Abstract
In this paper, we present the model of assessment of higher education school academic staff members rating based on the complex use of Harrington’s desirability function and a fuzzy inference system. Four main directions of activity were evaluated within the framework of the proposed model: pedagogical activity; science and research; organization activity and other activity types. Each of the proposed types of activities was assessed separately using the Harrington desirability function based on prior determined scores, where the general activity index of the appropriate score was calculated as the weighted average of the private desirability values. The Mamdani inference algorithm with triangular and trapezoidal membership functions was used within the framework of the fuzzy inference process implementation to evaluate the general rating score of the appropriate academic staff member. The base of rules was proposed to form the output for general rating scores calculated based on the individual rating scores. The output of the system was presented for each of the appropriate activity types separately and based on the assessment of the general rating score that contained the separate rating scores as the components. To our best mind, the proposed model can help us to optimize the functional possibilities of higher school system operation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Babanezhad, M., Behroyan, I., Nakhjiri, A., Marjani, A., Shirazian, S.: Performance and application analysis of ANFIS artificial intelligence for pressure prediction of nanofluid convective flow in a heated pipe. Sci. Rep. 11(1), Article no. 902 (2021). https://doi.org/10.1038/s41598-020-79628-w
Babichev, S., Durnyak, B., Zhydetskyy, V., Pikh, I., Senkivskyy, V.: Application of optics density-based clustering algorithm using inductive methods of complex system analysis. In: IEEE 2019 14th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2019 - Proceedings, pp. 169–172 (2019). https://doi.org/10.1109/STC-CSIT.2019.8929869
Babichev, S., Škvor, J.: Technique of gene expression profiles extraction based on the complex use of clustering and classification methods. Diagnostics 10(8), Article no. 584 (2020). https://doi.org/10.3390/diagnostics10080584
Chyrun, L., Kravets, P., Garasym, O., Gozhyj, A., Kalinina, I.: Cryptographic information protection algorithm selection optimization for electronic governance IT project management by the analytic hierarchy process based on nonlinear conclusion criteria. In: CEUR Workshop Proceedings, vol. 2565, pp. 205–220 (2020)
Gu, X., Angelov, P., Zhao, Z.: Self-organizing fuzzy inference ensemble system for big streaming data classification. Knowl.-Based Syst. 218, Article no. 106870 (2021). https://doi.org/10.1016/j.knosys.2021.106870
Harrington, J.: The desirability function. Ind. Qual. Control 21(10), 494–498 (1965)
Izonin, I., Tkachenko, R., Verhun, V., et al.: An approach towards missing data management using improved GRNN-SGTM ensemble method. Int. J. Eng. Sci. Technol. (2020, in Press). https://doi.org/10.1016/j.jestch.2020.10.005
Kobets, V., Liubchenko, V., Popovych, I., Koval, S.: Institutional aspects of integrated quality assurance of study programs at HEI using ICT. In: CEUR Workshop Proceedings, vol. 2833, pp. 83–92 (2020)
Kurniawan, D., Utama, D.: Decision support model using FIM Sugeno for assessing the academic performance. Adv. Sci. Technol. Eng. Syst. 6(1), 605–611 (2021). https://doi.org/10.25046/aj060165
Lytvyn, V., Gozhyj, A., Kalinina, I., et al.: An intelligent system of the content relevance at the example of films according to user needs. In: CEUR Workshop Proceedings, vol. 2516, pp. 1–23 (2019)
Lytvyn, V., Salo, T., Vysotska, V., et al.: Identifying textual content based on thematic analysis of similar texts in big data. In: IEEE 2019 14th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2019 - Proceedings, vol. 2, pp. 84–91 (2019). https://doi.org/10.1109/STC-CSIT.2019.8929808
Marasanov, V., Sharko, A., Sharko, A., Stepanchikov, D.: Modeling of energy spectrum of acoustic-emission signals in dynamic deformation processes of medium with microstructure. In: 2019 IEEE 39th International Conference on Electronics and Nanotechnology, ELNANO 2019 - Proceedings, pp. 718–723 (2019). https://doi.org/10.1109/ELNANO.2019.8783809
Marasanov, V., Stepanchikov, D., Sharko, A., Sharko, A.: Technique of system operator determination based on acoustic emission method. Adv. Intell. Syst. Comput. 1246, 3–22 (2021). https://doi.org/10.1007/978-3-030-54215-3_1
Marasanov, V., Sharko, A., Sharko, A.: Energy spectrum of acoustic emission signals in coupled continuous media. J. Nano Electron. Phys. 11(3), Article no. 03027 (2019). https://doi.org/10.21272/jnep.11(3).03028
Rzheuskyi, A., Kutyuk, O., Vysotska, V., et al.: The architecture of distant competencies analyzing system for it recruitment. In: IEEE 2019 14th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2019 - Proceedings, vol. 3, pp. 254–261 (2019). https://doi.org/10.1109/STC-CSIT.2019.8929762
Tkachenko, R., Izonin, I., Kryvinska, N., et al.: An approach towards increasing prediction accuracy for the recovery of missing IoT data based on the GRNN-SGTM ensemble. Sensors (Switzerland) 20(9), Article no. 2625 (2020). https://doi.org/10.3390/s20092625
Xu, S., Feng, N., Liu, K., Liang, Y., Liu, X.: A weighted fuzzy process neural network model and its application in mixed-process signal classification. Expert Syst. Appl. 172, Article no. 114642 (2021). https://doi.org/10.1016/j.eswa.2021.114642
Zadeh, L.A., Abbasov, A.M., Shahbazova, S.N.: Fuzzy-based techniques in human-like processing of social network data. Int. J. Uncertainty Fuzziness Knowl.-Based Syst. 23, 1–14 (2015)
Zadeh, L.: Possibility theory and soft data analysis. In: Mathematical Frontiers of the Social and Policy Sciences, pp. 69–129 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Babichev, S., Spivakovsky, A., Omelchuk, S., Kobets, V. (2022). A Model for Assessing the Rating of Higher Education School Academic Staff Members Based on the Fuzzy Inference System. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 77. Springer, Cham. https://doi.org/10.1007/978-3-030-82014-5_30
Download citation
DOI: https://doi.org/10.1007/978-3-030-82014-5_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-82013-8
Online ISBN: 978-3-030-82014-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)