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 ...

In complex industrial production environments, the efficacy of fault diagnostic techniques has become increasingly important and can enhance the reliability and safety of systems. In recent years, the ...

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 ...

Figure 1. Conceptual overview about Variational Autoencoder Modular Bayesian Network VAMBN) approach: In a first step, a low dimensional representation of known modules of variables is learned via ...

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 ...

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 ...

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, ...