## 3 Proposed Model
\begin{tabular}{c} \hline Begin \\ // Initialize the parameters \\  Initialize the size_population, size_archive, size_elite, size_pool_mating, generation_max,  
Probability_mutation, probability_crossover, pool_offspring, pool_mating, pool_gene;  
Initialize the utilisations_set, slots_total, utilizations_no, slots_total, utilisations_power,  
price_electricity;  
For \(i=1\) to size_population  
For each appliance \(j\) in the appliances_set  
For \(t=1\) to slots_totals;  
Allocate timeslot to the utilisation \(j\) within realistic time slots  
End For  
Initialize the following: Iteration = 1, generation_current = 1;  
For generation_current = 1 to generation_max  
For each chromosome \(i\) in the pool_parent  
For \(t=1\) to slots_total  
Determine the total load at time interval’t’  
End For  
Find the best chromosomes  
Copy the best chromosomes into the pool_offspring  
Update the current generation counter as generation_current = generation_current+1;  
Return chromosome fitness value in the pool_parent  
Goto GA Block // Given in Appendix A  
If generation_current equals generation_max then  
Terminate the execution cycle  
Output optimal chromosome  
Else  
Goto Parent Selection Block // Given in Appendix A  
Continue with the execution cycle  
End if  
\(\bullet\) Reinforcement  
Essential genes are selected and inserted into the offspring population. This selection will preserve and update the crucial genes in the successive chromosome population. Each generation computes the population diversity. Then, the Err\({}_{\text{div,std}}\) and Dist\({}_{\text{std,j}}\) are calculated. The GA-FIS is calculated from the variation between existing variety, and the diversity goal then selects xbase. ### Methodology for Image Classification  
The major steps involved in the image classification approach are given as follows:  
* Pre-processing. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Err\({}_{\text{disstd}}\)** & **Dist\({}_{\text{std},j}\)** & **Close** & **Medium** & **Far** \\ \hline Positive High & 0.7 & 0.8 & 0.9 \\ Positive Low & 0.5 & 0.6 & 0.7 \\ Fit & 0 & 0 & 0 \\ Negative High & 0.3 & 0.4 & 0.5 \\ Negative Low & 0.1 & 0.2 & 0.3 \\ \hline \hline \end{tabular}  
* **Algorithm 3.** Pseudocode of GA–fuzzy inference system.  
\end{table} 
Table 3: The fuzzy rule base. * Feature selection. * Classification. Initially, the images are gathered from the camera placed in the waste bin. Then, a filter is applied for image enhancement. Afterwards, from every image, features are collected. The selected features are sent to optimize fuzzy inference system (OFIS). * Image preprocessing: Images are captured from the waste bin. Then, the images are enhanced by sending them to the pre-processing unit, and the image is divided into an R, G, and B setup. The green band has more data in comparison to other colors, so the noise is reduced by deploying the green (G) band. * Feature selection: The color attributes and the texture are selected from the image. In this approach, from every image five features are extracted, namely the mean, standard deviation, area, perimeter, and eccentricity. * Mean (F1): The image with high brightness represents a higher mean, and the dark image represents a decreased mean value. The mean is calculated as given in Equation (9): \[F_{j}=\frac{1}{N}\sum_{i=1}^{N}Q_{ji}\] (9)
* Standard deviation (F2): SD is the term used to represent square root of the variance. Low variance is obtained if the contrast is less, and high variance results for a high-contrast image. It is calculated as given in Equation (10): \[F_{2}=\sqrt{\left(\frac{1}{N}\sum_{l=1}^{N}\left(Q_{ji}-F_{j}\right)^{2}\right)}\] (10)
* Area (F3): A particular region is extracted from the complete image. A scalar value which represents the number of pixels in a specific area is determined. \[\text{Y }=\text{ }\left\{\text{FIS}_{1},\text{ }\text{FIS}_{2},\text{ }\dots,\text{ }\text{FIS}_{\text{d}}\right\}\] (11) where \(\text{FIS}_{\text{d}}\) specifies the optimal FIS system in d\({}^{\text{th}}\) dimension.