Vishal Jain is an associate professor at Bharati Vidyapeeth's Institute of Computer Applications and Management (BVICAM), New Delhi, India. He has more than 350 research citation indices with Google Scholar (h-index score 9 and i-10 index 9). He has authored more than 70 research papers in reputed conferences and journals indexed by Web of Science and Scopus, as well as authored and edited more than 10 books with various international publishers. His research areas include information retrieval, semantic web, ontology engineering, data mining, adhoc networks, and sensor networks. Ritika Wason is currently working as an associate professor at Bharati Vidyapeeth's Institute of Computer Applications and Management (BVICAM), New Delhi. She completed her PhD degree in Computer Science from Sharda University. She has more than 10 years of teaching experience and has authored as well as edited several books in computer science and has been a recipient of many awards and honors. Jyotir Moy Chatterjee is currently an assistant professor in the IT department at Lord Buddha Education Foundation (Asia Pacific University of Technology & Innovation), Kathmandu, Nepal. He has completed M. Tech from Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha and B. Tech in Computer Science & Engineering from Dr. MGR Educational & Research Institute, Chennai. His research interests include the cloud computing, big data, privacy preservation, data mining, Internet of Things, machine learning. Dac-Nhuong Le, PhD is the Head-Deputy of Faculty of Information Technology, Haiphong University, Vietnam. He has a total academic teaching experience of 10 years with many publications in reputed international conferences, journals and online book chapter contributions. He researches interests span the optimization and algorithmic mathematics underpinnings of network communication, security and vulnerability, network performance analysis, and cloud computing.

• Preface xixAcknowledgment xxiii1 Role of Ontology in Health Care 1Sonia Singla1.1 Introduction 21.2 Ontology in Diabetes 31.2.1 Ontology Process 41.2.2 Impediments of the Present Investigation 51.3 Role of Ontology in Cardiovascular Diseases 61.4 Role of Ontology in Parkinson Diseases 81.4.1 The Spread of Disease With Age and Onset of Disease 101.4.2 Cost of PD for Health Care, Household 111.4.3 Treatment and Medicines 111.5 Role of Ontology in Depression 131.6 Conclusion 151.7 Future Scope 15References 152 A Study on Basal Ganglia Circuit and Its Relation With Movement Disorders 19Dinesh Bhatia2.1 Introduction 192.2 Anatomy and Functioning of Basal Ganglia 212.2.1 The Striatum-Major Entrance to Basal Ganglia Circuitry 222.2.2 Direct and Indirect Striatofugal Projections 232.2.3 The STN: Another Entrance to Basal Ganglia Circuitry 252.3 Movement Disorders 262.3.1 Parkinson Disease 262.3.2 Dyskinetic Disorder 272.3.3 Dystonia 282.4 Effect of Basal Ganglia Dysfunctioning on Movement Disorders 292.5 Conclusion and Future Scope 31References 313 Extraction of Significant Association Rules Using Pre- and Post-Mining Techniques—An Analysis 37M. Nandhini and S. N. Sivanandam3.1 Introduction 383.2 Background 393.2.1 Interestingness Measures 393.2.2 Pre-Mining Techniques 403.2.2.1 Candidate Set Reduction Schemes 403.2.2.2 Optimal Threshold Computation Schemes 413.2.2.3 Weight-Based Mining Schemes 423.2.3 Post-Mining Techniques 423.2.3.1 Rule Pruning Schemes 433.2.3.2 Schemes Using Knowledge Base 433.3 Methodology 443.3.1 Data Preprocessing 443.3.2 Pre-Mining 463.3.2.1 Pre-Mining Technique 1: Optimal Support and Confidence Threshold Value Computation Using PSO 463.3.2.2 Pre-Mining Technique 2: Attribute Weight Computation Using IG Measure 483.3.3 Association Rule Generation 503.3.3.1 ARM Preliminaries 503.3.3.2 WARM Preliminaries 523.3.4 Post-Mining 563.3.4.1 Filters 563.3.4.2 Operators 583.3.4.3 Rule Schemas 583.4 Experiments and Results 593.4.1 Parameter Settings for PSO-Based Pre-Mining Technique 603.4.2 Parameter Settings for PAW-Based Pre-Mining Technique 603.5 Conclusions 63References 654 Ontology in Medicine as a Database Management System 69Shobowale K. O.4.1 Introduction 704.1.1 Ontology Engineering and Development Methodology 724.2 Literature Review on Medical Data Processing 724.3 Information on Medical Ontology 754.3.1 Types of Medical Ontology 754.3.2 Knowledge Representation 764.3.3 Methodology of Developing Medical Ontology 764.3.4 Medical Ontology Standards 774.4 Ontologies as a Knowledge-Based System 784.4.1 Domain Ontology in Medicine 794.4.2 Brief Introduction of Some Medical Standards 814.4.2.1 Medical Subject Headings (MeSH) 814.4.2.2 Medical Dictionary for Regulatory Activities (MedDRA) 814.4.2.3 Medical Entities Dictionary (MED) 814.4.3 Reusing Medical Ontology 824.4.4 Ontology Evaluation 854.5 Conclusion 864.6 Future Scope 86References 875 Using IoT and Semantic Web Technologies for Healthcare and Medical Sector 91Nikita Malik and Sanjay Kumar Malik5.1 Introduction 925.1.1 Significance of Healthcare and Medical Sector and Its Digitization 925.1.2 e-Health and m-Health 925.1.3 Internet of Things and Its Use 945.1.4 Semantic Web and Its Technologies 965.2 Use of IoT in Healthcare and Medical Domain 985.2.1 Scope of IoT in Healthcare and Medical Sector 985.2.2 Benefits of IoT in Healthcare and Medical Systems 1005.2.3 IoT Healthcare Challenges and Open Issues 1005.3 Role of SWTs in Healthcare Services 1015.3.1 Scope and