Preface xvii 1 Internet of Medical Things—State-of-the-Art 1Kishor Joshi and Ruchi Mehrotra 1.1 Introduction 2 1.2 Historical Evolution of IoT to IoMT 2 1.2.1 IoT and IoMT—Market Size 4 1.3 Smart Wearable Technology 4 1.3.1 Consumer Fitness Smart Wearables 4 1.3.2 Clinical-Grade Wearables 5 1.4 Smart Pills 7 1.5 Reduction of Hospital-Acquired Infections 8 1.5.1 Navigation Apps for Hospitals 8 1.6 In-Home Segment 8 1.7 Community Segment 9 1.8 Telehealth and Remote Patient Monitoring 9 1.9 IoMT in Healthcare Logistics and Asset Management 12 1.10 IoMT Use in Monitoring During COVID-19 13 1.11 Conclusion 14 References 15 2 Issues and Challenges Related to Privacy and Security in Healthcare Using IoT, Fog, and Cloud Computing 21Hritu Raj, Mohit Kumar, Prashant Kumar, Amritpal Singh and Om Prakash Verma 2.1 Introduction 22 2.2 Related Works 23 2.3 Architecture 25 2.3.1 Device Layer 25 2.3.2 Fog Layer 26 2.3.3 Cloud Layer 26 2.4 Issues and Challenges 26 2.5 Conclusion 29 References 30 3 Study of Thyroid Disease Using Machine Learning 33Shanu Verma, Rashmi Popli and Harish Kumar 3.1 Introduction 34 3.2 Related Works 34 3.3 Thyroid Functioning 35 3.4 Category of Thyroid Cancer 36 3.5 Machine Learning Approach Toward the Detection of Thyroid Cancer 37 3.5.1 Decision Tree Algorithm 38 3.5.2 Support Vector Machines 39 3.5.3 Random Forest 39 3.5.4 Logistic Regression 39 3.5.5 Naïve Bayes 40 3.6 Conclusion 41 References 41 4 A Review of Various Security and Privacy Innovations for IoT Applications in Healthcare 43Abhishek Raghuvanshi, Umesh Kumar Singh and Chirag Joshi 4.1 Introduction 44 4.1.1 Introduction to IoT 44 4.1.2 Introduction to Vulnerability, Attack, and Threat 45 4.2 IoT in Healthcare 46 4.2.1 Confidentiality 46 4.2.2 Integrity 46 4.2.3 Authorization 46 4.2.4 Availability 47 4.3 Review of Security and Privacy Innovations for IoT Applications in Healthcare, Smart Cities, and Smart Homes 48 4.4 Conclusion 54 References 54 5 Methods of Lung Segmentation Based on CT Images 59Amit Verma and Thipendra P. Singh 5.1 Introduction 59 5.2 Semi-Automated Algorithm for Lung Segmentation 60 5.2.1 Algorithm for Tracking to Lung Edge 60 5.2.2 Outlining the Region of Interest in CT Images 62 5.2.2.1 Locating the Region of Interest 62 5.2.2.2 Seed Pixels and Searching Outline 62 5.3 Automated Method for Lung Segmentation 63 5.3.1 Knowledge-Based Automatic Model for Segmentation 63 5.3.2 Automatic Method for Segmenting the Lung CT Image 64 5.4 Advantages of Automatic Lung Segmentation Over Manual and Semi-Automatic Methods 64 5.5 Conclusion 65 References 65 6 Handling Unbalanced Data in Clinical Images 69Amit Verma 6.1 Introduction 70 6.2 Handling Imbalance Data 71 6.2.1 Cluster-Based Under-Sampling Technique 72 6.2.2 Bootstrap Aggregation (Bagging) 75 6.3 Conclusion 76 References 76 7 IoT-Based Health Monitoring System for Speech-Impaired People Using Assistive Wearable Accelerometer 81Ishita Banerjee and Madhumathy P. 7.1 Introduction 82 7.2 Literature Survey 84 7.3 Procedure 86 7.4 Results 93 7.5 Conclusion 97 References 97 8 Smart IoT Devices for the Elderly and People with Disabilities 101K. N. D. Saile and Kolisetti Navatha 8.1 Introduction 101 8.2 Need for IoT Devices 102 8.3 Where Are the IoT Devices Used? 103 8.3.1 Home Automation 103 8.3.2 Smart Appliances 104 8.3.3 Healthcare 104 8.4 Devices in Home Automation 104 8.4.1 Automatic Lights Control 104 8.4.2 Automated Home Safety and Security 104 8.5 Smart Appliances 105 8.5.1 Smart Oven 105 8.5.2 Smart Assistant 105 8.5.3 Smart Washers and Dryers 106 8.5.4 Smart Coffee Machines 