## 4 Results and Discussion  
We used the deep learning TensorFlow framework in the Python 3.7 programming platform with a Windows 10 Pro operating system, Intel(R) Core (TM) with i7 [EMAIL_ADDRESS] GHz, and Nvidia GTX 1060 with a 24G memory graphics card to implement the model architecture and perform the experiments on two datasets. We have used TensorFlow, OpenCV, Pandas, Numpy, and Matplotlib Python libraries for computational graphics, image processing, and mathematical processing of data and visualization of various parameters [5]. We used an Adam optimizer with Nesterov momentum called Nesterov-accelerated adaptive moment (Nadam) estimation [28] with an initial learning rate of 0.001 to improve high fluctuations in the loss and enhance the cost function's convergence process. We tuned the hyperparameters to achieve good training of the model. The details of hyperparameters used while training the model to recognize 36 classes are shown in Table 1.  
Figure 6: Flowchart of the proposed CBAM-CNN model. We performed five-fold cross-validation to achieve unbiased results and avoid overfitting while training. This method divides the dataset into five equal folds, in which a fold is unseen data for testing. The remaining folds are for training. The number of samples for each class in the test set for each fold will vary due to randomness in the process of k-fold cross-validation. We assessed our proposed architecture's efficiency using accuracy, precision, recall, and F1-score for individual folds. Then, we average these metrics across all the folds to assess your model's performance. This averaging process helps smooth out the effects of sample size variation and gives a more reliable estimate of your model's performance. This variation is due to the random partitioning of our dataset into folds. The precision is the ratio of the total count of precisely identified positive observations to the total number of values classified as positive. Recall is the fraction of accurate predictions to the overall correct classes. The F1-score is a widely used metric to measure a model's efficiency, also known as the balanced mean of the recall and precision metrics [29]. The mean accuracy of all folds is calculated for both datasets, as shown in Table 2. We computed the precision, recall, and F1-score using the following Equations (10)-(12). \[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{ FP}} \tag{10}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{ FN}} \tag{11}\]  
\[\text{F1}-\text{score}=2*\frac{\text{precision}*\text{recall}}{\text{ precision}+\text{ recall}} \tag{12}\]  
where \"TP\", \"FP\", and \"FN\" are true positives, false positives, and false negatives, respectively. Figures 7 and 8 represent the values of recall, precision, F1-score, and recognition accuracy of our proposed model on both datasets for fold two. Figure 9 shows the accuracy and loss graphs of both datasets for training and validation data. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Input size & \(128\times 128\) \\ Initial Learning Rate & 0.001 \\ Optimizer & Nadam \\ Batch Size & 32 \\ Cost Function & Categorical Cross-entropy \\ Epoch & 30 \\ Beta 1 & 0.9 \\ Beta 2 & 0.999 \\ Epsilon & \(1\times 10^{-8}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hyperparameter Details. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Folds** & **Dataset 1** & **Dataset 2** \\ \hline
1 & 99.10 & 99.69 \\
2 & 99.22 & 99.77 \\
3 & 99.24 & 99.79 \\
4 & 99.22 & 99.81 \\
5 & 99.30 & 99.83 \\ Average Accuracy (\%) & 99.22 & 99.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The average recognition rate for both datasets. It is observed from Figure 9 that the loss is reduced after each iteration and reaches a minimum value, and the accuracy metric is attaining the maximum value. In the ASL dataset, the accuracy and loss stabilized after the 25 th epoch. In the same way, both metrics stabilize after the 10 th epoch for the ISL database.