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The authors looked at the difference in investing in stock vs. mutual funds.

The authors studied the ability of deep learning models to predict droughts in the midwestern United States.

The authors looked at ways that seizures in patients with glioblastoma could be treated using C. elegans as a model system.

The authors looked the ability of sound sensors to predict clogged pipes when the sound intensity data is run through a machine learning algorithm.

Determination of Penalty Kick Success in Soccer

The authors design and test an easy-to-use and cost-effective mobile app-based alert system to help senior citizens rapidly communicate with caregivers in emergencies or when in need of assistance.

The authors develop a machine learning method to reduce misclassification of objects in safety-critical applications such as medical diagnosis.

Using machine learning to predict the risk of algae bloom

The authors looked at different treatments to clean up rainwater collected at home. They found that chlorine treatment and treatment with hydrogen peroxide catalyzed by yeast showed similar potential for cleaning up contaminated rainwater, but that further studies are needed to better assess impact on specific contaminant levels still present.

This study uses interpretable machine learning models, lasso and ridge regression with Shapley analysis, to identify key sales drivers for Corporación Favorita, Ecuador’s largest grocery chain. The results show that macroeconomic factors, especially labor force size, have the greatest impact on sales, though geographic and seasonal variables like city altitude and holiday proximity also play important roles. These insights can help businesses focus on the most influential market conditions to enhance competitiveness and profitability.