Читать книгу Fundamentals and Methods of Machine and Deep Learning - Pradeep Singh - Страница 2
Table of Contents
Оглавление1 Cover
4 Preface
5 1 Supervised Machine Learning: Algorithms and Applications 1.1 History 1.2 Introduction 1.3 Supervised Learning 1.4 Linear Regression (LR) 1.5 Logistic Regression 1.6 Support Vector Machine (SVM) 1.7 Decision Tree 1.8 Machine Learning Applications in Daily Life 1.9 Conclusion References
6 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
7 3 Model Evaluation 3.1 Introduction 3.2 Model Evaluation 3.3 Metric Used in Regression Model 3.4 Confusion Metrics 3.5 Correlation 3.6 Natural Language Processing (NLP) 3.7 Additional Metrics 3.8 Summary of Metric Derived from Confusion Metric 3.9 Metric Usage 3.10 Pro and Cons of Metrics 3.11 Conclusion References
8 4 Analysis of M-SEIR and LSTM Models for the Prediction of COVID-19 Using RMSLE 4.1 Introduction 4.2 Survey of Models 4.3 Methodology 4.4 Experimental Results 4.5 Conclusion 4.6 Future Work References
9 5 The Significance of Feature Selection Techniques in Machine Learning 5.1 Introduction 5.2 Significance of Pre-Processing 5.3 Machine Learning System 5.4 Feature Extraction Methods 5.5 Feature Selection 5.6 Merits and Demerits of Feature Selection 5.7 Conclusion References
10 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 6.4 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 6.8 Challenges and Future of Healthcare Systems Based on ML and DL 6.9 Conclusion References
11 7 Detection of Diabetic Retinopathy Using Ensemble Learning Techniques 7.1 Introduction 7.2 Related Work 7.3 Methodology 7.4 Proposed Models 7.5 Experimental Results and Analysis 7.6 Conclusion References
12 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 8.4 Feature Selection 8.5 ML Classifiers Techniques 8.6 Hyperparameter Tuning 8.7 Dataset Description 8.8 Experiments and Results 8.9 Analysis 8.10 Conclusion References
13 9 A Novel Convolutional Neural Network Model to Predict Software Defects 9.1 Introduction 9.2 Related Works 9.3 Theoretical Background 9.4 Experimental Setup 9.5 Conclusion and Future Scope References
14 10 Predictive Analysis of Online Television Videos Using Machine Learning Algorithms 10.1 Introduction 10.2 Proposed Framework 10.3 Feature Selection 10.4 Classification 10.5 Online Incremental Learning 10.6 Results and Discussion 10.7 Conclusion References
15 11 A Combinational Deep Learning Approach to Visually Evoked EEG-Based Image Classification 11.1 Introduction 11.2 Literature Review 11.3 Methodology 11.4 Result and Discussion 11.5 Conclusion References
16 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 12.3 Results and Discussion 12.4 Conclusions References
17 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 13.5 Results and Discussion 13.6 Conclusion References
18 14 Measuring Urban Sprawl Using Machine Learning 14.1 Introduction 14.2 Literature Survey 14.3 Remotely Sensed Images 14.4 Feature Selection 14.5 Classification Using Machine Learning Algorithms 14.6 Results 14.7 Discussion and Conclusion Acknowledgements References
19 15 Application of Deep Learning Algorithms in Medical Image Processing: A Survey 15.1 Introduction 15.2 Overview of Deep Learning Algorithms 15.3 Overview of Medical Images 15.4 Scheme of Medical Image Processing 15.5 Anatomy-Wise Medical Image Processing With Deep Learning 15.6 Conclusion References
20 16 Simulation of Self-Driving Cars Using Deep Learning 16.1 Introduction 16.2 Methodology 16.3 Hardware Platform 16.4 Related Work 16.5 Pre-Processing 16.6 Model 16.7 Experiments 16.8 Results 16.9 Conclusion References
21 17 Assistive Technologies for Visual, Hearing, and Speech Impairments: Machine Learning and Deep Learning Solutions 17.1 Introduction 17.2 Visual Impairment 17.3 Verbal and Hearing Impairment 17.4 Conclusion and Future Scope References
22 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 18.4 Hybrid Architectures for Multi-Sensor Data Processing 18.5 Conclusion References
23 Index
