SVM Kernel Function Research Articles

Computer-Aided Diagnosis System for Automated Detection of Mri Brain Tumors

Detection and classification of brain tumors in manual or traditional way is an area which could be improved by having such automated detection and clarification system for brain tumors. In this paper, enhanced Computer-Aided Diagnosis CAD software system is introduced for brain tumor detection and classification. Total of 229 brain MRI images was taken as dataset for the purpose of this research; those dataset images include 105 normal brain MRI images, and 124 abnormal brain MRI images. Proposed CAD system is specialized for Meningioma brain tumor detection and classification, and the technique could be generalized and implemented for Glioma, and Pituitary brain tumors as well, and the whole system was implemented using MATLAB software. We started by cropping the region of interest (ROI) of dataset images. Then, feature extraction was implemented using first order statistical features, as well as using of some wavelets filters in combination with the former. T-test is used to exclude features of no statistical significance (p-value < 0.05). After that, different types of classifiers were used to separate the normal set from the abnormal one. Note that, we used an iterative approach to by changing features with many runs until we got best performance, where, best accuracy results were gotten with SVM-Kernel Function (Linear), KNN-1, KNN-3, and KNN-5 classifiers. Note also that, we used convolutional neural networks (CNN) from Deep Learning toolbox of MATLAB as a control method to compare, where the images were fed directly to the CNN. The results were evaluated using performance assessment techniques which are Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy, Error Rate, and Area Under the Curve (AUC) of Reciever Operator Characteristic (ROC). With SVM classifier, the best gotten accuracy results were 91 % with CNN classifier, 82% with SVM classifier, and 77 % with KNN classifier. Furthermore, it was very beneficial to find such feature extraction techniques which gave acceptable accuracy results with three different classifiers; this was the case two times as mentioned the study. All proposed CAD system areas was developed and implemented using MATLAB software.

Research on New Word Recognition Based on Weibo Information and SVM

In order to effectively recognize Weibo new words, this paper proposes a Weibo new word recognition method based on Weibo information and SVM. Firstly, positive and negative samples are extracted from the Weibo corpus and the training corpus annotated with part of speech. Next, the word features of these samples are vectorized and trained through support vector machines to obtain support vectors for new word classification. Finally, it is vectorized and inputted into the trained support vector machine classifier to obtain the recognized Weibo new words. By comparing the experimental results, the optimal fusion feature combination and SVM kernel function are obtained.

Phonetic Feature Extraction and Recognition Model in Korean Pronunciation Practice Based on AdaBoost

This paper proposes a phonetic feature extraction and recognition model in Korean pronunciation exercises based on AdaBoot moments. Feature extraction algorithms have a great influence on speech emotion recognition algorithms, among which Mel-frequency Cepstral Coefficients (MFCC) is the most commonly used feature in speech emotion recognition. The feature extraction method and the influence of SVM kernel function and parameter selection on the recognition result are studied, and the existing speech feature extraction algorithms and their respective advantages and disadvantages are analyzed, as well as the influence of different kernel functions, kernel parameters and penalty parameters on the recognition performance. An improved grid optimization method is used to further improve the recognition time of voice information.

Support Vector Machine for Credit System: The Effect of Parameter Optimization and Training Sample

The credit assessment system is an essential part of modern financial institutions, and most of them have adopted different models to perform the task according to their specific needs. Support Vector Machine has been widespread and proved an efficient classifier, especially for relatively small datasets in recent years. When using SVM, data processing, choosing an appropriate kernel function, and tuning parameters can largely affect its performance. The most popular kernel function of SVM is the Radial basis function (RBF), and its main parameters are the regularization parameter, C, and the kernel coefficient, . Our study based on the South German credit dataset demonstrates that parameter optimization and an appropriate ratio of the size of the training dataset to the size of the testing dataset could significantly improve the performance of SVM.

