Browse Articles

The authors looked at how the length of an audio clip used of a song impacted the ability to properly classify it by musical genre.

The authors developed a quantum inspired model for stock market fluctuations.

This study explored how green spaces, climate, traffic, and air quality (GCTA) collectively influence asthma-related emergency department visits in the U.S using machine learning models and explainable AI.

This article describes the classification of medical text data using vector databases and text embedding. Various large language models were used to generate this medical data for the classification task.

In this paper, Thomas et al. introduce a new, affordable way to study characteristics of rocket motors using small-scale rocket motors.

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

In this study, the authors test whether providing a larger dataset of glass transition temperatures (T_g) to train the machine-learning platform Polymer Genome would improve its accuracy. Polymer Genome is a machine learning based data-driven informatics platform for polymer property prediction and T_g is one property needed to design new polymers in silico. They found that training the model with their larger, curated dataset improved the algorithm's T_g, providing valuable improvements to this useful platform.

Plastic pollution and energy shortages are pressing issues in today’s world. The authors examined whether waste plastic pyrolysis fuels are similar to conventional diesel and, thus, a plausible alternative fuel. Results showed that waste plastic pyrolysis fuels did not match up to diesel overall, though several fuels came close in calorific value.

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

This article discusses Alopecia areata, an autoimmune disorder causing sudden hair loss due to the immune system mistakenly attacking hair follicles. The article introduces the use of deep learning (DL) techniques, particularly convolutional neural networks (CNN), for classifying images of healthy and alopecia-affected hair. The study presents a comparative analysis of newly optimized CNN models with existing ones, trained on datasets containing images of healthy and alopecia-affected hair. The Inception-Resnet-v2 model emerged as the most effective for classifying Alopecia Areata.