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exponentially weighted moving average</title> <meta name="description" content="Search results for: exponentially weighted moving average"> <meta name="keywords" content="exponentially weighted moving average"> <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 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exponentially weighted moving average</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6262</span> Optimal Design for SARMA(P,Q)L Process of EWMA Control Chart</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yupaporn%20Areepong">Yupaporn Areepong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main goal of this paper is to study Statistical Process Control (SPC) with Exponentially Weighted Moving Average (EWMA) control chart when observations are serially-correlated. The characteristic of control chart is Average Run Length (ARL) which is the average number of samples taken before an action signal is given. Ideally, an acceptable ARL of in-control process should be enough large, so-called (ARL0). Otherwise it should be small when the process is out-of-control, so-called Average of Delay Time (ARL1) or a mean of true alarm. We find explicit formulas of ARL for EWMA control chart for Seasonal Autoregressive and Moving Average processes (SARMA) with Exponential white noise. The results of ARL obtained from explicit formula and Integral equation are in good agreement. In particular, this formulas for evaluating (ARL0) and (ARL1) be able to get a set of optimal parameters which depend on smoothing parameter (λ) and width of control limit (H) for designing EWMA chart with minimum of (ARL1). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=average%20run%20length" title="average run length">average run length</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20parameters" title=" optimal parameters"> optimal parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=exponentially%20weighted%20moving%20average%20%28EWMA%29" title=" exponentially weighted moving average (EWMA)"> exponentially weighted moving average (EWMA)</a>, <a href="https://publications.waset.org/abstracts/search?q=control%20chart" title=" control chart"> control chart</a> </p> <a href="https://publications.waset.org/abstracts/10653/optimal-design-for-sarmapql-process-of-ewma-control-chart" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10653.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">560</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">6261</span> Distribution-Free Exponentially Weighted Moving Average Control Charts for Monitoring Process Variability </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chen-Fang%20Tsai">Chen-Fang Tsai</a>, <a href="https://publications.waset.org/abstracts/search?q=Shin-Li%20Lu"> Shin-Li Lu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Distribution-free control chart is an oncoming area from the statistical process control charts in recent years. Some researchers have developed various nonparametric control charts and investigated the detection capability of these charts. The major advantage of nonparametric control charts is that the underlying process is not specifically considered the assumption of normality or any parametric distribution. In this paper, two nonparametric exponentially weighted moving average (EWMA) control charts based on nonparametric tests, namely NE-S and NE-M control charts, are proposed for monitoring process variability. Generally, weighted moving average (GWMA) control charts are extended by utilizing design and adjustment parameters for monitoring the changes in the process variability, namely NG-S and NG-M control charts. Statistical performance is also investigated on NG-S and NG-M control charts with run rules. Moreover, sensitivity analysis is performed to show the effects of design parameters under the nonparametric NG-S and NG-M control charts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Distribution-free%20control%20chart" title="Distribution-free control chart">Distribution-free control chart</a>, <a href="https://publications.waset.org/abstracts/search?q=EWMA%20control%20charts" title=" EWMA control charts"> EWMA control charts</a>, <a href="https://publications.waset.org/abstracts/search?q=GWMA%20control%20charts" title=" GWMA control charts"> GWMA control charts</a> </p> <a href="https://publications.waset.org/abstracts/88638/distribution-free-exponentially-weighted-moving-average-control-charts-for-monitoring-process-variability" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88638.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">272</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">6260</span> An EWMA P-Chart Based on Improved Square Root Transformation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saowanit%20Sukparungsee">Saowanit Sukparungsee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Generally, the traditional Shewhart p chart has been developed by for charting the binomial data. This chart has been developed using the normal approximation with condition as low defect level and the small to moderate sample size. In real applications, however, are away from these assumptions due to skewness in the exact distribution. In this paper, a modified Exponentially Weighted Moving Average (EWMA) control chat for detecting a change in binomial data by improving square root transformations, namely ISRT p EWMA control chart. The numerical results show that ISRT p EWMA chart is superior to ISRT p chart for small to moderate shifts, otherwise, the latter is better for large shifts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=number%20of%20defects" title="number of defects">number of defects</a>, <a href="https://publications.waset.org/abstracts/search?q=exponentially%20weighted%20moving%20average" title=" exponentially weighted moving average"> exponentially weighted moving average</a>, <a href="https://publications.waset.org/abstracts/search?q=average%20run%20length" title=" average run length"> average run length</a>, <a href="https://publications.waset.org/abstracts/search?q=square%20root%20transformations" title=" square root transformations"> square root transformations</a> </p> <a href="https://publications.waset.org/abstracts/10613/an-ewma-p-chart-based-on-improved-square-root-transformation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10613.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">440</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">6259</span> The Moment of the Optimal Average Length of the Multivariate Exponentially Weighted Moving Average Control Chart for Equally Correlated Variables</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Edokpa%20Idemudia%20Waziri">Edokpa Idemudia Waziri</a>, <a href="https://publications.waset.org/abstracts/search?q=Salisu%20S.%20Umar"> Salisu S. Umar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Hotellng’s T^2 is a well-known statistic for detecting a shift in the mean vector of a multivariate normal distribution. Control charts based on T have been widely used in statistical process control for monitoring a multivariate process. Although it is a powerful tool, the T statistic is deficient when the shift to be detected in the mean vector of a multivariate process is small and consistent. The Multivariate Exponentially Weighted Moving Average (MEWMA) control chart is one of the control statistics used to overcome the drawback of the Hotellng’s T statistic. In this paper, the probability distribution of the Average Run Length (ARL) of the MEWMA control chart when the quality characteristics exhibit substantial cross correlation and when the process is in-control and out-of-control was derived using the Markov Chain algorithm. The derivation of the probability functions and the moments of the run length distribution were also obtained and they were consistent with some existing results for the in-control and out-of-control situation. By simulation process, the procedure identified a class of ARL for the MEWMA control when the process is in-control and out-of-control. From our study, it was observed that the MEWMA scheme is quite adequate for detecting a small shift and a good way to improve the quality of goods and services in a multivariate situation. It was also observed that as the in-control average run length ARL0¬ or the number of variables (p) increases, the optimum value of the ARL0pt increases asymptotically and as the magnitude of the shift σ increases, the optimal ARLopt decreases. Finally, we use the example from the literature to illustrate our method and demonstrate its efficiency. