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Big data analytics in cognitive social media and literary texts : theory and praxis / / edited by Sanjiv Sharma, Valiur Rahaman, and G. R. Sinha
| Big data analytics in cognitive social media and literary texts : theory and praxis / / edited by Sanjiv Sharma, Valiur Rahaman, and G. R. Sinha |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (316 pages) |
| Disciplina | 610.285 |
| Soggetto topico |
Digital humanities
Social media |
| Soggetto genere / forma | Electronic books. |
| ISBN | 981-16-4729-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgements -- Introduction -- Contents -- About the Editors -- 1 The Concept of Cognitive Social Media and Cognitive Literary Studies -- 1.1 Introduction -- 1.2 Cognitive and Its Aspects -- 1.3 Literature and the Function of Literary Studies -- 1.4 Poetry -- 1.5 Prose -- 1.6 Fiction or Novel -- 1.7 Works of Art into Text -- 1.8 Translation as Cognitive Literary Text -- 1.9 Translation as Literary Texts -- 1.10 Social and Political Factors of Shahid's and Faiz's Poetry -- 1.11 Translation as a Medium of Reformation -- 1.12 Translation as Cultural Human Capital -- 1.13 Big Data Analytics in Literary Texts -- 1.14 Why Big Data? -- 1.15 Criticism of Big Data Analytics -- 1.16 Types of Analysis and W5 Formula in BDA -- 1.17 BDA in Humanities and Social Sciences -- 1.18 Cyberculture -- 1.19 Emergence of Digital Humanities and Big Data Analytics -- 1.20 Conclusion -- References -- 2 Big Data Analytics for Market Prediction via Consumer Insight -- 2.1 Big Data -- 2.1.1 Big Data Types -- 2.1.2 Storage of Big Data -- 2.1.3 Three Characteristics of Big Data -- 2.1.4 Uncertainty Prediction Using Big Data -- 2.2 Big Data Analytics -- 2.2.1 Big Data Analytics Types -- 2.3 Key Performance Indicators -- 2.3.1 Types of KPIs -- 2.4 Segmentation -- 2.4.1 Segmentation as Clustering -- 2.4.2 Segmentation as Classification -- 2.5 Targeting -- 2.5.1 Behavioral Approach -- 2.5.2 Demographics -- 2.5.3 Transaction Response -- 2.5.4 Sentimental Manifestation -- 2.5.5 Targeting Via Traditional Approach Versus Big Data Approach -- 2.6 Positioning -- 2.7 Association Rules -- 2.8 Case Studies -- 2.8.1 Walmart -- 2.8.2 eBay -- 2.8.3 Alibaba -- 2.8.4 Amazon -- 2.8.5 McDonald's -- 2.9 Conclusion -- References -- 3 Deconstructive Big Data Analytics: Literary Texts Analysis Through Atlas.ti Software -- 3.1 Introduction: What is Deconstruction?.
3.2 Deconstruction is Not Only a Method -- 3.3 Transdisciplinary Facets of Deconstruction -- 3.4 Deconstruction in Literary Theory -- 3.5 Meta-Discourse: Levonorgestrel Implications of Philosophical Discourses -- 3.6 Why Are the Literary Texts Complex? -- 3.7 Fluidities of Literary Texts and Semanalysis -- 3.8 Understanding Deconstruction for Big Data Analytics -- 3.9 Reading Philosophy as Literature: Case Study-1 -- 3.10 Reading Literature as Philosophy: Case Study-2 -- 3.11 Reading Literature Increases Analytic Skills -- 3.12 Deconstructive Big Data Analytics -- 3.13 Conclusion -- References -- 4 Study of Big Data Analytics Tool: Apache Spark -- 4.1 Notion of Machine Learning with Big Data -- 4.1.1 Sources of Big Data -- 4.1.2 Big Data Characteristics -- 4.1.3 Applications of Big Data -- 4.2 Apache Spark -- 4.2.1 Architecture of Apache Spark Components and API -- 4.2.2 Difference Between Hadoop and Apache Spark -- 4.2.3 Basic Programming in Apache Spark -- 4.2.4 Basic Functions in Apache Spark -- 4.2.5 Calculating Sum Using Apache Spark -- 4.2.6 Calculating Mean Using Apache Spark -- 4.2.7 Calculating Standard Deviation Using Apache Spark -- 4.3 Data Frame in Apache Spark -- 4.3.1 Data Frame Operations Using Apache Spark -- 4.3.2 Python Spark SQL (Pyspark) -- 4.4 Unsupervised Learning with Apache Spark -- 4.4.1 Introduction to Clustering -- 4.4.2 Introduction to K-Means Clustering -- 4.4.3 Objective of K-Means -- 4.4.4 Using K-means in Apache SparkML -- 4.5 Supervised Learning with Apache Spark -- 4.5.1 Linear Regression -- 4.5.2 Steps to Create a Regression -- 4.5.3 Programming Demonstration Using R Language -- 4.5.4 Logistic Regression -- 4.5.5 Logistic Regression with Apache SparkML -- 4.6 Conclusion -- References. 