Learn how to scale up your LSTM model with tips and tricks such as mini-batches, dropout, bidirectional LSTMs, attention mechanisms, and pre-trained embeddings.

The transportation department relies on accurate traffic forecasting for effective decision-making. However, determining relevant parameters for existing traffic flow prediction models poses ...

This research work incorporates historical data and various predictive models to forecast the future prices of stocks traded in financial markets based on factors such as market trends, company ...

2) A neural network model using Deleuze’s theoretical guidance is created. In this article, an LSTM-RNN-FNN model is utilized to predict the load in future 5 h based on the load value in the last 24 h ...

A deep prediction model for COVID-19 using XGBoost-SIRVD-LSTM is presented. The suggested approach combines Susceptible-Infected-Recovered-Vaccinated-Deceased (SIRVD), and a deep learning model, which ...