Sigmoid Kernel Function Research Articles

Leak diagnosis of pipeline based on empirical mode decomposition and support vector machine

The pipeline is used as a medium of transportation in global gas and oil industries, providing the most efficient, convenient and transportation method for natural gas and oil from downstream to upstream production of the economical mode of the power station, refineries, and domestic needs. However, the pipeline leakages become a major concern as their failure may contribute to operational and economic loss as well as environmental pollution. This paper proposed a system to detect pipe fault at different locations. Empirical Mode Decomposition (EMD) was applied for feature extraction using energy and kurtosis. The one-against-one (OAO) and one-against-all (OAA) multiclass SVM with radial basis function (RBF), polynomial and sigmoid kernel functions were implemented in order to classify the multiple fault locations from the extracted features. RBF kernel function recorded the highest classification accuracy for both OAO and OAA approaches with 97.77% and 96.29%, respectively, followed by slightly reduced accuracy for sigmoid whereas significantly low accuracy for the polynomial kernel. The outputs were further analysed to justify the performance of the classifiers. From all the cases, it was observed that OAO-SVM with RBF kernel performed the best for pipe fault diagnosis.

Destek Vektör Makineleri İçin Dengesiz Veri Yöntemlerinin Karşılaştırılması

Objective: The biggest problem we encounter when applying classification algorithms is that the classification categories are not equally distributed. Eight different re-sampling methods were used for balancing the dataset. Material and Methods: Support Vector Machines (SVM) were used to compare these methods. SVM are supervised learning models with associated learning algorithms that analyze data used for categorization and regression analysis. The main function of the algorithm is to find the best line, or hyperplane, which divides the data into two classes. SVM is basically a linear classifier that classifies linearly separable data, but, in general, the feature vectors might not be linearly separable. To overcome this issue, what is now called kernel trick was used. Results: This article presents a comparative study of different kernel functions (linear, radial, and sigmoid) for unbalanced data. The myocardial infarction dataset which was taken from the Github were classified by 10-fold cross validation to increase the performance. Accuracy, sensitivity, specificity, precision, g-mean and F score were used for comparing the methods. The analysis was carried out by R software. Conclusion: As a conclusion, the results of performance metrics for the original data increased through random over sampling examples re-sampling methods for linear and sigmoid kernel functions. Smote method performed better in the case of radial kernel. In general, the unbalance in the data in classification algorithms gives biased results and this should be eliminated.

Prediction of Formation Quality of Inconel 625 Clads Using Support Vector Regression

The process parameters of pulsed tungsten inert gas (PTIG) have a significant influence on the formation quality, mechanical properties and corrosion resistance of the weld overlay. The PTIG was utilized to deposit Inconel 625 clads with various combinations of the process parameters, which were determined by the central composite design (CCD) method. Based on the experimental results, the relationship between process parameters of PTIG and formation quality of the Inconel 625 clads was established using support vector regression (SVR) with different kernel functions, including polynomial kernel function, radial basis function (RBF) kernel function, and sigmoid kernel function. The results indicate that the kernel functions have a great influence on the prediction of height, width and dilution. The models with RBF kernel function feature the best goodness of fitting and the most accurate against the other SVR models for estimating the height and the dilution. However, the model with polynomial kernel function is superior to the other SVR models for predicting the width. Meanwhile, the prediction performance of the SVR models was compared with the general regression analysis. The results demonstrate that the optimized SVR model is much better than the general regression model in the prediction performance.

