Data science has recently gained much attention for a number of reasons, Big Data is the most significant among them. Scientists (from almost all disciplines including physics, chemistry, biology, and sociology, among others) and engineers (from all fields including civil, environmental,chemical, and mechanical, among others) are faced with challenges posed by data volume, variety, and velocity, or Big Data.
The book contains quantitative research, case studies, conceptual papers, and model papers, review papers, theoretical backing, etc. This book will cover data science and its application to interdisciplinary science.
This book will prove e valuable for graduate students, researchers, academicians, and professionals in information science, business, health, planning, manufacturing, and other areas who are interested in exploring the ever-expanding research on Data Science.
Chapter-01 provides a detailed survey and comparative analysis of various methodologies in the prediction of rainfall over multiple countries.
Chapter -02 focuses s on applying clustering for gaining the benefits of evolutionary computation to process large-scale data and based on optimality, the performance of the datasets can be measured.
Chapter-05 presents an investigation of the data obtained from IoT sensors and observed that

PREFACE i

LIST OF CONTRIBUTORS ii

CHAPTER 1 A COMPREHENSIVE STUDY AND ANALYSIS ON PREDICTION OF RAINFALL ACROSS MULTIPLE COUNTRIES USING MACHINE LEARNING C. Kishor Kumar Reddy, P.R. Anisha and Nguyen Gia Nhu 1

INTRODUCTION 1

RELEVANT WORK 3

DISCUSSION 9

CONCLUSION 23

CONSENT FOR PUBLICATION 24

CONFLICT OF INTEREST 24

ACKNOWLEDGEMENT 24

REFERENCES 24

CHAPTER 2 A NOVEL APPROACH FOR CLUSTERING LARGE-SCALE CLOUD DATA USING COMPUTATIONAL MECHANISM Zdzislaw Polkowski, Jyoti Prakash Mishra and Sambit Kumar Mishra 28

INTRODUCTION 28

REVIEW OF LITERATURE 29

IMPLEMENTATION USING GENETIC ALGORITHM 32

STRATEGIES OF EVALUATION OF QUERY PLANS RELATED TO LARGE SCALE DATA 32

ALGORITHM 33

EXPERIMENTAL ANALYSIS 34

DISCUSSION AND FUTURE DIRECTION 37

CONCLUSION 37

CONSENT FOR PUBLICATION 37

CONFLICT OF INTEREST 37

ACKNOWLEDGEMENT 37

REFERENCES 37

CHAPTER3 SECURE COMMUNICATION OVER IN-VEHICLE NETWORK USING MESSAGE AUTHENTICATION Manjunath Managuli, Sudha Slake, Pankaja S. Kadalgi and Gouri C. Khadabadi 40

INTRODUCTION 40

      Background for Vehicle Security 41

      Hacking Incidents on Vehicles 41

      Economic Value at Risk Due to Poor Security Investments 42

      Security Goals 42

      Security Attacks 44

      Techniques to Implement Security Mechanisms 45

      Network Security Model 46

      Security by Design 47

      Cybersecurity Concept for Connected Car 47

      Designing Secure Automotive Systems 49

      Security by Design across CAR Development Lifecycle 50

      Vehicle Communication Buses 51

      Format of Request and Response Messages 52

      Internal Key used for Decryption and Encryption 54

      AES Algorithm 54

      Sequence of Key Update Procedure 55

      Routine Control (31 hex) Service 55

      Steps Involved in Key Update 56

      Step 1： 57

      Step 2： 57

      Step 3： 57

      Step 4： 58

      STEP 5： 58

      Step 6： 58

      Step 7： 59

      Step 8： 59

DESIGN AND IMPLEMENTATION 59

      Overview of the AUTOSAR Standard 59

      AUTOSAR Architecture Overview 60

      AUTOSAR Software Architecture and Features for Security 61

      Design Flow within AUTOSAR Security Software Modules 62

      Implementation of in-vehicle Message Authentication 63

      Sequence Diagram Authentication during Direct Transmission 63

      Sequence Diagram Verification during Direct Reception 63

      Introduction to DaVinci Developer tool 64

      Introduction to DaVinci Configurator Pro Tool 65

      Introduction to CANoe Tool Environment 65

      Test Setup and CAN message Data Base for Verification 66

      Software Flashing Method 66

      Secret Key Storage into the Target Hardware Memory 68

      Verification Result for Message Authentication 69

      MAC Messages 70

      Additional Test Methods for Cyber Security Verification and Validation 70

CONCLUSION 71

CONSENT FOR PUBLICATION 71

CONFLICT OF INTEREST 71

ACKNOWLEDGEMENT 71

REFERENCES 71

CHAPTER 4 A DECISION MODEL FOR RELIABILITY ANALYSIS OF AGRICULTURAL SENSOR DATA FOR SMART IRRIGATION 4.0 Subhash Mondal, Samrat Podder and Diganta Sengupta

