Deep Learning and its Applications
Published on 15. October 2021
205 pages
978-1-68507-246-9 (ISBN)
Description
In just the past five years, deep learning has taken the world by surprise, driving rapid progress in fields as diverse as computer vision, natural language processing, automatic speech recognition, etc. This book presents an introduction to deep learning and various applications of deep learning such as recommendation systems, text recognition, diabetic retinopathy prediction of breast cancer, prediction of epilepsy, sentiment, fake news detection, software defect prediction and protein function prediction.
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Arvind Kumar Tiwari
Deep Learning and its Applications
Book
11/2021
Nova Science Publishers Inc
€210.94
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Content
- Intro
- 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
- Blank Page
- Blank Page
