BGR

This Free Tool Gives Your Headphones An Audiophile-Approved Tune-Up

There are literally thousands of headphones available on the market — from affordable over-ear Bluetooth headphones to finely tuned in-ear monitors, and everything in between. If you've read our ...
IEEE

Integrating Prototype Learning With Graph Convolution Network for Effective Active Hyperspectral Image Classification

Abstract: In recent years, active learning (AL) methods have provided a feasible approach to alleviate the problem of limited labeled samples in deep learning projects. Existing AL algorithms ...
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

MixHop and N-GCN

Recent methods generalize convolutional layers from Euclidean domains to graph-structured data by approximating the eigenbasis of the graph Laplacian. The computationally-efficient and broadly-used ...
Nature

Graph convolution network for fraud detection in bitcoin transactions

Anti-money laundering has been an issue in our society from the beginning of time. It simply refers to certain regulations and laws set by the government to uncover illegal money, which is passed as ...
Nature

AAGCN: a graph convolutional neural network with adaptive feature and topology learning

In recent years, there has been a growing prevalence of deep learning in various domains, owing to advancements in information technology and computing power. Graph neural network methods within deep ...
Scientific Research Publishing

Multi-Head Attention Spatial-Temporal Graph Neural Networks for Traffic Forecasting ()

Accurate traffic prediction is crucial for an intelligent traffic system (ITS). However, the excessive non-linearity and complexity of the spatial-temporal correlation in traffic flow severely limit ...
GitHub

TransGCN: a semi-supervised graph convolution network-based framework to infer protein translocations in spatio-temporal proteomics

the main.py can be run from the command line interface with the following commands, where -f (dataset file) and -r (the number of parid replicates) are the two required parameters. An example command ...
IEEE

A Graph Convolution Network-Based Model for Prioritizing Personalized Cancer Driver Genes of Individual Patients

Abstract: Cancer driver genes are mutated genes that play a key role in the growth of cancer cells. Accurately identifying the cancer driver genes helps us understand cancer’s pathogenesis and develop ...
Frontiers

Efficient graph convolutional networks for seizure prediction using scalp EEG

Epilepsy is a chronic brain disease that causes persistent and severe damage to the physical and mental health of patients. Daily effective prediction of epileptic seizures is crucial for epilepsy ...
Frontiers

Multi-Dimensional Enhanced Seizure Prediction Framework Based on Graph Convolutional Network

In terms of seizure prediction, how to fully mine relational data information among multiple channels of epileptic EEG? This is a scientific research subject worthy of further exploration. Recently, ...