An accurate prediction of imbalance prices is crucial for making well-informed decisions within short-term energy markets. This study proposes a two-stage probabilistic framework for the prediction of ...

Abstract: We propose a soft gradient boosting framework for sequential regression that embeds a learnable linear feature transform within the boosting procedure. At each boosting iteration, we train a ...

Background Patients with heart failure (HF) frequently suffer from undetected declines in cardiorespiratory fitness (CRF), which significantly increases their risk of poor outcomes. However, current ...

A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...

Recently, a research team led by Prof. Zhao Bangchuan from the Institute of Solid State Physics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, in collaboration with Prof. Xiao Yao ...

Researchers at Central South University in China have developed a new model to improve ultra-short-term photovoltaic (PV) power prediction, as detailed in their publication in Frontiers in Energy. In ...

ABSTRACT: Accurate prediction of survey response rates is essential for optimizing survey design and ensuring high-quality data collection. Traditional methods often struggle to capture the complexity ...

Early Risk Signals: Credit Card Delinquency Watch - AI-powered predictive analytics for proactive credit risk management. Machine learning models (Random Forest & Gradient Boosting) analyze behavioral ...

Michael Burry, the investor made famous by "The Big Short" who recently roiled the market with a tech short bet, is accusing some of America's largest technology companies of using aggressive ...

Investors focused on the Federal Reserve’s midweek policy decision to cut interest rates by a quarter point — and on the chances of no further rate reduction in December — may be overlooking another ...