bivariate features, obtained from multichannel electroencephalogram (eeg) recordings, quantify the relation between different brain regions. studies based on bivariate features have shown optimistic results for tackling epileptic seizure prediction problem in patients suffering from refractory epilepsy. a new bivariate approach using univariate features is proposed here. differences and ratios ...

As a new type of dam, roller compacted concrete dam (RCCD) develops very fast in recent years. Deformation plays an important role in the RCCD’s safety. The deformation of RCCD is influenced mainly by three parts: water pressure, temperature and time effect. As to any of the three parts, there are many factors. Therefore, the deformation of RCCD is a complicated system. The least squares suppor...

klinkenberg permeability is an important parameter in tight gas reservoirs. there are conventional methods for determining it, but these methods depend on core permeability. cores are few in number, but well logs are usually accessible for all wells and provide continuous information. in this regard, regression methods have been used to achieve reliable relations between log readings and klinke...

The Bayesian evidence framework is applied in this paper to least squares support vector machine (LS-SVM) regression in order to infer nonlinear models for predicting a financial time series and the related volatility. On the first level of inference, a statistical framework is related to the LS-SVM formulation which allows one to include the time-varying volatility of the market by an appropri...

Abstract This paper proposes a method which using density index function to sparse LS-SVM in highdimensional feature space, and gives a new method which takes each sample point as a clustering center to make hypersphere, so as to determine the fuzzy membership function in high-dimensional feature space, thus to establish a new fuzzy least squares support vector machine model, So it is different...

Abstract- With the advancement and development of computer network technologies, the way for intruders has become smoother; therefore, to detect threats and attacks, the importance of intrusion detection systems (IDS) as one of the key elements of security is increasing. One of the challenges of intrusion detection systems is managing of the large amount of network traffic features. Removing un...

The aim of this study is to develop the Bayesian Least Squares Support Vector Machine (LS-SVM) classifiers, for preoperatively predicting the malignancy of ovarian tumors. We describe how to perform parameter estimation, input variable selection for LS-SVM within the evidence framework. The issue of computing the posterior class probability for risk minimization decision making is addressed. Th...

in recent years, the needs of the internet are felt in lives of all people.  accordingly, many studies have been done on security in virtual environment. old technics such as firewalls, authentication and encryption could not provide internet security completely; so, intrusion detection system is created as a new solution and a defense wall in cyber environment. many studies were performed on d...

internet applications spreading and its high usage popularity result insignificant increasing of cyber-attacks. consequently, network security has becomea matter of importance and several methods have been developed for these attacks.for this purpose, intrusion detection systems (ids) are being used to monitor theattacks occurred on computer networks. data mining techniques, machinelearning, ne...

porosity is considered as an important petrophysical parameter in characterizing reservoirs, calculating in-situ oil reserves, and production evaluation. nowadays, using intelligent techniques has become a popular method for porosity estimation. support vector machine (svm) a new intelligent method with a great generalization potential of modeling non-linear relationships has been introduced fo...