Metaheuristic Algorithms in Industry 4.0
- Available for pre-order. Item will ship after September 20, 2021
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.
- 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.
Table of Contents
About the Editors
List of Contributors
A Review on Cyber Physical Systems and Smart Computing: Bibliometric Analysis
Deepak Sharma, Prashant K. Gupta, Javier Andreu-Perez
Design Optimization of Close-Fitting Free-Standing Acoustic Enclosure Using Jaya Algorithm
Ashish Khachane and Vijaykumar Jatti
A metaheuristic scheme for secure control of cyber-physical systems
Application of Salp Swarm Algorithm to Solve Constrained Optimization Problems with Dynamic Penalty Approach in Real Life Problems
Omkar Kulkarni1, G. M. Kakandikar, V. M. Nandedkar
Optimization of Robot Path Planning Using Advanced Optimization Techniques
R. V. Rao, S. Patel
Semi-Empirical Modeling and JAYA Optimization of White Layer Thickness during Electrical Discharge Machining of NiTi Alloy
Mahendra Uttam Gaikwad, Krishnamoorthy A, Vijaykumar S Jatti
Analysis of convolutional neural network architectures and their applications in industry 4.0
Gaurav Bansod, Shardul Khandekar, Soumya Khurana
EMD Based triaging of Pulmonary Diseases Using Chest Radiographs (X-Rays)
Niranjan Chavan, Priya Ranjan, Uday Kumar, Kumar Dron Shrivastav and Rajiv Janardhanan
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, Keshav N. Nandurkar
A Metaheuristic Optimization Algorithm Based Speed Controller for Brushless DC Motor: Industrial Case Studies
K.Vanchinathan, P. Sathiskumar and N.Selvaganesan
Predictive Analysis of Cellular Networks: A Survey
Nilakshee Rajule, Radhika Menon, Anju Kulkarni
Optimization Techniques and Algorithms for Dental Implants: A Comprehensive Review
Niharika Karnika, Pankaj Dhatraka
Pritesh Shah is an Associate Professor at the Symbiosis Institute of Technology, Symbiosis International (Deemed University), India
Ravi Sekhar is an Associate 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 Créteil, where he leads the Image and Signal Processing team in the Laboratoire Images, Signaux et Systèmes Intelligents (LiSSi).