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expectation</title> <meta name="description" content="Search results for: conditional expectation"> <meta name="keywords" content="conditional expectation"> <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 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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="conditional expectation"> <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> 568</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: conditional expectation</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">568</span> Characterization of Probability Distributions through Conditional Expectation of Pair of Generalized Order Statistics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zubdahe%20Noor">Zubdahe Noor</a>, <a href="https://publications.waset.org/abstracts/search?q=Haseeb%20Athar"> Haseeb Athar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this article, first a relation for conditional expectation is developed and then is used to characterize a general class of distributions F(x) = 1-e^(-ah(x)) through conditional expectation of difference of pair of generalized order statistics. Some results are reduced for particular cases. In the end, a list of distributions is presented in the form of table that are compatible with the given general class. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generalized%20order%20statistics" title="generalized order statistics">generalized order statistics</a>, <a href="https://publications.waset.org/abstracts/search?q=order%20statistics" title=" order statistics"> order statistics</a>, <a href="https://publications.waset.org/abstracts/search?q=record%20values" title=" record values"> record values</a>, <a href="https://publications.waset.org/abstracts/search?q=conditional%20expectation" title=" conditional expectation"> conditional expectation</a>, <a href="https://publications.waset.org/abstracts/search?q=characterization" title=" characterization"> characterization</a> </p> <a href="https://publications.waset.org/abstracts/22898/characterization-of-probability-distributions-through-conditional-expectation-of-pair-of-generalized-order-statistics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22898.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">460</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">567</span> Nonparametric Quantile Regression for Multivariate Spatial Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20H.%20Arnaud%20Kanga">S. H. Arnaud Kanga</a>, <a href="https://publications.waset.org/abstracts/search?q=O.%20Hili"> O. Hili</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Dabo-Niang"> S. Dabo-Niang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Spatial prediction is an issue appealing and attracting several fields such as agriculture, environmental sciences, ecology, econometrics, and many others. Although multiple non-parametric prediction methods exist for spatial data, those are based on the conditional expectation. This paper took a different approach by examining a non-parametric spatial predictor of the conditional quantile. The study especially observes the stationary multidimensional spatial process over a rectangular domain. Indeed, the proposed quantile is obtained by inverting the conditional distribution function. Furthermore, the proposed estimator of the conditional distribution function depends on three kernels, where one of them controls the distance between spatial locations, while the other two control the distance between observations. In addition, the almost complete convergence and the convergence in mean order q of the kernel predictor are obtained when the sample considered is alpha-mixing. Such approach of the prediction method gives the advantage of accuracy as it overcomes sensitivity to extreme and outliers values. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=conditional%20quantile" title="conditional quantile">conditional quantile</a>, <a href="https://publications.waset.org/abstracts/search?q=kernel" title=" kernel"> kernel</a>, <a href="https://publications.waset.org/abstracts/search?q=nonparametric" title=" nonparametric"> nonparametric</a>, <a href="https://publications.waset.org/abstracts/search?q=stationary" title=" stationary"> stationary</a> </p> <a href="https://publications.waset.org/abstracts/109937/nonparametric-quantile-regression-for-multivariate-spatial-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/109937.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">154</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">566</span> Expectation and Satisfaction of Health Spa Business Service, Ranong Province, Thailand</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Supattra%20Pranee">Supattra Pranee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purposes of this research are to study the current business of health spa and to study the customers’ level of expectation as well as level of satisfaction of the health spa business in Ranong, Thailand. This paper drew upon data collected from health spa customers by using questionnaire. In addition, an in-depth interview was utilized to collect data from health spa entrepreneurs. The findings revealed that the health spa business is growing very fast and the coming ASEAN Economic Community (AEC) will ameliorate the business growth and increase the customer base. There is a need to improve staff’s ability to communicate in English. However, the economic size of Ranong province is still small which has resulted in the hesitation of investors to increase their investment in this business. The findings also revealed four categories of level of expectation and satisfaction as follows: (1) Service: overall, customers had a high expectation with a mean of 3.80 and 0.873 SD and a high level of satisfaction with a mean of 3.66 and 0.704 SD. (2) Staff: overall, customers had a high expectation with a mean of 3.95 and 0.865 SD and a high level of satisfaction with a mean of 3.84 and 0.783 SD. (3) Product, Equipment, and Tools: overall, customers had a high expectation with a mean of 4.02 and 0.913 SD and a high level of satisfaction with a mean of 3.88 and 0.772 SD. (4) Place, Atmosphere, and Environment: overall, customers had a high expectation with a mean of 3.95 and 0.906 SD and a high level of satisfaction with a mean of 3.86 and 0.785 SD. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=expectation" title="expectation">expectation</a>, <a href="https://publications.waset.org/abstracts/search?q=health%20spa%20business" title=" health spa business"> health spa business</a>, <a href="https://publications.waset.org/abstracts/search?q=satisfaction" title=" satisfaction"> satisfaction</a>, <a href="https://publications.waset.org/abstracts/search?q=ranong%20province" title=" ranong province"> ranong province</a> </p> <a href="https://publications.waset.org/abstracts/2609/expectation-and-satisfaction-of-health-spa-business-service-ranong-province-thailand" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2609.