T. Ananth Kumar - Böcker

This reference text discusses various security techniques and challenges for cloud data protection from both software and hardware aspects.The text provides readers with an overview of cloud computing, beginning with historical perspectives on mainframe computers and early networking protocols, moving to current issues such as security of hardware and networks, performance, evolving IoT areas, edge computing, etc. It also deals with threat detection and incident response in cloud security. It covers important topics including operational security agitations in cloud computing, cyber artificial intelligence (AI) platform for cloud security, and security concerns of virtualization in cloud computing.The book will serve as a useful resource for graduate students and professionals in the fields of electrical engineering, electronics engineering, computer science, and information technology.

This reference text discusses various security techniques and challenges for cloud data protection from both software and hardware aspects.The text provides readers with an overview of cloud computing, beginning with historical perspectives on mainframe computers and early networking protocols, moving to current issues such as security of hardware and networks, performance, evolving IoT areas, edge computing, etc. It also deals with threat detection and incident response in cloud security. It covers important topics including operational security agitations in cloud computing, cyber artificial intelligence (AI) platform for cloud security, and security concerns of virtualization in cloud computing.The book will serve as a useful resource for graduate students and professionals in the fields of electrical engineering, electronics engineering, computer science, and information technology.

This book highlights various evolutionary algorithm techniques for various medical conditions and introduces medical applications of evolutionary computation for real-time diagnosis.Evolutionary Intelligence for Healthcare Applications presents how evolutionary intelligence can be used in smart healthcare systems involving big data analytics, mobile health, personalized medicine, and clinical trial data management. It focuses on emerging concepts and approaches and highlights various evolutionary algorithm techniques used for early disease diagnosis, prediction, and prognosis for medical conditions. The book also presents ethical issues and challenges that can occur within the healthcare system.Researchers, healthcare professionals, data scientists, systems engineers, students, programmers, clinicians, and policymakers will find this book of interest.

This book highlights various evolutionary algorithm techniques for various medical conditions and introduces medical applications of evolutionary computation for real-time diagnosis.Evolutionary Intelligence for Healthcare Applications presents how evolutionary intelligence can be used in smart healthcare systems involving big data analytics, mobile health, personalized medicine, and clinical trial data management. It focuses on emerging concepts and approaches and highlights various evolutionary algorithm techniques used for early disease diagnosis, prediction, and prognosis for medical conditions. The book also presents ethical issues and challenges that can occur within the healthcare system.Researchers, healthcare professionals, data scientists, systems engineers, students, programmers, clinicians, and policymakers will find this book of interest.

This book provides a comprehensive discussion of deep learning (DL) in medical and healthcare applications, including the fundamentals and current advances in medical image analysis, DL methods for medical image analysis, and DL-based clinical computer-aided diagnosis systems. It further presents algorithms, models, software, and tools in the field of bioinformatics.This book:Presents mathematical principles of DL algorithms such as convolutional neural networks and recurrent neural networksDiscusses applications of DL such as hyperparameter optimization and multimodal DL for bioinformaticsShowcases how algorithms are applied to a broad range of application areas, including microscopy and pathologyCovers DL techniques such as deep feedforward networks, sequence modeling, and convolutional networksExamines the importance of deep learning in biomedical image processing and enhancing biological diagnosisIt will serve as an ideal reference text for senior undergraduates, graduate students, and academic researchers in the areas such as electrical engineering, electronics, and communications engineering, computer engineering, and information technology.

SIMULATIONS AND ANALYSIS of Mathematical Methods Written and edited by a group of international experts in the field, this exciting new volume covers the state of the art of real-time applications of computer science using mathematics. This breakthrough edited volume highlights the security, privacy, artificial intelligence, and practical approaches needed by engineers and scientists in all fields of science and technology. It highlights the current research, which is intended to advance not only mathematics but all areas of science, research, and development, and where these disciplines intersect. As the book is focused on emerging concepts in machine learning and artificial intelligence algorithmic approaches and soft computing techniques, it is an invaluable tool for researchers, academicians, data scientists, and technology developers. The newest and most comprehensive volume in the area of mathematical methods for use in real-time engineering, this groundbreaking new work is a must-have for any engineer or scientist’s library. Also useful as a textbook for the student, it is a valuable contribution to the advancement of the science, both a working handbook for the new hire or student, and a reference for the veteran engineer.

