A. Suresh, PhD is an associate professor, Department of the Networking and Communications, SRM Institute of Science & Technology, Kattankulathur, Tamil Nadu, India. He has nearly two decades of experience in teaching and his areas of specialization are data mining, artificial intelligence, image processing, multimedia, and system software. He has published 6 patents and more than 100 papers in international journals.S. Vimal, PhD is an assistant professor in the Department of Artificial Intelligence & DS, Ramco Institute of Technology, Tamilnadu, India. He is the editor of 3 books and guest-edited multiple journal special issues. He has more than 15 years of teaching experience. Y. Harold Robinson, PhD is currently working in the School of Technology and Engineering, Vellore Institute of Technology, Vellore, India. He has published more than 50 papers in various international journals and presented more than 70 papers in both national and international conferences. Dhinesh Kumar Ramaswami, BE in Computer Science, is a Senior Consultant at Capgemini America Inc. He has over 9 years of experience in software development and specializes in various .net technologies. He has published more than 15 papers in international journals and national and international conferences. R. Udendhran, PhD is an assistant professor, Department of Computer Science and Engineering at Sri Sairam Institute of Technology, Chennai, Tamil Nadu, India. He has published about 20 papers in international journals.

• Preface xv1 Probabilistic Optimization of Machine Learning Algorithms for Heart Disease Prediction 1Jaspreet Kaur, Bharti Joshi and Rajashree Shedge1.1 Introduction 21.1.1 Scope and Motivation 31.2 Literature Review 41.2.1 Comparative Analysis 51.2.2 Survey Analysis 51.3 Tools and Techniques 101.3.1 Description of Dataset 111.3.2 Machine Learning Algorithm 121.3.3 Decision Tree 141.3.4 Random Forest 151.3.5 Naive Bayes Algorithm 161.3.6 K Means Algorithm 181.3.7 Ensemble Method 181.3.7.1 Bagging 191.3.7.2 Boosting 191.3.7.3 Stacking 191.3.7.4 Majority Vote 191.4 Proposed Method 201.4.1 Experiment and Analysis 201.4.2 Method 221.5 Conclusion 25References 262 Cancerous Cells Detection in Lung Organs of Human Body: IoT-Based Healthcare 4.0 Approach 29Rohit Rastogi, D.K. Chaturvedi, Sheelu Sagar, Neeti Tandon and Mukund Rastogi2.1 Introduction 302.1.1 Motivation to the Study 302.1.1.1 Problem Statements 312.1.1.2 Authors’ Contributions 312.1.1.3 Research Manuscript Organization 312.1.1.4 Definitions 322.1.2 Computer-Aided Diagnosis System (CADe or CADx) 322.1.3 Sensors for the Internet of Things 322.1.4 Wireless and Wearable Sensors for Health Informatics 332.1.5 Remote Human’s Health and Activity Monitoring 332.1.6 Decision-Making Systems for Sensor Data 332.1.7 Artificial Intelligence and Machine Learning for Health Informatics 342.1.8 Health Sensor Data Management 342.1.9 Multimodal Data Fusion for Healthcare 352.1.10 Heterogeneous Data Fusion and Context-Aware Systems: A Context-Aware Data Fusion Approach for Health-IoT 352.2 Literature Review 352.3 Proposed Systems 372.3.1 Framework or Architecture of the Work 382.3.2 Model Steps and Parameters 382.3.3 Discussions 392.4 Experimental Results and Analysis 392.4.1 Tissue Characterization and Risk Stratification 392.4.2 Samples of Cancer Data and Analysis 402.5 Novelties 422.6 Future Scope, Limitations, and Possible Applications 422.7 Recommendations and Consideration 432.8 Conclusions 43References 433 Computational Predictors of the Predominant Protein Function: SARS-CoV-2 Case 47Carlos Polanco, Manlio F. Márquez and Gilberto Vargas-Alarcón3.1 Introduction 483.2 Human Coronavirus Types 493.3 The SARS-CoV-2 Pandemic Impact 503.3.1 RNA Virus vs DNA Virus 513.3.2 The Coronaviridae Family 513.3.3 The SARS-CoV-2 Structural Proteins 523.3.4 Protein Representations 523.4 Computational Predictors 533.4.1 Supervised Algorithms 533.4.2 Non-Supervised Algorithms 543.5 Polarity Index Method® 543.5.1 The PIM® Profile 543.5.2 Advantages 553.5.3 Disadvantages 553.5.4 SARS-CoV-2 Recognition Using PIM® Profile 553.6 Future Implications 593.7 Acknowledgments 60References 604 Deep Learning in Gait Abnormality Detection: Principles and Illustrations 63Saikat Chakraborty, Sruti Sambhavi and Anup Nandy4.1 Introduction 634.2 Background 654.2.1 LSTM 654.2.1.1 Vanilla LSTM 654.2.1.2 Bidirectional LSTM 664.3 Related Works 674.4 Methods 684.4.1 Data Collection and Analysis 684.4.2 Results and Discussion 694.5 Conclusion and Future Work 714.6 Acknowledgments 71References 715 Broad Applications of Network Embeddings in Computational Biology, Genomics, Medicine, and Health 73Akanksha Jaiswar, Devender Arora, Manisha Malhotra, Abhimati Shukla and Nivedita Rai5.1 Introduction 745.2 Types of Biological Networks 765.3 Methodologies in Network Embedding 765.4 Attributed and Non-Attributed Network Embedding 825.5 Applications of Network Embedding in Computational Biology 835.5.1 Understanding Genomic and Protein Interaction via Network Alignment 835.5.2 Pharmacogenomics 845.5.2.1 Drug-Target Interaction Prediction 845.5.2.2 Drug-Drug Interaction 845.5.2.3 Drug-Disease Interaction Prediction 855.5.2.4 Analysis of Adverse Drug Reaction 855.5.3 Function Prediction 865.5.4 Community Detection 865.5.5 