106 8.5.5 Smart Refrigerator 106 8.6 Healthcare 106 8.6.1 Smart Watches 107 8.6.2 Smart Thermometer 107 8.6.3 Smart Blood Pressure Monitor 107 8.6.4 Smart Glucose Monitors 107 8.6.5 Smart Insulin Pump 108 8.6.6 Smart Wearable Asthma Monitor 108 8.6.7 Assisted Vision Smart Glasses 109 8.6.8 Finger Reader 109 8.6.9 Braille Smart Watch 109 8.6.10 Smart Wand 109 8.6.11 Taptilo Braille Device 110 8.6.12 Smart Hearing Aid 110 8.6.13 E-Alarm 110 8.6.14 Spoon Feeding Robot 110 8.6.15 Automated Wheel Chair 110 8.7 Conclusion 112 References 112 9 IoT-Based Health Monitoring and Tracking System for Soldiers 115Kavitha N. and Madhumathy P. 9.1 Introduction 116 9.2 Literature Survey 117 9.3 System Requirements 118 9.3.1 Software Requirement Specification 119 9.3.2 Functional Requirements 119 9.4 System Design 119 9.4.1 Features 121 9.4.1.1 On-Chip Flash Memory 122 9.4.1.2 On-Chip Static RAM 122 9.4.2 Pin Control Block 122 9.4.3 UARTs 123 9.4.3.1 Features 123 9.4.4 System Control 123 9.4.4.1 Crystal Oscillator 123 9.4.4.2 Phase-Locked Loop 124 9.4.4.3 Reset and Wake-Up Timer 124 9.4.4.4 Brown Out Detector 125 9.4.4.5 Code Security 125 9.4.4.6 External Interrupt Inputs 125 9.4.4.7 Memory Mapping Control 125 9.4.4.8 Power Control 126 9.4.5 Real Monitor 126 9.4.5.1 GPS Module 126 9.4.6 Temperature Sensor 127 9.4.7 Power Supply 128 9.4.8 Regulator 128 9.4.9 LCD 128 9.4.10 Heart Rate Sensor 129 9.5 Implementation 129 9.5.1 Algorithm 130 9.5.2 Hardware Implementation 130 9.5.3 Software Implementation 131 9.6 Results and Discussions 133 9.6.1 Heart Rate 133 9.6.2 Temperature Sensor 135 9.6.3 Panic Button 135 9.6.4 GPS Receiver 135 9.7 Conclusion 136 References 136 10 Cloud-IoT Secured Prediction System for Processing and Analysis of Healthcare Data Using Machine Learning Techniques 137G. K. Kamalam and S. Anitha 10.1 Introduction 138 10.2 Literature Survey 139 10.3 Medical Data Classification 141 10.3.1 Structured Data 142 10.3.2 Semi-Structured Data 142 10.4 Data Analysis 142 10.4.1 Descriptive Analysis 142 10.4.2 Diagnostic Analysis 143 10.4.3 Predictive Analysis 143 10.4.4 Prescriptive Analysis 143 10.5 ML Methods Used in Healthcare 144 10.5.1 Supervised Learning Technique 144 10.5.2 Unsupervised Learning 145 10.5.3 Semi-Supervised Learning 145 10.5.4 Reinforcement Learning 145 10.6 Probability Distributions 145 10.6.1 Discrete Probability Distributions 146 10.6.1.1 Bernoulli Distribution 146 10.6.1.2 Uniform Distribution 147 10.6.1.3 Binomial Distribution 147 10.6.1.4 Normal Distribution 148 10.6.1.5 Poisson Distribution 148 10.6.1.6 Exponential Distribution 149 10.7 Evaluation Metrics 150 10.7.1 Classification Accuracy 150 10.7.2 Confusion Matrix 150 10.7.3 Logarithmic Loss 151 10.7.4 Receiver Operating Characteristic Curve, or ROC Curve 152 10.7.5 Area Under Curve (AUC) 152 10.7.6 Precision 153 10.7.7 Recall 153 10.7.8 F1 Score 153 10.7.9 Mean Absolute Error 154 10.7.10 Mean Squared Error 154 10.7.11 Root Mean Squared Error 155 10.7.12 Root Mean Squared Logarithmic Error 155 10.7.13 R-Squared/Adjusted R-Squared 156 10.7.14 Adjusted R-Squared 156 10.8 Proposed Methodology 156 10.8.1 Neural Network 158 10.8.2 Triangular Membership Function 158 10.8.3 Data Collection 159 10.8.4 Secured Data Storage 159 10.8.5 Data Retrieval and Merging 161 10.8.6 Data Aggregation 162 10.8.7 Data Partition 162 10.8.8 Fuzzy Rules for Prediction of Heart Disease 163 10.8.9 Fuzzy Rules for Prediction of Diabetes 164 10.8.10 Disease Prediction With Severity and Diagnosis 165 10.9 Experimental Results 166 10.10 Conclusion 169 References 169 11 CloudIoT-Driven