# Learning at Your Fingertips: An Innovative IoT-Based AI-Powered Braille Learning System
## 4 Results and Discussions
Note that with non-freeze weights, the accuracy increased by 6.55% compared with the highest reported accuracy in Table 1.2 It should be clear here that the experiment was performed on the Arabic braille language dataset without augmentation. The experiment is then repeated using the optimized deep learning algorithms using the proposed non-freeze weight approach but this time on the combined Arabic braille language dataset with the augmented dataset. The results are shown in Table 1.3 The results \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Accuracy** & **Precision** & **Recall** & **F1 Measure** \\ \hline
**VGG19** & 99.28\% & 0.991 & 0.992 & 0.992 \\
**VGG16** & 99.68\% & 0.993 & 0.991 & 0.994 \\
**DenseNet** & 96.51\% & 0.960 & 0.964 & 0.965 \\
**AlexNet** & 99.13\% & 0.989 & 0.988 & 0.989 \\
**GoogleNet** & 98.70\% & 0.984 & 0.981 & 0.981 \\
**LeNet** & 86.23\% & 0.858 & 0.860 & 0.861 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results of the proposed non-freeze weight-based CNN models for the Arabic braille language data. Figure 16: LeNet loss learning curves for training and validation. Figure 15: GoogleNet loss learning curves for training and validation. indicate yet another increase in accuracy due to expanding the dataset size. The increase of 0.3% is actually significant as compared to results in Table 2 and dramatically significant as compared to results in Table 1 where the difference is 1.35%. The increase in accuracy is extremely important because this a proposed system that will serve for assistive learning for the visually impaired and they have no way of comparing the audio translation with the original unless they go to the traditional time-consuming touch-and-feel approach. Table 3 shows that the highest reported accuracy was again achieved using VGG16 with an average accuracy of 99.98%, precision of 99.4%, recall of 99.5%, and F1-measure of 99.7%. The lowest accuracy is again reported using th GoogleNet with an average value of 88.5%. The confusion matrix-based comparison obtained for the various experiments performed above with the best-performing VGG16 model is shown in Figures 17-19. These are the confusion matrices for the experiments performed on the Arabic braille language dataset. Figure 17 shows the confusion matrix for the basic VGG16 applied to the Arabic braille language dataset. It is noticed that even with the basic VGG16, the accuracy is high but it can be optimized to achieve better results because the application we are targeting is for the specific purpose of assistive learning technology for the visually impaired. Thus, an optimal solution can only be achieved as we approach approximately 100% on various complex datasets of the Arabic braille language dataset. Figure 18 shows the confusion matrix using the Optimized VGG16 model with the proposed transfer learning approach. It is noticed that the confusion matrix showed better results but still can stand for improvement for the optimal solution. Therefore, Figure 19 shows the confusion matrix using the optimized VGG16 along with the proposed transfer learning approach, which resulted in a further increase of accuracy. Similarly, the best-performing model has also been tested using the English braille language dataset. According to Table 4, the highest achieved accuracy was 99.92% by Vgg16. Note that Table 4 shows the results of the experiment of applying the proposed non-freeze weight approach with the optimized CNN models on the combined dataset of the English braille language dataset with augmentation. The highest accuracy was achieved using VGG16 and reported as 99.92%, with a precision of 99.5%, recall of 99.4%, and Fe-Score of 99.5%. The lowest accuracy of 86.79% was reported when using the LeNet. The VGG16 took 5 h and 20 min for training which is slightly less than the VGG19 model and relatively more than other compared models. It has been noticed that the individual braille image test computational time was approximately the same for all models on the proposed device.