Cryptographic Primitives Optimization Based on the Concepts of the Residue Number System and Finite Ring Neural Network-Reference-Cited by-同舟云学术

Cryptographic Primitives Optimization Based on the Concepts of the Residue Number System and Finite Ring Neural Network

Published:2021 Issue: Volume: Page:241-253
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
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Author:

Tchernykh Andrei^ORCID,Babenko Mikhail^ORCID,Pulido-Gaytan Bernardo^ORCID,Shiryaev Egor^ORCID,Golimblevskaia Elena^ORCID,Avetisyan Arutyun^ORCID,Hung Nguyen Viet^ORCID,Cortés-Mendoza Jorge M.^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-85672-4_18

Reference18 articles.

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3. Vinogradov, I.M.: Elements of Number Theory. Courier Dover Publications (2016)

4. Yu, D.-J., Hu, J., Tang, Z.-M., Shen, H.-B., Yang, J., Yang, J.-Y.: Improving protein-ATP binding residues prediction by boosting SVMs with random under-sampling. Neurocomputing 104, 180–190 (2013). https://doi.org/10.1016/j.neucom.2012.10.012

5. Hu, J., Li, Y., Yan, W.-X., Yang, J.-Y., Shen, H.-B., Yu, D.-J.: KNN-based dynamic query-driven sample rescaling strategy for class imbalance learning. Neurocomputing 191, 363–373 (2016). https://doi.org/10.1016/j.neucom.2016.01.043

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