Machine Learning

The Facial Expression Dataset (Sri Lankan) is a culturally specific dataset created to enhance the accuracy of emotion recognition models in Sri Lankan contexts. Existing datasets, often based on foreign samples, fail to account for cultural differences in facial expressions, affecting model performance. This dataset bridges that gap, using high-quality data sourced from over 100 video clips of professional Sri Lankan actors to ensure expressive and clear facial imagery.

The SINEW (Sensors in Home for Elderly Wellbeing) dataset consists of 15 high-level biomarker features, derived from raw sensor readings collected by in-home sensors used for predictive modeling research: SINEW Weekly Biomarker.

This dataset was collected for a study focused on the early detection of mild cognitive impairment, providing an opportunity for timely intervention before it progresses to Alzheimer's disease.

Two thousand MTJ samples in total were included in the dataset for analysis; First, shuffle the dataset randomly to eliminate bias, then split it into ten equal folds of 200 samples each. For each iteration of cross-validation, use nine folds for training and one for testing, rotating the test fold across all ten groups so that every sample is tested once. 

This study presents a English-Luganda parallel corpus comprising over 2,000 sentence pairs, focused on financial decision-making and products. The dataset draws from diverse sources, including social media platforms (TikTok comments and Twitter posts from authoritative accounts like Bank of Uganda and Capital Markets Uganda), as well as fintech blogs (Chipper Cash and Xeno). The corpus covers a range of financial topics, including bonds, loans, and unit trust funds, providing a comprehensive resource for financial language processing in both English and Luganda.