Browse Articles

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

This manuscript explores the performance of five different machine learning models in classifying brain tumors from a dataset of MRI scans. The authors find that several of the models showed >90% accuracy. Thus, the authors suggest that machine learning models demonstrate potential for effective implementation in clinical settings, including as a diagnostic tool that can be used to complement the expertise of neuroradiologists.

The authors survey high school students regarding preferences for taking notes by hand versus typing.

The authors looked at the ability to detect heart disease before the onset of severe clinical symptoms.

The authors looked at different factors, such as age, pre-existing conditions, and geographic region, and their ability to predict what an individual's health insurance premium would be.

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.

Here, the authors investigated the most efficient way to position magnets to hold the most pieces of paper on the surface of a refrigerator. They used a regression model along with an artificial neural network to identify the most efficient positions of four magnets to be at the vertices of a rectangle.

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.

In this study, the authors tested the ability and accuracy of a neural net to identify patterns in complex number matrices.

The Tor network allows individuals to secure their online identities by encrypting their traffic, however it is vulnerable to fingerprinting attacks that threaten users' online privacy. In this paper, the authors develop a new video fingerprinting model to explore how well video streaming can be fingerprinted in Tor. They found that their model could distinguish which one of 50 videos a user was hypothetically watching on the Tor network with 85% accuracy, demonstrating that video fingerprinting is a serious threat to the privacy of Tor users.