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Section I Health IoT Data Analytics
1. Introduction to IoT
2. Charateristics of IoT data
3. Health IoT Data Analytics Methods
4. Health IoT Architecture
5. Health IoT Data Analytics Challenges
6. Health IoT Data Analytics opportunities
Section II IoT Services in Health Industry
7. IoT for Patients
8. IoT for Physicians
9. IoT for Hospitals
10. IoT for Health Insurance Companies
11. IoT for Privacy and security
Section III Applications of IoT for Human
12. New Born
16. Smart health services in smart cities
Section IV Applications of IoT for Animals
17. Live Stock
18. Pet Animals
19. Wild Animals
20. Animal Monitoring
IoT Based Data Analytics for the Healthcare Industry: Techniques and Applications explores recent advances in the analysis of healthcare industry data through IoT data analytics. The book covers the analysis of ubiquitous data generated by the healthcare industry, from a wide range of sources, including patients, doctors, hospitals, and health insurance companies. The book provides AI solutions and support for healthcare industry end-users who need to analyze and manipulate this vast amount of data. These solutions feature deep learning and a wide range of intelligent methods, including simulated annealing, tabu search, genetic algorithm, ant colony optimization, and particle swarm optimization.
The book also explores challenges, opportunities, and future research directions, and discusses the data collection and pre-processing stages, challenges and issues in data collection, data handling, and data collection set-up. Healthcare industry data or streaming data generated by ubiquitous sensors cocooned into the IoT requires advanced analytics to transform data into information. With advances in computing power, communications, and techniques for data acquisition, the need for advanced data analytics is in high demand.
- Provides state-of-art methods and current trends in data analytics for the healthcare industry
- Addresses the top concerns in the healthcare industry using IoT and data analytics, and machine learning and deep learning techniques
- Discusses several potential AI techniques developed using IoT for the healthcare industry
- Explores challenges, opportunities, and future research directions, and discusses the data collection and pre-processing stages
Researchers, academicians, and experts working in IoT in Health Industry, Health Management, Biomedical Engineering, Medical Applications, and Analysis, Application of Machine Learning and Deep Learning in Health, Big data-based learning techniques, Integrated approach of IoT and Artificial Intelligence for the Healthcare industry. Scientists, Researchers, Practitioners, Professional from Government and Industries, Cloud-centric applications-based industry, Industries planning to deploy advance analytics (DL) on Industrial Data
- No. of pages:
- © Academic Press 2021
- 1st October 2020
- Academic Press
- Paperback ISBN:
Sanjay Kumar Singh received the M.Tech. degree in computer applications from the IIT (ISM), Dhanbad, India, in 1995, and the Ph.D. degree in computer science and engineering from Uttar Pradesh Technical University, Lucknow, India, in 2004. He is currently a Professor with the Department of Computer Science and Engineering, IIT (BHU) Varanasi, Varanasi, India. He has authored or coauthored more than 150 national and international journal publications, book chapters, and conference papers. His current research interests include biometrics, computer vision, image and video processing, pattern recognition, and artificial intelligence. Dr. Singh is a member of the ACM and Computer Society of India. He is a Guest Editorial Board Member, a reviewer for various journals, and a TPC Member for various conferences.
IIT (ISM), Dhanbad, India
Ravi Shankar Singh is Associate Professor at Department of Computer Science and Engineering, IIT (BHU), Varanasi, India. He received his B. Tech., M. Tech. and Ph.D., all in Computer Science and Engineering. His research interests include Algorithms and High Performance Computing. He has authored several research publications and one book. He has conducted many workshops/seminars in various areas of Computer Science and Engineering. He has served as reviewer of many reputed international journals.
IIT (BHU), Varanasi, India
Anil Kumar Pandey completed his master degree in Mathematics and Postgraduate Diploma in Computer Science Application. He has done Master in Computer Science and Computer Application. He has done Ph.D in Computer Science He is working as Programmer in Banaras Hindu University. He has about 32 years experience of teaching and Research. Dr. Pandey have exposure of community based data analysis. He has assisted more than 20 Ph.D. students in Science, Humanities and Medicine. He has expertise in relational data base management system, AI and machine learning and IOT.
Banaras Hindu University, India
Sandeep S. Udmale received the B.E. degree in computer engineering from the University of Mumbai, Mumbai, India, in 2006, and the M.Tech. degree in computer engineering from Dr. Babasaheb Ambedkar Technological University, Raigad, India, in 2009. He is currently pursuing the Ph.D. degree in computer science and engineering with IIT (BHU) Varanasi, Varanasi, India. He is currently an Assistant Professor with the Department of Computer Engineering and Information Technology, Veermata Jijabai Technological Institute, Mumbai, India. His current research interests include machine learning, data science, and optimization and pattern analysis. Mr. Udmale is a member of the ACM and Computer Society of India.
University of Mumbai, Mumbai, India
Ankit Chaudhary is an Assistant Professor at Dept. of Computer Science, The University of Missouri at Saint Louis. He received his B.Tech., M.Eng. and Ph.D., all in Computer Engineering. His research interests include Data Science, Computer Vision and Cyber Security. He has authored seventy research publications and two books. He is an Associate Editor of Computers and Electrical Engineering, an Elsevier Journal. Also he is on the Editorial Board of several International Journals and serves as Program Chair/TPC in many Conferences. He served as federal grant reviewer and also a reviewer for Journals including IEEE Trans. on Image Processing, IEEE Trans. on Multimedia, IEEE Trans. on Visualization & Computer Graphics, IET Computer Vision, IET Image Processing, ACM Trans. on Interactive Intelligent Systems, Signal Image and Video Processing, Robotics and Autonomous Systems.
The University of Missouri, Saint Louis, USA
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