Combined empirical mode decomposition and phase space reconstruction based psychologically stressed and non-stressed state classification from cardiac sound signals

The psychological stress and associated mental health conditions causes high socioeconomic impacts on society and the onset of pandemic worsened the situation making it imperative to timely detect psychological stress. This paper presents a novel framework for using a combination of empirical mode decomposition (EMD) and phase space reconstruction (PSR) analysis to capture non-linear, non-stationary dynamics of cardiac sound signals acquired from fifty-four healthy male adults for detecting psychological stress. The time interval among successive S1 peaks of acquired cardiac sound signals is extracted to obtain Interbeat Interval signal used for decomposition to Intrinsic Mode Functions (IMFs) using EMD technique. Thereafter, the feature vectors namely- largest singular value, smallest singular value from two-dimensional PSRs of IMFs and the mean value of Euclidean distance, standard deviation of Euclidean distance from three-dimensional PSRs are extracted to detect psychologically stressed state. The non-parametric Kruskal-Wallis statistical test is applied to select statistically significant features that are fed to Decision Tree, Naïve Bayes and Support Vector Machine classifiers for classifying stressed and non-stressed state with fivefold cross-validation to make it a reliable system. The average accuracy, sensitivity and specificity achieved is 97.14%, 99.8% and 94% respectively using SVM and Radial Basis Function kernel function. The proposed framework performed better on the dataset in comparison to ratio of low-frequency to high-frequency (LF/HF) power parameter of Electrocardiography signal. The use of easy to acquire, cost-effective cardiac sound signals for detecting psychological stress makes proposed framework feasible for rural healthcare centres of developing economies, homecare and telemedicine.

Homogenous Multiple Classifier System for Software Quality Assessment Based on Support Vector Machine

In today's society, almost all human endeavours depend on software products. Lack of quality software is one of the software industry's most important problems. Hence, it would be beneficial to access the quality of software to improve and enhance software products while increasing customer satisfaction. This paper assesses software product quality using a Support Vector Machine-based ensemble classifier. The ISO/IEC-9126 (International Organization for Standardization 2001) software quality (SQ) framework was adopted in this work. Dimension reduction of the product metric category dataset and the entire PM dataset was conducted using linear discriminant analysis (LDA). SVM kernel functions (linear, quadratic, cubic, fine gaussian, medium gaussian and coarse gaussian) were used to model each classifier. The combinations of the results from the multiple SVMs used AdaBoost, bagging, and random subspace ensemble methods for the assessment of SQ. All three ensemble learning methods performed better than the individual SVM, however, the bagging stood out with an accuracy of 93.0%. Hence, it was adopted in the fusion of the SVM results and classification of SQ into classes. Results from the confusion matrix and receivers’ operating characteristics were greater than 97.99% and confirm significant improvements with an ensemble of homogenous classifiers based on SVM.

Classification of Transgenic Mice by Retinal Imaging Using SVMS.

Alzheimer's disease is the neuro disorder which characterized by means of Amyloid– β (A β) in brain. However, accurate detection of this disease is a challenging task since the pathological issues of brain are complex in identification. In this paper, the changes associated with the retinal imaging for Alzheimer's disease are classified into two classes such as wild-type (WT) and transgenic mice model (TMM). For testing, optical coherence tomography (OCT) images are used to classify into two groups. The classification is implemented by support vector machines with the optimum kernel selection using a genetic algorithm. Among several kernel functions of SVM, the radial basis kernel function provides the better classification result. In order to deal with an effective classification using SVM, texture features of retinal images are extracted and selected. The overall accuracy reached 92% and 91% of precision for the classification of transgenic mice.

