Devdiscourse

Stacked AI model improves credit default forecasting

A new study suggests that lenders may get their strongest overall read on credit default risk by combining several machine learning models rather than relying on a single algorithm. The researchers ...
Renal & Urology News

Machine Learning Helps Predict MPA Exposure in Childhood-Onset Lupus Nephritis

This is the largest real-world analysis of mycophenolic acid in pediatric lupus nephritis to date, providing a decision-support system to help balance efficacy and safety.
GitHub

LLM4AD: Large Language Model for Algorithm Design

LLM4AD is an open-source Python-based Platform leveraging Large Language Models (LLMs) for Automatic Algorithm Design (AD). Please refer to the paper [LLM4AD] for detailed information, including the ...
IEEE

Influence-Balanced XGBoost: Improving XGBoost for Imbalanced Data Using Influence Functions

Abstract: Decision tree boosting algorithms, such as XGBoost, have demonstrated superior predictive performance on tabular data for supervised learning compared to neural networks. However, recent ...
IEEE

Hardware Trojan Detection with Explainable Graph Learning Using XGBoost Algorithm

Abstract: In today’s world of interdependencies, hardware security is viewed as a crucial component in system designs and production. As Integrated circuits (ICs) become more complex with the ...
Frontiers

Risk assessment of drug-associated miscarriage using XGBoost and SHAP explainability: a real-world pharmacovigilance analysis based on the FAERS database

Miscarriage is a common and serious adverse pregnancy outcome. Assessing drug-associated miscarriage risk is essential for medication safety in pregnancy. Using the FDA Adverse Event Reporting System ...