The proposed pipeline integrates a probabilistic attention approach.

The proposed pipeline integrates a probabilistic attention approach.

<p>The parameter <i>L</i> represents the sequence length processed by the Conv1D layer, and <i>k</i> represents the number of feature maps generated within each attention module.</p>

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Bibliographic Details
Main Author:	Dong Wang (73290) (author)
Other Authors:	Jian Lian (19690960) (author), Chengjiang Li (11430895) (author), Yanlei Wang (235566) (author)
Published:	2025
Subjects:	Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified reduced computational complexity recurrent neural network liquid level detection feature engineering techniques experimental results demonstrate enhance model performance efficient resource management deep learning predictions applying rigorous preprocessing time series forecasting capture temporal dependencies bidirectional long short natural gas production term memory model gas production term memory term dependencies handle long accurate forecasting xlink "> study proposes study contributes strong candidate sequential data recent advancement providing insights operational planning new dataset innovative self future research energy sector dataset comparing critical parameters comprehensive dataset attention mechanism art algorithms
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