Machine Learning Algorithms and Applications

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.

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. 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