## 3 Results  
The results from the relevant analysis showed that all three pre-trained CNN models performed well on the mineral classification task, with the MobileNet model achieving the highest overall accuracy of 98.98% and a loss of 0.0202, demonstrating its efficiency and precision. The Xception model followed with an accuracy of 98.69%, but had a higher loss of 0.0455, indicating less confident predictions. The VGG16 model achieved an accuracy of 96.51% and the highest loss of 0.1188 (Table 2). Figure 2(a) shows the loss decreasing for both training and validation as epochs progress, demonstrating effective learning across all models. The MobileNet model exhibits the steepest initial decline in loss, indicating faster convergence compared to the Xception and the VGG16 model. Among the models, the MobileNet model achieves the lowest validation loss, signifying better generalization and consistent performance on unseen data. In contrast, the VGG16 model had higher training and validation losses, suggesting slower convergence, while the Xception model balances convergence speed and validation loss, outperforming the VGG16 model but slightly underperforming the MobileNet model. The small gap between training and validation loss for all models indicates minimal overfitting, with MobileNet showing the most efficient loss profile overall. Similar Figure 2(b) illustrates a steady improvement in accuracy for both training and validation as epochs progress, consistent with the observed decreasing loss. All models converge toward high \begin{table}
\begin{tabular}{c c} \hline
**Key Features** & **Description** \\ \hline \multirow{3}{*}{Model and Data Setup} & A pre-trained model classifies mineral types from detected regions. \\  & Haar Cascade Classifier detects potential mineral regions in video frames. \\  & A labels.txt file provides the class labels for the minerals. \\ \hline Mineral Information & Dictionaries store descriptions and additional details about each mineral. \\  & These provide context about the minerals’ uses or properties. \\ \hline \multirow{3}{*}{Game Mechanics} & The game poses questions linking minerals to real-world associations \\  & (e.g., “Which mineral is used in watches?”). \\  & Tracks the user’s score and progresses \\ \hline \multirow{3}{*}{Real-Time Detection and Classification} & The camera feed is analyzed in real-time to detect minerals using Haar Cascade Classifier. \\  & Detected regions are preprocessed and classified by the deep learning model, \\  & displaying confidence scores. \\  & If the correct mineral is identified with \textgreater{}80\% confidence, a congratulatory message \\  & is shown, and the score is updated. \\ \hline \end{tabular}
\end{table} 
Table 1: Key features of the interactive mineral detection game, Mineral Quest. \begin{table}
\begin{tabular}{c c c} \hline
**Model** & **Overall Accuracy** & **Overall Loss** \\ \hline VGG16 & 0.9651 & 0.1188 \\ Xception & 0.9869 & 0.0455 \\ MobileNet & 0.9898 & 0.0202 \\ \hline \end{tabular}
\end{table} 
Table 2: Classification metrics across all models. validation accuracy by the final epochs, with MobileNet achieving the highest validation accuracy, consistently outperforming Xception and VGG16. Xception demonstrates slightly lower accuracy compared to MobileNet but outperforms VGG16, highlighting its superior feature extraction capabilities. VGG16 lags behind the other models in both training and validation accuracy, reflecting slower convergence and possibly greater model complexity. MobileNet's rapid increase in validation accuracy during the initial epochs underscores its efficiency and lightweight design. The next phase of analysis involved calculating the performance metrics, which include precision, recall and F1 score, and demonstrated the effectiveness of all three models in classifying the seven mineral types (Table 3).