Internet of Healthcare Things: Machine Learning for Security and Privacy

The book addresses privacy and security issues providing solutions through authentication and authorization mechanisms, blockchain, fog computing, machine learning algorithms, so that machine learning-enabled IoT devices can deliver information concealed in data for fast, computerized responses and enhanced decision-making.

The main objective of this book is to motivate healthcare providers to use telemedicine facilities for monitoring patients in urban and rural areas and gather clinical data for further research. To this end, it provides an overview of the Internet of Healthcare Things (IoHT) and discusses one of the major threats posed by it, which is the data security and data privacy of health records. Another major threat is the combination of numerous devices and protocols, precision time, data overloading, etc. In the IoHT, multiple devices are connected and communicate through certain protocols. Therefore, the application of emerging technologies to mitigate these threats and provide secure data communication over the network is discussed. This book also discusses the integration of machine learning with the IoHT for analyzing huge amounts of data for predicting diseases more accurately. Case studies are also given to verify the concepts presented in the book.

Audience

Researchers and industry engineers in computer science, artificial intelligence, healthcare sector, IT professionals, network administrators, cybersecurity experts.

About the Author

Kavita Sharma, PhD is an associate professor in the Department of CSE at Galgotias College of Engineering and Technology, Greater Noida, India. She has 4 patents (2 Granted and 2 published), published 6 books and 50 research articles in international journals and conferences. Her area of interest includes information and cyber security, mobile computing, IoT security, data analytics and machine learning.

Yogita Gigras, PhD is an assistant professor in the Department of CSE & IT, School of Engineering & Technology of The North Cap University, Haryana, India. She has published more than 30 research papers in peer-reviewed international journals and conferences and has more than 12 years of teaching experience at both post and undergraduate level.

Vishnu Sharma, PhD is Head of Department and Professor in Computer Science and Engineering at Galgotias College of Engineering and Technology Greater Noida, UP, India. He has published more than 50 research papers in international and national journals and conferences as well as two books on mobile computing. He has more than 21 years of teaching experience in engineering institutes and universities.

D. Jude Hemanth, PhD is at the Department of ECE, Karunya University, Coimbatore, India. He has authored more than 100 research papers in SCIE/Scopus indexed international journals conferences as well as authored 1 book and edited 11 others.

Ramesh Chandra (Poonia), PhD is an associate professor in the Department of Computer Science, CHRIST (Deemed to be University), Bangalore, Karnataka, India. He has authored more than 65 research papers in SCIE/Scopus indexed international journals conferences as well as authored 6 books.

Series

Machine Learning in Biomedical Science and Healthcare Informatics

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