Network Denoising 875.5.6 Analysis of Multi-Omics Data 875.6 Limitations of Network Embedding in Biology 875.7 Conclusion and Outlook 89References 896 Heart Disease Classification Using Regional Wall Thickness by Ensemble Classifier 99Prakash J., Vinoth Kumar B. and Sandhya R.6.1 Introduction 1006.2 Related Study 1016.3 Methodology 1036.3.1 Pre-Processing 1036.3.2 Region of Interest Extraction 1046.3.3 Segmentation 1056.3.4 Feature Extraction 1066.3.5 Disease Classification 1076.4 Implementation and Result Analysis 1086.4.1 Dataset Description 1086.4.2 Testbed 1086.4.3 Discussion 1086.4.3.1 K-Fold Cross-Validation 1106.4.3.2 Confusion Matrix 1106.5 Conclusion 115References 1157 Deep Learning for Medical Informatics and Public Health 117K. Aditya Shastry, Sanjay H. A., Lakshmi M. and Preetham N.7.1 Introduction 1187.2 Deep Learning Techniques in Medical Informatics and Public Health 1217.2.1 Autoencoders 1227.2.2 Recurrent Neural Network 1237.2.3 Convolutional Neural Network (CNN) 1247.2.4 Deep Boltzmann Machine 1267.2.5 Deep Belief Network 1277.3 Applications of Deep Learning in Medical Informatics and Public Health 1287.3.1 The Use of DL for Cancer Diagnosis 1287.3.2 DL in Disease Prediction and Treatment 1297.3.3 Future Applications 1337.4 Open Issues Concerning DL in Medical Informatics and Public Health 1357.5 Conclusion 139References 1408 An Insight Into Human Pose Estimation and Its Applications 147Shambhavi Mishra, Janamejaya Channegowda and Kasina Jyothi Swaroop8.1 Foundations of Human Pose Estimation 1478.2 Challenges to Human Pose Estimation 1498.2.1 Motion Blur 1508.2.2 Indistinct Background 1518.2.3 Occlusion or Self-Occlusion 1518.2.4 Lighting Conditions 1518.3 Analyzing the Dimensions 1528.3.1 2D Human Pose Estimation 1528.3.1.1 Single-Person Pose Estimation 1538.3.1.2 Multi-Person Pose Estimation 1538.3.2 3D Human Pose Estimation 1538.4 Standard Datasets for Human Pose Estimation 1548.4.1 Pascal VOC (Visual Object Classes) Dataset 1568.4.2 KTH Multi-View Football Dataset I 1568.4.3 KTH Multi-View Football Dataset II 1568.4.4 MPII Human Pose Dataset 1578.4.5 BBC Pose 1578.4.6 COCO Dataset 1578.4.7 J-HMDB Dataset 1588.4.8 Human3.6M Dataset 1588.4.9 DensePose 1588.4.10 AMASS Dataset 1598.5 Deep Learning Revolutionizing Pose Estimation 1598.5.1 Approaches in 2D Human Pose Estimation 1598.5.2 Approaches in 3D Human Pose Estimation 1638.6 Application of Human Pose Estimation in Medical Domains 1658.7 Conclusion 166References 1679 Brain Tumor Analysis Using Deep Learning: Sensor and IoT-Based Approach for Futuristic Healthcare 171Rohit Rastogi, D.K. Chaturvedi, Sheelu Sagar, Neeti Tandon and Akshit Rajan Rastogi9.1 Introduction 1729.1.1 Brain Tumor 1729.1.2 Big Data Analytics in Health Informatics 1729.1.3 Machine Learning in Healthcare 1739.1.4 Sensors for Internet of Things 1739.1.5 Challenges and Critical Issues of IoT in Healthcare 1749.1.6 Machine Learning and Artificial Intelligence for Health Informatics 1749.1.7 Health Sensor Data Management 1759.1.8 Multimodal Data Fusion for Healthcare 1759.1.9 Heterogeneous Data Fusion and Context-Aware Systems a Context-Aware Data Fusion Approach for Health-IoT 1769.1.10 Role of Technology in Addressing the Problem of Integration of Healthcare System 1769.2 Literature Survey 1779.3 System Design and Methodology 1799.3.1 System Design 1799.3.2 CNN Architecture 1809.3.3 Block Diagram 1819.3.4 Algorithm(s) 1819.3.5 Our Experimental Results, Interpretation, and Discussion 1839.3.6 Implementation Details 1839.3.7 Snapshots of Interfaces 1849.3.8 Performance Evaluation 1869.3.9 Comparison with Other Algorithms 1869.4 Novelty in Our Work 1869.5 Future Scope, Possible Applications, and Limitations 1889.6 Recommendations and Consideration 1889.7 Conclusions 188References 18910 Study of Emission From Medicinal Woods to Curb Threats of Pollution and Diseases: Global Healthcare Paradigm Shift in 21st Century 191Rohit Rastogi, Mamta Saxena, Devendra Kr. Chaturvedi, Sheelu Sagar, Neha Gupta, Harshit Gupta, Akshit Rajan Rastogi, Divya Sharma, Manu Bhardwaj and Pranav Sharma10.1 Introduction 19210.1.1 Scenario of Pollution and Need to Connect with Indian Culture 19210.1.2 Global Pollution Scenario 19210.1.3 Indian Crisis on Pollution and Worrying Stats 19310.1.4 Efforts Made to Curb Pollution World Wide 19410.1.5 Indian Ancient Vedic Sciences to Curb Pollution and Related Disease 19610.1.6 The Yajna Science: A Boon to Human Race From Rishi-Muni 19610.1.7 The Science of Mantra Associated With Yajna and Its Scientific Effects 19710.1.8 Effect of Different Woods and Cow Dung Used in Yajna 19710.1.9 Use of Sensors and IoT to Record Experimental Data 19810.1.10 Analysis and Pattern Recognition by ML and AI 19910.2 Literature Survey 20010.3 The Methodology and Protocols Followed 20110.4 Experimental Setup of an Experiment 20210.5 Results and Discussions 20210.5.1 Mango 20210.5.2 Bargad 20310.6 Applications of Yagya and Mantra Therapy in Pollution Control and Its Significance 20710.7 Future Research Perspectives 20710.8 Novelty of Our Research 20810.9 Recommendations 20810.10 Conclusions 209References 20911 An Economical Machine Learning Approach for Anomaly Detection in IoT Environment 215Ambika N.11.1 Introduction 21511.2 Literature Survey 21811.3 Proposed Work 