Machine learning algorithms and applications. (2021)

• Record Type: Book
• Further Information: Note: Edited by Mettu Srinivas, G. Sucharitha, Anjanna Matta.
• Editors: Srinivas, Mettu; Sucharitha, G; Matta, Anjanna
• Contents: Acknowledgments xv Preface xvii Part 1: Machine Learning for Industrial Applications 1 1 A Learning-Based Visualization Application for Air Quality Evaluation During COVID-19 Pandemic in Open Data Centric Services 3; Priyank Jain and Gagandeep Kaur 1.1 Introduction 4 1.1.1 Open Government Data Initiative 4 1.1.2 Air Quality 4 1.1.3 Impact of Lockdown on Air Quality 5 1.2 Literature Survey 5 1.3 Implementation Details 6 1.3.1 Proposed Methodology 7 1.3.2 System Specifications 8 1.3.3 Algorithms 8 1.3.4 Control Flow 10 1.4 Results and Discussions 11 1.5 Conclusion 21 References 21 2 Automatic Counting and Classification of Silkworm Eggs Using Deep Learning 23; Shreedhar Rangappa, Ajay A. and G. S. Rajanna 2.1 Introduction 23 2.2 Conventional Silkworm Egg Detection Approaches 24 2.3 Proposed Method 25 2.3.1 Model Architecture 26 .3.2 Foreground-Background Segmentation 28 2.3.3 Egg Location Predictor 30 2.3.4 Predicting Egg Class 31 2.4 Dataset Generation 35 2.5 Results 35 2.6 Conclusion 37 Acknowledgment 38 References 38 3 A Wind Speed Prediction System Using Deep Neural Networks 41; Jaseena K. U. and Binsu C. Kovoor 3.1 Introduction 42 3.2 Methodology 45 3.2.1 Deep Neural Networks 45 3.2.2 The Proposed Method 47 3.2.2.1 Data Acquisition 47 3.2.2.2 Data Pre-Processing 48 3.2.2.3 Model Selection and Training 50 3.2.2.4 Performance Evaluation 51 3.2.2.5 Visualization 51 3.3 Results and Discussions 52 3.3.1 Selection of Parameters 52 3.3.2 Comparison of Models 53 3.4 Conclusion 57 References 57 4 Res-SE-Net: Boosting Performance of ResNets by Enhancing Bridge Connections 61; Varshaneya V., S. Balasubramanian and Darshan Gera 4.1 Introduction 61 4.2 Related Work 62 4.3 Preliminaries 63 4.3.1 ResNet 63 4.3.2 Squeeze-and-Excitation Block 64 4.4 Proposed Model 66 4.4.1 Effect of Bridge Connections in ResNet 66 4.4.2 Res-SE-Net: Proposed Architecture 67 4.5 Experiments 68 4.5.1 Datasets 68 4.5.2 Experimental Setup 68 4.6 Results 69 4.7 Conclusion 73 References 74 5 Hitting the Success Notes of Deep Learning 77; Sakshi Aggarwal, Navjot Singh and K.K. Mishra 5.1 Genesis 78 5.2 The Big Picture: Artificial Neural Network 79 5.3 Delineating the Cornerstones 80 5.3.1 Artificial Neural Network vs. Machine Learning 80 5.3.2 Machine Learning vs. Deep Learning 81 5.3.3 Artificial Neural Network vs. Deep Learning 81 5.4 Deep Learning Architectures 82 5.4.1 Unsupervised Pre-Trained Networks 82 5.4.2 Convolutional Neural Networks 83 5.4.3 Recurrent Neural Networks 84 5.4.4 Recursive Neural Network 85 5.5 Why is CNN Preferred for Computer Vision Applications? 85 5.5.1 Convolutional Layer 86 5.5.2 Nonlinear Layer 86 5.5.3 Pooling Layer 87 5.5.4 Fully Connected Layer 87 5.6 Unravel Deep Learning in Medical Diagnostic Systems 89 5.7 Challenges and Future Expectations 94 5.8 Conclusion 94 References 95 6 Two-Stage Credit Scoring Model Based on Evolutionary Feature Selection and Ensemble Neural Networks 99; Diwakar Tripathi, Damodar Reddy Edla, Annushree Bablani and Venkatanareshbabu Kuppili 6.1 Introduction 100 6.1.1 Motivation 100 6.2 Literature Survey 101 6.3 Proposed Model for Credit Scoring 103 6.3.1 Stage-1: Feature Selection 104 6.3.2 Proposed Criteria Function 