Pradeep Kumar Garg – författare

Artificial Intelligence: Technologies, Applications, and Challenges is an invaluable resource for readers to explore the utilization of Artificial Intelligence, applications, challenges, and its underlying technologies in different applications areas. Using a series of present and future applications, such as indoor-outdoor securities, graphic signal processing, robotic surgery, image processing, character recognition, augmented reality, object detection and tracking, intelligent traffic monitoring, emergency department medical imaging, and many more, this publication will support readers to get deeper knowledge and implementing the tools of Artificial Intelligence.

The book offers comprehensive coverage of the most essential topics, including:

This book will be an ideal resource for IT professionals, researchers, under or post-graduate students, practitioners, and technology developers who are interested in gaining insight to the Artificial Intelligence with deep learning, IoT and machine learning, critical applications domains, technologies, and solutions to handle relevant challenges.

Artificial Intelligence: Technologies, Applications, and Challenges is an invaluable resource for readers to explore the utilization of Artificial Intelligence, applications, challenges, and its underlying technologies in different applications areas. Using a series of present and future applications, such as indoor-outdoor securities, graphic signal processing, robotic surgery, image processing, character recognition, augmented reality, object detection and tracking, intelligent traffic monitoring, emergency department medical imaging, and many more, this publication will support readers to get deeper knowledge and implementing the tools of Artificial Intelligence.

The book offers comprehensive coverage of the most essential topics, including:

This book will be an ideal resource for IT professionals, researchers, under or post-graduate students, practitioners, and technology developers who are interested in gaining insight to the Artificial Intelligence with deep learning, IoT and machine learning, critical applications domains, technologies, and solutions to handle relevant challenges.

This book provides readers to the vision of Society 5.0, which was originally proposed in the fifth Basic Science and Technology Plan by Japan’s government for a technology-based, human-centered society, emerging from the fourth industrial revolution (Industry 4.0). The implementation of AI and other modern techniques in a smart society requires automated data scheduling and analysis using smart applications, a smart infrastructure, smart systems, and a smart network.

Features

This book is aimed at graduate and post graduate students, researchers, academicians working in the field of computer science, artificial intelligence, and machine learning.

This book provides readers to the vision of Society 5.0, which was originally proposed in the fifth Basic Science and Technology Plan by Japan’s government for a technology-based, human-centered society, emerging from the fourth industrial revolution (Industry 4.0). The implementation of AI and other modern techniques in a smart society requires automated data scheduling and analysis using smart applications, a smart infrastructure, smart systems, and a smart network.

Features

This book is aimed at graduate and post graduate students, researchers, academicians working in the field of computer science, artificial intelligence, and machine learning.

This book presents the latest developments in deep learning-enabled healthcare tools and technologies and offers practical ideas for using the IoT with deep learning (motion-based object data) to deal with human dynamics and challenges including critical application domains, technologies, medical imaging, drug discovery, insurance fraud detection and solutions to handle relevant challenges. This book covers real-time healthcare applications, novel solutions, current open challenges, and the future of deep learning for next-generation healthcare. It includes detailed analysis of the utilization of the IoT with deep learning and its underlying technologies in critical application areas of emergency departments such as drug discovery, medical imaging, fraud detection, Alzheimer''s disease, and genomes.

Postgraduate students and researchers in the departments of computer science, working in the areas of the IoT, deep learning, machine learning, image processing, big data, cloud computing, and remote sensing will find this book useful.

This book presents the latest developments in deep learning-enabled healthcare tools and technologies and offers practical ideas for using the IoT with deep learning (motion-based object data) to deal with human dynamics and challenges including critical application domains, technologies, medical imaging, drug discovery, insurance fraud detection and solutions to handle relevant challenges. This book covers real-time healthcare applications, novel solutions, current open challenges, and the future of deep learning for next-generation healthcare. It includes detailed analysis of the utilization of the IoT with deep learning and its underlying technologies in critical application areas of emergency departments such as drug discovery, medical imaging, fraud detection, Alzheimer''s disease, and genomes.

Postgraduate students and researchers in the departments of computer science, working in the areas of the IoT, deep learning, machine learning, image processing, big data, cloud computing, and remote sensing will find this book useful.

