Neutrosophic Sets and Systems
Abstract
Advanced uncertainty modeling tools have emerged due to the growing complexity of real-world decision environments. Complex Single-Valued Neutrosophic Sets (CSV-NSs) use special functions to represent truth, uncertainty, and falsehood, making it easier to show unclear, conflicting, and vague information. CSV NSs, which consider both size and direction of uncertainty, let one more precisely combine and make decisions by using complex numbers. This work presents robust approaches for combining information, which rely on the Aczel-Alsina (A-A) operator and power-weighted strategies specifically designed for CSV-NS. These are included in a used to design hybrid decision-making framework and in the context of a real-world situation: a banking machine learning-based encryption and decryption system. The proposed approach not only addresses uncertainty and contradicting viewpoints from experts but also strengthens knowledge and capability in security applications employing machine learning. In terms of flexibility, computational efficiency, and decision quality, experimental validation attests to the superiority of the suggested approach over conventional techniques.
Recommended Citation
Kamran, Muhammad; Muhammad Shazib Hameed; Muhammad Tahir; Said Broumi; and Nurullayev Mirolim Nosirovich. "Complex Neutrosophic Aczel-Alsina Aggregation-Based Hybrid Decision Framework for Machine Learning Encryption in Banking." Neutrosophic Sets and Systems 98, 1 (2026). https://digitalrepository.unm.edu/nss_journal/vol98/iss1/12