# Learning at Your Fingertips: An Innovative IoT-Based AI-Powered Braille Learning System
## 3 Proposed Methodology
### Convolutional Neural Network-Based Transfer Learning  
Convolutional Neural Network (CNN) is an algorithm widely used in computer vision and deep learning. The algorithm takes an image as input and assigns significance to several objects in that image to distinguish one from the other. CNN algorithm requires minimal pre-processing compared to other classification methodologies. The CNN-based  
Figure 3: Sample braille for the Arabic braille language for English alphabets. Figure 2: Sample braille for the Arabic braille language for Arabic alphabets. models are generally divided into three major layers: the convolutional layer, the pooling layer, and the fully connected layer [17; 18]. The algorithm begins with reducing the image into an easily processed form while preventing the loss of significant features. This aids the creation of an architecture that can learn features and is scalable to interpret new datasets. In the convolutional layer, the Kernel/Filter, K, is the element performing the convolution operation in the first part of the layer. The filter traverses the image by moving to the right until it covers the full width and then down until it covers all pixels. The goal of convolution operations is to extract high-level features of an image. The results are of two types: dimensionality is either increased or stays the same by applying the same padding or convolved features reduced in dimensionality by applying valid padding. CNN may have multiple convolutional layers. The first layer captures the low-level features and with additional layers, it adapts the high-level features. This builds a system that can interpret images. The next layer is the pooling layer, where the spatial size of the convolved feature is reduced to minimize the computational power necessary for data processing through dimensionality reduction. The pooling layer extracts rotational and positional invariant dominant features for model training. Pooling has two types: average pooling and max pooling. Average pooling computes the average of all the values from the section of the image covered by the kernel. On the contrary, max pooling selects the maximum value from the section covered by the kernel and implements a noise suppressant. The convolutional layer and the pooling layer compose the ith layer of a CNN. Each architecture has a unique number of layers depending on the complexity of the image. Increasing the number of layers assists in capturing additional low-level details but requires more computational power. Now, the model can interpret the features and complete the first stage of the architecture to then move to the next stage and feed the classification model. In the third and last stage or the fully-connected layer, the image is flattened into a column vector and is fed into the neural network. The model then differentiates between significant and insignificant features and classifies them using the SoftMax classification technique. With each layer, the model increases in complexity and can identify more sections of a photo. Earlier layers extract simpler features and later ones extract more elements used to identify the object [19]. ConvNet includes several architectures such as LeNet, AlexNet, DenseNet, GoogleNet, and VGGNet [20]. These models are widely adopted as transfer learning to retrain the models with the new datasets for different applications. AlexNet is an extension of LeNet with a deeper architecture. It has eight layers in total: five convolutional layers and three fully connected layers. All layers are connected to a ReLU activation function. AlexNet employs data augmentation and dropout techniques to prevent overfitting due to excessive parameters. DenseNet can be considered an extension of ResNet, where the output of a previous layer is added to a subsequent layer. DenseNet proposes concatenating the outputs of previous layers with subsequent layers, which enhances the distinction in the input of succeeding layers, thereby increasing efficiency.