Healthcare: Review, Architecture, Security Implications, and Open Research Issues 173Junaid Latief Shah, Heena Farooq Bhat and Asif Iqbal Khan 11.1 Introduction 174 11.2 Background Elements 180 11.2.1 Security Comparison Between Traditional and IoT Networks 185 11.3 Secure Protocols and Enabling Technologies for CloudIoT Healthcare Applications 187 11.3.1 Security Protocols 187 11.3.2 Enabling Technologies 188 11.4 CloudIoT Health System Framework 191 11.4.1 Data Perception/Acquisition 192 11.4.2 Data Transmission/Communication 193 11.4.3 Cloud Storage and Warehouse 194 11.4.4 Data Flow in Healthcare Architecture - A Conceptual Framework 194 11.4.5 Design Considerations 197 11.5 Security Challenges and Vulnerabilities 199 11.5.1 Security Characteristics and Objectives 200 11.5.1.1 Confidentiality 202 11.5.1.2 Integrity 202 11.5.1.3 Availability 202 11.5.1.4 Identification and Authentication 202 11.5.1.5 Privacy 203 11.5.1.6 Light Weight Solutions 203 11.5.1.7 Heterogeneity 203 11.5.1.8 Policies 203 11.5.2 Security Vulnerabilities 203 11.5.2.1 IoT Threats and Vulnerabilities 205 11.5.2.2 Cloud-Based Threats 208 11.6 Security Countermeasures and Considerations 214 11.6.1 Security Countermeasures 214 11.6.1.1 Security Awareness and Survey 214 11.6.1.2 Security Architecture and Framework 215 11.6.1.3 Key Management 216 11.6.1.4 Authentication 217 11.6.1.5 Trust 218 11.6.1.6 Cryptography 219 11.6.1.7 Device Security 219 11.6.1.8 Identity Management 220 11.6.1.9 Risk-Based Security/Risk Assessment 220 11.6.1.10 Block Chain–Based Security 220 11.6.1.11 Automata-Based Security 220 11.6.2 Security Considerations 234 11.7 Open Research Issues and Security Challenges 237 11.7.1 Security Architecture 237 11.7.2 Resource Constraints 238 11.7.3 Heterogeneous Data and Devices 238 11.7.4 Protocol Interoperability 238 11.7.5 Trust Management and Governance 239 11.7.6 Fault Tolerance 239 11.7.7 Next-Generation 5G Protocol 240 11.8 Discussion and Analysis 240 11.9 Conclusion 241 References 242 12 A Novel Usage of Artificial Intelligence and Internet of Things in Remote-Based Healthcare Applications 255V. Arulkumar, D. Mansoor Hussain, S. Sridhar and P. Vivekanandan 12.1 Introduction Machine Learning 256 12.2 Importance of Machine Learning 256 12.2.1 ML vs. Classical Algorithms 258 12.2.2 Learning Supervised 259 12.2.3 Unsupervised Learning 261 12.2.4 Network for Neuralism 263 12.2.4.1 Definition of the Neural Network 263 12.2.4.2 Neural Network Elements 263 12.3 Procedure 265 12.3.1 Dataset and Seizure Identification 265 12.3.2 System 265 12.4 Feature Extraction 266 12.5 Experimental Methods 266 12.5.1 Stepwise Feature Optimization 266 12.5.2 Post-Classification Validation 268 12.5.3 Fusion of Classification Methods 268 12.6 Experiments 269 12.7 Framework for EEG Signal Classification 269 12.8 Detection of the Preictal State 270 12.9 Determination of the Seizure Prediction Horizon 271 12.10 Dynamic Classification Over Time 272 12.11 Conclusion 273 References 273 13 Use of Machine Learning in Healthcare 275V. Lakshman Narayana, R. S. M. Lakshmi Patibandla, B. Tarakeswara Rao and Arepalli Peda Gopi 13.1 Introduction 276 13.2 Uses of Machine Learning in Pharma and Medicine 276 13.2.1 Distinguish Illnesses and Examination 277 13.2.2 Drug Discovery and Manufacturing 277 13.2.3 Scientific Imaging Analysis 278 13.2.4 Twisted Therapy 278 13.2.5 AI to Know-Based Social Change 278 13.2.6 Perception Wellness Realisms 279 13.2.7 Logical Preliminary and Exploration 279 13.2.8 Publicly Supported Perceptions Collection 279 13.2.9 Better Radiotherapy 280 