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<p>Preface xv</p> <p><b>1 Era of Computational Cognitive Techniques in Healthcare Systems 1<br /></b><i>Deependra Rastogi, Varun Tiwari, Shobhit Kumar and Prabhat Chandra Gupta</i></p> <p>1.1 Introduction 2</p> <p>1.2 Cognitive Science 3</p> <p>1.3 Gap Between Classical Theory of Cognition 4</p> <p>1.4 Cognitive Computing’s Evolution 6</p> <p>1.5 The Coming Era of Cognitive Computing 7</p> <p>1.6 Cognitive Computing Architecture 9</p> <p>1.6.1 The Internet-of-Things and Cognitive Computing 10</p> <p>1.6.2 Big Data and Cognitive Computing 11</p> <p>1.6.3 Cognitive Computing and Cloud Computing 13</p> <p>1.7 Enabling Technologies in Cognitive Computing 13</p> <p>1.7.1 Reinforcement Learning and Cognitive Computing 13</p> <p>1.7.2 Cognitive Computing with Deep Learning 15</p> <p>1.7.2.1 Relational Technique and Perceptual Technique 15</p> <p>1.7.2.2 Cognitive Computing and Image Understanding 16</p> <p>1.8 Intelligent Systems in Healthcare 17</p> <p>1.8.1 Intelligent Cognitive System in Healthcare (Why and How) 20</p> <p>1.9 The Cognitive Challenge 32</p> <p>1.9.1 Case Study: Patient Evacuation 32</p> <p>1.9.2 Case Study: Anesthesiology 32</p> <p>1.10 Conclusion 34</p> <p>References 35</p> <p><b>2 Proposal of a Metaheuristic Algorithm of Cognitive Computing for Classification of Erythrocytes and Leukocytes in Healthcare Informatics 41<br /></b><i>Ana Carolina Borges Monteiro, Reinaldo Padilha França, Rangel Arthur and Yuzo Iano</i></p> <p>2.1 Introduction 42</p> <p>2.2 Literature Concept 44</p> <p>2.2.1 Cognitive Computing Concept 44</p> <p>2.2.2 Neural Networks Concepts 47</p> <p>2.2.3 Convolutional Neural Network 49</p> <p>2.2.4 Deep Learning 52</p> <p>2.3 Materials and Methods (Metaheuristic Algorithm Proposal) 55</p> <p>2.4 Case Study and Discussion 57</p> <p>2.5 Conclusions with Future Research Scopes 60</p> <p>References 61</p> <p><b>3 Convergence of Big Data and Cognitive Computing in Healthcare 67<br /></b><i>R. Sathiyaraj, U. Rahamathunnisa, M.V. Jagannatha Reddy and T. Parameswaran</i></p> <p>3.1 Introduction 68</p> <p>3.2 Literature Review 70</p> <p>3.2.1 Role of Cognitive Computing in Healthcare Applications 70</p> <p>3.2.2 Research Problem Study by IBM 73</p> <p>3.2.3 Purpose of Big Data in Healthcare 74</p> <p>3.2.4 Convergence of Big Data with Cognitive Computing 74</p> <p>3.2.4.1 Smart Healthcare 74</p> <p>3.2.4.2 Big Data and Cognitive Computing-Based Smart Healthcare 75</p> <p>3.3 Using Cognitive Computing and Big Data, a Smart Healthcare Framework for EEG Pathology Detection and Classification 76</p> <p>3.3.1 EEG Pathology Diagnoses 76</p> <p>3.3.2 Cognitive–Big Data-Based Smart Healthcare 77</p> <p>3.3.3 System Architecture 79</p> <p>3.3.4 Detection and Classification of Pathology 80</p> <p>3.3.4.1 EEG Preprocessing and Illustration 80</p> <p>3.3.4.2 CNN Model 80</p> <p>3.3.5 Case Study 81</p> <p>3.4 An Approach to Predict Heart Disease Using Integrated Big Data and Cognitive Computing in Cloud 83</p> <p>3.4.1 Cloud Computing with Big Data in Healthcare 86</p> <p>3.4.2 Heart Diseases 87</p> <p>3.4.3 Healthcare Big Data Techniques 88</p> <p>3.4.3.1 Rule Set Classifiers 88</p> <p>3.4.3.2 Neuro Fuzzy Classifiers 89</p> <p>3.4.3.3 Experimental Results 91</p> <p>3.5 Conclusion 92</p> <p>References 93</p> <p><b>4 IoT for Health, Safety, Well-Being, Inclusion, and Active Aging 97<br /></b><i>R. Indrakumari, Nilanjana Pradhan, Shrddha Sagar and Kiran