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">303</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">565</span> Combining the Dynamic Conditional Correlation and Range-GARCH Models to Improve Covariance Forecasts</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Piotr%20Fiszeder">Piotr Fiszeder</a>, <a href="https://publications.waset.org/abstracts/search?q=Marcin%20Fa%C5%82dzi%C5%84ski"> Marcin Fałdziński</a>, <a href="https://publications.waset.org/abstracts/search?q=Peter%20Moln%C3%A1r"> Peter Molnár</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The dynamic conditional correlation model of Engle (2002) is one of the most popular multivariate volatility models. However, this model is based solely on closing prices. It has been documented in the literature that the high and low price of the day can be used in an efficient volatility estimation. We, therefore, suggest a model which incorporates high and low prices into the dynamic conditional correlation framework. Empirical evaluation of this model is conducted on three datasets: currencies, stocks, and commodity exchange-traded funds. The utilisation of realized variances and covariances as proxies for true variances and covariances allows us to reach a strong conclusion that our model outperforms not only the standard dynamic conditional correlation model but also a competing range-based dynamic conditional correlation model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=volatility" title="volatility">volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=DCC%20model" title=" DCC model"> DCC model</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20and%20low%20prices" title=" high and low prices"> high and low prices</a>, <a href="https://publications.waset.org/abstracts/search?q=range-based%20models" title=" range-based models"> range-based models</a>, <a href="https://publications.waset.org/abstracts/search?q=covariance%20forecasting" title=" covariance forecasting"> covariance forecasting</a> </p> <a href="https://publications.waset.org/abstracts/107388/combining-the-dynamic-conditional-correlation-and-range-garch-models-to-improve-covariance-forecasts" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/107388.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">183</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">564</span> CPPI Method with Conditional Floor: The Discrete Time Case</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hachmi%20Ben%20Ameur">Hachmi Ben Ameur</a>, <a href="https://publications.waset.org/abstracts/search?q=Jean%20Luc%20Prigent"> Jean Luc Prigent</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose an extension of the CPPI method, which is based on conditional floors. In this framework, we examine in particular the TIPP and margin based strategies. These methods allow keeping part of the past gains and protecting the portfolio value against future high drawdowns of the financial market. However, as for the standard CPPI method, the investor can benefit from potential market rises. To control the risk of such strategies, we introduce both Value-at-Risk (VaR) and Expected Shortfall (ES) risk measures. For each of these criteria, we show that the conditional floor must be higher than a lower bound. We illustrate these results, for a quite general ARCH type model, including the EGARCH (1,1) as a special case. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CPPI" title="CPPI">CPPI</a>, <a href="https://publications.waset.org/abstracts/search?q=conditional%20floor" title=" conditional floor"> conditional floor</a>, <a href="https://publications.waset.org/abstracts/search?q=ARCH" title=" ARCH"> ARCH</a>, <a href="https://publications.waset.org/abstracts/search?q=VaR" title=" VaR"> VaR</a>, <a href="https://publications.waset.org/abstracts/search?q=expected%20ehortfall" title=" expected ehortfall"> expected ehortfall</a> </p> <a href="https://publications.waset.org/abstracts/43188/cppi-method-with-conditional-floor-the-discrete-time-case" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43188.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">305</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">563</span> A Hazard Rate Function for the Time of Ruin</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sule%20Sahin">Sule Sahin</a>, <a href="https://publications.waset.org/abstracts/search?q=Basak%20Bulut%20Karageyik"> Basak Bulut Karageyik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduces a hazard rate function for the time of ruin to calculate the conditional probability of ruin for very small intervals. We call this function the force of ruin (FoR). We obtain the expected time of ruin and conditional expected time of ruin from the exact finite time ruin probability with exponential claim amounts. Then we introduce the FoR which gives the conditional probability of ruin and the condition is that ruin has not occurred at time t. We analyse the behavior of the FoR function for different initial surpluses over a specific time interval. We also obtain FoR under the excess of loss reinsurance arrangement and examine the effect of reinsurance on the FoR. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=conditional%20time%20of%20ruin" title="conditional time of ruin">conditional time of ruin</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20time%20ruin%20probability" title=" finite time ruin probability"> finite time ruin probability</a>, <a href="https://publications.waset.org/abstracts/search?q=force%20of%20ruin" title=" force of ruin"> force of ruin</a>, <a href="https://publications.waset.org/abstracts/search?q=reinsurance" title=" reinsurance"> reinsurance</a> </p> <a href="https://publications.waset.org/abstracts/55648/a-hazard-rate-function-for-the-time-of-ruin" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55648.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">405</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">562</span> Bayesian Analysis of Change Point Problems Using Conditionally Specified Priors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Golnaz%20Shahtahmassebi">Golnaz Shahtahmassebi</a>, <a href="https://publications.waset.org/abstracts/search?q=Jose%20Maria%20Sarabia"> Jose Maria Sarabia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this talk, we introduce a new class of conjugate prior distributions obtained from conditional specification methodology. We illustrate the application of such distribution in Bayesian change point detection in Poisson processes. We obtain the posterior distribution of model parameters using a general bivariate distribution with gamma conditionals. Simulation from the posterior is readily implemented using a Gibbs sampling algorithm. The Gibbs sampling is implemented even when using conditional densities that are incompatible or only compatible with an improper joint density. The application of such methods will be demonstrated using examples of simulated and real data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=change%20point" title="change point">change point</a>, <a href="https://publications.waset.org/abstracts/search?q=bayesian%20inference" title=" bayesian inference"> bayesian inference</a>, <a href="https://publications.waset.org/abstracts/search?q=Gibbs%20sampler" title=" Gibbs sampler"> Gibbs sampler</a>, <a href="https://publications.waset.org/abstracts/search?q=conditional%20specification" title=" conditional specification"> conditional specification</a>, <a href="https://publications.waset.org/abstracts/search?q=gamma%20conditional%20distributions" title=" gamma conditional distributions"> gamma conditional distributions</a> </p> <a href="https://publications.waset.org/abstracts/141782/bayesian-analysis-of-change-point-problems-using-conditionally-specified-priors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141782.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">189</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">561</span> Handling Missing Data by Using Expectation-Maximization and Expectation-Maximization with Bootstrapping for Linear Functional Relationship Model </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adilah%20Abdul%20Ghapor">Adilah Abdul Ghapor</a>, <a href="https://publications.waset.org/abstracts/search?q=Yong%20Zulina%20Zubairi"> Yong Zulina Zubairi</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20H.%20M.%20R.%20Imon"> A. H. M. R. Imon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Missing value problem is common in statistics and has been of interest for years. This article considers two modern techniques in handling missing data for linear functional relationship model (LFRM) namely the Expectation-Maximization (EM) algorithm and Expectation-Maximization with Bootstrapping (EMB) algorithm using three performance indicators; namely the mean absolute error (MAE), root mean square error (RMSE) and estimated biased (EB). In this study, we applied the methods of imputing missing values in two types of LFRM namely the full model of LFRM and in LFRM when the slope is estimated using a nonparametric method. Results of the simulation study suggest that EMB algorithm performs much better than EM algorithm in both models. We also illustrate the applicability of the approach in a real data set. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=expectation-maximization" title="expectation-maximization">expectation-maximization</a>, <a href="https://publications.waset.org/abstracts/search?q=expectation-maximization%20with%20bootstrapping" title=" expectation-maximization with bootstrapping"> expectation-maximization with bootstrapping</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20functional%20relationship%20model" title=" linear functional relationship model"> linear functional relationship model</a>, <a href="https://publications.waset.org/abstracts/search?q=performance%20indicators" title=" performance indicators"> performance indicators</a> </p> <a href="https://publications.waset.org/abstracts/31370/handling-missing-data-by-using-expectation-maximization-and-expectation-maximization-with-bootstrapping-for-linear-functional-relationship-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31370.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">455</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">560</span> On Generalized Cumulative Past Inaccuracy Measure for Marginal and Conditional Lifetimes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amit%20Ghosh">Amit Ghosh</a>, <a href="https://publications.waset.org/abstracts/search?q=Chanchal%20Kundu"> Chanchal Kundu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, the notion of past cumulative inaccuracy (CPI) measure has been proposed in the literature as a generalization of cumulative past entropy (CPE) in univariate as well as bivariate setup. In this paper, we introduce the notion of CPI of order α (alpha) and study the proposed measure for conditionally specified models of two components failed at different time instants called generalized conditional CPI (GCCPI). We provide some bounds using usual stochastic order and investigate several properties of GCCPI. The effect of monotone transformation on this proposed measure has also been examined. Furthermore, we characterize some bivariate distributions under the assumption of conditional proportional reversed hazard rate model. Moreover, the role of GCCPI in reliability modeling has also been investigated for a real-life problem. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cumulative%20past%20inaccuracy" title="cumulative past inaccuracy">cumulative past inaccuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=marginal%20and%20conditional%20past%20lifetimes" title=" marginal and conditional past lifetimes"> marginal and conditional past lifetimes</a>, <a href="https://publications.waset.org/abstracts/search?q=conditional%20proportional%20reversed%20hazard%20rate%20model" title=" conditional proportional reversed hazard rate model"> conditional proportional reversed hazard rate model</a>, <a href="https://publications.waset.org/abstracts/search?q=usual%20stochastic%20order" title=" usual stochastic order"> usual stochastic order</a> </p> <a href="https://publications.waset.org/abstracts/79608/on-generalized-cumulative-past-inaccuracy-measure-for-marginal-and-conditional-lifetimes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79608.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">252</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">559</span> Estimating the Volatilite of Stock Markets in Case of Financial Crisis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gultekin%20Gurcay">Gultekin Gurcay</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, effects and responses of stock were analyzed. This analysis was done periodically. The dimensions of the financial crisis impact on the stock market were investigated by GARCH model. In this context, S&P 500 stock market is modeled with DAX, NIKKEI and BIST100. In this way, The effects of the changing in S&P 500 stock market were examined on European and Asian stock markets. Conditional variance coefficient will be calculated through garch model. The scope of the crisis period, the conditional covariance coefficient will be analyzed comparatively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=conditional%20variance%20coefficient" title="conditional variance coefficient">conditional variance coefficient</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20crisis" title=" financial crisis"> financial crisis</a>, <a href="https://publications.waset.org/abstracts/search?q=garch%20model" title=" garch model"> garch model</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20market" title=" stock market"> stock market</a> </p> <a href="https://publications.waset.org/abstracts/40843/estimating-the-volatilite-of-stock-markets-in-case-of-financial-crisis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40843.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">558</span> Potential Energy Expectation Value for Lithium Excited State (1s2s3s)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khalil%20H.%20Al-Bayati">Khalil H. Al-Bayati</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20Nasma"> G. Nasma</a>, <a href="https://publications.waset.org/abstracts/search?q=Hussein%20Ban%20H.%20Adel"> Hussein Ban H. Adel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of the present work is to calculate the expectation value of potential energy <V> for different spin states (ααα ≡ βββ, αβα ≡ βαβ) and compare it with spin states (αββ, ααβ ) for lithium excited state (1s2s3s) and Li-like ions (Be+, B+2) using Hartree-Fock wave function by partitioning technique. The result of inter particle expectation value shows linear behaviour with atomic number and for each atom and ion the <V> shows the trend ααα < ααβ < αββ < αβα. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lithium%20excited%20state" title="lithium excited state">lithium excited state</a>, <a href="https://publications.waset.org/abstracts/search?q=potential%20energy" title=" potential energy"> potential energy</a>, <a href="https://publications.waset.org/abstracts/search?q=1s2s3s" title=" 1s2s3s"> 1s2s3s</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%20physics" title=" mathematical physics "> mathematical physics </a> </p> <a href="https://publications.waset.org/abstracts/5085/potential-energy-expectation-value-for-lithium-excited-state-1s2s3s" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5085.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">489</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">557</span> Modern Imputation Technique for Missing Data in Linear Functional Relationship Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adilah%20Abdul%20Ghapor">Adilah Abdul Ghapor</a>, <a href="https://publications.waset.org/abstracts/search?q=Yong%20Zulina%20Zubairi"> Yong Zulina Zubairi</a>, <a href="https://publications.waset.org/abstracts/search?q=Rahmatullah%20Imon"> Rahmatullah Imon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Missing value problem is common in statistics and has been of interest for years. This article considers two modern techniques in handling missing data for linear functional relationship model (LFRM) namely the Expectation-Maximization (EM) algorithm and Expectation-Maximization with Bootstrapping (EMB) algorithm using three performance indicators; namely the mean absolute error (MAE), root mean square error (RMSE) and estimated biased (EB). In this study, we applied the methods of imputing missing values in the LFRM. Results of the simulation study suggest that EMB algorithm performs much better than EM algorithm in both models. We also illustrate the applicability of the approach in a real data set. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=expectation-maximization" title="expectation-maximization">expectation-maximization</a>, <a href="https://publications.waset.org/abstracts/search?q=expectation-maximization%20with%20bootstrapping" title=" expectation-maximization with bootstrapping"> expectation-maximization with bootstrapping</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20functional%20relationship%20model" title=" linear functional relationship model"> linear functional relationship model</a>, <a href="https://publications.waset.org/abstracts/search?q=performance%20indicators" title=" performance indicators"> performance indicators</a> </p> <a href="https://publications.waset.org/abstracts/44261/modern-imputation-technique-for-missing-data-in-linear-functional-relationship-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44261.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">399</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">556</span> Evidence of Conditional and Unconditional Cooperation in a Public Goods Game: Experimental Evidence from Mali</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maria%20Laura%20Alzua">Maria Laura Alzua</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20Adelaida%20Lopera"> Maria Adelaida Lopera</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper measures the relative importance of conditional cooperation and unconditional cooperation in a large public goods experiment conducted in Mali. We use expectations about total public goods provision to estimate a structural choice model with heterogeneous preferences. While unconditional cooperation can be captured by common preferences shared by all participants, conditional cooperation is much more heterogeneous and depends on unobserved individual factors. This structural model, in combination with two experimental treatments, suggests that leadership and group communication incentivize public goods provision through different channels. First, We find that participation of local leaders effectively changes individual choices through unconditional cooperation. A simulation exercise predicts that even in the most pessimistic scenario in which all participants expect zero public good provision, 60% would still choose to cooperate. Second, allowing participants to communicate fosters conditional cooperation. The simulations suggest that expectations are responsible for around 24% of the observed public good provision and that group communication does not necessarily ameliorate public good provision. In fact, communication may even worsen the outcome when expectations are low. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=conditional%20cooperation" title="conditional cooperation">conditional cooperation</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20choice%20model" title=" discrete choice model"> discrete choice model</a>, <a href="https://publications.waset.org/abstracts/search?q=expectations" title=" expectations"> expectations</a>, <a href="https://publications.waset.org/abstracts/search?q=public%20goods%20game" title=" public goods game"> public goods game</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20coefficients%20model" title=" random coefficients model"> random coefficients model</a> </p> <a href="https://publications.waset.org/abstracts/43314/evidence-of-conditional-and-unconditional-cooperation-in-a-public-goods-game-experimental-evidence-from-mali" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43314.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">306</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">555</span> Facial Expression Recognition Using Sparse Gaussian Conditional Random Field</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammadamin%20Abbasnejad">Mohammadamin Abbasnejad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The analysis of expression and facial Action Units (AUs) detection are very important tasks in fields of computer vision and Human Computer Interaction (HCI) due to the wide range of applications in human life. Many works have been done during the past few years which has their own advantages and disadvantages. In this work, we present a new model based on Gaussian Conditional Random Field. We solve our objective problem using ADMM and we show how well the proposed model works. We train and test our work on two facial expression datasets, CK+, and RU-FACS. Experimental evaluation shows that our proposed approach outperform state of the art expression recognition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20Conditional%20Random%20Field" title="Gaussian Conditional Random Field">Gaussian Conditional Random Field</a>, <a href="https://publications.waset.org/abstracts/search?q=ADMM" title=" ADMM"> ADMM</a>, <a href="https://publications.waset.org/abstracts/search?q=convergence" title=" convergence"> convergence</a>, <a href="https://publications.waset.org/abstracts/search?q=gradient%20descent" title=" gradient descent"> gradient descent</a> </p> <a href="https://publications.waset.org/abstracts/26245/facial-expression-recognition-using-sparse-gaussian-conditional-random-field" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26245.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">356</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">554</span> Surveillance Video Summarization Based on Histogram Differencing and Sum Conditional Variance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nada%20Jasim%20Habeeb">Nada Jasim Habeeb</a>, <a href="https://publications.waset.org/abstracts/search?q=Rana%20Saad%20Mohammed"> Rana Saad Mohammed</a>, <a href="https://publications.waset.org/abstracts/search?q=Muntaha%20Khudair%20Abbass"> Muntaha Khudair Abbass </a> </p> <p class="card-text"><strong>Abstract:</strong></p> For more efficient and fast video summarization, this paper presents a surveillance video summarization method. The presented method works to improve video summarization technique. This method depends on temporal differencing to extract most important data from large video stream. This method uses histogram differencing and Sum Conditional Variance which is robust against to illumination variations in order to extract motion objects. The experimental results showed that the presented method gives better output compared with temporal differencing based summarization techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=temporal%20differencing" title="temporal differencing">temporal differencing</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20summarization" title=" video summarization"> video summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=histogram%20differencing" title=" histogram differencing"> histogram differencing</a>, <a href="https://publications.waset.org/abstracts/search?q=sum%20conditional%20variance" title=" sum conditional variance"> sum conditional variance</a> </p> <a href="https://publications.waset.org/abstracts/54404/surveillance-video-summarization-based-on-histogram-differencing-and-sum-conditional-variance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54404.