Benefits of Incorporating Semantics in Healthcare 1015.3.2 Ontologies and Datasets for Healthcare and Medical Domain 1035.3.3 Challenges in the Use of SWTs in Healthcare Sector 1045.4 Incorporating IoT and/or SWTs in Healthcare and Medical Sector 1065.4.1 Proposed Architecture or Framework or Model 1065.4.2 Access Mechanisms or Approaches 1085.4.3 Applications or Systems 1095.5 Healthcare Data Analytics Using Data Mining and Machine Learning 1105.6 Conclusion 1125.7 Future Work 113References 1136 An Ontological Model, Design, and Implementation of CSPF for Healthcare 117Pooja Mohan6.1 Introduction 1176.2 Related Work 1196.3 Mathematical Representation of CSPF Model 1226.3.1 Basic Sets of CSPF Model 1236.3.2 Conditional Contextual Security and Privacy Constraints 1236.3.3 CSPF Model States CsetofStates 1246.3.4 Permission Cpermission 1246.3.5 Security Evaluation Function (SEFcontexts) 1246.3.6 Secure State 1256.3.7 CSPF Model Operations 1256.3.7.1 Administrative Operations 1256.3.7.2 Users’ Operations 1276.4 Ontological Model 1276.4.1 Development of Class Hierarchy 1276.4.1.1 Object Properties of Sensor Class 1296.4.1.2 Data Properties 1296.4.1.3 The Individuals 1296.5 The Design of Context-Aware Security and Privacy Model for Wireless Sensor Network 1296.6 Implementation 1336.7 Analysis and Results 1356.7.1 Inference Time/Latency/Query Response Time vs. No. of Policies 1356.7.2 Average Inference Time vs. Contexts 1366.8 Conclusion and Future Scope 137References 1387 Ontology-Based Query Retrieval Support for E-Health Implementation 143Aatif Ahmad Khan and Sanjay Kumar Malik7.1 Introduction 1437.1.1 Health Care Record Management 1447.1.1.1 Electronic Health Record 1447.1.1.2 Electronic Medical Record 1457.1.1.3 Picture Archiving and Communication System 1457.1.1.4 Pharmacy Systems 1457.1.2 Information Retrieval 1457.1.3 Ontology 1467.2 Ontology-Based Query Retrieval Support 1467.3 E-Health 1507.3.1 Objectives and Scope 1507.3.2 Benefits of E-Health 1517.3.3 E-Health Implementation 1517.4 Ontology-Driven Information Retrieval for E-Health 1547.4.1 Ontology for E-Heath Implementation 1557.4.2 Frameworks for Information Retrieval Using Ontology for E-Health 1577.4.3 Applications of Ontology-Driven Information Retrieval in Health Care 1587.4.4 Benefits and Limitations 1607.5 Discussion 1607.6 Conclusion 164References 1648 Ontology-Based Case Retrieval in an E-Mental Health Intelligent Information System 167Georgia Kaoura, Konstantinos Kovas and Basilis Boutsinas8.1 Introduction 1678.2 Literature Survey 1708.3 Problem Identified 1738.4 Proposed Solution 1748.4.1 The PAVEFS Ontology 1748.4.2 Knowledge Base 1798.4.3 Reasoning 1808.4.4 User Interaction 1828.5 Pros and Cons of Solution 1838.5.1 Evaluation Methodology and Results 1838.5.2 Evaluation Methodology 1858.5.2.1 Evaluation Tools 1868.5.2.2 Results 1878.6 Conclusions 1898.7 Future Scope 190References 1909 Ontology Engineering Applications in Medical Domain 193Mariam Gawich and Marco Alfonse9.1 Introduction 1939.2 Ontology Activities 1959.2.1 Ontology Learning 1959.2.2 Ontology Matching 1959.2.3 Ontology Merging (Unification) 1959.2.4 Ontology Validation 1969.2.5 Ontology Verification 1969.2.6 Ontology Alignment 1969.2.7 Ontology Annotation 1969.2.8 Ontology Evaluation 1969.2.9 Ontology Evolution 1969.3 Ontology Development Methodologies 1979.3.1 TOVE 1979.3.2 Methontology 1989.3.3 Brusa et al. Methodology 1989.3.4 UPON Methodology 1999.3.5 Uschold and King Methodology 2009.4 Ontology Languages 2039.4.1 RDF-RDF Schema 2039.4.2 OWL 2059.4.3 OWL 2 2059.5 Ontology Tools 2089.5.1 Apollo 2089.5.2 NeON 2099.5.3 Protégé 2109.6 Ontology Engineering Applications in Medical Domain 2129.6.1 Ontology-Based Decision Support System (DSS) 2139.6.1.1 OntoDiabetic 2139.6.1.2 Ontology-Based CDSS for Diabetes Diagnosis 2149.6.1.3 Ontology-Based Medical DSS within E-Care Telemonitoring Platform 2159.6.2 Medical Ontology in the Dynamic Healthcare Environment 2169.6.3 Knowledge Management Systems 2179.6.3.1 Ontology-Based System for Cancer Diseases 2179.6.3.2 Personalized Care System for Chronic Patients at Home 2189.7 Ontology Engineering Applications in Other Domains 2199.7.1 Ontology Engineering Applications in E-Commerce 2199.7.1.1 Automated Approach to Product Taxonomy Mapping in E-Commerce 2199.7.1.2 LexOnt Matching Approach 2219.7.2 Ontology Engineering Applications in Social Media Domain 2229.7.2.1 Emotive Ontology Approach 2229.7.2.2 Ontology-Based Approach for Social Media Analysis 2249.7.2.3 Methodological Framework for Semantic Comparison of Emotional Values 225References 22610 Ontologies on Biomedical Informatics 233Marco Alfonse and Mariam Gawich10.1 Introduction 23310.2 Defining Ontology 23410.3 Biomedical Ontologies and Ontology-Based Systems 23510.3.1 MetaMap 23510.3.2 GALEN 23610.3.3 NIH-CDE 23610.3.4 LOINC 23710.3.5 Current Procedural Terminology (CPT) 23810.3.6 Medline Plus Connect 23810.3.7 Gene Ontology 23910.3.8 UMLS 24010.3.9 SNOMED-CT 24010.3.10 OBO Foundry 24010.3.11 Textpresso 24010.3.12 National Cancer Institute Thesaurus 241References 24111 Machine Learning Techniques Best for Large Data Prediction: A Case Study of Breast Cancer Categorical Data: k-Nearest Neighbors 245Yagyanath Rimal11.1 Introduction 24611.2 R Programming 25011.3 Conclusion 255References 25512 Need of Ontology-Based Systems in Healthcare System 257Tshepiso Larona Mokgetse12.1 Introduction 25812.2 What is Ontology? 