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absolute error</title> <meta name="description" content="Search results for: mean absolute error"> <meta name="keywords" content="mean absolute error"> <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="mean absolute error"> <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> 2312</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: mean absolute error</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2312</span> Composite Forecasts Accuracy for Automobile Sales in Thailand</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Watchareeporn%20Chaimongkol">Watchareeporn Chaimongkol</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we compare the statistical measures accuracy of composite forecasting model to estimate automobile customer demand in Thailand. A modified simple exponential smoothing and autoregressive integrate moving average (ARIMA) forecasting model is built to estimate customer demand of passenger cars, instead of using information of historical sales data. Our model takes into account special characteristic of the Thai automobile market such as sales promotion, advertising and publicity, petrol price, and interest rate for loan. We evaluate our forecasting model by comparing forecasts with actual data using six accuracy measurements, mean absolute percentage error (MAPE), geometric mean absolute error (GMAE), symmetric mean absolute percentage error (sMAPE), mean absolute scaled error (MASE), median relative absolute error (MdRAE), and geometric mean relative absolute error (GMRAE). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=composite%20forecasting" title="composite forecasting">composite forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=simple%20exponential%20smoothing%20model" title=" simple exponential smoothing model"> simple exponential smoothing model</a>, <a href="https://publications.waset.org/abstracts/search?q=autoregressive%20integrate%20moving%20average%20model%20selection" title=" autoregressive integrate moving average model selection"> autoregressive integrate moving average model selection</a>, <a href="https://publications.waset.org/abstracts/search?q=accuracy%20measurements" title=" accuracy measurements"> accuracy measurements</a> </p> <a href="https://publications.waset.org/abstracts/6189/composite-forecasts-accuracy-for-automobile-sales-in-thailand" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6189.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">362</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">2311</span> A Comparative Analysis of ARIMA and Threshold Autoregressive Models on Exchange Rate</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Diteboho%20Xaba">Diteboho Xaba</a>, <a href="https://publications.waset.org/abstracts/search?q=Kolentino%20Mpeta"> Kolentino Mpeta</a>, <a href="https://publications.waset.org/abstracts/search?q=Tlotliso%20Qejoe"> Tlotliso Qejoe</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper assesses the in-sample forecasting of the South African exchange rates comparing a linear ARIMA model and a SETAR model. The study uses a monthly adjusted data of South African exchange rates with 420 observations. Akaike information criterion (AIC) and the Schwarz information criteria (SIC) are used for model selection. Mean absolute error (MAE), root mean squared error (RMSE) and mean absolute percentage error (MAPE) are error metrics used to evaluate forecast capability of the models. The Diebold –Mariano (DM) test is employed in the study to check forecast accuracy in order to distinguish the forecasting performance between the two models (ARIMA and SETAR). The results indicate that both models perform well when modelling and forecasting the exchange rates, but SETAR seemed to outperform ARIMA. <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=error%20metrices" title=" error metrices"> error metrices</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20selection" title=" model selection"> model selection</a>, <a href="https://publications.waset.org/abstracts/search?q=SETAR" title=" SETAR"> SETAR</a> </p> <a href="https://publications.waset.org/abstracts/57052/a-comparative-analysis-of-arima-and-threshold-autoregressive-models-on-exchange-rate" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57052.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">244</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">2310</span> Forecasting Performance Comparison of Autoregressive Fractional Integrated Moving Average and Jordan Recurrent Neural Network Models on the Turbidity of Stream Flows</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daniel%20Fulus%20Fom">Daniel Fulus Fom</a>, <a href="https://publications.waset.org/abstracts/search?q=Gau%20Patrick%20Damulak"> Gau Patrick Damulak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, the Autoregressive Fractional Integrated Moving Average (ARFIMA) and Jordan Recurrent Neural Network (JRNN) models were employed to model the forecasting performance of the daily turbidity flow of White Clay Creek (WCC). The two methods were applied to the log difference series of the daily turbidity flow series of WCC. The measurements of error employed to investigate the forecasting performance of the ARFIMA and JRNN models are the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). The outcome of the investigation revealed that the forecasting performance of the JRNN technique is better than the forecasting performance of the ARFIMA technique in the mean square error sense. The results of the ARFIMA and JRNN models were obtained by the simulation of the models using MATLAB version 8.03. The significance of using the log difference series rather than the difference series is that the log difference series stabilizes the turbidity flow series than the difference series on the ARFIMA and JRNN. