## 3 The Proposed Model
### Feature Extraction Module  
At the initial stage, the SSA with NASNetLarge model functions as a feature extraction model in which the hyperparameter tuning process is performed by the SSA. For determining the optimal convolution architecture for the dataset, a search algorithm can be used. Neural architecture search (NAS) is the most important search technique that the authors deployed in this network. Child network is shown to accomplish some accuracy on a validation set; in other words, it is used for convergence [25]. The subsequent accuracy value is utilized to upgrade the controller that consecutively generates better architecture over time. The policy gradient takes place to update the controller weight. A new searching space was designed, which permits the better architecture found on the CIFAR-10 dataset (available at [[http://www.cs.toronto.edu/~kriz/cifar.html](http://www.cs.toronto.edu/~kriz/cifar.html)]([http://www.cs.toronto.edu/~kriz/cifar.html](http://www.cs.toronto.edu/~kriz/cifar.html)), accessed on 12 February 2022) that generalized for large, high-resolution image datasets from the range of computation environments. To adapt input of depth of filtering and spatial dimension, this cell is sequentially stacked. In this technique, the convolution net overall architecture is predefined manually. They are composed of convolution cells that possess a similar shape as the original but are weighted in a different way. Two kinds of convolution cells have been taking place for rapidly developing scalable architecture for images of any size: (1) convolution cells return a feature map with a 2-fold reduction in width and height, and (2) convolution cells produce a feature map with the similar dimension. Figure 2 depicts the framework of NasNetLarge. Figure 1: Block diagram of MDTL-ICDDC technique. These two kinds of convolution cells are represented as Normal Cell and Reduction Cell, correspondingly. The primary process used for the cell's input gives a two-step stride to minimalize the cell's width and height. The convolution cells support striding because they consider each operation. The Normal as well as Reduction Cells architecture that the controller RNN search for dissimilar to convolution net. The searching region is utilized for searching each cell shape. There are 2 hidden states (HS), namely h (i) and h (i \(-1\)), presented for every cell in the searching space. Initially, the HS is the outcome of 2 cells in the prior 2 lower layers or input image, correspondingly. The controller RNN make recursive prediction on the remaining convolution cells on the basis of 2 primary HSs. The controller prediction for all the cells is ordered into B blocks, with every block comprising five prediction steps implemented by five discrete SoftMax classifications representing different selections of block elements. Step 1. Choose an HS from h (i), h (i \(-1\)), or the set of formerly generate HS. Step 2. In the similar option as in Step1, choose the next HS. Step 3. In Step 1, select the HS that the authors need to employ. Step 4. After, choose an HS in Step2, choose an operation to employ. Step 5. Define how the output from Steps 3 & 4 would be integrated to make a novel HS. It can be helpful to apply the recently generated HS as an input for the subsequent block. For the optimal hyperparameter tuning process, the SSA is utilized. It was initially established as a swarm intelligence process [26]; it can be stimulated by the foraging performance of the salp swarm that forms a chain. Similar to other algorithms, it finds an optimum solution via the cooperation and division of salps. The population is divided into the following categories: the leader and followers. Initially, the leaders lead the direction of the population, and next the follower follows them; consequently, the population forms a chain. The population \(X\) includes \(N\) agents are established as a matrix using D columns  
Figure 2: Architecture of NasNetLarge. and N rows.