In this paper, we propose a novel low-complex unsupervised model for anomaly detection (AD) within ST preprocessed LiDAR data named CNN-BiLSTM VAE that combines variational ... a symmetric ...

AI-powered adversaries have redefined what fast looks like. Credential stuffing at machine speed. Behavioral mimicry that ...

Together, these approaches reflect a shift from linear authorship to iterative co-creation, challenging the model of the architect as sole author, in which architecture emerges not as a fixed ...

🧠 Deep learning methods: Autoencoder (AE), Variational Autoencoder (VAE) 🧪 Classical methods: Isolation Forest, One-Class SVM, Local Outlier Factor, Elliptic Envelope Built as a portfolio-grade, ...

A networked, intrinsically-safe, fire and gas detection system from Autronica ... gas and safety critical control systems architecture designed by U.K.- based systems supplier Silvertech, and ...

There was an error while loading. Please reload this page. A collection of papers on anomaly detection (tabular data/time series/image/video/graph/text/log) with the ...

The increasing complexity of modern chemical engineering processes presents significant challenges for timely and accurate anomaly detection. Traditional model-based and statistical methods often ...

This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily ...