Smart Systems for Industrial Applications (Artificial Intelligence and Soft Computing for Industrial Transformation)

399 Pages·2022·44.21 MB·English

by  N. Rengarajan (editor), C. Venkatesh (editor), P. Ponmurugan (editor), S. Balamurugan (editor)

Smart Systems for
Industrial Applications
Scrivener Publishing
100 Cummings Center, Suite 541J
Beverly, MA 01915-6106
Artificial Intelligence and Soft Computing for Industrial Transformation
Series Editor: Dr S. Balamurugan ([email protected])
Scope: Artificial Intelligence and Soft Computing Techniques play an impeccable role in industrial
transformation. The topics to be covered in this book series include Artificial Intelligence, Machine Learning,
Deep Learning, Neural Networks, Fuzzy Logic, Genetic Algorithms, Particle Swarm Optimization,
Evolutionary Algorithms, Nature Inspired Algorithms, Simulated Annealing, Metaheuristics, Cuckoo
Search, Firefly Optimization, Bio-inspired Algorithms, Ant Colony Optimization, Heuristic Search
T echniques, Reinforcement Learning, Inductive Learning, Statistical Learning, Supervised and Unsupervised
Learning, Association Learning and Clustering, Reasoning, Support Vector Machine, Differential Evolution
Algorithms, Expert Systems, Neuro Fuzzy Hybrid Systems, Genetic Neuro Hybrid Systems, Genetic Fuzzy
Hybrid Systems and other Hybridized Soft Computing Techniques and their applications for Industrial
Transformation. The book series is aimed to provide comprehensive handbooks and reference books
for the benefit of scientists, research scholars, students and industry professional working towards next
generation industrial transformation.
Publishers at Scrivener
Martin Scrivener ([email protected])
Phillip Carmical ([email protected])
Smart Systems for
Industrial Applications
Edited by
C. Venkatesh, N. Rengarajan,
P. Ponmurugan
and
S. Balamurugan
This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA
and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA
© 2022 Scrivener Publishing LLC
For more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-76200-3
Cover image: Pixabay.Com
Cover design by Russell Richardson
Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines
Printed in the USA
10 9 8 7 6 5 4 3 2 1
Contents
Preface xvii
1 AI-Driven Information and Communication Technologies,
Services, and Applications for Next-Generation
Healthcare System 1
Vijayakumar Ponnusamy, A. Vasuki, J. Christopher Clement
and P. Eswaran
1.1 Introduction: Overview of Communication Technology
and Services for Healthcare 2
1.2 AI-Driven Communication Technology in Healthcare 6
1.2.1 Technologies Empowering in Healthcare 6
1.2.2 AI in Diagnosis 7
1.2.3 Conversion Protocols 8
1.2.4 AI in Treatment Assistant 9
1.2.5 AI in the Monitoring Process 10
1.2.6 Challenges of AI in Healthcare 10
1.3 AI-Driven mHealth Communication System and Services 10
1.3.1 Embedding of Handheld Imaging Platforms With
mHealth Devices 12
1.3.2 The Adaptability of POCUS in Telemedicine 12
1.4 AI-Driven Body Area Network Communication
Technologies and Applications 13
1.4.1 Features 16
1.4.2 Communication Architecture of Wireless Body
Area Networks 16
1.4.3 Role of AI in WBAN Architecture 17
1.4.4 Medical Applications 18
1.4.5 Nonmedical Applications 18
1.4.6 Challenges 18
v
vi Contents
1.5 AI-Driven IoT Device Communication Technologies
and Healthcare Applications  20
1.5.1 AI’s and IoT’s Role in Healthcare 20
1.5.2 Creating Efficient Communication Framework
for Remote Healthcare Management  21
1.5.3 Developing Autonomous Capability is Key
for Remote Healthcare Management 22
1.5.4 Enabling Data Privacy and Security in the Field
of Remote Healthcare Management 24
1.6 AI-Driven Augmented and Virtual Reality–Based
Communication Technologies and Healthcare Applications 25