SUSTAINABLE MANAGEMENT OF ELECTRONIC WASTEWritten and edited by a group of industry professionals, this new volume provides cutting-edge insights into how the sustainability of managing electronic waste can be achieved, for engineers, scientists, and students.As a result of the rapid advancement of technology and the globalization of the economy, waste electrical and electronic equipment (WEEE) management has become increasingly important. Manufacturers are especially concerned about the proper disposal of their waste, and researchers need to identify the obstacles and enablers that stand in the way of implementing a long-term WEEE management system in order to develop a long-term WEEE management system. Further, the literature did not adequately capture the perspectives of multiple stakeholders while also identifying the enablers required for the development of sustainable WEEE management policies, which was particularly important in developing countries.This volume fills a gap in the literature by considering the perspectives of multiple stakeholders to identify enablers of sustainable WEEE management in emerging economies which was previously unexplored. This book focuses on the most recent technological advancements for the twenty-first century, emphasizing the synergies that exist between computer science, bioinformatics, and other sciences. The research and development of artificial intelligence, machine learning, blockchain technologies, quantum computing with cryptography, nanotechnology, sensors based on biotechnology, Internet of Things devices, nature-inspired algorithms, computer vision techniques, computational biology, and other topics are covered in this book, along with their applications in the fields of science, engineering, physical science, and economics. Modern environmental techniques are among the most innovative innovations emerging as a result of the insatiable demand for health standards in the modern world.

This book provides a comprehensive exploration of computational intelligence techniques and their applications, offering valuable insights into advanced information processing, machine learning concepts, and their impact on agile manufacturing systems. Computational Intelligence presents a new concept for advanced information processing. Computational Intelligence (CI) is the principle, architecture, implementation, and growth of machine learning concepts that are physiologically and semantically inspired. Computational Intelligence methods aim to develop an approach to evaluating and creating flexible processing of human information, such as sensing, understanding, learning, recognizing, and thinking. The Artificial Neural Network simulates the human nervous system’s physiological characteristics and has been implemented numerically for non-linear mapping. Fuzzy Logic Systems simulate the human brain’s psychological characteristics and have been used for linguistic translation through membership functions and bioinformatics. The Genetic Algorithm simulates computer evolution and has been applied to solve problems with optimization algorithms for improvements in diagnostic and treatment technologies for various diseases. To expand the agility and learning capacity of manufacturing systems, these methods play essential roles. This book will express the computer vision techniques that make manufacturing systems more flexible, efficient, robust, adaptive, and productive by examining many applications and research into computational intelligence techniques concerning the main problems in design, making plans, and manufacturing goods in agile manufacturing systems.

Discover the essential insights and practical applications of explainable AI in healthcare that will empower professionals and enhance patient trust with Explainable AI in the Healthcare Industry, a must-have resource. Explainable AI (XAI) has significant implications for the healthcare industry, where trust, accountability, and interpretability are crucial factors for the adoption of artificial intelligence. XAI techniques in healthcare aim to provide clear and understandable explanations for AI-driven decisions, helping healthcare professionals, patients, and regulatory bodies to better comprehend and trust the AI models’ outputs. Explainable AI in the Healthcare Industry presents a comprehensive exploration of the critical role of explainable AI in revolutionizing the healthcare industry. With the rapid integration of AI-driven solutions in medical practice, understanding how these models arrive at their decisions is of paramount importance. The book delves into the principles, methodologies, and practical applications of XAI techniques specifically tailored for healthcare settings.