22911.4 Analysis of the Work 23011.5 Conclusion 231References 23112 Indian Science of Yajna and Mantra to Cure Different Diseases: An Analysis Amidst Pandemic With a Simulated Approach 235Rohit Rastogi, Mamta Saxena, Devendra Kumar Chaturvedi, Mayank Gupta, Puru Jain, Rishabh Jain, Mohit Jain, Vishal Sharma, Utkarsh Sangam, Parul Singhal and Priyanshi Garg12.1 Introduction 23612.1.1 Different Types of Diseases 23612.1.1.1 Diabetes (Madhumeha) and Its Types 23612.1.1.2 TTH and Stress 23712.1.1.3 Anxiety 23712.1.1.4 Hypertension 23712.1.2 Machine Vision 23712.1.2.1 Medical Images and Analysis 23812.1.2.2 Machine Learning in Healthcare 23812.1.2.3 Artificial Intelligence in Healthcare 23912.1.3 Big Data and Internet of Things (IoT) 23912.1.4 Machine Learning in Association with Data Science and Analytics 23912.1.5 Yajna Science 24012.1.6 Mantra Science 24012.1.6.1 Positive Impact of Recital of Gayatri Mantra and OM Chanting 24112.1.6.2 Significance of Mantra on Indian Culture and Mythology 24112.1.7 Usefulness and Positive Aspect of Yoga Asanas and Pranayama 24112.1.8 Effects of Yajna and Mantra on Human Health 24212.1.9 Impact of Yajna in Reducing the Atmospheric Solution 24212.1.10 Scientific Study on Impact of Yajna on Air Purification 24312.1.11 Scientific Meaning of Religious and Manglik Signs 24412.2 Literature Survey 24412.3 Methodology 24612.4 Results and Discussion 24912.5 Interpretations and Analysis 25012.6 Novelty in Our Work 25812.7 Recommendations 25912.8 Future Scope and Possible Applications 26012.9 Limitations 26112.10 Conclusions 26112.11 Acknowledgments 262References 26213 Collection and Analysis of Big Data From Emerging Technologies in Healthcare 269Nagashri K., Jayalakshmi D. S. and Geetha J.13.1 Introduction 26913.2 Data Collection 27113.2.1 Emerging Technologies in Healthcare and Its Applications 27113.2.1.1 RFID 27213.2.1.2 WSN 27313.2.1.3 IoT 27413.2.2 Issues and Challenges in Data Collection 27713.2.2.1 Data Quality 27713.2.2.2 Data Quantity 27713.2.2.3 Data Access 27813.2.2.4 Data Provenance 27813.2.2.5 Security 27813.2.2.6 Other Challenges 27913.3 Data Analysis 28013.3.1 Data Analysis Approaches 28013.3.1.1 Machine Learning 28013.3.1.2 Deep Learning 28113.3.1.3 Natural Language Processing 28113.3.1.4 High-Performance Computing 28113.3.1.5 Edge-Fog Computing 28213.3.1.6 Real-Time Analytics 28213.3.1.7 End-User Driven Analytics 28213.3.1.8 Knowledge-Based Analytics 28313.3.2 Issues and Challenges in Data Analysis 28313.3.2.1 Multi-Modal Data 28313.3.2.2 Complex Domain Knowledge 28313.3.2.3 Highly Competent End-Users 28313.3.2.4 Supporting Complex Decisions 28313.3.2.5 Privacy 28413.3.2.6 Other Challenges 28413.4 Research Trends 28413.5 Conclusion 286References 28614 A Complete Overview of Sign Language Recognition and Translation Systems 289Kasina Jyothi Swaroop, Janamejaya Channegowda and Shambhavi Mishra14.1 Introduction 28914.2 Sign Language Recognition 29014.2.1 Fundamentals of Sign Language Recognition 29014.2.2 Requirements for the Sign Language Recognition 29214.3 Dataset Creation 29314.3.1 American Sign Language 29314.3.2 German Sign Language 29614.3.3 Arabic Sign Language 29714.3.4 Indian Sign Language 29814.4 Hardware Employed for Sign Language Recognition 29914.4.1 Glove/Sensor-Based Systems 29914.4.2 Microsoft Kinect–Based Systems 30014.5 Computer Vision–Based Sign Language Recognition and Translation Systems 30214.5.1 Image Processing Techniques for Sign Language Recognition 30214.5.2 Deep Learning Methods for Sign Language Recognition 30414.5.3 Pose Estimation Application to Sign Language Recognition 30514.5.4 Temporal Information in Sign Language Recognition and Translation 30614.6 Sign Language Translation System—A Brief Overview 30714.7 Conclusion 309References 310Index 315

A. Suresh, PhD is an associate professor, Department of the Networking and Communications, SRM Institute of Science & Technology, Kattankulathur, Tamil Nadu, India. He has nearly two decades of experience in teaching and his areas of specialization are data mining, artificial intelligence, image processing, multimedia, and system software. He has published 6 patents and more than 100 papers in international journals.S. Vimal, PhD is an assistant professor in the Department of Artificial Intelligence & DS, Ramco Institute of Technology, Tamilnadu, India. He is the editor of 3 books and guest-edited multiple journal special issues. He has more than 15 years of teaching experience. Y. Harold Robinson, PhD is currently working in the School of Technology and Engineering, Vellore Institute of Technology, Vellore, India. He has published more than 50 papers in various international journals and presented more than 70 papers in both national and international conferences. Dhinesh Kumar Ramaswami, BE in Computer Science, is a Senior Consultant at Capgemini America Inc. He has over 9 years of experience in software development and specializes in various .net technologies. He has published more than 15 papers in international journals and national and international conferences. R. Udendhran, PhD is an assistant professor, Department of Computer Science and Engineering at Sri Sairam Institute of Technology, Chennai, Tamil Nadu, India. He has published about 20 papers in international journals.

