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Here, the authors sought to develop a new metric to evaluate the efficacy of baseball pitchers using machine learning models. They found that the frequency of balls, was the most predictive feature for their walks/hits allowed per inning (WHIP) metric. While their machine learning models did not identify a defining trait, such as high velocity, spin rate, or types of pitches, they found that consistently pitching within the strike zone resulted in significantly lower WHIPs.

The authors looked at different models of semantic segmentation to determine which may be best used in the future for segmentation of CT scans to help diagnose certain conditions.

Although commonly associated with cryptocurrency, blockchains offer security that other databases could benefit from. These student authors tested a blockchain database framework, and by tracking runtime of four independent variables, they prove this framework is feasible for application.

In this study, the authors investigate just how effectively paper towels can absorb different types of liquid and whether changing the properties of the towel (such as folding it) affects absorbance. Using variables of either different liquid types or the folded state of the paper towels, they used thorough approaches to make some important and very useful conclusions about optimal ways to use paper towels. This has important implications as we as a society continue to use more and more paper towels.

In this study, the authors hypothesized that closed-circuit television images could be stored with improved resolution by using enhanced deep residual (EDSR) networks.

White pieces make the first move in chess games, and there are several opening strategies and consequent defense strategies that white and black pieces, respectively, can take . The author of this paper investigated whether taking a specific opening and defense strategy, as well as playing as white vs. black, can increase the chances of winning the game, by playing against various human and computer opponents.

The authors test different machine learning algorithms to remove background noise from audio to help people with hearing loss differentiate between important sounds and distracting noise.

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.

Here, the authors investigated methods to reduce noise in audio composed of real-word sounds. They specifically used two spectral subtraction noise reduction algorithms: stationary and non-stationary finding notable differences in noise improvements depending on the noise sources.

The authors use pictures of candy wrappers and neural networks to improve nutritional accuracy of diet-tracking apps.