## 2 Related Work
### Preliminary
RBF Kernel has been applied in different field of study such as detection of network intrusion in network security [28], enzyme discrimination in acidic and alkaline compound composition in biomedical engineering [29], emotion recognition from geometric facial features in digital image processing [30], stock market index predictions in the financial market [31], battery management system for optimized energy utility in electronic engineering [32], short-term wind power prediction in renewable energy [33], predicting traffic flow in smart city [34], load sharing and voltage compensation of micro-grids in electricity power distribution in electricity management [35] and data mining for regions susceptible landslide in China in the field of geographical information systems [36]. * Sigmoid Kernel \(F(a,b)=tanh(ca^{T}b+h)\), also known as Hyperbolic Tangent Similarity Function, transits from zero and moves in the direction of the \(x\) solution, as long as that data is sufficient in the direction of \(x\). However, far in the direction of greater values, \(x\) gets high similarity values [14]. Recent studies have shown the application of the following fields of research, such as protein sequence classification in Biomedical research [37], network anomalies Detection for intelligent power substation [38], landmark recognition in digital image processing [39] and facial expressions recognition for videos [40]. Choosing the correct kernel type is a non-trivial task and may depend on specific tasks at hand. No matter which kernel is chosen, one needs to turn the kernel parameter to get good performance of its classifier [41]. A popular parameter tuning technique includes K-Fold Cross validation [42]. K-Fold Cross-Validation is used to evaluate the performance of the machine learning algorithm. Cross-Validation gives the most mileage on the training data and performance metric are averaged across K-Fold [17]. Advantages of using SVMs include [43]: They are effective in high dimension spaces [18]; SVMs use the subset of training points in the decision function on support vector; it is memory efficient; different kernel functions can be specified for various decision functions; and kernel functions can be added together to achieve even more complex hyperplane. SVMs also have some drawbacks such as, if the number of features is greater than the number of samples, the algorithm is likely to give poor performance [44]. SVMs also do not directly provide probability estimates, these are calculated using expensive techniques such as the K-Fold Cross Validation. SVM algorithm has numerous application for example: medical imaging, regressional modeling, image interpolation, medical classification tasks, financial industry time series prediction, financial analysis, application in neural networks for coding theory and practice, faulty diagnosis, page ranking and benchmarking, and objection recognition [45]. Recent studies have shown SVM is not only a classification technique but also an algorithm that can detect fault and anomalies [2], identify outliers [46], estimate and predict link quality [47; 48], be used as an energy efficient routing method [43], and monitor and detect structural damage [42]. [PERSON] et al. [49] applied a data-driven hyperparameter optimization technique to detect anomaly in sensor networks using one-class support vector machines wth radial bias functions kernelized. [PERSON] et al. [42] used a three optimization-algorithm grid search, partial swarm optimization and Gaussian kernel function parameters for damage detection on architectural structures. When the number of features are too large SVM performs poorly. [PERSON] and [PERSON] [44] proposed an approach based on iteratively adjusting a bound on the \(l_{1}\)-norm of the classifier vector to force the number of selected features to converge towards the desired maximum limit.