AI techniques in extremal combinatory for robust error detection

Bodke, Minal; Saoji, Saurabh; Vaidya, Vanisha P.; Sabirov, Sardor; Goyal, Shweta; Mohan, E.

doi:10.47974/JDMSC-2513

Open Access Research Article

AI techniques in extremal combinatory for robust error detection

Minal Bodkeminal.bodke@pccoer.inDepartment of Computer EngineeringPCET’S Pimpri Chinchwad College of Engineering and ResearchPune, Maharashtra, 412101, IndiaView full profile → , ^*Saurabh SaojiCorresponding authorsaurabh.saoji22@gmail.comDepartment of Computer EngineeringNutan Maharashtra Institute of Engineering and TechnologyPune, Maharashtra, 410507, IndiaView full profile → , Vanisha P. Vaidyavpvaidya@sipnaengg.ac.inDepartment of Information TechnologySipna College of Engineering and TechnologyAmravati, Maharashtra, 440701, IndiaView full profile → , Sardor Sabirovsabirov_sardor@mamunedu.uzDepartment of General Professional SciencesMamun UniversityKhiva, 220900, UzbekistanView full profile → , Shweta Goyalshwetugoyal@gmail.comDepartment of Electrical EngineeringGraphic Era Deemed to be UniversityDehradun, Uttarakhand, 248002, IndiaView full profile → , E. Mohanemohan1971@gmail.comDepartment of ECESaveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai, Tamil Nadu, 602105, IndiaView full profile →

* Corresponding author · click or hover a name for details

pp. 691–700Vol. 29Issue 2-AFebruary 2026DOI: 10.47974/JDMSC-2513 Crossmark XML

Received:: 08 Apr 2025
Published Online:: 31 Dec 2025
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-2513
Pages:: 691–700

Abstract

This work focuses at how to construct strong mistake detection systems by combining sophisticated AI techniques with extremal combinatory concepts. The study uses mathematical frameworks based on vector measure spaces and orthogonal sequences to develop new combinatorial models that can discover complicated patterns of mistakes. The proposed method uses AI-driven algorithms that work properly with combinatorial systems to make popularity greater accurate and proof against noise. Its miles centered on the way to efficiently describe data and use characteristic engineering to file complex combinatorial mistakes patterns. This proves that it can be used for actual-world conversation and data shop systems.

Keywords

Extremal combinatory AI-driven algorithms Vector metric space Orthogonal sequences Feature engineering

Subject Classifications

31A1033E30

References

[1] H. Wang, Z. Wang, M. Du, F. Yang, Z. Zhang, S. Ding, P. Mardziel, and X. Hu, “Score-CAM: Score-weighted visual explanations for convolutional neural networks,” arXiv preprint arXiv:1910.01279 (2020).
[2] A. K. Dombrowski, M. Alber, C. Anders, M. Ackermann, K. R. Müller, and P. Kessel, “Explanations can be manipulated and geometry is to blame,” arXiv preprint arXiv:1906.07983 (2019).
[3] B. S. Madan, N. J. Zade, N. P. Lanke, S. S. Pathan, S. N. Ajani, and P. Khobragade, “Self-supervised transformer networks: Unlocking new possibilities for label-free data,” Panam. Math. J., vol. 34, no. 4, pp. 194–210 (2024).
[4] A. Uddin, M. Monira, W. Shin, T. Chung, and S. H. Bae, “Saliencymix: A saliency guided data augmentation strategy for better regularization,” arXiv preprint arXiv:2006.01791 (2020).
[5] P. S. Takawale, K. N. Dhekale, S. B. Gore, P. S. Magar, and P. S. Manekar, “CurrencyShield: A survey of deep learning applications in currency risk mitigation,” Int. J. Adv. Comput. Theory Eng., vol. 13, no. 2, pp. 33–38 (Mar. 2025).
[6] S. Gurung and S. Chauhan, “A survey of black-hole attack mitigation techniques in MANET: Merits, drawbacks, and suitability,” Wirel. Netw., vol. 26, pp. 1981–2011 (2020).
[7] A. I. Hussein, M. I. Hussein, and O. S. Hlail, “AI-generated privacy-preserving protocols for cross-cloud data sharing and collaboration,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 28, no. 4-B, pp. 1265–1279 (2025).
[8] A. A. Abidalzahra and H. R. Yassein, “A new designing of MaTRU cryptosystem,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 28, no. 2, pp. 361–367 (2025).
[9] A. Hussain, S. Nazir, F. Khan, L. Nkenyereye, A. Ullah, S. Khan, S. Verma, and Kavita, “A resource efficient hybrid Proxy Mobile IPv6 extension for next generation IoT networks,” IEEE Internet Things J. (2021).
[10] S. H. Kulkarni, V. V. Deshmukh, R. K. Moje, and S. H. Lavate, “Applied discrete mathematics in developing efficient cryptographic algorithms for enhancing information security in WSN,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 28, no. 5-A, pp. 1733–142 (2025).

Views: 202Downloads: 146Citations: 0