D. Sumathi, PhD, 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. T. Poongodi, PhD, 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. B. Balamurugan, PhD, 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. Lakshmana Kumar Ramasamy, PhD, 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.

• Preface xv1 Era of Computational Cognitive Techniques in Healthcare Systems 1Deependra Rastogi, Varun Tiwari, Shobhit Kumar and Prabhat Chandra Gupta1.1 Introduction 21.2 Cognitive Science 31.3 Gap Between Classical Theory of Cognition 41.4 Cognitive Computing’s Evolution 61.5 The Coming Era of Cognitive Computing 71.6 Cognitive Computing Architecture 91.6.1 The Internet-of-Things and Cognitive Computing 101.6.2 Big Data and Cognitive Computing 111.6.3 Cognitive Computing and Cloud Computing 131.7 Enabling Technologies in Cognitive Computing 131.7.1 Reinforcement Learning and Cognitive Computing 131.7.2 Cognitive Computing with Deep Learning 151.7.2.1 Relational Technique and Perceptual Technique 151.7.2.2 Cognitive Computing and Image Understanding 161.8 Intelligent Systems in Healthcare 171.8.1 Intelligent Cognitive System in Healthcare (Why and How) 201.9 The Cognitive Challenge 321.9.1 Case Study: Patient Evacuation 321.9.2 Case Study: Anesthesiology 321.10 Conclusion 34References 352 Proposal of a Metaheuristic Algorithm of Cognitive Computing for Classification of Erythrocytes and Leukocytes in Healthcare Informatics 41Ana Carolina Borges Monteiro, Reinaldo Padilha França, Rangel Arthur and Yuzo Iano2.1 Introduction 422.2 Literature Concept 442.2.1 Cognitive Computing Concept 442.2.2 Neural Networks Concepts 472.2.3 Convolutional Neural Network 492.2.4 Deep Learning 522.3 Materials and Methods (Metaheuristic Algorithm Proposal) 552.4 Case Study and Discussion 572.5 Conclusions with Future Research Scopes 60References 613 Convergence of Big Data and Cognitive Computing in Healthcare 67R. Sathiyaraj, U. Rahamathunnisa, M.V. Jagannatha Reddy and T. Parameswaran3.1 Introduction 683.2 Literature Review 703.2.1 Role of Cognitive Computing in Healthcare Applications 703.2.2 Research Problem Study by IBM 733.2.3 Purpose of Big Data in Healthcare 743.2.4 Convergence of Big Data with Cognitive Computing 743.2.4.1 Smart Healthcare 743.2.4.2 Big Data and Cognitive Computing-Based Smart Healthcare 753.3 Using Cognitive Computing and Big Data, a Smart Healthcare Framework for EEG Pathology Detection and Classification 763.3.1 EEG Pathology Diagnoses 763.3.2 Cognitive–Big Data-Based Smart Healthcare 773.3.3 System Architecture 793.3.4 Detection and Classification of Pathology 803.3.4.1 EEG Preprocessing and Illustration 803.3.4.2 CNN Model 803.3.5 Case Study 813.4 An Approach to Predict Heart Disease Using Integrated Big Data and Cognitive Computing in Cloud 833.4.1 Cloud Computing with Big Data in Healthcare 863.4.2 Heart Diseases 873.4.3 Healthcare Big Data Techniques 883.4.3.1 Rule Set Classifiers 883.4.3.2 Neuro Fuzzy Classifiers 893.4.3.3 Experimental Results 913.5 Conclusion 92References 934 IoT for Health, Safety, Well-Being, Inclusion, and Active Aging 97R. Indrakumari, Nilanjana Pradhan, Shrddha Sagar and Kiran Singh4.1 Introduction 984.2 The Role of Technology in an Aging Society 994.3 Literature Survey 1004.4 Health Monitoring 1014.5 Nutrition Monitoring 1054.6 Stress-Log: An IoT-Based Smart Monitoring System 1064.7 Active Aging 1084.8 Localization 1084.9 Navigation Care 1114.10 Fall Monitoring 1134.10.1 Fall Detection System Architecture 1144.10.2 Wearable Device 1144.10.3 Wireless Communication Network 1144.10.4 Smart IoT Gateway 1154.10.5 Interoperability 1154.10.6 Transformation of Data 1154.10.7 Analyzer for Big Data 1154.11 Conclusion 115References 1165 Influence of Cognitive Computing in Healthcare Applications 121Lucia Agnes Beena T. and Vinolyn Vijaykumar5.1 Introduction 1225.2 Bond Between Big Data and Cognitive Computing 1245.3 Need for Cognitive Computing in Healthcare 1265.4 Conceptual Model Linking Big Data and Cognitive Computing 1285.4.1 Significance of Big Data 1285.4.2 The Need for Cognitive Computing 1295.4.3 The Association Between the Big Data and Cognitive Computing 1305.4.4 The Advent of Cognition in Healthcare 1325.5 IBM’s Watson and Cognitive Computing 1335.5.1 Industrial Revolution with Watson 1345.5.2 The IBM’s Cognitive Computing Endeavour in Healthcare 1355.6 Future Directions 1375.6.1 Retail 1385.6.2 Research 1395.6.3 Travel 1395.6.4 Security and Threat Detection 1395.6.5 Cognitive Training Tools 1405.7 Conclusion 141References 1416 An Overview of the Computational Cognitive from a Modern