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Dr. Mandeep Kaur is an Assistant Professor in the Department of Electronics and Communication Engineering at Punjabi University, Patiala, India. Her research interests include Computer Vision, Deep Learning, Pattern Recognition, Intelligent Transportation Systems, and Artificial Intelligence.
Traffic Sign Recognition (TSR) is an essential element of Intelligent Transportation Systems (ITS), Advanced Driver Assistance Systems (ADAS), and driverless vehicles. Precise identification of traffic signs facilitates safer driving, superior traffic management, and heightened awareness among road users. Conventional image-processing methods frequently encounter difficulties due to fluctuations in lighting, atmospheric conditions, obstructions, motion blur, and intricate backdrops. Deep Learning (DL) has transformed traffic sign identification through enhanced feature extraction, categorisation, and real-time detection capabilities. Advanced models, including Convolutional Neural Networks (CNNs), Residual Networks (ResNet), Vision Transformers (ViTs), and hybrid deep learning architectures, have exhibited remarkable efficacy in the recognition and classification of traffic signs under varied situations. This chapter examines the principles, methodology, datasets, architectures, applications, problems, and future trajectories of deep learning-based traffic sign recognition systems, highlighting their significance in intelligent transportation and autonomous driving technologies.
The incorporation of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) into the healthcare sector has profoundly altered illness detection, medical imaging analysis, clinical decision-making assistance, and patient risk evaluation. Nonetheless, singular AI systems might encounter constraints while processing diverse, noisy, high-dimensional, and multimodal healthcare information. Hybrid Artificial Intelligence models mitigate these constraints by integrating several algorithms, learning methodologies, or specialised knowledge into a cohesive diagnostic structure. These models can amalgamate convolutional neural networks, recurrent neural networks, transformers, ensemble learning, fuzzy logic, optimisation techniques, and clinical expertise to enhance diagnostic precision and resilience. This section offers an extensive examination of hybrid AI frameworks for smart healthcare diagnostics. It examines sources of healthcare data, diagnostic processes, hybrid AI frameworks, medical imaging analysis, electronic health record evaluation, disease forecasting, multimodal diagnostics, interpretable AI, uncertainty assessment, privacy-conscious learning, edge and cloud healthcare infrastructures, and clinical decision-making support.Ultimately, the discourse encompasses nascent research trajectories pertaining to multimodal foundation models, federated learning, digital twins, generative AI, and personalised medicine.
Agentic Artificial Intelligence (Agentic AI) is a novel framework in artificial intelligence that broadens traditional AI functionalities from specialised prediction and content creation to autonomous decision-making and action aligned with specific objectives. An autonomous AI system is capable of sensing its surroundings, analysing data, reasoning about goals, devising strategies, engaging with external instruments, performing actions, assessing results, and modifying its future conduct in response to feedback. Recent advancements in extensive language models, generative AI, reinforcement learning, retrieval-augmented generation, multimodal learning, and tool-utilizing frameworks have expedited the evolution of intelligent autonomous agents. This chapter delineates the principles of Agentic AI and explores its progression from rule-based frameworks, machine learning, and deep learning to generative and autonomous AI structures. The principles outlined in this chapter establish a conceptual framework for comprehending the evolution and practical implementation of intelligent autonomous AI systems.