## 4 Discussion
Despite being more efficient than traditional methods, the system's computational demands pose challenges in resource-constrained environments. Scalability is another issue, particularly when expanding to a broader range of mineral types or processing large datasets in real-time. Notably, the database used in this study contained minerals with distinct visual features, making them easier to differentiate; consequently, the models have not been tested on visually similar samples, which may affect their generalizability. To enhance the model's validity and improve its generalization capabilities, future efforts should prioritize diversifying the dataset to include mineral samples that are more difficult to differentiate. This could involve incorporating a wider variety of mineral types, textures, and lusters, as well as samples captured under diverse environmental conditions, such as varying lighting setups and backgrounds. Furthermore, expanding the dataset to include edge cases--such as minerals with overlapping or ambiguous physical characteristics--would provide a more rigorous evaluation of the model's robustness. Such enhancements would ensure the model performs reliably and consistently in real-world applications. Building on these efforts to enhance the dataset, this study employed a stacking ensemble approach to harness the complementary strengths of individual CNN models for mineral classification. Stacking ensembles are particularly effective as they combine the outputs of multiple models to create a meta-model, leveraging the diverse capabilities of individual classifiers to improve overall performance. By aggregating predictions from multiple CNNs, the stacking ensemble reduces overfitting and increases the robustness of classification results, demonstrating its suitability for this domain. While the stacking ensemble has shown significant promise, recent advancements in fusion methods, such as alpha-integration, present alternative approaches for high-level fusion. Alpha-integration, as outlined by [PERSON] et al. [56], employs both linear and nonlinear fusion techniques, enhanced by a graph-based regularization term. This term minimizes mean-square error and reduces the dispersion of fused statistics, improving the separation between data points under different detection hypotheses. Such an approach has been demonstrated to enhance classification performance in various applications by optimizing the fusion process. Future research could explore the application of alpha-integration to mineral classification tasks. Its ability to fine-tune the fusion of complementary features from multiple CNN models may prove invaluable in addressing challenges such as class imbalance, overlapping physical characteristics, and complex mineral textures. Furthermore, in our case the ensemble approach could be optimized for scalability and real-time performance by leveraging three pretrained models and focusing solely on optimizing the stacking ensemble's meta-model, the lightweight logistic regression, without requiring additional training phases. Techniques like model pruning and quantization can also be applied to pretrained models to reduce computational demands while avoiding retraining. Further efficiency gains can be achieved by sharing initial layers across models or implementingdynamic model selection to streamline processing. However, although these optimizations improve computational efficiency and scalability, they may introduce trade-offs, such as slight reductions in accuracy. Careful tuning is required to strike a balance between maintaining performance and meeting the resource constraints of real-time applications. The transparency and interpretability offered by Grad-CAM visualizations could be further leveraged in future work by integrating these heatmaps into interactive educational tools like the Mineral Quest quiz game. For instance, after a player answer correctly, Grad-CAM heatmaps could be displayed to demonstrate how the model arrived at its decision.