An Effective and Practical Probabilistic Simplified Neutrosophic Approach for Accurate and Reliable Innovative Design Evaluation for Digital Media Arts

The evaluation and classification of digital artworks remain a challenging task due to the inherent uncertainty, subjectivity, and imprecision in artistic design. Traditional computational techniques are no longer effective and fail to capture the ambiguity and probabilistic behavior associated with artistic evaluation, gradually becoming untrusted for innovative design assessment. To that end, we propose a Probabilistic Simplified Neutrosophic Set (PSNS)-based convolutional art classifier, that integrates neutrosophic logic to foster the representational power of simple Convolutional Network classifier to be able to learn and make a correct decision about uncertain art evaluation scenarios. With the encapsulation of PSNS, we can introduce probabilistic truth, indeterminacy, and falsity values to efficiently model the intrinsic uncertainties in digital media art evaluation. To validate our claims, we carefully choose a public case of digital art classification to conduct experiments to analyze the performance of the proposed approach. The quantitative results prove that our approach not only accentuates the applicability but also can improve decision-making through evaluating and refining the quality of elderly care services.