Building tools of an intelligent decision support system to identify cultural values.
Keywords:
decision support system, data mining, identification of cultural values, neural networks.
Abstract
The article discusses the problem of developing an intelligent decision support system for identifying cultural values, the issues of choosing and justifying methods and tools for its construction. The methods of research and construction of complex objects, the prospects of using modern artificial neural networks as a tool for the development of an intelligent decision support system are considered. The prospects and ways of further research and use of this subject area are also identified.
References
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Martynenko A., Moroz B., Hulina I., & Syrotkina O. (2020). Conceptual model of an intelligent decision support system to identify cultural values. COMPUTER-INTEGRATED TECHNOLOGIES: EDUCATION, SCIENCE, PRODUCTION, (40), 51-57. http://cit-journal.com.ua/index.php/cit/article/view/156
Martynenko A., Moroz B., Gulina I. “Intelligent decision support system for the identification of cultural values”. IV All-Ukrainian scientific-practical conference “Prospective areas of modern electronics, information and computer systems (MEICS-2019)”. Dnipro, November 27-29, 2019 http://meics.dnure.dp.ua/files/MEICS-2019.pdf
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Martynenko A., Moroz B., Hulina I., & Syrotkina O. (2020). Conceptual model of an intelligent decision support system to identify cultural values. COMPUTER-INTEGRATED TECHNOLOGIES: EDUCATION, SCIENCE, PRODUCTION, (40), 51-57. http://cit-journal.com.ua/index.php/cit/article/view/156
Martynenko A., Moroz B., Gulina I. “Intelligent decision support system for the identification of cultural values”. IV All-Ukrainian scientific-practical conference “Prospective areas of modern electronics, information and computer systems (MEICS-2019)”. Dnipro, November 27-29, 2019 http://meics.dnure.dp.ua/files/MEICS-2019.pdf
Komartsova L.G. "Methods and models in decision support systems at the initial stage of design of distributed computing systems" [Electronic resource] // Access mode: http://www.ict.nsc.ru/ws/Lyap2001/2250/index.html#ft
Haykin S. Neural networks. Complete course 2nd ed. Per. from English. - M.: Publishing house "Williams", 2006. - 1104 p.
Rutkovskaya D., Pilinsky M., Rutkovsky L. "Neural networks, genetic algorithms and fuzzy systems" 2nd ed., Stereotype. 2013.- 384 p.
Bodyansky E.V., Rudenko O.G. Artificial Neural Networks: Architectures, Learning, Applications. Kharkov: Teletekh, 2004. - 369 p.
Artificial neural networks (ANN) [Electronic resource] // Access mode: https://www.it.ua/ru/knowledge-base/technology-innovation/iskusstvennye-nejronnye-seti-ins
Neural network software [Electronic resource] // Access mode: https://bookflow.ru/nejrosetevoe-programmnoe-obespechenie
I.V. Ilyin, K.V. Gudkov ANALYSIS OF SOFTWARE FOR DEEP LEARNING OF ARTIFICIAL NEURAL NETWORKS [Electronic resource] // Access mode: http://www.penzgtu.ru/fileadmin/filemounts/confcit/articles/spring_2018/04.pdf
Top 9 Frameworks in the AI World [Electronic resource] // Access mode: https://geekflare.com/ai-frameworks/
A. Pavlenko. Types of neural networks. The principle of their operation and scope [Electronic resource] // Access mode: https: //otus.ru/nest/post/1263/
Neural networks [Electronic resource] // Access mode: http://wiki.mvtom.ru/index.php
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Published
2020-12-15
How to Cite
MartynenkoА., Moroz, B., & НulinаІ. (2020). Building tools of an intelligent decision support system to identify cultural values . COMPUTER-INTEGRATED TECHNOLOGIES: EDUCATION, SCIENCE, PRODUCTION, (41), 71-75. https://doi.org/10.36910/6775-2524-0560-2020-41-12
Section
Automation and Control
