AZoSensors on MSN

AI maps heat inside steelmaking’s critical sintering process beds

The Temporal Fusion Transformer model provides near-real-time insights into sintering temperatures, addressing critical challenges in steelmaking processes.
IEEE

Lightweight Real-Time Anomaly Detection and Prediction for Surveillance Videos Using a Self-Supervised Hybrid Autoencoder and Dynamic Graph CNN-LSTM Framework

Abstract: Anomaly detection in surveillance videos is a critical task for ensuring public safety, requiring both high accuracy and real-time efficiency. This study presents a novel lightweight anomaly ...
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 ...
Frontiers

Image steganalysis using LSTM fused convolutional neural networks for secure telemedicine

Deep learning-based image steganalysis has progressed in recent times, with efforts more concerted toward prioritizing detection accuracy over lightweight frameworks. In the context of AI-driven ...
IEEE

Thick Cloud Removal of Landsat Time-Series Using Convolutional LSTM With Embedded Residual Modules

Abstract: Extensive cloud contamination severely hinders the interpretation of optical remote sensing images. Existing cloud removal methods focus primarily on the reconstruction of individual cloudy ...
Scientific Research Publishing

Unsupervised Time-Series Signal Analysis with Autoencoders and Vision Transformers: A Review of Architectures and Applications ()

The rapid growth of unlabeled time-series data in domains such as wireless communications, radar, biomedical engineering, and the Internet of Things (IoT) has driven advancements in unsupervised ...