Federated Intelligent System for Healthcare

This practical guide gives valuable insights for integrating advanced technologies in healthcare, empowering researchers to effectively navigate and implement federated systems to enhance patient care.
Federated Intelligent Systems for Healthcare: A Practical Guide explores the integration of federated learning and intelligent systems within the healthcare domain. This volume provides an in-depth understanding of how federated systems enhance healthcare practices, detailing their principles, technologies, challenges, and opportunities. Additionally, this book addresses secure and privacy-preserving sharing of medical data, applications of artificial intelligence and machine learning in healthcare, and ethical considerations surrounding the adoption of these advanced technologies. With a focus on practical implementation and real-world use cases, Federated Intelligent Systems for Healthcare: A Practical Guide equips healthcare professionals, researchers, and technology experts with the knowledge needed to navigate the complexities of federated intelligent systems in healthcare and harness their potential to transform patient care and medical advancements.
Readers will find the book: - Provides cutting-edge research from industry experts to unlock the future of healthcare with innovative insights that embrace federated intelligence and shape the future; - Presents novel technologies and conceptual and visionary-based scenarios; - Discusses real-world case studies and implementations that illustrate how federated intelligence is practically applied across various healthcare scenarios, from personalized diagnostics to population-level insights; - Stands as a pioneer in the exploration of federated intelligent systems in healthcare.
Audience
Data scientists, IT, healthcare and business professionals working towards innovations in the healthcare sector. The book will be especially helpful to students and educators.

S. Rakesh Kumar, PhD, is an assistant professor in the Department of Computer Science and Engineering at the Gandhi Institute of Technology and Management, Visakhapatnam, India. He has published four books and over 50 articles in international journals and conference proceedings. His research interests include artificial intelligence, machine learning, and big data applications.
N. Gayathri, PhD, is an assistant professor in the Department of Computer Science and Engineering at the Gandhi Institute of Technology and Management, Visakhapatnam, India. She has published four books and over 50 articles in international journals and serves as a guest editor and reviewer for several journals of repute. Her research interests include big data analytics, Internet of Things, and machine learning.
Seifedine Kadry, PhD, is a professor in the Department of Applied Data Science at Noroff University and Lebanese American University. He serves as an ABET program evaluator, distinguished speaker of the Institute of Electrical and Electronics Engineers Computer Society, and a fellow of several other international societies. His research focuses on data science, education using technology, system prognostics, stochastic systems, and applied mathematics.