Fundamentals and Methods of Machine and Deep Learning

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Fundamentals and Methods of Machine and Deep Learning, Wiley
Algorithms, Tools, and Applications
Von Pradeep Singh, im heise Shop in digitaler Fassung erhältlich
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FUNDAMENTALS AND METHODS OF MACHINE AND DEEP LEARNING

THE BOOK PROVIDES A PRACTICAL APPROACH BY EXPLAINING THE CONCEPTS OF MACHINE LEARNING AND DEEP LEARNING ALGORITHMS, EVALUATION OF METHODOLOGY ADVANCES, AND ALGORITHM DEMONSTRATIONS WITH APPLICATIONS.Over the past two decades, the field of machine learning and its subfield deep learning have played a main role in software applications development. Also, in recent research studies, they are regarded as one of the disruptive technologies that will transform our future life, business, and the global economy. The recent explosion of digital data in a wide variety of domains, including science, engineering, Internet of Things, biomedical, healthcare, and many business sectors, has declared the era of big data, which cannot be analysed by classical statistics but by the more modern, robust machine learning and deep learning techniques. Since machine learning learns from data rather than by programming hard-coded decision rules, an attempt is being made to use machine learning to make computers that are able to solve problems like human experts in the field. The goal of this book is to present a??practical approach by explaining the concepts of machine learning and deep learning algorithms with applications. Supervised machine learning algorithms, ensemble machine learning algorithms, feature selection, deep learning techniques, and their applications are discussed. Also included in the eighteen chapters is unique information which provides a clear understanding of concepts by using algorithms and case studies illustrated with applications of machine learning and deep learning in different domains, including disease prediction, software defect prediction, online television analysis, medical image processing, etc. Each of the chapters briefly described below provides both a chosen approach and its implementation. AUDIENCEResearchers and engineers in artificial intelligence, computer scientists as well as software developers. PRADEEP SINGH PHD, is an assistant professor in the Department of Computer Science Engineering, National Institute of Technology, Raipur, India. His current research interests include machine learning, deep learning, evolutionary computing, empirical studies on software quality, and software fault prediction models. He has more than 15 years of teaching experience with many publications in reputed international journals, conferences, and book chapters.

Preface xix

1 SUPERVISED MACHINE LEARNING: ALGORITHMS AND APPLICATIONS 1

Shruthi H. Shetty, Sumiksha Shetty, Chandra Singh and Ashwath Rao

1.1 History 2

1.2 Introduction 2

1.3 Supervised Learning 4

1.4 Linear Regression (LR) 5

1.4.1 Learning Model 6

1.4.2 Predictions With Linear Regression 7

1.5 Logistic Regression 8

1.6 Support Vector Machine (SVM) 9

1.7 Decision Tree 11

1.8 Machine Learning Applications in Daily Life 12

1.8.1 Traffic Alerts (Maps) 12

1.8.2 Social Media (Facebook) 13

1.8.3 Transportation and Commuting (Uber) 13

1.8.4 Products Recommendations 13

1.8.5 Virtual Personal Assistants 13

1.8.6 Self-Driving Cars 14

1.8.7 Google Translate 14

1.8.8 Online Video Streaming (Netflix) 14

1.8.9 Fraud Detection 14

1.9 Conclusion 15

References 15

2 ZONOTIC DISEASES DETECTION USING ENSEMBLE MACHINE LEARNING ALGORITHMS 17

Bhargavi K.

