## 4 Results and Discussion
### Validation and Accuracy of the Model  
In the current study, we used Keras as an API for elevated neural networks. Keras is built in Python and can be run on TensorFlow. The sequential model is Keras's most basic model. It contains a logarithmic array of layers. During the training of the CNN model, we backpropagated data points that were not relevant with one data point, but with a large sample size. Furthermore, the data appears in different dimensions, and we need to generalize it; thus, a third dimension must be introduced so that the data can be restructured for further analysis.  
During the training phase, the computer is taught a lot about the data and the labels associated with it. We categorize the data by comparing it to each component in the training set and then transferring the label mostly from the closest training point. During the validation phase, the algorithm will memorize every part of the training set. It will then compare every feature of the validation set to each element of the training data to assess accuracy. The loss precision and validation efficiency were provided for each period. Analyzing the accuracy rate and loss, the complete accuracy for training accuracy was 98.76 percent after 20 epochs; the training loss was relatively low at 0.0413. The validation accuracy was 90%, with a validation loss of 0.70. The training accuracy and loss were evaluated during the training phase. They indicate how efficiently the model performed while being trained on the data. During training, this figure generally increases. Figure 6 depicts the relationship between accuracy and the number of epochs. The pattern in the graphs reveals that efficiency continued to increase in the last few epochs. This suggests that the model might be trained further but is not overfitting.  
\begin{table}
\begin{tabular}{l l l} \hline
**Layer (Type)** & **Output Shape** & **No. Parameter** \\ \hline Cov2d (Conv2D) & (None, 222, 222, 32) & 896 \\ \hline Activation (Activation) & (None, 222, 222, 32) & 0 \\ \hline Max\_polling2d (Maxpolling2D) & (None, 111, 111, 32) & 0 \\ \hline conv2d\_1 (Conv2D) & (None, 109, 109, 64) & 18,496 \\ \hline Activation\_1 (Activation) & (None, 109, 109, 64) & 0 \\ \hline Max\_polling2d\_1 (Maxpolling2D) & (None, 54, 54, 64) & 0 \\ \hline conv2d\_2 (Conv2D) & (None, 52, 52, 128) & 73,856 \\ \hline Activation\_2 (Activation) & (None, 52, 52, 128) & 0 \\ \hline Max\_polling2d\_12 (Maxpolling2D) & (None, 26, 26, 128) & 0 \\ \hline Flatten (flatten) & (None, 86,528) & 0 \\ \hline dense & (None, 256) & 22,151,424 \\ \hline Activation\_3 (Activation) & (None, 256) & 0 \\ \hline Dropout (Dropout) & (None, 256) & 0 \\ \hline Dense\_1 (Dense) & (None, 64) & 16,448 \\ \hline Activation\_4 (Activation) & (None, 64) & 0 \\ \hline Dropout\_1 (Dropout) & (None, 64) & 0 \\ \hline Dense\_2 (Dense) & (None, 2) & 130 \\ \hline Activation\_5 (Activation) & (None, 2) & 0 \\ \hline \end{tabular} Total params: 22,261,250. Trainable params: 22,261,250. Non-trainable params: 0.  
\end{table}
Table 6: Model summary.  
Figure 7 indicates that the method has comparable performance and that the training phase ends at the earliest epoch if the concurrent plots separate reliably. A confusion matrix is a table that is frequently used to specify the conclusion of a classification model on a testing dataset wherein real analysis is performed. It provides a visualization of an algorithm's results. Consequently, our model becomes confused with recyclable waste images and classifies them as organic, as shown in Figure 8. The precise prediction of waste quantity and quality is crucial for the design of smart waste management systems.  
Figure 6: Evaluating the proposed CNN model’s accuracy. (**a**) has epochs = 3, (**b**) has epochs = 5, (**c**) has epochs = 10, (**d**) has epochs = 20, (**e**) has epoch = 50.