Industry 4.0 and Machine Learning
Concepts, Strategies, and Innovations for Modern Manufacturing
Will be published approx. on 24. September 2026
432 pages
E-Book
978-1-040-56711-1 (ISBN)
Description
This book explores how machine learning algorithms can be applied to Industry 4.0 technologies to enhance their capabilities. It discusses how machine learning can be used for predictive maintenance in smart factories, optimizing supply chain management, and improving quality control through advanced data analytics and also includes:
Presents advanced machine learning techniques such as deep learning, reinforcement learning, and ensemble methods specifically tailored for Industry 4.0 applications.
Explores the integration of machine learning with other Industry 4.0 technologies such as the Internet of Things, big data analytics, cyber-physical systems, and cloud computing.
Showcases in-depth case studies and real-world examples from various industrial sectors that illustrate successful implementations of machine learning in Industry 4.0.
Addresses key challenges faced in implementing Industry 4.0 technologies, such as data integration, interoperability, cybersecurity, and scalability.
Discusses artificial intelligence-driven automation, digital twins, autonomous systems, and the implications of these technologies for the future of manufacturing and industrial engineering.
The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communication engineering, computer science and engineering, manufacturing engineering, and industrial engineering.
Presents advanced machine learning techniques such as deep learning, reinforcement learning, and ensemble methods specifically tailored for Industry 4.0 applications.
Explores the integration of machine learning with other Industry 4.0 technologies such as the Internet of Things, big data analytics, cyber-physical systems, and cloud computing.
Showcases in-depth case studies and real-world examples from various industrial sectors that illustrate successful implementations of machine learning in Industry 4.0.
Addresses key challenges faced in implementing Industry 4.0 technologies, such as data integration, interoperability, cybersecurity, and scalability.
Discusses artificial intelligence-driven automation, digital twins, autonomous systems, and the implications of these technologies for the future of manufacturing and industrial engineering.
The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communication engineering, computer science and engineering, manufacturing engineering, and industrial engineering.
More details
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Illustrations
106 Tables, black and white; 152 Line drawings, black and white; 53 Halftones, black and white; 205 Illustrations, black and white
ISBN-13
978-1-040-56711-1 (9781040567111)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Vivek Singh Kushwah | Ashish Bagwari | K. Vasanth
Industry 4.0 and Machine Learning
Concepts, Strategies, and Innovations for Modern Manufacturing
Book
approx. 09/2026
1st Edition
CRC Press
€216.50
Not yet published
Persons
Vivek Singh Kushwah is presently working as Professor in the Department of Electronics and Communications Engineering, at Chaitanya Bharathi Institute of Technology affiliated to Osmania University, Hyderabad, India. He is a senior member of the Institute of Electrical and Electronics Engineers and a fellow of IETE. His areas of research interest include microwave engineering, microstrip antennas, and microstrip filters. He has published more than sixty research papers in journals of national and international reputation. He has also published more than twenty papers in conferences.
Ashish Bagwari is the head of the Electronics and Communications Engineering Department at the Women Institute of Technology Dehradun, India. His areas of research interest include cognitive radio networks, mobile communication, sensor networks, wireless networks, analog communication, wireless and 5G communication, digital communication, and mobile ad-hoc networks. He has published more than one hundred and twenty research papers in journals and conferences of national and international reputation.
K. Vasanth serves as Associate professor and head in the Department of Electronics and Communications Engineering, at Chaitanya Bharathi Institute of Technology affiliated to Osmania University, Hyderabad, India. His areas of research interest include signal processing, image processing, video processing, and VLSI signal processing. He has published more than a hundred research papers in journals and conferences of national and international reputation.
Jorge Luis Victoria Barbosa serves as full Professor of applied computing at the University of Vale do Rio dos Sinos (UNISINOS), head of the university's Mobile Computing Lab, and a researcher at the Brazilian Council for Scientific and Technological Development. His main research interest is context prediction using context histories, mainly through similarity and pattern analysis. He has published research papers in journals and conferences of national and international repute.
Jose Alfredo Herrera Quispe serves as the Principal professor, and Director of the School of Computer Science, at National Major University of San Marcos, Peru. He is part of the professionalization program at MIT (Massachusetts Institute of Technology - USA), where he completed a ""Master's in Innovation & Design Thinking"". His lines of research are Artificial Intelligence, Data Mining, and Computing applied to the environment. He has published research papers in journals and conferences of national and international repute.
