Tag: anomaly detection

Researchers have developed a feedback-guided framework that adapts decision thresholds instead of retraining classifiers, achieving near-equal accuracy with lower false ...

A new GeroScience study shows that motoric cognitive risk syndrome carries a multidimensional gait signature, detectable through machine-learning anomaly scores, ...

A new cross-domain handover framework for vehicular networks combines one-time anonymous credentials with graph-attention and federated anomaly detection, achieving strong ...

Researchers have developed a hybrid deep learning architecture combining graph neural networks, Transformers, and variational autoencoders to detect and explain ...

Researchers at the University of Tabriz have developed GCAT, a graph-convolutional adversarial transformer that detects anomalies in multivariate sensor data ...

Researchers in India have unveiled a federated learning framework that combines reinforcement learning, tunicate swarm optimization, differential privacy and blockchain ...

A new graph-structured Transformer model trained with self-supervised learning and community-consensus pseudo-labeling achieves record F1-scores for detecting cryptocurrency money laundering ...

A new self-supervised framework called AnomalyMHKD combines self-distillation, cross-architecture distillation and dynamic teacher freezing to detect industrial anomalies with up ...

Researchers at Yonsei University have developed a prompt-tuning method that lets the vision-language model CLIP detect and localize industrial defects ...

Researchers have developed a CNN-LSTM deep learning framework that compresses 52 sensor streams from a large hydro-turbine unit into six ...