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=auto%20regressive" title="auto regressive">auto regressive</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20absolute%20error" title=" mean absolute error"> mean absolute error</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=root%20square%20mean%20error" title=" root square mean error"> root square mean error</a> </p> <a href="https://publications.waset.org/abstracts/75328/forecasting-performance-comparison-of-autoregressive-fractional-integrated-moving-average-and-jordan-recurrent-neural-network-models-on-the-turbidity-of-stream-flows" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75328.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">268</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">2309</span> Application of Adaptive Neuro Fuzzy Inference Systems Technique for Modeling of Postweld Heat Treatment Process of Pressure Vessel Steel AASTM A516 Grade 70</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Omar%20Al%20Denali">Omar Al Denali</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelaziz%20Badi"> Abdelaziz Badi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The ASTM A516 Grade 70 steel is a suitable material used for the fabrication of boiler pressure vessels working in moderate and lower temperature services, and it has good weldability and excellent notch toughness. The post-weld heat treatment (PWHT) or stress-relieving heat treatment has significant effects on avoiding the martensite transformation and resulting in high hardness, which can lead to cracking in the heat-affected zone (HAZ). An adaptive neuro-fuzzy inference system (ANFIS) was implemented to predict the material tensile strength of post-weld heat treatment (PWHT) experiments. The ANFIS models presented excellent predictions, and the comparison was carried out based on the mean absolute percentage error between the predicted values and the experimental values. The ANFIS model gave a Mean Absolute Percentage Error of 0.556 %, which confirms the high accuracy of the model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=prediction" title="prediction">prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=post-weld%20heat%20treatment" title=" post-weld heat treatment"> post-weld heat treatment</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20neuro-fuzzy%20inference%20system" title=" adaptive neuro-fuzzy inference system"> adaptive neuro-fuzzy inference system</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20absolute%20percentage%20error" title=" mean absolute percentage error"> mean absolute percentage error</a> </p> <a href="https://publications.waset.org/abstracts/148849/application-of-adaptive-neuro-fuzzy-inference-systems-technique-for-modeling-of-postweld-heat-treatment-process-of-pressure-vessel-steel-aastm-a516-grade-70" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148849.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">153</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">2308</span> Forecasting Models for Steel Demand Uncertainty Using Bayesian Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Watcharin%20Sangma">Watcharin Sangma</a>, <a href="https://publications.waset.org/abstracts/search?q=Onsiri%20Chanmuang"> Onsiri Chanmuang</a>, <a href="https://publications.waset.org/abstracts/search?q=Pitsanu%20Tongkhow"> Pitsanu Tongkhow</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A forecasting model for steel demand uncertainty in Thailand is proposed. It consists of trend, autocorrelation, and outliers in a hierarchical Bayesian frame work. The proposed model uses a cumulative Weibull distribution function, latent first-order autocorrelation, and binary selection, to account for trend, time-varying autocorrelation, and outliers, respectively. The Gibbs sampling Markov Chain Monte Carlo (MCMC) is used for parameter estimation. The proposed model is applied to steel demand index data in Thailand. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) criteria are used for model comparison. The study reveals that the proposed model is more appropriate than the exponential smoothing method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=forecasting%20model" title="forecasting model">forecasting model</a>, <a href="https://publications.waset.org/abstracts/search?q=steel%20demand%20uncertainty" title=" steel demand uncertainty"> steel demand uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20Bayesian%20framework" title=" hierarchical Bayesian framework"> hierarchical Bayesian framework</a>, <a href="https://publications.waset.org/abstracts/search?q=exponential%20smoothing%20method" title=" exponential smoothing method"> exponential smoothing method</a> </p> <a href="https://publications.waset.org/abstracts/10196/forecasting-models-for-steel-demand-uncertainty-using-bayesian-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10196.