À propos des auteurs
Dr. Danish Ather is an accomplished book editor with a robust academic and professional background. Currently as Professor at Amity University in Tashkent, Uzbekistan, Dr. Ather brings over 18 years of experience in teaching, research, and administration. He holds dual Ph.D. degrees in Computer Science and Computer Science & Engineering. His editorial expertise is highlighted through his role as Editor of the proceedings submitted yearly to IEEE USA for the SMART Series conferences. Dr. Ather has authored a book titled "Level up Your Programming with Core Dragon" and published 54 research papers in international journals and conferences, with 15 indexed in Scopus. He is a Senior Member of the IEEE Society and has received numerous awards, including a Certificate of Appreciation from the Ministry of Digital Technologies, Republic of Uzbekistan. His technical proficiency and dedication to advancing knowledge in fields such as IoT, AI, and programming make him a valuable asset in the realm of academic publishing.
Vishal Jain is presently working as an Associate Professor at Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, U. P. India. Before that, he has worked for several years as an Associate Professor at Bharati Vidyapeeth's Institute of Computer Applications and Management (BVICAM), New Delhi. He has more than 14 years of experience in the academics. He obtained Ph.D (CSE), M.Tech (CSE), MBA (HR), MCA, MCP and CCNA. He has authored more than 90 research papers in reputed conferences and journals, including Web of Science and Scopus. He has authored and edited more than 30 books with various reputed publishers, including Elsevier, Springer, Apple Academic Press, CRC, Taylor and Francis Group, Scrivener, Wiley, Emerald, NOVA Science and IGI-Global. His research areas include information retrieval, semantic web, ontology engineering, data mining, ad hoc networks, and sensor networks. He received a Young Active Member Award for the year 2012-13 from the Computer Society of India, Best Faculty Award for the year 2017 and Best Researcher Award for the year 2019 from BVICAM, New Delhi.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Enabling Collaborative Health Intelligence With Federated Learning
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Hardcover. Etat : new. Hardcover. In the era of data-driven healthcare, the ability to gather insights from diverse data sources is critical for advancing precision medicine and improving patient outcomes. However, concerns around data privacy and security compliance often hinder the analysis of sensitive health information. Federated Learning offers a great solution by enabling collaborative health intelligence across institutions without requiring data to leave local environments. By training machine learning models on decentralized data while preserving confidentiality, federated learning empowers healthcare stakeholders to build robust and generalizable models. Enabling Collaborative Health Intelligence With Federated Learning explores the confluence of federated learning and artificial intelligence as a blueprint for the future of medical care. This book bridges the gap between cutting-edge technological developments in federated learning and the pressing needs of AI-driven medical care. Covering topics such as health intelligence, data analysis, and data security, this book is an excellent resource for academics, healthcare practitioners, policy makers, industry leaders, and graduate students. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9798337333069
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Hardcover. Etat : new. Hardcover. In the era of data-driven healthcare, the ability to gather insights from diverse data sources is critical for advancing precision medicine and improving patient outcomes. However, concerns around data privacy and security compliance often hinder the analysis of sensitive health information. Federated Learning offers a great solution by enabling collaborative health intelligence across institutions without requiring data to leave local environments. By training machine learning models on decentralized data while preserving confidentiality, federated learning empowers healthcare stakeholders to build robust and generalizable models. Enabling Collaborative Health Intelligence With Federated Learning explores the confluence of federated learning and artificial intelligence as a blueprint for the future of medical care. This book bridges the gap between cutting-edge technological developments in federated learning and the pressing needs of AI-driven medical care. Covering topics such as health intelligence, data analysis, and data security, this book is an excellent resource for academics, healthcare practitioners, policy makers, industry leaders, and graduate students. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9798337333069
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Hardcover. Etat : new. Hardcover. In the era of data-driven healthcare, the ability to gather insights from diverse data sources is critical for advancing precision medicine and improving patient outcomes. However, concerns around data privacy and security compliance often hinder the analysis of sensitive health information. Federated Learning offers a great solution by enabling collaborative health intelligence across institutions without requiring data to leave local environments. By training machine learning models on decentralized data while preserving confidentiality, federated learning empowers healthcare stakeholders to build robust and generalizable models. Enabling Collaborative Health Intelligence With Federated Learning explores the confluence of federated learning and artificial intelligence as a blueprint for the future of medical care. This book bridges the gap between cutting-edge technological developments in federated learning and the pressing needs of AI-driven medical care. Covering topics such as health intelligence, data analysis, and data security, this book is an excellent resource for academics, healthcare practitioners, policy makers, industry leaders, and graduate students. 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. N° de réf. du vendeur 9798337333069
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Buch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In the era of data-driven healthcare, the ability to gather insights from diverse data sources is critical for advancing precision medicine and improving patient outcomes. However, concerns around data privacy and security compliance often hinder the analysis of sensitive health information. Federated Learning offers a great solution by enabling collaborative health intelligence across institutions without requiring data to leave local environments. By training machine learning models on decentralized data while preserving confidentiality, federated learning empowers healthcare stakeholders to build robust and generalizable models. Enabling Collaborative Health Intelligence With Federated Learning explores the confluence of federated learning and artificial intelligence as a blueprint for the future of medical care. This book bridges the gap between cutting-edge technological developments in federated learning and the pressing needs of AI-driven medical care. Covering topics such as health intelligence, data analysis, and data security, this book is an excellent resource for academics, healthcare practitioners, policy makers, industry leaders, and graduate students. N° de réf. du vendeur 9798337333069
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