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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 ...

Student dropout represents a social, resource and time loss for everyone involved. By identifying students with the potential to evade, it is possible to take the necessary measures to prevent that ...

Metabolite identification from 1D 1H NMR spectra is a major challenge in NMR-based metabolomics. This study introduces NMRformer, a Transformer-based deep learning framework for accurate peak ...

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 ...

LSTM networks can capture long-term dependencies in EEG signals, which is crucial for epileptic seizure detection. Nonetheless, RNNs have high computational complexity, longer training times, and are ...

In this paper, a 1D CNN-LSTM model is proposed for epileptic seizure recognition through EEG signal analysis. The proposed model combines a 1D CNN and an LSTM to construct an end-to-end network that ...

If you’ve read about unsupervised learning techniques before, you may have come across the term “autoencoder”. Autoencoders are one of the primary ways that unsupervised learning models are developed.