A. Jose Anand – författare

Mobile, wearable, and self-driving telephones are just a few examples of modern distributed networks that generate enormous amount of information every day. Due to the growing computing capacity of these devices as well as concerns over the transfer of private information, it has become important to process the part of the data locally by moving the learning methods and computing to the border of devices. Federated learning has developed as a model of education in these situations. Federated learning (FL) is an expert form of decentralized machine learning (ML). It is essential in areas like privacy, large-scale machine education and distribution. It is also based on the current stage of ICT and new hardware technology and is the next generation of artificial intelligence (AI). In FL, central ML model is built with all the data available in a centralised environment in the traditional machine learning. It works without problems when the predictions can be served by a central server. Users require fast responses in mobile computing, but the model processing happens at the sight of the server, thus taking too long. The model can be placed in the end-user device, but continuous learning is a challenge to overcome, as models are programmed in a complete dataset and the end-user device lacks access to the entire data package. Another challenge with traditional machine learning is that user data is aggregated at a central location where it violates local privacy policies laws and make the data more vulnerable to data violation. This book provides a comprehensive approach in federated learning for various aspects.

Mobile, wearable, and self-driving telephones are just a few examples of modern distributed networks that generate enormous amount of information every day. Due to the growing computing capacity of these devices as well as concerns over the transfer of private information, it has become important to process the part of the data locally by moving the learning methods and computing to the border of devices. Federated learning has developed as a model of education in these situations. Federated learning (FL) is an expert form of decentralized machine learning (ML). It is essential in areas like privacy, large-scale machine education and distribution. It is also based on the current stage of ICT and new hardware technology and is the next generation of artificial intelligence (AI). In FL, central ML model is built with all the data available in a centralised environment in the traditional machine learning. It works without problems when the predictions can be served by a central server. Users require fast responses in mobile computing, but the model processing happens at the sight of the server, thus taking too long. The model can be placed in the end-user device, but continuous learning is a challenge to overcome, as models are programmed in a complete dataset and the end-user device lacks access to the entire data package. Another challenge with traditional machine learning is that user data is aggregated at a central location where it violates local privacy policies laws and make the data more vulnerable to data violation. This book provides a comprehensive approach in federated learning for various aspects.

A digital twin represents the indistinguishable digital counterpart of the physical object to simulate, monitor and test with real time synchronization. This book presents the framework and important key aspects of digital twins, including various technologies with coverage of the digital twins in various industry and business applications. It provides a background of modeling and simulation, computer sensor technology and other areas required creating the next wave of digital twins.Features:Presents exclusive material on industrial and business applications of digital twinsIncludes diversified digital twin applications with use casesFocuses on tools and methods for digital twins, platforms, application domains and industriesEmphasizes advances and cutting-edge technologies throughoutReviews artificial intelligence (AI), fog/edge computing, industrial automation, blockchains and the Internet of Things (IoT)This book is aimed at researchers and graduate students in cloud computing, simulation, the IoT and computer engineering.

In today’s digital age, the risks to data and infrastructure have increased in both range and complexity. As a result, companies need to adopt cutting-edge artificial intelligence (AI) solutions to effectively detect and counter potential threats. This handbook fills the existing knowledge gap by bringing together a team of experts to discuss the latest advancements in security systems powered by AI. The handbook offers valuable insights on proactive strategies, threat mitigation techniques, and comprehensive tactics for safeguarding sensitive data.Handbook of AI-Driven Threat Detection and Prevention: A Holistic Approach to Security explores AI-driven threat detection and prevention, and covers a wide array of topics such as machine learning algorithms, deep learning, natural language processing, and so on. The holistic view offers a deep understanding of the subject matter as it brings together insights and contributions from experts from around the world and various disciplines including computer science, cybersecurity, data science, and ethics. This comprehensive resource provides a well-rounded perspective on the topic and includes real-world applications of AI in threat detection and prevention emphasized through case studies and practical examples that showcase how AI technologies are currently being utilized to enhance security measures. Ethical considerations in AI-driven security are highlighted, addressing important questions related to privacy, bias, and the responsible use of AI in a security context. The investigation of emerging trends and future possibilities in AI-driven security offers insights into the potential impact of technologies like quantum computing and blockchain on threat detection and prevention.This handbook serves as a valuable resource for security professionals, researchers, policymakers, and individuals interested in understanding the intersection of AI and security. It equips readers with the knowledge and expertise to navigate the complex world of AI-driven threat detection and prevention. This is accomplished by synthesizing current research, insights, and real-world experiences.

