Currently, most real-world time series datasets are multivariate and are rich in dynamical information of the underlying system. Such datasets are attracting much attention; therefore, the need for ...

Detecting abnormal cardiac rhythms in ECG signals using unsupervised deep learning — trained exclusively on normal heartbeats, evaluated on a held-out set of normal and abnormal sequences. This ...

This implements an supervised anomaly detection system for detecting network and vehicular intrusions using an LSTM-based autoencoder. The model is trained only on normal traffic and flags anomalies ...

With the rapid advancement of synthetic speech technologies, detecting deepfake audio has become essential for preventing impersonation and misinformation. This study aims to enhance detection ...

Abstract: This paper proposes a hybrid Transformer-LSTM autoencoder for student final grade prediction, addressing the limitations of existing models that capture either short-term or long-term ...

Abstract: Trajectory prediction is a significant function in many areas, such as autonomous driving, robotic navigation, surveillance, and human-robot interaction. The precise prediction of the future ...

We promised working AI Prototypes. Here's the first one. The LSTM autoencoder from Striim Labs takes a different approach to anomaly detection. Instead of defining every possible anomaly upfront with ...