## 4 Results
### Experimental Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multicolumn{3}{c}{**Depot**} & \multicolumn{6}{c}{**Zones**} \\ \hline
**Depot No.** & **Depot Capacity** & **Latitude** & **Longitude** & **Zone No.** &
\begin{tabular}{c} **Demand** \\ **(Waste Volume)** \\ \end{tabular} & **Latitude** & **Longitude** \\ \hline
1 & 1200 & \(-\)6.85 & 107.59 & 3 & 39.49 & \(-\)6.88 & 107.58 \\
2 & 1200 & \(-\)6.93 & 107.61 & 4 & 45.39 & \(-\)6.88 & 107.57 \\  & & & 5 & 14 & \(-\)6.86 & 107.58 \\  & & & 6 & 16 & \(-\)6.87 & 107.60 \\  & & & 7 & 4.5 & \(-\)6.86 & 107.60 \\  & & & 8 & 9.08 & \(-\)6.92 & 107.62 \\  & & & 9 & 27.37 & \(-\)6.91 & 107.62 \\  & & & 10 & 1.78 & \(-\)6.91 & 107.60 \\  & & & 11 & 31.93 & \(-\)6.91 & 107.61 \\  & & & 12 & 4 & \(-\)6.91 & 107.60 \\  & & & 13 & 33.39 & \(-\)6.92 & 107.65 \\  & & & 14 & 43.56 & \(-\)6.92 & 107.64 \\ \hline \end{tabular}
\end{table} 
Table 5: An example of depot and zone attributes. Figure 6: Solution representations for chromosome-1. Table 6 specifies the experimental settings of the proposed GA-FIS parameters, namely population size (_PS_), dimension (_N_), crossover rate, and mutant factor. The analysis from the experimental results is that there is consistency through 10, 30, and 50 dimensions. Figures 8 and 9 show the membership functions for the values of close, medium, and far with regard to input distance and error. Each of the color lines represent different range of membership function. For example, in Figure 9, the blue color signifies the range from 0 to 0.4 input resulting in a maximum membership, to which it can be categorized as close. The red color signifies the range of medium values between 0.2 to 0.8 input resulting in a maximum membership and green color signifies far value range from 0.6 to 1 input which can result in a maximum membership function. The proposed GA-FIS is analyzed with different diversity curves for various generations, namely, 10, 30, and 50, respectively. The diversity curves of multiple dimensions are compared, as shown in Figures 10-12, respectively. In all these figures, the blue line represents the diversity results for the traditional GA and the red line represents the diversity results for the proposed GA-FIS. Thus, it expresses that the control diversity technique is useful and independent of the dimensions. It is inferred from the figures that the individuals of GA-FIS begin to converge because of the depreciation of diversity. The GA-FIS expands the diversity level if the value is below the goal. Therefore, if a variety is handled appropriately, the proposed approach results in superior performance. Figure 11: Diversity curve with thirty (30) generations. Figure 9: Membership functions of input \(Err_{disc,std}\). Figure 13 shows that the hardware component of the monitoring system is shown in working condition. It is inferred from Figure 14 that the monitoring system successfully manages to detect the distance between the bottom side of the lid of the bin and the garbage level of the container. Figure 14: Distance measuring sensor placed on the inner lid of smart bin. Figure 12: Diversity curve with fifty (50) generations. Figure 13: Implementation of the waste monitoring system. Figure 15 illustrates the successful build-up of code in our waste segregation system. Once initialized, the garbage is placed on top of the smart bin. Figure 16 shows that when metal objects are placed as waste on the smart bin, the servo motors rotate properly and segregate the waste, with the values being 32 g for the metal ball considered for demonstration. Figure 17 shows the waste segregation system displaying an alert \"Trash can is full\" on the webpage. Therefore, the bin has to be cleaned. Figure 18 shows the alert message \"Trash is empty\", and the user can resume operations. A fuzzy inference system with GA is integrated for image classification. This classification distinguishes between recyclable and non-recyclable garbage waste.