Federated Learning in Metaverse Healthcare: Personalized Medicine and Wellness - Softcover

Softcover

ISBN 10: 0443337896 ISBN 13: 9780443337895

Verlag: Academic Press, 2025

Inhaltsangabe

Federated Learning in Metaverse Healthcare: Personalized Medicine and Wellness explores the integration of the metaverse with healthcare, offering immersive experiences and personalized care. The book introduces federated learning, emphasizing its advantages over traditional centralized machine learning in healthcare. It provides a historical context and discusses the technological advancements that led to the emergence of metaverse healthcare. Privacy-preserving methods crucial for protecting sensitive healthcare data within federated learning environments are also examined, underscoring the importance of secure communication protocols. Other important points include the transformation of healthcare delivery through virtual environments, remote consultations, and immersive experiences.

The role of telemedicine in facilitating remote diagnostics and consultations via virtual platforms, and the applications of augmented reality wearables for real-time health monitoring and wellness tracking are detailed. Additionally, the book discusses federated learning's ability to deliver personalized treatment plans tailored to individual patient needs, its role in predictive modeling for disease risks and prevention, as well as virtual health coaches offering personalized guidance for wellness management. The challenges and ethical dilemmas of metaverse healthcare and federated learning, along with potential solutions, are also considered.

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Über die Autorinnen und Autoren

Dr. Shubham Mahajan is an academic and researcher, member of IEEE, ACM, and IAENG. He earned a B.Tech from Baba Ghulam Shah Badshah University, an M.Tech from Chandigarh University, and a PhD from Shri Mata Vaishno Devi University. He is currently Assistant Professor at Amity University, Haryana. His research spans artificial intelligence and image processing, including video compression, image segmentation, fuzzy entropy, nature-inspired optimization, data mining, machine learning, robotics, and optical communications. He holds patents internationally and has published widely in high-impact venues; he has edited several Scopus-indexed books. He has received multiple awards for research excellence and travel support from IEEE, among others. He has served as IEEE Campus Ambassador at premier institutes and promotes international collaborations. He participates in technical program committees and editorial boards for conferences and journals, shaping discourse in AI and image processing.

Jyotir Moy Chatterjee is currently an Assistant Professor in Department of Computer Science and Engineering at Graphic Era (Deemed to be University), in Dehradun, India. He also serves as a Visiting Faculty member in Information Technology at Lord Buddha Education Foundation, which is affiliated with the Asia Pacific University of Technology & Innovation in Kathmandu, Nepal. His research interests focus on advancements in Machine Learning and Deep Learning.

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Federated Learning in Metaverse Healthcare: Personalized Medicine and Wellness explores the integration of the metaverse with healthcare, offering immersive experiences and personalized care. It introduces federated learning, highlighting its advantages over centralized machine learning in healthcare. The historical context and technological advancements that have led to the emergence of metaverse healthcare are explored, along with privacy-preserving methods crucial for protecting sensitive healthcare data in federated learning environments. The transformation of healthcare delivery through virtual environments, remote consultations, and immersive experiences is discussed, as well as the role of telemedicine in facilitating remote diagnostics and consultations through virtual platforms. The applications of augmented reality wearables in real-time health monitoring and wellness tracking are explored. The architecture and components of federated learning systems within metaverse healthcare environments are detailed, emphasizing the importance of secure communication protocols in safeguarding healthcare data. Federated learning's ability to deliver personalized treatment plans tailored to individual patient needs, as well as its role in predictive modeling for disease risks and prevention strategies, is examined. Virtual health coaches leveraging federated learning algorithms to provide personalized guidance and support for wellness management are also discussed. The challenges and ethical dilemmas inherent in metaverse healthcare and federated learning are considered, along with potential solutions. Finally, the future of metaverse healthcare and federated learning is speculated, highlighting emerging trends and areas for further research and development.

Details zur Ausgabe

• Verlag: Academic Press
• Erscheinungsdatum: 2025
• Sprache: Englisch
• ISBN 10: 0443337896
• ISBN 13: 9780443337895
• Einband: Taschenbuch
• Auflage: 1
• Anzahl der Seiten: 388
• Herausgeber: Mahajan
• Kontakt zum Hersteller: Academic Press; https://shop.elsevier.com/; ; 230 PARK AVENUE, SUITE 800; NY; NY; 10169; USA
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