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TÜRKİYE'NİN ALMANYA'YA PAMUKLU ÇORAP İHRACATININ DERİN ÖĞRENME YAKLAŞIMI İLE TAHMİNİ

Year 2024, Volume: 31 Issue: 135, 174 - 181, 30.09.2024

Ahmet Özbek , Çağatay Teke

https://doi.org/10.7216/teksmuh.1486577

Abstract

Pamuklu çoraplar Türkiye için stratejik bir ihraç ürünüdür. Bu nedenle, bu çalışmanın amacı Türkiye'nin dünyanın en büyük pamuklu çorap pazarı olan Almanya'ya ihracatını tahmin etmektir. Bu amaca ulaşmak için, literatür analiz edilerek ihracatın belirleyicileri tespit edilmiştir. Daha sonra, bu faktörlerin Türkiye'nin Almanya'ya pamuklu çorap ihracatı açısından önemini belirlemek için uzman görüşüne başvurulmuştur. Uzman görüşü sonucunda belirlenen faktörlerden oluşturulan derin öğrenme modeli kullanılarak Türkiye'nin Almanya'ya çorap ihracatının tahmini gerçekleştirilmiştir. Tahmin ile %96' lık bir başarı oranı elde edildi.

Keywords

Derin Öğrenme MLP yapay sinir ağı pamuklu çorap ihracat tahmini

References

  • Yildiz, A. & Özbek, A., (2020). Selection of socks export markets for Turkey using multi-criteria decision making methods. Sigma Journal of Engineering and Natural Sciences, 38(2), 795-815.
  • Avşar, İ. İ., & Ecemı̇ş, O., (2023). Forecast of turkey’s import and export by data mining methods. Osmaniye Korkut Ata University Journal of The Institute of Science and Technology, 6(3), 1890–1907. https://doi.org/10.47495/okufbed.1220874
  • Awasthi, S., SenGupta, I., Wilson, W., & Lakkakula, P., (2022). Machine learning and neural network based model predictions of soybean export shares from US Gulf to China. Statistical Analysis and Data Mining, 15(6), 707–721. https://doi.org/10.1002/ sam.11595
  • Bin, J., & Tianli, X., (2020). Forecast of export demand based on artificial neural network and fuzzy system theory. Journal of Intelligent & Fuzzy Systems, 39(2), 1701–1709. https://doi.org/ 10.3233/ jifs-179944
  • Çakan, V. A., (2020). Forecasts for Turkey Fresh Fig Production and Dried Fig Export: ARIMA Model Approach. Journal of Tekirdag Agricultural Faculty, 17(3), 357–368. https://doi.org/ 10.33462/jotaf.684893
  • Han, Z., Zhu, Z., Zhao, S., & Dai, W., (2022). Research on nonlinear forecast and influencing factors of foreign trade export based on support vector neural network. Neural Computing & Applications, 34(4), 2611–2622. https://doi.org/10.1007/s00521-021-05900-3
  • Ishaq, M., Ping, Q., Haq, Z., Li, C., & Tong, C., (2016). Maximum residue limits and agrifood exports of China: choosing the best estimation technique. Agricultural Economics, 62(2), 78–92. https://doi.org/10.17221/17/2015-agricecon
  • Islam, T., (2016). An empirical estimation of export and import demand functions using bilateral trade data: The case of Bangladesh. Journal of Commerce and Management Thought, 7(3), 526. https://doi.org/10.5958/0976-478x.2016.00030.6
  • Nooraeni, R., Nickelson, J., Rahmadian, E., & Yudho, N. P., (2022). New recommendation to predict export value using big data and machine learning technique. Statistical Journal of the IAOS, 38(1), 277–290. https://doi.org/10.3233/sji-210855
  • Sun, M., & Yang, H., (2023). Forecasting model of fishery import and export trade data using deep learning method. 2023 International Conference on Blockchain Technology and Applications (ICBTA).
  • Ahmadpour Kasgari, A., Divsalar, M., Javid, M. R., & Ebrahimian, S. J., (2013). Prediction of bankruptcy Iranian corporations through artificial neural network and Probit-based analyses. Neural Computing & Applications, 23 (3–4), 927–936. https://doi.org/10.1007/s00521-012-1017-z
  • Ko, P., & Lin, P., (2008). Resource allocation neural network in portfolio selection. Expert Systems with Applications, 35(1–2), 330–337. https://doi.org/10.1016/j.eswa.2007.07.031