5 Contemporary Social Media and IoT-Based Pandemic Control: Exploring Possibilities of Big Data Analytics for Healthcare Governance -- 5.1 Introduction: Social Media for Contemporary Healthcare -- 5.2 The Pandemic: Coronavirus -- 5.3 Social Media and the Current Situation -- 5.4 Big Data Analytics -- 5.5 Big Data Analytics Observation Through Social Media for Covid-19 -- 5.6 Tools Used for Big Data Analytics -- 5.7 Big Data to Cure Covid-19 -- 5.7.1 Coronavirus Diagnosis/Treatment -- 5.7.2 Challenges that Comes During This Pandemic -- 5.8 Discussion -- 5.8.1 Process Involved in IoT for Healthcare -- 5.8.2 Interconnected Hospital -- 5.8.3 Internet of HealthCare -- 5.8.4 Covidapp -- 5.8.5 Registration -- 5.8.6 Map Positioning -- 5.8.7 Start Consultation -- 5.8.8 Diagnosis -- 5.8.9 Confirmed Diagnosis -- 5.8.10 Suspected Diagnosis -- 5.8.11 Treatment -- 5.8.12 Mild -- 5.8.13 Moderated -- 5.8.14 Severe -- 5.8.15 Challenges -- 5.9 Conclusion -- References -- 6 Analyzing Women Health-Related Quality of Life Using Sentiment Analysis on Social Media -- 6.1 Introduction -- 6.2 Literature Study -- 6.3 Sentiment Analysis -- 6.3.1 Emotion-Based Sentiment Word Extraction -- 6.3.2 Sentiment Analysis Using Classifiers -- 6.4 Experiment Results -- 6.4.1 Training Data Collection -- 6.4.2 Emotion-Based Classifiers -- 6.5 Conclusion -- References -- 7 Necessity of Big Data Analytics in Social Media for Questioning the Existence and Survival of Women and the Marginalized People -- 7.1 Introduction -- 7.2 Aesthetics of Existence -- 7.3 Women and the Marginalized During Pandemic -- 7.4 Gender-Biased Violence and IPV During Pandemic -- 7.5 The Marginalized Dislocation -- 7.6 Why Big Data Analytics for Social and Psychological Impact of the Pandemic -- 7.7 Women's Condition -- 7.8 Meaning of the "Marginalized": Notions from Philosophy to Public. 7.9 Deconstructing the Marginality and Minority Writings -- 7.10 Women's Suffering Challenged Through Digital NGO -- 7.11 Conclusion -- References -- 8 Big Data Analytics and Cybersecurity: Emerging Trends -- 8.1 Introduction -- 8.2 Fundamentals of Big Data -- 8.3 Cybersecurity in Cloud Computing -- 8.4 Big Data Technologies in Cybersecurity Analytics -- 8.4.1 Supervised Method -- 8.4.2 Unsupervised Method -- 8.5 Emerging Trends in Cybersecurity Analytics in 2019/2020 -- 8.6 Conclusion -- References -- 9 Seizing the Networked Crime: Legal Framework for the Governance of Social Media Crimes in India -- 9.1 Introduction -- 9.2 Objectives -- 9.3 Methods -- 9.4 Sources of Data and Design -- 9.5 Review of Literature -- 9.6 Results and Discussion -- 9.7 Provisions of Information Technology Act, 2000 Related to Social Media -- 9.8 Provisions Related to Social Media in the Indian Penal Code -- 9.9 Draft Personal Data Protection Bill, 2019 -- 9.9.1 Jammu and Kashmir Case -- 9.9.2 Sushant Singh Rajput Death Probe Case -- 9.9.3 Prashant Bhushan Case -- 9.9.4 Ban of Certain Social Media Applications Case -- 9.10 Future Prospective -- 9.11 Conclusion -- References -- 10 Toxic Masculinity and Inherent Misogyny on Social Media: Preventive Laws and Indian Judicial Approach -- 10.1 Introduction -- 10.2 Toxic Masculinity and Inherent Misogyny -- 10.3 Toxic Masculinity and Social Media -- 10.4 Preventive Legislation -- 10.4.1 Resolution L. 13 of UNHRC -- 10.4.2 Information Technology Act (2000) -- 10.4.3 Indian Penal Code (1860) -- 10.5 Judicial Activism in Toxic Masculinity -- 10.6 Conclusion -- References -- 11 Quantifying Human Sentiments Using Qualitative Approach with Semantic-Sentiment Analysis -- 11.1 Introduction -- 11.1.1 Increasing Importance of Human Sentiments for Business and Academic Research -- 11.2 The Building Concepts -- 11.2.1 Human Sentiments. 