Evaluating the Performance of Various SVM Kernel Functions Based on Basic Features Extracted from KDDCUP'99 Dataset by Random Forest Method for Detecting DDoS Attacks

Evaluating the Performance of Various SVM Kernel Functions Based on Basic Features Extracted from KDDCUP'99 Dataset by Random Forest Method for Detecting DDoS Attacks

Identification of COVID-19 from Cough Sounds Using Non-Linear Analysis and Machine Learning

Automatic diagnosis of COVID-19 has an active role in reducing the spread of the disease by minimizing interaction with people. Machine learning models using various signals and images form the basis of automatic diagnosis. This study presents the machine learning based models for detecting COVID-19 infection using ‘Virufy’ dataset containing cough sound signals labeled as COVID-19 and Non-COVID-19. Since the number of COVID positive coughs in the set is less than those of COVID negative, firstly, data balancing was performed with the ADASYN oversampling technique in the study. Then, features were extracted by non-linear analysis of cough sounds using Multifractal Detrended Fluctuation Analysis (MDFA), Lempel–Ziv Complexity (LZC) and entropy measures. Later, the most effective features were selected by ReliefF method. Finally, five machine learning algorithms, namely Support Vector Machine with Radial Basis Function (SVM-RBF), Random Forest (RF), Adaboost, Artificial Neural Network (ANN), k Nearest Neighbor (kNN) were used to identify cough sounds as COVID-19 or Non-COVID19. As a result of the study, the cough sounds of COVID-19 patients and Non-COVID19 subjects were identified with 95.8% classification accuracy thanks to the RBF kernel function of SVM and the selected effective features. With this classifier, 93.1% sensitivity, 98.6% specificity, 98.6% precision, 0.92 kappa statistical values and 93.2% area under the ROC curve were obtained.

Gait based Parkinson’s disease diagnosis and severity rating using multi-class support vector machine

To diagnose Parkinson’s disease (PD) in a clinical setting, generally clinicians utilize several clinical manifestations such as motor impairments and non-motor symptoms and predict the severity level based on unified Parkinson’s disease rating scale (UPDRS). Such a clinical evaluation highly depends on the expertise and experience of the clinicians and results in variation in assessment among clinicians. Hence, to assist the clinicians to diagnose the PD and rate the severity level, we present a gait classification based decision support system using multi-class support vector machine (MCSVM). As the gait alterations are the initial manifestations of PD, we utilize the publicly available vertical ground reaction force (VGRF) dataset and perform the kinematic analysis to extract the spatiotemporal features. To identify the prominent gait biomarkers, this work utilizes a correlation based feature selection approach and employs multi-regression approach to normalize the gait time series data. Moreover, transforming the multi-class classification problem into multiple binary classification problem using one-versus-one (OVO) strategy, the proposed PD severity rating framework tests the performance of four SVM kernel functions for three different walking tests. Experimental results highlight that the quadratic SVM classifier offers an average accuracy of 98.65% and outperforms several other state-of-the-art methods that utilized gait dataset for PD diagnosis.

TAYLOR–MONARCH BUTTERFLY OPTIMIZATION-BASED SUPPORT VECTOR MACHINE FOR ACUTE LYMPHOBLASTIC LEUKEMIA CLASSIFICATION WITH BLOOD SMEAR MICROSCOPIC IMAGES

Acute lymphoblastic leukemia (ALL) is a serious hematological neoplasis that is characterized by the development of immature and abnormal growth of lymphoblasts. However, microscopic examination of bone marrow is the only way to achieve leukemia detection. Various methods are developed for automatic leukemia detection, but these methods are costly and time-consuming. Hence, an effective leukemia detection approach is designed using the proposed Taylor–monarch butterfly optimization-based support vector machine (Taylor–MBO-based SVM). However, the proposed Taylor–MBO is designed by integrating the Taylor series and MBO, respectively. The sparking process is designed to perform the automatic segmentation of blood smear images by estimating optimal threshold values. By extracting the features, such as texture features, statistical, and grid-based features from the segmented smear image, the performance of classification is increased with less training time. The kernel function of SVM is enabled to perform the leukemia classification such that the proposed Taylor–MBO algorithm accomplishes the training process of SVM. However, the proposed Taylor–MBO-based SVM obtained better performance using the metrics, such as accuracy, sensitivity, and specificity, with 94.5751, 95.526, and 94.570%, respectively.

