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American Journal of Business and Operations Research
Volume 9 , Issue 1, PP: 17-25 , 2023 | Cite this article as | XML | Html |PDF

Title

The Use of Intelligent Mathematical Models for Regional Investment Distribution Processes Analysis

  Jamshid S. Tukhtabaev 1 * ,   Umid A. Otajanov 2 ,   Shakhnoza T. Nurullaeva 3 ,   Saodat A. Saydullaeva 4 ,   Gulshan M. Abdulxayeva 5

1  Tashkent State University of Economics, Uzbekistan
    (jamshidtukhtabaev@gmail.com)

2  Tashkent State University of Economics, Uzbekistan
    (umid.otajanov25@hotmail.com)

3  Tashkent State University of Economics, Uzbekistan
    (shakhnoza.nurullaeva32@gmail.com)

4  Tashkent State University of Economics, Uzbekistan
    (Saodat.saydullaeva@hotmail.com)

5  Tashkent State University of Economics, Uzbekistan
    (Gulshan.abdulxayeva@hotmail.com)


Doi   :   https://doi.org/10.54216/AJBOR.090102

Received: May 08, 2022 Accepted: January 18, 2023

Abstract :

In this article, the artificial neural network mathematical model is used for Regional Investment Distribution Processes Analysis in the regions of the Republic of Uzbekistan; forecasts are made using this model, and the results are compared with the results determined using the trend and panel methods, and the preferred method is defined. Multi-layer perceptron, radial-basis grid, generalized-regression grid, and recurrent grid can be used to solve the forecasting problem. In the study, a program for intellectual analysis and forecasting of socio-economic development indicators of the regions of the Republic of Uzbekistan using the generalized regression network was developed. This program makes it possible to extract the most necessary factors from other methods even in the presence of multi-factor indicators and determine the future forecast result under their influence. According to the results of the forecast determined using the intellectual mathematical model developed because of the research, by 2025, the volume of the gross regional product of Andijan region is expected to be equal to 95607.34 billion soums, and in Samarkand region, it is expected to be equal to 80419.73 billion soums, in Tashkent city it is expected to be 259301.8 billion soums. According to the error levels of the results determined by the intellectual mathematical method, it represents an average error of 1.13% compared to the forecast period. If we determine the result of the trend by the level of error, it is equal to an average of 4.96% error compared to the forecast period, and these results prove the superiority of the intellectual mathematical method developed in the research.

Keywords :

Intelligent models; gross regional product; investment; model; neural network; layer; intellectual mathematical model; investment potential forecast.

References :

[1]  Decree of the President of the Republic of Uzbekistan No. 4702 “On the introduction of a rating assessment system for socio-economic development of regions” dated 01.05.2020. 

[2]  Investments with Gordon J. Alexander and Jeffrey Bailey, Prentice -Hall, (1999).

[3]  Lawrence D. Gitman, Michael D. Jonk. Basic Investing. – M., “Delo”, (2007).

[4]  Economics by Campbell McConnell, Stanley Brue McGraw-Hill Education, (2006).

[5]  Söhnke Bartram, Jürgen Branke, Mehrshad Motahari (2020)  / Artificial Intelligence in Asset Management. Center for Economic Policy Research Portal, London. https://portal.cepr.org/discussion-paper/16116 

[6]  A.Nazif Catik, Mehmet Karaçuka.  A Comparative Analysis of Alternative Univariate Time Series Models in Forecasting Turkish Inflation, (DICE), Düsseldorf, Germany, Heinrich-Heine-Universität Düsseldorf (2011).

[7]  Michael  Furtwaengler.  Model-Independent  Estimation  of  Optimal  Hedging  Strategies  with  Deep  Neural Networks Tobias. Dissertations. University of Wisconsin-Milwaukee. USA (May, 2019).

[8]  Samuel Björklund, Tobias Uhlin.  Artificial neural networks for financial time series prediction and portfolio optimization, SE, "LIU-IEI-TEK-A" (2017).

[9]  Martin T. Hagan,  Howard B. Demuth, Mark H. Beale.  Neural Network Design (2nd Edition), Frisco, Texas(2014).

[10]  Zaychenko Yu.P. Fuzzy model and method in the intellectual systems. (Ukraine) "Slovo" (2008).

[11]  Kruglov  V.V.,  Borisov  V.V.  Artificial  neural  networks.  Theory  and  practice.  2ndpublication.  –  M.: "Goryachaya Line-Telecom" (2002).

[12]  Barsky  A.B.  Neural  Networks:  Recognition,  Management,  and  Decision  Making.  –  M.:  "Finance  and Statistics" (2004).

[13]  Gulyamov S.S., Abdullaev A.M. National economic forecasting and modeling. – T.: "Science and technology"(2007).

