In the context of these analyses, Graph Neural Networks (GNNs) emerge as powerful tools for considering the proximity of sample neighbors in anomaly detection and data classification ... between input ...

In this paper, for the first time, we introduce autoencoder neural networks into WSN to solve the anomaly detection problem. We design a two-part algorithm that resides on sensors and the IoT cloud ...

Dr. James McCaffrey of Microsoft Research tackles 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. Data anomaly ...

The results show that the autoencoder is superior to traditional anomaly detection algorithms in several aspects. First, the AE method employs a non-linear activation function in the encoder/decoder, ...

In the context of these analyses, Graph Neural Networks (GNNs) emerge as powerful tools for considering the proximity of sample neighbors in anomaly detection and data classification ... between input ...

In this paper, for the first time, we introduce autoencoder neural networks into WSN to solve the anomaly detection problem. We design a two-part algorithm that resides on sensors and the IoT cloud ...

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 ...