Metaheuristic Algorithms in Industry 4.0 provides a guiding light to engineers, researchers, students, faculty and other professionals engaged in exploring and implementing industry 4.0 solutions in various systems and processes.
Due to increasing industry 4.0 practices, massive industrial process data is now available for researchers for modelling and optimization. Artificial Intelligence methods can be applied to the ever-increasing process data to achieve robust control against foreseen and unforeseen system fluctuations. Smart computing techniques, machine learning, deep learning, computer vision, for example, will be inseparable from the highly automated factories of tomorrow. Effective cybersecurity will be a must for all Internet of Things (IoT) enabled work and office spaces.
This book addresses metaheuristics in all aspects of Industry 4.0. It covers metaheuristic applications in IoT, cyber physical systems, control systems, smart computing, artificial intelligence, sensor networks, robotics, cybersecurity, smart factory, predictive analytics and more.
Key features:
- Includes industrial case studies.
- Includes chapters on cyber physical systems, machine learning, deep learning, cybersecurity, robotics, smart manufacturing and predictive analytics.
- surveys current trends and challenges in metaheuristics and industry 4.0.
Metaheuristic Algorithms in Industry 4.0 provides a guiding light to engineers, researchers, students, faculty and other professionals engaged in exploring and implementing industry 4.0 solutions in various systems and processes.
Contents
Preface
Editors
Contributors
1. A Review on Cyber Physical Systems and Smart Computing: Bibliometric
Analysis
Deepak Sharma, Prashant K. Gupta, and Javier Andreu-Perez
2. Design Optimization of Close-Fitting Free-Standing Acoustic Enclosure
Using Jaya Algorithm
Ashish Khachane and Vijaykumar S. Jatti
3. A Metaheuristic Scheme for Secure Control of Cyber-Physical Systems
Tua A. Tamba
4. Application of Salp Swarm Algorithm to Solve Constrained Optimization
Problems with Dynamic Penalty Approach in Real-Life Problems
Omkar Kulkarni, G. M. Kakandikar, and V. M. Nandedkar
5. Optimization of Robot Path Planning Using Advanced Optimization
Techniques
R. V. Rao and S. Patel
6. Semi-Empirical Modeling and Jaya Optimization of White Layer Thickness
during Electrical Discharge Machining of NiTi Alloy
Mahendra Uttam Gaikwad, A. Krishnamoorthy, and Vijaykumar S. Jatti
7. Analysis of Convolution Neural Network Architectures and
Their Applications in Industry 4.0
Gaurav Bansod, Shardul Khandekar, and Soumya Khurana
8. EMD-Based Triaging of Pulmonary Diseases Using Chest Radiographs (X-Rays)
Niranjan Chavan, Priya Ranjan, Uday Kumar, Kumar Dron Shrivastav, and Rajiv
Janardhanan
9. Adaptive Neuro Fuzzy Inference System to Predict Material Removal Rate
during Cryo-Treated Electric Discharge Machining
Vaibhav S. Gaikwad, Vijaykumar S. Jatti, Satish S. Chinchanikar, and Keshav
N. Nandurkar
10. A Metaheuristic Optimization Algorithm-Based Speed Controller for
Brushless DC Motor: Industrial Case Study
K. Vanchinathan, P. Sathiskumar, and N. Selvaganesan
11. Predictive Analysis of Cellular Networks: A Survey
Nilakshee Rajule, Radhika Menon, and Anju Kulkarni
12. Optimization Techniques and Algorithms for Dental Implants A
Comprehensive Review
Niharika Karnik and Pankaj Dhatrak
Index
Pritesh Shah is an Associate Professor at the Symbiosis Institute of Technology, Symbiosis International (Deemed University), India.
Ravi Sekhar is an Assistant Professor at the Symbiosis Institute of Technology, Symbiosis International (Deemed University), India.
Anand J. Kulkarni is an Associate Professor at the Symbiosis Center for Research and Innovation, Symbiosis International (Deemed University), India.
Patrick Siarry is a Professor of Automatics and Informatics at the University of Paris-Est Creteil, where he leads the Image and Signal Processing team in the Laboratoire Images, Signaux et Systemes Intelligents (LiSSi).
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