Utilizing fog computing and explainable deep learning techniques for gestational diabetes prediction
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1433-3058
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
online resource.
- Erschienen in
-
Utilizing fog computing and explainable deep learning techniques for gestational diabetes prediction ; day:20 ; month:12 ; year:2022 ; pages:1-20
Neural computing & applications ; (20.12.2022), 1-20
- Urheber
-
El-Rashidy, Nora
ElSayed, Nesma E.
El-Ghamry, Amir
Talaat, Fatma M.
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1007/s00521-022-08007-5
- URN
-
urn:nbn:de:101:1-2023030221101521226618
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
14.08.2025, 10:55 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- El-Rashidy, Nora
- ElSayed, Nesma E.
- El-Ghamry, Amir
- Talaat, Fatma M.
- SpringerLink (Online service)
Ähnliche Objekte (12)
Prediction of gestational diabetes based on explainable deep learning and fog computing
Retraction Note: Prediction of gestational diabetes based on explainable deep learning and fog computing
Utilizing technologies of fog computing in educational IoT systems: privacy, security, and agility perspective
Notions of architecture in fog computing
Resource pooling in vehicular fog computing
Scalable Distributed Computing Hierarchy: Cloud, Fog and Dew Computing
Reliablity and Security for Fog Computing Systems
Scheduling Algorithms in Fog Computing: A Survey
A queuing theory model for fog computing
Out of the fog: fog computing-enabled AI to support smart marketing management
2nd Workshop on Fog Computing and the IoT (Fog-IoT 2020)
P2P Fog Computing: Enhancing Fog-based IoT Scenarios with Distributed Hash Tables
Prediction of gestational diabetes based on explainable deep learning and fog computing
Retraction Note: Prediction of gestational diabetes based on explainable deep learning and fog computing
Utilizing technologies of fog computing in educational IoT systems: privacy, security, and agility perspective
Notions of architecture in fog computing
Resource pooling in vehicular fog computing
Scalable Distributed Computing Hierarchy: Cloud, Fog and Dew Computing
Reliablity and Security for Fog Computing Systems
Scheduling Algorithms in Fog Computing: A Survey
A queuing theory model for fog computing
Out of the fog: fog computing-enabled AI to support smart marketing management
2nd Workshop on Fog Computing and the IoT (Fog-IoT 2020)
P2P Fog Computing: Enhancing Fog-based IoT Scenarios with Distributed Hash Tables
Prediction of gestational diabetes based on explainable deep learning and fog computing
Retraction Note: Prediction of gestational diabetes based on explainable deep learning and fog computing
Utilizing technologies of fog computing in educational IoT systems: privacy, security, and agility perspective
Notions of architecture in fog computing
Resource pooling in vehicular fog computing
Scalable Distributed Computing Hierarchy: Cloud, Fog and Dew Computing
Reliablity and Security for Fog Computing Systems
Scheduling Algorithms in Fog Computing: A Survey
A queuing theory model for fog computing
Out of the fog: fog computing-enabled AI to support smart marketing management
2nd Workshop on Fog Computing and the IoT (Fog-IoT 2020)