Using the Recall, Precision, and F1-score metrics, three model types—naive deep learning models, advanced deep learning models, and the proposed framework—are compared for multiclassification of the Pathological minor class in an imbalanced CTG test dataset.

<p>Using the Recall, Precision, and F1-score metrics, three model types—naive deep learning models, advanced deep learning models, and the proposed framework—are compared for multiclassification of the Pathological minor class in an imbalanced CTG test dataset.</p>

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Amin Ullah (12015113) (author)
مؤلفون آخرون:	Qazi Mazhar Ul Haq (21866718) (author), Zabeeh Ullah (21866721) (author), Jaroslav Frnda (14564125) (author), Muhammad Shahid Anwar (19660537) (author)
منشور في:	2025
الموضوعات:	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
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الوصف
الملخص:	<p>Using the Recall, Precision, and F1-score metrics, three model types—naive deep learning models, advanced deep learning models, and the proposed framework—are compared for multiclassification of the Pathological minor class in an imbalanced CTG test dataset.</p>

Using the Recall, Precision, and F1-score metrics, three model types—naive deep learning models, advanced deep learning models, and the proposed framework—are compared for multiclassification of the Pathological minor class in an imbalanced CTG test dataset.

مواد مشابهة