R. Nidhya, PhD, is an assistant professor in the Department of Computer Science & Engineering, Madanapalle Institute of Technology & Science, affiliated to Jawaharlal Nehru Technical University, Anantapuram, India. She has published many research articles in SCI journals and her research interests include wireless body area networks, network security, and data mining.Manish Kumar, PhD, is an assistant professor in the School of Computer Science & Engineering, VIT Chennai. His research interests include soft computing applications for bioinformatics problems and computational intelligence. S. Balamurugan, PhD, is the Director of Research and Development, Intelligent Research Consultancy Services (iRCS), Coimbatore, Tamilnadu, India. He is also Director of the Albert Einstein Engineering and Research Labs (AEER Labs), as well as Vice-Chairman, Renewable Energy Society of India (RESI), India. He has published 45 books, 200+ international journals/ conferences, and 35 patents.

• Preface xv1 Machine Learning–Assisted Remote Patient Monitoring with Data Analytics 1Vinutha D. C., Kavyashree and G. T. Raju1.1 Introduction 21.1.1 Traditional Patient Monitoring System 21.1.2 Remote Monitoring System 31.1.3 Challenges in RPM 41.2 Literature Survey 51.2.1 Machine Learning Approaches in Patient Monitoring 71.3 Machine Learning in RPM 81.3.1 Support Vector Machine 91.3.2 Decision Tree 101.3.3 Random Forest 111.3.4 Logistic Regression 111.3.5 Genetic Algorithm 121.3.6 Simple Linear Regression 121.3.7 KNN Algorithm 131.3.8 Naive Bayes Algorithm 141.4 System Architecture 151.4.1 Data Collection 161.4.2 Data Pre-Processing 171.4.3 Apply Machine Learning Algorithm and Prediction 181.5 Results 211.6 Future Enhancement 231.7 Conclusion 24References 242 A Survey on Recent Computer-Aided Diagnosis for Detecting Diabetic Retinopathy 27Priyadharsini C., Jagadeesh Kannan R. and Farookh Khadeer Hussain2.1 Introduction 282.2 Diabetic Retinopathy 282.2.1 Features of DR 282.2.2 Stages of DR 292.3 Overview of DL Models 312.3.1 Convolution Neural Network 312.3.2 Autoencoders 322.3.3 Boltzmann Machine and Deep Belief Network 322.4 Data Set 332.5 Performance Metrics 342.6 Literature Survey 362.6.1 Segmentation of Blood Vessels 362.6.2 Optic Disc Feature 492.6.3 Lesion Detections 502.6.3.1 Exudate Detection 502.6.3.2 MA and HM 512.6.4 DR Classification 512.7 Discussion and Future Directions 522.8 Conclusion 53References 533 A New Improved Cryptography Method-Based e-Health Application in Cloud Computing Environment 59Dipesh Kumar, Nirupama Mandal and Yugal Kumar3.1 Introduction 603.1.1 Contribution 613.2 Motivation 623.3 Related Works 623.4 Challenges 643.5 Proposed Work 643.6 Proposed Algorithm for Encryption 663.6.1 Demonstration of Encryption Algorithm 663.6.1.1 When the Number of Columns Selected in the Table is Even 663.6.1.2 When the Number of Columns Selected in the Table is Odd 693.6.2 Flowchart for Encryption 723.7 Algorithm for Decryption 733.7.1 Demonstration of Decryption Algorithm 733.7.1.1 When the Number of Columns Selected in the Table is Even 733.7.1.2 When the Number of Columns Selected in the Table is Odd 753.7.2 Flowchart of Decryption Algorithm 783.8 Experiment and Result 783.9 Conclusion 80References 804 Cutaneous Disease Optimization Using Teledermatology Underresourced Clinics 85Supriya M., Murugan K., Shanmugaraja T. and Venkatesh T.4.1 Introduction 864.2 Materials and Methods 874.2.1 Clinical Setting and Teledermatology Workflow 874.2.2 Study Design, Data Collection, and Analysis 874.3 Proposed System 884.3.1 Teledermatology in an Underresourced Clinic 884.3.2 Teledermatology Consultations from Uninsured Patients 894.3.3 Teledermatology for Patients Lacking Access to Dermatologists 904.3.4 Teledermatologist Management from Nonspecialists 924.3.5 Segment Factors of Referring PCPs and Their Patients 934.3.6 Teledermatology Operational