{"product_id":"telehealthcare-9781119841760","title":"TeleHealthcare","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Machine Learning–Assisted Remote Patient Monitoring with Data Analytics 1\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eVinutha D. C., Kavyashree and G. T. Raju\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 2\u003c\/p\u003e \u003cp\u003e1.1.1 Traditional Patient Monitoring System 2\u003c\/p\u003e \u003cp\u003e1.1.2 Remote Monitoring System 3\u003c\/p\u003e \u003cp\u003e1.1.3 Challenges in RPM 4\u003c\/p\u003e \u003cp\u003e1.2 Literature Survey 5\u003c\/p\u003e \u003cp\u003e1.2.1 Machine Learning Approaches in Patient Monitoring 7\u003c\/p\u003e \u003cp\u003e1.3 Machine Learning in RPM 8\u003c\/p\u003e \u003cp\u003e1.3.1 Support Vector Machine 9\u003c\/p\u003e \u003cp\u003e1.3.2 Decision Tree 10\u003c\/p\u003e \u003cp\u003e1.3.3 Random Forest 11\u003c\/p\u003e \u003cp\u003e1.3.4 Logistic Regression 11\u003c\/p\u003e \u003cp\u003e1.3.5 Genetic Algorithm 12\u003c\/p\u003e \u003cp\u003e1.3.6 Simple Linear Regression 12\u003c\/p\u003e \u003cp\u003e1.3.7 KNN Algorithm 13\u003c\/p\u003e \u003cp\u003e1.3.8 Naive Bayes Algorithm 14\u003c\/p\u003e \u003cp\u003e1.4 System Architecture 15\u003c\/p\u003e \u003cp\u003e1.4.1 Data Collection 16\u003c\/p\u003e \u003cp\u003e1.4.2 Data Pre-Processing 17\u003c\/p\u003e \u003cp\u003e1.4.3 Apply Machine Learning Algorithm and Prediction 18\u003c\/p\u003e \u003cp\u003e1.5 Results 21\u003c\/p\u003e \u003cp\u003e1.6 Future Enhancement 23\u003c\/p\u003e \u003cp\u003e1.7 Conclusion 24\u003c\/p\u003e \u003cp\u003eReferences 24\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 A Survey on Recent Computer-Aided Diagnosis for Detecting Diabetic Retinopathy 27\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePriyadharsini C., Jagadeesh Kannan R. and Farookh Khadeer Hussain\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 28\u003c\/p\u003e \u003cp\u003e2.2 Diabetic Retinopathy 28\u003c\/p\u003e \u003cp\u003e2.2.1 Features of DR 28\u003c\/p\u003e \u003cp\u003e2.2.2 Stages of DR 29\u003c\/p\u003e \u003cp\u003e2.3 Overview of DL Models 31\u003c\/p\u003e \u003cp\u003e2.3.1 Convolution Neural Network 31\u003c\/p\u003e \u003cp\u003e2.3.2 Autoencoders 32\u003c\/p\u003e \u003cp\u003e2.3.3 Boltzmann Machine and Deep Belief Network 32\u003c\/p\u003e \u003cp\u003e2.4 Data Set 33\u003c\/p\u003e \u003cp\u003e2.5 Performance Metrics 34\u003c\/p\u003e \u003cp\u003e2.6 Literature Survey 36\u003c\/p\u003e \u003cp\u003e2.6.1 Segmentation of Blood Vessels 36\u003c\/p\u003e \u003cp\u003e2.6.2 Optic Disc Feature 49\u003c\/p\u003e \u003cp\u003e2.6.3 Lesion Detections 50\u003c\/p\u003e \u003cp\u003e2.6.3.1 Exudate Detection 50\u003c\/p\u003e \u003cp\u003e2.6.3.2 MA and HM 51\u003c\/p\u003e \u003cp\u003e2.6.4 DR Classification 51\u003c\/p\u003e \u003cp\u003e2.7 Discussion and Future Directions 52\u003c\/p\u003e \u003cp\u003e2.8 Conclusion 53\u003c\/p\u003e \u003cp\u003eReferences 53\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 A New Improved Cryptography Method-Based e-Health Application in Cloud Computing Environment 59\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDipesh Kumar, Nirupama Mandal and Yugal Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 60\u003c\/p\u003e \u003cp\u003e3.1.1 Contribution 61\u003c\/p\u003e \u003cp\u003e3.2 Motivation 62\u003c\/p\u003e \u003cp\u003e3.3 Related