Uncertain probability in information systems changed over time

El-Ghoul, M. M.; Gabr, M. K.; Kozae, Abdel Monaem M.; Teamah, A. M.

doi:10.22075/ijnaa.2022.6034

Uncertain probability in information systems changed over time

Document Type : Research Paper

Authors

M. M. El-Ghoul

M. K. Gabr

Abdel Monaem M. Kozae

A. M. Teamah

Department of Mathematics, Faculty of Science, Tanta University, Tanta, Egypt

10.22075/ijnaa.2022.6034

Abstract

In the information age, the decision-making process depends on data analysis. Most life problems are characterized by indeterminacy. In this research, we study data tables that change with time (changing information systems) and we use the concept of uncertain probabilities to introduce stochastic variables in data tables that changed over time. The uncertain probabilities are based on constructing neighborhoods on the set of objects in information systems and use topological approximations. Some examples are given to indicate the suggested approach. These concepts help in obtaining decisions using mathematical models in which no codes are used, or converting verbal variables into digital, and this helps to make approximations close to reality.

Keywords

Information system

uncertain probability

stochastic variables

[1] M.E. Abd El-Monsef, A.M. Kozae and R.A. Abu-Gdairi, Generalized rough probability in topological spaces, Int. J. Contemp. Math. Sci. 6 (2011) 1099–1110.

[2] M.E. Abd El-Monsef, A. Kozae and M. J. Miqelan, Near rough probability in topologized stochastic approximation spaces, J. Al-Azhar Gaza 12 (2010) 59–69.

[3] K. Aydogan and M. Ozmen, Attribute reduction in stochastic information systems based on α-dominance, J. Inf. Technol. 10(2) (2017) 211–219.

[4] A.T. Bjorvand, Time Series and Rough Sets, Master’s Thesis, Knowledge Systems Group, Department of Computer Systems and Telematics, Faculty of Electrical Engineering and Computer Science, The Norwegian Institute of Technology, The University of Trondheim, 1996.

[5] R. Intan, On the relation of probability, fuzziness, rough and evidence theory, Commun. Comput. Inf. Sci. 516 (2015) 1–15.

[6] W. Kotłowski, K. Dembczyński, S. Greco, and R. Słowiński, Stochastic dominance-based rough set model for ordinal classification, Inf. Sci. 178 (21) 4019–4037.

[7] A. Luay Al-Swidi, A.H. Ali and H.K. Al-Abbasi, Rough probability in topological spaces, Math. Theory Model. 3 (2013) 2224–0522.

[8] S. Menge and G. Lajio, A framework for stochastic system modelling and analysis, Electron. Notes Theor. Comput. Sci. 154 (2006) 73–78.

[9] S. Sedghi, N. Shobe, D.W. Lee and S. Firouzian, Set-valued mapping and rough probability, Math. Sci. Lett. Int. J. 1 (2018) 55–59.

[10] Q. Vu Bui, S. Ben Amor and M. Bui, Stochastic pretopology as a tool for complex networks analysis, J. Inf. Telecommun. 3(2) (2019) 135–155.

[11] W. Ziarko, Stochastic approach to rough set theory, Int. Conf. Rough Sets and Current Trends in Comput., Springer, Berlin, Heidelberg, 4259 (2006) 38–48.