Perspective, Its Techniques and Application Potential in Healthcare Systems 145Reinaldo Padilha França, Ana Carolina Borges Monteiro, Rangel Arthur and Yuzo Iano6.1 Introduction 1466.2 Literature Concept 1486.2.1 Cognitive Computing Concept 1486.2.1.1 Application Potential 1516.2.2 Cognitive Computing in Healthcare 1536.2.3 Deep Learning in Healthcare 1576.2.4 Natural Language Processing in Healthcare 1606.3 Discussion 1626.4 Trends 1636.5 Conclusions 164References 1657 Protecting Patient Data with 2F- Authentication 169G. S. Pradeep Ghantasala, Anu Radha Reddy and R. Mohan Krishna Ayyappa7.1 Introduction 1707.2 Literature Survey 1757.3 Two-Factor Authentication 1777.3.1 Novel Features of Two-Factor Authentication 1787.3.2 Two-Factor Authentication Sorgen 1787.3.3 Two-Factor Security Libraries 1797.3.4 Challenges for Fitness Concern 1807.4 Proposed Methodology 1817.5 Medical Treatment and the Preservation of Records 1867.5.1 Remote Method of Control 1877.5.2 Enabling Healthcare System Technology 1877.6 Conclusion 189References 1908 Data Analytics for Healthcare Monitoring and Inferencing 197Gend Lal Prajapati, Rachana Raghuwanshi and Rambabu Raghuwanshi8.1 An Overview of Healthcare Systems 1988.2 Need of Healthcare Systems 1988.3 Basic Principle of Healthcare Systems 1998.4 Design and Recommended Structure of Healthcare Systems 1998.4.1 Healthcare System Designs on the Basis of these Parameters 2008.4.2 Details of Healthcare Organizational Structure 2018.5 Various Challenges in Conventional Existing Healthcare System 2028.6 Health Informatics 2028.7 Information Technology Use in Healthcare Systems 2038.8 Details of Various Information Technology Application Use in Healthcare Systems 2038.9 Healthcare Information Technology Makes it Possible to Manage Patient Care and Exchange of Health Information Data, Details are Given Below 2048.10 Barriers and Challenges to Implementation of Information Technology in Healthcare Systems 2058.11 Healthcare Data Analytics 2068.12 Healthcare as a Concept 2068.13 Healthcare’s Key Technologies 2078.14 The Present State of Smart Healthcare Application 2078.15 Data Analytics with Machine Learning Use in Healthcare Systems 2088.16 Benefit of Data Analytics in Healthcare System 2108.17 Data Analysis and Visualization: COVID-19 Case Study in India 2108.18 Bioinformatics Data Analytics 2228.18.1 Notion of Bioinformatics 2228.18.2 Bioinformatics Data Challenges 2228.18.3 Sequence Analysis 2228.18.4 Applications 2238.18.5 COVID-19: A Bioinformatics Approach 2248.19 Conclusion 224References 2259 Features Optimistic Approach for the Detection of Parkinson’s Disease 229R. Shantha Selva Kumari, L. Vaishalee and P. Malavikha9.1 Introduction 2309.1.1 Parkinson’s Disease 2309.1.2 Spect Scan 2319.2 Literature Survey 2329.3 Methods and Materials 2339.3.1 Database Details 2339.3.2 Procedure 2349.3.3 Pre-Processing Done by PPMI 2359.3.4 Image Analysis and Features Extraction 2359.3.4.1 Image Slicing 2359.3.4.2 Intensity Normalization 2379.3.4.3 Image Segmentation 2399.3.4.4 Shape Features Extraction 2409.3.4.5 SBR Features 2419.3.4.6 Feature Set Analysis 2429.3.4.7 Surface Fitting 2429.3.5 Classification Modeling 2439.3.6 Feature Importance Estimation 2469.3.6.1 Need for Analysis of Important Features 2469.3.6.2 Random Forest 2479.4 Results and Discussion 2489.4.1 Segmentation 2489.4.2 Shape Analysis 2499.4.3 Classification 2499.5 Conclusion 252References 25310 Big Data Analytics in Healthcare 257Akanksha Sharma, Rishabha Malviya and Ramji Gupta10.1 Introduction 25810.2 Need for Big Data Analytics 26010.3 Characteristics of Big Data 26410.3.1 Volume 26410.3.2 Velocity 26510.3.3 Variety 26510.3.4 Veracity 26510.3.5 Value 26510.3.6 Validity 26510.3.7 Variability 26610.3.8 Viscosity 26610.3.9 Virality 26610.3.10 Visualization 26610.4 Big Data Analysis in Disease Treatment and Management 26710.4.1 For Diabetes 26710.4.2 For Heart Disease 26810.4.3 For Chronic Disease 27010.4.4 For Neurological Disease 27110.4.5 For Personalized Medicine 27110.5 Big Data: Databases and Platforms in Healthcare 27910.6 Importance of Big Data in Healthcare 28510.6.1 Evidence-Based Care 28510.6.2 Reduced Cost of Healthcare 28510.6.3 Increases the Participation of Patients in the Care Process 28510.6.4 The Implication in Health Surveillance 28510.6.5 Reduces Mortality Rate 28510.6.6 Increase of Communication Between Patients and Healthcare Providers 28610.6.7 Early Detection of Fraud and Security Threats in Health Management 28610.6.8 Improvement in the Care Quality 28610.7 Application of Big Data Analytics 28610.7.1 Image Processing 28610.7.2 Signal