Fundamentals and Methods of Machine and Deep Learning

Fundamentals and Methods of Machine and Deep Learning

by Pradeep Singh
Released March 2022
Publisher(s): Wiley-Scrivener
ISBN: 9781119821250

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Book description

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.

Audience

Researchers and engineers in artificial intelligence, computer scientists as well as software developers.

Table of contents

  1. Cover
  2. Title page
  3. Copyright
  4. Preface
  5. 1 Supervised Machine Learning: Algorithms and Applications
    1. 1.1 History
    2. 1.2 Introduction
    3. 1.3 Supervised Learning
    4. 1.4 Linear Regression (LR)
    5. 1.5 Logistic Regression
    6. 1.6 Support Vector Machine (SVM)
    7. 1.7 Decision Tree
    8. 1.8 Machine Learning Applications in Daily Life
    9. 1.9 Conclusion
    10. References
  6. 2 Zonotic Diseases Detection Using Ensemble Machine Learning Algorithms
    1. 2.1 Introduction
    2. 2.2 Bayes Optimal Classifier
    3. 2.3 Bootstrap Aggregating (Bagging)
    4. 2.4 Bayesian Model Averaging (BMA)
    5. 2.5 Bayesian Classifier Combination (BCC)
    6. 2.6 Bucket of Models
    7. 2.7 Stacking
    8. 2.8 Efficiency Analysis
    9. 2.9 Conclusion
    10. References
  7. 3 Model Evaluation
    1. 3.1 Introduction
    2. 3.2 Model Evaluation
    3. 3.3 Metric Used in Regression Model
    4. 3.4 Confusion Metrics
    5. 3.5 Correlation
    6. 3.6 Natural Language Processing (NLP)
    7. 3.7 Additional Metrics
    8. 3.8 Summary of Metric Derived from Confusion Metric
    9. 3.9 Metric Usage
    10. 3.10 Pro and Cons of Metrics
    11. 3.11 Conclusion
    12. References
  8. 4 Analysis of M-SEIR and LSTM Models for the Prediction of COVID-19 Using RMSLE
    1. 4.1 Introduction
    2. 4.2 Survey of Models
    3. 4.3 Methodology
    4. 4.4 Experimental Results
    5. 4.5 Conclusion
    6. 4.6 Future Work
    7. References
  9. 5 The Significance of Feature Selection Techniques in Machine Learning
    1. 5.1 Introduction
    2. 5.2 Significance of Pre-Processing
    3. 5.3 Machine Learning System
    4. 5.4 Feature Extraction Methods
    5. 5.5 Feature Selection
    6. 5.6 Merits and Demerits of Feature Selection
    7. 5.7 Conclusion
    8. References
  10. 6 Use of Machine Learning and Deep Learning in Healthcare—A Review on Disease Prediction System
    1. 6.1 Introduction to Healthcare System
    2. 6.2 Causes for the Failure of the Healthcare System
    3. 6.3 Artificial Intelligence and Healthcare System for Predicting Diseases
    4. 6.4 Facts Responsible for Delay in Predicting the Defects
    5. 6.5 Pre-Treatment Analysis and Monitoring
    6. 6.6 Post-Treatment Analysis and Monitoring
    7. 6.7 Application of ML and DL
    8. 6.8 Challenges and Future of Healthcare Systems Based on ML and DL
    9. 6.9 Conclusion
    10. References
  11. 7 Detection of Diabetic Retinopathy Using Ensemble Learning Techniques
    1. 7.1 Introduction
    2. 7.2 Related Work
    3. 7.3 Methodology
    4. 7.4 Proposed Models
    5. 7.5 Experimental Results and Analysis
    6. 7.6 Conclusion
    7. References
  12. 8 Machine Learning and Deep Learning for Medical Analysis—A Case Study on Heart Disease Data
    1. 8.1 Introduction
    2. 8.2 Related Works
    3. 8.3 Data Pre-Processing
    4. 8.4 Feature Selection
    5. 8.5 ML Classifiers Techniques
