IEEE

Spatio–Temporal Bidirectional Gated Graph Convolutional Network for Skeleton Action Recognition in Dynamic Complex Environments

Abstract: Graph convolutional networks (GCNs) have achieved remarkable success in skeleton-based action recognition. However, most existing studies mainly rely on historical and current data in ...
Frontiers

DEG-BRIN-GCN: interpretable graph convolutional framework with differentially expressed genes brain region interaction network prior for AD diagnosis

Due to the intricate dynamic coupling between molecular networks and brain regions, early diagnosis and pathological mechanism analysis of Alzheimer's disease (AD) remain highly challenging. To ...
IEEE

Multiscale Gaussian Process-Driven Graph Convolutional Neural Network for Polarimetric SAR Image Classification

Abstract: By focusing on the structure exploration and information propagation from non-Euclidean data space, graph convolutional neural network (GCN), which can extract abundant and discriminative ...
Forbes

TSA Warning—Do Not Use These Networks On Your Smartphone

TSA warning comes to life as man jailed for attack. Updated on Dec. 5 with expert security guidance for smartphone users when traveling, including how to avoid connecting to dangerous networks. Almost ...
ZDNet

The right way to run an internet speed tests (and how to use the results for faster Wi-Fi)

Accelerate your tech game Paid Content How the New Space Race Will Drive Innovation How the metaverse will change the future of work and society Managing the ...
GitHub

Haidi927/Event-extraction-and-Traffic-flow-prediction

Traditional traffic flow prediction mostly relies on historical traffic data, ignoring the impact of emergencies (such as accidents, construction, and extreme weather) on traffic. This project ...