73

INTRODUCTION 73

LITERATURE SURVEY 75

PROPOSED METHODOLOGY 77

      Dataset Acquisition 78

      Dataset Pre-Processing 79

      Framework 80

      Algorithm 81

      Parameter Estimation 81

      Modeling/Training Stage 82

      Hyper-Parameter Tuning 83

EXPERIMENTAL RESULT & ANALYSIS 83

      Precision 83

      Recall 84

      F1.Score 84

      Comparative Analysis 84

CONCLUSION 87

CONSENT FOR PUBLICATION 88

CONFLICT OF INTEREST 88

ACKNOWLEDGEMENT 88

REFERENCES 88

CHAPTER 5 MACHINE LEARNING BASED SMART ELECTRICITY MONITORING & FAULT DETECTION FOR SMART CITY 4.0 ECOSYSTEM Subhash Mondal, Suharta Banerjee, Sugata Ghosh, Adrija Dasgupta and Diganta Sengupta 90

INTRODUCTION 90

RELATED WORKS 91

PROPOSED FRAMEWORK 94

      Electricity Prediction Module 95

      Threshold Calculation Module 96

      Fault Detection Module 97

EXPERIMENTAL RESULT & ANALYSIS 97

CONCLUSION100

CONSENT FOR PUBLICATION100

CONFLICT OF INTEREST100

ACKNOWLEDGEMENT100

REFERENCES100

CHAPTER 6 INVESTIGATING THE EFFECTIVENESS OF MOBILE LEARNING IN HIGHER EDUCATION V. Kalaiarasi, D. Alamelu and N. Venugopal 103

INTRODUCTION 103

MODEL CONSTRUCTION AND DEVELOPMENT OF HYPOTHESIS 105

      Technology Acceptance and Learner Satisfaction 105

      System Success and Learner satisfaction 105

      Environmental Factors and Learner satisfaction 106

      Technology Acceptance and Learner Intention 106

      System Success and Learner Intention 107

      Environmental Factors and Learner Intention 107

      Learner Satisfaction and M-learning effectiveness 107

      Learner Intention and M-learning effectiveness 108

METHODOLOGY 108

      Operational Design 108

      Data Collection 110

      Instrument Development 110

RESULT 111

      Data Analysis and Results - Qualitative Study 111

      Technology Acceptance 111

      System Success 112

      Environmental Factors 112

      Learner Satisfaction 113

      LearnerIntention 113

      M-Learning Effectiveness 114

      Data Analysis and Results-Quantitative 114

      SEM in VPLS 115

      Results of Hypothesis Testing 116

DISCUSSION AND CONCLUSION 117

ABBREVIATIONS 118

REFERENCES 119

CHAPTER 7 SOCIO-ECONOMY OF COASTAL FISHING COMMUNITY OF SOUTHERN COAST OF ODISHA： A CASE STUDY T. Padmavati 123

INTRODUCTION 123

INFORMATION AND METHODOLOGY 126

RESULT AND DISCUSSION 126

      Overall population, geography, and literacy of Odisha 126

      Origin, present status, geography, and administrative classification of Ganjam 127

      Census (Govt. of India) 2011 128

      Ganjam District Population 129

      Ganjam District Population Growth Rate 129

      Ganjam District Density 129

      Ganjam Literacy Rate 129

      Ganjam Sex Ratio 129

      Ganjam Child Population 129

      Ganjam District Urban Population 130

      Ganjam District Rural Population 131

      Education Facilities 131

      Socio-economic status of the coastal total fishing community of Ganjam 131

      Fishing Activities 132

      Assets of the Fishermen 132

      Fishing Fleets 132

      Fishing craft 132

      Fishing gear and method 134

      Fish Harvest 134

      Fish Marketing and Preservation 136

      Problems Encountered in Fish Marketing 137

      Socio-economics 137

      Welfare Schemes 137

      Role of Different Banks in Financing Fishermen 138

      Fisheries Co-operatives 138

      Geomorphology 139

      Potential Fishing Zone (PFZ) Advisories using Remote Sensing Technology for Reduction

      of Fuel Consumption and Search Time and Improvement of Catch 140

      Socio-economic Situation of Fisherwomen in Ganjam District： A Case Study 142

      Significant Problems Associated with the Fisherwomen Community 143

      Lack of Empowerment among Women 143

      Inadequate Systems and Techniques to Support Fisher Women Micro-enterprises 143

      Lack of Capacity Building, Skills, and Institution 144

      Coastal Fishing Community at Gopalpur-on-sea (the Most Important Coastal Site for Fshing

      and Tourism of Ganjam District)： A Particular Case Study 144

      Ongoing Problems and Subsequent Demands of the Coastal Fishing Community of Gopalpur-on-sea 145

CONCLUSION 146

CONSENT FOR PUBLICATION 147

CONFLICT OF INTEREST 147

ACKNOWLEDGEMENTS 147

REFERENCES 148

CHAPTER 8 FILTERING TECHNIQUES FOR REMOVING NOISE FROM ECG SIGNALS K. Manimekalai and A. Kavitha 149

