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Utilizing fog computing and explainable deep learning techniques for gestational diabetes prediction

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Bibliographic Details
Main Authors: Nora, El-Rashidy, Nesma E., ElSayed, Amir, El-Ghamry
Format: Book
Language:English
Published: Springer 2023
Subjects:
GDM
(i) IoT Layer
Online Access:https://link.springer.com/article/10.1007/s00521-022-08007-5
https://dlib.phenikaa-uni.edu.vn/handle/PNK/7358
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spelling oai:localhost:PNK-73582023-03-31T01:40:14Z Utilizing fog computing and explainable deep learning techniques for gestational diabetes prediction Nora, El-Rashidy Nesma E., ElSayed Amir, El-Ghamry GDM (i) IoT Layer CC BY Gestational diabetes mellitus (GDM) is one of the pregnancy complications that poses a significant risk on mothers and babies as well. GDM usually diagnosed at 22–26 of gestation. However, the early prediction is desirable as it may contribute to decrease the risk. The continuous monitoring for mother’s vital signs helps in predicting any deterioration during pregnancy. The originality of this paper is to provide comprehensive framework for pregnancy women monitoring. The proposed Data Replacement and Prediction Framework consists of three layers which are: (i) IoT Layer, (ii) Fog Layer, and (iii) Cloud Layer. 2023-03-31T01:40:14Z 2023-03-31T01:40:14Z 2023 Book https://link.springer.com/article/10.1007/s00521-022-08007-5 https://dlib.phenikaa-uni.edu.vn/handle/PNK/7358 en application/pdf Springer
institution Digital Phenikaa
collection Digital Phenikaa
language English
topic GDM
(i) IoT Layer
spellingShingle GDM
(i) IoT Layer
Nora, El-Rashidy
Nesma E., ElSayed
Amir, El-Ghamry
Utilizing fog computing and explainable deep learning techniques for gestational diabetes prediction
description CC BY
format Book
author Nora, El-Rashidy
Nesma E., ElSayed
Amir, El-Ghamry
author_facet Nora, El-Rashidy
Nesma E., ElSayed
Amir, El-Ghamry
author_sort Nora, El-Rashidy
title Utilizing fog computing and explainable deep learning techniques for gestational diabetes prediction
title_short Utilizing fog computing and explainable deep learning techniques for gestational diabetes prediction
title_full Utilizing fog computing and explainable deep learning techniques for gestational diabetes prediction
title_fullStr Utilizing fog computing and explainable deep learning techniques for gestational diabetes prediction
title_full_unstemmed Utilizing fog computing and explainable deep learning techniques for gestational diabetes prediction
title_sort utilizing fog computing and explainable deep learning techniques for gestational diabetes prediction
publisher Springer
publishDate 2023
url https://link.springer.com/article/10.1007/s00521-022-08007-5
https://dlib.phenikaa-uni.edu.vn/handle/PNK/7358
_version_ 1761912523954585600
score 8.891053

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