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National projects and government programmes

Functional algorithm for evaluating and modelling using the Data Science methodology

Bibliographic Data

ID21780260
AuthorsОлим Астанакулов (0000-0002-0536-1214, corresponding author)
Year2020
Volume183
Issue5-6
Pages51-59
Publication date2020-06-04
Open AccessYes
TypeARTICLE
VenueEconomic Annals-ХХI (JOURNAL)
Journal identifiersISSN: 1728-6220 • E-ISSN: 1728-6239
PublisherInstitute of Society Transformation sp. z o.o. (PUBLISHER)
DOI10.21003/ea.v183-05
OpenAlexW3119704717
LanguageEN
Citations received1

Programme and target planning procedures in Russia have a lot of shortcomings, related to the selection of priority goals, establishment of criteria for evaluating the effectiveness of target programmes, as well as achievement of goals, development of a system of performance indicators, and so on. In addition, the problem of the lack of a high-quality theoretical and legislative framework for the transition to budget expenditures planning in accordance with the principles of result-oriented budgeting remains urgent. The purpose of this paper is to develop a functional fuzzy computing algorithm for modelling the evaluation of government programmes using neural networks. As a part of this work, we obtained stable results in the form of creating a neural network that can analyze government projects using a multi-criteria method, taking into account the root-mean-square error, with an accuracy of up to 95%. The analysis criteria cover all effective areas for predicting the correct use of the government projects by implementing them in the government systems

Algorithm · Artificial neural network · Fuzzy logic · Legislature · Machine learning · Management science · Operations research · Political science · Process management · Computer Science · Digitalization and Economic Development in Agriculture · Economic and Technological Developments in Russia · Economic and Technological Systems Analysis · Engineering · Artificial Intelligence

  • Tourism as a catalyst for regional development

    Open Access•Олим Астанакулов, Sanjar Goyipnazarov et al.•Economic Annals-ХХI•2023

Citations per year0.33
Citation span2023 - 2023 (1)
Highly citedNo

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