The authors develop a new method for training machine learning algorithms to differentiate between hate speech and cultural speech in online platforms.

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

In this article the authors look at the lung microbiome in patients with cystic fibrosis to determine what the major bacterial species present are.

To address whether odor sensory circuits are organized topographically, the authors investigate whether the neuronal responses to similar odors amongst different mice mapped similarly in brain.

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

In this article, the authors looked at developing a strategy that would allow for earlier diagnosis of Diabetes as that improves long-term outcomes. They were able to find that BMI, tricep skin fold thickness, and blood pressure are the risk factors with the highest accuracy in predicting diabetes risk.

In this study, the authors developed and assessed the accuracy of a machine learning algorithm to identify skin cancers using images of biopsies.

Here, in an effort to develop a model to predict future groundwater levels, the authors tested a tree-based automated artificial intelligence (AI) model against other methods. Through their analysis they found that groundwater levels in Texas aquifers are down significantly, and found that tree-based AI models most accurately predicted future levels.

In this study, the authors conducted a statistical investigation into the history of position-based racial segregation in the NFL. Specifically, they focused on the cornerback position, which they hypothesized would be occupied disproportionately by black players due to their historical stereotyping as more suitable for positions requiring extreme athletic ability. Using publicly available datasets on the demographics of NFL players over the past several decades, they confirmed their hypothesis that the cornerback position is skewed towards black players. They additionally discovered that, unlike in the quarterback position, this trend has shown no sign of decreasing over time.

In this work, the authors investigate the accuracy with which two different population growth models can predict population growth over time. They apply the Malthusian law or Logistic law to US population from 1951 until 2019. To assess how closely the growth model fits actual population data, a least-squared curve fit was applied and revealed that the Logistic law of population growth resulted in smaller sum of squared residuals. These findings are important for ensuring optimal population growth models are implemented to data as population forecasting affects a country's economic and social structure.