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Machine Learning and the Internet of Medical Things in Healthcare

  • ID: 5180529
  • Book
  • April 2021
  • 332 Pages
  • Elsevier Science and Technology
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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 health care techniques, offering mathematical and conceptual background on the latest technologies and describing machine learning techniques and the emerging platform of Internet of Medical Things used by practitioners and researchers worldwide. It includes sections on deep feed forward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology. Finally, the book offers research perspectives, covering the convergence of machine learning and IoT, along with the application of these technologies.
  • 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 that are all supported by real-world case studies
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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

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Singh, Krishna Kant
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).
Elhoseny, Mohamed
Dr. Mohamed Elhoseny is an Assistant Professor at the Department of Computer Science, College of Computer & Information Technology, American University in the Emirates (AUE). Dr. Elhoseny is an ACM Distinguished Speaker and IEEE Senior Member. He received his Ph.D. in Computers and Information from Mansoura University/University of North Texas through a joint scientific program. Dr. Elhoseny is the founder and the Editor-in-Chief of IJSSTA journal published by IGI Global. Also, he is an Associate Editor at IEEE Journal of Biomedical and Health Informatics, IEEE Access, Scientific Reports, IEEE Future Directions, Remote Sensing, and International Journal of E-services and Mobile Applications. Moreover, he served as the co-chair, the publication chair, the program chair, and a track chair for several international conferences published by recognized publishers such as IEEE and Springer. Dr. Elhoseny is the Editor-in-Chief of the Studies in Distributed Intelligence Springer Book Series, the Editor-in-Chief of The Sensors Communication for Urban Intelligence CRC Press-Taylor& Francis Book Series, and the Editor-in-Chief of The Distributed Sensing and Intelligent Systems CRC Press-Taylor& Francis Book Series.
Singh, Akansha
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.
Elngar, Ahmed
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.
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