In this study, we developed a high-resolution method for IMS data reconstruction using a window-based Adversarial Autoencoder (AAE) method. We acquired IMS data from partial cerebellum regions of mice ...

The neural autoencoder anomaly detection technique presented in this article is just one of many ways to look for data anomalies. The technique assumes you are working with tabular data, such as log ...

One-Class anomaly detection aims to detect anomalies from normal samples using a model trained on normal data. With recent advancements in deep learning, researchers have designed efficient one-class ...

Unsupervised anomaly detection uses an unlabeled test set of data. It involves training a machine learning (ML) model to identify normal behavior using an unlabeled dataset.

Accurate detection of anomalies in multivariate time series data has attracted much attention due to its importance in a wide range of applications. Since it is difficult to obtain accurately labeled ...

An AI-based anomaly detection tool went live in September, which is used to uncover suspicious transactions in the clearing and settlement process between banks.” Criminals are persistently finding ...