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

Über die Autorinnen und Autoren

Dr. Shubham Mahajan, a distinguished member of prestigious organizations such as IEEE, ACM, and IAENG, boasts an impressive academic and professional background. He earned his B.Tech. degree from Baba Ghulam Shah Badshah University, his M.Tech. degree from Chandigarh University, and his Ph.D. degree from Shri Mata Vaishno Devi University (SMVDU) in Katra, India.

Dr. Mahajan has a remarkable track record in the field of artificial intelligence and image processing, holding an impressive portfolio of eleven Indian patents, as well as one Australian and one German patent. His contributions to the field are further evidenced by his extensive publication record, which includes over 100+ articles published in peer-reviewed journals, conferences and 10+ books. His research interests span a wide array of topics, encompassing image processing, video compression, image segmentation, fuzzy entropy, nature-inspired computing methods, optimization, data mining, machine learning, robotics, and optical communication. Notably, his dedication and expertise have earned him the 'Best Research Paper Award' from ICRIC 2019, published by Springer in the LNEE series.

In recognition of his exceptional achievements, Dr. Mahajan has received numerous accolades and honours throughout his career. These include the Best Student Award in 2019, the IEEE Region-10 Travel Grant Award in 2019, the 2nd runner-up prize in the IEEE RAS HACKATHON in 2019 (held in Bangladesh), the IEEE Student Early Researcher Conference Fund (SERCF) in 2020, the Emerging Scientist Award in 2021, and the IEEE Signal Processing Society Professional Development Grant in 2021. His commitment to excellence in research was further underscored by his receipt of the Excellence in Research Award in 2023.

Dr. Mahajan's impact extends beyond the realm of academia. He has served as a Campus Ambassador for IEEE, representing esteemed institutions such as IIT Bombay, Kanpur, Varanasi, Delhi, as well as various multinational corporations. His active engagement in fostering international research collaborations reflects his enthusiasm for advancing the frontiers of knowledge and innovation on a global scale.

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.