The increasing accuracy of deep neural networks for solving problems such as speech and image recognition has stoked attention and research devoted to deep learning and AI more generally.

In this paper, a content-based video anomaly detection algorithm (COVAD) is proposed, and its network structure is modified based on the original memory-based video anomaly detection algorithm.

In this study, we explore an image-based method to automate the manual anomaly detection process on quality control plots using deep learning. To do this we trained a Convolutional Neural Network (CNN ...

Anomaly detection is one of the more difficult and underserved operational areas in the asset-servicing sector of financial institutions.

Anomaly detection algorithms are leading the charge to take organizations away from the limitations of manually monitoring datasets. In its place is a wave of solutions that can not only make use of ...

In a recent study, a research team from Chung-Ang University, Korea presents open research questions related to anomaly detection using deep learning and curates open-access time series datasets, an ...

Unsupervised anomaly detection algorithms include Autoencoders, K-means, Gaussian Mixture Modelling (GMMs), hypothesis tests-based analysis, and Principal Component Analysis (PCAs).