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=average%20run%20length" title="average run length">average run length</a>, <a href="https://publications.waset.org/abstracts/search?q=markov%20chain" title=" markov chain"> markov chain</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20exponentially%20weighted%20moving%20average" title=" multivariate exponentially weighted moving average"> multivariate exponentially weighted moving average</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20smoothing%20parameter" title=" optimal smoothing parameter"> optimal smoothing parameter</a> </p> <a href="https://publications.waset.org/abstracts/42648/the-moment-of-the-optimal-average-length-of-the-multivariate-exponentially-weighted-moving-average-control-chart-for-equally-correlated-variables" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42648.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">422</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">6258</span> EWMA and MEWMA Control Charts for Monitoring Mean and Variance in Industrial Processes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=L.%20A.%20Toro">L. A. Toro</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Prieto"> N. Prieto</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20J.%20Vargas"> J. J. Vargas </a> </p> <p class="card-text"><strong>Abstract:</strong></p> There are many control charts for monitoring mean and variance. Among these, the X y R, X y S, S2 Hotteling and Shewhart control charts, for mentioning some, are widely used for monitoring mean a variance in industrial processes. In particular, the Shewhart charts are based on the information about the process contained in the current observation only and ignore any information given by the entire sequence of points. Moreover, that the Shewhart chart is a control chart without memory. Consequently, Shewhart control charts are found to be less sensitive in detecting smaller shifts, particularly smaller than 1.5 times of the standard deviation. These kind of small shifts are important in many industrial applications. In this study and effective alternative to Shewhart control chart was implemented. In case of univariate process an Exponentially Moving Average (EWMA) control chart was developed and Multivariate Exponentially Moving Average (MEWMA) control chart in case of multivariate process. Both of these charts were based on memory and perform better that Shewhart chart while detecting smaller shifts. In these charts, information the past sample is cumulated up the current sample and then the decision about the process control is taken. The mentioned characteristic of EWMA and MEWMA charts, are of the paramount importance when it is necessary to control industrial process, because it is possible to correct or predict problems in the processes before they come to a dangerous limit. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=control%20charts" title="control charts">control charts</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20exponentially%20moving%20average%20%28MEWMA%29" title=" multivariate exponentially moving average (MEWMA)"> multivariate exponentially moving average (MEWMA)</a>, <a href="https://publications.waset.org/abstracts/search?q=exponentially%20moving%20average%20%28EWMA%29" title=" exponentially moving average (EWMA)"> exponentially moving average (EWMA)</a>, <a href="https://publications.waset.org/abstracts/search?q=industrial%20control%20process" title=" industrial control process"> industrial control process</a> </p> <a href="https://publications.waset.org/abstracts/38377/ewma-and-mewma-control-charts-for-monitoring-mean-and-variance-in-industrial-processes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38377.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">355</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">6257</span> On Periodic Integer-Valued Moving Average Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aries%20Nawel">Aries Nawel</a>, <a href="https://publications.waset.org/abstracts/search?q=Bentarzi%20Mohamed"> Bentarzi Mohamed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with the study of some probabilistic and statistical properties of a Periodic Integer-Valued Moving Average Model (PINMA_{S}(q)). The closed forms of the mean, the second moment and the periodic autocovariance function are obtained. Furthermore, the time reversibility of the model is discussed in details. Moreover, the estimation of the underlying parameters are obtained by the Yule-Walker method, the Conditional Least Square method (CLS) and the Weighted Conditional Least Square method (WCLS). A simulation study is carried out to evaluate the performance of the estimation method. Moreover, an application on real data set is provided. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=periodic%20integer-valued%20moving%20average" title="periodic integer-valued moving average">periodic integer-valued moving average</a>, <a href="https://publications.waset.org/abstracts/search?q=periodically%20correlated%20process" title=" periodically correlated process"> periodically correlated process</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20reversibility" title=" time reversibility"> time reversibility</a>, <a href="https://publications.waset.org/abstracts/search?q=count%20data" title=" count data"> count data</a> </p> <a href="https://publications.waset.org/abstracts/132956/on-periodic-integer-valued-moving-average-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132956.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">202</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">6256</span> A Data Science Pipeline for Algorithmic Trading: A Comparative Study in Applications to Finance and Cryptoeconomics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Luyao%20Zhang">Luyao Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Tianyu%20Wu"> Tianyu Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiayi%20Li"> Jiayi Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Carlos-Gustavo%20Salas-Flores"> Carlos-Gustavo Salas-Flores</a>, <a href="https://publications.waset.org/abstracts/search?q=Saad%20Lahrichi"> Saad Lahrichi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recent advances in AI have made algorithmic trading a central role in finance. However, current research and applications are disconnected information islands. We propose a generally applicable pipeline for designing, programming, and evaluating algorithmic trading of stock and crypto tokens. Moreover, we provide comparative case studies for four conventional algorithms, including moving average crossover, volume-weighted average price, sentiment analysis, and statistical arbitrage. Our study offers a systematic way to program and compare different trading strategies. Moreover, we implement our algorithms by object-oriented programming in Python3, which serves as open-source software for future academic research and applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=algorithmic%20trading" title="algorithmic trading">algorithmic trading</a>, <a href="https://publications.waset.org/abstracts/search?q=AI%20for%20finance" title=" AI for finance"> AI for finance</a>, <a href="https://publications.waset.org/abstracts/search?q=fintech" title=" fintech"> fintech</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=moving%20average%20crossover" title=" moving average crossover"> moving average crossover</a>, <a href="https://publications.waset.org/abstracts/search?q=volume%20weighted%20average%20price" title=" volume weighted average price"> volume weighted average price</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20arbitrage" title=" statistical arbitrage"> statistical arbitrage</a>, <a href="https://publications.waset.org/abstracts/search?q=pair%20trading" title=" pair trading"> pair trading</a>, <a href="https://publications.waset.org/abstracts/search?q=object-oriented%20programming" title=" object-oriented programming"> object-oriented programming</a>, <a href="https://publications.waset.org/abstracts/search?q=python3" title=" python3"> python3</a> </p> <a href="https://publications.waset.org/abstracts/146823/a-data-science-pipeline-for-algorithmic-trading-a-comparative-study-in-applications-to-finance-and-cryptoeconomics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146823.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">147</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">6255</span> A Comparative Analysis of Grade Weighted Average and Comprehensive Examination Result of Non Board Passers and Board Passers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rob%20Gesley%20Capistrano">Rob Gesley Capistrano</a>, <a href="https://publications.waset.org/abstracts/search?q=Jasper%20James%20Isaac"> Jasper James Isaac</a>, <a href="https://publications.waset.org/abstracts/search?q=Rose%20Mae%20Moralda"> Rose Mae Moralda</a>, <a href="https://publications.waset.org/abstracts/search?q=Therese%20Anne%20Peleo"> Therese Anne Peleo</a>, <a href="https://publications.waset.org/abstracts/search?q=Danica%20Rillo"> Danica Rillo</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20Virginia%20Santillian"> Maria Virginia Santillian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the valuable things that shows the intelligence among individuals is the academic background specifically their Grade Weighted Average and the significant result of the Comprehensive Examination. The general objective of the researchers to this study is to determine if there is a significant difference between General Weighted Average and Comprehensive Examination Result of Psychometrician Board Passers and Non-Board Passers. The respondents of this study composed of board passers and non-board passers. The researchers used purposive sampling technique. The result utilized by using T-test Independent Sample to determine the comparison of General Weighted Average and Comprehensive Examination Result of Board Passers and Non Board Passers. At the end, it concluded that the General Weighted Average of Board Passers and Non-Board Passers shows that there is no significant difference, but the average showed a minimal variation. The Comprehensive Examination Result of Board Passers and Non-Board Passers result revealed that there is a significant difference. The performance of comprehensive examination that will test the overall knowledge of an individual and will determine whose more proficient will likely to have a higher score. The result of the comprehensive examination had an impact in the passing performance of board examination. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=board%20passers" title="board passers">board passers</a>, <a href="https://publications.waset.org/abstracts/search?q=comprehensive%20examination%20result" title=" comprehensive examination result"> comprehensive examination result</a>, <a href="https://publications.waset.org/abstracts/search?q=grade%20weighted%20average" title=" grade weighted average"> grade weighted average</a>, <a href="https://publications.waset.org/abstracts/search?q=non%20board%20passers" title=" non board passers"> non board passers</a> </p> <a href="https://publications.waset.org/abstracts/101314/a-comparative-analysis-of-grade-weighted-average-and-comprehensive-examination-result-of-non-board-passers-and-board-passers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/101314.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">191</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">6254</span> Phase II Monitoring of First-Order Autocorrelated General Linear Profiles</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yihua%20Wang">Yihua Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yunru%20Lai"> Yunru Lai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Statistical process control has been successfully applied in a variety of industries. In some applications, the quality of a process or product is better characterized and summarized by a functional relationship between a response variable and one or more explanatory variables. A collection of this type of data is called a profile. Profile monitoring is used to understand and check the stability of this relationship or curve over time. The independent assumption for the error term is commonly used in the existing profile monitoring studies. However, in many applications, the profile data show correlations over time. Therefore, we focus on a general linear regression model with a first-order autocorrelation between profiles in this study. We propose an exponentially weighted moving average charting scheme to monitor this type of profile. The simulation study shows that our proposed methods outperform the existing schemes based on the average run length criterion. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autocorrelation" title="autocorrelation">autocorrelation</a>, <a href="https://publications.waset.org/abstracts/search?q=EWMA%20control%20chart" title=" EWMA control chart"> EWMA control chart</a>, <a href="https://publications.waset.org/abstracts/search?q=general%20linear%20regression%20model" title=" general linear regression model"> general linear regression model</a>, <a href="https://publications.waset.org/abstracts/search?q=profile%20monitoring" title=" profile monitoring"> profile monitoring</a> </p> <a href="https://publications.waset.org/abstracts/67610/phase-ii-monitoring-of-first-order-autocorrelated-general-linear-profiles" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67610.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">6253</span> Identification of Classes of Bilinear Time Series Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anthony%20Usoro">Anthony Usoro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, two classes of bilinear time series model are obtained under certain conditions from the general bilinear autoregressive moving average model. Bilinear Autoregressive (BAR) and Bilinear Moving Average (BMA) Models have been identified. From the general bilinear model, BAR and BMA models have been proved to exist for q = Q = 0, => j = 0, and p = P = 0, => i = 0 respectively. These models are found useful in modelling most of the economic and financial data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autoregressive%20model" title="autoregressive model">autoregressive model</a>, <a href="https://publications.waset.org/abstracts/search?q=bilinear%20autoregressive%20model" title=" bilinear autoregressive model"> bilinear autoregressive model</a>, <a href="https://publications.waset.org/abstracts/search?q=bilinear%20moving%20average%20model" title=" bilinear moving average model"> bilinear moving average model</a>, <a href="https://publications.waset.org/abstracts/search?q=moving%20average%20model" title=" moving average model"> moving average model</a> </p> <a href="https://publications.waset.org/abstracts/56430/identification-of-classes-of-bilinear-time-series-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56430.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">407</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">6252</span> A Study on the False Alarm Rates of MEWMA and MCUSUM Control Charts When the Parameters Are Estimated</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Umar%20Farouk%20Abbas">Umar Farouk Abbas</a>, <a href="https://publications.waset.org/abstracts/search?q=Danjuma%20Mustapha"> Danjuma Mustapha</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamisu%20Idi"> Hamisu Idi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is now a known fact that quality is an important issue in manufacturing industries. A control chart is an integrated and powerful tool in statistical process control (SPC). The mean µ and standard deviation σ parameters are estimated. In general, the multivariate exponentially weighted moving average (MEWMA) and multivariate cumulative sum (MCUSUM) are used in the detection of small shifts in joint monitoring of several correlated variables; the charts used information from past data which makes them sensitive to small shifts. The aim of the paper is to compare the performance of Shewhart xbar, MEWMA, and MCUSUM control charts in terms of their false rates when parameters are estimated with autocorrelation. A simulation was conducted in R software to generate the average run length (ARL) values of each of the charts. After the analysis, the results show that a comparison of the false alarm rates of the charts shows that MEWMA chart has lower false alarm rates than the MCUSUM chart at various levels of parameter estimated to the number of ARL0 (in control) values. Also noticed was that the sample size has an advert effect on the false alarm of the control charts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=average%20run%20length" title="average run length">average run length</a>, <a href="https://publications.waset.org/abstracts/search?q=MCUSUM%20chart" title=" MCUSUM chart"> MCUSUM chart</a>, <a href="https://publications.waset.org/abstracts/search?q=MEWMA%20chart" title=" MEWMA chart"> MEWMA chart</a>, <a href="https://publications.waset.org/abstracts/search?q=false%20alarm%20rate" title=" false alarm rate"> false alarm rate</a>, <a href="https://publications.waset.org/abstracts/search?q=parameter%20estimation" title=" parameter estimation"> parameter estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a> </p> <a href="https://publications.waset.org/abstracts/106504/a-study-on-the-false-alarm-rates-of-mewma-and-mcusum-control-charts-when-the-parameters-are-estimated" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/106504.