This study presents an integrated framework combining 1D-CNN-LSTM-Autoencoder-based anomaly detection with identity authentication using machine learning classifiers. The 1D-CNN-LSTM Autoencoder ...

This repository contains the implementation for my third major project, "Optimized CNN and CRNN for Advanced Classification and Regression," developed as part of my application for the Computer Vision ...

1D CNN for Sea/Land/Cloud Classification of Hyperspectral Images from the HYPSO-2 Satellite This project implements the 1D-JustoLiuNet for classification tasks on hyperspectral satellite image ...

The proposed 1D-CNN achieved a mean classification accuracy of 90.4% with 13 particle classes. According to the experimental results, the combination of SPMS and 1D-CNN enables real-time collection, ...

Here, we designed a novel hybrid framework called CHLNET, which combines a one-dimensional convolutional neural network (1D CNN) and support vector regression (SVR). The 1D CNN is used to extract ...

In chemical plants and other industrial facilities, the rapid and accurate detection of the root causes of process faults is essential for the prevention of unknown accidents. This study focused on ...

Firstly, the raw EEG signal data is pre-processed; then, the 1D CNN and LSTM are introduced, respectively; finally, the 1D CNN-LSTM model is designed and applied for epileptic seizure recognition.