Learning a forward mapping that relates stimuli to the corresponding brain activation measured by functional magnetic resonance imaging (fMRI) is termed as estimating encoding models. Computational ...

This projects detect Anomalous Behavior through live CCTV camera feed to alert the police or local authority for faster response time. We are using Spatio Temporal AutoEncoder and more importantly ...

Thus, we propose an ECG anomaly detection framework (ECG-AAE) based on an adversarial autoencoder and temporal convolutional network (TCN) which consists of three modules (autoencoder, discriminator, ...

Applying Convolutional Auto-Encoder in Trading In this project, I try to build a model to extract useful patterns in financial timeseries for predicting the directions of future price movements.

Here, we introduce a hybrid Convolutional LSTM (ConvLSTM) model framework to evaluate how the addition of spatiotemporal data can potentially improve flash flood predictions in Ellicott City, Maryland ...

A Convolutional Variational Autoencoder (CVAE) was developed for this purpose. We demonstrate the efficacy of our approach using the transient data generated from the simulations. The simulation data ...