Skip to main content
SpringerLink
Log in

The Classification of Turkish Economic Growth by Artificial Neural Network Algorithms

  • Conference paper
  • First Online:
Computational Science and Its Applications – ICCSA 2017 (ICCSA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10405))

Included in the following conference series:

Abstract

The development of globalization means that economies of the world are placing more importance on international trade. The increase in the variety of goods or services and the implementation of deregulation policies by many countries have made increases in international trade unavoidable. This study investigates the relationship between Turkey’s international trade balance and economic growth between the years of 1960 and 2015 by using data obtained by the Turkish Statistical Institute. Two data sets were used in this study to identify the factors that affect the Turkish international trade balance and economic growth. Data Set 1 was formed by combining the parameters that make up international trade balance and those that determine economic growth. Data Set 2 consists only of parameters that define international trade balance. The aim of this study is to be able to identify the relationship between the parameters that define international trade balance and economic growth in a computerized system. In order to achieve this, Data Set 1 and Data Set 2 were subjected to various Artificial Neural Network methods such as Feed Forward Back Propagation and Cascade Forward Back Propagation algorithms and were classified in accordance with the international trade volume parameters. At the conclusion of the experimental work, the accuracy of the Feed Forward Back Propagation and Cascade Forward Back Propagation algorithms obtained from the test operation in the classification process was calculated. As a result, the study has classified the factors that influence the growth of the economy and international trade in Turkey.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Jorgenson, D., Gollop, F.M., Fraumeni, B.: Productivity and US Economic Growth, p. 169. Elsevier, Amsterdam (2016)

    Google Scholar 

  2. Solow, R.M.: Resources and economic growth. Am. Econ. 61(1), 52–60 (2016)

    Google Scholar 

  3. Lu, X., Guo, K., Dong, Z., Wang, X.: Financial development and relationship evolvement among money supply, economic growth and inflation: a comparative study from the US and China. Appl. Econ. 49(10), 1032–1045 (2017)

    Article  Google Scholar 

  4. Greiner, A., Semmler, W., Gong, G.: The Forces of Economic Growth: A Time Series Perspective. Princeton University Press, Princeton (2016)

    Google Scholar 

  5. Srinivasan, A., Jayalakshmi, G.: Probabilistic analysis on time to recruitment for a single grade man power system when the breakdown threshold has two components using a different policy of recruitment. Indian J. Appl. Res. 5(7), 1–3 (2016)

    Google Scholar 

  6. Jacop, M.W.: The intensity of trade creation and trade diversion in COMESA, ECCAS and ECOWAS: a comparative analysis. J. Afr. Econ. 14(1), 117–141 (2005)

    Article  Google Scholar 

  7. Aguilar, C.A.: Trade analysis of specific agri-food commodities using a gravity model, michigan state university department of agricultural economics. Master of Science Thesis, Michigan (2006)

    Google Scholar 

  8. Dimitri, D.G., Balazs, H., Elina, R.: Foreign direct investment in European transition economies - the role of policies. IMF Working Papers 20431, 26–42 (2007)

    Google Scholar 

  9. Tenreyro, S.: On the trade impact of nominal exchange rate volatility. J. Dev. Econ. 82(2), 4 (2007)

    Article  Google Scholar 

  10. Allen, J.T.: The foreign direct investment-exports relationship: a US-Mexico analysis using the gravity model. Northern Illinois University, Doctor of Philosophy Dissertation, Dekalb, Illinois (2007)

    Google Scholar 

  11. Arize, A.C., Osang, T., Slottje, D.J.: Exchange-rate volatility in Latin America and its impact on foreign trade. Int. Rev. Econ. Finan. 17, 33–44 (2008)

    Article  Google Scholar 

  12. Kayumova, N.O.: How Exchange Rate Volatility Affects on the Main Export Goods of Uzbekistan? (2013). doi:10.12955/ejbe.v5i0.166

  13. Wang, S.C.: Artificial neural network. In: Wang, S.C. (ed.) Interdisciplinary Computing in Java Programming, vol. 743, pp. 81–100. Springer, US (2003)

    Chapter  Google Scholar 

  14. Akerkar, R., Sajja, P.: Knowledge-Based Systems. Jones & Bartlett Publishers (2010)

    Google Scholar 

  15. Mila\(\check{c}\)i\(\grave{c}\), L., Jovi\(\grave{c}\), S., Vujovi\(\grave{c}\), T., Miljkovi\(\grave{c}\), J.: Application of artificial neural network with extreme learning machine for economic growth estimation. Phys. A Stat. Mech. Appl. 465, 285–288 (2017)

    Google Scholar 

  16. Skiba, M., Mrówczyńska, M., Bazan-Krzywoszańska, M.: Modeling the economic dependence between town development policy and increasing energy effectiveness with neural networks. Case study: the town of Zielona Gra. Appl. Energy 188, 356–366 (2017)

    Article  Google Scholar 

  17. Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques, pp. 398–406. Elsevier, Amsterdam (2011)

    Google Scholar 

  18. Wang, L., Zeng, Y., Chen, T.: Back propagation neural network with adaptive differential evolution algorithm for time series forecasting. Expert Syst. Appl. 42(2), 855–863 (2015)

    Article  Google Scholar 

  19. Karaca, Y., Hayta, Ş.: Application and comparison of ANN and SVM for diagnostic classification for cognitive functioning. Appl. Math. Sci. 10(64), 3187–3199 (2016)

    Google Scholar 

  20. Qiao, J., Li, F., Han, H., Li, W.: Constructive algorithm for fully connected cascade feedforward neural networks. Neurocomputing 182, 154–164 (2016)

    Article  Google Scholar 

  21. Qiu, M., Song, Y., Akagi, F.: Application of artificial neural network for the prediction of stock market returns: the case of the Japanese stock market. Chaos Solitons Fractals 85, 1–7 (2016)

    Article  MathSciNet  Google Scholar 

  22. Peters, E.E.: Fractal Market Analysis Applying Chaos Theory to Investment and Economics. Wiley, Hoboken (1994)

    Google Scholar 

  23. Li, M.: Fractal time series—a tutorial review. Math. Probl. Eng. 2010 (2009). doi:10.1155/2010/157264

  24. Loffredo, M.I.: Testing chaos and fractal properties in economic time series. In: International Mathematica Symposium (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Visiting Engineering School (DEIM), Tuscia University, Viterbo, Italy

    Yeliz Karaca

  2. Department of Computer Engineering, Halic University, Istanbul, Turkey

    Şengül Bayrak

  3. Department of Computer Engineering, Kemerburgaz University, Istanbul, Turkey

    Emrullah Fatih Yetkin

Authors
  1. Yeliz Karaca
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Şengül Bayrak
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Emrullah Fatih Yetkin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yeliz Karaca .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Trieste, Trieste, Italy

    Giuseppe Borruso

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  9. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  10. University of Trieste, Trieste, Italy

    Alfredo Cuzzocrea

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Karaca, Y., Bayrak, Ş., Yetkin, E.F. (2017). The Classification of Turkish Economic Growth by Artificial Neural Network Algorithms. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10405. Springer, Cham. https://doi.org/10.1007/978-3-319-62395-5_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-62395-5_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62394-8

  • Online ISBN: 978-3-319-62395-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation