Complex triple-valued neutrosophic soft sets and their topological framework with AI-driven signal-template analysis

Authors

  • Raed Hatamleh
    Department of Mathematics, Faculty of Science, Jadara University, P.O. Box 733, Irbid 21110, Jordan
  • Diana Amin Mohammad Mahmoud
    Department of Mathematics, College of Arts and Sciences, Amman Arab University, P.O. Box 2234, Amman 11953, Jordan
  • Haitham Qawaqneh
    Department of Basic Science, Al-Zaytoonah University of Jordan, Amman 11733, Jordan
  • Hind Y. Saleh
    Department of Mathematics, College of Education, University of Duhok, Duhok 42001, Iraq
  • Alaa M. Abd El-latif
    Department of Mathematics, College of Science, Northern Border University, Arar 91431, Saudi Arabia
  • Eman Almuhur
    Department of Mathematics, Faculty of Science, Applied Science Private University, Amman, Jordan
  • Aqeedat Hussain
    Department of Mathematics, Institute of Numerical Sciences, Gomal University, Dera Ismail Khan 29050, KPK, Pakistan
  • Arif Mehmood
    Department of Mathematics, Institute of Numerical Sciences, Gomal University, Dera Ismail Khan 29050, KPK, Pakistan
  • Cris L. Armada
    Vietnam National University Ho Chi Minh City, Linh Trung Ward, Thu Duc City, Ho Chi Minh City, Vietnam
    Department of Applied Mathematics, Faculty of Applied Science, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet, Ward 14, District 10, Ho Chi Minh City, Vietnam

Keywords:

Complex triple-valued neutrosophic soft sets, Complex triple-valued neutrosophic soft topology, Cotangent similarity measures, Data visualization techniques

Abstract

This research proposes basic operations on complex triple-valued neutrosophic soft sets (CTVNSs) and defines complex triple-valued neutrosophic soft topological spaces (CTVNSTSs) with interior and closure operators. The study also presents an application to signal-template matching using cotangent similarity. Visual comparisons of four signals (S1--S4) and four templates (T1--T4) show that T4 is the most similar template, T2 is the least similar, and T1 and T3 are moderately similar. Normalization affects dominance and makes T1 relevant in some comparisons. Correlation analysis and clustering using principal component analysis and K-means (K=2) identify one cluster (S1, S2 , and S3) and one outlier (S4). These results indicate that cotangent similarity, together with data visualization and normalization, can effectively support the analysis of complex data.

Dimensions

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fig 6

Published

2026-06-22

How to Cite

Complex triple-valued neutrosophic soft sets and their topological framework with AI-driven signal-template analysis. (2026). Journal of the Nigerian Society of Physical Sciences, 8(3), 3261. https://doi.org/10.46481/jnsps.2026.3261

Issue

Volume 8, Issue 3, August 2026

Section

Mathematics & Statistics
Copyright & Licensing

Copyright (c) 2026 Raed Hatamleh, Diana Amin Mohammad Mahmoud, Haitham Qawaqneh, Hind Y. Saleh, Alaa M. Abd El-latif, Eman Almuhur, Aqeedat Hussain, Arif Mehmood, Cris L. Armada (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Complex triple-valued neutrosophic soft sets and their topological framework with AI-driven signal-template analysis. (2026). Journal of the Nigerian Society of Physical Sciences, 8(3), 3261. https://doi.org/10.46481/jnsps.2026.3261

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