## 4 The Neural Network Configuration
### Framework Model of Deep Learning (DL) and Support Vector Machine (SVM)  
The basic framework of ANN requires three layers (Input, Hidden, and Output) [67], as shown in Figure 6. The input layer is the first layer where data/features are obtained, and some standardization techniques are used to restrict inputs to a certain set. The structured inputs make the neural network work easier, resulting in better accuracy. Depending on the network application, the hidden layer (intermediate or invisible) may be a collection of layers. Such layers are responsible for identifying the pattern of a process or device. Most operations of the neural network are carried out in these layers. The output layer also contains neurons representing the outputs of the final network generated from the previous layers of neuron processing. The basics of ANN's composition involve neuronal interconnection and layer formation. Therefore, they may categorize their temperament into four different types. Such types include feed-forward single-layer, mesh, recurrent, and feed-forward multi-layer networks. DL is a class of machine learning algorithms that slowly extract higher-level features from the raw input using multiple layers. For example, lower layers can identify edges in image processing, whereas higher layers can identify human-relevant concepts, such as digits, letters, or faces [56]. The term 'DL' was introduced by [PERSON] in 1986 [68; 69], then followed by [PERSON] and his colleagues in 2000 in the form of Boolean threshold neurons [70; 71]. The hidden layers (see Figure 7) perform complex mathematical computations based on the number of inputs. The disadvantages of the DL model include the requirements of a large dataset and large amounts of computational power in both training and testing [72]. The concept of SVM was originated and developed by [PERSON] and [PERSON] [75] to introduce a classifier derived from the theory of statistical learning. It has since been shown to be very robust, and also has been used as an intuitive model representation to detect outliers. SVM is a supervised technique in machine learning used for problems of classification, as well as regression. Even with few examples, SVM performs well and has good accuracy. Because of these benefits, SVM differs from other techniques of machine learning [75; 76; 77]. In addition, the following order prov  
Figure 6: The basic ANN layer with interconnected nodes (reproduced from [73]). Figure 7: The framework of DL model (reproduced from [74]). of the principles of SVM [78]. SVM's goal is to find a function \(f\) that has a maximum \(\varepsilon\) deviation from the actual targets \(x_{n}\) for all the training data, and is as flat as possible. The functions \(f\) can be represented by the following equation for the training data:  
\[f(x)=\left\langle\omega,x\right\rangle+b \tag{1}\]  
where \(\omega<X\) and \(b<R\); \(<\),\(>\) refers to the dot product in \(X\); \(\omega\) is the weight vector; \(b\) is the scalar threshold. SVM determines the regression function in accordance with statistical theory by minimizing an objective function. The regression function parameters \(x\) and \(b\) are calculated by minimizing the regularized function of risk as follows:  
\[\frac{1}{2}\parallel\omega\parallel\dot{\Leftarrow}+C\sum_{i=1}^{n}(\xi_{i} +\xi_{i}^{*}) \tag{2}\]  
Subject to,  
\[\left\{ \begin{array}{c}y_{i}-\left\langle\omega,\varphi(x)\right\rangle-b \ \leq\ \varepsilon+\xi_{i}\\ \left\langle\omega,\varphi(x)\right\rangle+b-y_{i}\ \leq\ \varepsilon+\xi_{i}^{*}\\ \xi_{i}^{*},\ \xi_{i}^{*}\ \geq 0,\ i=1,2\ldots n\end{array} \right\} \tag{3}\]  
where \(C\) is the pre-specified SVM tolerance parameter, \(\xi_{i}\) and \(\xi_{i}^{*}\) are slack variables in determining the degree to which data points will be penalized if the error is larger than precision parameter \(\varepsilon\). \(\varepsilon\) is the insensitive loss function. The SVM training algorithm will rely only on the data in high-dimensional space via dot products.