Search results

Filters

  • Journals
  • Authors
  • Keywords
  • Date
  • Type

Search results

Number of results: 3
items per page: 25 50 75
Sort by:
Download PDF Download RIS Download Bibtex

Abstract

Large sets of articles are evaluated by predefined measures such as the article numbers and h-indexes. All of these indicators are scalars and refer rather to one discipline or the comprehensive science. Thus, according to disciplinary categories in scientific databases, the distribution has become too rigid for current science needs, dynamically growing towards inter- and trans-disciplinarity. We propose a new method of calculating the impact on knowledge of articles and their citations, creating citation networks, and using one of the optimistic fuzzy aggregation norms to estimate the contribution to the knowledge considering the citation inheritance of citing papers to cited papers (paper children to the paper-parents). Due to this method, we produced the contribution vectors for various disciplines/subdisciplines based on articles and their citations of publications belonging to the considered disciplines. We can prepare the scientific profiles of papers and disciplines based on the contribution vectors. Moreover, we can evaluate how much citations matter in the development of science. Applying this method, we can estimate the contribution to the considered research field caused by papers and their citations from different areas of science. The proposed method might be applicable in the assessment of developing concepts.
Go to article

Authors and Affiliations

Aleksandra Mreła
1
ORCID: ORCID
Veslava Osińska
2
ORCID: ORCID
Oleksandr Sokolov
3
ORCID: ORCID

  1. Institute of Informatics, Kazimierz Wielki University in Bydgoszcz, Kopernika 1, 85-074 Bydgoszcz, Poland
  2. Institute of Information and Communication Research, Nicolaus Copernicus University in Torun, W. Bojarskiego 1, 87-100 Torun, Poland
  3. Department of Informatics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University in TorunDepartment of Informatics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University in Toruń
Download PDF Download RIS Download Bibtex

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.
Go to article

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.
Go to article

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
Download PDF Download RIS Download Bibtex

Abstract

The growing interest in green energy observed in recent years has become the basis for pilot studies on its electricity production role in Poland. The diagnostic survey method allowed us to learn about young people’s opinions on renewable energy sources in the context of four identified research areas (the need for RES, planning its installation, costs, environmental impact). The authors proposed a method based on fuzzy logic (fuzzy relations and optimistic fuzzy aggregation norms) to develop and interpret the survey results to understand the selected community’s knowledge about the importance of RES (or not) in the national energy system. The survey shows that although there is no significant difference between respondents in all research areas, rural women are more interested in using green technologies. They have a high self-awareness of their beneficial effects on the environment. Rural respondents, compared to those from the cities, are willing (despite the high cost of equipment) to invest their capital to purchase green energy carriers, which is dictated by their lower knowledge about the forms of external support. Depending on the residence place, respondents selected various government aid programs for renewable energy. People from the city decided mainly on those that would improve the air’s comfort and quality in their place of residence. On the other hand, the rural areas’ inhabitants focused their attention on the aid possibilities, which would reduce the energy costs of the farms they run in the future. All the respondents agree that investments in clean energy (coming from natural sources) will translate into broadly understood environmental protection, bringing mutual benefits for everyone.
Go to article

Authors and Affiliations

Jolanta Barbara Cichowska
1
Aleksandra Mreła
2
ORCID: ORCID
Oleksandr Sokolov
3
ORCID: ORCID

  1. Faculty of Civil and Environmental Engineering and Architecture, University of Science and Technology in Bydgoszcz, Poland
  2. Institute of Informatics, Kazimierz Wielki University in Bydgoszcz, Poland
  3. Department of Informatics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University in Toruń, Poland

This page uses 'cookies'. Learn more