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In this study, the authors seek to improve a machine learning algorithm used for image classification: identifying male and female images. In addition to fine-tuning the classification model, they investigate how accuracy is affected by their changes (an important task when developing and updating algorithms). To determine accuracy, a set of images is used to train the model and then a separate set of images is used for validation. They found that the validation accuracy was close to the training accuracy. This study contributes to the expanding areas of machine learning and its applications to image identification.

The cosmic microwave background (CMB) is faint electromagnetic radiation left over from early stages in the formation of the universe. In order to analyze the CMB, scientists need to remove from electromagnetic data foreground radiation that contaminates CMB datasets. In this study, students utilize extensive updated datasets to analyze the correlation between CMB maps and Faraday RM and WMAP sky maps.

The authors test the effectiveness of machine learning to predict onset of cardiovascular disease.

The authors looked at biomarkers in glioblastoma patients they hypothesized to be correlated with survival rate. Ultimately they did not find hMSH2 or hMSH6, genes involved in mismatch repair, to be significantly associated with outcomes related to increased survival.

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.

Here, recognizing the difficulty that visually impaired people may have differentiating United States currency, the authors sought to use artificial intelligence (AI) models to identify US currencies. With a one-stage AI they reported a test accuracy of 89%, finding that multi-level deep learning models did not provide any significant advantage over a single-level AI.

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.

Over the last few decades, childhood stunting has persisted as a major global challenge. This study hypothesized that TPTO (Tree-based Pipeline Optimization Tool), an AutoML (automated machine learning) tool, would outperform all pre-existing machine learning models and reveal the positive impact of economic prosperity, strong familial traits, and resource attainability on reducing stunting risk. Feature correlation plots revealed that maternal height, wealth indicators, and parental education were universally important features for determining stunting outcomes approximately two years after birth. These results help inform future research by highlighting how demographic, familial, and socio-economic conditions influence stunting and providing medical professionals with a deployable risk assessment tool for predicting childhood stunting.

To explore the parent-child relationship during college planning, authors surveyed high school juniors from two private schools (boarding school vs. non-boarding parochial school). After coding, survey answers indicate students at boarding schools were found to have greater fear of parental control and disappointment, while students at non-boarding parochial schoolexpressed a greater need for parental assistance.

The authors looked at the emissions from a small, carbureted engine that was being powered by a mix of ethanol and methanol compared to E10 gasoline. The found that across all four pollutants measured, the ethanol-methanol mixture resulted in less emissions compared to the E10 fuel.