2.1 Introduction 18

2.2 Bayes Optimal Classifier 19

2.3 Bootstrap Aggregating (Bagging) 21

2.4 Bayesian Model Averaging (BMA) 22

2.5 Bayesian Classifier Combination (BCC) 24

2.6 Bucket of Models 26

2.7 Stacking 27

2.8 Efficiency Analysis 29

2.9 Conclusion 30

References 30

3 MODEL EVALUATION 33

Ravi Shekhar Tiwari

3.1 Introduction 34

3.2 Model Evaluation 34

3.2.1 Assumptions 36

3.2.2 Residual 36

3.2.3 Error Sum of Squares (Sse) 37

3.2.4 Regression Sum of Squares (Ssr) 37

3.2.5 Total Sum of Squares (Ssto) 37

3.3 Metric Used in Regression Model 38

3.3.1 Mean Absolute Error (Mae) 38

3.3.2 Mean Square Error (Mse) 39

3.3.3 Root Mean Square Error (Rmse) 41

3.3.4 Root Mean Square Logarithm Error (Rmsle) 42

3.3.5 R-Square (R2) 45

3.3.5.1 Problem With R-Square (R2) 46

3.3.6 Adjusted R-Square (R2) 46

3.3.7 Variance 47

3.3.8 AIC 48

3.3.9 BIC 49

3.3.10 ACP, Press, and R2-Predicted 49

3.3.11 Solved Examples 51

3.4 Confusion Metrics 52

3.4.1 How to Interpret the Confusion Metric? 53

3.4.2 Accuracy 55

3.4.2.1 Why Do We Need the Other Metric Along With Accuracy? 56

3.4.3 True Positive Rate (TPR) 56

3.4.4 False Negative Rate (FNR) 57

3.4.5 True Negative Rate (TNR) 57

3.4.6 False Positive Rate (FPR) 58

3.4.7 Precision 58

3.4.8 Recall 59

3.4.9 Recall-Precision Trade-Off 60

3.4.10 F1-Score 61

3.4.11 F-Beta Sore 61

3.4.12 Thresholding 63

3.4.13 AUC - ROC 64

3.4.14 AUC - PRC 65

3.4.15 Derived Metric From Recall, Precision, and F1-Score 67

3.4.16 Solved Examples 68

3.5 Correlation 70

3.5.1 Pearson Correlation 70

3.5.2 Spearman Correlation 71

3.5.3 Kendall’s Rank Correlation 73

3.5.4 Distance Correlation 74

3.5.5 Biweight Mid-Correlation 75

3.5.6 Gamma Correlation 76

3.5.7 Point Biserial Correlation 77

3.5.8 Biserial Correlation 78

3.5.9 Partial Correlation 78

3.6 Natural Language Processing (NLP) 78

3.6.1 N-Gram 79

3.6.2 BELU Score 79

3.6.2.1 BELU Score With N-Gram 80

3.6.3 Cosine Similarity 81

3.6.4 Jaccard Index 83

3.6.5 ROUGE 84

3.6.6 NIST 85

3.6.7 SQUAD 85

3.6.8 MACRO 86

3.7 Additional Metrics 86

3.7.1 Mean Reciprocal Rank (MRR) 86

3.7.2 Cohen Kappa 87

3.7.3 Gini Coefficient 87

3.7.4 Scale-Dependent Errors 87

3.7.5 Percentage Errors 88

3.7.6 Scale-Free Errors 88

3.8 Summary of Metric Derived from Confusion Metric 89

3.9 Metric Usage 90

3.10 Pro and Cons of Metrics 94

3.11 Conclusion 95

References 96

4 ANALYSIS OF M-SEIR AND LSTM MODELS FOR THE PREDICTION OF COVID-19 USING RMSLE 101

Archith S., Yukta C., Archana H.R. and Surendra H.H.

4.1 Introduction 101

4.2 Survey of Models 103

4.2.1 SEIR Model 103

4.2.2 Modified SEIR Model 103

4.2.3 Long Short-Term Memory (LSTM) 104

4.3 Methodology 106

4.3.1 Modified SEIR 106

4.3.2 LSTM Model 108

4.3.2.1 Data Pre-Processing 108

4.3.2.2 Data Shaping 109

4.3.2.3 Model Design 109

4.4 Experimental Results 111

4.4.1 Modified SEIR Model 111

4.4.2 LSTM Model 113

4.5 Conclusion 116

4.6 Future Work 116

References 118

5 THE SIGNIFICANCE OF FEATURE SELECTION TECHNIQUES IN MACHINE LEARNING 121

N. Bharathi, B.S. Rishiikeshwer, T. Aswin Shriram, B. Santhi and G.R. Brindha

5.1 Introduction 122

5.2 Significance of Pre-Processing 122

5.3 Machine Learning System 123

5.3.1 Missing Values 123

5.3.2 Outliers 123

5.3.3 Model Selection 124

5.4 Feature Extraction Methods 124

5.4.1 Dimension Reduction 125

5.4.1.1 Attribute Subset Selection 126

5.4.2 Wavelet Transforms 127

5.4.3 Principal Components Analysis 127

5.4.4 Clustering 128

5.5 Feature Selection 128

5.5.1 Filter Methods 129

5.5.2 Wrapper Methods 129

5.5.3 Embedded Methods 130

5.6 Merits and Demerits of Feature Selection 131

5.7 Conclusion 131

References 132

6 USE OF MACHINE LEARNING AND DEEP LEARNING IN HEALTHCARE—A REVIEW ON DISEASE PREDICTION SYSTEM 135

Radha R. and Gopalakrishnan R.

