## 3 Results  
In this study, the viscosity values of CuO nanofluids at various pH and temperatures for the nano-liquids obtained using the nanomaterials produced are inversely proportional to the temperature values between 20 and 60 \({}^{\circ}\)C. Figure 6 shows an inverse relationship between temperature and viscosity. According to Figure 6, the viscosity value increased when the pH value increased, but the viscosity value decreased when the temperature increased. Figure 7 shows the average velocity-viscosity values at a Reynolds number of 2300 and at a temperature of 20 \({}^{\circ}\)C. Figure 7 is the viscosity-average velocity graph of the nanofluids obtained by adding CuO at different percentages. According to this graph generated, at different pH values, the viscosity value increased as the pH ratio increased. At the same time, the higher the CuO ratio, the higher the viscosity value. The increase in the viscosity value due to the increase in the average velocity value can be explained by the increase in the amount of CuO. Figure 6: The experimental measurement of the viscosity of the CuO solution as a function of T with various pH values and weight % values. Figure 7: The average velocity-viscosity change of nanofluids obtained at different pH and CuO ratios. The error rates for ADTree and MLP predicted models created for viscosity values of CuO nanofluids were shown in Table 3.  
In the literature, different studies have been performed to create a predictive model of various nanofluidic viscosities by using computational intelligence methods. These studies are shown in Table 4.  
Table 4 shows the nanoacetic type, the number of data values, the predictive method, the type of error analysis and the results of the error analysis. In our study, the results of the RMSE error analysis of the methods used to estimate the viscosity of the nanofluids have less error than the results of the RMSE error analysis of the studies in Table 4.  
Figures 8 and 9 show the viscosity values estimated by computational intelligence methods and the viscosity values calculated from the experimental study. Figures 8 and 9 show the experimental and predictive data. The data given in the form of plus are predictive and the data given in square form are experimental. The predictive viscosity values are the values obtained using the input parameters specified in Section 2.3 with MLP and DT, which is a machine learning algorithm. The fact that the experimental data and the predictive data are close to each other indicate a more accurate estimation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Nanofluids** & **Number of Values** & **Method** & **Error Analysis** & **Error Analysis** & **Reference** \\ \hline Al\({}_{2}\)O\({}_{3}\), TiO\({}_{2}\), SiO\({}_{2}\) and & 3144 & MLP & RMSE & 0.1 & [PERSON] et al. [13] \\ \hline Multi-walled Carbon & & & & & \\ Nanotubes & 268 & ANN & MSE & 0.28 & [PERSON] et al. [14] \\ MWCNTs/water & & & & & \\ \hline Al\({}_{2}\)O\({}_{3}\), CuO, TiO\({}_{2}\) and & 801 & LS-SVM & RMSE & 37.084 & [PERSON] et al. [15] \\ SiO\({}_{2}\) Nanofluids & & & & & \\ \hline Al\({}_{2}\)O\({}_{3}\)–H\({}_{2}\)O, CuO–H\({}_{2}\)O, & 381 & GA-ANN & MSE & 2.48 & [PERSON] et al. [16] \\ SiO\({}_{2}\)–H\({}_{2}\)O, SiO\({}_{2}\)-EtOH & & & & & \\ \hline Different Nanofluids & 1620 & MLP & MSE & 0.09 & [PERSON] et al. [17] \\ \hline TiO\({}_{2}\)/SAE 50 & & & & & \\ Nano-lubricant & 251 & LS-SWM & RMSE & 0.58 & [PERSON] et al. [18] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The predictive models of nanofluids viscosity in the literature. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Kernel Models** & **MSE** & **RMSE** & **MAPE** \\ \hline Alternating Decision Tree & 0.056 & 0.1436 & 0.0384 \\ Multilayer Perceptron & 0.023 & 0.0745 & 0.0204 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The error rates. In the computational intelligence methods used to estimate the viscosity values, the estimated values are similar to actual values.