• Preface xv1 Probabilistic Optimization of Machine Learning Algorithms for Heart Disease Prediction 1Jaspreet Kaur, Bharti Joshi and Rajashree Shedge1.1 Introduction 21.1.1 Scope and Motivation 31.2 Literature Review 41.2.1 Comparative Analysis 51.2.2 Survey Analysis 51.3 Tools and Techniques 101.3.1 Description of Dataset 111.3.2 Machine Learning Algorithm 121.3.3 Decision Tree 141.3.4 Random Forest 151.3.5 Naive Bayes Algorithm 161.3.6 K Means Algorithm 181.3.7 Ensemble Method 181.3.7.1 Bagging 191.3.7.2 Boosting 191.3.7.3 Stacking 191.3.7.4 Majority Vote 191.4 Proposed Method 201.4.1 Experiment and Analysis 201.4.2 Method 221.5 Conclusion 25References 262 Cancerous Cells Detection in Lung Organs of Human Body: IoT-Based Healthcare 4.0 Approach 29Rohit Rastogi, D.K. Chaturvedi, Sheelu Sagar, Neeti Tandon and Mukund Rastogi2.1 Introduction 302.1.1 Motivation to the Study 302.1.1.1 Problem Statements 312.1.1.2 Authors’ Contributions 312.1.1.3 Research Manuscript Organization 312.1.1.4 Definitions 322.1.2 Computer-Aided Diagnosis System (CADe or CADx) 322.1.3 Sensors for the Internet of Things 322.1.4 Wireless and Wearable Sensors for Health Informatics 332.1.5 Remote Human’s Health and Activity Monitoring 332.1.6 Decision-Making Systems for Sensor Data 332.1.7 Artificial Intelligence and Machine Learning for Health Informatics 342.1.8 Health Sensor Data Management 342.1.9 Multimodal Data Fusion for Healthcare 352.1.10 Heterogeneous Data Fusion and Context-Aware Systems: A Context-Aware Data Fusion Approach for Health-IoT 352.2 Literature Review 352.3 Proposed Systems 372.3.1 Framework or Architecture of the Work 382.3.2 Model Steps and Parameters 382.3.3 Discussions 392.4 Experimental Results and Analysis 392.4.1 Tissue Characterization and Risk Stratification 392.4.2 Samples of Cancer Data and Analysis 402.5 Novelties 422.6 Future Scope, Limitations, and Possible Applications 422.7 Recommendations and Consideration 432.8 Conclusions 43References 433 Computational Predictors of the Predominant Protein Function: SARS-CoV-2 Case 47Carlos Polanco, Manlio F. Márquez and Gilberto Vargas-Alarcón3.1 Introduction 483.2 Human Coronavirus Types 493.3 The SARS-CoV-2 Pandemic Impact 503.3.1 RNA Virus vs DNA Virus 513.3.2 The Coronaviridae Family 513.3.3 The SARS-CoV-2 Structural Proteins 523.3.4 Protein Representations 523.4 Computational Predictors 533.4.1 Supervised Algorithms 533.4.2 Non-Supervised Algorithms 543.5 Polarity Index Method® 543.5.1 The PIM® Profile 543.5.2 Advantages 553.5.3 Disadvantages 553.5.4 SARS-CoV-2 Recognition Using PIM® Profile 553.6 Future Implications 593.7 Acknowledgments 60References 604 Deep Learning in Gait Abnormality Detection: Principles and Illustrations 63Saikat Chakraborty, Sruti Sambhavi and Anup Nandy4.1 Introduction 634.2 Background 654.2.1 LSTM 654.2.1.1 Vanilla LSTM 654.2.1.2 Bidirectional LSTM 664.3 Related Works 674.4 Methods 684.4.1 Data Collection and Analysis 684.4.2 Results and Discussion 694.5 Conclusion and Future Work 714.6 Acknowledgments 71References 715 Broad Applications of Network Embeddings in Computational Biology, Genomics, Medicine, and Health 73Akanksha Jaiswar, Devender Arora, Manisha Malhotra, Abhimati Shukla and Nivedita Rai5.1 Introduction 745.2 Types of Biological Networks 765.3 Methodologies in Network Embedding 765.4 Attributed and Non-Attributed Network Embedding 825.5 Applications of Network Embedding in Computational Biology 835.5.1 Understanding Genomic and Protein Interaction via Network Alignment 835.5.2 Pharmacogenomics 845.5.2.1 Drug-Target Interaction Prediction 845.5.2.2 Drug-Drug Interaction 845.5.2.3 Drug-Disease Interaction Prediction 855.5.2.4 Analysis of Adverse Drug Reaction 855.5.3 Function Prediction 865.5.4 Community Detection 865.5.5 Network Denoising 875.5.6 Analysis of Multi-Omics Data 875.6 Limitations of Network Embedding in Biology 875.7 Conclusion and Outlook 89References 896 Heart Disease Classification Using Regional Wall Thickness by Ensemble Classifier 99Prakash J., Vinoth Kumar B. and Sandhya R.6.1 Introduction 1006.2 Related Study 1016.3 Methodology 1036.3.1 Pre-Processing 1036.3.2 Region of Interest Extraction 1046.3.3 Segmentation 1056.3.4 Feature Extraction 1066.3.5 Disease Classification 1076.4 Implementation and Result Analysis 1086.4.1 Dataset Description 1086.4.2 Testbed 1086.4.3 Discussion 1086.4.3.1 K-Fold Cross-Validation 1106.4.3.2 Confusion Matrix 1106.5 Conclusion 115References 1157 Deep Learning for Medical Informatics and Public Health 117K. Aditya Shastry, Sanjay H. A., Lakshmi M. and Preetham N.7.1 Introduction 1187.2 Deep Learning Techniques in Medical Informatics and Public Health 1217.2.1 Autoencoders 1227.2.2 Recurrent Neural Network 1237.2.3 Convolutional Neural Network (CNN) 1247.2.4 Deep Boltzmann Machine 1267.2.5 Deep Belief Network 1277.3 Applications of Deep Learning in Medical Informatics and Public Health 1287.3.1 The Use of DL for Cancer Diagnosis 1287.3.2 DL in Disease Prediction and Treatment 1297.3.3 Future Applications 1337.4 Open Issues Concerning DL in Medical Informatics and Public Health 1357.5 Conclusion 139References 1408 An Insight Into Human Pose Estimation and Its Applications 147Shambhavi Mishra, Janamejaya Channegowda and Kasina Jyothi Swaroop8.1 Foundations of Human Pose Estimation 1478.2 Challenges to Human Pose Estimation 1498.2.1 Motion Blur 1508.2.2 Indistinct Background 1518.2.3 Occlusion or Self-Occlusion 