Transformer Incipient fault prediction using Support Vector Machine (SVM)

The power transformer is an important link in the power system. Utilities will face a huge loss if a fault occurs transformer. The outage can cause loss to the industry sector. Transformer incipient fault can be predicted using Dissolved Gas Analysis (DGA) based on gas ratios. The current work is an effort to use SVM to predict transformer incipient fault more precisely. DGA data of various transformer oil samples were collected and analyzed to select the best SVM kernel function and kernel factor to be used and to observe the prediction accuracy.

Identification of pumpkin powdery mildew based on image processing PCA and machine learning

A method based on image processing, principal component analysis (PCA) and machine learning was proposed to identify pumpkin powdery mildew and improve the identification accuracy of pumpkin powdery mildew. The leaf-disease image was processed by the color feature compositing and detection method,in order to segment the lesion more accurately. Then 20 feature values are extracted, including the texture features, color features and morphological features of the segmented lesions, and the 20-dimensional original feature parameters were simplified to 3-dimensional feature parameters via the PCA method. Finally, three different SVM kernel functions are used to construct the classification model to compare the original feature parameters and principal component feature parameters separately. Experiment results show that the effect of illumination angle and intensity can be effectively eliminated after the super red feature calculation, and that the SVM model based on PCA and polynomial kernel function has a better recognition effect on pumpkin powdery mildew. The recognition rate is 97.3%, which is 3.15% higher than that of the traditional original characteristic parameters. Based on this finding, it can be concluded that this machine learning model can achieve accurate lesions identification.

The Application of the Positive Semi-Definite Kernel Space for SVM in Quality Prediction

Background:A transformation toward 4th Generation Industrial Revolution (Industry 4.0) is being led by Germany based on Cyber-Physical System-enabled manufacturing and service innovation. Smart manufacturing is an important feature of Industry 4.0 which uses the networked manufacturing systems for smart production. Current manufacturing systems (5M1E systems) require deeper mining of the data which is generated from manufacturing process.Objective:To map low-dimensional embedding into the input space would meet the requirement of “kernel trick” to solve a problem in feature space. On the other hand, the distance can be calculated more precisely.Methods:In this research, we proposed a positive semi-definite kernel space by using a constant additive method based on a kernel view of ISOMAP. There were 6 steps in the algorithm.Results:The classification precision of KMLSVM was better than SVM in the enterprise data set, in which SVM selected the RBF kernel and optimized its parameters.Conclusion:We adopted the additive constant method in kernel space construction and the positive semi-definite kernel was built. The typical mixed data set of an enterprise was used in simulation. We compared the SVM and KMLSVM in this data set and optimized the SVM kernel function parameters. The simulation results demonstrated the KMLSVM was a better algorithm in mix type data set than SVM.

Extending Capture Range for Piston Error in Segmented Primary Mirror Telescopes Based on Wavelet Support Vector Machine With Improved Particle Swarm Optimization

In the co-phasing techniques applied to the segmented telescope, a Shack-Hartmann wavefront sensor cannot accurately detect the piston error of the segment. Although the phase diversity (PD) algorithm can detect the piston error of each segment, it fails to reconstruct the wavefront quickly, and its dynamic range is small. Other technologies, such as prisms or micro-lens arrays, will significantly increase the complexity and construction cost of the optical system. Moreover, they may also introduce non-common path errors. In this study, we propose an approach to address this challenging problem via curvature sensing. This method uses multi-wavelength to eliminate the influence of $2\pi $ ambiguity and improve the capture range of co-phasing detection. However, curvature sensing is easily influenced by atmospheric seeing. We propose a wavelet support vector machine optimized via particle swarm optimization (PSO-WSVM) method to deal with this problem, and to improve the application scope of curvature sensing. We reshape SVM with a wavelet kernel function, and improve the PSO algorithm. We train the SVM to build a prediction model to distinguish the piston error range of each pair of adjacent segments and surpass $2\pi $ ambiguity. First, we obtain defocused images by means of the convolution technique. Second, we propose a prediction model based on SVM. We select the correlation coefficient between the sampling signal and the template signal at different wavelengths as the input vector, and we choose a wavelet basis function as the kernel function of SVM. Third, we improve the PSO algorithm with the exponential decreasing inertia weight (EDIW) to tune the parameters of SVM. Finally, we perform a simulation experiment on a real optical system model based on the Keck telescope. The results indicate that the performance of this method is better than that of other state-of-the-art SVM-based classifiers, and it works rapidly during the observation.

