<p>Preface xv</p> <p><b>1 Probabilistic Optimization of Machine Learning Algorithms for Heart Disease Prediction 1<br /></b><i>Jaspreet Kaur, Bharti Joshi and Rajashree Shedge</i></p> <p>1.1 Introduction 2</p> <p>1.1.1 Scope and Motivation 3</p> <p>1.2 Literature Review 4</p> <p>1.2.1 Comparative Analysis 5</p> <p>1.2.2 Survey Analysis 5</p> <p>1.3 Tools and Techniques 10</p> <p>1.3.1 Description of Dataset 11</p> <p>1.3.2 Machine Learning Algorithm 12</p> <p>1.3.3 Decision Tree 14</p> <p>1.3.4 Random Forest 15</p> <p>1.3.5 Naive Bayes Algorithm 16</p> <p>1.3.6 K Means Algorithm 18</p> <p>1.3.7 Ensemble Method 18</p> <p>1.3.7.1 Bagging 19</p> <p>1.3.7.2 Boosting 19</p> <p>1.3.7.3 Stacking 19</p> <p>1.3.7.4 Majority Vote 19</p> <p>1.4 Proposed Method 20</p> <p>1.4.1 Experiment and Analysis 20</p> <p>1.4.2 Method 22</p> <p>1.5 Conclusion 25</p> <p>References 26</p> <p><b>2 Cancerous Cells Detection in Lung Organs of Human Body: IoT-Based Healthcare 4.0 Approach 29<br /></b><i>Rohit Rastogi, D.K. Chaturvedi, Sheelu Sagar, Neeti Tandon and Mukund Rastogi</i></p> <p>2.1 Introduction 30</p> <p>2.1.1 Motivation to the Study 30</p> <p>2.1.1.1 Problem Statements 31</p> <p>2.1.1.2 Authors’ Contributions 31</p> <p>2.1.1.3 Research Manuscript Organization 31</p> <p>2.1.1.4 Definitions 32</p> <p>2.1.2 Computer-Aided Diagnosis System (CADe or CADx) 32</p> <p>2.1.3 Sensors for the Internet of Things 32</p> <p>2.1.4 Wireless and Wearable Sensors for Health Informatics 33</p> <p>2.1.5 Remote Human’s Health and Activity Monitoring 33</p> <p>2.1.6 Decision-Making Systems for Sensor Data 33</p> <p>2.1.7 Artificial Intelligence and Machine Learning for Health Informatics 34</p> <p>2.1.8 Health Sensor Data Management 34</p> <p>2.1.9 Multimodal Data Fusion for Healthcare 35</p> <p>2.1.10 Heterogeneous Data Fusion and Context-Aware Systems: A Context-Aware Data Fusion Approach for Health-IoT 35</p> <p>2.2 Literature Review 35</p> <p>2.3 Proposed Systems 37</p> <p>2.3.1 Framework or Architecture of the Work 38</p> <p>2.3.2 Model Steps and Parameters 38</p> <p>2.3.3 Discussions 39</p> <p>2.4 Experimental Results and Analysis 39</p> <p>2.4.1 Tissue Characterization and Risk Stratification 39</p> <p>2.4.2 Samples of Cancer Data and Analysis 40</p> <p>2.5 Novelties 42</p> <p>2.6 Future Scope, Limitations, and Possible Applications 42</p> <p>2.7 Recommendations and Consideration 43</p> <p>2.8 Conclusions 43</p> <p>References 43</p> <p><b>3 Computational Predictors of the Predominant Protein Function: SARS-CoV-2 Case 47<br /></b><i>Carlos Polanco, Manlio F. Márquez and Gilberto Vargas-Alarcón</i></p> <p>3.1 Introduction 48</p> <p>3.2 Human Coronavirus Types 49</p> <p>3.3 The SARS-CoV-2 Pandemic Impact 50</p> <p>3.3.1 RNA Virus vs DNA Virus 51</p> <p>3.3.2 The <i>Coronaviridae </i>Family 51</p> <p>3.3.3 The SARS-CoV-2 Structural Proteins 52</p> <p>3.3.4 Protein Representations 52</p> <p>3.4 Computational Predictors 53</p> <p>3.4.1 Supervised Algorithms 53</p> <p>3.4.2 Non-Supervised Algorithms 54</p> <p>3.5 Polarity Index Method<sup>®</sup> 54</p> <p>3.5.1 The PIM<sup>®</sup> Profile 54</p> <p>3.5.2 Advantages 