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">222</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">6251</span> An Accelerated Stochastic Gradient Method with Momentum</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Liang%20Liu">Liang Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaopeng%20Luo"> Xiaopeng Luo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose an accelerated stochastic gradient method with momentum. The momentum term is the weighted average of generated gradients, and the weights decay inverse proportionally with the iteration times. Stochastic gradient descent with momentum (SGDM) uses weights that decay exponentially with the iteration times to generate the momentum term. Using exponential decay weights, variants of SGDM with inexplicable and complicated formats have been proposed to achieve better performance. However, the momentum update rules of our method are as simple as that of SGDM. We provide theoretical convergence analyses, which show both the exponential decay weights and our inverse proportional decay weights can limit the variance of the parameter moving directly to a region. Experimental results show that our method works well with many practical problems and outperforms SGDM. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=exponential%20decay%20rate%20weight" title="exponential decay rate weight">exponential decay rate weight</a>, <a href="https://publications.waset.org/abstracts/search?q=gradient%20descent" title=" gradient descent"> gradient descent</a>, <a href="https://publications.waset.org/abstracts/search?q=inverse%20proportional%20decay%20rate%20weight" title=" inverse proportional decay rate weight"> inverse proportional decay rate weight</a>, <a href="https://publications.waset.org/abstracts/search?q=momentum" title=" momentum"> momentum</a> </p> <a href="https://publications.waset.org/abstracts/133507/an-accelerated-stochastic-gradient-method-with-momentum" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133507.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">162</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">6250</span> A Data-Driven Monitoring Technique Using Combined Anomaly Detectors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fouzi%20Harrou">Fouzi Harrou</a>, <a href="https://publications.waset.org/abstracts/search?q=Ying%20Sun"> Ying Sun</a>, <a href="https://publications.waset.org/abstracts/search?q=Sofiane%20Khadraoui"> Sofiane Khadraoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Anomaly detection based on Principal Component Analysis (PCA) was studied intensively and largely applied to multivariate processes with highly cross-correlated process variables. Monitoring metrics such as the Hotelling's T2 and the Q statistics are usually used in PCA-based monitoring to elucidate the pattern variations in the principal and residual subspaces, respectively. However, these metrics are ill suited to detect small faults. In this paper, the Exponentially Weighted Moving Average (EWMA) based on the Q and T statistics, T2-EWMA and Q-EWMA, were developed for detecting faults in the process mean. The performance of the proposed methods was compared with that of the conventional PCA-based fault detection method using synthetic data. The results clearly show the benefit and the effectiveness of the proposed methods over the conventional PCA method, especially for detecting small faults in highly correlated multivariate data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data-driven%20method" title="data-driven method">data-driven method</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20control" title=" process control"> process control</a>, <a href="https://publications.waset.org/abstracts/search?q=anomaly%20detection" title=" anomaly detection"> anomaly detection</a>, <a href="https://publications.waset.org/abstracts/search?q=dimensionality%20reduction" title=" dimensionality reduction"> dimensionality reduction</a> </p> <a href="https://publications.waset.org/abstracts/30241/a-data-driven-monitoring-technique-using-combined-anomaly-detectors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30241.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">299</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">6249</span> New Segmentation of Piecewise Moving-Average Model by Using Reversible Jump MCMC Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Suparman">Suparman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper addresses the problem of the signal segmentation within a Bayesian framework by using reversible jump MCMC algorithm. The signal is modelled by piecewise constant Moving-Average (MA) model where the numbers of segments, the position of change-point, the order and the coefficient of the MA model for each segment are unknown. The reversible jump MCMC algorithm is then used to generate samples distributed according to the joint posterior distribution of the unknown parameters. These samples allow calculating some interesting features of the posterior distribution. The performance of the methodology is illustrated via several simulation results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=piecewise" title="piecewise">piecewise</a>, <a href="https://publications.waset.org/abstracts/search?q=moving-average%20model" title=" moving-average model"> moving-average model</a>, <a href="https://publications.waset.org/abstracts/search?q=reversible%20jump%20MCMC" title=" reversible jump MCMC"> reversible jump MCMC</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20segmentation" title=" signal segmentation"> signal segmentation</a> </p> <a href="https://publications.waset.org/abstracts/53614/new-segmentation-of-piecewise-moving-average-model-by-using-reversible-jump-mcmc-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53614.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">227</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">6248</span> Battery Control with Moving Average Algorithm to Smoothen the Intermittent Output Power of Photovoltaic Solar Power Plants in Off-Grid Configuration</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Gillfran%20Samual">Muhammad Gillfran Samual</a>, <a href="https://publications.waset.org/abstracts/search?q=Rinaldy%20Dalimi"> Rinaldy Dalimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Fauzan%20Hanif%20Jufri"> Fauzan Hanif Jufri</a>, <a href="https://publications.waset.org/abstracts/search?q=Budi%20Sudiarto"> Budi Sudiarto</a>, <a href="https://publications.waset.org/abstracts/search?q=Ismi%20Rosyiana%20Fitri"> Ismi Rosyiana Fitri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Solar energy is increasingly recognized as an important future energy source due to its abundant availability and renewable nature. However, the intermittent nature of solar energy can cause fluctuations in the electricity produced, making it difficult to guarantee a stable and reliable electricity supply. One solution that can be implemented is to use batteries in a photovoltaic solar power plant system with a Moving Average control algorithm, which can help smooth and reduce fluctuations in solar power output power. The parameter that can be adjusted in the Moving Average algorithm is the window size or the arithmetic average width of the photovoltaic output power over time. This research evaluates the effect of a change of window size parameter in the Moving Average algorithm on the resulting smoothed photovoltaic output power and the technical effects on batteries, i.e., power and energy usage. Based on the evaluation, it is found that the increase of window size parameter will slow down the response of photovoltaic output power to changes in irradiation and increase the smoothing quality of the intermittent photovoltaic output power. In addition, increasing the window size will reduce the maximum power received on the load side, and the amount of energy used by the battery during the power smoothing process will increase, which, in turn, increases the required battery capacity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=battery" title="battery">battery</a>, <a href="https://publications.waset.org/abstracts/search?q=intermittent" title=" intermittent"> intermittent</a>, <a href="https://publications.waset.org/abstracts/search?q=moving%20average" title=" moving average"> moving average</a>, <a href="https://publications.waset.org/abstracts/search?q=photovoltaic" title=" photovoltaic"> photovoltaic</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20smoothing" title=" power smoothing"> power smoothing</a> </p> <a href="https://publications.waset.org/abstracts/186512/battery-control-with-moving-average-algorithm-to-smoothen-the-intermittent-output-power-of-photovoltaic-solar-power-plants-in-off-grid-configuration" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186512.