Handbook of Artificial Intelligence Applications for Industrial Sustainability examines the influence of AI and how it can be used in several industries to improve corporate performance, reduce security concerns, improve customer experience, and ultimately generate value for customers and maximize profits.

The subject of Artificial Intelligence (AI) is continuing on its journey of affecting each and every individual and will keep on this path in the times to come. This handbook is a collection of topics on the application of artificial intelligence applications for sustainability in different areas. It provides an insight into the various uses of concepts and practical examples for different domains all in one place, which makes it unique and important for the potential reader. Handbook of Artificial Intelligence Applications for Industrial Sustainability: Concepts and Practical Examples examines the influence of AI and how it can be used in several industries to improve corporate performance, reduce security concerns, improve customer experience, and ultimately generate value for customers and maximize profits. The handbook offers practical examples, concepts, and applications that provide an easy understanding and implementation process. It provides AI applications in many fields, such as sustainable credit decisions, cyber security and fraud prevention, warehouse management, and much more. This handbook will provide insight to customers, managers, professionals, engineers, researchers, and students on the various uses of AI and sustainability in different domains. All of this needed information compiled into one handbook makes it unique and important for the engineering, business, and computer science communities.

In the industry of agriculture, farmers are facing a challenge worldwide: the need to simultaneously achieve substantial crop yields and mitigate the adverse environmental effects caused by persistent threats from agricultural parasites. The escalating demand for food in tandem with population expansion exacerbates this intricate dilemma, highlighting the shortcomings of conventional approaches to insect management. As climate change, the development of pest resistance, and the call for reduced chemical inputs intensify, a fundamental change in our approach to pest management becomes imperative. Revolutionizing Pest Management for Sustainable Agriculture , is an exploration into the convergence of technology and tradition, revealing how data-driven methodologies and state-of-the-art technologies are transforming the field of agricultural pest management. Revolutionizing Pest Management for Sustainable Agriculture serves as more than a compilation of developments; it is a strategic guide for policymakers, researchers, and farmers navigating the complexities of contemporary agriculture responsibly. With an objective to bridge the gap between traditional pest management and innovative technology, the book provides practical strategies, case studies, and valuable insights, inviting readers to explore the symbiotic relationship between technology and soil cultivation, paving the way for a paradigm shift in the agricultural industry. This carefully crafted resource is designed for a diverse audience, including agricultural researchers, Agri-tech professionals, policymakers, and educators, empowering them with the knowledge and resources needed to embrace smart solutions, contributing to increased productivity, reduced environmental impact, and the sustainability of agricultural systems.

A Biologist’s Guide to Artificial Intelligence: Building the Foundations of Artificial Intelligence and Machine Learning for Achieving Advancements in Life Sciences provides an overview of the basics of Artificial Intelligence for life science biologists. In 14 chapters/sections, readers will find an introduction to Artificial Intelligence from a biologist’s perspective, including coverage of AI in precision medicine, disease detection, and drug development. The book also gives insights into the AI techniques used in biology and the applications of AI in food, and in environmental, evolutionary, agricultural, and bioinformatic sciences. Final chapters cover ethical issues surrounding AI and the impact of AI on the future. This book covers an interdisciplinary area and is therefore is an important subject matter resource and reference for researchers in biology and students pursuing their degrees in all areas of Life Sciences. It is also a useful title for the industry sector and computer scientists who would gain a better understanding of the needs and requirements of biological sciences and thus better tune the algorithms. Helps biologists succeed in understanding the concepts of Artificial Intelligence and machine learning Equips with new data mining strategies an easy interface into the world of Artificial Intelligence Enables researchers to enhance their own sphere of researching Artificial Intelligence

Artificial intelligence and the use of computational methods to extract information from data are providing adequate tools to monitor and predict water pollutants and water quality issues faster and more accurately. Smart sensors and machine learning models help detect and monitor dispersion and leakage of pollutants before they reach groundwater. With contributions from experts in academia and industries, who give a unified treatment of AI methods and their applications in water science, this book help governments, industries, and homeowners not only address water pollution problems more quickly and efficiently, but also gain better insight into the implementation of more effective remedial measures. FEATURES Provides cutting-edge AI applications in water sector. Highlights the environmental models used by experts in different countries. Discusses various types of models using AI and its tools for achieving sustainable development in water and groundwater. Includes case studies and recent research directions for environmental issues in water sector. Addresses future aspects and innovation in AI field related to watersustainability. This book will appeal to scientists, researchers, and undergraduate and graduate students majoring in environmental or computer science and industry professionals in water science and engineering, environmental management, and governmental sectors. It showcases artificial intelligence applications in detecting environmental issues, with an emphasis on the mitigation and conservation of water and underground resources.

