Population demographic patterns in PFAS-neurological health research
The authors analyzed racial and ethnic representation in studies on PFAS and neurological health outcomes.
Read More...Predicting clogs in water pipelines using sound sensors and machine learning linear regression
The authors looked the ability of sound sensors to predict clogged pipes when the sound intensity data is run through a machine learning algorithm.
Read More...Differentiating characteristics in exoplanet host stars
The authors looked at what conditions with host stars favor development of exoplanets.
Read More...Ethanol levels in foods ensuing culinary preparation
The authors investigated alcohol content of foods during preparation and when ready to serve to determine how much alcohol remained in the food.
Read More...Gender differences in social media, sleep, and cognition in U.S. teens
The authors survey teenagers within the United States regarding the effect of social media use on sleep quality and attention span.
Read More...Modeling the moving sofas in circular hallways using geometric methods
Investigation of the largest rigid shape that can be moved through a circular hallway of unit width with an arbitrary turn angle
Read More...Mitigating open-set misclassification in a colorectal cancer detecting neural network
The authors develop a machine learning method to reduce misclassification of objects in safety-critical applications such as medical diagnosis.
Read More...An assessment of controllable etiological factors involved in neonatal seizure using a Monte Carlo model
The authors used Monte Carlo simulations to assess the impacts of various factors on neonatal seizure risk.
Read More...Class distinctions in automated domestic waste classification with a convolutional neural network
Domestic waste classification using convolutional neural network
Read More...Depression detection in social media text: leveraging machine learning for effective screening
Depression affects millions globally, yet identifying symptoms remains challenging. This study explored detecting depression-related patterns in social media texts using natural language processing and machine learning algorithms, including decision trees and random forests. Our findings suggest that analyzing online text activity can serve as a viable method for screening mental disorders, potentially improving diagnosis accuracy by incorporating both physical and psychological indicators.
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