Intelligent IoT Systems in Personalized Health Care (Cognitive Data Science in Sustainable Computing) - Softcover

Über die Autorinnen und Autoren

Prof. Arun Kumar Sangaiah received his PhD from the School of Computer Science and Engineering, VIT University, Vellore, India. He is currently a Full Professor with National Yunlin University of Science and Technology, Taiwan. He is also a Professor at the School of Computing Science and Engineering, VIT University, Vellore, India. His areas of research interest include machine learning, Internet of Things, Sustainable Computing. He has published more than 300 research articles in refereed journals, 11 edited books, one patent (held and filed), as well as four projects funded by MOST-TAIWAN, one funded by Ministry of IT of India, and several international projects (CAS, Guangdong Research fund, Australian Research Council). Dr. Sangaiah has received many awards, Yushan Young Scholar, Clarivate Top 1% Highly Cited Researcher (2021,2022, 2023), Top 2% Scientist (Standord Report-2020,2021,2022, 2023), PIFI-CAS fellowship, Top-10 outstanding researcher, CSI significant Contributor etc. He is also serving as Editor-in-Chief and/or Associate Editor of various reputed ISI journals. Dr. Sangaiah is a visiting scientist (2018-2019) with Chinese Academy of Sciences (CAS), China and visiting researcher of Université Paris-Est (UPEC), France (2019-2020) and etc.

Subhas Mukhopadhyay holds a B.E.E. (gold medallist), M.E.E., Ph.D. (India) and Doctor of Engineering (Japan). He has over 30+ years of teaching, industrial and research experience.

Currently he is working as a Professor of Mechanical/Electronics Engineering, Macquarie University, Australia and is the Discipline Leader of the Mechatronics Engineering Degree Programme. Before joining Macquarie he worked as Professor of Sensing Technology, Massey University, New Zealand. His fields of interest include Smart Sensors and sensing technology, instrumentation techniques, wireless sensors and network, Internet of Things, numerical field calculation, electromagnetics etc. He has supervised over 40 postgraduate students and over 100 Honours students. He has examined over 50 postgraduate theses.

He has published over 450 papers in different international journals and conference proceedings, written eight books and forty book chapters and edited seventeen conference proceedings. He has also edited thirty books with Springer-Verlag and twenty four journal special issues. He has organized over 20 international conferences as either General Chairs/co-chairs or Technical Programme Chair. He has delivered 340 presentations including keynote, invited, tutorial and special lectures.

He is a Fellow of IEEE (USA), a Fellow of IET (UK), a Fellow of IETE (India), a Topical Editor of IEEE Sensors journal, and an associate editor of IEEE Transactions on Instrumentation and Measurements. He is a Distinguished Lecturer of the IEEE Sensors Council from 2017 to 2019. He is the Founding chair of IEEE IMS NSW chapter.

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Intelligent IoT Systems in Personalized Health Care delivers a significant forum for the technical advancement of IoMT learning in parallel computing environment across biomedical engineering diversified domains and its applications. Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources.

The book presents novel in-depth fundamental research contributions either from a methodological/application perspective in understanding the fusion of AI with IoT and their capabilities in solving a diverse range of problems in for biomedical engineering and its real-world personalized health care applications.

Managing the gathered knowledge and applying it to multiple domains of personalized medical systems including healthcare predictive analytics, mining, recommendation systems, medical image processing, pattern recognition and predictions using AI paradigms is the major strength of this book.

The book is well suited for researchers exploring the significance of IoT based architecture to perform predictive analytics of user activities in sustainable health.