Ashish Bagwari is the head of the Electronics and Communications Engineering Department at the Women Institute of Technology Dehradun, India. His areas of research interest include cognitive radio networks, mobile communication, sensor networks, wireless networks, analog communication, wireless and 5G communication, digital communication, and mobile ad-hoc networks. He has published more than one hundred and twenty research papers in journals and conferences of national and international reputation.
K. Vasanth serves as Associate professor and head in the Department of Electronics and Communications Engineering, at Chaitanya Bharathi Institute of Technology affiliated to Osmania University, Hyderabad, India. His areas of research interest include signal processing, image processing, video processing, and VLSI signal processing. He has published more than a hundred research papers in journals and conferences of national and international reputation.
Jorge Luis Victoria Barbosa serves as full Professor of applied computing at the University of Vale do Rio dos Sinos (UNISINOS), head of the university's Mobile Computing Lab, and a researcher at the Brazilian Council for Scientific and Technological Development. His main research interest is context prediction using context histories, mainly through similarity and pattern analysis. He has published research papers in journals and conferences of national and international repute.
Jose Alfredo Herrera Quispe serves as the Principal professor, and Director of the School of Computer Science, at National Major University of San Marcos, Peru. He is part of the professionalization program at MIT (Massachusetts Institute of Technology - USA), where he completed a ""Master's in Innovation & Design Thinking"". His lines of research are Artificial Intelligence, Data Mining, and Computing applied to the environment. He has published research papers in journals and conferences of national and international repute.
Content
1. A Contactless Health-Monitoring System Using Deep Learning Models. 2. Deep Learning Techniques and Models to Predict Alzheimer's Disease: A Review. 3. Late Fusion-Assisted Multi-Modal Emotion Recognition from Audio-Visual Data Using Machine Learning Techniques. 4. Research on Optimal Scheduling of Multi-Energy Microgrids Based on a Modified Particle Swarm Optimization Algorithm. 5. Real-Time Implementation of Scale-Invariant Feature Transform (SIFT) Algorithm-Based Video Stitching Technique to Assist Car Drivers. 6. AI-Driven Workforce Optimization to Improve Productivity and Decision-Making in Industry 4.0. 7. Smart Farming with an Intelligent Pesticide and Fertilizer Recommendation System Based on TPF-CNN. 8. Cloud-based Integrated Air and Water Quality Monitoring System. 9. Gesture-Controlled Smart Home Interface with Real-Time Hand Tracking. 10. Integrating AI and Cobots: Bridging Human and Machine Collaboration in Industry 5.0. 11. Innovative Engineering of a Precision-Controlled Thermal Processing System for Cardiac Stimulation Conductor Insulation Manufacturing. 12. Optimization of Material Processing and Data Collection for 3D Construction Printing. 13. Predictive Maintenance in Industry 4.0 with AI-Driven Monitoring Systems. 14. Robotics and Autonomous Systems: AI-Driven Innovation in Industry. 15. AI-Powered Sign Language to Speech Synthesis with a Robotic Avatar. 16. Machine-Learning-Based Approaches for Predicting Child Mortality. 17. Sign Sense: Real-Time Image Categorization and Object Detection: A Review. 18. AI-Driven Motion Planning for Efficient Oil Palm Harvesting with Robotic Manipulators. 19. Enhancing Energy Efficiency in Manufacturing with AI and IoT-Enabled Smart Factories. 20. Data-Driven Railway Safety: Deep Learning Models for Automated Wheel Fault Detection and Object Segmentation. 21. Comparative Analysis of Fuzzy Logic and Machine Learning in Human-Robot Interaction. 22. Machine Learning Models for Crop Management and Disease Detection. 23. Comparative Performance Analysis of Machine Learning Algorithms for Detecting Specific Pollutants in Air. 24. Dimensional Fit-to-Size Adaptive Automated Packaging System. 25. Fork Spy: Real-Time Monitoring and Developer Engagement in GitHub Forks. 26. Energy-Efficient Automation Platform for Industry 4.0 Based on an AI Algorithm.
