Using the Recall, Precision, and F1-score metrics, three model types are compared: naïve deep learning models, advanced deep learning models, and the proposed framework for multiclassification of Suspicious minor class in an imbalanced CTG test dataset.
<p>Using the Recall, Precision, and F1-score metrics, three model types are compared: naïve deep learning models, advanced deep learning models, and the proposed framework for multiclassification of Suspicious minor class in an imbalanced CTG test dataset.</p>
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2025
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| _version_ | 1852018011193475072 |
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| author | Amin Ullah (12015113) |
| author2 | Qazi Mazhar Ul Haq (21866718) Zabeeh Ullah (21866721) Jaroslav Frnda (14564125) Muhammad Shahid Anwar (19660537) |
| author2_role | author author author author |
| author_facet | Amin Ullah (12015113) Qazi Mazhar Ul Haq (21866718) Zabeeh Ullah (21866721) Jaroslav Frnda (14564125) Muhammad Shahid Anwar (19660537) |
| author_role | author |
| dc.creator.none.fl_str_mv | Amin Ullah (12015113) Qazi Mazhar Ul Haq (21866718) Zabeeh Ullah (21866721) Jaroslav Frnda (14564125) Muhammad Shahid Anwar (19660537) |
| dc.date.none.fl_str_mv | 2025-07-31T17:57:09Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0328099.g014 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Using_the_Recall_Precision_and_F1-score_metrics_three_model_types_are_compared_na_ve_deep_learning_models_advanced_deep_learning_models_and_the_proposed_framework_for_multiclassification_of_Suspicious_minor_class_in_an_imbalanced_CTG_test_d/29732718 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biotechnology Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified uhb dataset confirms proposed framework solves proposed framework outperforms assessing fetal health improved clinical connectivity algorithms brings intelligence advanced dl technique advanced dl models iomt devices struggle advanced monitoring capabilities making capabilities clinical data artificial intelligence prenatal monitoring xlink "> wasserstein distance validation using study proposes study focuses small classes significance compared serious problem preventing complications pregnant women powerful solution powered networks medical things iomt systems information imbalance healthcare industry gan ), findings highlight f1 score efficiently managing defined networking deep learning controlling iomt based iomt based gans art solutions |
| dc.title.none.fl_str_mv | Using the Recall, Precision, and F1-score metrics, three model types are compared: naïve deep learning models, advanced deep learning models, and the proposed framework for multiclassification of Suspicious minor class in an imbalanced CTG test dataset. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>Using the Recall, Precision, and F1-score metrics, three model types are compared: naïve deep learning models, advanced deep learning models, and the proposed framework for multiclassification of Suspicious minor class in an imbalanced CTG test dataset.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_a8aab957775eb7bba1f3bbb78e38ea06 |
| identifier_str_mv | 10.1371/journal.pone.0328099.g014 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29732718 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Using the Recall, Precision, and F1-score metrics, three model types are compared: naïve deep learning models, advanced deep learning models, and the proposed framework for multiclassification of Suspicious minor class in an imbalanced CTG test dataset.Amin Ullah (12015113)Qazi Mazhar Ul Haq (21866718)Zabeeh Ullah (21866721)Jaroslav Frnda (14564125)Muhammad Shahid Anwar (19660537)BiotechnologyScience PolicySpace ScienceBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifieduhb dataset confirmsproposed framework solvesproposed framework outperformsassessing fetal healthimproved clinical connectivityalgorithms brings intelligenceadvanced dl techniqueadvanced dl modelsiomt devices struggleadvanced monitoring capabilitiesmaking capabilitiesclinical dataartificial intelligenceprenatal monitoringxlink ">wasserstein distancevalidation usingstudy proposesstudy focusessmall classessignificance comparedserious problempreventing complicationspregnant womenpowerful solutionpowered networksmedical thingsiomt systemsinformation imbalancehealthcare industrygan ),findings highlightf1 scoreefficiently managingdefined networkingdeep learningcontrolling iomtbased iomtbased gansart solutions<p>Using the Recall, Precision, and F1-score metrics, three model types are compared: naïve deep learning models, advanced deep learning models, and the proposed framework for multiclassification of Suspicious minor class in an imbalanced CTG test dataset.</p>2025-07-31T17:57:09ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0328099.g014https://figshare.com/articles/figure/Using_the_Recall_Precision_and_F1-score_metrics_three_model_types_are_compared_na_ve_deep_learning_models_advanced_deep_learning_models_and_the_proposed_framework_for_multiclassification_of_Suspicious_minor_class_in_an_imbalanced_CTG_test_d/29732718CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/297327182025-07-31T17:57:09Z |
| spellingShingle | Using the Recall, Precision, and F1-score metrics, three model types are compared: naïve deep learning models, advanced deep learning models, and the proposed framework for multiclassification of Suspicious minor class in an imbalanced CTG test dataset. Amin Ullah (12015113) Biotechnology Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified uhb dataset confirms proposed framework solves proposed framework outperforms assessing fetal health improved clinical connectivity algorithms brings intelligence advanced dl technique advanced dl models iomt devices struggle advanced monitoring capabilities making capabilities clinical data artificial intelligence prenatal monitoring xlink "> wasserstein distance validation using study proposes study focuses small classes significance compared serious problem preventing complications pregnant women powerful solution powered networks medical things iomt systems information imbalance healthcare industry gan ), findings highlight f1 score efficiently managing defined networking deep learning controlling iomt based iomt based gans art solutions |
| status_str | publishedVersion |
| title | Using the Recall, Precision, and F1-score metrics, three model types are compared: naïve deep learning models, advanced deep learning models, and the proposed framework for multiclassification of Suspicious minor class in an imbalanced CTG test dataset. |
| title_full | Using the Recall, Precision, and F1-score metrics, three model types are compared: naïve deep learning models, advanced deep learning models, and the proposed framework for multiclassification of Suspicious minor class in an imbalanced CTG test dataset. |
| title_fullStr | Using the Recall, Precision, and F1-score metrics, three model types are compared: naïve deep learning models, advanced deep learning models, and the proposed framework for multiclassification of Suspicious minor class in an imbalanced CTG test dataset. |
| title_full_unstemmed | Using the Recall, Precision, and F1-score metrics, three model types are compared: naïve deep learning models, advanced deep learning models, and the proposed framework for multiclassification of Suspicious minor class in an imbalanced CTG test dataset. |
| title_short | Using the Recall, Precision, and F1-score metrics, three model types are compared: naïve deep learning models, advanced deep learning models, and the proposed framework for multiclassification of Suspicious minor class in an imbalanced CTG test dataset. |
| title_sort | Using the Recall, Precision, and F1-score metrics, three model types are compared: naïve deep learning models, advanced deep learning models, and the proposed framework for multiclassification of Suspicious minor class in an imbalanced CTG test dataset. |
| topic | Biotechnology Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified uhb dataset confirms proposed framework solves proposed framework outperforms assessing fetal health improved clinical connectivity algorithms brings intelligence advanced dl technique advanced dl models iomt devices struggle advanced monitoring capabilities making capabilities clinical data artificial intelligence prenatal monitoring xlink "> wasserstein distance validation using study proposes study focuses small classes significance compared serious problem preventing complications pregnant women powerful solution powered networks medical things iomt systems information imbalance healthcare industry gan ), findings highlight f1 score efficiently managing defined networking deep learning controlling iomt based iomt based gans art solutions |