25912.3 Need for Ontology in Healthcare Systems 26012.3.1 Primary Healthcare 26212.3.1.1 Semantic Web System 26212.3.2 Emergency Services 26312.3.2.1 Service-Oriented Architecture 26312.3.2.2 IOT Ontology 26412.3.3 Public Healthcare 26512.3.3.1 IOT Data Model 26512.3.4 Chronic Disease Healthcare 26612.3.4.1 Clinical Reminder System 26612.3.4.2 Chronic Care Model 26712.3.5 Specialized Healthcare 26812.3.5.1 E-Health Record System 26812.3.5.2 Maternal and Child Health 26912.3.6 Cardiovascular System 27012.3.6.1 Distributed Healthcare System 27012.3.6.2 Records Management System 27012.3.7 Stroke Rehabilitation 27112.3.7.1 Patient Information System 27112.3.7.2 Toronto Virtual System 27112.4 Conclusion 272References 27213 Exploration of Information Retrieval Approaches With Focus on Medical Information Retrieval 275Mamata Rath and Jyotir Moy Chatterjee13.1 Introduction 27613.1.1 Machine Learning-Based Medical Information System 27813.1.2 Cognitive Information Retrieval 27813.2 Review of Literature 27913.3 Cognitive Methods of IR 28113.4 Cognitive and Interactive IR Systems 28613.5 Conclusion 288References 28914 Ontology as a Tool to Enable Health Internet of Things Viable 5G Communication Networks 293Nidhi Sharma and R. K. Aggarwal14.1 Introduction 29314.2 From Concept Representations to Medical Ontologies 29514.2.1 Current Medical Research Trends 29614.2.2 Ontology as a Paradigm Shift in Health Informatics 29614.3 Primer Literature Review 29714.3.1 Remote Health Monitoring 29814.3.2 Collecting and Understanding Medical Data 29814.3.3 Patient Monitoring 29814.3.4 Tele-Health 29914.3.5 Advanced Human Services Records Frameworks 29914.3.6 Applied Autonomy and Healthcare Mechanization 30014.3.7 IoT Powers the Preventive Healthcare 30114.3.8 Hospital Statistics Control System (HSCS) 30114.3.9 End-to-End Accessibility and Moderateness 30114.3.10 Information Mixing and Assessment 30214.3.11 Following and Alerts 30214.3.12 Remote Remedial Assistance 30214.4 Establishments of Health IoT 30314.4.1 Technological Challenges 30414.4.2 Probable Solutions 30614.4.3 Bit-by-Bit Action Statements 30714.5 Incubation of IoT in Health Industry 30714.5.1 Hearables 30814.5.2 Ingestible Sensors 30814.5.3 Moodables 30814.5.4 PC Vision Innovation 30814.5.5 Social Insurance Outlining 30814.6 Concluding Remarks 309References 30915 Tools and Techniques for Streaming Data: An Overview 313K. Saranya, S. Chellammal and Pethuru Raj Chelliah15.1 Introduction 31415.2 Traditional Techniques 31515.2.1 Random Sampling 31515.2.2 Histograms 31615.2.3 Sliding Window 31615.2.4 Sketches 31715.2.4.1 Bloom Filters 31715.2.4.2 Count-Min Sketch 31715.3 Data Mining Techniques 31715.3.1 Clustering 31815.3.1.1 STREAM 31815.3.1.2 BRICH 31815.3.1.3 CLUSTREAM 31915.3.2 Classification 31915.3.2.1 Naïve Bayesian 31915.3.2.2 Hoeffding 32015.3.2.3 Very Fast Decision Tree 32015.3.2.4 Concept Adaptive Very Fast Decision Tree 32015.4 Big Data Platforms 32015.4.1 Apache Storm 32115.4.2 Apache Spark 32115.4.2.1 Apache Spark Core 32115.4.2.2 Spark SQL 32215.4.2.3 Machine Learning Library 32215.4.2.4 Streaming Data API 32215.4.2.5 GraphX 32315.4.3 Apache Flume 32315.4.4 Apache Kafka 32315.4.5 Apache Flink 32615.5 Conclusion 327References 32816 An Ontology-Based IR for Health Care 331J. P. Patra, Gurudatta Verma and Sumitra Samal16.1 Introduction 33116.2 General Definition of Information Retrieval Model 33316.3 Information Retrieval Model Based on Ontology 33416.4 Literature Survey 33616.5 Methodolgy for IR 339References 344

Vishal Jain is an associate professor at Bharati Vidyapeeth's Institute of Computer Applications and Management (BVICAM), New Delhi, India. He has more than 350 research citation indices with Google Scholar (h-index score 9 and i-10 index 9). He has authored more than 70 research papers in reputed conferences and journals indexed by Web of Science and Scopus, as well as authored and edited more than 10 books with various international publishers. His research areas include information retrieval, semantic web, ontology engineering, data mining, adhoc networks, and sensor networks. Ritika Wason is currently working as an associate professor at Bharati Vidyapeeth's Institute of Computer Applications and Management (BVICAM), New Delhi. She completed her PhD degree in Computer Science from Sharda University. She has more than 10 years of teaching experience and has authored as well as edited several books in computer science and has been a recipient of many awards and honors. Jyotir Moy Chatterjee is currently an assistant professor in the IT department at Lord Buddha Education Foundation (Asia Pacific University of Technology & Innovation), Kathmandu, Nepal. He has completed M. Tech from Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha and B. Tech in Computer Science & Engineering from Dr. MGR Educational & Research Institute, Chennai. His research interests include the cloud computing, big data, privacy preservation, data mining, Internet of Things, machine learning. Dac-Nhuong Le, PhD is the Head-Deputy of Faculty of Information Technology, Haiphong University, Vietnam. He has a total academic teaching experience of 10 years with many publications in reputed international conferences, journals and online book chapter contributions. He researches interests span the optimization and algorithmic mathematics underpinnings of network communication, security and vulnerability, network performance analysis, and cloud computing.

