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Artificial Intelligence in Education: 25th International Conference, AIED 2024, Recife, Brazil, July 8–12, 2024, Proceedings, Part I (Lecture Notes in Computer Science #14829)
by Olga C. Santos Ig Ibert Bittencourt Irene-Angelica Chounta Andrew M. Olney Zitao LiuThis book constitutes the refereed proceedings of the 25th International Conference on Artificial Intelligence in Education, AIED 2024, held in Recife, Brazil, in July 8–12, 2024, Proceedings. The 49 full papers and 27 short papers presented in this book were carefully reviewed and selected from 334 submissions. The papers present results in high-quality research on intelligent systems and the cognitive sciences for the improvement and advancement of education.
Artificial Intelligence in Education: 25th International Conference, AIED 2024, Recife, Brazil, July 8–12, 2024, Proceedings, Part II (Lecture Notes in Computer Science #14830)
by Olga C. Santos Ig Ibert Bittencourt Irene-Angelica Chounta Andrew M. Olney Zitao LiuThis book constitutes the refereed proceedings of the 25th International Conference on Artificial Intelligence in Education, AIED 2024, held in Recife, Brazil, in July 8–12, 2024, Proceedings. The 49 full papers and 27 short papers presented in this book were carefully reviewed and selected from 334 submissions. The papers present result in high-quality research on intelligent systems and the cognitive sciences for the improvement and advancement of education.
Artificial Intelligence in Education: 24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, Proceedings (Lecture Notes in Computer Science #13916)
by Ning Wang Genaro Rebolledo-Mendez Noboru Matsuda Olga C. Santos Vania DimitrovaThis book constitutes the refereed proceedings of the 24th International Conference on Artificial Intelligence in Education, AIED 2023, held in Tokyo, Japan, during July 3-7, 2023. This event took place in hybrid mode. The 53 full papers and 26 short papers presented in this book were carefully reviewed and selected from 311 submissions. The papers present result in high-quality research on intelligent systems and the cognitive sciences for the improvement and advancement of education. The conference was hosted by the prestigious International Artificial Intelligence in Education Society, a global association of researchers and academics specializing in the many fields that comprise AIED, including, but not limited to, computer science, learning sciences, and education.
Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky: 25th International Conference, AIED 2024, Recife, Brazil, July 8–12, 2024, Proceedings, Part II (Communications in Computer and Information Science #2151)
by Olga C. Santos Ig Ibert Bittencourt Irene-Angelica Chounta Andrew M. Olney Zitao LiuThis volume constitutes poster papers and late breaking results presented during the 25th International Conference on Artificial Intelligence in Education, AIED 2024, which took place in Recife, Brazil, during July 8–12, 2024. The 18 full papers and 92 short papers were carefully reviewed and selected from 200 submissions. They are organized in topical sections as follows: Part One: Blue Sky, Industry, Innovation and Practitioner, WideAIED and Late-Breaking Results. Part Two: Late-Breaking Results, Doctoral Consortium, Workshops and Tutorials.
Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky: 25th International Conference, AIED 2024, Recife, Brazil, July 8–12, 2024, Proceedings, Part I (Communications in Computer and Information Science #2150)
by Olga C. Santos Ig Ibert Bittencourt Irene-Angelica Chounta Andrew M. Olney Zitao LiuThis volume constitutes poster papers and late breaking results presented during the 25th International Conference on Artificial Intelligence in Education, AIED 2024, which took place in Recife, Brazil, during July 8–12, 2024. The 18 full papers and 92 short papers were carefully reviewed and selected from 200 submissions. They are organized in topical sections as follows: Part One: Blue Sky, Industry, Innovation and Practitioner, WideAIED and Late-Breaking Results. Part Two: Late-Breaking Results, Doctoral Consortium, Workshops and Tutorials.
Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky: 24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, Proceedings (Communications in Computer and Information Science #1831)
by Ning Wang Genaro Rebolledo-Mendez Vania Dimitrova Noboru Matsuda Olga C. SantosThis volume constitutes poster papers and late breaking results presented during the 24th International Conference on Artificial Intelligence in Education, AIED 2023, Tokyo, Japan, July 3–7, 2023.The 65 poster papers presented were carefully reviewed and selected from 311 submissions. This set of posters was complemented with the other poster contributions submitted for the Poster and Late Breaking results track of the AIED 2023 conference.
