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.
Predicting change-prone object-oriented software using source code metrics is an area that has attracted several researchers attention. However, predicting change-prone web services in terms of changes in the WSDL (Web Service Description Language) Interface using source code metrics implementing the services is a relatively unexplored area. We conduct a case-study on change proneness prediction on...
This paper proposes the design of antipodal Vivaldi antennas using the kernel regression method. The kernel regression is applied for training a cost function model to predict the next sample with improved cost values, and the information of the predicted sample is employed to re-train the model. This process is repeated until the cost value converges to our design goal. The shapes of the tapered...
Smart grid infrastructure is an integration of advanced communication, sensing and computing techniques into the existing physical electrical grid. It is emerging as a critical cyber-physical system (CPS) infrastructure. CPS comes across as systems with a tight integration between the physical and the cyber layers in addition to the communication network. This exposes it to a risk of mis-operation...
With the successive increase in usage of vehicles, severe traffic congestion is on the rise. This in turn leads to increase in environmental pollution and accidents which ultimately affects the safety, time consumed and money spent of the transport users. The solution to this critical problem is traffic flow prediction depending on which traffic control measures and traffic management can be done...
An efficient wind speed forecasting algorithm based on the efficient Polynomial kernel ridge extreme learning machine is proposed in this paper. This algorithm can be defined as PK-RELM. The effectiveness of this proposed algorithm has been validated in this paper by comparing it with sigmoid kernel (SK-RELM) model. In order to compute the output weight vector in chunks and to improve the stability...
The combination of observed data and dynamical models of mean-field type over networked systems is a challenging problem because of non-linearity, high dimensionality and partial observations. In many networked systems, the effective extraction and utilization of the information contained in observed data should be accomplished by exploiting the availability of accurate predictive, proactive tools...
The objective of this study is to create a forecast model for the buying and selling points of stocks, using a support vector machine (SVM) model in order to create a highly accurate prediction. The trials compare four Kernel functions of SVM, consisting of Dot function, Radial Basis Function (RBF), Sigmoid function, and Polynomial function to evaluate which Kernel would provide the most accurate...
While the current supernova (SN) photometric classification system is based on high resolution spectroscopic observations, the next generation of large scale surveys will be based on photometric light curves of supernovae gathered at an unprecedented rate. Developing an efficient method for SN photometric classification is critical to cope with the rapid growth of data volumes in current astronomical...
General predictive models do not provide a measure of confidence in predictions without Bayesian assumptions. A way to circumvent potential restrictions is to use conformal methods for constructing non-parametric confidence regions, that offer guarantees regarding validity. In this paper we provide a detailed description of a computationally efficient conformal procedure for Kernel Ridge Regression...
As one of the machine learning methods that has been widely used in recent years, SVM can be applied to pattern classification and nonlinear regression. This paper proposes the basic modeling process by using SVM, and introduces the processing technique of dimension reduction by using MATLAB and principal component analysis method, and provides the process of classification forecasting by using SVM...
most cancers at early stages show no obvious symptoms and curative treatment is not an option any more when cancer is diagnosed. Therefore, making accurate predictions for the risk of early cancer has become urgently necessary in the field of medicine. In this paper, our purpose is to fully utilize real-world routine physical examination data to analyze the most discriminative features of cancer based...
Simulation is widely used to predict the performance of complex systems. The main drawback of simulation is that it is slow in execution and the related compute experiments can be very expensive. On the other hand, analytical methods are used to rapidly provide performance estimates, but they are often approximate because of their restrictive assumptions. Recently, Extended Kernel Regression (EKR)...
We present performance prediction studies and trade-offs of Smoothed Particle Hydrodynamics (SPH) codes that rely on a Hashed OctTree data structure to efficiently respond to neighborhood queries. We use the Performance Prediction Toolkit (PPT) to (i) build a loop-structure model (SPHSim) of an SPH code, where parameters capture the specific physics of the problem and method controls that SPH offers,...
In this paper, we study the effects of using smoothed variance estimates in place of the sample variances on the performance of stochastic kriging (SK). Different variance estimation methods are investigated and it is shown through numerical examples that such a replacement leads to improved predictive performance of SK. An SK-based dual metamodeling approach is further proposed to obtain an efficient...
Network traffic volume estimation and prediction is an important research topic that attracts persistent attention from the networking community and the machine learning community. Although there has been extensive work on estimating or predicting the traffic matrix using time series models, low rank matrix decomposition et. al, to the best of our knowledge, there is few work investigating the problem...
In business, consumers interest, behavior, product profits are the insights required to predict the future of business with the current data or historical data. These insights can be generated with the statistical techniques for the purpose of forecasting. The statistical techniques can be evaluated for the predictive model based on the requirements of the data. The prediction and forecasting are...
Based on the least squares support vector regression and the Nystrom approximation, it becomes possible to apply a nonlinear model for the prediction problem of water quality with large sample. This is done by using a reduce rank approximation of the nonlinear mapping induced by the primal kernel matrix, with an active selection of support vectors based on an unsupervised kernel clustering algorithm...
It is always desirable to be able to manage level of water in river, dam, and reservoir. Models have been constructed for predicting the level of these bodies of water, and good models can help increase the effectiveness of water management. Presently, the model that is employed by the Hydrographic Department of the Royal Thai Navy for predicting the level of water in Chao Phraya river is a harmonic...
Online social media networks play important roles for people to share opinions, communicate with others. One of important features behind these activities is trust. This paper investigates the trust model in Online social media networks. Considering the interaction between two users and the reputation in the social networks, this trust model gives a definition about the trust value between two users...
We propose Lumos+, an analytical framework for power and performance modeling of accelerator-rich heterogeneous architectures. As accelerators proliferate, the search space becomes too expensive for brute-force search. We describe a novel and highly accurate genetic search algorithm. We then use Lumos+ to explore the tradeoffs between using fixed-function accelerators and reconfigurable logic blocks,...
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.