Early auxiliary screening of cerebral infarction based on lacrimal Raman spectroscopy and SVM algorithm

In this study, we propose a method of tear Raman spectroscopy combined with support vector machine (SVM) to accurately, quickly and economically diagnose cerebral infarction. Tear samples from 15 patients with cerebral infarction and 47 healthy volunteers were assessed, and their Raman spectra were compared. Principal component analysis (PCA) was used for feature extraction. Then, support vector machine (SVM) was used to establish an efficient diagnostic model. To improve the accuracy of the SVM diagnostic model, we used the linear, polynomial, RBF and sigmoid kernel functions of SVM for discrimination and evaluation, and the best results were obtained when the RBF kernel was used, with an accuracy of 96.77 %, a specificity of 100 %, and a sensitivity of 86.67 %. Our results show that the combination of tear Raman spectroscopy and the SVM algorithm has great potential for screening cerebral infarction. This technology is expected to aid in the development of a portable, economic and high accuracy early-assisted screening device for cerebral infarction.

A Rapid Diagnostic Grading System for Cucumber Downy Mildew Based on Visible Light - Hyperspectral Imaging System

Downy mildew, a kind of cucumber disease with a high spread rate and harmfulness that is more common in the world, has a great influence on the yield of cucumbers. The rapid identification of its symptoms and the rapid classification of the post-disease characters are of great significance to the rapid diagnosis of cucumber frost mold and the proper treatment of medicine after the disease. In order to quickly and accurately classify the occurrence and the degree of cucumber downy mildew, a rapid diagnosis and classification method of cucumber downy mildew based on visible light - high spectral imaging technology was proposed in this paper. In addition, the stepwise regression method and PCA were used to reduce and extract the feature information of sensitive bands. Two kinds of acquired feature information are used as the input of the model to construct the disease degree classification detection model of the SVM classification model. The model based on the stepwise regression method is used to classify and identify downy mildew and normal leaves. In this model, the accuracy of the Sigmoid kernel function classification test is the highest, reaching 95.00%, and the recognition rate of different degrees of cucumber downy mildew disease leaves as high as 93.88, which has a high classification detection accuracy. The results show that the rapid diagnosis and classification of cucumber downy mildew can be realized by using the visible light spectral imaging system combined with the automatic classification model of SVM, which provides a new method and reference for solving the problem of cucumber downy mildew in time.

Intelligent Setting Method of Reagent Dosage Based on Time Series Froth Image in Zinc Flotation Process

It is well known that the change of the reagent dosage during the flotation process will cause the froth image to change continuously with time. Therefore, an intelligent setting method based on the time series froth image in the zinc flotation process is proposed. Firstly, the sigmoid kernel function is used to estimate the cumulative distribution function of bubble size, and the cumulative distribution function shape is characterized by sigmoid kernel function parameters. Since the reagent will affect the froth image over a period of time, the time series of bubble size cumulative distribution function is processed by the ELMo model and the dynamic feature vectors are output. Finally, XGBoost is used to establish the nonlinear relationship modeling between reagent dosage and dynamic feature vectors. Industrial experiments have proved the effectiveness of the proposed method.

Predicting jet-grout column diameter to mitigate the environmental impact using an artificial intelligence algorithm

This paper describes an approach for predicting the diameter of a jet-grout column using the support vector regression (SVR) technique, which is regarded as a novel learning machine based upon recent advances in statistical theory, in which the combined effects of the construction (construction methods and jetting parameters) and soil properties (soil type and shearing resistance) are considered. Four different kernel functions, namely, a linear kernel function, polynomial kernel function, radial basis kernel function, and sigmoid kernel function, are integrated into the SVR technique. A large amount of field measured data on the diameter of jet-grout column are retrieved from the published literature for training and testing purposes. The results indicate that the SVR technique with a radial basis kernel function provides predictions closest to the measured results, whereas the prepared design charts enable the ability to significantly widen the application of the proposed approach to the areas of ground improvement and environmental protection.

