Abstract: Estimating effort in software development is a crucial aspect that affects the overall success of the project. Traditional methods such as Use Case Point (UCP) are often less accurate, and ...

The promise of artificial intelligence in credit scoring is undeniable. By analyzing vast, non-traditional datasets from utility payments to transactional behavior machine learning models promise to ...

The work, led by Professor Gurminder Singh of the Department of Mechanical Engineering, and published across two studies covering ceramics and copper, focused on oven-sintered additive manufacturing.

Abstract: Recently, SHapley Additive exPlanations (SHAP) has been widely utilized in various research domains. This is particularly evident in application fields, where SHAP analysis serves as a ...

While atmospheric turbulence is a familiar culprit of rough flights, the chaotic movement of turbulent flows remains an unsolved problem in physics. To gain insight into the system, a team of ...

This project implements an advanced machine learning pipeline for high-dimensional financial time series forecasting with comprehensive interpretability analysis. The system predicts next-day S&P 500 ...

Artificial intelligence is deeply embedded in the daily workings of financial institutions, whether analyzing credit risk, automating underwriting, flagging fraud, or generating investment insights.

This study proposes a dual-architecture Explainable Artificial Intelligence (XAI) framework designed to unify risk scoring methodologies across corporate and retail lending domains. The framework ...

Scientists in China have developed a novel missingness-aware power forecasting method that leverages signal decomposition, multi-scale covariate interaction, and multi-domain collaborative transfer ...