization of AI (artificial intelligence) and ML (machine learning) in education pedagogy will undoubtedly enhance transformative changes in academic pedagogical engagements. Most interestingly, they are perceived to transform traditional instructional activities into digitized and seamless ones for effective and efficient education. To further explore the discourse, this study tries to elucidate the dramatic rise in trends and the constant evolution of AI and ML in education pedagogy applying a bibliometrics analysis. Historically, since the emergence of AI, its influence, functions, and applications have been transcending from one form to another in the sequence of artificial intelligence (simple level of AI) to applied artificial intelligence, to machine learning, and to deep learning. From our study outcome, numerous scholars predicted that AI and ML will continue to transform beyond their current state to more sophisticated tools. A bibliometric analysis was performed with the extracted articles on artificial intelligence, machine learning, and education pedagogy from the Scopus database. The downloaded data were analyzed with bibliometrics research tools such as VOSviewer (Var 1.6.6) and R packages. For this study exploration, a total of one thousand, one hundred and thirty-eight (1138) documents were authored by two thousand, eight hundred (2800) researchers and published in six-hundred and thirty-five (635) journals from 2000 to 2021, with 5.984 as the average citations per document from sixty-two (62) countries. Our study concluded that school administrators should promote the use of AI and ML devices to promote quality pedagogical services in the academic environment. Most especially, education policymakers are advised to promulgate policies that will support the acceptability and usability of artificial intelligence and machine learning in academic environment.","2023-01-01","2024-12-03 03:24:45","2024-12-03 03:24:45","","100655","","1","8","","Social Sciences & Humanities Open","","","","","","","","","","","","","","","","","","","Machine learning; Bibliometrics; Education; Artificial intelligence; Pedagogy","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"8WAK7Z8L","journalArticle","2024","Yu, Chih-Lung; Wen, Hao-Ming; Ko, Po-Chang; Shu, Ming-Hung; Wu, Yi-Sui","Automatic construction and optimization method of enterprise data asset knowledge graph based on graph attention network","Journal of Radiation Research and Applied Sciences","","1687-8507","10.1016/j.jrras.2024.101023","https://www.sciencedirect.com/science/article/pii/S1687850724002073","Traditional knowledge graph construction methods often rely on a large amount of human intervention and specialized knowledge, which seems inefficient and error-prone when dealing with massive, multi-source, and heterogeneous data assets of enterprises. To address this issue, a Multi Neighborhood Awareness Network (MNAN) model was proposed, which combines a multi language pre training model based on Bidirectional Encoder Representations from Transformers (BERT) and a Graph Convolutional Neural Network (GCN). By introducing an improved attention mechanism to deeply explore the neighborhood characteristics of entities, the accuracy and efficiency of entity matching are enhanced. In addition, to improve the integrity of the knowledge graph, a dynamic graph attention network (RDGAT) model based on graph attention network fusion relationship features is proposed. Through dynamic attention mechanism and relationship fusion strategy, deep learning of relationship features is carried out to optimize the completion performance of the knowledge graph spectrum. The performance test results show that the Hits@1 metric of the multi-neighborhood perceptual network model is close to 90% as the proportion of aligned entity seeds increases to 50%; the average inverse rank of the dynamic graph attention network on the test dataset improves by 6.3%. The experimental results show that the research-proposed knowledge graph fusion and complementation model exhibits significant effectiveness in automatically identifying and integrating large-scale, multi-source heterogeneous data at the enterprise level, and can significantly improve the automation level and accuracy of knowledge graph construction. The research method has achieved significant results in the data asset knowledge graph of petroleum enterprises and other fields, providing strong support for applications such as intelligent search, recommendation systems, and semantic analysis.","2024-09-01","2024-12-03 03:24:45","2024-12-03 03:24:45","","101023","","3","17","","Journal of Radiation Research and Applied Sciences","","","","","","","","","","","","","","","","","","","Knowledge graph; Attention mechanism; Enterprise data analysis; Entity embedding; Graph convolutional; Multi domain perception","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"3SD86YTK","journalArticle","2024","Chen, Chunmei","Evaluation on Collaborative Control Algorithm for Automotive Braking Based on Artificial Intelligence Simulation","The 11th International Conference on Applications and Techniques in Cyber Intelligence","","1877-0509","10.1016/j.procs.2024.10.129","https://www.sciencedirect.com/science/article/pii/S1877050924029296","Nowadays, cars have become the mainstream means of transportation, and traffic accidents caused by cars often occur on the road. Therefore, its safety is very important and has also attracted the attention of the general public. The braking system of a car is a very important part of its composition and structure, which determines the smoothness and safety of the car. The automobile braking system is a complex nonlinear system, which has multiple inputs, multiple outputs, uncertainties and multiple interference sources. Due to the complex relationship between input, interference, and output, the uncertainty of internal and external parameters in automobiles makes it very difficult to maintain appropriate braking. In order to improve the stability and safety of automobiles, this article conducts research on artificial intelligence technology, collaborative control algorithms, and automobile braking systems. The aim is to strengthen automobile braking systems through artificial intelligence technology and design an intelligent braking system to ensure smooth and safe driving of automobiles. Experiments have shown that the intelligent braking system can maintain a constant speed and maintain relative stability during emergency braking. The intelligent braking system can automatically detect accident prone road sections for reminders and speed reduction, and also automatically detect the situation around the car to give a warning. The experimental results show that when the car's speed reaches 25m/s or above (90km/h), the warning distance of the system is nearly 150 meters, which can fully ensure the safety of the driver; when driving at low speeds, the warning distance would not be too long to avoid affecting the driver's driving experience.","2024-01-01","2024-12-03 03:24:45","2024-12-03 03:24:45","","1070-1079","","","247","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial Intelligence Technology; Automotive Braking System; Collaborative Control Algorithm; Intelligent Braking System; Nonlinear System","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SCA725UJ","journalArticle","2024","Jiang, Wenshuo; Zhao, Zhigang","Trends in Research on AI-aided drug discovery from 2009 to 2023: a 15-year Bibliometric Analysis","Intelligent Pharmacy","","2949-866X","10.1016/j.ipha.2024.09.001","https://www.sciencedirect.com/science/article/pii/S2949866X2400090X","Purpose In recent years, the rapid advancement of artificial intelligence technology has brought opportunities for the acceleration and improvement of the drug discovery process by aiding in all stages of drug discovery like drug target identification and validation, virtual screening, de novo drug design, and ADMET property prediction. The present study aims to provide an overview of the developing tendency, cooperation, and influence of academic groups and individuals, hotspots, and crucial problems in the field of AI-aided drug discovery using bibliometric methods. Methods Publications on AI-aided drug discovery published from January 1, 2009, to December 31, 2023, were retrieved from the Web of Science core collection. The document type was limited to articles or reviews, and the language was set to English. Citespace was used to conduct the bibliometric analysis. Results A total of 9700 publications were included, and the number of them generally increased over time, with a rapid increase tendency since 2018. The US and China were the leading countries in this field. The Chinese Academy of Sciences was the most influential institution. Ekins, Sean was the most productive author and Hou, Tingjun formed the largest cooperation network. Networks and clusters of keywords highlighted terms like “virtual screening”, “expression” and “drug delivery” as focused topics, and burst analysis showed that “support vector machines”, and “classification” received the longest attention. Meanwhile the keywords “sars cov 2”, “molecular design” and “clinical trials” were hotspots in recent years. The content analysis of the co-cited literature identified the significant questions to be tackled in future research. Conclusions This study offers a comprehensive landscape of the global contributions given to this increasingly important and prolific field of research and points out several areas that might be addressed by future research to better develop the field of AI-aided drug discovery.","2024-09-04","2024-12-03 03:24:45","2024-12-03 03:24:45","","","","","","","Intelligent Pharmacy","","","","","","","","","","","","","","","","","","","bibliometric analysis; artificial intelligence; drug discovery","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"V2XTQJWF","journalArticle","2024","Italiani, Paolo; Frisoni, Giacomo; Moro, Gianluca; Carbonaro, Antonella; Sartori, Claudio","Evidence, my Dear Watson: Abstractive dialogue summarization on learnable relevant utterances","Neurocomputing","","0925-2312","10.1016/j.neucom.2023.127132","https://www.sciencedirect.com/science/article/pii/S0925231223012559","Abstractive dialogue summarization requires distilling and rephrasing key information from noisy multi-speaker documents. Combining pre-trained language models with input augmentation techniques has recently led to significant research progress. However, existing solutions still struggle to select relevant chat segments, primarily relying on open-domain and unsupervised annotators not tailored to the actual needs of the summarization task. In this paper, we propose DearWatson, a task-aware utterance-level annotation framework for improving the effectiveness and interpretability of pre-trained dialogue summarization models. Precisely, we learn relevant utterances in the source document and mark them with special tags, that then act as supporting evidence for the generated summary. Quantitative experiments are conducted on two datasets made up of real-life messenger conversations. The results show that DearWatson allows model attention to focus on salient tokens, achieving new state-of-the-art results in three evaluation metrics, including semantic and factuality measures. Human evaluation proves the superiority of our solution in semantic consistency and recall. Finally, extensive ablation studies confirm each module’s importance, also exploring different annotation strategies and parameter-efficient fine-tuning of large generative language models.","2024-03-01","2024-12-03 03:24:45","2024-12-03 03:24:45","","127132","","","572","","Neurocomputing","","","","","","","","","","","","","","","","","","","Text classification; Abstractive dialogue summarization; Gumbel-softmax trick; Input augmentation; Interpretable natural language processing","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"7ARJYHP9","journalArticle","2024","Madanaguli, Arun; Sjödin, David; Parida, Vinit; Mikalef, Patrick","Artificial intelligence capabilities for circular business models: Research synthesis and future agenda","Technological Forecasting and Social Change","","0040-1625","10.1016/j.techfore.2023.123189","https://www.sciencedirect.com/science/article/pii/S0040162523008740","This study explores the interlink between AI capabilities and circular business models (CBMs) through a literature review. Extant literature reveals that AI can act as efficiency catalyst, empowering firms to implement CBM. However, the journey to harness AI for CBM is fraught with challenges as firms grapple with the lack of sophisticated processes and routines to tap into AI's potential. The fragmented literature leaves a void in understanding the barriers and development pathways for AI capabilities in CBM contexts. Bridging this gap, adopting a capabilities perspective, this review intricately brings together four pivotal capabilities: integrated intelligence capability, process automation and augmentation capability, AI infrastructure and platform capability, and ecosystem orchestration capability as drivers of AI-enabled CBM. These capabilities are vital to navigating the multi-level barriers to utilizing AI for CBM. The key contribution of the study is the synthesis of an AI-enabled CBM framework, which not only summarizes the results but also sets the stage for future explorations in this dynamic field.","2024-03-01","2024-12-03 03:24:45","2024-12-03 03:24:45","","123189","","","200","","Technological Forecasting and Social Change","","","","","","","","","","","","","","","","","","","Artificial intelligence; Business model innovation; AI future research agenda; Circular business models","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"T4B547NN","journalArticle","2024","Wang, Sijing; Zhou, Ruoyu; Ren, Yijia; Jiao, Meiyuan; Liu, Honglai; Lian, Cheng","Advanced data-driven techniques in AI for predicting lithium-ion battery remaining useful life: A comprehensive review","Green Chemical Engineering","","2666-9528","10.1016/j.gce.2024.09.001","https://www.sciencedirect.com/science/article/pii/S2666952824000645","As artificial intelligence (AI) technology evolves, data-driven approaches are gaining attention in predicting lithium-ion battery's remaining useful life (RUL). Indeed, accurate RUL prediction is challenging, primarily because of the complex nature of the work and dynamic shifts in model parameters. To address these challenges, this article comprehensively explores five significant publicly accessible lithium-ion battery datasets, encompassing diverse usage conditions and battery types, offering researchers a rich repository of experimental data. In particular, we not only provide detailed information and access addresses for each dataset, but also present, for the first time, four innovative methods for battery aging health factor extraction. These methods, based on advanced AI techniques, are able to effectively identify and quantify key indicators of battery performance degradation, thereby enhancing the precision and dependability of RUL prediction. Additionally, the article identifies major challenges faced by current predictive techniques, including data quality, model generalization capabilities, and computational cost, highlighting the need for research focused on dataset diversity, multiple algorithm fusion, and hybrid physical-data-driven models to enhance prediction accuracy. We believe that this review will help researchers gain a comprehensive understanding of RUL estimation methods and promote the development of AI in battery.","2024-09-03","2024-12-03 03:24:45","2024-12-03 03:24:45","","","","","","","Green Chemical Engineering","","","","","","","","","","","","","","","","","","","Artificial intelligence; Data quality; Data-driven approaches; Health factor extraction; Remaining useful life","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"WS2QQMJ8","journalArticle","2023","Garsuault, Delphine; El Messaoudi, Sanaa; Prabakaran, Mookkan; Cheong, Ian; Boulanger, Anthony; Schmitt-Boulanger, Marion","Detection of several respiratory viruses with Surface-Enhanced Raman Spectroscopy coupled with Artificial Intelligence","Clinical Spectroscopy","","2666-0547","10.1016/j.clispe.2023.100025","https://www.sciencedirect.com/science/article/pii/S2666054723000029","Diagnoses of viral infections are a challenge when facing a crisis like COVID-19, where their speed and reliability are critical to minimize diseases spread. The gold standard of diagnostics, quantitative Polymerase Chain Reaction, is time- and reagent-consuming and requires qualified personnel. Therefore, it is necessary to find new detection techniques to overcome these barriers. Surface Enhanced Raman Spectroscopy (SERS) is a detection method, based on light and metallic particles admixed with the samples, already used in different fields of research. In this study, we discriminate three respiratory viruses using a combination of SERS and Artificial Intelligence (AI). Our technique appears to be fast, reproducible, and reliable, achieving between 95 % and 100 % of accuracy, standing out as a powerful tool usable for viral diagnostics.","2023-12-01","2024-12-03 03:24:45","2024-12-03 03:24:45","","100025","","","5","","Clinical Spectroscopy","","","","","","","","","","","","","","","","","","","Artificial intelligence; SARS-CoV-2; SERS; Virus detection","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"U776LX4H","journalArticle","2024","Tang, Dongyang","Systematic training of table tennis players' physical performance based on artificial intelligence technology and data fusion of sensing devices","SLAS Technology","","2472-6303","10.1016/j.slast.2024.100151","https://www.sciencedirect.com/science/article/pii/S2472630324000335","This research emphasises the value of physical training for table tennis players, particularly as ball speed and spin rate decline and emphasises how important intensity quality is to the game. Chinese table tennis players' dual identities place greater demands on the general growth of their learning and training as a crucial component of talent development preparation. Athletes' general quality, competitive level, and ability to avoid sports injuries are all improved by scientific and focused physical training. In order to achieve the functions of intelligent camera, multi-angle broadcasting, and 3D scene reproduction, this study combines the physical training model of artificial intelligence. This gives the audience a more engaging and in-depth viewing experience. More feature extraction of the match footage is made possible by deep learning and convolutional neural networks when combined with large-scale video data, greatly enhancing the match information for viewers. The experimental findings demonstrate that the accuracy of table tennis human technical movement recognition reaches 98.88 % based on the enhanced AM-Softmax classification algorithm.","2024-08-01","2024-12-03 03:24:45","2024-12-03 03:24:45","","100151","","4","29","","SLAS Technology","","","","","","","","","","","","","","","","","","","Data simulation; Physical training; Sensing device; Table tennis","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"6MZZ8QLT","journalArticle","2024","Ng, Jeremy Y.; Cramer, Holger; Lee, Myeong Soo; Moher, David","Traditional, complementary, and integrative medicine and artificial intelligence: Novel opportunities in healthcare","Integrative Medicine Research","","2213-4220","10.1016/j.imr.2024.101024","https://www.sciencedirect.com/science/article/pii/S2213422024000040","The convergence of traditional, complementary, and integrative medicine (TCIM) with artificial intelligence (AI) is a promising frontier in healthcare. TCIM is a patient-centric approach that combines conventional medicine with complementary therapies, emphasizing holistic well-being. AI can revolutionize healthcare through data-driven decision-making and personalized treatment plans. This article explores how AI technologies can complement and enhance TCIM, aligning with the shared objectives of researchers from both fields in improving patient outcomes, enhancing care quality, and promoting holistic wellness. This integration of TCIM and AI introduces exciting opportunities but also noteworthy challenges. AI may augment TCIM by assisting in early disease detection, providing personalized treatment plans, predicting health trends, and enhancing patient engagement. Challenges at the intersection of AI and TCIM include data privacy and security, regulatory complexities, maintaining the human touch in patient-provider relationships, and mitigating bias in AI algorithms. Patients' trust, informed consent, and legal accountability are all essential considerations. Future directions in AI-enhanced TCIM include advanced personalized medicine, understanding the efficacy of herbal remedies, and studying patient-provider interactions. Research on bias mitigation, patient acceptance, and trust in AI-driven TCIM healthcare is crucial. In this article, we outlined that the merging of TCIM and AI holds great promise in enhancing healthcare delivery, personalizing treatment plans, preventive care, and patient engagement. Addressing challenges and fostering collaboration between AI experts, TCIM practitioners, and policymakers, however, is vital to harnessing the full potential of this integration.","2024-03-01","2024-12-03 03:24:45","2024-12-03 03:24:45","","101024","","1","13","","Integrative Medicine Research","","","","","","","","","","","","","","","","","","","Artificial intelligence; Complementary and integrative medicine; Novel opportunities in healthcare; Traditional medicine","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2LUXEABW","journalArticle","2024","Durgun, Yeliz","Real-time water quality monitoring using AI-enabled sensors: Detection of contaminants and UV disinfection analysis in smart urban water systems","Journal of King Saud University - Science","","1018-3647","10.1016/j.jksus.2024.103409","https://www.sciencedirect.com/science/article/pii/S1018364724003215","This study introduces a novel method for assessing water quality, employing a cutting-edge sensor system integrated with artificial intelligence (AI) technologies. Addressing the global challenge of water scarcity and pollution, the research focuses on the innovative use of spectroscopic analysis for real-time water quality monitoring. The study evaluates the effectiveness of this system in distinguishing between clean, contaminated, and UV-disinfected water samples, highlighting its precision in detecting variations in water quality. Central to the research is the deployment of advanced machine learning algorithms, including Random Forest, Support Vector Machines (SVM), and Neural Networks, to process and classify spectral data. These models demonstrate remarkable accuracy in real-time classification, underscoring the synergy between AI and environmental science in addressing critical public health issues. Significantly, the study showcases the potential of UV disinfection in water treatment, as evidenced by the spectral changes observed in disinfected water samples. This aspect of the research emphasizes the role of spectral analysis in verifying the efficacy of water treatment processes. Overall, this study paves the way for more sophisticated and accessible water quality monitoring systems, offering a promising solution to one of the most pressing environmental challenges. The integration of AI and spectral analysis in this research offers a breakthrough in ensuring a safe water supply and effective water resource management. This study utilizes advanced machine learning algorithms, including Random Forest, Support Vector Machines (SVM), and Neural Networks, for water quality assessment. These models process and classify spectral data with high precision, highlighting variations in water quality.","2024-10-01","2024-12-03 03:24:45","2024-12-03 03:24:45","","103409","","9","36","","Journal of King Saud University - Science","","","","","","","","","","","","","","","","","","","Artificial intelligence in environmental monitoring; Machine learning in water safety; Multispectral spectroscopic sensors; Water quality assessment","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"P7E3HQKB","journalArticle","2023","Śliwa, Piotr; Krzos, Grzegorz","How To Teach Artificial Intelligence To Manage Our Organizations?","27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems (KES 2023)","","1877-0509","10.1016/j.procs.2023.10.479","https://www.sciencedirect.com/science/article/pii/S187705092301637X","Undoubtedly, Artificial Intelligence (AI) is going mainstream. More and more AI agents come into existence to augment human agents in their work by synthesizing a gigantic body of knowledge in a conversational interface (e.g., ChatGPT), generating art from a provided description (e.g., Stable Diffusion), creating software code based on a provided description (e.g., Codex), just to name a few. It becomes evident that at some point an AI agent will similarly help human managers in their daily operations, and, when it reaches the level of artificial general intelligence (AGI), unlock completely new levels of performance and sustainability. The authors used the critical review method and identified a research gap concerning the development of a generalized, numerical model of an organization and its environment that could be applied in machine learning pipelines, and effectively support managers in the key management functions.","2023-01-01","2024-12-03 03:24:45","2024-12-03 03:24:45","","4795-4804","","","225","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","machine learning; sustainability; artificial intelligence; management; artificial general intelligence; intelligent agents; organizational model","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"3GQV5GIP","journalArticle","2023","Tang, Hanzhang","Evaluation method of singing pronunciation quality based on artificial intelligence technology","3rd International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy","","1877-0509","10.1016/j.procs.2023.11.060","https://www.sciencedirect.com/science/article/pii/S1877050923018847","Since people have limited ability to judge the quality of their own singing and pronunciation, it is difficult to find problems on their own after reaching a certain level, so they need external help. However, external help also comes from human beings in the past and has many limitations. Therefore, in order to provide better help, this paper will carry out research from the perspective of artificial intelligence. In this paper, the basic concepts of artificial intelligence technology are discussed, and then the evaluation ideas of singing pronunciation quality on the technical level are put forward. Finally, the method system is designed.","2023-01-01","2024-12-03 03:24:45","2024-12-03 03:24:45","","526-532","","","228","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial intelligence technology; Pronunciation evaluation; Singing pronunciation quality","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2XGUQBM2","journalArticle","2024","Ou, Guanyong; Tang, Yuxuan; Liu, Jiexiang; Hao, Yabin; Chen, Zhi; Huang, Ting; Li, Shaxi; Niu, Shiyu; Peng, Yun; Feng, Jiaqi; Tu, Hongwei; Yang, Yang; Zhang, Han; Liu, Yingxia","Automated robot and artificial intelligence-powered wastewater surveillance for proactive mpox outbreak prediction","Biosafety and Health","","2590-0536","10.1016/j.bsheal.2024.07.002","https://www.sciencedirect.com/science/article/pii/S2590053624000855","In the wake of the largest-ever recorded outbreak of mpox in terms of magnitude and geographical spread in human history since May 2022, we innovatively developed an automated online sewage virus enrichment and concentration robot for disease tracking. Coupled with an artificial intelligence (AI) model, our research aims to estimate mpox cases based on the concentration of the monkeypox virus (MPXV) in wastewater. Our research has revealed a compelling link between the levels of MPXV in wastewater and the number of clinically confirmed mpox infections, a finding that is reinforced by the ability of our AI prediction model to forecast cases with remarkable precision, capturing 87 % of the data’s variability. However, it is worth noting that this high precision in predictions may be related to the relatively high frequency of data acquisition and the relatively non-mobile isolated environment of the hospital itself. In conclusion, this study represents a significant step forward in our ability to track and respond to mpox outbreaks. It has the potential to revolutionize public health surveillance by utilizing innovative technologies for disease surveillance and prediction.","2024-08-01","2024-12-03 03:24:46","2024-12-03 03:24:46","","225-234","","4","6","","Biosafety and Health","","","","","","","","","","","","","","","","","","","Artificial intelligence (AI) model; Automated online sewage virus enrichment robot; Early warning system; Monkeypox virus (MPXV); Mpox","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"EASM6QXR","journalArticle","2024","Sánchez, Omar; Castañeda, Karen; Vidal-Méndez, Sofía; Carrasco-Beltrán, Daniela; Lozano-Ramírez, Natalia E.","Exploring the influence of linear infrastructure projects 4.0 technologies to promote sustainable development in smart cities","Results in Engineering","","2590-1230","10.1016/j.rineng.2024.102824","https://www.sciencedirect.com/science/article/pii/S259012302401079X","Industry 4.0 technologies have a high potential to improve the planning and execution of linear projects such as roads, bridges and railroads. These technologies can help reduce emissions, enhance operations' efficiency, and improve users' quality of life. Despite their potential to promote sustainability in smart cities, there is a lack of research focused on analysing their impact. Therefore, this paper aims to identify 4.0 technologies related to linear projects and examine their influence on smart cities' sustainable development. The research method involved four main stages: identification of 4.0 technologies in linear projects, questionnaire design and application, influence analysis, and factor analysis. A systematic literature review identified a set of 4.0 technologies that favour smart cities' sustainable development. Subsequently, 66 experts were consulted to determine which of these technologies have the most significant influence on smart cities' sustainable development, highlighting data analysis and management, intelligent traffic control systems, and artificial intelligence. This study makes three main contributions: (1) it identifies thirty-seven 4.0 technologies associated with linear projects that promote sustainable development in smart cities; (2) it characterises the 4.0 technologies with the most significant influence on the sustainability of smart cities; and (3) it proposes nine principal components into which interrelated 4.0 technologies that promote sustainable development can be grouped. This study provides valuable evidence for smart city managers and offers guidance for the efficient adoption of 4.0 technologies in linear projects.","2024-09-01","2024-12-03 03:24:46","2024-12-03 03:24:46","","102824","","","23","","Results in Engineering","","","","","","","","","","","","","","","","","","","Sustainable development; Smart cities; Construction 4.0; Influence analysis; Linear infrastructure","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"L3HNAGVC","journalArticle","2024","Ramkumar Prabhu, M.; Sivaraman, R.; Nagabhooshanam, N.; Sampath Kumar, R.; Salunkhe, Satish S.","Empowering artificial intelligence-based multi-biometric image sensor for human identification","Measurement: Sensors","","2665-9174","10.1016/j.measen.2024.101082","https://www.sciencedirect.com/science/article/pii/S2665917424000588","Artificial intelligence (AI) and sensor technology developments have sparked revolutionary shifts in a number of fields, including biometric Identification. In order to improve human identification processes, this research offers a novel method that integrates AI and many biometric image sensors. The accuracy, robustness, and susceptibility to spoofing assaults of conventional single-modal biometric systems are among their many drawbacks. To overcome these challenges, we introduce a secure multi-biometric system that relies on feature-level fusion to identify users. In the preprocessing step, fingerprint images undergo Min-Max normalization to mitigate variations in image quality. In order to extract high-level features from both raw Electrocardiogram (ECG) signals and Min-Max normalized fingerprint images, ResNet50, a deep convolutional neural network, is used. These extracted feature vectors are able to distinguish between the two modalities. We proposed boosted Xgboost as a classifier for authentication in the identification steps to improve performance. The proposed approach is simulated using Python. A comparison study for improved Xgboost is presented using measures for accuracy, precision-recall, and F1-Score. Across all comparative metrics, the technique achieves much better performance. According to experimental findings, the suggested multi-biometric systems are more effective, dependable, and robust than the existing multi-biometric authentication systems.","2024-06-01","2024-12-03 03:24:46","2024-12-03 03:24:46","","101082","","","33","","Measurement: Sensors","","","","","","","","","","","","","","","","","","","Boosted Xgboost, Artificial intelligence (AI); Human Identification; Multi-biometric Image sensor","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"6EARTND2","journalArticle","2023","Fan, Hu","Research on innovation and application of 5G using artificial intelligence-based image and speech recognition technologies","Journal of King Saud University - Science","","1018-3647","10.1016/j.jksus.2023.102626","https://www.sciencedirect.com/science/article/pii/S1018364723000885","Many sectors have been fundamentally altered by the entrance of the 5G era due to the rapid advancement of information technology and computer technology. A fresh wave of digital media art (DMA) creation and invention has taken place in the current context. DMA is a brand-new field of study that brings together art and digital technology in a powerful way. It is a modern, multidisciplinary, and versatile art topic that is merged with other art themes. Humans used to be the primary means of creating digital media like animation. The labor of creating media content from raw sources is progressively being replaced by computers with advancements in artificial intelligence (AI) technology. Virtual reality (VR) technology's popularity has rapidly spread beyond the computer area to other parts of life, and it has also evolved into a new approach to DMA. Art will surely be highly influenced in the AI age, but we must also recognize the new developments brought to art by technical advancement in the 5G and AI. Hence, in this work, we examine the properties of virtual reality technology as well as the two most widely utilized approaches, artificial intelligence-based image recognition technology (AI-IRT) and artificial intelligence-based speech recognition technology (AI-SRT). We investigate and practice them in detail. We also compared these technologies to conventional teaching methods and discovered that visuals and pictures were considerably more responsive and enlightening to students than traditional teaching methods.","2023-05-01","2024-12-03 03:24:46","2024-12-03 03:24:46","","102626","","4","35","","Journal of King Saud University - Science","","","","","","","","","","","","","","","","","","","Artificial intelligence (AI); Virtual reality (VR); Artificial intelligence-based image recognition technology (AI-IRT); Artificial intelligence-based speech recognition technology (AI-SRT); Digital media art (DMA)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"43QJ6YDA","journalArticle","2024","Wang, Guansu; Kumar, Sameer; Huang, Zhihong; Liu, Ruoyi","Water resource management and policy evaluation in Middle Eastern countries: Achieving sustainable development goal 6","Desalination and Water Treatment","","1944-3986","10.1016/j.dwt.2024.100829","https://www.sciencedirect.com/science/article/pii/S1944398624203398","This study explores the challenges and management strategies of water resources in 15 Middle Eastern countries, framed within the context of Sustainable Development Goal 6 (SDG6). This study used 123 water resource policies in Middle Eastern countries as data and employed a pre-trained large language model based on RoBERTa, trained on the SNLI and MNLI datasets, to perform text classification tasks. In combination with the TF-IDF algorithm for keyword extraction, this approach is used to systematically evaluate and compare the water resource policies of these countries. The results reveal that the region faces significant water stress, exacerbated by high population growth, climate variability, and political instability. While countries like Kuwait and the UAE utilize advanced desalination technologies to mitigate water scarcity, policy gaps in sanitation and transboundary cooperation persist. Jordan's innovative partnerships for water resource management highlight the potential of collaborative frameworks to enhance regional water security. The findings suggest that comprehensive water management strategies, including technological innovation and public-private partnerships, are essential for addressing the region's pressing water challenges. This research contributes to understanding the complexities of water governance in arid regions and offers practical implications for policymakers aiming to achieve sustainable water resource management.","2024-10-01","2024-12-03 03:24:46","2024-12-03 03:24:46","","100829","","","320","","Desalination and Water Treatment","","","","","","","","","","","","","","","","","","","Policy; Middle Eastern Countries; SDG 6; Water resource management","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XK5V7TTX","journalArticle","2024","Zhang, Xiaobin","Global research on artificial intelligence in thyroid-associated ophthalmopathy: A bibliometric analysis","Advances in Ophthalmology Practice and Research","","2667-3762","10.1016/j.aopr.2023.11.002","https://www.sciencedirect.com/science/article/pii/S2667376223000616","Purpose To provide an overview of global publications on artificial intelligence (AI) in thyroid-associated ophthalmopathy (TAO) through bibliometric analysis. Methods Publications related to AI in TAO from inception until April 2023 were retrieved from the Web of Science database. The trends of publications and citations, publishing performance, collaboration among countries and institutions, and the funding agencies, relevant research domains, leading journals, hotspots and their evolution were identified. Results A total of 55 publications were included for analysis. The number of publications and citations continued to grow since 1998, with a significant acceleration of growth after 2020. China is the most productive country with the highest number of productive institutions, followed by the United States. European countries have the most extensive collaboration. The most relevant research domain was radiology, nuclear medicine & medical imaging. The European Journal of Radiology was one of the most productive journals, with the most influential articles published. ""Thyroid-associated ophthalmopathy"" and ""neural network"" maintain hotspots during the entire period. Studies were more focused on clinical features during 1998 and 2016, clinical features and medical data during 2017 and 2020, and medical data and AI techniques during 2021 and 2023. Conclusions This study summarized the global research status regarding AI in TAO in terms of trends, countries, institutions, research domains, journals, and key topics. AI has shown great potential in TAO. Sponsored by funding agencies such as NSFC, China has become the most productive country in the field of AI in TAO. Our findings help researchers better understand the development of this field and provide valuable clues for future research directions.","2024-02-01","2024-12-03 03:24:46","2024-12-03 03:24:46","","1-7","","1","4","","Advances in Ophthalmology Practice and Research","","","","","","","","","","","","","","","","","","","Bibliometric analysis; Artificial intelligence; Global publications; Thyroid-associated ophthalmopathy","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"8WMQUAR3","journalArticle","2024","Aggarwal, Swati; Mittal, Anshul","Futuristic hospitality conceptualized: DASH - Decentralized Autonomous and Smart Hotel system","Journal of Open Innovation: Technology, Market, and Complexity","","2199-8531","10.1016/j.joitmc.2024.100223","https://www.sciencedirect.com/science/article/pii/S2199853124000179","Ubiquitous hospitality has stimulated a rise in expeditions to inspiring getaways and extraordinary destinations. It is imperative for hotels to create a distinct and immersive experience leveraging disruptive technologies like Artificial Intelligence, Machine Learning, Internet of Things, and Blockchain, while ensuring economic affordability for patrons. This study introduces an autonomous and agile smart hotel system, DASH-Decentralized Autonomous and Smart Hotel system, operating on a pay-per-use model, meticulously tracking the usage of amenities and utilities for patrons. The proposed system is an amalgamation of Internet of Things, Robots, Artificial Intelligence, and Blockchain, each enhancing trust, transparency, and underlying operations while reducing workforce and operational costs. This work distinguishes itself by focusing on the complete automation of hotels rather than merely complementing current activities with these technologies. Method: This research hinges on an extensive literature review, deepening the understanding of contemporary hospitality systems, technological advances, and emerging trends. Building on these insights, this work employed conceptual modelling to create a robust framework for the DASH system, strategically integrating decentralization, autonomy, and intelligence. This method bridges visionary ideals with practical implementation, shaping the future of hospitality through cutting-edge technology integration. Main Findings: DASH, as a Decentralized Autonomous and Smart Hotel System, successfully integrates disruptive technologies to create an innovative and automated hospitality experience. The pay-per-use model proves effective in tracking amenity and utility usage, ensuring a cost-efficient and tailored service for patrons. The amalgamation of Internet of Things, Robots, Artificial Intelligence, and Blockchain enhances trust, transparency, and operational efficiency within the hospitality institution. The study's approach of steering towards complete hotel automation distinguishes it from existing literature, showcasing a unique and forward-thinking perspective on the implementation of disruptive technologies in the hospitality sector.","2024-03-01","2024-12-03 03:24:46","2024-12-03 03:24:46","","100223","","1","10","","Journal of Open Innovation: Technology, Market, and Complexity","","","","","","","","","","","","","","","","","","","Internet of Things; Robots; Blockchain; Automation in hospitality; Autonomous hotels; Pay per use model; Smart hotel; Smart tourism","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DKYCFH6P","journalArticle","2024","Obreja, Dragoș M.; Rughiniș, Răzvan; Rosner, Daniel","Mapping the conceptual structure of innovation in artificial intelligence research: A bibliometric analysis and systematic literature review","Journal of Innovation & Knowledge","","2444-569X","10.1016/j.jik.2024.100465","https://www.sciencedirect.com/science/article/pii/S2444569X24000052","This study uses bibliometric analysis and a systematic literature review to map the conceptual structure of artificial intelligence innovations (AI-I) in the social sciences between 2000 and 2023. It explicitly focuses on non-economic aspects conducive to AI-I, namely social, technological, cultural, sustainable, personal, moral, and ethical. Our analysis reveals that 1225 articles and proceeding papers have been published, and terms such as “technology,” “big data,” “management,” “performance,” “future,” and “impact” are the most frequently used when discussing innovation and AI. According to our time-zone analysis, the last two years have shown a significant emphasis on concepts such as “transformation,” “corporate social responsibility,” and “resource-based view.” In terms of citations, the countries that receive the highest number of references in the AI-I field are the United Kingdom, the United States, Germany, Australia, and China. The most prolific authors in terms of publications are David Teece, Erik Brynjolfsson, and Anjan Chatterjee. Given that most studies highlight the economic side of AI-I, we selected the most prolific 163 articles from all social science research areas. These studies legitimize the main non-economic aspects that highlight both certainties and uncertainties conducive to such innovations. Although the technological component is the most popular in our analysis of the non-economic aspects of the AI-I subfield, we find an important emphasis on ethical/moral dimensions conducive to slow innovation principles. We also observe a growing interest in the cultural dimension, specifically exploring potential factors that can lead to better human acceptance of these innovations.","2024-01-01","2024-12-03 03:24:46","2024-12-03 03:24:46","","100465","","1","9","","Journal of Innovation & Knowledge","","","","","","","","","","","","","","","","","","","Big data; Bibliometrics; Artificial intelligence; AI innovation; Conceptual structure","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"PMS49SPA","journalArticle","2024","Duwadi, Saroj; Cautinho, Carlos","ChatGPT based recommendation system for retail shops","International Conference on Industry Sciences and Computer Science Innovation","","1877-0509","10.1016/j.procs.2024.05.103","https://www.sciencedirect.com/science/article/pii/S1877050924011190","The rapid growth of e-commerce platforms has emphasized the significance of personalized recommendation systems in enhancing user engagement and satisfaction. This research paper presents the development and evaluation of an innovative Product Recommendation System that leverages advanced Artificial Intelligence (AI) techniques to provide tailored product suggestions. The primary objective is to create a user-centric experience by integrating an AI assistant, enabling natural and interactive interactions. Through a comprehensive survey conducted to understand customer behaviour while purchasing product using AI, the study aims to assess the system's effectiveness in delivering accurate recommendations and providing a seamless purchasing experience. The paper contributes to the field by showcasing the practical implementation of AI-driven recommendation systems, highlighting their potential to transform e-commerce interactions.","2024-01-01","2024-12-03 03:24:46","2024-12-03 03:24:46","","253-260","","","237","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Machine learning; ChatGPT; Artificial Intelligence (AI); AI assistant; E-commerce; Personalized recommendation systems; Retail business","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JQA2DM2S","journalArticle","2024","Azeroual, Otmane; Schöpfel, Joachim; Störl, Uta; Marušić, Ana","Ethical aspects using AI in CRIS","16th International Conference on Current Research Information Systems (CRIS 2024)","","1877-0509","10.1016/j.procs.2024.11.058","https://www.sciencedirect.com/science/article/pii/S1877050924032691","The integration of artificial intelligence (AI) into current research information systems (CRIS) is becoming increasingly prevalent, driven by the need for enhanced operational efficiency and data accessibility. While AI offers promising opportunities for improving CRIS functionalities, it also raises significant ethical challenges that must be addressed. This paper examines the ethical implications of AI implementation in CRIS, focusing on issues such as autonomy, discrimination, data protection, and user rights. Drawing on insights from machine, technology, digital, and robot ethics, the paper explores strategies for mitigating these challenges and fostering responsible AI use in CRIS. Additionally, it highlights the impact of AI technology on CRIS staff and emphasizes the importance of considering societal and personal effects alongside technological advancements. By raising awareness of ethical considerations and promoting ethical AI practices, this paper aims to contribute to the development and acceptance of AI technologies in CRIS, particularly in the light of forthcoming regulations such as the EU AI Act.","2024-01-01","2024-12-03 03:24:46","2024-12-03 03:24:46","","150-159","","","249","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","ethics; Artificial intelligence (AI); current research information systems (CRIS); data protection; responsible AI; user rights","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DHZ62NDQ","journalArticle","2024","El-Bouzaidi, Youssra El Idrissi; Abdoun, Otman","Artificial Intelligence for Sustainable Dermatology in Smart Green Cities: Exploring Deep Learning Models for Accurate Skin Lesion Recognition","International Symposium on Green Technologies and Applications (ISGTA’2023)","","1877-0509","10.1016/j.procs.2024.05.026","https://www.sciencedirect.com/science/article/pii/S1877050924010421","Smart and sustainable dermatology takes on a new dimension within Green Smart Cities with the integration of artificial intelligence (AI) into dermatological diagnosis. This study explores the success of deep learning models in accurately recognizing skin lesions, focusing on the use of the HAM10000 dataset. Our comparative analysis highlights the crucial impact of network architecture choices, data augmentation, and preprocessing on model performance. The results reveal that models leveraging transfer learning and fine-tuning on pre-trained networks excel in precision, underscoring their relevance in the context of smart green health. We also address opportunities for improvement in model generalization across diverse datasets and skin types. These findings provide a foundation for the development of more accurate skin lesion recognition models aligned with the principles of Green Smart Health, contributing to faster diagnostics, improved patient care, and ultimately, healthier Green Smart Cities. This work opens avenues for future research, such as exploring of the effectiveness of deep learning techniques in diverse health contexts and the integration of clinical data for more personalized dermatological diagnostics within Green Smart Cities.","2024-01-01","2024-12-03 03:24:46","2024-12-03 03:24:46","","233-240","","","236","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial intelligence; Skin cancer; Convolutional neural networks; Medical Diagnostics; Smart Healthcare","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CKCYRZPR","journalArticle","2024","Guttmann, Mike; Ge, Mouzhi","Research Agenda of Ethical Recommender Systems based on Explainable AI","The 15th International Conference on Ambient Systems, Networks and Technologies Networks (ANT) / The 7th International Conference on Emerging Data and Industry 4.0 (EDI40), April 23-25, 2024, Hasselt University, Belgium","","1877-0509","10.1016/j.procs.2024.06.032","https://www.sciencedirect.com/science/article/pii/S1877050924012687","In the digital era, recommender systems (RS) have become an integral part of our daily interactions, exerting a significant impact on users and society. However, this also raises ethical challenges related to RS that should be considered. Addressing these challenges requires the application of explainable artificial intelligence (XAI) models to make RS more understandable. Based on the current state-of-the-art literature, this paper aims to provide a comprehensive overview of XAI for RS and its ethical implications, with the aim of proposing a research agenda for ethical RS based on XAI. The findings of the literature review show that neural network-based RS have received much attention in terms of offering explanations, while there is a research gap in explaining context-based RS and in evaluating explanations. In addition, a set of ethical challenges for RS are discussed by exploring how explanations for recommendations can contribute to the ethical use of RS.","2024-01-01","2024-12-03 03:24:46","2024-12-03 03:24:46","","328-335","","","238","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Ethics; Human-computer interaction; Explainable artificial intelligence; Explanations; Recommender systems","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"8MC96T5B","journalArticle","2024","Kim, Young-Tak; Kim, Hayom; So, Mingyeong; Kong, Jooheon; Kim, Keun-Tae; Hong, Je Hyeong; Son, Yunsik; Sa, Jason K.; Do, Synho; Han, Jae-Ho; Kim, Jung Bin","Differentiating loss of consciousness causes through artificial intelligence-enabled decoding of functional connectivity","NeuroImage","","1053-8119","10.1016/j.neuroimage.2024.120749","https://www.sciencedirect.com/science/article/pii/S1053811924002465","Differential diagnosis of acute loss of consciousness (LOC) is crucial due to the need for different therapeutic strategies despite similar clinical presentations among etiologies such as nonconvulsive status epilepticus, metabolic encephalopathy, and benzodiazepine intoxication. While altered functional connectivity (FC) plays a pivotal role in the pathophysiology of LOC, there has been a lack of efforts to develop differential diagnosis artificial intelligence (AI) models that feature the distinctive FC change patterns specific to each LOC cause. Three approaches were applied for extracting features for the AI models: three-dimensional FC adjacency matrices, vectorized FC values, and graph theoretical measurements. Deep learning using convolutional neural networks (CNN) and various machine learning algorithms were implemented to compare classification accuracy using electroencephalography (EEG) data with different epoch sizes. The CNN model using FC adjacency matrices achieved the highest accuracy with an AUC of 0.905, with 20-s epoch data being optimal for classifying the different LOC causes. The high accuracy of the CNN model was maintained in a prospective cohort. Key distinguishing features among the LOC causes were found in the delta and theta brain wave bands. This research advances the understanding of LOC's underlying mechanisms and shows promise for enhancing diagnosis and treatment selection. Moreover, the AI models can provide accurate LOC differentiation with a relatively small amount of EEG data in 20-s epochs, which may be clinically useful.","2024-08-15","2024-12-03 03:24:46","2024-12-03 03:24:46","","120749","","","297","","NeuroImage","","","","","","","","","","","","","","","","","","","Explainable artificial intelligence; EEG coherence network; Functional connectivity; Graph theory; Loss of consciousness","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ABFD2JHU","journalArticle","2024","Giammanco, Avri; Bychkov, Andrey; Schallenberg, Simon; Tsvetkov, Tsvetan; Fukuoka, Junya; Pryalukhin, Alexey; Mairinger, Fabian; Seper, Alexander; Hulla, Wolfgang; Klein, Sebastian; Quaas, Alexander; Büttner, Reinhard; Tolkach, Yuri","Fast-Track Development and Multi-Institutional Clinical Validation of an Artificial Intelligence Algorithm for Detection of Lymph Node Metastasis in Colorectal Cancer","Modern Pathology","","0893-3952","10.1016/j.modpat.2024.100496","https://www.sciencedirect.com/science/article/pii/S0893395224000760","Lymph node metastasis (LNM) detection can be automated using artificial intelligence (AI)-based diagnostic tools. Only limited studies have addressed this task for colorectal cancer (CRC). This study aimed to develop of a clinical-grade digital pathology tool for LNM detection in CRC using the original fast-track framework. The training cohort included 432 slides from one department. A segmentation algorithm detecting 8 relevant tissue classes was trained. The test cohorts consisted of materials from 5 pathology departments digitized by 4 different scanning systems. A high-quality, large training data set was generated within 7 days and a minimal amount of annotation work using fast-track principles. The AI tool showed very high accuracy for LNM detection in all cohorts, with sensitivity, negative predictive value, and specificity ranges of 0.980 to 1.000, 0.997 to 1.000, and 0.913 to 0.990, correspondingly. Only 5 of 14,460 analyzed test slides with tumor cells over all cohorts were classified as false negative (3/5 representing clusters of tumor cells in lymphatic vessels). A clinical-grade tool was trained in a short time using fast-track development principles and validated using the largest international, multi-institutional, multiscanner cohort of cases to date, showing very high precision for LNM detection in CRC. We are releasing a part of the test data sets to facilitate academic research.","2024-06-01","2024-12-03 03:24:46","2024-12-03 03:24:46","","100496","","6","37","","Modern Pathology","","","","","","","","","","","","","","","","","","","artificial intelligence; digital pathology; colorectal cancer; lymph node; metastasis detection; validation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"HHVHEJCI","journalArticle","2024","Li, Xing; Zhao, Haiping; Feng, Yiming; Li, Jinze; Zhao, Yunfei; Wang, Xiao","Research on key technologies of high energy efficiency and low power consumption of new data acquisition equipment of power Internet of Things based on artificial intelligence","International Journal of Thermofluids","","2666-2027","10.1016/j.ijft.2024.100575","https://www.sciencedirect.com/science/article/pii/S266620272400017X","Energy efficiency is a critical problem that drives consideration of smart cities and urban areas' development. Energy security and the smart environment face enormous problems because of the dramatic rise in energy consumption brought on by rising population levels and the widespread use of new data-collecting technologies. Traditional smart grids can be updated with IoT-based smart metering (SM) and advanced metering infrastructure (AMI) technologies by revealing previously hidden information about electrical power by implementing a communication system between utilities and consumers during the power transaction process. The smart distribution and energy consumption in smart city environments are strongly supported by the Internet of Things (IoT) and Artificial Intelligence (AI). Hence, this paper suggests the IoT and AI-assisted Smart Metering System (IoT–AI–SMS) as a new data acquisition equipment for predicting energy consumption in smart cities. The information is taken from Energy Efficiency Datasets to examine smart cities' energy consumption. This research offers a Recurrent Neural Network (RNN) for load forecasting using smart meter data. This technique allows training a single model with all participating smart meters without exchanging local information. Considering the customers' needs, the model developed scheduled the controllable loads and offered optimal dispatch of distributed generation in the smart grid.","2024-02-01","2024-12-03 03:24:46","2024-12-03 03:24:46","","100575","","","21","","International Journal of Thermofluids","","","","","","","","","","","","","","","","","","","Internet of Things; Artificial intelligence; Optimization; Energy efficiency; Load forecasting; Low power consumption; Recurrent neural network; Smart grid; Smart meter","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2LH7IS7E","journalArticle","2023","Schmidt, Matthew; Glaser, Noah; Palmer, Heath; Schmidt, Carla; Xing, Wanli","Through the lens of artificial intelligence: A novel study of spherical video-based virtual reality usage in autism and neurotypical participants","Computers & Education: X Reality","","2949-6780","10.1016/j.cexr.2023.100041","https://www.sciencedirect.com/science/article/pii/S2949678023000351","The current study explores the use of computer vision and artificial intelligence (AI) methods for analyzing 360-degree spherical video-based virtual reality (SVVR) data. The study aimed to explore the potential of AI, computer vision, and machine learning methods (including entropy analysis, Markov chain analysis, and sequential pattern mining), in extracting salient information from SVVR video data. The research questions focused on differences and distinguishing characteristics of autistic and neurotypical usage characteristics in terms of behavior sequences, object associations, and common patterns, and the extent to which the predictability and variability of findings might distinguish the two participant groups and provide provisional insights into the dynamics of their usage behaviors. Findings from entropy analysis suggest the neurotypical group showed greater homogeneity and predictability, and the autistic group displayed significant heterogeneity and variability in behavior. Results from the Markov Chains analysis revealed distinct engagement patterns, with autistic participants exhibiting a wide range of transition probabilities, suggesting varied SVVR engagement strategies, and with the neurotypical group demonstrating more predictable behaviors. Sequential pattern mining results indicated that the autistic group engaged with a broader spectrum of classes within the SVVR environment, hinting at their attraction to a diverse set of stimuli. This research provides a preliminary foundation for future studies in this area, as well as practical implications for designing effective SVVR learning interventions for autistic individuals.","2023-12-01","2024-12-03 03:24:47","2024-12-03 03:24:47","","100041","","","3","","Computers & Education: X Reality","","","","","","","","","","","","","","","","","","","Data mining; Artificial Intelligence; Computer vision; Spherical video-based virtual reality","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"IJG78K48","journalArticle","2024","Mallinger, Kevin; Corpaci, Luiza; Neubauer, Thomas; Tikász, Ildikó E.; Goldenits, Georg; Banhazi, Thomas","Breaking the barriers of technology adoption: Explainable AI for requirement analysis and technology design in smart farming","Smart Agricultural Technology","","2772-3755","10.1016/j.atech.2024.100658","https://www.sciencedirect.com/science/article/pii/S2772375524002636","Understanding the factors that drive and hinder technology adoption is critical for companies that try to access customer segments or governmental agencies that want to foster economic, ecological, or social change. By assessing the technological readiness of customer groups, common and individual barriers or opportunities for technology adoption can be observed and translated into technological requirements, business strategies, or policy interventions. Current approaches to assessing such barriers do not provide information on which factors influence technological readiness more than others, limiting the prioritization of targeted technological or political interventions. This research introduces an Explainable Machine Learning (XAI) approach to overcome this limitation. It exemplifies its usability for the Precision Livestock Farming domain, particularly for smart technologies incorporating novel advances in Artificial Intelligence and Internet of Things. A random forest machine learning model is introduced to identify clusters of different farmers' technological readiness based on the available features (survey questions). XAI techniques are then deployed to understand the influence of individual features on the prediction outcome, highlighting factors that increase or decrease technological readiness of farmers. The results are assessed for their potential for requirement and business analysis while providing targeted suggestions for technology design.","2024-12-01","2024-12-03 03:24:47","2024-12-03 03:24:47","","100658","","","9","","Smart Agricultural Technology","","","","","","","","","","","","","","","","","","","Explainable Artificial Intelligence; Precision livestock farming; Technology barriers; Technology design; User attitudes","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JSKQWBFQ","journalArticle","2024","Maleki, Erfan; Bagherifard, Sara; Unal, Okan; Guagliano, Mario","Hybrid Intelligence approach to study post-processing impact on the mechanical performance of notched additively manufactured AlSi10Mg","Materials & Design","","0264-1275","10.1016/j.matdes.2024.113462","https://www.sciencedirect.com/science/article/pii/S0264127524008372","This study introduces a Hybrid Intelligence approach to investigate the Process-Structure-Property-Performance (PSSP) relationship in additively manufactured (AM) materials, specifically focusing on V-notched laser powder bed fused (L-PBF) AlSi10Mg specimens. The Humen Intelligence (HI) component managed the design, manufacturing processes, post-processing, structural characterization, mechanical testing, and data collection. In parallel, Artificial Intelligence (AI), utilizing advanced machine learning (ML) algorithms, performed tasks related to prediction, sensitivity analysis, and parametric analysis. AI identified patterns and developed predictive models that provided deeper insights into how process parameters affect material properties and performance. This integration of HI and AI enabled a more thorough exploration of these relationships; data collected from our previous research were complemented with new experiments conducted to assess the effects of various heat treatments (HTs) and surface post-treatments (SPTs) on the fatigue behavior of the specimens. The techniques applied included stress relief (SR), T6 thermal treatments, sand blasting (SB), shot peening (SP), severe vibratory peening (SVP), laser shock peening (LSP), tumble finishing (TF), abrasive flow machining (AFM), chemical polishing (CP), electrochemical polishing (ECP), and chemical milling (CM), along with their combinations. A total of 54 different post-processing techniques were examined in this study. The experimental data, covering surface texture, microstructure, porosity, hardness, and residual stress, were used to develop an ML model that analyzed the fatigue behavior of the specimens. This approach represents a significant advancement toward integrated mechanistic and data-driven materials engineering, offering valuable insights for optimizing fatigue performance in practical applications.","2024-12-01","2024-12-03 03:24:47","2024-12-03 03:24:47","","113462","","","248","","Materials & Design","","","","","","","","","","","","","","","","","","","Machine learning; Additive manufacturing; Fatigue behavior; Hybrid intelligence; Tensile properties","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"QLW9S3ME","journalArticle","2024","Wilendra, Wendra; Nadlifatin, Reny; Kusumawulan, Cahya Khairani","ChatGPT: The AI Game-Changing Revolution in Marketing Strategy for the Indonesian Cosmetic Industry","Seventh Information Systems International Conference (ISICO 2023)","","1877-0509","10.1016/j.procs.2024.03.091","https://www.sciencedirect.com/science/article/pii/S1877050924004496","The Indonesian cosmetic industry has experienced rapid growth in recent years, requiring the adoption of innovative marketing strategies to remain competitive. This article explores the potential of ChatGPT, an advanced AI language model, in revolutionizing marketing strategy within the Indonesian cosmetic industry. Employing a qualitative research approach and thematic analysis, this study investigates AI-driven marketing strategies, product development, personalization in marketing, ethical considerations, and future research directions. The findings suggest that ChatGPT can significantly enhance customer targeting, market segmentation, pricing strategies, and promotion, as well as contribute to product development and personalized marketing efforts. However, ethical concerns and future research directions warrant further exploration in this rapidly evolving field.","2024-01-01","2024-12-03 03:24:47","2024-12-03 03:24:47","","1012-1019","","","234","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","ChatGPT; Artificial Intelligence; Indonesian Cosmetic Industry; Marketing Intelligence; Marketing Strategy","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"9DQJLAXU","journalArticle","2024","Lami, Kris; Yoon, Han-Seung; Parwani, Anil V.; Pham, Hoa Hoang Ngoc; Tachibana, Yuri; Linhart, Chaim; Grinwald, Maya; Vecsler, Manuela; Fukuoka, Junya","Validation of prostate and breast cancer detection artificial intelligence algorithms for accurate histopathological diagnosis and grading: a retrospective study with a Japanese cohort","Pathology","","0031-3025","10.1016/j.pathol.2024.02.009","https://www.sciencedirect.com/science/article/pii/S0031302524001016","Summary Prostate and breast cancer incidence rates have been on the rise in Japan, emphasising the need for precise histopathological diagnosis to determine patient prognosis and guide treatment decisions. However, existing diagnostic methods face numerous challenges and are susceptible to inconsistencies between observers. To tackle these issues, artificial intelligence (AI) algorithms have been developed to aid in the diagnosis of prostate and breast cancer. This