Metaheuristic Algorithms in Industry 4.0
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Metaheuristic Algorithms in Industry 4.0: (Advances in Metaheuristics)

Metaheuristic Algorithms in Industry 4.0: (Advances in Metaheuristics)

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About the Book

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.

Table of Contents:
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


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Product Details
  • ISBN-13: 9781000435948
  • Publisher: Taylor & Francis Ltd
  • Publisher Imprint: CRC Press
  • Language: English
  • ISBN-10: 1000435946
  • Publisher Date: 28 Sep 2021
  • Binding: Digital (delivered electronically)
  • Series Title: Advances in Metaheuristics


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