Evolution of Machine Learning and Internet of Things Applications in Biomedical Engineering

By: Arun Kumar Rana (Editor), Vishnu Sharma (Editor), Sanjeev Kumar Rana (Editor)

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Hardcover | 30 October 2024

At a Glance

Hardcover
304 Pages

Dimensions(cm)
23.39 x 15.6 x 1.75

Hardcover

RRP $242.00

$171.25

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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 for 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.

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