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

The authors looked at potential downstream effects of raising the minimum wage. Specifically they focused on taxable wages, employment, and firm counts.

The authors develop a method for detecting fake AI-generated images from real images.

The authors looked at variables associated with identity fraud in the US. They found that national unemployment rate and online banking usage are among significant variables that explain identity fraud.

In this article the authors looked at different attributes of apps within the Google Play store to determine how those may impact the overall app rating out of five stars. They found that review count, amount of storage needed and when the app was last updated to be the most influential factors on an app's rating.

The authors compare the predicted versus actual geyser eruption times for the Old Faithful and Beehive Geysers at Yellowstone National Park.

The authors explore how diversity in data sets contributes to bias in artificial intelligence.

Chess engines, or computer programs built to play chess, outperform even the best human players. Kaushikan and Park investigate the inner workings of these chess engines by studying popular chess engines' evaluations of which side of a chess match is most likely to win, and how this is affected by the number of pieces and controlled squares on each side.

The authors develop and test a machine learning algorithm for predicting diabetes diagnoses.

The authors looked at using genetic algorithms to look at the Indian labor market and what features might best explain any variation seen. They found that features such as economic growth and household consumption, among others, best explained variation.