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Pablo Benalcazar, Małgorzata Krawczyk and Jacek Kamiński

] Available at: www.eia.gov/forecasts/ieo/pdf/0484(2016).pdf [Accessed: 7.06.2017]. Ermis et al. 2007 - Ermis, K., Midilli, A., Dincer, I. and Rosen, M.A. 2007. Artificial neural network analysis of world green energy use. Energy Policy 35, 1731-1743. doi: 10.1016/j.enpol.2006.04.015. Günay, M.E. 2016. Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: Case of Turkey. Energy Policy 90, 92-101. doi: 10.1016/j.enpol.2015

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B. Dziuba

( 2008 ) Structural studies on exopolysaccharides produced by three different propionibacteria strains. Carbohydr Res 343: 726-745. Dziuba B ( 2007 ) Identification of Lactobacillus strains at the species level using FTIR spectroscopy and artificial neural networks. Pol J Food Nutr Sci 57: 301-306. Dziuba B, Babuchowski A, Nałęcz D, Niklewicz M ( 2007a ) Identification of lactic acid bacteria using FTIR spectroscopy and cluster analysis. Inter Dairy J 17: 183-189. Dziuba B, Babuchowski A, Niklewicz M ( 2007b

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Eren Bas

Applications, 37, 2010, 7449-7455. [9] C.H. Aladag, E. Egrioglu, U. Yolcu, Forecast combination by using artificial neural networks, Neural Processing Letters, 32 (3), 2010, 269–276. [10] G. Zhang, B.E. Patuwo, Y.M. Hu, Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14, 1998, 35-62. [11] R. Sharda, Neural networks for the MS/OR analyst: An application bibliography, Interfaces, 24 (2), 1994, 116–130. [12] A.S. Weigend, B.A. Huberman, D.E. Rumelhart, Predict- ing the future: A connectionist

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Esra Akdeniz, Erol Egrioglu, Eren Bas and Ufuk Yolcu

References [1] R.N. Yadav, P.K. Kalra, J. John, Time series prediction with single multiplicative neuron model, Applied Soft Computing, 7, 2007, 1157-1163. [2] E. Egrioglu, C.H. Aladag, U. Yolcu, and E. Bas, Recurrent multiplicative neuron model artificial neural network for non-linear time series forecasting, Neural Processing Letters 41(2), 2015, 249-258. [3] O. Gundogdu, E. Egrioglu, C.H. Aladag, and U. Yolcu, Multiplicative neuron model artificial neural network based on gauss activation function, Neural Computing and Applications 27

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Aleksejs Zorins and Pēteris Grabusts

References [1] Spread the sign. [Online]. Available: https://www.spreadthesign.com/us/aboutus/ [Accessed: May 11, 2016]. [2] The Latvian Sign Language Development Department. [Online]. Available: http://rc.lns.lv/index.php [Accessed: May 11, 2016]. [3] A. Zorins and P. Grabusts, “Review of sign language recognition systems based on artificial neural networks,” in MENDEL 2016 conference proceedings, Brno, Czech Republic, 2016. [4] The Leap Motion Store. [Online]. Available: http

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Andrzej Wit and Adam Czaplicki

, 24 (1), 46-53. DOI: 10.1016/j.gaitpost.2005.07.005. Barton, G., Lisboa, P., Lees, A., Attfield, S., Gait quality assessment using self-organising artificial neural networks. Gait Posture , 2007, 25 (3), 374-379. DOI: 10.1016/j.gaitpost.2006.05.003. Lees, A., Technique analysis in sports: a critical review. J Sports Sci , 2002, 20 (10), 813-828. Graupe, D., Kordylewski, H., Artificial neural network control of FES in paraplegics for patient responsive ambulation. IEEE Trans Biomed Eng

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Assad Farooq, Muhammad Ilyas Sarwar, Muhammad Azeem Ashraf, Danish Iqbal, Azmat Hussain and Samander Malik

). Relationships between micronaire, fineness, and maturity. Part-I. Fundamentals. Journal of Cotton Science, 9, 81-88. [18] Gordon, S.G. & G.R.S. Naylor. (2004). Instrumentation for rapid direct measurement of cotton fibre fineness and maturity. [19] Erbil, Y., O. Babaarslan & İ.Ilhan. (2018). A comparative prediction for tensile properties of ternary blended open-end rotor yarns using regression and neural network models. The Journal of The Textile Institute, 109(4): 560-568.” [20] Kanat, Z.E. & N. Özdil. (2018). Application of artificial neural network (ANN

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Jarosław Frączek, Sławomir Francik, Zbigniew Ślipek and Adrian Knapczyk

:978-3-540-22980. Fang, Q., Hanna, M.A., Haque, E., Spillman, C.K. (2000). Neural network modeling of energy requirements for size reduction of wheat. Transactions Of The ASAE Volume: 43, Issue: 4, 947-952. Francik, S., Frączek, J. (2001). Model development of the external friction of granular vegetable materials on the basis of artificial neural networks. International Agrophysics, 15, 231-236. Frączek, J. (2003). Wpływ kształtu nasion na wartość powierzchni kontaktu. Inżynieria Rolnicza, 9(51), 81-88. Frączek, J., Kaczorowski, J. i

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Rodrigo Coral, Carlos A. Flesch, Cesar A. Penz, Mauro Roisenberg and Antonio L. S. Pacheco

References [1] Haykin, S. (1999). Neural Networks: A Comprehensive Foundation. 2nd ed., Prentice Hall. [2] Singaram, L. (2011). ANN prediction models for mechanical properties of AZ61 MG alloy fabricated by equal channel angular pressing. Int. J. of Res. and Reviews in Appl. Sciences, (8), 337−345. [3] Ghobadian, B., Rahimi, H., Nikbakht, A.M., Najafi, G., Yusaf, T.F. (2009). Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network. Renew

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I. Uygur, A. Cicek, E. Toklu, R. Kara and S. Saridemir

). [8] I. Uygur, Archives of Metal. & Mater. 56(1), 109 (2011). [9] D. Karayel, Journal of Materials Processing Technology 209, 3125 (2009). [10] E. Oztemel, Artificial Neural Network. Istanbul, Papatya Publishing, 2003. [11] G. Najafi, B. Ghobadian, T. Tavakoli, D.R. Buttsworth, T.F. Yusa f, M. Faizollahnejad, Applied Energy 86, 630 (2009). [12] N. Pasadakis, S. Sourligas, C. Foteinopoulo s, Fuel 85, 1131 (2006). [13] G. Liu, L. Wang, H. Qu, H. Shen, X. Zhang, Fuel 86, 2551 (2007