A comparative study of ML algorithms for anti-money laundering (AML) detection using the IBM AML dataset. Implemented Decision Trees, Random Forests, XGBoost, LGBM, SGD, Logistic Regression, and SV ...

Introduction We collect composition-aware model preferences from multiple models and employ an iterative feedback learning approach to enable the progressive self-refinement of both the base diffusion ...

Land use regression (LUR) models assess air pollution exposure but often struggle with transferability (predicting concentrations in areas without measurements) and generalizability (capturing spatial ...

Wisconsin brothers build chemical-free water park using nature Land of Natura relies on plants, fish, and bacteria to clean water without a drop of chlorine ...

This work investigates the effectiveness of Explainable AI (XAI) approaches, specifically Kernel SHAP, to increase the transparency and trustworthiness of machine learning models for heart disease ...

As a sensitivity analysis, we performed 1:1 nearest‐neighbor propensity score matching without replacement, using logistic regression‐derived scores based on age, body mass index, and eGFR.