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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
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.
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Veröffentlichungsjahr: 2022
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Table of Contents
Cover
Title page
Copyright
Preface
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
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
3.3 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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Index
End User License Agreement
Guide
Cover
Table of Contents
Title page
Copyright
Preface
Begin Reading
Index
End User License Agreement
List of Tables
Chapter 3
Table 3.1 Calculation and derived value from the predicted and actual values.
Table 3.2 Predicted probability value from model and actual value.
Table 3.3 Predicting class value using the threshold.
Table 3.4 Document information and cosine similarity.
Table 3.5 Metric derived from confusion metric.
Table 3.6 Metric usage.
Table 3.7 Metric pros and cons.
Chapter 4
Table 4.1 Model summary.
Table 4.2 Predicted data.
Chapter 7
Table 7.1 Literature survey of Diabetic Retinopathy.
Table 7.2 Retinopathy grades in the Kaggle dataset.
Table 7.3 Accuracy for binary classification using machine learning techniques.
Table 7.4 Accuracy for multiclass classification using machine learning techniqu...
Chapter 8
Table 8.1 Description of each feature in the dataset.
Table 8.2 Sample dataset.
Table 8.3 Experiments description.
Table 8.4 Accuracy scores (in %) of all classifiers on different data size.
Table 8.5 Accuracy scores (in %) of all classifiers on different data size.
Table 8.6 Accuracy scores (in %) of all classifiers on different data size.
Table 8.7 Logit model statistical test.
Table 8.8 Chi-square test.
Chapter 9
Table 9.1 Characteristics of the NASA data sets.
Table 9.2 Attribute information of the 21 features of PROMISE repository [13].
Table 9.3 Performance comparison for the data set KC1.
Table 9.4 Performance comparison for the data set KC3.
Table 9.5 Performance comparison for the data set PC1.
Table 9.6 Performance comparison for the data set PC2.
Table 9.7 Confusion matrix analysis for the KC1, KC3, PC1, and PC2 data sets (TP...
Chapter 10
Table 10.1 Classifiers vs. classification accuracy.
Table 10.2 Performance metrics of the recommended classifier.
Table 10.3 Confusion matrix.
Chapter 11
Table 11.1 Dataset description.
Table 11.2 Architecture of proposed convolutional neural network.
Table 11.3 Classification accuracy (%) with two proposed models on two different...
Chapter 12
Table 12.1 Description of ULB credit card transaction dataset.
Table 12.2 Confusion matrix [7].
Table 12.3 Result summary for all the implemented models.
Table 12.4 Confusion matrix results for all the implemented models.
Chapter 13
Table 13.1 Activation functions.
Table 13.2 Optimizers.
Table 13.3 Performance: optimizer vs. activation functions.
Chapter 14
Table 14.1 General confusion matrix for two class problems.
Table 14.2 Confusion matrix for a ML classifier.
Table 14.3 Confusion matrix for a k-NN classifier.
Table 14.4 Average precision, recall, F1-score, and accuracy.
Chapter 15
Table 15.1 Summary of datasets used in the survey.
Table 15.2 Summary of papers in brain tumor classification using DL.
Table 15.3 Paper summary—cancer detection in lung nodule by DL.
Table 15.4 Paper summary—classification of breast cancer by DL.
Table 15.5 Paper summary on heart disease prediction using DL.
Table 15.6 COVID-19 prediction paper summary.
Chapter 16
Table 16.1 CNN architecture.
Table 16.2 Model definition.
Table 16.3 Model results.
Chapter 17
Table 17.1 Comparison of sensors for obstacle detection in ETA inspired from [16...
Table 17.2 A comparison between few wearables.
Table 17.3 Sensor based methods from literature.
Table 17.4 Vision based approaches.
Chapter 18
Table 18.1 Hybrid deep architectures for remote sensing.
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Fundamentals and Methods of Machine and Deep Learning
Algorithms, Tools and Applications
Edited by
Pradeep Singh
Department of Computer Science Engineering, National Institute of Technology, Raipur, India
This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2022 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
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Limit of Liability/Disclaimer of WarrantyWhile the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials, or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read.
Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-82125-0
Cover image: Pixabay.ComCover design by Russell Richardson
Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines
Printed in the USA
10 9 8 7 6 5 4 3 2 1
Preface
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 analyzed 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.
Chapter 1 assists in learning supervised machine learning algorithms and their applications.
Chapter 2 discusses the detection of zonotic diseases using ensemble machine learning algorithms.
Chapter 3 provides machine learning model evaluation techniques.
Chapter 4 analyzes MSEIR and LSTM models for the prediction of COVID-19 using RMSLE.
Chapter 5discusses the significance of feature selection techniques in machine learning.
Chapter 6 provides insight into the development of disease prediction systems using machine learning and deep learning.
Chapter 7 discusses the detection of diabetic retinopathy using ensemble learning techniques.
Chapter 8 presents a case study for medical analysis of heart disease using machine learning and deep learning.
Chapter 9 discusses a novel convolutional neural network model to predict software defects.
Chapter 10 familiarizes the reader with the process of predictive analysis on online television videos using machine learning algorithms.
Chapter 11 discusses a combinational deep learning approach to visually evoked EEG-based image classification.
Chapter 12 gives a comparative analysis of machine learning algorithms with balancing techniques for credit card fraud detection.
Chapter 13 describes crack detection in civil structures using deep learning.
Chapter 14 discusses measuring urban sprawl using machine learning.
Chapter 15 is all about the applications of deep learning algorithms in medical image processing.
Chapter 16 assists in understanding the simulation of self-driving cars based on deep learning.
Chapter 17 discusses assistive technologies for visual hearing and speech impairments using machine learning and deep learning solutions.
Chapter 18 provides insight into the role of deep learning in remote sensing.
Finally, I would like to express my heartfelt thanks to all authors, reviewers, and the team at Scrivener Publishing for their kind co-operation extended during the various stages of processing this book.
Pradeep SinghNovember 2021
2Zonotic Diseases Detection Using Ensemble Machine Learning Algorithms
Bhargavi K.
Department of Computer Science and Engineering, Siddaganga Institute of Technology, Tumakuru, India
Abstract
Zonotic diseases are a kind of infectious disease which spreads from animals to humans; the disease usually spreads from infectious agents like virus, prion and bacteria. The identification and controlling the spread of zonotic disease is challenging due to several issues which includes no proper symptoms, signs of zoonoses are very similar, improper vaccination of animals, and poor knowledge among people about animal health. Ensemble machine learning uses multiple machine learning algorithms, to arrive at better performance, compared to individual/stand-alone machine learning algorithms. Some of the potential ensemble learning algorithms like Bayes optimal classifier, bootstrap aggregating (bagging), boosting, Bayesian model averaging, Bayesian model combination, bucket of models, and stacking are helpful in identifying zonotic diseases. Hence, in this chapter, the application of potential ensemble machine learning algorithms in identifying zonotic diseases is discussed with their architecture, advantages, and applications. The efficiency achieved by the considered ensemble machine learning techniques is compared toward the performance metrics, i.e., throughput, execution time, response time, error rate, and learning rate. From the analysis, it is observed that the efficiency achieved by Bayesian model combination, stacking, and Bayesian model combination are high in identifying of the zonotic diseases.
Keywords: Zonotic disease, ensemble machine learning, Bayes optimal classifier, bagging, boosting, Bayesian model averaging, Bayesian model combination, stacking