• Preface xixAcknowledgment xxiii1 Role of Ontology in Health Care 1Sonia Singla1.1 Introduction 21.2 Ontology in Diabetes 31.2.1 Ontology Process 41.2.2 Impediments of the Present Investigation 51.3 Role of Ontology in Cardiovascular Diseases 61.4 Role of Ontology in Parkinson Diseases 81.4.1 The Spread of Disease With Age and Onset of Disease 101.4.2 Cost of PD for Health Care, Household 111.4.3 Treatment and Medicines 111.5 Role of Ontology in Depression 131.6 Conclusion 151.7 Future Scope 15References 152 A Study on Basal Ganglia Circuit and Its Relation With Movement Disorders 19Dinesh Bhatia2.1 Introduction 192.2 Anatomy and Functioning of Basal Ganglia 212.2.1 The Striatum-Major Entrance to Basal Ganglia Circuitry 222.2.2 Direct and Indirect Striatofugal Projections 232.2.3 The STN: Another Entrance to Basal Ganglia Circuitry 252.3 Movement Disorders 262.3.1 Parkinson Disease 262.3.2 Dyskinetic Disorder 272.3.3 Dystonia 282.4 Effect of Basal Ganglia Dysfunctioning on Movement Disorders 292.5 Conclusion and Future Scope 31References 313 Extraction of Significant Association Rules Using Pre- and Post-Mining Techniques—An Analysis 37M. Nandhini and S. N. Sivanandam3.1 Introduction 383.2 Background 393.2.1 Interestingness Measures 393.2.2 Pre-Mining Techniques 403.2.2.1 Candidate Set Reduction Schemes 403.2.2.2 Optimal Threshold Computation Schemes 413.2.2.3 Weight-Based Mining Schemes 423.2.3 Post-Mining Techniques 423.2.3.1 Rule Pruning Schemes 433.2.3.2 Schemes Using Knowledge Base 433.3 Methodology 443.3.1 Data Preprocessing 443.3.2 Pre-Mining 463.3.2.1 Pre-Mining Technique 1: Optimal Support and Confidence Threshold Value Computation Using PSO 463.3.2.2 Pre-Mining Technique 2: Attribute Weight Computation Using IG Measure 483.3.3 Association Rule Generation 503.3.3.1 ARM Preliminaries 503.3.3.2 WARM Preliminaries 523.3.4 Post-Mining 563.3.4.1 Filters 563.3.4.2 Operators 583.3.4.3 Rule Schemas 583.4 Experiments and Results 593.4.1 Parameter Settings for PSO-Based Pre-Mining Technique 603.4.2 Parameter Settings for PAW-Based Pre-Mining Technique 603.5 Conclusions 63References 654 Ontology in Medicine as a Database Management System 69Shobowale K. O.4.1 Introduction 704.1.1 Ontology Engineering and Development Methodology 724.2 Literature Review on Medical Data Processing 724.3 Information on Medical Ontology 754.3.1 Types of Medical Ontology 754.3.2 Knowledge Representation 764.3.3 Methodology of Developing Medical Ontology 764.3.4 Medical Ontology Standards 774.4 Ontologies as a Knowledge-Based System 784.4.1 Domain Ontology in Medicine 794.4.2 Brief Introduction of Some Medical Standards 814.4.2.1 Medical Subject Headings (MeSH) 814.4.2.2 Medical Dictionary for Regulatory Activities (MedDRA) 814.4.2.3 Medical Entities Dictionary (MED) 814.4.3 Reusing Medical Ontology 824.4.4 Ontology Evaluation 854.5 Conclusion 864.6 Future Scope 86References 875 Using IoT and Semantic Web Technologies for Healthcare and Medical Sector 91Nikita Malik and Sanjay Kumar Malik5.1 Introduction 925.1.1 Significance of Healthcare and Medical Sector and Its Digitization 925.1.2 e-Health and m-Health 925.1.3 Internet of Things and Its Use 945.1.4 Semantic Web and Its Technologies 965.2 Use of IoT in Healthcare and Medical Domain 985.2.1 Scope of IoT in Healthcare and Medical Sector 985.2.2 Benefits of IoT in Healthcare and Medical Systems 1005.2.3 IoT Healthcare Challenges and Open Issues 1005.3 Role of SWTs in Healthcare Services 1015.3.1 Scope and Benefits of Incorporating Semantics in Healthcare 1015.3.2 Ontologies and Datasets for Healthcare and Medical Domain 1035.3.3 Challenges in the Use of SWTs in Healthcare Sector 1045.4 Incorporating IoT and/or SWTs in Healthcare and Medical Sector 1065.4.1 Proposed Architecture or Framework or Model 1065.4.2 Access Mechanisms or Approaches 1085.4.3 Applications or Systems 1095.5 Healthcare Data Analytics Using Data Mining and Machine Learning 1105.6 Conclusion 1125.7 Future Work 113References 1136 An Ontological Model, Design, and Implementation of CSPF for Healthcare 117Pooja Mohan6.1 Introduction 1176.2 Related Work 1196.3 Mathematical Representation of CSPF Model 1226.3.1 Basic Sets of CSPF Model 1236.3.2 Conditional Contextual Security and Privacy Constraints 1236.3.3 CSPF Model States CsetofStates 1246.3.4 Permission Cpermission 1246.3.5 Security Evaluation Function (SEFcontexts) 1246.3.6 Secure State 1256.3.7 CSPF Model Operations 1256.3.7.1 Administrative Operations 1256.3.7.2 Users’ Operations 1276.4 Ontological Model 1276.4.1 Development of Class Hierarchy 1276.4.1.1 Object Properties of Sensor Class 1296.4.1.2 Data Properties 1296.4.1.3 The Individuals 1296.5 The Design of Context-Aware Security and Privacy Model for Wireless Sensor Network 1296.6 Implementation 1336.7 Analysis and Results 1356.7.1 Inference Time/Latency/Query Response Time vs. No. of Policies 1356.7.2 Average Inference Time vs. Contexts 1366.8 Conclusion and Future Scope 137References 1387 Ontology-Based Query Retrieval Support for E-Health Implementation 143Aatif Ahmad Khan and Sanjay Kumar Malik7.1 Introduction 1437.1.1 Health Care Record Management 1447.1.1.1 Electronic Health Record 1447.1.1.2 Electronic Medical Record 1457.1.1.3 Picture Archiving and Communication System 1457.1.1.4 Pharmacy Systems 1457.1.2 Information Retrieval 1457.1.3 Ontology 1467.2 Ontology-Based