Processing 28710.7.3 Genomics 28810.7.4 Bioinformatics Applications 28910.7.5 Clinical Informatics Application 29110.8 Conclusion 293References 29411 Case Studies of Cognitive Computing in Healthcare Systems: Disease Prediction, Genomics Studies, Medical Image Analysis, Patient Care, Medical Diagnostics, Drug Discovery 303V. Sathananthavathi and G. Indumathi11.1 Introduction 30411.1.1 Glaucoma 30411.2 Literature Survey 30611.3 Methodology 30911.3.1 Sclera Segmentation 31011.3.1.1 Fully Convolutional Network 31111.3.2 Pupil/Iris Ratio 31311.3.2.1 Canny Edge Detection 31411.3.2.2 Mean Redness Level (MRL) 31511.3.2.3 Red Area Percentage (RAP) 31611.4 Results and Discussion 31711.4.1 Feature Extraction from Frontal Eye Images 31811.4.1.1 Level of Mean Redness (MRL) 31811.4.1.2 Percentage of Red Area (RAP) 31811.4.2 Images of the Frontal Eye Pupil/Iris Ratio 31811.4.2.1 Histogram Equalization 31911.4.2.2 Morphological Reconstruction 31911.4.2.3 Canny Edge Detection 31911.4.2.4 Adaptive Thresholding 32011.4.2.5 Circular Hough Transform 32111.4.2.6 Classification 32211.5 Conclusion and Future Work 324References 32512 State of Mental Health and Social Media: Analysis, Challenges, Advancements 327Atul Pankaj Patil, Kusum Lata Jain, Smaranika Mohapatra and Suyesha Singh12.1 Introduction 32812.2 Introduction to Big Data and Data Mining 32812.3 Role of Sentimental Analysis in the Healthcare Sector 33012.4 Case Study: Analyzing Mental Health 33212.4.1 Problem Statement 33212.4.2 Research Objectives 33312.4.3 Methodology and Framework 33312.4.3.1 Big 5 Personality Model 33312.4.3.2 Openness to Explore 33412.4.3.3 Methodology 33512.4.3.4 Detailed Design Methodologies 34012.4.3.5 Work Done Details as Required 34112.5 Results and Discussion 34312.6 Conclusion and Future 345References 34613 Applications of Artificial Intelligence, Blockchain, and Internet-of-Things in Management of Chronic Disease 349Geetanjali, Rishabha Malviya, Rajendra Awasthi, Pramod Kumar Sharma, Nidhi Kala, Vinod Kumar and Sanjay Kumar Yadav13.1 Introduction 35013.2 Artificial Intelligence and Management of Chronic Diseases 35113.3 Blockchain and Healthcare 35413.3.1 Blockchain and Healthcare Management of Chronic Disease 35513.4 Internet-of-Things and Healthcare Management of Chronic Disease 35813.5 Conclusions 360References 36014 Research Challenges and Future Directions in Applying Cognitive Computing in the Healthcare Domain 367BKSP Kumar Raju Alluri14.1 Introduction 36714.2 Cognitive Computing Framework in Healthcare 37114.3 Benefits of Using Cognitive Computing for Healthcare 37214.4 Applications of Deploying Cognitive Assisted Technology in Healthcare Management 37414.4.1 Using Cognitive Services for a Patient’s Healthcare Management 37514.4.2 Using Cognitive Services for Healthcare Providers 37614.5 Challenges in Using the Cognitive Assistive Technology in Healthcare Management 37714.6 Future Directions for Extending Heathcare Services Using CATs 38014.7 Addressing CAT Challenges in Healthcare as a General Framework 38414.8 Conclusion 384References 385Index 391

D. Sumathi, PhD, 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. T. Poongodi, PhD, 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. B. Balamurugan, PhD, 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. Lakshmana Kumar Ramasamy, PhD, 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.

• Preface xv1 Era of Computational Cognitive Techniques in Healthcare Systems 1Deependra Rastogi, Varun Tiwari, Shobhit Kumar and Prabhat Chandra Gupta1.1 Introduction 21.2 Cognitive Science 31.3 Gap Between Classical Theory of Cognition 41.4 Cognitive Computing’s Evolution 61.5 The Coming Era of Cognitive Computing 71.6 Cognitive Computing Architecture 91.6.1 The Internet-of-Things and Cognitive Computing 101.6.2 Big Data and Cognitive Computing 111.6.3 Cognitive Computing and Cloud Computing 131.7 Enabling Technologies in Cognitive Computing 131.7.1 Reinforcement Learning and Cognitive Computing 131.7.2 Cognitive Computing with Deep Learning 151.7.2.1 Relational Technique and Perceptual Technique 151.7.2.2 Cognitive Computing and Image Understanding 161.8 Intelligent Systems in Healthcare 171.8.1 Intelligent Cognitive System in Healthcare (Why and How) 201.9 The Cognitive Challenge 321.9.1 Case Study: Patient Evacuation 321.9.2 Case Study: Anesthesiology 321.10 Conclusion 34References 352 Proposal of a Metaheuristic Algorithm of Cognitive Computing for Classification of Erythrocytes and Leukocytes in Healthcare Informatics 41Ana Carolina Borges Monteiro, Reinaldo Padilha França, Rangel Arthur and Yuzo Iano2.1 Introduction 422.2 Literature Concept 442.2.1 Cognitive Computing Concept 442.2.2 Neural Networks Concepts 472.2.3 Convolutional Neural Network 492.2.4 Deep Learning 522.3 Materials and Methods (Metaheuristic Algorithm Proposal) 552.4 Case Study and Discussion 572.5 Conclusions with Future Research Scopes 60References 613 Convergence of Big Data and Cognitive Computing in Healthcare 67R. Sathiyaraj, U. Rahamathunnisa, M.V. Jagannatha Reddy and T. Parameswaran3.1 Introduction 683.2 Literature Review 703.2.1 Role of Cognitive Computing in Healthcare Applications 703.2.2 Research Problem Study by IBM 733.2.3 Purpose of Big Data in Healthcare 743.2.4 Convergence of Big Data with Cognitive Computing 743.2.4.1 Smart Healthcare 743.2.4.2 Big Data and Cognitive Computing-Based Smart Healthcare 753.3 Using Cognitive Computing and Big Data, a Smart Healthcare Framework for EEG Pathology Detection and Classification 763.3.1 EEG Pathology Diagnoses 763.3.2 Cognitive–Big Data-Based Smart Healthcare 773.3.3 System Architecture 793.3.4 Detection and Classification of Pathology 803.3.4.1 EEG Preprocessing and Illustration 803.3.4.2 CNN Model 803.3.5 Case Study 813.4 An Approach to Predict Heart Disease Using Integrated Big Data and Cognitive Computing in Cloud 833.4.1 Cloud Computing with Big Data in Healthcare 863.4.2 Heart Diseases 873.4.3 Healthcare Big Data Techniques 883.4.3.1 Rule Set Classifiers 883.4.3.2 Neuro Fuzzy Classifiers 893.4.3.3 Experimental Results 913.5 Conclusion 92References 934 IoT for Health, Safety, Well-Being, Inclusion, and Active Aging 97R. Indrakumari, Nilanjana Pradhan, Shrddha Sagar and Kiran Singh4.1 Introduction 984.2 The Role of Technology in an Aging Society 994.3 Literature Survey 1004.4 Health Monitoring 1014.5 Nutrition Monitoring 1054.6 Stress-Log: An IoT-Based Smart Monitoring System 1064.7 Active Aging 1084.8 Localization 1084.9 Navigation Care 1114.10 Fall Monitoring 1134.10.1 Fall Detection System Architecture 1144.10.2 Wearable Device 1144.10.3 Wireless Communication Network 1144.10.4 Smart IoT Gateway 1154.10.5 Interoperability 1154.10.6 Transformation of Data 1154.10.7 Analyzer for Big Data 1154.11 Conclusion 115References 1165 Influence of Cognitive Computing in Healthcare Applications 121Lucia Agnes Beena T. and Vinolyn Vijaykumar5.1 Introduction 1225.2 Bond Between Big Data and Cognitive Computing 1245.3 Need for Cognitive Computing in Healthcare 1265.4 Conceptual Model Linking Big Data and Cognitive Computing 1285.4.1 Significance of Big Data 1285.4.2 The Need for Cognitive Computing 1295.4.3 The Association Between the Big Data and Cognitive Computing 1305.4.4 The Advent of Cognition in Healthcare 1325.5 IBM’s Watson and Cognitive Computing 1335.5.1 Industrial Revolution with Watson 1345.5.2 The IBM’s Cognitive Computing Endeavour in Healthcare 1355.6 Future Directions 1375.6.1 Retail 1385.6.2 Research 1395.6.3 Travel 1395.6.4 Security and Threat Detection 1395.6.5 Cognitive Training Tools 1405.7 Conclusion 141References 1416 An Overview of the Computational Cognitive from a Modern Perspective, Its Techniques and Application Potential in Healthcare Systems 145Reinaldo Padilha França, Ana Carolina Borges Monteiro, Rangel Arthur and Yuzo Iano6.1 Introduction 1466.2 Literature Concept 1486.2.1 Cognitive Computing Concept 1486.2.1.1 Application Potential 1516.2.2 Cognitive Computing in Healthcare 1536.2.3 Deep Learning in Healthcare 1576.2.4 Natural Language Processing in Healthcare 1606.3 Discussion 1626.4 Trends 1636.5 Conclusions 164References 1657 Protecting Patient Data with 2F- Authentication 169G. S. Pradeep Ghantasala, Anu Radha Reddy and R. Mohan Krishna Ayyappa7.1 Introduction 1707.2 Literature Survey 1757.3 Two-Factor Authentication 1777.3.1 Novel Features of Two-Factor Authentication 1787.3.2 Two-Factor Authentication Sorgen 1787.3.3 Two-Factor Security Libraries 1797.3.4 Challenges for Fitness Concern 1807.4 Proposed Methodology 1817.5 Medical Treatment and the Preservation of Records 1867.5.1 Remote Method of Control 1877.5.2 Enabling Healthcare System Technology 1877.6 Conclusion 189References 1908 Data Analytics for Healthcare Monitoring and Inferencing 197Gend Lal Prajapati, Rachana Raghuwanshi and Rambabu Raghuwanshi8.1 An Overview of Healthcare Systems 1988.2 Need of Healthcare Systems 1988.3 Basic Principle of Healthcare Systems 1998.4 Design and Recommended Structure of Healthcare Systems 1998.4.1 Healthcare System Designs on the Basis of these Parameters 2008.4.2 Details of Healthcare Organizational Structure 2018.5 Various Challenges in Conventional Existing Healthcare System 