[1] W. S. McCulloch, W. Pitts, A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics 5 (1943) 115-133.
[2] S. Ding, H. Li, C. Su, J. Yu, F. Jin, Evolutionary artificial neural networks: a review. Artificial Intelligence Review 39 (2013) 251-260.
[3] R. Tanty, T. S. Desmukh, Application of artificial neural network in hydrology—A review. Int. J. Eng. Technol. Res 4 (2015) 184-188.
[4] Z. Zhang, K. Friedrich, Artificial neural networks applied to polymer composites: a review. Composites Science and technology 63 (2003) 2029-2044.
[5] P. J. Lisboa, A. F. Taktak, The use of artificial neural networks in decision support in cancer: a systematic review. Neural networks 19 (2006) 408-415.
[6] A. K. Yadav, S. Chandel, Solar radiation prediction using Artificial Neural Network techniques: A review. Renewable and sustainable energy reviews 33 (2014) 772-781.
[7] R. Kumar, R. Aggarwal, J. Sharma, Energy analysis of a building using artificial neural network: A review. Energy and Buildings 65 (2013) 352-358.
[8] P. Sharma, T. Bhatti, A review on electrochemical double-layer capacitors. Energy conversion and management 51 (2010) 2901-2912.
[9] A. B. Çolak, T. Güzel, O. Yıldız, M. Özer, An experimental study on determination of the shottky diode current-voltage characteristic depending on temperature with artificial neural network. Physica B: Condensed Matter, (2021) 412852.
[10] E. Rhoderick, R. Williams, Oxford, 1988.
11. Q. Liu, S. Lau, A review of the metal–GaN contact technology. Solid-State Electronics 42, 677-691 (1998).
[12] P. Blom, R. Wolf, J. Cillessen, M. Krijn, Ferroelectric schottky diode. Physical Review Letters 73 (1994) 2107.
[13] V. Rideout, A review of the theory, technology and applications of metal-semiconductor rectifiers. Thin Solid Films 48 (1978) 261-291.
[14] J. H. Zhao, K. Sheng, R. C. Lebron-Velilla, Silicon carbide schottky barrier diode. International journal of high speed electronics and systems 15 (2005) 821-866.
[15] T. Güzel, A. K. Bilgili, M. Özer, Investigation of inhomogeneous barrier height for Au/n-type 6H-SiC Schottky diodes in a wide temperature range. Superlattices and Microstructures 124 (2018) 30-40.
[16] X. She, A. Q. Huang, Ó. Lucía, B. Ozpineci, Review of silicon carbide power devices and their applications. IEEE Transactions on Industrial Electronics 64 (2017) 8193-8205.
[17] X. Wang, J. Qi, M. Yang, G. Zhang, Analysis of 600 V/650 V SiC schottky diodes at extremely high temperatures. CPSS Transactions on Power Electronics and Applications 5 (2020) 11-17.
[18] S. Lim et al., Highly Reliable Inference System of Neural Networks Using Gated Schottky Diodes. IEEE Journal of the Electron Devices Society 7 (2019) 522-528.
[19] A. Rabehi et al., Optimal estimation of Schottky diode parameters using a novel optimization algorithm: Equilibrium optimizer. Superlattices and Microstructures 146 (2020) 106665.
[20] A. Mellit, S. Sağlam, S. A. Kalogirou, Artificial neural network-based model for estimating the produced power of a photovoltaic module. Renewable Energy 60 (2013) 71-78.
[21] M. O. Alade, High Temperature Electronic Properties of a Microwave Frequency Sensor–GaN Schottky Diode. Adv. Phys. Theor. Appl 15 (2013) 47-53.
[22] A. Darwish et al., Optoelectronic performance and artificial neural networks (ANNs) modeling of n-InSe/p-Si solar cell. Superlattices and Microstructures 83 (2015) 299-309.
[23] M. Mittal, B. Bora, S. Saxena, A. M. Gaur, Performance prediction of PV module using electrical equivalent model and artificial neural network. Solar Energy 176 (2018) 104-117.
[24] A. Liang, Y. Xu, S. Jia, G. Sun, in 2008 International Conference on Microwave and Millimeter Wave Technology. IEEE 2, (2008) 558-561.
[25] A.B. Çolak, A novel comparative analysis between the experimental and numeric methods on viscosity of zirconium oxide nanofluid: Developing optimal artificial neural network and new mathematical model, Powder Technology 381 (2021) 338 – 351.
[26] A.A. Rahman, X. Zhang, Prediction of oscillatory heat transfer coefficient for a thermoacoustic heat exchanger through artificial neural network technique, International Journal of Heat and Mass Transfer 124 (2018) 1088 – 1096.
[27] A.B. Çolak, K. Akçaözoğlu, S. Akçaözoğlu, G. Beller, Artificial intelligence approach in predicting the effect of elevated temperature on the mechanical properties of PET aggregate mortars: An experimental study, Arabian Journal for Science and Engineering 46 (2021) 4867 – 4881.
[28] Z. Pang, F. Niu, Z. O’Neill, Solar radiation prediction using recurrent neural network and
artificial neural network: A case study with comparisons, Renewable Energy 156 (2020) 279 – 289.
[29] A.B. Çolak, Prediction of infection and death ratio of CoVID-19 virus in Turkey by using artificial neural network (ANN), Coronaviruses 2:1 (2021) 106 – 112.