Comparation Between Linear and Polynomial Kernel Function For Ovarium Cancer Classification

Ovarium cancer is a kind of cancer disease which often attacks a woman. Ovarium cancer consists of benign and malignant type. Based on the data set obtained, this research try to classify the data set with support vector machine algorithm. The principle of SVM is how to identify a hyperplanes with maximal margin. Hyperplanes is a border line between the data. Here, we will used two kernel function in SVM, linear and polynomial kernel function. Next We will compare both of them algorithm to reach the hyperplanes with maximal margin. The hyperplanes then used to predict the class of data testing given. Finally, comparation result will be obtained from confussion matrix and accuration calculation for each kernel function while used to classify the ovarium cancer data set. Accuration result with Linear kernel function is 68.98% for first schema and 66.67% for second schema. Then accuration result with Polynomial Kernel Function is 83.79% for both of schema.

Adaptive image denoising based on support vector machine and wavelet description

Adaptive image denoising method decomposes the original image into a series of basic pattern feature images on the basis of wavelet description and constructs the support vector machine regression function to realize the wavelet description of the original image. The support vector machine method allows the linear expansion of the signal to be expressed as a nonlinear function of the parameters associated with the SVM. Using the radial basis kernel function of SVM, the original image can be extended into a MEXICAN function and a residual trend. This MEXICAN represents a basic image feature pattern. If the residual does not fluctuate, it can also be represented as a characteristic pattern. If the residuals fluctuate significantly, it is treated as a new image and the same decomposition process is repeated until the residuals obtained by the decomposition do not significantly fluctuate. Experimental results show that the proposed method in this paper performs well; especially, it satisfactorily solves the problem of image noise removal. It may provide a new tool and method for image denoising.

WOA based Selection and Parameter Optimization of SVM Kernel Function for Underwater Target Classification

The identification and classification of noise sources in the ocean is a challenging task due to a myriad of impediments introduced by the complicated oceanic environment. The underwater target recognition system essentially has to identify underwater targets of interest that are heavily masked by the oceanic noise. The characteristic acoustic signatures of the underwater targets of interest, are patterned by feature recognition algorithms operating on data captured by hydrophone. In this paper, an SVM based classifier is used to distinguish between 4 classes of acoustic targets and attempt is made towards improving the performance of the classifier by automating the selection of kernel and algorithmic parameters of the underlying classifier through Whale Optimization Algorithm (WOA). Keywords: Underwater target classifier, Support Vector machines, Kernel Function, Whale Optimization Algorithm.

A Comparative Study of SVM Kernel Functions Based on Polynomial Coefficients and V-Transform Coefficients

A Comparative Study of SVM Kernel Functions Based on Polynomial Coefficients and V-Transform Coefficients

Study on Prediction of the Peasants’ Income Based on SVM

China is a big agricultural country, effective prediction of peasants’ income is very important. This study mainly uses the SVM theory to predict the peasants’ income. By analyzing the influence factors of peasants’ income, establishes the index system, that is corresponding relationship of peasants’ income and factors of social influence, According to this index system, designs the prediction method of peasants’ income based on SVM. Bases on the statistical data of social factors and peasants’ income between 1990-2012 in china, to train the SVM model, at the same time, the kernel function and parameters of SVM used were setting and compared. The experimental results show that the accuracy of RBF function is 90.7%, the time is 98ms, has higher accuracy and faster computing speed.