Complex triple-valued neutrosophic soft sets and their topological framework with AI-driven signal-template analysis
Keywords:
Complex triple-valued neutrosophic soft sets, Complex triple-valued neutrosophic soft topology, Cotangent similarity measures, Data visualization techniquesAbstract
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.
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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)

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