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Ever-growing popularity of smartphones has led to the equivalent growth in their related attacks and vulnerability exploitations. Especially, Android, one of the prominent smartphone operating system has grown its market share exponentially since its release in 2008. It is reported that malware targeting Google's Android platform has increased nearly six-fold in the third quarter of 2012. In this...
Recent years, people pay more attention on their culture and art education. Traditional art paintings as a major part of the culture of every country have been studied to identify the history and culture of a country. There are some applications have achieved to analyze the content or emotion of paintings. However, there are few people making a system that can classify paintings based on the author...
Security of computers and the networks that connect them is increasingly becoming of great significance. As an effect, building effective intrusion detection models with good accuracy and real-time performance are essential. In this paper we propose a new data mining based technique for intrusion detection using Cost-sensitive classification and Support Vector Machines. We introduced an algorithm...
Variety of Web Service discovery algorithms had been investigated for improvement of the retrieval quality. Combining the several algorithms according to their strong points, is proposed as enabling more refined discovery consequence. Now, many researches as OWL-Mx are sited as examples, had already shown the method that join together and conclude for the specific domain. However, there are no way...
Since system identification is closely related to control theory it is quite convenient that common tools of control may prove to be useful for identification as well. Semidefinite programming is now considered as a standard tool in control theory, however its applications for identification purposes are rare. This paper shows how L1 identification of the ARX model structure can be formulated as a...
Support Vector Machine (SVM) is a useful technique for data classification with successful applications in different fields of bioinformatics, image segmentation, data mining, etc. A key problem of these methods is how to choose an optimal kernel and how to optimize its parameters in the learning process of SVM. The objective of this study is to propose a Genetic Algorithm approach for parameter optimization...
Automatic document classification is becoming an important research field with the rapid increase of electronic documents. The main purpose of this research is to construct an accurate document classifier based on support vector machines (SVM), which is known as the state of the art algorithm for document classification. The rough null space (RNS) based approach is also known as a good linear approach...
Since reform and opening up to the outside world, rapid industrialization and urbanization in China have played an important role in the increase of energy consumption. China became the second energy consumption country all over the world in 2008. As a big country in energy consumption, forecasting energy consumption is one of the most important tools for energy policy setting. Although there are...
Against the low efficiency of training on large-scale SVM, a reduction approach is proposed. This paper presents a new samples reduction method, called bistratal reduction method (BRM). BRM has two levels. The first level is coarse-grained reduction. It deletes the redundant clusters with KDC reduction. The second level is fine-grained reduction. It picks out the support vectors from the clusters...
In safety engineering, lower and upper explosion limits are the important indices to evaluate the safety of multi-component explosive gas mixture such as hydrogen and methane. There is a nonlinear dependence of explosion limits on the composition (components and theirs concentration) of multi-component explosive gas mixture. Therefore, a least square support vector regression (LS-SVR) model was proposed...
The forecast of grain production is not only an important resource for establishing agriculture policy but also is of great significance to ensure our nation's food security based on studying the change rule of our country grain production system. In view of the fact that the complexity and incomplete information of grain production system, the primary factors influencing the grain production is decided...
This paper uses Lib-SVM algorithm of RBF kernel and linear kernel to develop a model for detecting regulating-profits financial statement fraud with the data of 112 Chinese listed companies. It turns out that the prediction accuracy of Lib-SVM algorithm for RBF kernel function model is 86.667%, the overall accuracy is 87.5%. And the prediction accuracy of the Lib-SVM linear kernel function model is...
The food problem is a global problem and many countries in the world recognize the food issue that is related to a global safety problem of this century. In order to overcome some deficiencies of traditional risk pre-warning model of food in these aspects of hypotheses, sample size and generalization capabilities, etc., this article combines support vector machine classification(SVM)theory with pre-warning...
This article directs at coal manufacturing cost factors applying support vector machine (SVM) theory, it establishes forecasting model of coal manufacturing cost using wavelet neural network after reducting attribute of influence factors, in order to forecast the coal manufacturing cost effectively.
It is deficiency to use accuracy as a measurement to evaluate model classifying ability. This paper proposes a measurement method which uses the area under the ROC curve, or AUC value, to evaluate the performance of the model. Furthermore, applying cross validation and grid-search methods, through designed algorithms, to build an optimization of support vector machines medical prediction model. The...
Support vector machines (SVM) is a widely used method which can treat problems involving small sample, devilish learning, and high dimension. The current paper conduct a multivariate SVM in a total-factor production framework, and the GDP per capita, capital stock and labor are taken as the independent variables and the energy consumption is the dependent variable. The Gaussian radial basis function...
A real wind power generation system is given in this paper. SVM control strategy and vector control is applied for generator side converter and doubly fed induction generator respectively. First the mathematical models of the wind turbine rotor, drive train, generator side converter are described. Then the control strategy of generator side converter system is given in detail. Finally the simulation...
In this paper, an explainable prediction model is established to select the optimum features and parameters, then the selected optimum parameters are applied to predicting potential customer churning in one foreign telecom company, discovering that the model not only achieves a desirable prediction but is also explainable through selected features, and that a balanced relation between accuracy and...
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