On the combination of kernel principal component analysis and neural networks for process indirect control

ABSTRACT A new adaptive kernel principal component analysis (KPCA) for non-linear discrete system control is proposed. The proposed approach can be treated as a new proposition for data pre-processing techniques. Indeed, the input vector of neural network controller is pre-processed by the KPCA method. Then, the obtained reduced neural network controller is applied in the indirect adaptive control. The influence of the input data pre-processing on the accuracy of neural network controller results is discussed by using numerical examples of the cases of time-varying parameters of single-input single-output non-linear discrete system and multi-input multi-output system. It is concluded that, using the KPCA method, a significant reduction in the control error and the identification error is obtained. The lowest mean squared error and mean absolute error are shown that the KPCA neural network with the sigmoid kernel function is the best.

Industrial Security Solution for Virtual Reality

In order to protect industrial safety, improve the operation stability of the industrial control system, conduct the response measures for network environment attacked by the external world, and realize simulation in virtual reality environment, in this study, class and sample weighted C-support vector machine (CSWC-SVM) algorithm is first proposed using SVM. Then, the intrusion detection model of industrial control network is built based on the CSWC-SVM algorithm. Finally, KDD CUP 1999 data are introduced to carry out simulation experiments on the algorithm model constructed in this study in the virtual reality simulation environment. The results show when the penalty factor of the polynomial kernel function, radial basis kernel function, and sigmoid kernel function is 104, the average number of support vectors is 45, 46, and 37, respectively; the average training time are about 0.43, 0.45, and 0.47 s, and the average test time is about 9.7, 9.9, and 10.2 s, respectively; the average recognition accuracy is about 85.7%, 86.2%, and 86.7%, and the false positive rate is 3.8%, 2.8%, and 2.3%, respectively; the accuracy of the CSWC-SVM algorithm in different sample sizes (1000-6000) can be kept above 90%. The operation error rate of the CSWC-SVM algorithm is lower than that of C-SVM, C-SVM, and RS-SVM algorithms under different validation data sets. After dimension reduction, the classification accuracy of the CSWC-SVM algorithm is higher than that of C-SVM and WC-SVM algorithms. The weight value increases from 0 to 200, and the number of model errors on 1000, 2000, and 3000 pieces of data decreases significantly. When the weight value is 200, the number of errors drops to 0, and the classification accuracy reaches 100%. In a word, the CSWC-SVM algorithm constructed in this study performs well in response to the attack of the industrial control system in the virtual reality simulation environment, which provides practical significance for the application of virtual reality in industrial monitoring.

Performance of Support Vector Machine Kernels (SVM-K) on Breast Cancer (BC) Dataset

Breast cancer (BC) most diagnosed invasive disorder and important cause of casualty for women worldwide. Indian contest BC most commonly spread disease among females. This problem is more alarming to economically developing country like India. Government of India made a lot of effort to make aware the women of the country, but despite of availability of diagnostic tool, prediction of disease in real situation is still a puzzle for researchers. Timely detection and categorization of BC using the evolving techniques like Machine Learning (ML) can show a significant role in BC identification and this could be a preventive policy which effectively reduces the risk of BC patients. Although there are four Kernels in ML, are widely in use but their performance varies with the kind of data available. In this study we, apply four different Kernels such as Linear Kernel (LK), Polynomial Kernel (PK), Sigmoid Kernel (SK) and Radial Basis Function Kernel (RBFK) on BC dataset. We estimated the performance of Support Vector Machine Kernels (SVM-K) on BC dataset .The basic idea is to check the exactness of SVM-K to classify WBCD in terms of effectiveness with respect to accuracy, runtime, specificity and precision. The investigations outcome displays that RBFK provides greater accuracy with minimal errors

Mobile Cancer Prophecy System to Assist Patients: Big Data Analysis and Design

The growth of cancer in India is growing hastily in recent years. Efficient monitoring and medication procedures are needed in high demand. Recent research states diagnose of cancer during its early break through will prevent mortality. The evolution of smart mobile devices paves its mutual focus in healthcare sectors. In this paper, an Intellectual model of disease diagnosis using the advantage of smart mobile devices has been proposed. This mobile based cancer diagnosis model uses a cloud environment for disease prediction and analysis. The Principal Component Analysis (PCA) technique is utilized to confiscate the superfluous features and choose the most appropriate features. Using the optimized features, cancer disease classification is accomplished using Support Vector Machines with sigmoid kernel function. SVM classifies the patients as normal and abnormal and the evaluated results are conveyed to the patients as well as the respective medical practitioners. The accuracy achieved through proposed model is satisfiable in comparison with other existing methods. Proposed Model incorporates with big data technologies to address the current issues of cancer system.

Spatial prediction of landslide susceptibility in Taleghan basin, Iran

Identifying landslide-susceptible zones is warranted to prevent and mitigate associated hazards in mountainous regions, where a landslide is a destructive type of erosion. A landslide susceptibility map was developed for the Taleghan basin based on frequency ratio (FR), logistic regression (LR), maximum entropy (MaxEnt), and support vector machine (SVM) with radial base (RBF), sigmoid (SIG), linear (LN), and polynomial (PL) kernel functions. To this end, an inventory map with 166 landslide locations was prepared and partitioned into 70% and 30% to train and validate the models, respectively. Subsequently, the models were designed based on 13 factors including elevation, slope degree, slope aspect, distance to stream, Stream Power Index, Topographic Wetness Index, Stream Transport Index, distance to fault, lithology, soil texture, land use, distance to road and precipitation. The performance of the methods was assessed using the area under the receiver operating characteristic curve, the Seed Cell Area Index (SCAI), the precision index (P). Moreover, statistical measures including sensitivity, specificity, and accuracy were calculated. Friedman test was also applied to confirm significant statistical differences among the seven models employed in this research. The validation results showed that MaxEnt had the maximum area under the curve (0.812). The results obtained using the models LR, FR, PL-SVM, SIG-SVM, LN-SVM, and RBF-SVM were 0.807, 0.732, 0.679, 0.663, 0.643 and 0.660, respectively. The obtained P index showed the better performance of MaxEnt and LR models. Moreover, the trend of changes in the SCAI values, from low- to high-susceptibility, indicated that the MaxEnt and LR models had the best performance. Decision makers can effectively use the findings of the present study to mitigate the financial and human costs resulting from the landslides.

Assimilation of Standard Regularizer Contextual Model and Composite Kernel with Fuzzy-based Noise Classifier

The paper assay the effect of assimilating smoothness prior contextual model and composite kernel function with fuzzy based noise classifier using remote sensing data. The concept of the composite kernel has been taken by fusing two kernels together to improve the classification accuracy. Gaussian and Sigmoid kernel functions have opted for kernel composition. As a contextual model, Markov Random Field (MRF) Standard regularization model (smoothness prior) has been studied with the composite kernel-based Noise Classifier. Comparative analysis of new classifier with the conventional construes increase in overall accuracy.

Estimation of forest development stage and crown closure using different classification methods and satellite images: A case study from Turkey

The objective of this study is to estimate stand development stages (SDS) and stand crown closures (SCC) of forest using different classification methods (maximum likelihood, support vector machine: linear, polynomial, radial and sigmoid kernel functions and artificial neural network) based on satellite imagery of different resolution (Landsat 7 ETM+ and IKONOS). The results showed that SDS and SCC were estimated with Landsat 7 ETM+ image using the artificial neural network with a 0.83 and 0.78 kappa statistic value, and 92.57 and 89.77% overall accuracy assessments, respectively. On the other hand, SDS and SCC were predicted with IKONOS image using support vector machine (polynomial) method with a 0.94 and 0.88 kappa statistic value, and 95.95 and 91.17% overall accuracy assessments, respectively. Our results demonstrated that IKONOS satellite image and support vector machine (polynomial) method produced a better estimation of SDS and SCC as compared to Landsat 7 ETM+ and other supervised classification methods used in this study.

