Contents
Preface
List of Reviewers
Chapter 1
Application of Deep Learning in Recommendation System
Abstract
Introduction
Background and Terminologies
Recommendation System
Deep Learning Techniques
Autoencoder
Recurrent Neural Network
Convolution Neural Network
Restricted Boltzmann Machine
Application of Deep Learning in Recommendation System
1. Collaborative Filtering Recommendation Systems Based on Deep Neural Networks
1.1. Collaborative Filtering Method Based on Generative Adversarial Network
1.2. Recurrent Neural Network Based Collaborative Filtering Method
1.3. Collaborative Filtering Method Bssed on Autoencoders
1.4. Collaborative Filtering Method Based on Restricted Boltzmann Machine
2. Content-Based Recommendation Systems Based on Deep Neural Networks
3. Hybrid Recommendation System Based on Deep Neural Networks
4. Social Network-Based Recommendation System Using Deep Neural Networks
5. Context-Aware Recommendation Systems Based on Deep Neural Networks
6. Applications
References
Chapter 2
Deep Learning Based Approaches for Text Recognition
Abstract
Introduction
Preprocessing
Segmentation
Feature Extraction
Classification
Post-Processing
Deep Learning Approaches for Text Recognition
Convolutional Neural Network (CNN)
Recurrent Neural Network (RNN)
Long Short Term Memory (LSTM)
Summarized Table for Literature Review
Conclusion
References
Chapter 3
Applications of Deep Learning in Diabetic Retinopathy Detection
Abstract
Introduction
Deep Learning in the Detection of Diabetic Retinopathy
Diabetic Retinopathy (DR)
Severity Levels of DR
Metrics for Evaluation
Databases Available
Process of Detection of DR Using Deep Learning
DR Screening Systems
Binary Classification
Multi-Level Classification
Lesion Based Classification
Vessel Based Classification
Conclusion
References
Chapter 4
Deep Learning Approaches for the Prediction of Breast Cancer
Abstract
Introduction
Related Work
Feature Extraction Techniques
Deep Learning Techniques
Convolutional Neural Networks (CNNs)
Artificial Neural Networks (ANNs)
Support Vector Machines (SVMs)
Deep CNN
Conclusion
References
Chapter 5
Deep Learning Techniques for the Prediction of Epilepsy
Abstract
Introduction
Artificial Intelligence
Machine Learning
Deep Learning
Deep Learning Models
Convolutional Neural Network
Recurrent Neural Network
Long Short Term Memory
Generative Adversarial Network
Epileptic Seizures
Electroencephalogram (EEG)
Application of Electroencephalogram (EEG)
Epilepsy Symptoms
Related Work
Feature Selection
Methodology
Performance Evaluation
Confusion Matrix
Evaluation Parameters
Accuracy
Precision
Recall
F-Measure
Specificity
Result Analysis
Conclusion
References
Chapter 6
Deep Learning and Its Applications
Abstract
Chapter 7
An Introduction to Sentiment Analysis Using Deep Learning Techniques
Abstract
1. Introduction
2. Embeddings
3. Sentiment Classification at the Sentence Level
3.1. Convolutional Neural Networks for Textual Dataset
3.2. Recurrent Neural Networks for Textual Dataset
3.3. Recursive Neural Networks for Textual Dataset
4. Sentiment Analysis at the Document Level
5. Sentiment Analysis on a Finer Scale
5.1. Opinion Mining
5.2. Sentiment Analysis with a Purpose
5.3. Sentiment Analysis at the Aspect Level
5.4. Stance Detection for the Textual Dataset
5.5. Sarcasm Identification
Conclusion
References
Chapter 8
Deep Learning Techniques in Protein-Protein Interaction
Abstract
1. Introduction
2. Protein
3. Protein-Protein Interaction
4. Types of Protein-Protein Interaction
Homo-Oligomers
Hetero-Oligomers
Stable
Transient
Covalent
Non-Covalent
5. Methodologies Used in Protein-Protein Interaction
5.1. Deep Learning
5.2. Approaches of Deep Learning
Supervised Learning
Unsupervised Learning
Hybrid Learning
Reinforcement Learning
5.3. Deep Learning Technique
Stochastic Gradient Descent
Batch Normalization
Back Propagation
Max-Pooling
Dropout
Transfer Learning
Skip-Gram
Neural Network
Convolutional Neural Networks
Recurrent Neural Network
Long Short Term Memory Networks: (LSTMs)
6. Challenges and Issues
7. Application
Conclusion
References
Chapter 9
Various Machine Learning Techniques for Software Defect Prediction
Abstract
Introduction
Software Defect
Types of Software Defects
Software Defect Prediction
Brief History of Software Defect Prediction Studies
Defect/Bug Life Cycle
Different Categories of Machine Learning
Supervised Learning
Unsupervised Learning
Semi-Supervised Learning
Reinforcement Learning
Software Defect Prediction Approaches
Within-Project Defect Prediction
Cross-Project Defect Prediction
Just-in-Time Defect Prediction
Performance Evaluation of SoDP
False Positive Rate
Accuracy
Precision
Recall/True Positive Rate
F-Measure/Score
Area under the Curve (AUC)
Receiver Operating Characteristic (ROC)
Case 1
Case 2
Case 3
Case 4
Conclusion
References
About the Editor
Index
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