Artificial Intelligence Iot And Fuzzy Systems For Sustainable Development And Industry 5 0
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Artificial Intelligence, IoT, and Fuzzy Systems for Sustainable Development and Industry 5.0
Author: Sayed Abdulhayan
language: en
Publisher: Deep Science Publishing
Release Date: 2025-06-19
In an era where sustainability is paramount, technology emerges as a critical driver for transformative change across industries. This book explores cutting-edge technological innovations that support sustainable development and align with global goals. Beginning with fuzzy systems in industrial engineering, we delve into intelligent approaches that optimize complex processes. The following chapters examine broader technological innovations fostering sustainability, green strategies in evolving supply chains and e-commerce aligned with SDG 9, and advanced technologies such as IoT, AI, and blockchain revolutionizing sustainable farming practices. This comprehensive compilation aims to inspire researchers, practitioners, and policymakers to harness innovative technologies for building resilient, eco-friendly systems that meet present needs without compromising the future.
AI-Powered IoT in the Energy Industry
AI-Powered IoT in the Energy Industry: Digital Technology and Sustainable Energy Systems looks at opportunities to employ cutting-edge applications of artificial intelligence (AI), the Internet of Things (IoT), and Machine Learning (ML) in designing and modeling energy and renewable energy systems. The book's main objectives are to demonstrate how big data can help with energy efficiency and demand reduction, increase the usage of renewable energy sources, and assist in transitioning from a centralized system to a distributed, efficient, and embedded energy system. Contributions cover the fundamentals of the renewable energy sector, including solar, wind, biomass, and hydrogen, as well as building services and power generation systems. Chapters also examine renewable energy property prediction methods and discuss AI and IoT prediction models for biomass thermal properties. Covers renewable energy sector fundamentals; Explains the application of big data in distributed energy domains; Discusses AI and IoT prediction methods and models.
Sustainable Development in the Digital Era
Collaborative manufacturing and management are a fundamental requirement in today’s digital age, to enable an innovative and sustainable development of companies. This is possible and highly recommended not just in the context of traditional companies, but further in cyber physical systems and in the context of extended, distributed, networked and virtual organizations. The book provides fundamental methodologies, models, methods, tools, and platforms about collaborative engineering, to support manufacturing and management processes and practices, aligned with the current requirements underlying Industry 4.0, and Society 5.0. It describes the application of collaborative management paradigms, about dynamic, distributed, integrated, intelligent, predictive, parallel, and real-time based approaches and tools to enable collaborating entities, including suppliers, business partners and other stakeholders, to develop projects and solve problems that are becoming increasingly more complex and challenging currently. Such collaborative processes and practices require companies and underlying stakeholders to be connected, and to further communicate, and share data, problems, and expertise, and other kind of resources, along with concerns, difficulties, and challenges, requiring co-learning, and the co-creation of knowledge, processes, methods, and systems to interactively support projects and problem solving. The book reviews the advances and provides case studies to assist scientists, practitioners and students in high standard manufacturing management processes and practices, to properly handle daily problems and challenges, with a special focus on the use of recent paradigms and tools to support manufacturing management decision making, through innovative methodologies and approaches for permitting researchers to learn, develop further work, and become advanced practitioners and promoters of collaborative management.