## 3 Proposed Frameworks
The CNN model contains a convolution layer, as shown in Figure 3. There are layers for initial step in the process of extracting various traits from the input photographs. Convolution is a mathematical operation that occurs at the layer between the image being input and a filter of a particular size MxM. Sliding the filter over the input image yields the dot product between the filter's elements and those of the input image with respect to the filter's size (MxM). The output, known as the feature map, provides information about the image, including its corners and edges. The subsequent layers include additional features from the input image, and the feature map is later provided. The second layer is the pooling layer, in which the primary objective is to reduce the size of the convolved feature map to decrease computational costs. This is achieved by minimizing the connections between layers and working individually on each feature map. Figure 1: The framework of the proposed model and model transfer to online learning environment setting. Figure 2: Image processing for proposed model. Depending on the technique used, many types of pooling procedures are available. Max pooling uses the largest component from the feature map. Average pooling determines the average of the components in an image segment with a predetermined size. Sum pooling determines the overall total of the components in the designated section. Typically, the pooling layer acts as a link between the convolutional layer and the FC layer. Finally, the weights and biases compose the fully connected (FC) layer, which connects the neurons of two layers. These levels are the final few in CNN designs and are frequently inserted before the output layer. Activation functions are one of the most essential components of CNN models for each model called homogenous ensemble convolution neural network. his paper comprises between 6 and 7-ensemble CNN models, which encompass the optimization parameters setting for each CNN model as Table 2. \begin{table}
\begin{tabular}{c c c} \hline
**CNN Models** & **Layers** & **Parameters Setting** \\ \hline DCNN & 9 & Optimizer = [PERSON], learning rate = 0.001, beta\_1 = 0.9, beta\_2 = 0.999, \\  & & epsilon = 1 \(\times\) 10\({}^{-7}\), epoch = 100, batch size = 64 \\ EfficientNetB2 & 342 & Optimizer = [PERSON], learning rate = 0.001, beta\_1 = 0.9, beta\_2 = 0.999, \\  & & epsilon = 1 \(\times\) 10\({}^{-7}\), epoch = 100, batch size = 64 \\ InceptionResNetV2 & 164 & Optimizer = [PERSON], learning rate = 0.005, beta\_1 = 0.9, beta\_2 = 0.999, \\  & & epsilon = 1 \(\times\) 10\({}^{-7}\), epoch = 100, batch size = 32 \\ RestNet50 & 50 & Optimizer = [PERSON], learning rate = 0.005, beta\_1 = 0.9, beta\_2 = 0.999, \\  & & epsilon = 1 \(\times\) 10\({}^{-7}\), epoch = 100, batch size = 64 \\ Xception & 71 & Optimizer = [PERSON], learning rate = 1 \(\times\) 10\({}^{-3}\), beta\_1 = 0.9, beta\_2 = 0.999, \\  & & epsilon = 1 \(\times\) 10\({}^{-7}\), epoch = 100, Batch size = 64 \\  & & Optimizer = [PERSON], learning rate = 0.0001, beta\_1 = 0.9, beta\_2 = 0.999, epsilon = 1 \(\times\) 10\({}^{-8}\), epoch = 100, Batch size = 32 \\ VGG16. & 16 & Optimizer = SGD, learning\_rate = 0.01, momentum = 0.9, nesterov = True, \\  & & epoch = 100, batch size = 32 \\ \hline \end{tabular}
\end{table} 
Table 2: The structure of CNN models and optimize parameter setting of models. Figure 3: Architecture of the homogenous ensemble convolution neural network. The ensemble model is activated to include decision multiple-class labels from the ensemble CNN model. The output p\({}^{\text{i}}\) is the probability vector of CNNi. Given a sample x, the highest argument of probability x and majority vote from the n-ensemble model are predicted following Equation (1).