## 3 Results  
The training model was observed until 100 epochs, using the ADAM optimizer [34]. At the end of the hundred periods of training, the accuracy found on the test data exceeded 97%. The source codes of 100 web pages were collected to train the model; each of these was given feedback on the quality of the source code itself, using the 10-value rating scale ranging from 1 (poor quality) to 10 (excellent quality). By applying the Dropout technique [35], where randomly selected neurons are ignored during training, it is guaranteed a correct generalization of the system in processing new source codes not previously used. The Loss and the Accuracy trend of the training LSTM phase are illustrated in Figure 7a,b, respectively. By comparing the training and testing plots, it was observed that the training epoch number to achieve the best results was higher if compared with the testing epoch number: this was due to the initial training of the LSTM network and was a function of the complexity of the web page. The epoch number of the testing efficiency decreases, thus confirming the correct structuration of the training dataset and a certain similarity of the testing web pages with the training ones. The designer front-end interface of the developed platform is illustrated in Figure 8, where it is possible to distinguish the following fields:  
1. Command to select an HTML web page source for the training and for the testing data process;
2. Training command related to training process;
3. LSTM score visualizing the output of Figure 3;
4. User behavior analysis as separate evaluation of the UX (UX dashboard). Figure 9a illustrates an example of user navigation embedding the GUI for the scoring (user feedback). The same figure also indicates an example of a post- processing reconstruction of a mouse hot map, indicating mouse movement distributions in the analyzed web page. Figure 9b illustrates the web page structuration of Figure 9a, embedding the related tags. All data of mouse movement, number of clicks \(n_{c}\), and navigation times \(T\) are stored in to the SQL database, and visualized by the UX dashboard of Figure 10a, also indicating the prediction of the UX trend for each web page as a regression. By this dashboard, it is possible to estimate the outliers. Thus, the platform provides additional information through graphical dashboard results, as the regression analysis between the score associated with mouse movements and the score of the actions performed (movements and clicks). Appendix C describes the code used for the tracking of mouse movement and time spent during the web page navigation, reporting data of an experimental test. This analysis allows understanding furthermore if the movement of the mouse, and the number of actions, are correlated or not. The outliers are also predicted by means of the ANN-MLPnetwork. The forecast algorithm adopted a dataset divided into a training set (80%) and test set (20%). The ANN-MLP trains on the first UX values of a specific web page and performs a prediction on the last values. The prediction results are related to the next 21 days after the last day of the available data. As discussed in [36] about ANN-MLP Rapid Miner data processing, only a few days are more reliable. In the analyzed case, the first predicted days present similar values and consecutively are more reliable. Figure 10b illustrates an example of a prediction of a possible UX outlier in the next four days, where no anomalous behaviors are predicted. Figure 8: “ANDUIA” platform GUI of the web designer interface. Figure 7: LSTM performance of the training phase (the training dataset is the 95% of the whole dataset): (**a**) Loss trend versus epoch number. (**b**) Accuracy trend versus epoch number. LSTM performance of the testing phase: (**c**) Loss trend versus epoch number. (**d**) Accuracy trend versus epoch number. Figure 9: (a) Score user: example of the user interface for the scoring provided during his web page navigation.