2028.6 Health Informatics 2028.7 Information Technology Use in Healthcare Systems 2038.8 Details of Various Information Technology Application Use in Healthcare Systems 2038.9 Healthcare Information Technology Makes it Possible to Manage Patient Care and Exchange of Health Information Data, Details are Given Below 2048.10 Barriers and Challenges to Implementation of Information Technology in Healthcare Systems 2058.11 Healthcare Data Analytics 2068.12 Healthcare as a Concept 2068.13 Healthcare’s Key Technologies 2078.14 The Present State of Smart Healthcare Application 2078.15 Data Analytics with Machine Learning Use in Healthcare Systems 2088.16 Benefit of Data Analytics in Healthcare System 2108.17 Data Analysis and Visualization: COVID-19 Case Study in India 2108.18 Bioinformatics Data Analytics 2228.18.1 Notion of Bioinformatics 2228.18.2 Bioinformatics Data Challenges 2228.18.3 Sequence Analysis 2228.18.4 Applications 2238.18.5 COVID-19: A Bioinformatics Approach 2248.19 Conclusion 224References 2259 Features Optimistic Approach for the Detection of Parkinson’s Disease 229R. Shantha Selva Kumari, L. Vaishalee and P. Malavikha9.1 Introduction 2309.1.1 Parkinson’s Disease 2309.1.2 Spect Scan 2319.2 Literature Survey 2329.3 Methods and Materials 2339.3.1 Database Details 2339.3.2 Procedure 2349.3.3 Pre-Processing Done by PPMI 2359.3.4 Image Analysis and Features Extraction 2359.3.4.1 Image Slicing 2359.3.4.2 Intensity Normalization 2379.3.4.3 Image Segmentation 2399.3.4.4 Shape Features Extraction 2409.3.4.5 SBR Features 2419.3.4.6 Feature Set Analysis 2429.3.4.7 Surface Fitting 2429.3.5 Classification Modeling 2439.3.6 Feature Importance Estimation 2469.3.6.1 Need for Analysis of Important Features 2469.3.6.2 Random Forest 2479.4 Results and Discussion 2489.4.1 Segmentation 2489.4.2 Shape Analysis 2499.4.3 Classification 2499.5 Conclusion 252References 25310 Big Data Analytics in Healthcare 257Akanksha Sharma, Rishabha Malviya and Ramji Gupta10.1 Introduction 25810.2 Need for Big Data Analytics 26010.3 Characteristics of Big Data 26410.3.1 Volume 26410.3.2 Velocity 26510.3.3 Variety 26510.3.4 Veracity 26510.3.5 Value 26510.3.6 Validity 26510.3.7 Variability 26610.3.8 Viscosity 26610.3.9 Virality 26610.3.10 Visualization 26610.4 Big Data Analysis in Disease Treatment and Management 26710.4.1 For Diabetes 26710.4.2 For Heart Disease 26810.4.3 For Chronic Disease 27010.4.4 For Neurological Disease 27110.4.5 For Personalized Medicine 27110.5 Big Data: Databases and Platforms in Healthcare 27910.6 Importance of Big Data in Healthcare 28510.6.1 Evidence-Based Care 28510.6.2 Reduced Cost of Healthcare 28510.6.3 Increases the Participation of Patients in the Care Process 28510.6.4 The Implication in Health Surveillance 28510.6.5 Reduces Mortality Rate 28510.6.6 Increase of Communication Between Patients and Healthcare Providers 28610.6.7 Early Detection of Fraud and Security Threats in Health Management 28610.6.8 Improvement in the Care Quality 28610.7 Application of Big Data Analytics 28610.7.1 Image Processing 28610.7.2 Signal Processing 28710.7.3 Genomics 28810.7.4 Bioinformatics Applications 28910.7.5 Clinical Informatics Application 29110.8 Conclusion 293References 29411 Case Studies of Cognitive Computing in Healthcare Systems: Disease Prediction, Genomics Studies, Medical Image Analysis, Patient Care, Medical Diagnostics, Drug Discovery 303V. Sathananthavathi and G. Indumathi11.1 Introduction 30411.1.1 Glaucoma 30411.2 Literature Survey 30611.3 Methodology 30911.3.1 Sclera Segmentation 31011.3.1.1 Fully Convolutional Network 31111.3.2 Pupil/Iris Ratio 31311.3.2.1 Canny Edge Detection 31411.3.2.2 Mean Redness Level (MRL) 31511.3.2.3 Red Area Percentage (RAP) 31611.4 Results and Discussion 31711.4.1 Feature Extraction from Frontal Eye Images 31811.4.1.1 Level of Mean Redness (MRL) 31811.4.1.2 Percentage of Red Area (RAP) 31811.4.2 Images of the Frontal Eye Pupil/Iris Ratio 31811.4.2.1 Histogram Equalization 31911.4.2.2 Morphological Reconstruction 31911.4.2.3 Canny Edge Detection 31911.4.2.4 Adaptive Thresholding 32011.4.2.5 Circular Hough Transform 32111.4.2.6 Classification 32211.5 Conclusion and Future Work 324References 32512 State of Mental Health and Social Media: Analysis, Challenges, Advancements 327Atul Pankaj Patil, Kusum Lata Jain, Smaranika Mohapatra and Suyesha Singh12.1 Introduction 32812.2 Introduction to Big Data and Data Mining 32812.3 Role of Sentimental Analysis in the Healthcare Sector 33012.4 Case Study: Analyzing Mental Health 33212.4.1 Problem Statement 33212.4.2 Research Objectives 33312.4.3 Methodology and