[30] W. Gao, J. L.G. Guirao, B. Basavanagoud, J. Wu, Partial multi-dividing ontology learning algorithm, Inf. Sci. 467 (2018) 35–58.
[31] W. Gao, W. Wang, D. Dimitrov, Y. Wang, Nano properties analysis via fourth multiplicative ABC indicator calculating, Arab. J. Chem. 11 (2018) 793–801.
[32] W. Gao, H. Wu, M.K. Siddiqui, A.Q. Baig, Study of biological networks using graph theory, Saudi J. Biol. Sci. 25 (2018) 1212–1219.
[33] W. Gao, J.L.G. Guirao, M. Abdel-Aty, W. Xi, An independent set degree condition for fractional critical deleted graphs, Dis. Cont. Dyn. Syst.-S 12 (2019) 877–886.
[34] W. Gao, D. Dimitrov, H. Abdo, Tight independent set neighborhood union condition for fractional critical deleted graphs and ID deleted graphs, Dis. Cont. Dyn. Syst.-S 12 (2019) 711–721.
[35] E. Bas, V.R. Uslu, E. Egrioglu, Robust learning algorithm for multiplicative neuron model artificial neural networks, Expert Systems With Applications 56 (2016) 80–88.
[36] M. Vakili, S.Khosrojerdi, P. Aghajannezhad, M.Yahyaei, A hybrid artificial neural network-genetic algorithm modeling approach for viscosity estimation of graphene nanoplatelets nanofluid using experimental data, International Communications in Heat and Mass Transfer 82 (2017) 40–48.
[37] M. Bahiraei, S. Heshmatian, H. Moayedi, Artificial intelligence in the field of nanofluids: A review on applications and potential future directions, Powder Technology 353 (2019) 276–301.
[38] J. Park, I.W. Sandberg, Universal approximation using radial-basis-function networks, Neural Comput. 3 (1991) 246–257.
[39] M.V. Valueva, N.N. Nagornov, P.A. Lyakhov, G.V. Valuev, N.I. Chervyakov, Application of the residue number system to reduce hardware costs of the convolutional neural network implementation, Math. Comp. Simul. 177 (2020) 232–243.
[40] S. Garg, A.M. Shariff, M.S. Shaikh, B. Lal, H. Suleman, N. Faiqa, Experimental data, thermodynamic and neural network modeling of CO2 solubility in aqueous sodium salt of l-phenylalanine, J. CO2 Utilization 19 (2017) 146–156.
[41] A.B. Çolak, An experimental study on the comparative analysis of the effect of the number of data on the error rates of artificial neural networks, International Journal of Energy Research, 45(1) (2021) 478 – 500.
[42] A. Barati-Harooni, A. Najafi-Marghmaleki, An accurate RBF-NN model for estimation of
Viscosity of nanofluids, J. Mol. Liq. 224 (2016) 580–588.
[43] S.H. Rostamian, M. Biglari, S. Saedodin, M.H. Esfe, An inspection of thermal conductivity of CuO-SWCNTs hybrid nanofluid versus temperature and concentration using experimental data, ANN modeling and new correlation, J. Mol. Liq. 231 (2017) 364–369.
[44] F. Esmaeilzadeh, A.S. Teja, A. Bakhtyari, The thermal conductivity, viscosity, and cloud points of bentonite nanofluids with n-pentadecane as the base fluid, J. Mol. Liq. 300 (2020) 112307.
[45] H. Bonakdari, A.H. Zaji, Open channel junction velocity prediction by using a hybrid self-neuron adjustable artificial neural network, Flow Measurement and Instrumentation 49 (2016) 46–51.
[46] A.B. Çolak, Developing optimal artificial neural network (ANN) to predict the specific heat of water based yttrium oxide (Y2O3) nanofluid according to the experimental data and proposing new correlation, Heat Transfer Research, 51(17) (2020) 1565 – 1586.
[47] E. Ahmadloo, S. Azizi, Prediction of thermal conductivity of various nanofluids using artificial neural network, International Communications in Heat and Mass Transfer 74 (2016) 69–75.
[48] A.B. Çolak, Experimental study for thermal conductivity of water-based zirconium oxide nanofluid: Developing optimal artificial neural network and proposing new correlation, International Journal of Energy Research, 45(2) (2020) 2912 – 2930.
[49] A.B. Çolak, O. Yıldız, M. Bayrak, B.S. Tezekici, Experimental study for predicting the specific heat of water based Cu-Al2O3 hybrid nanofluid using artificial neural network and proposing new correlation, International Journal of Energy Research 44(9), (2020) 7198-7215.
[50] B. Gunduz, I. Yahia, F. Yakuphanoglu, Electrical and photoconductivity properties of p-Si/P3HT/Al and p-Si/P3HT: MEH-PPV/Al organic devices: Comparison study. Microelectronic Engineering 98 (2012) 41-57.
[51] V. R. Reddy, Electrical properties of Au/polyvinylidene fluoride/n-InP Schottky diode with polymer interlayer. Thin Solid Films 556 (2014) 300-306.
[52] S. Forrest, P. Schmidt, Semiconductor analysis using organic‐on‐inorganic contact barriers. I. Theory of the effects of surface states on diode potential and ac admittance. Journal of applied physics 59 (1986) 513-525.