13.2.10 Incidence Forecast 280 13.3 The Ongoing Preferences of ML in Human Services 281 13.4 The Morals of the Use of Calculations in Medicinal Services 284 13.5 Opportunities in Healthcare Quality Improvement 288 13.5.1 Variation in Care 288 13.5.2 Inappropriate Care 289 13.5.3 Prevents Care–Associated Injurious and Death for Carefrontation 289 13.5.4 The Fact That People Are Unable to do What They Know Works 289 13.5.5 A Waste 290 13.6 A Team-Based Care Approach Reduces Waste 290 13.7 Conclusion 291 References 292 14 Methods of MRI Brain Tumor Segmentation 295Amit Verma 14.1 Introduction 295 14.2 Generative and Descriptive Models 296 14.2.1 Region-Based Segmentation 300 14.2.2 Generative Model With Weighted Aggregation 300 14.3 Conclusion 302 References 303 15 Early Detection of Type 2 Diabetes Mellitus Using Deep Neural Network–Based Model 305Varun Sapra and Luxmi Sapra 15.1 Introduction 306 15.2 Data Set 307 15.2.1 Data Insights 308 15.3 Feature Engineering 310 15.4 Framework for Early Detection of Disease 312 15.4.1 Deep Neural Network 313 15.5 Result 314 15.6 Conclusion 315 References 315 16 A Comprehensive Analysis on Masked Face Detection Algorithms 319Pranjali Singh, Amitesh Garg and Amritpal Singh 16.1 Introduction 320 16.2 Literature Review 321 16.3 Implementation Approach 325 16.3.1 Feature Extraction 325 16.3.2 Image Processing 325 16.3.3 Image Acquisition 325 16.3.4 Classification 325 16.3.5 MobileNetV2 326 16.3.6 Deep Learning Architecture 326 16.3.7 LeNet-5, AlexNet, and ResNet-50 326 16.3.8 Data Collection 326 16.3.9 Development of Model 327 16.3.10 Training of Model 328 16.3.11 Model Testing 328 16.4 Observation and Analysis 328 16.4.1 CNN Algorithm 328 16.4.2 SSDNETV2 Algorithm 330 16.4.3 SVM 331 16.5 Conclusion 332 References 333 17 IoT-Based Automated Healthcare System 335Darpan Anand and Aashish Kumar 17.1 Introduction 335 17.1.1 Software-Defined Network 336 17.1.2 Network Function Virtualization 337 17.1.3 Sensor Used in IoT Devices 338 17.2 SDN-Based IoT Framework 341 17.3 Literature Survey 343 17.4 Architecture of SDN-IoT for Healthcare System 344 17.5 Challenges 345 17.6 Conclusion 347 References 347 Index 351

This book offers a complete package involving the incubation of machine learning, AI, and IoT in healthcare that is beneficial for researchers, healthcare professionals, scientists, and technologists. The applications and challenges of machine learning and artificial intelligence in the Internet of Things (IoT) for healthcare applications are comprehensively covered in this book. IoT generates big data of varying data quality; intelligent processing and analysis of this big data are the keys to developing smart IoT applications, thereby making space for machine learning (ML) applications. Due to its computational tools that can substitute for human intelligence in the performance of certain tasks, artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Since IoT platforms provide an interface to gather data from various devices, they can easily be deployed into AI/ML systems. The value of AI in this context is its ability to quickly mesh insights from data and automatically identify patterns and detect anomalies in the data that smart sensors and devices generate—information such as temperature, pressure, humidity, air quality, vibration, and sound—that can be really helpful to rapid diagnosis. Audience This book will be of interest to researchers in artificial intelligence, the Internet of Things, machine learning as well as information technologists working in the healthcare sector.