Logistic Regression is a widely used model in Machine Learning. It is used in binary classification, where output variable ...

A new study in Small introduces OptiMate, a machine learning model that predicts optical properties and identifies ...

A review by researchers at Tongji University and the University of Technology Sydney published in Frontiers of Computer Science, highlights the powerful role of graph neural networks (GNNs) in ...

At ARVO 2025, in Salt Lake City, Utah, Patipol Tiyajamorn, talked about his poster on using graph neural networks to identify ...

Somatic hypermutation (SHM) of immunoglobulin variable (V) regions modulates antibody-antigen affinity is initiated by activation-induced cytidine deaminase (AID) on single-stranded DNA (ssDNA).

Abstract: This article proposes the operating status prediction model at electric vehicle (EV) charging stations based on the spatiotemporal graph convolutional network (SGCN). The SGCN combines graph ...

Features model training, evaluation metrics, visualizations, and a web interface for real-time predictions. Includes comprehensive documentation and Jupyter notebooks. This project leverages deep ...

The graphs show 99 possible scenarios (grey lines), that are produced by the Bureau's climate long-range forecast model, which represent the range of outcomes that may occur over the forecast period.

Unleash data-driven marketing with this customer segmentation project powered by K-Means clustering. We take raw customer data (like age, income, and spending behavior), clean it, visualize it, and ...