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Software metrics can be used as a indicator of the presence of software vulnerabilities. These metrics have been used with machine learning to predict source code prone to contain vulnerabilities. Although it is not possible to find the exact location of the flaws, the models can show which components require more attention during inspections and testing. Each new technique uses his own evaluation...
the noisy and complex nature of many biological signals such as the electroencephalogram (EEG) has long constituted a major challenge in terms of analysis and prediction for single and multivariate problems. Nonlinear signal modeling, despite its widespread applicability, often shows limited success whenever the signal is contaminated with noise or is time varying in nature. We herein introduce a...
Estimating the failure probabilities of SRAM memory cells using Monte Carlo or Importance Sampling techniques is expensive in the number of SPICE simulations needed. This paper presents a methodology for estimating the dynamic margin failure probabilities by building a surrogate model of the dynamic margin using Gaussian Process regression. Additive kernel functions that can extrapolate the margin...
In order to master the running status messages of natural gas pipeline system, predict the possible time and which equipment of the system may be go wrong, and change the equipment in time, avoid the failure and economic losses caused by the system failure. so, the prediction of system reliability is very important to system manage and maintenance. For traditional reliability prediction method, such...
We present the simulation of the application of the Model-based Predictive Control (MPC) of the drum level in a Combined Cycle Plant in order to minimize the time for reaching the highest capacity of plant, around 225 MW. In contrast to others control techniques, our simulation yields that the MPC has shown capabilities as to reach its expected power in about 40 minutes before than PID, time which...
The fusion approaches with multi-model ensemble can present a better performance than the simple approaches with single model in Prognostics and Health Management (PHM). Bayesian Model Averaging (BMA) is a very useful ensemble method in these fusion approaches because of its ability of uncertainty quantification. A fusion model based on BMA and relevance vector machine (RVM) is presented in this paper...
Recent lithography optimizations demand higher accuracy and cause longer runtime. Optical proximity correction (OPC) and sub-resolution assist feature (SRAF) insertion, for example, take a few days due to lengthy lithography simulations and high pattern density. Etch proximity correction (EPC) is another example of intensive optimization due to a complex physical model of etching process. Machine...
In order to solve the failure prognostics problem of electronic system, a method of fast relevance vector machine (FRVM) based on improved fruit fly optimization algorithm (FOA) is proposed. Grey data generation operation is introduced to process the original data and the output data for enhancing the regularity and reducing the randomness. Furthermore, the kernel function parameter of FRVM model...
In this paper, we use a combination of support vector machine to improve the Standard SVM, which combine different kernel functions to improve the SVM' learning ability and generalization ability, thereby improving the performance of a combination SVM kernel function, and avoiding the assertiveness of the single prediction model. Combination forecasting model to make joint decisions on the results,...
Replacing a portion of current light-duty vehicles (LDVs) with plug-in hybrid electric vehicles (PHEVs) offers the possibility to reduce the dependence on fossil fuels together with environmental and economic benefits. However, charging a myriad of PHEVs will certainly introduce a huge new load to the power grid. In the framework of the development of a smarter grid, the primary focus of the present...
The paper presents a comparative analysis of possibilities for assessment of the freshness of widespread foodstuffs like white brined cheese, yellow cheese, meat and bacon. The freshness is represented by the time of storage in specific conditions (dark room with temperature of 20°C). The time of storage is assessed using regression predictive models of features, related to the freshness product and...
To sustain and excel in competitive global market, organizations often bank on high productivity and world class quality. Endeavor of this research is to comprehend and model the manufacturing process of Electrical Discharge Machining (EDM) equipment product in order to increase productivity. Outcome of EDM operation is strongly influenced by various process parameters. The paper presents a framework...
We proposed a novel model to predict human's visual attention when free-viewing webpages. Compared with natural images, webpages are usually full of salient regions such as logos, text, and faces, while few of them attract human's attention in a short sight. Moreover, webpages perform distinct viewing patterns which are quite different from the natural images. In this paper, we introduced multi-features...
A sparse Laguerre-Volterra autoregressive model has been developed as feature extraction from subdural human EEG data for seizure prediction in temporal lobe epilepsy. The use of Laguerre-Volterra kernel can compactly yield an autoregressive model of longer system memory without increasing the number of the coefficients. In 6 sets of seizure, we used a sparse Laguerre-Volterra autoregressive model...
Valve-sparing aortic root reconstruction is an up- and-coming approach for patients suffering from aortic valve insufficiencies which promises to significantly reduce complications. However, the success of the treatment strongly depends on the challenging task of choosing the correct size of the prosthesis, for which, up to now, surgeons solely have to rely on their experience. Here, we present a...
The use of the direct evaluation of the Gaussian Process, using the square exponential function kernel prediction at the given data points is often misleading towards evaluation of the fit, given by the coefficient of determination. The predicted value at the data points when using the Gaussian Process, is almost at all cases equal to the original value. As such, interpretation problems arise when...
In order to enhance the precision of biofouling estimation, this paper uses Least Squares Support Vector Machine (LSSVM) to establish a prediction model based on the estimator with radial basis function kernel. The main Influencing factors include pH, conductivity, total number of bacteria, dissolved oxygen, TN, NH3-N was selected as the input variable, Biofouling as the output variable. The results...
In recent years, higher education has been gaining importance in graduate students to make successful careers. So, academic organizations are given utmost importance for quality in academics to build the careers of the students. Faculty performance plays a vital role in academic institutions. In this paper, the performance of faculty members is evaluated on the basis of different parameters are taken...
Kernel-based machine learning methods are gaining increasing interest in flow modeling and prediction in recent years. Gaussian process (GP) is one example of such kernel-based methods, which can provide very good performance for nonlinear problems. In this work, we apply GP regression to flow modeling and prediction of athletes in ski races, but the proposed framework can be generally applied to...
There has been a dramatic increase in the sharing of opinions and information across different web platforms and social media, especially online product reviews. Cloud web portals, such as getApp.com, were designed to amalgamate cloud service information and to also examine how consumers evaluate their experience of using cloud computing products. The current literature shows the growing importance...
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