Fundamentals and Methods of Machine and Deep Learning: Algorithms, Tools, and Applications
- Length: 480 pages
- Edition: 1
- Language: English
- Publisher: Wiley-Scrivener
- Publication Date: 2022-03-02
- ISBN-10: 1119821258
- ISBN-13: 9781119821250
- Sales Rank: #0 (See Top 100 Books)
UNDAMENTALS 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.
Audience
Researchers and engineers in artificial intelligence, computer scientists as well as software developers.
Cover Half-Title Page Series Page Title Page Copyright Page Contents Preface 1 Supervised Machine Learning: Algorithms and Applications 1.1 History 1.2 Introduction 1.3 Supervised Learning 1.4 Linear Regression (LR) 1.4.1 Learning Model 1.4.2 Predictions With Linear Regression 1.5 Logistic Regression 1.6 Support Vector Machine (SVM) 1.7 Decision Tree 1.8 Machine Learning Applications in Daily Life 1.8.1 Traffic Alerts (Maps) 1.8.2 Social Media (Facebook) 1.8.3 Transportation and Commuting (Uber) 1.8.4 Products Recommendations 1.8.5 Virtual Personal Assistants 1.8.6 Self-Driving Cars 1.8.7 Google Translate 1.8.8 Online Video Streaming (Netflix) 1.8.9 Fraud Detection 1.9 Conclusion References 2 Zonotic Diseases Detection Using Ensemble Machine Learning Algorithms 2.1 Introduction 2.2 Bayes Optimal Classifier 2.3 Bootstrap Aggregating (Bagging) 2.4 Bayesian Model Averaging (BMA) 2.5 Bayesian Classifier Combination (BCC) 2.6 Bucket of Models 2.7 Stacking 2.8 Efficiency Analysis 2.9 Conclusion References 3 Model Evaluation 3.1 Introduction 3.2 Model Evaluation 3.2.1 Assumptions 3.2.2 Residual 3.2.3 Error Sum of Squares (Sse) 3.2.4 Regression Sum of Squares (Ssr) 3.2.5 Total Sum of Squares (Ssto) 3.3 Metric Used in Regression Model 3.3.1 Mean Absolute Error (Mae) 3.3.2 Mean Square Error (Mse) 3.3.3 Root Mean Square Error (Rmse) 3.3.4 Root Mean Square Logarithm Error (Rmsle) 3.3.5 R-Square (R2) 3.3.6 Adjusted R-Square (R2) 3.3.7 Variance 3.3.8 AIC 3.3.9 BIC 3.3.10 ACP, Press, and R2-Predicted 3.3.11 Solved Examples 3.4 Confusion Metrics 3.4.1 How to Interpret the Confusion Metric? 3.4.2 Accuracy 3.4.3 True Positive Rate (TPR) 3.4.4 False Negative Rate (FNR) 3.4.5 True Negative Rate (TNR) 3.4.6 False Positive Rate (FPR) 3.4.7 Precision 3.4.8 Recall 3.4.9 Recall-Precision Trade-Off 3.4.10 F1-Score 3.4.11 F-Beta Sore 3.4.12 Thresholding 3.4.13 AUC-ROC 3.4.14 AUC-PRC 3.4.15 Derived Metric From Recall, Precision, and F1-Score 3.4.16 Solved Examples 3.5 Correlation 3.5.1 Pearson Correlation 3.5.2 Spearman Correlation 3.5.3 Kendall’s Rank Correlation 3.5.4 Distance Correlation 3.5.5 Biweight Mid-Correlation 3.5.6 Gamma Correlation 3.5.7 Point Biserial Correlation 3.5.8 Biserial Correlation 3.5.9 Partial Correlation 3.6 Natural Language Processing (NLP) 3.6.1 N-Gram 3.6.2 BELU Score 3.6.3 Cosine Similarity 3.6.4 Jaccard Index 3.6.5 ROUGE 3.6.6 NIST 3.6.7 SQUAD 3.6.8 MACRO 3.7 Additional Metrics 3.7.1 Mean Reciprocal Rank (MRR) 3.7.2 Cohen Kappa 3.7.3 Gini Coefficient 3.7.4 Scale-Dependent Errors 3.7.5 Percentage Errors 3.7.6 Scale-Free Errors 3.8 Summary of Metric Derived from Confusion Metric 3.9 Metric Usage 3.10 Pro and Cons of Metrics 3.11 Conclusion References 4 Analysis of M-SEIR and LSTM Models for the Prediction of COVID-19 Using RMSLE 4.1 Introduction 4.2 Survey of Models 4.2.1 SEIR Model 4.2.2 Modified SEIR Model 4.2.3 Long Short-Term Memory (LSTM) 4.3 Methodology 4.3.1 Modified SEIR 4.3.2 LSTM Model 4.4 Experimental Results 4.4.1 Modified SEIR Model 4.4.2 LSTM Model 4.5 Conclusion 4.6 Future Work References 5 The Significance of Feature Selection Techniques in Machine Learning 5.1 Introduction 5.2 Significance of Pre-Processing 5.3 Machine Learning System 