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Dr. V. Balaraju is an Assistant Professor in the Department of Electrical and Electronics Engineering at MJR College of Engineering and Technology, Andhra Pradesh, India. His research interests include IoT, Intelligent Systems, Smart Infrastructure, Embedded Technologies, and Engineering Applications of Artificial Intelligence.
Railway transit is an essential element of contemporary infrastructure, necessitating ongoing surveillance to guarantee operating safety, dependability, and efficiency. Conventional railway track inspection techniques are frequently laborious, protracted, and incapable of delivering real-time condition evaluations. The Internet of Things (IoT) has emerged as a transformative technology for intelligent railway track monitoring by facilitating continuous data collecting, remote surveillance, predictive maintenance, and real-time defect identification. IoT-based monitoring systems employ intelligent sensors, wireless communication networks, cloud computing, edge analytics, and artificial intelligence to evaluate track conditions, identify anomalies, and avert mishaps. This chapter examines the architecture, technology, methodology, applications, difficulties, and future prospects of smart railway track monitoring systems utilising IoT sensors. It emphasises the role of intelligent monitoring technologies in augmenting railway safety, decreasing maintenance expenses, and enhancing transportation efficiency.
Quantum Machine Learning (QML) integrates the computational prowess of quantum computing with the predictive skills of machine learning to tackle intricate engineering challenges that are difficult for traditional computing systems. Utilising quantum phenomena including superposition, entanglement, and quantum parallelism, QML algorithms can analyse high-dimensional data, optimise intricate systems, and expedite learning processes. Engineering fields such as manufacturing, robotics, energy systems, materials science, telecommunications, and smart infrastructure are progressively investigating QML for improved decision-making and predictive modelling. This chapter delineates the essential principles, structures, algorithms, and practical applications of Quantum Machine Learning within the field of engineering. It also analyses contemporary obstacles, implementation frameworks, and prospective research avenues, emphasising the revolutionary capacity of QML in addressing next-generation engineering issues.
The rising prevalence and severity of natural and anthropogenic disasters underscore the necessity for intelligent systems that facilitate swift, precise, and data-informed decision-making. Artificial Intelligence (AI)-driven Decision Support Systems (DSS) amalgamate machine learning, deep learning, Internet of Things (IoT), remote sensing, Geographic Information Systems (GIS), cloud computing, big data analytics, drones, and digital twin technologies to enhance disaster preparedness, risk evaluation, emergency response, and post-disaster recovery. These systems evaluate real-time environmental, geographical, and operational data to forecast disasters, optimise resource distribution, improve situational awareness, and coordinate emergency operations. AI-driven Decision Support Systems empower governments, emergency management agencies, healthcare professionals, and humanitarian organisations to make prompt and educated decisions, therefore reducing loss of life, infrastructure damage, and economic repercussions. This chapter examines the principles, designs, applications, problems, and future trajectories of AI-driven decision support systems in disaster management and emergency response, highlighting their significance in fostering resilient and catastrophe-prepared communities.
Generative Artificial Intelligence (GenAI) is transforming the media, communication, and creative sectors by facilitating intelligent content generation, tailored user experiences, automated production processes, and improved audience interaction. Generative AI, driven by sophisticated foundation models, large language models (LLMs), generative adversarial networks (GANs), diffusion models, and multimodal AI systems, facilitates the rapid and innovative production of text, images, audio, video, music, animation, and interactive digital experiences. Media organisations, publishers, advertising agencies, broadcasters, filmmakers, game developers, and digital artists are utilising Generative AI to enhance content creation, refine communication strategies, decrease operational expenses, and promote innovation. This chapter examines the principles, technologies, applications, problems, ethical considerations, and future trajectories of Generative AI within media, communication, and creative sectors, emphasising its revolutionary impact on the future of digital creativity and content ecosystems.