To monitor fetal distress monitoring, many CTG devices are placed in rooms within a distributed SDN-IoT architecture.

To monitor fetal distress monitoring, many CTG devices are placed in rooms within a distributed SDN-IoT architecture.

<p>After this, the control layer receives the data gathered from these devices for additional processing. We apply a Deep Learning model in this three-layered architecture to identify and categorize anomalies</p>

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Bibliographic Details
Main Author:	Amin Ullah (12015113) (author)
Other Authors:	Qazi Mazhar Ul Haq (21866718) (author), Zabeeh Ullah (21866721) (author), Jaroslav Frnda (14564125) (author), Muhammad Shahid Anwar (19660537) (author)
Published:	2025
Subjects:	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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