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process mixture model</title> <meta name="description" content="Search results for: Dirichlet process mixture model"> <meta name="keywords" content="Dirichlet process mixture model"> <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> 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action="https://publications.waset.org/abstracts/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 process mixture model"> <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> 29331</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Dirichlet process mixture model</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29331</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/abstracts/search?q=Mario%20Gianni">Mario Gianni</a>, <a href="https://publications.waset.org/abstracts/search?q=Manuel%20A.%20Ruiz%20Garcia"> Manuel A. Ruiz Garcia</a>, <a href="https://publications.waset.org/abstracts/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/abstracts/search?q=Dirichlet%20processes" title="Dirichlet processes">Dirichlet processes</a>, <a href="https://publications.waset.org/abstracts/search?q=gaussian%20mixture%20models" title=" gaussian mixture models"> gaussian mixture models</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20motion%20patterns" title=" learning motion patterns"> learning motion patterns</a>, <a href="https://publications.waset.org/abstracts/search?q=tracked%20robots%20for%20urban%20search%20and%20rescue" title=" tracked robots for urban search and rescue"> tracked robots for urban search and rescue</a> </p> <a href="https://publications.waset.org/abstracts/45613/learning-the-dynamics-of-articulated-tracked-vehicles" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45613.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">449</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29330</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/abstracts/search?q=Wanhyun%20Cho">Wanhyun Cho</a>, <a href="https://publications.waset.org/abstracts/search?q=Soonja%20Kang"> Soonja Kang</a>, <a href="https://publications.waset.org/abstracts/search?q=Sangkyoon%20Kim"> Sangkyoon Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Soonyoung%20Park"> Soonyoung Park</a> </p> <p class="card-text"><strong>Abstract:</strong></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 class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=human%20action%20recognition" title="human action recognition">human action recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20HMM" title=" Bayesian HMM"> Bayesian HMM</a>, <a href="https://publications.waset.org/abstracts/search?q=Dirichlet%20process%20mixture%20model" title=" Dirichlet process mixture model"> Dirichlet process mixture model</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian-Wishart%20emission%20model" title=" Gaussian-Wishart emission model"> Gaussian-Wishart emission model</a>, <a href="https://publications.waset.org/abstracts/search?q=Variational%20Bayesian%20inference" title=" Variational Bayesian inference"> Variational Bayesian inference</a>, <a href="https://publications.waset.org/abstracts/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/abstracts/search?q=KTH%20dataset" title=" KTH dataset"> KTH dataset</a> </p> <a href="https://publications.waset.org/abstracts/49713/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/abstracts/49713.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">353</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29329</span> An Infinite Mixture Model for Modelling Stutter Ratio in Forensic Data Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20A.%20C.%20S.%20Sampath%20Fernando">M. A. C. S. Sampath Fernando</a>, <a href="https://publications.waset.org/abstracts/search?q=James%20M.%20Curran"> James M. Curran</a>, <a href="https://publications.waset.org/abstracts/search?q=Renate%20Meyer"> Renate Meyer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Forensic DNA analysis has received much attention over the last three decades, due to its incredible usefulness in human identification. The statistical interpretation of DNA evidence is recognised as one of the most mature fields in forensic science. Peak heights in an Electropherogram (EPG) are approximately proportional to the amount of template DNA in the original sample being tested. A stutter is a minor peak in an EPG, which is not masking as an allele of a potential contributor, and considered as an artefact that is presumed to be arisen due to miscopying or slippage during the PCR. Stutter peaks are mostly analysed in terms of stutter ratio that is calculated relative to the corresponding parent allele height. Analysis of mixture profiles has always been problematic in evidence interpretation, especially with the presence of PCR artefacts like stutters. Unlike binary and semi-continuous models; continuous models assign a probability (as a continuous weight) for each possible genotype combination, and significantly enhances the use of continuous peak height information resulting in more efficient reliable interpretations. Therefore, the presence of a sound methodology to distinguish between stutters and real alleles is essential for the accuracy of the interpretation. Sensibly, any such method has to be able to focus on modelling stutter peaks. Bayesian nonparametric methods provide increased flexibility in applied statistical modelling. Mixture models are frequently employed as fundamental data analysis tools in clustering and classification of data and assume unidentified heterogeneous sources for data. In model-based clustering, each unknown source is reflected by a cluster, and the clusters are modelled using parametric models. Specifying the number of components in finite mixture models, however, is practically difficult even though the calculations are relatively simple. Infinite mixture models, in contrast, do not require the user to specify the number of components. Instead, a Dirichlet process, which is an infinite-dimensional generalization of the Dirichlet distribution, is used to deal with the problem of a number of components. Chinese restaurant process (CRP), Stick-breaking process and Pólya urn scheme are frequently used as Dirichlet priors in Bayesian mixture models. In this study, we illustrate an infinite mixture of simple linear regression models for modelling stutter ratio and introduce some modifications to overcome weaknesses associated with CRP. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chinese%20restaurant%20process" title="Chinese restaurant process">Chinese restaurant process</a>, <a href="https://publications.waset.org/abstracts/search?q=Dirichlet%20prior" title=" Dirichlet prior"> Dirichlet prior</a>, <a href="https://publications.waset.org/abstracts/search?q=infinite%20mixture%20model" title=" infinite mixture model"> infinite mixture model</a>, <a href="https://publications.waset.org/abstracts/search?q=PCR%20stutter" title=" PCR stutter"> PCR stutter</a> </p> <a href="https://publications.waset.org/abstracts/57612/an-infinite-mixture-model-for-modelling-stutter-ratio-in-forensic-data-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57612.