Federated Learning for Neural Disorders in Healthcare 6.0
CRC Press (Verlag)
978-1-032-96887-2 (ISBN)
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.
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.
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.
| Erscheinungsdatum | 10.04.2025 |
|---|---|
| Reihe/Serie | Future Generation Information Systems |
| Zusatzinfo | 41 Tables, black and white; 64 Line drawings, black and white; 11 Halftones, black and white; 75 Illustrations, black and white |
| Verlagsort | London |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Gewicht | 930 g |
| Themenwelt | Mathematik / Informatik ► Informatik ► Software Entwicklung |
| Medizin / Pharmazie ► Physiotherapie / Ergotherapie ► Orthopädie | |
| Technik ► Elektrotechnik / Energietechnik | |
| Technik ► Medizintechnik | |
| ISBN-10 | 1-032-96887-7 / 1032968877 |
| ISBN-13 | 978-1-032-96887-2 / 9781032968872 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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