… , there is a need for better anomaly detection frameworks that can address this … In this paper,
we propose an anomaly detection framework for VANETs based on deep neural networks (…

… neural network to measure similarities and detect anomalies … We then detect anomalies
based on the nearest neighbors … kinds of anomalies in different scenes and detect anomalous …

… anomaly detection … anomaly detection remains a big challenge. This paper proposes
OmniAnomaly, a stochastic recurrent neural network for multivariate time series anomaly detection …

… anomaly detection method based on graph neural network and dynamic threshold (GNN-DTAN).
Firstly, we build the graph neural network … the data, and the anomaly score between the …

… an artificial neural network based methodology for anomaly detection tailored to the …
anomaly detection methods applicable to this platform. We propose an artificial neural networks …

… In order to overcome these effects, we detect anomalies in the recorded data by learning
the … learning classification based techniques in detecting anomalies associated with the fuel …

… To overcome the above deficiencies and improve the data anomaly detection accuracy
further, in this paper, we designed a CNN-based anomaly detection method that fused and …

… In this work, we propose a novel DeepOC neural network that is applied to anomalous event
detection in complex scenes. As a one-stage model, DeepOC simultaneously learns a one…

… We propose to use autoencoder neural networks for undertaking the anomaly detection
task in WSN. We leverage the reconstruction ability of autoencoders and design a twopart …

… When building systems for detecting anomalous observations, … of a model for detecting
anomalous values of a fixed indicator … anomaly index and the subsequent application of a neural …