The project concerns the anomaly detection in credit cards transactions using machine learning models and Autoencoders. The main aim of this project is predict whether a given transaction was a fraud ...

The project focuses on detecting anomalies in images using autoencoder neural networks. An autoencoder learns to reconstruct normal images and can classify images as ...

We benchmark variants of our approach against state-of-the-art Autoencoders for anomaly detection by using a recently developed experimental dataset provided by the ASHRAE Research Project RP-1312.

The study reviews approaches to log-based anomaly detection, focusing on deep learning methods, especially those using pretrained LLMs. Traditional techniques include reconstruction-based methods ...