This project enables the generation of novel, valid, and drug-like molecules as SMILES strings, using a two-stage approach: Stage 1: Train an LSTM model on a large SMILES dataset for next-token ...

Objective: To compare the application of the ARIMA model, the Long Short-Term Memory (LSTM) model and the ARIMA-LSTM model in forecasting foodborne disease incidence. Methods: Monthly case data of ...

In a new comparative analysis of artificial intelligence applications in retail, researchers have revealed that advanced deep learning models can dramatically enhance the accuracy of demand ...

Implement Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks optimized for time series forecasting and sequence modeling.

With the widespread application of lithium-ion batteries in electric vehicles and energy storage systems, health monitoring and remaining useful life prediction have become critical components of ...

Abstract: Long Short-Term Memory (LSTM) networks are particularly useful in recommender systems since user preferences change over time. Unlike traditional recommender models which assume static ...

This study proposes a hybrid modeling approach that integrates a Physics Informed Neural Network (PINN) and a long short-term memory (LSTM) network to predict river water temperature in a defined ...

ABSTRACT: Accurate precipitation forecasting is crucial for mitigating the impacts of extreme weather events and enhancing disaster preparedness. This study evaluates the performance of Long ...