INTRODUCTION 149

ARTIFACTS 150

      Types of Artifact in ECG Signal 151

      Power Line Interference 152

      Muscle Contractions 153

      Electrode Motion Artifacts 153

      Baseline Wandering 154

      Reversed Lead 154

ECG RECORDING CONDITIONS 155

      Calibration of the Equipment 155

      Recording Procedure 156

      ECG Signal Filtering 156

      Decomposition 158

      Discrete Wavelet Transform based Decomposition 159

ALGORITHM： DWT DECOMPOSITION 160

      Denoising of ECG Signal 161

      Hard and Soft Thresholding 162

      Wavelet Thresholding 162

      EMD-Thresholding 164

      Wavelet-based Thresholding 164

      Wavelet Frequency Thresholding 164

      ECG Signal Filtering Techniques 166

      Derivative Base Filters 166

EVALUATION CRITERIA FOR DENOISING 167

      Signal to Noise Ratio 167

      Mean Square Error 167

EXPERIMENTAL RESULTS 168

CONCLUSION 170

CONSENT FOR PUBLICATION 171

CONFLICT OF INTEREST 171

ACKNOWLEDGEMENT 171

REFERENCES 171

CHAPTER 9 DEEP LEARNING TECHNIQUES FOR BIOMEDICAL RESEARCH AND SIGNIFICANT GENE IDENTIFICATION USING NEXT GENERATION SEQUENCING (NGS)DATA：-A REVIEW Debasish Swapnesh Kumar Nayak, Jayashankar Das and Tripti Swarnkar 172

INTRODUCTION 173

BACKGROUND 176

177

DNA SEQUENCING 179

      Sanger Sequencing 180

      Next Generation Sequencing (The Rising Trend) 180

NGS GENE EXPRESSION DATA (STRUCTURE, CHARACTER, AND CHALLENGES) 181

QCTOOLSFORNGSDATA PRE-PROCESSING 183

MACHINE LEARNING TECHNIQUES FOR NGS DATA ANALYSIS 187

      Various Datamining Methods for Sequence data 188

      Taxonomy of Datamining, ML, and DL Techniques used for NGS data Analysis 188

MACHINE LEARNING TECHNIQUES FOR NGS FEATURE SELECTION 189

      Filter Method 190

      Wrapper Method 191

      Embedded Method 191

      Hybrid Method 192

      Ensemble Method 192

FEATURE EXTRACTION TECHNIQUES FOR NGS DATA 193

      Correlation-based Feature Selection (CFS) 194

      Fast Correlation-Based Filter (FCBF) 194

      INTERACT 195

      Information Gain 195

      ReliefF 195

      Minimum Redundancy Maximum Relevance (mRMR) 195

      LASSO (Least Absolute Shrinkage and Selection Operator) 196

      Elastic Net(E-Net) 196

      Random Forest(RF) 196

ISSUES AND OPPORTUNITIES WITH TRADITIONAL MACHINE LEARNING 196

DEEP LEARNING (THE EMERGING TREND) 197

      The Revolution of Deep Learning 198

DEEP LEARNING APPROACH FOR NGS DATA ANALYSIS 199

      Artificial Neural Network (ANN) 199

      Convolutional Neural Network (CNN) 201

      Deep Neural Network (DNN) 202

      Feedforward Neural Network (FNN) 202

      Recurrent Neural Network (RNN) 204

SIGNIFICANT GENE IDENTIFICATION AND ANNOTATION 205

SUMMARY OF DL METHODS USED FOR NGS DATA ANALYSIS 206

CRITICAL OBSERVATION 208

      Data Volume 208

      Data Quality 208

      The Curse of Dimensionality 208

      Interpretability 209

      Domain Complexity 209

      Biological Annotation 209

CONCLUSION AND FUTURE SCOPE 210

CONSENT FOR PUBLICATION 210

CONFLICT OF INTEREST 210

ACKNOWLEDGEMENT 211

REFERENCES 211

CHAPTER 10 BREAST CANCER DETECTION USING MACHINE LEARNING CONCEPTS Fahmina Taranum and K. Sridevi 217

INTRODUCTION 218

      Background 218

      Undertaking Thorough Medical History 218

      Imaging Tests 218

      Advanced Test 219

      Classification Using the Techniques 219

      Dataset 219

      PROPOSED SYSTEM 220

      Problem Statement 220

      Objectives 221

      Why WDBC? 221

LITERATURE SURVEY 222

      Technological Development 222

      Dataset used in the Research 223

      Related Work 224

METHODOLOGIES 225

      Learning Algorithms225

      Measuring the Effectiveness of the Models 225

      Processing of Patterns 226

RESULTS AND DISCUSSION 227

CONCLUSION 236

CONSENT FOR PUBLICATION 237

CONFLICT OF INTEREST 237

ACKNOWLEDGEMENT 237

REFERENCES 237

SUBJECT INDEX 239

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