study focuses on validating the performance of two such algorithms, Galen Prostate and Galen Breast, in a Japanese cohort, with a particular focus on the grading accuracy and the ability to differentiate between invasive and non-invasive tumours. The research entailed a retrospective examination of 100 consecutive prostate and 100 consecutive breast biopsy cases obtained from a Japanese institution. Our findings demonstrated that the AI algorithms showed accurate cancer detection, with AUCs of 0.969 and 0.997 for the Galen Prostate and Galen Breast, respectively. The Galen Prostate was able to detect a higher Gleason score in four adenocarcinoma cases and detect a previously unreported cancer. The two algorithms successfully identified relevant pathological features, such as perineural invasions and lymphovascular invasions. Although further improvements are required to accurately differentiate rare cancer subtypes, these findings highlight the potential of these algorithms to enhance the precision and efficiency of prostate and breast cancer diagnosis in Japan. Furthermore, this validation paves the way for broader adoption of these algorithms as decision support tools within the Asian population.","2024-08-01","2024-12-03 03:24:47","2024-12-03 03:24:47","","633-642","","5","56","","Pathology","","","","","","","","","","","","","","","","","","","Artificial intelligence; breast cancer; digital pathology; validation; Japanese cohort; prostate cancer","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"MDN5T42M","journalArticle","2024","Ma, Guangjian","Key Technologies of Intelligence in Network Security Construction and System Development","The 4th International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy","","1877-0509","10.1016/j.procs.2024.09.053","https://www.sciencedirect.com/science/article/pii/S187705092402060X","In today's rapidly developing information technology, people's attention to network security is also constantly increasing. In the context of the new era, in order to better manage, protect, coordinate, and evaluate network resources and information, it is necessary to apply intelligent technology. Starting from the problems existing in the current network security construction and system development, this paper conducts in-depth analysis and research on the application of intelligent key technologies in the network security construction and system development. The results show that intelligent key technologies can improve the confidentiality of the Internet security systems from 91.12% to 95.86%, and the effect is very significant. It is hoped to provide some reference and reference for relevant staff, further improve the efficiency and quality of network security construction and system development, and ensure the safety of the lives and property of the general public.","2024-01-01","2024-12-03 03:24:47","2024-12-03 03:24:47","","431-439","","","243","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial Intelligence; Key Technologies for Intelligence; Network Security Construction; Security System Development","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"UU3K494F","journalArticle","2023","Velasquez-Camacho, Luisa; Etxegarai, Maddi; de-Miguel, Sergio","Implementing Deep Learning algorithms for urban tree detection and geolocation with high-resolution aerial, satellite, and ground-level images","Computers, Environment and Urban Systems","","0198-9715","10.1016/j.compenvurbsys.2023.102025","https://www.sciencedirect.com/science/article/pii/S0198971523000881","Urban forests are becoming increasingly important for human well-being as they provide ecosystem services that contribute to improving well-being of city dwellers and to addressing climate change. However, despite their importance, there is an information gap in most of the world's urban forests due to the high cost and complexity of conducting standard forest inventories in urban environments. New technologies based on artificial intelligence can represent a smart and efficient alternative to costly traditional inventories. In this paper, we present an approach based on deep learning algorithms for the detection, counting, and geopositioning of trees using a combination of ground-level and aerial/satellite imagery. We tested several convolutional networks, exploring different combinations of hyperparameters and adjusting the query distance between ground-level images, detection radius, and various resolutions of satellite and aerial images. Our methodology is able to detect and accurately locate 79% of the urban street tree with a positional accuracy of 60 cm to the center of the canopy. Additionally, this approach allows us to determine the availability of photographs of urban trees, indicating from which Google Street View image each tree is visible. Our research provides a scalable and replicable solution to the scarcity of urban tree data and information worldwide, demonstrating the potential of artificial intelligence to revolutionize the way in which we inventory and monitor urban forests.","2023-10-01","2024-12-03 03:24:47","2024-12-03 03:24:47","","102025","","","105","","Computers, Environment and Urban Systems","","","","","","","","","","","","","","","","","","","Remote sensing; Artificial intelligence; Urban forest; Urban forest inventory; Urban management","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ATDF4RWC","journalArticle","2024","Lin, Jiun-Shiung; Chen, Kun-Huang","A novel decision support system based on computational intelligence and machine learning: Towards zero-defect manufacturing in injection molding","Journal of Industrial Information Integration","","2452-414X","10.1016/j.jii.2024.100621","https://www.sciencedirect.com/science/article/pii/S2452414X24000657","Real-time monitoring solutions have gained popularity across industries due to the advent of Industry 4.0, AI, and big data enhancing the efficiency of industrial production and equipment decisions. Machine learning models that possess computing intelligence and interpretability provide superior predictive capabilities compared to manual adjustments, resulting in cost savings and manufacturing high-quality products. This study proposes a zero-defect manufacturing decision support system based on computational intelligence feature selection combined with interpretable machine learning. The decision support system integrates Particle Swarm Optimization (PSO) and the C4.5 decision tree method, abbreviated as PSO+C4.5, to enable the continuous monitoring of the injection molding process in real-time, considering production parameter information and collected data quality, guiding the decision-making process for implementing zero-defect manufacturing (ZDM). In contrast to existing research, our innovative methodology relies on computational intelligence techniques for extracting features and employs interpretable machine learning prediction models. In terms of quality prediction, our empirical findings show that the suggested method accomplishes the optimal balance between interpretability and predictive performance (Accuracy: 0.9889, Sensitivity: 0.9869, and Specificity: 0.9935). These characteristics can directly support maintenance personnel and operators in optimizing the processing quality process.","2024-07-01","2024-12-03 03:24:47","2024-12-03 03:24:47","","100621","","","40","","Journal of Industrial Information Integration","","","","","","","","","","","","","","","","","","","Industry 4.0; Big data; Artificial intelligence; Injection molding; Particle swarm optimization; Zero defect manufacturing","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RNSBLE69","journalArticle","2024","Fu, Yao; Weng, Zhenjie","Navigating the ethical terrain of AI in education: A systematic review on framing responsible human-centered AI practices","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100306","https://www.sciencedirect.com/science/article/pii/S2666920X24001097","With the rapid development of artificial intelligence (AI) in recent years, there has been an increasing number of studies on integrating AI in various educational contexts, ranging from early childhood to higher education. Although systematic reviews have widely reported the effects of AI on teaching and learning, limited reviews have examined and defined responsible AI in education (AIED). To fill this gap, we conducted a convergent systematic mixed studies review to analyze key themes emerging from primary research. Following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines, we searched Scopus and Web of Science and identified 40 empirical studies that satisfied our inclusion criteria. Specifically, we used four criteria for the screening process: (1) the study's full text was available in English; (2) the study was published before April 10th, 2024 in peer-reviewed journals or conference proceedings; (3) the study was primary research that collected original data and applied qualitative, quantitative, or mixed-methods as the study methodology; and (4) the study had a clear focus on ethical and/or responsible AI in one or multiple educational context(s). Our findings identified essential stakeholders and characteristics of responsible AI in K-20 educational contexts and expanded understanding of responsible human-centered AI (HCAI). We unveiled characteristics vital to HCAI, encompassing Fairness and Equity, Privacy and Security, Non-maleficence and Beneficence, Agency and Autonomy, and Transparency and Intelligibility. In addition, we provided suggestions on how to achieve responsible HCAI via collaborative efforts of stakeholders, including roles of users (e.g., students and educators), developers, researchers, and policy and decision-makers.","2024-12-01","2024-12-03 03:24:47","2024-12-03 03:24:47","","100306","","","7","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Educational contexts; Responsible human-centered artificial intelligence; Systematic mixed studies review","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KK7YVUYQ","journalArticle","2024","Pang, Jiayun; Pine, Alexander W. R.; Sulemana, Abdulai","Using natural language processing (NLP)-inspired molecular embedding approach to predict Hansen solubility parameters††Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d3dd00119a","Digital Discovery","","2635-098X","10.1039/d3dd00119a","https://www.sciencedirect.com/science/article/pii/S2635098X24000020","Hansen solubility parameters (HSPs) have three components, δd, δp and δh, accounting for dispersion forces, polar forces, and hydrogen bonding of a molecule, which were designed to better understand how molecular structure affects miscibility/solubility. HSP is widely used throughout the pipeline of pharmaceutical research and yet has not been as well studied computationally as the aqueous solubility. In the current study, we predicted HSPs using only the SMILES of molecules and utilise the molecular embedding approach inspired by Natural Language Processing (NLP). Two pre-trained deep learning models – Mol2Vec and ChemBERTa have been used to derive the embeddings. A dataset of ∼1200 organic molecules with experimentally determined HSPs was used as the labelled dataset. Upon finetuning, the ChemBERTa model “learned” relevant molecular features and shifted attention to functional groups that give rise to the relevant HSPs. The finetuned ChemBERTa model outperforms both the Mol2Vec model and the baseline Morgan fingerprint method albeit not to a significant extent. Interestingly, the embedding models can predict δd significantly better than δh and δp and overall, the accuracy of predicted HSPs is lower than the well-benchmarked ESOL aqueous solubility. Our study indicates that the extent of transfer learning leveraged from the pre-trained models is related to the labelled molecular properties. It also highlights how δp and δh may have large intrinsic errors in the way they are defined and therefore introduces inherent limitations to their accurate prediction using machine learning models. Our work reveals several interesting findings that will help explore the potential of BERT-based models for molecular property prediction. It may also guide the possible refinement of the Hansen solubility framework, which will generate a wide impact across the pharmaceutical industry and research.","2024-01-17","2024-12-03 03:24:47","2024-12-03 03:24:47","","145-154","","1","3","","Digital Discovery","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"6YYC573Z","journalArticle","2024","Felfeli, Tina; Huang, Ryan S.; Lee, Tin-Suet Joan; Lena, Eleanor R.; Basilious, Amy; Lamoureux, Daniel; Khalid, Shuja","Assessment of predictive value of artificial intelligence for ophthalmic diseases using electronic health records: A systematic review and meta-analysis","JFO Open Ophthalmology","","2949-8899","10.1016/j.jfop.2024.100124","https://www.sciencedirect.com/science/article/pii/S2949889924000485","Purpose The application of artificial intelligence (AI) in ophthalmology has shown significant promise across various clinical domains. This study addresses the need for assessing the predictive value of AI models utilizing electronic health records (EHRs) for diagnosis, prognostication and management of ocular diseases. Methods A search was conducted using Ovid MEDLINE, Ovid EMBASE, and Cochrane Central for relevant studies published between January 2010 to February 2023 on predictive value of AI algorithms in ophthalmic EHRs. The study followed the Preferred Reporting Items for a Systematic Review and Meta-analysis (PRISMA) guidelines, with a protocol registered on Prospero (registration number: CRD42022303128). A bivariate random effects model was used to perform the meta-analysis. The ROBINS-I tool was used to assess methodological quality and applicability of the included studies. Results Out of 4968 initial records, 41 studies met the inclusion criteria, comprising a total of 639,637 patients, with an average disease prevalence of 11%. The studies exhibited a diagnostic odds ratio of 18.527 (95% CI: 9.654–35.556), sensitivity of 0.811 (95% CI: 0.751−0.859), specificity of 0.812 (95% CI: 0.736−0.87) and Grading of Recommendations, Assessment, Development and Evaluation (GRADE) moderate. Likelihood ratios (LR+ and LR−) were 4.316 (95% CI: 2.938–6.339) and 0.233 (95% CI: 0.169−0.322), respectively. False positive rate was 0.188 (95% CI: 0.13−0.264). Inter-rate concordance for ROBINS-I scoring had a kappa score of 0.83. Out of the 41 studies, 22 had an overall low risk of bias, and 19 had a moderate risk of bias. There was a low to moderate quality of body of evidence for the reported outcomes. Conclusion This meta-analysis affirms the substantial potential of AI models utilizing EHRs for predictive modeling and clinical management of ocular diseases. Future research should emphasize external validation and standardized reporting for better implementation of AI in ophthalmic practice.","2024-09-01","2024-12-03 03:24:47","2024-12-03 03:24:47","","100124","","","7","","JFO Open Ophthalmology","","","","","","","","","","","","","","","","","","","Artificial intelligence; Ophthalmology; Electronic medical record; Diagnostic; Electronic health record","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"4EFAZSNU","journalArticle","2024","Farea, Ali Hamid; Alhazmi, Omar H.; Kucuk, Kerem","Advanced Optimized Anomaly Detection System for IoT Cyberattacks Using Artificial Intelligence","Computers, Materials and Continua","","1546-2218","10.32604/cmc.2023.045794","https://www.sciencedirect.com/science/article/pii/S1546221824001334","While emerging technologies such as the Internet of Things (IoT) have many benefits, they also pose considerable security challenges that require innovative solutions, including those based on artificial intelligence (AI), given that these techniques are increasingly being used by malicious actors to compromise IoT systems. Although an ample body of research focusing on conventional AI methods exists, there is a paucity of studies related to advanced statistical and optimization approaches aimed at enhancing security measures. To contribute to this nascent research stream, a novel AI-driven security system denoted as “AI2AI” is presented in this work. AI2AI employs AI techniques to enhance the performance and optimize security mechanisms within the IoT framework. We also introduce the Genetic Algorithm Anomaly Detection and Prevention Deep Neural Networks (GAADPSDNN) system that can be implemented to effectively identify, detect, and prevent cyberattacks targeting IoT devices. Notably, this system demonstrates adaptability to both federated and centralized learning environments, accommodating a wide array of IoT devices. Our evaluation of the GAADPSDNN system using the recently complied WUSTL-IIoT and Edge-IIoT datasets underscores its efficacy. Achieving an impressive overall accuracy of 98.18% on the Edge-IIoT dataset, the GAADPSDNN outperforms the standard deep neural network (DNN) classifier with 94.11% accuracy. Furthermore, with the proposed enhancements, the accuracy of the unoptimized random forest classifier (80.89%) is improved to 93.51%, while the overall accuracy (98.18%) surpasses the results (93.91%, 94.67%, 94.94%, and 94.96%) achieved when alternative systems based on diverse optimization techniques and the same dataset are employed. The proposed optimization techniques increase the effectiveness of the anomaly detection system by efficiently achieving high accuracy and reducing the computational load on IoT devices through the adaptive selection of active features.","2024-02-27","2024-12-03 03:24:47","2024-12-03 03:24:47","","1525-1545","","2","78","","Computers, Materials and Continua","","","","","","","","","","","","","","","","","","","security; Internet of Things; artificial intelligence; anomaly detection and prevention system; optimization techniques","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"MZSY8CTZ","journalArticle","2024","Graham, Byron; Bonner, Karen","The role of institutions in early-stage entrepreneurship: An explainable artificial intelligence approach","Journal of Business Research","","0148-2963","10.1016/j.jbusres.2024.114567","https://www.sciencedirect.com/science/article/pii/S0148296324000717","Although the importance of institutional conditions in fostering entrepreneurship is well established, less is known about the dominance of institutional dimensions, their predictive ability, and more complex non-linear relationships. To overcome the limitations of traditional regression approaches in addressing these gaps we apply techniques from explainable artificial intelligence to study the dominance and non-linearity of institutional dimensions in predicting country-level early-stage entrepreneurship. Eight machine learning algorithms are applied to matched data from the Global Entrepreneurship Monitor, Index of Economic Freedom, and World Bank across 573 observations from 81 countries. Findings from the most accurate random forest model reveal considerable non-linearity in the relationships between institutional dimensions and entrepreneurship, as well as heterogeneity in the importance of individual dimensions, with an overall trend towards the dominance of cultural-cognitive institutions. These findings contribute to institutional theory and highlight important areas where machine learning methods can contribute to entrepreneurship research and policy.","2024-03-01","2024-12-03 03:24:47","2024-12-03 03:24:47","","114567","","","175","","Journal of Business Research","","","","","","","","","","","","","","","","","","","Machine learning; Entrepreneurship; Institutional theory; Explainable artificial intelligence; Entrepreneurship policy","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"9XL2WRE6","journalArticle","2024","Ma, Mac Zewei; Chen, Sylvia Xiaohua; Wang, Xijing","Collective pronouns, collective health actions: Predicting pandemic precautionary measures through online first-person plural pronoun usage across U.S. states","Social Science & Medicine","","0277-9536","10.1016/j.socscimed.2024.117167","https://www.sciencedirect.com/science/article/pii/S0277953624006208","The COVID-19 pandemic has underscored the role of group identification in shaping collective health behaviors. Using the novel Pronoun-Influenced Collective Health Model — an integrated framework combining elements from health and social psychology theories — we investigated the relationship between online first-person plural pronoun usage and adherence to COVID-19 preventive measures across the United States. Analyzing weekly Google Trends data on English (Study 1) and Spanish (Study 2) first-person pronoun searches, alongside data on adherence to pandemic precautionary measures from early 2020 to late 2022, we found significant positive associations between relative first-person plural pronoun search volumes and adherence to social distancing, stay-at-home orders, vaccination rates, and proactive disease prevention information seeking. These associations remained robust after adjusting for potential confounding factors. A mini meta-analysis (Study 3) confirmed the consistency of our findings, revealing no significant moderation effects by language context or ecological-socio-cultural factors, suggesting broad generalizability. The implications of this research highlight the potential for tracking online collective language as a valuable indicator of and proxy for societal-level health engagement during crises. This novel digital linguistics approach, synergistically combining applied health and social psychology with big data from digital platforms such as Google, offers powerful tools for monitoring collective health actions across linguistic and cultural boundaries during large-scale health crises.","2024-09-01","2024-12-03 03:24:47","2024-12-03 03:24:47","","117167","","","357","","Social Science & Medicine","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"4TKBRNRQ","journalArticle","2024","Grüble, Tobias; Stetter, Ralf; Schuchter, Timo; Till, Markus; Rudolph, Stephan","Combined Geometric and Kinetic Data Model in Model-Based Systems Engineering of Robotic Cells","34th CIRP Design Conference","","2212-8271","10.1016/j.procir.2024.03.005","https://www.sciencedirect.com/science/article/pii/S2212827124006802","The main intention of the presented research is the development of an engineering framework that allows the automated generation of a combined geometric and kinetic digital twin of a robotic cell. The engineering framework is based on graph-based design languages and an appropriate compiler for their translation in order to realize a novel form of a machine-executable V-model for Model-Based Systems Engineering (MBSE). In this novel machine-executable MBSE process, the primary process management objects are no longer some documents but abstract process descriptions that can be automatically compiled into concrete product models. For such a holistic MBSE process, the models have to be enriched with physical behavior and performance information. The method is illustrated with a use-case of the engineering process of robotic cells. The starting point of the automated synthesis of the robotic cell with its resources is the abstract modelling by means of object-oriented programming. The use-case illustrates a pick-and-place operation that requires a specific geometry of a monolithic gripper. The gripper geometry synthesis is based on design automation with topology optimization, which is also realized inside the executable V-model. A large number of synthesis results from the geometry generating processes can be included in automated kinetic simulations, thus generating digital twins for a large solution spectrum to achieve an overall holistic optimization. Further, a digital twin created from geometrical data and kinetic simulation enables process monitoring based on physical values (joint forces, pressure, etc.) which then can be used to predict failure or evaluate new/optimized motion sequences.","2024-01-01","2024-12-03 03:24:48","2024-12-03 03:24:48","","156-161","","","128","","Procedia CIRP","","","","","","","","","","","","","","","","","","","behavior modelling; graph-based design languages; model based systems engineering","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JAB9PRZD","journalArticle","2024","Mertala, Pekka; Fagerlund, Janne","Finnish 5th and 6th graders’ misconceptions about artificial intelligence","International Journal of Child-Computer Interaction","","2212-8689","10.1016/j.ijcci.2023.100630","https://www.sciencedirect.com/science/article/pii/S2212868923000673","Research on children’s initial conceptions of AI is in an emerging state, which, from a constructivist viewpoint, challenges the development of pedagogically sound AI-literacy curricula, methods, and materials. To contribute to resolving this need in the present paper, qualitative survey data from 195 children were analyzed abductively to answer the following three research questions: What kind of misconceptions do Finnish 5th and 6th graders’ have about the essence AI?; 2) How do these misconceptions relate to common misconception types?; and 3) How profound are these misconceptions? As a result, three misconception categories were identified: 1) Non-technological AI, in which AI was conceptualized as peoples’ cognitive processes (factual misconception); 2) Anthropomorphic AI, in which AI was conceptualized as a human-like entity (vernacular, non-scientific, and conceptual misconception); and 3) AI as a machine with a pre-installed intelligence or knowledge (factual misconception). Majority of the children evaluated their AI-knowledge low, which implies that the misconceptions are more superficial than profound. The findings suggest that context-specific linguistic features can contribute to students' AI misconceptions. Implications for future research and AI literacy education are discussed.","2024-03-01","2024-12-03 03:24:48","2024-12-03 03:24:48","","100630","","","39","","International Journal of Child-Computer Interaction","","","","","","","","","","","","","","","","","","","Machine learning; Artificial intelligence; Student; AI literacy; Misconceptions","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"BYHIZYDC","journalArticle","2023","Gallofré Ocaña, Marc; Opdahl, Andreas L.","A Software Reference Architecture for Journalistic Knowledge Platforms","Knowledge-Based Systems","","0950-7051","10.1016/j.knosys.2023.110750","https://www.sciencedirect.com/science/article/pii/S0950705123005002","Newsrooms and journalists today rely on many different artificial-intelligence, big-data and knowledge-based systems to support efficient and high-quality journalism. However, making the different systems work together remains a challenge, calling for new unified journalistic knowledge platforms. A software reference architecture for journalistic knowledge platforms could help news organisations by capturing tried-and-tested best practices and providing a generic blueprint for how their IT infrastructure should evolve. To the best of our knowledge, no suitable architecture has been proposed in the literature. Therefore, this article proposes a software reference architecture for integrating artificial intelligence and knowledge bases to support journalists and newsrooms. The design of the proposed architecture is grounded on the research literature and on our experiences with developing a series of prototypes in collaboration with industry. Our aim is to make it easier for news organisations to evolve their existing independent systems for news production towards integrated knowledge platforms and to direct further research. Because journalists and newsrooms are early adopters of integrated knowledge platforms, our proposal can hopefully also inform architectures in other domains with similar needs.","2023-09-27","2024-12-03 03:24:48","2024-12-03 03:24:48","","110750","","","276","","Knowledge-Based Systems","","","","","","","","","","","","","","","","","","","Big data; Artificial intelligence; Knowledge graphs; Journalism; Newsrooms; Software reference architecture","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ZPFWGCYY","journalArticle","2024","Abbasi, Mohammad Hossein; Somai, Melek; Saber, Hamidreza","The trend of artificial intelligence application in medicine and neurology; the state-of-the-art systematic scoping review 2010–2022","Intelligence-Based Medicine","","2666-5212","10.1016/j.ibmed.2024.100179","https://www.sciencedirect.com/science/article/pii/S2666521224000462","Background Artificial Intelligence (AI) is an increasingly popular research focus for multiple areas of science. The trend of using AI-based clinical research in different fields of medicine and defining the shortcomings of those trials will guide researchers and future studies. Methods We systematically reviewed trials registered in ClinicalTrials.gov that apply AI in clinical research. We explored the trend of AI-applied clinical research and described the design and conduct of such trials. Also, we considered high-quality trials to represent their enrollees’ and other characteristics. Results Our search yielded 839 trials involving a direct application of AI, among which 330 (39.3 %) trials were interventional, and the rest were observational (60.7 %). Most of the studies aimed to improve diagnosis (70.2 %); in less than a quarter of trials, management was targeted (22.8 %), and AI was implemented in an acute setting (13 %). Gastrointestinal, cardiovascular, and neurology were the significant fields of medicine with the application of AI in their research. High-quality published AI trials showed good generalizability in terms of their enrollees’ characteristics, with an average age of 52.46 years old and 50.28 % female participants. Conclusion The incorporation of AI in different fields of medicine needs to be more balanced, and attempts should be made to broaden the spectrum of AI-based clinical research and to improve its deployment in real-world practice.","2024-01-01","2024-12-03 03:24:48","2024-12-03 03:24:48","","100179","","","10","","Intelligence-Based Medicine","","","","","","","","","","","","","","","","","","","Machine learning; Artificial intelligence; Clinical research; Brain-computer interface; Neurology","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"NYSH3JY8","journalArticle","2024","Hariguna, Taqwa; Ruangkanjanases, Athapol","Assessing the impact of artificial intelligence on customer performance: a quantitative study using partial least squares methodology","Data Science and Management","","2666-7649","10.1016/j.dsm.2024.01.001","https://www.sciencedirect.com/science/article/pii/S2666764924000018","The purpose of this research is to examine the impact of artificial intelligence (AI) on customer performance and identify the factors contributing to its effectiveness by employing a quantitative approach, specifically the partial least squares method, to test the hypotheses and explore the relationships between various variables. The findings indicate that effective business practices and successful AI assimilation have a positive impact on customer performance. Additionally, the results of this study provide valuable insights for both academic and practical communities. This study highlights the importance of specific variables, such as organizational and customer agility, customer experience, customer relationship quality, and customer performance in AI assimilation. By exploring these variables, it contributes significantly to the academic, managerial, and social aspects of AI and its impact on customer performance.","2024-09-01","2024-12-03 03:24:48","2024-12-03 03:24:48","","155-163","","3","7","","Data Science and Management","","","","","","","","","","","","","","","","","","","AI assimilation; Customer performance; Customer relationship quality","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"69VHVJRA","journalArticle","2024","Muteba, Franck","Digital twin (DT)-based predictive maintenance of a 6G communication network","The 15th International Conference on Ambient Systems, Networks and Technologies Networks (ANT) / The 7th International Conference on Emerging Data and Industry 4.0 (EDI40), April 23-25, 2024, Hasselt University, Belgium","","1877-0509","10.1016/j.procs.2024.06.058","https://www.sciencedirect.com/science/article/pii/S1877050924012948","This research leverages the potential of digital twin technology to present a novel method of predictive maintenance for 6G communication networks. With the increasing need for 6G networks to be consistently reliable and always available, this essay emphasises the need for more advanced maintenance techniques. The seamless integration of digital twin technology makes it possible to monitor, analyse, and simulate the 6G network in real time. This innovation makes it easier to use artificial intelligence to fine-tune maintenance schedules and anticipate maintenance requirements in advance. The paper fervently promotes the use of digital twin technology as a vital instrument for raising the bar for maintenance requirements in the context of 6G networks.","2024-01-01","2024-12-03 03:24:48","2024-12-03 03:24:48","","544-549","","","238","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Machine learning; Artificial intelligence; Digital Twin; Predictive maintenance; 6G network","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SIWMUC9H","journalArticle","2024","Peschel, Ella; Krotsetis, Susanne; Seidlein, Anna-Henrikje; Nydahl, Peter","Opening Pandora’s box by generating ICU diaries through artificial intelligence: A hypothetical study protocol","Intensive and Critical Care Nursing","","0964-3397","10.1016/j.iccn.2024.103661","https://www.sciencedirect.com/science/article/pii/S0964339724000417","Background Patients and families on Intensive Care Units (ICU) benefit from ICU diaries, enhancing their coping and understanding of their experiences. Staff shortages and a limited amount of time severely restrict the application of ICU diaries. To counteract this limitation, generating diary entries from medical and nursing records using an artificial intelligence (AI) might be a solution. Design and purpose Protocol for a hypothetical multi-center, mixed method study to identify the usability and impact of AI-generated ICU diaries, compared with hand-written diaries. Method A hand-written ICU diary will be written for patients with expected length of stay ≥ 72 h by trained nursing staff and families. Additionally at discharge, the medical and nursing records are analyzed by an AI software, transformed into understandable, empathic diary entries, and printed as diary. Based on an appointment with patients within 3 months, diaries are read in randomized order by trained clinicians with the patients and families. Patients and families will be interviewed about their experiences of reading both diaries. In addition, usability of diaries will be evaluated by a questionnaire. Expected findings and results Patients and families describe the similarities and differences of language and the content of the different diaries. In addition, concerns can be expressed about the generation and data processing by AI. Implications for practice Professional nursing involves empathic communication, patient-centered care, and evidence-based interventions. Diaries, beneficial for ICU patients and families, could potentially be generated by Artificial Intelligence, raising ethical and professional considerations about AI's role in complementing or substituting nurses in diary writing. Conclusions Generating AI-based entries for ICU diaries is feasible, but raises serious questions about nursing ethics, empathy, data protection, and values of professional nurses. Researchers and developers shall discuss these questions in detail, before starting such projects and opening Pandora’s box, that can never be closed afterwards.","2024-06-01","2024-12-03 03:24:48","2024-12-03 03:24:48","","103661","","","82","","Intensive and Critical Care Nursing","","","","","","","","","","","","","","","","","","","Ethics; Protocol; Artificial Intelligence; Coping; Intensive care diary","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"55KB67S3","journalArticle","2024","Butt, Naveed Anwer; Awais, Mian Muhammad; Shahzadi, Samra; Kim, Tai-hoon; Ashraf, Imran","Towards the development of believable agents: Adopting neural architectures and adaptive neuro-fuzzy inference system via playback of human traces","Journal of King Saud University - Computer and Information Sciences","","1319-1578","10.1016/j.jksuci.2024.102182","https://www.sciencedirect.com/science/article/pii/S1319157824002714","Artificial intelligence (AI) research on video games primarily focused on the imitation of human-like behavior during the past few years. Moreover, to increase the perceived worth of amusement and gratification, there is an enormous rise in the demand for intelligent agents that can imitate human players and video game characters. However, the agents developed using the majority of current approaches are perceived as rather more mechanical, which leads to frustration, and more importantly, failure in engagement. On that account, this study proposes an imitation learning framework to generate human-like behavior for more precise and accurate reproduction. To build a computational model, two learning paradigms are explored, artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS). This study utilized several variations of ANN, including feed-forward, recurrent, extreme learning machines, and regressions, to simulate human player behavior. Furthermore, to find the ideal ANFIS, grid partitioning, subtractive clustering, and fuzzy c-means clustering are used for training. The results demonstrate that ANFIS hybrid intelligence systems trained with subtractive clustering are overall best with an average accuracy of 95%, followed by fuzzy c-means with an average accuracy of 87%. Also, the believability of the obtained AI agents is tested using two statistical methods, i.e., the Mann–Whitney U test and the cosine similarity analysis. Both methods validate that the observed behavior has been reproduced with high accuracy.","2024-10-01","2024-12-03 03:24:48","2024-12-03 03:24:48","","102182","","8","36","","Journal of King Saud University - Computer and Information Sciences","","","","","","","","","","","","","","","","","","","Artificial neural networks; Machine learning; Adaptive neuro-fuzzy inference system; Game AI; Imitation learning","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SB4N4DZY","journalArticle","2024","Masiero, Sara; Qosaj, Jovista; Cutrona, Vincenzo","Digital Datasheet model: enhancing value of AI digital platforms","5th International Conference on Industry 4.0 and Smart Manufacturing (ISM 2023)","","1877-0509","10.1016/j.procs.2024.01.015","https://www.sciencedirect.com/science/article/pii/S1877050924000152","Digital AI platforms/marketplaces play a pivotal role in providing artificial intelligence applications for the manufacturing market. However, existing research on how AI platforms integrate and deploy AI solutions is limited. Additionally, Europe is grappling with the absence of a functional ecosystem capable enhancing the value generated by these AI technologies. This work proposes a digital datasheet model designed to facilitate value exchange among digital AI platforms/marketplaces, module providers, and end-users, enabling a new type of interaction for integrating AI assets, developing adaptable AI systems, meeting customer needs. The applicability is tested with an AI module of the Kitt4sme project.","2024-01-01","2024-12-03 03:24:48","2024-12-03 03:24:48","","149-158","","","232","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial Intelligence; AI Digital platforms and Marketplaces; Digital datasheet model; Manufacturing SMEs","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KG8R336T","journalArticle","2023","Suereth, Russell","Considering caring as a safeguard in artificial intelligence","New Techno Humanities","","2664-3294","10.1016/j.techum.2024.01.002","https://www.sciencedirect.com/science/article/pii/S2664329424000025","The focus of this research is to consider whether a safeguard of caring can be designed into an artificial intelligent system. According to movies, books, and research experts, a superintelligence could harm humans in devastating ways. The purpose of safeguards is to keep such harmful situations from happening. The problem with safeguards in AI is that they are challenging to design. This article considers whether caring can be a safeguard in AI. It examines caring in our human world and how it has been vital to our existence. It also considers what caring could look like in AI and how we could begin to think about designing care in these systems. Additionally, it provides an overview of the LIDA cognitive architecture as a model for designing care in AI systems. The article employs a methodology focusing on a caring frame of mind and a caring environment for our work and research. This article contributes to the current research by creating a greater awareness of care as a safeguard and establishing an initial step toward designing care in AI. It shows that care is an essential aspect of our interactions with the world and how care can be a safeguard in AI.","2023-12-01","2024-12-03 03:24:48","2024-12-03 03:24:48","","135-139","","2","3","","New Techno Humanities","","","","","","","","","","","","","","","","","","","Artificial intelligence; Safeguards; AI ethics; AI design; Caring; Superintelligence","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"MPV25WLB","journalArticle","2023","Janbi, Nourah; Katib, Iyad; Mehmood, Rashid","Distributed artificial intelligence: Taxonomy, review, framework, and reference architecture","Intelligent Systems with Applications","","2667-3053","10.1016/j.iswa.2023.200231","https://www.sciencedirect.com/science/article/pii/S266730532300056X","Artificial intelligence (AI) research and market have grown rapidly in the last few years, and this trend is expected to continue with many potential advancements and innovations in this field. One of the emerging AI research directions is Distributed Artificial Intelligence (DAI). It has been motivated by technological advances in communication, networking, and hardware, together with the nature of data being generated from connected, distributed, and diverse objects. DAI is expected to create a fertile environment for innovative, advanced, robust, and scalable approaches for AI supporting the vision of smart societies. In this paper, we explore state of the art on DAI and identify the opportunities and challenges of provisioning distributed AI as a service (DAIaaS). We provide a taxonomy and a comprehensive review covering the literature from 2016 to 2022. It comprises various aspects of DAI, including AI workflow, distribution paradigms, supporting infrastructure, management techniques, and applications. Based on the gained insights from the conducted review, we propose Imtidad, a framework for provisioning DAIaaS over the cloud, fog, and edge layers. We refine this framework and propose the Imtidad software Reference Architecture (RA) for designing and deploying DAI services. In addition, we extended the framework and developed a future networking infrastructure transformation framework, as it is one of the main enablers for DAI. This framework and RA can be used as guidance facilitating the transition to the future DAI, where DAI is decoupled from the design and development of smart applications. This paper, including the proposed framework, RA, taxonomy, and detailed review, is expected to have an extensive impact on DAI research and accelerate innovations in this area.","2023-05-01","2024-12-03 03:24:48","2024-12-03 03:24:48","","200231","","","18","","Intelligent Systems with Applications","","","","","","","","","","","","","","","","","","","cloud computing; Artificial intelligence; Federated learning; 6th generation (6 g) networks; Data parallelism; Distributed artificial intelligence; Distributed inference, distributed decision making; Distributed pre-processing; Distributed training; edge computing; Fog computing; Future networking; Hybrid parallelism; Model parallelism; Multi-layer architecture; Pipelined parallelism","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"47IBKCSE","journalArticle","2024","Fang, Shibiao; Tu, Wenrong; Lu, Weigang","Artificial intelligence vision technology application in sustainability evaluation of solar-driven distillation device","Environmental Technology & Innovation","","2352-1864","10.1016/j.eti.2024.103731","https://www.sciencedirect.com/science/article/pii/S2352186424002074","The process of transforming brackish water into freshwater utilizing solar thermal energy is referred to as solar-driven distillation device, or solar still. Such device without fossil fuel provides significant environmental and health benefits by reducing air pollutants and remedying brackish water. The yield of purified water from conventional solar stills remains insufficient, prompting the necessity for research to enhance it by understanding the coupling effect between steam flow, heat transfer, and mass transfer. As such, computer-aided brackish water treatment comparison of the hemispherical solar still and other multi-slope solar stills is conducted in this paper, and an automatic steam&heat flow detection algorithm is developed to solve the problem of difficult data acquisition for gas-liquid transport processes. Firstly, double slope, four slope, and hemispherical solar stills are exposed to the sun in outdoor experiments, therefore the solar thermal performance of each still is analyzed through pairwise comparisons. Secondly, a large amount of experimental image data is input into neural network for training, and by continuously adjusting network parameters, the network can accurately recognize different types of images. Finally, the image to be recognized is input into the trained neural network, which outputs the category labels of the image to achieve automatic image recognition. Based on the data above, the best structure of solar-driven device is hemispherical structure, due to that the hemispherical solar still possesses the best performance in terms of distilled water yield, energy efficiency, exergy efficiency and energy payback.","2024-11-01","2024-12-03 03:24:48","2024-12-03 03:24:48","","103731","","","36","","Environmental Technology & Innovation","","","","","","","","","","","","","","","","","","","Renewable energy; Brackish water treatment; Carbon neutrality; Image recognition artificial intelligence; Solar-driven distillation device; Sustainable environment","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KMU2EV92","journalArticle","2024","Kulal, Abhinandan; Rahiman, Habeeb Ur; Suvarna, Harinakshi; Abhishek, N.; Dinesh, Sahana","Enhancing public service delivery efficiency: Exploring the impact of AI","Journal of Open Innovation: Technology, Market, and Complexity","","2199-8531","10.1016/j.joitmc.2024.100329","https://www.sciencedirect.com/science/article/pii/S2199853124001239","This study aims to investigate the impact of Artificial Intelligence (AI) adoption on public service delivery efficiency in India. It addresses a significant gap in existing literature by investigating the impact of AI adoption on public service delivery efficiency in India, a context that has not been extensively explored. Through a comparative analysis approach, the study assesses the effectiveness of AI applications in enhancing public service delivery. The quantitative research design employed in the study draws on previous literature on AI integration in governance and focuses on Chief Information Officers (CIOs) as primary respondents. The findings reveal significant improvements in citizen-centric services and municipal processes due to AI adoption. However, the impact on human-centric aspects is found to be moderate. The study also underscores the importance of infrastructure readiness for successful AI implementation. Notably, only 25 % of organizations were found to be possessing advanced technological infrastructure. This research is original in its focus on Chief Information Officers (CIOs) as primary respondents and its comparative analysis approach to assess the effectiveness of AI applications in enhancing public service delivery. This study offers valuable insights for policymakers and practitioners. Emphasizing the need for effective policies and infrastructure development, it highlights the potential of AI to eliminate corruption risks and enhance overall efficiency and transparency in public service delivery mechanisms.","2024-09-01","2024-12-03 03:24:48","2024-12-03 03:24:48","","100329","","3","10","","Journal of Open Innovation: Technology, Market, and Complexity","","","","","","","","","","","","","","","","","","","Artificial Intelligence (AI); Efficiency Impact; Maturity Level; Public Service Delivery","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"FAG3ZXQE","journalArticle","2023","Savci, Pinar; Das, Bihter","Prediction of the customers' interests using sentiment analysis in e-commerce data for comparison of Arabic, English, and Turkish languages","Journal of King Saud University - Computer and Information Sciences","","1319-1578","10.1016/j.jksuci.2023.02.017","https://www.sciencedirect.com/science/article/pii/S131915782300054X","In the business world, large companies that can achieve continuity in innovation gain a significant competitive advantage. The sensitivity of these companies to follow and monitor news sources in e-commerce, social media, and forums provides important information to businesses in the decision-making process. With the large amount of data shared in these resources, sentiment analysis can be made from people's comments about services and products, users' emotions can be extracted and important feedback can be obtained. All of this is of course possible with accurate sentiment analysis. In this study, new data sets were created for Turkish, English, and Arabic, and for the first time, comparative sentiment analysis was performed from texts in three different languages. In addition, a very comprehensive study was presented to the researchers by comparing the performances of both the pre-trained language models for Turkish, Arabic, and English, as well as the deep learning and machine learning models. Our paper will guide researchers working on sentiment analysis about which methods will be more successful in texts written in different languages, which contain different types and spelling mistakes, which factors will affect the success, and how much these factors will affect the performance.","2023-03-01","2024-12-03 03:24:48","2024-12-03 03:24:48","","227-237","","3","35","","Journal of King Saud University - Computer and Information Sciences","","","","","","","","","","","","","","","","","","","Deep learning; Machine learning; Natural language processing; Sentiment analysis; E-commerce; Pre-trained language models","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XEAXN53D","journalArticle","2023","Chen, Dinah; Ran, Emma Anran; Tan, Ting Fang; Ramachandran, Rithambara; Li, Fei; Cheung, Carol; Yousefi, Siamak; Tham, Clement C.Y.; Ting, Daniel S.W.; Zhang, Xiulan; Al-Aswad, Lama A.","Applications of Artificial Intelligence and Deep Learning in Glaucoma","Asia-Pacific Journal of Ophthalmology","","2162-0989","10.1097/APO.0000000000000596","https://www.sciencedirect.com/science/article/pii/S2162098923007776","Diagnosis and detection of progression of glaucoma remains challenging. Artificial intelligence–based tools have the potential to improve and standardize the assessment of glaucoma but development of these algorithms is difficult given the multimodal and variable nature of the diagnosis. Currently, most algorithms are focused on a single imaging modality, specifically screening and diagnosis based on fundus photos or optical coherence tomography images. Use of anterior segment optical coherence tomography and goniophotographs is limited. The majority of algorithms designed for disease progression prediction are based on visual fields. No studies in our literature search assessed the use of artificial intelligence for treatment response prediction and no studies conducted prospective testing of their algorithms. Additional challenges to the development of artificial intelligence–based tools include scarcity of data and a lack of consensus in diagnostic criteria. Although research in the use of artificial intelligence for glaucoma is promising, additional work is needed to develop clinically usable tools.","2023-01-01","2024-12-03 03:24:48","2024-12-03 03:24:48","","80-93","","1","12","","Asia-Pacific Journal of Ophthalmology","","","","","","","","","","","","","","","","","","","deep learning; artificial intelligence; glaucoma; optical coherent tomography; visual fields","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"6ZBT5K2G","journalArticle","2024","Hargreaves, Christopher; Breitinger, Frank; Dowthwaite, Liz; Webb, Helena; Scanlon, Mark","DFPulse: The 2024 digital forensic practitioner survey","Forensic Science International: Digital Investigation","","2666-2817","10.1016/j.fsidi.2024.301844","https://www.sciencedirect.com/science/article/pii/S2666281724001719","This paper reports on the largest survey of digital forensic practitioners to date (DFPulse) conducted from March to May 2024 resulting in 122 responses. The survey collected information about practitioners' operating environments, the technologies they encounter, investigative techniques they use, the challenges they face, the degree to which academic research is accessed and useful to the practitioner community, and their suggested future research directions. The paper includes quantitative and qualitative results from the survey and a discussion of the implications for academia, the improvements that can be made, and future research directions.","2024-12-01","2024-12-03 03:24:48","2024-12-03 03:24:48","","301844","","","51","","Forensic Science International: Digital Investigation","","","","","","","","","","","","","","","","","","","Artificial intelligence; Challenges; Future directions; Digital forensics; Practitioner survey","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"9FCPDHC4","journalArticle","2024","Miró-Nicolau, Miquel; Jaume-i-Capó, Antoni; Moyà-Alcover, Gabriel","Assessing fidelity in XAI post-hoc techniques: A comparative study with ground truth explanations datasets","Artificial Intelligence","","0004-3702","10.1016/j.artint.2024.104179","https://www.sciencedirect.com/science/article/pii/S0004370224001152","The evaluation of the fidelity of eXplainable Artificial Intelligence (XAI) methods to their underlying models is a challenging task, primarily due to the absence of a ground truth for explanations. However, assessing fidelity is a necessary step for ensuring a correct XAI methodology. In this study, we conduct a fair and objective comparison of the current state-of-the-art XAI methods by introducing three novel image datasets with reliable ground truth for explanations. The primary objective of this comparison is to identify methods with low fidelity and eliminate them from further research, thereby promoting the development of more trustworthy and effective XAI techniques. Our results demonstrate that XAI methods based on the direct gradient calculation and the backpropagation of output information to input yield higher accuracy and reliability compared to methods relying on perturbation based or Class Activation Maps (CAM). However, these methods tend to generate more noisy saliency maps. These findings have significant implications for the advancement of XAI methods, enabling the elimination of erroneous explanations and fostering the development of more robust and reliable XAI.","2024-10-01","2024-12-03 03:24:48","2024-12-03 03:24:48","","104179","","","335","","Artificial Intelligence","","","","","","","","","","","","","","","","","","","Explainable Artificial Intelligence (XAI); Fidelity; Objective evaluation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SG8PI9JX","journalArticle","2024","Cubric, Marija; Li, Feng","Bridging the ‘Concept–Product’ gap in new product development: Emerging insights from the application of artificial intelligence in FinTech SMEs","Technovation","","0166-4972","10.1016/j.technovation.2024.103017","https://www.sciencedirect.com/science/article/pii/S0166497224000671","Building on the literature on the concept-product gap in new product development, we examine how FinTech SMEs are developing Artificial Intelligence (AI)-based innovations and which organisational or project factors best contribute to the acceleration of AI innovation. The empirical evidence collected from interviews with key stakeholders, practitioners’ forums, and public company documents yields two distinct approaches that differ in their potential for accelerating innovation and reducing the concept-product gap. From a contingency perspective, these two approaches are expanded into four distinct development process configurations, contingent on the business development stage, reliance on 3rd party platforms, availability of high volumes of data, investment level, organisational agility, and level of novelty. The resulting process typology could be used as a diagnostic tool for FinTech SMEs interested in effectively leveraging AI innovation. Using contingency theory, we further develop these insights into a new theoretical framework to explain how AI innovation development unfolds in FinTech SMEs and the rationale for different implementations. Our new process typology and theoretical model can help researchers investigate the mechanisms underlying technological innovation processes. We further identify the specific reasons why the potential of AI for creating new services and disrupting incumbents via digital startups has not been fully realised even in contexts with significant investment and support from public and private business development programmes. This field is still rapidly evolving, and thus, new areas for future research are also highlighted.","2024-06-01","2024-12-03 03:24:48","2024-12-03 03:24:48","","103017","","","134","","Technovation","","","","","","","","","","","","","","","","","","","Artificial intelligence (AI); FinTech; New product development (NPD); Small and medium enterprise (SME)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"HADBN4JI","journalArticle","2024","Nguyen, Anne X.; Joly-Chevrier, Maxine; Hébert, Mélanie; Jabbour, Gilbert; Lee, Aaron Y.; Duval, Renaud; Hardy, Isabelle","The involvement of clinicians in the most highly cited publications on artificial intelligence in ophthalmology indexed journals","AJO International","","2950-2535","10.1016/j.ajoint.2024.100018","https://www.sciencedirect.com/science/article/pii/S2950253524000182","Purpose Significant advances in artificial intelligence (AI) have led to promising applications in ophthalmology. This study highlights the involvement of clinicians in the most cited ophthalmology publications on AI in ophthalmology journals indexed by Web of Science. Methods Articles examining AI in ophthalmology journals were processed from Web of Science. After selecting relevant articles, we performed bibliometric analyses at the article and author levels as of March 2024. The primary outcome measure was the number of citations per article. Secondary outcomes included article measures (publication year, subspecialties, article type, databases, imaging) and author attributes (gender, academic metrics, location). Results The top 100 publications were cited between 58 and 734 times, with a median of 91 citations. Publication reprint addresses were mainly based in America (44) and in Europe (22). Common subspecialties were retina (60), glaucoma (44) and cornea (18). Most imaging modalities were fundus photography (47), optical coherence tomography (47) and visual fields (19). 