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Evaluation of Students Performance using Hierarchical Fuzzy Inference System

by Abdulkadir Abdullahi, Wang Peng, Abdullahi S. Saheed
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 177 - Number 23
Year of Publication: 2019
Authors: Abdulkadir Abdullahi, Wang Peng, Abdullahi S. Saheed
10.5120/ijca2019919690

Abdulkadir Abdullahi, Wang Peng, Abdullahi S. Saheed . Evaluation of Students Performance using Hierarchical Fuzzy Inference System. International Journal of Computer Applications. 177, 23 ( Dec 2019), 39-45. DOI=10.5120/ijca2019919690

@article{ 10.5120/ijca2019919690,
author = { Abdulkadir Abdullahi, Wang Peng, Abdullahi S. Saheed },
title = { Evaluation of Students Performance using Hierarchical Fuzzy Inference System },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2019 },
volume = { 177 },
number = { 23 },
month = { Dec },
year = { 2019 },
issn = { 0975-8887 },
pages = { 39-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume177/number23/31040-2019919690/ },
doi = { 10.5120/ijca2019919690 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:46:44.651789+05:30
%A Abdulkadir Abdullahi
%A Wang Peng
%A Abdullahi S. Saheed
%T Evaluation of Students Performance using Hierarchical Fuzzy Inference System
%J International Journal of Computer Applications
%@ 0975-8887
%V 177
%N 23
%P 39-45
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Fuzzy Inference Systems (FIS) has often been used to evaluate performance using few input variables as a result of fear for rules explosion. This problem is solved using Hierarchical Fuzzy Inference System (HFIS); a divide-and-conquer approach that drastically reduce the number of rules at the same time preserved the fuzzy logic reasoning. As a result, this study explore the potential of this tool in details by applying it to evaluate students’ exam records. The proposed model is compared to classical one and results show that HFIS is more promising from the perspective of simplicity and precision. However, for optimum results, the study suggests training FIS with neural networks and emerging optimization algorithms.

References
  1. Z. Lotfi Aliaska, “Fuzzy Sets,” Inf. Control, no. 8, pp. 338–353, 1965.
  2. B. S. Ionescu and C. Prichici, “Potential Beneficiaries of Cloud Account- ing technology : small or large Companies ?,” Manager, no. 17, pp. 282–292, 2013.
  3. P. Kaushal, N. Mohan, P. S. S, and hu, “Relevancy of Fuzzy Concept in Mathematics,” Int. J. Innov. Manag. Technol., vol. 1, no. 3, pp. 1–4, 2010.
  4. K. Tai, A. R. El-Sayed, M. Biglarbegian, C. I. Gonzalez, O. Castillo, and S. Mahmud, “Review of recent type-2 fuzzy controller applications,” Algorithms, vol. 9, no. 2, 2016.
  5. C. Wang, “A Study of Membership Functions on Mamdani-Type Fuzzy Inference S,” Theses Diss., vol. Paper 1665, 2015.
  6. A. Çebİ and H. Karal, “An application of fuzzy analytic hierarchy process ( FAHP ) for evaluating students ’ project,” vol. 12, no. 3, pp. 120–132, 2017.
  7. C. -m. Au Yeung and H.-F. Leung, “A Formal Model of Ontology for Handling Fuzzy Membership and Typicality of Instances,” Comput. J., vol. 53, no. 3, pp. 316–341, Mar. 2010.
  8. R. S. Yadav, A. K. Soni, and S. Pal, “A study of academic performance evaluation using fuzzy logic techniques,” 2014 Int. Conf. Comput. Sustain. Glob. Dev. INDIACom 2014, no. May, pp. 48–53, 2014.
  9. W. Xu, S. Zheng, and Z. Zhang, “The Application of Fuzzy Decision in the Management for Undergraduates,” vol. 68, no. Ssmi 2018, pp. 276–281, 2019.
  10. V. Ojha, A. Abraham, and V. Snášel, “Heuristic Design of Fuzzy Inference Systems: A Review of Three Decades of Research,” Eng. Appl. Artif. Intell., vol. 85, pp. 845–864, 2019.
  11. R. Makkar and C. Renu Makkar, “Application of fuzzy logic: A literature review Renu Makkar,” Int. J. Stat. Appl. Math., vol. 3, no. 1, pp. 357–359, 2018.
  12. G. Hayward and V. Davidson, “ANALYST Fuzzy logic applications,” pp. 1304–1306, 2003.
  13. P. Roseline, N. Ganesan, B. Clarence, and J. M. Tauro, “A Study of Applications of Fuzzy Logic in Various Domains of Agricultural Sciences,” Int. J. Comput. Appl., pp. 975–8887, 2015.
  14. L. Ouboubker, M. Khafallah, and J. Lamterkati, “DTC two level and three level inverters of Induction Motor with speed control using Fuzzy Logic Controller,” no. March, 2014.
  15. J. Azimjonov, “EVALUATION OF DISTANCE LEARNING STUDENTS PERFORMANCE USING,” no. October, 2016.
  16. S. H. Muhammad, S. S. Abdullahi, and M. ABDULRASHEED, “The Need to Accelerate Cloud Adoption in Developing Countries of Africa.” .
  17. P. Gupta, “Applications of Fuzzy Logic in Daily life,” vol. 3, no. June 1989, pp. 66–70, 2017.
  18. N. ARSLAN NAMLI and O. ŞENKAL, “Using the Fuzzy Logic in Assessing the Programming Performance of Students,” Int. J. Assess. Tools Educ., vol. 5, no. 4, pp. 701–712, 2018.
  19. A. Shaout and M. K. Yousif, “Performance Evaluation – Methods and Techniques Survey,” Int. J. Comput. Inf. Technol., vol. 03, no. 05, p. 2279764, 2014.
  20. D. Wang, X.-J. Zeng, and J. A. Keane, “A Survey of Hierarchical Fuzzy Systems (Invited Paper),” 2006.
  21. R. R. Yager, “On a Hierarchical Structure for Fuzzy Modeling and Control,” IEEE Trans. Syst. Man Cybern., vol. 23, no. 4, pp. 1189–1197, 1993.
Index Terms

Computer Science
Information Sciences

Keywords

Hierarchical Fuzzy Inference System (HFIS) Fuzzy logic Membership function student performance MATLAB

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