Main description:

Provides a comprehensive guide about how to use machine vision for Industry 4.0 applications like analysis of images for automated inspections, object detection, object tracking etc.

Includes case studies of Robotics Internet of Things with its current and future applications in Healthcare, Agriculture, Transportation, etc.

It highlights the inclusion of impaired people in industry, like intelligent assistant that helps deaf-mute people to transmit instructions and warnings in a manufacturing process.

It examines the significant technological advancements in machine vision for industrial Internet of things and explores the commercial benefits using the real world applications from healthcare to transportation.

Provides a conceptual framework of Machine vision for the various Industrial applications.

Addresses scientific aspects for a wider audience such as senior and junior engineers, undergraduate and post-graduate students, researchers, and anyone else interested in the trends, development, and opportunities for the Machine Vision for Industry 4.0 applications.

Contents:

Chapter 1. Challenges in Industry 4.0 for Machine Vision: A Conceptual Framework, A Review, and Numerous case studies, Chapter 2. Practical issues in robotics internet of things, Chapter 3. Role of sensing techniques in precision agriculture, Chapter 4. Perspectives on Deep Learning Techniques for Industrial IoT, Chapter 5. Missing person locator and identifier using artificial intelligence and supercomputing techniques proposal, Chapter 6. Inclusion of Impaired People in Industry 4.0: An Approach to Recognize Orders of Deaf-mute Supervisors through an Intelligent Sign Language Recognition System, Chapter 7. A Deep Learning Approach to Classify the Causes of Depression from Reddit Posts, Chapter 8. Psychiatric ChatBOT for COVID-19 using Machine Learning Approaches, Chapter 9. An Analysis of Drug - Drug Interaction (DI) using Machine Learning Techniques in Drug Development Process, Chapter 10. Image Processing based Fire Detection using IOT devices, Chapter 11. Crowd Estimation in Train by using Machine Vision, Chapter 12. Analysis of Machine Learning Algorithm to predict Wine Quality, Chapter 13. Machine Vision in Industry 4.0: Applications, Challenges and Future Directions, Chapter 14. Industry 5.0: The Integration of Modern Technologies