Works 62\u003c\/p\u003e \u003cp\u003e3.4 Challenges 64\u003c\/p\u003e \u003cp\u003e3.5 Proposed Work 64\u003c\/p\u003e \u003cp\u003e3.6 Proposed Algorithm for Encryption 66\u003c\/p\u003e \u003cp\u003e3.6.1 Demonstration of Encryption Algorithm 66\u003c\/p\u003e \u003cp\u003e3.6.1.1 When the Number of Columns Selected in the Table is Even 66\u003c\/p\u003e \u003cp\u003e3.6.1.2 When the Number of Columns Selected in the Table is Odd 69\u003c\/p\u003e \u003cp\u003e3.6.2 Flowchart for Encryption 72\u003c\/p\u003e \u003cp\u003e3.7 Algorithm for Decryption 73\u003c\/p\u003e \u003cp\u003e3.7.1 Demonstration of Decryption Algorithm 73\u003c\/p\u003e \u003cp\u003e3.7.1.1 When the Number of Columns Selected in the Table is Even 73\u003c\/p\u003e \u003cp\u003e3.7.1.2 When the Number of Columns Selected in the Table is Odd 75\u003c\/p\u003e \u003cp\u003e3.7.2 Flowchart of Decryption Algorithm 78\u003c\/p\u003e \u003cp\u003e3.8 Experiment and Result 78\u003c\/p\u003e \u003cp\u003e3.9 Conclusion 80\u003c\/p\u003e \u003cp\u003eReferences 80\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Cutaneous Disease Optimization Using Teledermatology Underresourced Clinics 85\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSupriya M., Murugan K., Shanmugaraja T. and Venkatesh T.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 86\u003c\/p\u003e \u003cp\u003e4.2 Materials and Methods 87\u003c\/p\u003e \u003cp\u003e4.2.1 Clinical Setting and Teledermatology Workflow 87\u003c\/p\u003e \u003cp\u003e4.2.2 Study Design, Data Collection, and Analysis 87\u003c\/p\u003e \u003cp\u003e4.3 Proposed System 88\u003c\/p\u003e \u003cp\u003e4.3.1 Teledermatology in an Underresourced Clinic 88\u003c\/p\u003e \u003cp\u003e4.3.2 Teledermatology Consultations from Uninsured Patients 89\u003c\/p\u003e \u003cp\u003e4.3.3 Teledermatology for Patients Lacking Access to Dermatologists 90\u003c\/p\u003e \u003cp\u003e4.3.4 Teledermatologist Management from Nonspecialists 92\u003c\/p\u003e \u003cp\u003e4.3.5 Segment Factors of Referring PCPs and Their Patients 93\u003c\/p\u003e \u003cp\u003e4.3.6 Teledermatology Operational Considerations 94\u003c\/p\u003e \u003cp\u003e4.3.7 Instruction of PCPs 94\u003c\/p\u003e \u003cp\u003e4.4 Challenges 95\u003c\/p\u003e \u003cp\u003e4.5 Results and Discussion 95\u003c\/p\u003e \u003cp\u003e4.5.1 Challenges of Referring to Teledermatology Services 96\u003c\/p\u003e \u003cp\u003eReferences 98\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Cognitive Assessment Based on Eye Tracking Using Device-Embedded Cameras via Tele-Neuropsychology 101\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eShanmugaraja T., Venkatesh T., Supriya M. and Murugan K.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 102\u003c\/p\u003e \u003cp\u003e5.2 Materials and Methods 102\u003c\/p\u003e \u003cp\u003e5.3 Framework Elements 102\u003c\/p\u003e \u003cp\u003e5.3.1 Eye Tracker Camera 102\u003c\/p\u003e \u003cp\u003e5.3.2 Test Construction 103\u003c\/p\u003e \u003cp\u003e5.3.3 Web Camera 106\u003c\/p\u003e \u003cp\u003e5.3.4 Camera for Eye Tracking 106\u003c\/p\u003e \u003cp\u003e5.4 Proposed System 106\u003c\/p\u003e \u003cp\u003e5.4.1 Camera for Tracking Eye 106\u003c\/p\u003e \u003cp\u003e5.4.2 Web Camera 108\u003c\/p\u003e \u003cp\u003e5.4.3 Scoring 108\u003c\/p\u003e \u003cp\u003e5.4.4 Eye Tracking Camera 108\u003c\/p\u003e \u003cp\u003e5.4.5 Web Camera Human-Coded