The Data Science Lab. Data Anomaly Detection Using a Neural Autoencoder with C#. 04/15/2024; Data anomaly detection is the process of examining a set of source data to find data items that are ...

The autoencoder is built using TensorFlow and Keras, and the data is preprocessed using scikit-learn. Project Structure autoencoder_anomaly_detection.py : Main script that generates synthetic data, ...

This repository provides an approach for detecting anomalies in unlabeled transactional data using Autoencoders. The primary objective is to identify businesses with abnormal transaction patterns, ...

Learn how autoencoders and GANs can help you with anomaly detection and data compression, and what are their differences and trade-offs.

This work aims at analyzing how provenance data can be used in anomaly detection by employing autoencoder networks which is a crucial operation for securing the various sectors through validation of ...

Anomalies in sensor data caused by errors or cyberattacks can cause severe accidents. To address the issue, this study proposed an innovative anomaly detection algorithm, namely the LSTM Autoencoder ...

The neural autoencoder anomaly detection technique presented in this article is just one of many ways to look for data anomalies. The technique assumes you are working with tabular data, such as log ...