AUTHORS: Ashnil Mandaliya, Manoj Sahni, Rajkumar Verma
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A lot of career opportunities are available for high school students. But choosing a right career becomes a most difficult task. The objective of this paper is to help students to choose suitable career after completion of High School. It is based on the student’s perception of their own and their Teacher’s marks using generalized intuitionistic fuzzy divergence measure. The intuitionistic fuzzy divergence measure is used in table to predict careers suitable for students using the aggregated intuitionistic fuzzy values. The aggregated marks of students and teachers perception are converted in the form of intuitionistic fuzzy values and are shown in table. The final table shows the fuzzy divergence values and the minimum value in the table will be the preferable choice of the students to choose career depending on the marks given by students and teachers perception in six subjects – Maths, Physics, Chemistry, Biology, Computer and English.
KEYWORDS: Fuzzy sets, Intuitionistic Fuzzy Sets, Aggregator operator, Divergence Measure, Career Determination.
REFERENCES:
[1] Zadeh, L.A., Fuzzy Sets, Inform. and Control,
Vol. 8, 1965, pp. 338 -353.
[2] Atanassov, K.T., Intuitionistic Fuzzy sets,
Fuzzy Sets Syst., Vol. 20, 1986, pp. 87 – 96.
[3] Atanassov, K.T., Intuitionistic Fuzzy Sets Past,
Present and Future, Proceedings of the 3rd
Conference of the European Society for Fuzzy
Logic and Technology, Zittau, Germany,
September, 2003, pp. 10 – 12.
[4] Ejegwa, P.A., Akubo, A.J., Joshua, O.M.,
Intuitionistic Fuzzy Set and Its Application in
Career Determination via Normalized
Euclidean Distance Method, Eur. Sci. J., Vol.
10, No. 15, 2014, pp. 529 – 536.
[5] Yager, R.R., Some Aspects of Intuitionistic
Fuzzy Sets, Fuzzy Optim. Decis. Making, Vol.
8, 2009, pp. 67 – 90.
[6] Xu Z., Yager, R.R., Dynamic Intuitionistic
Fuzzy Multi-attribute Decision Making, Int. J.
Approx. Reason., Vol. 48, 2008, pp. 246 – 262.
[7] Szmidt, E., Kacprzyk, J., Intuitionistic Fuzzy
Sets in Some Medical Applications, Sofia 22-
23 Sept. 2001, NIFS 7, Vol. 4, 2001, pp. 58 –
64.
[8] Kozae, A.M., Elshenawy A., Omran, M.,
Intuitionistic Fuzzy Set and Its Application in
Selecting Specialization: A Case Study for
Engineering Students, Int. J. Math. Anal. Appl.,
Vol. 2, No. 6, 2015, pp. 74 – 78.
[9] Szmidt E., Kacprzyk, J., Distances between
Intuitionistic Fuzzy Sets, Fuzzy Sets and
Systems, Vol. 114, No. 3, 2000, pp. 505 – 518.
[10] Szmidt E., Kacprzyk, J., Distances between
Intuitionistic Fuzzy sets: A straight forward
approaches may not work,” 3rd International
IEEE Conference Intelligent Systems, 2006.
[11] Hatzimichailidis, A.G., Papakostas G.A.,
Kaburlasos, V.G.: A Novel Distance Measure
of Intuitionistic Fuzzy Sets and Its Application
to Pattern Recognition Problems, International
Journal of Intelligent Systems, Vol. 27, 2012,
pp. 396 – 409.
[12] Dubois D., Prade, H., A Review of Fuzzy Set
Aggregation Connectives, Inf. Sci., Vol. 36,
1985, pp. 85 – 121.
[13] Xu, Z., Intuitionistic Fuzzy Aggregation
Operators, IEEE Transactions on Fuzzy
Systems, Vol. 15, No. 6, 2007, pp. 1179 – 1187.
[14] Yager, R.R.: On Ordered Weighted Averaging
Aggregation Operators in Multicriteria
Decision Making, IEEE Transactions on
Systems, Man and Cybernetics, Vol. 18, No. 1,
1988, pp.183 – 190.
[15] Verma R., Sharma, B.D., On Generalized
Intuitionistic Fuzzy Divergence (Relative
Information) and Their Properties, J. Uncertain
Systems, Vol. 6, No. 4, 2012, pp. 308 – 320.
