Mritunjay Rai – författare

Traditional surveillance systems struggle to process large volumes of visual data, identify specific objects or behaviors, and adapt to dynamic environments. Computational intelligence, which encompasses techniques like artificial intelligence (AI), machine learning (ML), and computer vision, offers powerful tools to address these challenges by enabling automated analysis, pattern recognition, and decision-making based on visual data. Computational Intelligence in Surveillance Systems Using Image Processing addresses the unique challenges and ethical considerations of applying AI and ML, offering a nuanced understanding of the regulatory landscape. It provides insights into the responsible development and deployment of technologies to unlock the transformative potential of computational intelligence to revolutionize surveillance systems and advance the capabilities of security and monitoring across various sectors.Discusses emerging trends, potential challenges, and areas for future research, providing a roadmap for scholars looking to contribute to the evolving field of image processingExplains how AI and ML algorithms can be applied to analyze and interpret visual data captured by surveillance camerasConsiders the challenges and considerations associated with deploying computational intelligence in surveillance, including privacy concerns, ethical considerations, and technical limitationsExplores specific use cases and applications where computational intelligence can enhance surveillance capabilities, such as object detection, activity recognition, anomaly detection, and predictive analytics

This book gives comprehensive insights into the application of AI, machine learning, and deep learning in developing efficient and optimal surveillance systems for both indoor and outdoor environments, addressing the evolving security challenges in public and private spaces. Mathematical Models Using Artificial Intelligence for Surveillance Systems aims to collect and publish basic principles, algorithms, protocols, developing trends, and security challenges and their solutions for various indoor and outdoor surveillance applications using artificial intelligence (AI). The book addresses how AI technologies such as machine learning (ML), deep learning (DL), sensors, and other wireless devices could play a vital role in assisting various security agencies. Security and safety are the major concerns for public and private places in every country. Some places need indoor surveillance, some need outdoor surveillance, and, in some places, both are needed. The goal of this book is to provide an efficient and optimal surveillance system using AI, ML, and DL-based image processing. The blend of machine vision technology and AI provides a more efficient surveillance system compared to traditional systems. Leading scholars and industry practitioners are expected to make significant contributions to the chapters. Their deep conversations and knowledge, which are based on references and research, will result in a wonderful book and a valuable source of information.

Revolutionizing Agricultural Quality Control with AI, Image Processing, and Computational Intelligence Techniques As the global demand for high-quality, sustainable agricultural products increases, advanced technology becomes critical in meeting these challenges. Computational Intelligence and Image Processing in Agriculture explores how innovative technologies are transforming agricultural quality evaluation. Combining foundational concepts with practical applications, this comprehensive text delves into innovative techniques to improve accuracy, efficiency, and sustainability in quality control. Addressing key challenges faced by researchers, practitioners, and industry professionals, contributions from leading experts in AI, agriculture, and computational intelligence provide a deep understanding of technologies such as deep learning, computer vision, and AI-driven robotics. Real-world examples, step-by-step tutorials, and code snippets make the concepts accessible and actionable, while coverage of emerging trends and future directions highlights the evolving landscape of agricultural technology. Offering interdisciplinary insights and practical tools to modernize evaluation techniques, reduce waste, enhance food safety, and meet the growing demands of sustainable farming practices, this book: Addresses challenges and solutions for real-time monitoring systems in agricultureHighlights cutting-edge applications such as AI-driven robotics and LiDAR in farmingEmphasizes sustainability and environmental impact through technological innovation Offers detailed coverage of image analysis algorithms for quality controlDiscusses ethical and environmental implications of technology in agricultureThis book is ideal for advanced undergraduate and graduate courses in agricultural engineering, computer science, and AI applications. It is also an essential reference for professionals including agricultural scientists, AI practitioners, and quality control experts.

Machine Learning Based Air Traffic Surveillance System Using Image Processing analyses how advanced machine learning algorithms and image processing technologies are revolutionising air-traffic management. By integrating real-time visual data analysis with sophisticated artificial intelligence techniques, this book highlights the potential to enhance situational awareness, safety, and efficiency in managing increasingly complex and congested airspaces. It delves into the use of convolutional neural networks (CNNs) and deep learning models to identify, track, and analyse aircraft movements, offering precise and actionable insights for air-traffic controllers.This comprehensive resource combines theoretical foundations with practical applications, including real-world case studies and discussions on system implementation. It addresses critical aspects such as object detection, anomaly identification, and trajectory prediction, alongside regulatory, ethical, and cybersecurity considerations. With its blend of cutting-edge research and practical insights, this book is an invaluable guide for professionals, researchers, and students in aerospace engineering, artificial intelligence, and computer vision, providing a roadmap for advancing air-traffic surveillance and management in the era of intelligent systems.

AI and Deep Learning Enabled Surveillance System Using Image Processing is an essential resource for anyone interested in the convergence of advanced technologies to enhance surveillance capabilities. This book explores in detail how artificial intelligence and deep learning can be applied to image processing, offering a transformative approach to modern surveillance systems. It covers foundational concepts of AI and deep learning, explaining how these technologies are reshaping the way we understand and utilize image data. The book emphasizes practical implementation, guiding readers through the integration of AI algorithms with image processing techniques to create sophisticated, real-time surveillance solutions.Through in-depth discussions and real-world case studies, the book highlights the applications of AI in various surveillance contexts, such as public safety, traffic monitoring, and access control. Readers will learn about the latest advancements in neural networks, object detection, and anomaly detection, gaining insights into developing and deploying intelligent surveillance systems. Additionally, the book addresses critical ethical and privacy considerations, ensuring that readers are aware of the balance between enhanced security and individual privacy rights. This comprehensive guide is an invaluable tool for professionals and researchers aiming to harness the power of AI and deep learning in the field of surveillance.

AI and Deep Learning Enabled Surveillance System Using Image Processing is an essential resource for anyone interested in the convergence of advanced technologies to enhance surveillance capabilities. This book explores in detail how artificial intelligence and deep learning can be applied to image processing, offering a transformative approach to modern surveillance systems. It covers foundational concepts of AI and deep learning, explaining how these technologies are reshaping the way we understand and utilize image data. The book emphasizes practical implementation, guiding readers through the integration of AI algorithms with image processing techniques to create sophisticated, real-time surveillance solutions.Through in-depth discussions and real-world case studies, the book highlights the applications of AI in various surveillance contexts, such as public safety, traffic monitoring, and access control. Readers will learn about the latest advancements in neural networks, object detection, and anomaly detection, gaining insights into developing and deploying intelligent surveillance systems. Additionally, the book addresses critical ethical and privacy considerations, ensuring that readers are aware of the balance between enhanced security and individual privacy rights. This comprehensive guide is an invaluable tool for professionals and researchers aiming to harness the power of AI and deep learning in the field of surveillance.