1518.2.4 Lighting Conditions 1518.3 Analyzing the Dimensions 1528.3.1 2D Human Pose Estimation 1528.3.1.1 Single-Person Pose Estimation 1538.3.1.2 Multi-Person Pose Estimation 1538.3.2 3D Human Pose Estimation 1538.4 Standard Datasets for Human Pose Estimation 1548.4.1 Pascal VOC (Visual Object Classes) Dataset 1568.4.2 KTH Multi-View Football Dataset I 1568.4.3 KTH Multi-View Football Dataset II 1568.4.4 MPII Human Pose Dataset 1578.4.5 BBC Pose 1578.4.6 COCO Dataset 1578.4.7 J-HMDB Dataset 1588.4.8 Human3.6M Dataset 1588.4.9 DensePose 1588.4.10 AMASS Dataset 1598.5 Deep Learning Revolutionizing Pose Estimation 1598.5.1 Approaches in 2D Human Pose Estimation 1598.5.2 Approaches in 3D Human Pose Estimation 1638.6 Application of Human Pose Estimation in Medical Domains 1658.7 Conclusion 166References 1679 Brain Tumor Analysis Using Deep Learning: Sensor and IoT-Based Approach for Futuristic Healthcare 171Rohit Rastogi, D.K. Chaturvedi, Sheelu Sagar, Neeti Tandon and Akshit Rajan Rastogi9.1 Introduction 1729.1.1 Brain Tumor 1729.1.2 Big Data Analytics in Health Informatics 1729.1.3 Machine Learning in Healthcare 1739.1.4 Sensors for Internet of Things 1739.1.5 Challenges and Critical Issues of IoT in Healthcare 1749.1.6 Machine Learning and Artificial Intelligence for Health Informatics 1749.1.7 Health Sensor Data Management 1759.1.8 Multimodal Data Fusion for Healthcare 1759.1.9 Heterogeneous Data Fusion and Context-Aware Systems a Context-Aware Data Fusion Approach for Health-IoT 1769.1.10 Role of Technology in Addressing the Problem of Integration of Healthcare System 1769.2 Literature Survey 1779.3 System Design and Methodology 1799.3.1 System Design 1799.3.2 CNN Architecture 1809.3.3 Block Diagram 1819.3.4 Algorithm(s) 1819.3.5 Our Experimental Results, Interpretation, and Discussion 1839.3.6 Implementation Details 1839.3.7 Snapshots of Interfaces 1849.3.8 Performance Evaluation 1869.3.9 Comparison with Other Algorithms 1869.4 Novelty in Our Work 1869.5 Future Scope, Possible Applications, and Limitations 1889.6 Recommendations and Consideration 1889.7 Conclusions 188References 18910 Study of Emission From Medicinal Woods to Curb Threats of Pollution and Diseases: Global Healthcare Paradigm Shift in 21st Century 191Rohit Rastogi, Mamta Saxena, Devendra Kr. Chaturvedi, Sheelu Sagar, Neha Gupta, Harshit Gupta, Akshit Rajan Rastogi, Divya Sharma, Manu Bhardwaj and Pranav Sharma10.1 Introduction 19210.1.1 Scenario of Pollution and Need to Connect with Indian Culture 19210.1.2 Global Pollution Scenario 19210.1.3 Indian Crisis on Pollution and Worrying Stats 19310.1.4 Efforts Made to Curb Pollution World Wide 19410.1.5 Indian Ancient Vedic Sciences to Curb Pollution and Related Disease 19610.1.6 The Yajna Science: A Boon to Human Race From Rishi-Muni 19610.1.7 The Science of Mantra Associated With Yajna and Its Scientific Effects 19710.1.8 Effect of Different Woods and Cow Dung Used in Yajna 19710.1.9 Use of Sensors and IoT to Record Experimental Data 19810.1.10 Analysis and Pattern Recognition by ML and AI 19910.2 Literature Survey 20010.3 The Methodology and Protocols Followed 20110.4 Experimental Setup of an Experiment 20210.5 Results and Discussions 20210.5.1 Mango 20210.5.2 Bargad 20310.6 Applications of Yagya and Mantra Therapy in Pollution Control and Its Significance 20710.7 Future Research Perspectives 20710.8 Novelty of Our Research 20810.9 Recommendations 20810.10 Conclusions 209References 20911 An Economical Machine Learning Approach for Anomaly Detection in IoT Environment 215Ambika N.11.1 Introduction 21511.2 Literature Survey 21811.3 Proposed Work 22911.4 Analysis of the Work 23011.5 Conclusion 231References 23112 Indian Science of Yajna and Mantra to Cure Different Diseases: An Analysis Amidst Pandemic With a Simulated Approach 235Rohit Rastogi, Mamta Saxena, Devendra Kumar Chaturvedi, Mayank Gupta, Puru Jain, Rishabh Jain, Mohit Jain, Vishal Sharma, Utkarsh Sangam, Parul Singhal and Priyanshi Garg12.1 Introduction 23612.1.1 Different Types of Diseases 23612.1.1.1 Diabetes (Madhumeha) and Its Types 23612.1.1.2 TTH and Stress 23712.1.1.3 Anxiety 23712.1.1.4 Hypertension 23712.1.2 Machine Vision 23712.1.2.1 Medical Images and Analysis 23812.1.2.2 Machine Learning in Healthcare 23812.1.2.3 Artificial Intelligence in Healthcare 23912.1.3 Big Data and Internet of Things (IoT) 23912.1.4 Machine Learning in Association with Data Science and Analytics 23912.1.5 Yajna Science 24012.1.6 Mantra Science 24012.1.6.1 Positive Impact of Recital of Gayatri Mantra and OM Chanting 24112.1.6.2 Significance of Mantra on Indian Culture and Mythology 24112.1.7 Usefulness and Positive Aspect of Yoga Asanas and Pranayama 24112.1.8 Effects of Yajna and Mantra on Human Health 24212.1.9 Impact of Yajna in Reducing the Atmospheric Solution 24212.1.10 Scientific Study on Impact of Yajna on Air Purification 24312.1.11 Scientific Meaning of Religious and Manglik Signs 24412.2 Literature Survey 24412.3 Methodology 24612.4 Results and Discussion 24912.5 Interpretations and Analysis 25012.6 Novelty in Our Work 25812.7 Recommendations 25912.8 Future Scope and Possible Applications 26012.9 Limitations 26112.10 Conclusions 26112.11 Acknowledgments 262References 26213 Collection and Analysis of Big Data From Emerging Technologies in Healthcare 269Nagashri K., Jayalakshmi D. S. and Geetha J.13.1 Introduction 26913.2 Data Collection 27113.2.1 Emerging Technologies