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References [1] Yegnanarayana B. (2009). Artificial neural networks. PHI Learning Pvt. Ltd. [2] Patterson D. W. (1998). Artificial neural networks: theory and applications. Prentice Hall PTR [3] Pastuszak Z. (26-02-2015). Zarządzanie logistyczne. Podstawowe definicje [in Polish], http://umcs.net.pl/index.php?act=Attach&type=post&id=2800 [4] Chaberek M. (2006). Główne problemy badawcze w zakresie rozwoju logistyki na obszarze Unii Europejskiej, Zeszyty Naukowe Uniwersytetu Szczecińskiego, seria Ekonomiczne Problemy Usług nr 3, vol. 435, pp. 17-22 [in Polish] [5

.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 approach, International Journal of Neural Systems, 1, 1990, 193

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://store-us.leapmotion.com/ [Accessed: May 11, 2016]. [5] L. Fausett, Fundamentals of

Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems . Helion. Golenia M., Zagajewski B., Ochtyra A., 2005, Zastosowanie sztucznych sieci neuronowych do aktualizacji map pokrycia terenu Corine. “Polski Przegląd Kartograficzny” T. 47, nr 3-4, pp. 257–266. Goodfellow I., Bengio Y., Courville A., 2009, Deep Learning. Boston : Massachusetts Institute of Technology (MIT). Khaze S. R., Mohammed M., Hojjatkhah S., 2013, Application of artificial neural networks in estimating participation in elections . “International Journal of

-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) for the prediction of thermal resistance of knitted fabrics at different moisture

., 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 Ślipek, Z. (2000). Pomiar rzeczywistej powierzchni kontaktu trących się materiałów

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(4), 2015, 927-935 [4] D

References Acreman M. C., Sinclair C. D. 1986: Classification of drainage basins according to their physical characteristics; an application for flood frequency analysis in Scotland. J. Hydrol., 84 , 365-380. Aqil M., Kita I., Yano A., Nishiyama S., 2007: A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff. Journal of Hydrology, 337 , 1-2, 22-34. Anctil F., Michel C., Perrin C., Andreassian V., 2004: A soil moisture index as an auxiliary ANN input for stream flow forecasting

REFERENCES ARE 2014. Statystyka Elektroenergetyki Polskiej [Polish electric power statistics]. ISSN 1232-2415. B agheri B odaghabadi M., M artinez -C asasnovas J.A., S alehi M.H., M ohammadi J., E sfandiarpoor B orujeni I., T oomanian N., G andomkar A. 2015. Digital soil mapping using artificial neural networks and terrain-related attributes. Pedosphere. Vol. 25. Iss. 4 p. 580–591. DOI: 10.1016/S1002-0160(15)30038-2. Ć wik J., M ielińczuk J. 2009. Statystyczne systemy uczące się. Ćwiczenia w oparciu o pakiet R. Warszawa. Oficyna Wydaw. PW. ISBN 978

. Mensah, Potential applications of system identification techniques in pavement performance modeling. Proceedings of the Second International Symposium on Maintenance and Rehabilitation of Pavements and Technological Control, National Center for Asphalt Technology, Auburn, Alabama, 2001. 6. S. Chou, T.K. Pellinen, Assessment of construction smoothness specification pay factor limits using artificial neural network modeling. Journal of Transportation Engineering, 131, 563-70, 2005. 7. R.A. Tarefder, L. White, M. Zaman, Development and application of a rut prediction