6.1 Introduction to Healthcare System 136

6.2 Causes for the Failure of the Healthcare System 137

6.3 Artificial Intelligence and Healthcare System for Predicting Diseases 138

6.3.1 Monitoring and Collection of Data 140

6.3.2 Storing, Retrieval, and Processing of Data 141

6.4 Facts Responsible for Delay in Predicting the Defects 142

6.5 Pre-Treatment Analysis and Monitoring 143

6.6 Post-Treatment Analysis and Monitoring 145

6.7 Application of ML and DL 145

6.7.1 ML and DL for Active Aid 145

6.7.1.1 Bladder Volume Prediction 147

6.7.1.2 Epileptic Seizure Prediction 148

6.8 Challenges and Future of Healthcare Systems Based on ML and DL 148

6.9 Conclusion 149

References 150

7 DETECTION OF DIABETIC RETINOPATHY USING ENSEMBLE LEARNING TECHNIQUES 153

Anirban Dutta, Parul Agarwal, Anushka Mittal, Shishir Khandelwal and Shikha Mehta

7.1 Introduction 153

7.2 Related Work 155

7.3 Methodology 155

7.3.1 Data Pre-Processing 155

7.3.2 Feature Extraction 161

7.3.2.1 Exudates 161

7.3.2.2 Blood Vessels 161

7.3.2.3 Microaneurysms 162

7.3.2.4 Hemorrhages 162

7.3.3 Learning 163

7.3.3.1 Support Vector Machines 163

7.3.3.2 K-Nearest Neighbors 163

7.3.3.3 Random Forest 164

7.3.3.4 AdaBoost 164

7.3.3.5 Voting Technique 164

7.4 Proposed Models 165

7.4.1 AdaNaive 165

7.4.2 AdaSVM 166

7.4.3 AdaForest 166

7.5 Experimental Results and Analysis 167

7.5.1 Dataset 167

7.5.2 Software and Hardware 167

7.5.3 Results 168

7.6 Conclusion 173

References 174

8 MACHINE LEARNING AND DEEP LEARNING FOR MEDICAL ANALYSIS—A CASE STUDY ON HEART DISEASE DATA 177

Swetha A.M., Santhi B. and Brindha G.R.

8.1 Introduction 178

8.2 Related Works 179

8.3 Data Pre-Processing 181

8.3.1 Data Imbalance 181

8.4 Feature Selection 182

8.4.1 Extra Tree Classifier 182

8.4.2 Pearson Correlation 183

8.4.3 Forward Stepwise Selection 183

8.4.4 Chi-Square Test 184

8.5 ML Classifiers Techniques 184

8.5.1 Supervised Machine Learning Models 185

8.5.1.1 Logistic Regression 185

8.5.1.2 SVM 186

8.5.1.3 Naive Bayes 186

8.5.1.4 Decision Tree 186

8.5.1.5 K-Nearest Neighbors (KNN) 187

8.5.2 Ensemble Machine Learning Model 187

8.5.2.1 Random Forest 187

8.5.2.2 AdaBoost 188

8.5.2.3 Bagging 188

8.5.3 Neural Network Models 189

8.5.3.1 Artificial Neural Network (ANN) 189

8.5.3.2 Convolutional Neural Network (CNN) 189

8.6 Hyperparameter Tuning 190

8.6.1 Cross-Validation 190

8.7 Dataset Description 190

8.7.1 Data Pre-Processing 193

8.7.2 Feature Selection 195

8.7.3 Model Selection 196

8.7.4 Model Evaluation 197

8.8 Experiments and Results 197

8.8.1 Study 1: Survival Prediction Using All Clinical Features 198

8.8.2 Study 2: Survival Prediction Using Age, Ejection Fraction and Serum Creatinine 198

8.8.3 Study 3: Survival Prediction Using Time, Ejection Fraction, and Serum Creatinine 199

8.8.4 Comparison Between Study 1, Study 2, and Study 3 203

8.8.5 Comparative Study on Different Sizes of Data 204

8.9 Analysis 206

8.10 Conclusion 206

References 207

9 A NOVEL CONVOLUTIONAL NEURAL NETWORK MODEL TO PREDICT SOFTWARE DEFECTS 211

Kumar Rajnish, Vandana Bhattacharjee and Mansi Gupta

9.1 Introduction 212

9.2 Related Works 213

9.2.1 Software Defect Prediction Based on Deep Learning 213

9.2.2 Software Defect Prediction Based on Deep Features 214

9.2.3 Deep Learning in Software Engineering 214

9.3 Theoretical Background 215

9.3.1 Software Defect Prediction 215

9.3.2 Convolutional Neural Network 216

9.4 Experimental Setup 218

9.4.1 Data Set Description 218

9.4.2 Building Novel Convolutional Neural Network (NCNN) Model 219

9.4.3 Evaluation Parameters 222

9.4.4 Results and Analysis 224

9.5 Conclusion and Future Scope 230

References 233

10 PREDICTIVE ANALYSIS ON ONLINE TELEVISION VIDEOS USING MACHINE LEARNING ALGORITHMS 237

Rebecca Jeyavadhanam B., Ramalingam V.V., Sugumaran V. and Rajkumar D.

