The training time of the BI-GRU model using three different datasets

The training time of the BI-GRU model using three different datasets

<p>The training time of the BI-GRU model using three different datasets</p>

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Bibliographic Details
Main Author:	Hiam Alquran (17636906) (author)
Other Authors:	Yazan Al-Issa (18934001) (author), Mohammed Alsalatie (21087775) (author), Shefa Tawalbeh (21087778) (author)
Published:	2025
Subjects:	Cell Biology Biotechnology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified proposed approach achieved mechanical vibrations occurring including digital filtering facilitate blood circulation empirical mode decomposition different cardiac structures deep learning algorithms cardiology challenge 2016 body &# 8217 lub sound ), dub sound ), segmenting phonocardiogram signals circor digiscope phonocardiogram circor digiscop dataset deep learning models pcg signal underwent gru ), bidirectional pcg signal mithsdb ), sound frequencies pcg ). mithsdb dataset three well term memory systolic region study compared software tool segmentation process processing steps paper represents namely s1 massachusetts institute known datasets highest level first investigation directional long diastolic region aforementioned datasets 5 %.
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Summary:	<p>The training time of the BI-GRU model using three different datasets</p>

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