  • Chang, R.-I., Chiu, Y.-H., & Lin, J.-W., (2020). Two-stage classification of tuberculosis culture diagnosis using convolutional neural network with transfer learning. The Journal of Supercomputing, 76(11), 8641–8656. https://doi.org/10.1007/ s11227-020-03152-x
  • Etebari, F., & Najafi, A. A., (2016). Intelligent choice-based network revenue management. Scientia Iranica, 23(2), 747–756. https://doi.org/10.24200/sci.2016.3860
  • Tsai, C., & Wu, J., (2008). Using neural network ensembles for bankruptcy prediction and credit scoring. Expert Systems with Applications, 34(4), 2639–2649. https://doi.org/10.1016/j.eswa. 2007.05.019
  • Wang, N., Chen, J., Xiao, H., Wu, L., Jiang, H., & Zhou, Y., (2019). Application of artificial neural network model in diagnosis of Alzheimer’s disease. BMC Neurology, 19(1). https://doi.org/ 10.1186/s12883-019-1377-4
  • Alam, T., (2019). Forecasting exports and imports through artificial neural network and autoregressive integrated moving average. Decision Science Letters, 249–260. https://doi.org/ 10.5267/j.dsl.2019.2.001
  • Eşı̇dı̇r, K. A., & Gür, Y. E., (2023). Turkish plastics industry import forecast with artificial neural networks: April-December 2023. The Academic Elegance, 10(23), 91–114. https://doi.org/10.58884/akademik-hassasiyetler.1307536
  • Okkan, U., (2011). Application of Levenberg-Marquardt optimization algorithm based Multilayer Neural Networks for hydrological time series modeling. An International Journal of Optimization and Control Theories & Applications (IJOCTA), 1(1), 53–63. https://doi.org/10.11121/ijocta.01.2011.0038
  • Prasad, V. K., Bhattacharya, P., Bhavsar, M., Verma, A., Tanwar, S., Sharma, G., Bokoro, P. N., & Sharma, R., (2022). ABV-CoViD: An ensemble forecasting model to predict availability of beds and ventilators for COVID-19 like pandemics. IEEE Access: Practical Innovations, Open Solutions, 10, 74131–74151. https://doi.org/ 10.1109/access.2022.3190497
  • Ratnasih, C., & Sulbahri, R. A., (2022). Full Costing Method model and Variable Costing Method against cement price determination (case in Indonesia). European Journal of Business and Management Research, 7(2), 284–288. https://doi.org/10.24018/ ejbmr.2022.7.2.1378
  • Ersen, N., Akyüz, İ., & Bayram, B. Ç., (2019). The forecasting of the exports and imports of paper and paper products of Turkey using Box-Jenkins method. Eurasian Journal of Forest Science, 7(1), 54–65. https://doi.org/10.31195/ejejfs.502397
  • Gür, Y. E., & Eşı̇dı̇r, K. A, (2024). Estimation of turkish trout export with arima and multilayer perceptron models. Turkish Studies-Economy, 19(1), 187–206. https://doi.org/10.7827/ turkishstudies.70444
  • Gür, Y. E., & Eşidir, K. A., (2024). Forecasting Türkiye's paper and paper products sector import using artificial neural networks. Hitit Journal of Social Sciences, 17(2), 206-224. https://doi.org/ 10.17218/hititsbd.1327799
  • Özbek, A., & Akalın, M., (2011). The prediction of Turkey’s denim trousers export to Germany with ANN models. Textile and Apparel, 21(4), 313-322.

Prediction of Turkey's cotton sock exports to Germany using deep learning approach

Year 2024, Volume: 31 Issue: 135, 174 - 181, 30.09.2024

Ahmet Özbek , Çağatay Teke

https://doi.org/10.7216/teksmuh.1486577

Abstract

Cotton socks are a strategic export product for Turkey. Therefore, the aim of this study is to forecast Turkey's exports to Germany, the world's largest cotton socks market. In order to achieve this objective, the determinants of exports were identified by analysing the literature. Then, expert opinion was sought to determine the importance of these factors for Turkey's cotton socks exports to Germany. Using the deep learning model created from the factors determined as a result of the expert opinion, the prediction of the export of Turkish socks to Germany was realised. A success rate of 96% was achieved with the prediction.

