That's why I implemented a 5 step process to apply Shap values to an autoencoder output to explain which features were the most important for the recognized anomaly, and which on the other hand, were ...

The trained neural autoencoder is subjected to a sanity check by computing the MSE for the 40-item validation dataset. The MSE is 0.0017 which is very close to the MSE of the dataset being reduced, ...

Merchant Fraud Detection System Using Anomaly Detection Techniques This repository contains a Merchant Fraud Detection System designed to detect suspicious transaction patterns and flag anomalous ...

The proposed MA-HBC system consists of multiple autoencoder-based transceivers trained jointly to optimize overall network performance. It supports various data rates ranging from 164 kbps to 5.25 ...

Another strength of using the deep autoencoder for feature extraction is that it can extract features from non-quantizable questionnaire responses (e.g., dietary habit survey questionnaire), which ...

The autoencoder is an unsupervised deep neural network that learns a compressed representation from the input data and reconstructs an output that is as similar as possible to the original data.

In the last decade, automatic writer identification using a convolutional neural network (CNN) has been well studied. For further performance improvement of the writer identification task, a ...