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

SMART SYSTEMS FOR INDUSTRIAL APPLICATIONS The prime objective of this book is to provide an insight into the role and advancements of artificial intelligence in electrical systems and future challenges. The book covers a broad range of topics about AI from a multidisciplinary point of view, starting with its history and continuing on to theories about artificial vs. human intelligence, concepts, and regulations concerning AI, human-machine distribution of power and control, delegation of decisions, the social and economic impact of AI, etc. The prominent role that AI plays in society by connecting people through technologies is highlighted in this book. It also covers key aspects of various AI applications in electrical systems in order to enable growth in electrical engineering. The impact that AI has on social and economic factors is also examined from various perspectives. Moreover, many intriguing aspects of AI techniques in different domains are covered such as e-learning, healthcare, smart grid, virtual assistance, etc. Audience The book will be of interest to researchers and postgraduate students in artificial intelligence, electrical and electronic engineering, as well as those engineers working in the application areas such as healthcare, energy systems, education, and others.

Table of 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 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 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 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 6 Introduction of Artificial Intelligence 141 R. Vishalakshi and S. Mangai 6.1 Introduction 142 6.1.1 Intelligence 142 6.1.2 Types of Intelligence 143 6.1.3 A Brief History of Artificial Intelligence From 1923 till 2000 144 6.2 Introduction to the Philosophy Behind Artificial Intelligence 145 6.2.1 Programming With and Without AI 147 6.3 Basic Functions of Artificial Intelligence 147 6.3.1 Categories of Artificial Intelligence 148 6.3.1.1 Reactive Machines 148 6.3.1.2 Limited Memory 148 6.3.1.3 Theory of Mind 149 6.3.1.4 Self-Awareness 149 6.4 Existing Technology and Its Review 149 6.4.1 Tesla’s Autopilot 149 6.4.2 Boxever 150 6.4.3 Fin Gesture 150 6.4.4 AI Robot 152 6.4.5 Vinci 153 6.4.6 AI Glasses 153 6.4.7 Affectiva 153 6.4.8 AlphaGo Beats 154 6.4.9 Cogito 154 6.4.10 Siri and Alexa 155 6.4.11 Pandora’s 157 6.5 Objectives 157 6.5.1 Major Goals 157 6.5.2 Need for Artificial Intelligence 158 6.5.3 Distinction Between Artificial Intelligence and Business Intelligence 158 6.6 Significance of the Study 159 6.6.1 Segments of Master Frameworks 160 6.6.1.1 User Interface 162 6.6.1.2 Expert Systems 163 6.6.1.3 Inference Engine 163 6.6.1.4 Voice Recognition 164 6.6.1.5 Robots 164 6.7 Discussion 164 6.7.1 Artificial Intelligence and Design Practice 164 6.8 Applications of AI 167 6.8.1 AI Has Been Developing a Huge Number of Tools Necessary to Find a Solution to the Most Challenging Problems in Computer Science 168 6.8.2 Future of AI 168 6.9 Conclusion 169 References 170 7 Artificial Intelligence in Healthcare: Algorithms and Decision Support Systems 173 S. Palanivel Rajan and M.Paranthaman 7.1 Introduction 173 7.2 Machine Learning Work Flow and Applications in Healthcare 176 7.2.1 Formatting and Cleaning Data 177 7.2.2 Supervised and Unsupervised Learning 178 7.2.3 Linear Discriminant Analysis 178 7.2.4 K-Nearest Neighbor 179 7.2.5 K-Means Clustering 180 7.2.6 Random Forest 181 7.2.7 Decision Tree 181 7.2.8 Support Vector Machine 182 7.2.9 Artificial Neural Network 183 7.2.10 Natural Language Processing 184 7.2.11 Deep Learning 185 7.2.12 Ensembles 186 7.3 Commercial Decision Support Systems Based on AI 187 7.3.1 Personal Genome Diagnostics 188 7.3.2 Tempus 188 7.3.3 iCarbonX—Manage Your Digital Life 189 7.3.4 H 2 O.ai 189 7.3.5 Google DeepMind 189 7.3.6 Buoy Health 189 7.3.7 PathAI 190 7.3.8 Beth Israel Deaconess Medical Center 190 7.3.9 Bioxcel Therapeutics 190 7.3.10 Berg 191 7.3.11 Enlitic 191 7.3.12 Deep Genomics 191 7.3.13 Freenome 192 7.3.14 CloudMedX 192 7.3.15 Proscia 192 7.4 Conclusion 193 References 193 8 Smart