Artificial Intelligence in Education: The Power and Dangers of ChatGPT in the Classroom (Studies in Big Data #144)
by Amina Al-Marzouqi Said A. Salloum Mohammed Al-Saidat Ahmed Aburayya Babeet GuptaThis book aims to bring together a collection of innovative and cutting-edge research that addresses the various challenges in the application and theoretical aspects of ChatGPT in education. ChatGPT is a large language model developed by OpenAI that has the ability to generate human-like text based on a prompt. This has significant potential for use in the field of education, as it allows for the creation of personalized, interactive learning experiences, automating assessment and grading, and more. In e-learning, ChatGPT is used to provide instant feedback and support to students, as well as generate interactive conversations in the target language for language learning. It is also integrated with existing learning management systems and educational technology platforms to enhance their capabilities. In research, ChatGPT is used for natural language processing and sentiment analysis to gather insights on student learning experiences and educational outcomes. However, it is important to note that there are also ethical and privacy concerns that come with using language models like ChatGPT in education, such as data protection and the potential for bias. Overall, the use of ChatGPT in education has the potential to revolutionize the way we learn, teach, and access information. The book seeks to publish original manuscripts that cover a broad range of topics, from the development of new chatbot technologies and their integration into the classroom, to the examination of the ethical and pedagogical implications of these systems. By compiling the latest developments in the field and highlighting new areas for exploration, this book provides valuable insights and perspectives for researchers, educators, and practitioners working in the field of ChatGPT and education. The ultimate goal is to advance the understanding of ChatGPT and its role in education and to promote its effective and responsible use in the classroom and beyond.
Artificial Intelligence in Education Technologies: Proceedings of 2022 3rd International Conference on Artificial Intelligence in Education Technology (Lecture Notes on Data Engineering and Communications Technologies #154)
by Eric C. K. Cheng Tianchong Wang Tim Schlippe Grigorios N. BeligiannisThis edited book is a collection of selected research papers presented at the 2022 3rd International Conference on Artificial Intelligence in Education Technology (AIET 2022), held in Wuhan, China, on July 1–3, 2022. AIET establishes a platform for AI in education researchers to present research, exchange innovative ideas, propose new models, as well as demonstrate advanced methodologies and novel systems. The book is divided into five main sections – 1) AI in Education in the Post-COVID New Norm, 2) Emerging AI Technologies, Methods, Systems and Infrastructure, 3) Innovative Practices of Teaching and Assessment Driven by AI and Education Technologies, 4) Curriculum, Teacher Professional Development and Policy for AI in Education, and 5) Issues and Discussions on AI In Education and Future Development. Through these sections, the book provides a comprehensive picture of the current status, emerging trends, innovations, theory, applications, challenges and opportunities of current AI in education research. This timely publication is well aligned with UNESCO’s Beijing Consensus on Artificial Intelligence (AI) and Education. It is committed to exploring how AI may play a role in bringing more innovative practices, transforming education in the post-pandemic new norm and triggering an exponential leap toward the achievement of the Education 2030 Agenda. Providing broad coverage of recent technology-driven advances and addressing a number of learning-centric themes, the book is an informative and useful resource for researchers, practitioners, education leaders and policy-makers who are involved or interested in AI and education.
