## 3 Materials and Methods
### Feature Extraction  
The presented MODAE-RCM technique exploited the MFFO with the NASNet model for feature extraction. The NASNet Mobile technique was a recently accomplished DL method with 5,326,716 variables, which will exhibit maximum reliability. The NASNet structure has a building block, and a group of blocks was jointly integrated for cell formation. The search space involved in the NASNet was the factorization of a network to cells, which was divided into blocks. The kind of blocks and cell count are not predetermined [18]. However, they must be improved for the dataset which is chosen. The probable function of the block includes max pooling, separable convolution, average pooling, identity map, convolution, and many more. The blocks are capable of mapping two inputs into output featured mapping. The network growth can be focused on three attributes, namely the amount of cells that are stacked (N), the count of filters from the primary layer (F), and the cell infrastructure.  
In order to regulate the hyperparameters of the NASNet method, the MFFO algorithm was applied to it. Generally, FFO is easier to design; however, it suffers from local optimal problems [19]. The flight direction and distance of osphresis foraging are not regular, and blind flight can decrease the searching act of the FFO method. Therefore, the MFFO technique was designed by the use of Levi flight into osphresis foraging for the purpose of adjusting the direction and distance of the FFO algorithm. Levi flight is a type of walking stage among short distance searches and, sporadically, long distance walks. Therefore, it could raise population diversity and extend the searching area, which causes the FFO algorithm to escape from local optima problems and decrease the possibility of earlier convergence. In addition, a condition probability \(P_{a}\) is defined as vision foraging for the challenging optimum location that can be defined in order to enhance the searching accuracy of the FFO algorithm. The algorithmic process of the MFFO algorithm is defined as the following:  
Step 1: initializing parameters. Fix maximum number of iterations as \(G_{\max}\), the FF swarm size as \(N\), and FF location \(X_{i,0}\), \(Y_{i,0}\) and optimum position \(X_{b,0}\), \(Y_{b,0}\) arbitrarily in the interval of \([0,\ 1]\).  
Step 2: upgrade the location of the FF by the Levi flight:  
\[X_{i,G}=X_{b,G-1}\pm(X_{i,G-1}-X_{b,G-1})\otimes L(\beta),\ i=1,2,\ldots n \tag{1}\]  
\[Y_{i,G}=Y_{b,G-1}\pm(Y_{i,G-1}-Y_{b,G-1})\otimes L(\beta),\ i=1,2,\ldots n\]  
Step 3: execute the FFO process.  
Step 4: produce an arbitrary number \(P_{t}\).  
Figure 1: Workflow of MODAE-RCM system.  
Step 5: compute the difference between optimum solutions \(Xb_{estindx,G}\), \(Y_{estindx,G}\) and the worst solution \(X_{nortsintdx,G}\) and \(Y_{nortsintdx,G}\) of the population.  
[worstSmell worstindex] = max(\(F_{i,G}\))  
\[X_{b,G} =X_{bestindex,G}-\frac{G_{\max}}{G}\log\biggl{(}\frac{G}{G_{\max}} \biggr{)}(X_{bestindex,\ G}-X_{nortsindex,\ G}) \tag{2}\] \[Y_{b,G} =Y_{bestindex,G}-\frac{G_{\max}}{G}\log\biggl{(}\frac{G}{G_{\max} }\biggr{)}(Y_{bestindex,\ G}-X_{nortsindex,\ G})\]  
Step 6: perform optimization and reiterate steps 2-4 to determine if smell concentration is superior to earlier. Then, the process is terminated upon reaching maximum accuracy or fixed number of iterations \(G_{\max}\).  
The MFFO algorithm will derive a fitness function (FF) for obtaining enhanced classifier outcomes. It would determine a positive value for denoting a superior outcome of candidate solutions. In this work, the reduced classifier error rate will be denoted as the FF, as given in Equation (3).  
\[\begin{split}& fitness(x_{i})=ClassifierErrorRate(x_{i})\\ &=\frac{number\ of\ misclassified\ samples}{Total\ number\ of\ samples}*100\end{split} \tag{3}\]