Researchers have developed a hybrid surrogate model for iso-octanol oxidation to iso-octanal that integrates data-driven ...

A new study published in Genome Research presents an interpretable artificial intelligence framework that improves both the accuracy and transparency of genomic prediction, a key challenge in fields ...

With the integration of machine learning techniques into the medical field, there has been a sea change in the last couple of years, and a new era of innovation seems to usher in precision healthcare ...

Background Transcatheter aortic valve replacement (TAVR) has increasingly emerged as one of the primary treatments for ...

Researchers are testing whether natural language processing can detect hidden psychological distress in childhood cancer ...

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 ...

Stroke is one of the leading causes of death and disability worldwide, making early screening and risk prediction crucial. Traditional methods have limitations in handling nonlinear relationships ...

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 ...

Bacteriocins offer a promising solution to antibiotic resistance, possessing the ability to target a wide range of bacteria with precision. Thus, there is an urgent need for a computational model to ...

As an assignment under the module of Applied Machine Learning, our task was to create 3 classification models and compete on a private Kaggle competition. The data provided had 97 masked variables. I ...

Abstract: Distributed Denial of Service (DDoS) attacks pose an ongoing threat to networked systems, often disrupting services and compromising data integrity. This study presents a real-time approach ...