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 ...

Using a deep-learning model designed for high-dimensional data, KAUST researchers have shown that it is possible to predict emergency department overcrowding from complex hospital records. This ...

Early detection and molecular characterization of disease progression are persistent challenges in modern medicine. Although genomic and transcriptomic profiling of tissue lesions and precursors has ...

Data anomaly detection is the process of examining a set of source data to find data items that are different in some way from the majority of the source items. There are many different types of ...