Career selection of students using hybridized distance measure based on picture fuzzy set and rough set theory

  • Rekha Sahu School of Computer Engineering, KIIT University, Bhubaneswar, India
  • Satya Ranjan Dash School of Computer Engineering, KIIT University, Bhubaneswar, India
  • Sujit Das Deptartment of Computer Science and Engineering, National Institute of Technology Warangal, Warangal, India
Keywords: Picture fuzzy set, distance measure, rough set, indiscernible relation, students’ career

Abstract

Since the future of the society depends upon the role of students and their services construct the prosperous and advanced society, so suitable career selection for the students' is considered to be an important problem to explore. As per psychology, if a student has the required capability, and positive attitudes towards a subject in terms of interest, attitude, memory, knowledge, environment, and career, then the student will achieve more in that subject. To consider this kind of uncertain issues, picture fuzzy set and rough set are found to be appropriate due to their inherent characteristics to deal with incomplete and imprecise information. In this study, picture fuzzy set and rough set-based approaches are proposed to help the student to choose an appropriate subject and consequently provide a good service or contribution to the society particularly in that domain. The main purpose of the article is to analyse student's features in terms of career, memory, interest, knowledge, environment and attitude and then predict the appropriate stream for making the career comfortable so that the student can conveniently explore much in that area. To select students' career, a hybridized distance measure under picture fuzzy environment is proposed where the evaluating information regarding students, subjects and student's features are given in picture fuzzy numbers. In this paper, two types of hybridization approaches are proposed which are the hybridization of Hausdorff and Hamming distance measures and hybridization of Hausdorff and Euclidean distance measures. Next, we apply rough set theory to determine whether a particular subject is appropriate for a student even if there is controversy to select a stream. Lower and higher approximation with boundary region of rough set theory is used to manage inconsistency situations. Finally, two case studies are demonstrated to validate the applicability of the proposed idea.

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References

Alkaya, S. A., Yaman, Ş., & Simones, J. (2018). Professional values and career choice of nursing students. Nursing ethics, 25(2), 243-252.

Aspert, N., Santa-Cruz, D., & Ebrahimi, T. (2002) Mesh: Measuring errors between surfaces using the hausdorff distance. In Proceedings. IEEE international conference on multimedia and expo (ICME), Iausanne, Switzerland, August 26-29, 2002 (Vol. 1, pp. 705-708).

Babajide, O. J., & Esther, O. O. (2020). Factors Influencing Career Choice Among Students Of The Schools Of Nursing And Midwifery In Akure. Lautech Journal Of Nursing (LJN), 7(1), 91-96.

Batool, T., & Raiz, J. (2020). Exploring Parents Involvement in University Students Education. Journal of Business and Social Review in Emerging Economies, 6(1), 187-196.

Burns, M. K., Davidson, K., Zaslofsky, A. F., Parker, D. C., & Maki, K. E. (2018). The relationship between acquisition rate for words and working memory, short-term memory, and reading skills: Aptitude-by-treatment or skill-by-treatment interaction?. Assessment for Effective Intervention, 43(3), 182-192.

Carrico, C., Matusovich, H. M., & Paretti, M. C. (2019). A qualitative analysis of career choice pathways of college-oriented rural central Appalachian high school students. Journal of Career Development, 46(2), 94-111.

Cuong, B. C. (2014). Picture fuzzy sets. Journal of Computer Science and Cybernetics, 30(4), 409-420.

Cuong, B. C., & Kreinovich, V. (2013). Picture Fuzzy Sets-a new concept for computational intelligence problems. Proceedings of the Third World Congress on Information and Communication Technologies (WICT), Hanoi, Vietnam, December 15-18, 2013, pp. 1-6.

Cuong, B. C., & Thong, P. H. (2018). Picture Fuzzy Rough Soft Sets: a new concept for Soft computing problems. Proceedings of the 7th International Conference on High Performance Scientific Computing (HPSC), Hanoi, Vietnam, March 19-23, 2018.

Cuong, B. C., & Thong, P. H. (2018). Two new concepts" Picture Fuzzy Rough Soft Sets" and" Picture Fuzzy Dynamic Systems" in Picture Fuzzy Systems. Proceedings of the 5th NAFOSTED Conference on Information and Computer Science (NICS), Ho Chi Minh City, Vietnam, November 23-24, 2018, pp. 87-92.

Das, S., Malakar, D., Kar, S., Pal, T. (2018). A brief review and future outline on decision making using fuzzy soft set, International Journal of Fuzzy System Applications, 7 (2), 1-43.

De, A., Das, S., Kar, S. (2019). Multiple Attribute Decision Making Based on Probabilistic Interval-valued Intuitionistic Hesitant Fuzzy Set and Extended TOPSIS Method. Journal of Intelligent & Fuzzy Systems, 37 (4), 5229-5248.

Dutta, P. (2018). Medical diagnosis based on distance measures between picture fuzzy sets. International Journal of Fuzzy System Applications (IJFSA), 7(4), 15-36.

Goel, S., Angeli, F., Dhirar, N., Singla, N., & Ruwaard, D. (2018). What motivates medical students to select medical studies: a systematic literature review. BMC Medical Education, 18(1), 1-10.

Griffin, B., & Hu, W. (2019). Parental career expectations: effect on medical students’ career attitudes over time. Medical education, 53(6), 584-592.

Guido, R. M. D. (2013). Attitude and motivation towards learning physics. International Journal of Engineering Research & Technology (IJERT), 2(11), arXiv:1805.02293.

Hannula, M. S. (2002). Attitude towards mathematics: Emotions, expectations and values. Educational studies in Mathematics, 49(1), 25-46.

