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Machine Learning and the Internet of Medical Things in Healthcare discusses the applications and challenges of machine learning for healthcare applications. The book provides a platform for presenting machine learning-enabled healthcare techniques and offers a mathematical and conceptual background of the latest technology. It describes machine learning techniques along with the emerging platform of the Internet of Medical Things used by practitioners and researchers worldwide.
The book includes deep feed forward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology. It also presents the concepts of the Internet of Things, the set of technologies that develops traditional devices into smart devices. Finally, the book offers research perspectives, covering the convergence of machine learning and IoT. It also presents the application of these technologies in the development of healthcare frameworks.
- Provides an introduction to the Internet of Medical Things through the principles and applications of machine learning
- Explains the functions and applications of machine learning in various applications such as ultrasound imaging, biomedical signal processing, robotics, and biomechatronics
- Includes coverage of the evolution of healthcare applications with machine learning, including Clinical Decision Support Systems, artificial intelligence in biomedical engineering, and AI-enabled connected health informatics, supported by real-world case studies
Academic research on: Biomedical Engineering, Computer Science, and researchers in machine learning, computational intelligence, as well as clinicians and researchers in various medical research and clinical settings
1. Machine Learning Architecture and Framework
2. Machine Learning in Healthcare: Review, Opportunities and Challenges
3. Machine Learning for Biomedical Signal Processing
4. Artificial Intelligence in Medicine
5. Diagnosing of Disease Using Machine Learning
6. A Novel Approach of Telemedicine for Managing Fetal Condition based on Machine Learning Technology from Iot Based Wearable Medical Device
7. Iot Based Healthcare Delivery Services to Promote Transparency and Patient Satisfaction in a Corporate Hospital
8. Examining Diabetic Subjects on Their Correlation with TTH and CAD: A Statistical Approach on Exploratory Results
9. Cancer Prediction and Diagnosis Hinged on HCML in IOMT Environment
10. Parameterization Techniques for Automatic Speech Recognition System
11. Impact of Big Data in Healthcare System: A Quick Look into Electronic Health Record Systems
- No. of pages:
- © Academic Press 2021
- 14th April 2021
- Academic Press
- Paperback ISBN:
- eBook ISBN:
Dr. Krishna Kant Singh is working as Associate Professor in Electronics & Communication Engineering in KIET Group of Institutions, Delhi-NCR, India. He has wide teaching and research experience. Dr. Singh has acquired B.Tech, M.Tech, and Ph.D (IIT Roorkee) in the area of image processing and remote sensing. He has authored more than 70 research papers in Scopus and SCIE indexed journals of repute. He has also authored 25 technical books. He is also an associate editor of Journal of Intelligent & Fuzzy Systems (SCIE Indexed), IEEE ACCESS (SCIE Indexed) and Guest Editor of Open Computer Science. He is also member of Editorial board of Applied Computing & Geoscience (Elsevier).
Professor, Faculty of Engineering & Technology, Jain (Deemed-to-be University), Bengaluru, India
Dr. Mohamed Elhoseny is currently an assistant professor at the Faculty of Computers and Information, Mansoura University and a researcher at the CoVIS Lab, Department of Computer Science and Engineering, University of North Texas. Dr. Elhoseny is the Director of Distributed Sensing and Intelligent Systems Lab, Mansoura University, in Egypt, and has over 100 ISI journal articles, conference proceedings, book chapters, and six books published by Springer and Taylor & Francis. He serves as the Editor-in-Chief for International Journal of Smart Sensor Technologies and Applications, IGI Global, and is an associate editor of several journals such as IEEE Access, IEEE Future Directions, PLOS One, Remote Sensing, and International Journal of E-services and Mobile Applications, IGI Global. His research interests include Network Security, Cryptography, Machine Learning Techniques, Internet of Things, and Quantum Computing.
College of Computer Information Technology, American University in the Emirates, Dubai, United Arab Emirates
Dr. Akansha Singh is B.Tech, M.Tech and PhD in Computer Science. She received her PhD from IIT Roorkee in the area of image processing and machine learning. Currently, she is working as Associate Professor in Department of Computer Science and Engineering, ASET, Amity University, Noida. She has to her credit more than 70 research papers, 20 books and numerous conference papers. She has been the editor for books on emerging topics with publishers like Elsevier, Taylor and Francis, Wiley etc. Dr. Singh has served as reviewer and technical committee member for multiple conferences and journals of High Repute. She is also the Associate Editor for IEEE Access journal which is an SCI journal with impact factor of 4.018. Dr. Singh has also undertaken government funded project as Principal Investigator. Her research areas include image processing, remote sensing, IoT and machine learning.
Department of CSE, ASET, Amity University Uttar Pradesh, Noida, India
Dr. Ahmed A. Elngar is currently an Assistant Professor at the Faculty of Computers and Artificial Intelligence, Computer Science Department, Beni-Suef University, Beni-Suef City, 62511, Egypt. Dr. Elngar is the Director of Technological and Informatics Studies Center (TISC) and is the Founder and Head of Scientific Innovation Research Group (SIRG) at Beni-Suef University. He is a Co-Director of the International Ranking Office, Beni Suef University. Dr. Elngar is a Director of Beni-Suef University Electronic Portal, He is IEEE member of Beni-Suef Section. Also, he is Managing Editor of Journal of CyberSecurity and Information Management (JCIM). Dr. Elngar is the co-author of five books, including Detecting Network Intrusion Using Computational Intelligence and Computational Intelligence for Confidential Authentication Systems, both from Lambert Academic Publishing, as well as Empowering Artificial Intelligence Through Machine Learning and Deep Learning and IoT in Healthcare Systems: Paradigms and Applications, both forthcoming from Apple Academic Press. Dr Elngar is a collaborative researcher – he is a member of the Egyptian Mathematical Society (EMS) and International Rough Set Society (IRSS). His research areas include computational intelligence, medical image analysis, security, authentication, cryptography, animal identification and multimedia data mining.
Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni Suef City, Egypt
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