IEEE

Machine Learning-Based Anomaly Detection of Correlated Sensor Data: An Integrated Principal Component Analysis-Autoencoder Approach

Abstract: This research proposes a lightweight hybrid approach for anomaly detection in correlated IoT sensor data, combining PCA for fast monitoring and Autoencoders for deeper analysis. Validated on ...
IEEE

Autoencoder-based Temporal Convolutional Network for Real-Time Anomaly Detection in Large-Scale Network Traffic

Abstract: Nowadays, the rapid growth of network traffic has needed the development of efficient anomaly detection systems to ensure network security and reliability. However, the existing One-Class ...
techxplore

New training method helps AI models handle messy, varied medical image data

Hospitals do not always have the opportunity to collect data in tidy, uniform batches. A clinic may have a handful of carefully labeled images from one scanner while holding thousands of unlabeled ...
GitHub

autoencoder-anomaly-detection

This repo contains all my Deep Learning semester work, including implementations of FNNs, CNNs, autoencoders, CBOW, and transfer learning. I explored TensorFlow, Keras, PyTorch, and Theano while ...
GitHub

A New Hybrid Model for Improving Outlier Detection Using Combined Autoencoder and Variational Autoencoder (AVE)

This repository contains the source code, scripts, and supplementary materials for the paper: "A New Hybrid Model for Improving Outlier Detection Using Combined Autoencoder and Variational Autoencoder ...