## 4 Results
### Discussion  
The comparison of least cost obtained by the GA-FIS with GA for the four different sets of smart bin loads is shown in Figure 20. The efficiency of GA-FIS over GA is measured in terms of percentage of cost lessened concerning four smart bin loads.  
It is evident from Figures 21-24 that GA-FIS has significant cost benefits because of the better fitness evolution for each of the smart loads one to four.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classes** & **Accuracy** & **Precision** & **Recall** \\ \hline Empty & 95.93 & 97.56 & 96.70 \\ Partial & 92.31 & 92.75 & 90 \\ Full & 98.09 & 99.73 & 99.19 \\ Overall & 95.44 & 96.68 & 93.96 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance metrics of the proposed model on the various states of the smart bin.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Class** & **Paper** & **Metal** & **Plastic** & **Cardboard** & **Glass** & **Total** & **Correct \%** \\ \hline Paper & 166 & 0 & 0 & 0 & 0 & 166 & 100 \\ Metal & 0 & 130 & 0 & 0 & 0 & 130 & 100 \\ Plastic & 0 & 0 & 140 & 10 & 0 & 150 & 93.33 \\ Cardboard & 0 & 0 & 0 & 146 & 22 & 168 & 86.9 \\ Glass & 0 & 0 & 2 & 18 & 149 & 169 & 88.16 \\ Total & 166 & 130 & 142 & 164 & 171 & 773 & \\ Correct \% & 100 & 100 & 98.59 & 89.02 & 87.1 & & 94.92 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Confusion matrix of different waste element segregation using proposed model.  
Figure 20: Performance analysis of GA-FIS with GA.  
Figure 23: Fitness evolution graphs of GA_FIS and GA for smart load 3.  
Figure 21: Fitness evolution graphs of GA_FIS and GA for smart load 1.  
Figure 22: Fitness evolution graphs of GA_FIS and GA for smart load 2.  
Table 10 shows the proposed GA-FIS model for waste management categorizing the various waste materials under recyclable and others. This improves the efficiency of our proposed approach.  
Figure 25 illustrates the accuracy results of various recyclable items in percentage. It is inferred that most of the items are classified successfully in the proposed system. However, a few items are incorrectly classified, such as soft plastic cups, long boxes, and small boxes, with accuracy rates of 80, 90, and 90 percent, respectively. The reason for the low accuracy is that the minimal weight is not counted by the weight sensor; however, when the item is placed, the pressure weight is recorded.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Set** & **Element** & **Number** \\ \hline \multirow{8}{*}{**Recyclable**} & Paper & Books & 5 \\  & & Cups & 5 \\  & & Boxes & 4 \\  & & General bottles & 6 \\  & Plastic & Shampoo bottles & 4 \\  & & Pen & 1 \\  & & Watch & 1 \\  & & Cans & 7 \\  & & Key & 1 \\  & Metal & Scissor & 1 \\  & & Beer cap & 1 \\  & Glass & Bottle & 4 \\
**Sum** & 4 & 12 & 40 \\  & & Apple & 1 \\  & & Banana & 1 \\  & Fruit/vegetable & Carrot & 1 \\  & & Cabbage & 1 \\
**Others** & & Rose & 1 \\  & & Others & 1 \\  & & Egg & 1 \\  & Kitchen waste & Lunch box & 1 \\  & & Trash bag & 1 \\  & others & Bowl & 1 \\
**Sum** & 3 & 10 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Categorization of waste materials using the proposed GA-FIS.  
Figure 24: Fitness evolution graphs of GA_FIS and GA for smart load 4.  
Table 11 shows the proposed model compared with existing smart garbage systems that have deployed machine learning classifiers and deep learning techniques as documented in the literature. It is inferred that the intelligent GA-FIS model is superior in terms of accuracy, precision, and recall, as the integration of GA with FIS results in optimal accuracy by determining the best chromosomes for prediction. The GA-FIS learning and prediction times were 3 and 10 s, respectively, while those times were 223 and 345 s for the GA. Therefore, in the proposed work GA-FIS performs better, as it is widely suited for dynamic environments.