Empower your practice with this definitive resource that bridges the gap between artificial intelligence and biomechanics, providing the essential tools and knowledge to optimize assessments, personalize treatment plans, and predict recovery outcomes in the rapidly evolving landscape of modern physiotherapy. The integration of artificial intelligence (AI) with biomechanics in physiotherapy represents a transformative shift in the healthcare landscape, driven by rapid technological advancement and an increasing emphasis on personalized, data-driven care. Over the past decade, AI has progressed from theoretical exploration to practical clinical application, enabling enhanced decision-making and improved patient outcomes. This book examines the intersection of artificial intelligence and physiotherapy with a focused emphasis on biomechanics, exploring how AI can optimize biomechanical assessments, support individualized treatment planning, and predict patient progress in clinical settings. As demand grows for AI-driven innovation in rehabilitation, this volume serves as an essential resource for physiotherapists, clinicians, and researchers seeking to understand and adopt these emerging technologies to advance practice and improve rehabilitation outcomes.

For industry practitioners, academic researchers, and governance professionals alike, this book offers both clarity and depth in one of the most important domains of modern technology. As AI matures, trust and risk management will define its success—and this book lays the groundwork for achieving that vision. As AI continues to permeate sectors ranging from healthcare to finance, ensuring that these systems are not only powerful but also accountable, transparent, and secure, is more critical than ever. This book offers a vital exploration into the intersection of trustworthiness, risk mitigation, and security governance in artificial intelligence systems, serving as a definitive guide for professionals, researchers, and policymakers striving to build, deploy, and manage AI responsibly in high-stakes environments. Using a comprehensive approach, it explores how to integrate technical safeguards, organizational practices, and regulatory alignment to manage the unique risks posed by AI, including algorithmic bias, data misuse, adversarial attacks, and opaque decision-making. The result is a strategic approach that not only identifies vulnerabilities, but also promotes resilient, auditable, and trustworthy AI ecosystems. At its core, AI TRiSM is a forward-looking concept that embraces the realities of AI in production environments. The framework moves beyond traditional static models of governance to propose dynamic, adaptive controls that evolve alongside AI systems. Through real-world case studies, the book outlines how tools like model cards, bias audits, and zero-trust architectures can be embedded into the AI development lifecycle. Readers will find the volume: Introduces concepts to stay ahead of regulations and build trustworthy AI systems that customers and stakeholders can rely on;Addresses security threats, bias, and compliance gaps to avoid costly AI failures;Explores proven frameworks and best practices to deploy AI responsibly and strategies to outperform;Provides comprehensive guidance through real-world case studies and contributions from industry and academia.Audience AI and machine learning engineers, data scientists, cybersecurity and risk management specialists, academics, researchers, and policymakers specializing in AI ethics, security, and risk management.

The book presents powerful optimization approaches for integrating AI into daily life.This book explores how heuristic and metaheuristic methodologies have revolutionized the fields of robotics and machine learning. The book covers the wide range of tools and methods that have emerged as part of the AI revolution, from state-of-the-art decision-making algorithms for robots to data-driven machine learning models. Each chapter offers a meticulous examination of the theoretical foundations and practical applications of mathematical optimization, helping readers understand how these methods are transforming the field of technology.This book is an invaluable resource for researchers, practitioners, and students. It makes AI optimization accessible and comprehensible, equipping the next generation of innovators with the knowledge and skills to further advance robotics and machine learning. While artificial intelligence constantly evolves, this book sheds light on the path ahead.

This book is intended for a technical audience with advanced learning and in-depth analysis of the latest real-world developments in the field of quantum computing. The book is focused on modeling emerging drone-based applications. The methods of developing faster drive with the new trending technology of quantum genetic heuristics, artificial intelligence, and machine-based applications are discussed. Researchers will find novel ways to secure the data and Quantum drone networks. The book examines the most promising exploratory quantum-computed drone that optimizes, secures, and dynamically analyses various sectors, including healthcare, the educational industry, finance, transportation, and manufacturing.