Estimating forest carbon fluxes using four different data-driven techniques based on long-term eddy covariance measurements: Model comparison and evaluation

With the recent availability of large amounts of data from the global flux towers across different terrestrial ecosystems based on the eddy covariance technique, the use of data-driven techniques has been viable. In this study, two advanced techniques, namely adaptive neuro-fuzzy inference system (ANFIS) and extreme learning machine (ELM), were developed and investigated for their viability in estimating daily carbon fluxes at the ecosystem level. All the data used in this study were based upon the long-term chronosequence observations derived from the flux towers in eight forest ecosystems. Both ANFIS and ELM methods were further compared with the most widely used artificial neural network (ANN) and support vector machine (SVM) methods. Moreover, we also focused on probing into the effects of internal parameters on their corresponding approaches. Our estimates showed that most variation in each carbon flux could be effectively explained by the developed models at almost all the sites. Moreover, the forecasting accuracy of each method was strongly dependent upon their respective internal algorithms. The best training function for ANN model can be acquired through the trial and error procedure. The SVM model with the radial basis kernel function performed considerably better than the SVM models with the polynomial and sigmoid kernel functions. The hybrid ELM models achieved similar predictive accuracy for the three fluxes and were consistently superior to the original ELM models with different transfer functions. In most instances, both the subtractive clustering and fuzzy c-means algorithms for the ANFIS models outperformed the most popular grid partitioning algorithm. It was demonstrated that the newly proposed ELM and ANFIS models were able to produce comparable estimates to the ANN and SVM models for forecasting terrestrial carbon fluxes.

A self-adaptive frequency selection common spatial pattern and least squares twin support vector machine for motor imagery electroencephalography recognition

Motor imagery brain-computer interface (BCI) systems require accurate and fast recognition of brain activity patterns for reliable communication and interaction. Achieving this accuracy is still a challenge because of the low signal-to-noise ratio in electroencephalography signals and high variability of sensorimotor rhythms. To address this need, we proposed a novel scheme that combined a frequency band selection common spatial pattern algorithm and a particle swarm optimization least squares twin support vector machine classifier for recognition of motor imagery patterns. We used self-adaptive artifact removal and the common spatial pattern method to obtain the most distinguishable features. To improve the classification results, we investigated linear, polynomial, sigmoid, wavelet and gaussian kernel functions for the nonlinear least squares twin support vector machine classifier. Particle swarm optimization, chaotic particle swarm optimization, a genetic algorithm and a quantum genetic algorithm were compared and used to tune the hyper-parameters for the classifier. To evaluate our proposed method, we used BCI Competition IV data sets 2A and 2B. Experimental results showed that for our method, the average recognition accuracy of data set 2A is increased by 6.10%, 6.71%, 3.87%, 4.01%, 2.55% and 4.86% compared with the results obtained by regularization projection twin support vector machine, twin support vector machine, support vector machine, linear discriminant analysis, back propagation and probabilistic neural network, respectively. Using data set 2B, the average recognition accuracy achieved by our method was greater by 4.73%, 5.46%, 4.45%, 4.10%, 8.62% and 4.27%, respectively. The standard deviation of the accuracy values of the two data sets decreased. Furthermore, compared with the traditional support vector machines, our proposed method achieved a faster central processing unit running time for training classifiers.

An intelligent method of cancer prediction based on mobile cloud computing

In recent years, mobile medical services have gradually become the common focus of the communications and healthcare industries. People can get medical information and services efficiently and conveniently anytime, anywhere. However, due to the limitations of the mobile terminal’s own computing and storage capabilities, mobile therapy is greatly challenged. Therefore, we propose a mobile medical health system based on cloud computing. Firstly, principal component analysis was used to obtain representative features. Then, a simplified feature subset was applied to support vector machine (SVM) based on Sigmoid kernel function. The data set was categorized by SVM as cancer patient and normal object experimental results show that the method is improved in accuracy, sensitivity, and specificity.

