Internet of Things and machine learning for type I and type II diabetes : use cases /

Provides a medium of exchange of expertise and addresses the concerns, needs, and problems associated with Type I and Type II diabetes. Expert contributions come from researchers across biomedical, data mining, and deep learning. This is an essential resource for both the AI and Biomedical research...

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Bibliographic Details
Corporate Author: ScienceDirect (Online service)
Other Authors: Dash, Sujata, 1964- (Editor), Pani, Subhendu Kumar, 1980- (Editor), Susilo, Willy (Editor), Tse, Gary M. (Editor), Cheung, Bernard Man Yung (Editor)
Format: eBook
Language:English
Published: London ; San Diego, CA : Academic Press, [2024]
Subjects:
Online Access:Connect to the full text of this electronic book
Description
Summary:Provides a medium of exchange of expertise and addresses the concerns, needs, and problems associated with Type I and Type II diabetes. Expert contributions come from researchers across biomedical, data mining, and deep learning. This is an essential resource for both the AI and Biomedical research community, crossing various sectors for broad coverage of the concepts, themes, and instrumentalities of this important and evolving area. Coverage includes IoT, AI, Deep Learning, Machine Learning and Big Data Analytics for diabetes and health informatics.
Physical Description:1 online resource
Bibliography:Includes bibliographical references and index.
ISBN:9780323956932
0323956939