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

Here, recognizing the difficulty associated with tracking the progression of dementia, the authors used machine learning models to predict between the presence of cognitive normalcy, mild cognitive impairment, and Alzheimer's Disease, based on blood DNA methylation levels, sex, and age. With four machine learning models and two dataset dimensionality reduction methods they achieved an accuracy of 53.33%.

The authors use the Lanczos algorithm to computationally solve the Schrodinger equation for 2D potentials with a Python program

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

These authors mathematically deduce a model that explains the interesting (and unintuitive) physical phenomenon that occurs when water falls.

The authors set to test a system that would help with the dehumidification and overall management of greehouses.

In this study, the authors tested the ability and accuracy of a neural net to identify patterns in complex number matrices.

Using a convolution neural network, these authors show machine learning can clinically diagnose breast cancer with high accuracy.

Here, beginning from an initial interest in the possibility to use a computer to automatically solve a geometry diagram parser, the authors developed their own Fast Geometry Diagram Parser (FastGDP) that uses clustering and corner information. They compared their own methods to a more widely available, method, GeoSolver, finding their own to be an order of magnitude faster in most cases that they considered.

In this study, the authors present proposed cryptographic controls for election sites with the hypothesis that this will mitigate risk and remediate vulnerabilities.