Considerations 944.3.7 Instruction of PCPs 944.4 Challenges 954.5 Results and Discussion 954.5.1 Challenges of Referring to Teledermatology Services 96References 985 Cognitive Assessment Based on Eye Tracking Using Device-Embedded Cameras via Tele-Neuropsychology 101Shanmugaraja T., Venkatesh T., Supriya M. and Murugan K.5.1 Introduction 1025.2 Materials and Methods 1025.3 Framework Elements 1025.3.1 Eye Tracker Camera 1025.3.2 Test Construction 1035.3.3 Web Camera 1065.3.4 Camera for Eye Tracking 1065.4 Proposed System 1065.4.1 Camera for Tracking Eye 1065.4.2 Web Camera 1085.4.3 Scoring 1085.4.4 Eye Tracking Camera 1085.4.5 Web Camera Human-Coded Scoring 1085.5 Subjects 1095.5.1 Characteristics of Subject 1095.6 Methodology 1105.6.1 Analysis of Data 1105.7 Results 1105.8 Discussion 1125.9 Conclusion 114References 1156 Fuzzy-Based Patient Health Monitoring System 117Venkatesh T., Murugan K., Supriya M., Shanmugaraja T. and Rekha Chakravarthi6.1 Introduction 1186.1.1 General Problem 1196.1.2 Existing Patient Monitoring and Diagnosis Systems 1196.1.3 Fuzzy Logic Systems 1206.2 System Design 1226.2.1 Hardware Requirements 1226.2.1.1 Functional Requirements 1236.2.1.2 Nonfunctional Specifications 1256.3 Software Architecture 1256.3.1 The Data Acquisition Unit (DAQ) Application Programmable Interface (API) 1266.3.2 Flowchart—API 1286.3.3 Foreign Tag IDs 1296.3.4 Database Manager 1306.3.5 Database Designing 1306.3.6 The Fuzzy Logic System 1316.3.6.1 Introduction to Fuzzy Logic 1316.3.6.2 The Modified Prior Alerting Score (MPAS) 1326.3.6.3 Structure of the Fuzzy Logic System 1346.3.7 Designing a System in Fuzzy 1356.3.7.1 Input Variables 1356.3.7.2 The Output Variable 1386.4 Results and Discussion 1406.4.1 Hardware Sensors Validation 1406.4.2 Implementations, Testing, and Evaluation of the Fuzzy Logic Engine 1416.4.3 Normal Group (NRM) 1466.4.4 Low Risk Group 1466.4.5 High Risk Group (HRG) 1536.5 Conclusions and Future Work 1556.5.1 Summary and Concluding Remarks 1556.5.2 Future Directions 155References 1557 Artificial Intelligence: A Key for Detecting COVID-19 Using Chest Radiography 159C. Vinothini, P. Anitha, Priya J., Abirami A. and Akash S.7.1 Introduction 1607.2 Related Work 1627.2.1 Traditional Approach 1627.2.2 Deep Learning–Based Approach 1637.3 Materials and Methods 1637.3.1 Data Set and Data Pre-Processing 1637.3.2 Proposed Model 1657.4 Experiment and Result 1717.4.1 Experiment Setup 1717.4.2 Comparison with Other Models 1737.5 Results 1747.6 Conclusion 175References 1768 An Efficient IoT Framework for Patient Monitoring and Predicting Heart Disease Based on Machine Learning Algorithms 179Shanthi S., Nidhya R., Uma Perumal and Manish Kumar8.1 Introduction 1808.2 Literature Survey 1828.3 Machine Learning Algorithms 1838.4 Problem Statement 1848.5 Proposed Work 1858.5.1 Data Set Description 1858.5.2 Collection of Values Through Sensor Nodes 1868.5.3 Storage of Data in Cloud 1878.5.4 Prediction with Machine Learning Algorithms 1888.5.4.1 Data Cleaning and Preparation 1888.5.4.2 Data Splitting 1898.5.4.3 Training and Testing 1898.5.5 Machine Learning Algorithms 1898.5.5.1 Naive Bayes Algorithm 1898.5.5.2 Decision Tree Algorithm 1908.5.5.3 K-Neighbors Classifier 1918.5.5.4 Logistic Regression 1928.6 Performance Analysis and Evaluation 1928.7 Conclusion 197References 1979 BABW: Biometric-Based Authentication Using DWT and FFNN 201R. Kingsy Grace, M.S. Geetha Devasena and R. Manimegalai9.1 Introduction 2029.2 Literature Survey 2039.3 BABW: Biometric Authentication Using Brain Waves 2089.4 Results and Discussion 2119.5 Conclusion 215References 21610 Autism Screening Tools With Machine Learning and Deep Learning Methods: A Review 221Pavithra D., Jayanthi A. N., Nidhya R. and Balamurugan S.10.1 Introduction 22210.2 Autism Screening Methods 22310.2.1 Autism Screening Instrument for Educational Planning—3rd Version 22410.2.2 Quantitative Checklist