The D-CNN-LSTM Autoencoder method optimizes the anomaly detection rate for all of the anomalies, specifically in the case of low magnitude anomalies, enhancing F1-score up to 18.12% in single types of ...

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 ...

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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 ...