Data Science and Machine Learning

​Research Track: Random Padding Data Augmentation.- Unsupervised Fraud Detection on Sparse Rating Networks.- Semi-Supervised Model-Based Clustering for Ordinal Data.- Damage GAN: A Generative Model for Imbalanced Data.- Text-Conditioned Graph Generation Using Discrete Graph Variational Autoencoders.- Boosting QA Performance through SA-Net and AA-Net with the Read+Verify Framework.- Anomaly Detection Algorithms: Comparative Analysis and Explainability Perspectives.- Towards Fairness and Privacy: A Novel Data Pre-processing Optimization Framework for Non-binary Protected Attributes.- MStoCast: Multimodal Deep Network for Stock Market Forecast..- Few Shot and Transfer Learning with Manifold Distributed Datasets.- Mitigating The Adverse Effects of Long-tailed Data on Deep Learning Models.- Shapley Value Based Feature Selection to Improve Generalization of Genetic Programming for High-Dimensional Symbolic Regression.- Hybrid Models for Predicting Cryptocurrency Price Using Financial and Non-Financial Indicators.- Application Track: Multi-Dimensional Data Visualization for Analyzing Materials.- Law in Order: An Open Legal Citation Network for New Zealand.- Enhancing Resource Allocation in IT Projects: The Potentials of Deep Learning-Based Recommendation Systems and Data-Driven Approaches.- A Comparison of One-Class versus Two-Class Machine Learning Models for Wildfire Prediction in California.- Skin Cancer Detection with Multimodal Data: A Feature Selection Approach Using Genetic Programming.- Comparison of Interpolation Techniques for Prolonged Exposure Estimation: A Case Study on Seven years of Daily Nitrogen Oxide in Greater Sydney.- Detecting Asthma Presentations from Emergency Department Notes:An Active Learning Approach.