Scoring 108\u003c\/p\u003e \u003cp\u003e5.5 Subjects 109\u003c\/p\u003e \u003cp\u003e5.5.1 Characteristics of Subject 109\u003c\/p\u003e \u003cp\u003e5.6 Methodology 110\u003c\/p\u003e \u003cp\u003e5.6.1 Analysis of Data 110\u003c\/p\u003e \u003cp\u003e5.7 Results 110\u003c\/p\u003e \u003cp\u003e5.8 Discussion 112\u003c\/p\u003e \u003cp\u003e5.9 Conclusion 114\u003c\/p\u003e \u003cp\u003eReferences 115\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Fuzzy-Based Patient Health Monitoring System 117\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eVenkatesh T., Murugan K., Supriya M., Shanmugaraja T. and Rekha Chakravarthi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 118\u003c\/p\u003e \u003cp\u003e6.1.1 General Problem 119\u003c\/p\u003e \u003cp\u003e6.1.2 Existing Patient Monitoring and Diagnosis Systems 119\u003c\/p\u003e \u003cp\u003e6.1.3 Fuzzy Logic Systems 120\u003c\/p\u003e \u003cp\u003e6.2 System Design 122\u003c\/p\u003e \u003cp\u003e6.2.1 Hardware Requirements 122\u003c\/p\u003e \u003cp\u003e6.2.1.1 Functional Requirements 123\u003c\/p\u003e \u003cp\u003e6.2.1.2 Nonfunctional Specifications 125\u003c\/p\u003e \u003cp\u003e6.3 Software Architecture 125\u003c\/p\u003e \u003cp\u003e6.3.1 The Data Acquisition Unit (DAQ) Application Programmable Interface (API) 126\u003c\/p\u003e \u003cp\u003e6.3.2 Flowchart—API 128\u003c\/p\u003e \u003cp\u003e6.3.3 Foreign Tag IDs 129\u003c\/p\u003e \u003cp\u003e6.3.4 Database Manager 130\u003c\/p\u003e \u003cp\u003e6.3.5 Database Designing 130\u003c\/p\u003e \u003cp\u003e6.3.6 The Fuzzy Logic System 131\u003c\/p\u003e \u003cp\u003e6.3.6.1 Introduction to Fuzzy Logic 131\u003c\/p\u003e \u003cp\u003e6.3.6.2 The Modified Prior Alerting Score (MPAS) 132\u003c\/p\u003e \u003cp\u003e6.3.6.3 Structure of the Fuzzy Logic System 134\u003c\/p\u003e \u003cp\u003e6.3.7 Designing a System in Fuzzy 135\u003c\/p\u003e \u003cp\u003e6.3.7.1 Input Variables 135\u003c\/p\u003e \u003cp\u003e6.3.7.2 The Output Variable 138\u003c\/p\u003e \u003cp\u003e6.4 Results and Discussion 140\u003c\/p\u003e \u003cp\u003e6.4.1 Hardware Sensors Validation 140\u003c\/p\u003e \u003cp\u003e6.4.2 Implementations, Testing, and Evaluation of the Fuzzy Logic Engine 141\u003c\/p\u003e \u003cp\u003e6.4.3 Normal Group (NRM) 146\u003c\/p\u003e \u003cp\u003e6.4.4 Low Risk Group 146\u003c\/p\u003e \u003cp\u003e6.4.5 High Risk Group (HRG) 153\u003c\/p\u003e \u003cp\u003e6.5 Conclusions and Future Work 155\u003c\/p\u003e \u003cp\u003e6.5.1 Summary and Concluding Remarks 155\u003c\/p\u003e \u003cp\u003e6.5.2 Future Directions 155\u003c\/p\u003e \u003cp\u003eReferences 155\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Artificial Intelligence: A Key for Detecting COVID-19 Using Chest Radiography 159\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eC. Vinothini, P. Anitha, Priya J., Abirami A. and Akash S.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 160\u003c\/p\u003e \u003cp\u003e7.2 Related Work 162\u003c\/p\u003e \u003cp\u003e7.2.1 Traditional Approach 162\u003c\/p\u003e \u003cp\u003e7.2.2 Deep Learning–Based Approach 163\u003c\/p\u003e \u003cp\u003e7.3 Materials and Methods 163\u003c\/p\u003e \u003cp\u003e7.3.1 Data Set and Data Pre-Processing 163\u003c\/p\u003e \u003cp\u003e7.3.2 Proposed Model 165\u003c\/p\u003e \u003cp\u003e7.4 Experiment and Result 171\u003c\/p\u003e \u003cp\u003e7.4.1 Experiment Setup 171\u003c\/p\u003e \u003cp\u003e7.4.2 Comparison with Other Models 173\u003c\/p\u003e \u003cp\u003e7.5 Results 174\u003c\/p\u003e \u003cp\u003e7.6 Conclusion 175\u003c\/p\u003e \u003cp\u003eReferences 176\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 An Efficient IoT Framework for Patient Monitoring and Predicting Heart Disease Based on Machine Learning Algorithms 179\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eShanthi S., Nidhya R., Uma Perumal and Manish Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 180\u003c\/p\u003e \u003cp\u003e8.2 Literature Survey 182\u003c\/p\u003e \u003cp\u003e8.3 Machine Learning Algorithms 183\u003c\/p\u003e \u003cp\u003e8.4 Problem Statement 184\u003c\/p\u003e \u003cp\u003e8.5 Proposed Work 185\u003c\/p\u003e \u003cp\u003e8.5.1 Data Set Description 185\u003c\/p\u003e \u003cp\u003e8.5.2 Collection of Values Through Sensor Nodes 186\u003c\/p\u003e \u003cp\u003e8.5.3 Storage of Data in Cloud 187\u003c\/p\u003e \u003cp\u003e8.5.4 Prediction with Machine Learning Algorithms 188\u003c\/p\u003e \u003cp\u003e8.5.4.1 Data Cleaning and Preparation 188\u003c\/p\u003e \u003cp\u003e8.5.4.2 Data Splitting 189\u003c\/p\u003e \u003cp\u003e8.5.4.3 Training and Testing 189\u003c\/p\u003e \u003cp\u003e8.5.5 Machine Learning Algorithms 189\u003c\/p\u003e \u003cp\u003e8.5.5.1 Naive Bayes Algorithm 189\u003c\/p\u003e \u003cp\u003e8.5.5.2 Decision Tree Algorithm 190\u003c\/p\u003e \u003cp\u003e8.5.5.3 K-Neighbors Classifier 191\u003c\/p\u003e \u003cp\u003e8.5.5.4 Logistic Regression 192\u003c\/p\u003e \u003cp\u003e8.6 Performance Analysis and Evaluation 192\u003c\/p\u003e \u003cp\u003e8.7 Conclusion 197\u003c\/p\u003e \u003cp\u003eReferences 197\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 BABW: Biometric-Based Authentication Using DWT and FFNN 201\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eR. Kingsy Grace, M.S. Geetha Devasena and R. Manimegalai\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 202\u003c\/p\u003e \u003cp\u003e9.2 Literature Survey 203\u003c\/p\u003e \u003cp\u003e9.3 BABW: Biometric Authentication Using Brain Waves 208\u003c\/p\u003e \u003cp\u003e9.4 Results and Discussion 211\u003c\/p\u003e \u003cp\u003e9.5 Conclusion 215\u003c\/p\u003e \u003cp\u003eReferences 216\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Autism Screening Tools With Machine Learning and Deep Learning Methods: A Review 221\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePavithra D., Jayanthi A. N., Nidhya R. and Balamurugan S.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 222\u003c\/p\u003e \u003cp\u003e10.2 Autism Screening Methods 223\u003c\/p\u003e \u003cp\u003e10.2.1 Autism Screening Instrument for Educational Planning—3rd Version 224\u003c\/p\u003e \u003cp\u003e10.2.2 Quantitative Checklist for Autism in Toddlers 224\u003c\/p\u003e \u003cp\u003e10.2.3 Autism Behavior Checklist 224\u003c\/p\u003e \u003cp\u003e10.2.4 Developmental Behavior Checklist-Early Screen 225\u003c\/p\u003e \u003cp\u003e10.2.5 Childhood Autism Rating Scale Version 2 225\u003c\/p\u003e \u003cp\u003e10.2.6 Autism Spectrum Screening Questionnaire (ASSQ) 226\u003c\/p\u003e \u003cp\u003e10.2.7 Early Screening for Autistic Traits 226\u003c\/p\u003e \u003cp\u003e10.2.8 Autism Spectrum Quotient 226\u003c\/p\u003e \u003cp\u003e10.2.9 Social Communication Questionnaire 227\u003c\/p\u003e \u003cp\u003e10.2.10 Child Behavior Check List 