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">330</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29328</span> Evaluating Traffic Congestion Using the Bayesian Dirichlet Process Mixture of Generalized Linear Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ren%20Moses">Ren Moses</a>, <a href="https://publications.waset.org/abstracts/search?q=Emmanuel%20Kidando"> Emmanuel Kidando</a>, <a href="https://publications.waset.org/abstracts/search?q=Eren%20Ozguven"> Eren Ozguven</a>, <a href="https://publications.waset.org/abstracts/search?q=Yassir%20Abdelrazig"> Yassir Abdelrazig</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study applied traffic speed and occupancy to develop clustering models that identify different traffic conditions. Particularly, these models are based on the Dirichlet Process Mixture of Generalized Linear regression (DML) and change-point regression (CR). The model frameworks were implemented using 2015 historical traffic data aggregated at a 15-minute interval from an Interstate 295 freeway in Jacksonville, Florida. Using the deviance information criterion (DIC) to identify the appropriate number of mixture components, three traffic states were identified as free-flow, transitional, and congested condition. Results of the DML revealed that traffic occupancy is statistically significant in influencing the reduction of traffic speed in each of the identified states. Influence on the free-flow and the congested state was estimated to be higher than the transitional flow condition in both evening and morning peak periods. Estimation of the critical speed threshold using CR revealed that 47 mph and 48 mph are speed thresholds for congested and transitional traffic condition during the morning peak hours and evening peak hours, respectively. Free-flow speed thresholds for morning and evening peak hours were estimated at 64 mph and 66 mph, respectively. The proposed approaches will facilitate accurate detection and prediction of traffic congestion for developing effective countermeasures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=traffic%20congestion" title="traffic congestion">traffic congestion</a>, <a href="https://publications.waset.org/abstracts/search?q=multistate%20speed%20distribution" title=" multistate speed distribution"> multistate speed distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20occupancy" title=" traffic occupancy"> traffic occupancy</a>, <a href="https://publications.waset.org/abstracts/search?q=Dirichlet%20process%20mixtures%20of%20generalized%20linear%20model" title=" Dirichlet process mixtures of generalized linear model"> Dirichlet process mixtures of generalized linear model</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20change-point%20detection" title=" Bayesian change-point detection"> Bayesian change-point detection</a> </p> <a href="https://publications.waset.org/abstracts/67198/evaluating-traffic-congestion-using-the-bayesian-dirichlet-process-mixture-of-generalized-linear-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67198.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">294</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29327</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/abstracts/search?q=Wanhyun%20Cho">Wanhyun Cho</a>, <a href="https://publications.waset.org/abstracts/search?q=Soonja%20Kang"> Soonja Kang</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanggoon%20Kim"> Sanggoon Kim</a>, <a href="https://publications.waset.org/abstracts/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 an 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/abstracts/search?q=multinomial%20dirichlet%20classification%20model" title="multinomial dirichlet classification model">multinomial dirichlet classification model</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20process%20priors" title=" Gaussian process priors"> Gaussian process priors</a>, <a href="https://publications.waset.org/abstracts/search?q=variational%20Bayesian%20approximation" title=" variational Bayesian approximation"> variational Bayesian approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=importance%20sampling" title=" importance sampling"> importance sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=approximate%20posterior%20distribution" title=" approximate posterior distribution"> approximate posterior distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=marginal%20likelihood%20evidence" title=" marginal likelihood evidence"> marginal likelihood evidence</a> </p> <a href="https://publications.waset.org/abstracts/33816/multinomial-dirichlet-gaussian-process-model-for-classification-of-multidimensional-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33816.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">444</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29326</span> Closed Forms of Trigonometric Series Interms of Riemann’s ζ Function and Dirichlet η, λ, β Functions or the Hurwitz Zeta Function and Harmonic Numbers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Slobodan%20B.%20Tri%C4%8Dkovi%C4%87">Slobodan B. Tričković</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We present the results concerned with trigonometric series that include sine and cosine functions with a parameter appearing in the denominator. We derive two types of closed-form formulas for trigonometric series. At first, for some integer values, as we know that Riemann’s ζ function and Dirichlet η, λ equal zero at negative even integers, whereas Dirichlet’s β function equals zero at negative odd integers, after a certain number of members, the rest of the series vanishes. Thus, a trigonometric series becomes a polynomial with coefficients involving Riemann’s ζ function and Dirichlet η, λ, β functions. On the other hand, in some cases, one cannot immediately replace the parameter with any positive integer because we shall encounter singularities. So it is necessary to take a limit, so in the process, we apply L’Hospital’s rule and, after a series of rearrangements, we bring a trigonometric series to a form suitable for the application of Choi-Srivastava’s theorem dealing with Hurwitz’s zeta function and Harmonic numbers. In this way, we express a trigonometric series as a polynomial over Hurwitz’s zeta function derivative. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dirichlet%20eta%20lambda%20beta%20functions" title="Dirichlet eta lambda beta functions">Dirichlet eta lambda beta functions</a>, <a href="https://publications.waset.org/abstracts/search?q=Riemann%27s%20zeta%20function" title=" Riemann's zeta function"> Riemann's zeta function</a>, <a href="https://publications.waset.org/abstracts/search?q=Hurwitz%20zeta%20function" title=" Hurwitz zeta function"> Hurwitz zeta function</a>, <a href="https://publications.waset.org/abstracts/search?q=Harmonic%20numbers" title=" Harmonic numbers"> Harmonic numbers</a> </p> <a href="https://publications.waset.org/abstracts/167649/closed-forms-of-trigonometric-series-interms-of-riemanns-z-function-and-dirichlet-i-l-v-functions-or-the-hurwitz-zeta-function-and-harmonic-numbers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167649.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">103</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29325</span> Existence of Positive Solutions to a Dirichlet Second Order Boundary Value Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Sufian%20Jusoh">Muhammad Sufian Jusoh</a>, <a href="https://publications.waset.org/abstracts/search?q=Mesliza%20Mohamed"> Mesliza Mohamed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we investigate the existence of positive solutions for a Dirichlet second order boundary value problem by applying the Krasnosel'skii fixed point theorem on compression and expansion of cones. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Krasnosel%27skii%20fixed%20point%20theorem" title="Krasnosel'skii fixed point theorem">Krasnosel'skii fixed point theorem</a>, <a href="https://publications.waset.org/abstracts/search?q=positive%20solutions" title=" positive solutions"> positive solutions</a>, <a href="https://publications.waset.org/abstracts/search?q=Dirichlet%20boundary%20value%20problem" title=" Dirichlet boundary value problem"> Dirichlet boundary value problem</a>, <a href="https://publications.waset.org/abstracts/search?q=Dirichlet%20second%20order%20%20boundary%20problem" title=" Dirichlet second order boundary problem"> Dirichlet second order boundary problem</a> </p> <a href="https://publications.waset.org/abstracts/16347/existence-of-positive-solutions-to-a-dirichlet-second-order-boundary-value-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16347.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">417</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29324</span> Process Simulation of 1-Butene Separation from C4 Mixture by Extractive Distillation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Naeem">Muhammad Naeem</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdulrahman%20A.%20Al-Rabiah"> Abdulrahman A. Al-Rabiah</a>, <a href="https://publications.waset.org/abstracts/search?q=Wasif%20Mughees"> Wasif Mughees</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Technical mixture of C4 containing 1-butene and n-butane are very close to each other with regard to their boiling points i.e. -6.3°C for 1-butene and -1°C for n-butane. Extractive distillation process is used for the separation of 1-butene from the existing mixture of C4. The solvent is the essential of extractive distillation, and an appropriate solvent plays an important role in the process economy of extractive distillation. Aspen Plus has been applied for the separation of these hydrocarbons as a simulator. Moreover, NRTL activity coefficient model was used in the simulation. This model indicated that the material balances in this separation process were accurate for several solvent flow rates. Mixture of acetonitrile and water used as a solvent and 99% pure 1-butene was separated. This simulation proposed the ratio of the feed to solvent as 1: 7.9 and 15 plates for the solvent recovery column. Previously feed to solvent ratio was more than this and the number of proposed plates were 30, which shows that the separation process can be economized. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extractive%20distillation" title="extractive distillation">extractive distillation</a>, <a href="https://publications.waset.org/abstracts/search?q=1-butene" title=" 1-butene"> 1-butene</a>, <a href="https://publications.waset.org/abstracts/search?q=aspen%20plus" title=" aspen plus"> aspen plus</a>, <a href="https://publications.waset.org/abstracts/search?q=ACN%20solvent" title=" ACN solvent"> ACN solvent</a> </p> <a href="https://publications.waset.org/abstracts/5813/process-simulation-of-1-butene-separation-from-c4-mixture-by-extractive-distillation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5813.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">544</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29323</span> A Learning-Based EM Mixture Regression Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yi-Cheng%20Tian">Yi-Cheng Tian</a>, <a href="https://publications.waset.org/abstracts/search?q=Miin-Shen%20Yang"> Miin-Shen Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The mixture likelihood approach to clustering is a popular clustering method where the expectation and maximization (EM) algorithm is the most used mixture likelihood method. In the literature, the EM algorithm had been used for mixture regression models. However, these EM mixture regression algorithms are sensitive to initial values with a priori number of clusters. In this paper, to resolve these drawbacks, we construct a learning-based schema for the EM mixture regression algorithm such that it is free of initializations and can automatically obtain an approximately optimal number of clusters. Some numerical examples and comparisons demonstrate the superiority and usefulness of the proposed learning-based EM mixture regression algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering" title="clustering">clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=EM%20algorithm" title=" EM algorithm"> EM algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20mixture%20model" title=" Gaussian mixture model"> Gaussian mixture model</a>, <a href="https://publications.waset.org/abstracts/search?q=mixture%20regression%20model" title=" mixture regression model"> mixture regression model</a> </p> <a href="https://publications.waset.org/abstracts/25163/a-learning-based-em-mixture-regression-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25163.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">510</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29322</span> Business Process Mashup</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fethia%20Zenak">Fethia Zenak</a>, <a href="https://publications.waset.org/abstracts/search?q=Salima%20Benbernou"> Salima Benbernou</a>, <a href="https://publications.waset.org/abstracts/search?q=Linda%20Zaoui"> Linda Zaoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, many companies are based on process development from scratch to achieve their business goals. The process development is not trivial and the main objective of enterprise managing processes is to decrease the software development time. Several concepts have been proposed in the field of business process-based reused development, known as BP Mashup. This concept consists of reusing existing business processes which have been modeled in order to respond to a particular goal. To meet user process requirements, our contribution is to mix parts of processes as 'processes fragments' components to build a new process (i.e. process mashup). The main idea of our paper is to offer graphical framework tool for both creating and running processes mashup. Allow users to perform a mixture of fragments, using a simple interface with set of graphical mixture operators based on a proposed formal model. A process mashup and mixture behavior are described within a new specification of a high-level language, language for process mashup (BPML). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=business%20process" title="business process">business process</a>, <a href="https://publications.waset.org/abstracts/search?q=mashup" title=" mashup"> mashup</a>, <a href="https://publications.waset.org/abstracts/search?q=fragments" title=" fragments"> fragments</a>, <a href="https://publications.waset.org/abstracts/search?q=bp%20mashup" title=" bp mashup"> bp mashup</a> </p> <a href="https://publications.waset.org/abstracts/4098/business-process-mashup" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4098.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">635</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29321</span> Olefin and Paraffin Separation Using Simulations on Extractive Distillation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Naeem">Muhammad Naeem</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdulrahman%20A.%20Al-Rabiah"> Abdulrahman A. Al-Rabiah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Technical mixture of C4 containing 1-butene and n-butane are very close to each other with respect to their boiling points i.e. -6.3°C for 1-butene and -1°C for n-butane. Extractive distillation process is used for the separation of 1-butene from the existing mixture of C4. The solvent is the essential of extractive distillation, and an appropriate solvent shows an important role in the process economy of extractive distillation. Aspen Plus has been applied for the separation of these hydrocarbons as a simulator; moreover NRTL activity coefficient model was used in the simulation. This model indicated that the material balances in this separation process were accurate for several solvent flow rates. Mixture of acetonitrile and water used as a solvent and 99 % pure 1-butene was separated. This simulation proposed the ratio of the feed to solvent as 1 : 7.9 and 15 plates for the solvent recovery column, previously feed to solvent ratio was more than this and the proposed plates were 30, which can economize the separation process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extractive%20distillation" title="extractive distillation">extractive distillation</a>, <a href="https://publications.waset.org/abstracts/search?q=1-butene" title=" 1-butene"> 1-butene</a>, <a href="https://publications.waset.org/abstracts/search?q=Aspen%20Plus" title=" Aspen Plus"> Aspen Plus</a>, <a href="https://publications.waset.org/abstracts/search?q=ACN%20solvent" title=" ACN solvent "> ACN solvent </a> </p> <a href="https://publications.waset.org/abstracts/10500/olefin-and-paraffin-separation-using-simulations-on-extractive-distillation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10500.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">447</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29320</span> Topic Modelling Using Latent Dirichlet Allocation and Latent Semantic Indexing on SA Telco Twitter Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Phumelele%20Kubheka">Phumelele Kubheka</a>, <a href="https://publications.waset.org/abstracts/search?q=Pius%20Owolawi"> Pius Owolawi</a>, <a href="https://publications.waset.org/abstracts/search?q=Gbolahan%20Aiyetoro"> Gbolahan Aiyetoro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Twitter is one of the most popular social media platforms where users can share their opinions on different subjects. As of 2010, The Twitter platform generates more than 12 Terabytes of data daily, ~ 4.3 petabytes in a single year. For this reason, Twitter is a great source for big mining data. 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 represented in Table 1. A higher topic coherence score indicates better performance of the model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=latent%20Dirichlet%20allocation" title=" latent Dirichlet allocation"> latent Dirichlet allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=latent%20semantic%20indexing" title=" latent semantic indexing"> latent semantic indexing</a>, <a href="https://publications.waset.org/abstracts/search?q=telco" title=" telco"> telco</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20modeling" title=" topic modeling"> topic modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=twitter" title=" twitter"> twitter</a> </p> <a href="https://publications.waset.org/abstracts/147818/topic-modelling-using-latent-dirichlet-allocation-and-latent-semantic-indexing-on-sa-telco-twitter-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147818.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">150</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29319</span> Gas Pressure Evaluation through Radial Velocity Measurement of Fluid Flow Modeled by Drift Flux Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aicha%20Rima%20Cheniti">Aicha Rima Cheniti</a>, <a href="https://publications.waset.org/abstracts/search?q=Hatem%20Besbes"> Hatem Besbes</a>, <a href="https://publications.waset.org/abstracts/search?q=Joseph%20Haggege"> Joseph Haggege</a>, <a href="https://publications.waset.org/abstracts/search?q=Christophe%20Sintes"> Christophe Sintes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we consider a drift flux mixture model of the blood flow. The mixture consists of gas phase which is carbon dioxide and liquid phase which is an aqueous carbon dioxide solution. This model was used to determine the distributions of the mixture velocity, the mixture pressure, and the carbon dioxide pressure. These theoretical data are used to determine a measurement method of mean gas pressure through the determination of radial velocity distribution. This method can be applicable in experimental domain. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mean%20carbon%20dioxide%20pressure" title="mean carbon dioxide pressure">mean carbon dioxide pressure</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20mixture%20pressure" title=" mean mixture pressure"> mean mixture pressure</a>, <a href="https://publications.waset.org/abstracts/search?q=mixture%20velocity" title=" mixture velocity"> mixture velocity</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20velocity" title=" radial velocity"> radial velocity</a> </p> <a href="https://publications.waset.org/abstracts/52258/gas-pressure-evaluation-through-radial-velocity-measurement-of-fluid-flow-modeled-by-drift-flux-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52258.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">324</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29318</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/abstracts/search?q=Khaled%20Moaddy">Khaled Moaddy</a> </p> <p class="card-text"><strong>Abstract:</strong></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 class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=standard%20finite%20difference%20schemes" title="standard finite difference schemes">standard finite difference schemes</a>, <a href="https://publications.waset.org/abstracts/search?q=non-standard%20schemes" title=" non-standard schemes"> non-standard schemes</a>, <a href="https://publications.waset.org/abstracts/search?q=Laplace%20equation" title=" Laplace equation"> Laplace equation</a>, <a href="https://publications.waset.org/abstracts/search?q=Dirichlet%20boundary%20conditions" title=" Dirichlet boundary conditions"> Dirichlet boundary conditions</a> </p> <a href="https://publications.waset.org/abstracts/119718/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/abstracts/119718.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">132</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29317</span> Evaluation of Carbon Dioxide Pressure through Radial Velocity Difference in Arterial Blood Modeled by Drift Flux Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aicha%20Rima%20Cheniti">Aicha Rima Cheniti</a>, <a href="https://publications.waset.org/abstracts/search?q=Hatem%20Besbes"> Hatem Besbes</a>, <a href="https://publications.waset.org/abstracts/search?q=Joseph%20Haggege"> Joseph Haggege</a>, <a href="https://publications.waset.org/abstracts/search?q=Christophe%20Sintes"> Christophe Sintes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we are interested to determine the carbon dioxide pressure in the arterial blood through radial velocity difference. The blood was modeled as a two phase mixture (an aqueous carbon dioxide solution with carbon dioxide gas) by Drift flux model and the Young-Laplace equation. The distributions of mixture velocities determined from the considered model permitted the calculation of the radial velocity distributions with different values of mean mixture pressure and the calculation of the mean carbon dioxide pressure knowing the mean mixture pressure. The radial velocity distributions are used to deduce a calculation method of the mean mixture pressure through the radial velocity difference between two positions which is measured by ultrasound. The mean carbon dioxide pressure is then deduced from the mean mixture pressure. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mean%20carbon%20dioxide%20pressure" title="mean carbon dioxide pressure">mean carbon dioxide pressure</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20mixture%20pressure" title=" mean mixture pressure"> mean mixture pressure</a>, <a href="https://publications.waset.org/abstracts/search?q=mixture%20velocity" title=" mixture velocity"> mixture velocity</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20velocity%20difference" title=" radial velocity difference"> radial velocity difference</a> </p> <a href="https://publications.waset.org/abstracts/51601/evaluation-of-carbon-dioxide-pressure-through-radial-velocity-difference-in-arterial-blood-modeled-by-drift-flux-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51601.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">421</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29316</span> OLED Encapsulation Process Using Low Melting Point Alloy and Epoxy Mixture by Instantaneous Discharge</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kyung%20Min%20Park">Kyung Min Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Cheol%20Hee%20Moon"> Cheol Hee Moon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study we are to develop a sealing process using a mixture of a LMPA and an epoxy for the atmospheric OLED sealing process as a substitute for the thin-film process. Electrode lines were formed on the substrates, which were covered with insulating layers and sacrificial layers. A mixture of a LMPA and an epoxy was screen printed between the two electrodes. In order to generate a heat for the melting of the mixture, Joule heating method was used. Were used instantaneous discharge process for generating Joule heating. Experimental conditions such as voltage, time and constituent of the electrode were varied to optimize the heating conditions. As a result, the mixture structure of this study showed a great potential for a low-cost, low-temperature, atmospheric OLED sealing process as a substitute for the thin-film process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=organic%20light%20emitting%20diode" title="organic light emitting diode">organic light emitting diode</a>, <a href="https://publications.waset.org/abstracts/search?q=encapsulation" title=" encapsulation"> encapsulation</a>, <a href="https://publications.waset.org/abstracts/search?q=low%20melting%20point%20alloy" title=" low melting point alloy"> low melting point alloy</a>, <a href="https://publications.waset.org/abstracts/search?q=joule%20heat" title=" joule heat"> joule heat</a> </p> <a href="https://publications.waset.org/abstracts/17279/oled-encapsulation-process-using-low-melting-point-alloy-and-epoxy-mixture-by-instantaneous-discharge" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17279.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">549</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29315</span> Text Mining of Twitter Data Using a Latent Dirichlet Allocation Topic Model and Sentiment Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sidi%20Yang">Sidi Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Haiyi%20Zhang"> Haiyi Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Twitter is a microblogging platform, where millions of users daily share their attitudes, views, and opinions. Using a probabilistic Latent Dirichlet Allocation (LDA) topic model to discern the most popular topics in the Twitter data is an effective way to analyze a large set of tweets to find a set of topics in a computationally efficient manner. Sentiment analysis provides an effective method to show the emotions and sentiments found in each tweet and an efficient way to summarize the results in a manner that is clearly understood. The primary goal of this paper is to explore text mining, extract and analyze useful information from unstructured text using two approaches: LDA topic modelling and sentiment analysis by examining Twitter plain text data in English. These two methods allow people to dig data more effectively and efficiently. LDA topic model and sentiment analysis can also be applied to provide insight views in business and scientific fields. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=text%20mining" title="text mining">text mining</a>, <a href="https://publications.waset.org/abstracts/search?q=Twitter" title=" Twitter"> Twitter</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20model" title=" topic model"> topic model</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a> </p> <a href="https://publications.waset.org/abstracts/95281/text-mining-of-twitter-data-using-a-latent-dirichlet-allocation-topic-model-and-sentiment-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95281.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">179</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29314</span> Mathematical Modeling of the AMCs Cross-Contamination Removal in the FOUPs: Finite Element Formulation and Application in FOUP’s Decontamination</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20Santatriniaina">N. Santatriniaina</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Deseure"> J. Deseure</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Q.%20Nguyen"> T. Q. Nguyen</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Fontaine"> H. Fontaine</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Beitia"> C. Beitia</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Rakotomanana"> L. Rakotomanana</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, with the increasing of the wafer's size and the decreasing of critical size of integrated circuit manufacturing in modern high-tech, microelectronics industry needs a maximum attention to challenge the contamination control. The move to 300 mm is accompanied by the use of Front Opening Unified Pods for wafer and his storage. In these pods an airborne cross contamination may occur between wafers and the pods. A predictive approach using modeling and computational methods is very powerful method to understand and qualify the AMCs cross contamination processes. This work investigates the required numerical tools which are employed in order to study the AMCs cross-contamination transfer phenomena between wafers and FOUPs. Numerical optimization and finite element formulation in transient analysis were established. Analytical solution of one dimensional problem was developed and the calibration process of physical constants was performed. The least square distance between the model (analytical 1D solution) and the experimental data are minimized. The behavior of the AMCs intransient analysis was determined. The model framework preserves the classical forms of the diffusion and convection-diffusion equations and yields to consistent form of the Fick's law. The adsorption process and the surface roughness effect were also traduced as a boundary condition using the switch condition Dirichlet to Neumann and the interface condition. The methodology is applied, first using the optimization methods with analytical solution to define physical constants, and second using finite element method including adsorption kinetic and the switch of Dirichlet to Neumann condition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=AMCs" title="AMCs">AMCs</a>, <a href="https://publications.waset.org/abstracts/search?q=FOUP" title=" FOUP"> FOUP</a>, <a href="https://publications.waset.org/abstracts/search?q=cross-contamination" title=" cross-contamination"> cross-contamination</a>, <a href="https://publications.waset.org/abstracts/search?q=adsorption" title=" adsorption"> adsorption</a>, <a href="https://publications.waset.org/abstracts/search?q=diffusion" title=" diffusion"> diffusion</a>, <a href="https://publications.waset.org/abstracts/search?q=numerical%20analysis" title=" numerical analysis"> numerical analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=wafers" title=" wafers"> wafers</a>, <a href="https://publications.waset.org/abstracts/search?q=Dirichlet%20to%20Neumann" title=" Dirichlet to Neumann"> Dirichlet to Neumann</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20elements%20methods" title=" finite elements methods"> finite elements methods</a>, <a href="https://publications.waset.org/abstracts/search?q=Fick%E2%80%99s%20law" title=" Fick’s law"> Fick’s law</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/7628/mathematical-modeling-of-the-amcs-cross-contamination-removal-in-the-foups-finite-element-formulation-and-application-in-foups-decontamination" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7628.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">506</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29313</span> Focus-Latent Dirichlet Allocation for Aspect-Level Opinion Mining</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohsen%20Farhadloo">Mohsen Farhadloo</a>, <a href="https://publications.waset.org/abstracts/search?q=Majid%20Farhadloo"> Majid Farhadloo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Aspect-level opinion mining that aims at discovering aspects (aspect identification) and their corresponding ratings (sentiment identification) from customer reviews have increasingly attracted attention of researchers and practitioners as it provides valuable insights about products/services from customer's points of view. Instead of addressing aspect identification and sentiment identification in two separate steps, it is possible to simultaneously identify both aspects and sentiments. In recent years many graphical models based on Latent Dirichlet Allocation (LDA) have been proposed to solve both aspect and sentiment identifications in a single step. Although LDA models have been effective tools for the statistical analysis of document collections, they also have shortcomings in addressing some unique characteristics of opinion mining. Our goal in this paper is to address one of the limitations of topic models to date; that is, they fail to directly model the associations among topics. Indeed in many text corpora, it is natural to expect that subsets of the latent topics have higher probabilities. We propose a probabilistic graphical model called focus-LDA, to better capture the associations among topics when applied to aspect-level opinion mining. Our experiments on real-life data sets demonstrate the improved effectiveness of the focus-LDA model in terms of the accuracy of the predictive distributions over held out documents. Furthermore, we demonstrate qualitatively that the focus-LDA topic model provides a natural way of visualizing and exploring unstructured collection of textual data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aspect-level%20opinion%20mining" title="aspect-level opinion mining">aspect-level opinion mining</a>, <a href="https://publications.waset.org/abstracts/search?q=document%20modeling" title=" document modeling"> document modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=Latent%20Dirichlet%20Allocation" title=" Latent Dirichlet Allocation"> Latent Dirichlet Allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=LDA" title=" LDA"> LDA</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a> </p> <a href="https://publications.waset.org/abstracts/121483/focus-latent-dirichlet-allocation-for-aspect-level-opinion-mining" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/121483.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">94</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29312</span> A Time-Varying and Non-Stationary Convolution Spectral Mixture Kernel for Gaussian Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kai%20Chen">Kai Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Shuguang%20Cui"> Shuguang Cui</a>, <a href="https://publications.waset.org/abstracts/search?q=Feng%20Yin"> Feng Yin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Gaussian process (GP) with spectral mixture (SM) kernel demonstrates flexible non-parametric Bayesian learning ability in modeling unknown function. In this work a novel time-varying and non-stationary convolution spectral mixture (TN-CSM) kernel with a significant enhancing of interpretability by using process convolution is introduced. A way decomposing the SM component into an auto-convolution of base SM component and parameterizing it to be input dependent is outlined. Smoothly, performing a convolution between two base SM component yields a novel structure of non-stationary SM component with much better generalized expression and interpretation. The TN-CSM perfectly allows compatibility with the stationary SM kernel in terms of kernel form and spectral base ignored and confused by previous non-stationary kernels. On synthetic and real-world datatsets, experiments show the time-varying characteristics of hyper-parameters in TN-CSM and compare the learning performance of TN-CSM with popular and representative non-stationary GP. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20process" title="Gaussian process">Gaussian process</a>, <a href="https://publications.waset.org/abstracts/search?q=spectral%20mixture" title=" spectral mixture"> spectral mixture</a>, <a href="https://publications.waset.org/abstracts/search?q=non-stationary" title=" non-stationary"> non-stationary</a>, <a href="https://publications.waset.org/abstracts/search?q=convolution" title=" convolution"> convolution</a> </p> <a href="https://publications.waset.org/abstracts/131675/a-time-varying-and-non-stationary-convolution-spectral-mixture-kernel-for-gaussian-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/131675.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">196</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29311</span> Prevention of Biocompounds and Amino Acid Losses in Vernonia amygdalina duringPost Harvest Treatment Using Hot Oil-Aqueous Mixture</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nneka%20Nkechi%20Uchegbu">Nneka Nkechi Uchegbu</a>, <a href="https://publications.waset.org/abstracts/search?q=Temitope%20Omolayo%20Fasuan"> Temitope Omolayo Fasuan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study investigated how to reduce bio-compounds and amino acids in V. amygdalina leaf during processing as a functional food ingredient. Fresh V. amygdalina leaf was processed using thermal oil-aqueous mixtures (soybean oil: aqueous and palm oil: aqueous) at 1:40 and 130 (v/v), respectively. Results indicated that the hot soybean oil-aqueous mixture was the most effective in preserving the bio-compounds and amino acids with retention potentials of 80.95% of the bio-compounds at the rate of 90-100%. Hot palm oil-aqueous mixture retained 61.90% of the bio-compounds at the rate of 90-100% and hot aqueous retained 9.52% of the bio-compounds at the same rate. During the debittering process, seven new bio-compounds were formed in the leaves treated with hot soybean oil-aqueous mixture, six in palm oil-aqueous mixture, and only four in hot aqueous leaves. The bio-compounds in the treated leaves have potential functions as antitumor, antioxidants, antihistaminic, anti-ovarian cancer, anti-inflammatory, antiarthritic, hepatoprotective, antihistaminic, haemolytic 5-α reductase inhibitor, nt, immune-stimulant, diuretic, antiandrogenic, and anaemiagenic. Alkaloids and polyphenols were retained at the rate of 81.34-98.50% using oil: aqueous mixture while aqueous recorded the rate of 33.47-41.46%. Most of the essential amino acids were retained at a rate above 90% through the aid of oil. The process is scalable and could be employed for domestic and industrial applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=V.%20amygdalina%20leaf" title="V. amygdalina leaf">V. amygdalina leaf</a>, <a href="https://publications.waset.org/abstracts/search?q=bio-compounds" title=" bio-compounds"> bio-compounds</a>, <a href="https://publications.waset.org/abstracts/search?q=oil-aqueous%20mixture" title=" oil-aqueous mixture"> oil-aqueous mixture</a>, <a href="https://publications.waset.org/abstracts/search?q=amino%20acids" title=" amino acids"> amino acids</a> </p> <a href="https://publications.waset.org/abstracts/147830/prevention-of-biocompounds-and-amino-acid-losses-in-vernonia-amygdalina-duringpost-harvest-treatment-using-hot-oil-aqueous-mixture" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147830.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">146</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29310</span> Unsupervised Reciter Recognition Using Gaussian Mixture Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Alwosheel">Ahmad Alwosheel</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Alqaraawi"> Ahmed Alqaraawi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work proposes an unsupervised text-independent probabilistic approach to recognize Quran reciter voice. It is an accurate approach that works on real time applications. This approach does not require a prior information about reciter models. It has two phases, where in the training phase the reciters' acoustical features are modeled using Gaussian Mixture Models, while in the testing phase, unlabeled reciter's acoustical features are examined among GMM models. Using this approach, a high accuracy results are achieved with efficient computation time process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Quran" title="Quran">Quran</a>, <a href="https://publications.waset.org/abstracts/search?q=speaker%20recognition" title=" speaker recognition"> speaker recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=reciter%20recognition" title=" reciter recognition"> reciter recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20Mixture%20Model" title=" Gaussian Mixture Model"> Gaussian Mixture Model</a> </p> <a href="https://publications.waset.org/abstracts/46532/unsupervised-reciter-recognition-using-gaussian-mixture-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46532.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">380</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29309</span> Differential Transform Method: Some Important Examples</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Jamil%20Amir">M. Jamil Amir</a>, <a href="https://publications.waset.org/abstracts/search?q=Rabia%20Iqbal"> Rabia Iqbal</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Yaseen"> M. Yaseen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we solve some differential equations analytically by using differential transform method. For this purpose, we consider four models of Laplace equation with two Dirichlet and two Neumann boundary conditions and K(2,2) equation and obtain the corresponding exact solutions. The obtained results show the simplicity of the method and massive reduction in calculations when one compares it with other iterative methods, available in literature. It is worth mentioning that here only a few number of iterations are required to reach the closed form solutions as series expansions of some known functions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=differential%20transform%20method" title="differential transform method">differential transform method</a>, <a href="https://publications.waset.org/abstracts/search?q=laplace%20equation" title=" laplace equation"> laplace equation</a>, <a href="https://publications.waset.org/abstracts/search?q=Dirichlet%20boundary%20conditions" title=" Dirichlet boundary conditions"> Dirichlet boundary conditions</a>, <a href="https://publications.waset.org/abstracts/search?q=Neumann%20boundary%20conditions" title=" Neumann boundary conditions"> Neumann boundary conditions</a> </p> <a href="https://publications.waset.org/abstracts/18605/differential-transform-method-some-important-examples" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18605.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">537</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29308</span> The Effects of the Waste Plastic Modification of the Asphalt Mixture on the Permanent Deformation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Soheil%20Heydari">Soheil Heydari</a>, <a href="https://publications.waset.org/abstracts/search?q=Ailar%20Hajimohammadi"> Ailar Hajimohammadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Nasser%20Khalili"> Nasser Khalili</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The application of plastic waste for asphalt modification is a sustainable strategy to deal with the enormous plastic waste generated each year and enhance the properties of asphalt. The modification is either practiced by the dry process or the wet process. In the dry process, plastics are added straight into the asphalt mixture, and in the wet process, they are mixed and digested into bitumen. In this article, the effects of plastic inclusion in asphalt mixture, through the dry process, on the permanent deformation of the asphalt are investigated. The main waste plastics that are usually used in asphalt modification are taken into account, which is linear, low-density polyethylene, low-density polyethylene, high-density polyethylene, and polypropylene. Also, to simulate a plastic waste stream, different grades of each virgin plastic are mixed and used. For instance, four different grades of polypropylene are mixed and used as representative of polypropylene. A precisely designed mixing condition is considered to dry-mix the plastics into the mixture such that the polymer was melted and modified by the later introduced binder. In this mixing process, plastics are first added to the hot aggregates and mixed three times in different time intervals, then bitumen is introduced, and the whole mixture is mixed three times in fifteen minutes intervals. Marshall specimens were manufactured, and dynamic creep tests were conducted to evaluate the effects of modification on the permanent deformation of the asphalt mixture. Dynamic creep is a common repeated loading test conducted at different stress levels and temperatures. Loading cycles are applied to the AC specimen until failure occurs; with the amount of deformation constantly recorded, the cumulative, permanent strain is determined and reported as a function of the number of cycles. The results of this study showed that the dry inclusion of the waste plastics is very effective in enhancing the resistance against permanent deformation of the mixture. However, the mixing process must be precisely engineered to melt the plastics, and a homogenous mixture is achieved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=permanent%20deformation" title="permanent deformation">permanent deformation</a>, <a href="https://publications.waset.org/abstracts/search?q=waste%20plastics" title=" waste plastics"> waste plastics</a>, <a href="https://publications.waset.org/abstracts/search?q=low-density%20polyethene" title=" low-density polyethene"> low-density polyethene</a>, <a href="https://publications.waset.org/abstracts/search?q=high-density%20polyethene" title=" high-density polyethene"> high-density polyethene</a>, <a href="https://publications.waset.org/abstracts/search?q=polypropylene" title=" polypropylene"> polypropylene</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20low-density%20polyethene" title=" linear low-density polyethene"> linear low-density polyethene</a>, <a href="https://publications.waset.org/abstracts/search?q=dry%20process" title=" dry process"> dry process</a> </p> <a href="https://publications.waset.org/abstracts/152076/the-effects-of-the-waste-plastic-modification-of-the-asphalt-mixture-on-the-permanent-deformation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152076.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">88</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29307</span> An Automatic Speech Recognition Tool for the Filipino Language Using the HTK System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=John%20Lorenzo%20Bautista">John Lorenzo Bautista</a>, <a href="https://publications.waset.org/abstracts/search?q=Yoon-Joong%20Kim"> Yoon-Joong Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the development of a Filipino speech recognition tool using the HTK System. The system was trained from a subset of the Filipino Speech Corpus developed by the DSP Laboratory of the University of the Philippines-Diliman. The speech corpus was both used in training and testing the system by estimating the parameters for phonetic HMM-based (Hidden-Markov Model) acoustic models. Experiments on different mixture-weights were incorporated in the study. The phoneme-level word-based recognition of a 5-state HMM resulted in an average accuracy rate of 80.13 for a single-Gaussian mixture model, 81.13 after implementing a phoneme-alignment, and 87.19 for the increased Gaussian-mixture weight model. The highest accuracy rate of 88.70% was obtained from a 5-state model with 6 Gaussian mixtures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Filipino%20language" title="Filipino language">Filipino language</a>, <a href="https://publications.waset.org/abstracts/search?q=Hidden%20Markov%20Model" title=" Hidden Markov Model"> Hidden Markov Model</a>, <a href="https://publications.waset.org/abstracts/search?q=HTK%20system" title=" HTK system"> HTK system</a>, <a href="https://publications.waset.org/abstracts/search?q=speech%20recognition" title=" speech recognition"> speech recognition</a> </p> <a href="https://publications.waset.org/abstracts/10240/an-automatic-speech-recognition-tool-for-the-filipino-language-using-the-htk-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10240.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">480</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29306</span> Particle Size Distribution Estimation of a Mixture of Regular and Irregular Sized Particles Using Acoustic Emissions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ejay%20Nsugbe">Ejay Nsugbe</a>, <a href="https://publications.waset.org/abstracts/search?q=Andrew%20Starr"> Andrew Starr</a>, <a href="https://publications.waset.org/abstracts/search?q=Ian%20Jennions"> Ian Jennions</a>, <a href="https://publications.waset.org/abstracts/search?q=Cristobal%20Ruiz-Carcel"> Cristobal Ruiz-Carcel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This works investigates the possibility of using Acoustic Emissions (AE) to estimate the Particle Size Distribution (PSD) of a mixture of particles that comprise of particles of different densities and geometry. The experiments carried out involved the mixture of a set of glass and polyethylene particles that ranged from 150-212 microns and 150-250 microns respectively and an experimental rig that allowed the free fall of a continuous stream of particles on a target plate which the AE sensor was placed. By using a time domain based multiple threshold method, it was observed that the PSD of the particles in the mixture could be estimated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=acoustic%20emissions" title="acoustic emissions">acoustic emissions</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20sizing" title=" particle sizing"> particle sizing</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20monitoring" title=" process monitoring"> process monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20processing" title=" signal processing"> signal processing</a> </p> <a href="https://publications.waset.org/abstracts/68042/particle-size-distribution-estimation-of-a-mixture-of-regular-and-irregular-sized-particles-using-acoustic-emissions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68042.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">352</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29305</span> Analysis of Pangasinan State University: Bayambang Students’ Concerns Through Social Media Analytics and Latent Dirichlet Allocation Topic Modelling Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Matthew%20John%20F.%20Sino%20Cruz">Matthew John F. Sino Cruz</a>, <a href="https://publications.waset.org/abstracts/search?q=Sarah%20Jane%20M.%20Ferrer"> Sarah Jane M. Ferrer</a>, <a href="https://publications.waset.org/abstracts/search?q=Janice%20C.