𝗖𝗼𝗺𝗽𝗹𝗲𝘅 𝗧𝗿𝗶𝗽𝗹𝗲-𝗩𝗮𝗹𝘂𝗲𝗱 𝗡𝗲𝘂𝘁𝗿𝗼𝘀𝗼𝗽𝗵𝗶𝗰 𝗦𝗼𝗳𝘁 𝗦𝗲𝘁𝘀 𝗮𝗻𝗱 𝗧𝗵𝗲𝗶𝗿 𝗧𝗼𝗽𝗼𝗹𝗼𝗴𝗶𝗰𝗮𝗹 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝘄𝗶𝘁𝗵 𝗔𝗜-𝗗𝗿𝗶𝘃𝗲𝗻 𝗦𝗶𝗴𝗻𝗮𝗹-𝗧𝗲𝗺𝗽𝗹𝗮𝘁𝗲 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀
This study introduces complex triple-valued neutrosophic soft sets and develops a topological framework for handling uncertainty, ambiguity, and complex-valued information. It establishes new operations, interior and closure operators, and applies the framework to AI-driven signal-template analysis using cotangent similarity measures. Through normalization, correlation analysis, principal component analysis, and K-means clustering, the study identifies template T4 as the closest match, T2 as the least similar, and S4 as an outlier among the signals. The work contributes to uncertainty modelling, neutrosophic topology, AI-assisted signal analysis, pattern recognition, and complex data classification.
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