Framework 33312.4.3.1 Big 5 Personality Model 33312.4.3.2 Openness to Explore 33412.4.3.3 Methodology 33512.4.3.4 Detailed Design Methodologies 34012.4.3.5 Work Done Details as Required 34112.5 Results and Discussion 34312.6 Conclusion and Future 345References 34613 Applications of Artificial Intelligence, Blockchain, and Internet-of-Things in Management of Chronic Disease 349Geetanjali, Rishabha Malviya, Rajendra Awasthi, Pramod Kumar Sharma, Nidhi Kala, Vinod Kumar and Sanjay Kumar Yadav13.1 Introduction 35013.2 Artificial Intelligence and Management of Chronic Diseases 35113.3 Blockchain and Healthcare 35413.3.1 Blockchain and Healthcare Management of Chronic Disease 35513.4 Internet-of-Things and Healthcare Management of Chronic Disease 35813.5 Conclusions 360References 36014 Research Challenges and Future Directions in Applying Cognitive Computing in the Healthcare Domain 367BKSP Kumar Raju Alluri14.1 Introduction 36714.2 Cognitive Computing Framework in Healthcare 37114.3 Benefits of Using Cognitive Computing for Healthcare 37214.4 Applications of Deploying Cognitive Assisted Technology in Healthcare Management 37414.4.1 Using Cognitive Services for a Patient’s Healthcare Management 37514.4.2 Using Cognitive Services for Healthcare Providers 37614.5 Challenges in Using the Cognitive Assistive Technology in Healthcare Management 37714.6 Future Directions for Extending Heathcare Services Using CATs 38014.7 Addressing CAT Challenges in Healthcare as a General Framework 38414.8 Conclusion 384References 385Index 391

D. Sumathi, PhD, 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. T. Poongodi, PhD, 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. B. Balamurugan, PhD, 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. Lakshmana Kumar Ramasamy, PhD, 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.

• Preface xv1 Era of Computational Cognitive Techniques in Healthcare Systems 1Deependra Rastogi, Varun Tiwari, Shobhit Kumar and Prabhat Chandra Gupta1.1 Introduction 21.2 Cognitive Science 31.3 Gap Between Classical Theory of Cognition 41.4 Cognitive Computing’s Evolution 61.5 The Coming Era of Cognitive Computing 71.6 Cognitive Computing Architecture 91.6.1 The Internet-of-Things and Cognitive Computing 101.6.2 Big Data and Cognitive Computing 111.6.3 Cognitive Computing and Cloud Computing 131.7 Enabling Technologies in Cognitive Computing 131.7.1 Reinforcement Learning and Cognitive Computing 131.7.2 Cognitive Computing with Deep Learning 151.7.2.1 Relational Technique and Perceptual Technique 151.7.2.2 Cognitive Computing and Image Understanding 161.8 Intelligent Systems in Healthcare 171.8.1 Intelligent Cognitive System in Healthcare (Why and How) 201.9 The Cognitive Challenge 321.9.1 Case Study: Patient Evacuation 321.9.2 Case Study: Anesthesiology 321.10 Conclusion 34References 352 Proposal of a Metaheuristic Algorithm of Cognitive Computing for Classification of Erythrocytes and Leukocytes in Healthcare Informatics 41Ana Carolina Borges Monteiro, Reinaldo Padilha França, Rangel Arthur and Yuzo Iano2.1 Introduction 422.2 Literature Concept 442.2.1 Cognitive Computing Concept 442.2.2 Neural Networks Concepts 472.2.3 Convolutional Neural Network 492.2.4 Deep Learning 522.3 Materials and Methods (Metaheuristic Algorithm Proposal) 552.4 Case Study and Discussion 572.5 Conclusions with Future Research Scopes 60References 613 Convergence of Big Data and Cognitive Computing in Healthcare 67R. Sathiyaraj, U. Rahamathunnisa, M.V. Jagannatha Reddy and T. Parameswaran3.1 Introduction 683.2 Literature Review 703.2.1 Role of Cognitive Computing in Healthcare Applications 703.2.2 Research Problem Study by IBM 733.2.3 Purpose of Big Data in Healthcare 743.2.4 Convergence of Big Data with Cognitive Computing 743.2.4.1 Smart Healthcare 743.2.4.2 Big Data and Cognitive Computing-Based Smart Healthcare 753.3 Using Cognitive Computing and Big Data, a Smart Healthcare Framework for EEG Pathology Detection and Classification 763.3.1 EEG Pathology Diagnoses 763.3.2 Cognitive–Big Data-Based Smart Healthcare 773.3.3 System Architecture 793.3.4 Detection and Classification of Pathology 803.3.4.1 EEG Preprocessing and Illustration 803.3.4.2 CNN Model 803.3.5 Case Study 813.4 An Approach to Predict Heart Disease Using Integrated Big Data and Cognitive Computing in Cloud 833.4.1 Cloud Computing with Big Data in Healthcare 863.4.2 Heart Diseases 873.4.3 Healthcare Big Data Techniques 883.4.3.1 Rule Set Classifiers 883.4.3.2 Neuro Fuzzy Classifiers 893.4.3.3 Experimental Results 913.5 Conclusion 92References 934 IoT for