Seminal work on proving the block model threshold conjecture [2] has provided rigorous conditions under which community detection is theoretically feasible. Such results are critical as they ...

This demo highlights how one can use a semi-supervised machine learning technique based on autoencoder to detect an anomaly ... The way one can use trained autoencoders for anomaly detection is that ...

autoencoder model built with TensorFlow/Keras to learn normal patterns from your metrics and identify deviations. The system includes scripts for data collection, preprocessing, model training, and ...

In this study, we applied the anomaly detection method based on sparse structure learning of the element correlation within MD trajectories to identify important features associated with state ...

To address this challenge, we investigate an explainable anomaly-based intrusion detection system (IDS) that translates the inference process of the Autoencoder into the high-fidelity allow-list rule ...

To address these challenges, we propose a novel hybrid machine anomaly detection methodology that integrates an innovative pretext task-based self-supervised learning framework with vibration ...

School of Information Science and Engineering, Shandong Normal University, Jinan, P. R. China ...

School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, P. R. China ...