FUZZY LOGIC PROCEDURE FOR DRAWING UP A PSYCHOLOGICAL PROFILE OF LEARNERS FOR BETTER PERCEPTION IN COURSES

Authors

  • Krasimir Ognyanov Slavyanov "Vasil Levski" National Military University, Bulgaria (BG)

DOI:

https://doi.org/10.17770/etr2019vol2.4073

Keywords:

fuzzy inference system, fuzzy rule, membership function, psychological profile

Abstract

This article offers an original classification procedure based on Mamdani fuzzy inference system (FIS) dedicated to compute multiple criterions each from different type of psychological profiles. The modelling and information analysis of the FIS are developed to draw a general conclusion from several psychological criterions in order to provide better pre-course lecturer preparation and thus better students’ perception. Simulation experiments are carried out in MATLAB environment.

Downloads

Download data is not yet available.

References

Atanasov, V.T., Smart Educational Cluster Conceptualization, Proceedings of International Scientific Conference, Shumen, October 02th -04th, 2018, pp.173-181, ISSN 2367-7902

http://www.aadcf.nvu.bg/scientific_events/dft2018/V.%20T.%20Atanasov.pdf

Gardner H., Frames of Mind: The Theory of Multiple Intelligences, New York : Basic Books, ©1983. ISBN-978-0-465-02433-9, https://doi.org/10.1177/001698628502900212

Zadeh, L.A., Outline of a new approach to the analysis of complex systems and decision processes, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 3, No. 1, Jan. 1973, pp. 28-44. https://doi.org/10.1109/TSMC.1973.5408575

Zadeh, L.A., Fuzzy logic, Computer (Volume: 21, Issue: 4, April 1988), pp. 83 – 93. https://doi.org/10.1109/2.53

Al Ganideh S. F.; El Refae G. A., Socio- psychological variables as antecedents to consumer ethnocentrism: A fuzzy logic based analysis study, 2010 Annual Meeting of the North American Fuzzy Information Processing Society, 12-14 July 2010, ISBN: 978-1-4244-7858-3, 2010,1-6. https://doi.org/10.1109/NAFIPS.2010.5548274

Lefly M., Austin V., Match'em: using fuzzy logic to profile criminals, Proceedings of 6th International Fuzzy Systems Conference, 1997, vol.1, 305 – 311. https://doi.org/10.1109/FUZZY.1997.616386

Di Nuovo A., Di Nuovo S., Buono S., Cutello V., Benefits of fuzzy logic in the assessment of intellectual disability, 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2014, 1843 – 1850.

https://doi.org/10.1109/FUZZ-IEEE.2014.6891834

Zhang J., Wang X., Mahfouf M., Linkens D.A., Fuzzy Logic based Identification of Operator Functional States Using Multiple Physiological and Performance Measures, 2008 International Conference on BioMedical Engineering and Informatics, 2008 , Vol.1, 570 – 574.

https://doi.org/10.1109/BMEI.2008.181

Petrova A. K., Methods and algorithms of decision making support in the process of experts' qualification evaluation for the completion of centers of professional certification, 2017 IEEE II International Conference on Control in Technical Systems (CTS), 2017, 384 – 386.

https://doi.org/10.1109/CTSYS.2017.8109575

Fernandez S., Ito T., Driver classification for intelligent transportation systems using fuzzy logic, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), 2016, 1212 – 1216.

https://doi.org/10.1109/ITSC.2016.7795711

Hosseininezhad F., Hosseininezhad F., Balalpour M., A Fuzzy Expert System for performance evaluation of HRM with 360 degree feedback approach (Case study: An Iranian IT company), Computer Sciences and Convergence Information Technology (ICCIT), 6th International Conference, Dec. 2011. https://ieeexplore.ieee.org/document/6316664

Doctor F., Hagras H., Roberts D. Callaghan V., A type-2 fuzzy based system for handling the uncertainties in group decisions for ranking job applicants within Human Resources systems, Fuzzy Systems, FUZZ-IEEE 2008, 2008, 481 – 488. https://doi.org/10.1109/FUZZY.2008.4630412

Slavyanov K.O., An algorithm of fuzzy inference system for human resources selection tools, “Society. Integration. Education”, Proceedings of the International Scientific Conference. Volume V, Rezekne, May 25th-26th, 2018. 445-454. http://dx.doi.org/10.17770/sie2018vol1.3311

Vasileva M., Lambeva M., “Intelligent methods for attestation of scientific and teaching staff. Rasch model”, Proceedings of the 11th International Scientific and Practical Conference “Environment. Technology. Resources”, June 15-17, 2017, Rezekne, Latvia, Volume II, pp 171 – 175,

http://dx.doi.org/10.17770/etr2017vol2.2665

Bloom B.S., Engelhart M.D., Furst E.J., Hill W.H., Krathwohl, D.R., Taxonomy of Educational Objectives: The Classification of Educational Goals, Handbook I: Cognitive domain. New York: David McKay Company, 1956

Mamdani E.H., Assilian S., An experiment in linguistic synthesis with a fuzzy logic controller, International Journal of Man-Machine Studies, Vol. 7, No. 1, 1975, pp. 1-13.

https://doi.org/10.1016/S0020-7373(75)80002-2

Nikolov, L. "Wireless Network Vulnerabilities Estimation", International Scientific Journal "Security and Future", Vol. 2 (2018), Issue 2, pg(s) 80-82 http://confsec.eu/sbornik/cs-2-2018.pdf

Bedzhev B., Tasheva Z., Stoyanov B., Bogdanov R., An algorithm for lossless signal processing (2007) Proceedings of the Third Workshop - 2005 IEEE Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2005, art. no. 4062184, pp. 498-502. https://doi.org/10.1109/IDAACS.2005.283033

Downloads

Published

2019-06-20

Issue

Vol. 2 (2019): Environment. Technology. Resources. Proceedings of the 12th International Scientific and Practical Conference. Volume 2

Section

Information Technologies

How to Cite

[1]
K. O. Slavyanov, “FUZZY LOGIC PROCEDURE FOR DRAWING UP A PSYCHOLOGICAL PROFILE OF LEARNERS FOR BETTER PERCEPTION IN COURSES”, ETR, vol. 2, pp. 136–140, Jun. 2019, doi: 10.17770/etr2019vol2.4073.