Malka N. Halgamuge – författare

Artificial intelligence (AI) has become deeply embedded in modern life, with autonomous systems now controlling many crucial functions in healthcare, transportation, and industry. However, the incredible power of AI also enables new forms of manipulation, deception, and harm. Mitigating the Risks of AI Deepfakes explores the dark side of AI and its dangerous implications for human wellbeing.Spanning over 250 pages, this book examines how attackers can exploit AI systems to spread misinformation, steal identities, endanger lives through faulty diagnostics, and jeopardize public safety through products like self-driving cars. The chapters also dive into the national security risks posed by deepfakes and the use of AI to create compelling fake audio/video content used for political sabotage and blackmail. They explain how deepfakes could be uniquely disruptive in conservative societies that depend on reputation and visual evidence.While increased reliance on AI delivers efficiency and insight, unchecked development poses catastrophic risks that outweigh the benefits. By assembling a comprehensive overview of the threats we face alongside potential solutions, this book sounds an alarm regarding the dark side of progress while laying out a blueprint for mitigating dangers. The chapters synthesize cutting-edge research on fighting back against AI manipulation through innovations in machine learning, deep learning, reinforcement learning and federated learning.Ultimately, Mitigating the Risks of AI Deepfakes serves as an essential call to action for consumers, companies, and governments to confront the hazards of AI proliferation head-on. If left unaddressed, the sinister potential of systems we depend on could endanger civil liberties and human life. We must work proactively to guard the public interest as intelligent algorithms grow more advanced. This book equips readers with the knowledge to meet that challenge.

Artificial intelligence (AI) has become deeply embedded in modern life, with autonomous systems now controlling many crucial functions in healthcare, transportation, and industry. However, the incredible power of AI also enables new forms of manipulation, deception, and harm. Mitigating the Risks of AI Deepfakes explores the dark side of AI and its dangerous implications for human wellbeing.Spanning over 250 pages, this book examines how attackers can exploit AI systems to spread misinformation, steal identities, endanger lives through faulty diagnostics, and jeopardize public safety through products like self-driving cars. The chapters also dive into the national security risks posed by deepfakes and the use of AI to create compelling fake audio/video content used for political sabotage and blackmail. They explain how deepfakes could be uniquely disruptive in conservative societies that depend on reputation and visual evidence.While increased reliance on AI delivers efficiency and insight, unchecked development poses catastrophic risks that outweigh the benefits. By assembling a comprehensive overview of the threats we face alongside potential solutions, this book sounds an alarm regarding the dark side of progress while laying out a blueprint for mitigating dangers. The chapters synthesize cutting-edge research on fighting back against AI manipulation through innovations in machine learning, deep learning, reinforcement learning and federated learning.Ultimately, Mitigating the Risks of AI Deepfakes serves as an essential call to action for consumers, companies, and governments to confront the hazards of AI proliferation head-on. If left unaddressed, the sinister potential of systems we depend on could endanger civil liberties and human life. We must work proactively to guard the public interest as intelligent algorithms grow more advanced. This book equips readers with the knowledge to meet that challenge.

Artificial intelligence (AI) has become deeply embedded in modern life, with autonomous systems now controlling many crucial functions in healthcare, transportation, and industry. However, the incredible power of AI also enables new forms of manipulation, deception, and harm. Mitigating the Risks of AI Deepfakes explores the dark side of AI and its dangerous implications for human wellbeing.Spanning over 250 pages, this book examines how attackers can exploit AI systems to spread misinformation, steal identities, endanger lives through faulty diagnostics, and jeopardize public safety through products like self-driving cars. The chapters also dive into the national security risks posed by deepfakes and the use of AI to create compelling fake audio/video content used for political sabotage and blackmail. They explain how deepfakes could be uniquely disruptive in conservative societies that depend on reputation and visual evidence.While increased reliance on AI delivers efficiency and insight, unchecked development poses catastrophic risks that outweigh the benefits. By assembling a comprehensive overview of the threats we face alongside potential solutions, this book sounds an alarm regarding the dark side of progress while laying out a blueprint for mitigating dangers. The chapters synthesize cutting-edge research on fighting back against AI manipulation through innovations in machine learning, deep learning, reinforcement learning and federated learning.Ultimately, Mitigating the Risks of AI Deepfakes serves as an essential call to action for consumers, companies, and governments to confront the hazards of AI proliferation head-on. If left unaddressed, the sinister potential of systems we depend on could endanger civil liberties and human life. We must work proactively to guard the public interest as intelligent algorithms grow more advanced. This book equips readers with the knowledge to meet that challenge.