Query Retrieval Support 1467.3 E-Health 1507.3.1 Objectives and Scope 1507.3.2 Benefits of E-Health 1517.3.3 E-Health Implementation 1517.4 Ontology-Driven Information Retrieval for E-Health 1547.4.1 Ontology for E-Heath Implementation 1557.4.2 Frameworks for Information Retrieval Using Ontology for E-Health 1577.4.3 Applications of Ontology-Driven Information Retrieval in Health Care 1587.4.4 Benefits and Limitations 1607.5 Discussion 1607.6 Conclusion 164References 1648 Ontology-Based Case Retrieval in an E-Mental Health Intelligent Information System 167Georgia Kaoura, Konstantinos Kovas and Basilis Boutsinas8.1 Introduction 1678.2 Literature Survey 1708.3 Problem Identified 1738.4 Proposed Solution 1748.4.1 The PAVEFS Ontology 1748.4.2 Knowledge Base 1798.4.3 Reasoning 1808.4.4 User Interaction 1828.5 Pros and Cons of Solution 1838.5.1 Evaluation Methodology and Results 1838.5.2 Evaluation Methodology 1858.5.2.1 Evaluation Tools 1868.5.2.2 Results 1878.6 Conclusions 1898.7 Future Scope 190References 1909 Ontology Engineering Applications in Medical Domain 193Mariam Gawich and Marco Alfonse9.1 Introduction 1939.2 Ontology Activities 1959.2.1 Ontology Learning 1959.2.2 Ontology Matching 1959.2.3 Ontology Merging (Unification) 1959.2.4 Ontology Validation 1969.2.5 Ontology Verification 1969.2.6 Ontology Alignment 1969.2.7 Ontology Annotation 1969.2.8 Ontology Evaluation 1969.2.9 Ontology Evolution 1969.3 Ontology Development Methodologies 1979.3.1 TOVE 1979.3.2 Methontology 1989.3.3 Brusa et al. Methodology 1989.3.4 UPON Methodology 1999.3.5 Uschold and King Methodology 2009.4 Ontology Languages 2039.4.1 RDF-RDF Schema 2039.4.2 OWL 2059.4.3 OWL 2 2059.5 Ontology Tools 2089.5.1 Apollo 2089.5.2 NeON 2099.5.3 Protégé 2109.6 Ontology Engineering Applications in Medical Domain 2129.6.1 Ontology-Based Decision Support System (DSS) 2139.6.1.1 OntoDiabetic 2139.6.1.2 Ontology-Based CDSS for Diabetes Diagnosis 2149.6.1.3 Ontology-Based Medical DSS within E-Care Telemonitoring Platform 2159.6.2 Medical Ontology in the Dynamic Healthcare Environment 2169.6.3 Knowledge Management Systems 2179.6.3.1 Ontology-Based System for Cancer Diseases 2179.6.3.2 Personalized Care System for Chronic Patients at Home 2189.7 Ontology Engineering Applications in Other Domains 2199.7.1 Ontology Engineering Applications in E-Commerce 2199.7.1.1 Automated Approach to Product Taxonomy Mapping in E-Commerce 2199.7.1.2 LexOnt Matching Approach 2219.7.2 Ontology Engineering Applications in Social Media Domain 2229.7.2.1 Emotive Ontology Approach 2229.7.2.2 Ontology-Based Approach for Social Media Analysis 2249.7.2.3 Methodological Framework for Semantic Comparison of Emotional Values 225References 22610 Ontologies on Biomedical Informatics 233Marco Alfonse and Mariam Gawich10.1 Introduction 23310.2 Defining Ontology 23410.3 Biomedical Ontologies and Ontology-Based Systems 23510.3.1 MetaMap 23510.3.2 GALEN 23610.3.3 NIH-CDE 23610.3.4 LOINC 23710.3.5 Current Procedural Terminology (CPT) 23810.3.6 Medline Plus Connect 23810.3.7 Gene Ontology 23910.3.8 UMLS 24010.3.9 SNOMED-CT 24010.3.10 OBO Foundry 24010.3.11 Textpresso 24010.3.12 National Cancer Institute Thesaurus 241References 24111 Machine Learning Techniques Best for Large Data Prediction: A Case Study of Breast Cancer Categorical Data: k-Nearest Neighbors 245Yagyanath Rimal11.1 Introduction 24611.2 R Programming 25011.3 Conclusion 255References 25512 Need of Ontology-Based Systems in Healthcare System 257Tshepiso Larona Mokgetse12.1 Introduction 25812.2 What is Ontology? 25912.3 Need for Ontology in Healthcare Systems 26012.3.1 Primary Healthcare 26212.3.1.1 Semantic Web System 26212.3.2 Emergency Services 26312.3.2.1 Service-Oriented Architecture 26312.3.2.2 IOT Ontology 26412.3.3 Public Healthcare 26512.3.3.1 IOT Data Model 26512.3.4 Chronic Disease Healthcare 26612.3.4.1 Clinical Reminder System 26612.3.4.2 Chronic Care Model 26712.3.5 Specialized Healthcare 26812.3.5.1 E-Health Record System 26812.3.5.2 Maternal and Child Health 26912.3.6 Cardiovascular System 27012.3.6.1 Distributed Healthcare System 27012.3.6.2 Records Management System 27012.3.7 Stroke Rehabilitation 27112.3.7.1 Patient Information System 27112.3.7.2 Toronto Virtual System 27112.4 Conclusion 272References 27213 Exploration of Information Retrieval Approaches With Focus on Medical Information Retrieval 275Mamata Rath and Jyotir Moy Chatterjee13.1 Introduction 27613.1.1 Machine Learning-Based Medical Information System 27813.1.2 Cognitive Information Retrieval 27813.2 Review of Literature 27913.3 Cognitive Methods of IR 28113.4 Cognitive and Interactive IR Systems 28613.5 Conclusion 288References 28914 Ontology as a Tool to Enable Health Internet of Things Viable 5G Communication Networks 293Nidhi Sharma and R. K. Aggarwal14.1 Introduction 29314.2 From Concept Representations to Medical Ontologies 29514.2.1 Current Medical Research Trends 29614.2.2 Ontology as a Paradigm Shift in Health Informatics 29614.3 Primer Literature Review 29714.3.1 Remote Health Monitoring 29814.3.2 Collecting and Understanding Medical Data 29814.3.3 Patient Monitoring 29814.3.4 Tele-Health 29914.3.5 Advanced Human Services Records Frameworks 29914.3.6 Applied Autonomy and Healthcare Mechanization 30014.3.7 IoT Powers the Preventive Healthcare 30114.3.8 Hospital Statistics Control System (HSCS) 30114.3.9 