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The authors looked at how students performed on standardized tests when they were taught material via memorization vs. conceptual based approaches.

The authors combine fine needle aspiration biopsy and machine learning algorithms to develop a breast cancer detection method suitable for resource-constrained regions that lack access to mammograms.

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

The authors looked at beach nourishment (a way to combat erosion on coasts) and resulting grain size distribution. Their work is important to understand the dynamics of erosion and it's relation to wave action and the implications this has for efforts to mitigate coastal erosion.

Using machine learning to predict the risk of algae bloom

The authors looked at different ways to build computational resources that would analyze shooting form for basketball players.

The authors developed and tested machine learning methods to diagnose tuberculosis from pulmonary X-ray scans.

Droughts kill over 45,000 people yearly and affect the livelihoods of 55 million others worldwide, with climate change likely to worsen these effects. However, unlike other natural disasters (hurricanes, etc.), there is no early detection system that can predict droughts far enough in advance to be useful. Bora, Caulkins, and Joycutty tackle this issue by creating a drought prediction model.

The authors looked at the primary structure of lignin peroxidase in an attempt to identify mutations that would improve both the stability and solubility of the peroxidase protein. The goal is to engineer peroxidase enzymes that are stable to help break down polymers, such as PVC, into monomers that can be reused instead of going to landfills.

The authors use machine learning and electroencephalogram data to propose a method for improving epilepsy diagnosis.