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">350</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">2307</span> A Generalized Weighted Loss for Support Vextor Classification and Multilayer Perceptron</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Filippo%20Portera">Filippo Portera</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Usually standard algorithms employ a loss where each error is the mere absolute difference between the true value and the prediction, in case of a regression task. In the present, we present several error weighting schemes that are a generalization of the consolidated routine. We study both a binary classification model for Support Vextor Classification and a regression net for Multylayer Perceptron. Results proves that the error is never worse than the standard procedure and several times it is better. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=loss" title="loss">loss</a>, <a href="https://publications.waset.org/abstracts/search?q=binary-classification" title=" binary-classification"> binary-classification</a>, <a href="https://publications.waset.org/abstracts/search?q=MLP" title=" MLP"> MLP</a>, <a href="https://publications.waset.org/abstracts/search?q=weights" title=" weights"> weights</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</a> </p> <a href="https://publications.waset.org/abstracts/163661/a-generalized-weighted-loss-for-support-vextor-classification-and-multilayer-perceptron" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163661.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">95</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">2306</span> Statistical Tools for SFRA Diagnosis in Power Transformers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rahul%20Srivastava">Rahul Srivastava</a>, <a href="https://publications.waset.org/abstracts/search?q=Priti%20Pundir"> Priti Pundir</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20R.%20Sood"> Y. R. Sood</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajnish%20Shrivastava"> Rajnish Shrivastava</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For the interpretation of the signatures of sweep frequency response analysis(SFRA) of transformer different types of statistical techniques serves as an effective tool for doing either phase to phase comparison or sister unit comparison. In this paper with the discussion on SFRA several statistics techniques like cross correlation coefficient (CCF), root square error (RSQ), comparative standard deviation (CSD), Absolute difference, mean square error(MSE),Min-Max ratio(MM) are presented through several case studies. These methods require sample data size and spot frequencies of SFRA signatures that are being compared. The techniques used are based on power signal processing tools that can simplify result and limits can be created for the severity of the fault occurring in the transformer due to several short circuit forces or due to ageing. The advantages of using statistics techniques for analyzing of SFRA result are being indicated through several case studies and hence the results are obtained which determines the state of the transformer. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=absolute%20difference%20%28DABS%29" title="absolute difference (DABS)">absolute difference (DABS)</a>, <a href="https://publications.waset.org/abstracts/search?q=cross%20correlation%20coefficient%20%28CCF%29" title=" cross correlation coefficient (CCF)"> cross correlation coefficient (CCF)</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20square%20error%20%28MSE%29" title=" mean square error (MSE)"> mean square error (MSE)</a>, <a href="https://publications.waset.org/abstracts/search?q=min-max%20ratio%20%28MM-ratio%29" title=" min-max ratio (MM-ratio)"> min-max ratio (MM-ratio)</a>, <a href="https://publications.waset.org/abstracts/search?q=root%20square%20error%20%28RSQ%29" title=" root square error (RSQ)"> root square error (RSQ)</a>, <a href="https://publications.waset.org/abstracts/search?q=standard%20deviation%20%28CSD%29" title=" standard deviation (CSD)"> standard deviation (CSD)</a>, <a href="https://publications.waset.org/abstracts/search?q=sweep%20frequency%20response%20analysis%20%28SFRA%29" title=" sweep frequency response analysis (SFRA)"> sweep frequency response analysis (SFRA)</a> </p> <a href="https://publications.waset.org/abstracts/27968/statistical-tools-for-sfra-diagnosis-in-power-transformers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27968.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">697</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">2305</span> An Improved Model of Estimation Global Solar Irradiation from in situ Data: Case of Oran Algeria Region</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Houcine%20Naim">Houcine Naim</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelatif%20Hassini"> Abdelatif Hassini</a>, <a href="https://publications.waset.org/abstracts/search?q=Noureddine%20Benabadji"> Noureddine Benabadji</a>, <a href="https://publications.waset.org/abstracts/search?q=Alex%20Van%20Den%20Bossche"> Alex Van Den Bossche </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, two models to estimate the overall monthly average daily radiation on a horizontal surface were applied to the site of Oran (35.38 ° N, 0.37 °W). We present a comparison between the first one is a regression equation of the Angstrom type and the second model is developed by the present authors some modifications were suggested using as input parameters: the astronomical parameters as (latitude, longitude, and altitude) and meteorological parameters as (relative humidity). The comparisons are made using the mean bias error (MBE), root mean square error (RMSE), mean percentage error (MPE), and mean absolute bias error (MABE). This comparison shows that the second model is closer to the experimental values that the model of Angstrom. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=meteorology" title="meteorology">meteorology</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20radiation" title=" global radiation"> global radiation</a>, <a href="https://publications.waset.org/abstracts/search?q=Angstrom%20model" title=" Angstrom model"> Angstrom model</a>, <a href="https://publications.waset.org/abstracts/search?q=Oran" title=" Oran"> Oran</a> </p> <a href="https://publications.waset.org/abstracts/39754/an-improved-model-of-estimation-global-solar-irradiation-from-in-situ-data-case-of-oran-algeria-region" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39754.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">232</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">2304</span> Applying Genetic Algorithm in Exchange Rate Models Determination</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mehdi%20Rostamzadeh">Mehdi Rostamzadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Genetic Algorithms (GAs) are an adaptive heuristic search algorithm premised on the evolutionary ideas of natural selection and genetic. In this study, we apply GAs for fundamental and technical models of exchange rate determination in exchange rate market. In this framework, we estimated absolute and relative purchasing power parity, Mundell-Fleming, sticky and flexible prices (monetary models), equilibrium exchange rate and portfolio balance model as fundamental models and Auto Regressive (AR), Moving Average (MA), Auto-Regressive with Moving Average (ARMA) and Mean Reversion (MR) as technical models for Iranian Rial against European Union’s Euro using monthly data from January 1992 to December 2014. Then, we put these models into the genetic algorithm system for measuring their optimal weight for each model. These optimal weights have been measured according to four criteria i.e. R-Squared (R2), mean square error (MSE), mean absolute percentage error (MAPE) and root mean square error (RMSE).Based on obtained Results, it seems that for explaining of Iranian Rial against EU Euro exchange rate behavior, fundamental models are better than technical models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=exchange%20rate" title="exchange rate">exchange rate</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=fundamental%20models" title=" fundamental models"> fundamental models</a>, <a href="https://publications.waset.org/abstracts/search?q=technical%20models" title=" technical models"> technical models</a> </p> <a href="https://publications.waset.org/abstracts/47210/applying-genetic-algorithm-in-exchange-rate-models-determination" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47210.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">273</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">2303</span> Neural Network Supervisory Proportional-Integral-Derivative Control of the Pressurized Water Reactor Core Power Load Following Operation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Derjew%20Ayele%20Ejigu">Derjew Ayele Ejigu</a>, <a href="https://publications.waset.org/abstracts/search?q=Houde%20Song"> Houde Song</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaojing%20Liu"> Xiaojing Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work presents the particle swarm optimization trained neural network (PSO-NN) supervisory proportional integral derivative (PID) control method to monitor the pressurized water reactor (PWR) core power for safe operation. The proposed control approach is implemented on the transfer function of the PWR core, which is computed from the state-space model. The PWR core state-space model is designed from the neutronics, thermal-hydraulics, and reactivity models using perturbation around the equilibrium value. The proposed control approach computes the control rod speed to maneuver the core power to track the reference in a closed-loop scheme. The particle swarm optimization (PSO) algorithm is used to train the neural network (NN) and to tune the PID simultaneously. The controller performance is examined using integral absolute error, integral time absolute error, integral square error, and integral time square error functions, and the stability of the system is analyzed by using the Bode diagram. The simulation results indicated that the controller shows satisfactory performance to control and track the load power effectively and smoothly as compared to the PSO-PID control technique. This study will give benefit to design a supervisory controller for nuclear engineering research fields for control application. <p class="card-text"><strong>Keywords:</strong> <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=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=pressurized%20water%20reactor" title=" pressurized water reactor"> pressurized water reactor</a>, <a href="https://publications.waset.org/abstracts/search?q=supervisory%20controller" title=" supervisory controller"> supervisory controller</a> </p> <a href="https://publications.waset.org/abstracts/140523/neural-network-supervisory-proportional-integral-derivative-control-of-the-pressurized-water-reactor-core-power-load-following-operation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/140523.