A Hybrid Approach for Detecting AI-Generated Text in English
📂GitHub Repository
📂GitHub Repository
📂GitHub Repository
This project explores interpretable and explainable machine learning techniques applied to both tabular and image-based data.
This project focuses on working with complex healthcare data from the Physionet 2012 Challenge dataset, which contains 48 hours of intensive care data used to predict patient mortality. It involves handling noisy, sparse, and irregularly-sampled multi-variate data through preprocessing, exploration, supervised learning, representation learning, and leveraging large language models (LLMs) for embedding extraction and analysis. Contributions
This interactive visualization tool was developed as part of our Master’s project to help non-expert users explore relationships between attributes in tabular data through interactive binning techniques.