5.3.1 Missing Values 5.3.2 Outliers 5.3.3 Model Selection 5.4 Feature Extraction Methods 5.4.1 Dimension Reduction 5.4.2 Wavelet Transforms 5.4.3 Principal Components Analysis 5.4.4 Clustering 5.5 Feature Selection 5.5.1 Filter Methods 5.5.2 Wrapper Methods 5.5.3 Embedded Methods 5.6 Merits and Demerits of Feature Selection 5.7 Conclusion References 6 Use of Machine Learning and Deep Learning in Healthcare—A Review on Disease Prediction System 6.1 Introduction to Healthcare System 6.2 Causes for the Failure of the Healthcare System 6.3 Artificial Intelligence and Healthcare System for Predicting Diseases 6.3.1 Monitoring and Collection of Data 6.3.2 Storing, Retrieval, and Processing of Data 6.4 Facts Responsible for Delay in Predicting the Defects 6.5 Pre-Treatment Analysis and Monitoring 6.6 Post-Treatment Analysis and Monitoring 6.7 Application of ML and DL 6.7.1 ML and DL for Active Aid 6.8 Challenges and Future of Healthcare Systems Based on ML and DL 6.9 Conclusion References 7 Detection of Diabetic Retinopathy Using Ensemble Learning Techniques 7.1 Introduction 7.2 Related Work 7.3 Methodology 7.3.1 Data Pre-Processing 7.3.2 Feature Extraction 7.3.3 Learning 7.4 Proposed Models 7.4.1 AdaNaive 7.4.2 AdaSVM 7.4.3 AdaForest 7.5 Experimental Results and Analysis 7.5.1 Dataset 7.5.2 Software and Hardware 7.5.3 Results 7.6 Conclusion References 8 Machine Learning and Deep Learning for Medical Analysis—A Case Study on Heart Disease Data 8.1 Introduction 8.2 Related Works 8.3 Data Pre-Processing 8.3.1 Data Imbalance 8.4 Feature Selection 8.4.1 Extra Tree Classifier 8.4.2 Pearson Correlation 8.4.3 Forward Stepwise Selection 8.4.4 Chi-Square Test 8.5 ML Classifiers Techniques 8.5.1 Supervised Machine Learning Models 8.5.2 Ensemble Machine Learning Model 8.5.3 Neural Network Models 8.6 Hyperparameter Tuning 8.6.1 Cross-Validation 8.7 Dataset Description 8.7.1 Data Pre-Processing 8.7.2 Feature Selection 8.7.3 Model Selection 8.7.4 Model Evaluation 8.8 Experiments and Results 8.8.1 Study 1: Survival Prediction Using All Clinical Features 8.8.2 Study 2: Survival Prediction Using Age, Ejection Fraction and Serum Creatinine 8.8.3 Study 3: Survival Prediction Using Time, Ejection Fraction, and Serum Creatinine 8.8.4 Comparison Between Study 1, Study 2, and Study 3 8.8.5 Comparative Study on Different Sizes of Data 8.9 Analysis 8.10 Conclusion References 9 A Novel Convolutional Neural Network Model to Predict Software Defects 9.1 Introduction 9.2 Related Works 9.2.1 Software Defect Prediction Based on Deep Learning 9.2.2 Software Defect Prediction Based on Deep Features 9.2.3 Deep Learning in Software Engineering 9.3 Theoretical Background 9.3.1 Software Defect Prediction 9.3.2 Convolutional Neural Network 9.4 Experimental Setup 9.4.1 Data Set Description 9.4.2 Building Novel Convolutional Neural Network (NCNN) Model 9.4.3 Evaluation Parameters 9.4.4 Results and Analysis 9.5 Conclusion and Future Scope References 10 Predictive Analysis of Online Television Videos Using Machine Learning Algorithms 10.1 Introduction 10.1.1 Overview of Video Analytics 10.1.2 Machine Learning Algorithms 10.2 Proposed Framework 10.2.1 Data Collection 10.2.2 Feature Extraction 10.3 Feature Selection 10.4 Classification 10.5 Online Incremental Learning 10.6 Results and Discussion 10.7 Conclusion References 11 A Combinational Deep Learning Approach to Visually Evoked EEG-Based Image Classification 11.1 Introduction 11.2 Literature Review 11.3 Methodology 11.3.1 Dataset Acquisition 11.3.2 Pre-Processing and Spectrogram Generation 11.3.3 Classification of EEG Spectrogram Images With Proposed CNN Model 11.3.4 Classification of EEG Spectrogram