Browse Articles

In this study, the authors develop an architecture to implement in a cloud-based database used by law firms to ensure confidentiality, availability, and integrity of attorney documents while maintaining greater efficiency than traditional encryption algorithms. They assessed whether the architecture satisfies necessary criteria and tested the overall file sizes the architecture could process. The authors found that their system was able to handle larger file sizes and fit engineering criteria. This study presents a valuable new tool that can be used to ensure law firms have adequate security as they shift to using cloud-based storage systems for their files.

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.

Default opt-ins and social proof tags effect on decision making in an e-commerce context

The authors looked the ability to predict future stock prices using various machine learning models.

Scientists are always on the quest to improve the body's healing abilities and broken bones are no exception. In this article, the authors investigate properties of 3D-printed biocompatible polymers used to improve bone healing. With such efforts, we can hope to, one day, improve bone scaffolding materials in ways that make the natural healing processes more efficient, reducing the time needed for recovery from bone fractures.

The authors demonstrate computationally that two types of nanoplastics can bind to estrogen receptor beta and potentially disrupt endocrine systems.

The authors studied the ability of deep learning models to predict droughts in the midwestern United States.

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

The authors developed and tested machine learning methods to diagnose tuberculosis from pulmonary X-ray scans.

The authors looked at different models of semantic segmentation to determine which may be best used in the future for segmentation of CT scans to help diagnose certain conditions.