Artificial Intelligence in Education Technologies: Proceedings of 2023 4th International Conference on Artificial Intelligence in Education Technology (Lecture Notes on Data Engineering and Communications Technologies #190)
by Tim Schlippe Eric C. K. Cheng Tianchong WangThis book is a collection of selected research papers presented at the 2023 4th International Conference on Artificial Intelligence in Education Technology (AIET 2023), held in Berlin, Germany, on June 30 - July 2, 2023. AIET establishes a platform for AI in education researchers to present research, exchange innovative ideas, propose new models, as well as demonstrate advanced methodologies and novel systems. It is a timely and up-to-date publication responsive to the rapid development of AI technologies, practices and their increasingly complex interplay with the education domain. It promotes the cross-fertilisation of knowledge and ideas from researchers in various fields to construct the interdisciplinary research area of AI in Education. These subject areas include computer science, cognitive science, education, learning sciences, educational technology, psychology, philosophy, sociology, anthropology and linguistics. The feature of this book will contribute from diverse perspectives to form a dynamic picture of AI in Education. It also includes various domain-specific areas for which AI and other education technology systems have been designed or used in an attempt to address challenges and transform educational practice. This timely publication is in line with UNESCO’s Beijing Consensus on Artificial Intelligence and Education. It is committed to exploring how AI may play a role in bringing more innovative practices, transforming education, and triggering an exponential leap towards the achievement of the Education 2030 Agenda. Providing broad coverage of recent technology-driven advances and addressing a number of learning-centric themes, the book is an informative and useful resource for researchers, practitioners, education leaders and policy-makers who are involved or interested in AI and education.
Artificial Intelligence in Forecasting: Tools and Techniques
by Sachi Nandan Mohanty Preethi Nanjundan Tejaswini KarForecasting deals with the uncertainty of the future. To be effective, forecasting models should be timely available, accurate, reliable, and compatible with existing database. Accurate projection of the future is of vital importance in supply chain management, inventory control, economic condition, technology, growth trend, social change, political change, business, weather forecasting, stock price prediction, earthquake prediction, etc. AI powered tools and techniques of forecasting play a major role in improving the projection accuracy. The software running AI forecasting models use machine learning to improve accuracy. The software can analyse the past data and can make better prediction about the future trends with higher accuracy and confidence that favours for making proper future planning and decision. In other words, accurate forecasting requires more than just the matching of models to historical data.The book covers the latest techniques used by managers in business today, discover the importance of forecasting and learn how it's accomplished. Readers will also be familiarised with the necessary skills to meet the increased demand for thoughtful and realistic forecasts.
Artificial Intelligence in Forecasting: Tools and Techniques
Forecasting deals with the uncertainty of the future. To be effective, forecasting models should be timely available, accurate, reliable, and compatible with existing database. Accurate projection of the future is of vital importance in supply chain management, inventory control, economic condition, technology, growth trend, social change, political change, business, weather forecasting, stock price prediction, earthquake prediction, etc. AI powered tools and techniques of forecasting play a major role in improving the projection accuracy. The software running AI forecasting models use machine learning to improve accuracy. The software can analyse the past data and can make better prediction about the future trends with higher accuracy and confidence that favours for making proper future planning and decision. In other words, accurate forecasting requires more than just the matching of models to historical data.The book covers the latest techniques used by managers in business today, discover the importance of forecasting and learn how it's accomplished. Readers will also be familiarised with the necessary skills to meet the increased demand for thoughtful and realistic forecasts.
Artificial Intelligence in HCI: 3rd International Conference, AI-HCI 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Virtual Event, June 26 – July 1, 2022, Proceedings (Lecture Notes in Computer Science #13336)
by Helmut Degen Stavroula NtoaThis book constitutes the refereed proceedings of the Third International Conference on Artificial Intelligence in HCI, AI-HCI 2022, which was held as part of HCI International 2022 and took place virtually during June 26 – July 1, 2022. A total of 1271 papers and 275 posters included in the 39 HCII 2022 proceedings volumes. AI-HCI 2022 includes a total of 39 papers; they are grouped thematically as follows: Human-Centered AI; Explainable and Trustworthy AI; UX Design and Evaluation of AI-Enabled Systems; AI Applications in HCI.
Artificial Intelligence in HCI: 5th International Conference, AI-HCI 2024, Held as Part of the 26th HCI International Conference, HCII 2024, Washington, DC, USA, June 29–July 4, 2024, Proceedings, Part III (Lecture Notes in Computer Science #14736)
by Helmut Degen Stavroula NtoaThe three-volume book set LNAI 14734, 14735, and 14736 constitutes the refereed proceedings of 5th International Conference on Artificial Intelligence in HCI, AI-HCI 2024, held as part of the 26th International Conference, HCI International 2024, which took place in Washington, DC, USA, during June 29-July 4, 2024. The total of 1271 papers and 309 posters included in the HCII 2024 proceedings was carefully reviewed and selected from 5108 submissions. The AI-HCI 2024 proceedings were organized in the following topical sections: Part I: Human-centered artificial intelligence; explainability and transparency; AI systems and frameworks in HCI; Part II: Ethical considerations and trust in AI; enhancing user experience through AI-driven technologies; AI in industry and operations; Part III: Large language models for enhanced interaction; advancing human-robot interaction through AI; AI applications for social impact and human wellbeing.
