Ranya Batsyas

Arnav Samal, Ranya Batsyas

We present CapsoNet, a deep learning framework developed for the Capsule Vision 2024 Challenge, designed to perform multi-class abnormality classification in video capsule endoscopy (VCE) frames. CapsoNet leverages an ensemble of convolutional neural networks (CNNs) and transformer-based architectures to capture both local and global visual features. The model was trained and evaluated on a dataset of over 50,000 annotated frames spanning ten abnormality classes, sourced from three public and one private dataset. To address the challenge of class imbalance, we employed focal loss, weighted random sampling, and extensive data augmentation strategies. All models were fully fine-tuned to maximize performance within the ensemble. CapsoNet achieved a balanced accuracy of 86.34 percent and a mean AUC-ROC of 0.9908 on the official validation set, securing Team Seq2Cure 5th place in the competition. Our implementation is available at http://github.com/arnavs04/capsule-vision-2024

Ranya Batsyas, Ritesh Yaduwanshi

The expansion of digital payment systems has heightened both the scale and intricacy of online financial transactions, thereby increasing vulnerability to fraudulent activities. Detecting fraud effectively is complicated by the changing nature of attack strategies and the significant disparity between genuine and fraudulent transactions. This research introduces a machine learning-based fraud detection framework utilizing the PaySim synthetic financial transaction dataset. Following the CRISP-DM methodology, the study includes hypothesis-driven exploratory analysis, feature refinement, and a comparative assessment of baseline models such as Logistic Regression and tree-based classifiers like Random Forest, XGBoost, and Decision Tree. To tackle class imbalance, SMOTE is employed, and model performance is enhanced through hyperparameter tuning with GridSearchCV. The proposed framework provides a robust and scalable solution to enhance fraud prevention capabilities in FinTech transaction systems. Keywords: fraud detection, imbalanced data, HPO, SMOTE