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Prediction of adsorption efficiencies of Ni (II) in aqueous solutions with perlite via artificial neural networks

References Alkan, M. & Doğan, M. (2001). Adsorption of Copper(II) onto Perlite, Journal of Colloid and Interface Science, 243, pp. 280-291. ASCE, 2000, Artifi cial neural networks in hydrology. I: Preliminary concepts, Journal of Hydrologic Engineering, 5(2), pp. 115-123, ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. Bui, H.M., Duong, H.T.G. & Nguyen, C.D. (2016). Applying an artificial neural network to predict coagulation capacity of reactive dying wastewater by chitosan

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Predicting subsurface soil layering and landslide risk with Artificial Neural Networks: a case study from Iran

References Allen A. & Tadesse G. 2003: Geological setting and tectonic subdivision of the Neoproterozoic orogenic belt of Tuludimtu, western Ethiopia. J. African Earth Sci. 36, 1-2, 329-343. Agrawal G., Chameau J.A. & Bourdeau P.L. 1997: Assessing the liquefaction susceptibility at a site based on information from penetration testing. In: Kartam N., Flood I. & Garrett J.H. (Eds.): Artificial neural networks for civil engineers: fundamentals and applications. New York, 185

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Present Trends in Research on Application of Artificial Neural Networks in Agricultural Engineering

References Aghbashlo, M., Mobli, H., Rafiee, S., Madadlou, A. (2012). The use of artificial neural network to predict exergetic performance of spray drying process: A preliminary study. Computers and Electronics in Agriculture, 88, 32-43. Boniecki, P., Koszela, K., Piekarska-Boniecka, H., Weres, J., Zaborowicz, M., Kujawa, S., Majewski, A., Raba, B. (2015). Neural identification of selected apple pests. Computers and Electronics in Agriculture, 110, 9-16. Cobaner, M., Citakoglu, H., Kisi, O., Haktanir, T. (2014

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Application of artificial neural networks to predict the deflections of reinforced concrete beams

Biophysics, 1943, 5, 115–133. [8] S chabowicz K., Neural networks in the NDT identification of the strength of concrete , Archives of Civil Engineering, 2005, 51(3), 371–382. [9] S chabowicz K., H oła B., Application of artificial neural networks in predicting earthmoving machinery effectiveness ratios , Archives of Civil and Mechanical Engineering, 2008, 8(4), 73–84. [10] O chmański M., B zówka J., Back analysis of SCL tunnels based on Artificial Neural Network , Architecture, Civil Engineering, Environment – ACEE Journal, 2012, 3, 73

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Planning Training Loads for The 400 M Hurdles in Three-Month Mesocycles Using Artificial Neural Networks

loads. Artificial intelligence methods play an important role in planning training loads. These include, among others, artificial neural networks (ANNs), which are developed from the design and function of the neural systems of living organisms ( Bishop, 2006 ). Numerous studies have shown that the ANN is a means of predicting sports results which has a good predictive ability ( Edelmann-Nusser et al., 2002 ; Przednowek and Wiktorowicz, 2011 ; Wilk et al., 2015 ). Thus, the ANN enables a coach to model the future level of athlete’s performance and supports the

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Artificial neural network to predict the natural convection from vertical and inclined arrays of horizontal cylinders

Free Convection Heat Transfer from Horizontal Isothermal Cylinders Arranged in Vertical and Inclined Arrays. J. Heat Transfer Engineering. 28(5),460-471.DOI: 10.1080/01457630601165822. 11. Sozen, A. & Arcaklioglu, E. (2007).Exergy analysis of an ejector-absorption heat transformer using artificial neural network approach. Appl. Therm. Eng. 27(2-3), 481-491.DOI: 10.1016/j.applthermaleng.2006.06.012. 12. Deng, S. & Hwang, Y. (2006).Applying neural networks to the solution of forward and inverse heat conduction problems. Int. J. of

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Method of Measurement of Capacitance and Dielectric Loss Factor Using Artificial Neural Networks

, M. (1994). Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks , 5 (6), 989-993. [18] Glowacz, A., Glowacz, A., Glowacz, Z. (2014). Recognition of monochrome thermal images of synchronous motor with the application of quadtree decomposition and backpropagation neural network. Maintenance and Reliability ( Eksploatacja i Niezawodnosc ), 16 (1), 92-96. [19] Roj, J. (2014). Estimation of the artificial neural network uncertainty used for measurand reconstruction in a sampling transducer. IET Science

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Artificial Neural Network Approach for Modeling of Ni(Ii) Adsorption from Aqueous Solution by Peanut Shell

-manganese oxide coated kaolinite: non-linear isotherm and kinetics modeling. Appl Clay Sci. 2015;107:70-77. DOI: 10.1016/j.clay.2015.01.005. [35] Asl SMH, Ahmadi M, Ghiasvand M, Tardast A, Katal R. Artificial neural network (ANN) approach for modeling of Cr(VI) adsorption from aqueous solution by zeolite prepared from raw fly ash (ZFA). J Ind Eng Chem. 2013;19:1044-1055. DOI: 10.1016/j.jiec.2012.12.001. [36] Allen SJ, Gan Q, Matthews R, Johnson PA. Comparison of optimised isotherm models for basic dye adsorption by kuzdu. Bioresour Technol. 2003;88(2):143-152. DOI

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References [1] ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000 I) Artificial Neural Networks in hydrology: I: preliminary concepts. Journal of Hydrology Engineering. 5(2), 115-123. DOI: 10.1061/(ASCE)1084-0699(2000)5:2(115) [2] ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000 II) Artificial Neural Networks in hydrology: II: hydrological applications. Journal of Hydrology Engineering. 5(2), 124-137. DOI: 10.1061/(ASCE)1084

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The artificial neural network modelling of the piezoelectric actuator vibrations using laser displacement sensor

Actuator (PEA) using Simulink Software”, ICEEE Electrical and Electronic Engineering , 2017 4th International Conference, pp. 153-157, April 2017. [21] W. Qingming, Z. Qiang, Z. Chi and C. Gang, “Vibration Control of Block Forming Machine Based on an Artificial Neural Network”, 4th International Symposium on Neural Networks , Nanjing, China, June 2007. [22] X. Yang, W. Li, G. Ye and X. Su, “Hysteresis Modeling of Piezo Actuator Using Neural Networks”, IEEE International Conference on Robotics and Biomimetics (ROBIO 2008) , Bangkok, Thailand, February 2009

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