227\u003c\/p\u003e \u003cp\u003e10.2.11 Indian Scale for Assessment of Autism 227\u003c\/p\u003e \u003cp\u003e10.3 Machine Learning in ASD Screening and Diagnosis 228\u003c\/p\u003e \u003cp\u003e10.4 DL in ASD Diagnosis 238\u003c\/p\u003e \u003cp\u003e10.5 Conclusion 242\u003c\/p\u003e \u003cp\u003eReferences 242\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Drug Target Module Mining Using Biological Multifunctional Score-Based Coclustering 249\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eR. Gowri and R. Rathipriya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 249\u003c\/p\u003e \u003cp\u003e11.2 Literature Study 250\u003c\/p\u003e \u003cp\u003e11.3 Materials and Methods 253\u003c\/p\u003e \u003cp\u003e11.3.1 Biological Terminologies 253\u003c\/p\u003e \u003cp\u003e11.3.2 Functional Coherence 256\u003c\/p\u003e \u003cp\u003e11.3.3 Biological Significances 257\u003c\/p\u003e \u003cp\u003e11.3.4 Existing Approach: MR-CoC 257\u003c\/p\u003e \u003cp\u003e11.4 Proposed Approach: MR-CoCmulti 258\u003c\/p\u003e \u003cp\u003e11.4.1 Biological Score Measures for DTM 259\u003c\/p\u003e \u003cp\u003e11.4.2 Multifunctional Score-Based Co-Clustering Approach 259\u003c\/p\u003e \u003cp\u003e11.5 Experimental Analysis 264\u003c\/p\u003e \u003cp\u003e11.5.1 Experimental Results 265\u003c\/p\u003e \u003cp\u003e11.6 Discussion 280\u003c\/p\u003e \u003cp\u003e11.7 Conclusion 280\u003c\/p\u003e \u003cp\u003eAcknowledgment 281\u003c\/p\u003e \u003cp\u003eReferences 281\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 The Ascendant Role of Machine Learning Algorithms in the Prediction of Breast Cancer and Treatment Using Telehealth 285\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJothi K.R., Oswalt Manoj S., Ananya Singhal and Suruchi Parashar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 286\u003c\/p\u003e \u003cp\u003e12.1.1 Objective 287\u003c\/p\u003e \u003cp\u003e12.1.2 Description and Goals 287\u003c\/p\u003e \u003cp\u003e12.1.2.1 Data Exploration 288\u003c\/p\u003e \u003cp\u003e12.1.2.2 Data Pre-Processing 288\u003c\/p\u003e \u003cp\u003e12.1.2.3 Feature Scaling 288\u003c\/p\u003e \u003cp\u003e12.1.2.4 Model Selection and Evaluation 288\u003c\/p\u003e \u003cp\u003e12.2 Literature Review 289\u003c\/p\u003e \u003cp\u003e12.3 Architecture Design and Implementation 304\u003c\/p\u003e \u003cp\u003e12.4 Results and Discussion 310\u003c\/p\u003e \u003cp\u003e12.5 Conclusion 312\u003c\/p\u003e \u003cp\u003e12.6 Future Work 313\u003c\/p\u003e \u003cp\u003eReferences 314\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Remote Patient Monitoring: Data Sharing and Prediction Using Machine Learning 317\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMohammed Hameed Alhameed, S. Shanthi, Uma Perumal and Fathe Jeribi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 318\u003c\/p\u003e \u003cp\u003e13.1.1 Patient Monitoring in Healthcare System 318\u003c\/p\u003e \u003cp\u003e13.2 Literature Survey 321\u003c\/p\u003e \u003cp\u003e13.3 Problem Statement 322\u003c\/p\u003e \u003cp\u003e13.4 Machine Learning 322\u003c\/p\u003e \u003cp\u003e13.4.1 Introduction 322\u003c\/p\u003e \u003cp\u003e13.4.2 Cloud Computing 324\u003c\/p\u003e \u003cp\u003e13.4.3 Design and Architecture 325\u003c\/p\u003e \u003cp\u003e13.5 Proposed System 326\u003c\/p\u003e \u003cp\u003e13.6 Results and Discussions 331\u003c\/p\u003e \u003cp\u003e13.7 Privacy and Security