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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Nora, El-Rashidy | - |
| dc.contributor.author | Nesma E., ElSayed | - |
| dc.contributor.author | Amir, El-Ghamry | - |
| dc.date.accessioned | 2023-03-31T01:40:14Z | - |
| dc.date.available | 2023-03-31T01:40:14Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.uri | https://link.springer.com/article/10.1007/s00521-022-08007-5 | - |
| dc.identifier.uri | https://dlib.phenikaa-uni.edu.vn/handle/PNK/7358 | - |
| dc.description | CC BY | vi |
| dc.description.abstract | 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. | vi |
| dc.language.iso | en | vi |
| dc.publisher | Springer | vi |
| dc.subject | GDM | vi |
| dc.subject | (i) IoT Layer | vi |
| dc.title | Utilizing fog computing and explainable deep learning techniques for gestational diabetes prediction | vi |
| dc.type | Book | vi |
| Appears in Collections | OER - Công nghệ thông tin | |
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