Utilizing fog computing and explainable deep learning techniques for gestational diabetes prediction
CC BY
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| Định dạng: | Sách |
| Ngôn ngữ: | English |
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Springer
2023
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| Truy cập trực tuyến: | https://link.springer.com/article/10.1007/s00521-022-08007-5 https://dlib.phenikaa-uni.edu.vn/handle/PNK/7358 |
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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 |
8.891145 |