Singh</i></p> <p>4.1 Introduction 98</p> <p>4.2 The Role of Technology in an Aging Society 99</p> <p>4.3 Literature Survey 100</p> <p>4.4 Health Monitoring 101</p> <p>4.5 Nutrition Monitoring 105</p> <p>4.6 Stress-Log: An IoT-Based Smart Monitoring System 106</p> <p>4.7 Active Aging 108</p> <p>4.8 Localization 108</p> <p>4.9 Navigation Care 111</p> <p>4.10 Fall Monitoring 113</p> <p>4.10.1 Fall Detection System Architecture 114</p> <p>4.10.2 Wearable Device 114</p> <p>4.10.3 Wireless Communication Network 114</p> <p>4.10.4 Smart IoT Gateway 115</p> <p>4.10.5 Interoperability 115</p> <p>4.10.6 Transformation of Data 115</p> <p>4.10.7 Analyzer for Big Data 115</p> <p>4.11 Conclusion 115</p> <p>References 116</p> <p><b>5 Influence of Cognitive Computing in Healthcare Applications 121<br /></b><i>Lucia Agnes Beena T. and Vinolyn Vijaykumar</i></p> <p>5.1 Introduction 122</p> <p>5.2 Bond Between Big Data and Cognitive Computing 124</p> <p>5.3 Need for Cognitive Computing in Healthcare 126</p> <p>5.4 Conceptual Model Linking Big Data and Cognitive Computing 128</p> <p>5.4.1 Significance of Big Data 128</p> <p>5.4.2 The Need for Cognitive Computing 129</p> <p>5.4.3 The Association Between the Big Data and Cognitive Computing 130</p> <p>5.4.4 The Advent of Cognition in Healthcare 132</p> <p>5.5 IBM’s Watson and Cognitive Computing 133</p> <p>5.5.1 Industrial Revolution with Watson 134</p> <p>5.5.2 The IBM’s Cognitive Computing Endeavour in Healthcare 135</p> <p>5.6 Future Directions 137</p> <p>5.6.1 Retail 138</p> <p>5.6.2 Research 139</p> <p>5.6.3 Travel 139</p> <p>5.6.4 Security and Threat Detection 139</p> <p>5.6.5 Cognitive Training Tools 140</p> <p>5.7 Conclusion 141</p> <p>References 141</p> <p><b>6 An Overview of the Computational Cognitive from a Modern Perspective, Its Techniques and Application Potential in Healthcare Systems 145<br /></b><i>Reinaldo Padilha França, Ana Carolina Borges Monteiro, Rangel Arthur and Yuzo Iano</i></p> <p>6.1 Introduction 146</p> <p>6.2 Literature Concept 148</p> <p>6.2.1 Cognitive Computing Concept 148</p> <p>6.2.1.1 Application Potential 151</p> <p>6.2.2 Cognitive Computing in Healthcare 153</p> <p>6.2.3 Deep Learning in Healthcare 157</p> <p>6.2.4 Natural Language Processing in Healthcare 160</p> <p>6.3 Discussion 162</p> <p>6.4 Trends 163</p> <p>6.5 Conclusions 164</p> <p>References 165</p> <p><b>7 Protecting Patient Data with 2F- Authentication 169<br /></b><i>G. S. Pradeep Ghantasala, Anu Radha Reddy and R. Mohan Krishna Ayyappa</i></p> <p>7.1 Introduction 170</p> <p>7.2 Literature Survey 175</p> <p>7.3 Two-Factor Authentication 177</p> <p>7.3.1 Novel Features of Two-Factor Authentication 178</p> <p>7.3.2 Two-Factor Authentication Sorgen 178</p> <p>7.3.3 Two-Factor Security Libraries 179</p> <p>7.3.4 Challenges for Fitness Concern 180</p> <p>7.4 Proposed Methodology 181</p> <p>7.5 Medical Treatment and the Preservation of Records 186</p> <p>7.5.1 Remote Method of Control 187</p> <p>7.5.2 Enabling Healthcare System Technology 187</p> <p>7.6 Conclusion 189</p> <p>References 190</p> <p><b>8 Data Analytics for Healthcare Monitoring and Inferencing 197<br /></b><i>Gend Lal Prajapati, Rachana Raghuwanshi and Rambabu Raghuwanshi</i></p> <p>8.1 An Overview of Healthcare Systems 198</p> <p>8.2 Need of Healthcare Systems 198</p> <p>8.3 Basic Principle of Healthcare Systems 199</p> <p>8.4 Design and Recommended Structure of Healthcare Systems 199</p> <p>8.4.1 Healthcare System Designs on the