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The authors use machine learning to develop an evidence-based detection tool for identifying human trafficking.

The authors use machine learning and electroencephalogram data to propose a method for improving epilepsy diagnosis.

This study examined the relationship between citizenship status, racial background, and the use of marijuana and cigarettes among males in California using data from the 2017–2018 California Health Interview Survey. Findings indicated that non-citizens and naturalized citizens were less likely to use marijuana compared to US-born citizens, while Asian and Latino males were less likely to consume marijuana than White males. Additionally, various racial groups were more likely to smoke cigarettes compared to White males, suggesting that targeted health interventions based on citizenship status and race could be beneficial.

The authors compare the predicted versus actual geyser eruption times for the Old Faithful and Beehive Geysers at Yellowstone National Park.

The authors develop and test a machine learning algorithm for predicting diabetes diagnoses.

The authors trained a machine learning model to detect kidney stones based on characteristics of urine. This method would allow for detection of kidney stones prior to the onset of noticeable symptoms by the patient.

This study hypothesized that a machine learning model could accurately predict the severity of California wildfires and determine the most influential meteorological factors. It utilized a custom dataset with information from the World Weather Online API and a Kaggle dataset of wildfires in California from 2013-2020. The developed algorithms classified fires into seven categories with promising accuracy (around 55 percent). They found that higher temperatures, lower humidity, lower dew point, higher wind gusts, and higher wind speeds are the most significant contributors to the spread of a wildfire. This tool could vastly improve the efficiency and preparedness of firefighters as they deal with wildfires.

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).

With advancements in machine learning a large data scale, high throughput virtual screening has become a more attractive method for screening drug candidates. This study compared the accuracy of molecular descriptors from two cheminformatics Mordred and PaDEL, software libraries, in characterizing the chemo-structural composition of 53 compounds from the non-nucleoside reverse transcriptase inhibitors (NNRTI) class. The classification model built with the filtered set of descriptors from Mordred was superior to the model using PaDEL descriptors. This approach can accelerate the identification of hit compounds and improve the efficiency of the drug discovery pipeline.

In this work, the authors sought to provide an original experiment to investigate the conflict over whether males or females tend to exhibit greater accuracy or confidence in their memories. By using an online portal to obtain a convenience sample, the authors found that their results suggest that though males tend to be more confident regarding their memories, they may in fact remember fewer details. The authors suggest that these findings merit further research before making systematic changes regarding crime scene recall settings.