Muutke küpsiste eelistusi

E-raamat: Metaheuristic Algorithms in Industry 4.0 [Taylor & Francis e-raamat]

Edited by (Universite Paris-Est Creteil, France), Edited by , Edited by , Edited by (MIT World Peace University, Pune, India)
  • Formaat: 286 pages, 76 Tables, black and white; 65 Line drawings, color; 42 Line drawings, black and white; 3 Halftones, color; 6 Halftones, black and white; 68 Illustrations, color; 48 Illustrations, black and white
  • Sari: Advances in Metaheuristics
  • Ilmumisaeg: 29-Sep-2021
  • Kirjastus: CRC Press
  • ISBN-13: 9781003143505
Teised raamatud teemal:
  • Taylor & Francis e-raamat
  • Hind: 156,95 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Tavahind: 224,21 €
  • Säästad 30%
Metaheuristic Algorithms in Industry 4.0Suurem pilt
  • Formaat: 286 pages, 76 Tables, black and white; 65 Line drawings, color; 42 Line drawings, black and white; 3 Halftones, color; 6 Halftones, black and white; 68 Illustrations, color; 48 Illustrations, black and white
  • Sari: Advances in Metaheuristics
  • Ilmumisaeg: 29-Sep-2021
  • Kirjastus: CRC Press
  • ISBN-13: 9781003143505
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9781003143505
"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. 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.



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.

Preface vii
Editors xi
Contributors xiii
1 A Review on Cyber Physical Systems and Smart Computing: Bibliometric Analysis
1(32)
Deepak Sharma
Prashant K. Gupta
Javier Andreu-Perez
2 Design Optimization of Close-Fitting Free-Standing Acoustic Enclosure Using Jaya Algorithm
33(14)
Ashish Khachane
Vijaykumar S. Jatti
3 A Metaheuristic Scheme for Secure Control of Cyber-Physical Systems
47(26)
Tua A. Jamba
4 Application of Salp Swarm Algorithm to Solve Constrained Optimization Problems with Dynamic Penalty Approach in Real-Life Problems
73(10)
Omkar Kulkarni
G. M. Kakandikar
V. M. Nandedkar
5 Optimization of Robot Path Planning Using Advanced Optimization Techniques
83(44)
R. V. Rao
S. Patel
6 Semi-Empirical Modeling and Jaya Optimization of White Layer Thickness during Electrical Discharge Machining of NiTi Alloy
127(12)
Mahendra Uttam Gaikwad
A. Krishnamoorthy
Vijaykumar S. Jatti
7 Analysis of Convolution Neural Network Architectures and Their Applications in Industry 4.0
139(24)
Gaurav Bansod
Shardul Khandekar
Soumya Khurana
8 EMD-Based Triaging of Pulmonary Diseases Using Chest Radiographs (X-Rays)
163(18)
Niranjan Chavan
Priya Ranjan
Uday Kumar
Kumar Dron Shrivastav
Rajiv Janardhanan
9 Adaptive Neuro Fuzzy Inference System to Predict Material Removal Rate during Cryo-Treated Electric Discharge Machining
181(8)
Vaibhav S. Gaikwad
Vijaykumar S. Jatti
Satish S. Chinchanikar
Keshav N. Nandurkar
10 A Metaheuristic Optimization Algorithm-Based Speed Controller for Brushless DC Motor: Industrial Case Study
189(28)
K. Vanchinathan
P. Sathiskumar
N. Selvaganesan
11 Predictive Analysis of Cellular Networks: A Survey
217(44)
Nilakshee Rajule
Radhika Menon
Anju Kulkarni
12 Optimization Techniques and Algorithms for Dental Implants -- A Comprehensive Review
261(22)
Niharika Karnik
Pankaj Dhatrak
Index 283
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).
Püsilink: https://www.kriso.ee/db/9781003143505_pe.html
Märksõnad: