## 1 Introduction
They examined the change in the viscosity of the formulations with pH and indicated that the low viscosity values had a pH appropriate for the skin. There are many valuable studies in the literature for the estimation of nanofluid viscosity. [PERSON] et al. Calculated the viscosities of different nanoparticles prepared with a total of 11 nanoparticles in the range of 4 to 190 nm nanoparticle sizes in the range of 0% to 10% nanoparticle volumes in the temperature range of \(-\)35 to 80 \({}^{\circ}\)C. Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Genetic Algorithm have calculated prediction models using computational intelligence methods. They used nanofluid temperature, nanoparticle size, nanoparticle percentage, and nanofluids density for the input data used in computational intelligence models. They used the nanofluid viscosity for the output data. They use the root mean square error (RMSE) error analysis to determine the accuracy of the methods. In the computational intelligence methods used for viscosity estimation, the MLP method yielded a more accurate result than the other methods with an RMSE error rate of 0.1 [13]. [PERSON] et al. estimated the viscosity of multi-walled carbon nanotubes/water nanofluid with an optimal neural network using experimental data in their study. ANN used solid volume fraction and temperature values in the predictive model as input variables, and their viscosity values as output variables. ANN also used 268 data values to generate a predictive model for viscosity. They used MAPE and MSE error analysis for the accuracy of the model. They obtained a successful model according to the results of 0.28 MSE and 0.910 MAPE error analyses [14]. [PERSON] et al. Performed nanofluidic viscosity modeling with a total of 801 data values consisting of % volume nanoparticle values used for nanofluid temperature, nanoparticle size and Al\({}_{2}\)O\({}_{3}\), TiO\({}_{2}\), SiO\({}_{2}\) and CuO nanofluids. For modeling, Least Squares Support Vector Machines (LSSVM) have used computational intelligence. For the accuracy of the model, absolute absolute error (AARE) and RMSE error analysis were applied. The LSSVM method estimated the nanofluidic viscosity values with an analysis value of 3.7084 RMSE and a value of 2.1435 AARE for the error analyses [15]. [PERSON] et al., used an accurate and effective artificial neural network (ANN) based on the genetic algorithm (GA) predicted 8 different nanofluids viscosity. Genetic algorithm (GA) has been used to optimize neural network parameters. The results showed that the GANN model was in good agreement with the experimental data with a 2.48% mean square error (MSE) error analysis result. The results also indicated that the GANN model performed better than traditional neural networks in predicting the viscosity of nanofluids with a percentage of 39% accuracy [16]. [PERSON] et al., In their study, have developed a predictive model for the relative viscosity of various nanofluids using feedback-propagation MLP neural networks. The model used a total of 1620 experimental data values to train, test and verify the proposed network. As the input variables of the developed network, they chose the nanofluid temperature, shear rate values, nanoparticle size, particle structure (nanoparticle density) and particle concentration. As a result, they showed that the network they proposed was capable of accurately predicting viscosity with a general average square error (MSE) of 0.00901 [17]. [PERSON] et al., in their studies, formed the niofluid samples TiO\({}_{2}\)/SAE 50 nano-lubricant by a two-stage method using a mixer and ultrasonic device. To estimate the viscosity values of the nanofluids they obtained, they applied three artificial intelligence methods including the Genetic Algorithm-Radial Basic Function Neural Network (GA-RBF), Smallest Square Support Vector Machine (LS-SVM) and Gene Expression Programming (GEP). They used the RMSE error analysis to investigate the accuracy of the proposed models.