Deep Learning and Its Applications

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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