Preface xv 1 Quality Assurance in Data Science: Need, Challenges and Focus 1Jasmine K.S., Ajay D. K. and Aditya Raj 1.1 Introduction 1 1.2 Testing and Quality Assurance 3 1.3 Product Quality and Test Efforts 4 1.4 Data Masking in Data Model and Associated Risks 8 1.5 Prediction in Data Science 9 1.6 Role of Metrics in Evaluation 20 1.7 Quantity of Data in Quality Assurance 20 1.8 Identifying the Right Data Sources 20 1.9 Conclusion 21 2 Design and Implementation of Social Media Mining -- Knowledge Discovery Methods for Effective Digital Marketing Strategies 23Prashant Bhat and Pradnya Malaganve 2.1 Introduction 24 2.2 Literature Review 26 2.3 Novel Framework for Social Media Data Mining and Knowledge Discovery 29 2.4 Classification for Comparison Analysis 34 2.5 Clustering Methodology to Provide Digital Marketing Strategies 38 2.6 Experimental Results 43 2.7 Conclusion 45 3 A Study on Big Data Engineering Using Cloud Data Warehouse 49Manjunath T. N., Pushpa S. K., Ravindra S. Hegadi and Ananya Hathwar K. S. 3.1 Introduction 50 3.2 Comparison Study of Different Cloud Data Warehouses 51 3.3 Snowflake Cloud Data Warehouse 55 3.4 Google BigQuery Cloud Data Warehouse 58 3.5 Microsoft Azure Synapse Cloud Data Warehouse 61 3.6 Informatica Intelligent Cloud Services (IICS) 64 3.7 Conclusion 67 4 Data Mining with Cluster Analysis Through Partitioning Approach of Huge Transaction Data 71Sampath Kini K. and Karthik Pai B.H. 4.1 Introduction 72 4.2 Methodology Used in Proposed Cluster Analysis System 75 4.3 Literature Survey on Existing Systems 80 4.4 Conclusion 82 5 Application of Data Science in Macromodeling of Nonlinear Dynamical Systems 85Nagaraj S., Seshachalam D. and Jayalatha G. 5.1 Introduction 86 5.2 Nonlinear Autonomous Dynamical System 89 5.3 Nonlinear System - MOR 90 5.4 Data Science Life Cycle 92 5.5 Artificial Neural Network in Modeling 94 5.6 Neuron Spiking Model Using FitzHugh-Nagumo (F-N) System 99 5.7 Ring Oscillator Model 104 5.8 Nonlinear VLSI Interconnect Model Using Telegraph Equation 108 5.9 Macromodel Using Machine Learning 112 5.10 MOR of Dynamical Systems Using POD-ANN 115 5.11 Numerical Results 117 5.12 Conclusion 126 6 Comparative Analysis of Various Ensemble Approaches for Web Page Classification 137J. Dutta, Yong Woon Kim and Dalia Dominic 6.1 Introduction 138 6.2 Literature Survey 139 6.3 Material and Methods 144 6.4 Ensemble Classifiers 146 6.5 Results 148 6.6 Conclusion 169 7 Feature Engineering and Selection Approach Over Malicious Image 173P.M. Kavitha and B. Muruganantham 7.1 Introduction 173 7.2 Feature Engineering Techniques 176 7.3 Malicious Feature Engineering 182 7.4 Image Processing Technique 183 7.5 Image Processing Techniques for Analysis on Malicious Images 185 7.6 Conclusion 191 8 Cubic-Regression and Likelihood Based Boosting GAM to Model Drug Sensitivity for Glioblastoma 195Satyawant Kumar, Vinai George Biju, Ho-Kyoung Lee and Blessy Baby Mathew 8.1 Introduction 196 8.2 Literature Survey 198 8.3 Materials and Methods 201 8.4 Evaluations, Results and Discussions 209 9 Unobtrusive Engagement Detection through Semantic Pose Estimation and Lightweight ResNet for an Online Class Environment 225Michael Moses Thiruthuvanathan, Balachandran Krishnan and Madhavi Rangaswamy 9.1 Introduction 226 9.2 Related Work 230 9.3 Proposed Methodology 234 9.4 Experimentation 241 9.5 Results and Discussions 245 10 Building Rule Base for Decision Making -- A Fuzzy-Rough Approach 255Sabu M. K., Neeraj Krishna M. S. and Reshmi R. 10.1 Introduction 256 10.2 Literature Review 258 10.3 Discretization of the Dataset Using Fuzzy Set Theory 260 10.4 Description of the Dataset 260 10.5 Process Involved in Proposed Work 261 10.6 Experiment 262 10.7 Evaluation Result 267 10.8 Discussion 273 11 An Effective Machine Learning Approach to Model Healthcare Data 279Shaila H. Koppad, S. Anupama Kumar and Mohan Kumar 11.1 Introduction 280 11.2 Types of Data in Healthcare 281 11.3 Big Data in Healthcare 283 11.4 Different V’s of Big Data 284 11.5 About COPD 285 11.6 Methodology Implemented 290 12 Recommendation Engine for Retail Domain Using Machine Learning Techniques 303Chandrashekhara K. T., Gireesh Babu C. N. and Thungamani M. 12.1 Introduction 304 12.2 Proposed System 304 12.3 Results 312 12.3.1 ARIMA Forecasting 312 12.4 Conclusion 313 13 Mining Heterogeneous Lung Cancer from Computer Tomography (CT) Scan with the Confusion Matrix 317Denny Dominic and Krishnan Balachandran 13.1 Introduction 317 13.2 Literature Review 319 13.3 Methodology 320 13.4 Result 326 13.5 Conclusion and Future Scope 332 References 332 14 ML Algorithms and Their Approach on COVID-19 Data Analysis 335Kambaluru Ashok, Penumalli Anvesh Reddy and Kukatlapalli Pradeep Kumar 14.1 Introduction 336 14.2 DataSet 336 14.3 Types of Machine Learning Algorithms 338 14.4 Conclusion 348 15 Analysis and Design for the Early Stage Detection of Lung Diseases Using Machine Learning Algorithms 351Sindhu Madhuri, Mahesh T. R., Vivek V., Shashikala H. K. and C. Saravanan 15.1 Introduction 352 15.2 Machine Learning Algorithms 358 15.3 Evaluation Metrics and Comparative Results for Early Detection of Lung Diseases 364 15.4 Conclusion 369 16 Estimation of Cancer Risk through Artificial Neural Network 373K. Aditya Shastry, Sanjay H. A., Balaji N. and Karthik Pai B. H. 16.1 Introduction 373 16.2 Case Studies Related to Cancer Risk Estimation Using ANN 375 16.3 Datasets Used in Cancer Risk Estimation 388 16.4 Discussion 397 16.5 Future Scope 400 16.6 Conclusion 400 17 Applications and Advancements in Data Science and Analytics 409T. Mamatha, A. Balaram, B. Rama Subba Reddy, C. Shoba Bindu and M. Niranjanamurthy 17.1 Data Science and Analytics in Software Testing 410 17.2 Applications of Data Science and Analytics 411 17.3 Selenium Testing Tool in Data Science 419 17.4 Challenges and Advancements in Data Science 425 17.5 Data Science and Analytics Tools 430 17.6 Conclusion 438 References 439 About the Editors 441 Index 443

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