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Background: Accurate forecasting of lung cancer incidence is crucial for early prevention, effective medical resource allocation, and evidence-based policymaking. Objective: This study proposes 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: Seismic facies analysis, as a crucial step in the study of depositional facies, effectively delineates the distribution patterns of depositional facies between wells. To address the ...

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 ...

There is a bug in torch.export when exporting a model with LSTM layer. When running the following source code in Python, these two outputs of LSTM layer (h_n, c_n) don't match the expected shapes. The ...

Abstract: A novel forecasting model of the bidirectional LSTM with self-attention (Bi-LSTM-SA) is introduced to address the need to upgrade the accurate projection of electrical load forecasting. The ...

This Python application uses machine learning to predict stock prices and provide day trading suggestions. Initially built with a basic LSTM model, it has evolved through multiple enhancements to ...