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>
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , |
| منشور في: |
2025
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| الموضوعات: | |
| الوسوم: |
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| _version_ | 1852021333203877888 |
|---|---|
| author | Hiam Alquran (17636906) |
| author2 | Yazan Al-Issa (18934001) Mohammed Alsalatie (21087775) Shefa Tawalbeh (21087778) |
| author2_role | author author author |
| author_facet | Hiam Alquran (17636906) Yazan Al-Issa (18934001) Mohammed Alsalatie (21087775) Shefa Tawalbeh (21087778) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hiam Alquran (17636906) Yazan Al-Issa (18934001) Mohammed Alsalatie (21087775) Shefa Tawalbeh (21087778) |
| dc.date.none.fl_str_mv | 2025-04-14T20:08:44Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0320297.t007 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/The_training_time_of_the_BI-GRU_model_using_three_different_datasets/28791445 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | 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 %. |
| dc.title.none.fl_str_mv | The training time of the BI-GRU model using three different datasets |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>The training time of the BI-GRU model using three different datasets</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_d9155bc6f8e2b4340ae85e3e96558be7 |
| identifier_str_mv | 10.1371/journal.pone.0320297.t007 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28791445 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | The training time of the BI-GRU model using three different datasetsHiam Alquran (17636906)Yazan Al-Issa (18934001)Mohammed Alsalatie (21087775)Shefa Tawalbeh (21087778)Cell BiologyBiotechnologySpace ScienceBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedproposed approach achievedmechanical vibrations occurringincluding digital filteringfacilitate blood circulationempirical mode decompositiondifferent cardiac structuresdeep learning algorithmscardiology challenge 2016body &# 8217lub sound ),dub sound ),segmenting phonocardiogram signalscircor digiscope phonocardiogramcircor digiscop datasetdeep learning modelspcg signal underwentgru ), bidirectionalpcg signalmithsdb ),sound frequenciespcg ).mithsdb datasetthree wellterm memorysystolic regionstudy comparedsoftware toolsegmentation processprocessing stepspaper representsnamely s1massachusetts instituteknown datasetshighest levelfirst investigationdirectional longdiastolic regionaforementioned datasets5 %.<p>The training time of the BI-GRU model using three different datasets</p>2025-04-14T20:08:44ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0320297.t007https://figshare.com/articles/dataset/The_training_time_of_the_BI-GRU_model_using_three_different_datasets/28791445CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/287914452025-04-14T20:08:44Z |
| spellingShingle | The training time of the BI-GRU model using three different datasets Hiam Alquran (17636906) 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 %. |
| status_str | publishedVersion |
| title | The training time of the BI-GRU model using three different datasets |
| title_full | The training time of the BI-GRU model using three different datasets |
| title_fullStr | The training time of the BI-GRU model using three different datasets |
| title_full_unstemmed | The training time of the BI-GRU model using three different datasets |
| title_short | The training time of the BI-GRU model using three different datasets |
| title_sort | The training time of the BI-GRU model using three different datasets |
| topic | 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 %. |