76 studies were aimed at the development and evaluation of a diagnostic technology. Some private databases (44 %) and public databases (40 %) were specified. Among the 399 men and 163 women authors, 297 were physicians (52.9 %). Women and men had significantly different h-indexes (women: 23 [interquartile range (IQR): 13–46] vs. men: 38.5 [17–65]; P = 0.02) and number of published documents (women: 104 [32–277] vs. men: 188.5 [63.5–394]; P = 0.03). Conclusion The most influential articles in AI and ophthalmology by number of citations predominantly used AI for image recognition and improving diagnostic technology in retina followed by glaucoma. Physicians had a predominant role in these, highlighting the continued importance of clinician involvement in this research.","2024-07-06","2024-12-03 03:24:49","2024-12-03 03:24:49","","100018","","2","1","","AJO International","","","","","","","","","","","","","","","","","","","Bibliometrics; Artificial intelligence; Ophthalmology","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"BAT2HL6H","journalArticle","2024","Pramana, Rio; Jonathan, Marcel; Yani, Habel Steven; Sutoyo, Rhio","A Comparison of BiLSTM, BERT, and Ensemble Method for Emotion Recognition on Indonesian Product Reviews","9th International Conference on Computer Science and Computational Intelligence 2024 (ICCSCI 2024)","","1877-0509","10.1016/j.procs.2024.10.266","https://www.sciencedirect.com/science/article/pii/S1877050924030746","Emotion recognition within online product reviews is pivotal for enhancing business strategies and customer insights. Recognizing emotions in Indonesian, a language rich in nuances and expressions presents significant challenges due to its complex linguistic structure and the scarcity of tailored datasets. This study aims to bridge this gap by evaluating the effectiveness of Bidirectional Long Short-Term Memory (BiLSTM), Bidirectional Encoder Representations from Transformers (BERT), and ensemble methods in analyzing emotions from Indonesian product reviews using the PRDECT-ID dataset. Extensive fine-tuning across 23 BERT con- figurations and multiple BiLSTM preprocessing combinations was conducted to adapt these models to the Indonesian linguistic context. Each model was assessed based on the F1 score. The BiLSTM model was particularly effective in configurations with complex preprocessing, achieving an optimal F1 score of 61% through advanced noise removal, stemming, and a modified stop- words list. Conversely, the minimally preprocessed fine-tuned ’base-p2’ and ’large-p1’ BERT variants achieved F1 scores of 72% and 73%, respectively, both surpassing the previous best result of 71%. This research also explored 4 ensemble methods, combin- ing the strengths of the best-performing BiLSTM and BERT models using both soft-voting and stacked generalization techniques. The unweighted stacked generalization achieved a 74% F1 score, while the weighted method excelled with the highest F1 score of 75%, surpassing other models and highlighting the advantages of strategic model integration. This research significantly advances the development of NLP models for Indonesian text, demonstrating how tailored deep-learning approaches can effectively enhance emotion recognition accuracy.","2024-01-01","2024-12-03 03:24:49","2024-12-03 03:24:49","","399-408","","","245","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Natural Language Processing; Deep Learning; Emotion Recognition; Product Review","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"6KB4ZUQE","journalArticle","2024","Wu, Longsheng; Qi, Lingli; Lam, Johnny F.I.; Chen, Guanqiuyue","Climate policy and corporate artificial intelligence: Evidence from low-carbon city pilots in China","Environmental and Sustainability Indicators","","2665-9727","10.1016/j.indic.2024.100446","https://www.sciencedirect.com/science/article/pii/S2665972724001144","The Low-Carbon City Pilot (LCCP) program in China is a climate policy implemented at the city level, and its impact on the development of corporate artificial intelligence (AI) remains to be studied. The LCCP program aims to promote low-carbon transformation in multiple Chinese cities and serves as a quasi-natural experiment to determine whether similar city-level climate policies can foster the development of corporate AI. This study employs a multi-period differences-in-differences (DID) approach to evaluate the impact of China’s LCCP program on the development of corporate AI. By redirecting attention from national-level policies to city-level initiatives, this study provides unique insights into the localized effects of climate policies on technological innovation within firms. The research findings indicate that the LCCP program has a significant promoting effect on the development of corporate AI. These results underscore the potential of city-level climate policies, such as the LCCP program, to drive advancements in corporate AI technologies. By demonstrating a positive correlation between the implementation of the LCCP program and increased levels of AI development within companies, the study provides valuable insights into the intersection of environmental policies and technological innovation. Policymakers and businesses can use these findings to guide the design and implementation of initiatives aimed at promoting sustainable economic growth through enhanced AI adoption. This study not only contributes to the academic discourse on environmental policy and technological innovation but also offers practical recommendations for achieving a balance between environmental sustainability and technological advancement.","2024-09-01","2024-12-03 03:24:49","2024-12-03 03:24:49","","100446","","","23","","Environmental and Sustainability Indicators","","","","","","","","","","","","","","","","","","","Climate policy; Corporate artificial intelligence; Low-carbon city pilot (LCCP); Multi-period differences-in-differences","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"PRKKCLPF","journalArticle","2024","Quan, Yazhuo; Tytko, Tetiana; Hui, Bronson","Utilizing ASReview in screening primary studies for meta-research in SLA: A step-by-step tutorial","Research Methods in Applied Linguistics","","2772-7661","10.1016/j.rmal.2024.100101","https://www.sciencedirect.com/science/article/pii/S2772766124000077","Meta-research, including meta-analyses and systematic methodological reviews, has proven to be a useful tool for obtaining a comprehensive understanding of research questions by numerically summarizing data and methodological features in a given literature. As part of the review procedure, researchers select primary studies to be included in their analysis. However, this process is resource-intensive and prone to human error. In this tutorial, we introduce a practical application of artificial intelligence (AI), known as ASReview, that can facilitate the screening process. Using a simulated data set derived from a published meta-analysis, we offer step-by-step guidance on how to incorporate the tool into the screening process. We cover the essential steps, including the preparation of the data set, the import of the data set, the labeling of the study as relevant or irrelevant (for inclusion or not), as well as the saving of the results for the researcher's record and sharing for transparency in the spirit of open science. In addition, the tutorial addresses essential factors to consider in the AI-aided screening process, such as stopping rules. We acknowledge potential limitations of the tool and provide a couple of alternatives for interested readers. Our overall goal is to contribute to advancing and promoting meta-research in SLA by facilitating the screening process in the era of AI.","2024-04-01","2024-12-03 03:24:49","2024-12-03 03:24:49","","100101","","1","3","","Research Methods in Applied Linguistics","","","","","","","","","","","","","","","","","","","Artificial intelligence; Screening; Meta-analysis; Systematic Review","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"VTIRMRB5","journalArticle","2024","Karn, Shashank; Kotecha, Radhika; Pandey, Ritesh Kumar","Towards Sustainable Farming: Leveraging AIoT for Precision Water Management and Crop Yield Optimization","5th International Conference on Innovative Data Communication Technologies and Application (ICIDCA 2024)","","1877-0509","10.1016/j.procs.2024.03.266","https://www.sciencedirect.com/science/article/pii/S1877050924006264","Sustainable Development Goals (SDGs) by the United Nations have Zero Hunger and Responsible Consumption and Production as the two significant agriculture-related components. Conventional irrigation control systems, relying heavily on manual labour, exhibit shortcomings in minimizing farmer input dependency. Recognizing the critical interdependence of water resource management, crop yield optimization, and environmental preservation, this work contends that precision in water management is essential. While efforts to reuse treated wastewater offer potential water conservation solutions, they often compromise crop yield. Precisely calculating water requirements can enhance crop yield, reduce human involvement, eliminate potential decision-making errors, and address water conservation issues. Automating tasks like irrigation demands comprehensive knowledge of crop and soil characteristics, climate conditions, time factors, and physical parameters like temperature, soil moisture, and humidity. This research addresses the limitations of current agricultural practices by advocating for a paradigm shift towards sustainable farming through the integration of Artificial Intelligence of Things (AIoT). The proposed framework leverages the fusion of the Artificial Intelligence (AI) and Internet of Things (IoT) to automate irrigation by employing a comprehensive analysis of diverse parameters. Through empirical evaluation, this work demonstrates that AIoT-based precision water management not only improves crop yield but also reduces human intervention, addressing water conservation challenges, and fostering sustainable agriculture practices.","2024-01-01","2024-12-03 03:24:49","2024-12-03 03:24:49","","772-781","","","233","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial Intelligence; Sustainable Development Goals (SDGs); Artificial Intelligence of Things (AIoT); Internet of Things (IoT); Precision Water Management","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LHJCGLBT","journalArticle","2024","Alaeifar, Poopak; Pal, Shantanu; Jadidi, Zahra; Hussain, Mukhtar; Foo, Ernest","Current approaches and future directions for Cyber Threat Intelligence sharing: A survey","Journal of Information Security and Applications","","2214-2126","10.1016/j.jisa.2024.103786","https://www.sciencedirect.com/science/article/pii/S2214212624000899","Cyber Threat Intelligence (CTI) is essential knowledge concerning cyber and physical threats aimed at mitigating potential cyber attacks. The rapid evolution of Information and Communications Technology (ICT), the Internet of Things (IoT), and Industry 5.0 has spawned a multitude of sources regarding current or potential cyber threats against organizations. Consequently, CTI sharing among organizations holds considerable promise for facilitating swift responses to attacks and enabling mutual benefits through active participation. However, exchanging CTI among different organizations poses significant challenges, including legal and regulatory obligations, interoperability standards, and data reliability. The current CTI sharing landscape remains inadequately explored, hindering a comprehensive examination of organizations’ critical needs and the challenges they encounter during CTI sharing. This paper presents a comprehensive survey on CTI sharing, beginning with an exploration of CTI fundamentals and its advancements in assessing cyber and physical threats and threat actors from various perspectives. For instance, we discuss the benefits of CTI, its applications, and diverse CTI sharing architectures. Additionally, we extensively discuss a list of CTI sharing challenges and evaluate how available CTI sharing proposals address these challenges. Finally, we provide an inventory of unique future research directions to offer insightful guidelines for CTI sharing.","2024-06-01","2024-12-03 03:24:49","2024-12-03 03:24:49","","103786","","","83","","Journal of Information Security and Applications","","","","","","","","","","","","","","","","","","","Machine learning; Information sharing; Security; Artificial intelligence; Blockchain; Cyber threat intelligence","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"D66XGZUS","journalArticle","2024","Mentzingen, Hugo; António, Nuno; Bacao, Fernando; Cunha, Marcio","Textual similarity for legal precedents discovery: Assessing the performance of machine learning techniques in an administrative court","International Journal of Information Management Data Insights","","2667-0968","10.1016/j.jjimei.2024.100247","https://www.sciencedirect.com/science/article/pii/S2667096824000363","The importance of legal precedents in ensuring consistent jurisprudence is undisputed. Particularly in jurisdictions following the Common law, but even in Civil law systems, uniformity in case law requires adherence to precedents. However, with the growing volume of cases, manual identification becomes a bottleneck, prompting the need for automation. Leveraging the capabilities of natural language processing (NLP) and machine learning (ML), our study delves into the potential of automation in identifying similar cases indicative of precedents. Drawing from a unique, substantial dataset of legal cases from an administrative court in Brazil, we extensively evaluated over one hundred combinations of document representations and text vectorizations. Contrary to earlier studies that relied on minimal validation samples, ours employed a statistically significant sample vetted by legal experts. Our findings reveal that models focusing on granular text representations perform optimally, especially when extracting concepts and relations. Notably, while intricate models may not always guarantee superior outcomes, the importance of refining textual features cannot be understated. These findings pave the way for creating efficient decision support systems in judicial contexts and set a direction for future research aiming to integrate technology in legal decision-making.","2024-11-01","2024-12-03 03:24:49","2024-12-03 03:24:49","","100247","","2","4","","International Journal of Information Management Data Insights","","","","","","","","","","","","","","","","","","","Imbalanced data; Case similarity; Court automation; Language processing","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LHVCAXRT","journalArticle","2024","Haugland Sundkvist, Charlotte; Kulset, Ellen M.","Teaching accounting in the era of ChatGPT – The student perspective","Journal of Accounting Education","","0748-5751","10.1016/j.jaccedu.2024.100932","https://www.sciencedirect.com/science/article/pii/S0748575124000484","This study examines students’ perceived usefulness, intended future and current use of ChatGPT in a study context. Since the release of ChatGPT to the public, its potential impact on education, both positive and negative, has been heavily debated, but there is limited research on the students’ perception and use of ChatGPT. We find that students’ perceived usefulness of ChatGPT is increasing with variables such as general enthusiasm about new technology, ease of use, trustworthiness, and social influence from friends. The intention to use ChatGPT in a study context in the future is increasing with the same variables except for trustworthiness. Students who have previously used ChatGPT find it more likely that they will use it in a study context in the future. Several students seem to be aware that the answers from ChatGPT cannot always be trusted, but few mention that using ChatGPT in a study context can also have other negative effect on learning outcomes (i.e., if ChatGPT does much of the work instead of the student). The use of ChatGPT seems to depend upon type of course, and the students are more likely to use ChatGPT in other courses than accounting courses. We also find that students are most likely to use ChatGPT to answer course specific questions, and least likely to use it on exams.","2024-12-01","2024-12-03 03:24:49","2024-12-03 03:24:49","","100932","","","69","","Journal of Accounting Education","","","","","","","","","","","","","","","","","","","Large language models; AI; Technology acceptance; Accounting education; Students’ perceived usefulness of chatbots; Students’ use of chatbots","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"6IQEQN9F","journalArticle","2024","Wang, Yubo; Zhao, Xingang; Wang, Kangsheng; Chen, He; Wang, Yang; Yu, Hao; Li, Peng","A lightweight method of integrated local load forecasting and control of edge computing in active distribution networks","iScience","","2589-0042","10.1016/j.isci.2024.110271","https://www.sciencedirect.com/science/article/pii/S2589004224014962","Summary The strong resource constraints of edge-computing devices and the dynamic evolution of load characteristics put forward higher requirements for forecasting methods of active distribution networks. This paper proposes a lightweight adaptive ensemble learning method for local load forecasting and predictive control of active distribution networks based on edge computing in resource constrained scenarios. First, the adaptive sparse integration method is proposed to reduce the model scale. Then, the auto-encoder is introduced to downscale the model variables to further reduce computation time and storage overhead. An adaptive correction method is proposed to maintain the adaptability. Finally, a multi-timescale predictive control method for the edge side is established, which realizes the collaboration of local load forecasting and control. All cases can be deployed on an actual edge-computing device. Compared to other benchmark methods and the existing researches, the proposed method can minimize the model complexity without reducing the forecasting accuracy.","2024-08-16","2024-12-03 03:24:59","2024-12-03 03:24:59","","110271","","8","27","","iScience","","","","","","","","","","","","","","","","","","","artificial intelligence; electrical engineering; energy systems; network algorithm","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SVCI87RS","journalArticle","2024","Besinger, Philipp; Vejnoska, Daniel; Ansari, Fazel","Responsible AI (RAI) in Manufacturing: A Qualitative Framework","5th International Conference on Industry 4.0 and Smart Manufacturing (ISM 2023)","","1877-0509","10.1016/j.procs.2024.01.081","https://www.sciencedirect.com/science/article/pii/S1877050924000814","Artificial Intelligence (AI) has profound economic influence in manufacturing, but its unmindful integration can also pose societal and environmental risks. This paper provides a quantified overview of manufacturing areas that are highly advanced in AI capability research, such as maintenance. Integrating Responsible AI (RAI) in further studies of those areas is essential to mitigate risks and deliver business benefits. To enable this, manufacturing specific RAI dimensions are defined to represent accountability, explainability, fairness, human-centricity, sustainability (Green AI) and privacy & security. Further, a qualitative RAI framework consisting of responsibility areas (human involvement, decision making, business focus, system design) is proposed. Practical considerations to align the framework with manufacturing requirements are made by discussing it within an AI systems lifecycle.","2024-01-01","2024-12-03 03:24:59","2024-12-03 03:24:59","","813-822","","","232","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Manufacturing; Responsible Artifical Intelligence; Responsible Research and Innovation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"YYZA2YBU","journalArticle","2024","Menya, Edmond; Interdonato, Roberto; Owuor, Dickson; Roche, Mathieu","Explainable epidemiological thematic features for event based disease surveillance","Expert Systems with Applications","","0957-4174","10.1016/j.eswa.2024.123894","https://www.sciencedirect.com/science/article/pii/S0957417424007607","Event based disease surveillance (EBS) systems are biosurveillance systems that have the ability to detect and alert on (re)-emerging infectious diseases by monitoring acute public or animal health event patterns from sources such as blogs, online news reports and curated expert accounts. These information rich sources, however, are largely unstructured text data requiring novel text mining techniques to achieve EBS goals such as epidemiological text classification. The main objective of this research was to improve epidemiological text classification by proposing a novel technique of enriching thematic features using a weak supervision approach. In our approach, we train and test a mixed domain language model named EpidBioELECTRA to first enrich thematic features which are then used to improve epidemiological text classification. We train EpidBioELECTRA on a large dataset which we create consisting of 70,700 annotated documents that includes 70,400 labeled thematic features. We empirically compare EpidBioELECTRA with both general purpose language models and domain specific language models in the task of epidemiological corpus classification. Our findings shows that epidemiological classification systems work best with language models pre-trained using both epidemiological and biomedical corpora with a continual pre-training strategy. EpidBioELECTRA improves epidemiological document classification by 19.2 F1 score points as compared to its vanilla implementation BioELECTRA. We observe this by the comparison of BioELECTRA verses EpidBioELECTRA on our most challenging dataset PADI-WebXL where our approach records 92.33 precision score, 94.62 recall score and 93.46 F1 score. We also experiment the impact of increasing context length of train documents in epidemiological document classification and found out that this improves the classification task by 7.79 F1 score points as recorded by EpidBioELECTRA’s performance. We also compute Almost Stochastic Order (ASO) scores to track EpidBioELECTRA’s statistical dominance. In addition, we carry out ablation studies on our proposed thematic feature enrichment approach using explainable AI techniques. We present explanations for the most critical thematic features and how they influence epidemiological classification task We found out that biomedical features (such as mentions of names of diseases and symptoms) are the most influential while spatio-temporal features (such as the mention of date of a given disease outbreak) are the least influential in epidemiological document classification. Our model can easily be extended to fit other domains.","2024-09-15","2024-12-03 03:24:59","2024-12-03 03:24:59","","123894","","","250","","Expert Systems with Applications","","","","","","","","","","","","","","","","","","","Text mining; Corpus classification; Disease surveillance; Epidemiology intelligence","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"483TVLMF","journalArticle","2024","Park, Subin; Yoon, Hee; Yeon Kang, Soo; Joon Jo, Ik; Heo, Sejin; Chang, Hansol; Eun Park, Jong; Lee, Guntak; Kim, Taerim; Yeon Hwang, Sung; Park, Soyoung; Jin Chung, Myung","Artificial intelligence-based evaluation of carotid artery compressibility via point-of-care ultrasound in determining the return of spontaneous circulation during cardiopulmonary resuscitation","Resuscitation","","0300-9572","10.1016/j.resuscitation.2024.110302","https://www.sciencedirect.com/science/article/pii/S0300957224001965","Aim This study introduces RealCAC-Net, an artificial intelligence (AI) system, to quantify carotid artery compressibility (CAC) and determine the return of spontaneous circulation (ROSC) during cardiopulmonary resuscitation. Methods A prospective study based on data from a South Korean emergency department from 2022 to 2023 investigated carotid artery compressibility in adult patients with cardiac arrest using a novel AI model, RealCAC-Net. The data comprised 11,958 training images from 161 cases and 15,080 test images from 134 cases. RealCAC-Net processes images in three steps: TransUNet-based segmentation, the carotid artery compressibility measurement algorithm for improved segmentation and CAC calculation, and CAC-based classification from 0 (indicating a circular shape) to 1 (indicating high compression). The accuracy of the ROSC classification model was tested using metrics such as the dice similarity coefficient, intersection-over-union, precision, recall, and F1 score. Results RealCAC-Net, which applied the carotid artery compressibility measurement algorithm, performed better than the baseline model in cross-validation, with an average dice similarity coefficient of 0.90, an intersection-over-union of 0.84, and a classification accuracy of 0.96. The test set achieved a classification accuracy of 0.96 and an F1 score of 0.97, demonstrating its efficacy in accurately identifying ROSC in cardiac arrest situations. Conclusions RealCAC-Net enabled precise CAC quantification for ROSC determination during cardiopulmonary resuscitation. Future research should integrate this AI-enhanced ultrasound approach to revolutionize emergency care.","2024-09-01","2024-12-03 03:25:00","2024-12-03 03:25:00","","110302","","","202","","Resuscitation","","","","","","","","","","","","","","","","","","","Artificial intelligence; Cardiopulmonary resuscitation; Carotid artery; Point-of-care ultrasound; Pulse check; Return of spontaneous circulation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"V5UPULP3","journalArticle","2024","Ayanwale, Musa Adekunle; Adelana, Owolabi Paul; Molefi, Rethabile Rosemary; Adeeko, Olalekan; Ishola, Adebayo Monsur","Examining artificial intelligence literacy among pre-service teachers for future classrooms","Computers and Education Open","","2666-5573","10.1016/j.caeo.2024.100179","https://www.sciencedirect.com/science/article/pii/S266655732400020X","In the context of global integration and increasing reliance on Artificial Intelligence (AI) in education, evaluating the AI literacy of pre-service teachers is crucial. As future architects of educational systems, pre-service teachers must not only possess pedagogical expertise but also a strong foundation in AI literacy. This quantitative study examines AI literacy among 529 pre-service teachers in a Nigerian university, utilizing structural equation modeling (SEM) for comprehensive analysis. The research explores various dimensions of AI literacy, revealing that a profound understanding of AI significantly predicts positive outcomes in AI use, detection, ethics, creation, and problem-solving. However, no correlation exists between AI knowledge and emotion regulation or the assumption that active AI use enhances AI detection capabilities. The study identifies a trade-off between AI application and creation, emphasizing the ethical considerations intertwined with emotional and persuasive facets of AI use. It also supports the link between AI creation and problem-solving, emphasizing the foundational role of AI knowledge in shaping diverse aspects of AI literacy among pre-service teachers. The findings offer valuable insights for educators, administrators, policymakers, and researchers aiming to enhance AI literacy in pre-service teacher education programs.","2024-06-01","2024-12-03 03:25:00","2024-12-03 03:25:00","","100179","","","6","","Computers and Education Open","","","","","","","","","","","","","","","","","","","AI literacy; AI emotion regulation; Detect AI; Knowing and understanding AI; Pre-service teachers","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"AEK2G3MR","journalArticle","2023","Foroughi, Mahda; de Andrade, Bruno; Pereira Roders, Ana","Capturing public voices: The role of social media in heritage management","Habitat International","","0197-3975","10.1016/j.habitatint.2023.102934","https://www.sciencedirect.com/science/article/pii/S0197397523001947","Social media platforms have been increasingly used by locals and tourists to express their opinions about buildings, cities, and built heritage in particular. Most recently, scholars have been using social media to conduct innovative research on built heritage and heritage management. Still, the application of artificial intelligence (AI) methods to analyze social media data for heritage management is seldom explored. This paper investigates the potentials of short texts (sentences and hashtags) shared through social media as a data source and artificial intelligence methods for data analysis for revealing the cultural significance (values and attributes) of built heritage. The city of Yazd, Iran was taken as a case study, with the particular focus on windcatchers, key attributes conveying outstanding universal values, as inscribed on the UNESCO World Heritage List. This paper has three subsequent phases: 1) state of the art on the intersection of public participation in heritage management and social media research; 2) methodology of data collection and data analysis related to coding people's voices from Instagram and Twitter into values of windcatchers over the last ten-years; 3) preliminary findings on the comparison between opinions of locals and tourists, sentiment analysis, and its association with the values and attributes of windcatchers. Results indicate that the age value is recognized as the most important value by all interest groups, while the political value is the least acknowledged. Besides, the negative sentiments are scarcely reflected (e.g., critiques) in social media. Results confirm the potential of social media for heritage management in terms of (de)coding and measuring the cultural significance of built heritage for windcatchers and also other attributes in Yazd and other case studies and scales.","2023-12-01","2024-12-03 03:25:00","2024-12-03 03:25:00","","102934","","","142","","Habitat International","","","","","","","","","","","","","","","","","","","Artificial intelligence; Sentiment analysis; Public participation; Social media; Cultural significance","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"BHTYIN8X","journalArticle","2024","Kumar, Rajan; Joshi, Ablokit; Khan, Salman A.; Misra, Shikhar","Automated extraction of synthesis parameters of pulsed laser-deposited materials from scientific literature††Electronic supplementary information (ESI) available: Tables for the F1 scores for the individual entities on the validation dataset for the individual epochs using the MatSciBERT, MatSciBERT-CRF, and MatSciBERT-BiLSTM-CRF architecture. See DOI: https://doi.org/10.1039/.","Digital Discovery","","2635-098X","10.1039/d4dd00051j","https://www.sciencedirect.com/science/article/pii/S2635098X24000731","The materials science literature contains a large amount of reliable and high-quality data and automatically extracting useful information, including processing parameters and materials property data from this scientific literature continues to be a challenge. The development of new materials is typically based on experimental trial and error approach to identify the optimized processing parameters. In this work, we present an approach at the intersection of Natural Language Processing (NLP) and Materials Science, focusing on the extraction and analysis of materials and processing parameters associated with Pulsed Laser Deposition (PLD). Using the MatSciBERT (Bidirectional Encoder Representations from Transformers)-based architecture, we achieved precise identification and categorization of different PLD synthesis parameters, including, deposition temperature and pressure, laser energy, laser wavelength, thin film material and substrate, using the Named Entity Recognition (NER) model. This involved meticulous data acquisition from over 6000 research articles, followed by pre-processing, feature extraction, and model training. The trained NER model showcased impressive micro and macro F1 scores of 80.2% and 81.4%, respectively. This highlights the potential of Literature-based Discovery (LBD) approaches in expediting material discovery processes. The insights gained from this study are expected to drive advancements in materials research, streamlining information extraction processes by building a searchable database, and accelerating discoveries in the domain of Pulsed Laser Deposition.","2024-05-15","2024-12-03 03:25:00","2024-12-03 03:25:00","","944-953","","5","3","","Digital Discovery","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"YZWJRRX3","journalArticle","2023","Lada, Suddin; Chekima, Brahim; Karim, Mohd. Rahimie Abdul; Fabeil, Noor Fzlinda; Ayub, Mat Salleh; Amirul, Sharifah Milda; Ansar, Rudy; Bouteraa, Mohamed; Fook, Lim Ming; Zaki, Hafizah Omar","Determining factors related to artificial intelligence (AI) adoption among Malaysia's small and medium-sized businesses","Journal of Open Innovation: Technology, Market, and Complexity","","2199-8531","10.1016/j.joitmc.2023.100144","https://www.sciencedirect.com/science/article/pii/S2199853123002469","The purpose of the study is to examine the relationship between Competitive Pressure (CP), Top Management Commitment (TMC), Employee Adaptability (EA), External Support (ES), Organization Readiness (OR) and Artificial Intelligence Adoption (AIA) among SMES operating in Sabah, Malaysia. By employing judgemental sampling a total of 196 respondents were involved (i.e., owners or managers) in varied SME sectors such as services, manufacturing, construction, agriculture, and mining & quarrying. A survey questionnaire was used for data collection and analysed using Smart PLS 4. The results revealed that top management commitment and organization readiness have a significant relationship with AI adoption. However, competitive pressure, employee adaptability, and external support have an insignificant impact on AI adoption. This suggests that SME organizations may benefit from focusing on and enhancing TMC and OR practices to improve Al outcomes. Overall, these findings can guide decision-making and resource allocation, emphasizing the importance of OR and TMC in driving desired outcomes related to Al and highlighting areas where efforts may not yield significant effects. Based on present technological demands, practical implications and future research directions are also highlighted.","2023-12-01","2024-12-03 03:25:00","2024-12-03 03:25:00","","100144","","4","9","","Journal of Open Innovation: Technology, Market, and Complexity","","","","","","","","","","","","","","","","","","","SME; Artificial intelligence; Competitive pressure; Employee adaptability; External support; Organization readiness; Top management commitment","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"IF9BMNQ5","journalArticle","2023","P．S．, Dr. Varsha","How can we manage biases in artificial intelligence systems – A systematic literature review","International Journal of Information Management Data Insights","","2667-0968","10.1016/j.jjimei.2023.100165","https://www.sciencedirect.com/science/article/pii/S2667096823000125","Artificial intelligence is similar to human intelligence, and robots in organisations always perform human tasks. However, AI encounters a variety of biases during its operational process in the online economy. The coded algorithms helps in decision-making in firms with a variety of biases and ambiguity. The study is qualitative in nature and asserts that AI biases and vulnerabilities experienced by people across industries lead to gender biases and racial discrimination. Furthermore, the study describes the different types of biases and emphasises the importance of responsible AI in firms in order to reduce the risk from AI. The implications discuss how policymakers, managers, and employees must understand biases to improve corporate fairness and societal well-being. Future research can be carryout on consumer bias, bias in job automation and bias in societal data.","2023-04-01","2024-12-03 03:25:00","2024-12-03 03:25:00","","100165","","1","3","","International Journal of Information Management Data Insights","","","","","","","","","","","","","","","","","","","Artificial intelligence; Bias; AI ethics; AI systems; Responsible Ai; Vulnerabilities","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"HNN5XUIR","journalArticle","2024","Chotwanvirat, Phawinpon; Prachansuwan, Aree; Sridonpai, Pimnapanut; Kriengsinyos, Wantanee","Automated Artificial Intelligence–Based Thai Food Dietary Assessment System: Development and Validation","Current Developments in Nutrition","","2475-2991","10.1016/j.cdnut.2024.102154","https://www.sciencedirect.com/science/article/pii/S247529912400088X","Background Dietary assessment is a fundamental component of nutrition research and plays a pivotal role in managing chronic diseases. Traditional dietary assessment methods, particularly in the context of Thai cuisine, often require extensive training and may lead to estimation errors. Objectives To address these challenges, Institute of Nutrition, Mahidol University (INMU) iFood, an innovative artificial intelligence–based Thai food dietary assessment system, allows for estimating the nutritive values of dishes from food images. Methods INMU iFood leverages state-of-the-art technology and integrates a validated automated Thai food analysis system. Users can use 3 distinct input methods: food image recognition, manual input, and a convenient barcode scanner. This versatility simplifies the tracking of dietary intake while maximizing data quality at the individual level. The core improvement in INMU iFood can be attributed to 2 key factors, namely, the replacement of Yolov4-tiny with Yolov7 and the expansion of noncarbohydrate source foods in the training image data set. Results This combination significantly enhances the system’s ability to identify food items, especially in scenarios with closely packed food images, thus improving accuracy. Validation results showcase the superior performance of the INMU iFood integrated V7-based system over its predecessor, V4-based, with notable improvements in protein and fat estimation. Furthermore, INMU iFood addresses limitations by offering users the option to import additional food products via a barcode scanner, thus providing access to a vast database of nutritional information through Open Food Facts. This integration ensures users can track their dietary intake effectively, with expanded access to over 3000 food items added to or updated in the Open Food Facts database covering a wide variety of dietary choices. Conclusions INMU iFood is a promising tool for researchers, health care professionals, and individuals seeking to monitor their dietary intake within the context of Thai cuisine and for ultimately promoting better health outcomes and facilitating nutrition-related research.","2024-05-01","2024-12-03 03:25:00","2024-12-03 03:25:00","","102154","","5","8","","Current Developments in Nutrition","","","","","","","","","","","","","","","","","","","artificial intelligence; validation; dietary assessment; image-assisted dietary assessment; macronutrients; Thai food","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"IFWDYSMU","journalArticle","2024","Cavicchioli, Matteo; Moglia, Andrea; Pierelli, Ludovica; Pugliese, Giacomo; Cerveri, Pietro","Main challenges on the curation of large scale datasets for pancreas segmentation using deep learning in multi-phase CT scans: Focus on cardinality, manual refinement, and annotation quality","Computerized Medical Imaging and Graphics","","0895-6111","10.1016/j.compmedimag.2024.102434","https://www.sciencedirect.com/science/article/pii/S0895611124001113","Accurate segmentation of the pancreas in computed tomography (CT) holds paramount importance in diagnostics, surgical planning, and interventions. Recent studies have proposed supervised deep-learning models for segmentation, but their efficacy relies on the quality and quantity of the training data. Most of such works employed small-scale public datasets, without proving the efficacy of generalization to external datasets. This study explored the optimization of pancreas segmentation accuracy by pinpointing the ideal dataset size, understanding resource implications, examining manual refinement impact, and assessing the influence of anatomical subregions. We present the AIMS-1300 dataset encompassing 1,300 CT scans. Its manual annotation by medical experts required 938 h. A 2.5D UNet was implemented to assess the impact of training sample size on segmentation accuracy by partitioning the original AIMS-1300 dataset into 11 smaller subsets of progressively increasing numerosity. The findings revealed that training sets exceeding 440 CTs did not lead to better segmentation performance. In contrast, nnU-Net and UNet with Attention Gate reached a plateau for 585 CTs. Tests on generalization on the publicly available AMOS-CT dataset confirmed this outcome. As the size of the partition of the AIMS-1300 training set increases, the number of error slices decreases, reaching a minimum with 730 and 440 CTs, for AIMS-1300 and AMOS-CT datasets, respectively. Segmentation metrics on the AIMS-1300 and AMOS-CT datasets improved more on the head than the body and tail of the pancreas as the dataset size increased. By carefully considering the task and the characteristics of the available data, researchers can develop deep learning models without sacrificing performance even with limited data. This could accelerate developing and deploying artificial intelligence tools for pancreas surgery and other surgical data science applications.","2024-10-01","2024-12-03 03:25:00","2024-12-03 03:25:00","","102434","","","117","","Computerized Medical Imaging and Graphics","","","","","","","","","","","","","","","","","","","Artificial intelligence surgery; Artificial intelligence surgical planning; Deep learning pancreas segmentation; Medical imaging dataset acquisition; Medical imaging dataset curation; Pancreas dataset; Pancreas segmentation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"BLLBK94H","journalArticle","2023","Ikäheimo, Janne","Detecting pitfall systems in the Suomenselkä watershed, Finland, with airborne laser scanning and artificial intelligence","Journal of Archaeological Science: Reports","","2352-409X","10.1016/j.jasrep.2023.104216","https://www.sciencedirect.com/science/article/pii/S2352409X23003917","This article examines the use of airborne laser scanning data and semi-automatic detection algorithms to identify pitfall sites in the northern part of the Suomenselkä watershed in Finland. The results show that new sites can be effectively detected with these methods, even in areas recently surveyed archaeologically. Most of the previously known pitfall sites were also easily distinguishable from the data. The geographic location of the newly discovered sites confirmed previous interpretations of the prehistoric and historic hunting of cervids with pitfalls in the research area. Yet, further research is needed to refine the interpretations concerning the use and temporal sequence of pitfall rows both in Finland and elsewhere in Fennoscandia.","2023-10-01","2024-12-03 03:25:00","2024-12-03 03:25:00","","104216","","","51","","Journal of Archaeological Science: Reports","","","","","","","","","","","","","","","","","","","Artificial intelligence; Airborne laser scanning; Cervid hunting; Finnish archaeology; Pitfalls; Semi-automatic detection; Suomenselkä watershed","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"B34XNVZZ","journalArticle","2024","Doherty, G.; McLaughlin, L.; Hughes, C.; McConnell, J.; Bond, R.; McFadden, S.","Radiographer Education and Learning in Artificial Intelligence (REAL-AI): A survey of radiographers, radiologists, and students’ knowledge of and attitude to education on AI","Current Issues in Radiography Education","","1078-8174","10.1016/j.radi.2024.10.010","https://www.sciencedirect.com/science/article/pii/S1078817424003080","Introduction In Autumn 2023, amendments to the Health and Care Professions Councils (HCPC) Standards of Proficiency for Radiographers were introduced requiring clinicians to demonstrate awareness of the principles of AI and deep learning technology, and its application to practice’ (HCPC 2023; standard 12.25). With the rapid deployment of AI in departments, staff must be prepared to implement and utilise AI. AI readiness is crucial for adoption, with education as a key factor in overcoming fear and resistance. This survey aimed to assess the current understanding of AI among students and qualified staff in clinical practice. Methods A survey targeting radiographers (diagnostic and therapeutic), radiologists and students was conducted to gather demographic data and assess awareness of AI in clinical practice. Hosted online via JISC, the survey included both closed and open-ended questions and was launched in March 2023 at the European Congress of Radiology (ECR). Results A total of 136 responses were collected from participants across 25 countries and 5 continents. The majority were diagnostic radiographers 56.6 %, followed by students 27.2 %, dual-qualified 3.7 % and radiologists 2.9 %. Of the respondents, 30.1 % of respondents indicated that their highest level of qualification was a Bachelor's degree, 29.4 % stated that they are currently using AI in their role, whilst 27 % were unsure. Only 10.3 % had received formal AI training. Conclusion This study reveals significant gaps in training and understanding of AI among medical imaging staff. These findings will guide further research into AI education for medical imaging professionals. Implications for practice This paper lays foundations for future qualitative studies on the provision of AI education for medical imaging professionals, helping to prepare the workforce for the evolving role of AI in medical imaging.","2024-12-01","2024-12-03 03:25:00","2024-12-03 03:25:00","","79-87","","","30","","Radiography","","","","","","","","","","","","","","","","","","","Education; Medical imaging; Artificial intelligence (AI); Technology acceptance; CPD","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"VCCTQ553","journalArticle","2023","Murray, Jack D.; Lange, Justus J.; Bennett-Lenane, Harriet; Holm, René; Kuentz, Martin; O'Dwyer, Patrick J.; Griffin, Brendan T.","Advancing algorithmic drug product development: Recommendations for machine learning approaches in drug formulation","European Journal of Pharmaceutical Sciences","","0928-0987","10.1016/j.ejps.2023.106562","https://www.sciencedirect.com/science/article/pii/S0928098723001926","Artificial intelligence is a rapidly expanding area of research, with the disruptive potential to transform traditional approaches in the pharmaceutical industry, from drug discovery and development to clinical practice. Machine learning, a subfield of artificial intelligence, has fundamentally transformed in silico modelling and has the capacity to streamline clinical translation. This paper reviews data-driven modelling methodologies with a focus on drug formulation development. Despite recent advances, there is limited modelling guidance specific to drug product development and a trend towards suboptimal modelling practices, resulting in models that may not give reliable predictions in practice. There is an overwhelming focus on benchtop experimental outcomes obtained for a specific modelling aim, leaving the capabilities of data scraping or the use of combined modelling approaches yet to be fully explored. Moreover, the preference for high accuracy can lead to a reliance on black box methods over interpretable models. This further limits the widespread adoption of machine learning as black boxes yield models that cannot be easily understood for the purposes of enhancing product performance. In this review, recommendations for conducting machine learning research for drug product development to ensure trustworthiness, transparency, and reliability of the models produced are presented. Finally, possible future directions on how research in this area might develop are discussed to aim for models that provide useful and robust guidance to formulators.","2023-12-01","2024-12-03 03:25:00","2024-12-03 03:25:00","","106562","","","191","","European Journal of Pharmaceutical Sciences","","","","","","","","","","","","","","","","","","","Machine learning; Artificial intelligence; Computational pharmaceutics; Data-driven modelling; Drug formulation; Property prediction","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"PCXL6YVI","journalArticle","2024","Chachoui, Yasmine; Azizi, Nabiha; Hotte, Richard; Bensebaa, Tahar","Enhancing algorithmic assessment in education: Equi-fused-data-based SMOTE for balanced learning","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100222","https://www.sciencedirect.com/science/article/pii/S2666920X24000237","Recently, there has been a growing interest among researchers in enhancing the efficacy of learning through the utilization of diverse machine learning models within the field of artificial intelligence. However, imbalanced data distributions in educational datasets present a significant challenge to machine learning algorithms. This imbalance can result in biased models, untrustworthy outcomes, and poor performance. Data was gathered from a sample of 2176 first-year novice programming students in this study. Due to an alarming 76% failure rate, the imbalanced dataset was preprocessed before being oversampled with techniques such as SMOTE, SMOTE Borderline, SMOTE-ENN, and ADASYN. The proposed non-redundant synthetic data cooperation approach, named Equi-Fused-Data-based SMOTE, seeks to capitalize on the diversity of the obtained data by combining oversampled datasets. The balanced bagging model was then applied to the combined dataset to demonstrate the robustness of this approach. The promising results demonstrate the effectiveness of the Equi-Fused-Data-based SMOTE model, which achieved a higher Accuracy of 93.85%, a Precision, Recall and F1-score of 92,86%, and an AUC of 98.08%.","2024-06-01","2024-12-03 03:25:00","2024-12-03 03:25:00","","100222","","","6","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Machine learning; Artificial intelligence; Education datasets; Oversampling techniques; SMOTE","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"HUYMWZ7U","journalArticle","2024","Sembey, Ruchi; Hoda, Rashina; Grundy, John","Emerging technologies in higher education assessment and feedback practices: A systematic literature review","Journal of Systems and Software","","0164-1212","10.1016/j.jss.2024.111988","https://www.sciencedirect.com/science/article/pii/S0164121224000311","The use of Emerging Technologies, such as Artificial Intelligence (AI), Learning Analytics (LA) and Extended Reality (XR) applications, in higher education has proliferated in recent times, as these technologies are considered to have a significant impact on the future of postsecondary teaching and learning. We wanted to find out the emerging technologies used in computing education, its evaluation and effectiveness, and limitations and gaps for future research. We carried out a Systematic Literature Review study on the use of Emerging Technologies in higher education computing education to identify the state of the art in the use of these three groups of technologies for assessment and feedback practices. After systematic search and filtering from a search pool of 3038 studies published between 2016 and 2021, we selected 38 articles for detailed meta-analysis. Our findings reveal that 71% of the reviewed studies are journal articles, 50% studies focus on learning analytics, and the majority of the studies employ quantitative approaches. The results from this systematic review suggest that XR technologies have received least attention to date in computing education (amongst the emerging technologies considered for the review) and there is a lack of frameworks for design, evaluation and use of emerging technologies in higher education. The findings of this review will be beneficial for researchers and educators to obtain an in-depth understanding of the main areas of application of emerging technologies in higher education computing education, an inventory of emerging technology tools used for assessment and feedback, effectiveness indicators, and evaluation approaches that have been used. For evidence-based guidance on future assessment and feedback practices using emerging technologies, we also present a brief research agenda, drawing attention to the need to trial more XR, focus on formative assessment and feedback practices, better understand impact of human-centric issues and take more thoughtful consideration of ethics in the use of emerging technologies in computing education.","2024-05-01","2024-12-03 03:25:00","2024-12-03 03:25:00","","111988","","","211","","Journal of Systems and Software","","","","","","","","","","","","","","","","","","","Artificial intelligence; Feedback; Learning analytics; Emerging technologies; Assessment; Systematic literature review; Extended reality","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XVER8INE","journalArticle","2024","Oyege, Ivan; Sibitenda, Harriet; Bhaskar, Maruthi Sridhar Balaji","Deep learning applications for real-time and early detection of fall armyworm, African armyworm, and maize stem borer","Machine Learning with Applications","","2666-8270","10.1016/j.mlwa.2024.100596","https://www.sciencedirect.com/science/article/pii/S2666827024000720","The application of artificial intelligence for identifying Fall armyworm (Spodoptera frugiperda), African armyworm (Spodoptera exempta), and Maize stem borer (Busseola fusca) is critical due to the threats they pose to global food production. This study aims to evaluate and identify the most accurate and robust DL models in detecting and classifying these three significant agricultural pests. Seven traditional DL models: Convolutional Neural Network, Visual Geometry Group (VGG16), Residual Networks (ResNet50), MobileNetV2, InceptionV3, Deep Neural Network (DNN), and InceptionResNetV2 and the advanced You Look Only Once (YOLOv8) model were trained and tested using pest image datasets. The results showed that all traditional models except DNN had high accuracies ranging from 93.17% (InceptionResNetV2) to 99.43% (MobileNet) in training and testing, with losses ranging from 1.71% (MobileNetV2) to 24.99% (InceptionResNetV2). DNN had a slightly lower accuracy range of 55.27% to 56.39% and a loss range of 85.02% to 89.96% in training and testing. YOLOv8 emerged as the best and most robust model in the pest detection and classification tasks, achieving Precision and Recall scores ranging from 98.4% to 100% on single-class and multi-class classifications, making it highly suitable for real-world pest management applications. This research pioneers the use of DL for the classification and detection of maize stem borer, African armyworm and Fall armyworm, unique and separately addressing a critical gap in agricultural pest management in corn. With early and accurate pest identification, crop protection measures can be implemented efficiently. The findings lead to reduced crop damage and enhanced food security.","2024-12-01","2024-12-03 03:25:00","2024-12-03 03:25:00","","100596","","","18","","Machine Learning with Applications","","","","","","","","","","","","","","","","","","","Machine learning; Artificial intelligence; Augmentation; Agriculture; Busseola fusca; Pest identification; Spodoptera exempta; Spodoptera frugiperda","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"BLGHDUB4","journalArticle","2023","Dahou, Abdelhalim Hafedh; Cheragui, Mohamed Amine","DzNER: A large Algerian Named Entity Recognition dataset","Natural Language Processing Journal","","2949-7191","10.1016/j.nlp.2023.100005","https://www.sciencedirect.com/science/article/pii/S294971912300002X","Named Entity Recognition (NER) is a natural language processing (NLP) task that involves assigning labels like Person, Location, and Organization to words in text. While there is a good amount of annotated data available for NER in English and other European languages, this is not the case for Arabic and its dialects. The goal of the paper is to introduce DzNER, an Algerian dataset for NER that consists of more than 21,000 manually annotated sentences (over 220,000 tokens) from Algerian Facebook pages and YouTube channels, with a focus on three prominent classes. In this study, we provide a detailed analysis of the NER tag-set used in the dataset and show that it has a good balance of quantity, diversity, and coverage of different domains. For the proof of resource-effectiveness, we also demonstrate the effectiveness of the dataset by using various language models for the sequence labeling task of NER and comparing the results to existing datasets. According to our research and knowledge, currently no available dataset meets the standards of both variability and volume as well as DzNER. We hope that this dataset and the accompanying code and models will be useful for further research on NLP for Algerian dialect and fill the gap of low resources.","2023-06-01","2024-12-03 03:25:00","2024-12-03 03:25:00","","100005","","","3","","Natural Language Processing Journal","","","","","","","","","","","","","","","","","","","Dataset; Algerian dialect; Arabic; Low-resource language; Named Entity Recognition","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"P99BLYL5","journalArticle","2023","Xu, Guangxia; Liu, Lei; Dong, Jingnan","Vulnerability Detection of Ethereum Smart Contract Based on SolBERT-BiGRU-Attention Hybrid Neural Model","CMES - Computer Modeling in Engineering and Sciences","","1526-1492","10.32604/cmes.2023.026627","https://www.sciencedirect.com/science/article/pii/S1526149223003430","In recent years, with the great success of pre-trained language models, the pre-trained BERT model has been gradually applied to the field of source code understanding. However, the time cost of training a language model from zero is very high, and how to transfer the pre-trained language model to the field of smart contract vulnerability detection is a hot research direction at present. In this paper, we propose a hybrid model to detect common vulnerabilities in smart contracts based on a lightweight pre-trained language model BERT and connected to a bidirectional gate recurrent unit model. The downstream neural network adopts the bidirectional gate recurrent unit neural network model with a hierarchical attention mechanism to mine more semantic features contained in the source code of smart contracts by using their characteristics. Our experiments show that our proposed hybrid neural network model SolBERT-BiGRU-Attention is fitted by a large number of data samples with smart contract vulnerabilities, and it is found that compared with the existing methods, the accuracy of our model can reach 93.85%, and the Micro-F1 Score is 94.02%.","2023-04-21","2024-12-03 03:25:01","2024-12-03 03:25:01","","903-922","","1","137","","CMES - Computer Modeling in Engineering and Sciences","","","","","","","","","","","","","","","","","","","deep learning; blockchain security; pre-trained language model; recurrent neural network; Smart contract","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"85PUVMQF","journalArticle","2024","Khan, Zeshan Aslam; Waqar, Muhammad; Chaudhary, Naveed Ishtiaq; Raja, Muhammad Junaid Ali Asif; Khan, Saadia; Khan, Farrukh Aslam; Chaudhary, Iqra Ishtiaq; Raja, Muhammad Asif Zahoor","Fractional gradient optimized explainable convolutional neural network for Alzheimer's disease diagnosis","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e39037","https://www.sciencedirect.com/science/article/pii/S2405844024150682","Alzheimer's is one of the brain syndromes that steadily affects the brain memory. The early stage of Alzheimer's disease (AD) is referred to as mild cognitive impairment (MCI), and the growth of Alzheimer's is not certain in patients with MCI. The premature detection of Alzheimer's is crucial for maintaining healthy brain function and avoiding memory loss. Different multi-neural network architectures have been proposed by researchers for efficient and accurate AD detection. The absence of improved feature extraction mechanisms and unexplored efficient optimizers in complex benchmark architectures lead to an inefficient and inaccurate AD classification. Moreover, the standard convolutional neural network (CNN)-based architectures for Alzheimer's diagnosis lack interpretability in their predictions. An interpretable, simplified, yet effective deep learning model is required for the accurate classification of AD. In this study, a generalized fractional order-based CNN classifier with explainable artificial intelligence (XAI) capabilities is proposed for accurate, efficient, and interpretable classification of AD diagnosis. The proposed study (a) classifies AD accurately by incorporating unexplored pooling technique with enhanced feature extraction mechanism, (b) provides fractional order-based optimization approach for adaptive learning and fast convergence speed, and (c) suggests an interpretable method for proving the transparency of the model. The proposed model outperforms complex benchmark architectures with regard to accuracy using standard ADNI dataset. The proposed fractional order-based CNN classifier achieves an improved accuracy of 99 % as compared to the state-of-the-art models.","2024-10-30","2024-12-03 03:25:01","2024-12-03 03:25:01","","e39037","","20","10","","Heliyon","","","","","","","","","","","","","","","","","","","Explainable artificial intelligence; Convolutional neural network; Alzheimer's disease; Customized pooling; Fractional optimization; Neuroimaging","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LPGSAMGP","journalArticle","2024","Abburi, Radha; Hatai, Indranil; Jaros, Rene; Martinek, Radek; Babu, Thirunavukkarasu Arun; Babu, Sharmila Arun; Samanta, Sibendu","Adopting artificial intelligence algorithms for remote fetal heart rate monitoring and classification using wearable fetal phonocardiography","Applied Soft Computing","","1568-4946","10.1016/j.asoc.2024.112049","https://www.sciencedirect.com/science/article/pii/S1568494624008238","Fetal phonocardiography (FPCG) is a non-invasive Fetal Heart Rate (FHR) monitoring technique that can detect vibrations and murmurs in heart sounds. However, acquiring fetal heart sounds from a wearable FPCG device is challenging due to noise and artefacts. This research contributes a resilient solution to overcome the conventional issues by adopting Artificial Intelligence (AI) with FPCG for automated FHR monitoring in an end-to-end manner, named (AI-FHR). Four sequential methodologies were used to ensure reliable and accurate FHR monitoring. The proposed method removes low-frequency noises and high-frequency noises by using Chebyshev II high-pass filters and Enhanced Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ECEEMDAN) in combination with Phase Shifted Maximal Overlap Discrete Wavelet Transform (PS-MODWT) filters, respectively. The denoised signals are segmented to reduce complexity, and the segmentation is performed using multi-agent deep Q-learning (MA-DQL). The segmented signal is provided to reduce the redundancies in cardiac cycles using the Artificial Hummingbird Optimization (AHBO) algorithm. The segmented and non-redundant signals are converted into 3D spectrograms using a machine learning algorithm called variational auto-encoder-general adversarial networks (VAE-GAN). The feature extraction and classification are carried out by adopting a hybrid of the bidirectional gated recurrent unit (BiGRU) and the multi-boosted capsule network (MBCapsNet). The proposed method was implemented and simulated using MATLAB R2020a and validated by adopting effective validation metrics. The results demonstrate that the proposed method performed better than the current method with accuracy (81.34%), sensitivity (72%), F1-score (83%), Energy (0.808 J), and complexity index (13.34). Like other optimization methods, AHO needs precise parameter adjustment in order to function well. Its performance may be greatly impacted by the selection of parameters, including population size, exploration rate, and learning rate.","2024-11-01","2024-12-03 03:25:01","2024-12-03 03:25:01","","112049","","","165","","Applied Soft Computing","","","","","","","","","","","","","","","","","","","Classification; Artificial intelligence (AI); Segmentation; Fetal heart rate (FHR); Fetal phonocardiography (FPCG)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"AXWQ8IQ8","journalArticle","2024","Xie, Chenxi; Yang, Yueyuxiao; Yu, Hao; He, Qiushun; Yuan, Mingze; Dong, Bin; Zhang, Li; Yang, Meng","RNA velocity prediction via neural ordinary differential equation","iScience","","2589-0042","10.1016/j.isci.2024.109635","https://www.sciencedirect.com/science/article/pii/S2589004224008575","Summary RNA velocity is a crucial tool for unraveling the trajectory of cellular responses. Several approaches, including ordinary differential equations and machine learning models, have been proposed to interpret velocity. However, the practicality of these methods is constrained by underlying assumptions. In this study, we introduce SymVelo, a dual-path framework that effectively integrates high- and low-dimensional information. Rigorous benchmarking and extensive studies demonstrate that SymVelo is capable of inferring differentiation trajectories in developing organs, analyzing gene responses to stimulation, and uncovering transcription dynamics. Moreover, the adaptable architecture of SymVelo enables customization to accommodate intricate data and diverse modalities in forthcoming research, thereby providing a promising avenue for advancing our understanding of cellular behavior.","2024-04-19","2024-12-03 03:25:01","2024-12-03 03:25:01","","109635","","4","27","","iScience","","","","","","","","","","","","","","","","","","","Artificial intelligence; Neuroscience; Cell Engineering","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CLTMBGLP","journalArticle","2023","Horodyski, Piotr","Recruiter's perception of artificial intelligence (AI)-based tools in recruitment","Computers in Human Behavior Reports","","2451-9588","10.1016/j.chbr.2023.100298","https://www.sciencedirect.com/science/article/pii/S2451958823000313","Artificial intelligence (AI) technologies have advanced to the point where they can automate large parts of the hiring process and in consequence transform the role of recruiters or Human Resource professionals. However, there is little research on how recruiters perceive AI, and little is known about the motivations or factors that influence the use of AI in their workplaces. This study examined recruiters' intentions to use AI by extending the unified theory of acceptance and use of technology (UTAUT) to include the frequency of AI use and education. Data were obtained from a web-based survey with 238 demographically balanced participants. Hierarchical regression analysis was applied for data analysis and hypothesis testing. The results showed that behavioral intention was significantly and positively influenced by performance expectancy, and the moderating effect of frequency of AI use, while gender, age, experience, and education had no significant impact. Efficiency gains, time savings and automation emerged as the most important benefits, while lack of human judgment was the main disadvantage of AI use in recruitment.","2023-05-01","2024-12-03 03:25:01","2024-12-03 03:25:01","","100298","","","10","","Computers in Human Behavior Reports","","","","","","","","","","","","","","","","","","","Recruitment; Artificial intelligence (AI); UTAUT; Behavioral intention; Recruiter's perception","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RKQWF66M","journalArticle","2024","Al-Kfairy, Mousa; Mustafa, Dheya; Al-Adaileh, Ahmed; Zriqat, Samah; Sendaba, Obsa","User acceptance of AI voice assistants in Jordan’s telecom industry","Computers in Human Behavior Reports","","2451-9588","10.1016/j.chbr.2024.100521","https://www.sciencedirect.com/science/article/pii/S2451958824001544","Purpose: This study aims to understand factors influencing consumer acceptance of artificial intelligence (AI) voice assistants used in customer support within telecom companies in Jordan. Methodology: A survey was conducted involving 248 individuals who have experience with telecom support services. To evaluate consumer acceptance, the study incorporates the Unified Theory of Acceptance and Use of Technology (UTAUT) framework and extends it with attributes specific to AI, such as Perceived Reliability, Voice Quality, and Quality of Information. Advanced statistical methods, including structural equation modeling with SPSS AMOS 28 and SmartPLS, were utilized to analyze the collected data. Findings: The results revealed that Perceived Reliability and Quality of Information were significant predictors of AI voice assistant adoption in the telecom sector, while traditional factors such as Perceived Usefulness and Trust showed no significant impact. These findings suggest that performance-related elements play a more crucial role in user acceptance of AI in this context compared to earlier technological acceptance models. Implications: The study offers an expansion to traditional technology acceptance models by highlighting the importance of AI-specific attributes over conventional factors like Perceived Usefulness and Trust. For telecom operators in developing markets, this research provides guidance on enhancing customer engagement with AI voice assistants. It suggests focusing on improving the reliability and quality of information provided by AI systems to boost user acceptance. Originality/value: The study provides valuable insights into the changing dynamics of consumer acceptance of AI in customer support, emphasizing a shift toward performance-based criteria. Telecom companies in Jordan can use these findings to inform their AI adoption strategies and enhance customer satisfaction.","2024-12-01","2024-12-03 03:25:01","2024-12-03 03:25:01","","100521","","","16","","Computers in Human Behavior Reports","","","","","","","","","","","","","","","","","","","Artificial Intelligence (AI); UTAUT; Virtual assistant; Technology acceptance; AI voice assistant; Telecomunication","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"6548LG62","journalArticle","2023","Tieo, Sonia; Dezeure, Jules; Cryer, Anna; Lepou, Pascal; Charpentier, Marie J.E.; Renoult, Julien P.","Social and sexual consequences of facial femininity in a non-human primate","iScience","","2589-0042","10.1016/j.isci.2023.107901","https://www.sciencedirect.com/science/article/pii/S2589004223019788","Summary In humans, femininity shapes women’s interactions with both genders, but its influence on animals remains unknown. Using 10 years of data on a wild primate, we developed an artificial intelligence-based method to estimate facial femininity from naturalistic portraits. Our method explains up to 30% of the variance in perceived femininity in humans, competing with classical methods using standardized pictures taken under laboratory conditions. We then showed that femininity estimated on 95 female mandrills significantly correlated with various socio-sexual behaviors. Unexpectedly, less feminine female mandrills were approached and aggressed more frequently by both sexes and received more male copulations, suggesting a positive valuation of masculinity attributes rather than a perception bias. This study contributes to understand the role of femininity on animal’s sociality and offers a framework for non-invasive research on visual communication in behavioral ecology.","2023-10-20","2024-12-03 03:25:01","2024-12-03 03:25:01","","107901","","10","26","","iScience","","","","","","","","","","","","","","","","","","","Artificial intelligence; Biology of gender; Ethology","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CPZ7EG6K","journalArticle","2023","Tiozzo Fasiolo, Diego; Scalera, Lorenzo; Maset, Eleonora; Gasparetto, Alessandro","Towards autonomous mapping in agriculture: A review of supportive technologies for ground robotics","Robotics and Autonomous Systems","","0921-8890","10.1016/j.robot.2023.104514","https://www.sciencedirect.com/science/article/pii/S0921889023001537","This paper surveys the supportive technologies currently available for ground mobile robots used for autonomous mapping in agriculture. Unlike previous reviews, we describe state-of-the-art approaches and technologies aimed at extracting information from agricultural environments, not only for navigation purposes but especially for mapping and monitoring. The state-of-the-art platforms and sensors, the modern localization techniques, the navigation and path planning approaches, as well as the potentialities of artificial intelligence towards autonomous mapping in agriculture are analyzed. According to the findings of this review, many examples of recent mobile robots provide full navigation and autonomous mapping capability. Significant resources are currently devoted to this research area, in order to further improve mobile robot capabilities in this complex and challenging field.","2023-11-01","2024-12-03 03:25:01","2024-12-03 03:25:01","","104514","","","169","","Robotics and Autonomous Systems","","","","","","","","","","","","","","","","","","","Localization; Mapping; Artificial intelligence; Agriculture; Mobile robotics; Path planning","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"YVJTGK2C","journalArticle","2024","Zhao, Qinghong; Yang, Tongyu; Xu, Changyong; Hu, Jiaqi; Shuai, Yu; Zou, Hua; Hu, Wei","Automatic diagnosis for adenomyosis in ultrasound images by deep neural networks","European Journal of Obstetrics & Gynecology and Reproductive Biology","","0301-2115","10.1016/j.ejogrb.2024.07.046","https://www.sciencedirect.com/science/article/pii/S0301211524004019","Objective To present a new noninvasive technique for automatic diagnosis of adenomyosis, using a novel end-to-end unified network framework based on transformer networks. Study design This is a prospective descriptive study conducted at a university hospital.1654 patients were recruited to the study according to adenomyosis diagnosed by transvaginal ultrasound (TVS). For adenomyosis characteristics and ultrasound images, automatic identification of adenomyosis were performed based on deep learning methods. We called this unique technique A2DNet: Adenomyosis Auto Diagnosis Network. Results The A2DNet exhibits excellent performance in diagnosis of adenomyosis, achieving an accuracy of 92.33%, a precision of 96.06%, a recall of 91.71% and an F1 score of 93.80% in the test group. The confusion matrix of experimental results show that the A2DNet can achieve a correct diagnosis rate of 92% or more for both normal and adenomyosis samples, which demonstrate the superiority of the A2DNet comparing with the state-of-the-arts. Conclusion The A2DNet is a safe and effective technique to aid in automatic diagnosis of adenomyosis. The technique which is nondestructive and non-invasive, is new and unique due to the advantages of artificial intelligence.","2024-10-01","2024-12-03 03:25:01","2024-12-03 03:25:01","","128-134","","","301","","European Journal of Obstetrics & Gynecology and Reproductive Biology","","","","","","","","","","","","","","","","","","","Deep learning; Adenomyosis; Transformer networks; Ultrasound","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"5EPQLC56","journalArticle","2023","Shamim, Saqib; Yang, Yumei; Ul Zia, Najam; Khan, Zaheer; Shariq, Syed Muhammad","Mechanisms of cognitive trust development in artificial intelligence among front line employees: An empirical examination from a developing economy","Journal of Business Research","","0148-2963","10.1016/j.jbusres.2023.114168","https://www.sciencedirect.com/science/article/pii/S0148296323005271","Drawing upon insights from the trust literature, we conducted two empirical surveys with the front-line employees of firms in Pakistan investigating the factors influencing cognitive trust in artificial intelligence (AI). Study1 consisted of 46 in-depth interviews aimed at exploring factors influencing cognitive trust. Based on the findings of Study 1, we developed a framework to enhance employees’ cognitive trust in AI. We then conducted a quantitative survey (study 2) with 314 employees to validate the proposed model. The findings suggest that AI features positively influence the cognitive trust of employees, while work routine disruptions have negative impact on cognitive trust in AI. The effectiveness of data governance was also found to facilitate employees' trust in data governance and subsequently, employees' cognitive trust in AI. We contribute to the technology trust literature, especial in developing economics. We discuss the implications of our findings for both research and practice.","2023-11-01","2024-12-03 03:25:01","2024-12-03 03:25:01","","114168","","","167","","Journal of Business Research","","","","","","","","","","","","","","","","","","","Artificial intelligence; Cognitive trust; Data governance; Developing market; Disruption in work routines","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"NIUUI3BD","journalArticle","2024","Fandi, Ghaeth; Novák, Jaroslav; Chyský, Jan","Modeling electric Vehicle's and improving battery lifetime using AI tools case study: Postal cars","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e34792","https://www.sciencedirect.com/science/article/pii/S2405844024108237","The rapid development in the field of electric vehicles requires a careful evaluation of the design process. The presence of a simulation model of the electric vehicle can effectively detect many faulty areas during the development process without risks. The MATLAB and Simulink environment is considered one of the most important tools used in the simulation process. In this paper, we will present the model of electric car used for transporting postal parcels (postal cars). The model includes simulating the operation of a permanent magnet synchronous electric motor. We will assume that the car is moving according to a driving cycle. The results will show the torque forces required to achieve the required speed. We will further calculate the traction and the resistance forces during the driving cycle and the engine efficiency in addition. Perhaps the most important problem facing electric car designers is calculating the amount of energy consumed from the battery or hydrogen fuel, and this is what was achieved as the result of the simulation process in this research. In the end, use one of the artificial intelligence tools (fuzzy controller) to improve battery life by providing the electric car driver with an alert system that will increase the ability to monitor the battery condition and thus increase battery life. The benefit of this paper emerges in realizing the importance of modeling and using simulation using artificial intelligence in developing the design of the electric car, specially the electric motor and battery size, and thus achieving one of