Basis of these Parameters 200</p> <p>8.4.2 Details of Healthcare Organizational Structure 201</p> <p>8.5 Various Challenges in Conventional Existing Healthcare System 202</p> <p>8.6 Health Informatics 202</p> <p>8.7 Information Technology Use in Healthcare Systems 203</p> <p>8.8 Details of Various Information Technology Application Use in Healthcare Systems 203</p> <p>8.9 Healthcare Information Technology Makes it Possible to Manage Patient Care and Exchange of Health Information Data, Details are Given Below 204</p> <p>8.10 Barriers and Challenges to Implementation of Information Technology in Healthcare Systems 205</p> <p>8.11 Healthcare Data Analytics 206</p> <p>8.12 Healthcare as a Concept 206</p> <p>8.13 Healthcare’s Key Technologies 207</p> <p>8.14 The Present State of Smart Healthcare Application 207</p> <p>8.15 Data Analytics with Machine Learning Use in Healthcare Systems 208</p> <p>8.16 Benefit of Data Analytics in Healthcare System 210</p> <p>8.17 Data Analysis and Visualization: COVID-19 Case Study in India 210</p> <p>8.18 Bioinformatics Data Analytics 222</p> <p>8.18.1 Notion of Bioinformatics 222</p> <p>8.18.2 Bioinformatics Data Challenges 222</p> <p>8.18.3 Sequence Analysis 222</p> <p>8.18.4 Applications 223</p> <p>8.18.5 COVID-19: A Bioinformatics Approach 224</p> <p>8.19 Conclusion 224</p> <p>References 225</p> <p><b>9 Features Optimistic Approach for the Detection of Parkinson’s Disease 229<br /></b><i>R. Shantha Selva Kumari, L. Vaishalee and P. Malavikha</i></p> <p>9.1 Introduction 230</p> <p>9.1.1 Parkinson’s Disease 230</p> <p>9.1.2 Spect Scan 231</p> <p>9.2 Literature Survey 232</p> <p>9.3 Methods and Materials 233</p> <p>9.3.1 Database Details 233</p> <p>9.3.2 Procedure 234</p> <p>9.3.3 Pre-Processing Done by PPMI 235</p> <p>9.3.4 Image Analysis and Features Extraction 235</p> <p>9.3.4.1 Image Slicing 235</p> <p>9.3.4.2 Intensity Normalization 237</p> <p>9.3.4.3 Image Segmentation 239</p> <p>9.3.4.4 Shape Features Extraction 240</p> <p>9.3.4.5 SBR Features 241</p> <p>9.3.4.6 Feature Set Analysis 242</p> <p>9.3.4.7 Surface Fitting 242</p> <p>9.3.5 Classification Modeling 243</p> <p>9.3.6 Feature Importance Estimation 246</p> <p>9.3.6.1 Need for Analysis of Important Features 246</p> <p>9.3.6.2 Random Forest 247</p> <p>9.4 Results and Discussion 248</p> <p>9.4.1 Segmentation 248</p> <p>9.4.2 Shape Analysis 249</p> <p>9.4.3 Classification 249</p> <p>9.5 Conclusion 252</p> <p>References 253</p> <p><b>10 Big Data Analytics in Healthcare 257<br /></b><i>Akanksha Sharma, Rishabha Malviya and Ramji Gupta</i></p> <p>10.1 Introduction 258</p> <p>10.2 Need for Big Data Analytics 260</p> <p>10.3 Characteristics of Big Data 264</p> <p>10.3.1 Volume 264</p> <p>10.3.2 Velocity 265</p> <p>10.3.3 Variety 265</p> <p>10.3.4 Veracity 265</p> <p>10.3.5 Value 265</p> <p>10.3.6 Validity 265</p> <p>10.3.7 Variability 266</p> <p>10.3.8 Viscosity 266</p> <p>10.3.9 Virality 266</p> <p>10.3.10 Visualization 266</p> <p>10.4 Big Data Analysis in Disease Treatment and Management 267</p> <p>10.4.1 For Diabetes 267</p> <p>10.4.2 For Heart Disease 268</p> <p>10.4.3 For Chronic Disease 270</p> <p>10.4.4 For Neurological Disease 271</p> <p>10.4.5 For Personalized Medicine 271</p> <p>10.5 Big Data: Databases and Platforms in Healthcare 279</p> <p>10.6 Importance of Big Data in Healthcare 285</p> <p>10.6.1 Evidence-Based Care 285</p> <p>10.6.2 Reduced Cost of Healthcare 285</p> <p>10.6.3 Increases the Participation of Patients