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Journal of Discrete Mathematical Sciences and Cryptography cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0065·ISSN (Print): 0972-0529

Monthly Journal: Publishes theoretical and applied research in all areas of Discrete Mathematical Sciences, Cryptography, Combinatorics, Elliptic Curves and Information Security.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
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Open Access Research Article

ML-based verification systems using discrete mathematical models

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pp. 971–978Vol. 29Issue 2-BFebruary 2026DOI: 10.47974/JDMSC-2548 Crossmark XML
Received:
07 May 2025
Published Online:
13 Feb 2026
Article type:
Research Article
Language:
EN
Article no.:
JDMSC-2548
Pages:
971–978

Abstract

Verification systems are very important for making sure that computer processes are correct and reliable in many areas, such as hacking, software validation, and data quality checks. This study shows a new system that combines machine learning (ML) methods with discrete mathematical models to make testing more accurate and faster. The suggested system uses ideas from vector measure spaces and orthogonal sequences to describe difficult proof tasks on a solid mathematical base. Adding vectorial convergence qualities to the system lets it improve testing results over and over, which cuts down on the false positives and raises the rate of detection.

Keywords

Subject Classifications

31A1033E30

References

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