# Novel Cuckoo Search-Based Metaheuristic Approach for Deep Learning Prediction of Depression
## 3 Methods Used
### Proposed Method
This vector is then passed through a dropout layer to prevent overfitting, which randomly sets a fraction of the input units to zero during training. The output of the dropout layer is then passed through a fully connected layer with 128 neurons and the dense activation function. The kernel size determines the size of each convolutional neuron's receptive field, while the depth of the layer is determined by the number of filters. A common method for normalizing a layer's activations over a small batch of input data is batch normalization. Internal covariate shift, also known as a change in the distribution of layer activations during training, is successfully decreased by this normalization technique, improving model stability and generalization. In deep learning models, the rectified linear unit (ReLU) is a popular non-linear activation function. It is renowned for its computational effectiveness, simplicity, and capacity to improve neural networks' performance in a variety of applications. Max pooling is used in convolutional neural networks to down-sample the feature maps and decrease their spatial dimensions. This process extracts important characteristics from the incoming data to increase the network's effectiveness. An approach that lowers the model's



times. The cuckoo will seek for an egg before abandoning the nest and going on to another. The suggested model's classification accuracy is evaluated using the fitness function:

\[\text{Classification Accuracy }=\frac{\text{CC}}{\text{N}}\times 100 \tag{4}\]

Equation (4) involves two parameters: N, which refers to the total number of samples in the relevant class; and CC, the number of observations that were properly classified. To evaluate the proposed method's fitness function, the classifier's classification accuracy is considered. If the fitness degree value is greater than that of the previous answer, the previous result is discarded in favor of the current one. Otherwise, the previous solution is retained. Finally, the fitness function is described in Equation (5):

\[\text{Fitness (f) = Accuracy (f_{a})} \tag{5}\]

Accuracy: \(\text{Accuracy }=\text{TP}+\frac{\text{TN}}{\text{TP}}+\text{FP}+\text{FN}+ \text{TN}\). The accuracy of a model is an indicator of its performance. Precision is calculated as \(\frac{\text{True Positives}}{\text{True Positives }+\text{False Positives}}\). A high precision value suggests a better model. Recall is calculated as \(\frac{\text{True Positives}}{\text{True Positives }+\text{False Negatives}}\). A high recall value also suggests a better model. The F1 score is calculated as \(\frac{2(\text{Precision }\times\text{Recall})}{\text{Precision }+\text{Recall}}\). The lower the F1 score, the better the model is performing. Accuracy (fit): Using the data, the classifier's accuracy was assessed (f). The following list of PS-CS algorithm parameters used in our tests:

Table 3 presents a comprehensive list of parameters used in our proposed model, specifically for the cuckoo search algorithm (CS). Each \"nest\" in the algorithm represents a potential solution to the optimization problem. The optimization technique incorporates an \"abandoning\" mechanism to determine which nests to keep and which to replace with new ideas. By defining the total number of eggs deposited during each iteration using the \"Total number of eggs\" parameter, it is possible to strike a balance between discovering novel solutions and making use of those that already exist. The \"Total number of generations\" parameter specifies the greatest number of iterations for the method. The \"Limit\" parameter restricts the introduction of new solutions by specifying the number of times an egg may be abandoned in a nest before being removed. The \"Step size\" parameter regulates the rate of exploration by setting a maximum distance an egg can go in the search space during each iteration. The \"Mutation probability\" parameter also describes the likelihood that a mutation will occur in an egg, which can lead to the introduction of fresh ideas and increased diversity.