Intro
Preface
Contents
About the Editors
A Comprehensive Survey on Machine Reading Comprehension: Models, Benchmarked Datasets, Evaluation Metrics, and Trends
1 Introduction
2 Materials and Methods
2.1 Machine Reading Comprehension Tasks
2.2 Types of Machine Reading Comprehension Questions
2.3 Latest Trends on Machine Reading Comprehension
3 MRC Architecture, Modules, and Baseline Models
3.1 Word Embedding
3.2 Feature Extraction
3.3 Context-Question Interaction
3.4 Answer Prediction
4 Performance Evaluation Metrics
4.1 Accuracy

4.2 Exact Match
4.3 Precision and Recall
4.4 F1 Score (F Score or F Measure)
4.5 Recall-Oriented Understudy for Gisting Evaluation (ROUGE)
4.6 Bilingual Evaluation Understudy (BLEU)
4.7 Metric for Evaluation of Translation with Explicit Ordering (METEOR)
4.8 Human Equivalence Score (HEQ)
5 Summary and Conclusion
References
A Novel Feature Descriptor: Color Texture Description with Diagonal Local Binary Patterns Using New Distance Metric for Image Retrieval
1 Introduction
2 Proposed Method
2.1 Diagonal Local Binary Pattern for Color Image

3 Similarity Measures
3.1 Proposed New Distance Metric
4 Analysis and Result
4.1 Performance and Measures
4.2 Datasets
4.3 Obtained Results
5 Conclusion
References
OntoINT: A Framework for Ontology Integration Based on Entity Linking from Heterogeneous Knowledge Sources
1 Introduction
2 Related Works
3 Proposed System Architecture
4 Implementation and Performance Evaluation
5 Conclusions
References
KnowCommerce: A Semantic Web Compliant Knowledge-driven Paradigm for Product Recommendation in E-commerce
1 Introduction
2 Related Works

3 Proposed System Architecture
4 Implementation
5 Performance Evaluation and Results
6 Conclusion
References
Removal of Occlusion in Face Images Using PIX2PIX Technique for Face Recognition
1 Introduction
2 Related Works
3 Proposed Research Methodology
3.1 GAN and Pix2pix
3.2 Proposed Architecture
4 Experiments
4.1 Case 1: Training with Occluded Images in the Dataset
4.2 Case 2: Training with Images by Creating an Occlusion
4.3 Case 3: Training With Images by Creating an Occlusion with Discriminative Receptive Field of 64 × 64
5 Conclusion