Anomaly detection (AD) of gearboxes is essential for ensuring the operational safety and reliability of the loader. However, identifying anomalies in non-stationary signals remains challenging as ...

Discussion: The dual autoencoder model, which integrates reconstruction errors from both healthy and glaucomatous training data, demonstrated superior diagnostic accuracy compared to the single ...

Real-time vulnerability detection and anomaly reporting tools enable cybersecurity teams to swiftly identify and neutralize threats before they intensify.

An unsupervised autoencoder approach achieves moderate success for anomaly detection (accuracy = 0.881) but struggles with recall (0.070). These findings highlight the trade-off between detection ...

TransMamba: Time-Frequency Discriminative Feature Learning for Multivariate Time Series Anomaly Detection This repository contains the official PyTorch implementation of our paper: "TransMamba: ...

We explore how these models enable feature extraction, anomaly detection, and classification across diverse signal types, including electrocardiograms, radar waveforms, and IoT sensor data.

However, the potting process is irreversible; defects lead to costly scrap and operational delays. For this reason, this study introduces an anomaly detection method for the transponder potting ...

A New AI Tool Can Recreate Your Face Using Nothing But Your DNA New AI built by Chinese scientists can create 3D faces from DNA with alarming accuracy.

Acceldata, a leading provider of data observability and agentic data management solutions, is announcing a new capability designed to amplify the power of agentic reasoning within the company's xLake ...

Unsupervised Anomaly Detection in Multivariate Time Series across Heterogeneous Domains The widespread adoption of digital services, along with the scale and complexity at which they operate, has made ...