## 2 Related Work  
SVM initially was proposed by [PERSON] and his team in 1990 [22]. The concept of SVM is derived from neural networks, or we can say that SVM is a mathematical extension of neural networks. Both linear and non-linear datasets can be classified by SVM [23]. SVM performs classification by transforming data into different feature spaces and construct hyperplane on higher dimensions. SVM finds \(w\) and \(b\) to set optimal hyperplanes in feature space in the binary class. \[D_{w,b}(x)=w\varphi(x)+b, \tag{10}\]  
where \(w\) and \(b\) can be determined by Lagrange's multiplier [24]. The decision rule of SVM is, if \(D_{w,b}>0\) then classify as positive otherwise negative class. As we know in the binary classification problem, the training samples and the classes are given, and they all belong to \(R_{d}\), where the class labels are given as \(+1,-1\). SVM can be used for feature selection. Parameter optimization and feature selection are two primary perspectives to enhance the performance of the classifier. An innovative method based upon the genetic algorithm (GA) for feature selection and parameter optimization of support vector machine (SVM) is introduced to enhance the hospitalization cost model's prediction accuracy [25]. Another technique [26] presents a different feature adoption method to deal with machine learning's two main issues: class imbalance and high dimensionality. The proposed fixed strategy punishes the feature set's cardinality via the scaling factors method also is applied with two support vector machine (SVM) formulations intended to deal with the class-imbalanced problem, namely Cost-Sensitive SVM and Support Vector Data Description. A lot of work has been done on feature selection by using SVM [27; 28]. In [16] the author proposed a model called SVMRFE. They use the smallest weight to eradicate the features. SVM features selection method used for prostate histopathological grading. Features selection is mainly used for diagnosing histopathological images [29]. In this study, they use textual features that were obtained from nuclei cytoplasm and stroma. The first step of methodology was to categorize the tissue images. Based on the text of a single classifier and then ensemble learning model was applied by combining the values obtained by each classifier. For that purpose, they use SVM-RFE (Recursive Features Elimination) that integrates SVM-RFE with Absolute Cosine (AC). The results obtained by SVM-RFE (AC) were better than the results of simple SVM. SVM is used in real-life problems due to its strong kernel tricks. SVM with multi-domain features used for diagnosing rolling bearing [30]. Sensitive features extracted from the highly vibrated signal are still challenging tasks and for diagnosing rolling bearing. An existing classifier that uses single domain features is easy for extraction and recognition of low accuracy. To address this challenge of low accuracy, they proposed a new approach called SVM with multi-domain features that are consist of three stages, i.e., is features extraction, feature selection, and feature weighting. In [31] author proposed Digital Signal Processing platform and PCA-SVM Algorithm for rapid beef meat freshness prediction and identification. They used PCA and kernelized SVM algorithm and embedded system by DSP. In [32], the authors have used kernelized SVM for determining the effectiveness of treatment of tuberculosis. They used SVM to check the effectiveness of the drug. Traditional doctors provide solutions to these problems only on their own experience. Multiple real-life problems in the field of medical and signal processing have been solved by SVM. Intra-class validation and intra-class ambiguity in the feature space was tackled and resolved by [33]. The concept of large margin and local estimate classification was used. Most of the time, SVM was applied after feature selection. The concept of a single large sparse autoencoder for feature learning was used in [34] and then SVM was applied for classification.