Health, Safety, Well-Being, Inclusion, and Active Aging 97R. Indrakumari, Nilanjana Pradhan, Shrddha Sagar and Kiran Singh4.1 Introduction 984.2 The Role of Technology in an Aging Society 994.3 Literature Survey 1004.4 Health Monitoring 1014.5 Nutrition Monitoring 1054.6 Stress-Log: An IoT-Based Smart Monitoring System 1064.7 Active Aging 1084.8 Localization 1084.9 Navigation Care 1114.10 Fall Monitoring 1134.10.1 Fall Detection System Architecture 1144.10.2 Wearable Device 1144.10.3 Wireless Communication Network 1144.10.4 Smart IoT Gateway 1154.10.5 Interoperability 1154.10.6 Transformation of Data 1154.10.7 Analyzer for Big Data 1154.11 Conclusion 115References 1165 Influence of Cognitive Computing in Healthcare Applications 121Lucia Agnes Beena T. and Vinolyn Vijaykumar5.1 Introduction 1225.2 Bond Between Big Data and Cognitive Computing 1245.3 Need for Cognitive Computing in Healthcare 1265.4 Conceptual Model Linking Big Data and Cognitive Computing 1285.4.1 Significance of Big Data 1285.4.2 The Need for Cognitive Computing 1295.4.3 The Association Between the Big Data and Cognitive Computing 1305.4.4 The Advent of Cognition in Healthcare 1325.5 IBM’s Watson and Cognitive Computing 1335.5.1 Industrial Revolution with Watson 1345.5.2 The IBM’s Cognitive Computing Endeavour in Healthcare 1355.6 Future Directions 1375.6.1 Retail 1385.6.2 Research 1395.6.3 Travel 1395.6.4 Security and Threat Detection 1395.6.5 Cognitive Training Tools 1405.7 Conclusion 141References 1416 An Overview of the Computational Cognitive from a Modern Perspective, Its Techniques and Application Potential in Healthcare Systems 145Reinaldo Padilha França, Ana Carolina Borges Monteiro, Rangel Arthur and Yuzo Iano6.1 Introduction 1466.2 Literature Concept 1486.2.1 Cognitive Computing Concept 1486.2.1.1 Application Potential 1516.2.2 Cognitive Computing in Healthcare 1536.2.3 Deep Learning in Healthcare 1576.2.4 Natural Language Processing in Healthcare 1606.3 Discussion 1626.4 Trends 1636.5 Conclusions 164References 1657 Protecting Patient Data with 2F- Authentication 169G. S. Pradeep Ghantasala, Anu Radha Reddy and R. Mohan Krishna Ayyappa7.1 Introduction 1707.2 Literature Survey 1757.3 Two-Factor Authentication 1777.3.1 Novel Features of Two-Factor Authentication 1787.3.2 Two-Factor Authentication Sorgen 1787.3.3 Two-Factor Security Libraries 1797.3.4 Challenges for Fitness Concern 1807.4 Proposed Methodology 1817.5 Medical Treatment and the Preservation of Records 1867.5.1 Remote Method of Control 1877.5.2 Enabling Healthcare System Technology 1877.6 Conclusion 189References 1908 Data Analytics for Healthcare Monitoring and Inferencing 197Gend Lal Prajapati, Rachana Raghuwanshi and Rambabu Raghuwanshi8.1 An Overview of Healthcare Systems 1988.2 Need of Healthcare Systems 1988.3 Basic Principle of Healthcare Systems 1998.4 Design and Recommended Structure of Healthcare Systems 1998.4.1 Healthcare System Designs on the Basis of these Parameters 2008.4.2 Details of Healthcare Organizational Structure 2018.5 Various Challenges in Conventional Existing Healthcare System 2028.6 Health Informatics 2028.7 Information Technology Use in Healthcare Systems 2038.8 Details of Various Information Technology Application Use in Healthcare Systems 2038.9 Healthcare Information Technology Makes it Possible to Manage Patient Care and Exchange of Health Information Data, Details are Given Below 2048.10 Barriers and Challenges to Implementation of Information Technology in Healthcare Systems 2058.11 Healthcare Data Analytics 2068.12 Healthcare as a Concept 2068.13 Healthcare’s Key Technologies 2078.14 The Present State of Smart Healthcare Application 2078.15 Data Analytics with Machine Learning Use in Healthcare Systems 2088.16 Benefit of Data Analytics in Healthcare System 2108.17 Data Analysis and Visualization: COVID-19 Case Study in India 2108.18 Bioinformatics Data Analytics 2228.18.1 Notion of Bioinformatics 2228.18.2 Bioinformatics Data Challenges 2228.18.3 Sequence Analysis 2228.18.4 Applications 2238.18.5 COVID-19: A Bioinformatics Approach 2248.19 Conclusion 224References 2259 Features Optimistic Approach for the Detection of Parkinson’s Disease 229R. Shantha Selva Kumari, L. Vaishalee and P. Malavikha9.1 Introduction 2309.1.1 Parkinson’s Disease 2309.1.2 Spect Scan 2319.2 Literature Survey 2329.3 Methods and Materials 2339.3.1 Database Details 2339.3.2 Procedure 2349.3.3 Pre-Processing Done by PPMI 2359.3.4 Image Analysis and Features Extraction 2359.3.4.1 Image Slicing 2359.3.4.2 Intensity Normalization 2379.3.4.3 Image Segmentation 2399.3.4.4 Shape Features Extraction 2409.3.4.5 SBR