This English edition monograph is developed and updated from China's best-selling, and award-winning, book on Artificial Intelligence (AI). It covers the foundations as well as the latest developments of AI in a comprehensive and systematic manner. It is a valuable guide for students and researchers on artificial intelligence.A wide range of topics in AI are covered in this book with four distinct features. First of all, the book comprises a comprehensive system, covering the core technology of AI, including the basic theories and techniques of 'traditional' artificial intelligence, and the basic principles and methods of computational intelligence. Secondly, the book focuses on innovation, covering advanced learning methods for machine learning and deep learning techniques and other artificial intelligence that have been widely used in recent years. Thirdly, the theory and practice of the book are highly integrated. There are theories, techniques and methods, as well as many application examples, which will help readers to understand the artificial intelligence theory and its application development. Fourthly, the content structure of the book is quite characteristic, consisting of three parts: (i) knowledge-based artificial intelligence, (ii) data-based artificial intelligence, and (iii) artificial intelligence applications.It is closely related to the core elements of artificial intelligence, namely knowledge, data, algorithms, and computing powers. This reflects the authors' deep understanding of the artificial intelligence discipline.

In today's context of intricate global challenges, encompassing climate crises, resource scarcity, and social disparities, the imperative for sustainable development has never been more pressing. While academic scholars and researchers are instrumental in crafting solutions, they often grapple with the intricate balance between theoretical concepts and practical implementation. This gap impedes the transformation of innovative ideas into tangible societal progress, leaving a void where effective real-world strategies for cross-industry sustainability should flourish. "Fostering Cross-Industry Sustainability With Intelligent Technologies" seeks to bridge this divide. This book is more than just a collection of pages; it serves as a roadmap for those determined to make a tangible impact. It brings together a diverse group of esteemed experts from various disciplines, offering a comprehensive spectrum of actionable insights, all grounded in the ethical imperatives of inclusivity and environmental responsibility. Anchored in the United Nations Sustainable Development Goals (SDGs), this volume serves as a guiding star, channeling theoretical expertise into practical solutions. For academic scholars, scientists, innovators, and students alike, Fostering Cross-Industry Sustainability With Intelligent Technologies is the definitive guidepost. It fosters a profound understanding of the real-world implications of research, promoting interdisciplinary collaborations that transcend conventional boundaries. This comprehensive book presents a wealth of sustainable science and intelligent technology applications, all while emphasizing the importance of ethics and societal impact. With visionary insights woven throughout its pages, it calls upon humanity to envision a future where challenges transform into opportunities, and sustainable development becomes an attainable reality.

HANDBOOK OF INTELLIGENT COMPUTING AND OPTIMIZATION FOR SUSTAINABLE DEVELOPMENT This book provides a comprehensive overview of the latest breakthroughs and recent progress in sustainable intelligent computing technologies, applications, and optimization techniques across various industries. Optimization has received enormous attention along with the rapidly increasing use of communication technology and the development of user-friendly software and artificial intelligence. In almost all human activities, there is a desire to deliver the highest possible results with the least amount of effort. Moreover, optimization is a very well-known area with a vast number of applications, from route finding problems to medical treatment, construction, finance, accounting, engineering, and maintenance schedules in plants. As far as optimization of real-world problems is concerned, understanding the nature of the problem and grouping it in a proper class may help the designer employ proper techniques which can solve the problem efficiently. Many intelligent optimization techniques can find optimal solutions without the use of objective function and are less prone to local conditions. The 41 chapters comprising the Handbook of Intelligent Computing and Optimization for Sustainable Development by subject specialists, represent diverse disciplines such as mathematics and computer science, electrical and electronics engineering, neuroscience and cognitive sciences, medicine, and social sciences, and provide the reader with an integrated understanding of the importance that intelligent computing has in the sustainable development of current societies. It discusses the emerging research exploring the theoretical and practical aspects of successfully implementing new and innovative intelligent techniques in a variety of sectors, including IoT, manufacturing, optimization, and healthcare. Audience It is a pivotal reference source for IT specialists, industry professionals, managers, executives, researchers, scientists, and engineers seeking current research in emerging perspectives in the field of artificial intelligence in the areas of Internet of Things, renewable energy, optimization, and smart cities.

An analysis of different concepts and case studies in engineering disciplines such as chemical, civil, electrical, telecommunications and mechanical engineering, demonstrating how engineering systems and processes can leverage the power of AI to drive and achieve the UN SDGs.