## 3 The Proposed Model
### Feature Extraction  
In this work, the STO with the Xception model is exploited for feature extraction purposes. The DL technology is a familiar application developed from the ML approach with increasing multilayer FFNN. In the application of constrained hardware properties, multiple layers in traditional NN are constrained by learned parameters, and the relationship between the layers needs maximal computational time. With the formation of an advanced end system, it could be possible to train deep methodologies through multi-phase NN. The DL technology is developed from CNNs using a higher rate of function in huge applications such as speech examination, object prediction, image processing, and ML techniques. Additionally, CNN is a multilayer NN [23]. Moreover, CNN benefits from FE limiting the pre-processing step to a greater amount. Hence, it is not necessary to conduct a study to identify the features of an image. The CNN is composed of classification, Input, Relu, Pooling, Dropout, Convolution, and Fully Connected (FC) layers. In our study, the DL-based Xception module is used to extract the features from the facial image. The Xception model is similar to the inception module, where the inceptions are substituted with depthwise separate Conv. layer. Especially, Xception architecture is created based on the linear stack of a depthwise separate convolutional layer using a linear residual attached. In this method, pointwise and depthwise layers have been employed. In the depthwise layer, a spatial convolution layer manually occurs in the pointwise layer and channel of the input dataset, where a \(1\times 1\) convolution map the outcomes of new channel space in the applications of the depthwise convolution layer. Here, the STO algorithm is utilized to fine-tune the hyperparameters involved in the Xception model [24]. In this study, the STO algorithm is preferred over other optimization algorithms for the following reasons. The STO algorithm is capable of exploration, exploitation, and local optima avoidance. It can solve challenging constrained problems and is very competitive compared with other optimization algorithms. The STO technique was simulated by the attack performance of sooty term birds. Usually, the sooty terms live from the groups. It can utilize its intelligence for locating and attacking the target. The most important features of sooty terms are migration and assault behaviors. The subsequent offer insights as to sooty term birds:* The sooty term moved from the group in migration. To avoid a collision, the primary places of sooty terms were distinct. * In the groups, a sooty term with a minimal fitness level nevertheless traveled a similar distance to the fittest amongst them. * The sooty terms with minimal fitness are updated in their initial places on the basis of the fittest sooty terms. The sooty term must meet three requirements for the migration: SA was utilized for computing a novel searching agent place for avoiding collisions with their neighborhood searching agents (for instance, sooty terms). \[\overset{\rightarrow}{C}_{st}=s_{A}\cdot\overset{\rightarrow}{P}(z) \tag{3}\]  
In which \(\overset{\rightarrow}{C}_{st}\) signifies the place of sooty terms which do not collide with other terms. \(\overset{\rightarrow}{P}_{st}\) implies the existing place of sooty terms. \(z\) refers to the existing iteration, and \(s_{A}\) indicates the migration of sooty terms from the solution space. Next, the collision evasion, the searching agent converges from the path of the finest neighbor. \[\overset{\rightarrow}{M}_{st}=C_{B}\cdot\overset{\rightarrow}{P}_{st}(z) \left(\overset{\rightarrow}{P}_{bst}(z)-\overset{\rightarrow}{P}_{st}(z)\right) \tag{4}\]  
whereas \(\overset{\rightarrow}{M}_{st}\) signifies the special place of searching agents (that is, sooty term).