Artificial Intelligence in HCI: 5th International Conference, AI-HCI 2024, Held as Part of the 26th HCI International Conference, HCII 2024, Washington, DC, USA, June 29 – July 4, 2024, Proceedings, Part I (Lecture Notes in Computer Science #14734)
by Helmut Degen Stavroula NtoaThe three-volume book set LNAI 14734, 14735, and 14736 constitutes the refereed proceedings of 5th International Conference on Artificial Intelligence in HCI, AI-HCI 2024, held as part of the 26th International Conference, HCI International 2024, which took place in Washington, DC, USA, during June 29-July 4, 2024. The total of 1271 papers and 309 posters included in the HCII 2024 proceedings was carefully reviewed and selected from 5108 submissions. The AI-HCI 2024 proceedings were organized in the following topical sections: Part I: Human-centered artificial intelligence; explainability and transparency; AI systems and frameworks in HCI; Part II: Ethical considerations and trust in AI; enhancing user experience through AI-driven technologies; AI in industry and operations; Part III: Large language models for enhanced interaction; advancing human-robot interaction through AI; AI applications for social impact and human wellbeing.
Artificial Intelligence in HCI: 5th International Conference, AI-HCI 2024, Held as Part of the 26th HCI International Conference, HCII 2024, Washington, DC, USA, June 29–July 4, 2024, Proceedings, Part II (Lecture Notes in Computer Science #14735)
by Helmut Degen Stavroula NtoaThe three-volume book set LNAI 14734, 14735, and 14736 constitutes the refereed proceedings of 5th International Conference on Artificial Intelligence in HCI, AI-HCI 2024, held as part of the 26th International Conference, HCI International 2024, which took place in Washington, DC, USA, during June 29-July 4, 2024. The total of 1271 papers and 309 posters included in the HCII 2024 proceedings was carefully reviewed and selected from 5108 submissions. The AI-HCI 2024 proceedings were organized in the following topical sections: Part I: Human-centered artificial intelligence; explainability and transparency; AI systems and frameworks in HCI; Part II: Ethical considerations and trust in AI; enhancing user experience through AI-driven technologies; AI in industry and operations; Part III: Large language models for enhanced interaction; advancing human-robot interaction through AI; AI applications for social impact and human wellbeing.
Artificial Intelligence in Health
by Marianne SarazinUndeniable, inescapable, exhilarating and breaking free from the exclusive domain of science, artificial intelligence has become our main preoccupation. A major generator of new mathematical thinking, AI is the result of easy access to information and data, as facilitated by computer technology. Big Data has come to be seen as an unlimited source of knowledge, the use of which is still being fully explored, but its industrialization has swiftly followed in the footsteps of mathematicians; today's tools are increasingly designed to replace human beings, which comes with social and philosophical consequences. Drawing on examples of scientific work and the insights of experts, this book offers food for thought on the consequences and future of AI technology in education, health, the workplace and aging.
Artificial Intelligence in Health
by Marianne SarazinUndeniable, inescapable, exhilarating and breaking free from the exclusive domain of science, artificial intelligence has become our main preoccupation. A major generator of new mathematical thinking, AI is the result of easy access to information and data, as facilitated by computer technology. Big Data has come to be seen as an unlimited source of knowledge, the use of which is still being fully explored, but its industrialization has swiftly followed in the footsteps of mathematicians; today's tools are increasingly designed to replace human beings, which comes with social and philosophical consequences. Drawing on examples of scientific work and the insights of experts, this book offers food for thought on the consequences and future of AI technology in education, health, the workplace and aging.
