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Computational tools for sustainable industrial transformation

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Computational tools for sustainable industrial transformation edited by Ahmad H. Sabry, Nasri Sulaiman, Bashra Kadhim.
其他作者:	Sabry, Ahmad H.
出版者:	Singapore :Springer Nature Singapore :2025.
面頁冊數:	xxii, 263 p. :ill. (chiefly col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Computational intelligence.
電子資源:	https://doi.org/10.1007/978-981-95-0500-5
ISBN:	9789819505005$q(electronic bk.)

Computational tools for sustainable industrial transformation
Computational tools for sustainable industrial transformation [electronic resource] /edited by Ahmad H. Sabry, Nasri Sulaiman, Bashra Kadhim. - Singapore :Springer Nature Singapore :2025. - xxii, 263 p. :ill. (chiefly col.), digital ;24 cm. - Science for sustainable societies,2197-7356. - Science for sustainable societies..

Chapter 1. Advanced AI and Digital Twin Solutions: WOA, BERT, ST-GCN, PSO- Enhanced IoT Cybersecurity and Transformation -- Chapter 2. AI-Powered Blockchain and IoT Frameworks: Integrating DAG, LPWAN, SPNs, PSNR, and ECC for Smart Environmental Solutions -- Chapter 3. AI-Powered Multi-Scale Analysis of Urban Green Spaces Using OBIA, LULC Mapping, and Ecosystem Valuation for Human Settlement Sustainability -- Chapter 4. Industrial Robotics in Smart Manufacturing: Integrating PLM, AMRs, GNNs, CPS, and Energy-Efficient Systems -- Chapter 5. Revolutionizing Industrial Automation: Blockchain-IoT Convergence with Secure Data Sharing and Real-Time Monitoring for Smart Systems -- Chapter 6. Revolutionizing Urban Development in Smart Cities with Advanced Digital Twins: Integrating IoT, Multi-Model Simulations, and Geospatial Analytics -- Chapter 7. Running Industrial Workflow Applications in a Software-Defined Multi-Cloud Environment Using Neural Networks, MAS, PSO, MILP, and Game-Theoretic Models for Green Energy-Aware Scheduling -- Chapter 8. Transforming Urban Landscapes with AI: Utilizing Reforestation Drones, Ocean Cleanup Robotics, Predictive Climate Modelling, and Green Infrastructure to Build Resilient and Sustainable Cities of Tomorrow.

This book discusses how computational tools are revolutionizing sustainable industrial transformation. By integrating advanced technologies such as big data analytics, machine learning, digital twins, and IoT, this volume provides a comprehensive guide to optimizing industrial processes for enhanced efficiency and reduced environmental impact. The chapters cover critical topics including the principles of industrial efficiency, the application of digital twins in manufacturing, and the application of machine learning and AI for process optimization and predictive maintenance. Readers will also explore the benefits of big data analytics in monitoring sustainability metrics and the role of IoT in smart sensor networks. Through real-world case studies and expert contributions, this book offers actionable insights into how computational tools can revolutionize industrial practices. The material presented significantly advances sustainability science by addressing key challenges and opportunities in the transition towards smart and sustainable societies. Through the integration of computational methods with industrial transformation, the book offers innovative solutions to pressing sustainability issues such as resource depletion, environmental degradation, and social inequality. Designed for industrial engineers, managers, and academics across disciplines such as engineering, environmental science, and business management, this book offers practical guidance on implementing computational techniques to optimize processes and reduce environmental impact. It invites readers to think through critical questions about sustainable practices and provides actionable insights that can be directly applied within industrial settings. By bridging theoretical knowledge with practical application, this book serves as an essential resource for professionals seeking to drive sustainable change in industry.

ISBN: 9789819505005$q(electronic bk.)

Standard No.: 10.1007/978-981-95-0500-5doiSubjects--Topical Terms:

210824
Computational intelligence.

LC Class. No.: Q342 / .C66 2025

Dewey Class. No.: 006.3

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505 0 $a Chapter 1. Advanced AI and Digital Twin Solutions: WOA, BERT, ST-GCN, PSO- Enhanced IoT Cybersecurity and Transformation -- Chapter 2. AI-Powered Blockchain and IoT Frameworks: Integrating DAG, LPWAN, SPNs, PSNR, and ECC for Smart Environmental Solutions -- Chapter 3. AI-Powered Multi-Scale Analysis of Urban Green Spaces Using OBIA, LULC Mapping, and Ecosystem Valuation for Human Settlement Sustainability -- Chapter 4. Industrial Robotics in Smart Manufacturing: Integrating PLM, AMRs, GNNs, CPS, and Energy-Efficient Systems -- Chapter 5. Revolutionizing Industrial Automation: Blockchain-IoT Convergence with Secure Data Sharing and Real-Time Monitoring for Smart Systems -- Chapter 6. Revolutionizing Urban Development in Smart Cities with Advanced Digital Twins: Integrating IoT, Multi-Model Simulations, and Geospatial Analytics -- Chapter 7. Running Industrial Workflow Applications in a Software-Defined Multi-Cloud Environment Using Neural Networks, MAS, PSO, MILP, and Game-Theoretic Models for Green Energy-Aware Scheduling -- Chapter 8. Transforming Urban Landscapes with AI: Utilizing Reforestation Drones, Ocean Cleanup Robotics, Predictive Climate Modelling, and Green Infrastructure to Build Resilient and Sustainable Cities of Tomorrow.
520 $a This book discusses how computational tools are revolutionizing sustainable industrial transformation. By integrating advanced technologies such as big data analytics, machine learning, digital twins, and IoT, this volume provides a comprehensive guide to optimizing industrial processes for enhanced efficiency and reduced environmental impact. The chapters cover critical topics including the principles of industrial efficiency, the application of digital twins in manufacturing, and the application of machine learning and AI for process optimization and predictive maintenance. Readers will also explore the benefits of big data analytics in monitoring sustainability metrics and the role of IoT in smart sensor networks. Through real-world case studies and expert contributions, this book offers actionable insights into how computational tools can revolutionize industrial practices. The material presented significantly advances sustainability science by addressing key challenges and opportunities in the transition towards smart and sustainable societies. Through the integration of computational methods with industrial transformation, the book offers innovative solutions to pressing sustainability issues such as resource depletion, environmental degradation, and social inequality. Designed for industrial engineers, managers, and academics across disciplines such as engineering, environmental science, and business management, this book offers practical guidance on implementing computational techniques to optimize processes and reduce environmental impact. It invites readers to think through critical questions about sustainable practices and provides actionable insights that can be directly applied within industrial settings. By bridging theoretical knowledge with practical application, this book serves as an essential resource for professionals seeking to drive sustainable change in industry.
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