alharbi meshal (22 risultati)

HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

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Hardback. Condizione: New. New copy - Usually dispatched within 4 working days.

Condizione: New. 1st edition NO-PA16APR2015-KAP.

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Hardcover. Condizione: Brand New. 264 pages. 9.19x6.13x9.21 inches. In Stock.

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 192 pp. Englisch.

Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Hardcover. Condizione: new. Hardcover. Federated Learning (FL) represents a transformative leap in distributed machine learning by enabling multiple clients to collaboratively solve complex tasks without compromising data privacy. This innovative approach eliminates the need for centralized cloud storage, ensuring privacy-preserving data handling while offering smarter models, reduced latency, and enhanced power efficiency.This book serves as a comprehensive guide to the evolving field of Federated Learning, providing in-depth insights into its definition, architecture, and classification. It examines the distinctions between FL and traditional distributed learning paradigms through a comparative lens. The chapters explore key concepts, algorithmic advancements, and computational strategies that underpin the development of FL, with a particular focus on deep learning applications.Readers will find detailed discussions on critical topics such as horizontal and vertical FL, federated neural networks, federated reinforcement learning, and specialized algorithms like Federated LSTM and CNNs. By bridging theoretical foundations with practical implementations, the book also addresses common challenges in FL and presents potential pathways for future advancements.Aimed at researchers, academics, and practitioners, this book is valuable for understanding Federated Learning's role in shaping the future of privacy-conscious, intelligent machine learning systems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book aims to apply state-of-the-art advanced computational intelligence frameworks in healthcare. It presents recent and real-life applications of computationally intelligent healthcare. It also discusses problems and solutions to remote healthcare and emergency healthcare services. Healthcare Analytics and Advanced Computational Intelligence highlights modern ambient intelligence-enabled healthcare models along with advanced topics like quantum computing in healthcare and cryptomedical systems.Healthcare Analytics and Advanced Computational Intelligence examines designing the latest medical systems and models that will allow the societal acceptance of ambiance computing in healthcare, medical imaging, health analytics, machine intelligence, sensory computing, medical data analytics, disease detection, telemedicine, and their applications. It includes diverse case studies dealing with various clinical-based applications. These intelligent models are primarily structured to deal with complex real-world issues in clinical data analytics, by means of state-of-the-art techniques with general implementation, domain-specific solutions, or hybrid methods which integrate computational intelligence with conventional statistical methods.The book is written for researchers and academicians in diverse areas. Engineers from technical disciplines such as computer engineering are likely to purchase the book. Various sub-streams such as machine learning, big data analytics, healthcare analytics, and computational intelligence will find the book significant for their curriculum. 236 pp. Englisch.

HRD. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Sushruta Mishra is working as an Assistant Professor in the School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India. He pursued his MTech from IIIT, Bhubaneswar, Odisha, India, and has completed his PhD in Computer Sc.

HRD. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Federated Learning (FL) represents a transformative leap in distributed machine learning by enabling multiple clients to collaboratively solve complex tasks without compromising data privacy. This innovative approach eliminates the need for centralized cloud storage, ensuring privacy-preserving data handling while offering smarter models, reduced latency, and enhanced power efficiency. This book serves as a comprehensive guide to the evolving field of Federated Learning, providing in-depth insights into its definition, architecture, and classification. It examines the distinctions between FL and traditional distributed learning paradigms through a comparative lens. The chapters explore key concepts, algorithmic advancements, and computational strategies that underpin the development of FL, with a particular focus on deep learning applications. Readers will find detailed discussions on critical topics such as horizontal and vertical FL, federated neural networks, federated reinforcement learning, and specialized algorithms like Federated LSTM and CNNs. By bridging theoretical foundations with practical implementations, the book also addresses common challenges in FL and presents potential pathways for future advancements. Aimed at researchers, academics, and practitioners, this book is valuable for understanding Federated Learning's role in shaping the future of privacy-conscious, intelligent machine learning systems.Books on Demand GmbH, Überseering 33, 22297 Hamburg 192 pp. Englisch.

Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering.

Buch. Condizione: Neu. Federated Learning - A Systematic Review | A Systematic Review | Buch | Artificial Intelligence, Volume 33 | Englisch | 2025 | IntechOpen | EAN 9781836342120 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.

Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book aims to apply state-of-the-art advanced computational intelligence frameworks in healthcare. It presents recent and real-life applications of computationally intelligent healthcare. It also discusses problems and solutions to remote healthcare and emergency healthcare services. Healthcare Analytics and Advanced Computational Intelligence highlights modern ambient intelligence-enabled healthcare models along with advanced topics like quantum computing in healthcare and cryptomedical systems.Healthcare Analytics and Advanced Computational Intelligence examines designing the latest medical systems and models that will allow the societal acceptance of ambiance computing in healthcare, medical imaging, health analytics, machine intelligence, sensory computing, medical data analytics, disease detection, telemedicine, and their applications. It includes diverse case studies dealing with various clinical-based applications. These intelligent models are primarily structured to deal with complex real-world issues in clinical data analytics, by means of state-of-the-art techniques with general implementation, domain-specific solutions, or hybrid methods which integrate computational intelligence with conventional statistical methods.The book is written for researchers and academicians in diverse areas. Engineers from technical disciplines such as computer engineering are likely to purchase the book. Various sub-streams such as machine learning, big data analytics, healthcare analytics, and computational intelligence will find the book significant for their curriculum.