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HoRNS-CNN model: an energy-efficient fully homomorphic residue number system convolutional neural network model for privacy-preserving classification of dyslexia neural-biomarkers

Crossref DOI link: https://doi.org/10.1186/s40708-025-00256-z

Published Online: 2025-04-30

Published Print: 2025-12

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Usman, Opeyemi Lateef https://orcid.org/0000-0002-0788-5927
Muniyandi, Ravie Chandren https://orcid.org/0000-0002-8999-9548
Omar, Khairuddin https://orcid.org/0000-0003-1794-019X
Mohamad, Mazlyfarina https://orcid.org/0000-0002-0318-3282
Owoade, Ayoade Akeem

Kareem, Morufat Adebola
Funding

Funding for this research was provided by:

Fundamental Research Grant Scheme (FRGS) of the Ministry of Higher Education (FRGS/1/2021/ICT07/UKM/02/1)
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Text and Data Mining valid from 2025-04-30

Version of Record valid from 2025-04-30
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Article History

Received: 18 June 2024

Accepted: 20 March 2025

First Online: 30 April 2025

Declarations

:

: This article does not contain any studies with human participants or animals performed by any of the authors.

: The authors declare no competing interests.

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