This study focused on the analysis of partial slip effect of Arrhenius activation energy and temperature dependent viscosity on non-Newtonian Maxwell nanofluid bio-convective flow using artificial intelligence approach. Local Nusselt number, local Sherwood number and local density number values, which are dimensionless flow parameters, have been used to examine the said effect. Three different artificial neural network models have been developed using the numerically obtained data sets. Each of the feed forward back propagation multilayer perceptron network models has been developed with different input parameters. 80% of the data set has been used for training the model and 20% for the testing phase. The estimation performance of the network models developed with the Bayesian regularization training algorithm has been extensively analyzed, and the compatibility between the estimation values and the target data has been examined. The findings have shown that artificial neural network models have been developed to make predictions with high accuracy. In addition, artificial neural networks have also proven to be an ideal engineering tool that can be used to analyze the partial slip effect of non-Newtonian Maxwell nanofluids on bio-convective flow.

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