the most important goals of the United Nations of preserving the environment and reducing carbon emissions.","2024-08-15","2024-12-03 03:25:01","2024-12-03 03:25:01","","e34792","","15","10","","Heliyon","","","","","","","","","","","","","","","","","","","Energy consumption; Battery charge; Driver attention; Electric vehicle modeling; Fuzzy logic controller","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"UL5VSCEF","journalArticle","2024","Truhn, Daniel; Tayebi Arasteh, Soroosh; Saldanha, Oliver Lester; Müller-Franzes, Gustav; Khader, Firas; Quirke, Philip; West, Nicholas P.; Gray, Richard; Hutchins, Gordon G.A.; James, Jacqueline A.; Loughrey, Maurice B.; Salto-Tellez, Manuel; Brenner, Hermann; Brobeil, Alexander; Yuan, Tanwei; Chang-Claude, Jenny; Hoffmeister, Michael; Foersch, Sebastian; Han, Tianyu; Keil, Sebastian; Schulze-Hagen, Maximilian; Isfort, Peter; Bruners, Philipp; Kaissis, Georgios; Kuhl, Christiane; Nebelung, Sven; Kather, Jakob Nikolas","Encrypted federated learning for secure decentralized collaboration in cancer image analysis","Medical Image Analysis","","1361-8415","10.1016/j.media.2023.103059","https://www.sciencedirect.com/science/article/pii/S1361841523003195","Artificial intelligence (AI) has a multitude of applications in cancer research and oncology. However, the training of AI systems is impeded by the limited availability of large datasets due to data protection requirements and other regulatory obstacles. Federated and swarm learning represent possible solutions to this problem by collaboratively training AI models while avoiding data transfer. However, in these decentralized methods, weight updates are still transferred to the aggregation server for merging the models. This leaves the possibility for a breach of data privacy, for example by model inversion or membership inference attacks by untrusted servers. Somewhat-homomorphically-encrypted federated learning (SHEFL) is a solution to this problem because only encrypted weights are transferred, and model updates are performed in the encrypted space. Here, we demonstrate the first successful implementation of SHEFL in a range of clinically relevant tasks in cancer image analysis on multicentric datasets in radiology and histopathology. We show that SHEFL enables the training of AI models which outperform locally trained models and perform on par with models which are centrally trained. In the future, SHEFL can enable multiple institutions to co-train AI models without forsaking data governance and without ever transmitting any decryptable data to untrusted servers.","2024-02-01","2024-12-03 03:25:01","2024-12-03 03:25:01","","103059","","","92","","Medical Image Analysis","","","","","","","","","","","","","","","","","","","Artificial intelligence; Federated learning; Radiology; Histopathology; Homomorphic encryption; Privacy-preserving deep learning","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RKPXTS44","journalArticle","2024","Sanusi, Ismaila Temitayo; Ayanwale, Musa Adekunle; Tolorunleke, Adebayo Emmanuel","Investigating pre-service teachers’ artificial intelligence perception from the perspective of planned behavior theory","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100202","https://www.sciencedirect.com/science/article/pii/S2666920X24000031","There is a need for teachers who are prepared to teach Artificial Intelligence (AI) across the K-12 learning contexts. Owing to the dearth of teacher education programmes on AI, it is helpful to explore factors to be considered in designing an effective AI programme for future teachers. We posit that understanding how to encourage pre-service teachers to learn AI is thus critical for practitioners and policymakers while designing effective instructional AI teacher education programmes. This exploratory study examined the perceptions of pre-service teachers and their behavioral intention to learn AI, by identifying factors that might affect learning and promoting AI in teacher preparation programmes. This study proposed a research model supported by the theory of planned behavior and expanded with other constructs. The factors that were examined include basic knowledge of AI, subjective norm, AI for social good, perceived self-efficacy, self-transcendent goals, personal relevance, AI anxiety, behavioral intention to learn AI, and actual learning of AI. Using a duly validated questionnaire, we surveyed 796 pre-service teachers in Nigerian Universities. Through structural equation modeling approach analyses, our proposed model explains about 79% of the variance in pre-service teachers' intention to learn AI. Basic knowledge and subjective norm were found to be the most important determinant in pre-service teachers’ intention to learn AI. All our hypotheses were supported except for self-efficacy and personal relevance, personal relevance and social good, and behavioral intention and actual learning behavior. The findings provide practitioners, researchers, and policymakers with valuable information to consider in designing effective AI teacher education programmes.","2024-06-01","2024-12-03 03:25:01","2024-12-03 03:25:01","","100202","","","6","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Artificial intelligence; Behavioral intention; Pre-service teachers; Basic knowledge; Personal characteristics; Teacher education","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"W8AD8SXV","journalArticle","2024","Azab, Ahmed; Osman, Hany; Baki, Fazle","CAPP-GPT: A computer-aided process planning-generative pretrained transformer framework for smart manufacturing","52nd SME North American Manufacturing Research Conference (NAMRC 52)","","2213-8463","10.1016/j.mfglet.2024.09.009","https://www.sciencedirect.com/science/article/pii/S221384632400066X","Smart manufacturing (SM) constitutes the backbone of Industry 4.0 (I4.0), allowing for heightened autonomy of the various interacting cyber-physical systems, making the various entities on the production floor. Connectivity, a vital enabler, plays a crucial role through state-of-the-art Digital Twinning (DT) technologies driven by underlying innovations like the industrial Internet of Things, Cloud Computing, and advancements in sensory devices. DT, which plays a vital role in the various planning functions under the production and operations management umbrella, is being used in the developed combined CAPP-GPT (Computer-Aided Process Planning-Generative Pretrained Transformer) and production scheduling approach to address disruptions on the shopfloor and in self-healing of the manufacturing processes at a micro-CAPP level by optimally adapting the process parameters and the developed toolpath on the fly based on online process signature measurements. In a leap commensurate with that which has taken place in Natural Language Processing-Large Language Models (Chat-GPT), similar efforts are currently being undertaken to parse CAD data structures and blueprints, fusing operations research and predictive analytics algorithms to carry out setup planning as well as sequencing and grouping manufacturing sub-operations. A hybridized Optimization and Machine Learning (ML) approach is employed where Logical Analysis of Data is used to solve the problem heuristically, exploiting various generative and variant methods at heart. Another extension of this macro-CAPP problem is being tackled by integrating the problem with delayed product differentiation, lot-sizing, and transfer line balance for futuristic batch-production shops employing Hybrid Manufacturing (HM) and Smart Assembly. At the micro-CAPP level, HM process parameters are optimized using a comprehensive approach employing the Taguchi loss function to assess surface roughness, internal failure costs, and other criteria, including greenhouse gas emissions and expended energy. Online measurements of the process signatures are also employed to adapt the initial set of process parameters using different automatic control schemes. ML is used to identify the process parameters carrying simulations on Simulink before the system is deployed.","2024-10-01","2024-12-03 03:25:01","2024-12-03 03:25:01","","51-62","","","41","","Manufacturing Letters","","","","","","","","","","","","","","","","","","","Scheduling; Machine Learning; Smart Manufacturing; CAPP-GPT; Macro-CAPP; Maintenance 4.0; Micro-CAPP; Quality 4.0","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SSJGJXRP","journalArticle","2024","Bagdy-Bálint, Réka; Szabó, Gergely; Zováthi, Örkény H.; Zováthi, Bendegúz H.; Somorjai, Ábris; Köpenczei, Csenge; Rózsa, Noémi Katinka","Accuracy of automated analysis in cephalometry","Journal of Dental Sciences","","1991-7902","10.1016/j.jds.2024.09.012","https://www.sciencedirect.com/science/article/pii/S1991790224003234","Background/purpose Artificial intelligence (AI) has been widely used in medicine, including orthodontics. The aim of this study was to investigate the training process of a cascaded Convolutional Neural Network (CNN), built for landmark detection on various qualities of lateral cephalograms and to determine the speed, reliability and clinical accuracy of an algorithm for orthodontic diagnosis. Materials and methods The CNN model was trained on a total of 1600 lateral cephalograms. After each training datasets (input of 400, 800, 1200, 1600 images) were added, the model was evaluated on a test set containing 78 images of varying quality. We measured the accuracy of AI-based landmark detection by statistical analysis of intra- and interexaminer distance errors, as well as examiner versus model predictions, furthermore by prognosis of consecutive diagnostic failures. Results There was a clear improvement in time efficiency (5.25 min), and substantial improvements were observed during the training process. In terms of accuracy, based on Euclidean distance error measurements, the best model provided more consistent dot tracing than two different examiners or the same examiner on two different occasions. Angular (0.05°–1.86°) and proportional (3.14%) errors, measured by the best model, were considered clinically acceptable. Conclusion The application of a proper AI-algorithm for orthodontic cephalometric analysis results in lower variability between models than the variability observed among experts. AI predictions supported the examiners in finding the correct location of the specific landmarks more accurately and in less time as the training of the automatic prediction model improved. Further research could investigate the therapeutic consequences.","2024-10-08","2024-12-03 03:25:02","2024-12-03 03:25:02","","","","","","","Journal of Dental Sciences","","","","","","","","","","","","","","","","","","","Artificial intelligence; Automation; Diagnosis; Cephalometry; Clinical decision-making; Computer-assisted","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"X7HBLYBG","journalArticle","2024","AlJasmi, Amina Abdelqadir Mohamed; Ghonim, Hatem; Fahmy, Mohyi Eldin; Nair, Aswathy; Kumar, Shamie; Robert, Dennis; Mohamed, Afrah Abdikarim; Abdou, Hany; Srivastava, Anumeha; Reddy, Bhargava","Post-deployment performance of a deep learning algorithm for normal and abnormal chest X-ray classification: A study at visa screening centers in the United Arab Emirates","European Journal of Radiology Open","","2352-0477","10.1016/j.ejro.2024.100606","https://www.sciencedirect.com/science/article/pii/S2352047724000613","Background Chest radiographs (CXRs) are widely used to screen for infectious diseases like tuberculosis and COVID-19 among migrants. At such high-volume settings, manual CXR reporting is challenging and integrating artificial intelligence (AI) algorithms into the workflow help to rule out normal findings in minutes, allowing radiologists to focus on abnormal cases. Methods In this post-deployment study, all the CXRs acquired during the visa screening process across 33 centers in United Arab Emirates from January 2021 to June 2022 (18 months) were included. The qXR v2.1 chest X-ray interpretation software was used to classify the scans into normal and abnormal, and its agreement against radiologist was evaluated. Additionally, a digital survey was conducted among 20 healthcare professionals with prior AI experience to understand real-world implementation challenges and impact. Results The analysis of 1309,443 CXRs from 1309,431 patients (median age: 35 years; IQR [29–42]; 1030,071 males [78.7 %]) in this study revealed a Negative Predictive Value (NPV) of 99.92 % (95 % CI: 99.92, 99.93), Positive Predictive Value (PPV) of 5.06 % (95 % CI: 4.99, 5.13) and overall percent agreement of the AI with radiologists of 72.90 % (95 % CI: 72.82, 72.98). In the survey, majority (88.2 %) of the radiologists agreed to turnaround time reduction after AI integration, while 82 % suggested that the AI improved their diagnostic accuracy. Discussion In contrast with the existing studies, this research uses a substantially large data. A high NPV and satisfactory agreement with human readers indicate that AI can reliably identify normal CXRs, making it suitable for routine applications.","2024-12-01","2024-12-03 03:25:02","2024-12-03 03:25:02","","100606","","","13","","European Journal of Radiology Open","","","","","","","","","","","","","","","","","","","Artificial intelligence; Workflow; Abnormality; Agreement; Chest radiograph; Visa screening","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"NQTC2EKA","journalArticle","2024","Lo, Chung Kwan; Hew, Khe Foon; Jong, Morris Siu-yung","The influence of ChatGPT on student engagement: A systematic review and future research agenda","Computers & Education","","0360-1315","10.1016/j.compedu.2024.105100","https://www.sciencedirect.com/science/article/pii/S0360131524001143","ChatGPT, a state-of-the-art artificial intelligence (AI) chatbot, has gained considerable attention as a transformative yet controversial tool for enhancing teaching and learning experiences. Several reviews and numerous articles have been written about harnessing ChatGPT in education since its release on November 30, 2022. Besides summarising its strengths, weaknesses, opportunities, and threats (SWOT) as identified in previous systematic reviews of ChatGPT research, this systematic review aims to develop a new understanding of its influence on student engagement by synthesising the existing related research using a three-dimensional framework comprising behavioural, emotional, and cognitive aspects. We searched relevant databases and included 72 empirical studies published within one year of ChatGPT's initial release. The findings reveal robust but narrowly focused evidence related to behavioural engagement (i.e., work with ChatGPT) and disengagement (i.e., academic dishonesty). The evidence related to the emotional aspect is mixed, with instances of both engagement (e.g., satisfaction and interest/fun) and disengagement (e.g., disappointment and worry/anxiety). There is broad but weak evidence regarding cognitive engagement (e.g., increased understanding and positive self-perception) and disengagement (e.g., reduced critical thinking and overreliance). Our review uncovers several under-explored indicators of student engagement, pointing to the need for further research. Specifically, future studies could focus on students' study habits and attendance (behavioural engagement), social interaction (emotional engagement), and self-regulation and critical thinking (cognitive engagement) in ChatGPT-supported learning environments.","2024-10-01","2024-12-03 03:25:02","2024-12-03 03:25:02","","105100","","","219","","Computers & Education","","","","","","","","","","","","","","","","","","","Systematic review; Artificial intelligence; ChatGPT; OpenAI; Student engagement","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"FRBCYHTQ","journalArticle","2024","Dissler, Nina; Nogueira, Daniela; Keppi, Bertrand; Sanguinet, Pierre; Ozanon, Christophe; Geoffroy-Siraudin, Cendrine; Pollet-Villard, Xavier; Boussommier-Calleja, Alexandra","Artificial intelligence-powered assisted ranking of sibling embryos to increase first cycle pregnancy rate","Reproductive BioMedicine Online","","1472-6483","10.1016/j.rbmo.2024.103887","https://www.sciencedirect.com/science/article/pii/S1472648324000762","Research question Could EMBRYOLY, an artificial intelligence embryo evaluation tool, assist embryologists to increase first cycle pregnancy rate and reduce cycles to pregnancy for patients? Design Data from 11,988 embryos were collected via EMBRYOLY from 2666 egg retrievals (2019–2022) across 11 centres in France, Spain and Morocco using three time-lapse systems (TLS). Data from two independent clinics were also examined. EMBRYOLY's transformer-based model was applied to transferred embryos to evaluate ranking performances against pregnancy and birth outcomes. It was applied to cohorts to rank sibling embryos (including non-transferred) according to their likelihood of clinical pregnancy and to compute the agreement with the embryologist's highest ranked embryo. Its effect on time to pregnancy and first cycle pregnancy rate was evaluated on cohorts with multiple single blastocyst transfers, assuming the embryologist would have considered EMBRYOLY's ranking on the embryos favoured for transfer. Results EMBRYOLY's score correlated significantly with clinical pregnancies and live births for cleavage and blastocyst transfers. This held true for clinical pregnancies from blastocyst transfers in two independent clinics. In cases of multiple single embryo transfers, embryologists achieved a 19.8% first cycle pregnancy rate, which could have been improved to 44.1% with the adjunctive use of EMBRYOLY (McNemar's test: P < 0.001). This could have reduced cycles to clinical pregnancy from 2.01 to 1.66 (Wilcoxon test: P < 0.001). Conclusions EMBRYOLY's potential to enhance first cycle pregnancy rates when combined with embryologists' expertise is highlighted. It reduces the number of unsuccessful cycles for patients across TLS and IVF centres.","2024-07-01","2024-12-03 03:25:02","2024-12-03 03:25:02","","103887","","1","49","","Reproductive BioMedicine Online","","","","","","","","","","","","","","","","","","","artificial intelligence; birth; embryo evaluation; pregnancy; time to pregnancy; time-lapse incubator systems","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"4LENXJZN","journalArticle","2024","Abdollahi, Azam; Li, Deli; Deng, Jian; Amini, Ali","An explainable artificial-intelligence-aided safety factor prediction of road embankments","Engineering Applications of Artificial Intelligence","","0952-1976","10.1016/j.engappai.2024.108854","https://www.sciencedirect.com/science/article/pii/S0952197624010121","Despite the widespread application of data-centric techniques in Geotechnical Engineering, there is a rising need for building trust in the artificial intelligence (AI)-driven safety assessment of road embankments due to its so-called “black-box” nature. In addition, from the lens of limit equilibrium approaches, e.g., Bishop, Fellenius, Janbu and Morgenstern–Price, and finite element method, it is essential to carefully examine the interplay of both topological and physical/mechanical properties during the safety factor (FoS) predictions. First, aside from having conventional geotechnical inputs for soil in core and foundation and the height of embankments, this paper codifies geometric features innovatively. The number of slope types with different ratios including 1:1, 1.5:1 and 2:1 as well as the number of berms is introduced. Second, a pool of 19 machine learning (ML) techniques is effortlessly trained on the dataset using an automated ML (AutoML) pipeline to identify the most optimized ML algorithm. Finally, to achieve post-hoc interpretability for the internal mechanism of the input–output relationship unbiasedly, a game-theory-based explainable AI (XAI) method called Shapley additive explanations (SHAP) values is applied. SHAP-aided importance analysis provides human-interpretable insights and indicates height, California bearing ratio, slope type 2:1 and cohesion as the most influential parameters. Exclusively, analyzing hazardous embankments by classifying main and joint contributors exhibits a complex and highly variable influence on the FoS. This paper harnesses the power of XAI tools to enhance reliability and transparency for the rapid FoS prediction of slopes. It targets geotechnical researchers, practitioners, decision-makers, and the general public for the first time.","2024-10-01","2024-12-03 03:25:02","2024-12-03 03:25:02","","108854","","","136","","Engineering Applications of Artificial Intelligence","","","","","","","","","","","","","","","","","","","Explainable artificial intelligence; Shapley additive explanations; Automated machine learning; Geotechnical slope engineering; Interpretable machine learning; Safety factor calculation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KL69DIX7","journalArticle","2024","Waligórski, Marek; Kozak, Miłosław; Świetlicka, Aleksandra","Acoustic frequency-based method for high-speed aircraft combustion analysis and hybrid artificial intelligence diagnostics","Measurement","","0263-2241","10.1016/j.measurement.2024.115304","https://www.sciencedirect.com/science/article/pii/S0263224124011898","In the paper, a method of high-speed continuous combustion monitoring for jet aircrafts, based on acoustic data processing, is proposed. As part of the research, acoustic signals corresponding to the specific phases of the combustible mixture formation and its combustion were separated. They were assigned to the generated process energy value. In the result, parametric functions were obtained in multidimensional spaces of states and processes. Parameters and acoustic characteristics in the amplitude, frequency and JTFA domains constituted information about particular symptoms in the jet engine, thanks to which reference waveforms of signals and forms for specific malfunctions were mapped. The above graphic characteristics have been parameterized to determine their diagnostic reliability factors. The method is verified by empirical signals obtained from turbojet propulsion system in time and power dependent conditions. Appropriate exothermic combustion phases were assigned for the determined acoustic spectra, taking into account the obtained power. Then, representative diagnostic parameters transformed in the process values domain were calculated. Finally, monitoring functions and algorithms of combustion efficiency with determination of malfunctions sources (at dynamic conditions) are proposed. As a result, it is possible to identify first outbreaks of design and process failures during fuel injection and combustion in aircrafts, taking into account artificial intelligence methods. The obtained results indicate it is possible to adapt the acoustic characteristics of the spectra and their discrete representatives, for appropriate states and failures, to detect first dangerous symptoms in the aircraft during flight conditions (supporting OBD with the acoustic adaptive diagnostic procedures).","2024-09-30","2024-12-03 03:25:02","2024-12-03 03:25:02","","115304","","","237","","Measurement","","","","","","","","","","","","","","","","","","","Artificial intelligence; Acoustic empirical characteristics; Early symptoms identification; Highly turbulent combustion; On-board aircraft diagnosis","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"7T8G8YHJ","journalArticle","2023","Verma, Navdeep; Getenet, Dr Seyum; Dann, Dr Christopher; Shaik, Thanveer","Designing an artificial intelligence tool to understand student engagement based on teacher's behaviours and movements in video conferencing","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2023.100187","https://www.sciencedirect.com/science/article/pii/S2666920X23000668","Video conferencing is an effective tool that promotes interaction and collaboration, increasing student engagement in online learning. This study is the second phase of design-based research to create a tool to generate a report of engaging teaching videos using deep learning as an artificial intelligence (AI) methodology. In this second phase, the authors have applied the characteristics and indicators of engaging teaching videos identified in the first phase, reported in another study, to develop an Artificial Intelligence enabled tool. Twenty-five recorded lecture videos presented to higher education students were annotated based on the indicators and characteristics of engaging teaching videos. An AI expert has assisted the authors in creating the Artificial Intelligence-enabled tool from the reports generated by this manual annotation. With the assistance of this tool, the engagement enhancing teachers' behaviours and movements can be identified from recorded lecture videos, and a report can be generated on engaging teaching videos. For the classification task of video analysis, the deep learning model is adopted in this research. The model is trained with manually annotated videos and determines class imbalance issues and misleading metrics. The model was further improved by adopting the oversampling technique. The second version of the tool achieved promising outputs with average precision, recall, f1-score, and balanced accuracy of 68, 75, 73, and 79%, respectively, in classifying the annotated videos at the indicator level. The tool can assist the education institutes in creating moderation in the lecture delivery and whether the teachers are utilising the technology effectively. Additionally, this can help teachers recognise the presence or absence of engagement-enhancing behaviours and movements during their video conferencing sessions.","2023-01-01","2024-12-03 03:25:02","2024-12-03 03:25:02","","100187","","","5","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Artificial intelligence; Teachers' behaviours; Teachers' movements; Video conferencing","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2AQTTQVH","journalArticle","2024","Dhiman, Pummy; Kaur, Amandeep; Gupta, Deepali; Juneja, Sapna; Nauman, Ali; Muhammad, Ghulam","GBERT: A hybrid deep learning model based on GPT-BERT for fake news detection","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e35865","https://www.sciencedirect.com/science/article/pii/S2405844024118968","The digital era has expanded social exposure with easy internet access for mobile users, allowing for global communication. Now, people can get to know what is going on around the globe with just a click; however, this has also resulted in the issue of fake news. Fake news is content that pretends to be true but is actually false and is disseminated to defraud. Fake news poses a threat to harmony, politics, the economy, and public opinion. As a result, bogus news detection has become an emerging research domain to identify a given piece of text as genuine or fraudulent. In this paper, a new framework called Generative Bidirectional Encoder Representations from Transformers (GBERT) is proposed that leverages a combination of Generative pre-trained transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) and addresses the fake news classification problem. This framework combines the best features of both cutting-edge techniques—BERT's deep contextual understanding and the generative capabilities of GPT—to create a comprehensive representation of a given text. Both GPT and BERT are fine-tuned on two real-world benchmark corpora and have attained 95.30 % accuracy, 95.13 % precision, 97.35 % sensitivity, and a 96.23 % F1 score. The statistical test results indicate the effectiveness of the fine-tuned framework for fake news detection and suggest that it can be a promising approach for eradicating this global issue of fake news in the digital landscape.","2024-08-30","2024-12-03 03:25:02","2024-12-03 03:25:02","","e35865","","16","10","","Heliyon","","","","","","","","","","","","","","","","","","","Deep learning; Technology; Transformers; Internet access; Large language model; Text classification; Social media; Fake news detection","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"8JTC6EX7","journalArticle","2024","Fu, Guangtao; Savic, Dragan; Butler, David","Making Waves: Towards data-centric water engineering","Water Research","","0043-1354","10.1016/j.watres.2024.121585","https://www.sciencedirect.com/science/article/pii/S0043135424004871","Artificial intelligence (AI) is expected to transform many scientific disciplines, with the potential to significantly accelerate scientific discovery. This perspective calls for the development of data-centric water engineering to tackle water challenges in a changing world. Building on the historical evolution of water engineering from empirical and theoretical paradigms to the current computational paradigm, we argue that a fourth paradigm, i.e., data-centric water engineering, is emerging driven by recent AI advances. Here we define a new framework for data-centric water engineering in which data are transformed into knowledge and insight through a data pipeline powered by AI technologies. It is proposed that data-centric water engineering embraces three principles – data-first, integration and decision making. We envision that the development of data-centric water engineering needs an interdisciplinary research community, a shift in mindset and culture in the academia and water industry, and an ethical and risk framework to guide the development and application of AI. We hope this paper could inspire research and development that will accelerate the paradigm shift towards data-centric water engineering in the water sector and fundamentally transform the planning and management of water infrastructure.","2024-06-01","2024-12-03 03:25:02","2024-12-03 03:25:02","","121585","","","256","","Water Research","","","","","","","","","","","","","","","","","","","Artificial intelligence; Data-centric; Model-centric; Scientific paradigm; Water engineering","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"NQ9GE3BW","journalArticle","2024","Xu, Gan; Qiu, Yue; Qi, Jingyu","Artificial intelligence and labor demand: An empirical analysis of Chinese small and micro enterprises","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e33893","https://www.sciencedirect.com/science/article/pii/S2405844024099249","The widespread application of artificial intelligence (AI) technology has triggered a significant transformation in the economic structure and has brought profound changes to human society. As China promotes the digital transformation of industries, understanding how the investment in AI by small and micro enterprises (SMEs) affects labor demand, which is inextricably linked to “stable employment”, becomes an important question. This paper uses special data from 127 SMEs in 14 provinces from 2016 to 2020 and employs a two-way fixed effects model to study the impact of AI inputs on enterprises’ labor demand. The empirical results show that the impact of AI inputs on the labor demand of SMEs is not significant overall, but shows a significant negative effect in non-state-owned enterprises, private enterprises, and high-tech enterprises. There is a significant difference in the impact of AI inputs on the labor demand of different industries, with only the wholesale and retail industry demonstrating a significant positive impact. From the results of mechanism analysis, the substitution effect and creation effect of AI inputs on labor demand coexist, and in general, these two effects cancel each other out. However, the substitution effect dominates in some types of enterprises and industries. Finally, this paper discusses the government and enterprise coping strategies for the employment impact of AI applications based on empirical evidence and research results. This paper not only theoretically demonstrates that the impact of AI investment on firms' labor demand is uncertain, but also empirically demonstrates that Chinese firms' AI investment does not significantly affect firms' overall labor demand. This facilitates the government and enterprises to formulate strategies that can enhance the level of enterprise intelligence without impacting the labor market.","2024-07-15","2024-12-03 03:25:02","2024-12-03 03:25:02","","e33893","","13","10","","Heliyon","","","","","","","","","","","","","","","","","","","SMEs; AI inputs; Creation effect; Labor demand; Substitution effect","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"E53JM3TN","journalArticle","2024","Sánchez, Omar; Castañeda, Karen; Vidal-Méndez, Sofía; Carrasco-Beltrán, Daniela; Lozano-Ramírez, Natalia E.","Exploring the influence of linear infrastructure projects 4.0 technologies to promote sustainable development in smart cities","Results in Engineering","","2590-1230","10.1016/j.rineng.2024.102824","https://www.sciencedirect.com/science/article/pii/S259012302401079X","Industry 4.0 technologies have a high potential to improve the planning and execution of linear projects such as roads, bridges and railroads. These technologies can help reduce emissions, enhance operations' efficiency, and improve users' quality of life. Despite their potential to promote sustainability in smart cities, there is a lack of research focused on analysing their impact. Therefore, this paper aims to identify 4.0 technologies related to linear projects and examine their influence on smart cities' sustainable development. The research method involved four main stages: identification of 4.0 technologies in linear projects, questionnaire design and application, influence analysis, and factor analysis. A systematic literature review identified a set of 4.0 technologies that favour smart cities' sustainable development. Subsequently, 66 experts were consulted to determine which of these technologies have the most significant influence on smart cities' sustainable development, highlighting data analysis and management, intelligent traffic control systems, and artificial intelligence. This study makes three main contributions: (1) it identifies thirty-seven 4.0 technologies associated with linear projects that promote sustainable development in smart cities; (2) it characterises the 4.0 technologies with the most significant influence on the sustainability of smart cities; and (3) it proposes nine principal components into which interrelated 4.0 technologies that promote sustainable development can be grouped. This study provides valuable evidence for smart city managers and offers guidance for the efficient adoption of 4.0 technologies in linear projects.","2024-09-01","2024-12-03 03:25:02","2024-12-03 03:25:02","","102824","","","23","","Results in Engineering","","","","","","","","","","","","","","","","","","","Sustainable development; Smart cities; Construction 4.0; Influence analysis; Linear infrastructure","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"3V5VU6B6","journalArticle","2024","Swamy, H.A. Kumara; Ryu, Daesick; Kim, Hyunju; Sankar, M.; Do, Younghae","Exploring bioconvection dynamics within an inclined porous annulus: Integration of CFD and AI on the synergistic effects of hybrid nanofluids, oxytactic microorganisms, and magnetic field","International Communications in Heat and Mass Transfer","","0735-1933","10.1016/j.icheatmasstransfer.2024.107999","https://www.sciencedirect.com/science/article/pii/S0735193324007619","The current research integrates artificial intelligence (AI) with computational fluid dynamics (CFD) to explore the magneto-bioconvective phenomenon of hybrid nanofluid within an inclined porous annulus containing oxytactic microorganisms. Initially, we analyze bioconvection dynamics and energy distribution by simulating the nonlinear governing equations. Owing to the involvement of multiple variable parameters, simulation technique demands significant resources and time to determine the optimal parametric values. To surmount this challenge, CFD-ANN-GA method is proposed to identify the optimum parameter values. Leveraging 212 CFD dataset, we developed an artificial neural network (ANN) model, tested with new dataset, and achieved an accuracy of 99.03 % and 94.82 % respectively, for average Nusselt and Sherwood numbers. This highly accurate ANN model's predictions unveiled significant insights that remained elusive from the CFD data. The CFD data suggested that the parameter set: Ra=106,Rb=10,Ha=0,Da=10−1,Φ=45°,Pe=1,Le=1, experience maximum heat dissipation. However, the genetic algorithm (GA) recommended a different parameter set: Ra=106,Rb=10,Ha=0,Da=10−1.5644,Φ=40.67°,Pe=0.100388,Le=1, providing maximum thermal transport. Subsequently, the GA recommended parameter set has been tested through simulation technique and found to exhibit comparatively greater thermal transport. Furthermore, we identified the hierarchy of parameters influence on heat and oxygen mass transport within the enclosure by conducting sensitivity analysis.","2024-12-01","2024-12-03 03:25:02","2024-12-03 03:25:02","","107999","","","159","","International Communications in Heat and Mass Transfer","","","","","","","","","","","","","","","","","","","Artificial intelligence; Sensitivity analysis; Genetic algorithm; Hybrid nanofluid; Oxytactic microorganisms; Porous material","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"5ZRY376Z","journalArticle","2024","Okwu, M.O.; Otanocha, O.B.; Edward, B.A.; Oreko, B.U.; Oyekale, J.; Oyejide, O.J.; Osuji, J.; Maware, C.; Ezekiel, K.; Orikpete, O.F.","Investigating the Accuracy of Artificial Neural Network Models in Predicting Surface Roughness in Drilling Processes","5th International Conference on Industry 4.0 and Smart Manufacturing (ISM 2023)","","1877-0509","10.1016/j.procs.2024.02.020","https://www.sciencedirect.com/science/article/pii/S1877050924001972","In recent times, the application of Artificial Intelligence (AI) has become widespread across various fields, including machining operations like milling, drilling, shaping, turning, grinding, counter drilling, and counter sinking. Among the techniques used in these applications, Artificial Neural Networks (ANN) have gained popularity. This study focuses on utilizing ANN to optimize drilling hardened non-shrinking (OHNS) die steel. The methodology presented in this research involves the implementation of ANN to analyze the impact of drilling process parameters on surface roughness. MATLAB 2023 software was used to effectively train the neural network. The dataset for this study consists of input and output expressions derived from experimental analysis. The input data includes variables such as cutting speed, feedrate, drill size, and depth of cut. The objective of this study is to predict surface roughness based on input datasets, with 70% of the samples allocated as training data and the remaining 30% for testing and validation. The experimental results demonstrate that ANN provides a reliable prediction capability, with Coefficient of Determination (R2) and Mean Square Error (MSE) values of 0.997 and 0.231893, respectively. The low MSE value indicate better model performance, also the R2 value obtained indicate a strong correlation between the predicted and actual values, suggesting that the ANN model provides a satisfactory fit to the data, indicating a high level of confidence in the statistical results. Thus, it can be concluded that the results obtained from the ANN model are statistically significant, slightly superior to the classical model, and exhibit a good fit.","2024-01-01","2024-12-03 03:25:03","2024-12-03 03:25:03","","1982-1990","","","232","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial intelligence (AI); artificial neural networks (ANN); Computational Intelligence (CI); drilling operations; oil hardened non-shrinking (OHNS); Surface Roughness","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"NERCC8K7","journalArticle","2023","Montezuma, Diana; Oliveira, Sara P.; Neto, Pedro C.; Oliveira, Domingos; Monteiro, Ana; Cardoso, Jaime S.; Macedo-Pinto, Isabel","Annotating for Artificial Intelligence Applications in Digital Pathology: A Practical Guide for Pathologists and Researchers","Modern Pathology","","0893-3952","10.1016/j.modpat.2022.100086","https://www.sciencedirect.com/science/article/pii/S0893395222055260","Training machine learning models for artificial intelligence (AI) applications in pathology often requires extensive annotation by human experts, but there is little guidance on the subject. In this work, we aimed to describe our experience and provide a simple, useful, and practical guide addressing annotation strategies for AI development in computational pathology. Annotation methodology will vary significantly depending on the specific study’s objectives, but common difficulties will be present across different settings. We summarize key aspects and issue guiding principles regarding team interaction, ground-truth quality assessment, different annotation types, and available software and hardware options and address common difficulties while annotating. This guide was specifically designed for pathology annotation, intending to help pathologists, other researchers, and AI developers with this process.","2023-04-01","2024-12-03 03:25:03","2024-12-03 03:25:03","","100086","","4","36","","Modern Pathology","","","","","","","","","","","","","","","","","","","artificial intelligence; digital pathology; computational pathology; annotation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"955SB3U6","journalArticle","2023","Zhang, Ben; Ming, Chenxu","A patent portfolio value analysis based on intuitionistic fuzzy sets: An empirical analysis of artificial intelligence for healthcare","Journal of Open Innovation: Technology, Market, and Complexity","","2199-8531","10.1016/j.joitmc.2023.100124","https://www.sciencedirect.com/science/article/pii/S2199853123002263","Evaluating patent assets from the perspective of a patent portfolio is an emerging research direction and also a concrete practical example of open innovation theory, which presents the main form of encouraging more innovation entities to participate and further innovate by disclosing technological innovation information. For a better understanding of the patent portfolio value system at the quantitative level, this paper attempts to explore the relationship between the patent portfolio and the open innovation environment of industrial clusters, thus proposing a dynamic patent value evaluation method. Therefore, through focusing on the field of artificial intelligence for healthcare, this paper implements the evaluation process based on collecting relevant patent data and building an intuitionistic fuzzy set evaluation system. The evaluation results provide a value ranking for the patent portfolio, which helps decision-makers to carry out industrial patent layout planning and implement patent licensing strategies orienting to open innovation.","2023-09-01","2024-12-03 03:25:03","2024-12-03 03:25:03","","100124","","3","9","","Journal of Open Innovation: Technology, Market, and Complexity","","","","","","","","","","","","","","","","","","","Artificial intelligence for healthcare; Intuitionistic fuzzy set; Open innovation environment; Patent portfolio; Patent valuation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"863ETBGQ","journalArticle","2024","Musyaffi, Ayatulloh Michael; Adha, Maulana Amirul; Mukhibad, Hasan; Oli, Mario Colega","Improving students' openness to artificial intelligence through risk awareness and digital literacy: Evidence form a developing country","Social Sciences & Humanities Open","","2590-2911","10.1016/j.ssaho.2024.101168","https://www.sciencedirect.com/science/article/pii/S2590291124003656","The integration of technology, particularly artificial intelligence (AI) in learning process has the potential to enhance learning efficiency by offering diverse perspectives and quick information access. However, this reliance on AI can lead to overdependence, thereby diminishing the overall learning experience. This study aims to explore student acceptance and risk factors for learning using AI through the Technology Acceptance Model (TAM). This cross-sectional study surveyed 218 accounting students using a structured questionnaire. The internal consistency of latent constructs was verified through Cronbach's alpha, followed by exploratory factor analysis to ensure the unidimensionality of these constructs. The theoretical framework was tested using Partial Least Squares - Structural Equation Modeling (PLS-SEM). The result suggests that students with higher self-confidence demonstrate greater enthusiasm for learning AI, while digital literacy significantly influences the perceived ease of use. While accessibility is not prioritized, functionality and information accuracy are deemed more critical. The research model posits that perceived usefulness, perceived ease of use, self-efficacy, digital literacy, and perceived risk impact student acceptance of AI. Perceived usefulness and self-efficacy positively influence student acceptance, while perceived risk has a negative impact. Furthermore, it emphasizes the importance of increasing basic technological literacy among students in online learning environments. Despite these insights, the study is limited by its focus on accounting students, and future research should consider a broader demographic. This study contributes to the existing literature by highlighting the role of digital literacy and self-efficacy in AI adoption, offering valuable implications for educators and AI service providers.","2024-01-01","2024-12-03 03:25:03","2024-12-03 03:25:03","","101168","","","10","","Social Sciences & Humanities Open","","","","","","","","","","","","","","","","","","","Artificial intelligence; Digital literacy; Self-efficacy; Perceived risk; Accounting student","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"GWB69KPI","journalArticle","2024","Aggarwal, Swati; Mittal, Anshul","Futuristic hospitality conceptualized: DASH - Decentralized Autonomous and Smart Hotel system","Journal of Open Innovation: Technology, Market, and Complexity","","2199-8531","10.1016/j.joitmc.2024.100223","https://www.sciencedirect.com/science/article/pii/S2199853124000179","Ubiquitous hospitality has stimulated a rise in expeditions to inspiring getaways and extraordinary destinations. It is imperative for hotels to create a distinct and immersive experience leveraging disruptive technologies like Artificial Intelligence, Machine Learning, Internet of Things, and Blockchain, while ensuring economic affordability for patrons. This study introduces an autonomous and agile smart hotel system, DASH-Decentralized Autonomous and Smart Hotel system, operating on a pay-per-use model, meticulously tracking the usage of amenities and utilities for patrons. The proposed system is an amalgamation of Internet of Things, Robots, Artificial Intelligence, and Blockchain, each enhancing trust, transparency, and underlying operations while reducing workforce and operational costs. This work distinguishes itself by focusing on the complete automation of hotels rather than merely complementing current activities with these technologies. Method: This research hinges on an extensive literature review, deepening the understanding of contemporary hospitality systems, technological advances, and emerging trends. Building on these insights, this work employed conceptual modelling to create a robust framework for the DASH system, strategically integrating decentralization, autonomy, and intelligence. This method bridges visionary ideals with practical implementation, shaping the future of hospitality through cutting-edge technology integration. Main Findings: DASH, as a Decentralized Autonomous and Smart Hotel System, successfully integrates disruptive technologies to create an innovative and automated hospitality experience. The pay-per-use model proves effective in tracking amenity and utility usage, ensuring a cost-efficient and tailored service for patrons. The amalgamation of Internet of Things, Robots, Artificial Intelligence, and Blockchain enhances trust, transparency, and operational efficiency within the hospitality institution. The study's approach of steering towards complete hotel automation distinguishes it from existing literature, showcasing a unique and forward-thinking perspective on the implementation of disruptive technologies in the hospitality sector.","2024-03-01","2024-12-03 03:25:03","2024-12-03 03:25:03","","100223","","1","10","","Journal of Open Innovation: Technology, Market, and Complexity","","","","","","","","","","","","","","","","","","","Internet of Things; Robots; Blockchain; Automation in hospitality; Autonomous hotels; Pay per use model; Smart hotel; Smart tourism","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"3C6K89E9","journalArticle","2024","Zhang, Yongchang; Geng, Panpan","Artificial intelligence based smoke flow mechanism analysis and prediction patterns of fire for large space building","Alexandria Engineering Journal","","1110-0168","10.1016/j.aej.2024.05.061","https://www.sciencedirect.com/science/article/pii/S1110016824005301","The deposition and dynamics of smoke layers in large-space building fires are governed by a complex interplay of factors, making the prediction of such dynamics using traditional mathematical models challenging. In response, this study introduces a Back Propagation (BP) neural network model, devised from field simulation data, to efficiently forecast the temporal progression of smoke layers. It was observed that the model exhibits minimal training errors and high processing speeds, thereby fulfilling the stringent accuracy demands of fire engineering. Specifically, the model achieved a minimum relative error of 0.0005 and a maximum of 0.0845 across various prediction points, underscoring its reliability and precision. The ability of this BP neural network model to predict smoke layer changes significantly enhances the design optimization of smoke control systems swiftly and accurately in large buildings and supports rapid, informed decision-making during fire emergencies. Moreover, the model facilitates the development of engineering calculation models tailored for the quick prediction of fire smoke dynamics, which are essential for both theoretical research and practical applications. This approach not only conserves experimental resources but also advances the implementation of scientific, effective rescue operations in the event of large space building fires.","2024-08-01","2024-12-03 03:25:03","2024-12-03 03:25:03","","322-330","","","100","","Alexandria Engineering Journal","","","","","","","","","","","","","","","","","","","Artificial Intelligence (AI); Artificial Neural Network (ANN); Back propagation neural network; Large-scale building fires; Smoke layer","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"BLTKTA67","journalArticle","2024","Chellappa, Vigneshkumar; Luximon, Yan","Understanding the perception of design students towards ChatGPT","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100281","https://www.sciencedirect.com/science/article/pii/S2666920X24000845","The benefits of artificial intelligence (AI)-enabled language models, such as ChatGPT, have contributed to their growing popularity in education. However, there is currently a lack of evidence regarding the perception of ChatGPT, specifically among design students. This study aimed to understand the product design (PD) and user experience design (UXD) students' views on ChatGPT and focused on an Indian university. The study employed a survey research design, utilizing questionnaires as the primary data collection method. The collected data (n = 149) was analyzed using descriptive statistics (i.e., frequency, percentage, average, and standard deviation (SD). Inferential statistics (i.e., one-way ANOVA) was used to understand the significant differences between the programs of study, gender, and academic level. The findings indicate that the students expressed admiration for the capabilities of ChatGPT and found it to be an interesting and helpful tool for their studies. In addition, the students' motivation towards using ChatGPT was moderate. Furthermore, the study observed significant differences between PD and UXD students and differences based on gender and academic level on certain variables. Notably, UXD students reported that ChatGPT does not understand their questions well, and formulating effective prompts for the tool was more challenging than for PD students. Based on the findings, the study recommends how educators should consider integrating ChatGPT into design education curricula and pedagogical practices. The insights aim to contribute to refining the use of ChatGPT in educational settings and exploring avenues for improving its effectiveness, ultimately advancing the field of AI in design education.","2024-12-01","2024-12-03 03:25:03","2024-12-03 03:25:03","","100281","","","7","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Artificial intelligence; ChatGPT; Design education; Students' perceptions","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"NCI34RVK","journalArticle","2024","Imoniana, Joshua Onome; Cornachionne, Edgard; Reginato, Luciane; Silva, Washington Lopes","Relationships between (Un)known consequences of Artificial Intelligence usage in an organizational or societal context","The 15th International Conference on Ambient Systems, Networks and Technologies Networks (ANT) / The 7th International Conference on Emerging Data and Industry 4.0 (EDI40), April 23-25, 2024, Hasselt University, Belgium","","1877-0509","10.1016/j.procs.2024.06.100","https://www.sciencedirect.com/science/article/pii/S187705092401336X","This study investigates the relationships between (un)known consequences of artificial intelligence (AI) and usage in an organizational or societal context. The study adopts a qualitative approach and toes an interpretive constructivist perspective. Used the resources of AI to construct the data corpus on which content analysis was performed. Additionally, to triangulate we also drawn on interview of six specialists with varied knowledge as consultants, strategists, developers and users, and also data from Digital Week held by PwC. Results show that the consequences of AI are complex and multifaceted thus requiring thorough planning, constitution of GenAI teams among users from business focal points to pose adequate and diverse questions and build consistent algorithms in line with policies to mitigate risks of negative impacts. Results also show that consequences of AI are highly context-dependent and can vary based on the specific application and dataset. The conceptions that have implications on the unknow known consequences of AI are Automation, Improved efficiency, Enhanced decision making, Personalization, Cost reduction, Healthcare advancements, Autonomous decisions, Natural Language Processing (NLP), Predictive Maintenance, Environmental monitoring, Fraud detection and Education & Training. Others are Scientific Research, Customer Services, Accessibility, Agricultural baseline, Security and Entertainment. Overall, to harness the values, addressing the challenges is inevitable to avoid pitfalls by turning GenAI more inclusive through continuous experimentation and maximizing the benefits of AI. AI have left a lasting imprint on our society and will continue to do so as AI technologies advance and become more integrated into various aspects of our lives. This study contributes to the debate on AI and serves as a limelight to the academia, professionals, and policy makers.","2024-01-01","2024-12-03 03:25:03","2024-12-03 03:25:03","","833-840","","","238","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial Intelligence; (Un)known Consequences; IPA-Interpretive Phenomenographic Analysis","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CTN53D3Q","journalArticle","2024","Uzun, Burak; Tekinerdogan, Bedir","Detecting deviations in the code using architecture view-based drift analysis","Computer Standards & Interfaces","","0920-5489","10.1016/j.csi.2023.103774","https://www.sciencedirect.com/science/article/pii/S0920548923000557","Context One of the key requirements for the code is conformance with the architecture. Architectural drift implies the diverging of the implemented code from the architecture design of the system. Manually checking the consistency between the implemented code and architecture can be intractable and cumbersome for large-scale systems. Objective This article proposes a holistic, automated architecture drift analysis approach that explicitly focuses on the adoption of architecture views. The approach builds on, complements, and enhances existing architecture conformance analysis methods that do not adopt a holistic approach or fail to address the architecture viewpoints. Method A model-driven development approach is adopted in which architecture views are represented as specifications of domain-specific languages. The code in its turn, is analyzed, and the architectural view specifications are reconstructed, which are then automatically checked with the corresponding architecture models. Results To illustrate the approach, we have applied a systematic case study research for an architecture drift analysis of the business-to-customer (B2C) system within a large-scale software company. Conclusion The case study research showed that divergences and absences of architectural elements could be detected in a cost-effective manner with the proposed approach.","2024-01-01","2024-12-03 03:25:03","2024-12-03 03:25:03","","103774","","","87","","Computer Standards & Interfaces","","","","","","","","","","","","","","","","","","","Architecture drift; Architecture drift analysis; Model-driven development; Software architecture reconstruction","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"X8PV2W2J","journalArticle","2024","Luttmer, Michael; Weigold, Matthias; Thaler, Heiko; Dongus, Jürgen; Hopf, Anton","Towards data-driven quality monitoring for advanced metal inert gas welding processes in body-in-white","Journal of Manufacturing Systems","","0278-6125","10.1016/j.jmsy.2024.10.013","https://www.sciencedirect.com/science/article/pii/S0278612524002383","In recent years, numerous monitoring approaches have been developed in the field of intelligent welding manufacturing to predict quality-related characteristics using process data and artificial intelligence-based techniques. While most investigations have focused on welding steel with conventional gas metal arc welding processes, the welding of aluminum and its alloys using advanced process variants has been less explored. This work addresses this gap by investigating data-driven methods for fault diagnosis and detection in an advanced metal inert gas welding process commonly used in body-in-white manufacturing. To this end, electrical, acoustic, and spectroscopic signals were recorded from numerous welding tests simulating typical fault causes. Various predictive models, ranging from traditional machine learning algorithms to state-of-the-art deep learning techniques, were trained and evaluated for classifying faulty seams and identifying their root causes. The results demonstrate that combining sensor data enhances the performance of predictive models compared to using individual sensors alone. However, a deep learning approach based solely on electrical signals emerged as the best solution for both use cases, considering both the results and practical aspects. Overall, the experiments highlight the significant potential of data-driven techniques to enhance quality monitoring in advanced MIG welding processes, promoting their more widespread adoption in body-in-white manufacturing.","2024-12-01","2024-12-03 03:25:03","2024-12-03 03:25:03","","875-891","","","77","","Journal of Manufacturing Systems","","","","","","","","","","","","","","","","","","","Deep learning; Machine learning; Intelligent welding manufacturing; Metal inert gas welding; Quality monitoring","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"UTP67C23","journalArticle","2023","Gryncewicz, Wiesława; Zygała, Ryszard; Pilch, Agnieszka","AI in HRM: case study analysis. Preliminary research","27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems (KES 2023)","","1877-0509","10.1016/j.procs.2023.10.226","https://www.sciencedirect.com/science/article/pii/S1877050923013844","The article attempts to identify Artificial Intelligence (AI) algorithms in Human Resources Management (HRM) systems focusing particular attention on candidate selection, career building, and predicting employee attrition. The review examines case studies that demonstrate the benefits of AI in HRM, including enhancing employee engagement and satisfaction, improving recruitment processes, supporting decision-making and predicting employee retention. The research indicates that interpretable algorithms, such as decision trees, are frequently used in HRM solutions. The study emphasizes that AI should be viewed as a tool rather than a replacement for human judgment in HRM. Both the review and article highlight the growing trend of AI in HRM systems and the need for further research in this area to fully understand its impact on HRM practices and outcomes.","2023-01-01","2024-12-03 03:25:03","2024-12-03 03:25:03","","2351-2360","","","225","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial Intelligence Applications; Human Resource Management, Artificial Intelligence, Alghoritms","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"FKHDKLMN","journalArticle","2023","Dwivedi, Rahul; Nerur, Sridhar; Balijepally, Venugopal","Exploring artificial intelligence and big data scholarship in information systems: A citation, bibliographic coupling, and co-word analysis","International Journal of Information Management Data Insights","","2667-0968","10.1016/j.jjimei.2023.100185","https://www.sciencedirect.com/science/article/pii/S2667096823000320","This research explores extant research on artificial intelligence (AI) and big data published in the premier Information Systems (IS) journals over a period of 26 years (1997–2022), and it uses the techniques of citation analysis, bibliographic coupling, and co-word analysis. Citation analysis results reveal IS as the most cited reference discipline, followed by general business, organization science, and marketing. Two major topical clusters have been identified — problem domain-specific AI (e.g., predictive analytics, machine learning algorithms, and text mining) and organizational-specific AI (e.g., big data capabilities, firm performance, agility, and strategy). Co-word analysis revealed a gradual shift of scholarly interest from problem-domain-specific AI toward organizational-specific AI. Using the citation data, the most influential (cited) authors, (cited) articles, journals, institutions, and countries are identified. Gaps in extant research and future research paths are discussed.","2023-11-01","2024-12-03 03:25:03","2024-12-03 03:25:03","","100185","","2","3","","International Journal of Information Management Data Insights","","","","","","","","","","","","","","","","","","","Information systems; Artificial intelligence; Citation analysis; Bibliographic coupling; Big data research; Co-word analysis; Identifying reference disciplines; Intellectual structure; Performance analysis","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"6RC2PWJJ","journalArticle","2024","Niu, Zhi-Bin; Jia, Si-Yuan; Xu, Hong-He","Automated graptolite identification at high taxonomic resolution using residual networks","iScience","","2589-0042","10.1016/j.isci.2023.108549","https://www.sciencedirect.com/science/article/pii/S2589004223026263","Summary Graptolites, fossils significant for evolutionary studies and shale gas exploration, are traditionally identified visually by taxonomists due to their intricate morphologies and preservation challenges. Artificial intelligence (AI) holds great promise for transforming such meticulous tasks. In this paper, we demonstrate that graptolites can be identified with taxonomist accuracy using a deep learning model. We construct the most sophisticated and largest professional single organisms image dataset to date, which is composed of >34,000 images of 113 graptolite species annotated at pixel-level resolution to train the model, develop, and evaluate deep learning networks to classify graptolites. The model’s performance surpassed taxonomists in accuracy, time, and generalization, achieving 86% and 81% accuracy in identifying graptolite genus and species, respectively. This AI-based method, capable of recognizing minute morphological details better than taxonomists, can be integrated into web and mobile apps, extending graptolite identification beyond research institutes and enhancing shale gas exploration efficiency.","2024-01-19","2024-12-03 03:25:03","2024-12-03 03:25:03","","108549","","1","27","","iScience","","","","","","","","","","","","","","","","","","","Artificial intelligence; Earth sciences; Geology; Paleontology","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"9SZ7BZDI","journalArticle","2024","Anifowose, Babatunde; Anifowose, Fatai","Artificial intelligence and machine learning in environmental impact prediction for soil pollution management – case for EIA process","Environmental Advances","","2666-7657","10.1016/j.envadv.2024.100554","https://www.sciencedirect.com/science/article/pii/S2666765724000723","Scientific predictions are a key component of Environmental Impact Assessments (EIA), which can indicate the level of change within an environmental sphere (e.g., soil). As part of the EIA process, decision-making in mitigating complex environmental problems such as maintaining soil quality can be challenging, especially in data-sparse locations. Artificial Intelligence (AI) can ameliorate but the literature suggests that the deployment of Machine Learning (ML) techniques in soil research is concentrated mostly in developed countries. The potential of ML in managing soil pollution from complex mixture of heavy metals, petroleum hydrocarbons, and physicochemical factors is rarely explored. To address this research gap, we built robust models that increase the accuracy of impact prediction based on new experimental soil data from a data-sparse region of Africa (i.e., Nigeria). The algorithms applied are artificial neural networks (ANN), support vector regression (SVR), regression tree (RT), and random forest (RF). The study also implemented a multivariate linear regression (MLR) model as a baseline. Key findings include (a) the MLR model performed less than the machine learning models largely due to the nonlinearity of data; (b) Log-normalization helped to improve the predictive capability of all models as the effects of statistical variability were removed; (c) the RF model had the best performance in terms of correlation coefficient, mean absolute error, and root mean square error, and (d) the machine learning models showed improved performance with increased correlation and lower error between the actual and predicted soil electrical conductivity values. Our results imply that data sparsity may no longer be an excuse for the non-use of quantitative impact prediction in Environmental Impact Assessment (EIA) processes. This could change how EIAs are conducted and enhance sustainability in natural resource exploitation, globally. Future work will apply algorithms for automated feature selection to obtain optimal subset of soil quality measurements that will further improve the accuracy of the models.","2024-10-01","2024-12-03 03:25:03","2024-12-03 03:25:03","","100554","","","17","","Environmental Advances","","","","","","","","","","","","","","","","","","","Sustainability; Artificial Intelligence; Machine Learning; Environmental Data Science; Environmental Impact Assessment; Multivariate Linear Regression; Soil Quality","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"PNIZJAMS","journalArticle","2023","Mall, Pawan Kumar; Singh, Pradeep Kumar; Srivastav, Swapnita; Narayan, Vipul; Paprzycki, Marcin; Jaworska, Tatiana; Ganzha, Maria","A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities","Healthcare Analytics","","2772-4425","10.1016/j.health.2023.100216","https://www.sciencedirect.com/science/article/pii/S2772442523000837","Artificial Intelligence (AI) solutions have been widely used in healthcare, and recent developments in deep neural networks have contributed to significant advances in medical image processing. Much ongoing research is aimed at helping medical practitioners by providing automated systems to analyze images and diagnose acute diseases, such as brain tumors, bone cancer, breast cancer, bone fracture, and many others. This comprehensive review delivers an overview of recent advances in medical imaging using deep neural networks. In addition to the comprehensive literature review, a summary of openly available data sources and future research directions are outlined.","2023-12-01","2024-12-03 03:25:04","2024-12-03 03:25:04","","100216","","","4","","Healthcare Analytics","","","","","","","","","","","","","","","","","","","Machine learning; Artificial intelligence; Predictive analytics; Deep neural networks; Medical imaging diagnostic analytics","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"WGRHW8TY","journalArticle","2024","Zang, Yue; Qu, Mo; Pham, Duc Truong; Dixon, Roger; Goli, Farzaneh; Zhang, Yongquan; Wang, Yongjing","Robotic disassembly of electric vehicle batteries: Technologies and opportunities","Computers & Industrial Engineering","","0360-8352","10.1016/j.cie.2024.110727","https://www.sciencedirect.com/science/article/pii/S0360835224008490","The demand for electric vehicle (EV) battery services, such