Machine learning : algorithms and applications / Mohssen Mohammed, Muhammad Badruddin Khan, Eihab Bashier Mohammed Bashier.
By: Mohammed, Mohssen [author.]
Contributor(s): Khan, Muhammad Badruddin [author.] | Bashier, Eihab Bashier Mohammed [author.]
Language: English Publisher: Boca Raton : CRC Press, Taylor & Francis Group, [2021]Description: 1 online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9781119768852; 9781119769248; 9781119769262Subject(s): Machine learning | Computer algorithmsGenre/Form: Electronic books.DDC classification: 006.3/12 LOC classification: Q325.5 | .M63 2017Online resources: Full text is available at Wiley Online Library Click here to view| Item type | Current location | Home library | Call number | Status | Date due | Barcode | Item holds |
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COLLEGE LIBRARY | COLLEGE LIBRARY | 006.312 M7254 2021 (Browse shelf) | Available | CL-52990 |
ABOUT THE AUTHOR
Mettu Srinivas PhD from the Indian Institute of Technology Hyderabad, and is currently an assistant professor in the Department of Computer Science and Engineering, NIT Warangal, India.
G. Sucharitha PhD from KL University, Vijayawada and is currently an assistant professor in the Department of Electronics and Communication Engineering at ICFAI Foundation for Higher Education Hyderabad.
Anjanna Matta PhD from the Indian Institute of Technology Hyderabad and is currently an assistant professor in the Department of Mathematics at ICFAI Foundation for Higher Education Hyderabad.
Prasenjit Chatterjee PhD is an associate professor in the Mechanical Engineering Department at MCKV Institute of Engineering, India.
Includes bibliographical references and index.
TABLE OF 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 Conventional Methods 216
12.3.2.1 Local Orientation Estimation 216
12.3.2.2 Singular Region Detection 218
12.3.3 Proposed CNN Architecture 219
12.3.4 Dataset 221
12.3.5 Computational Environment 221
12.4 Results 222
12.4.1 Feature Extraction and Visualization 223
12.5 Conclusion 226
Acknowledgements 226
References 226
13 Facial Expression Recognition Using Fusion of Deep Learning and Multiple Features 229
M. Srinivas, Sanjeev Saurav, Akshay Nayak and Murukessan A. P.
13.1 Introduction 230
13.2 Related Work 232
13.3 Proposed Method 235
13.3.1 Convolutional Neural Network 236
13.3.1.1 Convolution Layer 236
13.3.1.2 Pooling Layer 237
13.3.1.3 ReLU Layer 238
13.3.1.4 Fully Connected Layer 238
13.3.2 Histogram of Gradient 239
13.3.3 Facial Landmark Detection 240
13.3.4 Support Vector Machine 241
13.3.5 Model Merging and Learning 242
13.4 Experimental Results 242
13.4.1 Datasets 242
13.5 Conclusion 245
Acknowledgement 245
References 245
Part 4: Machine Learning for Classification and Information Retrieval Systems 247
14 AnimNet: An Animal Classification Network using Deep Learning 249
Kanak Manjari, Kriti Singhal, Madhushi Verma and Gaurav Singal
14.1 Introduction 249
14.1.1 Feature Extraction 250
14.1.2 Artificial Neural Network 250
14.1.3 Transfer Learning 251
14.2 Related Work 252
14.3 Proposed Methodology 254
14.3.1 Dataset Preparation 254
14.3.2 Training the Model 254
14.4 Results 258
14.4.1 Using Pre-Trained Networks 259
14.4.2 Using AnimNet 259
14.4.3 Test Analysis 260
14.5 Conclusion 263
References 264
15 A Hybrid Approach for Feature Extraction From Reviews to Perform Sentiment Analysis 267
Alok Kumar and Renu Jain
15.1 Introduction 268
15.2 Related Work 269
15.3 The Proposed System 271
15.3.1 Feedback Collector 272
15.3.2 Feedback Pre-Processor 272
15.3.3 Feature Selector 272
15.3.4 Feature Validator 274
15.3.4.1 Removal of Terms From Tentative List of Features on the Basis of Syntactic Knowledge 274
15.3.4.2 Removal of Least Significant Terms on the Basis of Contextual Knowledge 276
15.3.4.3 Removal of Less Significant Terms on the Basis of Association With Sentiment Words 277
15.3.4.4 Removal of Terms Having Similar Sense 278
15.3.4.5 Removal of Terms Having Same Root 279
15.3.4.6 Identification of Multi-Term Features 279
15.3.4.7 Identification of Less Frequent Feature 279
15.3.5 Feature Concluder 281
15.4 Result Analysis 282
15.5 Conclusion 286
References 286
16 Spark-Enhanced Deep Neural Network Framework for Medical Phrase Embedding 289
Amol P. Bhopale and Ashish Tiwari
16.1 Introduction 290
16.2 Related Work 291
16.3 Proposed Approach 292
16.3.1 Phrase Extraction 292
16.3.2 Corpus Annotation 294
16.3.3 Phrase Embedding 294
16.4 Experimental Setup 297
16.4.1 Dataset Preparation 297
16.4.2 Parameter Setting 297
16.5 Results 298
16.5.1 Phrase Extraction 298
16.5.2 Phrase Embedding 298
16.6 Conclusion 303
References 303
17 Image Anonymization Using Deep Convolutional Generative Adversarial Network 305
Ashish Undirwade and Sujit Das
17.1 Introduction 306
17.2 Background Information 310
17.2.1 Black Box and White Box Attacks 310
17.2.2 Model Inversion Attack 311
17.2.3 Differential Privacy 312
17.2.3.1 Definition 312
17.2.4 Generative Adversarial Network 313
17.2.5 Earth-Mover (EM) Distance/Wasserstein Metric 316
17.2.6 Wasserstein GAN 317
17.2.7 Improved Wasserstein GAN (WGAN-GP) 317
17.2.8 KL Divergence and JS Divergence 318
17.2.9 DCGAN 319
17.3 Image Anonymization to Prevent Model Inversion Attack 319
17.3.1 Algorithm 321
17.3.2 Training 322
17.3.3 Noise Amplifier 323
17.3.4 Dataset 324
17.3.5 Model Architecture 324
17.3.6 Working 325
17.3.7 Privacy Gain 325
17.4 Results and Analysis 326
17.5 Conclusion 328
References 329
Index 331
Machine Learning Algorithms is for current and ambitious machine learning specialists looking to implement solutions to real-world machine learning problems. It talks entirely about the various applications of machine and deep learning techniques, with each chapter dealing with a novel approach of machine learning architecture for a specific application, and then compares the results with previous algorithms.
The book discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, sentiment analysis, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the user can easily move from the equations in the book to a computer program.
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