Anomaly detection is an active area of computer vision and widely applied in diverse fields. As known, it is a considerable challenge to collect abnormalities in practice. To tackle it, researchers ...

In the realm of infrastructure maintenance, a novel approach to detecting concrete bridge damage has emerged. Researchers ...

It is common for deep learning methods to be used in video anomaly detection to focus on analyzing video streams from just one camera with a single scenario. Using large-scale training data with high ...

Competitive endogenous RNA (ceRNA) regulatory networks (CENA) have advanced our understanding of noncoding RNAs’ roles in complex diseases, providing a theoretical basis for disease mechanisms.

Autoencoders are based on neural networks, and the network consists of two parts: an encoder and a decoder. Encoder compresses the N-dimensional input (e.g. a frame of sensor data) into an ...

This project implements a system for detecting anomalies in time series data collected from Prometheus. It uses an LSTM (Long Short-Term Memory) autoencoder model built with TensorFlow/Keras to learn ...