Anand Deshpande – författare

Artificial Intelligence (A) is transforming transportation by enabling intelligent, adaptive, and data-driven mobility systems capable of addressing growing urbanization and sustainability challenges. By leveraging machine learning, computer vision, natural language processing, and advanced data analytics, AI enhances traffic prediction, autonomous navigation, safety monitoring, and infrastructure management. These technologies allow transportation networks to respond in real time to changing conditions, reduce congestion and emissions, improve road safety, and optimize resource utilization. AI also plays a central role in integrating vehicles, users, and infrastructure into cohesive smart mobility ecosystems. As transportation systems evolve toward automation, connectivity, and sustainability, AI emerges as a foundational enabler for resilient, efficient, and future-ready transportation networks worldwide.

The book offers a comprehensive and forward-looking examination of AI’s expanding role in modern transportation. The book spans foundational principles and advanced innovations, beginning with intelligent transportation systems and progressing to specialized areas such as AI-driven driving analytics, smart traffic control, and augmented-reality-based safety navigation. It further explores emerging frontiers including autonomous vehicle intelligence, edge-AI frameworks, 6G-enabled mobility, quantum computing, generative AI for infrastructure design, and robust vehicle-to-vehicle communication systems. By integrating theory, applications, and future trends, the book presents a holistic vision of how AI can build safer, smarter, and more sustainable transportation ecosystems while supporting the broader evolution of smart cities.

This book is designed for researchers, engineers, industry professionals, urban planners, policymakers, and advanced students engaged in transportation, artificial intelligence, and smart city development. It serves as both a practical reference and a visionary guide for those seeking to understand, design, and deploy AI-enabled transportation solutions, while supporting informed decision-making and innovation in global mobility systems.

Artificial Intelligence (A) is transforming transportation by enabling intelligent, adaptive, and data-driven mobility systems capable of addressing growing urbanization and sustainability challenges. By leveraging machine learning, computer vision, natural language processing, and advanced data analytics, AI enhances traffic prediction, autonomous navigation, safety monitoring, and infrastructure management. These technologies allow transportation networks to respond in real time to changing conditions, reduce congestion and emissions, improve road safety, and optimize resource utilization. AI also plays a central role in integrating vehicles, users, and infrastructure into cohesive smart mobility ecosystems. As transportation systems evolve toward automation, connectivity, and sustainability, AI emerges as a foundational enabler for resilient, efficient, and future-ready transportation networks worldwide.

The book offers a comprehensive and forward-looking examination of AI’s expanding role in modern transportation. The book spans foundational principles and advanced innovations, beginning with intelligent transportation systems and progressing to specialized areas such as AI-driven driving analytics, smart traffic control, and augmented-reality-based safety navigation. It further explores emerging frontiers including autonomous vehicle intelligence, edge-AI frameworks, 6G-enabled mobility, quantum computing, generative AI for infrastructure design, and robust vehicle-to-vehicle communication systems. By integrating theory, applications, and future trends, the book presents a holistic vision of how AI can build safer, smarter, and more sustainable transportation ecosystems while supporting the broader evolution of smart cities.

This book is designed for researchers, engineers, industry professionals, urban planners, policymakers, and advanced students engaged in transportation, artificial intelligence, and smart city development. It serves as both a practical reference and a visionary guide for those seeking to understand, design, and deploy AI-enabled transportation solutions, while supporting informed decision-making and innovation in global mobility systems.

This book explores the application of deep learning techniques within a particularly difficult computational type of computer vision (CV) problem ─ super-resolution (SR). The authors present and discuss ways to apply computational intelligence (CI) methods to SR. The volume also explores the possibility of using different kinds of CV techniques to develop and enhance the tools/processes related to SR. The application areas covered include biomedical engineering, healthcare applications, medicine, histology, and material science. The book will be a valuable reference for anyone concerned with multiple multimodal images, especially professionals working in remote sensing, nanotechnology and immunology at research institutes, healthcare facilities, biotechnology institutions, agribusiness services, veterinary facilities, and universities.