55</p> <p>3.5.3 Disadvantages 55</p> <p>3.5.4 SARS-CoV-2 Recognition Using PIM<sup>®</sup> Profile 55</p> <p>3.6 Future Implications 59</p> <p>3.7 Acknowledgments 60</p> <p>References 60</p> <p><b>4 Deep Learning in Gait Abnormality Detection: Principles and Illustrations 63<br /></b><i>Saikat Chakraborty, Sruti Sambhavi and Anup Nandy</i></p> <p>4.1 Introduction 63</p> <p>4.2 Background 65</p> <p>4.2.1 LSTM 65</p> <p>4.2.1.1 Vanilla LSTM 65</p> <p>4.2.1.2 Bidirectional LSTM 66</p> <p>4.3 Related Works 67</p> <p>4.4 Methods 68</p> <p>4.4.1 Data Collection and Analysis 68</p> <p>4.4.2 Results and Discussion 69</p> <p>4.5 Conclusion and Future Work 71</p> <p>4.6 Acknowledgments 71</p> <p>References 71</p> <p><b>5 Broad Applications of Network Embeddings in Computational Biology, Genomics, Medicine, and Health 73<br /></b><i>Akanksha Jaiswar, Devender Arora, Manisha Malhotra, Abhimati Shukla and Nivedita Rai</i></p> <p>5.1 Introduction 74</p> <p>5.2 Types of Biological Networks 76</p> <p>5.3 Methodologies in Network Embedding 76</p> <p>5.4 Attributed and Non-Attributed Network Embedding 82</p> <p>5.5 Applications of Network Embedding in Computational Biology 83</p> <p>5.5.1 Understanding Genomic and Protein Interaction via Network Alignment 83</p> <p>5.5.2 Pharmacogenomics 84</p> <p>5.5.2.1 Drug-Target Interaction Prediction 84</p> <p>5.5.2.2 Drug-Drug Interaction 84</p> <p>5.5.2.3 Drug-Disease Interaction Prediction 85</p> <p>5.5.2.4 Analysis of Adverse Drug Reaction 85</p> <p>5.5.3 Function Prediction 86</p> <p>5.5.4 Community Detection 86</p> <p>5.5.5 Network Denoising 87</p> <p>5.5.6 Analysis of Multi-Omics Data 87</p> <p>5.6 Limitations of Network Embedding in Biology 87</p> <p>5.7 Conclusion and Outlook 89</p> <p>References 89</p> <p><b>6 Heart Disease Classification Using Regional Wall Thickness by Ensemble Classifier 99<br /></b><i>Prakash J., Vinoth Kumar B. and Sandhya R.</i></p> <p>6.1 Introduction 100</p> <p>6.2 Related Study 101</p> <p>6.3 Methodology 103</p> <p>6.3.1 Pre-Processing 103</p> <p>6.3.2 Region of Interest Extraction 104</p> <p>6.3.3 Segmentation 105</p> <p>6.3.4 Feature Extraction 106</p> <p>6.3.5 Disease Classification 107</p> <p>6.4 Implementation and Result Analysis 108</p> <p>6.4.1 Dataset Description 108</p> <p>6.4.2 Testbed 108</p> <p>6.4.3 Discussion 108</p> <p>6.4.3.1 K-Fold Cross-Validation 110</p> <p>6.4.3.2 Confusion Matrix 110</p> <p>6.5 Conclusion 115</p> <p>References 115</p> <p><b>7 Deep Learning for Medical Informatics and Public Health 117<br /></b><i>K. Aditya Shastry, Sanjay H. A., Lakshmi M. and Preetham N.</i></p> <p>7.1 Introduction 118</p> <p>7.2 Deep Learning Techniques in Medical Informatics and Public Health 121</p> <p>7.2.1 Autoencoders 122</p> <p>7.2.2 Recurrent Neural Network 123</p> <p>7.2.3 Convolutional Neural Network (CNN) 124</p> <p>7.2.4 Deep Boltzmann Machine 126</p> <p>7.2.5 Deep Belief Network 127</p> <p>7.3 Applications of Deep Learning in Medical Informatics and Public Health 128</p> <p>7.3.1 The Use of DL for Cancer Diagnosis 128</p> <p>7.3.2 DL in Disease Prediction and Treatment 129</p> <p>7.3.3 Future Applications 