Machine Intelligence Research Institute

Leakage-aware hybrid tabular deep learning with target-provenance auditing for calorie-expenditure prediction and constrained exercise food recommendation

Abstract Personalised exercise planning depends on calorie expenditure estimates derived from valid predictors. Published tabular recommenders rarely scrutinise the provenance of their outcome variable. We propose a leakage-aware, audit-first framework that combines hybrid tabula...

Seed yield optimization in soybean: integrating biochemical and environmental factors using TabPFN for precision agriculture

Precision agriculture seeks yields and optimizes the value of resources consumed by data-driven decisions. Soybean is an important crop worldwide, significant for food and industrial use. Traditional methods fail to accurately predicting seed yield because plant growth is complex...

Hybrid mammogram mass segmentation and classification using efficient handcrafted features and ensemble learning

Early detection and accurate classification of mammographic abnormalities are essential to reducing the mortality rate. The proposed work on mammogram mass segmentation addresses this issue using a hybrid approach that combines mean median iterative filtering, fuzzy logic, Multil...