Development of Cross-Platform Cognitive Tools Invariant to Problem Areas and their Integration into Intelligent Systems

Article Preview

Abstract:

Nowadays application and development of cognitive graphic tools for the usage in intelligent system of data and knowledge analysis, decision-making and its justification for different problem areas including material research are urgency. Most significantly developed cognitive graphics tools based on n-simplex which are invariant to problem areas are presented. Specificity of program realization of cognitive graphics tools which is invariant to problem areas is described. Most significant results are given and discussed. Future investigations are connected with the usage of new approach to rendering, cross-platform realization, improving cognitive features and expanding n-simplex family

You might also be interested in these eBooks

Info:

Periodical:

Pages:

609-616

Citation:

Online since:

February 2016

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2016 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] S.R. Niezgoda, A.K. Kanjarla, S.R. Kalidindi, Novel microstructure quantification framework for databasing, visualization, and analysis of microstructure data, Integrating Mater. and Manufacturing Innov., 2, 3 (2013) 1-27.

DOI: 10.1186/2193-9772-2-3

Google Scholar

[2] R.M. Axelrod, The Structure of Decision: Cognitive Maps of Political Elites, Princeton University Press, Princeton, (1976).

Google Scholar

[3] R.G. Basaker, T.L. Saati, Finite Graphs and Networks: an Introduction with Applications, Research Analysis Corp., Mc Graw Hill Company, NY-London-Toronto, (1965).

Google Scholar

[4] D.A. Pospelov, Cognitive graphics – a window into the new world, Software products and systems (in Russian), 2 (1992) 4-6.

Google Scholar

[5] D.A. Pospelov, Ten hot spots, in research on artificial intelligence, Intelligent systems (MSU) (in Russian), 1 (1996) 47-56.

Google Scholar

[6] D.A. Pospelov, L.V. Litvintseva, How to combine left and right, News of Artificial Intelligence (in Russian), 2 (1996) 66-71.

Google Scholar

[7] А.А. Zenkin, Cognitive Computer Graphics, Nauka, Мoscow (in Russian), (1991).

Google Scholar

[8] A.A. Zenkin, Knowledge-generating technologies of cognitive reality, News of Artificial Intelligence (in Russian), 2 (1996) 72-78.

Google Scholar

[9] V.А. Albu, V.F. Khoroshevskiy, COGR – cognitive graphics system, design, development, application, Russian Academy of Science Bulletin, Technical Cybernetics (in Russian), 5 (1990) 12-20.

Google Scholar

[10] A.A. Zenkin, Cognitive computer graphics, Directory of Artificial Intelligence. Book 2 Models and Methods, (in Russian), Moscow, 1990, pp.137-143.

Google Scholar

[11] B.A. Kobrinskiy, Why should we take in account imaginary thinking and intuition in medical expert systems, Artificial Intelligence – 96, Proc. of the 5th National Conf. with Int. Participation (in Russian), 2 (1996) 207-210.

Google Scholar

[12] A.E. Yankovskaya, Logical Tests and Means of Cognitive Graphics, LAP LAMBERT Academic Publishing (in Russian), Saarbrücken, (2011).

Google Scholar

[13] A.E. Yankovskaya, N.M. Krivdyuk, Cognitive graphics tool based on 3-simplex for decision-making and substantiation of decisions in intelligent system, Proc. of the IASTED Int. Conf. Technology for Education and Learning, 1 (2013) 463-469.

DOI: 10.2316/p.2013.808-017

Google Scholar

[14] A.E. Yankovskaya, A.I. Gedike, R.V. Ametov, A.M. Bleikher, IMSLOG-2002 software tool for supporting information technologies of test pattern recognition, Pattern Recognition and Image Analysis, 13 (2003) 650-657.

Google Scholar

[15] B. Wang, X. Feng, K.H. Chu, A novel graphical procedure based on ternary diagram for minimizing refinery consumption of fresh hydrogen, J. of Cleaner Production, 37 (2012) 202-210.

DOI: 10.1016/j.jclepro.2012.07.009

Google Scholar

[16] J. Podani, C. Ricotta, D. Schmera, A general framework for analyzing beta diversity, nestedness and related community-level phenomena based on abundance data, Ecological Complexity, 15 (2013) 52-61.

DOI: 10.1016/j.ecocom.2013.03.002

Google Scholar

[17] A.E. Yankovskaya, Test recognizing medical expert systems with elements of cognitive graphics, Computer Chronicle (in Russian), 8 (1994) 61-83.

Google Scholar

[18] S.V. Kondratenko, A.E. Yankovskaya, Pattern recognition system TRIANG to decision-making substantiation using cognitive graphics, Abstracts of the III Conference on Artificial Intelligence (in Russian), 1 (1992) 152-155.

Google Scholar

[19] A.V. Yamshanov, N.M. Krivdyuk, Specify of software implementation of cognitive graphic tools in intelligent and education systems, Proc. of the Conf. Prospects of Fundamental Science Development XI, (in Russian), 1 (2014) 706-709.

Google Scholar