Keywords

Deep learning MLP neural network cotton sock export prediction

References

  • Yildiz, A. & Özbek, A., (2020). Selection of socks export markets for Turkey using multi-criteria decision making methods. Sigma Journal of Engineering and Natural Sciences, 38(2), 795-815.
  • Avşar, İ. İ., & Ecemı̇ş, O., (2023). Forecast of turkey’s import and export by data mining methods. Osmaniye Korkut Ata University Journal of The Institute of Science and Technology, 6(3), 1890–1907. https://doi.org/10.47495/okufbed.1220874
  • Awasthi, S., SenGupta, I., Wilson, W., & Lakkakula, P., (2022). Machine learning and neural network based model predictions of soybean export shares from US Gulf to China. Statistical Analysis and Data Mining, 15(6), 707–721. https://doi.org/10.1002/ sam.11595
  • Bin, J., & Tianli, X., (2020). Forecast of export demand based on artificial neural network and fuzzy system theory. Journal of Intelligent & Fuzzy Systems, 39(2), 1701–1709. https://doi.org/ 10.3233/ jifs-179944
  • Çakan, V. A., (2020). Forecasts for Turkey Fresh Fig Production and Dried Fig Export: ARIMA Model Approach. Journal of Tekirdag Agricultural Faculty, 17(3), 357–368. https://doi.org/ 10.33462/jotaf.684893
  • Han, Z., Zhu, Z., Zhao, S., & Dai, W., (2022). Research on nonlinear forecast and influencing factors of foreign trade export based on support vector neural network. Neural Computing & Applications, 34(4), 2611–2622. https://doi.org/10.1007/s00521-021-05900-3
  • Ishaq, M., Ping, Q., Haq, Z., Li, C., & Tong, C., (2016). Maximum residue limits and agrifood exports of China: choosing the best estimation technique. Agricultural Economics, 62(2), 78–92. https://doi.org/10.17221/17/2015-agricecon
  • Islam, T., (2016). An empirical estimation of export and import demand functions using bilateral trade data: The case of Bangladesh. Journal of Commerce and Management Thought, 7(3), 526. https://doi.org/10.5958/0976-478x.2016.00030.6
  • Nooraeni, R., Nickelson, J., Rahmadian, E., & Yudho, N. P., (2022). New recommendation to predict export value using big data and machine learning technique. Statistical Journal of the IAOS, 38(1), 277–290. https://doi.org/10.3233/sji-210855
  • Sun, M., & Yang, H., (2023). Forecasting model of fishery import and export trade data using deep learning method. 2023 International Conference on Blockchain Technology and Applications (ICBTA).
  • Ahmadpour Kasgari, A., Divsalar, M., Javid, M. R., & Ebrahimian, S. J., (2013). Prediction of bankruptcy Iranian corporations through artificial neural network and Probit-based analyses. Neural Computing & Applications, 23 (3–4), 927–936. https://doi.org/10.1007/s00521-012-1017-z
  • Ko, P., & Lin, P., (2008). Resource allocation neural network in portfolio selection. Expert Systems with Applications, 35(1–2), 330–337. https://doi.org/10.1016/j.eswa.2007.07.031
  • Chang, R.-I., Chiu, Y.-H., & Lin, J.-W., (2020). Two-stage classification of tuberculosis culture diagnosis using convolutional neural network with transfer learning. The Journal of Supercomputing, 76(11), 8641–8656. https://doi.org/10.1007/ s11227-020-03152-x
  • Etebari, F., & Najafi, A. A., (2016). Intelligent choice-based network revenue management. Scientia Iranica, 23(2), 747–756. https://doi.org/10.24200/sci.2016.3860
  • Tsai, C., & Wu, J., (2008). Using neural network ensembles for bankruptcy prediction and credit scoring. Expert Systems with Applications, 34(4), 2639–2649. https://doi.org/10.1016/j.eswa. 2007.05.019
  • Wang, N., Chen, J., Xiao, H., Wu, L., Jiang, H., & Zhou, Y., (2019). Application of artificial neural network model in diagnosis of Alzheimer’s disease. BMC Neurology, 19(1). https://doi.org/ 10.1186/s12883-019-1377-4
  • Alam, T., (2019). Forecasting exports and imports through artificial neural network and autoregressive integrated moving average. Decision Science Letters, 249–260. https://doi.org/ 10.5267/j.dsl.2019.2.001
  • Eşı̇dı̇r, K. A., & Gür, Y. E., (2023). Turkish plastics industry import forecast with artificial neural networks: April-December 2023. The Academic Elegance, 10(23), 91–114. https://doi.org/10.58884/akademik-hassasiyetler.1307536
  • Okkan, U., (2011). Application of Levenberg-Marquardt optimization algorithm based Multilayer Neural Networks for hydrological time series modeling. An International Journal of Optimization and Control Theories & Applications (IJOCTA), 1(1), 53–63. https://doi.org/10.11121/ijocta.01.2011.0038
  • Prasad, V. K., Bhattacharya, P., Bhavsar, M., Verma, A., Tanwar, S., Sharma, G., Bokoro, P. N., & Sharma, R., (2022). ABV-CoViD: An ensemble forecasting model to predict availability of beds and ventilators for COVID-19 like pandemics. IEEE Access: Practical Innovations, Open Solutions, 10, 74131–74151. https://doi.org/ 10.1109/access.2022.3190497
  • Ratnasih, C., & Sulbahri, R. A., (2022). Full Costing Method model and Variable Costing Method against cement price determination (case in Indonesia). European Journal of Business and Management Research, 7(2), 284–288. https://doi.org/10.24018/ ejbmr.2022.7.2.1378
  • Ersen, N., Akyüz, İ., & Bayram, B. Ç., (2019). The forecasting of the exports and imports of paper and paper products of Turkey using Box-Jenkins method. Eurasian Journal of Forest Science, 7(1), 54–65. https://doi.org/10.31195/ejejfs.502397
  • Gür, Y. E., & Eşı̇dı̇r, K. A, (2024). Estimation of turkish trout export with arima and multilayer perceptron models. Turkish Studies-Economy, 19(1), 187–206. https://doi.org/10.7827/ turkishstudies.70444
  • Gür, Y. E., & Eşidir, K. A., (2024). Forecasting Türkiye's paper and paper products sector import using artificial neural networks. Hitit Journal of Social Sciences, 17(2), 206-224. https://doi.org/ 10.17218/hititsbd.1327799
  • Özbek, A., & Akalın, M., (2011). The prediction of Turkey’s denim trousers export to Germany with ANN models. Textile and Apparel, 21(4), 313-322.
There are 25 citations in total.

