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To best identify tuberculosis and pneumonia diagnoses in chest x-rays, the authors compare different deep learning convolution neural networks.

The authors test various machine learning models to improve the accuracy and efficiency of pneumonia diagnosis from X-ray images.

Levy & Levy tackle the optimization of the coronary artery bypass graft, a life-saving surgical technique that treats artery blockage due to coronary heart disease. The authors develop a novel auto-suturing method that saves time, allows for an increased number of sutures, and improves graft quality over hand suturing. The authors also show that increasing the number of sutures from four to five with their new method significantly improves graft quality. These promising findings may help improve outcomes for patients undergoing surgery to treat coronary heart disease.

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.

In this study the authors develop an app for faster chess game entry method to help chess learners improve their game. This culminated in the Augmented Reality Chess Analyzer (ARChessAnalyzer) which uses traditional image and vision techniques for chess board recognition and Convolutional Neural Networks (CNN) for chess piece recognition.

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.