# Learning at Your Fingertips: An Innovative IoT-Based AI-Powered Braille Learning System
## 4 Results and Discussions  
The original and augmented datasets were used in the experiments in order to increase the overall size of the dataset. Various metrics were used in order to evaluate the performance of the proposed methodology. These include recall, precision, accuracy, and F1 measure [22]. In the proposed model, the idea is to freeze the top twelve layers and unfreeze the remaining layers to retrain the unfrozen layers. The decision to freeze the initial layers and retrain the later layers was made to balance pre-trained knowledge while adapting to our specific task. The determination of optimal layers for freezing and retraining was based on systematic experimentation, aiming for a balance between prior knowledge and  
Figure 4: Fine-tuned VGG16 architectures for braille language detection. task-specific adaptation. This approach was applied to various deep learning models including VGG19, VGG16, DenseNet, AlexNet, GoogleNet, and LeNet. The proposed approach was applied to the combined dataset and to both the Arabic and English braille letters. In order to compare the performance of the proposed approach, the first experiment was performed using the original freeze weight of the original CNN models applied to the Arabic braille language dataset. The results are shown in Table 1. It should be noted that each letter has 500 images being used. These are divided into 300 images (60%) of each letter for training, 100 (20%) for validation, and 100 (20%) for testing. This percentage was used for all letters in both the Arabic and English braille alphabets. The experiments are performed for 30 epochs with a batch size of 512 with Adam optimizer and a learning rate of 0.001. Table 1 shows the results of the experiments of the original CNN models using freeze weight applied to the Arabic braille language dataset. The results indicated that the best accuracy was achieved using GoogleNet with an average value of 98.63% and 98.4%, 98.4%, and 98.1% for precision, recall, and F1-measure, respectively. The lowest accuracy was reported for the GoogleNet algorithm with an average accuracy of 94.50%. Figure 5, Figure 6, Figure 7, Figure 8, Figure 9, and Figure 10 show a comparison of the training and testing validation accuracies for VGG19, VGG16, DenseNet, AlexNet, GoogleNet, and LeNet, respectively. The comparison shows that both training and testing validation accuracies approach 100% as expected. These results indicate that the overfitting and the under-fitting problems were accounted for in this research with no under-fitting or overfitting problems reported. This is further proven and shown in Figure 11, Figure 12, Figure 13, Figure 14, Figure 15, and Figure 16 which show the comparison of the training and testing validation losses for VGG19. VGG16. DenseNet, AlexNet, GoogleNet, and LeNet, respectively. The comparison shows that both training and testing validation losses approached zero as expected. Figure 8: AlexNet accuracy learning curves for training and validation. Figure 6: VGG16 accuracy learning curves for training and validation. Figure 7: DenseNet accuracy learning curves for training and validation. Figure 11: VGG19 loss learning curves for training and validation. Figure 10: LeNet accuracy learning curves for training and validation. Figure 9: GoogleNet accuracy learning curves for training and validation. Figure 14: AlexNet loss learning curves for training and validation. Figure 12: VGG16 loss learning curves for training and validation. Table 1.0 DenseNet loss learning curves for training and validation. The experiments were then repeated on the same optimized deep learning algorithms using the proposed non-freeze weights approach with the Arabic braille language dataset, as shown in Table 1.1 The accuracy increased significantly, with the best accuracy achieved by the VGG16 with an average accuracy of 99.68%, a precision of 98.36%, a recall of 97.96%, and an F1-measure of 98.16%. The lowest accuracy was still reported for GoogleNet with an average accuracy of 98.70%.