For example, Nassif et al. (2021) split ML based anomaly detection into three broad categories: supervised anomaly detection where the process involves labelling a dataset and training a model to ...

We’ve come a long way from up/down monitoring. From factory floors to modern enterprise infrastructure, IT admins now require significantly more information than a simple check to determine whether a ...

AI integration in materials testing accelerates evaluations, improves accuracy, and enables predictive quality assurance, ...

UK, and Ben Byfield, Principal Consultant at Actica Consulting, exploring the future of digital twins for Operational ...

[1] Automatic oscillations detection and quantification in process control loops using linear predictive coding. Engineering Science and Technology an International Journal (2020).

Detecting anomalous events in videos is a challenging task due to their infrequent and unpredictable nature in real-world scenarios. In this paper, we propose SwinAnomaly, a video anomaly detection ...

This project implements a system for detecting anomalies in time series data collected from Prometheus. It uses an LSTM (Long Short-Term Memory) autoencoder model built with TensorFlow/Keras to learn ...

This project implements an AI-powered log anomaly detection system that analyzes logs stored in Elasticsearch. It uses machine learning to detect unusual patterns and frequencies in log data, with ...

Structure diagram of VQ-VAE. The input image is denoted as x, and the output of the encoder, ... By automating the process of seizure anomaly detection, SAnoDDPM can assist medical professionals in ...