Features 2419.3.4.6 Feature Set Analysis 2429.3.4.7 Surface Fitting 2429.3.5 Classification Modeling 2439.3.6 Feature Importance Estimation 2469.3.6.1 Need for Analysis of Important Features 2469.3.6.2 Random Forest 2479.4 Results and Discussion 2489.4.1 Segmentation 2489.4.2 Shape Analysis 2499.4.3 Classification 2499.5 Conclusion 252References 25310 Big Data Analytics in Healthcare 257Akanksha Sharma, Rishabha Malviya and Ramji Gupta10.1 Introduction 25810.2 Need for Big Data Analytics 26010.3 Characteristics of Big Data 26410.3.1 Volume 26410.3.2 Velocity 26510.3.3 Variety 26510.3.4 Veracity 26510.3.5 Value 26510.3.6 Validity 26510.3.7 Variability 26610.3.8 Viscosity 26610.3.9 Virality 26610.3.10 Visualization 26610.4 Big Data Analysis in Disease Treatment and Management 26710.4.1 For Diabetes 26710.4.2 For Heart Disease 26810.4.3 For Chronic Disease 27010.4.4 For Neurological Disease 27110.4.5 For Personalized Medicine 27110.5 Big Data: Databases and Platforms in Healthcare 27910.6 Importance of Big Data in Healthcare 28510.6.1 Evidence-Based Care 28510.6.2 Reduced Cost of Healthcare 28510.6.3 Increases the Participation of Patients in the Care Process 28510.6.4 The Implication in Health Surveillance 28510.6.5 Reduces Mortality Rate 28510.6.6 Increase of Communication Between Patients and Healthcare Providers 28610.6.7 Early Detection of Fraud and Security Threats in Health Management 28610.6.8 Improvement in the Care Quality 28610.7 Application of Big Data Analytics 28610.7.1 Image Processing 28610.7.2 Signal Processing 28710.7.3 Genomics 28810.7.4 Bioinformatics Applications 28910.7.5 Clinical Informatics Application 29110.8 Conclusion 293References 29411 Case Studies of Cognitive Computing in Healthcare Systems: Disease Prediction, Genomics Studies, Medical Image Analysis, Patient Care, Medical Diagnostics, Drug Discovery 303V. Sathananthavathi and G. Indumathi11.1 Introduction 30411.1.1 Glaucoma 30411.2 Literature Survey 30611.3 Methodology 30911.3.1 Sclera Segmentation 31011.3.1.1 Fully Convolutional Network 31111.3.2 Pupil/Iris Ratio 31311.3.2.1 Canny Edge Detection 31411.3.2.2 Mean Redness Level (MRL) 31511.3.2.3 Red Area Percentage (RAP) 31611.4 Results and Discussion 31711.4.1 Feature Extraction from Frontal Eye Images 31811.4.1.1 Level of Mean Redness (MRL) 31811.4.1.2 Percentage of Red Area (RAP) 31811.4.2 Images of the Frontal Eye Pupil/Iris Ratio 31811.4.2.1 Histogram Equalization 31911.4.2.2 Morphological Reconstruction 31911.4.2.3 Canny Edge Detection 31911.4.2.4 Adaptive Thresholding 32011.4.2.5 Circular Hough Transform 32111.4.2.6 Classification 32211.5 Conclusion and Future Work 324References 32512 State of Mental Health and Social Media: Analysis, Challenges, Advancements 327Atul Pankaj Patil, Kusum Lata Jain, Smaranika Mohapatra and Suyesha Singh12.1 Introduction 32812.2 Introduction to Big Data and Data Mining 32812.3 Role of Sentimental Analysis in the Healthcare Sector 33012.4 Case Study: Analyzing Mental Health 33212.4.1 Problem Statement 33212.4.2 Research Objectives 33312.4.3 Methodology and Framework 33312.4.3.1 Big 5 Personality Model 33312.4.3.2 Openness to Explore 33412.4.3.3 Methodology 33512.4.3.4 Detailed Design Methodologies 34012.4.3.5 Work Done Details as Required 34112.5 Results and Discussion 34312.6 Conclusion and Future 345References 34613 Applications of Artificial Intelligence, Blockchain, and Internet-of-Things in Management of Chronic Disease 349Geetanjali, Rishabha Malviya, Rajendra Awasthi, Pramod Kumar Sharma, Nidhi Kala, Vinod Kumar and Sanjay Kumar Yadav13.1 Introduction 35013.2 Artificial Intelligence and Management of Chronic Diseases 35113.3 Blockchain and Healthcare 35413.3.1 Blockchain and Healthcare Management of Chronic Disease 35513.4 Internet-of-Things and Healthcare Management of Chronic Disease 35813.5 Conclusions 360References 36014 Research Challenges and Future Directions in Applying Cognitive Computing in the Healthcare Domain 367BKSP Kumar Raju Alluri14.1 Introduction 36714.2 Cognitive Computing Framework in Healthcare 37114.3 Benefits of Using Cognitive Computing for Healthcare 37214.4 Applications of Deploying Cognitive Assisted Technology in Healthcare Management 37414.4.1 Using Cognitive Services for a Patient’s Healthcare Management 37514.4.2 Using Cognitive Services for Healthcare Providers 37614.5 Challenges in Using the Cognitive Assistive Technology in Healthcare Management 37714.6 Future Directions for Extending Heathcare Services Using CATs 38014.7 Addressing CAT Challenges in Healthcare as a General Framework 38414.8 Conclusion 384References 385Index 391