133</p> <p>7.4 Open Issues Concerning DL in Medical Informatics and Public Health 135</p> <p>7.5 Conclusion 139</p> <p>References 140</p> <p><b>8 An Insight Into Human Pose Estimation and Its Applications 147<br /></b><i>Shambhavi Mishra, Janamejaya Channegowda and Kasina Jyothi Swaroop</i></p> <p>8.1 Foundations of Human Pose Estimation 147</p> <p>8.2 Challenges to Human Pose Estimation 149</p> <p>8.2.1 Motion Blur 150</p> <p>8.2.2 Indistinct Background 151</p> <p>8.2.3 Occlusion or Self-Occlusion 151</p> <p>8.2.4 Lighting Conditions 151</p> <p>8.3 Analyzing the Dimensions 152</p> <p>8.3.1 2D Human Pose Estimation 152</p> <p>8.3.1.1 Single-Person Pose Estimation 153</p> <p>8.3.1.2 Multi-Person Pose Estimation 153</p> <p>8.3.2 3D Human Pose Estimation 153</p> <p>8.4 Standard Datasets for Human Pose Estimation 154</p> <p>8.4.1 Pascal VOC (Visual Object Classes) Dataset 156</p> <p>8.4.2 KTH Multi-View Football Dataset I 156</p> <p>8.4.3 KTH Multi-View Football Dataset II 156</p> <p>8.4.4 MPII Human Pose Dataset 157</p> <p>8.4.5 BBC Pose 157</p> <p>8.4.6 COCO Dataset 157</p> <p>8.4.7 J-HMDB Dataset 158</p> <p>8.4.8 Human3.6M Dataset 158</p> <p>8.4.9 DensePose 158</p> <p>8.4.10 AMASS Dataset 159</p> <p>8.5 Deep Learning Revolutionizing Pose Estimation 159</p> <p>8.5.1 Approaches in 2D Human Pose Estimation 159</p> <p>8.5.2 Approaches in 3D Human Pose Estimation 163</p> <p>8.6 Application of Human Pose Estimation in Medical Domains 165</p> <p>8.7 Conclusion 166</p> <p>References 167</p> <p><b>9 Brain Tumor Analysis Using Deep Learning: Sensor and IoT-Based Approach for Futuristic Healthcare 171<br /></b><i>Rohit Rastogi, D.K. Chaturvedi, Sheelu Sagar, Neeti Tandon and Akshit Rajan Rastogi</i></p> <p>9.1 Introduction 172</p> <p>9.1.1 Brain Tumor 172</p> <p>9.1.2 Big Data Analytics in Health Informatics 172</p> <p>9.1.3 Machine Learning in Healthcare 173</p> <p>9.1.4 Sensors for Internet of Things 173</p> <p>9.1.5 Challenges and Critical Issues of IoT in Healthcare 174</p> <p>9.1.6 Machine Learning and Artificial Intelligence for Health Informatics 174</p> <p>9.1.7 Health Sensor Data Management 175</p> <p>9.1.8 Multimodal Data Fusion for Healthcare 175</p> <p>9.1.9 Heterogeneous Data Fusion and Context-Aware Systems a Context-Aware Data Fusion Approach for Health-IoT 176</p> <p>9.1.10 Role of Technology in Addressing the Problem of Integration of Healthcare System 176</p> <p>9.2 Literature Survey 177</p> <p>9.3 System Design and Methodology 179</p> <p>9.3.1 System Design 179</p> <p>9.3.2 CNN Architecture 180</p> <p>9.3.3 Block Diagram 181</p> <p>9.3.4 Algorithm(s) 181</p> <p>9.3.5 Our Experimental Results, Interpretation, and Discussion 183</p> <p>9.3.6 Implementation Details 183</p> <p>9.3.7 Snapshots of Interfaces 184</p> <p>9.3.8 Performance Evaluation 186</p> <p>9.3.9 Comparison with Other Algorithms 186</p> <p>9.4 Novelty in Our Work 186</p> <p>9.5 Future Scope, Possible Applications, and Limitations 188</p> <p>9.6 Recommendations and Consideration 188</p> <p>9.7 Conclusions 188</p> <p>References 189</p> <p><b>10 Study of Emission From Medicinal Woods to Curb Threats of Pollution