Information Technologies for Assessing the Quality of IT-specialties Graduates' Training of University by Means of Fuzzy Logic and Neural Networks

• Autorzy: Azarova Anzhelika O. , Azarova Larysa E. , Pavlov Sergii V. , Savina Nataliia B. , Kaplun Iryna S. , Wójcik Waldemar , Smailova Saule , Kalizhanova Aliya

Identyfikatory

DOI

10.24425/ijet.2020.131893

• Języki publikacji: EN

Abstrakty

EN

The information technologies for assessing the quality of IT-specialties graduates' training of university by means of fuzzy logic and neural networks are developed in the article. It makes possible taking into account a wide set of estimation and output parameters, influence of the external and internal factors and allows to simplify the assessing process by means of modern mathematical apparatuses of artificial intelligence.

Słowa kluczowe

EN

information technologies; fuzzy logic; neural networks; quality of IT-specialties graduates' training

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2020
• Tom: Vol. 66, No. 3
• Strony: 411--416
• Opis fizyczny: Bibliogr. 12 poz., rys., tab., wykr.

Twórcy

autor

Azarova Anzhelika O.

• Vinnytsia National Technical University, Ukraine

autor

Azarova Larysa E.

• Vinnytsia National Technical University, Ukraine

autor

Pavlov Sergii V.

• Vinnytsia National Technical University, Ukraine

autor

Savina Nataliia B.

• National University of Water and Environmental Engineering, Rivne, Ukraine

autor

Kaplun Iryna S.

• Vinnytsia National Technical University, Ukraine

autor

Wójcik Waldemar

• Lublin University of Technology, Lublin, Poland

autor

Smailova Saule

• East Kazakhstan State Technical University named after D. Serikbayev, Ust-Kamenogorsk, Kazakhstan

autor

Kalizhanova Aliya

• Institute of Information and Computational Technologies CS MES RK and Al-Farabi Kazakh National University, Almaty, Kazakhstan

Bibliografia

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• [3] S. V. Yuhimchuk, and A. O. Azarova, Mathematical risk models for decision support systems, Vinnytsya, UNIVERSUM–Vinnitsa, 2003, pp. 188.
• [4] A. O. Azarova, and O. V. Antoniuk, Mathematical models of the evaluation of enterprise strategic potential and making decision concerning its improvement: monograph, Vinnitsa, VNTU, 2012, pp. 168.
• [5] V. V. Kruglov, and V. V. Borisov, Neural networks. Theory and practice, Moscow : Hotline - Telecom, pp. 382, 2002.
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• [7] A. P. Rotshtein, and H. B. Rakytyanska, “Diagnosis problem solving using fuzzy relations,” IEEE Transactions on Fuzzy Systems, vol. 16, no. 3, pp. 664-675, 2008.
• [8] L.I. Timchenko, “A multistage parallel-hierarchic network as a model of a neuronlike computation scheme,” Cybernetics and Systems Analysis, vol. 36, no. 2, pp. 251-267, 2000.
• [9] L. I. Timchenko, Y. F. Kutaev, V. P. Kozhemyako, and etc., “Method for training of a parallel-hierarchical network, based on population coding for processing of extended laser paths images,” Proceedings of SPIE 4790, 2002.
• [10] M. F. Kirichenko, Yu. V. Krak, and A. A. Polishchuk, “Pseudo inverse and projective matrices in problems of synthesis of functional transformers,” Cybernetics and Systems Analysis, vol. 40, no. 3, pp. 116-129, 2004.
• [11] V. Mosorov, T. Panskyi, S. Biedron, “Modified alternative decision rule in the pre-clustering algorithm,” Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Srodowiska – IAPGOS, vol. 6, no. 2, pp. 9–12, 2016.
• [12] Yu. V. Krak, “Dynamics of manipulation robots: Numerical-analytical method of formation and investigation of computational complexity,” Journal of Automation and Information Sciences, vol. 31, no. 1-3, pp. 121-128, 1999.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).