Abstract: The data produced by millions of connected devices and smart sensors in the Industrial Internet of Things (IIoT) is highly dynamic, large-scale, heterogeneous, and time-stamped. These ...

A comprehensive Edge AI and IoT anomaly detection system designed for research and educational purposes. This project demonstrates real-time anomaly detection using autoencoder-based models optimized ...

The research aim is to develop an intelligent agent for cybersecurity systems capable of detecting abnormal user behavior using deep learning methods and ensuring interpretability of decisions. A four ...

Data anomaly detection is the process of examining a set of source data to find data items that are different in some way from the majority of the source items. There are many different types of ...

The composition of metagenomic communities within the human body often reflects localized medical conditions such as upper respiratory diseases and gastrointestinal diseases. Fast and accurate ...

Automatic detection and alarm of abnormal electrocardiogram (ECG) events play an important role in an ECG monitor system; however, popular classification models based on supervised learning fail to ...

MST-VAE is an unsupervised learning approach for anomaly detection in multivariate time series. Inspired by InterFusion paper, we propose a simple yet effective multi-scale convolution kernels applied ...