and Diseases: Global Healthcare Paradigm Shift in 21st Century 191<br /></b><i>Rohit Rastogi, Mamta Saxena, Devendra Kr. Chaturvedi, Sheelu Sagar, Neha Gupta, Harshit Gupta, Akshit Rajan Rastogi, Divya Sharma, Manu Bhardwaj and Pranav Sharma</i></p> <p>10.1 Introduction 192</p> <p>10.1.1 Scenario of Pollution and Need to Connect with Indian Culture 192</p> <p>10.1.2 Global Pollution Scenario 192</p> <p>10.1.3 Indian Crisis on Pollution and Worrying Stats 193</p> <p>10.1.4 Efforts Made to Curb Pollution World Wide 194</p> <p>10.1.5 Indian Ancient Vedic Sciences to Curb Pollution and Related Disease 196</p> <p>10.1.6 The Yajna Science: A Boon to Human Race From Rishi-Muni 196</p> <p>10.1.7 The Science of Mantra Associated With Yajna and Its Scientific Effects 197</p> <p>10.1.8 Effect of Different Woods and Cow Dung Used in Yajna 197</p> <p>10.1.9 Use of Sensors and IoT to Record Experimental Data 198</p> <p>10.1.10 Analysis and Pattern Recognition by ML and AI 199</p> <p>10.2 Literature Survey 200</p> <p>10.3 The Methodology and Protocols Followed 201</p> <p>10.4 Experimental Setup of an Experiment 202</p> <p>10.5 Results and Discussions 202</p> <p>10.5.1 Mango 202</p> <p>10.5.2 Bargad 203</p> <p>10.6 Applications of Yagya and Mantra Therapy in Pollution Control and Its Significance 207</p> <p>10.7 Future Research Perspectives 207</p> <p>10.8 Novelty of Our Research 208</p> <p>10.9 Recommendations 208</p> <p>10.10 Conclusions 209</p> <p>References 209</p> <p><b>11 An Economical Machine Learning Approach for Anomaly Detection in IoT Environment 215<br /></b><i>Ambika N.</i></p> <p>11.1 Introduction 215</p> <p>11.2 Literature Survey 218</p> <p>11.3 Proposed Work 229</p> <p>11.4 Analysis of the Work 230</p> <p>11.5 Conclusion 231</p> <p>References 231</p> <p><b>12 Indian Science of Yajna and Mantra to Cure Different Diseases: An Analysis Amidst Pandemic With a Simulated Approach 235<br /></b><i>Rohit Rastogi, Mamta Saxena, Devendra Kumar Chaturvedi, Mayank Gupta, Puru Jain, Rishabh Jain, Mohit Jain, Vishal Sharma, Utkarsh Sangam, Parul Singhal and Priyanshi Garg</i></p> <p>12.1 Introduction 236</p> <p>12.1.1 Different Types of Diseases 236</p> <p>12.1.1.1 Diabetes (Madhumeha) and Its Types 236</p> <p>12.1.1.2 TTH and Stress 237</p> <p>12.1.1.3 Anxiety 237</p> <p>12.1.1.4 Hypertension 237</p> <p>12.1.2 Machine Vision 237</p> <p>12.1.2.1 Medical Images and Analysis 238</p> <p>12.1.2.2 Machine Learning in Healthcare 238</p> <p>12.1.2.3 Artificial Intelligence in Healthcare 239</p> <p>12.1.3 Big Data and Internet of Things (IoT) 239</p> <p>12.1.4 Machine Learning in Association with Data Science and Analytics 239</p> <p>12.1.5 Yajna Science 240</p> <p>12.1.6 Mantra Science 240</p> <p>12.1.6.1 Positive Impact of Recital of Gayatri Mantra and OM Chanting 241</p> <p>12.1.6.2 Significance of Mantra on Indian Culture and Mythology 241</p> <p>12.1.7 Usefulness and Positive Aspect of Yoga Asanas and Pranayama 241</p> <p>12.1.8 Effects of Yajna and Mantra on Human Health 242</p> <p>12.1.9 Impact of Yajna in Reducing the Atmospheric Solution 242</p> <p>12.1.10 Scientific Study on Impact of Yajna