in the Care Process 285</p> <p>10.6.4 The Implication in Health Surveillance 285</p> <p>10.6.5 Reduces Mortality Rate 285</p> <p>10.6.6 Increase of Communication Between Patients and Healthcare Providers 286</p> <p>10.6.7 Early Detection of Fraud and Security Threats in Health Management 286</p> <p>10.6.8 Improvement in the Care Quality 286</p> <p>10.7 Application of Big Data Analytics 286</p> <p>10.7.1 Image Processing 286</p> <p>10.7.2 Signal Processing 287</p> <p>10.7.3 Genomics 288</p> <p>10.7.4 Bioinformatics Applications 289</p> <p>10.7.5 Clinical Informatics Application 291</p> <p>10.8 Conclusion 293</p> <p>References 294</p> <p><b>11 Case Studies of Cognitive Computing in Healthcare Systems: Disease Prediction, Genomics Studies, Medical Image Analysis, Patient Care, Medical Diagnostics, Drug Discovery 303<br /></b><i>V. Sathananthavathi and G. Indumathi</i></p> <p>11.1 Introduction 304</p> <p>11.1.1 Glaucoma 304</p> <p>11.2 Literature Survey 306</p> <p>11.3 Methodology 309</p> <p>11.3.1 Sclera Segmentation 310</p> <p>11.3.1.1 Fully Convolutional Network 311</p> <p>11.3.2 Pupil/Iris Ratio 313</p> <p>11.3.2.1 Canny Edge Detection 314</p> <p>11.3.2.2 Mean Redness Level (MRL) 315</p> <p>11.3.2.3 Red Area Percentage (RAP) 316</p> <p>11.4 Results and Discussion 317</p> <p>11.4.1 Feature Extraction from Frontal Eye Images 318</p> <p>11.4.1.1 Level of Mean Redness (MRL) 318</p> <p>11.4.1.2 Percentage of Red Area (RAP) 318</p> <p>11.4.2 Images of the Frontal Eye Pupil/Iris Ratio 318</p> <p>11.4.2.1 Histogram Equalization 319</p> <p>11.4.2.2 Morphological Reconstruction 319</p> <p>11.4.2.3 Canny Edge Detection 319</p> <p>11.4.2.4 Adaptive Thresholding 320</p> <p>11.4.2.5 Circular Hough Transform 321</p> <p>11.4.2.6 Classification 322</p> <p>11.5 Conclusion and Future Work 324</p> <p>References 325</p> <p><b>12 State of Mental Health and Social Media: Analysis, Challenges, Advancements 327<br /></b><i>Atul Pankaj Patil, Kusum Lata Jain, Smaranika Mohapatra and Suyesha Singh</i></p> <p>12.1 Introduction 328</p> <p>12.2 Introduction to Big Data and Data Mining 328</p> <p>12.3 Role of Sentimental Analysis in the Healthcare Sector 330</p> <p>12.4 Case Study: Analyzing Mental Health 332</p> <p>12.4.1 Problem Statement 332</p> <p>12.4.2 Research Objectives 333</p> <p>12.4.3 Methodology and Framework 333</p> <p>12.4.3.1 Big 5 Personality Model 333</p> <p>12.4.3.2 Openness to Explore 334</p> <p>12.4.3.3 Methodology 335</p> <p>12.4.3.4 Detailed Design Methodologies 340</p> <p>12.4.3.5 Work Done Details as Required 341</p> <p>12.5 Results and Discussion 343</p> <p>12.6 Conclusion and Future 345</p> <p>References 346</p> <p><b>13 Applications of Artificial Intelligence, Blockchain, and Internet-of-Things in Management of Chronic Disease 349<br /></b><i>Geetanjali, Rishabha Malviya, Rajendra Awasthi, Pramod Kumar Sharma, Nidhi Kala, Vinod Kumar and Sanjay Kumar Yadav</i></p> <p>13.1 Introduction 350</p> <p>13.2 Artificial Intelligence and Management of Chronic Diseases 351</p> <p>13.3 Blockchain and Healthcare 354</p> <p>13.3.1 Blockchain and Healthcare Management of Chronic Disease 355</p> <p>13.4 Internet-of-Things and Healthcare Management of Chronic Disease 358</p> <p>13.5 Conclusions 360</p> <p>References 360</p> <p><b>14 Research Challenges and Future Directions in Applying Cognitive Computing in the Healthcare Domain 367<br /></b><i>BKSP Kumar Raju Alluri</i></p> <p>14.1 Introduction 367</p> <p>14.2 Cognitive Computing