Browse Articles

Head injuries are common in American football and the facemasks found on football helmets receive a lot of impact during contacts. This study investigates how effective they are at reducing concussion risk.

The purpose of this investigation is to develop a hydrogel to aid skin regeneration by creating an extracellular matrix for fibroblast growth with antibacterial and infection-detection properties. Authors developed two natural hydrogels based on pectin and potato peels and characterized the gels for fibroblast compatibility through rheology, scanning electron microscopy, swelling, degradation, and cell cytotoxicity assays. Overall, this experiment fabricated various hydrogels capable of acting as skin substitutes and counteracting infections to facilitate wound healing. Following further testing and validation, these hydrogels could help alleviate the 13-billion-dollar financial burden of foot ulcer treatment.

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 authors use machine learning on MRI images of brain tissue to predict tumor onset as an avenue for early detection of brain cancer.

This study compares the performance of two deep learning models, U-Net and DeconvNet, for segmenting gliomas from MRI scans.

Brain-Computer Interface (BCI) allows users, especially those with paralysis, to control devices through brain activity. This study explored using a custom transformer model to decode neural signals into handwritten text for individuals with limited motor skills, comparing its performance to a traditional RNN-based BCI.

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, 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.

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

The authors use pictures of candy wrappers and neural networks to improve nutritional accuracy of diet-tracking apps.