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The authors developed a quantum inspired model for stock market fluctuations.

The author looked at how dance styles can vary by individuals, even between a student and their teacher.

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

The authors looked at how soil temperature changes with fire to develop a sensor system that could aid in earlier detection of fires.

The authors looked at non-natural factors that influenced the spread rate of fire ants in multiple cities in China.

This study explored how green spaces, climate, traffic, and air quality (GCTA) collectively influence asthma-related emergency department visits in the U.S using machine learning models and explainable AI.

The authors looked at the ability to use audio clips to analyze the progression of Parkinson's disease.

In this study the authors looked at the ability of the Discrete Fourier Transform (DFT) to analyze different musical elements. They found that DFT is a powerful method to analyze recorded music.

Building on previous work by earlier astronomers, the authors investigate the correlation between the density and size of celestial objects in the universe, including neutron stars, galaxies, and galaxy clusters.

AI analysis of brain scans offers promise for helping doctors diagnose brain tumors. Haider and Drosis explore this field by developing machine learning models that classify brain scans as "cancer" or "non-cancer" diagnoses.