(DOWNLOAD) "Machine Learning and the Internet of Medical Things in Healthcare (Enhanced Edition)" by Krishna Kant Singh, Mohamed Elhoseny, Akansha Singh & Ahmed A. Elngar " Book PDF Kindle ePub Free
eBook details
- Title: Machine Learning and the Internet of Medical Things in Healthcare (Enhanced Edition)
- Author : Krishna Kant Singh, Mohamed Elhoseny, Akansha Singh & Ahmed A. Elngar
- Release Date : January 14, 2021
- Genre: Science & Nature,Books,Professional & Technical,Engineering,MZGenre.ProfessionalTechnical.Engineering.eBooks.ChemicalPetroleumEngineering,Medical,
- Pages : * pages
- Size : 16172 KB
Description
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 learningExplains the functions and applications of machine learning in various applications such as ultrasound imaging, biomedical signal processing, robotics, and biomechatronicsIncludes 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
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