Efficient smart system fuzzy logic model for determining candidates’ performances for university admission in Nigeria


  • P.O. Adebayo Federal Polytechnic Nasarawa, Nasarawa State, Nigeria
  • W.B. Yahya Department of Statistics, University of Ilorin, P.M.B. 1515, Ilorin, Nigeria
  • E.E. Akpan Federal Polytechnic Nasarawa, Nasarawa State, Nigeria
  • M.K. Garba Department of Statistics, University of Ilorin, P.M.B. 1515, Ilorin, Nigeria


Fuzzy Logic; Smart System Model; UTME; Post-UTME; UTME/‘O’Lpoints


This paper depicts adaptation of expert systems technology using fuzzy logic to handle qualitative and uncertain facts in the decision making process. Over the years, performance evaluations of students are based on qualitative facts, which are now becoming numerically inestimable as a result of uncertainty factors. Through fuzzy logic the qualitative terms like; low, medium and high; low, moderate and high were numerically weighted during the final decision making on students’ performance. The key parameters were given weights according to their priorities through mapping of numeric results from uncertain knowledge. Mathematical formulae were applied to calculate the numeric results at the final stage. In this way, the developed fuzzy expert system was demonstrated to be an effective tool for evaluating the performances of candidates seeking for admission into Nigeria tertiary institutions. This may also be adopted as a useful tool by stakeholders in government and Industry to predict the standard and long term expectations in the nation-building enterprise.


Adebayo, P.O., 2009. Implementation of Fuzzy Logic control Decision System: A case Study of Admission Progress in University of Ilorin. M.Sc. Thesis (Unpublished).

Akinyokun, O.C., 2002. Neuro-Fuzzy Expert System For Evaluation of Human Resource Performance, Inaugural Lecture. Federal Univ. Technol., Akure, Nigeria.

Amin, H., 2009. An Intelligent Frame work for teachers ‘performance evaluation at higher education institutions of Pakistan. M.Sc. Thesis. Inst. Inform. Technol.Kohat Univ. Sci. Technol. Kohat, NWFP, Islamic Republ. Pak., (Unpublished)

Bai, S.M., Chen, S.M., 2006. A new method for students‘ learning achievement using fuzzy membership functions. Proc. 11thconfer. Artific. Intell., Kaohsiung, Taiwan. Republ. China.

Chang, D.F., Sun, C.M., 1993. Fuzzy assessment of learning performance of junior high school students. Proceedings of the 1993 first national symposium on fuzzy theory and applications, Hsinchu, Taiwan. Republ. China., 1–10.

Chen, S.M., Lee, C.H., 1999. New methods for students’ evaluating using fuzzysets. Fuzzy Sets System., 104: 209–218.

Hapfelmeier, A., Yahya, W.B., Rosenberg, R., Ulm, K., 2012. Predictive modellingof gene Expression data. In: Handbook of Statistics in Clinical Oncology, 3rd ed., Edited by Crowley, J. A. Hoer. Chapman and Hall/CRC, New York, pp, 463-475.

Kazeem, K., 2004. Redressing Cultism in Higher Institution. Paper Pres. Univ. Benin.

Kazeem, K., Ige, O., 2010. Redressing The Growing Concern of The Education Sector in Nigeria. Edo j. Counsel., 3(1), pp 40 - 48

Mohammad, M.A., 2011. Learning with Local decision expert to consult next case study: Athemia Diagnosis. Inter. J. innovate. Comput. inform. Control., 7(1), 1-5

Odukoya, D., 2009. Formulation and Implementation of Educational Policies in Nigeria.URL: http://www.slideshare.net/ernwaca/formulation-and-implementation-of-educational-policies-in-nigeria. Accessed on: 20/08/2012.

Omolewa, M., 200l. The Challenges of Education in Nigeria. University Press, Ibadan.

Robert, O.D., 2010. Yar’adua 7-Point Agenda, the MDGs and Sustainable Development in Nigeria. Vol. 10 Issue 4(ver. 1,0) August 2010. URL: https://globaljournals.org/GJHSS_Volume10/1-Yaradua-7-Point-Agenda-the-Mdgs-and-Sustainable-Development-in-Nigeria.pdf Accessed on: 02/01/2015.

Wu, M.H., 2003. Research on applying fuzzy set theory and item response theory to evaluate learning performance. M.Sc. Thesis. Dep. Inform. Manag. Chaoyang Univ. Technol., Wufeng, Taichung County, Taiwan, Republ. China.

Yahya, W.B., 2009. Sequential dimension reduction and prediction methods with high-dimensional microarray data. Universitätsbibliothek, Ludwig- Maximilians-Universität, München, Germany. Ph.D. Thesis. URL: http://edoc.ub.uni-muenchen.de/10254.

Yahya, W.B., 2012. Genes Selection and Tumour Classifications in Cancer Research: A New Approach. Lambert Academ. Publ., Saarbrücken, Germany.

Yahya, W.B., Oladiipo, M.O., Jolayemi, E.T., 2012. A fast algorithm to construct neural networks classification models with high-dimensional genomic data. Annals. Comput. Sci.Ser., 10 (1):, 39-58.

Yahya, W.B., Rosenberg, R., Ulm, K., 2014. Microarray-based Classification of Histopathologic Responses of Locally Advanced Rectal Carcinomas to Neoadjuvant Radiochemotherapy Treatment. Turk. Klinikleri J. Biostat., 6(1), 8-23.

Yahya, W.B., Ulm, K., Fahrmeir, L., Hapfelmeier, A., 2011. k-SS: a sequential feature selection and prediction method in Microarray Study. Int’l. J. Artific. Intell., 6(S11), 19-47.

Zhang, B., 2007. A Fuzzy-Logic-Based Methodology for Batch Process Scheduling. URL: http://www.sys.virginia.edu /sieds06/papers/FMorningSession5.3.pdf. Access. on: 30/11/2014.

Zimmermann, H.J., 1996. Fuzzy Set Theory and its Applications. 3rd ed., Kluwer Academ. Publ., Boston.



How to Cite

P.O. Adebayo, W.B. Yahya, E.E. Akpan, & M.K. Garba. (2015). Efficient smart system fuzzy logic model for determining candidates’ performances for university admission in Nigeria. Scientific Journal of Pure and Applied Sciences, 4(1), 16-27. Retrieved from http://sjournals.com/index.php/sjpas/article/view/518



Computer and Information Science