    6. 8.6 Hyperparameter Tuning
    7. 8.7 Dataset Description
    8. 8.8 Experiments and Results
    9. 8.9 Analysis
    10. 8.10 Conclusion
    11. References
  13. 9 A Novel Convolutional Neural Network Model to Predict Software Defects
    1. 9.1 Introduction
    2. 9.2 Related Works
    3. 9.3 Theoretical Background
    4. 9.4 Experimental Setup
    5. 9.5 Conclusion and Future Scope
    6. References
  14. 10 Predictive Analysis of Online Television Videos Using Machine Learning Algorithms
    1. 10.1 Introduction
    2. 10.2 Proposed Framework
    3. 10.3 Feature Selection
    4. 10.4 Classification
    5. 10.5 Online Incremental Learning
    6. 10.6 Results and Discussion
    7. 10.7 Conclusion
    8. References
  15. 11 A Combinational Deep Learning Approach to Visually Evoked EEG-Based Image Classification
    1. 11.1 Introduction
    2. 11.2 Literature Review
    3. 11.3 Methodology
    4. 11.4 Result and Discussion
    5. 11.5 Conclusion
    6. References
  16. 12 Application of Machine Learning Algorithms With Balancing Techniques for Credit Card Fraud Detection: A Comparative Analysis
    1. 12.1 Introduction
    2. 12.2 Methods and Techniques
    3. 12.3 Results and Discussion
    4. 12.4 Conclusions
    5. References
  17. 13 Crack Detection in Civil Structures Using Deep Learning
    1. 13.1 Introduction
    2. 13.2 Related Work
    3. 13.3 Infrared Thermal Imaging Detection Method
    4. 13.4 Crack Detection Using CNN
    5. 13.5 Results and Discussion
    6. 13.6 Conclusion
    7. References
  18. 14 Measuring Urban Sprawl Using Machine Learning
    1. 14.1 Introduction
    2. 14.2 Literature Survey
    3. 14.3 Remotely Sensed Images
    4. 14.4 Feature Selection
    5. 14.5 Classification Using Machine Learning Algorithms
    6. 14.6 Results
    7. 14.7 Discussion and Conclusion
    8. Acknowledgements
    9. References
  19. 15 Application of Deep Learning Algorithms in Medical Image Processing: A Survey
    1. 15.1 Introduction
    2. 15.2 Overview of Deep Learning Algorithms
    3. 15.3 Overview of Medical Images
    4. 15.4 Scheme of Medical Image Processing
    5. 15.5 Anatomy-Wise Medical Image Processing With Deep Learning
    6. 15.6 Conclusion
    7. References
  20. 16 Simulation of Self-Driving Cars Using Deep Learning
    1. 16.1 Introduction
    2. 16.2 Methodology
    3. 16.3 Hardware Platform
    4. 16.4 Related Work
    5. 16.5 Pre-Processing
    6. 16.6 Model
    7. 16.7 Experiments
    8. 16.8 Results
    9. 16.9 Conclusion
    10. References
  21. 17 Assistive Technologies for Visual, Hearing, and Speech Impairments: Machine Learning and Deep Learning Solutions
    1. 17.1 Introduction
    2. 17.2 Visual Impairment
    3. 17.3 Verbal and Hearing Impairment
    4. 17.4 Conclusion and Future Scope
    5. References
  22. 18 Case Studies: Deep Learning in Remote Sensing
    1. 18.1 Introduction
    2. 18.2 Need for Deep Learning in Remote Sensing
    3. 18.3 Deep Neural Networks for Interpreting Earth Observation Data
    4. 18.4 Hybrid Architectures for Multi-Sensor Data Processing
    5. 18.5 Conclusion
    6. References
  23. Index
  24. End User License Agreement

Product information

  • Title: Fundamentals and Methods of Machine and Deep Learning
  • Author(s): Pradeep Singh
  • Release date: March 2022
  • Publisher(s): Wiley-Scrivener
  • ISBN: 9781119821250