Browse Articles

The authors looked at non-natural factors that influenced the spread rate of fire ants in multiple cities in China.

The authors looked at the ability to detect heart disease before the onset of severe clinical symptoms.

The authors looked at the ability of different machine learning algorithms to predict the level of financial corruption in different countries.

This study explored how green spaces, climate, traffic, and air quality (GCTA) collectively influence asthma-related emergency department visits in the U.S using machine learning models and explainable AI.

The authors used machine learning to predict the presence of anxiety and burnout in students based on facial expressions and demographic information.

This study compares the performance of two deep learning models, U-Net and DeconvNet, for segmenting gliomas from MRI scans.

The authors looked at different ways to build computational resources that would analyze shooting form for basketball players.

The authors developed and tested machine learning methods to diagnose tuberculosis from pulmonary X-ray scans.

This study examined the impact of erosion on the performance of a triangular aerofoil at a low Reynolds number (Re = 10,000), relevant for harsh conditions like those on Mars.

Depression affects millions globally, yet identifying symptoms remains challenging. This study explored detecting depression-related patterns in social media texts using natural language processing and machine learning algorithms, including decision trees and random forests. Our findings suggest that analyzing online text activity can serve as a viable method for screening mental disorders, potentially improving diagnosis accuracy by incorporating both physical and psychological indicators.