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Nagarathna A. is an Associate Professor of Law at the National Law School of India University, Bengaluru. Her expertise includes cyber law, intellectual property rights, digital governance, and legal studies. She has extensive experience in legal research and policy analysis. Her work contributes to the understanding of technology-driven legal and social challenges.
The fast urbanisation and rising population density have heightened the demand for sophisticated public safety and security systems in contemporary cities. Conventional surveillance systems frequently depend on manual oversight, which can be ineffective, labour-intensive, and susceptible to human error. Artificial Intelligence (AI) has emerged as a transformative tool for improving crime detection and urban surveillance via automated video analytics, facial recognition, anomaly detection, predictive policing, and real-time danger assessment. Integrating AI with computer vision, machine learning, deep learning, Internet of Things (IoT) devices, and smart city infrastructures enables urban surveillance systems to proactively detect suspicious behaviours, enhance law enforcement reaction times, and facilitate evidence-based decision-making. This chapter examines the technologies, designs, applications, difficulties, and future trajectories of AI-driven crime detection and urban surveillance systems, emphasising their contribution to the establishment of better and more secure urban settings.
This chapter provides an extensive examination of the integration of machine learning and Internet of Things technologies for the identification and alleviation of cyberbullying and digital harassment among adolescents. It stresses how important it is to use artificial intelligence, machine learning, and data-driven methods together to change how schools work today. The chapter also looks at real-time analytics, adaptive systems, and predictive modelling as examples of how to use these ideas in the real world to make teaching more successful and keep students interested. To make sure that the implementation is responsible, we carefully look at problems like data quality, scalability, privacy issues, and ethical concerns. There is also a discussion about how new technology will affect education systems that are equipped for the future. The chapter shows how intelligent systems can fill in the holes in traditional education by providing scalable and creative solutions that boost performance, make learning more accessible, and lead to long-term academic achievement in a variety of settings.