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The authors looked at different factors that influence specific gravity in beverages, including sweetener used, caffeine, carbonation, and sodium.

The authors looked at genes that were differentially expressed in patients who had and had not had febrile seizures to determine what differences in gene expression existed.

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 looked the ability to predict future stock prices using various machine learning models.

The authors looked at different factors, such as age, pre-existing conditions, and geographic region, and their ability to predict what an individual's health insurance premium would be.

The authors looked at the impact of calcium on iron absorption in mealworms, hypothesizing that calcium would inhibit iron absorption.

The authors evaluated the effect of concentration and variant of N-Acetylcysteine in hydrolyzing mucus.

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

Using machine learning to predict the risk of algae bloom

This study investigated how well food labels from 280 different brands across multiple food and drink categories in India and the US adhered to recommended nutritional labeling standards as outlined by the Codex Alimentarius.