Industry 4.0 and Machine Learning

Concepts, Strategies, and Innovations for Modern Manufacturing

Vivek Singh Kushwah, Ashish Bagwari, K. Vasanth, Jorge Luis Barbosa, Jose Alfredo Quispe, Jorge Luis Victória Barbosa, Jose Alfredo Herrera Quispe

• Publisher: CRC Press
• Year: 2026
• Print ISBN:
  9781041100881
• Edition: 1
• eBook ISBN:
  9781040622735
• Topic: Technology & Engineering
• Subtopic: Computer Engineering
• Index: Technology & Engineering

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:

• 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.

Table of contents

• Cover
• Half-Title Page
• Title Page
• Copyright Page
• Table of Contents
• About the authors
• Contributors
• Preface
• Chapter 1 A contactless health-monitoring system using deep learning models
• Chapter 2 Deep learning techniques and models to predict Alzheimer’s disease: A review
• Chapter 3 Late fusion-assisted multimodal emotion recognition from audio-visual data using machine learning techniques
• Chapter 4 Research on optimal scheduling of multi-energy microgrids based on a modified particle swarm optimization algorithm
• Chapter 5 Real-time implementation of the scale-invariant feature transform (SIFT) algorithm-based video stitching technique to assist car drivers
• Chapter 6 AI-driven workforce optimization to improve productivity and decision-making in industry 4.0
• Chapter 7 Smart farming with an intelligent pesticide and fertilizer recommendation system based on TPF-CNN
• Chapter 8 Cloud-based integrated air and water quality monitoring system
• Chapter 9 Integrating AI and cobots: Bridging human and machine collaboration in industry 5.0
• Chapter 10 Innovative engineering of a precision-controlled thermal processing system for cardiac stimulation conductor insulation manufacturing
• Chapter 11 Predictive maintenance in industry 4.0 with AI-driven monitoring systems
• Chapter 12 Robotics and autonomous systems: AI-driven innovation in industry
• Chapter 13 AI-powered sign language to speech synthesis with a robotic avatar
• Chapter 14 Machine-learning-based approaches for predicting child mortality
• Chapter 15 Sign sense: Real-time image categorization and object detection: a review
• Chapter 16 AI-driven motion planning for efficient oil palm harvesting with robotic manipulators
• Chapter 17 Enhancing energy efficiency in manufacturing with AI and IoT-enabled smart factories
• Chapter 18 Data-driven railway safety: Deep learning models for automated wheel fault detection and object segmentation
• Chapter 19 Comparative analysis of fuzzy logic and machine learning in human–robot interaction
• Chapter 20 Machine learning models for crop management and disease detection
• Chapter 21 Comparative performance analysis of machine learning algorithms for detecting specific pollutants in air
• Chapter 22 Dimensional fit-to-size adaptive automated packaging system
• Chapter 23 Fork spy: Real-time monitoring and developer engagement in GitHub forks
• Chapter 24 Energy-efficient automation platform for industry 4.0 based on an AI algorithm
• Index