This book discusses the use of machine vision and technologies in specific engineering case studies and focuses on how machine vision techniques are impacting every step of industrial processes and how smart sensors and cognitive big data analytics are supporting the automation processes in Industry 4.0 applications.
Industry 4.0, the Fourth Industrial Revolution, combines traditional manufacturing with automation and data exchange. Machine vision is used in the industry for reliable product inspections, quality control, and data capture solutions. It combines different technologies to provide important information from the acquisition and analysis of images for robot-based inspection and guidance.
Features
- Presents a comprehensive guide on how to use machine vision for Industry 4.0 applications, such as analysis of images for automated inspections, object detection, object tracking, and more
- Includes case studies of Robotics Internet of Things with its current and future applications in healthcare, agriculture, and transportation
- Highlights the inclusion of impaired people in the industry, for example, an intelligent assistant that helps deaf-mute individuals to transmit instructions and warnings in a manufacturing process
- Examines the significant technological advancements in machine vision for Industrial Internet of Things and explores the commercial benefits using real-world applications from healthcare to transportation
Discusses a conceptual framework of machine vision for various industrial applications
The book addresses scientific aspects for a wider audience such as senior and junior engineers, undergraduate and postgraduate students, researchers, and anyone interested in the trends, development, and opportunities for machine vision for Industry 4.0 applications.
This book discusses the use of machine vision and technologies in specific engineering case studies and focuses on how machine vision techniques are impacting every step of industrial processes and how smart sensors and cognitive big data analytics are supporting the automation processes in Industry 4.0 applications.
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
Dr. Roshani Raut is an Associate Professor in the Department of Information Technology and also handling the charge of Associate Dean International Relations at Pimpri Chinchwad College of Engineering, Pune, India. She completed her doctorate in computer science and engineering. She has more than 17 years of experience and is a member of ACM and ISTE. She has availed the research and workshop grants from BCUD, Pune University. She has presented more than 70 research communications in national and international conferences and journals and published 15 patents. She has been invited as a TPC member and session chair for various national and international conferences. She has served as editor for various books of IGI Global, CRC Press/Taylor & Francis, and Scrivener Wiley, etc. Her research area includes Artificial Intelligence, Machine Learning, Data Mining, Deep Learning, Internet of Things, and so on. Her details are available on https://roshaniraut531.wixsite.com/roshaniraut/
Dr. Salah-ddine Krit is an Associate Professor at the Polydisciplinary Faculty of Ouarzazate, Ibn Zohr University Agadir Morocco, He is currently the Director of Engineering Science and Energies Laboratory and the Chief of the Department of Mathematics, Informatics and Management. He received his PhD in software engineering from Sidi Mohammed Ben Abdellah University, Fez, Morroco. He worked as an Engineer Team Leader in audio and power management integrated circuits research, design, simulation and layout of analog and digital blocks dedicated for mobile and satellite communication systems. He has authored and co-authored over 130 journal articles, proceedings, and book chapters published by reputable publishers.
Dr. Prasenjit Chatterjee is an Associate Professor of Mechanical Engineering Department at the MCKV Institute of Engineering, India. He has published over 80 research papers in various international journals and has received numerous awards including Outstanding Researcher Award and University Gold Medal. He has been the guest editor of several special issues and has edited and authored several books on decision-making approaches and sustainability. He is the Lead Series Editor of International Perspectives on Decision Analysis and Operations Research, Emerald Group Publishing. Dr. Chatterjee is one of the developers of a new data-driven multiple-criteria decision-making method called Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS).
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