Federated Learning for Neural Disorders in Healthcare 6.0 (eBook, PDF)

Redaktion: Reddy C, Kishor Kumar; Nag, Anindya

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 414
• Erscheinungstermin: 14. Mai 2025
• Englisch
• ISBN-13: 9781040344712
• Artikelnr.: 73703711

Autorenporträt

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.

Inhaltsangabe

1. Federated Learning in Healthcare 6.0 Paradigm, Technologies and
Challenges. 2. Evolving Neural Disorders in the Era of Healthcare 6.0:
Classifications, Types, and Societal Impact. 3. Advancing Explainable AI in
Healthcare Methods, Applications, and Ethical Implications. 4. From Neurons
to Algorithms: Enhancing Machine Learning with Neuroscientific Insight. 5.
Optimizing Neural Disorder Treatment through Federated Learning and
Multi-Institutional Data Collaboration. 6. Harnessing Machine learning and
deep learning techniques for neuroimaging. 7. AI and Federated Learning:
Enhancing Cross-Institutional Research and Treatment Strategies for Neural
Disorders. 8. Ensuring data privacy and security in federated learning for
Healthcare data. 9. Federated Learning and Personalized Medicine: Tailoring
Neural Disorder Therapies in Healthcare 6.0. 10. Federated Machine Learning
And Augmented Reality (AR)/Virtual Reality (VR)-Based Framework For
Schizophrenia Diagnosis And Therapy. 11. Harnessing Deep Learning for the
Early Diagnosis of Dementia: A Transformative Approach in Neurological
Health. 12. Federated Learning Based Diagnosis of Epilepsy Disease in
Healthcare 6.0. 13. Early Detection of Alzheimer's: The Evolving Role of
MRI in Neuroimaging. 14. Federated Learning-Enabled CNN for Predicting and
Detecting Brain Tumors in Healthcare 6.0.