Indoor air quality (IAQ) and indoor air pollution (IAP) are a matter of concern in many countries because they can significantly influence the general health and well-being of those who spend most of their time inside, whether at home or work. Poor IAQ and repeated exposure to dangerous concentrations of pollutants can contribute significantly to the healthcare burden along with increased absenteeism and lost productivity worldwide.This book, Indoor Air Quality Assessment for Smart Environments, explores the problem of IAQ and highlights potential challenges, gaps, and opportunities in the field. As the title suggests, it focuses on assessing IAQ in smart environments using emerging technologies, such as the Internet of Things (IoT) and Wireless Sensor Networks (WSN), that can further contribute to the development of intelligent building management systems. The book contains 8 chapters, written by various experts in the field and addressing significant elements of IAQ management, including: definition, state-of-the-art and applications; sensing techniques; technological interventions and smart environments; smart monitoring devices; green and smart hospitals; health risks of nano building products; the optimization of household ventilation; and an assessment of smart environments.While providing a useful source of knowledge for researchers, policymakers, public health professionals and government agencies wishing to enhance the air quality in buildings, the book will also serve as a guide to building occupants who wish to take the necessary measures to enhance the built environment with improved ventilation arrangements.

This book presents Internet of Things (IoT) solutions monitoring and assessing a variety of applications areas for indoor air quality (IAQ). This book synthesizes recent developments, presents case studies, and discusses new methods in the area of air quality monitoring, all the while addressing public health concerns. The authors discuss the issues and solutions, including IoT systems that can provide a continuous flow of data retrieved from cost-effective sensors that can be used in multiple applications.The authors present the leading IoT technologies, applications, algorithms, systems, and future scope in this multi-disciplinary domain.

This book presents Internet of Things (IoT) solutions monitoring and assessing a variety of applications areas for indoor air quality (IAQ). This book synthesizes recent developments, presents case studies, and discusses new methods in the area of air quality monitoring, all the while addressing public health concerns. The authors discuss the issues and solutions, including IoT systems that can provide a continuous flow of data retrieved from cost-effective sensors that can be used in multiple applications.The authors present the leading IoT technologies, applications, algorithms, systems, and future scope in this multi-disciplinary domain.

This book presents high-quality research papers presented at International Conference on Artificial Intelligence and Cyber Security (ICAICS 2025), organized by School of Engineering and Technology, VIPS-TC, Delhi during 4 – 5 December 2025. The book covers innovative topics in machine learning, deep learning, and natural language processing, ethical hacking, cryptographic techniques, and cyber threat intelligence. The book also addresses contemporary challenges and highlights emerging trends.

The expected time of impact, also known as the mean first passage time (MFPT) to reach failure, is a critical metric in the management of natural disasters. The complexity of the dynamics governing natural disasters lead to stochastic behaviour. This book shows that state transitions of many such systems translate into random walks on their respective state spaces, biased and shaped by environmental inhomogeneity. Thus the probabilistic treatment of those random walks gives valuable insights of expected behaviour. A comprehensive case study of predicting cyclone induced flood is followed by a discussion of generic methods that predict MFPT addressing directional bias. This is followed by discussing MFPT prediction methods in systems showing network inhomogeneity. All presented methods are illustrated using real datasets of natural disasters. The book ends with a short discussion of possible future research areas introducing the problem of predicting MFPT for bush-fire propagation.

The expected time of impact, also known as the mean first passage time (MFPT) to reach failure, is a critical metric in the management of natural disasters. The complexity of the dynamics governing natural disasters lead to stochastic behaviour. This book shows that state transitions of many such systems translate into random walks on their respective state spaces, biased and shaped by environmental inhomogeneity. Thus the probabilistic treatment of those random walks gives valuable insights of expected behaviour. A comprehensive case study of predicting cyclone induced flood is followed by a discussion of generic methods that predict MFPT addressing directional bias. This is followed by discussing MFPT prediction methods in systems showing network inhomogeneity. All presented methods are illustrated using real datasets of natural disasters. The book ends with a short discussion of possible future research areas introducing the problem of predicting MFPT for bush-fire propagation.