as repair, remanufacturing, and recycling, is rising as more EVs enter the market. Disassembly is an essential step in these services, but it is usually done manually, which is slow and costly. Therefore, robots have been proposed to disassemble batteries. This work provides an extensive overview of existing technologies in EV battery disassembly and potential robotics. The technologies investigated in this paper focus on how robots can enhance automation and flexibility. This paper summarises the characteristics and applicability of the potential technologies and highlights promising research directions to stimulate further exploration by researchers.","2024-12-01","2024-12-03 03:25:04","2024-12-03 03:25:04","","110727","","","198","","Computers & Industrial Engineering","","","","","","","","","","","","","","","","","","","Sustainability; Artificial intelligence; Electric vehicle battery; Remanufacture; Robotic disassembly","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RIA397YL","journalArticle","2023","Greeda, J; Vinoba, V.","Agent-based Fuzzy Hamiltonian Graph Systems with Artificial Intelligence in the Private Region","3rd International Conference on Evolutionary Computing and Mobile Sustainable Networks (ICECMSN 2023)","","1877-0509","10.1016/j.procs.2023.12.116","https://www.sciencedirect.com/science/article/pii/S187705092302121X","In the form of a Hamiltonian graph, there is a Hamiltonian cycle. A fuzzy Hamiltonian cycle is one that departs from each of the vertices or nodes precisely once. A lot of research is being done to figure out how many fuzzy Hamiltonian cycles a fuzzy Hamiltonian graph has it becomes more difficult to recall all the many ways that the edges and vertices have been linked as the total number of vertex and edges grows for several capacities, artificial intelligent are going to complement or increase social facilities. The method is indeed simple to quickly determine whether an edge links the two vertexes when graphs are represented by adjacency matrices. This work proposes a new approach for detecting fuzzy Hamilton cycles with Artificial Intelligence (AI) as well as the level that defines the vertices outside a graph g. In order to show the algorithms utilizing the selected travel system of fuzzy graphs with Artificial Intelligence structure have been generated as well.","2023-01-01","2024-12-03 03:25:04","2024-12-03 03:25:04","","608-619","","","230","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial Intelligence; fuzzy Hamiltonian; states of travel system","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"FKASSRSY","journalArticle","2024","Wilendra, Wendra; Nadlifatin, Reny; Kusumawulan, Cahya Khairani","ChatGPT: The AI Game-Changing Revolution in Marketing Strategy for the Indonesian Cosmetic Industry","Seventh Information Systems International Conference (ISICO 2023)","","1877-0509","10.1016/j.procs.2024.03.091","https://www.sciencedirect.com/science/article/pii/S1877050924004496","The Indonesian cosmetic industry has experienced rapid growth in recent years, requiring the adoption of innovative marketing strategies to remain competitive. This article explores the potential of ChatGPT, an advanced AI language model, in revolutionizing marketing strategy within the Indonesian cosmetic industry. Employing a qualitative research approach and thematic analysis, this study investigates AI-driven marketing strategies, product development, personalization in marketing, ethical considerations, and future research directions. The findings suggest that ChatGPT can significantly enhance customer targeting, market segmentation, pricing strategies, and promotion, as well as contribute to product development and personalized marketing efforts. However, ethical concerns and future research directions warrant further exploration in this rapidly evolving field.","2024-01-01","2024-12-03 03:25:04","2024-12-03 03:25:04","","1012-1019","","","234","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","ChatGPT; Artificial Intelligence; Indonesian Cosmetic Industry; Marketing Intelligence; Marketing Strategy","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"6BBLKF9I","journalArticle","2023","Kinder, T.; Stenvall, J.; Koskimies, E.; Webb, H.; Janenova, S.","Local public services and the ethical deployment of artificial intelligence","Government Information Quarterly","","0740-624X","10.1016/j.giq.2023.101865","https://www.sciencedirect.com/science/article/pii/S0740624X23000655","Responding to growing criticism that the use of artificial intelligence in public services reinforces unethical activities such as discrimination, the paper presents two new cases from the cities in Finland, both self-describing as centres for the ethical use of AI. Structured by an ethical AI foresighting framework we explore how and why AI is being used in local public services and its outcomes, the degree to which current AI-enabled public services are ethically evaluated and whether ethical evaluation features in trends for future AI use. The research objectives are to demonstrate how AI is being deployed in cities claiming to be European centres for ethical AI use, to innovate new service models and to present a new framework, based on social learning to help analysis of ethics in AI-related innovation processes, in particular those enhancing accountability to citizens. In doing so, we show in practical terms how ethical decision-making processes are identified and responded to addressing explainability and understandability issues. We suggest that negative ethical results from AI use can be avoided, however this requires an ethos of citizen involvement in innovation processes and significant investment in times and attention to distribute learning and opinions between providers, technical partners and service users include an acknowledgment that technical partners learn from users as well as users learning from technical partners.","2023-10-01","2024-12-03 03:25:04","2024-12-03 03:25:04","","101865","","4","40","","Government Information Quarterly","","","","","","","","","","","","","","","","","","","Artificial intelligence; Local Goverment; Public services","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"FFIS8I53","journalArticle","2024","Jia, Jianxin; Zheng, Xiaorou; Wang, Yueming; Chen, Yuwei; Karjalainen, Mika; Dong, Shoubin; Lu, Runuo; Wang, Jianyu; Hyyppä, Juha","The effect of artificial intelligence evolving on hyperspectral imagery with different signal-to-noise ratio, spectral and spatial resolutions","Remote Sensing of Environment","","0034-4257","10.1016/j.rse.2024.114291","https://www.sciencedirect.com/science/article/pii/S0034425724003092","Hyperspectral images are increasingly being used in classification and identification. Data users prefer hyperspectral imagery with high spatial resolution, finer spectral resolution, and high signal-to-noise ratio (SNR). However, tradeoffs exist in these core parameters in imagery acquired by different hyperspectral sensor systems. Data users may find it difficult to utilize all the advantages of hyperspectral imagery. How to select hyperspectral data with optimal parameter configuration has been one of the essential issues for data users, which also affects the back-end applications. With advancements in computer science, various artificial intelligence algorithms from conventional machine learning to deep learning have been utilized for hyperspectral images classification and identification. Few researchers study the mechanism between the core parameters of hyperspectral imaging spectrometers and advanced artificial intelligence algorithms, which affects the application efficiency and accuracy. In this paper, we delved into the evolution of machine learning and deep learning models applied to imagery acquired by different hyperspectral sensor systems having different SNR, spectral, and spatial resolutions. Additionally, we also considered the tradeoffs among the core parameters of hyperspectral imagers. We used two conventional machine learning models, including the classification and regression tree (CART) and random forest (RF), two deep learning methods based on convolution neural network architectures—3D convolutional neural network (3D-CNN) and hamida, and two deep learning methods based on vision transformers architectures—transformer models vision transformer (VIT) and robust vision transformer (RVT), to compare the characteristics of different algorithms. In addition, five hyperspectral datasets with different species categories and scene distributions and aggregated datasets with different spatial resolutions, spectral resolutions, and SNRs were used to validate our study. The experimental results indicate that: (1) The overall accuracy (OA) using CART, RF, 3D-CNN, and VIT models decreased with coarser spectral resolution, but almost remained unchanged using the RVT classifier. The number of class and classification species affect the results. (2) The influence of spatial resolution on classification accuracy is related to the scene complexity, target size, and classification purpose. The coarser spatial resolution can achieve higher OA than the original spatial resolution for the uniform scene distribution. For the datasets with small objects and intersections of different species, OA first increased, plateaued, and then decreased with coarser spatial resolution. (3) The SNR has an obvious impact on OA for the CART and RF classifiers, and the impact decreased for deep learning models, especially for the VIT and RVT models, which were almost unaffected by SNR. Additionally, slight variations in experimental results were observed for datasets with different scene distributions and categories. Furthermore, we conducted a detailed analysis of the role of traditional machine learning and deep learning models in the experimental outcomes. The study can provide insights into understanding the relationship between the core parameters of hyperspectral imager and the artificial intelligence algorithms used for hyperspectral classification. It serves to bridge the knowledge gap between the front-end hyperspectral imager, mid-end model, and back-end applications, and further promote the development of hyperspectral imaging technology.","2024-09-01","2024-12-03 03:25:04","2024-12-03 03:25:04","","114291","","","311","","Remote Sensing of Environment","","","","","","","","","","","","","","","","","","","Artificial intelligence; Core parameters; Hyperspectral classification; Hyperspectral imaging spectrometer; Tradeoff","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"IZ75IMSA","journalArticle","2024","Li, Haiyang; Gui, Xinjing; Wang, Panpan; Yue, Yousong; Li, Han; Fan, Xuehua; Li, Xuelin; Liu, Ruixin","Research on rapid quality identification method of Panax notoginseng powder based on artificial intelligence sensory technology and multi-source information fusion technology","Food Chemistry","","0308-8146","10.1016/j.foodchem.2023.138210","https://www.sciencedirect.com/science/article/pii/S0308814623028285","Panax notoginseng powder (PNP) has high medicinal value and is widely used in the medical and health food industries. However, the adulteration of PNP in the market has dramatically reduced its efficacy. Therefore, this study intends to use artificial intelligence sensory (AIS) and multi-source information fusion (MIF) technology to try to establish a quality evaluation system for different grades of PNP and adulterated Panax notoginseng powder (AD-PNP). The highest accuracy rate reached 100% in identifying PNP grade and adulteration. In the prediction of adulteration ratio and total saponin content, the optimal determination coefficients of the test set were 0.9965 and 0.9948, respectively, and the root mean square errors were 0.0109 and 0.0123, respectively. Therefore, the grade identification method of PNP and the evaluation system of AD-PNP based on AIS and MIF technology can rapidly and accurately evaluate the quality of PNP.","2024-05-15","2024-12-03 03:25:04","2024-12-03 03:25:04","","138210","","","440","","Food Chemistry","","","","","","","","","","","","","","","","","","","Adulteration; Artificial intelligence sensory technology; Fast quality identification; Health food; Multi-source information fusion technology; powder","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"WN4MTELR","journalArticle","2023","Hu, Jincheng; Li, Jihao; Hou, Zhuoran; Jiang, Jingjing; Liu, Cunjia; Chu, Liang; Huang, Yanjun; Zhang, Yuanjian","Potential auto-driving threat: Universal rain-removal attack","iScience","","2589-0042","10.1016/j.isci.2023.107393","https://www.sciencedirect.com/science/article/pii/S2589004223014700","Summary Severe weather conditions pose a significant challenge for computer vision algorithms in autonomous driving applications, particularly regarding robustness. Image rain-removal algorithms have emerged as a potential solution by leveraging the power of neural networks to restore rain-free backgrounds in images. However, existing research overlooks the vulnerability concerns in neural networks, which exposes a potential threat to the intelligent perception of autonomous vehicles in rainy conditions. This paper proposes a universal rain-removal attack (URA) that exploits the vulnerability of image rain-removal algorithms. By generating a non-additive spatial perturbation, URA significantly diminishes scene restoration similarity and image quality. The imperceptible and generic perturbation employed by URA makes it a crucial tool for vulnerability detection in image rain-removal algorithms and a potential real-world AI attack method. Experimental results demonstrate that URA can reduce scene repair capability by 39.5% and image generation quality by 26.4%, effectively targeting state-of-the-art rain-removal algorithms.","2023-09-15","2024-12-03 03:25:04","2024-12-03 03:25:04","","107393","","9","26","","iScience","","","","","","","","","","","","","","","","","","","Computer science; Artificial intelligence; Artificial intelligence applications","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"S6FZYIRA","journalArticle","2024","Islam, Md. Touhidul; Hasan, Md. Mahadi; Redwanuzzaman, Md.; Hossain, Md. Kamal","Practices of artificial intelligence to improve the business in Bangladesh","Social Sciences & Humanities Open","","2590-2911","10.1016/j.ssaho.2023.100766","https://www.sciencedirect.com/science/article/pii/S2590291123003716","This research paper explores the practices of artificial intelligence (AI) and its impact on improving businesses in Bangladesh through the review of existing literature and primary data analysis. Additionally, a conceptual analysis has been conducted on key AI concepts and their potential applications in the Bangladeshi business context. The study's objectives include providing a detailed understanding of specific AI technologies, measuring the benefits of AI, assessing the challenges faced by the industry in implementing AI and finding probable solutions to overcome these challenges. Data were collected from 120 respondents from 10 types of businesses in Bangladesh and analyzed using MS Excel and SPSS (V. 25). According to the results, using AI in enterprises may have a significant positive impact on efficiency, decision-making, production, cost, fraud detection, and supply chain optimization. However, obstacles to AI deployment include a lack of qualified personnel, poor data quality, money, infrastructure, and legal frameworks. Businesses should raise employee understanding of AI, look for diversified financing and qualified personnel, work with the government on infrastructure support and legislation, address concerns about job displacement through training, and encourage employee acceptance of change in order to overcome these challenges. Businesses in Bangladesh may improve operations and competitiveness by using these techniques. Business executives, decision-makers, and academics interested in maximizing the potential of AI and enhancing business outcomes in Bangladesh might learn from this study.","2024-01-01","2024-12-03 03:25:04","2024-12-03 03:25:04","","100766","","","9","","Social Sciences & Humanities Open","","","","","","","","","","","","","","","","","","","Survey; Artificial intelligence; Bangladeshi business; Non-probabilistic convenience sampling","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"U9QAELAK","journalArticle","2024","Hartmann, Stefan; Brock, Jonathan; Kühn, Arno; Dumitrescu, Roman","Applying Artificial Intelligence in the Smart Factory: Lessons Learned from real-world use cases","57th CIRP Conference on Manufacturing Systems 2024 (CMS 2024)","","2212-8271","10.1016/j.procir.2024.10.062","https://www.sciencedirect.com/science/article/pii/S2212827124012150","The smart factory is a key concept of Industry 4.0, in which the manufacturing system is fully connected. This connection results in a large amount of data being generated. Artificial intelligence (AI) aims to create machines that are as intelligent as humans. Nowadays, machine learning is the most sophisticated approach within AI, essentially describing the possibility to find patterns in large amounts of data to, for example, predict machine failure. Thus, the potentials of AI in smart factories are plentiful and are frequently reported on in literature. However, organizations who want to deploy AI in their real-world smart factories face multiple challenges such as identifying the relevant data, missing management support, or headwinds from the workforce. In this paper, we report on the lessons learned from applying AI in the smart factory in over 50 real-world use cases. We conduct a research world café and 13 interviews to consolidate eleven lessons learned. We structure these lessons learned based on the common conception of people, technology, and organization. Our findings allow the research community to reflect on possible future research directions, and allow practitioners to avoid pitfalls when conducting AI projects in smart factories.","2024-01-01","2024-12-03 03:25:04","2024-12-03 03:25:04","","100-105","","","130","","Procedia CIRP","","","","","","","","","","","","","","","","","","","Industry 4.0; Data Analyitcs; Lessons Learned; Real-World; Smart Factory","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"BKRLSI3J","journalArticle","2024","Almutairi, Taghreed H.; Olatunji, Sunday O.","The utilization of AI in healthcare to predict no-shows for dental appointments: A case study conducted in Saudi Arabia","Informatics in Medicine Unlocked","","2352-9148","10.1016/j.imu.2024.101472","https://www.sciencedirect.com/science/article/pii/S2352914824000285","Artificial Intelligence (AI) refers to the development of computer systems that can perform tasks that typically require human intelligence. The utilization of AI in healthcare, particularly in dental clinics, has drawn attention to the issue of appointment no-shows. These no-shows have detrimental effects such as increased waiting times, limited-service access, and financial burden on healthcare providers. Therefore, optimizing the organization of dental clinics is crucial to effectively cater to a diverse patient population with varying dental needs, especially considering the projected rise in demand for dental care. To address the problem of appointment no-shows, the researchers proposed a programming model that harnesses machine learning algorithms. Three specific algorithms, namely Decision Trees, Random Forest, and Multilayer Perceptron, were employed, with the Multilayer Perceptron being used for the first time in this particular context. The researchers collected a dataset from five dental facilities specializing in nine areas and employed Explainable AI techniques to gain insights into the factors contributing to patient absences. The model's performance was evaluated using multiple metrics. The Decision Tree model exhibited favorable accuracy, achieving 79% precision, 94% recall, 86% F1-Score, and 84% AUC (Area Under the Curve). The Random Forest model demonstrated even higher accuracy, with 81% precision, 93% recall, 87% F1-Score, and 83% AUC. Similarly, the Multilayer Perceptron model attained an accuracy of 80% precision, 91% recall, 86% F1-Score, and 83% AUC.","2024-01-01","2024-12-03 03:25:04","2024-12-03 03:25:04","","101472","","","46","","Informatics in Medicine Unlocked","","","","","","","","","","","","","","","","","","","Patients; Artificial intelligence; Healthcare; Appointments; Dental; No-shows","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DILQAC9I","journalArticle","2024","Rodas-Trejo, Jenner; Ocampo-González, Paola","Assessment of ChatGPT's potential as an innovative tool in searching for information on wild mammals","Ecological Informatics","","1574-9541","10.1016/j.ecoinf.2024.102810","https://www.sciencedirect.com/science/article/pii/S1574954124003522","In November 2022 OpenAI launched ChatGPT, an Artificial Intelligence model capable of processing, retrieving, and organizing large amounts of data and identifying patterns, thereby generating text on various topics and contexts. In recent months ChatGPT has gained wide attention and adoption in the academic and scientific fields, so its use is being widely evaluated and discussed. In this connection, an evaluation of the performance of ChatGPT was carried out on the accuracy of the answers on general knowledge about wild mammals and the specific knowledge of 30 species. A descriptive study was carried out using three chats where twenty-one questions on general knowledge and three questions on specific knowledge were asked. The questions considered information on the taxonomy, natural history, and conservation status of each species, as well as concepts and indices commonly used in the study of wild mammals. The answers were compared with scientific literature and a value was assigned to later obtain the percentage of precision. The results showed a high precision in the specific knowledge of the species, with an average of 88 % correct answers. Precision varied by species, with species scoring close to 100 % and others scoring as low as 65 %. The taxonomy question had 100 % correct answers, the natural history questions 90 %, and the conservation status question 56 %. In the precision of the general knowledge answers in the study of wild mammals, a moderate precision of 73.54 % was obtained. The study shows that ChatGPT has high precision, so it can be a helpful tool in the search for information in research on wild mammals. On the other hand, concerns are raised about its applicability in the academic field, due to the risk of producing unreliable or biased results and generating inaccurate or misleading content, so it is important to take into account the limitations and risks associated with its use. It is suggested that further research and insight into accuracy be done to explore the full potential of ChatGPT.","2024-11-01","2024-12-03 03:25:04","2024-12-03 03:25:04","","102810","","","83","","Ecological Informatics","","","","","","","","","","","","","","","","","","","Artificial intelligence; Chatbots; Biodiversity; Wild mammals","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"8RN92EY5","journalArticle","2023","Kaufmann, Esther; Chacon, Alvaro; Kausel, Edgar E.; Herrera, Nicolas; Reyes, Tomas","Task-specific algorithm advice acceptance: A review and directions for future research","Special Issue on Human-AI Interaction","","2543-9251","10.1016/j.dim.2023.100040","https://www.sciencedirect.com/science/article/pii/S2543925123000141","Due to digitalization resulting in artificial intelligence advice, there are increasing studies on advice taking, exploring individual and task-relevant factors associated with the acceptance of algorithm advice. However, to our notice, there are no reviews of studies on the acceptance of algorithm advice that focus explicitly on a task level that consider methodological features and provide a quantitative measure of algorithm acceptance. Our review closes these research gaps. We evaluated 44 studies, 122 tasks, and 89,751 participants. Our review shows that algorithm aversion is present in 75% of the 122 considered tasks. In addition, our quantified measures underscore some shortcomings by the underrepresented individual, task, or methodological characteristics—for example, the expertise of advice takers and longitudinal studies. Finally, we provide valuable recommendations to continue research on algorithm acceptance.","2023-09-01","2024-12-03 03:25:04","2024-12-03 03:25:04","","100040","","3","7","","Data and Information Management","","","","","","","","","","","","","","","","","","","Artificial intelligence; Decision-making; Review; Advice-taking; Algorithm appreciation; Algorithm aversion; Tasks","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"7XTQGEQX","journalArticle","2024","Rjiba, Ameny; Belcadhi, Lilia Cheniti; Kasperiuniene, Judita","Ontological model for intelligent assessment in collaborative environment based on serious games","28th International Conference on Knowledge Based and Intelligent information and Engineering Systems (KES 2024)","","1877-0509","10.1016/j.procs.2024.09.719","https://www.sciencedirect.com/science/article/pii/S1877050924027881","In today’s collaborative learning environments, effective assessment plays an important role in assessing knowledge acquisition and fostering skill development. The integration of serious games adds an interactive and engaging dimension to these environments, offering opportunities for immersive learning experiences. This combination not only evaluates learners’ progress but also enhances their engagement and motivation, contributing to more effective educational outcomes. This research proposes an innovative ontological model specifically designed for intelligent assessment scenarios with serious games in collaborative environments. Our research aims to enhance learning experiences and skill development by integrating artificial intelligence, education, and serious games. The suggested ontological model incorporates stealthy assessment methods, personalization, and adaptivity to accurately represent the dynamics of collaborative serious gaming. Our approach fills in the gaps in the research by integrating personalized instruction, stealth assessment, and adaptive gameplay in a collaborative environment. We validate the model to make sure it is accurate and consistent and to make sure there are no logical conflicts. This work creates paths for future research, focusing on intelligent assessment and collaborative learning within the context of educational serious games.","2024-01-01","2024-12-03 03:25:04","2024-12-03 03:25:04","","3158-3167","","","246","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial Intelligence; Personalization; Adaptivity; Collaborative Learning; Intelligent Assessment; Ontological Model; Serious Games; Stealth Assessment","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"F7GXL8EN","journalArticle","2023","Liu, Xiaojun","Design of Wireless Communication Base Station Monitoring System Based on Artificial Intelligence and Network Security","3rd International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy","","1877-0509","10.1016/j.procs.2023.11.097","https://www.sciencedirect.com/science/article/pii/S1877050923019282","With the rapid popularization of the network, under the increasingly complex network security situation and the increasingly prominent network security problems, network security occupies an important field in the wireless communication base station monitoring system, and has become a hot research direction. It is to design a wireless communication base station monitoring system based on artificial intelligence and network security. In the experiment, using the supervised machine learning algorithm, the program of the wireless communication base station monitoring system is designed by setting the working frequency of the GSM-based wireless communication system to the wireless communication base station monitoring system.","2023-01-01","2024-12-03 03:25:04","2024-12-03 03:25:04","","1254-1261","","","228","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial Intelligence; Monitoring System; Network Security; Wireless Communication Base Station","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"T6ZKKYPA","journalArticle","2024","Haq, Mahmood Ul; Sethi, Muhammad Athar Javed; Aoun, Najib Ben; Alluhaidan, Ala Saleh; Ahmad, Sadique; farid, Zahid","CapsNet-FR: Capsule Networks for Improved Recognition of Facial Features","Computers, Materials and Continua","","1546-2218","10.32604/cmc.2024.049645","https://www.sciencedirect.com/science/article/pii/S1546221824002662","Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security, authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neural networks (CNNs), have shown promising results in the field of FR. However CNNs are easily fooled since they do not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networks as a more robust design capable of retaining pose information and spatial correlations to recognize objects more like the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, and so on, which are routed to higher-level capsules via a new routing by agreement algorithm. This provides capsule networks with viewpoint invariance, which has previously evaded CNNs. This research presents a FR model based on capsule networks that was tested using the LFW dataset, COMSATS face dataset, and own acquired photos using cameras measuring 128 × 128 pixels, 40 × 40 pixels, and 30 × 30 pixels. The trained model outperforms state-of-the-art algorithms, achieving 95.82% test accuracy and performing well on unseen faces that have been blurred or rotated. Additionally, the suggested model outperformed the recently released approaches on the COMSATS face dataset, achieving a high accuracy of 92.47%. Based on the results of this research as well as previous results, capsule networks perform better than deeper CNNs on unobserved altered data because of their special equivariance properties.","2024-05-15","2024-12-03 03:25:05","2024-12-03 03:25:05","","2169-2186","","2","79","","Computers, Materials and Continua","","","","","","","","","","","","","","","","","","","artificial intelligence; CapsNet; face recognition","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"BRNBYYJC","journalArticle","2024","Pesqueira, Antonio; de Bem Machado, Andreia; Bolog, Sama; Pereira, Rúben; Sousa, Maria José","Exploring the impact of EU tendering operations on future AI governance and standards in pharmaceuticals","Computers & Industrial Engineering","","0360-8352","10.1016/j.cie.2024.110655","https://www.sciencedirect.com/science/article/pii/S0360835224007770","This research examines the incorporation of artificial intelligence (AI) into the domain of tender management (TM) within the pharmaceutical industry, with a particular emphasis on operational efficiency, governance, and compliance with European regulatory standards. A comparative analysis of four companies—two that have adopted AI and two that have not—reveals significant discrepancies in the management of TM processes between AI-driven and traditional companies. The study employs the Delphi method to ascertain expert consensus on eight critical areas of AI governance, including data privacy, transparency, and ethical AI use. The findings indicate that companies integrating AI demonstrate enhanced decision-making capabilities, accelerated processing times, and enhanced stakeholder engagement. However, they also encounter challenges pertaining to ethical governance and regulatory compliance. The research highlights the necessity of aligning the adoption of AI with the latest European directives, such as the AI Act and General Data Protection Regulation (GDPR), to ensure both operational efficiency and adherence to ethical standards. The broader implications of the study underscore the necessity for pharmaceutical companies to develop robust governance frameworks, prioritize ethical considerations, and maintain regulatory compliance to fully leverage the potential of AI. Additionally, the study contributes to the ongoing scholarly discourse by providing empirical evidence on the interplay between AI, ethics, and governance, thereby encouraging further interdisciplinary research. This work emphasizes the critical role of strategic AI adoption in maintaining competitive advantage while safeguarding societal trust and adhering to legal requirements.","2024-12-01","2024-12-03 03:25:05","2024-12-03 03:25:05","","110655","","","198","","Computers & Industrial Engineering","","","","","","","","","","","","","","","","","","","Ethics; Artificial Intelligence (AI); Governance; Operational Efficiency; Pharmaceutical Industry; Tender Management (TM)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"A5BLK5RJ","journalArticle","2023","Duan, Siyu; Zhao, Yang","Knowledge graph analysis of artificial intelligence application research in nursing field based on visualization technology","Alexandria Engineering Journal","","1110-0168","10.1016/j.aej.2023.06.072","https://www.sciencedirect.com/science/article/pii/S1110016823005471","In order to assess the research status, hot issues of artificial intelligence in nursing to provide reference for scholars. This work conduct a quantitative analysis on related literature in WOS from 2011 to 2022 by mathematical and statistical methods, including publication trend, journals, author, institution, national and regional, keyword, literature co-citation. The hotspots and trends were revealed. The results showed that: (1) scholars' attention to it showed a steady increasing. (2) There were 20 journals published more than 6 papers, published 205 papers, that's 30.83 % of the total, Journal of Healthcare Engineering published the most papers, that's 8.78 %. (2) Kendrick Cato, Lisiane Pruinelli published the most papers, that's 2.95 %, which have strong cooperation. (3) Harvard Medical School, Columbia University and University of Minnesota published 114 papers, that's 8.21 % of the total, were the core unit, Harvard Med Sch, Columbia Univ and Univ Penn had strong cooperation. (4) The United States, China, Japan, Australia and United Kingdom are the top five countries for publishing papers, that's 80.3 % of the total. The cooperation degree of the each are 0.51, 0.36, 0.07, 0.04, and 0.06. (6) “electronic health record”, “risk prediction” and “supervised machine learning” are current research hotspots.","2023-08-01","2024-12-03 03:25:05","2024-12-03 03:25:05","","651-667","","","76","","Alexandria Engineering Journal","","","","","","","","","","","","","","","","","","","Bibliometrics; Nursing; Artificial intelligence; CiteSpace; Map of knowledge; Visual analysis","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"IDXMGAMU","journalArticle","2024","Tabuenca, Bernardo; Uche-Soria, Manuel; Greller, Wolfgang; Hernández-Leo, Davinia; Balcells-Falgueras, Paula; Gloor, Peter; Garbajosa, Juan","Greening smart learning environments with Artificial Intelligence of Things","Internet of Things","","2542-6605","10.1016/j.iot.2023.101051","https://www.sciencedirect.com/science/article/pii/S2542660523003748","This article investigates the functionality and applications of an Artificial Intelligence of Things (AIoT) system specifically designed for learning purposes. It presents three compelling case studies that pilot the AIoT system in various educational contexts. The first case study focuses on primary education and the use of a smart dashboard to monitor the state of plants in environmental awareness activities. In the second case study, conducted in higher education, variables such as CO2 levels, light intensity, and temperature are monitored to generate personalised recommendations for creating an optimal learning environment through tailored adjustments. The third case study explores the potential of plants to identify human presence and activity patterns in learning environments. By utilising the AIoT system’s capabilities, plant data is analysed to infer human presence and interactions. This innovative approach offers insights into understanding student behaviour and optimising learning environments based on real-time feedback from the plant ecosystem. Analysing these studies, the article deliberates on implications and future research opportunities in the realm of AI and IoT. It underscores the potential of AIoT systems in enhancing learning experiences, engaging students, and refining educational settings. The findings not only pave the way for future investigations, including model enhancements and privacy considerations but also emphasise AIoT’s potential in reshaping the educational landscape. This article serves as a valuable resource for researchers and practitioners keen on leveraging the synergy of AI and IoT in educational contexts.","2024-04-01","2024-12-03 03:25:05","2024-12-03 03:25:05","","101051","","","25","","Internet of Things","","","","","","","","","","","","","","","","","","","Internet of Things; Artificial Intelligence; Avatar; Predictive models; Environmental education; Learning activities; Plant biosensors; Smart Learning Environments","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"PI2M8W2T","journalArticle","2024","Peretz-Andersson, Einav; Tabares, Sabrina; Mikalef, Patrick; Parida, Vinit","Artificial intelligence implementation in manufacturing SMEs: A resource orchestration approach","International Journal of Information Management","","0268-4012","10.1016/j.ijinfomgt.2024.102781","https://www.sciencedirect.com/science/article/pii/S026840122400029X","Artificial intelligence (AI) is playing a leading role in the digital transformation of enterprises, particularly in the manufacturing industry where it has been responsible for a profound transformation in key business and production operations. Despite the accelerated growth of AI technologies, knowledge of the implementation of AI by small and medium-sized enterprises (SMEs) remains underexplored. Thus, this study seeks to examine how manufacturing SMEs orchestrate resources for AI implementation. Building on the resource orchestration (RO) theory and recent work on AI implementation, we investigate multiple case studies involving manufacturing SMEs in Sweden operating in the packaging, plastic, and metal sectors. Our findings indicate that SMEs structure a portfolio based on acquiring and accumulating AI resources. AI resources are bundled into learning and governance capabilities to leverage configurations for AI implementation. Through a dynamic process of AI resource orchestration, SMEs effectively leverage AI resources and capabilities by mobilising technologies, coordinating manufacturing processes, and empowering skilled people. This research contributes to existing practice and the academic literature on AI implementation, highlighting how SMEs orchestrate AI resources and capabilities to drive an organisation’s digital transformation whilst creating a competitive advantage.","2024-08-01","2024-12-03 03:25:05","2024-12-03 03:25:05","","102781","","","77","","International Journal of Information Management","","","","","","","","","","","","","","","","","","","Competitive advantage; Capabilities; Artificial intelligence; Manufacturing; AI; Digital transformation; Resource orchestration; Resources; Small and medium-sized enterprises (SMEs)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"X53FBGXS","journalArticle","2024","Raushan, K.; Mac Uidhir, T.; Llorens Salvador, M.; Norton, B.; Ahern, C.","A data-driven standardised generalisable methodology to validate a large energy performance Certification dataset: A case of the application in Ireland","Energy and Buildings","","0378-7788","10.1016/j.enbuild.2024.114774","https://www.sciencedirect.com/science/article/pii/S0378778824008909","Energy performance certificate (EPC) databases are crucial for analysing building stocks and informing relevant policy interventions across Europe. Initially designed for building-to-building energy efficiency assessments, EPCs and resultant EPC databases, are now used to inter alia, evaluate stock energy efficiency, monitor impacts of national policies, and estimate financial viability of interventions. Consumers use EPCs when seeking financial incentives like “green” mortgages, while financial institutions use EPCs to consider renovation costs in mortgage assessments. The range of stakeholders using EPC data thus extends beyond those who are expert in building energy rating. Errors in manually inputting data from on-site surveys into calculations can undermine EPC results. Currently, no standardised method exists for validating EPC datasets. This research introduces the first automated, data-driven validation of an EPC dataset. It adapts as the database evolves. Seventeen unique filters were developed, revealing that 30% of EPC entries were erroneous and/or outlier data, with 80% related to misassessment of geometrical features. Errors in one field often correlated with errors in others, indicating some Assessors’ low responsibility towards data quality. The validation method, automated through Python and R language scripts, align the Irish EPC database taxonomy with Irish and European Building Stock Observatories, facilitating consistent future reporting. Recommendations are provided to enhance EPC data quality.","2024-11-15","2024-12-03 03:25:05","2024-12-03 03:25:05","","114774","","","323","","Energy and Buildings","","","","","","","","","","","","","","","","","","","Data processing; Building energy stock modelling; Data cleaning; Data validation; Energy performance certification; EPC database","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"UUFE6FMA","journalArticle","2024","Pattnaik, Debidutta; Ray, Sougata; Raman, Raghu","Applications of artificial intelligence and machine learning in the financial services industry: A bibliometric review","Heliyon","","2405-8440","10.1016/j.heliyon.2023.e23492","https://www.sciencedirect.com/science/article/pii/S2405844023107006","This bibliometric review examines the research state of artificial intelligence (AI) and machine learning (ML) applications in the Banking, Financial Services, and Insurance (BFSI) sector. The study focuses on Scopus-indexed articles to identify key research clusters. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, 39,498 articles were screened, resulting in 1045 articles meeting the inclusion criteria. N-gram analysis identified 177 unique terms in the article titles and abstracts. Co-occurrence analysis revealed nine distinct clusters covering fintech, risk management, anti-money laundering, and actuarial science, among others. These clusters offer a comprehensive overview of the multifaceted research landscape. The identified clusters can guide future research and inform study design. Policymakers, researchers, and practitioners in the BFSI sector can benefit from the study’s findings, which identify research gaps and opportunities. This study contributes to the growing literature on bibliometrics, providing insights into AI and ML applications in the BFSI sector. The findings have practical implications, advancing our understanding of AI and ML’s role in benefiting academia and industry.","2024-01-15","2024-12-03 03:25:05","2024-12-03 03:25:05","","e23492","","1","10","","Heliyon","","","","","","","","","","","","","","","","","","","Bibliometrics; ML; AI; BFSI; Co-occurrence analysis; N-gram analysis","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"M3I95699","journalArticle","2023","Zhu, Wenbo; Gui, Renzhou; Guo, Ru","Unveiling the nexus and promoting integration of diverse factors: Prospects of big data-driven artificial intelligence technology in achieving carbon neutrality in Chongming District","Water-Energy Nexus","","2588-9125","10.1016/j.wen.2023.09.001","https://www.sciencedirect.com/science/article/pii/S2588912523000164","Climate change is one of the most pressing challenges facing the world today. The large amount of greenhouse gas emissions produced by human activities, especially the emission of carbon dioxide, is an important driving factor behind climate issues. Under the background of China’s “3060” decarbonization goal”, Chongming District in Shanghai is actively promoting the construction of a world-class ecological island and is committed to creating a carbon–neutral demonstration zone with global influence. However, Chongming District faces challenges as the mechanism of carbon-neutrality transition path remains unclear. The data related to evaluating carbon neutrality status are heterogeneous from multiple sources. It is difficult to effectively implement relevant evaluation and response measures, impeding the progress of its low-carbon transformation. In response to the aforementioned challenges, this paper will propose and discuss the potential methods based on the new generation of information technology, represented by big data and artificial intelligence. These technologies aim to facilitate the integration of diverse factors, including carbon, and explore the nexus among them, thus exploring pathways for low-carbon transformation, and ultimately achieving decarbonization goal in Chongming District. Hopefully, the research conducted in this paper will contribute to the efforts of China and the global community in addressing carbon-related challenges and advancing towards a more sustainable and low-carbon future.","2023-12-01","2024-12-03 03:25:05","2024-12-03 03:25:05","","112-121","","","6","","Water-Energy Nexus","","","","","","","","","","","","","","","","","","","Deep learning; Big data; Artificial intelligence; Multi-element nexus; New generation of information technology","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"49F3KQGB","journalArticle","2024","Baik, Seung Min; Hong, Kyung Sook; Lee, Jae-Myeong; Park, Dong Jin","Integrating ensemble and machine learning models for early prediction of pneumonia mortality using laboratory tests","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e34525","https://www.sciencedirect.com/science/article/pii/S2405844024105567","Background The recent use of artificial intelligence (AI) in medical research is noteworthy. However, most research has focused on medical imaging. Although the importance of laboratory tests in the clinical field is acknowledged by clinicians, they are undervalued in medical AI research. Our study aims to develop an early prediction AI model for pneumonia mortality, primarily using laboratory test results. Materials and methods We developed a mortality prediction model using initial laboratory results and basic clinical information of patients with pneumonia. Several machine learning (ML) models and a deep learning method—multilayer perceptron (MLP)—were selected for model development. The area under the receiver operating characteristic curve (AUROC) and F1-score were optimized to improve model performance. In addition, an ensemble model was developed by blending several models to improve the prediction performance. We used 80,940 data instances for model development. Results Among the ML models, XGBoost exhibited the best performance (AUROC = 0.8989, accuracy = 0.88, F1-score = 0.80). MLP achieved an AUROC of 0.8498, accuracy of 0.86, and F1-score of 0.75. The performance of the ensemble model was the best among the developed models, with an AUROC of 0.9006, accuracy of 0.90, and F1-score of 0.81. Several laboratory tests were conducted to identify risk factors that affect pneumonia mortality using the ""Feature importance"" technique and SHapley Additive exPlanations. We identified several laboratory results, including systolic blood pressure, serum glucose level, age, aspartate aminotransferase-to-alanine aminotransferase ratio, and monocyte-to-lymphocyte ratio, as significant predictors of mortality in patients with pneumonia. Conclusions Our study demonstrates that the ensemble model, incorporating XGBoost, CatBoost, and LGBM techniques, outperforms individual ML and deep learning models in predicting pneumonia mortality. Our findings emphasize the importance of integrating AI techniques to leverage laboratory test data effectively, offering a promising direction for advancing AI applications in medical research and clinical decision-making.","2024-07-30","2024-12-03 03:25:05","2024-12-03 03:25:05","","e34525","","14","10","","Heliyon","","","","","","","","","","","","","","","","","","","Artificial intelligence; Prediction; Laboratory test; Mortality; Pneumonia","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XMKU85Z5","journalArticle","2024","Azhar, Badril; Gunawan, Setiyo; Muharja, Maktum; Avian, Cries; Satrio, Dendy; Aparamarta, Hakun W.","Optimization of microwave-assisted extraction in the purification of triglycerides from non-edible crude Calophyllum inophyllum oil as biodiesel feedstock using artificial intelligence","South African Journal of Chemical Engineering","","1026-9185","10.1016/j.sajce.2023.12.001","https://www.sciencedirect.com/science/article/pii/S1026918523001208","Crude nyamplung Calophyllum inophyllum is a potential non-edible feedstock for biodiesel production. Calophyllum Inophyllum oil (CCIO) is a non-edible oil that has a high content of triglyceride (TG) and free fatty acid (FFA). This study aims to optimize microwave-assisted power and extraction time of triglyceride purification from C. inophyllum crude oil for biodiesel. This work deployed Artificial Intelligence (AI) algorithms consisting of eight Machine Learning (ML) algorithms and found the most accurate model, then optimized using the Particle Swarm Optimization (PSO) algorithm.The result of machine learning modelling Random Forest achieved higher accuracy in R-Square and lower Mean Square Error (MSE) than any other models. Overall, in R-Square average across all variables was 0.949 ± 0.026 and the MSE average of 0.097 ± 0.068. This result can be interpreted as a mean deviation between the predicted value and an accurate value of less than 0.1 for all variables. The optimum of the TG compound resulted in the power of 462.3 W and time of 39.12 min that equalled at 84.02% and FFA equalled at 6.92%. The TG have increased by 11% from the reference range, which states conventional methods from crude oil. Comparison with the MAE method has a minimum fitness value difference of 0.0006 but has a smaller accuracy of less than 1%. Implementing this prediction and optimization method can shorten the extraction time by 5.8 min and reduce energy consumption or system work by 130 kJ. This method can be used for input parameter model prediction and parameter optimization in purification for biodiesel feedstock. Further research can be carried out using other artificial intelligence methods to optimize biodiesel production.","2024-01-01","2024-12-03 03:25:06","2024-12-03 03:25:06","","312-321","","","47","","South African Journal of Chemical Engineering","","","","","","","","","","","","","","","","","","","Machine learning; Biodiesel; Calophyllum inophyllum; Microwave-assisted extraction; PSO","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"N8WPI9MF","journalArticle","2023","Li, Weifeng; Wang, Jing; Yan, Yan; Yuan, Peng; Cao, Changqi; Li, Shijie; Wu, Qi","Potential applications of ChatGPT in endoscopy: Opportunities and limitations","Gastroenterology & Endoscopy","","2949-7523","10.1016/j.gande.2023.06.001","https://www.sciencedirect.com/science/article/pii/S294975232300033X","ChatGPT is an artificial intelligence model that has the potential to revolutionize the field of endoscopy. It can rapidly summarize medical records, assist with diagnosis, provide patient communication support, and even understand endoscopic images. However, there are limitations, including the risk of inaccurate or inappropriate responses, privacy and security issues, and the potential to limit doctors' thinking. Further research and improvements are needed to ensure ChatGPT's safe use in the medical field. Overall, the potential benefits of ChatGPT in endoscopy are vast, and it has the ability to greatly improve the efficiency of diagnosis and treatment.","2023-07-01","2024-12-03 03:25:06","2024-12-03 03:25:06","","152-154","","3","1","","Gastroenterology & Endoscopy","","","","","","","","","","","","","","","","","","","Artificial intelligence; ChatGPT; GPT-4; Assistant; Endoscopy","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"N542ZMQZ","journalArticle","2023","Swami, Viren; Tran, Ulrich S.; Stieger, Stefan; Aavik, Toivo; Ranjbar, Hamed Abdollahpour; Adebayo, Sulaiman Olanrewaju; Afhami, Reza; Ahmed, Oli; Aimé, Annie; Akel, Marwan; Halbusi, Hussam Al; Alexias, George; Ali, Khawla F.; Alp-Dal, Nursel; Alsalhani, Anas B.; Álvares-Solas, Sara; Amaral, Ana Carolina Soares; Andrianto, Sonny; Aspden, Trefor; Argyrides, Marios; Aruta, John Jamir Benzon R.; Atkin, Stephen; Ayandele, Olusola; Baceviciene, Migle; Bahbouh, Radvan; Ballesio, Andrea; Barron, David; Bellard, Ashleigh; Bender, Sóley Sesselja; Beydağ, Kerime Derya; Birovljević, Gorana; Blackburn, Marie-Ève; Borja-Alvarez, Teresita; Borowiec, Joanna; Bozogáňová, Miroslava; Bratland-Sanda, Solfrid; Browning, Matthew H.E.M.; Brytek-Matera, Anna; Burakova, Marina; Çakır-Koçak, Yeliz; Camacho, Pablo; Camilleri, Vittorio Emanuele; Cazzato, Valentina; Cerea, Silvia; Chaiwutikornwanich, Apitchaya; Chaleeraktrakoon, Trawin; Chambers, Tim; Chen, Qing-Wei; Chen, Xin; Chien, Chin-Lung; Chobthamkit, Phatthanakit; Choompunuch, Bovornpot; Compte, Emilio J.; Corrigan, Jennifer; Cosmas, Getrude; Cowden, Richard G.; Czepczor-Bernat, Kamila; Czub, Marcin; da Silva, Wanderson Roberto; Dadfar, Mahboubeh; Dalley, Simon E.; Dany, Lionel; Datu, Jesus Alfonso D.; Berbert de Carvalho, Pedro Henrique; Coelho, Gabriel Lins de Holanda; De Jesus, Avila Odia S.; Debbabi, Sonia Harzallah; Dhakal, Sandesh; Di Bernardo, Francesca; Dimitrova, Donka D.; Dion, Jacinthe; Dixson, Barnaby; Donofrio, Stacey M.; Drysch, Marius; Du, Hongfei; Dzhambov, Angel M.; El-Jor, Claire; Enea, Violeta; Eskin, Mehmet; Farbod, Farinaz; Farrugia, Lorleen; Fian, Leonie; Fisher, Maryanne L.; Folwarczny, Michał; Frederick, David A.; Fuller-Tyszkiewicz, Matthew; Furnham, Adrian; García, Antonio Alías; Geller, Shulamit; Ghisi, Marta; Ghorbani, Alireza; Martinez, Maria Angeles Gomez; Gradidge, Sarah; Graf, Sylvie; Grano, Caterina; Gyene, Gyöngyvér; Hallit, Souheil; Hamdan, Motasem; Handelzalts, Jonathan E.; Hanel, Paul H.P.; Hawks, Steven R.; Hekmati, Issa; Helmy, Mai; Hill, Tetiana; Hina, Farah; Holenweger, Geraldine; Hřebíčková, Martina; Ijabadeniyi, Olasupo Augustine; Imam, Asma; İnce, Başak; Irrazabal, Natalia; Jankauskiene, Rasa; Jiang, Ding-Yu; Jiménez-Borja, Micaela; Jiménez-Borja, Verónica; Johnson, Evan M.; Jovanović, Veljko; Jović, Marija; Jović, Marko; Junqueira, Alessandra Costa Pereira; Kahle, Lisa-Marie; Kantanista, Adam; Karakiraz, Ahmet; Karkin, Ayşe Nur; Kasten, Erich; Khatib, Salam; Khieowan, Nuannut; Kimong, Patricia Joseph; Kiropoulos, Litza; Knittel, Joshua; Kohli, Neena; Koprivnik, Mirjam; Kospakov, Aituar; Król-Zielińska, Magdalena; Krug, Isabel; Kuan, Garry; Kueh, Yee Cheng; Kujan, Omar; Kukić, Miljana; Kumar, Sanjay; Kumar, Vipul; Lamba, Nishtha; Lauri, Mary Anne; Laus, Maria Fernanda; LeBlanc, Liza April; Lee, Hyejoo J.; Lipowska, Małgorzata; Lipowski, Mariusz; Lombardo, Caterina; Lukács, Andrea; Maïano, Christophe; Malik, Sadia; Manjary, Mandar; Baldó, Lidia Márquez; Martinez-Banfi, Martha; Massar, Karlijn; Matera, Camilla; McAnirlin, Olivia; Mebarak, Moisés Roberto; Mechri, Anwar; Meireles, Juliana Fernandes Filgueiras; Mesko, Norbert; Mills, Jacqueline; Miyairi, Maya; Modi, Ritu; Modrzejewska, Adriana; Modrzejewska, Justyna; Mulgrew, Kate E.; Myers, Taryn A.; Namatame, Hikari; Nassani, Mohammad Zakaria; Nerini, Amanda; Neto, Félix; Neto, Joana; Neves, Angela Noguiera; Ng, Siu-Kuen; Nithiya, Devi; O, Jiaqing; Obeid, Sahar; Oda-Montecinos, Camila; Olapegba, Peter Olamakinde; Olonisakin, Tosin Tunrayo; Omar, Salma Samir; Örlygsdóttir, Brynja; Özsoy, Emrah; Otterbring, Tobias; Pahl, Sabine; Panasiti, Maria Serena; Park, Yonguk; Patwary, Muhammad Mainuddin; Pethö, Tatiana; Petrova, Nadezhda; Pietschnig, Jakob; Pourmahmoud, Sadaf; Prabhu, Vishnunarayan Girishan; Poštuvan, Vita; Prokop, Pavol; Ramseyer Winter, Virginia L.; Razmus, Magdalena; Ru, Taotao; Rupar, Mirjana; Sahlan, Reza N.; Hassan, Mohammad Salah; Šalov, Anđela; Sapkota, Saphal; Sarfo, Jacob Owusu; Sawamiya, Yoko; Schaefer, Katrin; Schulte-Mecklenbeck, Michael; Seekis, Veya; Selvi, Kerim; Sharifi, Mehdi; Shrivastava, Anita; Siddique, Rumana Ferdousi; Sigurdsson, Valdimar; Silkane, Vineta; Šimunić, Ana; Singh, Govind; Slezáčková, Alena; Sundgot-Borgen, Christine; Ten Hoor, Gill; Tevichapong, Passagorn; Tipandjan, Arun; Todd, Jennifer; Togas, Constantinos; Tonini, Fernando; Tovar-Castro, Juan Camilo; Trangsrud, Lise Katrine Jepsen; Tripathi, Pankaj; Tudorel, Otilia; Tylka, Tracy L.; Uyzbayeva, Anar; Vally, Zahir; Vanags, Edmunds; Vega, Luis Diego; Vicente-Arruebarrena, Aitor; Vidal-Mollón, Jose; Vilar, Roosevelt; Villegas, Hyxia; Vintilă, Mona; Wallner, Christoph; White, Mathew P.; Whitebridge, Simon; Windhager, Sonja; Wong, Kah Yan; Yau, Eric Kenson; Yamamiya, Yuko; Yeung, Victoria Wai Lan; Zanetti, Marcelo Callegari; Zawisza, Magdalena; Zeeni, Nadine; Zvaríková, Martina; Voracek, Martin","Body appreciation around the world: Measurement invariance of the Body Appreciation Scale-2 (BAS-2) across 65 nations, 40 languages, gender identities, and age","Body Image","","1740-1445","10.1016/j.bodyim.2023.07.010","https://www.sciencedirect.com/science/article/pii/S1740144523001079","The Body Appreciation Scale-2 (BAS-2) is a widely used measure of a core facet of the positive body image construct. However, extant research concerning measurement invariance of the BAS-2 across a large number of nations remains limited. Here, we utilised the Body Image in Nature (BINS) dataset – with data collected between 2020 and 2022 – to assess measurement invariance of the BAS-2 across 65 nations, 40 languages, gender identities, and age groups. Multi-group confirmatory factor analysis indicated that full scalar invariance was upheld across all nations, languages, gender identities, and age groups, suggesting that the unidimensional BAS-2 model has widespread applicability. There were large differences across nations and languages in latent body appreciation, while differences across gender identities and age groups were negligible-to-small. Additionally, greater body appreciation was significantly associated with higher life satisfaction, being single (versus being married or in a committed relationship), and greater rurality (versus urbanicity). Across a subset of nations where nation-level data were available, greater body appreciation was also significantly associated with greater cultural distance from the United States and greater relative income inequality. These findings suggest that the BAS-2 likely captures a near-universal conceptualisation of the body appreciation construct, which should facilitate further cross-cultural research.","2023-09-01","2024-12-03 03:25:06","2024-12-03 03:25:06","","449-466","","","46","","Body Image","","","","","","","","","","","","","","","","","","","Body appreciation; Body appreciation Scale-2 (BAS-2); Cross-cultural; Measurement invariance; Multi-group confirmatory factor analysis (MG-CFA); Psychometrics; Structural analysis","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ZSBG2T9X","journalArticle","2024","Al-khresheh, Mohammad H.","Bridging technology and pedagogy from a global lens: Teachers’ perspectives on integrating ChatGPT in English language teaching","Computers and Education: Artificial Intelligence","","2666-920X","10.1016/j.caeai.2024.100218","https://www.sciencedirect.com/science/article/pii/S2666920X24000195","The rise of Artificial Intelligence in educational contexts has sparked both excitement and trepidation, highlighting the urgent need to investigate its implications, especially in English Language Teaching. In light of this context, this study aimed to find out how English language teachers perceive the pedagogical benefits and challenges posed by ChatGPT when incorporated into ELT and identify potential avenues for such digital innovations. Adopting a qualitative research design, data were purposively collected by distributing an open-ended questionnaire to 46 English language teachers from multiple countries. This sample was solicited via the academic and research platform ResearchGate, assuring a broad representation of academic ranks and teaching experience. Thematic analysis was used to unpack the rich textual responses. Findings revealed that, while teachers recognize ChatGPT's potential to facilitate personalized and dynamic learning interactions, they also harboured perceptible concerns regarding linguistic fidelity, potential overreliance on the tool, and the possibility of creativity suppression. In addition, the perceived limitations of the instrument in developing crucial language skills such as listening and speaking were highlighted. The data further emphasized the need for targeted professional development and agile curriculum adaptation to maximize the potential of ChatGPT and other AI tools. This research contributes to the burgeoning discourse on AI's interaction with ELT by incorporating a global perspective, making it invaluable for teachers, curriculum designers, and tech innovators. Limitations, recommendations and implications were provided.","2024-06-01","2024-12-03 03:25:06","2024-12-03 03:25:06","","100218","","","6","","Computers and Education: Artificial Intelligence","","","","","","","","","","","","","","","","","","","Artificial intelligence; ChatGPT; English language teaching; Curriculum adaptation; Digital innovations; Teachers' perceptions; Technology in education","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DM2HHAET","journalArticle","2023","Mikalef, Patrick; Islam, Najmul; Parida, Vinit; Singh, Harkamaljit; Altwaijry, Najwa","Artificial intelligence (AI) competencies for organizational performance: A B2B marketing capabilities perspective","Journal of Business Research","","0148-2963","10.1016/j.jbusres.2023.113998","https://www.sciencedirect.com/science/article/pii/S0148296323003569","The deployment of Artificial Intelligence (AI) has been accelerating in several fields over the past few years, with much focus placed on its potential in Business-to-Business (B2B) marketing. Early reports highlight promising benefits of AI in B2B marketing such as offering important insights into customer behaviors, identifying critical market insight, and streamlining operational inefficiencies. Nevertheless, there is a lack of understanding concerning how organizations should structure their AI competencies for B2B marketing, and how these ultimately influence organizational performance. Drawing on AI competencies and B2B marketing literature, this study develops a conceptual research model that explores the effect that AI competencies have on B2B marketing capabilities, and in turn on organizational performance. The proposed research model is tested using 155 survey responses from European companies and analyzed using partial least squares structural equation modeling. The results highlight the mechanisms through which AI competencies influence B2B marketing capabilities, as well as how the later impact organizational performance.","2023-09-01","2024-12-03 03:25:06","2024-12-03 03:25:06","","113998","","","164","","Journal of Business Research","","","","","","","","","","","","","","","","","","","Artificial intelligence; AI competencies; B2B marketing; Core competencies theory","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"V8GET5UK","journalArticle","2023","Algarni, Abeer D.","Bayesian Deep Learning Enabled Sentiment Analysis on Web Intelligence Applications","Computers, Materials and Continua","","1546-2218","10.32604/cmc.2023.026687","https://www.sciencedirect.com/science/article/pii/S1546221823002680","In recent times, web intelligence (WI) has become a hot research topic, which utilizes Artificial Intelligence (AI) and advanced information technologies on the Web and Internet. The users post reviews on social media and are employed for sentiment analysis (SA), which acts as feedback to business people and government. Proper SA on the reviews helps to enhance the quality of the services and products, however, web intelligence techniques are needed to raise the company profit and user fulfillment. With this motivation, this article introduces a new modified pigeon inspired optimization based feature selection (MPIO-FS) with Bayesian deep learning (BDL), named MPIO-BDL model for SA on WI applications. The presented MPIO-BDL model initially involved preprocessing and feature extraction take place using Term Frequency—Inverse Document Frequency (TF-IDF) technique to derive a useful set of information from the user reviews. Besides, the MPIO-FS model is applied for the selection of optimal feature subsets, which helps to enhance classification accuracy and reduce computation complexity. Moreover, the BDL model is employed to allocate the proper class labels of the applied user review data. A comprehensive experimental results analysis highlighted the improved classification efficiency of the presented model.","2023-03-27","2024-12-03 03:25:06","2024-12-03 03:25:06","","3399-3412","","2","75","","Computers, Materials and Continua","","","","","","","","","","","","","","","","","","","feature selection; artificial intelligence; Social media; Bayesian deep learning; data classification; web intelligence","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KRJHGQNL","journalArticle","2024","Bouqentar, Mohammed Amine; Terrada, Oumaima; Hamida, Soufiane; Saleh, Shawki; Lamrani, Driss; Cherradi, Bouchaib; Raihani, Abdelhadi","Early heart disease prediction using feature engineering and machine learning algorithms","Heliyon","","2405-8440","10.1016/j.heliyon.2024.e38731","https://www.sciencedirect.com/science/article/pii/S2405844024147627","Heart disease is one of the most widespread global health issues, it is the reason behind around 32 % of deaths worldwide every year. The early prediction and diagnosis of heart diseases are critical for effective treatment and sickness management. Despite the efforts of healthcare professionals, cardiovascular surgeons and cardiologists' misdiagnosis and misinterpretation of test results may happen every day. This study addresses the growing global health challenge raised by Cardiovascular Diseases (CVDs), which account for 32 % of all deaths worldwide, according to the World Health Organization (WHO). With the progress of Machine Learning (ML) and Deep Learning (DL) techniques as part of Artificial Intelligence (AI), these technologies have become crucial for predicting and diagnosing CVDs. This research aims to develop an ML system for the early prediction of cardiovascular diseases by choosing one of the powerful existing ML algorithms after a deep comparative analysis of several. To achieve this work, the Cleveland and Statlog heart datasets from international platforms are used in this study to evaluate and validate the system's performance. The Cleveland dataset is categorized and used to train various ML algorithms, including decision tree, random forest, support vector machine, logistic regression, adaptive boosting, and K-nearest neighbors. The performance of each algorithm is assessed based on accuracy, precision, recall, F1 score, and the Area Under the Curve metrics. Hyperparameter tuning approaches have been employed to find the best hyperparameters that reflect the optimal performance of the used algorithms based on different evaluation approaches including 10-fold cross-validation with a 95 % confidence interval. The study's findings highlight the potential of ML in improving the early prediction and diagnosis of cardiovascular diseases. By comparing and analyzing the performance of the applied algorithms on both the Cleveland and Statlog heart datasets, this research contributes to the advancement of ML techniques in the medical field. The developed ML system offers a valuable tool for healthcare professionals in the early prediction and diagnosis of cardiovascular diseases, with implications for the prediction and diagnosis of other diseases as well.","2024-10-15","2024-12-03 03:25:06","2024-12-03 03:25:06","","e38731","","19","10","","Heliyon","","","","","","","","","","","","","","","","","","","Deep learning; Machine learning; Classification; Artificial intelligence; Prediction; Cardiovascular diseases","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"545BSSG4","journalArticle","2024","Zhang, Yuezhou; Folarin, Amos A.; Dineley, Judith; Conde, Pauline; de Angel, Valeria; Sun, Shaoxiong; Ranjan, Yatharth; Rashid, Zulqarnain; Stewart, Callum; Laiou, Petroula; Sankesara, Heet; Qian, Linglong; Matcham, Faith; White, Katie; Oetzmann, Carolin; Lamers, Femke; Siddi, Sara; Simblett, Sara; Schuller, Björn W.; Vairavan, Srinivasan; Wykes, Til; Haro, Josep Maria; Penninx, Brenda W.J.H.; Narayan, Vaibhav A.; Hotopf, Matthew; Dobson, Richard J.B.; Cummins, Nicholas","Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model","Journal of Affective Disorders","","0165-0327","10.1016/j.jad.2024.03.106","https://www.sciencedirect.com/science/article/pii/S0165032724005305","Background Prior research has associated spoken language use with depression, yet studies often involve small or non-clinical samples and face challenges in the manual transcription of speech. This paper aimed to automatically identify depression-related topics in speech recordings collected from clinical samples. Methods The data