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Dr. Ashish Nagila is an Assistant Professor in the Department of Computer Science and Engineering at the School of Computer Science and Applications, IFTM University, Moradabad, India. His research interests include Artificial Intelligence, Deep Learning, intelligent transportation systems, computer vision, and predictive analytics. He actively contributes to academic research focused on intelligent computing solutions for real-world applications.
Road traffic collisions rank among the foremost contributors to deaths, injuries, and financial detriment globally. Accelerated urban development, rising automobile ownership, driver conduct, unfavourable weather conditions, inadequate road infrastructure, and traffic congestion have markedly heightened the intricacy of road safety administration. Traditional accident prevention methods predominantly depend on past accident documentation and human traffic surveillance, constraining their capacity to proactively recognise high-risk scenarios. The amalgamation of Deep Learning (DL), Artificial Intelligence (AI), Internet of Things (IoT), Computer Vision, Edge Computing, Cloud Computing, and Intelligent Transportation Systems (ITS) has revolutionised the forecasting and prevention of road accidents through real-time surveillance, astute risk evaluation, traffic behaviour analysis, and automated decision-making support. Deep learning frameworks like Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Vision Transformers (ViT), Graph Neural Networks (GNN), and Hybrid Deep Learning models markedly enhance the precision of accident forecasting by scrutinising traffic imagery, video feeds, sensor inputs, meteorological data, and vehicle telemetry. This chapter elucidates the principles, framework, deep learning methodologies, applications, obstacles, and prospective research avenues of deep learning for road accident prediction and prevention.
The Internet of Things (IoT) and cloud computing have emerged as essential technologies for the creation of intelligent, interconnected engineering systems. The Internet of Things facilitates the collection and exchange of real-time data across physical devices, sensors, machines, and infrastructure, whilst cloud computing offers scalable storage, processing, analytical, and service functionalities.This chapter elucidates the essential principles, frameworks, communication technologies, and applications of IoT and cloud computing in the context of intelligent engineering systems. It examines IoT sensor strata, network frameworks, edge and fog computing, cloud infrastructures, data governance, security measures, interoperability, and AI-driven analytics. The section also explores the amalgamation of IoT and cloud technology within intelligent manufacturing, urban environments, energy frameworks, transportation, healthcare, agriculture, and infrastructure oversight. Innovative methodologies including edge AI, digital twins, federated learning, 5G/6G communication, and serverless computing are likewise investigated. Ultimately, the chapter underscores significant obstacles and prospective research avenues for developing scalable, secure, intelligent, and sustainable IoT-cloud ecosystems.