Sustainable Development Goals (SDGs) give the UN a road-map for development, with Agenda 2030 as the target. It was built on the previously established Millennium Development Goals (MDGs). This book examines the supporting technologies needed to achieve SDG 2: reducing hunger and creating a better society. This much-needed book, the first of its type to offer a specific focus on the relationship between technology and SDG 2, will be valuable for academics working in the subject of global sustainable development. This book will also be useful for international organisations and representatives, who will be able to share knowledge on technological views to minimise hunger rates.

Sustainable Development Goals (SDGs) give the UN a road-map for development with Agenda 2030 as target. It was built on the previously established Millennium Development Goals (MDG). This book examines the supporting technologies needed to achieve the SDG 3 to ensure healthy lives and promote well-being for all at all ages. This much-needed book, which is the first of its type to offer a specific focus on the relationship between technology and the SDG 3, will be valuable for academicians working in the subject of global sustainable development. International organisations and representatives of national governments will be able to share knowledge on technological views to improve to health and wellbeing’s of human in all means.

This edited volume, Smart Technologies for Sustainable Development Goals: Quality Education, explores the transformative role of emerging technologies in advancing SDG 4—Quality Education. Through diverse chapters on AI, XR, gamification, adaptive learning, blockchain, and inclusive pedagogy, the book offers practical insights and research-based strategies for enhancing teaching, learning, and institutional excellence. It serves as a vital resource for educators, policymakers, and researchers striving to build resilient, equitable, and future-ready education systems.

Cloud computing has revolutionized digital infrastructure, but it has also introduced complex forensic challenges that demand intelligent, scalable, and automated investigative solutions. AI Driven Cloud Forensics: Advanced Techniques for Next Generation Digital Investigations provides a comprehensive exploration of how artificial intelligence, machine learning, big data analytics, and automation are transforming the future of digital forensics in cloud environments. This edited volume examines advanced methodologies for forensic acquisition in virtual machines, containers, and ephemeral environments; intelligent cloud architectures for secure investigations; and AI-powered frameworks for anomaly detection, fraud investigation, phishing detection, and log analysis. It highlights emerging approaches such as AutoML-enabled forensic systems, federated learning for communication-efficient anomaly detection, AI-centric privileged access management, and real-time detection of data fabrication attacks in decentralized systems. Beyond core forensic technologies, the book addresses interdisciplinary applications across smart micro-grids, dark web environments, digital supply chains, tourism systems, and e-discovery processes. By integrating automation with forensic intelligence, the contributors demonstrate how next-generation digital investigations can become more proactive, predictive, and adaptive. Special emphasis is placed on addressing challenges such as data volatility, multi-tenancy, encryption, jurisdictional constraints, and large-scale log analysis in distributed cloud ecosystems. The book also includes a bibliometric and science-mapping analysis to provide insights into evolving research trends in AI-driven cloud forensic automation. Designed for researchers, cybersecurity professionals, forensic analysts, legal practitioners, cloud architects, and postgraduate students, this volume bridges theoretical foundations with real-world implementations. It serves as a forward-looking reference for building resilient, trustworthy, and AI-enabled forensic ecosystems capable of responding to increasingly sophisticated cyber threats. By combining innovation with investigative rigor, AI Driven Cloud Forensics contributes to shaping the next generation of intelligent digital security and forensic readiness in cloud-driven environments.

Explores the role of emerging technologies in advancing SDG 5 – Gender Equality, a key pillar of the UN Agenda 2030. The book examines how artificial intelligence, big data, IoT, blockchain, and other smart technologies can bridge gender gaps and empower women across various sectors. Highlighting the role of technology to promote gender equality, this book will serve as a valuable resource for academics, policymakers, researchers, and international organizations working toward inclusive and equitable development. National governments, NGOs, and industry leaders will gain insights into technological innovations that can accelerate progress toward achieving SDG 5 and fostering a more gender-equitable future.

