Graph-Based Framework Detects IoT Anomalies Without Labels
A new self-supervised graph neural network framework, GraphContrast-IoT, improves anomaly detection in industrial IoT sensor networks by combining physical topology ...
A new self-supervised graph neural network framework, GraphContrast-IoT, improves anomaly detection in industrial IoT sensor networks by combining physical topology ...
Environmental scientists have combined Shannon entropy and machine learning to distinguish genuine wildfire smoke plumes from faulty ground-level fine particulate ...
A systematic review of 68 studies reveals that virtual and augmented reality tools are expanding beyond emergency response training into ...
A five-year editorial review documents the expansion of Discover Internet of Things alongside research challenges in artificial intelligence, edge analytics, ...
A new joint optimization framework improves incomplete multi-view clustering by integrating Tanimoto similarity, adaptive weighting, and dynamic compensation to handle ...
A new deep learning framework called DeepSIP improves air quality forecasting by effectively imputing missing sensor data and modeling complex ...
A new federated learning framework reads each school's own CO2 record to set its aggregation weight, matching centralized training on ...
A new review of 201 studies traces how bridge structural health monitoring evolved from classical Fourier and wavelet analysis to ...
A generative adversarial network combined with an unscented Kalman filter reconstructed missing air-quality data across New York City, cutting prediction ...
A 14-year study of 257 tall structures across Europe validates the EUCLID lightning detection network, finding a median location accuracy ...
© 2026 Scienmag - Science Magazine
© 2026 Scienmag - Science Magazine