Evaluating machine and deep learning techniques in predicting blood sugar levels within the E-health domain-Reference-Cited by-同舟云学术

Evaluating machine and deep learning techniques in predicting blood sugar levels within the E-health domain

Published:2023-11-09 Issue:1 Volume:35 Page:
ISSN:0954-0091
Container-title:Connection Science
language:en
Short-container-title:Connection Science

Author:

Di Martino Beniamino¹²³,Esposito Antonio¹,Pezzullo Gennaro Junior¹,Weng Tien-Hsiung⁴

Affiliation:

1. University of Campania Luigi Vanvitelli, Department of Engineering, Aversa, Italy

2. Department of Computer Science and Information Engineering, ASIA University, Taichung City, Taiwan

3. Department of Computer Science, University of Vienna, Vienna, Austria

4. Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan

Funder

Universitá degli Studi della Campania Luigi Vanvitelli

Publisher

Informa UK Limited

Subject

Artificial Intelligence,Human-Computer Interaction,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/09540091.2023.2279900

Reference20 articles.

1. Aliberti A. Bagatin A. Acquaviva A. Macii E. & Patti E. (2020). Data driven patient-specialized neural networks for blood glucose prediction. In 2020 IEEE international conference on multimedia & expo workshops (ICMEW) (pp. 1–6).

2. Aral A. Esposito A. Nagiyev A. Benkner S. Di Martino B. & Bochicchio M. A. (2023). Experiences in architectural design and deployment of ehealth and environmental applications for cloud-edge continuum. In International conference on advanced information networking and applications (pp. 136–145). Springer.

3. Machine learning techniques for prediction of multiple sclerosis progression

4. Diabetes M. (2022). Carelink system user guide . Retrieved September 10 2023. https://www.medtronicdiabetes.com/sites/default/files/library/download-library/workbooks/CareLink-System-User-Guide.pdf

5. Validating the integrity of Convolutional Neural Network predictions based on zero-knowledge proof

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