Ajay Pratap – författare

Reimagine the future of healthcare with a deep dive into hyperscale computing and distributed networks In AI-Driven Smart Healthcare: Powered by Hyperscale Computing and Next Generation Networks, a team of distinguished researchers delivers an insightful and practical discussion of the healthcare applications of artificial intelligence and fog-enabled next-generation networks. The book provides practical insights and methodologies for the design, development, and deployment of these technologies throughout the healthcare industry. Readers will explore key areas of recent advancement, including the Internet of Things, fog computing, artificial intelligence, machine learning, serverless computing, and blockchain in a way that allows them to assess the feasibility and scalability of a variety of technological healthcare solutions. The book also includes: A thorough introduction to the integration of AI and fog computing into smart healthcare systemsComprehensive explorations of how these technologies enhance healthcare delivery, with examples like remote patient monitoring and advanced diagnostic modelsPractical discussions of the advantages, challenges, and potential solutions associated with AI and fog computingAn interdisciplinary focus for professionals working at the intersection of AI, machine learning, fog computing, and healthcarePerfect for researchers, practitioners, and other healthcare stakeholders, AI-Driven Smart Healthcare will also benefit technologists, educators, hospital administrators, and other professionals with an interest in the application of the latest technologies to recurrent and significant issues in the field of healthcare.

Reimagine the future of healthcare with a deep dive into hyperscale computing and distributed networks

In AI-Driven Smart Healthcare: Powered by Hyperscale Computing and Next Generation Networks, a team of distinguished researchers delivers an insightful and practical discussion of the healthcare applications of artificial intelligence and fog-enabled next-generation networks. The book provides practical insights and methodologies for the design, development, and deployment of these technologies throughout the healthcare industry.

Readers will explore key areas of recent advancement, including the Internet of Things, fog computing, artificial intelligence, machine learning, serverless computing, and blockchain in a way that allows them to assess the feasibility and scalability of a variety of technological healthcare solutions.

The book also includes:

A thorough introduction to the integration of AI and fog computing into smart healthcare systemsComprehensive explorations of how these technologies enhance healthcare delivery, with examples like remote patient monitoring and advanced diagnostic modelsPractical discussions of the advantages, challenges, and potential solutions associated with AI and fog computingAn interdisciplinary focus for professionals working at the intersection of AI, machine learning, fog computing, and healthcare

Perfect for researchers, practitioners, and other healthcare stakeholders, AI-Driven Smart Healthcare will also benefit technologists, educators, hospital administrators, and other professionals with an interest in the application of the latest technologies to recurrent and significant issues in the field of healthcare.

Reimagine the future of healthcare with a deep dive into hyperscale computing and distributed networks

In AI-Driven Smart Healthcare: Powered by Hyperscale Computing and Next Generation Networks, a team of distinguished researchers delivers an insightful and practical discussion of the healthcare applications of artificial intelligence and fog-enabled next-generation networks. The book provides practical insights and methodologies for the design, development, and deployment of these technologies throughout the healthcare industry.

Readers will explore key areas of recent advancement, including the Internet of Things, fog computing, artificial intelligence, machine learning, serverless computing, and blockchain in a way that allows them to assess the feasibility and scalability of a variety of technological healthcare solutions.

The book also includes:

A thorough introduction to the integration of AI and fog computing into smart healthcare systemsComprehensive explorations of how these technologies enhance healthcare delivery, with examples like remote patient monitoring and advanced diagnostic modelsPractical discussions of the advantages, challenges, and potential solutions associated with AI and fog computingAn interdisciplinary focus for professionals working at the intersection of AI, machine learning, fog computing, and healthcare

Perfect for researchers, practitioners, and other healthcare stakeholders, AI-Driven Smart Healthcare will also benefit technologists, educators, hospital administrators, and other professionals with an interest in the application of the latest technologies to recurrent and significant issues in the field of healthcare.

This book offers a comprehensive, mathematically grounded introduction to multi-attribute decision-making (MADM) for undergraduate, graduate, and doctoral students, as well as researchers in engineering, management, economics, and biology. It bridges theory and practice, covering fundamental concepts, advanced methods, and MATLAB implementations. Topics include critical, subjective, objective, and comprehensive weighting methods, ranking techniques, and their integration for solving complex decision problems. The book addresses challenges such as ranking abnormalities, complexity, and applicability in emerging applications. It also explores fuzzy MADM for handling uncertainty and non-sharp boundaries, highlighting recent research directions and open challenges. Filling a gap in the literature, this book serves as a valuable resource for academics and professionals seeking to apply MADM in dynamic, interdisciplinary contexts.

This book offers a comprehensive, mathematically grounded introduction to multi-attribute decision-making (MADM) for undergraduate, graduate, and doctoral students, as well as researchers in engineering, management, economics, and biology. It bridges theory and practice, covering fundamental concepts, advanced methods, and MATLAB implementations. Topics include critical, subjective, objective, and comprehensive weighting methods, ranking techniques, and their integration for solving complex decision problems. The book addresses challenges such as ranking abnormalities, complexity, and applicability in emerging applications. It also explores fuzzy MADM for handling uncertainty and non-sharp boundaries, highlighting recent research directions and open challenges. Filling a gap in the literature, this book serves as a valuable resource for academics and professionals seeking to apply MADM in dynamic, interdisciplinary contexts.