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Visual Studio Magazine1 j

Data Anomaly Detection Using a Neural Autoencoder with C#

Dr. James McCaffrey of Microsoft Research tackles 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. Data anomaly ...
GitHub25d

Anomaly Detection in Network Traffic using LSTM Autoencoder

This project provides an unsupervised learning solution for detecting anomalies in time-series data, specifically demonstrated on synthetic network traffic. It leverages a Long Short-Term Memory (LSTM ...
GitHub3mnd

Prometheus Time Series Anomaly Detection with LSTM Autoencoder

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 ...
IEEE5d

A CNNs-Transformer Hybrid Masked Autoencoder With Contrastive Learning for Landslide Extraction

Abstract: Landslide extraction plays a critical role in disaster prevention and mitigation. However, acquiring sufficient landslide samples is often challenging, significantly limiting the performance ...
IEEE19d

Superpixel-Based Autoencoder-Like Nonnegative Tensor Factorization for Hyperspectral Unmixing

Abstract: Hyperspectral unmixing is significant for advancing remote sensing (RS) applications, aiming at extracting the spectra of pure materials (called endmembers) and obtaining their proportions ...