Challenges 333\u003c\/p\u003e \u003cp\u003e13.8 Conclusions and Future Enhancement 334\u003c\/p\u003e \u003cp\u003eReferences 335\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Investigations on Machine Learning Models to Envisage Coronavirus in Patients 339\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eR. Sabitha, J. Shanthini, R.M. Bhavadharini and S. Karthik\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 340\u003c\/p\u003e \u003cp\u003e14.2 Categories of ML Algorithms in Healthcare 341\u003c\/p\u003e \u003cp\u003e14.3 Why ML to Fight COVID-19? Tools and Techniques 343\u003c\/p\u003e \u003cp\u003e14.4 Highlights of ML Algorithms Under Consideration 344\u003c\/p\u003e \u003cp\u003e14.5 Experimentation and Investigation 349\u003c\/p\u003e \u003cp\u003e14.6 Comparative Analysis of the Algorithms 353\u003c\/p\u003e \u003cp\u003e14.7 Scope of Enhancement for Better Investigation 354\u003c\/p\u003e \u003cp\u003eReferences 356\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Healthcare Informatics: Emerging Trends, Challenges, and Analysis of Medical Imaging 359\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eG. Karthick and N.S. Nithya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Emerging Trends and Challenges in Healthcare Informatics 360\u003c\/p\u003e \u003cp\u003e15.1.1 Advanced Technologies in Healthcare Informatics 360\u003c\/p\u003e \u003cp\u003e15.1.2 Intelligent Smart Healthcare Devices Using IoT With DL 361\u003c\/p\u003e \u003cp\u003e15.1.3 Cyber Security in Healthcare Informatics 362\u003c\/p\u003e \u003cp\u003e15.1.4 Trends, Challenges, and Issues in Healthcare IT Analytics 363\u003c\/p\u003e \u003cp\u003e15.2 Performance Analysis of Medical Image Compression Using Wavelet Functions 364\u003c\/p\u003e \u003cp\u003e15.2.1 Introduction 364\u003c\/p\u003e \u003cp\u003e15.2.2 Materials and Methods 366\u003c\/p\u003e \u003cp\u003e15.2.3 Wavelet Basis Functions 367\u003c\/p\u003e \u003cp\u003e15.2.3.1 Haar Wavelet 367\u003c\/p\u003e \u003cp\u003e15.2.3.2 db Wavelet 368\u003c\/p\u003e \u003cp\u003e15.2.3.3 bior Wavelet 368\u003c\/p\u003e \u003cp\u003e15.2.3.4 rbio Wavelet 368\u003c\/p\u003e \u003cp\u003e15.2.3.5 Symlets Wavelet 369\u003c\/p\u003e \u003cp\u003e15.2.3.6 coif Wavelet 369\u003c\/p\u003e \u003cp\u003e15.2.3.7 dmey Wavelet 369\u003c\/p\u003e \u003cp\u003e15.2.3.8 fk Wavelet 369\u003c\/p\u003e \u003cp\u003e15.2.4 Compression Methods 370\u003c\/p\u003e \u003cp\u003e15.2.4.1 Embedded Zero-Trees of Wavelet Transform 370\u003c\/p\u003e \u003cp\u003e15.2.4.2 Set Partitioning in Hierarchical Trees 370\u003c\/p\u003e \u003cp\u003e15.2.4.3 Adaptively Scanned Wavelet Difference Reduction 370\u003c\/p\u003e \u003cp\u003e15.2.4.4 Coefficient Thresholding 371\u003c\/p\u003e \u003cp\u003e15.3 Results and Discussion 371\u003c\/p\u003e \u003cp\u003e15.3.1 Mean Square Error 371\u003c\/p\u003e \u003cp\u003e15.3.2 Peak Signal to Noise Ratio 371\u003c\/p\u003e \u003cp\u003e15.4 Conclusion 380\u003c\/p\u003e \u003cp\u003e15.4.1 Summary 380\u003c\/p\u003e \u003cp\u003eReferences 380\u003c\/p\u003e \u003cp\u003eIndex 383\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48866419704151,"sku":"9781119841760","price":153.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119841760.jpg?v=1722278556","url":"https:\/\/bookcurl.com\/products\/telehealthcare-9781119841760","provider":"Book Curl","version":"1.0","type":"link"}