This volume explores the transformative potential of emerging technologies in achieving SDG 6 – Clean Water and Sanitation, a foundational goal of the UN Agenda 2030. This volume investigates how smart technologies— including artificial intelligence, the Internet of Things, big data analytics, blockchain, and remote sensing—are reshaping water resource management, sanitation systems, and wastewater treatment across diverse geographies. As one of the first comprehensive works to address the synergy between technological innovation and sustainable water solutions, this book is an essential resource for academics, researchers, engineers, and policy professionals. It offers practical insights for national and local governments, NGOs, and industry leaders aiming to implement inclusive, efficient, and climate-resilient water and sanitation infrastructure. By bridging global gaps in access to clean water, this work supports the collective mission toward universal health, environmental sustainability, and human dignity.Key Features:Focuses on SDG 6 and Smart TechnologiesExplains Multidisciplinary and Global PerspectivesReviews Case Studies and Real-World ApplicationsDiscusses Policy and Implementation GuidanceBridges Academia and PracticeSupports UN 2030 Agenda and Climate Resilience

Artificial Intelligence-Based System Models in Healthcare provides a comprehensive and insightful guide to the transformative applications of AI in the healthcare system. This book is a groundbreaking exploration of the synergies between artificial intelligence and healthcare innovation. In an era where technological advancements are reshaping the landscape of medical practices, this book provides a comprehensive and insightful guide to the transformative applications of AI in healthcare systems. From conceptual foundations to practical implementations, the book serves as a roadmap for understanding the intricate relationships between AI-based system models and the evolution of healthcare delivery. The first section delves into the fundamental role of technology in reshaping the healthcare landscape. With a focus on daily life activities, decision support systems, vision-based management, and semantic frameworks, this section lays the groundwork for understanding the pivotal role of AI in revolutionizing traditional healthcare approaches. Each chapter offers a unique perspective, emphasizing the intricate integration of technology into healthcare ecosystems. The second section takes a deep dive into specific applications of AI, ranging from predictive analysis and machine learning to deep learning, image analysis, and biomedical text processing. With a focus on decision-making support systems, this section aims to demystify the complex world of AI algorithms in healthcare, offering valuable insights into their practical implications and potential impact on patient outcomes. The final section addresses the modernization of healthcare practices and envisions the future landscape of AI applications. From medical imaging and diagnostics to predicting ventilation needs in intensive care units, modernizing health record maintenance, natural language processing, chatbots for medical inquiries, secured health insurance management, and glimpses into the future, the book concludes by exploring the frontiers of AI-driven healthcare innovations. Audience This book is intended for researchers and postgraduate students in artificial intelligence and the biomedical and healthcare sectors. Medical administrators, policymakers and regulatory specialists will also have an interest.

This book stands out in the field due to its interdisciplinary approach, integrating cutting-edge computational methods with environmental sciences to offer practical solutions for sustainable development. One of the most important worldwide issues of our day is climate change, which calls for creative, data-driven, and scalable solutions. A thorough examination of how computational intelligence (CI) methods can be applied to different facets of climate change, from assessing its effects to developing mitigation and adaptation plans, can be found in the book. The chapters explain how these tools can be harnessed to tackle the complexities of climate change. The book contextualizes climate change as a data-rich problem, highlighting the challenges posed by its vast, heterogeneous, and dynamic datasets. The book also discusses about climate data analytics, which discusses the role of CI in managing and analyzing large-scale climate data collected from satellites, weather stations, and IoT devices. This book presents techniques for preprocessing, cleaning, and normalizing climate data to enhance its usability. This book details the use of computational intelligence for creating accurate climate models. This book explores techniques for predicting long-term trends, such as global warming and sea level rise, and short-term events like hurricanes and heatwaves. Case studies demonstrate how machine learning algorithms, such as random forests and support vector machines, have been used to refine predictive models. It presents vulnerability analysis frameworks that use CI to assess the susceptibility of regions, ecosystems, and communities to climate risks. The book discusses real-world applications, such as identifying drought-prone areas using neural networks or assessing flood risks using fuzzy logic systems. The book delves into the integration of CI with emerging technologies such as blockchain, quantum computing, and the Internet of Things (IoT) for more robust climate solutions.