11.2.2 Semantic Analysis -- 11.3 Quantifying Human Sentiments: Ways Implied from Research -- 11.3.1 Does Qualitative Research Methods Give an Edge? -- 11.3.2 Our Way -- 11.4 Illustration of Semantic-Sentiment Analysis -- 11.4.1 Problem Statement -- 11.4.2 Solution -- 11.4.3 Inference and Implication -- 11.5 Caselets for Practice -- 11.5.1 A Caselet of Academic Research -- 11.5.2 A Caselet of Business Research -- 11.6 Further Discussion -- 11.7 Conclusion -- Appendix A.1: Data Sources -- Appendix B.1: RStudio Code Block -- Appendix C.1: Hints for Solving the Caselets -- References -- 12 Cognitive Transformation Through Social Media -- 12.1 Introduction -- 12.2 Background -- 12.3 Statistics -- 12.4 Interacting Virtually and Communicating Globally -- 12.5 Interacting Virtually and Communicating Individually -- 12.6 Social Media and Social Structure -- 12.7 Popular Media and Social Relationships -- 12.8 Social Relations and Their Importance -- 12.9 Communication Media and Social Institutions -- 12.10 Connection Between Media and Public -- 12.11 Media and Society Model -- 12.12 Social Media and Social Dynamics Through Individual Morals -- 12.13 Conclusion -- References -- 13 Understanding Digital Diaspora as Cognitive Social Media: Necessity of Big Data Analytics for Peace and Harmony -- 13.1 Introduction: Diasporas and Digital Diasporas -- 13.2 Diaspora is a Social Formation -- 13.3 Digital Diasporas and Its Discontents -- 13.4 Logic of e-Diaspora, Digital Diaspora, and Big Data -- 13.5 Understanding Diasporic Experience in Literary Texts -- 13.6 Major Themes of Diaspora Writing -- 13.7 Digital Diaspora a World Phenomenon -- 13.7.1 Afghanistan Online -- 13.7.2 Somalinet -- 13.8 Features of Digital Diaspora -- 13.9 Cognitive Social Capital: The Benefits of Cognitive Social Media -- 13.10 Conclusion -- References. 14 Data Analytics of Psychological Distress and Coping Among Fresh Migrant from North Eastern Region to Bengaluru City. |
| Record Nr. | UNINA-9910502981403321 |
| Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Case-based Atlas of Cardiac Imaging [[electronic resource] /] / edited by Sanjiv Sharma
| Case-based Atlas of Cardiac Imaging [[electronic resource] /] / edited by Sanjiv Sharma |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (XI, 601 p. 419 illus., 236 illus. in color.) |
| Disciplina | 616.0757 |
| Soggetto topico |
Radiology
Cardiology Heart - Surgery Pediatrics Children - Surgery Cardiac Surgery Pediatric Surgery |
| ISBN | 981-9956-20-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 Chest Radiograph in heart disease -- Part 1 Congenital Heart Diseases -- 2 Algorithmic Approach to Imaging Diagnosis of Congenital Heart Disease -- 3 Imaging in Tetralogy of Fallot -- 4 Imaging in Double outlet right ventricle -- 5 Imaging in Tricuspid atresia -- 6 Imaging in Transposition of great arteries -- 7 Imaging in Truncus arteriosus -- 8 Imaging in Total anomalous pulmonary venous connections and Partial anomalous pulmonary venous connections -- 9 Imaging in Ebstein anomaly -- 10 Imaging in Hypoplastic left heart syndrome -- 11 Imaging in Ventricular septal defect -- 12 Imaging in atrioventricular canal defects -- 13 Imaging in Aorto-pulmonary window -- 14 Imaging in sinus of Valsalva aneurysm -- 15 Imaging of Patent ductus arteriosus -- 16 Imaging in Cor triatriatum -- 17 Imaging in Vascular rings -- 18 Imaging in Heterotaxy syndromes -- 19 Imaging of Coarctation of aorta Part 2 Cardiomyopathies -- 20 Cardiac MR Imaging in Myocarditis -- 21 Imaging in Cardiac Sarcoidosis -- 22 Cardiac MR Imaging in Non-ischemic dilated cardiomyopathy -- 23 Cardiac MR Imaging in Left ventricular non-compaction -- 24 Cardiac MR Imaging in Arrythmogenic ventricular cardiomyopathy -- 25 Cardiac MR Imaging in Hypertrophic cardiomyopathy -- 26 Imaging in Cardiac amyloidosis -- 27 Cardiac MR Imaging in Restrictive cardiomyopathy -- 28 Imaging in cardiac tuberculosis -- 29 Imaging in Iron overload cardiomyopathy -- 30 Cardiac MR Imaging in Ischemic cardiomyopathy -- 31 Imaging in Complications of Myocardial ischemia -- 32 Imaging in Takotsubo cardiomyopathy -- 33 Imaging in post heart transplant patients -- Part 3 Cardiac masses -- 34 Imaging approach to cardiac masses -- Part 4 Coronary artery anomalies -- 35 Imaging in Anomalies of Coronary Artery Origin -- 36 Imaging in Anomalies of coronary artery course -- 37 Imaging in Anomalies of coronary artery termination -- 38 Imaging in Coronary artery aneurysms -- Part 4 Miscellaneous -- 39 Advances in cardiovascular MRI in heart failure -- 40 Artificial Intelligence in Cardiac Imaging. |
| Record Nr. | UNINA-9910799250103321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||