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">348</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">553</span> Forecasting Electricity Spot Price with Generalized Long Memory Modeling: Wavelet and Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Souhir%20Ben%20Amor">Souhir Ben Amor</a>, <a href="https://publications.waset.org/abstracts/search?q=Heni%20Boubaker"> Heni Boubaker</a>, <a href="https://publications.waset.org/abstracts/search?q=Lotfi%20Belkacem"> Lotfi Belkacem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This aims of this paper is to forecast the electricity spot prices. First, we focus on modeling the conditional mean of the series so we adopt a generalized fractional -factor Gegenbauer process (k-factor GARMA). Secondly, the residual from the -factor GARMA model has used as a proxy for the conditional variance; these residuals were predicted using two different approaches. In the first approach, a local linear wavelet neural network model (LLWNN) has developed to predict the conditional variance using the Back Propagation learning algorithms. In the second approach, the Gegenbauer generalized autoregressive conditional heteroscedasticity process (G-GARCH) has adopted, and the parameters of the k-factor GARMA-G-GARCH model has estimated using the wavelet methodology based on the discrete wavelet packet transform (DWPT) approach. The empirical results have shown that the k-factor GARMA-G-GARCH model outperform the hybrid k-factor GARMA-LLWNN model, and find it is more appropriate for forecasts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electricity%20price" title="electricity price">electricity price</a>, <a href="https://publications.waset.org/abstracts/search?q=k-factor%20GARMA" title=" k-factor GARMA"> k-factor GARMA</a>, <a href="https://publications.waset.org/abstracts/search?q=LLWNN" title=" LLWNN"> LLWNN</a>, <a href="https://publications.waset.org/abstracts/search?q=G-GARCH" title=" G-GARCH"> G-GARCH</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a> </p> <a href="https://publications.waset.org/abstracts/75361/forecasting-electricity-spot-price-with-generalized-long-memory-modeling-wavelet-and-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75361.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">231</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">552</span> Forward Conditional Restricted Boltzmann Machines for the Generation of Music</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Johan%20Loeckx">Johan Loeckx</a>, <a href="https://publications.waset.org/abstracts/search?q=Joeri%20Bultheel"> Joeri Bultheel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, the application of deep learning to music has gained popularity. Its true potential, however, has been largely unexplored. In this paper, a new idea for representing the dynamic behavior of music is proposed. A ”forward” conditional RBM takes into account not only preceding but also future samples during training. Though this may sound controversial at first sight, it will be shown that it makes sense from a musical and neuro-cognitive perspective. The model is applied to reconstruct music based upon the first notes and to improvise in the musical style of a composer. Different to expectations, reconstruction accuracy with respect to a regular CRBM with the same order, was not significantly improved. More research is needed to test the performance on unseen data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=restricted%20boltzmann%20machine" title=" restricted boltzmann machine"> restricted boltzmann machine</a>, <a href="https://publications.waset.org/abstracts/search?q=music%20generation" title=" music generation"> music generation</a>, <a href="https://publications.waset.org/abstracts/search?q=conditional%20restricted%20boltzmann%20machine%20%28CRBM%29" title=" conditional restricted boltzmann machine (CRBM)"> conditional restricted boltzmann machine (CRBM)</a> </p> <a href="https://publications.waset.org/abstracts/19489/forward-conditional-restricted-boltzmann-machines-for-the-generation-of-music" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19489.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">522</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">551</span> VaR or TCE: Explaining the Preferences of Regulators</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Silvia%20Faroni">Silvia Faroni</a>, <a href="https://publications.waset.org/abstracts/search?q=Olivier%20Le%20Courtois"> Olivier Le Courtois</a>, <a href="https://publications.waset.org/abstracts/search?q=Krzysztof%20Ostaszewski"> Krzysztof Ostaszewski</a> </p> <p class="card-text"><strong>Abstract:</strong></p> While a lot of research concentrates on the merits of VaR and TCE, which are the two most classic risk indicators used by financial institutions, little has been written on explaining why regulators favor the choice of VaR or TCE in their set of rules. In this paper, we investigate the preferences of regulators with the aim of understanding why, for instance, a VaR with a given confidence level is ultimately retained. Further, this paper provides equivalence rules that explain how a given choice of VaR can be equivalent to a given choice of TCE. Then, we introduce a new risk indicator that extends TCE by providing a more versatile weighting of the constituents of probability distribution tails. All of our results are illustrated using the generalized Pareto distribution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generalized%20pareto%20distribution" title="generalized pareto distribution">generalized pareto distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20tail%20conditional%20expectation" title=" generalized tail conditional expectation"> generalized tail conditional expectation</a>, <a href="https://publications.waset.org/abstracts/search?q=regulator%20preferences" title=" regulator preferences"> regulator preferences</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20measure" title=" risk measure"> risk measure</a> </p> <a href="https://publications.waset.org/abstracts/146694/var-or-tce-explaining-the-preferences-of-regulators" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146694.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">169</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">550</span> Gap Analysis of Service Quality: The Veterinary Teaching Hospital, University of Peradeniya, Sri Lanka</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Preethi%20Sudarshanie%20Dassanayake">Preethi Sudarshanie Dassanayake</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20A.%20Sudath%20Weerasiri"> R. A. Sudath Weerasiri </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Objective: The objective of this study were to find out highest expectation and perception,highest gap between perception and expectation of service quality, and to find out such gaps between perception and expectation with regard to service quality dimensions were whether statistically significant. Methodology: This study carried out at the Out Patient Department (OPD) of the Veterinary Teaching Hospital (VTH), University of Peradeniya. Modified version of SERVQUAL with 22-pairs of items regarding expectation and perception of service quality in dimensions of tangible, reliability, responsiveness, assurance and empathy were included in Part 1 and the Part 2 of the questionnaire consisted of questions regarding socio-demographic factors. Sample size was 200 and sampling procedure was Systematic Random Sampling. Customers above 18 years of age, able to read, write and understand Sinhala or English language, visits more than twice in last six months and who willing to respond were selected. Findings: The analysis revealed customers expectations of service higher than the perceived for all 22- items of the SERVQUAL. This high expectation suggests that there is sufficient room for further improvement of service quality in all five dimensions. Originality/Value of the Paper: This study gave a new insight for poorly researched area of veterinary health service quality in Sri Lankan context. It provides hospital administrators and policy makers to develop strategies for further improvement of service quality according to customers' view. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=expectation" title="expectation">expectation</a>, <a href="https://publications.waset.org/abstracts/search?q=perception" title=" perception"> perception</a>, <a href="https://publications.waset.org/abstracts/search?q=service%20quality" title=" service quality"> service quality</a>, <a href="https://publications.waset.org/abstracts/search?q=SERVQUAL" title=" SERVQUAL"> SERVQUAL</a>, <a href="https://publications.waset.org/abstracts/search?q=veterinary%20health%20care" title=" veterinary health care"> veterinary health care</a> </p> <a href="https://publications.waset.org/abstracts/23754/gap-analysis-of-service-quality-the-veterinary-teaching-hospital-university-of-peradeniya-sri-lanka" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23754.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">469</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">549</span> A Comparative Study of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Extreme Value Theory (EVT) Model in Modeling Value-at-Risk (VaR)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Longqing%20Li">Longqing Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper addresses the inefficiency of the classical model in measuring the Value-at-Risk (VaR) using a normal distribution or a Student’s t distribution. Specifically, the paper focuses on the one day ahead Value-at-Risk (VaR) of major stock market’s daily returns in US, UK, China and Hong Kong in the most recent ten years under 95% confidence level. To improve the predictable power and search for the best performing model, the paper proposes using two leading alternatives, Extreme Value Theory (EVT) and a family of GARCH models, and compares the relative performance. The main contribution could be summarized in two aspects. First, the paper extends the GARCH family model by incorporating EGARCH and TGARCH to shed light on the difference between each in estimating one day ahead Value-at-Risk (VaR). Second, to account for the non-normality in the distribution of financial markets, the paper applies Generalized Error Distribution (GED), instead of the normal distribution, to govern the innovation term. A dynamic back-testing procedure is employed to assess the performance of each model, a family of GARCH and the conditional EVT. The conclusion is that Exponential GARCH yields the best estimate in out-of-sample one day ahead Value-at-Risk (VaR) forecasting. Moreover, the discrepancy of performance between the GARCH and the conditional EVT is indistinguishable. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Value-at-Risk" title="Value-at-Risk">Value-at-Risk</a>, <a href="https://publications.waset.org/abstracts/search?q=Extreme%20Value%20Theory" title=" Extreme Value Theory"> Extreme Value Theory</a>, <a href="https://publications.waset.org/abstracts/search?q=conditional%20EVT" title=" conditional EVT"> conditional EVT</a>, <a href="https://publications.waset.org/abstracts/search?q=backtesting" title=" backtesting"> backtesting</a> </p> <a href="https://publications.waset.org/abstracts/49589/a-comparative-study-of-generalized-autoregressive-conditional-heteroskedasticity-garch-and-extreme-value-theory-evt-model-in-modeling-value-at-risk-var" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49589.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">321</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">548</span> Visitor Expectation on a Tour Guide Business as Part of Promoting the Sustainability Tourism in Thailand</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kawinphat%20Lertpontmanee">Kawinphat Lertpontmanee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The tourism industry in Thailand is regarded as an energizer of the domestic economy for several years. With this reason, researchers aim to study Visitor Expectation on a Tour Guide Business as part of promoting the Sustainability Tourism in Thailand. The study collected data via questionnaires from the population of 400 samples who have experienced the tour guide and traveling business. The research was studied by divided samples into two main groups, male samples and female sample. There are differences on their average salary per month and expectation on the tour company as part of promoting the Sustainability Tourism of the country. The majority of samples expect that the tour guide company will present the awareness of operating their business without any impact to the environment, offering an appropriated quality trips and offering a valuable price. The tour guide companies were expected to be expanded and operated in regional level in order to strengthen the community economy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=expectation" title="expectation">expectation</a>, <a href="https://publications.waset.org/abstracts/search?q=tour%20guide%20business" title=" tour guide business"> tour guide business</a>, <a href="https://publications.waset.org/abstracts/search?q=sustainability%20tourism" title=" sustainability tourism"> sustainability tourism</a>, <a href="https://publications.waset.org/abstracts/search?q=sightseeing%20business" title=" sightseeing business"> sightseeing business</a> </p> <a href="https://publications.waset.org/abstracts/39962/visitor-expectation-on-a-tour-guide-business-as-part-of-promoting-the-sustainability-tourism-in-thailand" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39962.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">320</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">547</span> Use of Multistage Transition Regression Models for Credit Card Income Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Denys%20Osipenko">Denys Osipenko</a>, <a href="https://publications.waset.org/abstracts/search?q=Jonathan%20Crook"> Jonathan Crook</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Because of the variety of the card holders’ behaviour types and income sources each consumer account can be transferred to a variety of states. Each consumer account can be inactive, transactor, revolver, delinquent, defaulted and requires an individual model for the income prediction. The estimation of transition probabilities between statuses at the account level helps to avoid the memorylessness of the Markov Chains approach. This paper investigates the transition probabilities estimation approaches to credit cards income prediction at the account level. The key question of empirical research is which approach gives more accurate results: multinomial logistic regression or multistage conditional logistic regression with binary target. Both models have shown moderate predictive power. Prediction accuracy for conditional logistic regression depends on the order of stages for the conditional binary logistic regression. On the other hand, multinomial logistic regression is easier for usage and gives integrate estimations for all states without priorities. Thus further investigations can be concentrated on alternative modeling approaches such as discrete choice models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multinomial%20regression" title="multinomial regression">multinomial regression</a>, <a href="https://publications.waset.org/abstracts/search?q=conditional%20logistic%20regression" title=" conditional logistic regression"> conditional logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=credit%20account%20state" title=" credit account state"> credit account state</a>, <a href="https://publications.waset.org/abstracts/search?q=transition%20probability" title=" transition probability"> transition probability</a> </p> <a href="https://publications.waset.org/abstracts/19488/use-of-multistage-transition-regression-models-for-credit-card-income-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19488.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">486</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">546</span> Educational Leadership for Social Justice: Meeting UK Muslim Expectation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mochammad%20Thalut">Mochammad Thalut</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This essay discusses how educational leadership response the Muslims pupils’ problems and their expectation about education in the UK. As we know, the Muslims community in the country is increasing. However, the debate about educational leadership is still limited to the separation between religion and academic by westerns approach. It is found that there are four major problems of Muslims pupils that need to solve by the educational leader to provide social justice in education. Leader-teacher as an Islamic concept of the educational leader is an alternative approach that can be used by the educational leader to overcome the problems. In the end, it is strongly recommended to bring this issue to the leadership development program in the UK to give all aspiring heads understanding about Muslims expectation about education. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muslim" title="Muslim">Muslim</a>, <a href="https://publications.waset.org/abstracts/search?q=education" title=" education"> education</a>, <a href="https://publications.waset.org/abstracts/search?q=leadership" title=" leadership"> leadership</a>, <a href="https://publications.waset.org/abstracts/search?q=identity" title=" identity"> identity</a> </p> <a href="https://publications.waset.org/abstracts/73324/educational-leadership-for-social-justice-meeting-uk-muslim-expectation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73324.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">255</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">545</span> Ankaferd Blood Stopper (ABS) Has Protective Effect on Colonic Inflammation: An in Vitro Study in Raw 264.7 and Caco-2 Cells</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aysegul%20Alyamac">Aysegul Alyamac</a>, <a href="https://publications.waset.org/abstracts/search?q=Sukru%20Gulec"> Sukru Gulec</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ankaferd Blood Stopper (ABS) is a plant extract used to stop bleeding caused by injuries and surgical interventions. ABS also involved in wound healing of intestinal mucosal damage due to oxidative stress and inflammation. Inflammatory Bowel Disease (IBD) is a common chronic disorder of the gastrointestinal tract that causes abdominal pain, diarrhea, and gastrointestinal bleeding, and increases the risk of colon cancer. Inflammation is an essential factor in the development of IBD. The various studies have been performed about the physiological effects of ABS; however, ABS dependent mechanism on colonic inflammation has not been elucidated. Thus, the protective effect of ABS on colonic inflammation was investigated in this study. The Caco-2 and RAW 264.7 murine macrophage cells were used as a model of in vitro colonic inflammation. RAW 264.7 cells were treated with lipopolysaccharide (LPS) for 12 hours to induce the inflammation, and a conditional medium was obtained. Caco-2 cells were treated with 15 µl/ml ABS for 4 hours, then incubated with conditional medium and the cells also were incubated with 15 µl/ml ABS and conditional medium together for 4 hours. Tumor necrosis factor alpha (TNF-α) protein levels were targeted in testing inflammatory condition and its level was significantly increased (25 fold, p<0.001) compared to the control group by using Enzyme-Linked Immunosorbent Assay (ELISA) method. The COX-2 mRNA level was used as a marker gene to show the possible anti-inflammatory effect of ABS in Caco-2 cells. RAW cells-derived conditional medium significantly (3.3 fold, p<0.001) induced cyclooxygenase-2 (COX-2) mRNA levels in Caco-2 cells. The pretreatment of Caco-2 cells caused a significant decrease (3.3 fold, p<0.001) in COX-2 mRNA levels relative to conditional medium given group. Furthermore, COX-2 mRNA level was significantly reduced (4,7 fold, p<0.001) in ABS and conditional medium treated group. These results suggest that ABS might have an anti-inflammatory effect in vitro. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ankaferd%20blood%20stopper" title="Ankaferd blood stopper">Ankaferd blood stopper</a>, <a href="https://publications.waset.org/abstracts/search?q=CaCo-2" title=" CaCo-2"> CaCo-2</a>, <a href="https://publications.waset.org/abstracts/search?q=colonic%20inflammation" title=" colonic inflammation"> colonic inflammation</a>, <a href="https://publications.waset.org/abstracts/search?q=RAW%20264.7" title=" RAW 264.7"> RAW 264.7</a> </p> <a href="https://publications.waset.org/abstracts/121210/ankaferd-blood-stopper-abs-has-protective-effect-on-colonic-inflammation-an-in-vitro-study-in-raw-2647-and-caco-2-cells" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/121210.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">544</span> VaR Estimation Using the Informational Content of Futures Traded Volume</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amel%20Oueslati">Amel Oueslati</a>, <a href="https://publications.waset.org/abstracts/search?q=Olfa%20Benouda"> Olfa Benouda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> New Value at Risk (VaR) estimation is proposed and investigated. The well-known two stages Garch-EVT approach uses conditional volatility to generate one step ahead forecasts of VaR. With daily data for twelve stocks that decompose the Dow Jones Industrial Average (DJIA) index, this paper incorporates the volume in the first stage volatility estimation. Afterwards, the forecasting ability of this conditional volatility concerning the VaR estimation is compared to that of a basic volatility model without considering any trading component. The results are significant and bring out the importance of the trading volume in the VaR measure. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Garch-EVT" title="Garch-EVT">Garch-EVT</a>, <a href="https://publications.waset.org/abstracts/search?q=value%20at%20risk" title=" value at risk"> value at risk</a>, <a href="https://publications.waset.org/abstracts/search?q=volume" title=" volume"> volume</a>, <a href="https://publications.waset.org/abstracts/search?q=volatility" title=" volatility"> volatility</a> </p> <a href="https://publications.waset.org/abstracts/56021/var-estimation-using-the-informational-content-of-futures-traded-volume" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56021.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">285</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">543</span> Spatial Time Series Models for Rice and Cassava Yields Based on Bayesian Linear Mixed Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Panudet%20Saengseedam">Panudet Saengseedam</a>, <a href="https://publications.waset.org/abstracts/search?q=Nanthachai%20Kantanantha"> Nanthachai Kantanantha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a linear mixed model (LMM) with spatial effects to forecast rice and cassava yields in Thailand at the same time. A multivariate conditional autoregressive (MCAR) model is assumed to present the spatial effects. A Bayesian method is used for parameter estimation via Gibbs sampling Markov Chain Monte Carlo (MCMC). The model is applied to the rice and cassava yields monthly data which have been extracted from the Office of Agricultural Economics, Ministry of Agriculture and Cooperatives of Thailand. The results show that the proposed model has better performance in most provinces in both fitting part and validation part compared to the simple exponential smoothing and conditional auto regressive models (CAR) from our previous study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20method" title="Bayesian method">Bayesian method</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20mixed%20model" title=" linear mixed model"> linear mixed model</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20conditional%20autoregressive%20model" title=" multivariate conditional autoregressive model"> multivariate conditional autoregressive model</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20time%20series" title=" spatial time series"> spatial time series</a> </p> <a href="https://publications.waset.org/abstracts/11875/spatial-time-series-models-for-rice-and-cassava-yields-based-on-bayesian-linear-mixed-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11875.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">395</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">542</span> Arabic Character Recognition Using Regression Curves with the Expectation Maximization Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdullah%20A.