Intelligent faculty evaluation and ranking system based on N-framed plithogenic fuzzy hypersoft set and extended NR-TOPSIS

This paper proposes an intelligent faculty evaluation and ranking system in a fuzzy environment, with a focus on semester-wise evaluation rather than annual evaluation. This approach is warranted because a university’s academic goals may vary across semesters, affecting the selection and weights of performance indicators related to teaching effectiveness, research output, and official responsibilities. To achieve this objective, the authors introduce the concept of N-framed plithogenic hypersoft set (PHSS), where N represents the number of frames or semesters. Three types of N-framed PHSS are introduced, and an efficient rank reversal-free multi-criteria decision-making technique, namely NR-TOPSIS, is extended by embedding N-framed PHSS in the algorithm of NR-TOPSIS, termed as ENR-TOPSIS. The modified algorithm is capable of extracting data from a source file and producing the desired results. The procedure is implemented for faculty evaluation in a double-framed fuzzy environment, and sensitivity analysis is performed for the proposed ENR-TOPSIS. The developed framework combines intelligent systems that are adaptable and contain additional characteristics, making it more versatile and increasing its accuracy and transparency, in line with SDG-4, which focuses on quality education.