on Air Purification 243</p> <p>12.1.11 Scientific Meaning of Religious and Manglik Signs 244</p> <p>12.2 Literature Survey 244</p> <p>12.3 Methodology 246</p> <p>12.4 Results and Discussion 249</p> <p>12.5 Interpretations and Analysis 250</p> <p>12.6 Novelty in Our Work 258</p> <p>12.7 Recommendations 259</p> <p>12.8 Future Scope and Possible Applications 260</p> <p>12.9 Limitations 261</p> <p>12.10 Conclusions 261</p> <p>12.11 Acknowledgments 262</p> <p>References 262</p> <p><b>13 Collection and Analysis of Big Data From Emerging Technologies in Healthcare 269<br /></b><i>Nagashri K., Jayalakshmi D. S. and Geetha J.</i></p> <p>13.1 Introduction 269</p> <p>13.2 Data Collection 271</p> <p>13.2.1 Emerging Technologies in Healthcare and Its Applications 271</p> <p>13.2.1.1 RFID 272</p> <p>13.2.1.2 WSN 273</p> <p>13.2.1.3 IoT 274</p> <p>13.2.2 Issues and Challenges in Data Collection 277</p> <p>13.2.2.1 Data Quality 277</p> <p>13.2.2.2 Data Quantity 277</p> <p>13.2.2.3 Data Access 278</p> <p>13.2.2.4 Data Provenance 278</p> <p>13.2.2.5 Security 278</p> <p>13.2.2.6 Other Challenges 279</p> <p>13.3 Data Analysis 280</p> <p>13.3.1 Data Analysis Approaches 280</p> <p>13.3.1.1 Machine Learning 280</p> <p>13.3.1.2 Deep Learning 281</p> <p>13.3.1.3 Natural Language Processing 281</p> <p>13.3.1.4 High-Performance Computing 281</p> <p>13.3.1.5 Edge-Fog Computing 282</p> <p>13.3.1.6 Real-Time Analytics 282</p> <p>13.3.1.7 End-User Driven Analytics 282</p> <p>13.3.1.8 Knowledge-Based Analytics 283</p> <p>13.3.2 Issues and Challenges in Data Analysis 283</p> <p>13.3.2.1 Multi-Modal Data 283</p> <p>13.3.2.2 Complex Domain Knowledge 283</p> <p>13.3.2.3 Highly Competent End-Users 283</p> <p>13.3.2.4 Supporting Complex Decisions 283</p> <p>13.3.2.5 Privacy 284</p> <p>13.3.2.6 Other Challenges 284</p> <p>13.4 Research Trends 284</p> <p>13.5 Conclusion 286</p> <p>References 286</p> <p><b>14 A Complete Overview of Sign Language Recognition and Translation Systems 289<br /></b><i>Kasina Jyothi Swaroop, Janamejaya Channegowda and Shambhavi Mishra</i></p> <p>14.1 Introduction 289</p> <p>14.2 Sign Language Recognition 290</p> <p>14.2.1 Fundamentals of Sign Language Recognition 290</p> <p>14.2.2 Requirements for the Sign Language Recognition 292</p> <p>14.3 Dataset Creation 293</p> <p>14.3.1 American Sign Language 293</p> <p>14.3.2 German Sign Language 296</p> <p>14.3.3 Arabic Sign Language 297</p> <p>14.3.4 Indian Sign Language 298</p> <p>14.4 Hardware Employed for Sign Language Recognition 299</p> <p>14.4.1 Glove/Sensor-Based Systems 299</p> <p>14.4.2 Microsoft Kinect–Based Systems 300</p> <p>14.5 Computer Vision–Based Sign Language Recognition and Translation Systems 302</p> <p>14.5.1 Image Processing Techniques for Sign Language Recognition 302</p> <p>14.5.2 Deep Learning Methods for Sign Language Recognition 304</p> <p>14.5.3 Pose Estimation Application to Sign Language Recognition 305</p> <p>14.5.4 Temporal Information in Sign Language Recognition and Translation 306</p> <p>14.6 Sign Language Translation System—A Brief Overview 307</p> <p>14.7 Conclusion 309</p> <p>References 310</p> <p>Index 315</p>