Figure 1. Basic Autoencoder architecture, showing encoder and decoder components [22]. Figure 2. AE-based framework for signal reconstruction, highlighting latent space compression. Recent ...

The autoencoder utilizes these optimized features for anomaly detection, achieving efficient anomaly traffic detection functionality. The introduction of CBAM optimizes the feature extraction stage, ...

This project implements a real-time anomaly detection system using an LSTM Autoencoder to analyze continuous data streams. The system is designed to detect unusual patterns, such as exceptional values ...

ConvAE-LSTM: Convolutional Autoencoder Long Short-Term Memory Network for Smartphone-Based Human Activity Recognition Abstract: The self-regulated recognition of human activities from time-series ...

This repository provides the official implementations and experiments for our research in evolving domian generalization, including LSSAE and MMD-LSAE. Both of them employ a sequential autoencoder ...

In the new paper Sequencer: Deep LSTM for Image Classification, a research team from Rikkyo University and AnyTech Co., Ltd. examines the suitability of different inductive biases for computer vision ...

Intelligent Transportation Systems (ITS), especially Autonomous Vehicles (AVs), are vulnerable to security and safety issues that threaten the lives of the people. Unlike manual vehicles, the security ...