IEEE

Novel Predictive Factors to Boost Short-Term Electricity Demand Forecasting Using Machine Learning Models

Abstract: Accurate Short-Term Load Forecasting (STLF) is essential for effective operational planning, particularly for optimizing maintenance schedules, managing power generation capacity, and ...
Frontiers

Machine learning–guided feature selection and predictive model construction for attention-deficit/hyperactivity disorder

Background: Attention Deficit/Hyperactivity Disorder (ADHD) is a highly prevalent neurodevelopmental disorder, but its diagnosis remains constrained. This study aimed to identify potential candidate ...
EurekAlert!

Intelligent XGBoost framework enhances asphalt pavement skid resistance assessment

Researchers have developed a novel AI-driven framework using the XGBoost algorithm to accurately evaluate the skid resistance of asphalt pavements under various conditions. Published in Smart ...
IEEE

Enhancing Predictive Maintenance of Power Transformers Using Machine Learning

Abstract: The article enhancing the Predictive Maintenance of Power Transformers Using Machine Learning focuses on the application of machine learning algorithms to improve the accuracy of predicting ...
Frontiers

A comparative study of risk factors in predictive models for cognitive dysfunction in patients with leukoaraiosis based on machine learning algorithms

Objective: To explore the risk factors of cognitive dysfunction in patients with leukoaraiosis (LA) and to construct a predictive model using machine learning. Methods: A total of 273 patients with LA ...