## 3 The Proposed Model
### Feature Extraction  
To derive features, the presented AOTL-CDA3S technique utilized the SqueezeNet model to produce feature vectors. In general, CNN includes convolutional, pooling, and full connected (FC) layers [23]. Initially, the feature was extracted by employing several pooling and convolutional layers. Afterward, the mapping feature in the last convolution layers is changed to a 1D vector followed by the resultant layer classifying the input image. The network decreases the square variance between the predictive and classifier results and alters the weighted variable by BP. The neuron in every layer is orderly in three dimensions (width, depth, and height) so that depth defines the count of input feature mapping or  
Figure 1: Block diagram of the AOTL-CDA3S system. channel count of input images, and height and width signify the size of neurons. The convolutional layer comprises many convolutional filters and extracting features in the image with convolution approach. The convolution filters of the current layer convoluted the input feature mapping for removing local features and realizing the resultant feature mapping. Then, the non-linear feature mapping was realized by the activation function. The pooling or subsampling layers are the last convolutional layers. This applies a down sampling approach that is a specific value as an outcome in particular region. As the variable count for AlexNet and VGGNet enhances, the SqueezeNet network infrastructure is established that is minimally variable but maintains accuracy. The fire elements develop an important element in SqueezeNet. This element was separated to expand and squeeze infrastructure. The 1 \(\times\) 1 convolutional layer obtained considerable attention from the network infrastructure, thereby attaining data integration on the channel and a linear combination of many features mapping. If the count of input and output channels is superior, the convolution kernels develop well. Next, adding 1 \(\times\) 1 convolutional to all the single inception systems decreases the count of input channels, and the complex function and convolution kernel variable were reduced. Lastly, 1 \(\times\) 1 convolutional was added for increasing the extracting feature and the count of channels. When the sampling reduction approach can be delayed, a superior activation graph was offered to convolutional layer, so the higher activation graph reserves more data and offers superior classifier efficacy. For the hyperparameter optimization process, the AO algorithm is utilized. The AO approach mimics Aquila's social activity to catch the prey [24]. AO is a population-based optimized method which is based on other metaheuristic algorithms and begins by establishing an initial population \(X\) with \(N\) agent. The succeeding equation is exploited to implement these processes as explained in Algorithm 1. \[X_{ij}=r_{1}\times\left(UB_{j}-LB_{j}\right)+LB_{j},\ i=1,2,\ Nj=1,2,\ \ldots,\ Dim \tag{2}\]  
Here, \(UB_{j}\) and \(LB_{j}\) illustrate the restriction of search space. \(r_{1}\in[0,1]\) indicates the arbitrary number and \(Dim\) shows the dimension of agents. In this study, the subsequent stage is to implement exploitation and exploration until the optimum solution is determined. There are 2 phases in exploration and exploitation. The \(X_{b}\) optimum agent and (X) the average agent is employed in the exploration, and the mathematical expressions are given below:  
\[X_{i}(t+1)=X_{b}(t)\times\left(\frac{1-t}{T}\right)+\left(X_{M}(t)-X_{b}(t)*rand \right), \tag{3}\]  
\[X_{M}(t)=\frac{1}{N}\sum_{i=1}^{N}X(t),\ \forall j=1,2,\ \ldots,\ Dim \tag{4}\]  
The exploration phase can be controlled by means of using \(\left(\frac{1-t}{T}\right)\).