*3.7. Classification Using Conventional ML Classifiers*

The proposed CNN COV-Net architecture was used to extract features from the augmented dataset. We extracted features from the FC-1 layer, and details are shown in Table 2. After extracting the features, these features were passed as input to conventional ML classifiers to train them. Different ML classifiers like Naïve Bayes, decision trees, KNN, and SVM determine the robustness of the classification. The performance of these models was measured by classifying COVID-19, pneumonia-infected, and healthy patients. The accuracy of classification attained by using conventional ML classifiers performed better than the softmax function. This is because it extracted the most highlighted features from chest X-rays of different patients by using the most abstract feature extraction techniques.

### 3.7.1. SVM

SVM is a linear model. It can tackle linear and non-linear issues. Its basic idea is that it makes a line to separate two classes. New data components are assigned to one class based on predictive analysis. As a rule, a parallel classifier expects that the data being referred to contain two potential objective variables. It utilizes a procedure called kernel trick to change the data and then find boundaries between them. It groups data and trains models inside really limited levels of extremity, making a three-dimensional order model that simply follows the X/Y prescient axis [34].

$$\mathcal{L}(\boldsymbol{\gamma}, \boldsymbol{\alpha}, \boldsymbol{\beta}) = \frac{1}{2} \parallel \boldsymbol{\gamma} \parallel^2 - \sum \sum\_{i=1}^{m} \beta \mathbf{i} [\mathbf{y}i (\boldsymbol{\gamma} \cdot \mathbf{x} + \boldsymbol{\alpha}) \mathbf{1}] \tag{1}$$
