## 3 The Proposed Model
### Feature Extraction: NASNet Model  
Primarily, the proposed QWSA-HDLAR technique exploits the NASNet model to derive a collection of feature vectors. This is one of the very influential and famous technologies for handling smaller datasets through a pretrained network. A pretrained network was trained on massive datasets, generally in the tasks of classifying images; later, the architecture and weight were retained. If these primary datasets are relatively large and sufficiently widespread, then the feature subset that the pretrained network has learned could be helpful as a visual model. Thus, the feature might assist various computer vision tasks, although the new task can include completely different classifications from the primary task [20]. The transfer of learning from a pretrained network is exploited in two ways: fine-tuning and feature extraction. Feature extraction includes the convolution base of the pretrained network for extracting the novel data set feature and later training a novel classification on top of the output.  
The fine-tuning corresponds to the feature extraction model, which includes unfreezing the final layer of the frozen convolution base employed for extracting features. Then, the unfrozen layer is later retrained alongside the novel classification formerly learned in the feature extraction model. The fine-tuning model aims to alter the pretrained model's most abstract features to make them more pertinent to the novel task. The following steps are involved in this study:  
* A pretrained NASNet is considered, and the classification base is detached.
* The convolution base of pretrained models is frozen.
* A new CNN-BiGRU classification is added and trained on top of the convolution base of pretrained networks.
* C layers of the convolution base of pretrained networks are unfrozen.
* Finally, the unfrozen layer and novel classifiers are trained together.  
Equipped with engineering expertise and a large amount of computation power, Google launched NASNet [21] and devised the problem of searching for an optimal CNN model as a reinforcement learning (RL) problem. The RL model is a type of ML approach which allows an agent to discern the best action in virtual environments to attain the goal with feedback from its own experiences and actions. Furthermore, the concept was to search the optimal grouping of parameters of the provided number of layers, searching filter size, strides, output channel, etc. In the RL settings, the reward after every search action was the accuracy of the searched model on the provided datasets. In NASNet, using the general framework is predetermined; the cells or blocks are not predetermined by researchers. Rather, they searched through the RL search technique. The structure of the NASNet model is shown in Figure 1.  
Furthermore, the number of early convolution filters and motif repetitions N are free parameters and utilized for scaling. In particular, they are named reduction and normal cells. A reduction cell is a convolution cell that returns a feature map from which the feature  
Figure 1: NASNet architecture.  
map's width and height are minimized by a factor of 2, and a normal cell is a convolution cell that returns a feature map of similar dimensions. NASNet achieved advanced results in the ImageNet competition, but the computational power required for NASNet was bigger than what a small company only capable of using common methodologies could provide.