## 7 Conclusions
### Recommendations for Future Research  
The following areas are worth future investigation to enhance the CNN tool:  
* Enlarging and diversifying the training dataset to have more coverage over emotional expressions and demographic variability.
* Carrying out clinical validation studies that test the models in a real-world setting to refine their predictive capabilities.
* Combining multimodal data--physiological plus audio cues--with the aim of creating a more comprehensive system for emotion recognition.  
To improve emotion recognition accuracy and resilience to varying distortions of data, upcoming research must take the following into consideration:  
* Reinforcement Learning [32]: Reinforcement learning fosters generalization by providing real-time feedback and enabling trial-and-error learning, which is essential in achieving optimal performance.
* Ensemble Methods [33]: Ensemble methods, including bagging, boosting, and stacking, help combine models for improved performance.
* GANs [34]: GANs generate synthetic data to augment training datasets, which is an important component in enhancing the overall quality of a model.
* RNNs [35]: RNNs are used to analyze temporal patterns, thus aiding in better understanding emotional states.
* Hybrid Models: The merging of CNNs with SVMs or decision trees allows for handling different types of data, especially when it comes to complexity.
* Neural Architecture Search (NAS) [36]: Neural architecture search involves automating the discovery process towards attaining an optimal neural network structure that would be most suitable based on the nature of the dataset at hand.  
The use of these methods can considerably enhance models in emotion recognition and lead to the creation of a much better tool for clinical diagnosis.  
Ethical implications and patient acceptability could also be part of the scope of future studies on AI tools applied in clinical diagnostics. This will ensure such technologies are used to augment human judgment and not replace it.  
The use of CNN models in the diagnosis of psychosomatic illnesses contributes to the significant step that must be made at the crossroads between technology and health. Other technological breakthroughs such as the emotion recognition tool designed in this research have even more potential value as they can foster increased precision in diagnoses and effectiveness in treatment, leading to favorable patient outcomes and an elaborate understanding of how emotional states are tied to physical health (which will provide information on their co-dependency). However, we have to ensure these tools are developed with due regard for ethics, patient confidentiality, and--let us not forget--mechanisms that can strengthen the patient-doctor bond.  
Conceptualization, M.N. and A.R.; Methodology, Y.M.; Validation, M.N. and Y.M.; Investigation, M.N.; Writing--original draft, M.N.; Writing--review & editing, A.R. and Y.M.; Supervision, A.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The datasets presented in this article are not readily available because they are part of an ongoing PhD dissertation study. Requests to access the datasets should be directed to [PERSON].  
The authors declare no conflicts of interest.