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The authors looked at variables and their patterns and how those contribute to the properties of X-ray binaries.

The purpose of the study was to determine whether graph-based machine learning techniques, which have increased prevalence in the last few years, can accurately classify data into one of many clusters, while requiring less labeled training data and parameter tuning as opposed to traditional machine learning algorithms. The results determined that the accuracy of graph-based and traditional classification algorithms depends directly upon the number of features of each dataset, the number of classes in each dataset, and the amount of labeled training data used.

With the advance of technology, artificial intelligence (AI) is now applied widely in society. In the study of AI, machine learning (ML) is a subfield in which a machine learns to be better at performing certain tasks through experience. This work focuses on the convolutional neural network (CNN), a framework of ML, applied to an image classification task. Specifically, we analyzed the performance of the CNN as the type of neural activation function changes.

This study used hand-collected Greenhouse gas (GHG) emissions data from the Environmental Protection Agency (EPA) and aimed to understand the determinants and incentives of GHG emissions reduction. It explored how companies’ financials, Chief Executive Officer (CEO) compensation, and corporate governance affected GHG emissions. Results showed that companies reporting GHG emissions were wide-spread among the 48 industries represented by two-digit Standard Industrial Classification (SIC) codes.

Machine learning and deep learning techniques can be used to predict the early onset of breast cancer. The main objective of this analysis was to determine whether machine learning algorithms can be used to predict the onset of breast cancer with more than 90% accuracy. Based on research with supervised machine learning algorithms, Gaussian Naïve Bayes, K Nearest Algorithm, Random Forest, and Logistic Regression were considered because they offer a wide variety of classification methods and also provide high accuracy and performance. We hypothesized that all these algorithms would provide accurate results, and Random Forest and Logistic Regression would provide better accuracy and performance than Naïve Bayes and K Nearest Neighbor.

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.

In this study the authors looked at the ability of artificial intelligence to detect tempo, rhythm, and intonation of a piece played on violin. Technology such as this would allow for students to practice and get feedback without the need of a teacher.

In this study, the authors identify transcripts and gene networks that are changed after infection with the Middle East Respiratory Syndrome-related coronavirus (MERS-CoV).

The authors explore factors affecting graduation rates of students learning English as a second language across Florida counties.

In this article, the authors systematically study whether the type of a star is correlated with the number of planets it can support. Their study shows that medium-sized stars are likely to support more than one planet, just like the case in our solar system. They predict that, of the hundreds of planets beyond our solar system, 6% might be habitable. As humans work to travel further and further into space, some of those might truly be suited for human life.