Artificial Neural Networks in Pattern Recognition: 10th IAPR TC3 Workshop, ANNPR 2022, Dubai, United Arab Emirates, November 24–26, 2022, Proceedings

Preface
Organization
Contents
Learning Algorithms and Architectures
Graph Augmentation for Neural Networks Using Matching-Graphs
	1 Introduction and Related Work
	2 Theory and Basic Models
		2.1 Graphs
		2.2 Graph Edit Distance (GED)
		2.3 Graph Neural Networks (GNNs)
	3 Augment Training Sets by Means of Matching-Graphs
	4 Experimental Evaluation
		4.1 Experimental Setup
		4.2 Data Sets
		4.3 Validation of Metaparameters
		4.4 Test Results and Discussion
	5 Conclusion and Future Work
	References
A Novel Representation of Graphical Patterns for Graph Convolution Networks
	1 Introduction
	2 Related Work
	3 The GrapHisto
	4 Preliminary Experimental Evaluation
	5 Conclusion
	References
Minimizing Cross Intersections in Graph Drawing via Linear Splines
	1 Introduction
	2 Related Work
	3 Method
		3.1 Learning Non-differentiable Aesthetic Criteria: The Neural Aesthete
		3.2 Employing Splines to Improve Graph Readability
		3.3 Edge Crossing Optimization with Splines
		3.4 Stress Optimization with Splines
	4 Experiments
	5 Conclusion
	References
Multi-stage Bias Mitigation for Individual Fairness in Algorithmic Decisions
	1 Introduction
	2 Background and Related Work
		2.1 Statistical Definitions of Fairness
		2.2 Definitions of Individual Fairness
	3 Multi-stage Individual Fairness
		3.1 Notations
		3.2 Transformed Representation Learning
		3.3 Similarity Measure
		3.4 Fairness Measure
		3.5 Optimisation
	4 Data and Experiment
		4.1 Datasets
		4.2 Evaluation Measures
		4.3 Experimental Results
	5 Conclusion
	References
Do Minimal Complexity Least Squares Support Vector Machines Work?
	1 Introduction
	2 Minimal Complexity Least Squares Support Vector Machines
		2.1 Architecture
		2.2 Solving Subproblem 1
		2.3 Solving Subproblem 2
		2.4 Training Procedure
	3 Performance Evaluation
	4 Conclusions
	References
A Review of Capsule Networks in Medical Image Analysis
	1 Introduction
	2 Background on Capsule Networks
		2.1 Limitations of CNNs
		2.2 Advantages of Capsule Networks
		2.3 Capsule Network Architecture
	3 Applications of Capsule Networks on Medical Images
		3.1 Brain Injuries and Tumours
		3.2 Ophthalmology
		3.3 Cardiac Diseases
		3.4 Pulmonary Diseases
	4 Discussion
	5 Conclusions and Recommendations for Future Work
	References
Introducing an Atypical Loss: A Perceptual Metric Learning for Image Pairing
	1 Introduction
	2 Related Work
	3 Learning Atypical Perceptual Similarity
		3.1 The Baseline Triplet-Network
		3.2 The Atypical Perceptual Similarity
	4 Experimentation
		4.1 The TTL Benchmark
		4.2 Evaluation
	5 Conclusion
	References
Applications
Wavelet Scattering Transform Depth Benefit, An Application for Speaker Identification
	1 Introduction
	2 Related Work
		2.1 CNN-raw System
		2.2 SincNet
		2.3 HWSTCNN
	3 Experimental Setup
		3.1 Speaker Identification Text-Independent
		3.2 Speaker Identification Text-Dependent
	4 Results and Discussion
		4.1 Speaker Identification Text-Independent
		4.2 Speaker Identification Text-Dependent
	5 Conclusion
	References
Sequence-to-Sequence CNN-BiLSTM Based Glottal Closure Instant Detection from Raw Speech
	1 Introduction
	2 Data Description
		2.1 Speech Material
		2.2 GCI Detection Measures
	3 Models
		3.1 Baseline CNN-Based GCI Detection System
		3.2 Recurrent Neural Network-Based GCI Detection
		3.3 CNN-BiLSTM GCI Detection
	4 Results
		4.1 Comparison of Proposed Models
		4.2 Comparison of Different GCI Detection Models
	5 Conclusions
	References
Mono vs Multilingual BERT for Hate Speech Detection and Text Classification: A Case Study in Marathi
	1 Introduction
	2 Related Work
	3 Datasets
	4 Experiments
		4.1 Transformer Models
		4.2 Evaluation Results
	5 Conclusion
	References
Transformer-Encoder Generated Context-Aware Embeddings for Spell Correction
	1 Introduction
	2 Related Work
		2.1 Deep Learning Based Approaches to Spell Correction
	3 Proposed Method
		3.1 Model Architecture
		3.2 Model Training and Triplet Loss
	4 Experiment and Results
		4.1 Dataset
		4.2 Training, Evaluation and Baselines
		4.3 Results
	5 Conclusion and Future Work
	References
Assessment of Pharmaceutical Patent Novelty with Siamese Neural Networks
	1 Introduction
	2 Related Work
		2.1 Patent Content Analysis
		2.2 Patent Relationships
		2.3 Non-textual Analysis
	3 Proposed Method
		3.1 Data
		3.2 Pipeline for Patent Document Processing
		3.3 Creating Word Embeddings
		3.4 Siamese Deep Neural Network Model
	4 Results
		4.1 Experimental Setup
		4.2 Sentence and Document Embeddings Evaluation
		4.3 Similarity Detection Model Evaluation
		4.4 Ablation Studies
	5 Discussion and Conclusion
	References
White Blood Cell Classification of Porcine Blood Smear Images
	1 Introduction
	2 Methodology
		2.1 Dataset
		2.2 Model Implementation
		2.3 Performance Evaluation
	3 Results and Discussion
	4 Conclusion
	References
Medical Deepfake Detection using 3-Dimensional Neural Learning
	1 Introduction
	2 Dataset
	3 Proposed Methodology
		3.1 Detection Using Machine Learning
		3.2 Detection Using 3DCNN
	4 Results and Discussion
		4.1 Experimental Results
	5 Conclusion
	References
A Study on the Autonomous Detection of Impact Craters
	1 Introduction
	2 Background
	3 Dataset
	4 Experimental Setup
		4.1 Optimization Functions
		4.2 Training Strategy
	5 Results
	6 Conclusion and Future Work
	References
Utilization of Vision Transformer for Classification and Ranking of Video Distortions
	1 Introduction
	2 Materials and Methods
		2.1 Dataset Overview
		2.2 Vision Transformer – The Proposed Method
	3 Results and Discussion
		3.1 Experimental Setup
		3.2 Experimental Results
	4 Conclusion and Future Work
	References
Author Index