This paper explores effective methods for predicting gold prices, proposing three modeling strategies: a standalone Long Short-Term Memory (LSTM) network, a Convolutional Self-Attention (CSA) Network, ...

This software is provided for educational and research purposes only. It is NOT financial advice and should NOT be used for actual trading without: Proper financial licenses and regulatory compliance ...

In the context of global energy shortages, traditional energy sources face issues of limited reserves and high prices. As a result, the importance of energy storage technology is increasingly ...

This research presents a hybrid CNN-LSTM model for predicting cotton production using remote sensing data. The model integrates the spatial feature extraction capability of Convolutional Neural ...

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 ...

Landslides are one of the most prevalent natural geological disasters, causing significant economic losses, damaging public environments, and posing severe threats to human lives. Landslide ...

The goal of this project is to predict future stock prices by training on historical stock data. Please describe the specific objective here, such as: [e.g., to predict the closing price for a ...

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: In this paper, the LSTM model is used to predict the stock prices of the top 10 constituents of CSI 300 index and construct a portfolio. The empirical results show that the LSTM model ...