With rapid advancements in AI, this book reveals how AI can be a powerful tool in reducing pollution and fostering sustainability. It highlights the integration of geospatial techniques with AI for enhancing capabilities in mapping, analysis, and mitigation of environmental pollution. Starting with foundational concepts in AI, geospatial technology, and pollution, the book addresses air, water, soil and thermal pollution, emphasizing their harmful impacts. Through real-world case studies and advanced research, it showcases AI and geospatial technology''s revolutionary role in pollution mitigation, exploring AI-driven sensors, satellite imagery, and associated networks for precise and efficient pollution monitoring and management.

With rapid advancements in AI, this book reveals how AI can be a powerful tool in reducing pollution and fostering sustainability. It highlights the integration of geospatial techniques with AI for enhancing capabilities in mapping, analysis, and mitigation of environmental pollution. Starting with foundational concepts in AI, geospatial technology, and pollution, the book addresses air, water, soil and thermal pollution, emphasizing their harmful impacts. Through real-world case studies and advanced research, it showcases AI and geospatial technology''s revolutionary role in pollution mitigation, exploring AI-driven sensors, satellite imagery, and associated networks for precise and efficient pollution monitoring and management.

This book provides a comprehensive overview of the concept of digital twins, emphasising its strategic importance across various commercial domains. This book covers the fundamentals, data requirements, tools, and technologies essential for understanding and implementing digital twins. It discusses how digital twins are used for running simulations, analysing performance issues, and generating potential improvements to optimise business processes. The book explores the architecture, historical background, and real-time applications in sectors including urban planning, healthcare, smart cities, and manufacturing.

It is a reference book for researchers, scholars, and students who are working or interested in learning about digital twin technology.

This book provides a comprehensive overview of the concept of digital twins, emphasising its strategic importance across various commercial domains. This book covers the fundamentals, data requirements, tools, and technologies essential for understanding and implementing digital twins. It discusses how digital twins are used for running simulations, analysing performance issues, and generating potential improvements to optimise business processes. The book explores the architecture, historical background, and real-time applications in sectors including urban planning, healthcare, smart cities, and manufacturing.

It is a reference book for researchers, scholars, and students who are working or interested in learning about digital twin technology.

This book addresses the mapping of soil-landscape parameters in the geospatial domain. It begins by discussing the fundamental concepts, and then explains how machine learning and geomatics can be applied for more efficient mapping and to improve our understanding and management of ‘soil’. The judicious utilization of a piece of land is one of the biggest and most important current challenges, especially in light of the rapid global urbanization, which requires continuous monitoring of resource consumption. The book provides a clear overview of how machine learning can be used to analyze remote sensing data to monitor the key parameters, below, at, and above the surface. It not only offers insights into the approaches, but also allows readers to learn about the challenges and issues associated with the digital mapping of these parameters and to gain a better understanding of the selection of data to represent soil-landscape relationships as well as the complex and interconnected links between soil-landscape parameters under a range of soil and climatic conditions. Lastly, the book sheds light on using the network of satellite-based Earth observations to provide solutions toward smart farming and smart land management.

The book introduces a variety of latest techniques designed to represent, enhance, and empower multi-disciplinary approaches of geographic information system (GIS), artificial intelligence (AI), deep learning (DL), machine learning, and cloud computing research in healthcare. It provides a unique compendium of the current and emerging use of geospatial data for healthcare and reflects the diversity, complexity, and depth and breadth of this multi-disciplinary area. This book addresses various aspects of how smart healthcare devices can be used to detect and analyze diseases. Further, it describes various tools and techniques to evaluate the efficacy, suitability, and efficiency of geospatial data for health-related applications. It features illustrative case studies, including future applications and healthcare challenges. This book is beneficial for computer science and engineering students and researchers, medical professionals, and anyone interested in using geospatial data in healthcare. It is also intended for experts, offering them a valuable retrospective and a global vision for the future, as well as for non-experts who are curious to learn about this important subject. The book presents an effort to draw how we can build health-related applications using geospatial big data and their subsequent analysis.