IoMT-Enabled Real-Time Blood Glucose Prediction With Deep Learning and Edge Computing-Reference-Cited by-同舟云学术

IoMT-Enabled Real-Time Blood Glucose Prediction With Deep Learning and Edge Computing

Published:2023-03-01 Issue:5 Volume:10 Page:3706-3719
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Zhu Taiyu¹^ORCID,Kuang Lei¹^ORCID,Daniels John¹^ORCID,Herrero Pau¹^ORCID,Li Kezhi²^ORCID,Georgiou Pantelis¹^ORCID

Affiliation:

1. Center for Bio-Inspired Technology, Imperial College London, London, U.K.

2. Institute of Health Informatics, University College London, London, U.K.

Funder

Engineering and Physical Sciences Research Council

President’s Ph.D. Scholarship at Imperial College London, U.K.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/10049212/09681840.pdf?arnumber=9681840

Reference70 articles.

1. Deep residual time-series forecasting: Application to blood glucose prediction;rubin-falcone;Proc 5th Int Workshop Knowl Discovery Healthcare Data,2020

2. Joint Optimization of Offloading Utility and Privacy for Edge Computing Enabled IoT

3. Dilated recurrent neural network for short-time prediction of glucose concentration;chen;Proc 3rd Int Workshop Knowl Discovery Healthcare Data,2018

4. Edge-Computing Architectures for Internet of Things Applications: A Survey

5. GluNet: A Deep Learning Framework for Accurate Glucose Forecasting

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