## 5 Discussion
As the RMSE threshold becomes more restrictive, MOIBBO-CNN-LSTM maintains its advantage, requiring only 124 s for RMSE \(<\) 15 and 263 s for RMSE \(<\) 10, while other models experience a substantial increase in execution time. The NSGA-II-CNN-LSTM model, for example, takes 381 s for RMSE \(<\) 15 and 694 s for RMSE \(<\) 10, more than double the time required by the proposed model, highlighting the superior optimization strategy of MOIBBO. The inability of ViT, DRL, CNN, and LSTM to reach RMSE \(<\) 5 further underscores their limitations in predictive accuracy and computational efficiency. CNN and LSTM, despite their relatively fast initial convergence, fail to reach any threshold below RMSE \(<\) 10, indicating their restricted capacity to model complex SEE relationships in CF-mMIMO-based IoT networks. DRL and ViT, while capable of improving accuracy over time, exhibit significantly longer execution times, with DRL requiring more than 1000 s to reach RMSE \(<\) 10, demonstrating inefficiency in real-time applications. In contrast, the MOIBBO-CNN-LSTM model efficiently balances accuracy and computational cost, making it the most practical and effective solution to optimize security-aware energy efficiency in CF-mMIMO-based IoT networks. The RMSE values from Table 4 are also presented visually in Figure 10 to facilitate a clearer comparison between the models. As shown in the bar chart, the proposed MOIBBO-CNN-LSTM consistently achieves the lowest error across all training stages, while baseline models exhibit notably higher RMSE values. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **RMSE** & **R2** & **MAPE** & **Average Time (s)** \\ \hline MOIBBO-CNN-LSTM & 0.08 & 0.97 & 1.03\% & 962 \\ NSGA-II-CNN-LSTM & 3.27 & 0.92 & 6.96\% & 1241 \\ ViT & 6.12 & 0.86 & 8.32\% & 1012 \\ DRL & 9.39 & 0.84 & 11.43\% & 1317 \\ CNN & 11.27 & 0.82 & 15.96\% & 652 \\ LSTM & 13.91 & 0.80 & 19.36\% & 743 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of proposed models for SEE optimization in CF-mMIMO-based IoT networks. Table 5 presents the RMSE values recorded in different training epochs (50, 100, 200, and 300) for all evaluated models, illustrating their convergence behavior and overall learning efficiency. The results highlight how each model's prediction accuracy improves as training progresses, with lower RMSE values indicating better performance. The MOIBBO-CNN-LSTM model achieves the lowest RMSE across all epochs, demonstrating its ability to efficiently learn complex relationships in SEE optimization. By epoch 300, the proposed model attains an RMSE of just 0.08, significantly outperforming all other methods. The NSGA-II-CNN-LSTM model, although benefiting from evolutionary optimization, converges more slowly and stabilizes at a higher RMSE of 3.27 after 300 epochs. Although this approach improves over standalone DL methods, its performance remains inferior to MOIBBO-CNN-LSTM, reinforcing the advantage of MOIBBO's multi-objective optimization strategy. The ViT and DRL models, despite incorporating advanced architectures, struggle to reach competitive error rates, with RMSE values of 6.12 and 9.39 at epoch 300, respectively. These results suggest that while transformer-based and reinforcement learning approaches capture certain patterns, they are less effective for SEE prediction in CF-mMIMO-based IoT networks compared to the proposed hybrid model. The CNN and LSTM models show the weakest performance, with RMSE values of 11.27 and 13.91 at epoch 300, confirming their limitations in handling SEE trade-offs when used independently. Their slow convergence and high final error rates indicate that neither spatial feature extraction (CNN) nor sequential modeling (LSTM) alone is sufficient for this optimization task.