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">62</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">6247</span> Notes on Frames in Weighted Hardy Spaces and Generalized Weighted Composition Operators</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shams%20Alyusof">Shams Alyusof</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work is to enrich the studies of the frames due to their prominent role in pure mathematics as well as in applied mathematics and many applications in computer science and engineering. Recently, there are remarkable studies of operators that preserve frames on some spaces, and this research could be considered as an extension of such studies. Indeed, this paper is to we characterize weighted composition operators that preserve frames in weighted Hardy spaces on the open unit disk. Moreover, it shows that this characterization does not apply to generalized weighted composition operators on such spaces. Nevertheless, this study could be extended to provide more specific characterizations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=frames" title="frames">frames</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20weighted%20composition%20operators" title=" generalized weighted composition operators"> generalized weighted composition operators</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20Hardy%20spaces" title=" weighted Hardy spaces"> weighted Hardy spaces</a>, <a href="https://publications.waset.org/abstracts/search?q=analytic%20functions" title=" analytic functions"> analytic functions</a> </p> <a href="https://publications.waset.org/abstracts/156372/notes-on-frames-in-weighted-hardy-spaces-and-generalized-weighted-composition-operators" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156372.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">121</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">6246</span> Some Results for F-Minimal Hypersurfaces in Manifolds with Density</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Abdelmalek">M. Abdelmalek</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, we study the hypersurfaces of constant weighted mean curvature embedded in weighted manifolds. We give a condition about these hypersurfaces to be minimal. This condition is given by the ellipticity of the weighted Newton transformations. We especially prove that two compact hypersurfaces of constant weighted mean curvature embedded in space forms and with the intersection in at least a point of the boundary must be transverse. The method is based on the calculus of the matrix of the second fundamental form in a boundary point and then the matrix associated with the Newton transformations. By equality, we find the weighted elementary symmetric function on the boundary of the hypersurface. We give in the end some examples and applications. Especially in Euclidean space, we use the above result to prove the Alexandrov spherical caps conjecture for the weighted case. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=weighted%20mean%20curvature" title="weighted mean curvature">weighted mean curvature</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20manifolds" title=" weighted manifolds"> weighted manifolds</a>, <a href="https://publications.waset.org/abstracts/search?q=ellipticity" title=" ellipticity"> ellipticity</a>, <a href="https://publications.waset.org/abstracts/search?q=Newton%20transformations" title=" Newton transformations"> Newton transformations</a> </p> <a href="https://publications.waset.org/abstracts/160174/some-results-for-f-minimal-hypersurfaces-in-manifolds-with-density" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160174.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">93</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">6245</span> Inventory Optimization in Restaurant Supply Chain Outlets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Raja%20Kannusamy">Raja Kannusamy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The research focuses on reducing food waste in the restaurant industry. A study has been conducted on the chain of retail restaurant outlets. It has been observed that the food wastages are due to the inefficient inventory management systems practiced in the restaurant outlets. The major food items which are wasted more in quantity are being selected across the retail chain outlets. A moving average forecasting method has been applied for the selected food items so that their future demand could be predicted accurately and food wastage could be avoided. It has been found that the moving average prediction method helps in predicting forecasts accurately. The demand values obtained from the moving average method have been compared to the actual demand values and are found to be similar with minimum variations. The inventory optimization technique helps in reducing food wastage in restaurant supply chain outlets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=food%20wastage" title="food wastage">food wastage</a>, <a href="https://publications.waset.org/abstracts/search?q=restaurant%20supply%20chain" title=" restaurant supply chain"> restaurant supply chain</a>, <a href="https://publications.waset.org/abstracts/search?q=inventory%20optimisation" title=" inventory optimisation"> inventory optimisation</a>, <a href="https://publications.waset.org/abstracts/search?q=demand%20forecasting" title=" demand forecasting"> demand forecasting</a> </p> <a href="https://publications.waset.org/abstracts/152444/inventory-optimization-in-restaurant-supply-chain-outlets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152444.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">91</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">6244</span> Multi-Criteria Decision Approach to Performance Measurement Techniques Data Envelopment Analysis: Case Study of Kerman City’s Parks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20A.%20Abdollahi">Ali A. Abdollahi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> During the last several decades, scientists have consistently applied Multiple Criteria Decision-Making methods in making decisions about multi-faceted, complicated subjects. While making such decisions and in order to achieve more accurate evaluations, they have regularly used a variety of criteria instead of applying just one Optimum Evaluation Criterion. The method presented here utilizes both ‘quantity’ and ‘quality’ to assess the function of the Multiple-Criteria method. Applying Data envelopment analysis (DEA), weighted aggregated sum product assessment (WASPAS), Weighted Sum Approach (WSA), Analytic Network Process (ANP), and Charnes, Cooper, Rhodes (CCR) methods, we have analyzed thirteen parks in Kerman city. It further indicates that the functions of WASPAS and WSA are compatible with each other, but also that their deviation from DEA is extensive. Finally, the results for the CCR technique do not match the results of the DEA technique. Our study indicates that the ANP method, with the average rate of 1/51, ranks closest to the DEA method, which has an average rate of 1/49. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multiple%20criteria%20decision%20making" title="multiple criteria decision making">multiple criteria decision making</a>, <a href="https://publications.waset.org/abstracts/search?q=Data%20envelopment%20analysis%20%28DEA%29" title=" Data envelopment analysis (DEA)"> Data envelopment analysis (DEA)</a>, <a href="https://publications.waset.org/abstracts/search?q=Charnes%20Cooper%20Rhodes%20%28CCR%29" title=" Charnes Cooper Rhodes (CCR)"> Charnes Cooper Rhodes (CCR)</a>, <a href="https://publications.waset.org/abstracts/search?q=Weighted%20Sum%20Approach%20%28WSA%29" title=" Weighted Sum Approach (WSA)"> Weighted Sum Approach (WSA)</a> </p> <a href="https://publications.waset.org/abstracts/92557/multi-criteria-decision-approach-to-performance-measurement-techniques-data-envelopment-analysis-case-study-of-kerman-citys-parks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92557.