Tech Xplore

A hardware-software co-design can efficiently run AI on edge devices

A new hardware-software co-design increases AI energy efficiency and reduces latency, enabling real-time processing of ...
News-Medical.Net

Dynamic AI models detect body tipping points to forecast disease before symptoms

The editorial, "Dynamics-driven medical big data mining: dynamic approaches to early disease forecasting and individualized care," published in Intelligent Medicine (February 2026, Volume 6, Issue 1), ...
CoinDesk

Encryption Supremacy: Zcash and Privacy in the Age of Scale

CoinDesk Research maps five crypto privacy approaches and examines which models hold up as AI improves. Full coverage of ...
techxplore

Communication-aware neural networks could advance edge computing

Edge computing is an emerging IT architecture that enables the processing of data locally by smartphones, autonomous vehicles, local servers, and other IoT devices instead of sending it to be ...
IEEE

SEADGAT: A Heterogeneous Graph Attention Network with Cross Distillation for Encrypted Traffic Classification

Abstract: With the increasing severity of network security threats, encrypted traffic identification has become a core challenge in the field of network security. Graph Neural Networks (GNNs) have ...
EurekAlert!

PolyU develops novel AI graph neural network models to unravel interdisciplinary complexities in image recognition and neuroscience

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...
C&EN

Efficient Learning of Molecular Properties Using Graph Neural Networks Enhanced with Chemistry Knowledge

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Graph neural networks (GNNs) have emerged as a powerful tool in predicting molecular ...
Frontiers

Inferred global dense residue transition graphs from primary structure sequences enable protein interaction prediction via directed graph convolutional neural networks

Accurate prediction of protein-protein interactions (PPIs) is crucial for understanding cellular functions and advancing the development of drugs. While existing in-silico methods leverage direct ...
IEEE

Leveraging Graph Neural Networks and Multimodal Fusion for Enhanced Movie Genre Classification With Plots and Reviews

Abstract: Movie genre classification is essential for organizing cinematic content, improving recommendation systems, and supporting market analysis. Unimodal approaches relying solely on plot ...
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 ...