10.1 Introduction 238

10.1.1 Overview of Video Analytics 241

10.1.2 Machine Learning Algorithms 242

10.1.2.1 Decision Tree C4.5 243

10.1.2.2 J48 Graft 243

10.1.2.3 Logistic Model Tree 244

10.1.2.4 Best First Tree 244

10.1.2.5 Reduced Error Pruning Tree 244

10.1.2.6 Random Forest 244

10.2 Proposed Framework 245

10.2.1 Data Collection 246

10.2.2 Feature Extraction 246

10.2.2.1 Block Intensity Comparison Code 247

10.2.2.2 Key Frame Rate 248

10.3 Feature Selection 249

10.4 Classification 250

10.5 Online Incremental Learning 251

10.6 Results and Discussion 253

10.7 Conclusion 255

References 256

11 A COMBINATIONAL DEEP LEARNING APPROACH TO VISUALLY EVOKED EEG-BASED IMAGE CLASSIFICATION 259

Nandini Kumari, Shamama Anwar and Vandana Bhattacharjee

11.1 Introduction 260

11.2 Literature Review 262

11.3 Methodology 264

11.3.1 Dataset Acquisition 264

11.3.2 Pre-Processing and Spectrogram Generation 265

11.3.3 Classification of EEG Spectrogram Images With Proposed CNN Model 266

11.3.4 Classification of EEG Spectrogram Images With Proposed Combinational CNN+LSTM Model 268

11.4 Result and Discussion 270

11.5 Conclusion 272

References 273

12 APPLICATION OF MACHINE LEARNING ALGORITHMS WITH BALANCING TECHNIQUES FOR CREDIT CARD FRAUD DETECTION: A COMPARATIVE ANALYSIS 277

Shiksha

12.1 Introduction 278

12.2 Methods and Techniques 280

12.2.1 Research Approach 280

12.2.2 Dataset Description 282

12.2.3 Data Preparation 283

12.2.4 Correlation Between Features 284

12.2.5 Splitting the Dataset 285

12.2.6 Balancing Data 285

12.2.6.1 Oversampling of Minority Class 286

12.2.6.2 Under-Sampling of Majority Class 286

12.2.6.3 Synthetic Minority Over Sampling Technique 286

12.2.6.4 Class Weight 287

12.2.7 Machine Learning Algorithms (Models) 288

12.2.7.1 Logistic Regression 288

12.2.7.2 Support Vector Machine 288

12.2.7.3 Decision Tree 290

12.2.7.4 Random Forest 292

12.2.8 Tuning of Hyperparameters 294

12.2.9 Performance Evaluation of the Models 294

12.3 Results and Discussion 298

12.3.1 Results Using Balancing Techniques 299

12.3.2 Result Summary 299

12.4 Conclusions 305

12.4.1 Future Recommendations 305

References 306

13 CRACK DETECTION IN CIVIL STRUCTURES USING DEEP LEARNING 311

Bijimalla Shiva Vamshi Krishna, Rishiikeshwer B.S., J. Sanjay Raju, N. Bharathi, C. Venkatasubramanian and G.R. Brindha

13.1 Introduction 312

13.2 Related Work 312

13.3 Infrared Thermal Imaging Detection Method 314

13.4 Crack Detection Using CNN 314

13.4.1 Model Creation 316

13.4.2 Activation Functions (AF) 317

13.4.3 Optimizers 322

13.4.4 Transfer Learning 322

13.5 Results and Discussion 322

13.6 Conclusion 323

References 323

14 MEASURING URBAN SPRAWL USING MACHINE LEARNING 327

Keerti Kulkarni and P. A. Vijaya

14.1 Introduction 327

14.2 Literature Survey 328

14.3 Remotely Sensed Images 329

14.4 Feature Selection 331

14.4.1 Distance-Based Metric 331

14.5 Classification Using Machine Learning Algorithms 332

14.5.1 Parametric vs. Non-Parametric Algorithms 332

14.5.2 Maximum Likelihood Classifier 332

14.5.3 k-Nearest Neighbor Classifiers 334

14.5.4 Evaluation of the Classifiers 334

14.5.4.1 Precision 334

14.5.4.2 Recall 335

14.5.4.3 Accuracy 335

14.5.4.4 F1-Score 335

14.6 Results 335

14.7 Discussion and Conclusion 338

Acknowledgements 338

References 338

15 APPLICATION OF DEEP LEARNING ALGORITHMS IN MEDICAL IMAGE PROCESSING: A SURVEY 341

Santhi B., Swetha A.M. and Ashutosh A.M.

