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

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 decisionmaking 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 forthe 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.