## 4 Results
Furthermore, to evaluate the contribution of hybrid CNN-LSTM integration, two individual deep-learning models, CNN and LSTM, were used in the comparison. The standalone CNN model processes spatial features extracted from raw data without considering temporal dependencies, while the LSTM model captures sequential correlations in data without leveraging convolutional feature extraction. By including both CNN and LSTM individually, the comparative analysis demonstrates the advantages of their combined usage, particularly in scenarios where both spatial and temporal dependencies influence decision-making. This selection of benchmark models provides a comprehensive evaluation, ensuring that the proposed approach is assessed not only against evolutionary optimization methods but also against standalone and alternative deep-learning frameworks. To evaluate the effectiveness of the proposed MOIBBO-CNN-LSTM framework, a synthetic dataset was constructed by simulating a wide range of CF-mMIMO-based IoT network scenarios. Each scenario was generated by randomly initializing the key system parameters, including the number of APs, the number of IoT devices and eavesdroppers, the channel fading coefficients, the SNRs, and the transmission power levels. Based on these randomly generated parameters, the theoretical values of SEE were calculated using Equation (22), which encapsulates both the physical layer security and the energy efficiency of the system. The resulting dataset consisted of input-output pairs where the input captures the network configuration and the output corresponds to the calculated SEE. The generated dataset was used to train and validate the hybrid CNN-LSTM model, allowing it to learn the nonlinear mapping between network parameters and the corresponding SEE values. To quantify the effectiveness of the models, five key evaluation metrics were employed, ensuring a rigorous and well-rounded assessment. The first metric used is the root mean squared error (RMSE), which measures the squared difference between predicted and observed values. RMSE is particularly relevant for assessing prediction accuracy, as it penalizes larger errors more heavily, making it ideal for evaluating the ability of the CNN-LSTM model to accurately predict SEE in CF-mMIMO-based IoT networks. A lower RMSE value indicates that the model provides more precise estimations, reducing deviations from actual values. The second metric used is the mean absolute percentage error (MAPE), which quantifies the relative prediction error in percentage form, as expressed in Equation (44). This metric is particularly useful for assessing generalization across different data scales, as it provides an interpretable measure of accuracy in real-world scenarios. Given the variability in IoT network parameters, MAPE ensures that the model remains effective across diverse conditions. In addition to RMSE and MAPE, the coefficient of determination (\(R^{2}\)) was used as a statistical measure to evaluate the strength of the correlation between predicted and actual values. This metric, computed using Equation (45), determines how well the predictions align with real data trends. A higher \(R^{2}\) score, ideally close to 1, suggests that the model captures the variance in the dataset effectively, indicating strong predictive capabilities. This is particularly important in IoT applications, where understanding the underlying relationships between security, energy efficiency, and network parameters is critical for optimizing performance.