Abstract: Credit card fraud detection is a critical task in financial systems, requiring effective algorithms to accurately classify transactions as fraudulent or non-fraudulent. This paper proposes a ...
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 ...
With the in-depth digital transformation of the global shipping industry, the accurate prediction of smart port operation efficiency has become a key factor in enhancing the competitiveness of ...
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 ...
College of Engineering and IT, University of Dubai, Dubai, United Arab Emirates Climate change has significantly impacted vulnerable communities globally, with rising temperatures caused by greenhouse ...
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 ...
I've been trying to train a simple LSTM model in mlpack for a many-to-one prediction task — basically, it takes 50 points from a sine function and predicts the next one. But the model just doesn't ...
Shao, G. (2023) Prediction of Stock Prices Based on the LSTM Model. Atlantis Press International BV.
ABSTRACT: The Efficient Market Hypothesis postulates that stock prices are unpredictable and complex, so they are challenging to forecast. However, this study demonstrates that it is possible to ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback