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<!DOCTYPE html> <html lang="en" dir="ltr"> <head>  <script async src="https://www.googletagmanager.com/gtag/js?id=G-P63WKM1TM1"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-P63WKM1TM1'); </script>  <script type="text/javascript" > (function(m,e,t,r,i,k,a){m[i]=m[i]||function(){(m[i].a=m[i].a||[]).push(arguments)}; m[i].l=1*new Date(); for (var j = 0; j < document.scripts.length; j++) {if (document.scripts[j].src === r) { return; }} k=e.createElement(t),a=e.getElementsByTagName(t)[0],k.async=1,k.src=r,a.parentNode.insertBefore(k,a)}) (window, document, "script", "https://mc.yandex.ru/metrika/tag.js", "ym"); ym(55165297, "init", { clickmap:false, trackLinks:true, accurateTrackBounce:true, webvisor:false }); </script> <noscript><div><img src="https://mc.yandex.ru/watch/55165297" style="position:absolute; left:-9999px;" alt="" /></div></noscript>    <title>Search results for: Dirichlet kernel</title> <meta name="description" content="Search results for: Dirichlet kernel"> <meta name="keywords" content="Dirichlet kernel"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Dirichlet kernel"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 179</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Dirichlet kernel</h1> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">179</span> L1-Convergence of Modified Trigonometric Sums</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Sandeep%20Kaur%20Chouhan">Sandeep Kaur Chouhan</a>, <a href="https://publications.waset.org/search?q=Jatinderdeep%20Kaur"> Jatinderdeep Kaur</a>, <a href="https://publications.waset.org/search?q=S.%20S.%20Bhatia"> S. S. Bhatia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The existence of sine and cosine series as a Fourier series, their L1-convergence seems to be one of the difficult question in theory of convergence of trigonometric series in L1-metric norm. In the literature so far available, various authors have studied the L1-convergence of cosine and sine trigonometric series with special coefficients. In this paper, we present a modified cosine and sine sums and criterion for L1-convergence of these modified sums is obtained. Also, a necessary and sufficient condition for the L1-convergence of the cosine and sine series is deduced as corollaries. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Conjugate%20Dirichlet%20kernel" title="Conjugate Dirichlet kernel">Conjugate Dirichlet kernel</a>, <a href="https://publications.waset.org/search?q=Dirichlet%20kernel" title=" Dirichlet kernel"> Dirichlet kernel</a>, <a href="https://publications.waset.org/search?q=L1-convergence" title=" L1-convergence"> L1-convergence</a>, <a href="https://publications.waset.org/search?q=modified%20sums." title=" modified sums."> modified sums.</a> </p> <a href="https://publications.waset.org/10004897/l1-convergence-of-modified-trigonometric-sums" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10004897/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10004897/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10004897/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10004897/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10004897/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10004897/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10004897/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10004897/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10004897/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10004897/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10004897.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1222</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">178</span> Comparative Studies of Support Vector Regression between Reproducing Kernel and Gaussian Kernel</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Wei%20Zhang">Wei Zhang</a>, <a href="https://publications.waset.org/search?q=Su-Yan%20Tang"> Su-Yan Tang</a>, <a href="https://publications.waset.org/search?q=Yi-Fan%20Zhu"> Yi-Fan Zhu</a>, <a href="https://publications.waset.org/search?q=Wei-Ping%20Wang"> Wei-Ping Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Support vector regression (SVR) has been regarded as a state-of-the-art method for approximation and regression. The importance of kernel function, which is so-called admissible support vector kernel (SV kernel) in SVR, has motivated many studies on its composition. The Gaussian kernel (RBF) is regarded as a 鈥渂est" choice of SV kernel used by non-expert in SVR, whereas there is no evidence, except for its superior performance on some practical applications, to prove the statement. Its well-known that reproducing kernel (R.K) is also a SV kernel which possesses many important properties, e.g. positive definiteness, reproducing property and composing complex R.K by simpler ones. However, there are a limited number of R.Ks with explicit forms and consequently few quantitative comparison studies in practice. In this paper, two R.Ks, i.e. SV kernels, composed by the sum and product of a translation invariant kernel in a Sobolev space are proposed. An exploratory study on the performance of SVR based general R.K is presented through a systematic comparison to that of RBF using multiple criteria and synthetic problems. The results show that the R.K is an equivalent or even better SV kernel than RBF for the problems with more input variables (more than 5, especially more than 10) and higher nonlinearity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=admissible%20support%20vector%20kernel" title="admissible support vector kernel">admissible support vector kernel</a>, <a href="https://publications.waset.org/search?q=reproducing%20kernel" title=" reproducing kernel"> reproducing kernel</a>, <a href="https://publications.waset.org/search?q=reproducing%20kernel%20Hilbert%20space" title="reproducing kernel Hilbert space">reproducing kernel Hilbert space</a>, <a href="https://publications.waset.org/search?q=support%20vector%20regression." title=" support vector regression."> support vector regression.</a> </p> <a href="https://publications.waset.org/8559/comparative-studies-of-support-vector-regression-between-reproducing-kernel-and-gaussian-kernel" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/8559/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/8559/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/8559/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/8559/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/8559/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/8559/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/8559/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/8559/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/8559/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/8559/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/8559.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1595</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">177</span> Coefficients of Some Double Trigonometric Cosine and Sine Series</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Jatinderdeep%20Kaur">Jatinderdeep Kaur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the results of Kano from one dimensional cosine and sine series are extended to two dimensional cosine and sine series. To extend these results, some classes of coefficient sequences such as class of semi convexity and class R are extended from one dimension to two dimensions. Further, the function f(x, y) is two dimensional Fourier Cosine and Sine series or equivalently it represents an integrable function or not, has been studied. Moreover, some results are obtained which are generalization of Moricz鈥檚 results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Conjugate%20Dirichlet%20kernel" title="Conjugate Dirichlet kernel">Conjugate Dirichlet kernel</a>, <a href="https://publications.waset.org/search?q=conjugate%20Fejer%20kernel" title=" conjugate Fejer kernel"> conjugate Fejer kernel</a>, <a href="https://publications.waset.org/search?q=Fourier%20series" title=" Fourier series"> Fourier series</a>, <a href="https://publications.waset.org/search?q=Semi-convexity." title=" Semi-convexity."> Semi-convexity.</a> </p> <a href="https://publications.waset.org/10002758/coefficients-of-some-double-trigonometric-cosine-and-sine-series" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10002758/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10002758/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10002758/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10002758/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10002758/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10002758/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10002758/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10002758/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10002758/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10002758/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10002758.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2147</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">176</span> Adaptive Kernel Filtering Used in Video Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Rasmus%20Engholm">Rasmus Engholm</a>, <a href="https://publications.waset.org/search?q=Eva%20B.%20Vedel%20Jensen"> Eva B. Vedel Jensen</a>, <a href="https://publications.waset.org/search?q=Henrik%20Karstoft"> Henrik Karstoft</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In this paper we present a noise reduction filter for video processing. It is based on the recently proposed two dimensional steering kernel, extended to three dimensions and further augmented to suit the spatial-temporal domain of video processing. Two alternative filters are proposed - the time symmetric kernel and the time asymmetric kernel. The first reduces the noise on single sequences, but to handle the problems at scene shift the asymmetric kernel is introduced. The performance of both are tested on simulated data and on a real video sequence together with the existing steering kernel. The proposed kernels improves the Rooted Mean Squared Error (RMSE) compared to the original steering kernel method on video material.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Adaptive%20image%20filtering" title="Adaptive image filtering">Adaptive image filtering</a>, <a href="https://publications.waset.org/search?q=noise%20reduction" title=" noise reduction"> noise reduction</a>, <a href="https://publications.waset.org/search?q=kernel%20methods" title=" kernel methods"> kernel methods</a>, <a href="https://publications.waset.org/search?q=video%20processing." title=" video processing."> video processing.</a> </p> <a href="https://publications.waset.org/8499/adaptive-kernel-filtering-used-in-video-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/8499/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/8499/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/8499/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/8499/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/8499/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/8499/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/8499/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/8499/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/8499/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/8499/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/8499.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1470</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">175</span> Application of Formal Methods for Designing a Separation Kernel for Embedded Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Kei%20Kawamorita">Kei Kawamorita</a>, <a href="https://publications.waset.org/search?q=Ryouta%20Kasahara"> Ryouta Kasahara</a>, <a href="https://publications.waset.org/search?q=Yuuki%20Mochizuki"> Yuuki Mochizuki</a>, <a href="https://publications.waset.org/search?q=Kenichiro%20Noguchi"> Kenichiro Noguchi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>A separation-kernel-based operating system (OS) has been designed for use in secure embedded systems by applying formal methods to the design of the separation-kernel part. The separation kernel is a small OS kernel that provides an abstract distributed environment on a single CPU. The design of the separation kernel was verified using two formal methods, the B method and the Spin model checker. A newly designed semi-formal method, the extended state transition method, was also applied. An OS comprising the separation-kernel part and additional OS services on top of the separation kernel was prototyped on the Intel IA-32 architecture. Developing and testing of a prototype embedded application, a point-of-sale application, on the prototype OS demonstrated that the proposed architecture and the use of formal methods to design its kernel part are effective for achieving a secure embedded system having a high-assurance separation kernel.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=B%20method" title="B method">B method</a>, <a href="https://publications.waset.org/search?q=embedded%20systems" title=" embedded systems"> embedded systems</a>, <a href="https://publications.waset.org/search?q=extended%20state%20transition" title=" extended state transition"> extended state transition</a>, <a href="https://publications.waset.org/search?q=formal%20methods" title=" formal methods"> formal methods</a>, <a href="https://publications.waset.org/search?q=separation%20kernel" title=" separation kernel"> separation kernel</a>, <a href="https://publications.waset.org/search?q=Spin." title=" Spin."> Spin.</a> </p> <a href="https://publications.waset.org/8630/application-of-formal-methods-for-designing-a-separation-kernel-for-embedded-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/8630/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/8630/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/8630/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/8630/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/8630/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/8630/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/8630/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/8630/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/8630/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/8630/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/8630.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1925</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">174</span> Online Prediction of Nonlinear Signal Processing Problems Based Kernel Adaptive Filtering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Hamza%20Nejib">Hamza Nejib</a>, <a href="https://publications.waset.org/search?q=Okba%20Taouali"> Okba Taouali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents two of the most knowing kernel adaptive filtering (KAF) approaches, the kernel least mean squares and the kernel recursive least squares, in order to predict a new output of nonlinear signal processing. Both of these methods implement a nonlinear transfer function using kernel methods in a particular space named reproducing kernel Hilbert space (RKHS) where the model is a linear combination of kernel functions applied to transform the observed data from the input space to a high dimensional feature space of vectors, this idea known as the kernel trick. Then KAF is the developing filters in RKHS. We use two nonlinear signal processing problems, Mackey Glass chaotic time series prediction and nonlinear channel equalization to figure the performance of the approaches presented and finally to result which of them is the adapted one. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=KLMS" title="KLMS">KLMS</a>, <a href="https://publications.waset.org/search?q=online%20prediction" title=" online prediction"> online prediction</a>, <a href="https://publications.waset.org/search?q=KAF" title=" KAF"> KAF</a>, <a href="https://publications.waset.org/search?q=signal%20processing" title=" signal processing"> signal processing</a>, <a href="https://publications.waset.org/search?q=RKHS" title=" RKHS"> RKHS</a>, <a href="https://publications.waset.org/search?q=Kernel%20methods" title=" Kernel methods"> Kernel methods</a>, <a href="https://publications.waset.org/search?q=KRLS" title=" KRLS"> KRLS</a>, <a href="https://publications.waset.org/search?q=KLMS." title=" KLMS."> KLMS.</a> </p> <a href="https://publications.waset.org/10007683/online-prediction-of-nonlinear-signal-processing-problems-based-kernel-adaptive-filtering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10007683/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10007683/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10007683/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10007683/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10007683/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10007683/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10007683/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10007683/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10007683/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10007683/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10007683.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1052</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">173</span> A Non-Standard Finite Difference Scheme for the Solution of Laplace Equation with Dirichlet Boundary Conditions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Khaled%20Moaddy">Khaled Moaddy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In this paper, we present a fast and accurate numerical scheme for the solution of a Laplace equation with Dirichlet boundary conditions. The non-standard finite difference scheme (NSFD) is applied to construct the numerical solutions of a Laplace equation with two different Dirichlet boundary conditions. The solutions obtained using NSFD are compared with the solutions obtained using the standard finite difference scheme (SFD). The NSFD scheme is demonstrated to be reliable and efficient.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Standard%20finite%20difference%20schemes" title="Standard finite difference schemes">Standard finite difference schemes</a>, <a href="https://publications.waset.org/search?q=non%E2%80%93standard%20schemes" title=" non鈥搒tandard schemes"> non鈥搒tandard schemes</a>, <a href="https://publications.waset.org/search?q=Laplace%20equation" title=" Laplace equation"> Laplace equation</a>, <a href="https://publications.waset.org/search?q=Dirichlet%20boundary%20conditions." title=" Dirichlet boundary conditions."> Dirichlet boundary conditions.</a> </p> <a href="https://publications.waset.org/10011280/a-non-standard-finite-difference-scheme-for-the-solution-of-laplace-equation-with-dirichlet-boundary-conditions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10011280/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10011280/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10011280/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10011280/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10011280/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10011280/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10011280/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10011280/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10011280/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10011280/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10011280.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">667</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">172</span> A New Composition Method of Admissible Support Vector Kernel Based on Reproducing Kernel</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Wei%20Zhang">Wei Zhang</a>, <a href="https://publications.waset.org/search?q=Xin%20Zhao"> Xin Zhao</a>, <a href="https://publications.waset.org/search?q=Yi-Fan%20Zhu"> Yi-Fan Zhu</a>, <a href="https://publications.waset.org/search?q=Xin-Jian%20Zhang"> Xin-Jian Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Kernel function, which allows the formulation of nonlinear variants of any algorithm that can be cast in terms of dot products, makes the Support Vector Machines (SVM) have been successfully applied in many fields, e.g. classification and regression. The importance of kernel has motivated many studies on its composition. It-s well-known that reproducing kernel (R.K) is a useful kernel function which possesses many properties, e.g. positive definiteness, reproducing property and composing complex R.K by simple operation. There are two popular ways to compute the R.K with explicit form. One is to construct and solve a specific differential equation with boundary value whose handicap is incapable of obtaining a unified form of R.K. The other is using a piecewise integral of the Green function associated with a differential operator L. The latter benefits the computation of a R.K with a unified explicit form and theoretical analysis, whereas there are relatively later studies and fewer practical computations. In this paper, a new algorithm for computing a R.K is presented. It can obtain the unified explicit form of R.K in general reproducing kernel Hilbert space. It avoids constructing and solving the complex differential equations manually and benefits an automatic, flexible and rigorous computation for more general RKHS. In order to validate that the R.K computed by the algorithm can be used in SVM well, some illustrative examples and a comparison between R.K and Gaussian kernel (RBF) in support vector regression are presented. The result shows that the performance of R.K is close or slightly superior to that of RBF.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=admissible%20support%20vector%20kernel" title="admissible support vector kernel">admissible support vector kernel</a>, <a href="https://publications.waset.org/search?q=reproducing%20kernel" title=" reproducing kernel"> reproducing kernel</a>, <a href="https://publications.waset.org/search?q=reproducing%20kernel%20Hilbert%20space" title="reproducing kernel Hilbert space">reproducing kernel Hilbert space</a>, <a href="https://publications.waset.org/search?q=Green%20function" title=" Green function"> Green function</a>, <a href="https://publications.waset.org/search?q=support%20vectorregression" title=" support vectorregression"> support vectorregression</a> </p> <a href="https://publications.waset.org/4312/a-new-composition-method-of-admissible-support-vector-kernel-based-on-reproducing-kernel" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/4312/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/4312/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/4312/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/4312/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/4312/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/4312/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/4312/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/4312/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/4312/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/4312/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/4312.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1544</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">171</span> Numerical Study of Iterative Methods for the Solution of the Dirichlet-Neumann Map for Linear Elliptic PDEs on Regular Polygon Domains</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=A.%20G.%20Sifalakis">A. G. Sifalakis</a>, <a href="https://publications.waset.org/search?q=E.%20P.%20Papadopoulou"> E. P. Papadopoulou</a>, <a href="https://publications.waset.org/search?q=Y.%20G.%20Saridakis"> Y. G. Saridakis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A generalized Dirichlet to Neumann map is one of the main aspects characterizing a recently introduced method for analyzing linear elliptic PDEs, through which it became possible to couple known and unknown components of the solution on the boundary of the domain without solving on its interior. For its numerical solution, a well conditioned quadratically convergent sine-Collocation method was developed, which yielded a linear system of equations with the diagonal blocks of its associated coefficient matrix being point diagonal. This structural property, among others, initiated interest for the employment of iterative methods for its solution. In this work we present a conclusive numerical study for the behavior of classical (Jacobi and Gauss-Seidel) and Krylov subspace (GMRES and Bi-CGSTAB) iterative methods when they are applied for the solution of the Dirichlet to Neumann map associated with the Laplace-s equation on regular polygons with the same boundary conditions on all edges. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Elliptic%20PDEs" title="Elliptic PDEs">Elliptic PDEs</a>, <a href="https://publications.waset.org/search?q=Dirichlet%20to%20Neumann%20Map" title=" Dirichlet to Neumann Map"> Dirichlet to Neumann Map</a>, <a href="https://publications.waset.org/search?q=Global%20Relation" title=" Global Relation"> Global Relation</a>, <a href="https://publications.waset.org/search?q=Collocation" title=" Collocation"> Collocation</a>, <a href="https://publications.waset.org/search?q=Iterative%20Methods" title=" Iterative Methods"> Iterative Methods</a>, <a href="https://publications.waset.org/search?q=Jacobi" title=" Jacobi"> Jacobi</a>, <a href="https://publications.waset.org/search?q=Gauss-Seidel" title=" Gauss-Seidel"> Gauss-Seidel</a>, <a href="https://publications.waset.org/search?q=GMRES" title=" GMRES"> GMRES</a>, <a href="https://publications.waset.org/search?q=Bi-CGSTAB." title=" Bi-CGSTAB."> Bi-CGSTAB.</a> </p> <a href="https://publications.waset.org/14250/numerical-study-of-iterative-methods-for-the-solution-of-the-dirichlet-neumann-map-for-linear-elliptic-pdes-on-regular-polygon-domains" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/14250/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/14250/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/14250/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/14250/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/14250/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/14250/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/14250/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/14250/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/14250/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/14250/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/14250.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1712</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">170</span> A Bayesian Kernel for the Prediction of Protein- Protein Interactions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Hany%20Alashwal">Hany Alashwal</a>, <a href="https://publications.waset.org/search?q=Safaai%20Deris"> Safaai Deris</a>, <a href="https://publications.waset.org/search?q=Razib%20M.%20Othman"> Razib M. Othman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Understanding proteins functions is a major goal in the post-genomic era. Proteins usually work in context of other proteins and rarely function alone. Therefore, it is highly relevant to study the interaction partners of a protein in order to understand its function. Machine learning techniques have been widely applied to predict protein-protein interactions. Kernel functions play an important role for a successful machine learning technique. Choosing the appropriate kernel function can lead to a better accuracy in a binary classifier such as the support vector machines. In this paper, we describe a Bayesian kernel for the support vector machine to predict protein-protein interactions. The use of Bayesian kernel can improve the classifier performance by incorporating the probability characteristic of the available experimental protein-protein interactions data that were compiled from different sources. In addition, the probabilistic output from the Bayesian kernel can assist biologists to conduct more research on the highly predicted interactions. The results show that the accuracy of the classifier has been improved using the Bayesian kernel compared to the standard SVM kernels. These results imply that protein-protein interaction can be predicted using Bayesian kernel with better accuracy compared to the standard SVM kernels. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Bioinformatics" title="Bioinformatics">Bioinformatics</a>, <a href="https://publications.waset.org/search?q=Protein-protein%20interactions" title=" Protein-protein interactions"> Protein-protein interactions</a>, <a href="https://publications.waset.org/search?q=Bayesian%20Kernel" title=" Bayesian Kernel"> Bayesian Kernel</a>, <a href="https://publications.waset.org/search?q=Support%20Vector%20Machines." title=" Support Vector Machines."> Support Vector Machines.</a> </p> <a href="https://publications.waset.org/3698/a-bayesian-kernel-for-the-prediction-of-protein-protein-interactions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/3698/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/3698/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/3698/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/3698/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/3698/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/3698/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/3698/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/3698/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/3698/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/3698/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/3698.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2164</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">169</span> A Comparison of the Nonparametric Regression Models using Smoothing Spline and Kernel Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Dursun%20Aydin">Dursun Aydin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper study about using of nonparametric models for Gross National Product data in Turkey and Stanford heart transplant data. It is discussed two nonparametric techniques called smoothing spline and kernel regression. The main goal is to compare the techniques used for prediction of the nonparametric regression models. According to the results of numerical studies, it is concluded that smoothing spline regression estimators are better than those of the kernel regression. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Kernel%20regression" title="Kernel regression">Kernel regression</a>, <a href="https://publications.waset.org/search?q=Nonparametric%20models" title=" Nonparametric models"> Nonparametric models</a>, <a href="https://publications.waset.org/search?q=Prediction" title="Prediction">Prediction</a>, <a href="https://publications.waset.org/search?q=Smoothing%20spline." title=" Smoothing spline."> Smoothing spline.</a> </p> <a href="https://publications.waset.org/4537/a-comparison-of-the-nonparametric-regression-models-using-smoothing-spline-and-kernel-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/4537/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/4537/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/4537/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/4537/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/4537/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/4537/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/4537/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/4537/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/4537/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/4537/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/4537.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">3101</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">168</span> Generalization Kernel for Geopotential Approximation by Harmonic Splines</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Elena%20Kotevska">Elena Kotevska</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a generalization kernel for gravitational potential determination by harmonic splines. It was shown in [10] that the gravitational potential can be approximated using a kernel represented as a Newton integral over the real Earth body. On the other side, the theory of geopotential approximation by harmonic splines uses spherically oriented kernels. The purpose of this paper is to show that in the spherical case both kernels have the same type of representation, which leads us to conclusion that it is possible to consider the kernel represented as a Newton integral over the real Earth body as a kind of generalization of spherically harmonic kernels to real geometries. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Geopotential" title="Geopotential">Geopotential</a>, <a href="https://publications.waset.org/search?q=Reproducing%20Kernel" title=" Reproducing Kernel"> Reproducing Kernel</a>, <a href="https://publications.waset.org/search?q=Approximation" title=" Approximation"> Approximation</a>, <a href="https://publications.waset.org/search?q=Regular%20Surface" title=" Regular Surface"> Regular Surface</a> </p> <a href="https://publications.waset.org/8514/generalization-kernel-for-geopotential-approximation-by-harmonic-splines" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/8514/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/8514/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/8514/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/8514/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/8514/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/8514/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/8514/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/8514/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/8514/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/8514/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/8514.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1297</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">167</span> Kernel鈥檚 Parameter Selection for Support Vector Domain Description</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Mohamed%20EL%20Boujnouni">Mohamed EL Boujnouni</a>, <a href="https://publications.waset.org/search?q=Mohamed%20Jedra"> Mohamed Jedra</a>, <a href="https://publications.waset.org/search?q=Noureddine%20Zahid"> Noureddine Zahid</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Support Vector Domain Description (SVDD) is one of the best-known one-class support vector learning methods, in which one tries the strategy of using balls defined on the feature space in order to distinguish a set of normal data from all other possible abnormal objects. As all kernel-based learning algorithms its performance depends heavily on the proper choice of the kernel parameter. This paper proposes a new approach to select kernel's parameter based on maximizing the distance between both gravity centers of normal and abnormal classes, and at the same time minimizing the variance within each class. The performance of the proposed algorithm is evaluated on several benchmarks. The experimental results demonstrate the feasibility and the effectiveness of the presented method.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Gravity%20centers" title="Gravity centers">Gravity centers</a>, <a href="https://publications.waset.org/search?q=Kernel%E2%80%99s%20parameter" title=" Kernel鈥檚 parameter"> Kernel鈥檚 parameter</a>, <a href="https://publications.waset.org/search?q=Support%20Vector%20Domain%20Description" title=" Support Vector Domain Description"> Support Vector Domain Description</a>, <a href="https://publications.waset.org/search?q=Variance." title=" Variance."> Variance.</a> </p> <a href="https://publications.waset.org/17365/kernels-parameter-selection-for-support-vector-domain-description" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/17365/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/17365/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/17365/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/17365/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/17365/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/17365/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/17365/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/17365/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/17365/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/17365/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/17365.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1831</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">166</span> Finger Vein Recognition using PCA-based Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Sepehr%20Damavandinejadmonfared">Sepehr Damavandinejadmonfared</a>, <a href="https://publications.waset.org/search?q=Ali%20Khalili%20Mobarakeh"> Ali Khalili Mobarakeh</a>, <a href="https://publications.waset.org/search?q=Mohsen%20Pashna">Mohsen Pashna</a>, <a href="https://publications.waset.org/search?q="></a>, <a href="https://publications.waset.org/search?q=Jiangping%20Gou%0D%0ASayedmehran%20Mirsafaie%20Rizi"> Jiangping Gou Sayedmehran Mirsafaie Rizi</a>, <a href="https://publications.waset.org/search?q=Saba%20Nazari"> Saba Nazari</a>, <a href="https://publications.waset.org/search?q=Shadi%20Mahmoodi%20Khaniabadi"> Shadi Mahmoodi Khaniabadi</a>, <a href="https://publications.waset.org/search?q=Mohamad%20Ali%20Bagheri"> Mohamad Ali Bagheri </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper a novel algorithm is proposed to merit the accuracy of finger vein recognition. The performances of Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA), and Kernel Entropy Component Analysis (KECA) in this algorithm are validated and compared with each other in order to determine which one is the most appropriate one in terms of finger vein recognition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Biometrics" title="Biometrics">Biometrics</a>, <a href="https://publications.waset.org/search?q=finger%20vein%20recognition" title=" finger vein recognition"> finger vein recognition</a>, <a href="https://publications.waset.org/search?q=PrincipalComponent%20Analysis%20%28PCA%29" title=" PrincipalComponent Analysis (PCA)"> PrincipalComponent Analysis (PCA)</a>, <a href="https://publications.waset.org/search?q=Kernel%20Principal%20Component%20Analysis%28KPCA%29" title=" Kernel Principal Component Analysis(KPCA)"> Kernel Principal Component Analysis(KPCA)</a>, <a href="https://publications.waset.org/search?q=Kernel%20Entropy%20Component%20Analysis%20%28KPCA%29." title=" Kernel Entropy Component Analysis (KPCA)."> Kernel Entropy Component Analysis (KPCA).</a> </p> <a href="https://publications.waset.org/9030/finger-vein-recognition-using-pca-based-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9030/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9030/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9030/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9030/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9030/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9030/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9030/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9030/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9030/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9030/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9030.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2680</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">165</span> Face Recognition using Features Combination and a New Non-linear Kernel</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Essam%20Al%20Daoud">Essam Al Daoud</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To improve the classification rate of the face recognition, features combination and a novel non-linear kernel are proposed. The feature vector concatenates three different radius of local binary patterns and Gabor wavelet features. Gabor features are the mean, standard deviation and the skew of each scaling and orientation parameter. The aim of the new kernel is to incorporate the power of the kernel methods with the optimal balance between the features. To verify the effectiveness of the proposed method, numerous methods are tested by using four datasets, which are consisting of various emotions, orientations, configuration, expressions and lighting conditions. Empirical results show the superiority of the proposed technique when compared to other methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Face%20recognition" title="Face recognition">Face recognition</a>, <a href="https://publications.waset.org/search?q=Gabor%20wavelet" title=" Gabor wavelet"> Gabor wavelet</a>, <a href="https://publications.waset.org/search?q=LBP" title=" LBP"> LBP</a>, <a href="https://publications.waset.org/search?q=Non-linearkerner" title=" Non-linearkerner"> Non-linearkerner</a> </p> <a href="https://publications.waset.org/4963/face-recognition-using-features-combination-and-a-new-non-linear-kernel" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/4963/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/4963/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/4963/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/4963/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/4963/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/4963/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/4963/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/4963/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/4963/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/4963/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/4963.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1540</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">164</span> Region Segmentation based on Gaussian Dirichlet Process Mixture Model and its Application to 3D Geometric Stricture Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Jonghyun%20Park">Jonghyun Park</a>, <a href="https://publications.waset.org/search?q=Soonyoung%20Park"> Soonyoung Park</a>, <a href="https://publications.waset.org/search?q=Sanggyun%20Kim"> Sanggyun Kim</a>, <a href="https://publications.waset.org/search?q=Wanhyun%20Cho"> Wanhyun Cho</a>, <a href="https://publications.waset.org/search?q=Sunworl%20Kim"> Sunworl Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In general, image-based 3D scenes can now be found in many popular vision systems, computer games and virtual reality tours. So, It is important to segment ROI (region of interest) from input scenes as a preprocessing step for geometric stricture detection in 3D scene. In this paper, we propose a method for segmenting ROI based on tensor voting and Dirichlet process mixture model. In particular, to estimate geometric structure information for 3D scene from a single outdoor image, we apply the tensor voting and Dirichlet process mixture model to a image segmentation. The tensor voting is used based on the fact that homogeneous region in an image are usually close together on a smooth region and therefore the tokens corresponding to centers of these regions have high saliency values. The proposed approach is a novel nonparametric Bayesian segmentation method using Gaussian Dirichlet process mixture model to automatically segment various natural scenes. Finally, our method can label regions of the input image into coarse categories: “ground", “sky", and “vertical" for 3D application. The experimental results show that our method successfully segments coarse regions in many complex natural scene images for 3D.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Region%20segmentation" title="Region segmentation">Region segmentation</a>, <a href="https://publications.waset.org/search?q=tensor%20voting" title=" tensor voting"> tensor voting</a>, <a href="https://publications.waset.org/search?q=image-based%203D" title=" image-based 3D"> image-based 3D</a>, <a href="https://publications.waset.org/search?q=geometric%20structure" title=" geometric structure"> geometric structure</a>, <a href="https://publications.waset.org/search?q=Gaussian%20Dirichlet%20process%20mixture%20model" title=" Gaussian Dirichlet process mixture model"> Gaussian Dirichlet process mixture model</a> </p> <a href="https://publications.waset.org/7008/region-segmentation-based-on-gaussian-dirichlet-process-mixture-model-and-its-application-to-3d-geometric-stricture-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/7008/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/7008/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/7008/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/7008/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/7008/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/7008/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/7008/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/7008/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/7008/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/7008/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/7008.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1891</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">163</span> Human Action Recognition Using Variational Bayesian HMM with Dirichlet Process Mixture of Gaussian Wishart Emission Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Wanhyun%20Cho">Wanhyun Cho</a>, <a href="https://publications.waset.org/search?q=Soonja%20Kang"> Soonja Kang</a>, <a href="https://publications.waset.org/search?q=Sangkyoon%20Kim"> Sangkyoon Kim</a>, <a href="https://publications.waset.org/search?q=Soonyoung%20Park"> Soonyoung Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In this paper, we present the human action recognition method using the variational Bayesian HMM with the Dirichlet process mixture (DPM) of the Gaussian-Wishart emission model (GWEM). First, we define the Bayesian HMM based on the Dirichlet process, which allows an infinite number of Gaussian-Wishart components to support continuous emission observations. Second, we have considered an efficient variational Bayesian inference method that can be applied to drive the posterior distribution of hidden variables and model parameters for the proposed model based on training data. And then we have derived the predictive distribution that may be used to classify new action. Third, the paper proposes a process of extracting appropriate spatial-temporal feature vectors that can be used to recognize a wide range of human behaviors from input video image. Finally, we have conducted experiments that can evaluate the performance of the proposed method. The experimental results show that the method presented is more efficient with human action recognition than existing methods.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Human%20action%20recognition" title="Human action recognition">Human action recognition</a>, <a href="https://publications.waset.org/search?q=Bayesian%20HMM" title=" Bayesian HMM"> Bayesian HMM</a>, <a href="https://publications.waset.org/search?q=Dirichlet%20process%20mixture%20model" title=" Dirichlet process mixture model"> Dirichlet process mixture model</a>, <a href="https://publications.waset.org/search?q=Gaussian-Wishart%20emission%20model" title=" Gaussian-Wishart emission model"> Gaussian-Wishart emission model</a>, <a href="https://publications.waset.org/search?q=Variational%20Bayesian%20inference" title=" Variational Bayesian inference"> Variational Bayesian inference</a>, <a href="https://publications.waset.org/search?q=Prior%20distribution%20and%20approximate%20posterior%20distribution" title=" Prior distribution and approximate posterior distribution"> Prior distribution and approximate posterior distribution</a>, <a href="https://publications.waset.org/search?q=KTH%20dataset." title=" KTH dataset."> KTH dataset.</a> </p> <a href="https://publications.waset.org/10005884/human-action-recognition-using-variational-bayesian-hmm-with-dirichlet-process-mixture-of-gaussian-wishart-emission-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10005884/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10005884/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10005884/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10005884/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10005884/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10005884/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10005884/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10005884/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10005884/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10005884/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10005884.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1006</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">162</span> Study of Effective Moisture Diffusivity of Oak Acorn</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Habibeh%20Nalbandi">Habibeh Nalbandi</a>, <a href="https://publications.waset.org/search?q=Sadegh%20Seiiedlou"> Sadegh Seiiedlou</a>, <a href="https://publications.waset.org/search?q=Hamid%20R.%20Ghasemzadeh"> Hamid R. Ghasemzadeh</a>, <a href="https://publications.waset.org/search?q=Naser%20Hamdami"> Naser Hamdami</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>The purpose of present work was to study the drying kinetics of whole acorn and its kernel at different drying air temperatures and their effective moisture diffusivity. The results indicated that the drying time of whole acorn was 442, 206 and 188 min at the air temperature of 65, 75 and 85ºC, respectively. At the same temperatures, the drying time of kernel was 131, 56 and 76min. The results showed that the effect of drying air temperature increasing on the drying time reduction could not be significant on acorn drying at all conditions. The effective moisture diffusivity of whole acorn and kernel increased with increasing air temperature from 65 to 75ºC. However more air temperature increasing, led to decreasing this property of acorn kernel. The critical temperature of acorn drying was about 75°C in which acorn kernel had the highest effective moisture diffusivity.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Critical%20temperature" title="Critical temperature">Critical temperature</a>, <a href="https://publications.waset.org/search?q=Drying%20kinetics" title=" Drying kinetics"> Drying kinetics</a>, <a href="https://publications.waset.org/search?q=Moisture%20diffusivity" title=" Moisture diffusivity"> Moisture diffusivity</a>, <a href="https://publications.waset.org/search?q=Oak%20acorn." title=" Oak acorn. "> Oak acorn. </a> </p> <a href="https://publications.waset.org/16902/study-of-effective-moisture-diffusivity-of-oak-acorn" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/16902/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/16902/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/16902/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/16902/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/16902/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/16902/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/16902/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/16902/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/16902/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/16902/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/16902.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1907</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">161</span> Waste Lubricating Oil Treatment by Adsorption Process Using Different Adsorbents</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Nabil%20M.%20Abdel-Jabbar">Nabil M. Abdel-Jabbar</a>, <a href="https://publications.waset.org/search?q=Essam%20A.H.%20Al%20Zubaidy"> Essam A.H. Al Zubaidy</a>, <a href="https://publications.waset.org/search?q=Mehrab%20Mehrvar"> Mehrab Mehrvar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Waste lubricating oil re-refining adsorption process by different adsorbent materials was investigated. Adsorbent materials such as oil adsorbent, egg shale powder, date palm kernel powder, and acid activated date palm kernel powder were used. The adsorption process over fixed amount of adsorbent at ambient conditions was investigated. The adsorption/extraction process was able to deposit the asphaltenic and metallic contaminants from the waste oil to lower values. It was found that the date palm kernel powder with contact time of 4 h was able to give the best conditions for treating the waste oil. The recovered solvent could be also reused. It was also found that the activated bentonite gave the best physical properties followed by the date palm kernel powder. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=activated%20bentonite" title="activated bentonite">activated bentonite</a>, <a href="https://publications.waset.org/search?q=egg%20shale%20powder" title=" egg shale powder"> egg shale powder</a>, <a href="https://publications.waset.org/search?q=datepalm%20kernel%20powder" title=" datepalm kernel powder"> datepalm kernel powder</a>, <a href="https://publications.waset.org/search?q=used%20oil%20treatment" title=" used oil treatment"> used oil treatment</a>, <a href="https://publications.waset.org/search?q=used%20oilcharacteristics." title=" used oilcharacteristics."> used oilcharacteristics.</a> </p> <a href="https://publications.waset.org/9869/waste-lubricating-oil-treatment-by-adsorption-process-using-different-adsorbents" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9869/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9869/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9869/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9869/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9869/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9869/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9869/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9869/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9869/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9869/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9869.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">3833</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">160</span> Adaptive Kernel Principal Analysis for Online Feature Extraction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Mingtao%20Ding">Mingtao Ding</a>, <a href="https://publications.waset.org/search?q=Zheng%20Tian"> Zheng Tian</a>, <a href="https://publications.waset.org/search?q=Haixia%20Xu"> Haixia Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>The batch nature limits the standard kernel principal component analysis (KPCA) methods in numerous applications, especially for dynamic or large-scale data. In this paper, an efficient adaptive approach is presented for online extraction of the kernel principal components (KPC). The contribution of this paper may be divided into two parts. First, kernel covariance matrix is correctly updated to adapt to the changing characteristics of data. Second, KPC are recursively formulated to overcome the batch nature of standard KPCA.This formulation is derived from the recursive eigen-decomposition of kernel covariance matrix and indicates the KPC variation caused by the new data. The proposed method not only alleviates sub-optimality of the KPCA method for non-stationary data, but also maintains constant update speed and memory usage as the data-size increases. Experiments for simulation data and real applications demonstrate that our approach yields improvements in terms of both computational speed and approximation accuracy.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=adaptive%20method" title="adaptive method">adaptive method</a>, <a href="https://publications.waset.org/search?q=kernel%20principal%20component%20analysis" title=" kernel principal component analysis"> kernel principal component analysis</a>, <a href="https://publications.waset.org/search?q=online%20extraction" title=" online extraction"> online extraction</a>, <a href="https://publications.waset.org/search?q=recursive%20algorithm" title=" recursive algorithm"> recursive algorithm</a> </p> <a href="https://publications.waset.org/5153/adaptive-kernel-principal-analysis-for-online-feature-extraction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/5153/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/5153/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/5153/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/5153/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/5153/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/5153/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/5153/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/5153/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/5153/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/5153/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/5153.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1552</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">159</span> An Iterative Algorithm for KLDA Classifier </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=D.N.%20Zheng">D.N. Zheng</a>, <a href="https://publications.waset.org/search?q=J.X.%20Wang"> J.X. Wang</a>, <a href="https://publications.waset.org/search?q=Y.N.%20Zhao"> Y.N. Zhao</a>, <a href="https://publications.waset.org/search?q=Z.H.%20Yang"> Z.H. Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Linear discriminant analysis (LDA) can be generalized into a nonlinear form - kernel LDA (KLDA) expediently by using the kernel functions. But KLDA is often referred to a general eigenvalue problem in singular case. To avoid this complication, this paper proposes an iterative algorithm for the two-class KLDA. The proposed KLDA is used as a nonlinear discriminant classifier, and the experiments show that it has a comparable performance with SVM. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Linear%20discriminant%20analysis%20%28LDA%29" title="Linear discriminant analysis (LDA)">Linear discriminant analysis (LDA)</a>, <a href="https://publications.waset.org/search?q=kernel%20LDA%0D%0A%28KLDA%29" title=" kernel LDA (KLDA)"> kernel LDA (KLDA)</a>, <a href="https://publications.waset.org/search?q=conjugate%20gradient%20algorithm" title=" conjugate gradient algorithm"> conjugate gradient algorithm</a>, <a href="https://publications.waset.org/search?q=nonlinear%20discriminant%20classifier." title=" nonlinear discriminant classifier."> nonlinear discriminant classifier.</a> </p> <a href="https://publications.waset.org/4549/an-iterative-algorithm-for-klda-classifier" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/4549/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/4549/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/4549/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/4549/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/4549/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/4549/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/4549/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/4549/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/4549/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/4549/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/4549.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1959</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">158</span> Multinomial Dirichlet Gaussian Process Model for Classification of Multidimensional Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Wanhyun%20Cho">Wanhyun Cho</a>, <a href="https://publications.waset.org/search?q=Soonja%20Kang"> Soonja Kang</a>, <a href="https://publications.waset.org/search?q=Sangkyoon%20Kim"> Sangkyoon Kim</a>, <a href="https://publications.waset.org/search?q=Soonyoung%20Park"> Soonyoung Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We present probabilistic multinomial Dirichlet classification model for multidimensional data and Gaussian process priors. Here, we have considered efficient computational method that can be used to obtain the approximate posteriors for latent variables and parameters needed to define the multiclass Gaussian process classification model. We first investigated the process of inducing a posterior distribution for various parameters and latent function by using the variational Bayesian approximations and important sampling method, and next we derived a predictive distribution of latent function needed to classify new samples. The proposed model is applied to classify the synthetic multivariate dataset in order to verify the performance of our model. Experiment result shows that our model is more accurate than the other approximation methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Multinomial%20dirichlet%20classification%20model" title="Multinomial dirichlet classification model">Multinomial dirichlet classification model</a>, <a href="https://publications.waset.org/search?q=Gaussian%0D%0Aprocess%20priors" title=" Gaussian process priors"> Gaussian process priors</a>, <a href="https://publications.waset.org/search?q=variational%20Bayesian%20approximation" title=" variational Bayesian approximation"> variational Bayesian approximation</a>, <a href="https://publications.waset.org/search?q=Importance%0D%0Asampling" title=" Importance sampling"> Importance sampling</a>, <a href="https://publications.waset.org/search?q=approximate%20posterior%20distribution" title=" approximate posterior distribution"> approximate posterior distribution</a>, <a href="https://publications.waset.org/search?q=Marginal%20likelihood%0D%0Aevidence." title=" Marginal likelihood evidence."> Marginal likelihood evidence.</a> </p> <a href="https://publications.waset.org/10003081/multinomial-dirichlet-gaussian-process-model-for-classification-of-multidimensional-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10003081/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10003081/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10003081/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10003081/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10003081/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10003081/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10003081/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10003081/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10003081/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10003081/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10003081.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1615</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">157</span> Learning the Dynamics of Articulated Tracked Vehicles</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Mario%20Gianni">Mario Gianni</a>, <a href="https://publications.waset.org/search?q=Manuel%20A.%20Ruiz%20Garcia"> Manuel A. Ruiz Garcia</a>, <a href="https://publications.waset.org/search?q=Fiora%20Pirri"> Fiora Pirri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, we present a Bayesian non-parametric approach to model the motion control of ATVs. The motion control model is based on a Dirichlet Process-Gaussian Process (DP-GP) mixture model. The DP-GP mixture model provides a flexible representation of patterns of control manoeuvres along trajectories of different lengths and discretizations. The model also estimates the number of patterns, sufficient for modeling the dynamics of the ATV. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Dirichlet%20processes" title="Dirichlet processes">Dirichlet processes</a>, <a href="https://publications.waset.org/search?q=Gaussian%20processes" title=" Gaussian processes"> Gaussian processes</a>, <a href="https://publications.waset.org/search?q=robot%20control%0D%0Alearning" title=" robot control learning"> robot control learning</a>, <a href="https://publications.waset.org/search?q=tracked%20vehicles." title=" tracked vehicles."> tracked vehicles.</a> </p> <a href="https://publications.waset.org/10004630/learning-the-dynamics-of-articulated-tracked-vehicles" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10004630/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10004630/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10004630/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10004630/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10004630/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10004630/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10004630/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10004630/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10004630/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10004630/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10004630.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1783</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">156</span> Topic Modeling Using Latent Dirichlet Allocation and Latent Semantic Indexing on South African Telco Twitter Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Phumelele%20P.%20Kubheka">Phumelele P. Kubheka</a>, <a href="https://publications.waset.org/search?q=Pius%20A.%20Owolawi"> Pius A. Owolawi</a>, <a href="https://publications.waset.org/search?q=Gbolahan%20Aiyetoro"> Gbolahan Aiyetoro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Twitter is one of the most popular social media platforms where users share their opinions on different subjects. Twitter can be considered a great source for mining text due to the high volumes of data generated through the platform daily. Many industries such as telecommunication companies can leverage the availability of Twitter data to better understand their markets and make an appropriate business decision. This study performs topic modeling on Twitter data using Latent Dirichlet Allocation (LDA). The obtained results are benchmarked with another topic modeling technique, Latent Semantic Indexing (LSI). The study aims to retrieve topics on a Twitter dataset containing user tweets on South African Telcos. Results from this study show that LSI is much faster than LDA. However, LDA yields better results with higher topic coherence by 8% for the best-performing model in this experiment. A higher topic coherence score indicates better performance of the model.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Big%20data" title="Big data">Big data</a>, <a href="https://publications.waset.org/search?q=latent%20Dirichlet%20allocation" title=" latent Dirichlet allocation"> latent Dirichlet allocation</a>, <a href="https://publications.waset.org/search?q=latent%20semantic%20indexing" title=" latent semantic indexing"> latent semantic indexing</a>, <a href="https://publications.waset.org/search?q=Telco" title=" Telco"> Telco</a>, <a href="https://publications.waset.org/search?q=topic%20modeling" title=" topic modeling"> topic modeling</a>, <a href="https://publications.waset.org/search?q=Twitter." title=" Twitter."> Twitter.</a> </p> <a href="https://publications.waset.org/10012641/topic-modeling-using-latent-dirichlet-allocation-and-latent-semantic-indexing-on-south-african-telco-twitter-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10012641/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10012641/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10012641/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10012641/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10012641/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10012641/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10012641/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10012641/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10012641/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10012641/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10012641.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">461</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">155</span> Optimal Feature Extraction Dimension in Finger Vein Recognition Using Kernel Principal Component Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Amir%20Hajian">Amir Hajian</a>, <a href="https://publications.waset.org/search?q=Sepehr%20Damavandinejadmonfared"> Sepehr Damavandinejadmonfared</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In this paper the issue of dimensionality reduction is investigated in finger vein recognition systems using kernel Principal Component Analysis (KPCA). One aspect of KPCA is to find the most appropriate kernel function on finger vein recognition as there are several kernel functions which can be used within PCA-based algorithms. In this paper, however, another side of PCA-based algorithms -particularly KPCA- is investigated. The aspect of dimension of feature vector in PCA-based algorithms is of importance especially when it comes to the real-world applications and usage of such algorithms. It means that a fixed dimension of feature vector has to be set to reduce the dimension of the input and output data and extract the features from them. Then a classifier is performed to classify the data and make the final decision. We analyze KPCA (Polynomial, Gaussian, and Laplacian) in details in this paper and investigate the optimal feature extraction dimension in finger vein recognition using KPCA.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Biometrics" title="Biometrics">Biometrics</a>, <a href="https://publications.waset.org/search?q=finger%20vein%20recognition" title=" finger vein recognition"> finger vein recognition</a>, <a href="https://publications.waset.org/search?q=Principal%0D%0AComponent%20Analysis%20%28PCA%29" title=" Principal Component Analysis (PCA)"> Principal Component Analysis (PCA)</a>, <a href="https://publications.waset.org/search?q=Kernel%20Principal%20Component%20Analysis%0D%0A%28KPCA%29." title=" Kernel Principal Component Analysis (KPCA)."> Kernel Principal Component Analysis (KPCA).</a> </p> <a href="https://publications.waset.org/9999486/optimal-feature-extraction-dimension-in-finger-vein-recognition-using-kernel-principal-component-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9999486/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9999486/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9999486/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9999486/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9999486/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9999486/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9999486/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9999486/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9999486/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9999486/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9999486.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1962</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">154</span> Flame Kernel Growth and Related Effects of Spark Plug Electrodes: Fluid Motion Interaction in an Optically Accessible DISI Engine</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=A.%20Schirru">A. Schirru</a>, <a href="https://publications.waset.org/search?q=A.%20Irimescu"> A. Irimescu</a>, <a href="https://publications.waset.org/search?q=S.%20Merola"> S. Merola</a>, <a href="https://publications.waset.org/search?q=A.%20d%E2%80%99Adamo"> A. d鈥橝damo</a>, <a href="https://publications.waset.org/search?q=S.%20Fontanesi"> S. Fontanesi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>One of the aspects that are usually neglected during the design phase of an engine is the effect of the spark plug on the flow field inside the combustion chamber. Because of the difficulties in the experimental investigation of the mutual interaction between flow alteration and early flame kernel convection effect inside the engine combustion chamber, CFD-3D simulation is usually exploited in such cases. Experimentally speaking, a particular type of engine has to be used in order to directly observe the flame propagation process. In this study, a double electrode spark plug was fitted into an optically accessible engine and a high-speed camera was used to capture the initial stages of the combustion process. Both the arc and the kernel phases were observed. Then, a morphologic analysis was carried out and the position of the center of mass of the flame, relative to the spark plug position, was calculated. The crossflow orientation was chosen for the spark plug and the kernel growth process was observed for different air-fuel ratios. It was observed that during a normal cycle the flow field between the electrodes tends to transport the arc deforming it. Because of that, the kernel growth phase takes place away from the electrodes and the flame propagates with a preferential direction dictated by the flow field.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Combustion" title="Combustion">Combustion</a>, <a href="https://publications.waset.org/search?q=Kernel%20growth" title=" Kernel growth"> Kernel growth</a>, <a href="https://publications.waset.org/search?q=optically%20accessible%20engine" title=" optically accessible engine"> optically accessible engine</a>, <a href="https://publications.waset.org/search?q=spark-ignition%20engine" title=" spark-ignition engine"> spark-ignition engine</a>, <a href="https://publications.waset.org/search?q=spark%20plug%20orientation." title=" spark plug orientation."> spark plug orientation.</a> </p> <a href="https://publications.waset.org/10011106/flame-kernel-growth-and-related-effects-of-spark-plug-electrodes-fluid-motion-interaction-in-an-optically-accessible-disi-engine" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10011106/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10011106/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10011106/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10011106/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10011106/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10011106/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10011106/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10011106/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10011106/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10011106/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10011106.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">756</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">153</span> Genetic Algorithms and Kernel Matrix-based Criteria Combined Approach to Perform Feature and Model Selection for Support Vector Machines</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=A.