included 3919 English free-response speech recordings collected via smartphones from 265 participants with a depression history. We transcribed speech recordings via automatic speech recognition (Whisper tool, OpenAI) and identified principal topics from transcriptions using a deep learning topic model (BERTopic). To identify depression risk topics and understand the context, we compared participants' depression severity and behavioral (extracted from wearable devices) and linguistic (extracted from transcribed texts) characteristics across identified topics. Results From the 29 topics identified, we identified 6 risk topics for depression: ‘No Expectations’, ‘Sleep’, ‘Mental Therapy’, ‘Haircut’, ‘Studying’, and ‘Coursework’. Participants mentioning depression risk topics exhibited higher sleep variability, later sleep onset, and fewer daily steps and used fewer words, more negative language, and fewer leisure-related words in their speech recordings. Limitations Our findings were derived from a depressed cohort with a specific speech task, potentially limiting the generalizability to non-clinical populations or other speech tasks. Additionally, some topics had small sample sizes, necessitating further validation in larger datasets. Conclusion This study demonstrates that specific speech topics can indicate depression severity. The employed data-driven workflow provides a practical approach for analyzing large-scale speech data collected from real-world settings.","2024-06-15","2024-12-03 03:25:06","2024-12-03 03:25:06","","40-49","","","355","","Journal of Affective Disorders","","","","","","","","","","","","","","","","","","","Smartphone; Speech; Depression; Topic modeling; Automatic speech recognition","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"W6F64GPQ","journalArticle","2024","Giakoumoglou, Nikolaos; Björnfot, Tomas; Montes, David Suárez; Álvarez-Gil, María; Ilver, Dag; Pechlivani, Eleftheria Maria","Artificial Intelligence-based Flow Cytometer for Real-time Algae Monitoring","International Conference on Industry Sciences and Computer Science Innovation","","1877-0509","10.1016/j.procs.2024.05.111","https://www.sciencedirect.com/science/article/pii/S187705092401127X","Flow cytometry is a laser-based technology that rapidly detects and analyzes the chemical and physical characteristics of single cells or particles and is already well established in environmental and toxicological studies for microalgae and bacterial quantification. This study introduces an imaging Flow Cytometer (FC) system, designed specifically for the enhanced analysis of microalgae biomass populations and aggregate groups through Artificial Intelligence (AI) integration. The FC incorporates a single flow line, critical hardware components, and a trifurcated software setup. The system employs a multi-step process for counting algae units and an Artificial Neural Network (ANN) for classifying them in groups of two or four. To demonstrate its capabilities, the system was tested on its ability to capture, count, and categorize algal units, specifically the Desmodesmus sp. morphotype with high accuracy. Furthermore, the FC's capabilities were contrasted with traditional counting methods, validating its enhanced precision and efficiency against a hematocytometer. With its capability to provide rapid, accurate, and high-throughput analyses, this innovative FC paves the way for a revolutionary approach to cellular research.","2024-01-01","2024-12-03 03:25:07","2024-12-03 03:25:07","","320-327","","","237","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial Neural Network; Desmodesmus sp; Flow Cytometer; Microalgae","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RDDNDL2G","journalArticle","2024","Dong, Zhenjiang; Ge, Xin; Huang, Yuehua; Dong, Jiankuo; Xu, Jiang","EG-STC: An Efficient Secure Two-Party Computation Scheme Based on Embedded GPU for Artificial Intelligence Systems","Computers, Materials and Continua","","1546-2218","10.32604/cmc.2024.049233","https://www.sciencedirect.com/science/article/pii/S1546221824000237","This paper presents a comprehensive exploration into the integration of Internet of Things (IoT), big data analysis, cloud computing, and Artificial Intelligence (AI), which has led to an unprecedented era of connectivity. We delve into the emerging trend of machine learning on embedded devices, enabling tasks in resource-limited environments. However, the widespread adoption of machine learning raises significant privacy concerns, necessitating the development of privacy-preserving techniques. One such technique, secure multi-party computation (MPC), allows collaborative computations without exposing private inputs. Despite its potential, complex protocols and communication interactions hinder performance, especially on resource-constrained devices. Efforts to enhance efficiency have been made, but scalability remains a challenge. Given the success of GPUs in deep learning, leveraging embedded GPUs, such as those offered by NVIDIA, emerges as a promising solution. Therefore, we propose an Embedded GPU-based Secure Two-party Computation (EG-STC) framework for Artificial Intelligence (AI) systems. To the best of our knowledge, this work represents the first endeavor to fully implement machine learning model training based on secure two-party computing on the Embedded GPU platform. Our experimental results demonstrate the effectiveness of EG-STC. On an embedded GPU with a power draw of 5 W, our implementation achieved a secure two-party matrix multiplication throughput of 5881.5 kilo-operations per millisecond (kops/ms), with an energy efficiency ratio of 1176.3 kops/ms/W. Furthermore, leveraging our EG-STC framework, we achieved an overall time acceleration ratio of 5–6 times compared to solutions running on server-grade CPUs. Our solution also exhibited a reduced runtime, requiring only 60% to 70% of the runtime of previously best-known methods on the same platform. In summary, our research contributes to the advancement of secure and efficient machine learning implementations on resource-constrained embedded devices, paving the way for broader adoption of AI technologies in various applications.","2024-06-20","2024-12-03 03:25:07","2024-12-03 03:25:07","","4021-4044","","3","79","","Computers, Materials and Continua","","","","","","","","","","","","","","","","","","","edge computing; embedded GPU acceleration; privacy-preserving machine learning; Secure two-party computation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LXV6D847","journalArticle","2023","Ghaffarian, Saman; Taghikhah, Firouzeh Rosa; Maier, Holger R.","Explainable artificial intelligence in disaster risk management: Achievements and prospective futures","International Journal of Disaster Risk Reduction","","2212-4209","10.1016/j.ijdrr.2023.104123","https://www.sciencedirect.com/science/article/pii/S2212420923006039","Disasters can have devastating impacts on communities and economies, underscoring the urgent need for effective strategic disaster risk management (DRM). Although Artificial Intelligence (AI) holds the potential to enhance DRM through improved decision-making processes, its inherent complexity and ""black box"" nature have led to a growing demand for Explainable AI (XAI) techniques. These techniques facilitate the interpretation and understanding of decisions made by AI models, promoting transparency and trust. However, the current state of XAI applications in DRM, their achievements, and the challenges they face remain underexplored. In this systematic literature review, we delve into the burgeoning domain of XAI-DRM, extracting 195 publications from the Scopus and ISI Web of Knowledge databases, and selecting 68 for detailed analysis based on predefined exclusion criteria. Our study addresses pertinent research questions, identifies various hazard and disaster types, risk components, and AI and XAI methods, uncovers the inherent challenges and limitations of these approaches, and provides synthesized insights to enhance their explainability and effectiveness in disaster decision-making. Notably, we observed a significant increase in the use of XAI techniques for DRM in 2022 and 2023, emphasizing the growing need for transparency and interpretability. Through a rigorous methodology, we offer key research directions that can serve as a guide for future studies. Our recommendations highlight the importance of multi-hazard risk analysis, the integration of XAI in early warning systems and digital twins, and the incorporation of causal inference methods to enhance DRM strategy planning and effectiveness. This study serves as a beacon for researchers and practitioners alike, illuminating the intricate interplay between XAI and DRM, and revealing the profound potential of AI solutions in revolutionizing disaster risk management.","2023-11-01","2024-12-03 03:25:07","2024-12-03 03:25:07","","104123","","","98","","International Journal of Disaster Risk Reduction","","","","","","","","","","","","","","","","","","","Transparency; Data-driven decision making; Hazard and disaster type; Interpretable artificial intelligence; Resilience building","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"VU6MPTID","journalArticle","2024","Born, Cornelius; Wildmoser, Julian; Schwarz, Romy; Böttcher, Timo; Hein, Andreas; Krcmar, Helmut","Identifying potentials for Artificial Intelligence-based process support along the emergency department care pathway to alleviate overcrowding","CENTERIS – International Conference on ENTERprise Information Systems / ProjMAN - International Conference on Project MANagement / HCist - International Conference on Health and Social Care Information Systems and Technologies 2023","","1877-0509","10.1016/j.procs.2024.06.348","https://www.sciencedirect.com/science/article/pii/S1877050924015928","This study examines the challenge of overcrowding within Emergency Department (ED) processes and elucidates the potential for AI-based interventions to enhance patient care and operational efficiency. We used qualitative interviews conducted in two German hospitals to identify five challenges along the ED care pathway: limited demand predictability, task overload, information unavailability, lack of IT systems interoperability, and low process standardization. We propose AI-based solutions to target these issues. For example, demand forecasting models could optimize resource allocation during patient arrival, while AI-guided queries and Decision Support Systems could improve data quality and standardization in registration & triage and diagnosis & treatment, respectively. Additionally, AI-driven text recognition and monitoring could streamline information management and patient observation. While the study is constrained by its geographic and methodological scope, it is foundational work for future research, including Design Science Research approaches to validate and implement the proposed AI-based process aids in the ED.","2024-01-01","2024-12-03 03:25:07","2024-12-03 03:25:07","","1705-1712","","","239","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial Intelligence; Machine Learning; Crowding; Emergency Department; Overcrowding","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"54F7VSIL","journalArticle","2024","Zhong, Keyi; Jackson, Tom; West, Andrew; Cosma, Georgina","Natural Language Processing Approaches in Industrial Maintenance: A Systematic Literature Review","5th International Conference on Industry 4.0 and Smart Manufacturing (ISM 2023)","","1877-0509","10.1016/j.procs.2024.02.029","https://www.sciencedirect.com/science/article/pii/S1877050924002060","Industrial maintenance plays a crucial role in manufacturing by significantly reducing machine failure time and minimizing costs, especially in the revolution of Industry 4.0. Consequently, researchers and industrial engineers have continuously focused on this area. Manufacturing companies possess extensive maintenance reports or logs containing valuable textual information, which offers a new avenue for exploring effective industrial maintenance methods. Natural Language Processing (NLP), a subfield of Artificial Intelligence, has demonstrated remarkable potential in analyzing maintenance reports and achieving promising results in various tasks. This paper presents a comprehensive systematic literature review that specifically concentrates on the applications of NLP approaches employed in the field of industrial maintenance. Additionally, this review analyzed the datasets utilized in previous studies and the evaluation measures adopted, which can serve as a valuable resource for other researchers seeking potential solutions in maintenance. Furthermore, the paper discusses the challenges encountered in applying NLP to industrial maintenance and outlines future research directions in this domain. By conducting this systematic literature review, we provide a comprehensive understanding of the current state of NLP applications in industrial maintenance, identify gaps in the existing literature, and guide future research efforts in leveraging NLP techniques for enhanced maintenance practices.","2024-01-01","2024-12-03 03:25:08","2024-12-03 03:25:08","","2082-2097","","","232","","Procedia Computer Science","","","","","","","","","","","","","","","","","","","Artificial intelligence; Natural language processing; NLP; AI; Systematic literature review; Industrial maintenance","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"884ZZHXS","journalArticle","2024","Loomans, BAC; Mendes, FM; Vinayahalingam, S; Xi, T; Opdam, NJM; Kreulen, CM; Pereira-Cenci, T; Cenci, MS","Challenges in conducting clinical research in primary care dentistry","Journal of Dentistry","","0300-5712","10.1016/j.jdent.2024.104958","https://www.sciencedirect.com/science/article/pii/S0300571224001283","The integration of dentistry into primary health care is crucial for promoting patient well-being. However, clinical studies in dentistry face challenges, including issues with study design, transparency, and relevance to primary care. Clinical trials in dentistry often focus on specific issues with strict eligibility criteria, limiting the generalizability of findings. Randomized clinical trials (RCTs) face challenges in reflecting real-world conditions and using clinically relevant outcomes. The need for more pragmatic approaches and the inclusion of clinically relevant outcomes (CROs) is discussed, such as tooth loss or implant success. Solutions proposed include well-controlled observational studies, optimized data collection tools, and the integration of artificial intelligence (AI) for predictive modelling, computer-aided diagnostics and automated diagnosis. In this position paper advocates for more efficient trials with a focus on patient-centred outcomes, as well as the adoption of pragmatic study designs reflecting real-world conditions. Collaborative research networks, increased funding, enhanced data retrieval, and open science practices are also recommended. Technology, including intraoral scanners and AI, is highlighted for improving efficiency in dental research. AI is seen as a key tool for participant recruitment, predictive modelling, and outcome evaluation. However, ethical considerations and ongoing validation are emphasized to ensure the reliability and trustworthiness of AI-driven solutions in dental research. In conclusion, the efficient conduct of clinical research in primary care dentistry requires a comprehensive approach, including changes in study design, data collection, and analytical methods. The integration of AI is seen as pivotal in achieving these objectives in a meaningful and efficient way.","2024-05-01","2024-12-03 03:25:08","2024-12-03 03:25:08","","104958","","","144","","Journal of Dentistry","","","","","","","","","","","","","","","","","","","Artificial intelligence; Dentistry; Clinical trials; Primary care","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"J4K76CBL","journalArticle","2024","Sendekie, Ashenafi Kibret; Limenh, Liknaw Workie; Abate, Biruk Beletew; Chanie, Gashaw Sisay; Kassaw, Abebe Tarekegn; Tamene, Fasil Bayafers; Gete, Kalab Yigermal; Dagnew, Ephrem Mebratu","Artificial intelligence in community pharmacy practice: Pharmacists' perceptions, willingness to utilize, and barriers to implementation","Exploratory Research in Clinical and Social Pharmacy","","2667-2766","10.1016/j.rcsop.2024.100542","https://www.sciencedirect.com/science/article/pii/S2667276624001392","Background Artificial intelligence (AI) has a significant potential to impact pharmacy practices worldwide. This study investigates pharmacists' perceptions of AI's role in pharmacy practices, their willingness to adopt it, and perceived barriers to its implementation at community pharmacies in Ethiopia. Methods A cross-sectional study was conducted among community pharmacists in Ethiopia. Data were collected using a self-administered questionnaire. Independent samples t-test, one-way ANOVA, and post-hoc analyses were used to compare pharmacists' perception and willingness scores. A linear regression analysis examined the association of independent variables with pharmacists' perception of AI and willingness to utilize AI. A p-value <0.05 was considered statistically significant. Results Of 241 pharmacists approached, 225 (93.3 %) completed the survey. Overall, about two-thirds (67.1 % and 66.2 %) of community pharmacists had a high level of perception and willingness to use AI applications in pharmacy, respectively. Pharmacists with bachelor's degrees and above (β = 2.76: 95 % CI: 0.09, 5.01 vs. β = 1.79: 95 % CI: 0.05, 4.21), those who utilized scientific drug information sources (β = 2.45, 95 %: 0.17, 4.45 vs. β = 1.76, 95 % CI: 0.91, 3.89), pharmacists who had a previous exposure of AI (β = 1.02, 95 %: 0.03, 3.24 vs. β =1.13, 95 % CI: 0.07, 2.93), and those who with higher perceived AI knowledge (β =1.09, 95 % CI: 0.02, 2.46 vs. β = 1.14, 95 %CI: 0.17, 3.11) had significantly higher perception of AI and willingness to utilize it, respectively compared to their counterparts. Lack of internet availability (89.3 %), lack of AI-related software/hardware (88.2 %), and limited training (80.9 %) were the most frequently reported barriers by pharmacists to AI adoption. Over 90 % of pharmacists agreed on the importance of internet availability (93.3 %), policies/frameworks (91.6 %), and research/learning from others (89.3 %) for successful AI integration. Conclusion Despite positive perceptions and willingness from pharmacists, AI implementation in community pharmacies could be hindered by resource limitations, training gaps, skill constraints, and infrastructure issues. To facilitate adoption, enhancing knowledge and skills, and developing policies/frameworks are crucial.","2024-12-01","2024-12-03 03:25:08","2024-12-03 03:25:08","","100542","","","16","","Exploratory Research in Clinical and Social Pharmacy","","","","","","","","","","","","","","","","","","","Artificial intelligence; Perception; Community pharmacy; Ethiopia; Healthcare technology; Pharmacist; Pharmacy practice; Willingness","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"67ZIPNDP","journalArticle","2024","Li, Mahei Manhai; Reinhard, Philipp; Peters, Christoph; Oeste-Reiss, Sarah; Leimeister, Jan Marco","A Value Co-Creation Perspective on Data Labeling in Hybrid Intelligence Systems: A Design Study","Information Systems","","0306-4379","10.1016/j.is.2023.102311","https://www.sciencedirect.com/science/article/pii/S0306437923001473","The adoption of innovative technologies confronts IT-Service-Management (ITSM) with an increasing volume and variety of requests. Artificial intelligence (AI) possesses the potential to augment customer service employees. However, the training data for AI systems are annotated by domain experts with little interest in labeling correctly due to their limited perceived value. Ultimately, insufficient labeled data leads to diminishing returns in AI performance. Following a design science research approach, we provide a novel human-in-the-loop (HIL) design for ITSM support ticket recommendations by incorporating a value co-creation perspective. The design incentivizes ITSM agents to provide labels during their everyday ticket-handling procedures. We develop a functional prototype based on 17,120 support tickets provided by a pilot partner as an instantiation and evaluate the design through accuracy metrics and user evaluations. Our evaluation revealed that recommendations after label improvement showed increased user ratings, and users are willing to contribute their domain knowledge. We demonstrate that our design benefits for both human agent and AI systems in the form of hybrid intelligence service systems. Overall, our results emphasize agents' need for value-in-use by providing better results if they improve the labeling of support tickets pre-labeled by AI. Thus, we provide prescriptive knowledge of a novel HIL design that enables efficient and interactive labeling in the context of diverse applications of reinforcement learning systems.","2024-02-01","2024-12-03 03:25:08","2024-12-03 03:25:08","","102311","","","120","","Information Systems","","","","","","","","","","","","","","","","","","","machine learning; artificial intelligence; Hybrid intelligence; Human-in-the-loop; Interactive labeling; IT Service Management (ITSM); IT support; Value Co-creation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SG7JEWSL","journalArticle","2024","Delgado-Aguilera Jurado, Raquel; Ye, Xiaojie; Ortolá Plaza, Vicent; Zamarreño Suárez, María; Pérez Moreno, Francisco; Arnaldo Valdés, Rosa María","An introduction to the current state of standardization and certification on military AI applications","Journal of Air Transport Management","","0969-6997","10.1016/j.jairtraman.2024.102685","https://www.sciencedirect.com/science/article/pii/S0969699724001509","The main objective of this article is to provide an overview of the current state of development regarding certification and standardization efforts for Artificial Intelligence (AI) systems in military aviation. The incorporation of AI capabilities in the military holds the potential for significant strategic advantages in information and decision supremacy. However, AI also brings novel risks and safety considerations that existing certification processes are inadequate to address. Consequently, the need arises for the establishment of an entirely new certification framework, encompassing requirements and standardized processes tailored to the unique demands of AI safe-ty. During the development of such framework, the 7 High Level Requirements of the EU AI High Level Experts Group are taken as reference to develop the successive horizontal (cross-domain) and vertical (domain-specific) standards that would produce legal, robust and ethical AI. To facilitate the creation of a new AI certification framework in military aviation, a review has been done over traditional civil and military certification processes and the current AI certification progress under development, to present an overview of the key elements and processes involved. References from various levels (regulatory, industry, research) have been considered to provide an introduction to the prospective military AI certification framework.","2024-11-01","2024-12-03 03:25:08","2024-12-03 03:25:08","","102685","","","121","","Journal of Air Transport Management","","","","","","","","","","","","","","","","","","","Artificial intelligence; AI certification; AI requirements; AI standardization; AI taxonomy; AI trustworthiness; Military aviation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"FQ9XM9SC","journalArticle","2023","Acharjee1, Purnendu B.; Ghai, Bhupaesh; Elangovan, Muniyandy; Bhuvaneshwari, S.; Rastogi, Ravi; Rajkumar, P.","Exploring AI-driven approaches to drug discovery and development","The Scientific Temper","","0976-8653","10.58414/scientifictemper.2023.14.4.48","","The integration of artificial intelligence (AI) into drug discovery and development has ushered in a transformative era in pharmaceutical research. The research explores the profound impact of AI-driven approaches in drug discovery and development, demonstrating, that computational intelligence and biomedicine synergize to enhance innovation, efficiency, and precision in pharmaceutical science. AI's influence spans multiple phases of drug development, from target identification and validation to the optimization of drug candidates, while also facilitating personalized medicine and expediting drug repurposing. Recent studies underscore the precision and swiftness that AI brings to the discovery of drug candidates and the prediction of molecular properties, illustrating the potential advantages of AI in pharmaceutical research. However, AI's application in healthcare demands cautious consideration, as concerns such as model interpretability, ethical data usage, and regulatory frameworks loom large. The research also the critical need for ethical and secure data utilization. It investigates the methodology employed to create data visualizations that offer comprehensive insights into the advantages and disadvantages of AI algorithms in drug discovery. The analysis emphasizes that a judicious and context-specific approach to AI algorithm selection is essential to harness the transformative power of AI while mitigating its limitations.","2023","2024-12-03 03:35:57","2024-12-03 03:35:57","","1387–1393","","04","14","","","","","","","","","","","","","","","","","Citation Key: Acharjee12023","","","","personalized medicine; ai-driven drug discovery; ethical considerations; pharmaceutical research; regulatory frameworks; target identification","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"E22PKYJZ","journalArticle","2023","Qian, Zhaozhi; Davis, Rob; van der Schaar, Mihaela","Synthcity: a benchmark framework for diverse use cases of tabular synthetic data","Advances in Neural Information Processing Systems","","10495258","","","Accessible high-quality data is the bread and butter of machine learning research, and the demand for data has exploded as larger and more advanced ML models are built across different domains. Yet, real data often contain sensitive information, subject to various biases, and are costly to acquire, which compromise their quality and accessibility. Synthetic data have thus emerged as a complement, sometimes even a replacement, to real data for ML training. However, the landscape of synthetic data research has been fragmented due to the large number of data modalities (e.g., tabular data, time series data, images, etc.) and various use cases (e.g., privacy, fairness, data augmentation, etc.). This poses practical challenges in comparing and selecting synthetic data generators in different problem settings. To this end, we develop Synthcity, an open-source Python library that allows researchers and practitioners to perform one-click benchmarking of synthetic data generators across data modalities and use cases. In addition, Synthcity's plug-in style API makes it easy to incorporate additional data generators into the framework. Beyond benchmarking, it also offers a single access point to a diverse range of cutting-edge data generators. Through examples on tabular data generation and data augmentation, we illustrate the general applicability of Synthcity, and the insight one can obtain.","2023","2024-12-03 03:35:57","2024-12-03 03:35:57","","","","NeurIPS","36","","","","","","","","","","","","","","","","","Citation Key: Qian2023","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"D724KCK8","journalArticle","2023","Leaver, Tama; Srdarov, Suzanne","ChatGPT isn't magic: The hype and hypocrisy of generative artificial intelligence (AI) rhetoric","M/C journal","","1441-2616","","","Introduction Author Arthur C. Clarke famously argued that in science fiction literature “any sufficiently advanced technology is indistinguishable from magic” (Clarke). On 30 November 2022, technology company OpenAI publicly released their Large Language Model (LLM)-based chatbot ChatGPT (Chat Generative Pre-Trained Transformer), and instantly it was hailed as world-changing. Initial media stories about ChatGPT highlighted the speed with which it generated new material as evidence that this tool might be both genuinely creative and actually intelligent, in both exciting and disturbing ways. Indeed, ChatGPT is part of a larger pool of Generative Artificial Intelligence (AI) tools that can very quickly generate seemingly novel outputs in a variety of media formats based on text prompts written by users. Yet, claims that AI has become sentient, or has even reached a recognisable level of general intelligence, remain in the realm of science fiction, for now at least (Leaver). That has not stopped technology companies, scientists, and others from suggesting that super-smart AI is just around the corner. Exemplifying this, the same people creating generative AI are also vocal signatories of public letters that ostensibly call for a temporary halt in AI development, but these letters are simultaneously feeding the myth that these tools are so powerful that they are the early form of imminent super-intelligent machines. For many people, the combination of AI technologies and media hype means generative AIs are basically magical insomuch as their workings seem impenetrable, and their existence could ostensibly change the world. This article explores how the hype around ChatGPT and generative AI was deployed across the first six months of 2023, and how these technologies were positioned as either utopian or dystopian, always seemingly magical, but never banal. We look at some initial responses to generative AI, ranging from schools in Australia to picket lines in Hollywood. We offer a critique of the utopian/dystopian binary positioning of generative AI, aligning with critics who rightly argue that focussing on these extremes displaces the more grounded and immediate challenges generative AI bring that need urgent answers. Finally, we loop back to the role of schools and educators in repositioning generative AI as something to be tested, examined, scrutinised, and played with both to ground understandings of generative AI, while also preparing today's students for a future where these tools will be part of their work and cultural landscapes. Hype, Schools, and Hollywood In December 2022, one month after OpenAI launched ChatGPT, Elon Musk tweeted: “ChatGPT is scary good. We are not far from dangerously strong AI”. Musk's post was retweeted 9400 times, liked 73 thousand times, and presumably seen by most of his 150 million Twitter followers. This type of engagement typified the early hype and language that surrounded the launch of ChatGPT, with reports that “crypto” had been replaced by generative AI as the “hot tech topic” and hopes that it would be “‘transformative' for business” (Browne). By March 2023, global economic analysts at Goldman Sachs had released a report on the potentially transformative effects of generative AI, saying that it marked the “brink of a rapid acceleration in task automation that will drive labor cost savings and raise productivity” (Hatzius et al.). Further, they concluded that “its ability to generate content that is indistinguishable from human-created output and to break down communication barriers between humans and machines reflects a major advancement with potentially large macroeconomic effects” (Hatzius et al.). Speculation about the potentially transformative power and reach of generative AI technology was reinforced by warnings that it could also lead to “significant disruption” of the labour market, and the potential automation of up to 300 million jobs, with associated job losses for humans (Hatzius et al.). In addition, there was widespread buzz that ChatGPT's “rationalization process may evidence human-like cognition” (Browne), claims that were supported by the emergent language of ChatGPT. The technology was explained as being “trained” on a “corpus” of datasets, using a “neural network” capable of producing “natural language“” (Dsouza), positioning the technology as human-like, and more than ‘artificial' intelligence. Incorrect responses or errors produced by the tech were termed “hallucinations”, akin to magical thinking, which OpenAI founder Sam Altman insisted wasn't a word that he associated with sentience (Intelligencer staff). Indeed, Altman asserts that he rejects moves to “anthropomorphize” (Intelligencer staff) the technology; however, arguably the language, hype, and Altman's well-publicised misgivings about ChatGPT have had the combined effect of shaping our understanding of this generative AI as alive, vast, fast-moving, and potentially lethal to humanity. Unsurprisingly, the hype around the transformative effects of ChatGPT and its ability to generate ‘human-like' answers and sophisticated essay-style responses was matched by a concomitant panic throughout educational institutions. The beginning of the 2023 Australian school year was marked by schools and state education ministers meeting to discuss the emerging problem of ChatGPT in the education system (Hiatt). Every state in Australia, bar South Australia, banned the use of the technology in public schools, with a “national expert task force” formed to “guide” schools on how to navigate ChatGPT in the classroom (Hiatt). Globally, schools banned the technology amid fears that students could use it to generate convincing essay responses whose plagiarism would be undetectable with current software (Clarence-Smith). Some schools banned the technology citing concerns that it would have a “negative impact on student learning”, while others cited its “lack of reliable safeguards preventing these tools exposing students to potentially explicit and harmful content” (Cassidy). ChatGPT investor Musk famously tweeted, “It's a new world. Goodbye homework!”, further fuelling the growing alarm about the freely available technology that could “churn out convincing essays which can't be detected by their existing anti-plagiarism software” (Clarence-Smith). Universities were reported to be moving towards more “in-person supervision and increased paper assessments” (SBS), rather than essay-style assessments, in a bid to out-manoeuvre ChatGPT's plagiarism potential. Seven months on, concerns about the technology seem to have been dialled back, with educators more curious about the ways the technology can be integrated into the classroom to good effect (Liu et al.); however, the full implications and impacts of the generative AI are still emerging. In May 2023, the Writer's Guild of America (WGA), the union representing screenwriters across the US creative industries, went on strike, and one of their core issues were “regulations on the use of artificial intelligence in writing” (Porter). Early in the negotiations, Chris Keyser, co-chair of the WGA's negotiating committee, lamented that “no one knows exactly what AI's going to be, but the fact that the companies won't talk about it is the best indication we've had that we have a reason to fear it” (Grobar). At the same time, the Screen Actors' Guild (SAG) warned that members were being asked to agree to contracts that stipulated that an actor's voice could be re-used in future scenarios without that actor's additional consent, potentially reducing actors to a dataset to be animated by generative AI technologies (Scheiber and Koblin). In a statement issued by SAG, they made their position clear that the creation or (re)animation of any digital likeness of any part of an actor must be recognised as labour and properly paid, also warning that any attempt to legislate around these rights should be strongly resisted (Screen Actors Guild). Unlike the more sensationalised hype, the WGA and SAG responses to generative AI are grounded in labour relations. These unions quite rightly fear the immediate future where human labour could be augmented, reclassified, and exploited by, and in the name of, algorithmic systems. Screenwriters, for example, might be hired at much lower pay rates to edit scripts first generated by ChatGPT, even if those editors would really be doing most of the creative work to turn something clichéd and predictable into something more appealing. Rather than a dystopian world where machines do all the work, the WGA and SAG protests railed against a world where workers would be paid less because executives could pretend generative AI was doing most of the work (Bender). The Open Letter and Promotion of AI Panic In an open letter that received enormous press and media uptake, many of the leading figures in AI called for a pause in AI development since “advanced AI could represent a profound change in the history of life on Earth”; they warned early 2023 had already seen “an out-of-control race to develop and deploy ever more powerful digital minds that no one – not even their creators – can understand, predict, or reliably control” (Future of Life Institute). Further, the open letter signatories called on “all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4”, arguing that “labs and independent experts should use this pause to jointly develop and implement a set of shared safety protocols for advanced AI design and development that are rigorously audited and overseen by independent outside experts” (Future of Life Institute). Notably, many of the signatories work for the very companies involved in the “out-of-control race”. Indeed, while this letter could be read as a moment of ethical clarity for the AI industry, a more cynical reading might just be that in warning that their AIs could effectively destroy the world, th","2023","2024-12-03 03:35:57","2024-12-03 03:35:57","","1–6","","5","26","","","","","","","","","","","","","","","","","Citation Key: Leaver2023","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"S6QESB3D","journalArticle","2023","Musalamadugu, Tanmai Sree; Kannan, Hemachandran","Generative AI for medical imaging analysis and applications","Future Medicine AI","","","10.2217/fmai-2023-0004","","Generative AI plays a pivotal role in medical imaging analysis, enabling precise diagnosis, treatment planning and disease monitoring. Techniques like generative adversarial networks (GANs) and variational autoencoders (VAEs) enhance medical imaging by generating synthetic images, improving reconstruction, segmentation and facilitating disease diagnosis and treatment planning. Nonetheless, ethical, legal and regulatory concerns arise regarding patient privacy, data protection and fairness. This paper offers an overview of generative AI in medical imaging analysis, highlighting applications, challenges and case studies. It compares results with traditional methods and examines potential implications on healthcare policies. The paper concludes with recommendations for responsible implementation and suggests future research and development directions.","2023","2024-12-03 03:35:57","2024-12-03 03:35:57","","1–8","","","","","","","","","","","","","","","","","","","","Citation Key: Musalamadugu2023","","","","generative ai; ai; gan; generative adversarial network; medical imaging analysis; vae; variational","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JU85MESC","journalArticle","2023","Hussain, Muhammad","When, where, and which?: Navigating the intersection of computer vision and generative AI for strategic business integration","IEEE access : practical innovations, open solutions","","21693536","10.1109/ACCESS.2023.3332468","","In today's rapidly evolving digital landscape, Artificial Intelligence (AI) exerts a profound influence on our daily lives, from predictive text in emails to the ever-present virtual assistants like Alexa and Siri. This scholarly article embarks on a comprehensive exploration of the expansive world of Artificial Intelligence, with a keen focus on the domains of generative AI and computer vision. Our objective is to provide businesses with a nuanced and in-depth understanding of these critical AI subfields. By doing so, we empower organizations to make informed and strategic decisions regarding the adoption of generative AI and computer vision technologies. Our ultimate goal is to equip businesses with the knowledge and insights necessary to harness the potential of these AI domains effectively, driving innovation and bolstering their competitive edge in an increasingly technology-driven world.","2023","2024-12-03 03:35:57","2024-12-03 03:35:57","","127202–127215","","October","11","","IEEE Access","","","","","","","","","","","","","","","Citation Key: Hussain2023 Publisher: IEEE","","","","generative AI; automation; Computer vision; business intelligence; machine vision","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"HF3NK5D4","journalArticle","2023","Lee, Jaejun; Eom, So-youn; Lee, Junhee","Empowering game designers with generative ai","Iadis International Journal on Computer Science and Information Systems","","","10.33965/ijcsis_2023180213","","This paper explores how AI-based game design affects the game development process and the role structure of game development teams, with a particular focus on the changes it brings during the early game project proposal phase. We present a comparison of game design proposal cases conducted by a game designer over a span of four years, showcasing how the utilization of artificial intelligence is re-empowering game designers. We envision a empowered game designer capable of market sensing, artwork creation, playable prototype development, subsequent game analysis, and operations all by oneself or with a very small team, a ""super game designer"". This paper illustrates how AI is ushering in a new era for game designers, and discusses the potential to stimulate innovation within the game industry by fostering creativity and imagination of game designers.","2023","2024-12-03 03:35:57","2024-12-03 03:35:57","","213–230","","2","18","","","","","","","","","","","","","","","","","Citation Key: Lee2023","","","","generative ai; ai; game design; midjourney; novel ai","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"F2CKV4A2","journalArticle","2024","Zhang, Hong; Shao, Haijian","Exploring the latest applications of OpenAI and ChatGPT: An in-depth survey","Computer Modeling in Engineering & Sciences","","15261506","10.32604/cmes.2023.030649","","OpenAI and ChatGPT, as state-of-the-art language models driven by cutting-edge artificial intelligence technology, have gained widespread adoption across diverse industries. In the realm of computer vision, these models have been employed for intricate tasks including object recognition, image generation, and image processing, leveraging their advanced capabilities to fuel transformative breakthroughs. Within the gaming industry, they have found utility in crafting virtual characters and generating plots and dialogues, thereby enabling immersive and interactive player experiences. Furthermore, these models have been harnessed in the realm of medical diagnosis, providing invaluable insights and support to healthcare professionals in the realm of disease detection. The principal objective of this paper is to offer a comprehensive overview of OpenAI, OpenaiGym, ChatGPT, DALL E, stable diffusion, the pre-trained clip model, and other pertinent models in various domains, encompassing CLIP Text-to-Image, education, medical imaging, computer vision, social influence, natural language processing, software development, coding assistance, and Chatbot, among others. Particular emphasis will be placed on comparative analysis and examination of popular text-to-image and text-to-video models under diverse stimuli, shedding light on the current research landscape, emerging trends, and existing challenges within the domains of OpenAI and ChatGPT. Through a rigorous literature review, this paper aims to deliver a professional and insightful overview of the advancements, potentials, and limitations of these pioneering language models.","2024","2024-12-03 03:35:57","2024-12-03 03:35:57","","2061–2102","","3","138","","","","","","","","","","","","","","","","","Citation Key: Zhang2024","","","","chatgpt; dall e; openai; openai gym; stable diffusion; text-to-image; text-to-video","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ABWPHY2I","journalArticle","2023","George, Babu; Wooden, Ontario","Managing the strategic transformation of higher education through artificial intelligence","Administrative Sciences","","20763387","10.3390/admsci13090196","","Considering the rapid advancements in artificial intelligence (AI) and their potential implications for the higher education sector, this article seeks to critically evaluate the strategic adoption of AI in the framework of “smart universities”. We envisage these innovative institutions as the imminent evolution in higher education, harnessing AI and quantum technologies to reshape academic and administrative processes. The core presumption is that through such integration, universities can achieve personalized learning trajectories, enhanced accessibility, economic efficiency, and a boost in overall operational performance. However, venturing into this new educational paradigm necessitates a thorough exploration of potential pitfalls, including questions surrounding educational quality, potential job losses, risks of bias, privacy breaches, and safety concerns. Our primary objective is to offer a balanced assessment to aid stakeholders in making informed strategic decisions about endorsing and advancing the smart university model. A pivotal factor in this discourse is the acceptance of qualifications from AI-enriched institutions by employers, a variable that may drastically redefine the education sector's trajectory. Within the context of a comprehensive analysis of its broader societal impact, this article also delves into the ramifications of AI-driven innovations for historically Black colleges and universities (HBCUs).","2023","2024-12-03 03:35:58","2024-12-03 03:35:58","","","","9","13","","","","","","","","","","","","","","","","","Citation Key: George2023","","","","digital transformation; artificial intelligence; change management; educational sustainability; future of education; HBCUs; quantum technologies; smart university; strategic management","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"U2QX49YV","journalArticle","2024","Hasselgren, Catrin; Oprea, Tudor I.","Artificial intelligence for drug discovery: Are we there yet?","Annual Review of Pharmacology and Toxicology","","15454304","10.1146/annurev-pharmtox-040323-040828","","Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small-molecule drugs. AI technologies, such as generative chemistry, machine learning, and multiproperty optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.","2024","2024-12-03 03:35:58","2024-12-03 03:35:58","","527–550","","","64","","","","","","","","","","","","","","","","","Citation Key: Hasselgren2024 PMID: 37738505","","","","machine learning; deep learning; knowledge graphs; target identification; autoencoders; explainable AI; generative chemistry; multiproperty optimization; small-molecule drug discovery","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CI5WEEAB","journalArticle","2022","Appenzeller, Arno; Leitner, Moritz; Philipp, Patrick; Krempel, Erik; Beyerer, Jürgen","Privacy and utility of private synthetic data for medical data analyses","Applied Sciences (Switzerland)","","20763417","10.3390/app122312320","","The increasing availability and use of sensitive personal data raises a set of issues regarding the privacy of the individuals behind the data. These concerns become even more important when health data are processed, as are considered sensitive (according to most global regulations). Privacy Enhancing Technologies (PETs) attempt to protect the privacy of individuals whilst preserving the utility of data. One of the most popular technologies recently is Differential Privacy (DP), which was used for the 2020 U.S. Census. Another trend is to combine synthetic data generators with DP to create so-called private synthetic data generators. The objective is to preserve statistical properties as accurately as possible, while the generated data should be as different as possible compared to the original data regarding private features. While these technologies seem promising, there is a gap between academic research on DP and synthetic data and the practical application and evaluation of these techniques for real-world use cases. In this paper, we evaluate three different private synthetic data generators (MWEM, DP-CTGAN, and PATE-CTGAN) on their use-case-specific privacy and utility. For the use case, continuous heart rate measurements from different individuals are analyzed. This work shows that private synthetic data generators have tremendous advantages over traditional techniques, but also require in-depth analysis depending on the use case. Furthermore, it can be seen that each technology has different strengths, so there is no clear winner. However, DP-CTGAN often performs slightly better than the other technologies, so it can be recommended for a continuous medical data use case.","2022","2024-12-03 03:35:58","2024-12-03 03:35:58","","","","23","12","","","","","","","","","","","","","","","","","Citation Key: Appenzeller2022","","","","differential privacy; medical data; open source framework; private data processing; secondary use; synthetic data generation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"TE8FVD8P","journalArticle","2022","Appenzeller, Arno; Leitner, Moritz; Philipp, Patrick; Krempel, Erik; Beyerer, Jürgen","Privacy and utility of private synthetic data for medical data analyses","Applied Sciences (Switzerland)","","20763417","10.3390/app122312320","","The increasing availability and use of sensitive personal data raises a set of issues regarding the privacy of the individuals behind the data. These concerns become even more important when health data are processed, as are considered sensitive (according to most global regulations). Privacy Enhancing Technologies (PETs) attempt to protect the privacy of individuals whilst preserving the utility of data. One of the most popular technologies recently is Differential Privacy (DP), which was used for the 2020 U.S. Census. Another trend is to combine synthetic data generators with DP to create so-called private synthetic data generators. The objective is to preserve statistical properties as accurately as possible, while the generated data should be as different as possible compared to the original data regarding private features. While these technologies seem promising, there is a gap between academic research on DP and synthetic data and the practical application and evaluation of these techniques for real-world use cases. In this paper, we evaluate three different private synthetic data generators (MWEM, DP-CTGAN, and PATE-CTGAN) on their use-case-specific privacy and utility. For the use case, continuous heart rate measurements from different individuals are analyzed. This work shows that private synthetic data generators have tremendous advantages over traditional techniques, but also require in-depth analysis depending on the use case. Furthermore, it can be seen that each technology has different strengths, so there is no clear winner. However, DP-CTGAN often performs slightly better than the other technologies, so it can be recommended for a continuous medical data use case.","2022","2024-12-03 03:35:58","2024-12-03 03:35:58","","","","23","12","","","","","","","","","","","","","","","","","Citation Key: Appenzeller2022a","","","","differential privacy; medical data; open source framework; private data processing; secondary use; synthetic data generation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LM7R59D2","journalArticle","2024","Miletic, Marko; Sariyar, Murat","Challenges of using synthetic data generation methods for tabular microdata","Applied Sciences (Switzerland)","","20763417","10.3390/app14145975","","Featured Application: This study's findings hold significant implications for enhancing data privacy and utility in healthcare analytics. By evaluating synthetic data generation methods like CTGAN, TVAE, CopulaGAN and Copula across diverse medical datasets containing sensitive patient information, such as genetic profiles and medical histories, the research aims to improve the development of predictive models without compromising patient privacy. The generation of synthetic data holds significant promise for augmenting limited datasets while avoiding privacy issues, facilitating research, and enhancing machine learning models' robustness. Generative Adversarial Networks (GANs) stand out as promising tools, employing two neural networks—generator and discriminator—to produce synthetic data that mirrors real data distributions. This study evaluates GAN variants (CTGAN, CopulaGAN), a variational autoencoder, and copulas on diverse real datasets of different complexity encompassing numerical and categorical attributes. The results highlight CTGAN's sensitivity to training parameters and TVAE's robustness across datasets. Scalability challenges persist, with GANs demanding substantial computational resources. TVAE stands out for its high utility across all datasets, even for high-dimensional data, though it incurs higher privacy risks, which is indicative of the curse of dimensionality. While no single model universally excels, understanding the trade-offs and leveraging model strengths can significantly enhance synthetic data generation (SDG). Future research should focus on adaptive learning mechanisms, scalability enhancements, and standardized evaluation metrics to advance SDG methods effectively. Addressing these challenges will foster broader adoption and application of synthetic data.","2024","2024-12-03 03:35:58","2024-12-03 03:35:58","","","","14","14","","","","","","","","","","","","","","","","","Citation Key: Miletic2024","","","","generative adversarial networks; GAN; copula; synthetic data; variational autoencoder","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"8Z3RVY4V","journalArticle","2024","Subasi, Abdulhamit","Artificial intelligence in drug discovery and development","Applications of Artificial Intelligence in Healthcare and Biomedicine","","","10.1016/B978-0-443-22308-2.00018-4","","The demand for more effective and efficient methods to discover and create new therapeutic agents drives the ongoing evolution of the drug discovery and development industry. Artificial intelligence (AI) has become a potent tool in this field in recent years, transforming different phases of the drug discovery and development process. This chapter presents an application of AI in drug development and discovery, emphasizing its potential to accelerate the identification of novel drug candidates and enhance drug design. We go over the most important AI methods, including deep learning, and machine learning, as well as their uses in target identification. We also discuss the difficulties and restrictions that come with integrating AI into the pharmaceutical sector. The impact of AI on drug discovery and development, as well as its potential to completely disrupt how we find and deliver new treatments, are highlighted in the chapter's conclusion.","2024","2024-12-03 03:35:58","2024-12-03 03:35:58","","417–454","","August 2021","23","","","","","","","","","","","","","","","","","Citation Key: Subasi2024 ISBN: 9780443223082","","","","Deep learning; Machine learning; Drug development; Drug discovery; Artificial intelligence (AI); Target identification; Virtual screening; Lead optimization","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"7L55HG8H","journalArticle","2024","Tang, Xiangru; Dai, Howard; Knight, Elizabeth; Wu, Fang; Li, Yunyang; Li, Tianxiao; Gerstein, Mark","A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation","Briefings in Bioinformatics","","14774054","10.1093/bib/bbae338","","Artificial intelligence (AI)-driven methods can vastly improve the historically costly drug design process, with various generative models already in widespread use. Generative models for de novo drug design, in particular, focus on the creation of novel biological compounds entirely from scratch, representing a promising future direction. Rapid development in the field, combined with the inherent complexity of the drug design process, creates a difficult landscape for new researchers to enter. In this survey, we organize de novo drug design into two overarching themes: small molecule and protein generation. Within each theme, we identify a variety of subtasks and applications, highlighting important datasets, benchmarks, and model architectures and comparing the performance of top models. We take a broad approach to AI-driven drug design, allowing for both micro-level comparisons of various methods within each subtask and macro-level observations across different fields. We discuss parallel challenges and approaches between the two applications and highlight future directions for AI-driven de novo drug design as a whole. An organized repository of all covered sources is available at https://github.com/gersteinlab/GenAI4Drug.","2024","2024-12-03 03:35:58","2024-12-03 03:35:58","","","","4","25","","","","","","","","","","","","","","","","","Citation Key: Tang2024 arXiv: 2402.08703 PMID: 39007594 tex.arxivid: 2402.08703","","","","drug design; generative model; molecule generation; protein generation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JUXVCZLI","journalArticle","2024","Mariam, Zamara; Niazi, Sarfaraz K.; Magoola, Matthias","Unlocking the future of drug development: Generative AI, digital twins, and beyond","BioMedInformatics","","26737426","10.3390/biomedinformatics4020079","","This article delves into the intersection of generative AI and digital twins within drug discovery, exploring their synergistic potential to revolutionize pharmaceutical research and development. Through various instances and examples, we illuminate how generative AI algorithms, capable of simulating vast chemical spaces and predicting molecular properties, are increasingly integrated with digital twins of biological systems to expedite drug discovery. By harnessing the power of computational models and machine learning, researchers can design novel compounds tailored to specific targets, optimize drug candidates, and simulate their behavior within virtual biological environments. This paradigm shift offers unprecedented opportunities for accelerating drug development, reducing costs, and, ultimately, improving patient outcomes. As we navigate this rapidly evolving landscape, collaboration between interdisciplinary teams and continued innovation will be paramount in realizing the promise of generative AI and digital twins in advancing drug discovery.","2024","2024-12-03 03:35:59","2024-12-03 03:35:59","","1441–1456","","2","4","","","","","","","","","","","","","","","","","Citation Key: Mariam2024","","","","generative AI; digital twins; drug development; prospective analysis","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"UM2VUEDN","journalArticle","2022","Kokosi, Theodora; Harron, Katie","Synthetic data in medical research","BMJ Medicine","","","10.1136/bmjmed-2022-000167","","### Key messages ¿ Synthetic data have the potential to improve medical research while minimising the need to access personal data; Theodora Kokosi and Katie Harron explain what they are and how they are used. Demand to access high quality data at the individual level for medical and healthcare","2022","2024-12-03 03:35:59","2024-12-03 03:35:59","","e000167","","1","1","","","","","","","","","","","","","","","","","Citation Key: Kokosi2022","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DZT5UUKG","journalArticle","2023","Candelon, François; Gupta, Abhishek; Krayer, Lisa; Zhukov, Leonid","The CEO ' s guide to the generative AI revolution","Boston Consulting Group","","","","https://www.bcg.com/publications/2023/ceo-guide-to-ai-revolution?utm_source=newsletter&utm_medium=email&utm_campaign=exploring_the_benefits_of_people_analytics_at_wharton_people_analytics_conference&utm_term=2023-03-21","Generative AI has the potential to disrupt nearly every industry—promising both competitive advantage and creative destruction. CEOs, who are likely several steps removed from the technology itself, may feel uncertain about their next move. But the priority for leaders isn't to fully immerse themselves in the technology; instead, they should focus on how generative AI will impact their organizations and their industries, and what strategic choices will enable them to exploit opportunities and manage challenges. These choices are centered on three key pillars: Potential. Identify the uses cases that will differentiate your organization. People. Adapt your organizational structures and prepare your employees to support deployment. Policies. Set up ethical guardrails and legal protections. Each of these pillars involves short- and long-term considerations—and many unanswered questions. But CEOs need to prepare for the moment when their current business models become obsolete. Here's how to strategize for that future.","2023","2024-12-03 03:35:59","2024-12-03 03:35:59","","1–13","","","","","","","","","","","","","","","","","","","","Citation Key: Candelon2023","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"NTPN33BP","journalArticle","2024","Javan, Ramin; Cole, Jamie; Hsiao, Sabrina; Cronquist, Brennan; Monfared, Ashkan","Integration of AI-generated images in clinical otolaryngology","Curēus","","","10.7759/cureus.68313","","","2024","2024-12-03 03:35:59","2024-12-03 03:35:59","","6–11","","October 2023","16","","Cureus","","","","","","","","","","","","","","","Citation Key: Javan2024","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"8CXEJCZ9","journalArticle","2022","Hamid, Taezeen; Chhabra, Megha; Ravulakollu, Kiran; Singh, Prabjot; Dalal, Sunil; Dewan, Ritu","A review on artificial intelligence in orthopaedics","Proceedings of the 2022 9th International Conference on Computing for Sustainable Global Development, INDIACom 2022","","","10.23919/INDIACom54597.2022.9763178","","Artificial intelligence (AI) is a booming technology in today's world that is poised to usher in a new era in the globe by developing intelligent machines capable of solving complicated issues and making decisions based on pattern recognition. AI is impacting enormously various domains and one such domain is healthcare. As medical data is increasing exponentially, AI has the potential of taking over monotonous tasks which otherwise can lead to burnout among medical professionals. The question is can surgical robots replace human surgeons? AI assists radiologists to ameliorate diagnostic accuracy and averting human errors and observer fatigue. Deep learning models such as Convolutional Neural Networks (CNN), are being used for image classification. AI can be useful in saving human lives by analysing the medical records of patients and thus predicting the criticality in patients. The study of skeletal radiographs and the interpretation of radiologic images such as Magnetic Resonance Imaging (MRI) can both benefit from AI. The main aim of this paper is to present a comparative study and gap analysis of the recent work in the field of orthopaedics using AI. This will provide readers with an understanding of the different models that have been used in the field and the accuracy of those models.","2022","2024-12-03 03:35:59","2024-12-03 03:35:59","","365–369","","","","","","","","","","","","","","","","","","","","Citation Key: Hamid2022 ISBN: 9789380544441","","","","machine learning; Convolutional neural networks; magnetic resonance imaging; support vector machines","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"UQNHYNL8","journalArticle","2024","Dhudum, Rushikesh; Ganeshpurkar, Ankit; Pawar, Atmaram","Revolutionizing drug discovery: a comprehensive review of AI applications","Drugs and Drug Candidates","","","10.3390/ddc3010009","","The drug discovery and development process is very lengthy, highly expensive, and extremely complex in nature. Considering the time and cost constraints associated with conventional drug discovery, new methods must be found to enhance the declining efficiency of traditional approaches. Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Advancements in AI and machine learning (ML) techniques have revolutionized their applications to drug discovery and development. This review illuminates the profound influence of AI on diverse aspects of drug discovery, encompassing drug-target identification, molecular properties, compound analysis, drug development, quality assurance, and drug toxicity assessment. ML algorithms play an important role in testing systems and can predict important aspects such as the pharmacokinetics and toxicity of drug candidates. This review not only strengthens the theoretical foundation and development of this technology, but also explores the myriad challenges and promising prospects of AI in drug discovery and development. The combination of AI and drug discovery offers a promising strategy to overcome the challenges and complexities of the pharmaceutical industry.","2024","2024-12-03 03:35:59","2024-12-03 03:35:59","","148–171","","1","3","","","","","","","","","","","","","","","","","Citation Key: Dhudum2024","","","","machine learning; deep learning; artificial intelligence; drug discovery","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JYVYCB5L","journalArticle","2023","Sanjeeva, Polepaka; Reddy, Vanipenta Balasri Nitin; Goud, Jagirdar Indraj; Prasad, Aavula Guru; Pathani, Ashish","TEXT2AV - automated text to audio and video conversion","E3S Web of Conferences","","22671242","10.1051/e3sconf/202343001027","","The paper aims to develop a machine learning-based system that can automatically convert text to audio and text to video as per the user's request. Suppose Reading a large text is difficult for anyone, but this TTS model makes it easy by converting text into audio by producing the audio output by an avatar with lip sync to make it look more attractive and human-like interaction in many languages. The TTS model is built based on Waveform Recurrent Neural Networks (WaveRNN). It is a type of auto-regressive model that predicts future data based on the present. The system identifies the keywords in the input texts and uses diffusion models to generate high-quality video content. The system uses GAN (Generative Adversarial Network) to generate videos. Frame Interpolation is used to combine different frames into two adjacent frames to generate a slow- motion video. WebVid-20M, Image-Net, and Hugging-Face are the datasets used for Text video and LibriTTS corpus, and Lip Sync are the dataset used for text-to-audio. The System provides a user-friendly and automated platform to the user which takes text as input and produces either a high-quality audio or high-resolution video quickly and efficiently.","2023","2024-12-03 03:35:59","2024-12-03 03:35:59","","","","","430","","","","","","","","","","","","","","","","","Citation Key: Sanjeeva2023","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"YA4G7UYR","journalArticle","2023","Fathoni, Ahmad Faisal Choiril Anam","Leveraging generative AI solutions in art and design education: Bridging sustainable creativity and fostering academic integrity for innovative society","E3S Web of Conferences","","22671242","10.1051/e3sconf/202342601102","","Artificial intelligence (AI) has transformed art and design education, giving students new ways to create, explore, and learn. Unfortunately, there is fear among academicians that students will use AI, especially text-to-image generators like Midjourney or Dall-E, as an illegal shortcut in creating their work. This article examines how generative AI solutions, such as text-to-image generators, can help students create innovative and sustainable designs while promoting academic integrity. The article shows how AI in art and design education can equip students with the skills and knowledge to succeed in a rapidly changing digital landscape. This research uses a qualitative method by analyzing the apps and literature reviews in journals and documents related to the problems studied. Case studies show how AI-based solutions can help students create innovative and sustainable designs while promoting academic integrity. Integrating controlled AI- based approaches in art and design education can promote academic integrity, creativity, and sustainability. AI-based art and design education solutions may help society become more innovative and sustainable. This article concludes that art and design educators must embrace AI-based solutions to prepare students for a rapidly changing digital world.","2023","2024-12-03 03:35:59","2024-12-03 03:35:59","","","","","426","","","","","","","","","","","","","","","","","Citation Key: Fathoni2023","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"VKBI2GR5","journalArticle","2022","Gonzalez-Abril, Luis; Angulo, Cecilio; Ortega, Juan Antonio; Lopez-Guerra, José Luis","Statistical validation of synthetic data for lung cancer patients generated by using generative adversarial networks","Electronics (Switzerland)","","20799292","10.3390/electronics11203277","","The development of healthcare patient digital twins in combination with machine learning technologies helps doctors in therapeutic prescription and in minimally invasive intervention procedures. The confidentiality of medical records or limited data availability in many health domains are drawbacks that can be overcome with the generation of synthetic data conformed to real data. The use of generative adversarial networks (GAN) for the generation of synthetic data of lung cancer patients has been previously introduced as a tool to solve this problem in the form of anonymized synthetic patients. However, generated synthetic data are mainly validated from the machine learning domain (loss functions) or expert domain (oncologists). In this paper, we propose statistical decision making as a validation tool: Is the model good enough to be used? Does the model pass rigorous hypothesis testing criteria? We show for the case at hand how loss functions and hypothesis validation are not always well aligned.","2022","2024-12-03 03:35:59","2024-12-03 03:35:59","","1–15","","20","11","","","","","","","","","","","","","","","","","Citation Key: Gonzalez-Abril2022","","","","personalized medicine; generative adversarial network; lung cancer; validation tools","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DZT6TUWY","journalArticle","2023","Owan, Valentine Joseph; Abang, Kinsgley Bekom; Idika, Delight Omoji; Etta, Eugene Onor; Bassey, Bassey Asuquo","Exploring the potential of artificial intelligence tools in educational measurement and assessment","Eurasia Journal of Mathematics, Science and Technology Education","","13058223","10.29333/ejmste/13428","","Artificial intelligence (AI) is transforming various industries, and education is no exception. Rapid advancements in AI technology have become essential for educators and educational assessment professionals to enhance teaching and learning experiences. AI-powered educational assessment tools provide numerous benefits, including improving the accuracy and efficiency of assessments, generating personalized feedback for students, and enabling teachers to adapt their teaching strategies to meet the unique needs of each student. Therefore, AI has the potential to revolutionize the way education is delivered and assessed, ultimately leading to better educational outcomes for students. This paper explores the various applications of AI tools in educational measurement and assessment. Specifically, it discusses the integration of large language AI models in classroom assessment, in specific areas such as test purpose determination and specification, developing, test blueprint, test item generation/development, preparation of test instructions, item assembly/selection, test administration, test scoring, interpretation of test results, test analysis/appraisal, and reporting. It analyses the role of teachers in AI-based assessment and the challenges of using AI-powered tools in educational assessment. Finally, the paper presents strategies to address these challenges and enhance the effectiveness of AI in educational assessment. In conclusion, using AI in educational assessment has benefits and limitations. As such, educators, policymakers, and stakeholders must work together to develop strategies that maximize the benefits of AI in educational assessment while mitigating the associated risks. The application of AI in educational assessment can ultimately transform education, improve learning outcomes, and equip students with the skills needed to succeed in the 21st century.","2023","2024-12-03 03:36:00","2024-12-03 03:36:00","","","","8","19","","","","","","","","","","","","","","","","","Citation Key: Owan2023","","","","ChatGPT; technology; skills; educational assessment; reliability; validity","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ZYATW9GK","journalArticle","2023","Mao, Yaoli; Rafner, Janet; Wang, Yi; Sherson, Jacob","A hybrid intelligence approach to training generative design assistants: Partnership between human experts and AI enhanced co-creative tools","Frontiers in Artificial Intelligence and Applications","","18798314","10.3233/FAIA230078","","The emergence of generative design (GD) has introduced a new paradigm for co-creation between human experts and AI systems. Empirical findings have shown promising outcomes such as augmented human cognition and highly creative design products. Barriers still remain that prevent individuals from perceiving and adopting AI, entering into collaboration with AI and sustaining it over time. It is even more challenging for creative design industries to adopt and trust AI where these professionals value individual style and expression, and therefore require highly personalized and specialized AI assistance. In this paper, we present a holistic hybrid intelligence (HI) approach for individual experts to train and personalize their GD assistants on the fly. Our contribution to human-AI interaction is three-fold including i) a programmable common language between human and AI to represent the expert's design goals to the generative algorithm, ii) a human-centered continual training loop to seamlessly integrate AI-training into the expert's task workflow, iii) a hybrid intelligence narrative to address the psychological willingness to spend time and effort training such a virtual assistant. This integral approach enables individuals to directly communicate design goals to AI and seeks to create a psychologically safe space for adopting, training and improving AI without the fear of job-replacement. We concertize these constructs through a newly developed Hybrid Intelligence Technology Acceptance Model (HI-TAM). We used mixed methods to empirically evaluate this approach through the lens of HI-TAM with 8 architectural professionals working individually with a GD assistant to co-create floor plan layouts of office buildings. We believe that the proposed approach enables individual professionals, even non-technical ones, to adopt and trust AI-enhanced co-creative tools.","2023","2024-12-03 03:36:00","2024-12-03 03:36:00","","108–123","","","368","","","","","","","","","","","","","","","","","Citation Key: Mao2023 ISBN: 9781643683942","","","","Technology Acceptance Model; Communication; Personalization; Co-creation; Human AI language; Partnership; Tool adoption; Training generative AI assistants","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"WEVN5MLR","journalArticle","2023","BCG","How people create and destroy value with generative AI","BCG Henderson Institut","","","","","Generative AI will be a powerful enabler of competitive advantage for companies that crack the code of adoption. In a first-of-its-kind scientific experiment, we found that when GenAI is used in the right way, and for the right tasks, its capabilities are such that people's efforts to improve the quality of its output can backfire. But it isn't obvious when the new technology is (or is not) a good fit, and the persuasive abilities of the tool make it hard to spot a mismatch. This can have serious consequences: When it is used in the wrong way, for the wrong tasks, generative AI can cause significant value destruction.","2023","2024-12-03 03:36:00","2024-12-03 03:36:00","","1–16","","","","","","","","","","","","","","","","","","","","Citation Key: BCG2023","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"TG8BI66N","journalArticle","2024","Liu, Yonggang; Awang, Hapini; Mansor, Nur Suhaili","To explore sora's potential influence on future education","International Journal of Multidisciplinary and Current Educational Research","","","","","","2024","2024-12-03 03:36:00","2024-12-03 03:36:00","","98–105","","5","6","","","","","","","","","","","","","","","","","Citation Key: Liu2024","","","","generative artificial intelligence; education; visualization; gai; potential; sora","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"MVL3MKM8","journalArticle","2023","Andrianov, Alexander M.; Shuldau, Mikita A.; Furs, Konstantin V.; Yushkevich, Artsemi M.; Tuzikov, Alexander V.","AI-driven de novo design and molecular modeling for discovery of small-molecule compounds as potential drug candidates targeting SARS-CoV-2 main protease","International Journal of Molecular Sciences","","14220067","10.3390/ijms24098083","","Over the past three years, significant progress has been made in the development of novel promising drug candidates against COVID-19. However, SARS-CoV-2 mutations resulting in the emergence of new viral strains that can be resistant to the drugs used currently in the clinic necessitate the development of novel potent and broad therapeutic agents targeting different vulnerable spots of the viral proteins. In this study, two deep learning generative models were developed and used in combination with molecular modeling tools for de novo design of small molecule compounds that can inhibit the catalytic activity of SARS-CoV-2 main protease (Mpro), an enzyme critically important for mediating viral replication and transcription. As a result, the seven best scoring compounds that exhibited low values of binding free energy comparable with those calculated for two potent inhibitors of Mpro, via the same computational protocol, were selected as the most probable inhibitors of the enzyme catalytic site. In light of the data obtained, the identified compounds are assumed to present promising scaffolds for the development of new potent and broad-spectrum drugs inhibiting SARS-CoV-2 Mpro, an attractive therapeutic target for anti-COVID-19 agents.","2023","2024-12-03 03:36:00","2024-12-03 03:36:00","","","","9","24","","","","","","","","","","","","","","","","","Citation Key: Andrianov2023 PMID: 37175788","","","","deep learning; SARS-CoV-2; anti-SARS-CoV-2 drugs; binding free energy calculations; generative autoencoder; main protease; molecular docking; molecular dynamics; virtual screening","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RKD2PYX5","journalArticle","2022","Kokosi, Theodora; De Stavola, Bianca; Mitra, Robin; Frayling, Lora; Doherty, Aiden; Dove, Iain; Sonnenberg, Pam; Harron, Katie","An overview of synthetic administrative data for research","International Journal of Population Data Science","","23994908","10.23889/ijpds.v7i1.1727","","Use of administrative data for research and for planning services has increased over recent decades due to the value of the large, rich information available. However, concerns about the release of sensitive or personal data and the associated disclosure risk can lead to lengthy approval processes and restricted data access. This can delay or prevent the production of timely evidence. A promising solution to facilitate more efficient data access is to create synthetic versions of the original datasets which are less likely to hold confidential information and can minimise disclosure risk. Such data may be used as an interim solution, allowing researchers to develop their analysis plans on non-disclosive data, whilst waiting for access to the real data. We aim to provide an overview of the background and uses of synthetic data and describe common methods used to generate synthetic data in the context of UK administrative research. We propose a simplified terminology for categories of synthetic data (univariate, multivariate, and complex modality synthetic data) as well as a more comprehensive description of the terminology used in the existing literature and illustrate challenges and future directions for research.","2022","2024-12-03 03:36:00","2024-12-03 03:36:00","","","","1","7","","","","","","","","","","","","","","","","","Citation Key: Kokosi2022a PMID: 37650026","","","","synthetic data; administrative datasets; data confidentiality; data linkage; data utility; statistical disclosure control","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"8H2YLYYD","journalArticle","2022","Narayanan, Ravi Ram; Durga, Narayanamoorthy; Nagalakshmi, Sethuraman","Impact of artificial intelligence (AI) on drug discovery and product development","Indian Journal of Pharmaceutical Education and Research","","00195464","10.5530/ijper.56.3s.146","","Artificial Intelligence (AI) had transfigured different sectors in society, where the pharmaceutical sector is not an exceptional case. Pharmaceutical sectors have reached new heights with the emergence of these sophisticated technologies. The evolution of artificial intelligence in the pharmaceutical industry is in a growth phase opening the possibilities of discovering many new drugs. The diseases affecting humans are increasing tremendously whereas the drugs which are available to treat or cure are very much minimal. But this kind of scenario will not be present in the future because of the combination of artificial intelligence and the pharmaceutical industry which results in faster discovery of drugs with increased clinical outcomes. There was a shift in the paradigm of various stages in drug discovery because of the utilization of artificial intelligence. Each stage of drug discovery involves a certain timeline that can be cut down with the help of artificial intelligence. Many pharma companies are engaged with AI-based drug discovery approaches for treating various diseases like Parkinson's disease diabetes, Alzheimer's, Obsessive Compulsive Disorder, etc., AI is also being employed in product development for the fabrication of nanomedicines and nanorobots. Few AI-based drugs are already in the phase of clinical trials which indicates the growth of AI-driven drug discovery. In this review, we have highlighted the application of AI in drug discovery and product development of pharmaceuticals.","2022","2024-12-03 03:36:01","2024-12-03 03:36:01","","s387–s397","","3 Suppl.","56","","","","","","","","","","","","","","","","","Citation Key: Narayanan2022","","","","Machine learning; Drug discovery; Drug delivery; Algorithm; Era of machines","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DHQ5RZE8","journalArticle","2024","Al-kfairy, Mousa; Mustafa, Dheya; Kshetri, Nir; Insiew, Mazen; Alfandi, Omar","Ethical challenges and solutions of generative AI: An interdisciplinary perspective","Informatics","","22279709","10.3390/informatics11030058","","This paper conducts a systematic review and interdisciplinary analysis of the ethical challenges of generative AI technologies (N = 37), highlighting significant concerns such as privacy, data protection, copyright infringement, misinformation, biases, and societal inequalities. The ability of generative AI to produce convincing deepfakes and synthetic media, which threaten the foundations of truth, trust, and democratic values, exacerbates these problems. The paper combines perspectives from various disciplines, including education, media, and healthcare, underscoring the need for AI systems that promote equity and do not perpetuate social inequalities. It advocates for a proactive approach to the ethical development of AI, emphasizing the necessity of establishing policies, guidelines, and frameworks that prioritize human rights, fairness, and transparency. The paper calls for a multidisciplinary dialogue among policymakers, technologists, and researchers to ensure responsible AI development that conforms to societal values and ethical standards. It stresses the urgency of addressing these ethical concerns and advocates for the development of generative AI in a socially beneficial and ethically sound manner, contributing significantly to the discourse on managing AI's ethical implications in the modern digital era. The study highlights the theoretical and practical implications of these challenges and suggests a number of future research directions.","2024","2024-12-03 03:36:01","2024-12-03 03:36:01","","58","","3","11","","","","","","","","","","","","","","","","","Citation Key: Al-kfairy2024","","","","data protection; privacy; bias in ai; bias in AI; copyright; deepfakes; ethical challenges","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"BKI9KTLY","journalArticle","2022","Lameras, Petros; Arnab, Sylvester","Power to the teachers: An exploratory review on artificial intelligence in education","Information (Switzerland)","","20782489","10.3390/info13010014","","This exploratory review attempted to gather evidence from the literature by shedding light on the emerging phenomenon of conceptualising the impact of artificial intelligence in education. The review utilised the PRISMA framework to review the analysis and synthesis process encompassing the search, screening, coding, and data analysis strategy of 141 items included in the corpus. Key findings extracted from the review incorporate a taxonomy of artificial intelligence applications with associated teaching and learning practice and a framework for helping teachers to develop and self-reflect on the skills and capabilities envisioned for employing artificial intelligence in education. Implications for ethical use and a set of propositions for enacting teaching and learning using artificial intelligence are demarcated. The findings of this review contribute to developing a better understanding of how artificial intelligence may enhance teachers' roles as catalysts in designing, visualising, and orchestrating AI-enabled teaching and learning, and this will, in turn, help to proliferate AI-systems that render computational representations based on meaningful data-driven inferences of the pedagogy, domain, and learner models.","2022","2024-12-03 03:36:01","2024-12-03 03:36:01","","","","1","13","","","","","","","","","","","","","","","","","Citation Key: Lameras2022","","","","Teachers; Artificial intelligence in education; AIED ethics; AIED skills and competencies; AIED tools and applications","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"5CPFC48R","journalArticle","2024","Sufi, Fahim","Addressing data scarcity in the medical domain: a GPT-based approach for synthetic data generation and feature extraction","Information (Switzerland)","","20782489","10.3390/info15050264","","This research confronts the persistent challenge of data scarcity in medical machine learning by introducing a pioneering methodology that harnesses the capabilities of Generative Pre-trained Transformers (GPT). In response to the limitations posed by a dearth of labeled medical data, our approach involves the synthetic generation of comprehensive patient discharge messages, setting a new standard in the field with GPT autonomously generating 20 fields. Through a meticulous review of the existing literature, we systematically explore GPT's aptitude for synthetic data generation and feature extraction, providing a robust foundation for subsequent phases of the research. The empirical demonstration showcases the transformative potential of our proposed solution, presenting over 70 patient discharge messages with synthetically generated fields, including severity and chances of hospital re-admission with justification. Moreover, the data had been deployed in a mobile solution where regression algorithms autonomously identified the correlated factors for ascertaining the severity of patients' conditions. This study not only establishes a novel and comprehensive methodology but also contributes significantly to medical machine learning, presenting the most extensive patient discharge summaries reported in the literature. The results underscore the efficacy of GPT in overcoming data scarcity challenges and pave the way for future research to refine and expand the application of GPT in diverse medical contexts.","2024","2024-12-03 03:36:01","2024-12-03 03:36:01","","","","5","15","","","","","","","","","","","","","","","","","Citation Key: Sufi2024","","","","large language models; GPT; synthetic data generation; feature extraction; medical date labeling; prompt engineering","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"I4U2VWTE","journalArticle","2023","Skandarani, Youssef; Jodoin, Pierre Marc; Lalande, Alain","GANs for medical image synthesis: An empirical study","Journal of Imaging","","2313433X","10.3390/jimaging9030069","","Generative adversarial networks (GANs) have become increasingly powerful, generating mind-blowing photorealistic images that mimic the content of datasets they have been trained to replicate. One recurrent theme in medical imaging, is whether GANs can also be as effective at generating workable medical data, as they are for generating realistic RGB images. In this paper, we perform a multi-GAN and multi-application study, to gauge the benefits of GANs in medical imaging. We tested various GAN architectures, from basic DCGAN to more sophisticated style-based GANs, on three medical imaging modalities and organs, namely: cardiac cine-MRI, liver CT, and RGB retina images. GANs were trained on well-known and widely utilized datasets, from which their FID scores were computed, to measure the visual acuity of their generated images. We further tested their usefulness by measuring the segmentation accuracy of a U-Net trained on these generated images and the original data. The results reveal that GANs are far from being equal, as some are ill-suited for medical imaging applications, while others performed much better. The top-performing GANs are capable of generating realistic-looking medical images by FID standards, that can fool trained experts in a visual Turing test and comply to some metrics. However, segmentation results suggest that no GAN is capable of reproducing the full richness of medical datasets.","2023","2024-12-03 03:36:01","2024-12-03 03:36:01","","1–16","","3","9","","","","","","","","","","","","","","","","","Citation Key: Skandarani2023 arXiv: 2105.05318 tex.arxivid: 2105.05318","","","","GAN; adversarial; CT; heart; liver; MRI; retina","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"Q9CZBYUQ","journalArticle","2023","Kebaili, Aghiles; Lapuyade-Lahorgue, Jérôme; Ruan, Su","Deep learning approaches for data augmentation in medical imaging: a review","Journal of Imaging","","2313433X","10.3390/jimaging9040081","","Deep learning has become a popular tool for medical image analysis, but the limited availability of training data remains a major challenge, particularly in the medical field where data acquisition can be costly and subject to privacy regulations. Data augmentation techniques offer a solution by artificially increasing the number of training samples, but these techniques often produce limited and unconvincing results. To address this issue, a growing number of studies have proposed the use of deep generative models to generate more realistic and diverse data that conform to the true distribution of the data. In this review, we focus on three types of deep generative models for medical image augmentation: variational autoencoders, generative adversarial networks, and diffusion models. We provide an overview of the current state of the art in each of these models and discuss their potential for use in different downstream tasks in medical imaging, including classification, segmentation, and cross-modal translation. We also evaluate the strengths and limitations of each model and suggest directions for future research in this field. Our goal is to provide a comprehensive review about the use of deep generative models for medical image augmentation and to highlight the potential of these models for improving the performance of deep learning algorithms in medical image analysis.","2023","2024-12-03 03:36:01","2024-12-03 03:36:01","","","","4","9","","","","","","","","","","","","","","","","","Citation Key: Kebaili2023 arXiv: 2307.13125 tex.arxivid: 2307.13125","","","","deep learning; generative models; data augmentation; diffusion models; medical imaging; variational autoencoders","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"BDSEU9TD","journalArticle","2023","Wessel, Michael; Adam, Martin; Benlian, Alexander; Majchrzak, Ann; Thies, Ferdinand","Call for papers to the special issue : Generative AI and its transformative value for digital platforms","Journal of management in in","","","","","","2023","2024-12-03 03:36:01","2024-12-03 03:36:01","","1–6","","","","","","","","","","","","","","","","","","","","Citation Key: Wessel2023","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"AG99IS7Z","journalArticle","2023","Suresh, Krish; Cohen, Michael S.; Hartnick, Christopher J.; Bartholomew, Ryan A.; Lee, Daniel J.; Crowson, Matthew G.","Generation of synthetic tympanic membrane images: Development, human validation, and clinical implications of synthetic data","PLOS Digital Health","","27673170","10.1371/journal.pdig.0000202","http://dx.doi.org/10.1371/journal.pdig.0000202","Synthetic clinical images could augment real medical image datasets, a novel approach in otolaryngology-head and neck surgery (OHNS). Our objective was to develop a generative adversarial network (GAN) for tympanic membrane images and to validate the quality of synthetic images with human reviewers. Our model was developed using a state-of-the-art GAN architecture, StyleGAN2-ADA. The network was trained on intraoperative high-definition (HD) endoscopic images of tympanic membranes collected from pediatric patients undergoing myringotomy with possible tympanostomy tube placement. A human validation survey was administered to a cohort of OHNS and pediatrics trainees at our institution. The primary measure of model quality was the Frechet Inception Distance (FID), a metric comparing the distribution of generated images with the distribution of real images. The measures used for human reviewer validation were the sensitivity, specificity, and area under the curve (AUC) for humans' ability to discern synthetic from real images. Our dataset comprised 202 images. The best GAN was trained at 512x512 image resolution with a FID of 47.0. The progression of images through training showed stepwise “learning” of the anatomic features of a tympanic membrane. The validation survey was taken by 65 persons who reviewed 925 images. Human reviewers demonstrated a sensitivity of 66","2023","2024-12-03 03:36:01","2024-12-03 03:36:01","","1–10","","2","2","","","","","","","","","","","","","","","","","Citation Key: Suresh2023 ISBN: 1111111111","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"3PZC8WRP","journalArticle","2022","Thambawita, Vajira; Salehi, Pegah; Sheshkal, Sajad Amouei; Hicks, Steven A.; Hammer, Hugo L.; Parasa, Sravanthi; de Lange, Thomas; Halvorsen, Pål; Riegler, Michael A.","SinGAN-Seg: Synthetic training data generation for medical image segmentation","PLoS ONE","","19326203","10.1371/journal.pone.0267976","","Analyzing medical data to find abnormalities is a time-consuming and costly task, particularly for rare abnormalities, requiring tremendous efforts from medical experts. Therefore, artificial intelligence has become a popular tool for the automatic processing of medical data, acting as a supportive tool for doctors. However, the machine learning models used to build these tools are highly dependent on the data used to train them. Large amounts of data can be difficult to obtain in medicine due to privacy reasons, expensive and time-consuming annotations, and a general lack of data samples for infrequent lesions. In this study, we present a novel synthetic data generation pipeline, called SinGAN-Seg, to produce synthetic medical images with corresponding masks using a single training image. Our method is different from the traditional generative adversarial networks (GANs) because our model needs only a single image and the corresponding ground truth to train. We also show that the synthetic data generation pipeline can be used to produce alternative artificial segmentation datasets with corresponding ground truth masks when real datasets are not allowed to share. The pipeline is evaluated using qualitative and quantitative comparisons between real data and synthetic data to show that the style transfer technique used in our pipeline significantly improves the quality of the generated data and our method is better than other state-of-the-art GANs to prepare synthetic images when the size of training datasets are limited. By training UNet++ using both real data and the synthetic data generated from the SinGAN-Seg pipeline, we show that the models trained on synthetic data have very close performances to those trained on real data when both datasets have a considerable amount of training data. In contrast, we show that synthetic data generated from the SinGAN-Seg pipeline improves the performance of segmentation models when training datasets do not have a considerable amount of data. All experiments were performed using an open dataset and the code is publicly available on GitHub.","2022","2024-12-03 03:36:01","2024-12-03 03:36:01","","1–24","","5 May","17","","","","","","","","","","","","","","","","","Citation Key: Thambawita2022 arXiv: 2107.00471 ISBN: 1111111111 PMID: 35500005 tex.arxivid: 2107.00471","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KKWB6LVL","journalArticle","2022","Hahn, Waldemar; Schütte, Katharina; Schultz, Kristian; Wolkenhauer, Olaf; Sedlmayr, Martin; Schuler, Ulrich; Eichler, Martin; Bej, Saptarshi; Wolfien, Markus","Contribution of synthetic data generation towards an improved patient stratification in palliative care","Journal of Personalized Medicine","","20754426","10.3390/jpm12081278","","AI model development for synthetic data generation to improve Machine Learning (ML) methodologies is an integral part of research in Computer Science and is currently being transferred to related medical fields, such as Systems Medicine and Medical Informatics. In general, the idea of personalized decision-making support based on patient data has driven the motivation of researchers in the medical domain for more than a decade, but the overall sparsity and scarcity of data are still major limitations. This is in contrast to currently applied technology that allows us to generate and analyze patient data in diverse forms, such as tabular data on health records, medical images, genomics data, or even audio and video. One solution arising to overcome these data limitations in relation to medical records is the synthetic generation of tabular data based on real world data. Consequently, ML-assisted decision-support can be interpreted more conveniently, using more relevant patient data at hand. At a methodological level, several state-of-the-art ML algorithms generate and derive decisions from such data. However, there remain key issues that hinder a broad practical implementation in real-life clinical settings. In this review, we will give for the first time insights towards current perspectives and potential impacts of using synthetic data generation in palliative care screening because it is a challenging prime example of highly individualized, sparsely available patient information. Taken together, the reader will obtain initial starting points and suitable solutions relevant for generating and using synthetic data for ML-based screenings in palliative care and beyond.","2022","2024-12-03 03:36:01","2024-12-03 03:36:01","","1–15","","8","12","","","","","","","","","","","","","","","","","Citation Key: Hahn2022","","","","personalized medicine; synthetic data generation; GANs; palliative care; screening","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"U5YTKEJL","journalArticle","2024","Visan, Anita Ioana; Negut, Irina","Integrating artificial intelligence for drug discovery in the context of revolutionizing drug delivery","Life (Chicago, Ill. : 1978)","","20751729","10.3390/life14020233","","Drug development is expensive, time-consuming, and has a high failure rate. In recent years, artificial intelligence (AI) has emerged as a transformative tool in drug discovery, offering innovative solutions to complex challenges in the pharmaceutical industry. This manuscript covers the multifaceted role of AI in drug discovery, encompassing AI-assisted drug delivery design, the discovery of new drugs, and the development of novel AI techniques. We explore various AI methodologies, including machine learning and deep learning, and their applications in target identification, virtual screening, and drug design. This paper also discusses the historical development of AI in medicine, emphasizing its profound impact on healthcare. Furthermore, it addresses AI's role in the repositioning of existing drugs and the identification of drug combinations, underscoring its potential in revolutionizing drug delivery systems. The manuscript provides a comprehensive overview of the AI programs and platforms currently used in drug discovery, illustrating the technological advancements and future directions of this field. This study not only presents the current state of AI in drug discovery but also anticipates its future trajectory, highlighting the challenges and opportunities that lie ahead.","2024","2024-12-03 03:36:01","2024-12-03 03:36:01","","","","2","14","","Life","","","","","","","","","","","","","","","Citation Key: Visan2024","","","","machine learning; deep learning; artificial intelligence; drug discovery; drug repurposing; pharmaceutical AI","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"64K2BY33","journalArticle","2022","Figueira, Alvaro; Vaz, Bruno","Survey on synthetic data generation, evaluation methods and gans","Mathematics","","22277390","10.3390/math10152733","","Synthetic data consists of artificially generated data. When data are scarce, or of poor quality, synthetic data can be used, for example, to improve the performance of machine learning models. Generative adversarial networks (GANs) are a state-of-the-art deep generative models that can generate novel synthetic samples that follow the underlying data distribution of the original dataset. Reviews on synthetic data generation and on GANs have already been written. However, none in the relevant literature, to the best of our knowledge, has explicitly combined these two topics. This survey aims to fill this gap and provide useful material to new researchers in this field. That is, we aim to provide a survey that combines synthetic data generation and GANs, and that can act as a good and strong starting point for new researchers in the field, so that they have a general overview of the key contributions and useful references. We have conducted a review of the state-of-the-art by querying four major databases: Web of Sciences (WoS), Scopus, IEEE Xplore, and ACM Digital Library. This allowed us to gain insights into the most relevant authors, the most relevant scientific journals in the area, the most cited papers, the most significant research areas, the most important institutions, and the most relevant GAN architectures. GANs were thoroughly reviewed, as well as their most common training problems, their most important breakthroughs, and a focus on GAN architectures for tabular data. Further, the main algorithms for generating synthetic data, their applications and our thoughts on these methods are also expressed. Finally, we reviewed the main techniques for evaluating the quality of synthetic data (especially tabular data) and provided a schematic overview of the information presented in this paper.","2022","2024-12-03 03:36:01","2024-12-03 03:36:01","","1–41","","15","10","","","","","","","","","","","","","","","","","Citation Key: Figueira2022","","","","generative adversarial networks; synthetic data generation; evaluation of synthetic data","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"GUDQ4URW","journalArticle","2022","Chui, Michael; Roberts, Roger; Yee, Lareina","Generative AI is here : How tools like ChatGPT could change your business","Quantum Black, AI by McKinsey","","","","","Generative AI and other foundation models are changing the AI game, taking assistive technology to a new level, reducing application development time, and bringing powerful capabilities to nontechnical users.","2022","2024-12-03 03:36:01","2024-12-03 03:36:01","","5","","December","","","","","","","","","","","","","","","","","","Citation Key: Chui2022","","","","ChatGPT; Generative AI","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"WNWXENZD","journalArticle","2023","Gonzales, Aldren; Guruswamy, Guruprabha; Smith, Scott R.","Synthetic data in health care: A narrative review","PLOS Digital Health","","27673170","10.1371/journal.pdig.0000082","http://dx.doi.org/10.1371/journal.pdig.0000082","Data are central to research, public health, and in developing health information technology (IT) systems. Nevertheless, access to most data in health care is tightly controlled, which may limit innovation, development, and efficient implementation of new research, products, services, or systems. Using synthetic data is one of the many innovative ways that can allow organizations to share datasets with broader users. However, only a limited set of literature is available that explores its potentials and applications in health care. In this review paper, we examined existing literature to bridge the gap and highlight the utility of synthetic data in health care. We searched PubMed, Scopus, and Google Scholar to identify peer-reviewed articles, conference papers, reports, and thesis/dissertations articles related to the generation and use of synthetic datasets in health care. The review identified seven use cases of synthetic data in health care: a) simulation and prediction research, b) hypothesis, methods, and algorithm testing, c) epidemiology/public health research, d) health IT development, e) education and training, f) public release of datasets, and g) linking data. The review also identified readily and publicly accessible health care datasets, databases, and sandboxes containing synthetic data with varying degrees of utility for research, education, and software development. The review provided evidence that synthetic data are helpful in different aspects of health care and research. While the original real data remains the preferred choice, synthetic data hold possibilities in bridging data access gaps in research and evidence-based policymaking.","2023","2024-12-03 03:36:02","2024-12-03 03:36:02","","1–16","","1","2","","","","","","","","","","","","","","","","","Citation Key: Gonzales2023 ISBN: 1111111111","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"CRXPHRI2","journalArticle","2023","Mavrogenis, Andreas F.; Scarlat, Marius M.","Artificial intelligence publications: synthetic data, patients, and papers","International Orthopaedics","","14325195","10.1007/s00264-023-05830-w","https://doi.org/10.1007/s00264-023-05830-w","","2023","2024-12-03 03:36:02","2024-12-03 03:36:02","","1395–1396","","6","47","","","","","","","","","","","","","","","","","Citation Key: Mavrogenis2023 ISBN: 0123456789 PMID: 37162553 Publisher: Springer Berlin Heidelberg","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"69NPFXAR","journalArticle","2023","Huang, Gaofeng; Jafari, Amir Hossein","Enhanced balancing GAN: minority-class image generation","Neural Computing and Applications","","14333058","10.1007/s00521-021-06163-8","https://doi.org/10.1007/s00521-021-06163-8","Generative adversarial networks (GANs) are one of the most powerful generative models, but always require a large and balanced dataset to train. Traditional GANs are not applicable to generate minority-class images in a highly imbalanced dataset. Balancing GAN (BAGAN) is proposed to mitigate this problem, but it is unstable when images in different classes look similar, e.g., flowers and cells. In this work, we propose a supervised autoencoder with an intermediate embedding model to disperse the labeled latent vectors. With the enhanced autoencoder initialization, we also build an architecture of BAGAN with gradient penalty (BAGAN-GP). Our proposed model overcomes the unstable issue in original BAGAN and converges faster to high-quality generations. Our model achieves high performance on the imbalanced scale-down version of MNIST Fashion, CIFAR-10, and one small-scale medical image dataset. https://github.com/GH920/improved-bagan-gp.","2023","2024-12-03 03:36:02","2024-12-03 03:36:02","","5145–5154","","7","35","","","","","","","","","","","","","","","","","Citation Key: Huang2023 arXiv: 2011.00189 ISBN: 0052102106 Publisher: Springer London tex.arxivid: 2011.00189","","","","Data augmentation; GAN; Imbalanced data; Image generation; Medical image","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"TXI6JJMA","journalArticle","2024","Sauvola, Jaakko; Tarkoma, Sasu; Klemettinen, Mika; Riekki, Jukka; Doermann, David","Future of software development with generative AI","Automated Software Engineering","","15737535","10.1007/s10515-024-00426-z","https://doi.org/10.1007/s10515-024-00426-z","Generative AI is regarded as a major disruption to software development. Platforms, repositories, clouds, and the automation of tools and processes have been proven to improve productivity, cost, and quality. Generative AI, with its rapidly expanding capabilities, is a major step forward in this field. As a new key enabling technology, it can be used for many purposes, from creative dimensions to replacing repetitive and manual tasks. The number of opportunities increases with the capabilities of large-language models (LLMs). This has raised concerns about ethics, education, regulation, intellectual property, and even criminal activities. We analyzed the potential of generative AI and LLM technologies for future software development paths. We propose four primary scenarios, model trajectories for transitions between them, and reflect against relevant software development operations. The motivation for this research is clear: the software development industry needs new tools to understand the potential, limitations, and risks of generative AI, as well as guidelines for using it.","2024","2024-12-03 03:36:02","2024-12-03 03:36:02","","1–8","","1","31","","","","","","","","","","","","","","","","","Citation Key: Sauvola2024 ISBN: 0123456789 Publisher: Springer US","","","","Generative AI; Real-time digital economy; Software development","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KELQP6GK","journalArticle","2023","Mosquera, Lucy; El Emam, Khaled; Ding, Lei; Sharma, Vishal; Zhang, Xue Hua; Kababji, Samer El; Carvalho, Chris; Hamilton, Brian; Palfrey, Dan; Kong, Linglong; Jiang, Bei; Eurich, Dean T.","A method for generating synthetic longitudinal health data","BMC Medical Research Methodology","","14712288","10.1186/s12874-023-01869-w","https://doi.org/10.1186/s12874-023-01869-w","Getting access to administrative health data for research purposes is a difficult and time-consuming process due to increasingly demanding privacy regulations. An alternative method for sharing administrative health data would be to share synthetic datasets where the records do not correspond to real individuals, but the patterns and relationships seen in the data are reproduced. This paper assesses the feasibility of generating synthetic administrative health data using a recurrent deep learning model. Our data comes from 120,000 individuals from Alberta Health's administrative health database. We assess how similar our synthetic data is to the real data using utility assessments that assess the structure and general patterns in the data as well as by recreating a specific analysis in the real data commonly applied to this type of administrative health data. We also assess the privacy risks associated with the use of this synthetic dataset. Generic utility assessments that used Hellinger distance to quantify the difference in distributions between real and synthetic datasets for event types (0.027), attributes (mean 0.0417), Markov transition matrices (order 1 mean absolute difference: 0.0896, sd: 0.159; order 2: mean Hellinger distance 0.2195, sd: 0.2724), the Hellinger distance between the joint distributions was 0.352, and the similarity of random cohorts generated from real and synthetic data had a mean Hellinger distance of 0.3 and mean Euclidean distance of 0.064, indicating small differences between the distributions in the real data and the synthetic data. By applying a realistic analysis to both real and synthetic datasets, Cox regression hazard ratios achieved a mean confidence interval overlap of 68","2023","2024-12-03 03:36:03","2024-12-03 03:36:03","","1–21","","1","23","","","","","","","","","","","","","","","","","Citation Key: Mosquera2023 PMID: 36959532 Publisher: BioMed Central","","","","Data privacy; Synthetic data; Administrative health data; Data sharing","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"GBTYTRV8","journalArticle","2023","Parrot, Maud; Tajmouati, Hamza; da Silva, Vinicius Barros Ribeiro; Atwood, Brian Ross; Fourcade, Robin; Gaston-Mathé, Yann; Do Huu, Nicolas; Perron, Quentin","Integrating synthetic accessibility with AI-based generative drug design","Journal of Cheminformatics","","17582946","10.1186/s13321-023-00742-8","https://doi.org/10.1186/s13321-023-00742-8","Generative models are frequently used for de novo design in drug discovery projects to propose new molecules. However, the question of whether or not the generated molecules can be synthesized is not systematically taken into account during generation, even though being able to synthesize the generated molecules is a fundamental requirement for such methods to be useful in practice. Methods have been developed to estimate molecule “synthesizability”, but, so far, there is no consensus on whether or not a molecule is synthesizable. In this paper we introduce the Retro-Score (RScore), which computes a synthetic accessibility score of molecules by performing a full retrosynthetic analysis through our data-driven synthetic planning software Spaya, and its dedicated API: Spaya-API (https://spaya.ai). We start by comparing several synthetic accessibility scores to a binary “chemist score” as estimated by chemists on a bench of generated molecules, as a first experimental validation that the RScore is a reliable synthetic accessibility score. We then describe a pipeline to generate molecules that validate a list of targets while still being easy to synthesize. We further this idea by performing experiments comparing molecular generator outputs across a range of constraints and conditions. We show that the RScore can be learned by a Neural Network, which leads to a new score: RSPred. We demonstrate that using the RScore or RSPred as a constraint during molecular generation enables our molecular generators to produce more synthesizable solutions, with higher diversity. The open-source Python code containing all the scores and the experiments can be found on (https://github.com/iktos/generation-under-synthetic-constraint). Graphic Abstract: [Figure not available: see fulltext.]","2023","2024-12-03 03:36:03","2024-12-03 03:36:03","","1–17","","1","15","","","","","","","","","","","","","","","","","Citation Key: Parrot2023 Publisher: Springer International Publishing","","","","machine learning; In silico molecular generation; In-silico synthesizability; Retrosynthesis artificial intelligence","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"7ELZELGK","journalArticle","2023","Shimizu, Yugo; Ohta, Masateru; Ishida, Shoichi; Terayama, Kei; Osawa, Masanori; Honma, Teruki; Ikeda, Kazuyoshi","AI-driven molecular generation of not-patented pharmaceutical compounds using world open patent data","Journal of Cheminformatics","","17582946","10.1186/s13321-023-00791-z","https://doi.org/10.1186/s13321-023-00791-z","Developing compounds with novel structures is important for the production of new drugs. From an intellectual perspective, confirming the patent status of newly developed compounds is essential, particularly for pharmaceutical companies. The generation of a large number of compounds has been made possible because of the recent advances in artificial intelligence (AI). However, confirming the patent status of these generated molecules has been a challenge because there are no free and easy-to-use tools that can be used to determine the novelty of the generated compounds in terms of patents in a timely manner; additionally, there are no appropriate reference databases for pharmaceutical patents in the world. In this study, two public databases, SureChEMBL and Google Patents Public Datasets, were used to create a reference database of drug-related patented compounds using international patent classification. An exact structure search system was constructed using InChIKey and a relational database system to rapidly search for compounds in the reference database. Because drug-related patented compounds are a good source for generative AI to learn useful chemical structures, they were used as the training data. Furthermore, molecule generation was successfully directed by increasing and decreasing the number of generated patented compounds through incorporation of patent status (i.e., patented or not) into learning. The use of patent status enabled generation of novel molecules with high drug-likeness. The generation using generative AI with patent information would help efficiently propose novel compounds in terms of pharmaceutical patents. Scientific contribution: In this study, a new molecule-generation method that takes into account the patent status of molecules, which has rarely been considered but is an important feature in drug discovery, was developed. The method enables the generation of novel molecules based on pharmaceutical patents with high drug-likeness and will help in the efficient development of effective drug compounds.","2023","2024-12-03 03:36:03","2024-12-03 03:36:03","","1–11","","1","15","","","","","","","","","","","","","","","","","Citation Key: Shimizu2023 Publisher: Springer International Publishing","","","","Drug discovery; Database; Compound search; Molecular generation; Patented compounds; Reward function","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KIQ8CWL8","journalArticle","2024","Loeffler, Hannes H.; He, Jiazhen; Tibo, Alessandro; Janet, Jon Paul; Voronov, Alexey; Mervin, Lewis H.; Engkvist, Ola","Reinvent 4: Modern AI–driven generative molecule design","Journal of Cheminformatics","","17582946","10.1186/s13321-024-00812-5","https://doi.org/10.1186/s13321-024-00812-5","REINVENT 4 is a modern open-source generative AI framework for the design of small molecules. The software utilizes recurrent neural networks and transformer architectures to drive molecule generation. These generators are seamlessly embedded within the general machine learning optimization algorithms, transfer learning, reinforcement learning and curriculum learning. REINVENT 4 enables and facilitates de novo design, R-group replacement, library design, linker design, scaffold hopping and molecule optimization. This contribution gives an overview of the software and describes its design. Algorithms and their applications are discussed in detail. REINVENT 4 is a command line tool which reads a user configuration in either TOML or JSON format. The aim of this release is to provide reference implementations for some of the most common algorithms in AI based molecule generation. An additional goal with the release is to create a framework for education and future innovation in AI based molecular design. The software is available from https://github.com/MolecularAI/REINVENT4 and released under the permissive Apache 2.0 license. Scientific contribution. The software provides an open–source reference implementation for generative molecular design where the software is also being used in production to support in–house drug discovery projects. The publication of the most common machine learning algorithms in one code and full documentation thereof will increase transparency of AI and foster innovation, collaboration and education.","2024","2024-12-03 03:36:03","2024-12-03 03:36:03","","1–16","","1","16","","","","","","","","","","","","","","","","","Citation Key: Loeffler2024 Publisher: Springer International Publishing","","","","Transfer learning; Transformers; Generative AI; Reinforcement learning; Multi parameter optimization; Recurrent neural networks","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SP7N2EKL","journalArticle","2023","Li, Feng; Wang, Caohui","Artificial intelligence and edge computing for teaching quality evaluation based on 5G-enabled wireless communication technology","Journal of Cloud Computing","","2192113X","10.1186/s13677-023-00418-6","https://doi.org/10.1186/s13677-023-00418-6","Cloud computing and artificial intelligence are now widely used for classroom teaching in higher learning institutes. The digital teaching supported to ICT technologies in colleges serves as a central point for the advancement of modern education; and has become as a mode of instruction and an approach to teaching. Digital teaching has emerged as a major driving force in the advancement of digital economy and digitization of education in colleges. In this paper, we investigate the movable information management system utilized in the digital teaching using edge computing and 5G wireless communication technology. Furthermore, we explain the idea of a mobile data scheme and presents a teaching platform based on the edge computing and 5G-enabled wireless communication technology. The main objective of this work is to develop a digital teaching framework for college students that, in fact, enables digital teaching, the collection, and incorporation of teaching information, the provision of modern education, and sharing of resources. Cutting-edge technology advancements in the educational platform have the potential to improve 5G communication. To implement the cutting-edge technology, all types of technological devices, smart devices, and gadgets from the Internet of Things (IoT) platform are used. We evaluated the proposed system through reasonable assumptions and numerical simulations. The experimental results reveal that the suggested system has significantly improved the teaching efficiency with which digital teaching management is managed in colleges. Moreover, the edge and 5G technology can significantly improve the system performance, in terms of response time, that can be as high as 11.45","2023","2024-12-03 03:36:03","2024-12-03 03:36:03","","","","1","12","","","","","","","","","","","","","","","","","Citation Key: Li2023 ISBN: 1367702300418 Publisher: Springer Berlin Heidelberg","","","","Education; Students; Teaching; Edge computing; 5G; Classroom learning; Wireless communication technology","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"6FZ7TIGN","journalArticle","2022","Iqbal, Ahmed; Sharif, Muhammad; Yasmin, Mussarat; Raza, Mudassar; Aftab, Shabib","Generative adversarial networks and its applications in the biomedical image segmentation: a comprehensive survey","International Journal of Multimedia Information Retrieval","","2192662X","10.1007/s13735-022-00240-x","https://doi.org/10.1007/s13735-022-00240-x","Recent advancements with deep generative models have proven significant potential in the task of image synthesis, detection, segmentation, and classification. Segmenting the medical images is considered a primary challenge in the biomedical imaging field. There have been various GANs-based models proposed in the literature to resolve medical segmentation challenges. Our research outcome has identified 151 papers; after the twofold screening, 138 papers are selected for the final survey. A comprehensive survey is conducted on GANs network application to medical image segmentation, primarily focused on various GANs-based models, performance metrics, loss function, datasets, augmentation methods, paper implementation, and source codes. Secondly, this paper provides a detailed overview of GANs network application in different human diseases segmentation. We conclude our research with critical discussion, limitations of GANs, and suggestions for future directions. We hope this survey is beneficial and increases awareness of GANs network implementations for biomedical image segmentation tasks.","2022","2024-12-03 03:36:03","2024-12-03 03:36:03","","333–368","","3","11","","","","","","","","","","","","","","","","","Citation Key: Iqbal2022 Publisher: Springer London","","","","Generative adversarial network; GANs applications; GANs in medical image segmentation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"XLYZ9DJ2","journalArticle","2023","Dimitriadou, Eleni; Lanitis, Andreas","A critical evaluation, challenges, and future perspectives of using artificial intelligence and emerging technologies in smart classrooms","Smart Learning Environments","","21967091","10.1186/s40561-023-00231-3","https://doi.org/10.1186/s40561-023-00231-3","The term ""Smart Classroom"" has evolved over time and nowadays reflects the technological advancements incorporated in educational spaces. The rapid advances in technology, and the need to create more efficient and creative classes that support both in-class and remote activities, have led to the integration of Artificial Intelligence and smart technologies in smart classes. In this paper we discuss the concept of Artificial Intelligence in Education and present a literature review related to smart classroom technology, with an emphasis on emerging technologies such as AI-related technologies. As part of this survey key technologies related to smart classes used for effective class management that enhance the convenience of classroom environments, the use of different types of smart teaching aids during the educational process and the use of automated performance assessment technologies are presented. Apart from discussing a variety of technological accomplishments in each of the aforementioned areas, the role of AI is discussed, allowing the readers to comprehend the importance of AI in key technologies related to smart classes. Furthermore, through a SWOT analysis, the Strengths, Weaknesses, Opportunities, and Threats of adopting AI in smart classes are presented, while the future perspectives and challenges in utilizing AI-based techniques in smart classes are discussed. This survey targets educators and AI professionals so that the former get informed about the potential, and limitations of AI in education, while the latter can get inspiration from the challenges and peculiarities of educational AI-based systems.","2023","2024-12-03 03:36:03","2024-12-03 03:36:03","","","","1","10","","","","","","","","","","","","","","","","","Citation Key: Dimitriadou2023 Publisher: Springer Nature Singapore","","","","Educational technology; Artificial intelligence; Emerging technologies; Smart classroom; Smart environment","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ST2EEBWD","journalArticle","2022","You, Aram; Kim, Jin Kuk; Ryu, Ik Hee; Yoo, Tae Keun","Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey","Eye and Vision","","23260254","10.1186/s40662-022-00277-3","https://doi.org/10.1186/s40662-022-00277-3","Background: Recent advances in deep learning techniques have led to improved diagnostic abilities in ophthalmology. A generative adversarial network (GAN), which consists of two competing types of deep neural networks, including a generator and a discriminator, has demonstrated remarkable performance in image synthesis and image-to-image translation. The adoption of GAN for medical imaging is increasing for image generation and translation, but it is not familiar to researchers in the field of ophthalmology. In this work, we present a literature review on the application of GAN in ophthalmology image domains to discuss important contributions and to identify potential future research directions. Methods: We performed a survey on studies using GAN published before June 2021 only, and we introduced various applications of GAN in ophthalmology image domains. The search identified 48 peer-reviewed papers in the final review. The type of GAN used in the analysis, task, imaging domain, and the outcome were collected to verify the usefulness of the GAN. Results: In ophthalmology image domains, GAN can perform segmentation, data augmentation, denoising, domain transfer, super-resolution, post-intervention prediction, and feature extraction. GAN techniques have established an extension of datasets and modalities in ophthalmology. GAN has several limitations, such as mode collapse, spatial deformities, unintended changes, and the generation of high-frequency noises and artifacts of checkerboard patterns. Conclusions: The use of GAN has benefited the various tasks in ophthalmology image domains. Based on our observations, the adoption of GAN in ophthalmology is still in a very early stage of clinical validation compared with deep learning classification techniques because several problems need to be overcome for practical use. However, the proper selection of the GAN technique and statistical modeling of ocular imaging will greatly improve the performance of each image analysis. Finally, this survey would enable researchers to access the appropriate GAN technique to maximize the potential of ophthalmology datasets for deep learning research.","2022","2024-12-03 03:36:03","2024-12-03 03:36:03","","","","1","9","","","","","","","","","","","","","","","","","Citation Key: You2022 Publisher: BioMed Central","","","","Deep learning; Data augmentation; Generative adversarial network; Domain transfer; Ophthalmology image","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"WR8C5TBR","journalArticle","2022","Mukherkjee, Debadyuti; Saha, Pritam; Kaplun, Dmitry; Sinitca, Aleksandr; Sarkar, Ram","Brain tumor image generation using an aggregation of GAN models with style transfer","Scientific Reports","","20452322","10.1038/s41598-022-12646-y","https://doi.org/10.1038/s41598-022-12646-y","In the recent past, deep learning-based models have achieved tremendous success in computer vision-related tasks with the help of large-scale annotated datasets. An interesting application of deep learning is synthetic data generation, especially in the domain of medical image analysis. The need for such a task arises due to the scarcity of original data. Class imbalance is another reason for applying data augmentation techniques. Generative Adversarial Networks (GANs) are beneficial for synthetic image generation in various fields. However, stand-alone GANs may only fetch the localized features in the latent representation of an image, whereas combining different GANs might understand the distributed features. To this end, we have proposed AGGrGAN, an aggregation of three base GAN models—two variants of Deep Convolutional Generative Adversarial Network (DCGAN) and a Wasserstein GAN (WGAN) to generate synthetic MRI scans of brain tumors. Further, we have applied the style transfer technique to enhance the image resemblance. Our proposed model efficiently overcomes the limitation of data unavailability and can understand the information variance in multiple representations of the raw images. We have conducted all the experiments on the two publicly available datasets - the brain tumor dataset and the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 dataset. Results show that the proposed model can generate fine-quality images with maximum Structural Similarity Index Measure (SSIM) scores of 0.57 and 0.83 on the said two datasets.","2022","2024-12-03 03:36:04","2024-12-03 03:36:04","","1–16","","1","12","","","","","","","","","","","","","","","","","Citation Key: Mukherkjee2022 ISBN: 0123456789 PMID: 35650252 Publisher: Nature Publishing Group UK","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"9QLQBJKW","journalArticle","2023","Khader, Firas; Müller-Franzes, Gustav; Tayebi Arasteh, Soroosh; Han, Tianyu; Haarburger, Christoph; Schulze-Hagen, Maximilian; Schad, Philipp; Engelhardt, Sandy; Baeßler, Bettina; Foersch, Sebastian; Stegmaier, Johannes; Kuhl, Christiane; Nebelung, Sven; Kather, Jakob Nikolas; Truhn, Daniel","Denoising diffusion probabilistic models for 3D medical image generation","Scientific Reports","","20452322","10.1038/s41598-023-34341-2","https://doi.org/10.1038/s41598-023-34341-2","Recent advances in computer vision have shown promising results in image generation. Diffusion probabilistic models have generated realistic images from textual input, as demonstrated by DALL-E 2, Imagen, and Stable Diffusion. However, their use in medicine, where imaging data typically comprises three-dimensional volumes, has not been systematically evaluated. Synthetic images may play a crucial role in privacy-preserving artificial intelligence and can also be used to augment small datasets. We show that diffusion probabilistic models can synthesize high-quality medical data for magnetic resonance imaging (MRI) and computed tomography (CT). For quantitative evaluation, two radiologists rated the quality of the synthesized images regarding ""realistic image appearance"", ""anatomical correctness"", and ""consistency between slices"". Furthermore, we demonstrate that synthetic images can be used in self-supervised pre-training and improve the performance of breast segmentation models when data is scarce (Dice scores, 0.91 [without synthetic data], 0.95 [with synthetic data]).","2023","2024-12-03 03:36:04","2024-12-03 03:36:04","","1–12","","1","13","","","","","","","","","","","","","","","","","Citation Key: Khader2023 ISBN: 0123456789 PMID: 37147413 Publisher: Nature Publishing Group UK","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"3IT6QMER","journalArticle","2023","Azizi, Zahra; Lindner, Simon; Shiba, Yumika; Raparelli, Valeria; Norris, Colleen M.; Kublickiene, Karolina; Herrero, Maria Trinidad; Kautzky-Willer, Alexandra; Klimek, Peter; Gisinger, Teresa; Pilote, Louise; El Emam, Khaled","A comparison of synthetic data generation and federated analysis for enabling international evaluations of cardiovascular health","Scientific Reports","","20452322","10.1038/s41598-023-38457-3","https://doi.org/10.1038/s41598-023-38457-3","Sharing health data for research purposes across international jurisdictions has been a challenge due to privacy concerns. Two privacy enhancing technologies that can enable such sharing are synthetic data generation (SDG) and federated analysis, but their relative strengths and weaknesses have not been evaluated thus far. In this study we compared SDG with federated analysis to enable such international comparative studies. The objective of the analysis was to assess country-level differences in the role of sex on cardiovascular health (CVH) using a pooled dataset of Canadian and Austrian individuals. The Canadian data was synthesized and sent to the Austrian team for analysis. The utility of the pooled (synthetic Canadian + real Austrian) dataset was evaluated by comparing the regression results from the two approaches. The privacy of the Canadian synthetic data was assessed using a membership disclosure test which showed an F1 score of 0.001, indicating low privacy risk. The outcome variable of interest was CVH, calculated through a modified CANHEART index. The main and interaction effect parameter estimates of the federated and pooled analyses were consistent and directionally the same. It took approximately one month to set up the synthetic data generation platform and generate the synthetic data, whereas it took over 1.5 years to set up the federated analysis system. Synthetic data generation can be an efficient and effective tool for enabling multi-jurisdictional studies while addressing privacy concerns.","2023","2024-12-03 03:36:04","2024-12-03 03:36:04","","1–12","","1","13","","","","","","","","","","","","","","","","","Citation Key: Azizi2023 ISBN: 4159802338 PMID: 37460705 Publisher: Nature Publishing Group UK","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"TZJBULKP","journalArticle","2023","Müller-Franzes, Gustav; Niehues, Jan Moritz; Khader, Firas; Arasteh, Soroosh Tayebi; Haarburger, Christoph; Kuhl, Christiane; Wang, Tianci; Han, Tianyu; Nolte, Teresa; Nebelung, Sven; Kather, Jakob Nikolas; Truhn, Daniel","A multimodal comparison of latent denoising diffusion probabilistic models and generative adversarial networks for medical image synthesis","Scientific Reports","","20452322","10.1038/s41598-023-39278-0","https://doi.org/10.1038/s41598-023-39278-0","Although generative adversarial networks (GANs) can produce large datasets, their limited diversity and fidelity have been recently addressed by denoising diffusion probabilistic models, which have demonstrated superiority in natural image synthesis. In this study, we introduce Medfusion, a conditional latent DDPM designed for medical image generation, and evaluate its performance against GANs, which currently represent the state-of-the-art. Medfusion was trained and compared with StyleGAN-3 using fundoscopy images from the AIROGS dataset, radiographs from the CheXpert dataset, and histopathology images from the CRCDX dataset. Based on previous studies, Progressively Growing GAN (ProGAN) and Conditional GAN (cGAN) were used as additional baselines on the CheXpert and CRCDX datasets, respectively. Medfusion exceeded GANs in terms of diversity (recall), achieving better scores of 0.40 compared to 0.19 in the AIROGS dataset, 0.41 compared to 0.02 (cGAN) and 0.24 (StyleGAN-3) in the CRMDX dataset, and 0.32 compared to 0.17 (ProGAN) and 0.08 (StyleGAN-3) in the CheXpert dataset. Furthermore, Medfusion exhibited equal or higher fidelity (precision) across all three datasets. Our study shows that Medfusion constitutes a promising alternative to GAN-based models for generating high-quality medical images, leading to improved diversity and less artifacts in the generated images.","2023","2024-12-03 03:36:04","2024-12-03 03:36:04","","1–10","","1","13","","","","","","","","","","","","","","","","","Citation Key: Muller-Franzes2023 ISBN: 0123456789 PMID: 37495660 Publisher: Nature Publishing Group UK","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DVDK5DUA","journalArticle","2024","Kühnel, Lisa; Schneider, Julian; Perrar, Ines; Adams, Tim; Moazemi, Sobhan; Prasser, Fabian; Nöthlings, Ute; Fröhlich, Holger; Fluck, Juliane","Synthetic data generation for a longitudinal cohort study – evaluation, method extension and reproduction of published data analysis results","Scientific Reports","","20452322","10.1038/s41598-024-62102-2","https://doi.org/10.1038/s41598-024-62102-2","Access to individual-level health data is essential for gaining new insights and advancing science. In particular, modern methods based on artificial intelligence rely on the availability of and access to large datasets. In the health sector, access to individual-level data is often challenging due to privacy concerns. A promising alternative is the generation of fully synthetic data, i.e., data generated through a randomised process that have similar statistical properties as the original data, but do not have a one-to-one correspondence with the original individual-level records. In this study, we use a state-of-the-art synthetic data generation method and perform in-depth quality analyses of the generated data for a specific use case in the field of nutrition. We demonstrate the need for careful analyses of synthetic data that go beyond descriptive statistics and provide valuable insights into how to realise the full potential of synthetic datasets. By extending the methods, but also by thoroughly analysing the effects of sampling from a trained model, we are able to largely reproduce significant real-world analysis results in the chosen use case.","2024","2024-12-03 03:36:04","2024-12-03 03:36:04","","1–15","","1","14","","","","","","","","","","","","","","","","","Citation Key: Kuhnel2024 ISBN: 0123456789 PMID: 38909025 Publisher: Nature Publishing Group UK","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"X7FIN47Q","journalArticle","2023","Qian, Zhaozhi; Callender, Thomas; Cebere, Bogdan; Janes, Sam M; Navani, Neal; van der Schaar, Mihaela","Synthetic data for privacy-preserving clinical risk prediction","medRxiv : the preprint server for health sciences","","","","http://medrxiv.org/content/early/2023/05/24/2023.05.18.23290114.abstract","Synthetic data promise privacy-preserving data sharing for healthcare research and development. Compared with other privacy-enhancing approaches - such as federated learning - analyses performed on synthetic data can be applied downstream without modification, such that synthetic data can act in place of real data for a wide range of use cases. However, the role that synthetic data might play in all aspects of clinical model development remains unknown. In this work, we used state-of-the-art generators explicitly designed for privacy preservation to create a synthetic version of the UK Biobank before building prognostic models for lung cancer under several data release assumptions. We demonstrate that synthetic data can be effectively used throughout the modelling pipeline even without eventual access to the real data. Furthermore, we show the implications of different data release approaches on how synthetic data could be deployed within the healthcare system.Competing Interest StatementThe authors have declared no competing interest.Funding StatementThis work was supported by the International Alliance for Cancer Early Detection, a partnership between Cancer Research UK, Canary Center at Stanford University, the University of Cambridge, OHSU Knight Cancer Institute, University College London and the University of Manchester (reference EICEDAAP00012). TC is supported by the Wellcome Trust through a Wellcome Clinical PhD Training Fellowship. NN is supported by a Medical Research Council Clinical Academic Research Partnership (MR/T02481X/1). This work was partly undertaken at the University College London Hospitals University College London that received a proportion of 21 funding from the Department of Health&#039;s National Institute for Health Research (NIHR) Biomedical Research Centre&#039;s funding scheme. NN reports honoraria for non-promotional educational talks, conference support or advisory boards from Amgen, Astra Zeneca, Boehringer Ingelheim, Bristol Myers Squibb, Guardant Health, Janssen, Lilly, Merck Sharp &amp; Dohme, Olympus, OncLive, PeerVoice, Pfizer, and Takeda. SMJ receives support from the CRUK Lung Cancer Centre and the CRUK City of London Centre, the Rosetrees Trust, the Roy Castle Lung Cancer foundation, the Longfonds BREATH Consortia, MRC UKRMP2 Consortia, the Garfield Weston Trust and UCLH Charitable Foundation. SMJ has received fees for advisory board membership in the last three years from Astra-Zeneca, Bard1 Lifescience, and Johnson and Johnson. He has received grant income from Owlstone and GRAIL Inc. He has received assistance with travel to an academic meeting from Cheisi. This work was partly undertaken at UCL who received a proportion of funding from the Department of Health&#039;s NIHR Biomedical Research Centre&#039;s funding scheme. This work was supported by Azure sponsorship credits granted by Microsoft&#039;s AI for Good Research Lab. The funders had no role in the design or conduct of this study.