Hardcover. This reference text offers a relevant and thorough examination of the overlap between neuroscience and federated learning. It explores the complexities of utilizing federated learning algorithms for MRI data analysis, demonstrating how to improve the accuracy and efficiency of diagnostic procedures. The book covers topics such as the prediction and diagnosis of Alzheimers disease using neural networks and ensuring data privacy and security in federated learning for neural disorders.This book: Provides a thorough examination of the transformative impact of federated learning on the diagnosis, treatment, and understanding of brain disordersFocuses on combining federated learning with magnetic resonance imaging (MRI) data, which is a fundamental aspect of contemporary neuroimaging researchExamines the use of federated learning as a promising approach for collaborative data analysis in healthcare, with a focus on maintaining privacy and securityExplores the cutting-edge field of healthcare innovation by examining the interface of neuroscience and machine learning, with a specific focus on the breakthrough technique of federated learningOffers a comprehensive understanding of how federated learning may transform patient care, covering both theoretical ideas and practical examplesIt is primarily written for graduate students and academic researchers in electrical engineering, electronics, and communication engineering, computer science and engineering, and biomedical engineering. This text offers a relevant and thorough examination of the overlap between neuroscience and federated learning. It explores the complexities of utilizing federated learning algorithms for MRI data analysis, demonstrating how to improve the accuracy and efficiency of diagnostic procedures. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

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Detalles bibliogr�ficos

T�tulo: Federated Learning for Neural Disorders in Healthcare 6.0 (Hardcover)
Editor: Taylor & Francis Ltd, London
Fecha de publicaci�n: 2025
Idioma: Ingl�s
ISBN 10: 1032968877
ISBN 13: 9781032968872
Encuadernaci�n: Hardcover
Estado: new

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Sinopsis

This reference text offers a relevant and thorough examination of the overlap between neuroscience and federated learning. It explores the complexities of utilizing federated learning algorithms for MRI data analysis, demonstrating how to improve the accuracy and efficiency of diagnostic procedures. The book covers topics such as the prediction and diagnosis of Alzheimer’s disease using neural networks and ensuring data privacy and security in federated learning for neural disorders.

This book:

Provides a thorough examination of the transformative impact of federated learning on the diagnosis, treatment, and understanding of brain disorders
Focuses on combining federated learning with magnetic resonance imaging (MRI) data, which is a fundamental aspect of contemporary neuroimaging research
Examines the use of federated learning as a promising approach for collaborative data analysis in healthcare, with a focus on maintaining privacy and security
Explores the cutting-edge field of healthcare innovation by examining the interface of neuroscience and machine learning, with a specific focus on the breakthrough technique of federated learning
Offers a comprehensive understanding of how federated learning may transform patient care, covering both theoretical ideas and practical examples

It is primarily written for graduate students and academic researchers in electrical engineering, electronics, and communication engineering, computer science and engineering, and biomedical engineering.

Acerca del autor

Kishor Kumar Reddy C is currently working as an associate professor in the Department of Computer Science and Engineering, Stanley College of Engineering and Technology for Women, Hyderabad, India. He has research and teaching experience of more than 13 years. He has published more than 90 research papers in national and international conferences, book chapters, and journals indexed by SCIE, Scopus and others. He has authored two textbooks and 12 edited books. He is a member of ISTE, CSI, IAENG, UACEE, and IACSIT.

Anindya Nag obtained an M.Sc. in Computer Science and Engineering from Khulna University in Khulna, Bangladesh, and a B.Tech. in Computer Science and Engineering from Adamas University in Kolkata, India. He is currently a lecturer in the Department of Computer Science and Engineering at the Northern University of Business and Technology in Khulna, Bangladesh. His research focuses on health informatics, medical Internet of Things, neuroscience, and machine learning. He serves as a reviewer for numerous prestigious journals and international conferences. He has authored and co-authored around 32 publications, including journal articles, conference papers, book chapters, and has co-edited five books.

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