Cubic Spherical Neutrosophic Sets and Selection of Electric Truck Using Cosine Similarity Measure

The concepts of cubic spherical neutrosophic sets (CSNSs), introduced and investigated by Gomathi
 et al. [5], o er a geometric representation of collection of neutrosophic sets (NSs), enhancing their ability to
 capture uncertainty. The formulation characterizes information using points on a sphere with a de ned center
 and radius, providing a more precise depiction of fuzziness inherent in uncertain data. The cubic spherical
 neutrosophic Archimedean triangular norms(ATN) and conorms (ATCN), expanding the models capabilities
 to handle uncertainty. These algebraic operators enable the aggregation and combination of uncertain informa
tion, o ering a more comprehensive approach to decision-making. The research further presents a method for
 solving multiple-criteria decision-making problems within the cubic spherical neutrosophic context, leveraging
 the newly integrated norms and conorms. The algorithm utilizes the cosine similarity measure of cubic spher
ical neutrosophic sets, exempli ed through an application involving the selection of the most e ective electric
 truck. This extended framework provides decision-makers with enhanced tools to navigate complex decision
 landscapes amidst uncertainty, facilitating more informed and robust choices across diverse domains