# A Novel Fuzzy Kernel Extreme Learning Machine Algorithm in Classification Problems
## 5 Conclusions  
In summary, this study presents a novel and fundamental approach to the facial expression classification problem through the proposed Fuzzy Wavelet Mexican Hat Kernel Extreme Learning Machine (FWMHKELM) framework. The FWMHKELM framework combines high accuracy with efficient feature extraction techniques, offering an important contribution to the problem. As the study provides the basic structure, we would like to highlight several areas of improvement in the work that can guide future research. One problem is the random initialization of parameters, which can affect the consistency of the results. This problem can be addressed by using heuristic optimization algorithms such as Genetic Algorithms, Whale, or Grey Wolf Optimization to automatically determine optimal parameter values. With a better pre-processing approach, the area that is likely to be a face can be minimized. Instead of the Mexican hat wavelet used in the proposed structure, it can be integrated with different wavelet kernel structures (such as Morlet or Haar). Comparative analyses with deep learning architectures (e.g., ResNet or Vision Transformer) will be conducted in future studies to evaluate the competitiveness of the model more comprehensively. Thanks to the use of fuzzy logic in our study, we believe that the model has a certain level of interpretability. Fuzzy rules allow the decision mechanism to be analyzed with a certain degree of clarity. However, the combination of a fuzzy wavelet and an ELM may cause the model to be perceived as a \"black box\". Considering this point, in future studies, model stability can be supported with explainability techniques such as SHAP or LIME to support the explainability of the proposed model. With further refinements, the FWMHKELM framework has the potential to set a new benchmark in affective computing and pattern recognition. Its hybrid nature bridges the gap between traditional machine learning and modern deep learning, offering a scalable solution for applications requiring both high accuracy and explainability. Future work could also explore edge deployment for low-power devices, enabling real-time emotion detection in IoT and wearable technologies. In conclusion, this study lays a solid foundation for advancing facial expression classification systems. By addressing the outlined limitations and integrating cutting-edge techniques, the proposed framework can evolve into a versatile tool for both academic research and industrial applications, paving the way for innovative solutions in emotion-aware AI systems. Conceptualization, A.K.K. and O.O. ; data curation, A.K.K. ; formal analysis, A.K.K. and O.O. ; investigation, O.O. ; methodology, A.K.K. and O.O. ; project administration, O.O. ; resources, A.K.K. and O.O. ; software, A.K.K. and O.O. ; supervision, A.K.K. and O.O. ; validation, A.K.K. and O.O. ; visualization, A.K.K. and O.O. ; writing--original draft, A.K.K. ; writing--review and editing, A.K.K. and O.O. All authors have read and agreed to the published version of the manuscript. This study was supported by Eskisehir Technical University Scientific Research Project Commission under grant No. 21 DRP054. The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Ethics Committee of Eskisehir Technical University (E-87914409-050.03.04-22000209 and 21.11.2022). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. The first experiment utilizes an existing dataset from the literature. For the second experiment, the raw data supporting the conclusions of this article will be made available by the authors on request. The authors declare no conflicts of interest.