End-to-End Accessibility and Moderateness 30114.3.10 Information Mixing and Assessment 30214.3.11 Following and Alerts 30214.3.12 Remote Remedial Assistance 30214.4 Establishments of Health IoT 30314.4.1 Technological Challenges 30414.4.2 Probable Solutions 30614.4.3 Bit-by-Bit Action Statements 30714.5 Incubation of IoT in Health Industry 30714.5.1 Hearables 30814.5.2 Ingestible Sensors 30814.5.3 Moodables 30814.5.4 PC Vision Innovation 30814.5.5 Social Insurance Outlining 30814.6 Concluding Remarks 309References 30915 Tools and Techniques for Streaming Data: An Overview 313K. Saranya, S. Chellammal and Pethuru Raj Chelliah15.1 Introduction 31415.2 Traditional Techniques 31515.2.1 Random Sampling 31515.2.2 Histograms 31615.2.3 Sliding Window 31615.2.4 Sketches 31715.2.4.1 Bloom Filters 31715.2.4.2 Count-Min Sketch 31715.3 Data Mining Techniques 31715.3.1 Clustering 31815.3.1.1 STREAM 31815.3.1.2 BRICH 31815.3.1.3 CLUSTREAM 31915.3.2 Classification 31915.3.2.1 Naïve Bayesian 31915.3.2.2 Hoeffding 32015.3.2.3 Very Fast Decision Tree 32015.3.2.4 Concept Adaptive Very Fast Decision Tree 32015.4 Big Data Platforms 32015.4.1 Apache Storm 32115.4.2 Apache Spark 32115.4.2.1 Apache Spark Core 32115.4.2.2 Spark SQL 32215.4.2.3 Machine Learning Library 32215.4.2.4 Streaming Data API 32215.4.2.5 GraphX 32315.4.3 Apache Flume 32315.4.4 Apache Kafka 32315.4.5 Apache Flink 32615.5 Conclusion 327References 32816 An Ontology-Based IR for Health Care 331J. P. Patra, Gurudatta Verma and Sumitra Samal16.1 Introduction 33116.2 General Definition of Information Retrieval Model 33316.3 Information Retrieval Model Based on Ontology 33416.4 Literature Survey 33616.5 Methodolgy for IR 339References 344

Vishal Jain is an associate professor at Bharati Vidyapeeth's Institute of Computer Applications and Management (BVICAM), New Delhi, India. He has more than 350 research citation indices with Google Scholar (h-index score 9 and i-10 index 9). He has authored more than 70 research papers in reputed conferences and journals indexed by Web of Science and Scopus, as well as authored and edited more than 10 books with various international publishers. His research areas include information retrieval, semantic web, ontology engineering, data mining, adhoc networks, and sensor networks. Ritika Wason is currently working as an associate professor at Bharati Vidyapeeth's Institute of Computer Applications and Management (BVICAM), New Delhi. She completed her PhD degree in Computer Science from Sharda University. She has more than 10 years of teaching experience and has authored as well as edited several books in computer science and has been a recipient of many awards and honors. Jyotir Moy Chatterjee is currently an assistant professor in the IT department at Lord Buddha Education Foundation (Asia Pacific University of Technology & Innovation), Kathmandu, Nepal. He has completed M. Tech from Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha and B. Tech in Computer Science & Engineering from Dr. MGR Educational & Research Institute, Chennai. His research interests include the cloud computing, big data, privacy preservation, data mining, Internet of Things, machine learning. Dac-Nhuong Le, PhD is the Head-Deputy of Faculty of Information Technology, Haiphong University, Vietnam. He has a total academic teaching experience of 10 years with many publications in reputed international conferences, journals and online book chapter contributions. He researches interests span the optimization and algorithmic mathematics underpinnings of network communication, security and vulnerability, network performance analysis, and cloud computing.

• Preface xixAcknowledgment xxiii1 Role of Ontology in Health Care 1Sonia Singla1.1 Introduction 21.2 Ontology in Diabetes 31.2.1 Ontology Process 41.2.2 Impediments of the Present Investigation 51.3 Role of Ontology in Cardiovascular Diseases 61.4 Role of Ontology in Parkinson Diseases 81.4.1 The Spread of Disease With Age and Onset of Disease 101.4.2 Cost of PD for Health Care, Household 111.4.3 Treatment and Medicines 111.5 Role of Ontology in Depression 131.6 Conclusion 151.7 Future Scope 15References 152 A Study on Basal Ganglia Circuit and Its Relation With Movement Disorders 19Dinesh Bhatia2.1 Introduction 192.2 Anatomy and Functioning of Basal Ganglia 212.2.1 The Striatum-Major Entrance to Basal Ganglia Circuitry 222.2.2 Direct and Indirect Striatofugal Projections 232.2.3 The STN: Another Entrance to Basal Ganglia Circuitry 252.3 Movement Disorders 262.3.1 Parkinson Disease 262.3.2 Dyskinetic Disorder 272.3.3 Dystonia 282.4 Effect of Basal Ganglia Dysfunctioning on Movement Disorders 292.5 Conclusion and Future Scope 31References 313 Extraction of Significant Association Rules Using Pre- and Post-Mining Techniques—An Analysis 37M. Nandhini and S. N. Sivanandam3.1 Introduction 383.2 Background 393.2.1 Interestingness Measures 393.2.2 Pre-Mining Techniques 403.2.2.1 Candidate Set Reduction Schemes 403.2.2.2 Optimal Threshold Computation Schemes 413.2.2.3 Weight-Based Mining Schemes 423.2.3 Post-Mining Techniques 423.2.3.1 Rule Pruning Schemes 433.2.3.2 Schemes Using Knowledge Base 433.3 Methodology 443.3.1 Data Preprocessing 443.3.2 Pre-Mining 463.3.2.1 Pre-Mining Technique 1: Optimal Support and Confidence Threshold Value Computation Using PSO 463.3.2.2 Pre-Mining