in Healthcare and Its Applications 27113.2.1.1 RFID 27213.2.1.2 WSN 27313.2.1.3 IoT 27413.2.2 Issues and Challenges in Data Collection 27713.2.2.1 Data Quality 27713.2.2.2 Data Quantity 27713.2.2.3 Data Access 27813.2.2.4 Data Provenance 27813.2.2.5 Security 27813.2.2.6 Other Challenges 27913.3 Data Analysis 28013.3.1 Data Analysis Approaches 28013.3.1.1 Machine Learning 28013.3.1.2 Deep Learning 28113.3.1.3 Natural Language Processing 28113.3.1.4 High-Performance Computing 28113.3.1.5 Edge-Fog Computing 28213.3.1.6 Real-Time Analytics 28213.3.1.7 End-User Driven Analytics 28213.3.1.8 Knowledge-Based Analytics 28313.3.2 Issues and Challenges in Data Analysis 28313.3.2.1 Multi-Modal Data 28313.3.2.2 Complex Domain Knowledge 28313.3.2.3 Highly Competent End-Users 28313.3.2.4 Supporting Complex Decisions 28313.3.2.5 Privacy 28413.3.2.6 Other Challenges 28413.4 Research Trends 28413.5 Conclusion 286References 28614 A Complete Overview of Sign Language Recognition and Translation Systems 289Kasina Jyothi Swaroop, Janamejaya Channegowda and Shambhavi Mishra14.1 Introduction 28914.2 Sign Language Recognition 29014.2.1 Fundamentals of Sign Language Recognition 29014.2.2 Requirements for the Sign Language Recognition 29214.3 Dataset Creation 29314.3.1 American Sign Language 29314.3.2 German Sign Language 29614.3.3 Arabic Sign Language 29714.3.4 Indian Sign Language 29814.4 Hardware Employed for Sign Language Recognition 29914.4.1 Glove/Sensor-Based Systems 29914.4.2 Microsoft Kinect–Based Systems 30014.5 Computer Vision–Based Sign Language Recognition and Translation Systems 30214.5.1 Image Processing Techniques for Sign Language Recognition 30214.5.2 Deep Learning Methods for Sign Language Recognition 30414.5.3 Pose Estimation Application to Sign Language Recognition 30514.5.4 Temporal Information in Sign Language Recognition and Translation 30614.6 Sign Language Translation System—A Brief Overview 30714.7 Conclusion 309References 310Index 315

A. Suresh, PhD is an associate professor, Department of the Networking and Communications, SRM Institute of Science & Technology, Kattankulathur, Tamil Nadu, India. He has nearly two decades of experience in teaching and his areas of specialization are data mining, artificial intelligence, image processing, multimedia, and system software. He has published 6 patents and more than 100 papers in international journals.S. Vimal, PhD is an assistant professor in the Department of Artificial Intelligence & DS, Ramco Institute of Technology, Tamilnadu, India. He is the editor of 3 books and guest-edited multiple journal special issues. He has more than 15 years of teaching experience. Y. Harold Robinson, PhD is currently working in the School of Technology and Engineering, Vellore Institute of Technology, Vellore, India. He has published more than 50 papers in various international journals and presented more than 70 papers in both national and international conferences. Dhinesh Kumar Ramaswami, BE in Computer Science, is a Senior Consultant at Capgemini America Inc. He has over 9 years of experience in software development and specializes in various .net technologies. He has published more than 15 papers in international journals and national and international conferences. R. Udendhran, PhD is an assistant professor, Department of Computer Science and Engineering at Sri Sairam Institute of Technology, Chennai, Tamil Nadu, India. He has published about 20 papers in international journals.

• Preface xv1 Probabilistic Optimization of Machine Learning Algorithms for Heart Disease Prediction 1Jaspreet Kaur, Bharti Joshi and Rajashree Shedge1.1 Introduction 21.1.1 Scope and Motivation 31.2 Literature Review 41.2.1 Comparative Analysis 51.2.2 Survey Analysis 51.3 Tools and Techniques 101.3.1 Description of Dataset 111.3.2 Machine Learning Algorithm 121.3.3 Decision Tree 141.3.4 Random Forest 151.3.5 Naive Bayes Algorithm 161.3.6 K Means Algorithm 181.3.7 Ensemble Method 181.3.7.1 Bagging 191.3.7.2 Boosting 191.3.7.3 Stacking 191.3.7.4 Majority Vote 191.4 Proposed Method 201.4.1 Experiment and Analysis 201.4.2 Method 221.5 Conclusion 25References 262 Cancerous Cells Detection in Lung Organs of Human Body: IoT-Based Healthcare 4.0 Approach 29Rohit Rastogi, D.K. Chaturvedi, Sheelu Sagar, Neeti Tandon and Mukund Rastogi2.1 Introduction 302.1.1 Motivation to the Study 302.1.1.1 Problem Statements 312.1.1.2 Authors’ Contributions 312.1.1.3 Research Manuscript Organization 312.1.1.4 Definitions 322.1.2 Computer-Aided Diagnosis System (CADe or CADx) 322.1.3 Sensors for the Internet of Things 322.1.4 Wireless and Wearable Sensors for Health Informatics 332.1.5 Remote Human’s Health and Activity Monitoring 332.1.6 Decision-Making Systems for Sensor Data 332.1.7 Artificial Intelligence and Machine Learning for Health Informatics 342.1.8 Health Sensor Data Management 342.1.9 Multimodal Data Fusion for Healthcare 352.1.10 Heterogeneous Data Fusion and Context-Aware Systems: A Context-Aware Data Fusion Approach for Health-IoT 352.2 Literature Review 352.3 Proposed Systems 372.3.1 Framework or Architecture of the Work 382.3.2 Model Steps and Parameters 382.3.3 Discussions 392.4 Experimental Results and Analysis 392.4.1 Tissue Characterization and Risk Stratification 392.4.2 Samples of Cancer Data and Analysis 402.5 Novelties 422.6 Future Scope, Limitations, and Possible Applications 422.7 Recommendations and Consideration 432.8 Conclusions 