This 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 around the world.
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Arun Kumar Rana is currently Assistant Professor-3 in Galgotias College of Engineering and Technology, Greater Noida, India with more than 16 years of experience. His areas of interest includes image processing, wireless sensor network, Internet of Things, AI, and machine learning and embedded systems.
Vishnu Sharma is Professor and Dean CSE at ITS College of Engineering, Greater Noida India. Dr. Sharma completed his BTech, MTech, and PhD (CSE) in 2012 from the Government Autonomous Institute, Madhav Institute of Technology and Science (M.I.T.S.) Gwalior, MP, in Computer Science and Engineering, and was affiliated to Rajiv Gandhi Technical University, Bhopal, India. His areas of interest are mobile computing, cybersecurity, and advanced mobile computing.
Sanjeev Kumar Rana is Professor of Computer Science and Engineering at Maharishi Markandeshwar (deemed to be a university), Mullana-Ambala, India. He earned his PhD from Maharishi Markandeshwar University, Mullana-Ambala, India, in 2012. He is also a CISCO-certified instructor. His research interest includes distributed computing, network security, blockchain technology, and big data analytics.
Vijay Shanker Chaudhary is Assistant Professor (GCET, Greater Noida) and Researcher (photonic crystal fiber-based biosensors), He received his PhD from the Madan Mohan Malviya University of Technology, Gorakhpur, India. His research interests include photonic crystal fiber, optical fiber sensors, and terahertz sensing properties.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Hardcover. Etat : new. Hardcover. This book provides a platform for presenting machine learning (ML)-enabled healthcare techniques and offers a mathematical and conceptual background of the latest technology. It describes ML techniques along with the emerging platform of the Internet of Medical Things used by practitioners and researchers around the world.Evolution of Machine Learning and Internet of Things Applications in Biomedical Engineering discusses the Internet of Things (IoT) and ML devices that are deployed for enabling patient health tracking, various emergency issues, and the smart administration of patients. It looks at the problems of cardiac analysis in e-healthcare, explores the employment of smart devices aimed at different patient issues, and examines the usage of Arduino kits where the data can be transferred to the cloud for Internet-based uses. The book includes deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology. The authors also examine the role of IoT and ML in electroencephalography and magnetic resonance imaging, which play significant roles in biomedical applications. This book also incorporates the use of IoT and ML applications for smart wheelchairs, telemedicine, GPS positioning of heart patients, and smart administration with drug tracking. Finally, the book also presents the application of these technologies in the development of advanced healthcare frameworks.This book will be beneficial for new researchers and practitioners working in the biomedical and healthcare fields. It will also be suitable for a wide range of readers who may not be scientists but who are also interested in the practices of medical image retrieval and brain image segmentation. This 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 around the world. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9781032759234
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides a platform for presenting machine learning (ML)-enabled healthcare techniques and offers a mathematical and conceptual background of the latest technology. It describes ML techniques along with the emerging platform of the Internet of Medical Things used by practitioners and researchers around the world.Evolution of Machine Learning and Internet of Things Applications in Biomedical Engineering discusses the Internet of Things(IoT) and ML devices that are deployed for enabling patient health tracking, various emergency issues, and the smart administration of patients. It looks at the problems of cardiac analysis in e-healthcare, explores the employment of smart devices aimed at different patient issues, and examines the usage of Arduino kits where the data can be transferred to the cloud for Internet-based uses. The book includes deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology. The authors also examine the role of IoT and ML in electroencephalography and magnetic resonance imaging, which play significant roles in biomedical applications. This book also incorporates the use of IoT and ML applications for smart wheelchairs, telemedicine, GPS positioning of heart patients, and smart administration with drug tracking. Finally, the book also presents the application of these technologies in the development of advanced healthcare frameworks.This book will be beneficial for new researchers and practitioners working in the biomedical and healthcare fields. It will also be suitable for a wide range of readers who may not be scientists but who are also interested in the practices of medical image retrieval and brain image segmentation. 304 pp. Englisch. N° de réf. du vendeur 9781032759234
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