1.6.1 Clinical Applications of Communication-Based
AI and Augmented Reality 27
1.6.2 Surgical Applications of Communication-Based on
Artificial Intelligence and Augmented Reality 28
References 30
2 Pneumatic Position Servo System Using Multi-Variable
Multi-Objective Genetic Algorithm–Based
Fractional-Order PID Controller 33
D.Magdalin Mary, V.Vanitha and G.Sophia Jasmine
2.1 Introduction 34
2.2 Pneumatic Servo System 36
2.3 Existing System Analysis 38
2.4 Proposed Controller and Its Modeling 40
2.4.1 Modeling of Fractional-Order PID Controller 40
2.4.1.1 Fractional-Order Calculus 40
2.4.1.2 Fractional-Order PID Controller 42
2.5 Genetic Algorithm 43
2.5.1 GA Optimization Methodology 43
2.5.1.1 Initialization 44
2.5.1.2 Fitness Function 44
2.5.1.3 Evaluation and Selection 44
2.5.1.4 Crossover 45
2.5.1.5 Mutation 45
2.5.2 GA Parameter Tuning 46
2.6 Simulation Results and Discussion 47
2.6.1 MATLAB Genetic Algorithm Tool Box 47
2.6.2 Simulation Results 47
2.6.2.1 Reference = 500 (Error) 48
2.6.2.2 Reference = 500 52
Contents vii
2.6.2.3 Reference = 1,500 52
2.6.2.4 Analysis Report 56
2.7 Hardware Results 56
2.7.1 Reference = 500 58
2.7.2 Reference = 1,500 59
2.8 Conclusion 59
References 59
3 Improved Weighted Distance Hop Hyperbolic Prediction–Based
Reliable Data Dissemination (IWDH-HP-RDD) Mechanism
for Smart Vehicular Environments 63
Sengathir Janakiraman, M. Deva Priya
and A. Christy Jeba Malar
3.1 Introduction 64
3.2 Related Work 67
3.2.1 Extract of the Literature 70
3.3 Proposed Improved Weighted Distance Hop Hyperbolic
Prediction–Based Reliable Data Dissemination
(IWDH-HP-RDD) Mechanism for Smart
Vehicular Environments 71
3.4 Simulation Results and Analysis of the Proposed
IWDH-HP-RDD Scheme 79
3.5 Conclusion 89
References 90
4 Remaining Useful Life Prediction of Small and Large Signal
Analog Circuits Using Filtering Algorithms 93
Sathiyanathan M., Anandhakumar K., Jaganathan S.
and Subashkumar C. S.
4.1 Introduction 94
4.2 Literature Survey 95
4.3 System Architecture 98
4.4 Remaining Useful Life Prediction 99
4.4.1 Initialization 99
4.4.2 Proposal Distribution 100
4.4.3 Time Update 101
4.4.4 Relative Entropy in Particle Resampling 101
4.4.5 RUL Prediction 102
4.5 Results and Discussion 103
4.6 Conclusion 111
References 111
viii Contents
5 AI in Healthcare 115
S. Menaga and J. Paruvathavardhini
5.1 Introduction 116
5.1.1 What is Artificial Intelligence? 117
5.1.2 Machine Learning – Neural Networks
and Deep Learning 117
5.1.3 Natural Language Processing 119
5.2 Need of AI in Electronic Health Record 119
5.2.1 How Does AI/ML Fit Into EHR? 120
5.2.2 Natural Language Processing (NLP) 121
5.2.3 Data Analytics and Representation 122
5.2.4 Predictive Investigation 122
5.2.5 Administrative and Security Consistency 122
5.3 The Trending Role of AI in Pharmaceutical Development 123
5.3.1 Drug Discovery and Design 124
5.3.2 Diagnosis of Biomedical and Clinical Data 125
5.3.3 Rare Diseases and Epidemic Prediction 125
5.3.4 Applications of AI in Pharma 126
5.3.5 AI in Marketing 126
5.3.6 Review of the Companies That Use AI 126
5.4 AI in Surgery 127
5.4.1 3D Printing 127
5.4.2 Stem Cells 128
5.4.3 Patient Care 128
5.4.4 Training and Future Surgical Team 129
5.5 Artificial Intelligence in Medical Imaging 131
5.5.1 In Cardio Vascular Abnormalities 131
5.5.2 In Fractures and Musculoskeletal Injuries 132
5.5.3 In Neurological Diseases and Thoracic
Complications 133
5.5.4 In Detecting Cancers 134
5.6 AI in Patient Monitoring and Wearable Health Devices 134
5.6.1 Monitoring Health Through Wearable’s and
Personal Devices 135
5.6.2 Making Smartphone Selfies Into Powerful
Diagnostic Tools 136
5.7 Revolutionizing of AI in Medicinal Decision-Making
at the Bedside 137
5.8 Future of AI in Healthcare 137
5.9 Conclusion 139
References 139