Complex Neutrosophic Aczel-Alsina Aggregation-Based Hybrid Decision Framework for Machine Learning Encryption in Banking

Muhammad Kamran; Muhammad Shazib Hameed; Muhammad Tahir; Said Broumi; Nurullayev Mirolim Nosirovich

Authors

Muhammad Kamran Research Institute of Business Analytics and SCM, College of Management, Shenzhen University, China.
Muhammad Shazib Hameed Institute of Mathematics, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Punjab, Pakistan.
Muhammad Tahir Department of Mathematics, Institute of Numerical Sciences, Gomal University, Dera Ismail Khan, 29050, KPK, Pakistan.
Said Broumi Laboratory of Information Processing, Faculty of Science Ben MSik, University Hassan II, Casablanca, Morocco.
Nurullayev Mirolim Nosirovich Center for Research and Innovation, Asia International University, Yangibod MFY, G’ijduvon street, House 74, Bukhara, Uzbekistan.

Keywords:

CSV-NSs, Aczel-Alsina Operator, Decision-Making, Machine Learning

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.

DOI 10.5281/zenodo.18337977