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">218</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">6243</span> Breast Cancer Survivability Prediction via Classifier Ensemble</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Al-Badrashiny">Mohamed Al-Badrashiny</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelghani%20Bellaachia"> Abdelghani Bellaachia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a classifier ensemble approach for predicting the survivability of the breast cancer patients using the latest database version of the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute. The system consists of two main components; features selection and classifier ensemble components. The features selection component divides the features in SEER database into four groups. After that it tries to find the most important features among the four groups that maximizes the weighted average F-score of a certain classification algorithm. The ensemble component uses three different classifiers, each of which models different set of features from SEER through the features selection module. On top of them, another classifier is used to give the final decision based on the output decisions and confidence scores from each of the underlying classifiers. Different classification algorithms have been examined; the best setup found is by using the decision tree, Bayesian network, and Na¨ıve Bayes algorithms for the underlying classifiers and Na¨ıve Bayes for the classifier ensemble step. The system outperforms all published systems to date when evaluated against the exact same data of SEER (period of 1973-2002). It gives 87.39% weighted average F-score compared to 85.82% and 81.34% of the other published systems. By increasing the data size to cover the whole database (period of 1973-2014), the overall weighted average F-score jumps to 92.4% on the held out unseen test set. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classifier%20ensemble" title="classifier ensemble">classifier ensemble</a>, <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer%20survivability" title=" breast cancer survivability"> breast cancer survivability</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=SEER" title=" SEER"> SEER</a> </p> <a href="https://publications.waset.org/abstracts/42621/breast-cancer-survivability-prediction-via-classifier-ensemble" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42621.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">328</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">6242</span> Numerical Simulation of a Three-Dimensional Framework under the Action of Two-Dimensional Moving Loads</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jia-Jang%20Wu">Jia-Jang Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The objective of this research is to develop a general technique so that one may predict the dynamic behaviour of a three-dimensional scale crane model subjected to time-dependent moving point forces by means of conventional finite element computer packages. To this end, the whole scale crane model is divided into two parts: the stationary framework and the moving substructure. In such a case, the dynamic responses of a scale crane model can be predicted from the forced vibration responses of the stationary framework due to actions of the four time-dependent moving point forces induced by the moving substructure. Since the magnitudes and positions of the moving point forces are dependent on the relative positions between the trolley, moving substructure and the stationary framework, it can be found from the numerical results that the time histories for the moving speeds of the moving substructure and the trolley are the key factors affecting the dynamic responses of the scale crane model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=moving%20load" title="moving load">moving load</a>, <a href="https://publications.waset.org/abstracts/search?q=moving%20substructure" title=" moving substructure"> moving substructure</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20responses" title=" dynamic responses"> dynamic responses</a>, <a href="https://publications.waset.org/abstracts/search?q=forced%20vibration%20responses" title=" forced vibration responses"> forced vibration responses</a> </p> <a href="https://publications.waset.org/abstracts/37626/numerical-simulation-of-a-three-dimensional-framework-under-the-action-of-two-dimensional-moving-loads" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37626.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">6241</span> Post-Contrast Susceptibility Weighted Imaging vs. Post-Contrast T1 Weighted Imaging for Evaluation of Brain Lesions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sujith%20Rajashekar%20Swamy">Sujith Rajashekar Swamy</a>, <a href="https://publications.waset.org/abstracts/search?q=Meghana%20Rajashekara%20Swamy"> Meghana Rajashekara Swamy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Although T1-weighted gadolinium-enhanced imaging (T1-Gd) has its established clinical role in diagnosing brain lesions of infectious and metastatic origins, the use of post-contrast susceptibility-weighted imaging (SWI) has been understudied. This observational study aims to explore and compare the prominence of brain parenchymal lesions between T1-Gd and SWI-Gd images. A cross-sectional study design was utilized to analyze 58 patients with brain parenchymal lesions using T1-Gd and SWI-Gd scanning techniques. Our results indicated that SWI-Gd enhanced the conspicuity of metastatic as well as infectious brain lesions when compared to T1-Gd. Consequently, it can be used as an adjunct to T1-Gd for post-contrast imaging, thereby avoiding additional contrast administration. Improved conspicuity of brain lesions translates directly to enhanced patient outcomes, and hence SWI-Gd imaging proves useful to meet that endpoint. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=susceptibility%20weighted" title="susceptibility weighted">susceptibility weighted</a>, <a href="https://publications.waset.org/abstracts/search?q=T1%20weighted" title=" T1 weighted"> T1 weighted</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20lesions" title=" brain lesions"> brain lesions</a>, <a href="https://publications.waset.org/abstracts/search?q=gadolinium%20contrast" title=" gadolinium contrast"> gadolinium contrast</a> </p> <a href="https://publications.waset.org/abstracts/160957/post-contrast-susceptibility-weighted-imaging-vs-post-contrast-t1-weighted-imaging-for-evaluation-of-brain-lesions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160957.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">128</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">6240</span> A Study on Exploring and Prioritizing Critical Risks in Construction Project Assessment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Swetha">A. Swetha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study aims to prioritize and explore critical risks in construction project assessment, employing the Weighted Average Index method and Principal Component Analysis (PCA). Through extensive literature review and expert interviews, project assessment risk factors were identified across Budget and Cost Management Risk, Schedule and Time Management Risk, Scope and Planning Risk, Safety and Regulatory Compliance Risk, Resource Management Risk, Communication and Stakeholder Management Risk, and Environmental and Sustainability Risk domains. A questionnaire was distributed to stakeholders involved in construction activities in Hyderabad, India, with 180 completed responses analyzed using the Weighted Average Index method to prioritize risk factors. Subsequently, PCA was used to understand relationships between these factors and uncover underlying patterns. Results highlighted dependencies on critical resources, inadequate risk assessment, cash flow constraints, and safety concerns as top priorities, while factors like currency exchange rate fluctuations and delayed information dissemination ranked lower but remained significant. These insights offer valuable guidance for stakeholders to mitigate risks effectively and enhance project outcomes. By adopting systematic risk assessment and management approaches, construction projects in Hyderabad and beyond can navigate challenges more efficiently, ensuring long-term viability and resilience. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=construction%20project%20assessment%20risk%20factor" title="construction project assessment risk factor">construction project assessment risk factor</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20prioritization" title=" risk prioritization"> risk prioritization</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20average%20index" title=" weighted average index"> weighted average index</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20analysis" title=" principal component analysis"> principal component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=project%20risk%20factors" title=" project risk factors"> project risk factors</a> </p> <a href="https://publications.waset.org/abstracts/187450/a-study-on-exploring-and-prioritizing-critical-risks-in-construction-project-assessment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/187450.