## 4 Results
### Comparison with Other Approaches  
As shown in the presented literature, the results of SER algorithms vary greatly depending on the training and testing datasets. _The algorithm presented in the current work and integrated into the NeuroPredict platform_ differs greatly from the work presented in the studied literature because it _is based on data aggregated from four datasets_: Savee, Ravdess, Tess, and Crema-D. Another difference in current work as opposed to other works on SER algorithms is the fact that _the targeted classes are just the three main classes: positive, neutral, and negative_ emotions, as these are considered relevant in order to assess the health condition of the tested subjects (patients monitored using the NeuroPredict platform), as opposed to other applications of SER like human-computer interaction where more detailed emotion identification is relevant. In order to be able to perform a direct comparison between the current approach and approaches identified in the studied literature, _multiple classification models are built and tested_:  
* Models based on each of the initial datasets: models built and tested only on Ravdess, only on Crema-D, and only on Savee. * Models based on each of the initial datasets that use the original labeling (with the original number of classes) from each dataset and models based on each dataset using the new labeling and data aggregation for obtaining the targeted three classes: positive, neutral, and negative emotions. No work has been found in the studied literature for the aggregated three classes. As presented in Tables 6 and 7, the performances of the models are better when only targeting the three major classes, as the algorithm does not have to distinguish between different types of negative emotions or different types of positive emotions. For each of the tasks presented above, as for the algorithm that is the main purpose of the current work (the algorithm that is trained on the aggregated dataset that predicts one of the three classes: positive, neutral, or negative emotions), multiple model architectures have been used. These included _models with only convolutional layers, with and without batch normalization_, and _with bidirectional LSTM or GRU layers_, to identify the best model for the data and targeted emotions. Best results are synthesized in Table 6.  
For the aggregated dataset, as for each of the original datasets, the following approaches have been used:  
* Models using 1D CNN with and without batch normalization and varying the number of hidden layers and the number of fully connected layers;
* Models using a CNN architecture with two bidirectional LSTM layers with batch normalization;
* Models using a CNN architecture with two GRU layers with batch normalizations. All the models mentioned above have been created both on the aggregated dataset containing the detailed emotions and on the aggregated dataset containing only the three broad classes of emotions. Performance varies depending on the targeted number of emotions. Only the best results in terms of average accuracy are presented. Table 7 presents the best accuracies achieved on the aggregated comprehensive dataset and a short description of the architecture of the neural network that achieved the best results. As shown in Table 7, the best performance for recognizing detailed emotions, was achieved using a 1D CNN architecture, which included four convolutional layers and one fully connected layer with batch normalization. For the three broad emotion classes (positive, neutral, and negative), the best results were obtained with a CNN-LSTM approach. This is the model that is integrated into the NeuroPredict platform and is further described in the next section. As presented in Tables 6 and 7, when trained for detecting all detailed emotions, the average performances are lower compared to when targeting the three classes of emotions of interest to this study.