15.1 Introduction 342

15.2 Overview of Deep Learning Algorithms 343

15.2.1 Supervised Deep Neural Networks 343

15.2.1.1 Convolutional Neural Network 343

15.2.1.2 Transfer Learning 344

15.2.1.3 Recurrent Neural Network 344

15.2.2 Unsupervised Learning 345

15.2.2.1 Autoencoders 345

15.2.2.2 GANs 345

15.3 Overview of Medical Images 346

15.3.1 MRI Scans 346

15.3.2 CT Scans 347

15.3.3 X-Ray Scans 347

15.3.4 PET Scans 347

15.4 Scheme of Medical Image Processing 348

15.4.1 Formation of Image 348

15.4.2 Image Enhancement 349

15.4.3 Image Analysis 349

15.4.4 Image Visualization 349

15.5 Anatomy-Wise Medical Image Processing With Deep Learning 349

15.5.1 Brain Tumor 352

15.5.2 Lung Nodule Cancer Detection 357

15.5.3 Breast Cancer Segmentation and Detection 362

15.5.4 Heart Disease Prediction 364

15.5.5 COVID-19 Prediction 370

15.6 Conclusion 372

References 372

16 SIMULATION OF SELF-DRIVING CARS USING DEEP LEARNING 379

Rahul M. K., Praveen L. Uppunda, Vinayaka Raju S., Sumukh B. and C. Gururaj

16.1 Introduction 380

16.2 Methodology 380

16.2.1 Behavioral Cloning 380

16.2.2 End-to-End Learning 380

16.3 Hardware Platform 381

16.4 Related Work 382

16.5 Pre-Processing 382

16.5.1 Lane Feature Extraction 382

16.5.1.1 Canny Edge Detector 383

16.5.1.2 Hough Transform 383

16.5.1.3 Raw Image Without Pre-Processing 384

16.6 Model 384

16.6.1 CNN Architecture 385

16.6.2 Multilayer Perceptron Model 385

16.6.3 Regression vs. Classification 385

16.6.3.1 Regression 386

16.6.3.2 Classification 386

16.7 Experiments 387

16.8 Results 387

16.9 Conclusion 394

References 394

17 ASSISTIVE TECHNOLOGIES FOR VISUAL, HEARING, AND SPEECH IMPAIRMENTS: MACHINE LEARNING AND DEEP LEARNING SOLUTIONS 397

Shahira K. C., Sruthi C. J. and Lijiya A.

17.1 Introduction 397

17.2 Visual Impairment 398

17.2.1 Conventional Assistive Technology for the VIP 399

17.2.1.1 Way Finding 399

17.2.1.2 Reading Assistance 402

17.2.2 The Significance of Computer Vision and Deep Learning in AT of VIP 403

17.2.2.1 Navigational Aids 403

17.2.2.2 Scene Understanding 405

17.2.2.3 Reading Assistance 406

17.2.2.4 Wearables 408

17.3 Verbal and Hearing Impairment 410

17.3.1 Assistive Listening Devices 410

17.3.2 Alerting Devices 411

17.3.3 Augmentative and Alternative Communication Devices 411

17.3.3.1 Sign Language Recognition 412

17.3.4 Significance of Machine Learning and Deep Learning in Assistive Communication Technology 417

17.4 Conclusion and Future Scope 418

References 418

18 CASE STUDIES: DEEP LEARNING IN REMOTE SENSING 425

Emily Jenifer A. and Sudha N.

18.1 Introduction 426

18.2 Need for Deep Learning in Remote Sensing 427

18.3 Deep Neural Networks for Interpreting Earth Observation Data 427

18.3.1 Convolutional Neural Network 427

18.3.2 Autoencoder 428

18.3.3 Restricted Boltzmann Machine and Deep Belief Network 429

18.3.4 Generative Adversarial Network 430

18.3.5 Recurrent Neural Network 431

18.4 Hybrid Architectures for Multi-Sensor Data Processing 432

18.5 Conclusion 434

References 434

Index 439
Artikel-Details
Anbieter:
Wiley
Autor:
Pradeep Singh
Artikelnummer:
9781119821892
Veröffentlicht:
25.01.2022
Seitenanzahl:
480