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:The UK Biobank: https://www.ukbiobank.ac.uk/I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesAll data produced in the present study are available upon reasonable request to the authors https://www.ukbiobank.ac.uk/","2023","2024-12-03 03:36:04","2024-12-03 03:36:04","","2023.05.18.23290114","","","","","medRxiv","","","","","","","","","","","","","","","Citation Key: Qian2023a","","","","machine learning; synthetic data; 2; both industry and academia; consequently; constraints 1; data access remains; data is such that; emphasis; high-quality data; it is; leading to an increasing; medical advances are predicated; nevertheless; on data sharing in; on the availability of; risk-prediction; subject to country-specific legal; the sensitivity of medical; usually tightly controlled and","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"E7Z9GW9F","journalArticle","2022","İçen, Mustafa","The future of education utilizing artificial intelligence in Turkey","Humanities and Social Sciences Communications","","26629992","10.1057/s41599-022-01284-4","","This study examined the potential effects of artificial intelligence on Turkish education. A qualitative research approach was employed by posing an open-ended question to academics in order to attain this objective thanks to built-in capabilities for conducting complicated computer operations, cloud-based services, and conciliatory accession for agile network connections. This study emphasizes that Turkey is highly fragmented and consists of various business organizations at both the municipal and regional levels. The two main policy documents produced by the Turkish government suggest that colleges play a strong role in national and regional Artificial Intelligence (AI) strategies for workforce growth, with substantial consequences for AI adoption strategies. These documents include information on three well-known educational entities: The new oriental workgroups, recurrent neural networks, and classroom clustering. Significant aspects of Turkey's educational AI growth include a strong private education industry and a growing international interest. The investigation results revealed a decline in the level of understanding regarding the methods of using artificial intelligence, indicating the necessity for additional awareness-raising in Turkey.","2022","2024-12-03 03:36:04","2024-12-03 03:36:04","","1–10","","1","9","","","","","","","","","","","","","","","","","Citation Key: Icen2022","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"3IDAPB58","journalArticle","2023","Giuffrè, Mauro; Shung, Dennis L.","Harnessing the power of synthetic data in healthcare: innovation, application, and privacy","npj Digital Medicine","","23986352","10.1038/s41746-023-00927-3","","Data-driven decision-making in modern healthcare underpins innovation and predictive analytics in public health and clinical research. Synthetic data has shown promise in finance and economics to improve risk assessment, portfolio optimization, and algorithmic trading. However, higher stakes, potential liabilities, and healthcare practitioner distrust make clinical use of synthetic data difficult. This paper explores the potential benefits and limitations of synthetic data in the healthcare analytics context. We begin with real-world healthcare applications of synthetic data that informs government policy, enhance data privacy, and augment datasets for predictive analytics. We then preview future applications of synthetic data in the emergent field of digital twin technology. We explore the issues of data quality and data bias in synthetic data, which can limit applicability across different applications in the clinical context, and privacy concerns stemming from data misuse and risk of re-identification. Finally, we evaluate the role of regulatory agencies in promoting transparency and accountability and propose strategies for risk mitigation such as Differential Privacy (DP) and a dataset chain of custody to maintain data integrity, traceability, and accountability. Synthetic data can improve healthcare, but measures to protect patient well-being and maintain ethical standards are key to promote responsible use.","2023","2024-12-03 03:36:04","2024-12-03 03:36:04","","1–8","","1","6","","","","","","","","","","","","","","","","","Citation Key: Giuffre2023 Publisher: Springer US","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"J54C586L","journalArticle","2023","Gao, Cong; Killeen, Benjamin D.; Hu, Yicheng; Grupp, Robert B.; Taylor, Russell H.; Armand, Mehran; Unberath, Mathias","Synthetic data accelerates the development of generalizable learning-based algorithms for X-ray image analysis","Nature Machine Intelligence","","25225839","10.1038/s42256-023-00629-1","","Artificial intelligence (AI) now enables automated interpretation of medical images. However, AI's potential use for interventional image analysis remains largely untapped. This is because the post hoc analysis of data collected during live procedures has fundamental and practical limitations, including ethical considerations, expense, scalability, data integrity and a lack of ground truth. Here we demonstrate that creating realistic simulated images from human models is a viable alternative and complement to large-scale in situ data collection. We show that training AI image analysis models on realistically synthesized data, combined with contemporary domain generalization techniques, results in machine learning models that on real data perform comparably to models trained on a precisely matched real data training set. We find that our model transfer paradigm for X-ray image analysis, which we refer to as SyntheX, can even outperform real-data-trained models due to the effectiveness of training on a larger dataset. SyntheX provides an opportunity to markedly accelerate the conception, design and evaluation of X-ray-based intelligent systems. In addition, SyntheX provides the opportunity to test novel instrumentation, design complementary surgical approaches, and envision novel techniques that improve outcomes, save time or mitigate human error, free from the ethical and practical considerations of live human data collection.","2023","2024-12-03 03:36:04","2024-12-03 03:36:04","","294–308","","3","5","","","","","","","","","","","","","","","","","Citation Key: Gao2023 Publisher: Springer US","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JPBN4PN8","journalArticle","2022","Alrashedy, Halima Hamid N.; Almansour, Atheer Fahad; Ibrahim, Dina M.; Hammoudeh, Mohammad Ali A.","BrainGAN: Brain MRI image generation and classification framework using GAN architectures and CNN models","Sensors","","14248220","10.3390/s22114297","","Deep learning models have been used in several domains, however, adjusting is still required to be applied in sensitive areas such as medical imaging. As the use of technology in the medical domain is needed because of the time limit, the level of accuracy assures trustworthiness. Because of privacy concerns, machine learning applications in the medical field are unable to use medical data. For example, the lack of brain MRI images makes it difficult to classify brain tumors using image-based classification. The solution to this challenge was achieved through the application of Generative Adversarial Network (GAN)-based augmentation techniques. Deep Convolutional GAN (DCGAN) and Vanilla GAN are two examples of GAN architectures used for image generation. In this paper, a framework, denoted as BrainGAN, for generating and classifying brain MRI images using GAN architectures and deep learning models was proposed. Consequently, this study proposed an automatic way to check that generated images are satisfactory. It uses three models: CNN, MobileNetV2, and ResNet152V2. Training the deep transfer models with images made by Vanilla GAN and DCGAN, and then evaluating their performance on a test set composed of real brain MRI images. From the results of the experiment, it was found that the ResNet152V2 model outperformed the other two models. The ResNet152V2 achieved 99.09","2022","2024-12-03 03:36:05","2024-12-03 03:36:05","","","","11","22","","","","","","","","","","","","","","","","","Citation Key: Alrashedy2022 PMID: 35684918","","","","deep learning; image classification; brain MRI images; DCGANs; image generation; vanilla GANs","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DQXM6RPG","journalArticle","2023","Adewumi, Bamidele J,Onamade,Akintunde O.,Asaju, Opeyemi A.","Lead city university postgraduate multidisciplinary serial, (series 3) ______________________________________________________________________________________","Multidisciplinary conferenccetidisciplinary","","","","","","2023","2024-12-03 03:36:05","2024-12-03 03:36:05","","258–271","","Series 3","","","","","","","","","","","","","","","","","","Citation Key: AdewumiBamideleJOnamadeAkintundeO.Asaju2023","","","","virtual","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"F39WI3IE","journalArticle","2022","Limna, Pongsakorn; Jakwatanatham, Somporch; Siripipattanakul, Sutithep; Kaewpuang, Pichart; Sriboonruang, Patcharavadee","A review of artificial intelligence (AI) in education during the digital era","Advance Knowledge for Executives","","","","https://ssrn.com/abstract=4160798","Objective: Artificial intelligence (AI) plays a critical role in education. This paper aims to review the artificial intelligence adoption in learning and teaching during the digital era. Method: A narrative synthesis and a systematic literature review were conducted in this review article. The literature and information were obtained from various books and research articles on EBSCO, Google Scholar, Scopus, Web of Science, and ScienceDirect. The inclusion criteria were studies that clearly defined artificial intelligence in the education sector, were published Electronic copy available at: https://ssrn.com/abstract=4160798","2022","2024-12-03 03:36:05","2024-12-03 03:36:05","","1–9","","1","1","","","","","","","","","","","","","","","","","Citation Key: Limna2022 ISBN: 0000000332255","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LVLFQL52","journalArticle","2024","Doshi, Anil R.; Hauser, Oliver P.","Generative AI enhances individual creativity but reduces the collective diversity of novel content","Science Advances","","23752548","10.1126/sciadv.adn5290","","Creativity is core to being human. Generative artificial intelligence (AI)-including powerful large language models (LLMs)-holds promise for humans to be more creative by offering new ideas, or less creative by anchoring on generative AI ideas. We study the causal impact of generative AI ideas on the production of short stories in an online experiment where some writers obtained story ideas from an LLM. We find that access to generative AI ideas causes stories to be evaluated as more creative, better written, and more enjoyable, especially among less creative writers. However, generative AI-enabled stories are more similar to each other than stories by humans alone. These results point to an increase in individual creativity at the risk of losing collective novelty. This dynamic resembles a social dilemma: With generative AI, writers are individually better off, but collectively a narrower scope of novel content is produced. Our results have implications for researchers, policy-makers, and practitioners interested in bolstering creativity.","2024","2024-12-03 03:36:05","2024-12-03 03:36:05","","","","28","10","","","","","","","","","","","","","","","","","Citation Key: Doshi2024 arXiv: 2312.00506 PMID: 38996021 tex.arxivid: 2312.00506","","","","artificial intelligence; generative artificial intelligence; ethics; 1; 4535536; abstract; and music; art; com; creativity; creativity is fundamental to; electronic copy available at; experiment; genai; however; https; innovation and human expression; machine behavior; ssrn; technologies is; the emergence of generative; through literature","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"IW7Q758C","journalArticle","2023","Rane, Nitin","Role of ChatGPT and similar generative artificial intelligence (AI) in construction industry","SSRN Electronic Journal","","","10.2139/ssrn.4598258","","The integration of generative Artificial Intelligence (AI) systems, such as ChatGPT, into the construction industry represents a transformative force with profound implications. This research paper explores the multifaceted roles of these generative AI models within various facets of construction engineering and management, shedding light on their contributions, hurdles, and innovative potential. Generative AI, including ChatGPT is reshaping the construction industry across a multitude of domains. These AI models significantly contribute to design and Building Information Modeling (BIM) by generating intricate designs, optimizing layouts, and simulating construction processes. In the pursuit of energy-efficient and sustainable building construction, generative AI plays a pivotal role by suggesting eco-friendly materials, optimizing building parameters, and proposing innovative technologies. These AI models are indispensable in advancing construction materials and technologies, offering novel solutions to complex engineering challenges through data-driven ideation and innovation. Within the realm of construction automation and robotics, generative AI models streamline operations by generating automated workflows, strategies, and even controlling robotic systems. They contribute to the development of smart cities and infrastructure by assisting in urban planning, traffic management, and infrastructure optimization. They also play a crucial role in construction management and project control, assisting in project scheduling, cost estimation, and resource allocation, ultimately promoting efficient project execution. Furthermore, they enhance safety and risk management by analyzing historical data, generating safety protocols, and reducing workplace accidents. Quality control and defect detection benefit from the capabilities of generative AI, which employ both language understanding and image recognition to identify flaws and deviations from specifications. In supply chain and inventory management, these AI models optimize procurement processes, minimize waste, and ensure the seamless flow of materials. Ethical considerations, data privacy, and security concerns must be addressed, along with the necessity for specialized training and maintenance of these AI systems. ChatGPT and its generative AI counterparts have the potential to revolutionize the construction industry, rendering processes more efficient, sustainable, and resilient. Nevertheless, their careful implementation and management are essential to harness their full potential while addressing the associated challenges within the construction sector.","2023","2024-12-03 03:36:05","2024-12-03 03:36:05","","","","","","","","","","","","","","","","","","","","","","Citation Key: Rane2023","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"98UZD7SN","journalArticle","2024","Peta, Venkata Phanindra; Khambam, Sai Krishna Reddy; Kaluvakuri, Venkata Praveen Kumar","Unlocking the power of generative AI: Building creative applications with cloud-based large language models","SSRN Electronic Journal","","","10.2139/ssrn.4927234","","","2024","2024-12-03 03:36:05","2024-12-03 03:36:05","","2387–2399","","","12","","","","","","","","","","","","","","","","","Citation Key: Peta2024","","","","cloud computing; artificial intelligence; large language models; generative ai; ai; ai deployment; ai frameworks; cloud-based ai; computational; content generation; cost management; creative applications; data privacy; ethical; gpt-3; innovative solutions; llms; performance metrics; power; real-time scenarios; scalability; technical challenges; user engagement","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"UKMTZF9F","journalArticle","2024","Aggarwal, Kapil Kumar; Agrawal, Satakshi","Artificial intelligence and its role in financial market","Global Financial Analytics and Business Forecasting","","","","","Artificial intelligence has become the new wave of opportunities for the financial and banking sectors in the market. The growing opportunities in these sectors have also led to the increased risk of cyber-attacks to, which these organisations need to be aware of. The introduction of AI has made the ecosystem of the financial sector very uncertain, due to which opportunities like mergers and acquisition has also emerged. The infusion of AI and AI-based technology has led to the operations being cost efficient along with the multiple benefits and providing being multiple various improved efficiency. The scale of operations and the handling of the huge data sets pose the risk of data breach, which must be taken off. The use of algorithms, defined data sets, and human decision making based on the data arises the risk of biases in the functioning. The risk can be dealt with through the infusion of workforce both in the functioning of the AI and the human decision-making to improve the vigilance and thereby reducing the risk of biasness.","2024","2024-12-03 03:36:06","2024-12-03 03:36:06","","67–82","","","","","","","","","","","","","","","","","","","","Citation Key: Aggarwal2024 ISBN: 9798891132788","","","","Banking; Risk; Artificial intelligence (AI); Opportunities; Financial markets","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"2LKV46E9","journalArticle","2022","Ahmad, Sayed Fayaz; Alam, Muhammad Mansoor; Rahmat, Mohd Khairil; Mubarik, Muhammad Shujaat; Hyder, Syed Irfan","Academic and administrative role of artificial intelligence in education","Sustainability (Switzerland)","","20711050","10.3390/su14031101","","The aim of the article is to explore the academic and administrative applications of Artificial Intelligence. Teachers have the main responsibility of teaching in any educational setting. But there are various other tasks to be performed by the teachers as well. Besides academic duty, most of the teacher's time and educational resources are dedicated to administrative works. Artificial Intelligence Applications (AIA) are not only assisting education academically and administratively but also enhance their effectiveness. AIA provides help to teachers in various types of tasks in the shape of Learning Analytics (LA), Virtual Reality (VR), Grading/Assessments (G/A), and Admissions. It minimizes the administrative tasks of a teacher to invest more in teaching and guiding students. In the current era, where there are a lot of tasks associated with the teaching profession, AIA adds a significant contribution to enhance student learning, minimize the workload of a teacher, grade/assess the students effectively and easily, and to help in a lot of other administrative tasks. The study needs to be quantitatively checked to make it generalized and acceptable.","2022","2024-12-03 03:36:06","2024-12-03 03:36:06","","1–11","","3","14","","","","","","","","","","","","","","","","","Citation Key: Ahmad2022","","","","Admissions (A); Artificial Intelligence Applications (AIA); Grading/Assessments (G/A); Learning Analytics (LA); Personalized Education (PE)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"QCVUDMCA","journalArticle","2022","Hamal, Oussama; El Faddouli, Nour Eddine; Alaoui Harouni, Moulay Hachem; Lu, Joan","Artificial intelligent in education","Sustainability (Switzerland)","","20711050","10.3390/su14052862","","The application of Artificial Intelligence or AI in education has been the subject of academic research for more than 30 years. The field examines learning wherever it occurs, in traditional classrooms or at workplaces so to support formal education and lifelong learning. It combines interdisciplinary AI and learning sciences (such as education, psychology, neuroscience, linguistics, sociology and anthropology) in order to facilitate the development of effective adaptive learning environments and various flexible, inclusive tools. Nowadays, there are several new challenges in the field of education technology in the era of smart phones, tablets, cloud computing, Big Data, etc., whose current research questions focus on concepts such as ICT-enabled personalized learning, mobile learning, educational games, collaborative learning on social media, MOOCs, augmented reality application in education and so on. Therefore, to meet these new challenges in education, several fields of research using AI have emerged over time to improve teaching and learning using digital technologies. Moreover, each field of research is distinguished by its own vision and methodologies. In this article, to the authors present a state of the art finding in the fields of research of Artificial Intelligence in Education or AIED, Educational Data Mining or EDM and Learning Analytics or LA. We discuss their historical elements, definition attempts, objectives, adopted methodologies, application examples and challenges.","2022","2024-12-03 03:36:06","2024-12-03 03:36:06","","1–11","","5","14","","","","","","","","","","","","","","","","","Citation Key: Hamal2022","","","","Mobile learning; Personalized learning; AIED; Collaborative learning on social media MOOCs; EDM; Educational; LA","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"RNSMT8U3","journalArticle","2023","Bietsch, Dominik; Stahlbock, Robert; Voß, Stefan","Synthetic data as a proxy for real-world electronic health records in the patient length of stay prediction","Sustainability (Switzerland)","","20711050","10.3390/su151813690","","While generative artificial intelligence has gained popularity, e.g., for the creation of images, it can also be used for the creation of synthetic tabular data. This bears great potential, especially for the healthcare industry, where data are often scarce and underlie privacy restrictions. For instance, the creation of synthetic electronic health records (EHR) promises to improve the usage of machine learning algorithms, which usually work with large amounts of data. This also applies for the prediction of the patient length of stay (LOS), a key measure for hospitals. Thereby, the LOS represents one of the core tools for decision makers to plan the allocation of resources. Thus, this paper aims to add to the still-young research concerning the application of generative adversarial nets (GAN) on tabular EHR. It does that with the intention to leverage the advantages of synthetic data for the prediction of the LOS in order to contribute to the efficiency-enhancing and cost-saving aspirations of hospitals and insurance companies. Therefore, the applicability of synthetic data that is generated using GANs as a proxy for scarce real-world EHR for the patient LOS multi-class classification task is examined. In this context, the Conditional Tabular GAN (CTGAN) and the Copula GAN are selected as the underlying models as they are state-of-the-art GAN architectures designed for generating synthetic tabular data. The CTGAN is found to be the superior model for the underlying use case. Nevertheless, the paper shows that there is still room for improvement when applying state-of-the-art GAN architectures to clinical healthcare data.","2023","2024-12-03 03:36:06","2024-12-03 03:36:06","","1–30","","18","15","","","","","","","","","","","","","","","","","Citation Key: Bietsch2023","","","","generative artificial intelligence; healthcare industry; patient length of stay (LOS); synthetic electronic health records (EHR); synthetic tabular data","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"YC6ICGGM","journalArticle","2024","Leelavathi, R.; Surendhranatha, Reddy C.","ChatGPT in the classroom: navigating the generative AI wave in management education","Journal of Research in Innovative Teaching and Learning","","23977604","10.1108/JRIT-01-2024-0017","","Purpose: The study aims to explore the role of ChatGPT, an artificial intelligence (AI) language model, in the field of management education. Specifically, the goal is to evaluate ChatGPT's effectiveness in facilitating active learning, promoting critical thinking, and fostering creativity among students. Additionally, the study seeks to investigate the potential of ChatGPT as a novel tool for enhancing traditional teaching methods within the framework of management education. Design/methodology/approach: This research systematically explores ChatGPT's impact on student engagement in management education, considering AI integration benefits and limitations. Ethical dimensions, including information authenticity and bias, are scrutinized, alongside educators' roles in guiding AI-augmented learning. Findings: The study reveals ChatGPT's effectiveness in engaging students, nurturing critical thinking, and fostering creativity in management education. Ethical concerns regarding information authenticity and bias are addressed. Insights from student and teacher perceptions offer valuable pedagogical implications for AI's role in management education. Research limitations/implications: While this study offers valuable insights into the role of ChatGPT in management education, it is essential to acknowledge certain limitations. Firstly, the research primarily focuses on a specific AI model (ChatGPT), and findings may not be generalized to other AI language models. Additionally, the study relies on a specific set of educational contexts and may not fully capture the diverse landscape of management education globally. The duration of the research and the sample size could also impact the generalizability of the findings. Practical implications: The findings of this study hold practical significance for educators and institutions engaged in management education. The integration of ChatGPT into teaching strategies has the potential to improve active learning, critical thinking, and creativity. Educators can utilize this AI tool to diversify instructional methods and accommodate diverse learning styles. However, the practical implementation of AI in the classroom necessitates meticulous consideration of infrastructure, training, and ongoing support for both educators and students. Furthermore, institutions should proactively tackle ethical concerns and establish guidelines for the responsible use of AI in education. Social implications: The incorporation of AI, such as ChatGPT, in management education carries broader social implications. The study underscores the significance of addressing ethical concerns associated with AI, including issues related to information authenticity and bias. As AI becomes more widespread in educational settings, there is a necessity for societal discussions on the role of technology in shaping learning experiences. This research advocates for a thoughtful approach to AI adoption, emphasizing the importance of transparency, accountability, and inclusivity in the development and deployment of AI technologies within the educational sphere. The findings prompt reflections on the societal impact of AI-driven education and the potential consequences for students' skills, employment prospects, and societal values. Originality/value: Originality/Values: This research contributes to the academic discourse by systematically examining the role of ChatGPT in management education, providing insights into both its advantages and potential ethical challenges. The study offers original perspectives on the use of AI in educational settings, paving the way for well-informed decision-making that can shape the future of management education in the evolving landscape of technological progress.","2024","2024-12-03 03:36:06","2024-12-03 03:36:06","","","","","","","","","","","","","","","","","","","","","","Citation Key: Leelavathi2024 ISBN: 0120240017","","","","Artificial intelligence; ChatGPT; Management education; AI-Augmented; Classroom; Educational tool and future of learning; Navigating; Pedagogical innovation","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SJ3PZIIV","journalArticle","2023","Oluwafemi Ayotunde, Oke; Jamil, Dashty Ismil; Cavus, Nadire","the impact of artificial intelligence in foreign language learning using learning management systems: a systematic literature review","Information Technologies and Learning Tools","","2076-8184","10.33407/itlt.v95i3.5233","","Among numerous foreign languages, the English language is considered one of the major languages of the world. It has become a door opener for many people in their different fields and have led to advancements in career areas as it plays a major role in boosting confidence, improving connection, communication, and so on. It also gives people the opportunity for self-expression on a global standard level. Although the learning process of a foreign language can sometimes be difficult for both learners and teachers in terms of the various aspects of language learning, which includes; reading, writing, speaking, and listening skills. The methodology in this study implemented a systematic literature review using four popular scientific databases for searching relevant records for the purpose of the research. The results from searching the databases were integrated into the PRISMA flow diagram for the identification and extraction of high quality records that is relevant to the effects artificial intelligence has in the process of foreign language learning via the use of learning management system as the platform and medium for learning and teaching English as a foreign language. The systematic review research covers a span of 10 years, from 2011 to 2021. The most important finding in this systematic literature review is that the emergence of AI technology is helpful to both students and teachers in the learning of English as a foreign language using a learning management system as it improves and increases speaking, writing, reading, and listening skills processes and provides an easy, interesting, and personalized learning experience. The benefit of the study to shareholders; students and teachers, is to guide teachers on which AI tools to use and how they can be integrated or used into learning management systems to increase students' reading, writing, speaking, and listening skills in teaching and learning foreign languages, especially the English language.","2023","2024-12-03 03:36:06","2024-12-03 03:36:06","","215–228","","3","95","","","","","","","","","","","","","","","","","Citation Key: OluwafemiAyotunde2023 ISBN: 0000000221","","","","artificial intelligence; ai tools; english language learning; learning management","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"34TVPPSN","journalArticle","2022","Hutson, James; Jeevanjee, Theresa; Graaf, Vanessa Vander; Lively, Jason; Weber, Joseph; Weir, Graham; Arnone, Kathryn; Carnes, Geremy; Vosevich, Kathi; Plate, Daniel; Leary, Michael; Edele, Susan","Artificial intelligence and the disruption of higher education: Strategies for integrations across disciplines","Creative Education","","2151-4755","10.4236/ce.2022.1312253","","Artificial intelligence (AI) and its impact on society have received a great deal of attention in the past five years since the first Stanford AI100 report. AI already globally impacts individuals in critical and personal ways, and many industries will continue to experience disruptions as the full algorithmic effects are understood. Higher education is one of the industries that will be greatly impacted; consequently, many institutions have begun accelerating its adoption across disciplines to address the fast-approaching market shift. Recent advances with the technology are especially promising for its potential to create and scale personalized learning for students, to optimize strategies for learning outcomes, and to increase access to a more diverse populations. In the US alone, colleges are predicted to witness a 48","2022","2024-12-03 03:36:06","2024-12-03 03:36:06","","3953–3980","","12","13","","","","","","","","","","","","","","","","","Citation Key: Hutson2022","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"3Y6K8PIS","journalArticle","2024","Akiya, Ippei; Ishihara, Takuma; Yamamoto, Keiichi","Comparison of synthetic data generation techniques for control group survival data in oncology clinical trials: Simulation study","JMIR Medical Informatics","","","10.2196/55118","","Background: Synthetic patient data (SPD) generation for survival analysis in oncology trials holds significant potential for accelerating clinical development. Various machine learning methods, including classification and regression trees (CART), random forest (RF), Bayesian network (BN), and conditional tabular generative adversarial network (CTGAN), have been used for this purpose, but their performance in reflecting actual patient survival data remains under investigation. Objective: The aim of this study was to determine the most suitable SPD generation method for oncology trials, specifically focusing on both progression-free survival (PFS) and overall survival (OS), which are the primary evaluation end points in oncology trials. To achieve this goal, we conducted a comparative simulation of 4 generation methods, including CART, RF, BN, and the CTGAN, and the performance of each method was evaluated. Methods: Using multiple clinical trial data sets, 1000 data sets were generated by using each method for each clinical trial data set and evaluated as follows: (1) median survival time (MST) of PFS and OS; (2) hazard ratio distance (HRD), which indicates the similarity between the actual survival function and a synthetic survival function; and (3) visual analysis of Kaplan-Meier (KM) plots. Each method's ability to mimic the statistical properties of real patient data was evaluated from these multiple angles. Results: In most simulation cases, CART demonstrated the high percentages of MSTs for synthetic data falling within the 95","2024","2024-12-03 03:36:06","2024-12-03 03:36:06","","e55118–e55118","","","12","","","","","","","","","","","","","","","","","Citation Key: Akiya2024 Publisher: JMIR Medical Informatics","","","","machine learning; simulation; 1; https; 12; 2024; citation purposes; e55118; jmir; jmir med inform 2024; medinform; oncology clinical trial; org; p; page number not for; spd; survival analysis; synthetic patient data; vol","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"QXGS43B2","journalArticle","2023","Orchard, Tim; Tasiemski, Leszek","The rise of Generative AI and possible effects on the economy","Economics and Business Review","","24500097","10.18559/ebr.2023.2.732","","The aim of the paper is to analyse the likely implications of Generative AI (GAI) on various aspects of business and the economy. Amid the rapid growth and maturing of Generative AI technologies such as Large Language Models (like ChatGPT by OpenAI) a rapid growth of both immediate and potential applications can be seen. The implications for the economy and industries of this technological shift will be discussed. The foreseeable scenarios for the level and types of adoption that GAI might achieve - from useful analytical tool, invaluable assistant to the white-collar workers of the world to being trusted with a wide array of business and life-critical decision making. Both disruptive and premium service opportunities are foreseen. For instance, general purpose models may provide quality service - such as copywriting - to overserved customers leaving human writers as the premium option. In this context, overserved customers would be those who would be satisfied with a non-human, potentially less creative content. On the other hand highly specialized models - specifically trained in a given domain and with access to proprietary knowledge can possibly provide a premium service over that provided by human experts. It is expected that some jobs will be replaced by new AI applications. However, new workplaces will emerge. Not only the obvious expert-level data scientist roles but also low grade, ""model supervisors""- people training the models, assessing the quality of responses given and handling escalations. Lastly new cybercrime risks emerging from the rise of GAI are discussed.","2023","2024-12-03 03:36:06","2024-12-03 03:36:06","","9–26","","2","9","","","","","","","","","","","","","","","","","Citation Key: Orchard2023","","","","Generative AI; artificial intelligence (AI); business models; disruptive technology; Large Language Models (LLM)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"WL24HDL7","journalArticle","2023","Hutson, James; Nichols, Morgan Harper","Generative AI and algorithmic art: Disrupting the framing of meaning and rethinking the subject-object dilemma","Global Journal of Computer Science and Technology","","09754350","10.34257/gjcstdvol23is1pg55","","In the revision of treatments of contemporary art in the 21st century, art historians are recognizing 2022 as the dawn of the age of creative artificial intelligence (AI). The emergence of generative AI tools like ChatGPT and Stable Diffusion in late 2022 immediately disrupted the established practices of the art world, leading to debates about the validity of ""AI Art"" and the emergence of a new market for NFTs. However, fears regarding the ""death of the artist"" are unwarranted when considering the historical adoption of new technologies by artists, such as photography. The role of the artist will undoubtedly transform, and the definition of ""art"" will be redefined once again. To better understand how AI generative art will impact traditional art-making practices, this study will present an AI generative art development pipeline and provide recommendations for future technical and theoretical considerations of the subject-object dilemma in art through a post structuralist reading of reception theory.","2023","2024-12-03 03:36:06","2024-12-03 03:36:06","","55–61","","","","","","","","","","","","","","","","","","","","Citation Key: Hutson2023","","","","artificial intelligence; generative ai; co-creativity; creative process; human-ai creativity","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DP8N8GTB","journalArticle","2023","Jain, Moksh; Deleu, Tristan; Hartford, Jason; Liu, Cheng Hao; Garcia, Alex Hernandez; Bengio, Yoshua","GFlowNets for AI-driven scientific discovery","Digital Discovery","","2635098X","10.1039/d3dd00002h","","Tackling the most pressing problems for humanity, such as the climate crisis and the threat of global pandemics, requires accelerating the pace of scientific discovery. While science has traditionally relied on trial and error and even serendipity to a large extent, the last few decades have seen a surge of data-driven scientific discoveries. However, in order to truly leverage large-scale data sets and high-throughput experimental setups, machine learning methods will need to be further improved and better integrated in the scientific discovery pipeline. A key challenge for current machine learning methods in this context is the efficient exploration of very large search spaces, which requires techniques for estimating reducible (epistemic) uncertainty and generating sets of diverse and informative experiments to perform. This motivated a new probabilistic machine learning framework called GFlowNets, which can be applied in the modeling, hypotheses generation and experimental design stages of the experimental science loop. GFlowNets learn to sample from a distribution given indirectly by a reward function corresponding to an unnormalized probability, which enables sampling diverse, high-reward candidates. GFlowNets can also be used to form efficient and amortized Bayesian posterior estimators for causal models conditioned on the already acquired experimental data. Having such posterior models can then provide estimators of epistemic uncertainty and information gain that can drive an experimental design policy. Altogether, here we will argue that GFlowNets can become a valuable tool for AI-driven scientific discovery, especially in scenarios of very large candidate spaces where we have access to cheap but inaccurate measurements or too expensive but accurate measurements. This is a common setting in the context of drug and material discovery, which we use as examples throughout the paper.","2023","2024-12-03 03:36:06","2024-12-03 03:36:06","","557–577","","3","2","","","","","","","","","","","","","","","","","Citation Key: Jain2023 arXiv: 2302.00615 tex.arxivid: 2302.00615","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"QY64GQK5","journalArticle","2024","Sichani, Elnaz Karimian; Smith, Aaron; El Emam, Khaled; Mosquera, Lucy","Creating high-quality synthetic health data: Framework for model development and validation","JMIR Formative Research","","2561326X","10.2196/53241","","Background: Electronic health records are a valuable source of patient information that must be properly deidentified before being shared with researchers. This process requires expertise and time. In addition, synthetic data have considerably reduced the restrictions on the use and sharing of real data, allowing researchers to access it more rapidly with far fewer privacy constraints. Therefore, there has been a growing interest in establishing a method to generate synthetic data that protects patients' privacy while properly reflecting the data. Objective: This study aims to develop and validate a model that generates valuable synthetic longitudinal health data while protecting the privacy of the patients whose data are collected. Methods: We investigated the best model for generating synthetic health data, with a focus on longitudinal observations. We developed a generative model that relies on the generalized canonical polyadic (GCP) tensor decomposition. This model also involves sampling from a latent factor matrix of GCP decomposition, which contains patient factors, using sequential decision trees, copula, and Hamiltonian Monte Carlo methods. We applied the proposed model to samples from the MIMIC-III (version 1.4) data set. Numerous analyses and experiments were conducted with different data structures and scenarios. We assessed the similarity between our synthetic data and the real data by conducting utility assessments. These assessments evaluate the structure and general patterns present in the data, such as dependency structure, descriptive statistics, and marginal distributions. Regarding privacy disclosure, our model preserves privacy by preventing the direct sharing of patient information and eliminating the one-to-one link between the observed and model tensor records. This was achieved by simulating and modeling a latent factor matrix of GCP decomposition associated with patients. Results: The findings show that our model is a promising method for generating synthetic longitudinal health data that is similar enough to real data. It can preserve the utility and privacy of the original data while also handling various data structures and scenarios. In certain experiments, all simulation methods used in the model produced the same high level of performance. Our model is also capable of addressing the challenge of sampling patients from electronic health records. This means that we can simulate a variety of patients in the synthetic data set, which may differ in number from the patients in the original data. Conclusions: We have presented a generative model for producing synthetic longitudinal health data. The model is formulated by applying the GCP tensor decomposition. We have provided 3 approaches for the synthesis and simulation of a latent factor matrix following the process of factorization. In brief, we have reduced the challenge of synthesizing massive longitudinal health data to synthesizing a nonlongitudinal and significantly smaller data set.","2024","2024-12-03 03:36:06","2024-12-03 03:36:06","","","","","8","","","","","","","","","","","","","","","","","Citation Key: Sichani2024","","","","synthetic data; generative models; data utility; data privacy; data sharing; electronic health record; longitudinal; model development; model validation; tensor decomposition","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"6RRDDPE3","journalArticle","2023","Sira, Mariya","Generative ai takes centre stage: Revolutionizing productivity and reshaping industries","System Safety: Human - Technical Facility - Environment","","26575450","10.2478/czoto-2023-0007","","The growing prominence of Generative AI in discussions on artificial intelligence has significant implications for productivity and industry dynamics. This article aims to examine the transformative role of Generative AI, specifically focusing on its revolutionary impact on productivity and its influence on various industries. The objectives of this article include conducting a detailed analysis of how systems have greatly enhanced efficiency for developers and knowledge workers. By examining both the positive and negative aspects of the Generative AI movement, this article aims to provide valuable insights into the innovations driven by Generative AI and the advancements that contribute to its evolution. Through this exploration, the goal is to offer a comprehensive understanding of the current landscape, highlighting the opportunities and challenges presented by the rise of Generative AI in the management sphere.","2023","2024-12-03 03:36:07","2024-12-03 03:36:07","","57–65","","1","5","","","","","","","","","","","","","","","","","Citation Key: Sira2023 ISBN: 9788367405706","","","","Generative AI; challenges; opportunities; managerial approach","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"JRAWIADB","journalArticle","2023","Pan, Shaoyan; Wang, Tonghe; Qiu, Richard L.J.; Axente, Marian; Chang, Chih Wei; Peng, Junbo; Patel, Ashish B.; Shelton, Joseph; Patel, Sagar A.; Roper, Justin; Yang, Xiaofeng","2D medical image synthesis using transformer-based denoising diffusion probabilistic model","Physics in Medicine and Biology","","13616560","10.1088/1361-6560/acca5c","","Objective. Artificial intelligence (AI) methods have gained popularity in medical imaging research. The size and scope of the training image datasets needed for successful AI model deployment does not always have the desired scale. In this paper, we introduce a medical image synthesis framework aimed at addressing the challenge of limited training datasets for AI models. Approach. The proposed 2D image synthesis framework is based on a diffusion model using a Swin-transformer-based network. This model consists of a forward Gaussian noise process and a reverse process using the transformer-based diffusion model for denoising. Training data includes four image datasets: chest x-rays, heart MRI, pelvic CT, and abdomen CT. We evaluated the authenticity, quality, and diversity of the synthetic images using visual Turing assessments conducted by three medical physicists, and four quantitative evaluations: the Inception score (IS), Fréchet Inception Distance score (FID), feature similarity and diversity score (DS, indicating diversity similarity) between the synthetic and true images. To leverage the framework value for training AI models, we conducted COVID-19 classification tasks using real images, synthetic images, and mixtures of both images. Main results. Visual Turing assessments showed an average accuracy of 0.64 (accuracy converging to 50","2023","2024-12-03 03:36:07","2024-12-03 03:36:07","","","","10","68","","","","","","","","","","","","","","","","","Citation Key: Pan2023 PMID: 37015231 Publisher: IOP Publishing","","","","COVID-19; artificial intelligence; medical image synthesis; Swin-transformer-based network; transformer-based diffusion model","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KIBL9FWP","journalArticle","2023","Myles, Puja; Ordish, Johan; Tucker, Allan","The potential synergies between synthetic data and in silico trials in relation to generating representative virtual population cohorts","Progress in Biomedical Engineering","","25161091","10.1088/2516-1091/acafbf","","In silico trial methods promise to improve the path to market for both medicines and medical devices, targeting the development of products, reducing reliance on animal trials, and providing adjunct evidence to bolster regulatory submissions. In silico trials are only as good as the simulated data which underpins them, consequently, often the most difficult challenge when creating robust in silico models is the generation of simulated measurements or even virtual patients that are representative of real measurements and patients. This article digests the current state of the art for generating synthetic patient data outside the context of in silico trials and outlines potential synergies to unlock the potential of in silico trials using virtual populations, by exploiting synthetic patient data to model effects on a more diverse and representative population. Synthetic data could be defined as artificial data that mimic the properties and relationships in real data. Recent advances in synthetic data generation methodologies have allowed for the generation of high-fidelity synthetic data that are both statistically and clinically, indistinguishable from real patient data. Other experimental work has demonstrated that synthetic data generation methods can be used for selective sample boosting of underrepresented groups. This article will provide a brief outline of synthetic data generation approaches and discuss how evaluation frameworks developed to assess synthetic data fidelity and utility could be adapted to evaluate the similarity of virtual patients used for in silico trials, to real patients. The article will then discuss outstanding challenges and areas for further research that would advance both synthetic data generation methods and in silico trial methods. Finally, the article will also provide a perspective on what evidence will be required to facilitate wider acceptance of in silico trials for regulatory evaluation of medicines and medical devices, including implications for post marketing safety surveillance.","2023","2024-12-03 03:36:07","2024-12-03 03:36:07","","","","1","5","","","","","","","","","","","","","","","","","Citation Key: Myles2023","","","","synthetic data; in silico trials; medical devices","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KSP3VMFE","journalArticle","2022","Dorjsembe, Zolnamar; Odonchimed, Sodtavilan; Xiao, Furen","Three-dimensional medical image synthesis with denoising diffusion probabilistic models","Midl","","","","https://arxiv.org/abs/2102.09672.","Denoising diffusion probabilistic models (DDPM) have recently shown superior performance in image synthesis and have been extensively studied in various image processing tasks. In this work, we propose a 3D-DDPM for generating three-dimensional (3D) medical images. Different from previous studies, to the best of our knowledge, this work presents the first attempt to investigate the DDPM to enable 3D medical image synthesis. Our study examined the generation of high-resolution magnetic resonance images (MRI) of brain tumors. The proposed method is evaluated through experiments on a semi-public dataset, with both quantitative and qualitative tests showing promising results. Our code will be publicly available at https://github.com/DL-Circle/3D-DDPM.","2022","2024-12-03 03:36:07","2024-12-03 03:36:07","","2–4","","","","","","","","","","","","","","","","","","","","Citation Key: Dorjsembe2022","","","","Diffusion models; image synthesis; magnetic resonance imaging (MRI)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LDPSPWKA","journalArticle","2024","Kuo, Nicholas I.Hsien; Perez-Concha, Oscar; Hanly, Mark; Mnatzaganian, Emmanuel; Hao, Brandon; Di Sipio, Marcus; Yu, Guolin; Vanjara, Jash; Valerie, Ivy Cerelia; de Oliveira Costa, Juliana; Churches, Timothy; Lujic, Sanja; Hegarty, Jo; Jorm, Louisa; Barbieri, Sebastiano","Enriching data science and health care education: Application and impact of synthetic data sets through the health gym project","JMIR Medical Education","","23693762","10.2196/51388","","Large-scale medical data sets are vital for hands-on education in health data science but are often inaccessible due to privacy concerns. Addressing this gap, we developed the Health Gym project, a free and open-source platform designed to generate synthetic health data sets applicable to various areas of data science education, including machine learning, data visualization, and traditional statistical models. Initially, we generated 3 synthetic data sets for sepsis, acute hypotension, and antiretroviral therapy for HIV infection. This paper discusses the educational applications of Health Gym's synthetic data sets. We illustrate this through their use in postgraduate health data science courses delivered by the University of New South Wales, Australia, and a Datathon event, involving academics, students, clinicians, and local health district professionals. We also include adaptable worked examples using our synthetic data sets, designed to enrich hands-on tutorial and workshop experiences. Although we highlight the potential of these data sets in advancing data science education and health care artificial intelligence, we also emphasize the need for continued research into the inherent limitations of synthetic data.","2024","2024-12-03 03:36:07","2024-12-03 03:36:07","","1–15","","1","10","","","","","","","","","","","","","","","","","Citation Key: Kuo2024","","","","medical education; generative adversarial networks; data science; generative model; privacy; data privacy; accessibility; antiretroviral therapy (ART); data sets; educational purposes; health care AI; HIV; human immunodeficiency virus (HIV); hypotension; science education; sepsis","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"HNFGAXV8","journalArticle","2023","Lutfiani, Ninda; Wijono, Sutarto; Rahardja, Untung; Iriani, Ade; Aini, Qurotul; Septian, Rafly Ananda Dwi","A bibliometric study : Recommendation based on artificial intelligence for iLearning education","APTISI Transactions on Technopreneurship","","26568888","10.34306/att.v5i2.279","","Since most students begin their studies online, the LMS platform is frequently used. Universities and colleges play a crucial role in adopting many of its LMS platforms. A web-based application software package called Bibliometrics is used to design, test, and evaluate specific learning processes. LMS will be the dominant artificial intelligence-based solution for managing eLearning starting in early 2021. The principal objective of this project is to develop an artificial intelligence-powered LMS portal that enables students to continue studying and receive the most recent lessons from their teachers. Using the Communicate cloud software and Dialog Flow, a chatbot plugin system connected to the Google platform, and based on current needs, research bibliometrics was developed as an LMS project system. Students can interact with the chatbot anytime to satisfy their learning needs as long as they have an internet connection and a student ID card to access the dashboard. The LMS Platform, which was developed utilizing Bibliometrics and Artificial Intelligence approaches to help students access resources and complete teacher tasks, is a novelty in this work, according to earlier publications that have been evaluated.","2023","2024-12-03 03:36:07","2024-12-03 03:36:07","","109–117","","2","5","","","","","","","","","","","","","","","","","Citation Key: Lutfiani2023","","","","Bibliometrics; Chatbot; Artificial Intelligent; eLearning; LMS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"HKGNEHID","journalArticle","2024","Somasundaram, Naveena; M, Vigneshkumar; Pawar, Sanjay R.; Amutha, M.; S, Balu; V, Priya","AI-driven material design for tissue engineering a comprehensive approach integrating generative adversarial networks and high-throughput experimentation","The Scientific Temper","","0976-8653","10.58414/scientifictemper.2024.15.1.02","","This study presents an innovative AI-driven material design approach for tissue engineering, integrating generative adversarial networks (GANs) and high-throughput experimentation (HTE). The research methodology combines synthetic data generation, dimensionality reduction through principal component analysis (PCA), and model evaluation using a random forest classifier. The synthetic data, representative of diverse biomaterial structures, is generated with a three-class classification task. The model undergoes training on PCAtransformed and standardized synthetic data, with evaluation metrics including accuracy, precision, recall, and F1 score. Visualization through scatter plots, confusion matrices, and bar charts provides a comprehensive overview of the proposed approach's efficacy. Results demonstrate the GAN's capability to generate diverse synthetic data, the model's focused learning during training, and its subsequent generalization in the testing phase. Mathematical functions, including sine and cosine, further illustrate fundamental principles, while performance metrics confirm the model's proficiency in biomaterial classification. This research contributes to the evolving field of AI-driven material design, offering a systematic methodology and visual insights for accelerated and validated biomaterial discovery in tissue engineering applications.","2024","2024-12-03 03:36:07","2024-12-03 03:36:07","","1576–1580","","01","15","","","","","","","","","","","","","","","","","Citation Key: Somasundaram2024","","","","generative adversarial networks; classification; ai-driven material design; bannari amman institute; biomaterial; department of information technology; engineering; high-throughput experimentation; india; machine learning in tissue; of technology; sathyamangalam; tamil nadu; tissue engineering","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"VZIIERPF","journalArticle","2023","Gassman, Oliver; Haefner, Naomi","Generative AI and AI-based business model innovation a conversation with oliver gassmann and naomi haefner-interviewed by christian nielsen","Journal of Business Model","","","","","","2023","2024-12-03 03:36:07","2024-12-03 03:36:07","","46–50","","3","11","","","","","","","","","","","","","","","","","Citation Key: Gassman2023","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"7EYH7C9R","journalArticle","2023","Al-Tkhayneh, Khawlah M.; Alghazo, Emad M.; Tahat, Dina","The advantages and disadvantages of using artificial intelligence in education","Journal of Educational and Social Research","","22400524","10.36941/jesr-2023-0094","","This study aimed to identify the advantages and disadvantages of using artificial intelligence in education from the perspective of the students of Al Ain University. The researcher used the descriptive approach due to its compatibility to the study objectives. The study sample consisted of (184) students from Al Ain University in Al Ain City, who were selected using the random stratified sampling method. The questionnaire was developed and distributed by using Google Drive software. The results showed that most students think that artificial intelligence can improve learning personal experiences, process a considerable amount of data and improve task management. However, there have been different opinions about the ability of artificial intelligence to control students' behavior and direct learning, improve the efficiency of educational system, provide notes and reviews, reduce dependency on teachers, and enhance social interaction. Furthermore, several students expressed their concerns about the possible missing of traditional educational jobs, the costs of implementing artificial intelligence systems, the errors of programming and error-processing, and missing the human relationships in the classroom.","2023","2024-12-03 03:36:07","2024-12-03 03:36:07","","105–117","","4","13","","","","","","","","","","","","","","","","","Citation Key: Al-Tkhayneh2023","","","","model; artificial intelligence; education; Microsoft teams; students of Al Ain University","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"TZD4V5C5","journalArticle","2024","and; Roșca, Cosmina – Mihaela; Gortoescu, Ionuț Adrian; Tănase, Marius Radu","Artificial intelligence – powered video content generation tools","Romanian Journal of Petroleum & Gas Technology","","27345319","10.51865/jpgt.2024.01.10","","This article discusses the considerations of artificial intelligence-powered video content generation tools, exploring their applications, ethical considerations, and evaluation criteria. Through discussions of various artificial intelligence (AI) tools, including features, limitations, and implications, the authors analyze the evolving landscape of video creation in the digital age. Key themes include the ethical implications of deep fake technology and the importance of responsible AI principles, exemplified by Microsoft's guidelines. This paper identifies five of the most promoted free social media tools. Evaluation criteria for these tools, such as visual quality, relevance, coherence, authenticity, and transparency, are examined to assess the suitability of AI-generated videos. While AI offers promising opportunities, the discussion underscores the continued need for human oversight and ethical considerations to ensure the responsible use of AI technologies in video content generation.","2024","2024-12-03 03:36:08","2024-12-03 03:36:08","","131–144","","1","5 (76)","","","","","","","","","","","","","","","","","","","","","technology; ethical considerations; ai-powered video content generation; deep fake; evaluation criteria; responsible ai principles","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"5XATMKSX","journalArticle","2024","Huang, Decheng; Yang, Mingxuan; Wen, Xin; Xia, Siwei; Yuan, Bo","AI-driven drug discovery: Accelerating the development of novel therapeutics in biopharmaceuticals","Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)","","","10.60087/jklst.vol3.n3.p.206-224","","Artificial Intelligence (AI) has emerged as a transformative force in drug discovery, revolutionizing the biopharmaceutical industry's approach to developing novel therapeutics. This paper provides a comprehensive overview of AI-driven drug discovery, focusing on its applications in accelerating the development of innovative treatments. We examine the fundamental AI technologies employed in drug discovery, including machine learning algorithms, deep learning architectures, and natural language processing techniques. The paper analyzes the integration of AI across various stages of the drug discovery pipeline, from target identification to clinical trial design, highlighting significant improvements in efficiency and accuracy. We explore the impact of big data on AI-driven drug discovery, discussing the challenges and opportunities presented by multi-omics data integration, electronic health records mining, and the need for data standardization. The study also addresses ethical considerations and regulatory challenges associated with AI implementation in drug development. Finally, we present emerging trends and prospects for AI in biopharmaceuticals, emphasizing the importance of collaborative ecosystems and the potential for AI to revolutionize personalized medicine. This review synthesizes current research and industry practices, providing insights into the transformative potential of AI in drug discovery and the challenges that lie ahead in realizing its full potential.","2024","2024-12-03 03:36:08","2024-12-03 03:36:08","","206–224","","3","3","","","","","","","","","","","","","","","","","Citation Key: Huang2024","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"DC8CWLFT","journalArticle","2023","Gozalo-Brizuela, R","A survey of generative AI applications","Cornell University","","","","","","2023","2024-12-03 03:39:40","2024-12-03 03:39:40","","","","","","","","","","","","","","","","","","","","","","Citation Key: pop00018 Type: CITATION tex.note+duplicate-1: Query date: 2024-11-29 14:01:16","","","https://scholar.google.com/scholar?q=related:FOungXNiPFgJ:scholar.google.com/&scioq=generative+ai&hl=en&as_sdt=2007&as_ylo=2022&as_yhi=2024","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"755W542I","journalArticle","2023","Shukla, S","Creative computing and harnessing the power of generative artificial intelligence","Journal Environmental Sciences And Technology","","","","","","2023","2024-12-03 03:40:20","2024-12-03 03:40:20","","","","","","","","","","","","","","","","","","","","","","Citation Key: pop00262 Type: CITATION tex.note+duplicate-1: Query date: 2024-11-29 14:01:16","","","https://scholar.google.com/scholar?q=related:iZYsSa1zkwoJ:scholar.google.com/&scioq=generative+ai&hl=en&as_sdt=2007&as_ylo=2022&as_yhi=2024","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""