Internet Of Things Enabled Machine Learning For Biomedical Application
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Internet of Things enabled Machine Learning for Biomedical Application
The text begins by highlighting the benefits of the Internet of Things-enabled machine learning in the healthcare sector, examines the diagnosis of diseases using machine learning algorithms, and analyzes security and privacy issues in the healthcare systems using the Internet of Things. The text elaborates on image processing implementation for medical images to detect and classify diseases based on magnetic resonance imaging and ultrasound images. This book: · Covers the procedure to recognize emotions using image processing and the Internet of Things-enabled machine learning. · Highlights security and privacy issues in the healthcare system using the Internet of Things. · Discusses classification and implementation techniques of image segmentation. · Explains different algorithms of machine learning for image processing in a comprehensive manner. · Provides computational intelligence on the Internet of Things for future biomedical applications including lung cancer. It is primarily written for graduate students and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and biomedical engineering.
Internet of Things Enabled Machine Learning for Biomedical Application
"The text begins by highlighting the benefits of the internet of things enabled machine learning in the healthcare sector, examines the diagnosis of diseases using machine learning algorithms, and examines security and privacy issues in the healthcare systems using the internet of things. The text elaborates on image processing implementation for medical images to detect and classify diseases based on magnetic resonance imaging and ultrasound images. This book: Covers the procedure to recognize emotions using image processing and the internet of things-enabled machine learning. Highlights security and privacy issues in the healthcare system using the internet of things. Discusses classification and implementation techniques of image segmentation. Explains different algorithms of machine learning for image processing in a comprehensive manner. Provides computational intelligence on the internet of things for future biomedical applications including lung cancer. It is primarily written for graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and biomedical engineering"--
Machine Learning and the Internet of Medical Things in Healthcare
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