%20AlShaher">Abdullah A. AlShaher</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we demonstrate how regression curves can be used to recognize 2D non-rigid handwritten shapes. Each shape is represented by a set of non-overlapping uniformly distributed landmarks. The underlying models utilize 2<sup>nd</sup> order of polynomials to model shapes within a training set. To estimate the regression models, we need to extract the required coefficients which describe the variations for a set of shape class. Hence, a least square method is used to estimate such modes. We then proceed by training these coefficients using the apparatus Expectation Maximization algorithm. Recognition is carried out by finding the least error landmarks displacement with respect to the model curves. Handwritten isolated Arabic characters are used to evaluate our approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=character%20recognition" title="character recognition">character recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20curves" title=" regression curves"> regression curves</a>, <a href="https://publications.waset.org/abstracts/search?q=handwritten%20Arabic%20letters" title=" handwritten Arabic letters"> handwritten Arabic letters</a>, <a href="https://publications.waset.org/abstracts/search?q=expectation%20maximization%20algorithm" title=" expectation maximization algorithm"> expectation maximization algorithm</a> </p> <a href="https://publications.waset.org/abstracts/101427/arabic-character-recognition-using-regression-curves-with-the-expectation-maximization-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/101427.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">145</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">541</span> FMR1 Gene Carrier Screening for Premature Ovarian Insufficiency in Females: An Indian Scenario</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sarita%20Agarwal">Sarita Agarwal</a>, <a href="https://publications.waset.org/abstracts/search?q=Deepika%20Delsa%20Dean"> Deepika Delsa Dean</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Like the task of transferring photo images to artistic images, image-to-image translation aims to translate the data to the imitated data which belongs to the target domain. Neural Style Transfer and CycleGAN are two well-known deep learning architectures used for photo image-to-art image transfer. However, studies involving these two models concentrate on one-to-one domain translation, not one-to-multi domains translation. Our study tries to investigate deep learning architectures, which can be controlled to yield multiple artistic style translation only by adding a conditional vector. We have expanded CycleGAN and constructed Conditional CycleGAN for 5 kinds of categories translation. Our study found that the architecture inserting conditional vector into the middle layer of the Generator could output multiple artistic images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=genetic%20counseling" title="genetic counseling">genetic counseling</a>, <a href="https://publications.waset.org/abstracts/search?q=FMR1%20gene" title=" FMR1 gene"> FMR1 gene</a>, <a href="https://publications.waset.org/abstracts/search?q=fragile%20x-associated%20primary%20ovarian%20insufficiency" title=" fragile x-associated primary ovarian insufficiency"> fragile x-associated primary ovarian insufficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=premutation" title=" premutation"> premutation</a> </p> <a href="https://publications.waset.org/abstracts/118798/fmr1-gene-carrier-screening-for-premature-ovarian-insufficiency-in-females-an-indian-scenario" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/118798.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">130</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">540</span> Energy Calculation for Excited Lithium Atom in Position Space</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khalil%20H.%20Al-Bayati">Khalil H. Al-Bayati</a>, <a href="https://publications.waset.org/abstracts/search?q=Khalid%20Omar%20Al-Baiti"> Khalid Omar Al-Baiti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The energy expectation value <E> for Li-like ions systems (Li, Be+ and Be2+) hasbeen calculated and examined within the ground state (1s2sα)^2 S and the excited state (1s3sα)^2 S in position space. The partitioning technique of Hartree-Fock (H-F) has been used for existing wavefnctions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=energy%20expectation%20value" title="energy expectation value">energy expectation value</a>, <a href="https://publications.waset.org/abstracts/search?q=atomic%20systems" title=" atomic systems"> atomic systems</a>, <a href="https://publications.waset.org/abstracts/search?q=ground%20and%20excited%20states" title=" ground and excited states"> ground and excited states</a>, <a href="https://publications.waset.org/abstracts/search?q=Hartree-Fock%20approximation" title=" Hartree-Fock approximation"> Hartree-Fock approximation</a> </p> <a href="https://publications.waset.org/abstracts/11402/energy-calculation-for-excited-lithium-atom-in-position-space" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11402.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">617</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">539</span> Generative AI: A Comparison of Conditional Tabular Generative Adversarial Networks and Conditional Tabular Generative Adversarial Networks with Gaussian Copula in Generating Synthetic Data with Synthetic Data Vault</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lakshmi%20Prayaga">Lakshmi Prayaga</a>, <a href="https://publications.waset.org/abstracts/search?q=Chandra%20Prayaga.%20Aaron%20Wade"> Chandra Prayaga. Aaron Wade</a>, <a href="https://publications.waset.org/abstracts/search?q=Gopi%20Shankar%20Mallu"> Gopi Shankar Mallu</a>, <a href="https://publications.waset.org/abstracts/search?q=Harsha%20Satya%20Pola"> Harsha Satya Pola</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Synthetic data generated by Generative Adversarial Networks and Autoencoders is becoming more common to combat the problem of insufficient data for research purposes. However, generating synthetic data is a tedious task requiring extensive mathematical and programming background. Open-source platforms such as the Synthetic Data Vault (SDV) and Mostly AI have offered a platform that is user-friendly and accessible to non-technical professionals to generate synthetic data to augment existing data for further analysis. The SDV also provides for additions to the generic GAN, such as the Gaussian copula. We present the results from two synthetic data sets (CTGAN data and CTGAN with Gaussian Copula) generated by the SDV and report the findings. The results indicate that the ROC and AUC curves for the data generated by adding the layer of Gaussian copula are much higher than the data generated by the CTGAN. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=synthetic%20data%20generation" title="synthetic data generation">synthetic data generation</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20adversarial%20networks" title=" generative adversarial networks"> generative adversarial networks</a>, <a href="https://publications.waset.org/abstracts/search?q=conditional%20tabular%20GAN" title=" conditional tabular GAN"> conditional tabular GAN</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20copula" title=" Gaussian copula"> Gaussian copula</a> </p> <a href="https://publications.waset.org/abstracts/183000/generative-ai-a-comparison-of-conditional-tabular-generative-adversarial-networks-and-conditional-tabular-generative-adversarial-networks-with-gaussian-copula-in-generating-synthetic-data-with-synthetic-data-vault" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183000.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">82</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=conditional%20expectation&page=2">2</a></li> <li class="page-item"><a class="page-link" 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