105 6.3.3 Stage-2: Ensemble Classifier 106 6.4 Results and Discussion 107 6.4.1 Experimental Datasets and Performance Measures 107 6.4.2 Classification Results With Feature Selection 108 6.5 Conclusion 112 References 113 7 Enhanced Block-Based Feature Agglomeration Clustering for Video Summarization 117; Sreeja M. U. and Binsu C. Kovoor 7.1 Introduction 118 7.2 Related Works 119 7.3 Feature Agglomeration Clustering 122 7.4 Proposed Methodology 122 7.4.1 Pre-Processing 123 7.4.2 Modified Block Clustering Using Feature Agglomeration Technique 125 7.4.3 Post-Processing and Summary Generation 127 7.5 Results and Analysis 129 7.5.1 Experimental Setup and Data Sets Used 129 7.5.2 Evaluation Metrics 130 7.5.3 Evaluation 131 7.6 Conclusion 138 References 138 Part 2: Machine Learning for Healthcare Systems 141 8 Cardiac Arrhythmia Detection and Classification From ECG Signals Using XGBoost Classifier 143; Saroj Kumar Pandeyz, Rekh Ram Janghel and Vaibhav Gupta 8.1 Introduction 143 8.2 Materials and Methods 145 8.2.1 MIT-BIH Arrhythmia Database 146 8.2.2 Signal Pre-Processing 147 8.2.3 Feature Extraction 147 8.2.4 Classification 148 8.2.4.1 XGBoost Classifier 148 8.2.4.2 AdaBoost Classifier 149 8.3 Results and Discussion 149 8.4 Conclusion 155 References 156 9 GSA-Based Approach for Gene Selection from Microarray Gene Expression Data 159; Pintu Kumar Ram and Pratyay Kuila 9.1 Introduction 159 9.2 Related Works 161 9.3 An Overview of Gravitational Search Algorithm 162 9.4 Proposed Model 163 9.4.1 Pre-Processing 163 9.4.2 Proposed GSA-Based Feature Selection 164 9.5 Simulation Results 166 9.5.1 Biological Analysis 168 9.6 Conclusion 172 References 172 Part 3: Machine Learning for Security Systems 175 10 On Fusion of NIR and VW Information for Cross-Spectral Iris Matching 177; Ritesh Vyas, Tirupathiraju Kanumuri, Gyanendra Sheoran and Pawan Dubey 10.1 Introduction 177 10.1.1 Related Works 178 10.2 Preliminary Details 179 10.2.1 Fusion 181 10.3 Experiments and Results 182 10.3.1 Databases 182 10.3.2 Experimental Results 182 10.3.2.1 Same Spectral Matchings 183 10.3.2.2 Cross Spectral Matchings 184 10.3.3 Feature-Level Fusion 186 10.3.4 Score-Level Fusion 189 10.4 Conclusions 190 References 190 11 Fake Social Media Profile Detection 193; Umita Deepak Joshi, Vanshika, Ajay Pratap Singh, Tushar Rajesh Pahuja, Smita Naval and Gaurav Singal 11.1 Introduction 194 11.2 Related Work 195 11.3 Methodology 197 11.3.1 Dataset 197 11.3.2 Pre-Processing 198 11.3.3 Artificial Neural Network 199 11.3.4 Random Forest 202 11.3.5 Extreme Gradient Boost 202 11.3.6 Long Short-Term Memory 204 11.4 Experimental Results 204 11.5 Conclusion and Future Work 207 Acknowledgment 207 References 207 12 Extraction of the Features of Fingerprints Using Conventional Methods and Convolutional Neural Networks 211; E. M. V. Naga Karthik and Madan Gopal 12.1 Introduction 212 12.2 Related Work 213 12.3 Methods and Materials 215 12.3.1 Feature Extraction Using SURF 215 12.3.2 Feature Extraction Using Conve
• Edition: 1st
• Publisher Details: Hoboken : Wiley-Scrivener
• Publication Date: 2021
• Extent: 1 online resource
• Subjects: 006.31; Machine learning; Computer algorithms
• Languages: English
• ISBNs: 9781119769248
• Related ISBNs: 9781119768852
• Notes: Note: Description based on CIP data; resource not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
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