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Abstract

All universities are responsible for assessing the quality of education. One of the required factors is the results of the students’ research. The procedure involves, most often, the preparation of the questionnaire by the staff, which is voluntarily answered by students; then, the university staff uses the statistical methods to analyze data and prepare reports. The proposed EQE method by the application of the fuzzy relations and the optimistic fuzzy aggregation norm may show a closer connection between the students’ answers and the achieved results. Moreover, the objects obtained by the application of the EQE method can be visualized by using the t-SNE technique, cosine between vectors and distances of points in five-dimensional space.
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Bibliography

  1.  A. Mrówczyńska, A. Król, and P. Czech, “Artificial immune system in planning deliveries in a short time,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 67, no. 5, pp. 969–980, 2019, doi: 10.24425/bpas.2019.126630.
  2.  G. Kovacs, “Layout design for efficiency improvement and cost reduction,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 67, no. 3, pp. 547–555, 2019, doi: 10.24425/bpasts.2019.129653.
  3.  A. Zaborowski, “Data processing in self-controlling enterprise processes, “ Bull. Pol. Acad Sci. Tech. Sci, vol. 67, no. 1, pp. 3–20, 2019, doi: 10.24425/bpas.2019.127333.
  4.  M.J. Cobo, A.G. López-Herrera, E. Herrera-Viedma, and F. Herrera, “An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field,” J. Infom., 5, pp. 146–166, 2011, doi: 10.1016/j.joi.2010.10.002.
  5.  V. Osińska, O. Sokolov, and A. Mreła, “Nonlinear Estimation of Similarity Between Scientists’ Disciplinary Profiles. Case Study,” ZIN Studia Informacyjne, 57(2A), pp. 12–27, 2019, doi: 10.36702/zin.467.
  6.  Law on higher education (Dz.U. 2005 nr 164 poz. 1365). [On line]. Available: http://isap.sejm.gov.pl/isap.nsf/DocDetails. xsp?id=WDU20051641365. (Accessed: 30 Jun. 2019) [in Polish].
  7.  R. Biswas, “An application of fuzzy sets in students’ evaluation,” Fuzzy Sets Syst., vol. 74, no. 2, pp. 187–194, 1995, doi: 10.1016/0165- 0114(95)00063-Q.
  8.  S.M. Chen and C.H. Lee, “New methods for students’ evaluating using fuzzy sets,” Fuzzy Sets Syst., vol. 104, no. 2, pp. 209–2018, 1999.
  9.  J. Ma and D. Zhou, “Fuzzy set approach to the assessment of students-centered learning,” IEEE Trans. Educ., vol. 43, no. 2, pp. 237–241, 2000, doi: 10.1109/13.848079.
  10.  D. Molodsov, “Soft Set Theory – First Results,” Comput. Math. Appl., vol. 37, pp. 19–31, 1999, doi: 10.1016/S0898-1221(99)00056-5.
  11.  B. Ahmad and A. Kharal, “On Fuzzy Soft Sets,” Adv. Fuzzy Syst., vol. 2009, no. 4–9, pp. 1–6, 2019, doi: 10.1155/2009/586507.
  12.  P. Majumdar and S.K. Samanta, “A Generalized Fuzzy Soft Set Based Student Ranking System,” Int. J. Adv. Soft Comput. Appl., vol. 3, no. 3, pp. 42‒51, Nov. 2011. [Online]. Available: http://home.ijasca.com/data/documents/A-Generalised-Fuzzy-Soft-Set.pdf (Accessed: 20 Aug. 2019).
  13.  S. Weon and J. Kim, “Learning achievement evaluation strategy using fuzzy membership function,” in Proc. 31st ASEE/IEEE Frontiers in Education Conference, Reno, NV, USA, 2001, pp. T3A-19, doi: 10.1109/FIE.2001.963904. [Online]. Available: http://archive.fie- conference.org/fie2001/papers/1215.pdf (Accessed: 21 Aug. 2019).
  14.  S.M. Bai and S.M. Chen, “Evaluating students’ learning achievement using fuzzy membership functions and fuzzy rules,” Expert Syst. Appl., vol. 34, no. 1, pp. 399–410, 2008, doi: 10.1016/j.eswa.2006.09.010.
  15.  F. Dayan, M. Zulqarnain, and N. Hassan, “A Ranking Method for Students of Different Socio Economic Backgrounds Based on Generalized Fuzzy Soft Sets,” Int. J. Sci. Res. (IJSR), vol. 6, no 9, pp. 691‒694, Sep. 2017, [Online] Available: https://www.ijsr.net/search_index_ results_paperid.php?id=ART20176512. (Accessed: 20 Aug. 2019).
  16.  A. Mreła, O. Sokolov, and W. Urbaniak, “The method of learning outcomes assessment based on fuzzy relations,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 67, no. 3, pp. 527‒533, 2019, doi: 10.24425/bpasts.2019.129651.
  17.  Report on the quality of education in Adam Mickiewicz University in Poznań. [Online]. Available: http://brjk.amu.edu.pl/badanie-jakosci- ksztalcenia/badanie-jakosci-ksztalcenia-na-uam (Accessed: 30 Jun. 2019) [in Polish].
  18.  Report on the quality of education at Warsaw University of Technology, Faculty of Materials Science and Engineering. [Online]. Available: https://www.wim.pw.edu.pl/content/download/1668/14211/file/ankietyzacja.pdf (Accessed: 30 Jun. 2019) [in Polish].
  19.  Report on the quality of education at Kazimierz Wielki University in Bydgoszcz, [Online]. Available: https://www.ukw.edu.pl/download/34273/raport_oceny_ankietyzacja_2017_2018_ukw_bydgoszcz.pdf (Accessed: 30 Jun. 2019) [in Polish].
  20. [20]  L.A. Zadeh, “Fuzzy sets,” Inf.Control, vol. 8, no. 3, pp. 338–353, 1965, doi: 10.1016/S0019-9958(65)90241-X.
  21.  L.A. Zadeh, “The concept of a linguistic variable and its application to approximate reasoning –1,” Inf. Sci., vol. 8, pp. 199–249, 1975, doi: 10.1016/0020-0255(75)90036-5.
  22.  L. Rutkowski, Methods and techniques of artificial intelligence, PWN, Warsaw, pp. 1–452, 2012, [in Polish].
  23.  O. Sokolov, W. Osińska, A. Mreła, and W. Duch, “Modeling of Scientific Publications Disciplinary Collocation Based on Optimistic Fuzzy Aggregation Norms,” in Information Systems Architecture and Technology: Proceedings of 39th International Conference on Information Systems Architecture and Technology – ISAT 2018., eds. J. Światek, L. Borzemski and Z. Wilmowska, Advances in Intelligent Systems and Computing. Information Systems Architecture and Technology Part II, vol. 853, pp. 145–156, 2019, doi: 10.1007/978-3-319-99996-8.
  24.  L.J.P. Van der Maaten and G.E. Hinton, “Visualizing data using t-SNE,” J. Mach. Learn. Res., vol. 9, pp. 2579–2605, Nov. 2008, [Online]. Available: https://research.tilburguniversity.edu/en/publications/visualizing-high-dimensional-data-using-t-sne (Accessed: 21 Jun. 2019).
  25.  C.L. Hwang and K. Yoon, “Methods for multiple attribute decision making”, in Multiple Attribute Decision Making. Lecture Notes in Economics and Mathematical Systems, vol. 186. Springer, Berlin, Heidelberg, doi: 10.1007/978-3-642-48318-9_3.
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Authors and Affiliations

