## 3 Materials and Methods
### Methodology
#### 3.4.1 Convolutional Neural Network  
Deep learning is a branch of AI that enables computers to learn from data. Deep learning systems modify the input data by applying linear and nonlinear transformations several times to create abstract and discriminative representations. DL is based on techniques for the training of neural networks, which often include extremely large datasets and, in most instances, do not have any labels attached to them. When neural networks are used, the input is filtered via several hidden layers and nodes. These nodes process the input and send the results to the next tier. More layers in the network lead to a more accurate representation of the data. This technique simultaneously trains features and classifiers. The initial layers of the network perform the feature extraction and contain filter banks, non-linear transformation, and pooling layers. Meanwhile, the upper layers function as fully connected layers. This approach is often used by a number of object recognition systems for the purposes of feature extraction and categorization [29; 41]. Convolutional neural networks (CNN), recurrent neural networks (RNN), and Q-learning are components of deep learning. The most common type of neural network is the CNN or ConvNet. It has a deep feed-forward layer architecture and a capacity to generalize more effectively than other neural networks with fully connected layers. It can distinguish abstract characteristics and recognize objects efficiently. A CNN has three primary layers: convolutional, pooling, and fully connected (FC) (Figure 7). The first two layers, the convolution and pooling layers, are responsible for fundamental and local feature extraction, while the third layer (FC) transfers the retrieved characteristics to the final class classification array. Figure 8 describes the step-by-step procedures of the convolution and max-pooling operations. A CNN may extract information about objects based on their color, shape, and texture when the datasets include complex images of objects with different backgrounds [32; 78; 79; 39]. Constructing a fully functional CNN model from scratch is a difficult and time-consuming endeavor, requiring the careful consideration of layer construction and parameter choices. These tasks are non-intuitive, and a great deal of trial and error is needed to make a CNN efficient [80; 81]. However, scientists and researchers have spent years working on effective CNN models. CNNs like AlexNet, VGGNet, GoogleNet, and ResNet have shown superior performance to other non-intensive learning techniques in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). These models can categorize over 1000 objects, including aircraft, dogs, fish, plants, etc., and have been trained on over a million images. These models' categorization accuracy has reached a comparable level to human intellect. Because of their numerous benefits and desirable qualities, we opted to employ pre-trained models (CNNs) in our research [82]. The summary of the above-mentioned pre-trained CNN models, such as their salient features, number of layers, activation function, total parameter size, minimized error rate, and input size, are summarized in Table 6.  
Figure 7: A sample CNN’s block diagram. **AlexNet:** AlexNet is a well-known deep convolutional neural network (CNN) utilized in many different image identification and classification systems. It was initially proposed by [PERSON] et al. [83] in the ILSVRC 2012 contest. Its design architecture comprises eight main layers: the first five are convolutional layers and the other three are max-pooling layers. It also consists of other supporting layers: two normalization layers, two fully connected layers, and the last softmax layer. Every convolutional layer has convolutional