Browse Articles

This manuscript investigates the urban heat island (UHI) effect by utilizing two satellite datasets: Landsat (high spatial resolution, lower temporal resolution) and MODIS (lower spatial resolution, high temporal resolution). The authors hypothesized that Landsat would provide better spatial detail, while MODIS would better capture temporal variations. Their analysis in the Washington D.C.–Baltimore region supports these hypotheses, demonstrating that Landsat offers finer spatial details, whereas MODIS provides more consistent seasonal patterns and better detects heatwave frequencies.

The authors looked at the importance of different point breakdowns needed to win in a game of tennis.

The authors use machine learning and electroencephalogram data to propose a method for improving epilepsy diagnosis.

Using the data provided by the University of Twente High School Project on Astrophysics Research with Cosmics (HiSPARC), an analysis of locations for possible high-energy cosmic ray air showers was conducted. An example includes an analysis conducted of the high-energy rain shower recorded in January 2014 and the use of Stellarium™ to discern its location.

In this study, three models are used to test the hypothesis that data-centric artificial intelligence (AI) will improve the performance of machine learning.

This study examined behavioral and psychological predictors of burnout among high school and university students in Pakistan using survey data and machine-learning models. Shorter sleep and greater mental fatigue—especially fatigue—were associated with higher burnout risk, while a Random Forest model successfully identified students at risk of burnout.

The authors looked at the feasibility to predict wind speeds that will have less reliance on using historical data.

The authors train a neural network to detect text-based emotions including joy, sadness, anger, fear, love, and surprise.

The authors use machine learning to develop an evidence-based detection tool for identifying human trafficking.

Semantic segmentation - labelling each pixel in an image to a specific class- models require large amounts of manually labeled and collected data to train.