## 5 Research Results
### Prediction Results of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) Models
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Multilayer Perceptron (MLP)**} & \multicolumn{4}{c}{**Radial Basis Function (RBF)**} \\ \hline \hline \multirow{3}{*}{**Data Set**} & \multicolumn{3}{c}{**Existing Values**} & \multirow{3}{*}{**Correctly**} & \multicolumn{3}{c}{**Existing Values**} & **Correctly** \\ \cline{3-8}  & **Estimated** & & **I Wouldn’t** & **Classified** & **I Wouldn’t** & **I Wouldn’t** & **Classified** \\ \cline{2-8}  & **Values** & **Accept** & **Accept** & **Instances (\%)** & **Accept** & **Accept** & **Instances (\%)** \\ \hline \multirow{3}{*}{Training} & I would accept & 278 & 9 & 96.9\% & 272 & 15 & 94.8\% \\ \cline{2-8}  & I wouldn’t accept & 21 & 65 & 75.6\% & 31 & 60 & 65.9\% \\ \cline{2-8}  & Overall (\%) & 80.2\% & 19.8\% & 92.0\% & 80.2\% & 19.8\% & 87.8\% \\ \hline \multirow{3}{*}{Testing} & I would accept & 95 & 5 & 95.0\% & 80 & 10 & 88.9\% \\ \cline{2-8}  & I wouldn’t accept & 9 & 15 & 62.5\% & 12 & 11 & 47.8\% \\ \cline{2-8}  & Overall (\%) & 83.9\% & 16.1\% & 88.7\% & 81.4\% & 18.6\% & 80.5\% \\ \hline \multirow{3}{*}{Holdout} & I would accept & 84 & 15 & 84.8\% & 98 & 11 & 89.9\% \\ \cline{2-8}  & I wouldn’t accept & 14 & 16 & 53.3\% & 17 & 9 & 34.6\% \\ \cline{1-1} \cline{2-8}  & Overall (\%) & 76.0\% & 24.0\% & 77.5\% & 85.2\% & 14.8\% & 79.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Prediction of e-bikes acceptability using ANN models. recall of both models indicates a very good detection of positive responses in users who accept (MLP 0.95 and RBF 0.89) and slightly worse in users who do not accept the use of e-bikes (MLP 0.63 and RBF 0.47). In addition to the above, other performance measures of the model such as F1 indicate a significant predictive power in the form of precision and data memory when it comes to the answer \"I would accept\", which is very significant for the MLP model (0.93) and to a slightly lesser extent for the RBF (0.88) model. When classifying users with the answer \"I wouldn't accept\", the MLP model also indicates a higher accuracy of predicting users with this answer (0.68), which is not the case with RBF (0.50). Finally, including all values from the confusion matrix, the MCC value, known as [PERSON]'s correlation coefficient formula, indicates that the MLP model can better predict the answers \"I would accept\" and \"I wouldn't accept\" due to the existence of higher values (0.618) compared to the RBF model, which gives very poor results (0.380). Also, it indicates equal predictive power of both responses in both models. The last and one of the most significant parameters of the model is the ROC curve, which defines the quality of the classification model. With a value under the curve that can range from 0 (worst value) to 1 (best value), this value indicates the probability that a randomly selected user can be correctly rated or ranked if they are more likely to adopt the use of e-bikes. The measured area under the curve and the best score of 0.927 indicates a very good ability to classify users by the response-dependent MLP model, while this value for the RBF model is 0.897, indicating a very good classification ability. In addition to the presented analysis, the IBM SPSS Statistics 29.0.2.0 software enables the display of the predicted pseudo-probability of the MLP and RBF models for the two perceived classes (responses) of acceptability of e-bikes as a box plot diagram. This graph specifically illustrates the predictions for two classes of the dependent variable acceptability (the responses \"I would accept\" and \"I wouldn't accept\"). Also, it is important to point out that this graph shows box plots that categorize the predicted pseudo-probabilities based on the entire analyzed data set. As a rule, for each box plot in each different class, values above 0.5 can confirm correct predictions. In this regard, the box plot graph developed on the MLP and RBF models showed the predicted probability of the observed acceptability classes.