%20Perolini">A. Perolini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Feature and model selection are in the center of attention of many researches because of their impact on classifiers- performance. Both selections are usually performed separately but recent developments suggest using a combined GA-SVM approach to perform them simultaneously. This approach improves the performance of the classifier identifying the best subset of variables and the optimal parameters- values. Although GA-SVM is an effective method it is computationally expensive, thus a rough method can be considered. The paper investigates a joined approach of Genetic Algorithm and kernel matrix criteria to perform simultaneously feature and model selection for SVM classification problem. The purpose of this research is to improve the classification performance of SVM through an efficient approach, the Kernel Matrix Genetic Algorithm method (KMGA). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Feature%20and%20model%20selection" title="Feature and model selection">Feature and model selection</a>, <a href="https://publications.waset.org/search?q=Genetic%20Algorithms" title=" Genetic Algorithms"> Genetic Algorithms</a>, <a href="https://publications.waset.org/search?q=Support%20Vector%20Machines" title="Support Vector Machines">Support Vector Machines</a>, <a href="https://publications.waset.org/search?q=kernel%20matrix." title=" kernel matrix."> kernel matrix.</a> </p> <a href="https://publications.waset.org/1583/genetic-algorithms-and-kernel-matrix-based-criteria-combined-approach-to-perform-feature-and-model-selection-for-support-vector-machines" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/1583/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/1583/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/1583/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/1583/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/1583/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/1583/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/1583/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/1583/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/1583/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/1583/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/1583.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1597</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">152</span> Enhancing Predictive Accuracy in Pharmaceutical Sales Through an Ensemble Kernel Gaussian Process Regression Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Shahin%20Mirshekari">Shahin Mirshekari</a>, <a href="https://publications.waset.org/search?q=Mohammadreza%20Moradi"> Mohammadreza Moradi</a>, <a href="https://publications.waset.org/search?q=Hossein%20Jafari"> Hossein Jafari</a>, <a href="https://publications.waset.org/search?q=Mehdi%20Jafari"> Mehdi Jafari</a>, <a href="https://publications.waset.org/search?q=Mohammad%20Ensaf"> Mohammad Ensaf</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>This research employs Gaussian Process Regression (GPR) with an ensemble kernel, integrating Exponential Squared, Revised Mat茅rn, and Rational Quadratic kernels to analyze pharmaceutical sales data. Bayesian optimization was used to identify optimal kernel weights: 0.76 for Exponential Squared, 0.21 for Revised Mat茅rn, and 0.13 for Rational Quadratic. The ensemble kernel demonstrated superior performance in predictive accuracy, achieving an R虏 score near 1.0, and significantly lower values in MSE, MAE, and RMSE. These findings highlight the efficacy of ensemble kernels in GPR for predictive analytics in complex pharmaceutical sales datasets.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Gaussian%20Process%20Regression" title="Gaussian Process Regression">Gaussian Process Regression</a>, <a href="https://publications.waset.org/search?q=Ensemble%20Kernels" title=" Ensemble Kernels"> Ensemble Kernels</a>, <a href="https://publications.waset.org/search?q=Bayesian%20Optimization" title=" Bayesian Optimization"> Bayesian Optimization</a>, <a href="https://publications.waset.org/search?q=Pharmaceutical%20Sales%20Analysis" title=" Pharmaceutical Sales Analysis"> Pharmaceutical Sales Analysis</a>, <a href="https://publications.waset.org/search?q=Time%20Series%0D%0AForecasting" title=" Time Series Forecasting"> Time Series Forecasting</a>, <a href="https://publications.waset.org/search?q=Data%20Analysis." title=" Data Analysis."> Data Analysis.</a> </p> <a href="https://publications.waset.org/10013623/enhancing-predictive-accuracy-in-pharmaceutical-sales-through-an-ensemble-kernel-gaussian-process-regression-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10013623/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10013623/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10013623/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10013623/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10013623/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10013623/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10013623/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10013623/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10013623/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10013623/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10013623.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">111</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">151</span> Composite Kernels for Public Emotion Recognition from Twitter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Chien-Hung%20Chen">Chien-Hung Chen</a>, <a href="https://publications.waset.org/search?q=Yan-Chun%20Hsing"> Yan-Chun Hsing</a>, <a href="https://publications.waset.org/search?q=Yung-Chun%20Chang"> Yung-Chun Chang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>The Internet has grown into a powerful medium for information dispersion and social interaction that leads to a rapid growth of social media which allows users to easily post their emotions and perspectives regarding certain topics online. Our research aims at using natural language processing and text mining techniques to explore the public emotions expressed on Twitter by analyzing the sentiment behind tweets. In this paper, we propose a composite kernel method that integrates tree kernel with the linear kernel to simultaneously exploit both the tree representation and the distributed emotion keyword representation to analyze the syntactic and content information in tweets. The experiment results demonstrate that our method can effectively detect public emotion of tweets while outperforming the other compared methods.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Public%20emotion%20recognition" title="Public emotion recognition">Public emotion recognition</a>, <a href="https://publications.waset.org/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/search?q=composite%20kernel" title=" composite kernel"> composite kernel</a>, <a href="https://publications.waset.org/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a>, <a href="https://publications.waset.org/search?q=text%20mining." title=" text mining."> text mining.</a> </p> <a href="https://publications.waset.org/10009602/composite-kernels-for-public-emotion-recognition-from-twitter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10009602/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10009602/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10009602/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10009602/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10009602/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10009602/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10009602/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10009602/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10009602/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10009602/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10009602.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">773</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">150</span> On Discretization of Second-order Derivatives in Smoothed Particle Hydrodynamics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=R.%20Fatehi">R. Fatehi</a>, <a href="https://publications.waset.org/search?q=M.A.%20Fayazbakhsh"> M.A. Fayazbakhsh</a>, <a href="https://publications.waset.org/search?q=M.T.%20Manzari"> M.T. Manzari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Discretization of spatial derivatives is an important issue in meshfree methods especially when the derivative terms contain non-linear coefficients. In this paper, various methods used for discretization of second-order spatial derivatives are investigated in the context of Smoothed Particle Hydrodynamics. Three popular forms (i.e. "double summation", "second-order kernel derivation", and "difference scheme") are studied using one-dimensional unsteady heat conduction equation. To assess these schemes, transient response to a step function initial condition is considered. Due to parabolic nature of the heat equation, one can expect smooth and monotone solutions. It is shown, however in this paper, that regardless of the type of kernel function used and the size of smoothing radius, the double summation discretization form leads to non-physical oscillations which persist in the solution. Also, results show that when a second-order kernel derivative is used, a high-order kernel function shall be employed in such a way that the distance of inflection point from origin in the kernel function be less than the nearest particle distance. Otherwise, solutions may exhibit oscillations near discontinuities unlike the "difference scheme" which unconditionally produces monotone results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Heat%20conduction" title="Heat conduction">Heat conduction</a>, <a href="https://publications.waset.org/search?q=Meshfree%20methods" title=" Meshfree methods"> Meshfree methods</a>, <a href="https://publications.waset.org/search?q=Smoothed%20ParticleHydrodynamics%20%28SPH%29" title=" Smoothed ParticleHydrodynamics (SPH)"> Smoothed ParticleHydrodynamics (SPH)</a>, <a href="https://publications.waset.org/search?q=Second-order%20derivatives." title=" Second-order derivatives."> Second-order derivatives.</a> </p> <a href="https://publications.waset.org/14882/on-discretization-of-second-order-derivatives-in-smoothed-particle-hydrodynamics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/14882/apa" target="_blank" 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