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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 techniques in polynomial algebra for advanced signal processing

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* Corresponding author · click or hover a name for details

pp. 721–730Vol. 29Issue 2-AFebruary 2026DOI: 10.47974/JDMSC-2518 Crossmark XML
Received:
22 Apr 2025
Published Online:
31 Dec 2025
Article type:
Research Article
Language:
EN
Article no.:
JDMSC-2518
Pages:
721–730

Abstract

This study looks at how machine learning (ML) and polynomial math can be used together to improve advanced signal processing. The study creates new mathematical models that use the polynomial structures that are already present in signal data. These models combine vector metric spaces and orthogonal polynomial sequences to find features and show signals.  The suggested way is based on quickly getting polynomial features that keep the purity of the signal and allow for strong learning. Combining machine learning algorithms with polynomial algebraic structures makes it easier to analyse and handle complex signals. This solves problems with convergence and orthogonality in very large vector spaces.  The method offers better accuracy in jobs like signal classification, noise reduction, and rebuilding.

Keywords

Subject Classifications

31A1033E30

References

[1] M. Cho, J. Gim, J. H. Kim, and S. Kang, “Development of an artificial neural network model to predict the tensile strength of friction stir welding of dissimilar materials using cryogenic processes,” Appl. Sci., vol. 14, p. 9309 (2024).
[2] D. Petraki and N. Samaras, “Solving the nth degree polynomial matrix equation,” Journal of Interdisciplinary Mathematics, vol. 24, no. 5, pp. 1079–1092 (2021).
[3] I. R. S. Sabry, A. H. Idrisi, and A. H. I. Mourad, “Friction stir welding process parameters optimization through hybrid multi-criteria decision-making approach,” Int. Rev. Model. Simul. (IREMOS), vol. 14, pp. 75–84 (2021).
[4] D. Petraki and N. Samaras, “Solving the nth degree polynomial matrix equation,” Journal of Interdisciplinary Mathematics, vol. 24, no. 5, pp. 1079–1092 (2021).
[5] M. Marian and S. Tremmel, “Current trends and applications of machine learning in tribology—A review,” Lubricants, vol. 9, p. 86 (2021).
[6] S. R. Prabhu, A. Shettigar, M. A. Herbert, and S. S. Rao, “Parameter investigation and optimization of friction stir welded AA6061/TiO2 composites through TLBO,” Weld. World, vol. 66, pp. 93–103 (2022).
[7] A. Mishra, E. M. Sefene, and A. A. Tsegaw, “Process parameter optimization of friction stir welding on 6061AA using supervised machine learning regression-based algorithms,” arXiv preprint, arXiv:2109.00570 (2021).
[8] M. A. Constantin, M. D. Iordache, E. L. Nitu, M. Diakhaté, Y. Demmouche, M. Dhondt, and C. Bădulescu, “An efficient strategy for 3D numerical simulation of friction stir welding process of pure copper plates,” IOP Conf. Ser.: Mater. Sci. Eng., vol. 916, pp. 012021 (2020).
[9] S. C. Shiralashetti and S. Kumbinarasaiah, “CAS wavelets analytic solution and Genocchi polynomials numerical solutions for the integral and integro-differential equations,” Journal of Interdisciplinary Mathematics, vol. 22, no. 3, pp. 201–218 (2019).
[10] D. A. P. Prabhakar, A. Korgal, A. K. Shettigar, M. A. Herbert, M. P. G. Chandrashekharappa, D. Y. Pimenov, and K. Giasin, “A review of optimization and measurement techniques of the friction stir welding (FSW) process,” J. Manuf. Mater. Process., vol. 7, p. 181 (2023).
[11] A. Deogade, N. Shahu, R. Raibole, A. Pillare, and U. Shastrakar, “Design and analysis of a wheelchair for physically disabled individuals,” Int. J. Mech. Eng. Res. (IJMER), vol. 14, no. 1, pp. 19–25 (May 2025).
[12] T. B. Rao, C. S. P. Rao, and N. Baki, “Multi-objective optimisation of friction stir welding parameters: Integration of FEM and NSGA-II,” Int. J. Manuf. Res., vol. 16, pp. 82–101 (2021).
[13] S. Kumar, H. Singh, I. Gupta, and A. J. Gupta, “Symmetric encryption scheme based on quasigroup using chained mode of operation,” J. Discrete Math. Sci. Cryptogr., vol. 27, no. 8, pp. 2337–2364 (2024).

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