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 Supervised Machine Learning: Algorithms and Applications

2 Zonotic Diseases Detection Using Ensemble Machine Learning Algorithms

3 Model Evaluation

4 Analysis of M-SEIR and LSTM Models for the Prediction of COVID-19 Using RMSLE

5 The Significance of Feature Selection Techniques in Machine Learning

6 Use of Machine Learning and Deep Learning in Healthcare- A Review on Disease Prediction System

7 Detection of Diabetic Retinopathy Using Ensemble Learning Techniques

8 Machine Learning and Deep Learning for Medical Analysis- A Case Study on Heart Disease Data

9 A Novel Convolutional Neural Network Model to Predict Software Defects

10 Predictive Analysis of Online Television Videos Using Machine Learning Algorithms

11 A Combinational Deep Learning Approach to Visually Evoked EEG-Based Image Classification

12 Application of Machine Learning Algorithms With Balancing Techniques for Credit Card Fraud Detection: A Comparative Analysis

13 Crack Detection in Civil Structures Using Deep Learning

14 Measuring Urban Sprawl Using Machine Learning

15 Application of Deep Learning Algorithms in Medical Image Processing: A Survey

16 Simulation of Self-Driving Cars Using Deep Learning

17 Assistive Technologies for Visual, Hearing, and Speech Impairments: Machine Learning and Deep Learning Solutions

18 Case Studies: Deep Learning in Remote Sensing

About the Author

Pradeep Singh 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.