[14]  Tukhtabaev  J.S.  Econometric  Evaluation  of  Influential  Factors  to  Increasing  Labor  Efficiency  in  Textile Enterprises.  Webology,  Volume  18,  Special  Issue  on  Information.  Retrieval  and  Web  Search,  2021. https://www.webology.org/datacms/articles/20210129114502amWEB18024.pdf

[15]  Tillaeva B.R. et al. Ways of development of agriculture and processing industry enterprises manufacturing cooperation.  IOP  Conf.  Series:  Earth  and  Environmental  Science  1043  (2022)  012024.  doi:10.1088/1755 -1315/1043/1/012024 (https://iopscience.iop.org/article/10.1088/1755-1315/1043/1/012024) 

[16]  Alimov  R.X.,  Otajanov  U.A.  Use  of  neural  networks  in  investment  allocation  processes.  II  International scientific-practical conference. Action strategy of the Republic of Uzbekistan: prospects for macroeconomic stability, investment activity and innovative development. A collection of scientific lectures and articles. - T.: TSUE, (2019).

[17]  Baikhonov  B.T.  "Improving  the  methodology  of  econometric  modeling  of  intersectoral  distribution  of investments in the economy of Uzbekistan". - T., (2019).

[18]  Otajanov U.A. Improvement of methods of assessing the investment climate of the regions of the Republic of Uzbekistan // TEST: Engineering&Management. March-April, - p. 5489 (2020).

[19]  Based on the research, the calculations results made in the "intellectual analysis of factors" program created by the  author  based  on  the  information  of  the  State  Statistics  Committee  of  the  Republic  of  Uzbekistan (www.stat.uz)

[20]  Tukhtabaev J.S. et al. The role of industrial enterprises in ensuring food security. IOP Conf. Series: Earth and Environmental  Science  1043  (2022)  012023.  doi:10.1088/1755-1315/1043/1/012023 (https://iopscience.iop.org/article/10.1088/1755-1315/1043/1/012023)  


Cite this Article as :
Style #
MLA Jamshid S. Tukhtabaev, Umid A. Otajanov, Shakhnoza T. Nurullaeva, Saodat A. Saydullaeva, Gulshan M. Abdulxayeva. "The Use of Intelligent Mathematical Models for Regional Investment Distribution Processes Analysis." American Journal of Business and Operations Research, Vol. 9, No. 1, 2023 ,PP. 17-25 (Doi   :  https://doi.org/10.54216/AJBOR.090102)
APA Jamshid S. Tukhtabaev, Umid A. Otajanov, Shakhnoza T. Nurullaeva, Saodat A. Saydullaeva, Gulshan M. Abdulxayeva. (2023). The Use of Intelligent Mathematical Models for Regional Investment Distribution Processes Analysis. Journal of American Journal of Business and Operations Research, 9 ( 1 ), 17-25 (Doi   :  https://doi.org/10.54216/AJBOR.090102)
Chicago Jamshid S. Tukhtabaev, Umid A. Otajanov, Shakhnoza T. Nurullaeva, Saodat A. Saydullaeva, Gulshan M. Abdulxayeva. "The Use of Intelligent Mathematical Models for Regional Investment Distribution Processes Analysis." Journal of American Journal of Business and Operations Research, 9 no. 1 (2023): 17-25 (Doi   :  https://doi.org/10.54216/AJBOR.090102)
Harvard Jamshid S. Tukhtabaev, Umid A. Otajanov, Shakhnoza T. Nurullaeva, Saodat A. Saydullaeva, Gulshan M. Abdulxayeva. (2023). The Use of Intelligent Mathematical Models for Regional Investment Distribution Processes Analysis. Journal of American Journal of Business and Operations Research, 9 ( 1 ), 17-25 (Doi   :  https://doi.org/10.54216/AJBOR.090102)
Vancouver Jamshid S. Tukhtabaev, Umid A. Otajanov, Shakhnoza T. Nurullaeva, Saodat A. Saydullaeva, Gulshan M. Abdulxayeva. The Use of Intelligent Mathematical Models for Regional Investment Distribution Processes Analysis. Journal of American Journal of Business and Operations Research, (2023); 9 ( 1 ): 17-25 (Doi   :  https://doi.org/10.54216/AJBOR.090102)
IEEE Jamshid S. Tukhtabaev, Umid A. Otajanov, Shakhnoza T. Nurullaeva, Saodat A. Saydullaeva, Gulshan M. Abdulxayeva, The Use of Intelligent Mathematical Models for Regional Investment Distribution Processes Analysis, Journal of American Journal of Business and Operations Research, Vol. 9 , No. 1 , (2023) : 17-25 (Doi   :  https://doi.org/10.54216/AJBOR.090102)