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Abstract

Allocating university resources, especially defining the number of necessary student groups and laboratory classes is a hard task without knowing the exact number of students who will enroll in the given courses. This number usually depends on the exam results of the prerequisite courses. However, the planning of the next term has to be done some months before the end of the actual term. This paper presents the creation of a fuzzy model that can predict the student results in case of the Visual Programming course with an acceptable accuracy based on nine input factors describing the relevant history of the student. The model has a low complexity rule base containing only 28 rules and predicts the exam result using fuzzy rule interpolation based inference. The position of the rule consequent sets as well as the rule weights were tuned by particle swarm optimization. The root mean squared error expressed in percentage of the output range was less than 13% in case of all the training, validation and test datasets, which gives a satisfactory level of information for the planning of the number of student groups and laboratory classes in the next term in case of the next course that follows the examined Visual Programming course.

Keywords

Prediction exam results fuzzy rule interpolation particle swarm optimization

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References

Baranyi ,

L.T.

Koczy and

T.D.

Gedeon , A generalized concept for fuzzy rule interpolation, in: IEEE Transaction on Fuzzy Systems 12(6) (2004), 820–837.

S.M.

Chen and

Y.K.

Ko , Fuzzy interpolative reasoning for sparse fuzzy rule-based systems based on alpha-cuts and Transformations techniques, in: IEEE Transactions on Fuzzy Systems, 16(6) (2008), 1626–1648.

E.H.

Guechi ,

Lauber ,

Dambrine ,

Klancar and

Blazic , PDC control design for non-holonomic wheeled mobilerobots with delayed outputs, in: Journal of Intelligent and Robotic Systems 60(3-4) (2010), 395–414.

Z.H.

Huang and

Shen , Fuzzy interpolation with generalized representative values, Proceedings of the UK Workshop on Computational Intelligence, 2004, pp. 161–171.

Z.C.

Johanyak , A modified particle swarm optimization algorithm for the optimization of a fuzzy classification subsystem in a series hybrid electric vehicle, in: Technicki Vjesnik - Technical Gazette 24(Suppl. 2) (2017), 295–301.

Z.C.

Johanyak , New Initial Fuzzy System Generation Features in the SFMI Toolbox, Proceedings of the 5th IEEE International Symposium on Logistics and Industrial Informatics (LINDI), 2013, pp. 29–34.

Z.C.

Johanyak , Performance Enhancement of the Fuzzy Rule Interpolation Method FRISUV by Rule Weights, Proceedings of the 6th Gyor Symposium and 3rd Hungarian-Polish and 1st Hungarian-Romanian Joint Conference on Computational Intelligence, 2014, pp. 147–151.

Z.C.

Johanyak and

Kovacs , Fuzzy Rule Interpolation by the Least Squares Method, Proceedings of the 7th International Symposium of Hungarian Researchers on Computational Intelligence (HUCI), 2006, pp. 495–506.

Kennedy and

Eberhart , Particle Swarm Optimization, Proceedings of IEEE International Conference on Neural Networks, 1995, pp. 1942–1948.

10.

L.T.

Koczy and

Hirota , Approximate reasoning by linear rule interpolation and general approximation, in: International Journal of Approximative Reasoning 9 (1993), 197–225.

11.

Kovacs , Extending the Fuzzy Rule Interpolation "FIVE" by Fuzzy Observation, in: Advances in Soft Computing, Computational Intelligence, Theory and Applications, Bernd Reusch (Ed.), Springer Germany, 2006, pp. 485–497.

12.

E.H.

Mamdani and

Assilian , An experiment in linguistic synthesis with a fuzzy logic controller, in: International Journal of Man Machine Studies 7 (1975), 1–13.

13.

Moallem ,

B.S.

Mousavi and

S.Sh.

Naghibzadeh , Fuzzy inference system optimized by genetic algorithm for robust face and pose detection, in: International Journal of Artificial Intelligence 13(2) (2015), 73–88.

14.

Pokoradi and

Menyhart , Electric Vehicles' Battery Parameter Tolerances Analysis by Fuzzy Logic, Proceedings of thellth IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI), 2016, pp. 361–364.

15.

R.E.

Precup ,

M.C.

Sabau and

E.M.

Petriu , Nature-inspired optimal tuning of input membership functions of Takagi-Sugeno-Kang fuzzy models for anti-lock braking systems, in: Applied Soft Computing 27(2) (2015), 575–589.

16.

R.E.

Precup and

M.L.

Tomescu , Stable fuzzy logic control of a general class of chaotic systems, in: Neural Computing and Applications 26(3) (2015), 541–550.

17.

Shepard , A two dimensional interpolation function for irregularly spaced data, Proceedings of the 23rd ACM International Conference, 1968, pp. 517–524.

18.

Takagi and

Sugeno , Fuzzy identification of systems and its applications to modeling and control, in: IEEE Transactions on System, Man and Cybernetics 15(1) (1985), 116–132.

19.

Vascakand

Pal'a , Adaptation of Fuzzy Cognitive Maps for Navigation Purposes by Migration Algorithms, in: International Journal of Artificial Intelligence 8(S12) (2012), 20–37.

20.

Vincze , Fuzzy Rule Interpolation and Reinforcement Learning, Proceedings of the 15th IEEE International Symposium on Applied Machine Intelligence and Informatics, 2017, pp. 173–178.

21.

K.W.

Wong ,

Tikk ,

T.D.

Gedeon and

L.T.

Koczy , Fuzzy rule interpolation for multidimensional input spaces with applications, in: IEEE Transactions on Fuzzy Systems 13(6) (2005), 809–819.

22.

L.A.

Zadeh , Fuzzy sets, in: Information and Control 8(3) (1965), 338–353.

Fuzzy rule interpolation based model for student result prediction

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