In the realm of infrastructure maintenance, a novel approach to detecting concrete bridge damage has emerged. Researchers ...

Autoencoders are based on neural networks, and the network consists of two parts: an encoder and a decoder. Encoder compresses the N-dimensional input (e.g. a frame of sensor data) into an ...

To address these issues, this paper proposes an anomaly detection method for wind turbine operations based on an improved Auxiliary Classifier Generative Adversarial Network. The proposed approach ...

That said, applying a neural autoencoder anomaly detection system to tabular data is typically the best way to start. A limitation of the autoencoder architecture presented in this article is that it ...

Data Anomaly Detection Using a Neural Autoencoder with C#. 04/15/2024; Data anomaly detection is the process of examining a set of source data to find data items that are different in some way from ...

16. Schreyer M, Sattarov T, Schulze C, Bernd R, Damian B. Detection of accounting anomalies in the latent space using adversarial autoencoder neural networks, 2nd KDD workshop on anomaly detection in ...

Anomaly detection is a critical issue across several academic fields and real-world applications. Artificial neural networks have been proposed to detect anomalies from different input types, but ...