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Synopsis

Explainable IoT Application: A Demystification is an in-depth guide that examines the intersection of the Internet of Things (IoT) with AI and Machine Learning, focusing on the crucial need for transparency and interpretability in IoT systems. As IoT devices become more integrated into daily life, from smart homes to industrial automation, it is increasingly important to understand and trust the decisions they make. The book starts by covering the basics of IoT, highlighting its importance in modern technology and its wide-ranging applications in fields such as healthcare, transportation, and smart cities. It then delves into the concept of explainability, stressing the need to prevent IoT systems from being perceived as opaque, black-box operations. The authors explore various techniques and methods for achieving explainability, including rule-based systems and machine learning models, while also addressing the challenge of balancing explainability with performance. Through practical examples, the book shows how explainability can be successfully implemented in IoT applications, such as in smart healthcare systems.

 

Furthermore, the book addresses the significant challenges of securing IoT systems in an increasingly connected world. It examines the unique vulnerabilities that come with the widespread use of IoT devices, such as data breaches, cyberattacks, and privacy issues, and discusses the complexities of managing these risks. The authors emphasize the importance of implementing security strategies that strike a balance between fostering innovations and protecting user data. The book concludes with a comprehensive exploration of the challenges and opportunities in making IoT systems more transparent and interpretable, offering valuable insights for researchers, developers, and decision-makers aiming to create IoT applications that are both trustworthy and understandable.

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À propos de l?auteur

Dr. Sachi Nandan Mohanty was recognized as Top 2% World Scientists Ranking by Stanford University and Elsevier for years 2022, and 2023. He received his PostDoc from Indian Institute of Technology Kanpur, India in the year 2019 and Ph.D. from Indian Institute of Technology Kharagpur, India in the year 2015, with MHRD scholarship from Govt of India. He has authored/edited Forty two books, published by IEEE-Wiley, Springer, Wiley, CRC Press, NOVA and DeGruyter. His research areas include Data mining, Big Data Analysis, Cognitive Science, Fuzzy Decision Making, Brain-Computer Interface, Cognition, and Computational Intelligence. Prof. S N Mohanty has received four Best Paper Awards during his Ph.D. from International Conference at Benjing, China, and the other at International Conference on Soft Computing Applications organized by IIT Rookee in the year 2013. He has awarded Best thesis award first prize by Computer Society of India in the year 2015. He has guided 9 PhD Scholar, twenty three Post graduate student. He has published 241 International Journals of International repute. He has received 4th time International Travel support from Government of India Department of Science and Technology, SERB Funding for International travel for presenting research paper and Keynote specker. He has awarded by many awards and fellowships during his career and to name a few are and has been elected as FELLOW of Institute of Engineers, IETE, Ambassador European Alliance Innovation (EAI), and Senior member of IEEE Computer Society Hyderabad chapter. He has been awarded by many awards and fellowship during his career and to name a few are Prof. Ganesh Mishra Memorial Award on 61st Annual Technical Session & 19th Prof. Bhubaneswar Behera Lecturer on 23-24th June 2020 from The Institution of Engineers (India) Odisha State Center, Bhubaneswar. He is a member of professional bodies like IETE, CSI, IE, IEEE (Hyderabad section). He also the reviewer of Journal of Robotics and Autonomous Systems (Elsevier), Computational and Structural Biotechnology Journal (Elsevier), Artificial Intelligence Review (Springer), Spatial Information Research (Springer). He is leading as General chair of 2 international conferences such as ICISML, AIHC and editor in chief of international journal EAI Transaction on Intelligent System and Machine learning Application. Dr. Mohanty has gone for academic assignment to New York, Atlanta, Orlondo, Georgia, Paris, Slovakia, Singapore, Abu Dhabi, Saraja, Dubai, Malaysia, Istanbul, Viena, and Germany for delivering Key note talk and chair the sessions in International conferences with travel support from Department of Science & Technology, Government of India, New Delhi, India.

Dr. Suneeta Satpathy (Senior member, IEEE) is currently working as an Associate Professor, Center For AI & ML, SOA University, Bhubaneswar, Odisha. She has received her Ph.D. from Utkal University, Bhubaneswar, Odisha, in the year 2015, with Directorate of Forensic Sciences, MHA scholarship from Govt of India. Her research interests include Computer Forensics, Cyber Security, Data Fusion, Data Mining, Big Data analysis, and Decision Mining. She has edited books in association with Springer and Wiley and CRC AAP, NOVA Publications. In addition to research, she has guided many post-graduate and graduate students. She has published papers in many International Journals and conferences in repute. Her professional activities include roles as editorial board member and/or reviewer of Journal of Engineering Science, Advancement of Computer Technology and Applications, Robotics and Autonomous Systems (Elsevier), computational and Structural Biotechnology Journal (Elsevier), Journal of Big Data(Springer) as well as Inder Science Journals. She is an active member of CSI, ISTE, OITS, IE, Nikhil Bharat Shiksha Parisad and IEEE.

Prof. Xiaochun Cheng won full scholarship for all his university education, received the BEng Degree in Computer Engineering with first class degree in 1992, PhD in Computer Science with distinction in 1996. He has worked in UK since 1997. One project was funded with 16 Million Euro budget. He contributed for five times best conference paper awards so far. 6 his papers were in the top 1% of the academic field by Data from Essential Science Indicators. He won 3 times national competitions. He won national award for research. Two solutions were adopted nationally. He serves 7 IEEE Technical Committees and 3 BCS Specialist Groups. He currently works at Swansea University, UK.

Dr. Subhendu Kumar Pani is the Professor at Krupajal Engineering College. He is the Book Series Editor-in-Chief of the Advances in Computational Collective Intelligence(Routledge, Taylor & Francis Group), Advances in Intelligent Decision-Making, Systems Engineering, and Project Management(Routledge, Taylor & Francis Group), Advances in Artificial Intelligence and Robotics(AAP-CRC Press, Taylor & Francis Group), Emerging Trends in Biomedical Technologies and Health informatics(Routledge, Taylor & Francis Group), Advances in Intelligent and Scientific Computing (Scrivener-Wiley Publishing), Applied Artificial Intelligence in Data Science, Cloud Computing and IoT Frameworks(Bentham Science Press), Emerging methodologies and applications in Intelligent Computing(NOVA Science Publisher, USA) and he has served as a guest editor for a number of journals including the MDPI Journal of Sensors. Dr. Pani works in a multi-disciplinary environment involving Artificial Intelligence, Health informatics, Internet of things, Collective Computational Intelligence, Robotics, Social Computing Web intelligence, Web services, data mining and applied to various real world problems. In these areas he has authored / coauthored more than 300+ research publications out of which there are 40+ books covering various aspects of Computer Science. About 80+ publications are indexed by Scopus. He is actively involved in the organization of several academic conferences and member of many (more than 150) international program committees and several editorial review boards.For his research, he has won 5 researcher awards from different prestigious body. Dr. Pani received Ph.D. degree in Computer Science from Utkal University, Odisha, India (2013) and a Master of Technology Degree from KIIT University, India(2007). He is a fellow in Scientific Society of Advance Research and Social Change and a life member in IE, ISTE, ISCA, OBA.OMS, SMIACSIT, SMUACEE, CSI.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Explainable IoT Application: A Demystification is an in-depth guide that examines the intersection of the Internet of Things (IoT) with AI and Machine Learning, focusing on the crucial need for transparency and interpretability in IoT systems. As IoT devices become more integrated into daily life, from smart homes to industrial automation, it is increasingly important to understand and trust the decisions they make. The book starts by covering the basics of IoT, highlighting its importance in modern technology and its wide-ranging applications in fields such as healthcare, transportation, and smart cities. It then delves into the concept of explainability, stressing the need to prevent IoT systems from being perceived as opaque, black-box operations. The authors explore various techniques and methods for achieving explainability, including rule-based systems and machine learning models, while also addressing the challenge of balancing explainability with performance. Through practical examples, the book shows how explainability can be successfully implemented in IoT applications, such as in smart healthcare systems.Furthermore, the book addresses the significant challenges of securing IoT systems in an increasingly connected world. It examines the unique vulnerabilities that come with the widespread use of IoT devices, such as data breaches, cyberattacks, and privacy issues, and discusses the complexities of managing these risks. The authors emphasize the importance of implementing security strategies that strike a balance between fostering innovations and protecting user data. The book concludes with a comprehensive exploration of the challenges and opportunities in making IoT systems more transparent and interpretable, offering valuable insights for researchers, developers, and decision-makers aiming to create IoT applications that are both trustworthy and understandable. N° de réf. du vendeur 9783031748844