Homes and Smart Cities 199 C. N. Marimuthu and G. Arthy 8.1 Smart Homes 199 8.1.1 Introduction 199 8.1.2 Evolution of Smart Home 200 8.1.3 Smart Home Architecture 202 8.1.3.1 Smart Electrical Devices or Smart Plugs 202 8.1.3.2 Home Intelligent Terminals or Home Area Networks 203 8.1.3.3 Master Network 203 8.1.4 Smart Home Technologies 204 8.1.5 Smart Grid Technology 206 8.1.6 Smart Home Applications 206 8.1.6.1 Smart Home in the Healthcare of Elderly People 206 8.1.6.2 Smart Home in Education 207 8.1.6.3 Smart Lighting 207 8.1.6.4 Smart Surveillance 207 8.1.7 Advantages and Disadvantages of Smart Homes 207 8.2 Smart Cities 208 8.2.1 Introduction 208 8.2.2 Smart City Framework 209 8.2.3 Architecture of Smart Cities 210 8.2.4 Components of Smart Cities 211 8.2.4.1 Smart Technology 212 8.2.4.2 Smart Infrastructure 212 8.2.4.3 Smart Mobility 214 8.2.4.4 Smart Buildings 215 8.2.4.5 Smart Energy 216 8.2.4.6 Smart Governance 217 8.2.4.7 Smart Healthcare 218 8.2.5 Characteristics of Smart Cities 219 8.2.6 Challenges in Smart Cities 221 8.2.7 Conclusion 222 References 222 9 Application of AI in Healthcare 225 V. Priya and S. Prabu 9.1 Introduction 226 9.1.1 Supervised Learning Process 226 9.1.2 Unsupervised Learning Process 227 9.1.3 Semi-Supervised Learning Process 227 9.1.4 Reinforcement Learning Process 227 9.1.5 Healthcare System Using ml 228 9.1.6 Primary Examples of ML’s Implementation in the Healthcare 228 9.1.6.1 AI-Assisted Radiology and Pathology 228 9.1.6.2 Physical Robots for Surgery Assistance 229 9.1.6.3 With the Assistance of AI/ML Techniques, Drug Discovery 231 9.1.6.4 Precision Medicine and Preventive Healthcare in the Future 232 9.2 Related Works 232 9.2.1 In Healthcare, Data Driven AI Models 232 9.2.2 Support Vector Machine 233 9.2.3 Artificial Neural Networks 233 9.2.4 Logistic Regression 235 9.2.5 Random Forest 235 9.2.6 Discriminant Analysis 236 9.2.7 Naïve Bayes 236 9.2.8 Natural Language Processing 236 9.2.9 Tf-idf 236 9.2.10 Word Vectors 237 9.2.11 Deep Learning 237 9.2.12 Convolutional Neural Network 237 9.3 dl Frameworks for Identifying Disease 240 9.3.1 TensorFlow 240 9.3.2 High Level APIs 240 9.3.3 Estimators 240 9.3.4 Accelerators 241 9.3.5 Low Level APIs 241 9.4 Proposed Work 241 9.4.1 Application of AI in Finding Heart Disease 241 9.4.2 Data Pre-Processing and Classification of Heart Disease 241 9.5 Results and Discussions 244 9.6 Conclusion 246 References 246 10 Battery Life and Electric Vehicle Range Prediction 249 Ravikrishna S., Subash Kumar C. S. and Sundaram M. 10.1 Introduction 250 10.2 Different Stages of Electrification of Electric Vehicles 253 10.2.1 Starting and Stopping 253 10.2.2 Regenerative Braking 253 10.2.3 Motor Control 253 10.2.4 EV Drive 254 10.3 Estimating SoC 254 10.3.1 Cell Capacity 254 10.3.2 Calendar Life 255 10.3.3 Cycling Life 255 10.3.4 SoH Based on Capacity Fade 255 10.3.5 SoH Based on Power Fade 255 10.3.6 Open Circuit Voltage 255 10.3.7 Impedance Spectroscopy 255 10.3.8 Model-Based Approach 256 10.4 Kalman Filter 257 10.4.1 Sigma Point Kalman Filter 257 10.4.2 Six Step Process 258 10.5 Estimating SoH 260 10.6 Results and Discussion 262 10.7 Conclusion 267 References 267 11 AI-Driven Healthcare Analysis 269 N. Kasthuri and T. Meeradevi 11.1 Introduction 270 11.2 Literature Review 271 11.3 Feature Extraction 275 11.3.1 GLCM Feature Descriptors 275 11.4 Classifiers 276 11.4.1 Stochastic Gradient Descent Classifier 276 11.4.2 Naïve Bayes Classifier 276 11.4.3 K-Nearest Neighbor Classifier 277 11.4.4 Support Vector Machine Classifier 277 11.4.5 Random Forest Classifier 278 11.4.6 Working of Random Forest Algorithm 278 11.4.7 Convolutional Neural Network 278 11.4.7.1 Activation Function 281 11.4.7.2 Pooling Layer 281 11.4.7.3 Fully Connected Layer (FC) 281 11.5 Results and Conclusion 282 11.5.1 5,000 Images 282 11.5.2 10,000 Images 283 References 284 12 A Novel Technique for Continuous Monitoring of Fuel Adulteration 287 Rajalakshmy P., Varun R., Subha Hency Jose P. and Rajasekaran K. 12.1 Introduction 288 12.1.1 Literature Review 289 12.1.2 Overview 290 12.1.3 Objective 290 12.2 Existing Method 290 12.2.1 Module-1 Water 291 