for Autism in Toddlers 22410.2.3 Autism Behavior Checklist 22410.2.4 Developmental Behavior Checklist-Early Screen 22510.2.5 Childhood Autism Rating Scale Version 2 22510.2.6 Autism Spectrum Screening Questionnaire (ASSQ) 22610.2.7 Early Screening for Autistic Traits 22610.2.8 Autism Spectrum Quotient 22610.2.9 Social Communication Questionnaire 22710.2.10 Child Behavior Check List 22710.2.11 Indian Scale for Assessment of Autism 22710.3 Machine Learning in ASD Screening and Diagnosis 22810.4 DL in ASD Diagnosis 23810.5 Conclusion 242References 24211 Drug Target Module Mining Using Biological Multifunctional Score-Based Coclustering 249R. Gowri and R. Rathipriya11.1 Introduction 24911.2 Literature Study 25011.3 Materials and Methods 25311.3.1 Biological Terminologies 25311.3.2 Functional Coherence 25611.3.3 Biological Significances 25711.3.4 Existing Approach: MR-CoC 25711.4 Proposed Approach: MR-CoCmulti 25811.4.1 Biological Score Measures for DTM 25911.4.2 Multifunctional Score-Based Co-Clustering Approach 25911.5 Experimental Analysis 26411.5.1 Experimental Results 26511.6 Discussion 28011.7 Conclusion 280Acknowledgment 281References 28112 The Ascendant Role of Machine Learning Algorithms in the Prediction of Breast Cancer and Treatment Using Telehealth 285Jothi K.R., Oswalt Manoj S., Ananya Singhal and Suruchi Parashar12.1 Introduction 28612.1.1 Objective 28712.1.2 Description and Goals 28712.1.2.1 Data Exploration 28812.1.2.2 Data Pre-Processing 28812.1.2.3 Feature Scaling 28812.1.2.4 Model Selection and Evaluation 28812.2 Literature Review 28912.3 Architecture Design and Implementation 30412.4 Results and Discussion 31012.5 Conclusion 31212.6 Future Work 313References 31413 Remote Patient Monitoring: Data Sharing and Prediction Using Machine Learning 317Mohammed Hameed Alhameed, S. Shanthi, Uma Perumal and Fathe Jeribi13.1 Introduction 31813.1.1 Patient Monitoring in Healthcare System 31813.2 Literature Survey 32113.3 Problem Statement 32213.4 Machine Learning 32213.4.1 Introduction 32213.4.2 Cloud Computing 32413.4.3 Design and Architecture 32513.5 Proposed System 32613.6 Results and Discussions 33113.7 Privacy and Security Challenges 33313.8 Conclusions and Future Enhancement 334References 33514 Investigations on Machine Learning Models to Envisage Coronavirus in Patients 339R. Sabitha, J. Shanthini, R.M. Bhavadharini and S. Karthik14.1 Introduction 34014.2 Categories of ML Algorithms in Healthcare 34114.3 Why ML to Fight COVID-19? Tools and Techniques 34314.4 Highlights of ML Algorithms Under Consideration 34414.5 Experimentation and Investigation 34914.6 Comparative Analysis of the Algorithms 35314.7 Scope of Enhancement for Better Investigation 354References 35615 Healthcare Informatics: Emerging Trends, Challenges, and Analysis of Medical Imaging 359G. Karthick and N.S. Nithya15.1 Emerging Trends and Challenges in Healthcare Informatics 36015.1.1 Advanced Technologies in Healthcare Informatics 36015.1.2 Intelligent Smart Healthcare Devices Using IoT With DL 36115.1.3 Cyber Security in Healthcare Informatics 36215.1.4 Trends, Challenges, and Issues in Healthcare IT Analytics 36315.2 Performance Analysis of Medical Image Compression Using Wavelet Functions 36415.2.1 Introduction 36415.2.2 Materials and Methods 36615.2.3 Wavelet Basis Functions 36715.2.3.1 Haar Wavelet 36715.2.3.2 db Wavelet 36815.2.3.3 bior Wavelet 36815.2.3.4 rbio Wavelet 36815.2.3.5 Symlets Wavelet 36915.2.3.6 coif Wavelet 36915.2.3.7 dmey Wavelet 36915.2.3.8 fk Wavelet 36915.2.4 Compression Methods 37015.2.4.1 Embedded Zero-Trees of Wavelet Transform 37015.2.4.2 Set Partitioning in Hierarchical Trees 37015.2.4.3 Adaptively Scanned Wavelet Difference Reduction 37015.2.4.4 Coefficient Thresholding 37115.3 Results and Discussion 37115.3.1 Mean Square Error 37115.3.2 Peak Signal to Noise Ratio 37115.4 Conclusion 38015.4.1 Summary 380References 380Index 383