For software developers, choosing which technologies and skills to master next has never been more difficult. Experts offer ...

Background: Depression affects more than 350 million people globally. Traditional diagnostic methods have limitations. Analyzing textual data from social media provides new insights into predicting ...

In a world where asset managers strive to differentiate themselves from the competition and capture the attention of financial advisors, one approach has been systematic investing. Systematic ...

Abstract: Deep learning models are widely used in data-driven applications due to their high predictive performance, but their lack of interpretability limits their applicability in domains requiring ...

Experiment tracking is an essential part of modern machine learning workflows. Whether you’re tweaking hyperparameters, monitoring training metrics, or collaborating with colleagues, it’s crucial to ...

Two new studies from the Department of Computational Biomedicine at Cedars-Sinai are advancing what we know about using machine learning and big data to improve health care and medical research. Both ...

Abstract: Using machine learning (ML)-based prediction models could significantly improve the precision and effectiveness of traditional air quality models. This article provides a comprehensive ...

Objective: This study aims to develop and validate a machine learning model that integrates dietary antioxidants to predict cardiovascular disease (CVD) risk in diabetic patients. By analyzing the ...

Passive sensing via wearable devices and smartphones, combined with machine learning (ML), enables objective, continuous, and noninvasive mental health monitoring. Objective: This study aimed to ...

Interdisciplinary Graduate Program in Quantitative Biosciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States School of Chemistry and Biochemistry, Georgia Institute of ...