43References 433 Computational Predictors of the Predominant Protein Function: SARS-CoV-2 Case 47Carlos Polanco, Manlio F. Márquez and Gilberto Vargas-Alarcón3.1 Introduction 483.2 Human Coronavirus Types 493.3 The SARS-CoV-2 Pandemic Impact 503.3.1 RNA Virus vs DNA Virus 513.3.2 The Coronaviridae Family 513.3.3 The SARS-CoV-2 Structural Proteins 523.3.4 Protein Representations 523.4 Computational Predictors 533.4.1 Supervised Algorithms 533.4.2 Non-Supervised Algorithms 543.5 Polarity Index Method® 543.5.1 The PIM® Profile 543.5.2 Advantages 553.5.3 Disadvantages 553.5.4 SARS-CoV-2 Recognition Using PIM® Profile 553.6 Future Implications 593.7 Acknowledgments 60References 604 Deep Learning in Gait Abnormality Detection: Principles and Illustrations 63Saikat Chakraborty, Sruti Sambhavi and Anup Nandy4.1 Introduction 634.2 Background 654.2.1 LSTM 654.2.1.1 Vanilla LSTM 654.2.1.2 Bidirectional LSTM 664.3 Related Works 674.4 Methods 684.4.1 Data Collection and Analysis 684.4.2 Results and Discussion 694.5 Conclusion and Future Work 714.6 Acknowledgments 71References 715 Broad Applications of Network Embeddings in Computational Biology, Genomics, Medicine, and Health 73Akanksha Jaiswar, Devender Arora, Manisha Malhotra, Abhimati Shukla and Nivedita Rai5.1 Introduction 745.2 Types of Biological Networks 765.3 Methodologies in Network Embedding 765.4 Attributed and Non-Attributed Network Embedding 825.5 Applications of Network Embedding in Computational Biology 835.5.1 Understanding Genomic and Protein Interaction via Network Alignment 835.5.2 Pharmacogenomics 845.5.2.1 Drug-Target Interaction Prediction 845.5.2.2 Drug-Drug Interaction 845.5.2.3 Drug-Disease Interaction Prediction 855.5.2.4 Analysis of Adverse Drug Reaction 855.5.3 Function Prediction 865.5.4 Community Detection 865.5.5 Network Denoising 875.5.6 Analysis of Multi-Omics Data 875.6 Limitations of Network Embedding in Biology 875.7 Conclusion and Outlook 89References 896 Heart Disease Classification Using Regional Wall Thickness by Ensemble Classifier 99Prakash J., Vinoth Kumar B. and Sandhya R.6.1 Introduction 1006.2 Related Study 1016.3 Methodology 1036.3.1 Pre-Processing 1036.3.2 Region of Interest Extraction 1046.3.3 Segmentation 1056.3.4 Feature Extraction 1066.3.5 Disease Classification 1076.4 Implementation and Result Analysis 1086.4.1 Dataset Description 1086.4.2 Testbed 1086.4.3 Discussion 1086.4.3.1 K-Fold Cross-Validation 1106.4.3.2 Confusion Matrix 1106.5 Conclusion 115References 1157 Deep Learning for Medical Informatics and Public Health 117K. Aditya Shastry, Sanjay H. A., Lakshmi M. and Preetham N.7.1 Introduction 1187.2 Deep Learning Techniques in Medical Informatics and Public Health 1217.2.1 Autoencoders 1227.2.2 Recurrent Neural Network 1237.2.3 Convolutional Neural Network (CNN) 1247.2.4 Deep Boltzmann Machine 1267.2.5 Deep Belief Network 1277.3 Applications of Deep Learning in Medical Informatics and Public Health 1287.3.1 The Use of DL for Cancer Diagnosis 1287.3.2 DL in Disease Prediction and Treatment 1297.3.3 Future Applications 1337.4 Open Issues Concerning DL in Medical Informatics and Public Health 1357.5 Conclusion 139References 1408 An Insight Into Human Pose Estimation and Its Applications 147Shambhavi Mishra, Janamejaya Channegowda and Kasina Jyothi Swaroop8.1 Foundations of Human Pose Estimation 1478.2 Challenges to Human Pose Estimation 1498.2.1 Motion Blur 1508.2.2 Indistinct Background 1518.2.3 Occlusion or Self-Occlusion 1518.2.4 Lighting Conditions 1518.3 Analyzing the Dimensions 1528.3.1 2D Human Pose Estimation 1528.3.1.1 Single-Person Pose Estimation 1538.3.1.2 Multi-Person Pose Estimation 1538.3.2 3D Human Pose Estimation 1538.4 Standard Datasets for Human Pose Estimation 1548.4.1 Pascal VOC (Visual Object Classes) Dataset 1568.4.2 KTH Multi-View Football Dataset I 1568.4.3 KTH Multi-View Football Dataset II 1568.4.4 MPII Human Pose Dataset 1578.4.5 BBC Pose 1578.4.6 COCO Dataset 1578.4.7 J-HMDB Dataset 1588.4.8 Human3.6M Dataset 1588.4.9 DensePose 1588.4.10 AMASS Dataset 1598.5 Deep Learning Revolutionizing Pose Estimation 1598.5.1 Approaches in 2D Human Pose Estimation 1598.5.2 Approaches in 3D Human Pose Estimation 1638.6 Application of Human Pose Estimation in Medical Domains 1658.7 Conclusion 166References 1679 Brain Tumor Analysis Using Deep Learning: Sensor and IoT-Based Approach for Futuristic Healthcare 171Rohit Rastogi, D.K. Chaturvedi, Sheelu Sagar, Neeti Tandon and Akshit Rajan Rastogi9.1 Introduction 1729.1.1 Brain Tumor 1729.1.2 Big Data Analytics in Health Informatics 1729.1.3 Machine Learning in Healthcare 1739.1.4 Sensors for Internet of Things 1739.1.5 Challenges and Critical Issues of IoT in Healthcare 1749.1.6 Machine Learning and Artificial Intelligence for Health Informatics 1749.1.7 Health Sensor Data Management 1759.1.8 Multimodal Data Fusion for Healthcare 1759.1.9 Heterogeneous Data Fusion and Context-Aware Systems a Context-Aware Data Fusion Approach for Health-IoT 1769.1.10 Role of Technology in Addressing the Problem of Integration of Healthcare System 1769.2 Literature Survey 1779.3 System Design and Methodology 1799.3.1 System Design 1799.3.2 CNN Architecture 1809.3.3 Block Diagram 1819.3.4 Algorithm(s) 1819.3.5 Our Experimental Results, Interpretation, and Discussion 1839.3.6 Implementation Details 1839.3.7 Snapshots of Interfaces 1849.3.8 Performance Evaluation 1869.3.9 Comparison with Other Algorithms 1869.4 