| Description |
1 online resource (215 p.) |
| Contents |
Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Acknowledgments -- Contributors -- 1 Introduction to Data Mining -- References -- 2 Review of Latent Dirichlet Allocation to Understand Motivations to Share Conspiracy Theory: A Case Study of ""Plandemic"" During COVID-19 -- 2.1 Literature Review -- 2.1.1 Conspiracy Theories -- 2.1.2 Measuring Conspiracy Theories Beliefs -- 2.1.3 Textual Analysis and Text Mining -- 2.2 Methods -- 2.2.1 Data Collection -- 2.2.2 Executing Latent Dirichlet Allocation -- 2.2.2.1 Dataset Cleaning -- 2.2.2.2 Dataset Preprocessing |
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2.2.2.3 Preparing the Data -- 2.2.2.4 Determining Topics and Corresponding Tweets for the LDA Model -- 2.2.3 Thematic Analysis -- 2.3 Results -- 2.3.1 Results From LDA Tuning Run -- 2.3.2 Results From Thematic Analysis -- 2.4 Discussion -- 2.4.1 Observable Conspiracy Theories Motives -- 2.4.2 Limitations -- 2.4.3 Practical Implications and Conclusions -- References -- 3 Near Human-Level Style Transfer -- 3.1 Introduction -- 3.2 Methodology -- 3.3 Pre-Processing -- 3.4 Feature Extraction Using Transfer Learning -- 3.5 Performance Parameters -- 3.6 Content Loss -- 3.7 Style Loss |
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3.8 Total Variation Loss -- 3.9 Optimization -- 3.10 Super-Resolution -- 3.11 Results and Implementation -- Code -- 4 Semantics-Based Distributed Document Clustering -- 4.1 Introduction -- 4.2 Background and Related Work -- 4.3 Proposed Approach: Semantics-Based Distributed Document Clustering -- 4.3.1 Dataset Pre-Processing -- 4.3.2 Document Representation: Ontology-Based VSM -- 4.3.3 Distributed K-Means and Bisecting K-Means Algorithm for Document Clustering -- 4.4 Datasets and Experimental Description -- 4.4.1 Pre-Processed Datasets -- 4.4.2 Experimental Setup -- 4.5 Results and Discussion |
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4.5.1 Test Cases for Stability of Algorithms (Count of Clusters and Stability) -- 4.5.2 Test Cases for Accuracy/Quality of Syntactic and Semantic Analysis -- 4.5.3 Test Cases for Clustering Time -- 4.6 Conclusion and Future Scope -- References -- 5 Application of Machine Learning in Disease Prediction -- 5.1 Introduction -- 5.2 Literature Review -- 5.3 System Architecture -- 5.4 Algorithm -- 5.5 Dataset -- 5.6 Results and Discussion -- 5.7 Conclusion -- References -- 6 Federated Machine Learning-Based Bank Customer Churn Prediction -- 6.1 Introduction -- 6.2 Related Works -- 6.3 Dataset |
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6.4 Experimental Setup -- 6.5 Proposed Approach -- 6.6 Results -- 6.7 Challenges -- 6.7.1 Costly Communication -- 6.7.2 Detection of Malicious Clients -- 6.7.3 Privacy Concern -- 6.7.4 System Heterogeneity -- 6.8 Conclusion -- References -- 7 Challenges and Avenues in the Sophisticated Health-Care System -- 7.1 Introduction -- 7.2 Organization of the Chapters -- 7.3 The Challenges Faced By Health-Care Systems -- 7.3.1 Patients Predictions -- 7.3.2 Electronic Health Records (EHRs) -- 7.3.3 Real-Time Alerting -- 7.3.4 Patient Engagement |
| Notes |
Description based upon print version of record |
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7.3.5 Less Use of Health Data for Informed Strategic Planning |
| Form |
Electronic book
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| Author |
Shah, Neepa
|
|
Sawant, Vinaya
|
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Parolia, Neeraj
|
| ISBN |
9781000895483 |
|
1000895483 |
|
