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1. Combining IoT architectures in next generation healthcare computing systems
2. RFID-based unsupervised apnea detection in health care system
3. Designing a cooperative hierarchical model of interdiction median problem with protection and its solution approach: A case study of health-care network
4. Parallel machine learning and deep learning approaches for internet of medical things (IoMT)
5. Cloud-based IoMT framework for cardiovascular disease prediction and diagnosis in personalized E-health care
6. A study on security privacy issues and solutions in internet of medical things — A review
7. Application of computational intelligence models in IoMT big data for heart disease diagnosis in personalized health care
8. An improved canny detection method for detecting human flexibility
9. Prediction and classification of diabetes mellitus using genomic data
10. An application of cypher query-based dynamic rule-based decision tree over suicide statistics dataset with Neo4j
11. Exploring the possibilities of security and privacy issues in health-care IoT
Intelligent IoT Systems in Personalized Health Care delivers a significant forum for the technical advancement of IoMT learning in parallel computing environments across biomedical engineering diversified domains and its applications. Pursuing an interdisciplinary approach, the book focuses on methods used to identify and acquire valid, potentially useful knowledge sources. The book presents novel, in-depth, fundamental research contributions from a methodological/application perspective to help readers understand the fusion of AI with IoT and its capabilities in solving a diverse range of problems for biomedical engineering and its real-world personalized health care applications.
The book is well suited for researchers exploring the significance of IoT based architecture to perform predictive analytics of user activities in sustainable health.
- Presents novel, in-depth, fundamental research contributions from a methodological/application perspective to help readers understand the fusion of AI with IoT
- Illustrates state-of-the-art developments in new theories and applications of IoMT techniques as applied to parallel computing environments in biomedical engineering systems
- Presents concepts and technologies successfully used in the implementation of today's intelligent data-centric IoT systems and Edge-Cloud-Big data
Undergraduates, graduates and professionals in fields related to Healthcare, Engineering, Artificial Intelligence, and Computer Science
- No. of pages:
- © Academic Press 2020
- 6th November 2020
- Academic Press
- Paperback ISBN:
- eBook ISBN:
Dr. Arun Kumar Sangaiah received his Master of Engineering from Anna University and Ph.D. from VIT University, India. He is currently as 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. Moreover, he has holding visiting professor positions in China, France, Japan, South Korea. Further, he has been visited many research centers and universities in China, Japan, France, Singapore and South Korea for join collaboration towards research projects and publications. Dr. Sangaiah’s outstanding scientific production spans over 200+ contributions published in high standard ISI journals, such as IEEE-TII, IEEE-Communication Magazine, IEEE Systems and IEEE IoT. In addition, he has authored/edited 8 books (Elsevier, Springer and others) and edited 50 special issues in reputed ISI journals, such as IEEE-Communication Magazine, IEEE-TII, IEEE-IoT, ACM transaction on Intelligent Systems and Technology etc. He has also registered one Indian patent in the area of Computational Intelligence. His Google Scholar Citations reached 5000+ with h-index: 40+ and i10-index: 150+. Further, Dr. Sangaiah is responsible for EiC, Editorial Board Member and Associate Editor of many reputed ISI journals.Finally, he has received many awards that includes,Chinese Academy of Sciences-PIFI overseas visiting scientist award, UPEC-France Visiting Scholar award, Carrers-360 Top-10 Outstanding Researchers award and etc.
School of Computer Science, The University of Adelaide, Australia
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.
Professor of Mechanical/Electronics Engineering, Macquarie University, Australia
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