The training process for TV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs.

The training process for TV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs.

<p>The training process for TV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs.</p>

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Hiam Alquran (17636906) (author)
مؤلفون آخرون: Yazan Al-Issa (18934001) (author), Mohammed Alsalatie (21087775) (author), Shefa Tawalbeh (21087778) (author)
منشور في: 2025
الموضوعات:
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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