The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
B-cell epitope is the small portion of antigen surface that is identified by antibodies. Epitope prediction is the task of identification of antigen surface in one of the classes of epitopes and non-epitopes. The prediction of B-cell epitopes is affected by different scales (features) of amino acid samples, such as hydrophobicity, polarity and flexibility, and so, it is necessary to utilize an appropriate...
Petroleum and its products are complex mixture, and how to precise analysis its components is an important part of the oil industry. In this paper, we proposed a components forecasting methods for gasoline octane value prediction based on independent component analysis (ICA) and support vector machine (SVM). By evaluating the accuracy of the models with two feature optimization methods(principal component...
Since the maintainability data of equipment is less, a time sequence characteristic maintainability forecast method based on structural risk minimization principle is being present. Take existing maintainability information as the eigenvalue, the forecasts model is proposed. Screen the best Kernel variable according as the minimum mean square error. Compared with the actual value, the prediction results...
Protein phosphorylation, one of the most important types of post-translational modifications (PTMs), participates in multiple cellular processes. Accurate prediction on phosphorylaiton sites has become necessary, as many modifications are related to diseases and used as biomarkers. Currently a number of computational approaches only establish prediction models on sequence information. In this study,...
The reliability of wave prediction is a crucial issue in coastal, harbor and ocean engineering. Support vector machine (SVM) is an appropriate and suitable method for significant wave height (Hs) prediction due to its best versatility, robustness, and effectiveness. In this present work, only significant wave height (Hs) of previous time steps were used as predictors during the period 01-01-2004 to...
Machine learning (ML) techniques such as artificial neural network (ANN) and support vector machine (SVM) have been increasingly used to predict harmful algal blooms (HABs). In this paper, we use the biweekly data in Tolo Harbour, Hong Kong, and choose several machine learning methods to develop prediction models of algal blooms. Three different kinds of models are designed based on back-propagation...
An update algorithm of least squares support vector machine (LSSVM) is proposed to tackle the time-varying characteristics of the real industrial process. The process variations are concluded to two categories, and accordingly the samples adding and samples replacement are proposed to update the initial LSSVM model incrementally. Then the LSSVM model with proposed updating measures is applied in the...
Aiming at increasing the precision of tunnel settlement prediction, a modified support vector machine (SVM) based on the dynamic on-line sliding window (Dolsw) technique is proposed. In the prediction model, the historically observational settlement data act as the learning samples. The nonlinear relationship between settlement data and influencing variables is established on the basis of on-line...
Long-term (multi-step-ahead) time series prediction is a much more challenging task comparing to the short-term (one-step-ahead) time series prediction. This is due to the increasing uncertainty and the lack of knowledge about the future trend. In this paper, we propose a multi-model integration strategy to 1) generate predicted values using multiple predictive models; and then 2) integrate the predicted...
This paper deals with modelling on the nitrogen oxides (NOx) emission of a 600MW coal-fired boiler using artificial neural network (ANN), least squares support vector machine (LSSVM) and partial least squares (PLS) methods based on the experimental data. Some comparisons on the prediction accuracy, time consuming and some other aspects are also given. Two simulation cases are investigated to make...
The support vector machine(SVM) based on structural risk minimization is more and more widely used to solve the problems of small sample, nonlinear, high dimensional and local minimization attributes because of its good generalization. But the performance of SVM is influenced by the model parameters very much. At present there is not a unified method of model selection, which makes it troublesome...
Learning to predict communication faults from alarm sequences is an important, real-world problem in communication networks. There are various methods from the areas of statistics and data mining for this purpose. In order to improve predictive efficiency, we propose a prediction with Sparse Bayesian Method (PSBM) in this paper. Furthermore, we also provide the mathematical formulation of the approach...
The prediction strength of cement is an important task in civil engineering. In this study, the support vector machine (SVM), a novel type of learning algorithm based on statistical theory, has been used to predict the 28d strength of cement. The seven input variables used for the SVM model for prediction of strength are content of slag, SO3 content, cement fineness, 1d compressive strength and folding...
In oil and gas industry, prior prediction of certain properties is needed ahead of exploration and facility design. Viscosity and gas/oil ratio (GOR), are among those properties described through curves with their values varying over a specific range of reservoir pressures. However, the usual prediction approach could result into curves that are not consistent, exhibiting scattered behaviour as compared...
The price of crude oil is tied to major economic activities in all nations of the world, as a change in the price of crude oil invariably affects the cost of other goods and services. This has made the prediction of crude oil price a top priority for researchers and scientists alike. In this paper we present an intelligent system that predicts the price of crude oil. This system is based on Support...
This study proposed a novel HPSO-SVR model that hybridized the particle swarm optimization (PSO) and support vector regression (SVR) to improve the regression accuracy based on the type of kernel function and kernel parameter value optimization with a small and appropriate feature subset, which is then applied to forecast the monthly rainfall. This optimization mechanism combined the discrete PSO...
This paper studies the identification algorithm of parameters self adaptive SMO based on linear kernel function, and analyses its performance and advantages. For ARX model and long-term prediction model, the method is used to identify the model of main steam pressure of thermal system and dual-lane gas turbine engine of aero system. The simulation results show that the algorithm can effectively identify...
A prediction method of coal and gas outburst was presented based on the combination of attribute reduction function of rough set theory and nonlinear mapping characteristics of support vector machine. Firstly, attribute reduction and denoising were executed. Secondly, the training samples that have been processed were input to the support vector machine to train the model. Finally, the trained model...
In this paper, a novel nonlinear Radial Basis Function Neural Network (RBF-NN) ensemble model based on ν-Support Vector Machine (SVM) regression is presented for financial time series forecasting. In the process of ensemble modeling, the first stage the initial data set is divided into different training sets by used Bagging and Boosting technology. In the second stage, these training sets are input...
Long-term time series prediction is to predict the future values multi-step ahead. It has received more and more attention due to its applications in predicting stock prices, traffic status, power consumption, etc. In this paper, a k-nearest neighbors (k-NN) based least squares support vector machine (LS-SVM) framework is proposed to perform long-term time series prediction. A new distance function,...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.