Artificial Intelligence in Healthcare: Emphasis on Diabetes, Hypertension, and Depression Management (Intelligent Data-Driven Systems and Artificial Intelligence)
by Gourav Bathla Sanoj Kumar Harish Garg Deepika SainiThis book presents state-of-the-art research works for a better understanding of the advantages and limitations of AI techniques in the field of healthcare. It will further discuss artificial intelligence applications in depression, hypertension and diabetes management. The text also presents an artificial intelligence chatbot for depression, diabetes, and hypertension self-help.This book: Provides a structured overview of recent developments of artificial intelligence applications in the healthcare sector. Presents an in-depth understanding of how artificial intelligence techniques can be applied to diabetes management. Showcases supervised learning techniques based on datasets for depression management. Discusses artificial intelligence chatbot for diabetes, depression, and hypertension self-care. Highlights the importance of artificial intelligence in managing and predicting diabetes, hypertension, and depression. The text is primarily written for senior undergraduate, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, computer science and engineering, and biomedical engineering.
Artificial Intelligence in Healthcare
by Adam Bohr Kaveh MemarzadehArtificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data
Artificial Intelligence in Healthcare (Advanced Technologies and Societal Change)
by Lalit Garg Sebastian Basterrech Chitresh Banerjee Tarun K. SharmaThis book highlights the analytics and optimization issues in healthcare systems, proposes new approaches, and presents applications of innovative approaches in real facilities. In the past few decades, there has been an exponential rise in the application of swarm intelligence techniques for solving complex and intricate problems arising in healthcare. The versatility of these techniques has made them a favorite among scientists and researchers working in diverse areas. The primary objective of this book is to bring forward thorough, in-depth, and well-focused developments of hybrid variants of swarm intelligence algorithms and their applications in healthcare systems.
Artificial Intelligence in Healthcare: Emphasis on Diabetes, Hypertension, and Depression Management (Intelligent Data-Driven Systems and Artificial Intelligence)
This book presents state-of-the-art research works for a better understanding of the advantages and limitations of AI techniques in the field of healthcare. It will further discuss artificial intelligence applications in depression, hypertension and diabetes management. The text also presents an artificial intelligence chatbot for depression, diabetes, and hypertension self-help.This book: Provides a structured overview of recent developments of artificial intelligence applications in the healthcare sector. Presents an in-depth understanding of how artificial intelligence techniques can be applied to diabetes management. Showcases supervised learning techniques based on datasets for depression management. Discusses artificial intelligence chatbot for diabetes, depression, and hypertension self-care. Highlights the importance of artificial intelligence in managing and predicting diabetes, hypertension, and depression. The text is primarily written for senior undergraduate, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, computer science and engineering, and biomedical engineering.
Artificial Intelligence in Healthcare and Medicine
by Kayvan NajarianThis book provides a comprehensive overview of the recent developments in clinical decision support systems, precision health, and data science in medicine. The book targets clinical researchers and computational scientists seeking to understand the recent advances of artificial intelligence (AI) in health and medicine. Since AI and its applications are believed to have the potential to revolutionize healthcare and medicine, there is a clear need to explore and investigate the state-of-the-art advancements in the field. This book provides a detailed description of the advancements, challenges, and opportunities of using AI in medical and health applications. Over 10 case studies are included in the book that cover topics related to biomedical image processing, machine learning for healthcare, clinical decision support systems, visualization of high dimensional data, data security and privacy, bioinformatics, and biometrics. The book is intended for clinical researchers and computational scientists seeking to understand the recent advances of AI in health and medicine. Many universities may use the book as a secondary training text. Companies in the healthcare sector can greatly benefit from the case studies covered in the book. Moreover, this book also: Provides an overview of the recent developments in clinical decision support systems, precision health, and data science in medicine Examines the advancements, challenges, and opportunities of using AI in medical and health applications Includes 10 cases for practical application and reference Kayvan Najarian is a Professor in the Department of Computational Medicine and Bioinformatics, Department of Electrical Engineering and Computer Science, and Department of Emergency Medicine at the University of Michigan, Ann Arbor. Delaram Kahrobaei is the University Dean for Research at City University of New York (CUNY), a Professor of Computer Science and Mathematics, Queens College CUNY, and the former Chair of Cyber Security, University of York. Enrique Domínguez is a professor in the Department of Computer Science at the University of Malaga and a member of the Biomedical Research Institute of Malaga. Reza Soroushmehr is a Research Assistant Professor in the Department of Computational Medicine and Bioinformatics and a member of the Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor.