Novelty in Our Work 1869.5 Future Scope, Possible Applications, and Limitations 1889.6 Recommendations and Consideration 1889.7 Conclusions 188References 18910 Study of Emission From Medicinal Woods to Curb Threats of Pollution and Diseases: Global Healthcare Paradigm Shift in 21st Century 191Rohit Rastogi, Mamta Saxena, Devendra Kr. Chaturvedi, Sheelu Sagar, Neha Gupta, Harshit Gupta, Akshit Rajan Rastogi, Divya Sharma, Manu Bhardwaj and Pranav Sharma10.1 Introduction 19210.1.1 Scenario of Pollution and Need to Connect with Indian Culture 19210.1.2 Global Pollution Scenario 19210.1.3 Indian Crisis on Pollution and Worrying Stats 19310.1.4 Efforts Made to Curb Pollution World Wide 19410.1.5 Indian Ancient Vedic Sciences to Curb Pollution and Related Disease 19610.1.6 The Yajna Science: A Boon to Human Race From Rishi-Muni 19610.1.7 The Science of Mantra Associated With Yajna and Its Scientific Effects 19710.1.8 Effect of Different Woods and Cow Dung Used in Yajna 19710.1.9 Use of Sensors and IoT to Record Experimental Data 19810.1.10 Analysis and Pattern Recognition by ML and AI 19910.2 Literature Survey 20010.3 The Methodology and Protocols Followed 20110.4 Experimental Setup of an Experiment 20210.5 Results and Discussions 20210.5.1 Mango 20210.5.2 Bargad 20310.6 Applications of Yagya and Mantra Therapy in Pollution Control and Its Significance 20710.7 Future Research Perspectives 20710.8 Novelty of Our Research 20810.9 Recommendations 20810.10 Conclusions 209References 20911 An Economical Machine Learning Approach for Anomaly Detection in IoT Environment 215Ambika N.11.1 Introduction 21511.2 Literature Survey 21811.3 Proposed Work 22911.4 Analysis of the Work 23011.5 Conclusion 231References 23112 Indian Science of Yajna and Mantra to Cure Different Diseases: An Analysis Amidst Pandemic With a Simulated Approach 235Rohit Rastogi, Mamta Saxena, Devendra Kumar Chaturvedi, Mayank Gupta, Puru Jain, Rishabh Jain, Mohit Jain, Vishal Sharma, Utkarsh Sangam, Parul Singhal and Priyanshi Garg12.1 Introduction 23612.1.1 Different Types of Diseases 23612.1.1.1 Diabetes (Madhumeha) and Its Types 23612.1.1.2 TTH and Stress 23712.1.1.3 Anxiety 23712.1.1.4 Hypertension 23712.1.2 Machine Vision 23712.1.2.1 Medical Images and Analysis 23812.1.2.2 Machine Learning in Healthcare 23812.1.2.3 Artificial Intelligence in Healthcare 23912.1.3 Big Data and Internet of Things (IoT) 23912.1.4 Machine Learning in Association with Data Science and Analytics 23912.1.5 Yajna Science 24012.1.6 Mantra Science 24012.1.6.1 Positive Impact of Recital of Gayatri Mantra and OM Chanting 24112.1.6.2 Significance of Mantra on Indian Culture and Mythology 24112.1.7 Usefulness and Positive Aspect of Yoga Asanas and Pranayama 24112.1.8 Effects of Yajna and Mantra on Human Health 24212.1.9 Impact of Yajna in Reducing the Atmospheric Solution 24212.1.10 Scientific Study on Impact of Yajna on Air Purification 24312.1.11 Scientific Meaning of Religious and Manglik Signs 24412.2 Literature Survey 24412.3 Methodology 24612.4 Results and Discussion 24912.5 Interpretations and Analysis 25012.6 Novelty in Our Work 25812.7 Recommendations 25912.8 Future Scope and Possible Applications 26012.9 Limitations 26112.10 Conclusions 26112.11 Acknowledgments 262References 26213 Collection and Analysis of Big Data From Emerging Technologies in Healthcare 269Nagashri K., Jayalakshmi D. S. and Geetha J.13.1 Introduction 26913.2 Data Collection 27113.2.1 Emerging Technologies in Healthcare and Its Applications 27113.2.1.1 RFID 27213.2.1.2 WSN 27313.2.1.3 IoT 27413.2.2 Issues and Challenges in Data Collection 27713.2.2.1 Data Quality 27713.2.2.2 Data Quantity 27713.2.2.3 Data Access 27813.2.2.4 Data Provenance 27813.2.2.5 Security 27813.2.2.6 Other Challenges 27913.3 Data Analysis 28013.3.1 Data Analysis Approaches 28013.3.1.1 Machine Learning 28013.3.1.2 Deep Learning 28113.3.1.3 Natural Language Processing 28113.3.1.4 High-Performance Computing 28113.3.1.5 Edge-Fog Computing 28213.3.1.6 Real-Time Analytics 28213.3.1.7 End-User Driven Analytics 28213.3.1.8 Knowledge-Based Analytics 28313.3.2 Issues and Challenges in Data Analysis 28313.3.2.1 Multi-Modal Data 28313.3.2.2 Complex Domain Knowledge 28313.3.2.3 Highly Competent End-Users 28313.3.2.4 Supporting Complex Decisions 28313.3.2.5 Privacy 28413.3.2.6 Other Challenges 28413.4 Research Trends 28413.5 Conclusion 286References 28614 A Complete Overview of Sign Language Recognition and Translation Systems 289Kasina Jyothi Swaroop, Janamejaya Channegowda and Shambhavi Mishra14.1 Introduction 28914.2 Sign Language Recognition 29014.2.1 Fundamentals of Sign Language Recognition 29014.2.2 Requirements for the Sign Language Recognition 29214.3 Dataset Creation 29314.3.1 American Sign Language 29314.3.2 German Sign Language 29614.3.3 Arabic Sign Language 29714.3.4 Indian Sign Language 29814.4 Hardware Employed for Sign Language Recognition 29914.4.1 Glove/Sensor-Based Systems 29914.4.2 Microsoft Kinect–Based Systems 30014.5 Computer Vision–Based Sign Language Recognition and Translation Systems 30214.5.1 Image Processing Techniques for Sign Language Recognition 30214.5.2 Deep Learning Methods for Sign Language Recognition 30414.5.3 Pose Estimation Application to Sign Language Recognition 30514.5.4 Temporal Information in Sign Language Recognition and Translation 30614.6 Sign Language Translation System—A Brief Overview 30714.7 Conclusion 309References 310Index 315