Algorithm 1: Training and Evaluation Process with 5-fold Cross-Validation

1. Initialize Metrics List   . final_test_metrics = [] 2. Combine Training and Validation sets   . S = N train + N val        where S represents the dataset 3. 5-Fold Cross - Validation   . For i in {1, 2, 3, 4, 5}:     3.1. Data Splitting       $. {Train}_i$$=S – S_i$       $. {Val}_i$$={ S}_i$     3.2. Train Model        .Train the model on ${Train}_i$ and validate on ${Val}_i$       .Setup Callbacks and Optimizer     3.3. Evaluate on Test set (T) where T represents the testing data       .temp_metrics = Model. Evaluate (T)       .Append temp_metrics to final_test_metrics   4. Calculate Average Test Metrics      .Metrics final = $\frac{1}{5}{\displaystyle {\sum }_{i=1}^5}final\_test\_metrics\left[i\right]$ 5. Output   . Metrics final contains the average values on the set T