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Autonomous drone imaging combined with machine learning offers a promising approach for early detection of invasive species. In this study, students built an autonomous drone and compared three models: CNN, SGDC, and XGBoost, to identify Brassica nigra from aerial footage. Their results show that CNNs most effectively recognize key visual features, demonstrating strong potential for supporting conservation and invasive plant management.

The authors used developed a machine learning tool for studying social media discourse surrounding use of tobacco and cannabis.

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

Determination of Penalty Kick Success in Soccer

The authors looked at the immunogenicity of a newly developed pneumococcal conjugate vaccine compared to a previously developed one. They found the newly developed vaccine did elicit an immune response.

Using machine learning to predict the risk of algae bloom

Here the authors aimed to compare the effects of artificial Allura Red AC dye and natural cochineal dye on the growth of Escherichia coli and Bacillus coagulans bacteria. Their research found that only Allura Red AC dye significantly affected bacterial growth, specifically amplifying E. coli growth. Based on their results, they suggest that Allura Red AC dye may increase the growth of E. coli bacteria within the human gut.

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.

The authors use machine learning on MRI images of brain tissue to predict tumor onset as an avenue for early detection of brain cancer.

This study investigated the impact of pH on the efficiency of calcium phosphate, used as a drug delivery agent.