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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Explainable IoT Application: A Demystification is an in-depth guide that examines the intersection of the Internet of Things (IoT) with AI and Machine Learning, focusing on the crucial need for transparency and interpretability in IoT systems. As IoT devices become more integrated into daily life, from smart homes to industrial automation, it is increasingly important to understand and trust the decisions they make. The book starts by covering the basics of IoT, highlighting its importance in modern technology and its wide-ranging applications in fields such as healthcare, transportation, and smart cities. It then delves into the concept of explainability, stressing the need to prevent IoT systems from being perceived as opaque, black-box operations. The authors explore various techniques and methods for achieving explainability, including rule-based systems and machine learning models, while also addressing the challenge of balancing explainability with performance. Through practical examples, the book shows how explainability can be successfully implemented in IoT applications, such as in smart healthcare systems.Furthermore, the book addresses the significant challenges of securing IoT systems in an increasingly connected world. It examines the unique vulnerabilities that come with the widespread use of IoT devices, such as data breaches, cyberattacks, and privacy issues, and discusses the complexities of managing these risks. The authors emphasize the importance of implementing security strategies that strike a balance between fostering innovations and protecting user data. The book concludes with a comprehensive exploration of the challenges and opportunities in making IoT systems more transparent and interpretable, offering valuable insights for researchers, developers, and decision-makers aiming to create IoT applications that are both trustworthy and understandable. 487 pp. Englisch. N° de réf. du vendeur 9783031748844

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Buch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Explainable IoT Application: A Demystification is an in-depth guide that examines the intersection of the Internet of Things (IoT) with AI and Machine Learning, focusing on the crucial need for transparency and interpretability in IoT systems. As IoT devices become more integrated into daily life, from smart homes to industrial automation, it is increasingly important to understand and trust the decisions they make. The book starts by covering the basics of IoT, highlighting its importance in modern technology and its wide-ranging applications in fields such as healthcare, transportation, and smart cities. It then delves into the concept of explainability, stressing the need to prevent IoT systems from being perceived as opaque, black-box operations. The authors explore various techniques and methods for achieving explainability, including rule-based systems and machine learning models, while also addressing the challenge of balancing explainability with performance. Through practical examples, the book shows how explainability can be successfully implemented in IoT applications, such as in smart healthcare systems.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 496 pp. Englisch. N° de réf. du vendeur 9783031748844

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Hardcover. Etat : new. Hardcover. Explainable IoT Application: A Demystification is an in-depth guide that examines the intersection of the Internet of Things (IoT) with AI and Machine Learning, focusing on the crucial need for transparency and interpretability in IoT systems. As IoT devices become more integrated into daily life, from smart homes to industrial automation, it is increasingly important to understand and trust the decisions they make. The book starts by covering the basics of IoT, highlighting its importance in modern technology and its wide-ranging applications in fields such as healthcare, transportation, and smart cities. It then delves into the concept of explainability, stressing the need to prevent IoT systems from being perceived as opaque, black-box operations. The authors explore various techniques and methods for achieving explainability, including rule-based systems and machine learning models, while also addressing the challenge of balancing explainability with performance. Through practical examples, the book shows how explainability can be successfully implemented in IoT applications, such as in smart healthcare systems. Furthermore, the book addresses the significant challenges of securing IoT systems in an increasingly connected world. It examines the unique vulnerabilities that come with the widespread use of IoT devices, such as data breaches, cyberattacks, and privacy issues, and discusses the complexities of managing these risks. The authors emphasize the importance of implementing security strategies that strike a balance between fostering innovations and protecting user data. The book concludes with a comprehensive exploration of the challenges and opportunities in making IoT systems more transparent and interpretable, offering valuable insights for researchers, developers, and decision-makers aiming to create IoT applications that are both trustworthy and understandable. Explainable IoT Application: A Demystification is an in-depth guide that examines the intersection of the Internet of Things (IoT) with AI and Machine Learning, focusing on the crucial need for transparency and interpretability in IoT systems. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9783031748844

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