Estimating shear stress in a rectangular channel with rough boundaries using an optimized SVM method

The accurate simulation of wall and bed shear stresses in rectangular channels is one of the most important topics in hydraulic engineering. In this study, the support vector machine (SVM) soft computing technique is utilized to simulate the shear stress in a rectangular channel with rough boundaries. In the modeling procedure, eight different SVM models with linear, sigmoid, polynomial, multi-quadratic, Gaussian, rational quadratic, exponential and Laplacian kernel functions are examined. Finally, the different input combinations are assessed to introduce the most accurate SVM models in wall and bed shear stress prediction, whose output programs are also presented. The results are compared with experimental data and other researchers’ equations, and it is found that the SVM models with RMSE of 0.0447 and 0.0566 in wall and bed stress prediction, respectively, outperform previous equations.

SVM 기반 실리콘 웨이퍼 마이크로크랙의 분류성능 분석

In this paper, the classification rate of micro-cracks in silicon wafers was improved using a SVM. In case Ι, we investigated how feature data of micro-cracks and SVM parameters affect a classification rate. As a result, weighting vector and bias did not affect the classification rate, which was improved in case of high cost and sigmoid kernel function. Case II was performed using a more high quality image than that in case I. It was identified that learning data and input data had a large effect on the classification rate. Finally, images from cases I and II and another illumination system were used in case III. In spite of different condition images, good classification rates was achieved. Critical points for micro-crack classification improvement are SVM parameters, kernel function, clustered feature data, and experimental conditions. In the future, excellent results could be obtained through SVM parameter tuning and clustered feature data.

Mixed Pixel Wise Characterization Based on HDP-HMM and Hyperspectral Image Shape Detection using Hybrid Canny Edge Detection and WPDF

Hyper Spectral Imaging (HSI) gathers and processes information from across the electromagnetic spectrum. The information enclosed in hyperspectral data permits the characterization, recognition and classification of the land-covers with enhanced accuracy and robustness. On the other hand, quite a lot of vital complications must be considered during the classification process of hyperspectral data, among which the maximum quantity of spectral channels, the spatial unevenness of the spectral signature, shape discovery of the images and the value of data. Above all, the maximum quantity of spectral channels and low number of labeled training samples pose the setback of the curse of dimensionality and, accordingly, result in the possibility of overfitting the training data. With the aim of solving all these complications, in this study presented the framework of Support Vector Machine (SVM) together with Fuzzy Sigmoid Kernel Function (SVM-FSK) in the circumstance of HSI classification and analyzing their features in the hyperspectral domain. A Kernel Fisher Discriminant Analysis (KFDA) model is employed for the purpose of dimensionality reduction of HSI. The KFDA dimensionality reduction scheme depends on the selection of the kernel in a higher-dimensional HSI feature space. In order to enhance the gradient level of spatial information, employed Improved Empirical Mode Decomposition (IEMD) with Gaussian Firefly Algorithm (GFA) (IEMD-GFA) to boost the mixed pixel wise SVM-FSK classification accuracy. During the process of IEMD scheme, the identifiable of Intrinsic Mode Functions (IMFs) of spectral band, weight values of IMFs are computed with the help of GFA. In order to identify the shape of HSI, novel hybrid scheme depending on the canny operator and fuzzy entropy theory is formulated. This scheme computes the fuzzy entropy of gradients from an image to make a decision on the threshold for the canny operator. For the purpose of detecting the edges and to discover the shape of the object Weibull Probability Density Function (WPDF) scheme is used. The obtained both spectral and spatial pixels are classified using SVM-FSK and estimated by using Hierarchical Dirichlet Process (HDP)-Hidden Markov Model (HMM). The proposed SVM-FSK is assessed with hyperspectral AVIRIS Indian Pine dataset. It shows that the proposed dimensionality reduction with SVM-FSK classification shows improved classification accuracy in terms of parameters like overall accuracy, standard deviation and mean.