## 3 The Proposed Model
### Object Classification Module  
Next to the object detection process, the OSSR technique is utilized for classifying the objects into multiple classes. The OSRR is a faster batch learning model and offers optimal generalization efficiency [21]. OSRR could learn the training data chunk-by-chunk or one-by-one. As OSRR employs a batch learning model, it is utilized to classify microarray data. The online sequential learning in OSRR comprises two phases:  
Step 1 Initialization  
In this stage, a smaller part of trained data \(n_{0}=(x_{i},\;y_{i}),i=1,\ldots,N_{0}\) with \(N_{0}\in N\) is taken into account to initialize the learning method. The first resultant weight matrix is evaluated based on the RR approah by arbitrarily allocating the input weight \(w_{j}\) and bias \(b_{j}\), \(j=1,2,L\):  
\[\beta^{(0)}=Q_{0}H_{0}^{T}T^{0} \tag{13}\]  
where \(Q_{0}=\left(H_{0}^{T}H^{0}\right)^{-1}\) (19) and \(H^{0}\) represent the first hidden state output matrix. Step 2 sequential learning  
Once the new set of observations arrives \(n_{k+1}=(x_{i}y_{i})\),  
\[Y_{k+1}=\left[y\left(\sum_{l=0}^{k}N_{l}\right)+1,\ldots,y\left(\sum_{l=0}^{k+ 1}N_{l}\right)\right]^{T} \tag{14}\]  
where \((k+1)th\) denotes the chunk of the data and determines the partially hidden state output matrix \(H_{k+1}\)\(N_{k+1}\) denotes the number of instances present in the \((k+1)th\) chunk. Next, by utilizing the output weight upgrade equation as given in the following, the output weight matrix \(\beta^{(k+1)}\) could be calculated. \[Y_{k+1}=\left[y\left(\sum_{l=0}^{k}N_{l}\right)+1,\ldots,y\left(\sum_{l=0}^{k +1}N_{l}\right)\right]^{T} \tag{15}\]  
\[Q_{k+1}=Q_{K}-Q_{k},H_{k+1}^{T}(1+H_{k+1}Q,H_{k+1}^{T})^{-1}H_{k+1}Q_{k} \tag{16}\]  
\[\beta^{(k+1)}=\beta^{k}+Q_{k+1},H_{k+1}\left(T_{k+1}-H_{k+1}\beta^{k}\right) \tag{17}\]  
Every time a novel chunk of data is attained, the resultant weight matrix was upgraded based on the above equation. The upgraded equation is for one-by-one and chunk-by-chunk learning since the one-by-one data could be taken into account as a special case of the chunk-by-chunk when the chunk size = 1. To determine the weight and bias values of the OSSR technique, the WHO algorithm was utilized. The WHO approach arithmetically simulates and duplicates the social behavior of wild horses in nature [22]. Horses predominantly live in herds with stallions and several mares and foals. It can display different kinds of behavior, involving commanding, mating, pursuing, grazing, and dominating. Those stages for the WHO approach are shown in the following: Firstly, the population initialization is aliented into various groups. \(G\) denotes the amount of groups and \(N\) implies the count of the population. Everygroup has a leader (stallion), hence the count of stallions from the approach equals \(G\), and \((NG)\) indicates the residual population (mares and Foals) are distributed correspondingly amongst this group. Figure 2 depicts the flowchart of the WHO technique. \[X_{i,G}^{j}=2Z\ \text{cos}\ (2\pi RZ)\times\left(\text{{Stallion}}^{j}-X_{i,G}^{j} \right)+\text{{Stallion}}^{j} \tag{18}\]  
In which \(X_{i,G}^{j}\) signifies the existing position of the foal or mare group member, \(\text{{Stallion}}\) shows the stallion location, \(R\) indicate a uniform stochastic value within \([-2,2]\), and \(Z\) indicates the adaptive model estimated as follows:  
\[P=\overset{\rightarrow}{R_{1}}<TDR;\ IDX=(P==0) \tag{19}\]  
\[Z=R_{2}\Theta IDX+\overset{\rightarrow}{R_{3}}\Theta(\sim IDX) \tag{20}\]  
here \(P\) indicates a vector consisting of 0 to 1, \(\overset{\rightarrow}{R_{1}}\) and \(\overset{\rightarrow}{R_{3}}\) represent an arbitrary value within \([0,1]\), \(R_{2}\) shows a uniform arbitrary value within \([0,1]\), \(TDR\) indicates an adoptive variable that begins with 1 and reduces until it reaches 0 toward the end of algorithm execution as follows:  
\[TDR=1-it\times\left(\frac{1}{\textit{{maxit}}}\right) \tag{21}\]  
Figure 2: Flowchart of WHO algorithm.