Framework in Healthcare 371</p> <p>14.3 Benefits of Using Cognitive Computing for Healthcare 372</p> <p>14.4 Applications of Deploying Cognitive Assisted Technology in Healthcare Management 374</p> <p>14.4.1 Using Cognitive Services for a Patient’s Healthcare Management 375</p> <p>14.4.2 Using Cognitive Services for Healthcare Providers 376</p> <p>14.5 Challenges in Using the Cognitive Assistive Technology in Healthcare Management 377</p> <p>14.6 Future Directions for Extending Heathcare Services Using CATs 380</p> <p>14.7 Addressing CAT Challenges in Healthcare as a General Framework 384</p> <p>14.8 Conclusion 384</p> <p>References 385</p> <p>Index 391</p>

<p><b> D. Sumathi, PhD,</b> is an associate professor at VIT-AP University, Andhra Pradesh. She has an overall experience of 21 years out of which six years in the industry, and 15 years in the teaching field. Her research interests include cloud computing, network security, data mining, natural language processing, and the theoretical foundations of computer science. </p> <p><b> T. Poongodi, PhD,</b> is an associate professor in the Department of Computer Science and Engineering at Galgotias University, Delhi – NCR, India. She has more than 15 years of experience working in teaching and research. <p><b> B. Balamurugan, PhD,</b> is a professor in the School of Computing Science and Engineering at Galgotias University, Delhi – NCR, India. His focus is on engineering education, blockchain, and data sciences. He has published more than 30 books on various technologies and more than 150 research articles in SCI journals, conferences, and book chapters. <p><b>Lakshmana Kumar Ramasamy, PhD,</b> is leading the Machine Learning for Cyber Security team at Hindusthan College of Engineering and Technology, Coimbatore. Tamil Nadu, India. He is also allied with a company conducting specific training for Infosys Campus Connect, Oracle WDP, and Palo Alto Networks. He holds the Gold level partnership award from Infosys, India for bridging the gap between industry and academia in 2017.

<p><b>Applications of cognitive intelligence, advanced communication, and computational methods can drive healthcare research and enhance existing traditional methods in disease detection and management and prevention. </b></p> <p>As health is the foremost factor affecting the quality of human life, it is necessary to understand how the human body is functioning by processing health data obtained from various sources more quickly. Since an enormous amount of data is generated during data processing, a cognitive computing system could be applied to respond to queries, thereby assisting in customizing intelligent recommendations. This decision-making process could be improved by the deployment of cognitive computing techniques in healthcare, allowing for cutting-edge techniques to be integrated into healthcare to provide intelligent services in various healthcare applications. <p> This book tackles all these issues and provides insight into these diversified topics in the healthcare sector and shows the range of recent innovative research, in addition to shedding light on future directions in this area. <p><b> Audience</b> <p>The book will be very useful to a wide range of specialists including researchers, engineers, and postgraduate students in artificial intelligence, bioinformatics, information technology, as well as those in biomedicine.

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