Holloway-Friesen, H. (2018). Acculturation, enculturation, gender, and college environment on perceived career barriers among Latino/a college students. Journal of Career Development, 45(2), 117-131.

Kumbhar K., Das S. (2020). Solving Multi-attribute Decision-Making Problems Using Probabilistic Interval-Valued Intuitionistic Hesitant Fuzzy Set and Particle Swarm Optimization. In: Dutta D., Mahanty B. (eds) Numerical Optimization in Engineering and Sciences. Advances in Intelligent Systems and Computing, 979. Springer, Singapore. https://doi.org/10.1007/978-981-15-3215-3_14

Madden, A. D., Webber, S., Ford, N., & Crowder, M. (2018). The relationship between students’ subject preferences and their information behaviour. Journal of Documentation, 74(4), 692-721.

McKenzie, K., Murray, A., Cooper, M., Martin, R., Murray, K., Baguley, C., & Chiscop, A. (2020). An exploration of the factors influencing career choice in mental health. Journal of Clinical Nursing, 29(19-20), 3764-3773.

Natividad, M. C. B., Gerardo, B. D., & Medina, R. P. (2019). A fuzzy-based career recommender system for senior high school students in K to 12 education. MS&E, 482(1), 012025.

Nehmeh, G., & Kelly, A. (2018). Women physicists and sociocognitive considerations in career choice and persistence. Journal of Women and Minorities in Science and Engineering 24(2),95–119.

Nguyen, J., Sánchez-Hernández, G., Armisen, A., Agell, N., Rovira, X., & Angulo, C. (2018). A linguistic multi-criteria decision-aiding system to support university career services. Applied Soft Computing, 67, 933-940.

Nie, M., Yang, L., Sun, J., Su, H., Xia, H., Lian, D., & Yan, K. (2018). Advanced forecasting of career choices for college students based on campus big data. Frontiers of Computer Science, 12(3), 494-503.

Orewere, E. O. R. A., & Ojochogu, D. (2020). The Effect of Guidance and Counselling Services on Students Career Choice in Selected Secondary Schools of Jos Metropolis. International Journal of Education and Evaluation, 6(1), 34-42.

Pawlak, Z. (1995). Vagueness and uncertainty: a rough set perspective. Computational intelligence, 11(2), 227-232.

Peker, M., Gürüler, H., Şen, B., & İstanbullu, A. (2017). A new fuzzy logic-based career guidance system: WEB-CGS. Technical Gazette 24, 6(2017), 1863-1868.

Pratiwi, F., Syakurah, R. A., Yuliana, I., & Siburian, R. (2020, June). Relationships of Self-Efficacy, Outcome Expectation, Career Intention and Career Exploration in Nutrition Science Student’s Career Choice. In 2nd Sriwijaya International Conference of Public Health (SICPH 2019) (pp. 302-309). Atlantis Press.

Ramentol, E., Madera, J., & Rodríguez, A. (2019). Early detection of possible undergraduate drop out using a new method based on probabilistic rough set theory. In Uncertainty Management with Fuzzy and Rough Sets (pp. 211-232). Springer, Cham.

Si, A., Das, S., Kar, S. (2019). An approach to rank picture fuzzy numbers for decision making problems, Decision Making: Applications in Management and Engineering, 2(2), 54-64.

Si, A., Das, S., Kar, S. (2020). Extension of TOPSIS and VIKOR Method for Decision-Making Problems with Picture Fuzzy Number. In: Mandal J., Mukhopadhyay S. (eds) Proceedings of the Global AI Congress, Kolkata, September 12-14, 2019, Advances in Intelligent Systems and Computing, vol 1112. pp. 563-577.

Silseth, K. (2018). Students’ everyday knowledge and experiences as resources in educational dialogues. Instructional Science, 46(2), 291-313.

Son, L. H. (2016). Generalized picture distance measure and applications to picture fuzzy clustering. Applied Soft Computing, 46(C), 284-295.

Tan, A., Wu, W. Z., Qian, Y., Liang, J., Chen, J., & Li, J. (2018). Intuitionistic fuzzy rough set-based granular structures and attribute subset selection. IEEE Transactions on Fuzzy Systems, 27(3), 527-539.

Tugrul, F., Gezercan, M., & Citil, M. (2017). Application of intuitionistic fuzzy set in high school determination via normalized euclidean distance method. Notes on Intuitionistic Fuzzy Sets, 23(1), 42-47.

Van Dinh, N., Thao, N. X., & Chau, N. M. (2019). Distance and dissimilarity measure of picture fuzzy sets. PROCEEDING of Publishing House for Science and Technology, 1(1), 104-109.

Wang, J., Yang, M., Lv, B., Zhang, F., Zheng, Y., & Sun, Y. (2020). Influencing Factors of 10th Grade Students' Science Career Expectations: A Structural Equation Model. Journal of Baltic Science Education, 19(4), 675-686.

Wen, L., Yang, H., Bu, D., Diers, L., & Wang, H. (2018). Public accounting vs private accounting, career choice of accounting students in China. Journal of Accounting in Emerging Economies, 8(1), 124-140.

Yu, B., Cai, M., Dai, J., & Li, Q. (2020). A novel approach to predictive analysis using attribute-oriented rough fuzzy sets. Expert Systems with Applications, 161, 113644.

Published
2021-03-13
How to Cite
Sahu , R., Dash , S. R., & Das, S. (2021). Career selection of students using hybridized distance measure based on picture fuzzy set and rough set theory. Decision Making: Applications in Management and Engineering, 4(1), 104-126. https://doi.org/10.31181/dmame2104104s