Images With Proposed Combinational CNN+LSTM Model 11.4 Result and Discussion 11.5 Conclusion References 12 Application of Machine Learning Algorithms With Balancing Techniques for Credit Card Fraud Detection: A Comparative Analysis 12.1 Introduction 12.2 Methods and Techniques 12.2.1 Research Approach 12.2.2 Dataset Description 12.2.3 Data Preparation 12.2.4 Correlation Between Features 12.2.5 Splitting the Dataset 12.2.6 Balancing Data 12.2.7 Machine Learning Algorithms (Models) 12.2.8 Tuning of Hyperparameters 12.2.9 Performance Evaluation of the Models 12.3 Results and Discussion 12.3.1 Results Using Balancing Techniques 12.3.2 Result Summary 12.4 Conclusions 12.4.1 Future Recommendations References 13 Crack Detection in Civil Structures Using Deep Learning 13.1 Introduction 13.2 Related Work 13.3 Infrared Thermal Imaging Detection Method 13.4 Crack Detection Using CNN 13.4.1 Model Creation 13.4.2 Activation Functions (AF) 13.4.3 Optimizers 13.4.4 Transfer Learning 13.5 Results and Discussion 13.6 Conclusion References 14 Measuring Urban Sprawl Using Machine Learning 14.1 Introduction 14.2 Literature Survey 14.3 Remotely Sensed Images 14.4 Feature Selection 14.4.1 Distance-Based Metric 14.5 Classification Using Machine Learning Algorithms 14.5.1 Parametric vs. Non-Parametric Algorithms 14.5.2 Maximum Likelihood Classifier 14.5.3 k-Nearest Neighbor Classifiers 14.5.4 Evaluation of the Classifiers 14.6 Results 14.7 Discussion and Conclusion Acknowledgements References 15 Application of Deep Learning Algorithms in Medical Image Processing: A Survey 15.1 Introduction 15.2 Overview of Deep Learning Algorithms 15.2.1 Supervised Deep Neural Networks 15.2.2 Unsupervised Learning 15.3 Overview of Medical Images 15.3.1 MRI Scans 15.3.2 CT Scans 15.3.3 X-Ray Scans 15.3.4 PET Scans 15.4 Scheme of Medical Image Processing 15.4.1 Formation of Image 15.4.2 Image Enhancement 15.4.3 Image Analysis 15.4.4 Image Visualization 15.5 Anatomy-Wise Medical Image Processing With Deep Learning 15.5.1 Brain Tumor 15.5.2 Lung Nodule Cancer Detection 15.5.3 Breast Cancer Segmentation and Detection 15.5.4 Heart Disease Prediction 15.5.5 COVID-19 Prediction 15.6 Conclusion References 16 Simulation of Self-Driving Cars Using Deep Learning 16.1 Introduction 16.2 Methodology 16.2.1 Behavioral Cloning 16.2.2 End-to-End Learning 16.3 Hardware Platform 16.4 Related Work 16.5 Pre-Processing 16.5.1 Lane Feature Extraction 16.6 Model 16.6.1 CNN Architecture 16.6.2 Multilayer Perceptron Model 16.6.3 Regression vs. Classification 16.7 Experiments 16.8 Results 16.9 Conclusion References 17 Assistive Technologies for Visual, Hearing, and Speech Impairments: Machine Learning and Deep Learning Solutions 17.1 Introduction 17.2 Visual Impairment 17.2.1 Conventional Assistive Technology for the VIP 17.2.2 The Significance of Computer Vision and Deep Learning in AT of VIP 17.3 Verbal and Hearing Impairment 17.3.1 Assistive Listening Devices 17.3.2 Alerting Devices 17.3.3 Augmentative and Alternative Communication Devices 17.3.4 Significance of Machine Learning and Deep Learning in Assistive Communication Technology 17.4 Conclusion and Future Scope References 18 Case Studies: Deep Learning in Remote Sensing 18.1 Introduction 18.2 Need for Deep Learning in Remote Sensing 18.3 Deep Neural Networks for Interpreting Earth Observation Data 18.3.1 Convolutional Neural Network 18.3.2 Autoencoder 18.3.3 Restricted Boltzmann Machine and Deep Belief Network 18.3.4 Generative Adversarial Network 18.3.5 Recurrent Neural Network 18.4 Hybrid Architectures for Multi-Sensor Data Processing 18.5 Conclusion References Index
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