%20Francisco"> Janice C. Francisco</a> </p> <p class="card-text"><strong>Abstract:</strong></p> COVID-19 pandemic has affected more than 114 countries all over the world since it was considered a global health concern in 2020. Different sectors, including education, have shifted to remote/distant setups to follow the guidelines set to prevent the spread of the disease. One of the higher education institutes which shifted to remote setup is the Pangasinan State University (PSU). In order to continue providing quality instructions to the students, PSU designed Flexible Learning Model to still provide services to its stakeholders amidst the pandemic. The model covers the redesigning of delivering instructions in remote setup and the technology needed to support these adjustments. The primary goal of this study is to determine the insights of the PSU – Bayambang students towards the remote setup implemented during the pandemic and how they perceived the initiatives employed in relation to their experiences in flexible learning. In this study, the topic modelling approach was implemented using Latent Dirichlet Allocation. The dataset used in the study. The results show that the most common concern of the students includes time and resource management, poor internet connection issues, and difficulty coping with the flexible learning modality. Furthermore, the findings of the study can be used as one of the bases for the administration to review and improve the policies and initiatives implemented during the pandemic in relation to remote service delivery. In addition, further studies can be conducted to determine the overall sentiment of the other stakeholders in the policies implemented at the University. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=COVID-19" title="COVID-19">COVID-19</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20modelling" title=" topic modelling"> topic modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=students%E2%80%99%20sentiment" title=" students’ sentiment"> students’ sentiment</a>, <a href="https://publications.waset.org/abstracts/search?q=flexible%20learning" title=" flexible learning"> flexible learning</a>, <a href="https://publications.waset.org/abstracts/search?q=Latent%20Dirichlet%20allocation" title=" Latent Dirichlet allocation"> Latent Dirichlet allocation</a> </p> <a href="https://publications.waset.org/abstracts/154923/analysis-of-pangasinan-state-university-bayambang-students-concerns-through-social-media-analytics-and-latent-dirichlet-allocation-topic-modelling-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/154923.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">122</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29304</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/abstracts/search?q=Sandeep%20Kaur%20Chouhan">Sandeep Kaur Chouhan</a>, <a href="https://publications.waset.org/abstracts/search?q=Jatinderdeep%20Kaur"> Jatinderdeep Kaur</a>, <a href="https://publications.waset.org/abstracts/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/abstracts/search?q=conjugate%20Dirichlet%20kernel" title="conjugate Dirichlet kernel">conjugate Dirichlet kernel</a>, <a href="https://publications.waset.org/abstracts/search?q=Dirichlet%20kernel" title=" Dirichlet kernel"> Dirichlet kernel</a>, <a href="https://publications.waset.org/abstracts/search?q=L1-convergence" title=" L1-convergence"> L1-convergence</a>, <a href="https://publications.waset.org/abstracts/search?q=modified%20sums" title=" modified sums"> modified sums</a> </p> <a href="https://publications.waset.org/abstracts/44182/l1-convergence-of-modified-trigonometric-sums" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44182.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">354</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29303</span> The Determinants of Country Corruption: Unobserved Heterogeneity and Individual Choice- An empirical Application with Finite Mixture Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alessandra%20Marcelletti">Alessandra Marcelletti</a>, <a href="https://publications.waset.org/abstracts/search?q=Giovanni%20Trovato"> Giovanni Trovato</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Corruption in public offices is found to be the reflection of country-specific features, however, the exact magnitude and the statistical significance of its determinants effect has not yet been identified. The paper aims to propose an estimation method to measure the impact of country fundamentals on corruption, showing that covariates could differently affect the extent of corruption across countries. Thus, we exploit a model able to take into account different factors affecting the incentive to ask or to be asked for a bribe, coherently with the use of the Corruption Perception Index. We assume that discordant results achieved in literature may be explained by omitted hidden factors affecting the agents' decision process. Moreover, assuming homogeneous covariates effect may lead to unreliable conclusions since the country-specific environment is not accounted for. We apply a Finite Mixture Model with concomitant variables to 129 countries from 1995 to 2006, accounting for the impact of the initial conditions in the socio-economic structure on the corruption patterns. Our findings confirm the hypothesis of the decision process of accepting or asking for a bribe varies with specific country fundamental features. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Corruption" title="Corruption">Corruption</a>, <a href="https://publications.waset.org/abstracts/search?q=Finite%20Mixture%20Models" title=" Finite Mixture Models"> Finite Mixture Models</a>, <a href="https://publications.waset.org/abstracts/search?q=Concomitant%20Variables" title=" Concomitant Variables"> Concomitant Variables</a>, <a href="https://publications.waset.org/abstracts/search?q=Countries%20Classification" title=" Countries Classification"> Countries Classification</a> </p> <a href="https://publications.waset.org/abstracts/23112/the-determinants-of-country-corruption-unobserved-heterogeneity-and-individual-choice-an-empirical-application-with-finite-mixture-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23112.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">263</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29302</span> Syndromic Surveillance Framework Using Tweets Data Analytics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=David%20Ming%20Liu">David Ming Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Benjamin%20Hirsch"> Benjamin Hirsch</a>, <a href="https://publications.waset.org/abstracts/search?q=Bashir%20Aden"> Bashir Aden</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Syndromic surveillance is to detect or predict disease outbreaks through the analysis of medical sources of data. Using social media data like tweets to do syndromic surveillance becomes more and more popular with the aid of open platform to collect data and the advantage of microblogging text and mobile geographic location features. In this paper, a Syndromic Surveillance Framework is presented with machine learning kernel using tweets data analytics. Influenza and the three cities Abu Dhabi, Al Ain and Dubai of United Arabic Emirates are used as the test disease and trial areas. Hospital cases data provided by the Health Authority of Abu Dhabi (HAAD) are used for the correlation purpose. In our model, Latent Dirichlet allocation (LDA) engine is adapted to do supervised learning classification and N-Fold cross validation confusion matrix are given as the simulation results with overall system recall 85.595% performance achieved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Syndromic%20surveillance" title="Syndromic surveillance">Syndromic surveillance</a>, <a href="https://publications.waset.org/abstracts/search?q=Tweets" title=" Tweets"> Tweets</a>, <a href="https://publications.waset.org/abstracts/search?q=Machine%20Learning" title=" Machine Learning"> Machine Learning</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=Latent%20Dirichlet%20allocation%20%28LDA%29" title=" Latent Dirichlet allocation (LDA)"> Latent Dirichlet allocation (LDA)</a>, <a href="https://publications.waset.org/abstracts/search?q=Influenza" title=" Influenza"> Influenza</a> </p> <a href="https://publications.waset.org/abstracts/120850/syndromic-surveillance-framework-using-tweets-data-analytics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/120850.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">116</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Dirichlet%20process%20mixture%20model&page=2">2</a></li> <li class="page-item"><a class="page-link" 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