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...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824009591 |
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| author | Usman Afzal Muhammad Rayees Ahmad Nazek Alessa Nauman Raza Fathea M.O. Birkea Salem Alkhalaf Nader Omer |
| author_facet | Usman Afzal Muhammad Rayees Ahmad Nazek Alessa Nauman Raza Fathea M.O. Birkea Salem Alkhalaf Nader Omer |
| author_sort | Usman Afzal |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-be4a69bc65484d06b6ca584cfed55981 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-be4a69bc65484d06b6ca584cfed559812024-12-21T04:27:45ZengElsevierAlexandria Engineering Journal1110-01682024-12-011091828Intelligent faculty evaluation and ranking system based on N-framed plithogenic fuzzy hypersoft set and extended NR-TOPSISUsman Afzal0Muhammad Rayees Ahmad1Nazek Alessa2Nauman Raza3Fathea M.O. Birkea4Salem Alkhalaf5Nader Omer6Department of Mathematics, University of Management and Technology, Lahore, PakistanDepartment of Mathematics, University of Management and Technology, Lahore, PakistanDepartment of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Mathematics, University of the Punjab, Lahore, Pakistan; Department of Mathematics, Near East University, TRNC Mersin 10, Nicosia 99138, TurkeyDepartment of Mathematics, Faculty of Science, Northern Border University, Arar, Saudi ArabiaDepartment of Computer Engineering, College of Computer, Qassim University, Buraydah, Saudi ArabiaDepartment of Information Systems, College of Computing and Information Technology, University of Bisha, Bisha 61922, Saudi Arabia; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S1110016824009591Faculty evaluationMulti-criteria decision making (MCDM)Plithogenic hypersoft set (PHSS)N-framed PHSSExtended NR-TOPSIS |
| spellingShingle | Usman Afzal Muhammad Rayees Ahmad Nazek Alessa Nauman Raza Fathea M.O. Birkea Salem Alkhalaf Nader Omer Intelligent faculty evaluation and ranking system based on N-framed plithogenic fuzzy hypersoft set and extended NR-TOPSIS Alexandria Engineering Journal Faculty evaluation Multi-criteria decision making (MCDM) Plithogenic hypersoft set (PHSS) N-framed PHSS Extended NR-TOPSIS |
| title | Intelligent faculty evaluation and ranking system based on N-framed plithogenic fuzzy hypersoft set and extended NR-TOPSIS |
| title_full | Intelligent faculty evaluation and ranking system based on N-framed plithogenic fuzzy hypersoft set and extended NR-TOPSIS |
| title_fullStr | Intelligent faculty evaluation and ranking system based on N-framed plithogenic fuzzy hypersoft set and extended NR-TOPSIS |
| title_full_unstemmed | Intelligent faculty evaluation and ranking system based on N-framed plithogenic fuzzy hypersoft set and extended NR-TOPSIS |
| title_short | Intelligent faculty evaluation and ranking system based on N-framed plithogenic fuzzy hypersoft set and extended NR-TOPSIS |
| title_sort | intelligent faculty evaluation and ranking system based on n framed plithogenic fuzzy hypersoft set and extended nr topsis |
| topic | Faculty evaluation Multi-criteria decision making (MCDM) Plithogenic hypersoft set (PHSS) N-framed PHSS Extended NR-TOPSIS |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824009591 |
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