12.2.2 Module-2 Petrol 293 12.2.3 Petrol Density Measurement 293 12.2.4 Block Diagram 293 12.2.5 Components of the System 294 12.2.5.1 Pressure Instrument 294 12.2.5.2 Sensor 294 12.2.6 Personal Computer 295 12.2.7 Petrol Density Measurement Instrument Setup 295 12.2.7.1 Setup 1 296 12.2.7.2 Setup 2 298 12.2.7.3 Setup 3 298 12.2.7.4 Setup 4 298 12.2.7.5 Final Setup 298 12.3 Interfacing MPX2010DP with INA 114 299 12.3.1 I2C Bus Configuration for Honeywell Sensor 299 12.3.2 Pressure and Temperature Output Through I2C 300 12.4 Results and Discussion 302 12.5 Conclusion 303 References 304 13 Improved Merkle Hash and Trapdoor Function–Based Secure Mutual Authentication (IMH-TF-SMA) Mechanism for Securing Smart Home Environment 307 M. Deva Priya, Sengathir Janakiraman and A. Christy Jeba Malar 13.1 Introduction 308 13.2 Related Work 310 13.3 Proposed Improved Merkle Hash and Trapdoor Function– Based Secure Mutual Authentication (IMH-TF-SMA) Mechanism for Securing Smart Home Environment 316 13.3.1 Threat Model 317 13.3.2 IMH-TF-SMA Mechanism 317 13.3.2.1 Phase of Initialization 320 13.3.2.2 Phase of Addressing 320 13.3.2.3 Phase of Registration 320 13.3.2.4 Phase of Login Authentication 321 13.3.2.5 Phase of Session Agreement 321 13.4 Results and Discussion 325 13.5 Conclusion 330 References 330 14 Smart Sensing Technology 333 S. Palanivel Rajan and T. Abirami 14.1 Introduction 333 14.1.1 Sensor 333 14.1.1.1 Real-Time Example of Sensor 334 14.1.1.2 Definition of Sensors 335 14.1.1.3 Characteristics of Sensors 335 14.1.1.4 Classification of Sensors 336 14.1.1.5 Types of Sensors 336 14.1.2 IoT (Internet of Things) 340 14.1.2.1 Trends and Characteristics 340 14.1.2.2 Definition 340 14.1.2.3 Flow Chart of IoT 341 14.1.2.4 IoT Phases 341 14.1.2.5 Phase Chart 342 14.1.2.6 IoT Protocol 342 14.1.3 Wpan 343 14.1.3.1 IEEE 802.15.1 Overview 344 14.1.3.2 Bluetooth 344 14.1.3.3 History of Bluetooth 344 14.1.3.4 How Bluetooth Works 345 14.1.3.5 Bluetooth Specifications 345 14.1.3.6 Advantages of Bluetooth Technology 346 14.1.3.7 Applications 347 14.1.4 Zigbee (IEEE 802.15.4) 348 14.1.4.1 Introduction 348 14.1.4.2 Architecture of Zigbee 349 14.1.4.3 Zigbee Devices 351 14.1.4.4 Operating Modes of Zigbee 351 14.1.4.5 Zigbee Topologies 352 14.1.4.6 Applications of Zigbee Technology 353 14.1.5 Wlan 353 14.1.5.1 Introduction 353 14.1.5.2 Advantages of WLANs 355 14.1.5.3 Drawbacks of WLAN 355 14.1.6 Gsm 356 14.1.6.1 Introduction 356 14.1.6.2 Composition of GSM Networks 356 14.1.6.3 Security 358 14.1.7 Smart Sensor 358 14.1.7.1 Development History of Smart Sensors 358 14.1.7.2 Internal Parts of Smart Transmitter 359 14.1.7.3 Applications 361 14.1.8 Conclusion 365 References 365 Index 367

About the Author :
C. Venkatesh, PhD is Professor and Principal, Sengunthar Engineering College, India, and has 28 years of teaching experience. He has published 5 patents, about 80 research papers in international journals, and about 70 papers in international and national conferences. N. Rengarajan, PhD is Professor and Principal, Nandha Engineering College, India and has more than three decades of experience. He has published 8 patents, 70 papers in international journals, and 20 papers in national and international conferences. P. Ponmurugan, PhD is an associate professor, Sri Krishna College of Technology, India has almost a decade of experience in academics. He has published 11 patents and about 40 papers in international journals and conferences. He was awarded the “Best Young Engineer” by IEI – Erode Local Centre and “Young Scientist” by the International Association of Research and Developed Organization (IARDO). S. Balamurugan, PhD, SMIEEE and ACM Distinguished Speaker, received his PhD from Anna University, India. He has published 57 books, 300+ international journals/conferences, and 100 patents. He is the Director of the Albert Einstein Engineering and Research Labs. He is also the Vice-Chairman of the Renewable Energy Society of India (RESI). He is serving as a research consultant to many companies, startups, SMEs, and MSMEs. He has received numerous awards for research at national and international levels.