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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 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.

Domestic waste classification using convolutional neural network

The authors describe the design and testing of new guide RNAs targeting the DMPK gene, which is responsible for myotonic dystrophy.

Pediatric cancers pose unique challenges due to their rarity and distinct biological factors, emphasizing the need for accurate survival prediction to guide treatment. This study integrated generative AI and machine learning, including synthetic data, to analyze 9,184 pediatric cancer patients, identifying age at diagnosis, cancer types, and anatomical sites as significant survival predictors. The findings highlight the potential of AI-driven approaches to improve survival prediction and inform personalized treatment strategies, with broader implications for innovative healthcare applications.