Browse Articles

In this article the authors look at the ability of spices to reduce microbial load on a cutting surface by comparing growth of bacteria cultured before and after cleaning with various spice mixtures.

The authors monitored water quality over time looking for influences from construction and deforestation.

The authors design and test an easy-to-use and cost-effective mobile app-based alert system to help senior citizens rapidly communicate with caregivers in emergencies or when in need of assistance.

The authors develop a method for detecting fake AI-generated images from real images.

In this paper, we measured the privacy budgets and utilities of different differentially private mechanisms combined with different machine learning models that forecast traffic congestion at future timestamps. We expected the ANNs combined with the Staircase mechanism to perform the best with every value in the privacy budget range, especially with the medium high values of the privacy budget. In this study, we used the Autoregressive Integrated Moving Average (ARIMA) and neural network models to forecast and then added differentially private Laplacian, Gaussian, and Staircase noise to our datasets. We tested two real traffic congestion datasets, experimented with the different models, and examined their utility for different privacy budgets. We found that a favorable combination for this application was neural networks with the Staircase mechanism. Our findings identify the optimal models when dealing with tricky time series forecasting and can be used in non-traffic applications like disease tracking and population growth.

This study examined whether findings from laboratory studies of baseball cognition also apply during real games. Using 2024 MLB data from 30 players, researchers analyzed batting performance under different levels of feedback and anxiety.

Recent advances in generative AI have made it increasingly hard to distinguish real images from AI-generated ones. Traditional detection models using CNNs or U-net architectures lack precision because they overlook key spatial and frequency domain details. This study introduced a hybrid model combining Convolutional Neural Networks (CNN) with Fast Fourier Transform (FFT) to better capture subtle edge and texture patterns.

The authors investigate how well AI-detection machine learning models can detect real versus AI-generated art across different art styles.

Here, the authors investigated methods to reduce noise in audio composed of real-word sounds. They specifically used two spectral subtraction noise reduction algorithms: stationary and non-stationary finding notable differences in noise improvements depending on the noise sources.

The authors looked at the ability of machine learning algorithms to interpret language given their increasing use in moderating content on social media. Using an explainable model they were able to achieve 81% accuracy in detecting fake vs. real news based on language of posts alone.