Technique 2: Attribute Weight Computation Using IG Measure 483.3.3 Association Rule Generation 503.3.3.1 ARM Preliminaries 503.3.3.2 WARM Preliminaries 523.3.4 Post-Mining 563.3.4.1 Filters 563.3.4.2 Operators 583.3.4.3 Rule Schemas 583.4 Experiments and Results 593.4.1 Parameter Settings for PSO-Based Pre-Mining Technique 603.4.2 Parameter Settings for PAW-Based Pre-Mining Technique 603.5 Conclusions 63References 654 Ontology in Medicine as a Database Management System 69Shobowale K. O.4.1 Introduction 704.1.1 Ontology Engineering and Development Methodology 724.2 Literature Review on Medical Data Processing 724.3 Information on Medical Ontology 754.3.1 Types of Medical Ontology 754.3.2 Knowledge Representation 764.3.3 Methodology of Developing Medical Ontology 764.3.4 Medical Ontology Standards 774.4 Ontologies as a Knowledge-Based System 784.4.1 Domain Ontology in Medicine 794.4.2 Brief Introduction of Some Medical Standards 814.4.2.1 Medical Subject Headings (MeSH) 814.4.2.2 Medical Dictionary for Regulatory Activities (MedDRA) 814.4.2.3 Medical Entities Dictionary (MED) 814.4.3 Reusing Medical Ontology 824.4.4 Ontology Evaluation 854.5 Conclusion 864.6 Future Scope 86References 875 Using IoT and Semantic Web Technologies for Healthcare and Medical Sector 91Nikita Malik and Sanjay Kumar Malik5.1 Introduction 925.1.1 Significance of Healthcare and Medical Sector and Its Digitization 925.1.2 e-Health and m-Health 925.1.3 Internet of Things and Its Use 945.1.4 Semantic Web and Its Technologies 965.2 Use of IoT in Healthcare and Medical Domain 985.2.1 Scope of IoT in Healthcare and Medical Sector 985.2.2 Benefits of IoT in Healthcare and Medical Systems 1005.2.3 IoT Healthcare Challenges and Open Issues 1005.3 Role of SWTs in Healthcare Services 1015.3.1 Scope and Benefits of Incorporating Semantics in Healthcare 1015.3.2 Ontologies and Datasets for Healthcare and Medical Domain 1035.3.3 Challenges in the Use of SWTs in Healthcare Sector 1045.4 Incorporating IoT and/or SWTs in Healthcare and Medical Sector 1065.4.1 Proposed Architecture or Framework or Model 1065.4.2 Access Mechanisms or Approaches 1085.4.3 Applications or Systems 1095.5 Healthcare Data Analytics Using Data Mining and Machine Learning 1105.6 Conclusion 1125.7 Future Work 113References 1136 An Ontological Model, Design, and Implementation of CSPF for Healthcare 117Pooja Mohan6.1 Introduction 1176.2 Related Work 1196.3 Mathematical Representation of CSPF Model 1226.3.1 Basic Sets of CSPF Model 1236.3.2 Conditional Contextual Security and Privacy Constraints 1236.3.3 CSPF Model States CsetofStates 1246.3.4 Permission Cpermission 1246.3.5 Security Evaluation Function (SEFcontexts) 1246.3.6 Secure State 1256.3.7 CSPF Model Operations 1256.3.7.1 Administrative Operations 1256.3.7.2 Users’ Operations 1276.4 Ontological Model 1276.4.1 Development of Class Hierarchy 1276.4.1.1 Object Properties of Sensor Class 1296.4.1.2 Data Properties 1296.4.1.3 The Individuals 1296.5 The Design of Context-Aware Security and Privacy Model for Wireless Sensor Network 1296.6 Implementation 1336.7 Analysis and Results 1356.7.1 Inference Time/Latency/Query Response Time vs. No. of Policies 1356.7.2 Average Inference Time vs. Contexts 1366.8 Conclusion and Future Scope 137References 1387 Ontology-Based Query Retrieval Support for E-Health Implementation 143Aatif Ahmad Khan and Sanjay Kumar Malik7.1 Introduction 1437.1.1 Health Care Record Management 1447.1.1.1 Electronic Health Record 1447.1.1.2 Electronic Medical Record 1457.1.1.3 Picture Archiving and Communication System 1457.1.1.4 Pharmacy Systems 1457.1.2 Information Retrieval 1457.1.3 Ontology 1467.2 Ontology-Based Query Retrieval Support 1467.3 E-Health 1507.3.1 Objectives and Scope 1507.3.2 Benefits of E-Health 1517.3.3 E-Health Implementation 1517.4 Ontology-Driven Information Retrieval for E-Health 1547.4.1 Ontology for E-Heath Implementation 1557.4.2 Frameworks for Information Retrieval Using Ontology for E-Health 1577.4.3 Applications of Ontology-Driven Information Retrieval in Health Care 1587.4.4 Benefits and Limitations 1607.5 Discussion 1607.6 Conclusion 164References 1648 Ontology-Based Case Retrieval in an E-Mental Health Intelligent Information System 167Georgia Kaoura, Konstantinos Kovas and Basilis Boutsinas8.1 Introduction 1678.2 Literature Survey 1708.3 Problem Identified 1738.4 Proposed Solution 1748.4.1 The PAVEFS Ontology 1748.4.2 Knowledge Base 1798.4.3 Reasoning 1808.4.4 User Interaction 1828.5 Pros and Cons of Solution 1838.5.1 Evaluation Methodology and Results 1838.5.2 Evaluation Methodology 1858.5.2.1 Evaluation Tools 1868.5.2.2 Results 1878.6 Conclusions 1898.7 Future Scope 190References 1909 Ontology Engineering Applications in Medical Domain 193Mariam Gawich and Marco Alfonse9.1 Introduction 1939.2 Ontology Activities 1959.2.1 Ontology Learning 1959.2.2 Ontology Matching 1959.2.3 Ontology Merging (Unification) 1959.2.4 Ontology Validation 1969.2.5 Ontology Verification 1969.2.6 Ontology Alignment 1969.2.7 Ontology Annotation 1969.2.8 Ontology Evaluation 1969.2.9 Ontology Evolution 1969.3 Ontology Development Methodologies 1979.3.1 TOVE 1979.3.2 Methontology 1989.3.3 Brusa et al. Methodology 1989.3.4 UPON Methodology 1999.3.5 Uschold and King Methodology 2009.4 Ontology Languages 2039.4.1 RDF-RDF Schema 2039.4.2 OWL 2059.4.3 OWL 2 2059.5 Ontology Tools 2089.5.1 