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">40</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">6239</span> Magnetohydrodynamic Flow over an Exponentially Stretching Sheet</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Raj%20Nandkeolyar">Raj Nandkeolyar</a>, <a href="https://publications.waset.org/abstracts/search?q=Precious%20Sibanda"> Precious Sibanda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The flow of a viscous, incompressible, and electrically conducting fluid under the influence of aligned magnetic field acting along the direction of fluid flow over an exponentially stretching sheet is investigated numerically. The nonlinear partial differential equations governing the flow model is transformed to a set of nonlinear ordinary differential equations using suitable similarity transformation and the solution is obtained using a local linearization method followed by the Chebyshev spectral collocation method. The effects of various parameters affecting the flow and heat transfer as well as the induced magnetic field are discussed using suitable graphs and tables. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aligned%20magnetic%20field" title="aligned magnetic field">aligned magnetic field</a>, <a href="https://publications.waset.org/abstracts/search?q=exponentially%20stretching%20sheet" title=" exponentially stretching sheet"> exponentially stretching sheet</a>, <a href="https://publications.waset.org/abstracts/search?q=induced%20magnetic%20field" title=" induced magnetic field"> induced magnetic field</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetohydrodynamic%20flow" title=" magnetohydrodynamic flow"> magnetohydrodynamic flow</a> </p> <a href="https://publications.waset.org/abstracts/10795/magnetohydrodynamic-flow-over-an-exponentially-stretching-sheet" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10795.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">454</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">6238</span> Magnetohydrodynamic 3D Maxwell Fluid Flow Towards a Horizontal Stretched Surface with Convective Boundary Conditions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Y.%20Malika">M. Y. Malika</a>, <a href="https://publications.waset.org/abstracts/search?q=Farzana"> Farzana</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdul%20Rehman"> Abdul Rehman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study deals with the steady, 3D MHD boundary layer flow of a non-Newtonian Maxwell fluid flow due to a horizontal surface stretched exponentially in two lateral directions. The temperature at the boundary is assumed to be distributed exponentially and possesses convective boundary conditions. The governing nonlinear system of partial differential equations along with associated boundary conditions is simplified using a suitable transformation and the obtained set of ordinary differential equations is solved through numerical techniques. The effects of important involved parameters associated with fluid flow and heat flux are shown through graphs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=boundary%20layer%20flow" title="boundary layer flow">boundary layer flow</a>, <a href="https://publications.waset.org/abstracts/search?q=exponentially%20stretched%20surface" title=" exponentially stretched surface"> exponentially stretched surface</a>, <a href="https://publications.waset.org/abstracts/search?q=Maxwell%20fluid" title=" Maxwell fluid"> Maxwell fluid</a>, <a href="https://publications.waset.org/abstracts/search?q=numerical%20solution" title=" numerical solution"> numerical solution</a> </p> <a href="https://publications.waset.org/abstracts/23186/magnetohydrodynamic-3d-maxwell-fluid-flow-towards-a-horizontal-stretched-surface-with-convective-boundary-conditions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23186.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">589</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">6237</span> Study of ANFIS and ARIMA Model for Weather Forecasting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bandreddy%20Anand%20Babu">Bandreddy Anand Babu</a>, <a href="https://publications.waset.org/abstracts/search?q=Srinivasa%20Rao%20Mandadi"> Srinivasa Rao Mandadi</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Pradeep%20Reddy"> C. Pradeep Reddy</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Ramesh%20Babu"> N. Ramesh Babu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper quickly illustrate the correlation investigation of Auto-Regressive Integrated Moving and Average (ARIMA) and daptive Network Based Fuzzy Inference System (ANFIS) models done by climate estimating. The climate determining is taken from University of Waterloo. The information is taken as Relative Humidity, Ambient Air Temperature, Barometric Pressure and Wind Direction utilized within this paper. The paper is carried out by analyzing the exhibitions are seen by demonstrating of ARIMA and ANIFIS model like with Sum of average of errors. Versatile Network Based Fuzzy Inference System (ANFIS) demonstrating is carried out by Mat lab programming and Auto-Regressive Integrated Moving and Average (ARIMA) displaying is produced by utilizing XLSTAT programming. ANFIS is carried out in Fuzzy Logic Toolbox in Mat Lab programming. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ARIMA" title="ARIMA">ARIMA</a>, <a href="https://publications.waset.org/abstracts/search?q=ANFIS" title=" ANFIS"> ANFIS</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20surmising%20tool%20stash" title=" fuzzy surmising tool stash"> fuzzy surmising tool stash</a>, <a href="https://publications.waset.org/abstracts/search?q=weather%20forecasting" title=" weather forecasting"> weather forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=MATLAB" title=" MATLAB"> MATLAB</a> </p> <a href="https://publications.waset.org/abstracts/13743/study-of-anfis-and-arima-model-for-weather-forecasting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13743.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">419</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">6236</span> Using the Smith-Waterman Algorithm to Extract Features in the Classification of Obesity Status</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rosa%20Figueroa">Rosa Figueroa</a>, <a href="https://publications.waset.org/abstracts/search?q=Christopher%20Flores"> Christopher Flores</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Text categorization is the problem of assigning a new document to a set of predetermined categories, on the basis of a training set of free-text data that contains documents whose category membership is known. To train a classification model, it is necessary to extract characteristics in the form of tokens that facilitate the learning and classification process. In text categorization, the feature extraction process involves the use of word sequences also known as N-grams. In general, it is expected that documents belonging to the same category share similar features. The Smith-Waterman (SW) algorithm is a dynamic programming algorithm that performs a local sequence alignment in order to determine similar regions between two strings or protein sequences. This work explores the use of SW algorithm as an alternative to feature extraction in text categorization. The dataset used for this purpose, contains 2,610 annotated documents with the classes Obese/Non-Obese. This dataset was represented in a matrix form using the Bag of Word approach. The score selected to represent the occurrence of the tokens in each document was the term frequency-inverse document frequency (TF-IDF). In order to extract features for classification, four experiments were conducted: the first experiment used SW to extract features, the second one used unigrams (single word), the third one used bigrams (two word sequence) and the last experiment used a combination of unigrams and bigrams to extract features for classification. To test the effectiveness of the extracted feature set for the four experiments, a Support Vector Machine (SVM) classifier was tuned using 20% of the dataset. The remaining 80% of the dataset together with 5-Fold Cross Validation were used to evaluate and compare the performance of the four experiments of feature extraction. Results from the tuning process suggest that SW performs better than the N-gram based feature extraction. These results were confirmed by using the remaining 80% of the dataset, where SW performed the best (accuracy = 97.10%, weighted average F-measure = 97.07%). The second best was obtained by the combination of unigrams-bigrams (accuracy = 96.04, weighted average F-measure = 95.97) closely followed by the bigrams (accuracy = 94.56%, weighted average F-measure = 94.46%) and finally unigrams (accuracy = 92.96%, weighted average F-measure = 92.90%). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=comorbidities" title="comorbidities">comorbidities</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=obesity" title=" obesity"> obesity</a>, <a href="https://publications.waset.org/abstracts/search?q=Smith-Waterman%20algorithm" title=" Smith-Waterman algorithm"> Smith-Waterman algorithm</a> </p> <a href="https://publications.waset.org/abstracts/64616/using-the-smith-waterman-algorithm-to-extract-features-in-the-classification-of-obesity-status" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/64616.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">297</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">6235</span> Statistical and Analytical Comparison of GIS Overlay Modelings: An Appraisal on Groundwater Prospecting in Precambrian Metamorphics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tapas%20Acharya">Tapas Acharya</a>, <a href="https://publications.waset.org/abstracts/search?q=Monalisa%20Mitra"> Monalisa Mitra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Overlay modeling is the most widely used conventional analysis for spatial decision support system. Overlay modeling requires a set of themes with different weightage computed in varied manners, which gives a resultant input for further integrated analysis. In spite of the popularity and most widely used technique; it gives inconsistent and erroneous results for similar inputs while processed in various GIS overlay techniques. This study is an attempt to compare and analyse the differences in the outputs of different overlay methods using GIS platform with same set of themes of the Precambrian metamorphic to obtain groundwater prospecting in Precambrian metamorphic rocks. The objective of the study is to emphasize the most suitable overlay method for groundwater prospecting in older Precambrian metamorphics. Seven input thematic layers like slope, Digital Elevation Model (DEM), soil thickness, lineament intersection density, average groundwater table fluctuation, stream density and lithology have been used in the spatial overlay models of fuzzy overlay, weighted overlay and weighted sum overlay methods to yield the suitable groundwater prospective zones. Spatial concurrence analysis with high yielding wells of the study area and the statistical comparative studies among the outputs of various overlay models using RStudio reveal that the Weighted Overlay model is the most efficient GIS overlay model to delineate the groundwater prospecting zones in the Precambrian metamorphic rocks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20overlay" title="fuzzy overlay">fuzzy overlay</a>, <a href="https://publications.waset.org/abstracts/search?q=GIS%20overlay%20model" title=" GIS overlay model"> GIS overlay model</a>, <a href="https://publications.waset.org/abstracts/search?q=groundwater%20prospecting" title=" groundwater prospecting"> groundwater prospecting</a>, <a href="https://publications.waset.org/abstracts/search?q=Precambrian%20metamorphics" title=" Precambrian metamorphics"> Precambrian metamorphics</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20overlay" title=" weighted overlay"> weighted overlay</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20sum%20overlay" title=" weighted sum overlay "> weighted sum overlay </a> </p> <a href="https://publications.waset.org/abstracts/119978/statistical-and-analytical-comparison-of-gis-overlay-modelings-an-appraisal-on-groundwater-prospecting-in-precambrian-metamorphics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/119978.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">128</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">6234</span> Estimation and Forecasting with a Quantile AR Model for Financial Returns </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuzhi%20Cai">Yuzhi Cai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This talk presents a Bayesian approach to quantile autoregressive (QAR) time series model estimation and forecasting. We establish that the joint posterior distribution of the model parameters and future values is well defined. The associated MCMC algorithm for parameter estimation and forecasting converges to the posterior distribution quickly. We also present a combining forecasts technique to produce more accurate out-of-sample forecasts by using a weighted sequence of fitted QAR models. A moving window method to check the quality of the estimated conditional quantiles is developed. We verify our methodology using simulation studies and then apply it to currency exchange rate data. An application of the method to the USD to GBP daily currency exchange rates will also be discussed. The results obtained show that an unequally weighted combining method performs better than other forecasting methodology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=combining%20forecasts" title="combining forecasts">combining forecasts</a>, <a href="https://publications.waset.org/abstracts/search?q=MCMC" title=" MCMC"> MCMC</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20modelling" title=" quantile modelling"> quantile modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20forecasting" title=" quantile forecasting"> quantile forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20density%20functions" title=" predictive density functions"> predictive density functions</a> </p> <a href="https://publications.waset.org/abstracts/33437/estimation-and-forecasting-with-a-quantile-ar-model-for-financial-returns" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33437.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">347</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">6233</span> Effect of Magnetic Field on Mixed Convection Boundary Layer Flow over an Exponentially Shrinking Vertical Sheet with Suction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20S.%20P.%20M.%20Isa">S. S. P. M. Isa</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20M.%20Arifin"> N. M. Arifin</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Nazar"> R. Nazar</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Bachok"> N. Bachok</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20M.%20Ali"> F. M. Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=I.%20Pop"> I. Pop</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A theoretical study has been presented to describe the boundary layer flow and heat transfer on an exponentially shrinking sheet with a variable wall temperature and suction, in the presence of magnetic field. The governing nonlinear partial differential equations are converted into ordinary differential equations by similarity transformation, which are then solved numerically using the shooting method. Results for the skin friction coefficient, local Nusselt number, velocity profiles as well as temperature profiles are presented through graphs and tables for several sets of values of the parameters. The effects of the governing parameters on the flow and heat transfer characteristics are thoroughly examined. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=exponentially%20shrinking%20sheet" title="exponentially shrinking sheet">exponentially shrinking sheet</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20field" title=" magnetic field"> magnetic field</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed%20convection" title=" mixed convection"> mixed convection</a>, <a href="https://publications.waset.org/abstracts/search?q=suction" title=" suction"> suction</a> </p> <a href="https://publications.waset.org/abstracts/13072/effect-of-magnetic-field-on-mixed-convection-boundary-layer-flow-over-an-exponentially-shrinking-vertical-sheet-with-suction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13072.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">331</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=exponentially%20weighted%20moving%20average&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=exponentially%20weighted%20moving%20average&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=exponentially%20weighted%20moving%20average&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=exponentially%20weighted%20moving%20average&page=5">5</a></li> <li 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