The diagnosis of malaria remains one of the major hurdles to eradicating the disease, especially among poorer populations. Here, the authors use machine learning to improve the accuracy of deep learning algorithms that automate the diagnosis of malaria using images of blood smears from patients, which could make diagnosis easier and faster for many.

The authors compare neuroimaging datasets to identify potential new biomarkers for earlier detection of Alzheimer's disease.

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

In this study, the authors use aerosol optical depth data to determine if aerosol levels were lower in major metropolitan areas around the world during the COVID-19 pandemic.

In this study, the authors share their work on improving the frame rate of videos to reduce data sent to users with both 2D and 3D footage. This work helps improve the experience for both types of footage!

Autism spectrum disorder (ASD) is hard to correctly diagnose due to the very subjective nature of diagnosing it: behavior analysis. Due to this issue, we sought to find a machine learning-based method that diagnoses ASD without behavior analysis or helps reduce misdiagnosis.

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.

The authors investigate how well AI-detection machine learning models can detect real versus AI-generated art across different art styles.

Studying other galaxies can help us understand the origins of the universe. Here, the authors study a type of galaxies known as Green Peas gaining insights that could help inform our understanding of Lyman alpha emitters, one of the first types of galaxies that existed in the early universe.

In this study, the authors investigate the relationship between iron/hydrogen ratio [Fe/H] of a type of variable stars commonly used as reference points RR Lyrae stars and their light curves to see if one can determine the composition of these stars solely by measuring their light curve characteristics.