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

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 prediction- Covers 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 health- Covers both the theory and real-world applications of AI and nanotechnology in predicting pandemics- Discusses 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

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 mechanisms- Provides insights into emerging trends and future developments in consensus mechanisms and their potential implications for blockchain innovation and DApp expansion- Offers 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 applications- Includes 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.

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 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.

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.

Document from the year 2022 in the subject Computer Sciences - Artificial Intelligence, grade: B.Tech, Amity University (Amity School of Engineering and Technology), language: English, abstract: Unlock the secrets of intelligent systems and embark on a journey into the fascinating world of machine learning! This comprehensive guide provides a robust foundation in the core principles and practical applications of this transformative field. Delve into the fundamental concepts of learning systems, exploring the goals and diverse applications of machine learning across various industries. Master the art of preparing data for success, from selection and preprocessing to transformation techniques, and understand the critical role of training, test, and validation datasets in building robust models. Unravel the complexities of supervised and unsupervised learning, gaining insights into various algorithms and the unique challenges associated with each approach. Discover how to combat overfitting and ensure your models generalize effectively to new, unseen data. Explore a rich landscape of classification families, including linear and non-linear discriminative models, decision trees, conditional models like linear and logistic regression, generative models, and nearest neighbor algorithms. Sharpen your skills with an in-depth examination of logistic regression, mastering its function, representation, probability prediction, model learning processes, and data preparation requirements. Uncover the inner workings of the perceptron model and its learning algorithm. Finally, journey into the realm of statistical distributions with a focus on the exponential family, including normal, Poisson, exponential, Bernoulli, and binomial distributions, providing a crucial context for the probabilistic nature of many machine learning algorithms. Whether you're a student, a researcher, or a seasoned professional, this book equips you with the knowledge and skills to harness the power of machine learning and create intelligent solutions for a data-driven world. Explore essential topics such as data preprocessing, algorithm selection, model evaluation, and strategies for preventing overfitting, ensuring you build models that are both accurate and reliable. Learn how to leverage techniques like logistic regression and understand the nuances of perceptron models, providing you with practical tools for real-world applications. Dive deep into the mathematical foundations with a focus on the exponential family of distributions, solidifying your understanding of the statistical underpinnings of machine learning.

Document from the year 2023 in the subject Computer Sciences - Artificial Intelligence, Amity University (Amity School of Engineering and Technology), course: Information Security, Machine Learning, language: English, abstract: Imagine a world where digital content is seamlessly protected, its authenticity verifiable at a glance. This book plunges into the cutting-edge realm of digital watermarking, offering innovative solutions for securing multimedia in an increasingly interconnected world. Dive into the intricacies of image watermarking through the lens of advanced machine learning techniques, specifically Lagrangian Twin Support Vector Regression (LTSVR) and Genetic Algorithms (GA). Explore how these powerful algorithms are harnessed to create robust and imperceptible watermarks, ensuring copyright protection without compromising visual quality. Uncover the secrets of watermark embedding and extraction in both the Discrete Cosine Transform (DCT) and Wavelet Transform (WT) domains, comparing and contrasting the strengths of each approach. Delve into novel methodologies incorporating QR decomposition, further enhancing the efficiency and resilience of watermarking schemes. This book meticulously details the optimization of watermarking systems, balancing the critical factors of robustness against various attacks and imperceptibility to maintain the integrity of the original content. Whether you're a seasoned researcher or a curious newcomer, this exploration of digital watermarking provides a comprehensive understanding of the latest advancements in securing digital assets, paving the way for a future where intellectual property is safeguarded with unparalleled precision and sophistication. Discover how the fusion of machine learning and signal processing is revolutionizing copyright protection in the digital age. Explore practical applications and gain insights into the performance evaluation of digital watermarking schemes. This book is essential reading for anyone interested in the intersection of information security, image processing, and artificial intelligence, offering a glimpse into the future of digital rights management and content authentication. The journey begins here, where algorithms become the guardians of digital creativity, ensuring a fair and secure landscape for content creators and consumers alike, safeguarding against unauthorized duplication and distribution through sophisticated data-hiding strategies.

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.