Artificial Intelligence in Healthcare and Medicine
by Kayvan Najarian Delaram Kahrobaei Enrique Dominguez Reza SoroushmehrThis book provides a comprehensive overview of the recent developments in clinical decision support systems, precision health, and data science in medicine. The book targets clinical researchers and computational scientists seeking to understand the recent advances of artificial intelligence (AI) in health and medicine. Since AI and its applications are believed to have the potential to revolutionize healthcare and medicine, there is a clear need to explore and investigate the state-of-the-art advancements in the field. This book provides a detailed description of the advancements, challenges, and opportunities of using AI in medical and health applications. Over 10 case studies are included in the book that cover topics related to biomedical image processing, machine learning for healthcare, clinical decision support systems, visualization of high dimensional data, data security and privacy, bioinformatics, and biometrics. The book is intended for clinical researchers and computational scientists seeking to understand the recent advances of AI in health and medicine. Many universities may use the book as a secondary training text. Companies in the healthcare sector can greatly benefit from the case studies covered in the book. Moreover, this book also: Provides an overview of the recent developments in clinical decision support systems, precision health, and data science in medicine Examines the advancements, challenges, and opportunities of using AI in medical and health applications Includes 10 cases for practical application and reference Kayvan Najarian is a Professor in the Department of Computational Medicine and Bioinformatics, Department of Electrical Engineering and Computer Science, and Department of Emergency Medicine at the University of Michigan, Ann Arbor. Delaram Kahrobaei is the University Dean for Research at City University of New York (CUNY), a Professor of Computer Science and Mathematics, Queens College CUNY, and the former Chair of Cyber Security, University of York. Enrique Domínguez is a professor in the Department of Computer Science at the University of Malaga and a member of the Biomedical Research Institute of Malaga. Reza Soroushmehr is a Research Assistant Professor in the Department of Computational Medicine and Bioinformatics and a member of the Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor.
Artificial Intelligence in Healthcare Industry (Advanced Technologies and Societal Change)
by Jyotismita Talukdar Thipendra P. Singh Basanta BarmanThis book presents a systematic evolution of artificial intelligence (AI), its applications, challenges and solutions in the field of healthcare. The book mainly covers the foundations and various methods of learning in artificial intelligence with its application in healthcare industry. This book provides a comprehensive introduction to data analysis using AI as a tool in the generation, normalization and analysis of healthcare data in association with several evaluation techniques and accuracy measurements. The book is divided into three major sections describing the basic foundations of AI and its associated algorithms, history of artificial intelligence in healthcare, recent developments and several modeling techniques for the same. The last section of the book provides insights into several implementations and methods of evaluation and accuracy prediction for healthcare analysis in AI. Extensive use of data for analysis and prediction using several technologies has transformed the lives of normal people indirectly effecting our process to communicate, learn, work and socialize within the society. Thus, the book also provides an insight into the ethics of AI that is very vital in the process of implementation and evaluation of healthcare data. The book provides an organized analysis to a considerable part of data in a digitized society. In view of this, it covers the theory, methodology, perfection and verification of empirical work for health-related data processing. Particular attention is devoted to in-depth experiments and applications.
Artificial Intelligence in Industrial Applications: Approaches to Solve the Intrinsic Industrial Optimization Problems (Learning and Analytics in Intelligent Systems #25)
by Steven Lawrence Fernandes Tarun K. SharmaThis book highlights the analytics and optimization issues in industry, to propose new approaches, and to present applications of innovative approaches in real facilities. In the past few decades there has been an exponential rise in the application of artificial intelligence for solving complex and intricate problems arising in industrial domain. The versatility of these techniques have made them a favorite among scientists and researchers working in diverse areas. The book is edited to serve a broad readership, including computer scientists, medical professionals, and mathematicians interested in studying computational intelligence and their applications. It will also be helpful for researchers, graduate and undergraduate students with an interest in the fields of Artificial Intelligence and Industrial problems. This book will be a useful resource for researchers, academicians as well as professionals interested in the highly interdisciplinary field of Artificial Intelligence.