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.

This book presents Internet of Things (IoT) solutions monitoring and assessing a variety of applications areas for indoor air quality (IAQ).

This book presents Internet of Things (IoT) solutions monitoring and assessing a variety of applications areas for indoor air quality (IAQ).

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.