Multi-Valued Q-Neutrosophic Computation for Enhancing the Digital Media Content Creation and Editing with Computer Assistance

Abstract

 In the era of modern content production, evaluating digital creation tools across diverse 
hardware environments and user perspectives involves inherent vagueness, uncertainty, and 
subjectivity. This work Multi-valued Q-neutrosophic Computation (MV-QNC) approach for 
enhancing digital content creation by adapting to subjective artistic preferences. MV-QNC acts as 
a dependable tool to represent ambiguous, incomplete, unpredictable, and hesitant decision
making information and replicate the distribution features of all underlying assessment values. 
First, we introduce a definition of multi-valued Q-neutrosophic soft sets (MQNSS), along with 
extended combination operations, as well as necessity and possibility operations. Next, we 
present a methodological approach for multi-criteria group decision-making based on MV-QNC, 
that integrates a new multi-valued aggregation operator, as well as a balanced scoring function 
that accounts for the nature of different criteria. Finally, an explanatory example regarding Digital 
Content Creation is introduced to validate the proposed MV-QNC approach, and the results 
demonstrate their viability and legitimacy by comparison with other previous studies. 

DOI: 10.5281/zenodo.15200157