# Novel Cuckoo Search-Based Metaheuristic Approach for Deep Learning Prediction of Depression
## 3 Methods Used
### Proposed Method
The \"Crossover rate\" parameter, which defines the likelihood that two eggs will experience crossover and produce a new solution, increases the diversity of solutions being taken into consideration. The parameter values play a crucial role in determining the performance of the optimization algorithms. Changing these parameters can have a significant impact on the optimization results and finding the optimal parameter values is often a challenging task. In our study, we conducted a series of experiments to evaluate the performance of the PSO, CS, and PS-CS algorithms using different parameter values. ## 5 Experimental Result and Discussion

### Part A: Comparison of Proposed Approach Performance with Various CNN Based Deep Learning Models

In this section, a classification task performed to distinguish between depressed and non-depressed patients using CNN-based deep learning models and compared with the performance of proposed algorithm. The LeNet, ResNet, and VGG models are well-established CNN architectures widely used in image classification tasks. The training and validation dataset were split in an 80:20 ratio, respectively, to evaluate the performance of every model for the fair comparison. The same set of hyper parameters setting used across all models to ensure a fair assessment. The Adam optimizer with a learning rate of 0.001 was utilized, along with the binary cross entropy loss function. The models were trained for a total of 20 epochs, and loss, accuracy, precision, recall, and F1 score were among the computed assessment measures. In contrast, the proposed model is a novel architecture developed specifically for this task. The performance of each model was compared based on the evaluation metrics mentioned above. #### 5.1.1 Training and Validation Loss

The evaluation of deep learning models during the training process heavily relies on training and validation loss as crucial metrics. Training loss, on the other hand, evaluates the difference between the anticipated and actual values of the training data; validation loss assesses the difference between the predicted and actual values of the validation data. It is essential to consider both training and validation loss to gain an overall perspective on the model's performance visualization for the same, for the proposed algorithm is presented in Figure 4.

Figure 5 visualizes the overall comparison; we can observe that the proposed model indicates the lowest training loss of 0.45 and a validation loss of 0.66. This finding suggests that the proposed model is performing well and has the potential to generate reliable predictions on new, unseen data to conclude, based on the presented findings, the proposed model appears to be the most efficient among the listed models, demonstrating a balanced proportion of low training and validation loss. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Description** & **PSO** & **CS** & **PS-CS** \\ \hline w & Inertia weight & 0.65 & - & - \\ \hline C\({}_{1}\) & Learning factor & 1.2 & - & 1.2 \\ \hline C\({}_{2}\) & Learning factor & 2 & - & 2 \\ \hline N & Population size & 100 & 80 & - \\ \hline M & Iteration & 100 & 300 & 100 \\ \hline P\({}_{\text{c}}\) & Crossover probability & - & - & - \\ \hline P\({}_{\text{m}}\) & Variation probability & - & - & - \\ \hline w\({}_{\text{max}}\) & Maximum inertia weight & - & - & 0.9 \\ \hline w\({}_{\text{min}}\) & Minimum inertia weight & - & - & 0.3 \\ \hline N\({}_{\text{max}}\) & Maximum population size & - & - & 100 \\ \hline N\({}_{\text{min}}\) & Minimum population size & - & - & 20 \\ \hline P\({}_{\text{a}}\) & Abandonment rate & - & 0.25 & 0.25 \\ \hline P\({}_{\text{amin}}\) & Maximum probability & - & 0.2 & 0.2 \\ \hline P\({}_{\text{amax}}\) & Minimum probability & - & 0.6 & 0.6 \\ \hline \(\alpha\) & Step size & - & 0.03 & 0.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameter setting of the proposed algorithm.