Grzegorz Śmigielski
1
ORCID: ORCID
Aleksandra Mreła
1
ORCID: ORCID
Oleksandr Sokolov
2
ORCID: ORCID
Mykoła Nedashkovskyy
1
ORCID: ORCID

  1. Kazimierz Wielki University in Bydgoszcz, Institute of Informatics, ul. Kopernika 1, 85-074 Bydgoszcz, Poland
  2. Nicolaus Copernicus University in Toruń, Faculty of Physics, Astronomy and Informatics, ul. Grudziądzka 5, 87-100 Toruń, Poland
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Abstract

The paper presents Gupta's relational decomposition technique expanded on linguistic level. It allows to reduce the hardware cost of the fuzzy system or the computing time of the final result, especially when referring to First Aggregation Then Inference (FATI) relational systems or First Inference Then Aggregation (FITA) rule systems. The inference result of the hierarchical system using decomposition technique is more fuzzy than of the classical system. The paper describes a linguistic decomposition technique based on partitioning the knowledge base of the fuzzy inference system. It allows to decrease or even totally remove a redundant fuzziness of the inference result.

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Authors and Affiliations

B. Wyrwoł
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Abstract

The main idea of this article is the necessity to take into account the multi-variant technological and organizational solutions of individual construction works in order to ensure rational planning for the implementation of construction projects. In practice, selection of construction works most often limited to the evaluation of technological and organizational solutions on the basis of time and cost criteria. However, it should be remembered that construction projects usually have a complex technological and organizational structure. This fact may increase the durations and costs of individual works in relation to their planned durations and costs. Therefore, the authors propose to take into account the criterion of technological and organizational complexity of the assessed construction work. The article describes the procedure for the technological and organizational optimization of construction works. A numerical example of the method of selecting technological and organizational solutions with the use of a fuzzy relation of preferences is also presented. The article also proposes to combine the computational selection model with the network planning model in a graphic form. This approach expands the computational and decision-making possibilities of network models in the practice of planning construction projects.
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Authors and Affiliations

Nabi Ibadov
1
ORCID: ORCID
Sahib Farzaliyev
2
ORCID: ORCID
Irene Ladnykh
1
ORCID: ORCID

  1. Warsaw University of Technology, Faculty of Civil Engineering, Al. Armii Ludowej 16, 00-637 Warsaw, Poland
  2. Azerbaijan University of Architecture and Construction, Faculty of Construction, Ayna Sultanova 11, Baku, Azerbaijan

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