Frontiers

Graph-Guided Neural Networks: Revolutionizing Anomaly Detection in Chemical Engineering

The increasing complexity of modern chemical engineering processes presents significant challenges for timely and accurate anomaly detection. Traditional ...
Scientific Research Publishing

KAN-Transformer Fusion with Mixture of Experts for Temporal Imputation of Spatiotemporal Air Pollution Data ()

The Kolmogorov-Arnold Network (abbr. KAN) is a novel neural network architecture inspired by the Kolmogorov-Arnold ...
IEEE

GGCT-Net: A Dual-Branch Gated Graph Convolution and Grouped Cross-Attention Transformer Network for Hyperspectral Unmixing

Abstract: In recent years, deep learning-based hyperspectral unmixing (HU) techniques have garnered increasing attention and achieved significant progress. However, existing deep learning methods ...
Scientific Research Publishing

Graph Neural Networks for Anomaly Detection in Cloud Infrastructure ()

Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex dependencies ...
GitHub

Pull requests: Gabriele-Immordino/Spatio-temporal-Graph-Convolutional-Autoencoder-for-Transonic-Wing-Pressure-Distribution-Forecasting

Pull requests help you collaborate on code with other people. As pull requests are created, they’ll appear here in a searchable and filterable list. To get started, you should create a pull request.
IEEE

Encoder-Enhanced Graph Convolutional Network for Cross-Domain Mechanical Fault Diagnosis

Abstract: Graph Convolutional Network (GCN) has been widely applied in mechanical fault diagnosis due to its ability to extract features from data in non-Euclidean spaces. However, the local ...