Evolution and Advances in Computing Technologies for Industry 6.0: Technology, Practices, and Challenges

This book provides a comprehensive exploration of Industry 6.0, which marks the convergence of intelligent systems, machine learning (ML), deep learning, and human–robot collaboration (HRC) in various sectors. It focuses on how these technologies enable businesses to harness insights from vast datasets, optimize operations, forecast maintenance requirements, and mitigate outages. In this comprehensive book, the authors cover the major aspects of Industry 6.0, including the latest advances in technology, new applications, and the many challenges that accompany the process that is changing it. This book acts as a compass, guiding readers through the labyrinth of Industry 6.0, revealing the complex interactions between technologies, new applications, innovations, and business ecosystems. From a multidisciplinary perspective, the book explores the far-reaching impact of Industry 6.0 on the business world, revealing its potential to increase productivity, improve decision-making, and open new opportunities for development. Discusses the transformative impact of Industry 6.0 driven by the convergence of artificial intelligence (AI), ML, and the industrial internet of things (IIoT) in manufacturing Explores the transformative potential of HRC in modern manufacturing, emphasizing the ability of HRC to boost productivity, flexibility, and safety Compares several service models in cloud computing, with a focus on private, public, and community cloud deployment methods Presents information about autism spectrum disorder (ASD) in children and the advanced technology of convolutional neural networks used to detect autism in children Describes the accurate diagnosis of Alzheimer’s disease through integrating state-of-the-art technologies, especially deep learning Focuses on digital adaptation in India’s agricultural sector, especially Uttar Pradesh, highlighting challenges and prospects Presents a visionary leap into Agriculture 6.0, where technology and tradition converge for sustainable farming solutions Provides the mitigation strategies for climate change over multiple locations Explores the transformative impact of recent computational intelligence advancements in vehicle surveillance and recognition, particularly through CCTV analysis Focuses on the exploration and development of a supply chain network design (SCND) model of milk products This book will be of interest to researchers, academics, practitioners, technology providers, undergraduate and postgraduate students, scholars, consultants, advisors, and doctors. It can be used by undergraduate and postgraduate students pursuing Industry 6.0 programs in computer science and engineering across the globe.

Table of Contents:
1. Empowering the Factory of the Future: Integrating Artificial Intelligence, Machine Learning, and IoT Innovations in Industry 6.0. 2. Human–Robot Collaboration Analyzing the Challenges and Opportunities of Integrating Soft Computing Algorithms in Manufacturing Environments. 3. An Overview of Cloud Computing: Paradigm, Technologies, and Application. 4. Autism Spectrum Disorder Classification in Children Using Deep Learning Approaches in Healthcare 6.0. 5. Exploring Neurodegenerative Progression: Healthcare 6.0 Insights Using Sustainable Convolutional Neural Networks. 6. Digital Adaptation in Agricultural Sector: Challenges and Prospects in Uttar Pradesh, India. 7. Next‑Gen Agriculture 6.0: A Smart Solution Pioneering the Future of Farming. 8. Review of Study of Biochemical Water Parameters for Prediction and Analysis of Quality of Water Using Machine Learning Techniques in Industry 6.0. 9. Climate Change Mitigation Strategies Using Convolutional Neural Networks over Multiple Locations. 10. Driving Sustainable Practices in Industry 6.0: A Study on Article Detection with Fuzzy‑Wuzzy and Supervised Machine Learning Algorithms. 11. Evolution of Computational Intelligence for Vehicle Surveillance and Recognition in Industry 6.0: Recent Innovations, Practices, and Challenges. 12. Piecewise Mixed‑Integer Non‑Linear Optimization for Supply Chain Network Design (SCND) Model: Confirmation through Cluster Analysis. 13. Integrated Multi‑Item Multi‑Echelon Supply Chain Model: A Mixed‑Integer Heuristic Approach.