Details

Primary Language English
Subjects Textile Economy
Journal Section Articles
Authors

Ahmet Özbek 0000-0001-5015-8082

Çağatay Teke 0000-0002-6975-8544

Publication Date September 30, 2024
Submission Date May 19, 2024
Acceptance Date September 7, 2024
Published in Issue Year 2024 Volume: 31 Issue: 135

Cite

APA Özbek, A., & Teke, Ç. (2024). Prediction of Turkey’s cotton sock exports to Germany using deep learning approach. Tekstil Ve Mühendis, 31(135), 174-181. https://doi.org/10.7216/teksmuh.1486577
AMA Özbek A, Teke Ç. Prediction of Turkey’s cotton sock exports to Germany using deep learning approach. Tekstil ve Mühendis. September 2024;31(135):174-181. doi:10.7216/teksmuh.1486577
Chicago Özbek, Ahmet, and Çağatay Teke. “Prediction of Turkey’s Cotton Sock Exports to Germany Using Deep Learning Approach”. Tekstil Ve Mühendis 31, no. 135 (September 2024): 174-81. https://doi.org/10.7216/teksmuh.1486577.
EndNote Özbek A, Teke Ç (September 1, 2024) Prediction of Turkey’s cotton sock exports to Germany using deep learning approach. Tekstil ve Mühendis 31 135 174–181.
IEEE A. Özbek and Ç. Teke, “Prediction of Turkey’s cotton sock exports to Germany using deep learning approach”, Tekstil ve Mühendis, vol. 31, no. 135, pp. 174–181, 2024, doi: 10.7216/teksmuh.1486577.
ISNAD Özbek, Ahmet - Teke, Çağatay. “Prediction of Turkey’s Cotton Sock Exports to Germany Using Deep Learning Approach”. Tekstil ve Mühendis 31/135 (September 2024), 174-181. https://doi.org/10.7216/teksmuh.1486577.
JAMA Özbek A, Teke Ç. Prediction of Turkey’s cotton sock exports to Germany using deep learning approach. Tekstil ve Mühendis. 2024;31:174–181.
MLA Özbek, Ahmet and Çağatay Teke. “Prediction of Turkey’s Cotton Sock Exports to Germany Using Deep Learning Approach”. Tekstil Ve Mühendis, vol. 31, no. 135, 2024, pp. 174-81, doi:10.7216/teksmuh.1486577.
Vancouver Özbek A, Teke Ç. Prediction of Turkey’s cotton sock exports to Germany using deep learning approach. Tekstil ve Mühendis. 2024;31(135):174-81.