Apollo 2089.5.2 NeON 2099.5.3 Protégé 2109.6 Ontology Engineering Applications in Medical Domain 2129.6.1 Ontology-Based Decision Support System (DSS) 2139.6.1.1 OntoDiabetic 2139.6.1.2 Ontology-Based CDSS for Diabetes Diagnosis 2149.6.1.3 Ontology-Based Medical DSS within E-Care Telemonitoring Platform 2159.6.2 Medical Ontology in the Dynamic Healthcare Environment 2169.6.3 Knowledge Management Systems 2179.6.3.1 Ontology-Based System for Cancer Diseases 2179.6.3.2 Personalized Care System for Chronic Patients at Home 2189.7 Ontology Engineering Applications in Other Domains 2199.7.1 Ontology Engineering Applications in E-Commerce 2199.7.1.1 Automated Approach to Product Taxonomy Mapping in E-Commerce 2199.7.1.2 LexOnt Matching Approach 2219.7.2 Ontology Engineering Applications in Social Media Domain 2229.7.2.1 Emotive Ontology Approach 2229.7.2.2 Ontology-Based Approach for Social Media Analysis 2249.7.2.3 Methodological Framework for Semantic Comparison of Emotional Values 225References 22610 Ontologies on Biomedical Informatics 233Marco Alfonse and Mariam Gawich10.1 Introduction 23310.2 Defining Ontology 23410.3 Biomedical Ontologies and Ontology-Based Systems 23510.3.1 MetaMap 23510.3.2 GALEN 23610.3.3 NIH-CDE 23610.3.4 LOINC 23710.3.5 Current Procedural Terminology (CPT) 23810.3.6 Medline Plus Connect 23810.3.7 Gene Ontology 23910.3.8 UMLS 24010.3.9 SNOMED-CT 24010.3.10 OBO Foundry 24010.3.11 Textpresso 24010.3.12 National Cancer Institute Thesaurus 241References 24111 Machine Learning Techniques Best for Large Data Prediction: A Case Study of Breast Cancer Categorical Data: k-Nearest Neighbors 245Yagyanath Rimal11.1 Introduction 24611.2 R Programming 25011.3 Conclusion 255References 25512 Need of Ontology-Based Systems in Healthcare System 257Tshepiso Larona Mokgetse12.1 Introduction 25812.2 What is Ontology? 25912.3 Need for Ontology in Healthcare Systems 26012.3.1 Primary Healthcare 26212.3.1.1 Semantic Web System 26212.3.2 Emergency Services 26312.3.2.1 Service-Oriented Architecture 26312.3.2.2 IOT Ontology 26412.3.3 Public Healthcare 26512.3.3.1 IOT Data Model 26512.3.4 Chronic Disease Healthcare 26612.3.4.1 Clinical Reminder System 26612.3.4.2 Chronic Care Model 26712.3.5 Specialized Healthcare 26812.3.5.1 E-Health Record System 26812.3.5.2 Maternal and Child Health 26912.3.6 Cardiovascular System 27012.3.6.1 Distributed Healthcare System 27012.3.6.2 Records Management System 27012.3.7 Stroke Rehabilitation 27112.3.7.1 Patient Information System 27112.3.7.2 Toronto Virtual System 27112.4 Conclusion 272References 27213 Exploration of Information Retrieval Approaches With Focus on Medical Information Retrieval 275Mamata Rath and Jyotir Moy Chatterjee13.1 Introduction 27613.1.1 Machine Learning-Based Medical Information System 27813.1.2 Cognitive Information Retrieval 27813.2 Review of Literature 27913.3 Cognitive Methods of IR 28113.4 Cognitive and Interactive IR Systems 28613.5 Conclusion 288References 28914 Ontology as a Tool to Enable Health Internet of Things Viable 5G Communication Networks 293Nidhi Sharma and R. K. Aggarwal14.1 Introduction 29314.2 From Concept Representations to Medical Ontologies 29514.2.1 Current Medical Research Trends 29614.2.2 Ontology as a Paradigm Shift in Health Informatics 29614.3 Primer Literature Review 29714.3.1 Remote Health Monitoring 29814.3.2 Collecting and Understanding Medical Data 29814.3.3 Patient Monitoring 29814.3.4 Tele-Health 29914.3.5 Advanced Human Services Records Frameworks 29914.3.6 Applied Autonomy and Healthcare Mechanization 30014.3.7 IoT Powers the Preventive Healthcare 30114.3.8 Hospital Statistics Control System (HSCS) 30114.3.9 End-to-End Accessibility and Moderateness 30114.3.10 Information Mixing and Assessment 30214.3.11 Following and Alerts 30214.3.12 Remote Remedial Assistance 30214.4 Establishments of Health IoT 30314.4.1 Technological Challenges 30414.4.2 Probable Solutions 30614.4.3 Bit-by-Bit Action Statements 30714.5 Incubation of IoT in Health Industry 30714.5.1 Hearables 30814.5.2 Ingestible Sensors 30814.5.3 Moodables 30814.5.4 PC Vision Innovation 30814.5.5 Social Insurance Outlining 30814.6 Concluding Remarks 309References 30915 Tools and Techniques for Streaming Data: An Overview 313K. Saranya, S. Chellammal and Pethuru Raj Chelliah15.1 Introduction 31415.2 Traditional Techniques 31515.2.1 Random Sampling 31515.2.2 Histograms 31615.2.3 Sliding Window 31615.2.4 Sketches 31715.2.4.1 Bloom Filters 31715.2.4.2 Count-Min Sketch 31715.3 Data Mining Techniques 31715.3.1 Clustering 31815.3.1.1 STREAM 31815.3.1.2 BRICH 31815.3.1.3 CLUSTREAM 31915.3.2 Classification 31915.3.2.1 Naïve Bayesian 31915.3.2.2 Hoeffding 32015.3.2.3 Very Fast Decision Tree 32015.3.2.4 Concept Adaptive Very Fast Decision Tree 32015.4 Big Data Platforms 32015.4.1 Apache Storm 32115.4.2 Apache Spark 32115.4.2.1 Apache Spark Core 32115.4.2.2 Spark SQL 32215.4.2.3 Machine Learning Library 32215.4.2.4 Streaming Data API 32215.4.2.5 GraphX 32315.4.3 Apache Flume 32315.4.4 Apache Kafka 32315.4.5 Apache Flink 32615.5 Conclusion 327References 32816 An Ontology-Based IR for Health Care 331J. P. Patra, Gurudatta Verma and Sumitra Samal16.1 Introduction 33116.2 General Definition of Information Retrieval Model 33316.3 Information Retrieval Model Based on Ontology 33416.4 Literature Survey 33616.5 Methodolgy for IR 339References 344