Ashok Kumar Yadav – författare

Prediction of the Future Pandemic: Artificial Intelligence and Nanotechnology Approaches provides a comprehensive account of the applications, challenges, and breakthroughs in using AI and nanotechnology for pandemic prediction. The book emphasizes interdisciplinary collaboration among experts and a global perspective on the prediction of future pandemics. Sections provide a comprehensive exploration of cutting-edge methodologies that leverage AI and nanotechnology to predict, analyze, and combat future pandemics. This book aims to bridge the gap between theoretical concepts and practical applications, offering insights into the integration of these advanced technologies for more accurate, timely, and ethical pandemic predictions.Offers an in-depth examination of the integration of AI and nanotechnology in pandemic predictionCovers many nanotechnology applications in pandemic prediction, such as nanorobotics for targeted intervention, the role of nanomaterials in prevention strategies, viral detection/nanosensors in quarantine enforcement, and nanocomposite resilience (building pandemic-proof materials)Helps readers get to grips with the fast-evolving landscape of AI, nanotechnology, and global healthCovers both the theory and real-world applications of AI and nanotechnology in predicting pandemicsDiscusses the fast-changing connection between AI, nanotechnology, and pandemic prediction

Consensus: Fueling Blockchain Innovation and DApp Expansion explores the complexities of consensus mechanisms in order to shed light on emerging trends, best practices, and real-world applications that can fuel blockchain innovation while encouraging the dissemination of DApps across various industries. Additionally, the book bridges a crucial gap in the literature by providing in-depth insights into the role of consensus mechanisms in shaping the future of blockchain technology and decentralized applications. This book delves into the fundamentals of blockchain technology along with the roles and significance of vital consensus mechanisms, their underlying principles, formal specifications, functional characteristics, architecture, frameworks, and potential across diverse blockchain applications. Moreover, the book meticulously explores classification, performance metrics, and design parameters. It offers a comprehensive comparative analysis of these mechanisms, shedding light on their computational and communication complexity, strengths, weaknesses, and suitability. Additionally, the book delves into future research directions, highlighting emerging trends and areas requiring further investigation. It also addresses the efforts underway to address existing challenges and open issues within the realm of consensus mechanisms, ensuring a comprehensive understanding of the state-of-the-art in this pivotal aspect of blockchain technology. Due to the wide range of availability and evolving new consensus mechanisms, selecting an optimal and suitable consensus for a specific blockchain application is one of the crucial challenges in the development and innovation of blockchain systems. This book has also a discussion on appropriate selection algorithms based on multi-attribute decision-making for specific blockchain systems and DApps development.Provides readers with an in-depth understanding of the fundamental concepts of blockchain technology and its numerous essential consensus mechanismsProvides insights into emerging trends and future developments in consensus mechanisms and their potential implications for blockchain innovation and DApp expansionOffers a comprehensive examination of consensus mechanisms, highlighting their pivotal role in driving blockchain innovation and expanding decentralized applications (DApps)Presents practical insights into real-world implementations, alongside comparative analyses of different consensus models, empowering readers with decision-making tools to select the most suitable mechanism for their blockchain systems and applicationsIncludes discussions on multi-attribute decision-making for consensus selection, addressing a critical challenge faced by developers

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.

This book bridges the gap between conventional materials science and emerging data-driven methodologies, highlighting the integration of AI, machine learning, and deep learning technologies to enhance the design, analysis, and optimization of smart materials. It provides a holistic perspective essential for researchers, engineers, and students exploring the intersection of materials engineering and AI technologies.The book examines the connection between recent advancements in materials science and multiscale machine learning, facilitating predictive and prescriptive modeling for assessing material behavior based on composition, structure, and processing. It includes comprehensive discussions on smart material design, optimization, complexity analysis, and advanced computational methods for synthesizing and characterizing materials. Challenges in multiscale modeling, such as biologically inspired material design and the influence of nanotechnology on current trends, are thoroughly explored.Emphasizing the critical role of multiscale machine learning and nanotechnology in creating sustainable smart materials, the book also addresses the ethical implications of this research. It discusses opportunities and challenges in biomaterials, particularly in healthcare and biomedical applications, and anticipates future trends in machine learning for sustainable materials design. The book provides insights into how predictive and prescriptive modeling through machine learning can accelerate the material discovery process, guiding researchers toward promising candidates for further exploration.Serving as a roadmap for researchers and scientists, this book offers valuable insights into innovative approaches that support the future of materials science.