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">155</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">2302</span> Models Comparison for Solar Radiation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Djelloul%20Benatiallah">Djelloul Benatiallah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the current high consumption and recent industry growth, the depletion of fossil and natural energy supplies like oil, gas, and uranium is declining. Due to pollution and climate change, there needs to be a swift switch to renewable energy sources. Research on renewable energy is being done to meet energy needs. Solar energy is one of the renewable resources that can currently meet all of the world's energy needs. In most parts of the world, solar energy is a free and unlimited resource that can be used in a variety of ways, including photovoltaic systems for the generation of electricity and thermal systems for the generation of heatfor the residential sector's production of hot water. In this article, we'll conduct a comparison. The first step entails identifying the two empirical models that will enable us to estimate the daily irradiations on a horizontal plane. On the other hand, we compare it using the data obtained from measurements made at the Adrar site over the four distinct seasons. The model 2 provides a better estimate of the global solar components, with an absolute mean error of less than 7% and a correlation coefficient of more than 0.95, as well as a relative coefficient of the bias error that is less than 6% in absolute value and a relative RMSE that is less than 10%, according to a comparison of the results obtained by simulating the two models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=solar%20radiation" title="solar radiation">solar radiation</a>, <a href="https://publications.waset.org/abstracts/search?q=renewable%20energy" title=" renewable energy"> renewable energy</a>, <a href="https://publications.waset.org/abstracts/search?q=fossil" title=" fossil"> fossil</a>, <a href="https://publications.waset.org/abstracts/search?q=photovoltaic%20systems" title=" photovoltaic systems"> photovoltaic systems</a> </p> <a href="https://publications.waset.org/abstracts/157758/models-comparison-for-solar-radiation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157758.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">79</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">2301</span> Comparison of Different Intraocular Lens Power Calculation Formulas in People With Very High Myopia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xia%20Chen">Xia Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Yulan%20Wang"> Yulan Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> purpose: To compare the accuracy of Haigis, SRK/T, T2, Holladay 1, Hoffer Q, Barrett Universal II, Emmetropia Verifying Optical (EVO) and Kane for intraocular lens power calculation in patients with axial length (AL) ≥ 28 mm. Methods: In this retrospective single-center study, 50 eyes of 41 patients with AL ≥ 28 mm that underwent uneventful cataract surgery were enrolled. The actual postoperative refractive results were compared to the predicted refraction calculated with different formulas (Haigis, SRK/T, T2, Holladay 1, Hoffer Q, Barrett Universal II, EVO and Kane). The mean absolute prediction errors (MAE) 1 month postoperatively were compared. Results: The MAE of different formulas were as follows: Haigis (0.509), SRK/T (0.705), T2 (0.999), Holladay 1 (0.714), Hoffer Q (0.583), Barrett Universal II (0.552), EVO (0.463) and Kane (0.441). No significant difference was found among the different formulas (P = .122). The Kane and EVO formulas achieved the lowest level of mean prediction error (PE) and median absolute error (MedAE) (p < 0.05). Conclusion: The Kane and EVO formulas had a better success rate than others in predicting IOL power in high myopic eyes with AL longer than 28 mm in this study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cataract" title="cataract">cataract</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20calculation%20formulas" title=" power calculation formulas"> power calculation formulas</a>, <a href="https://publications.waset.org/abstracts/search?q=intraocular%20lens" title=" intraocular lens"> intraocular lens</a>, <a href="https://publications.waset.org/abstracts/search?q=long%20axial%20length" title=" long axial length"> long axial length</a> </p> <a href="https://publications.waset.org/abstracts/179273/comparison-of-different-intraocular-lens-power-calculation-formulas-in-people-with-very-high-myopia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/179273.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">83</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">2300</span> Age Estimation from Teeth among North Indian Population: Comparison and Reliability of Qualitative and Quantitative Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jasbir%20Arora">Jasbir Arora</a>, <a href="https://publications.waset.org/abstracts/search?q=Indu%20Talwar"> Indu Talwar</a>, <a href="https://publications.waset.org/abstracts/search?q=Daisy%20Sahni"> Daisy Sahni</a>, <a href="https://publications.waset.org/abstracts/search?q=Vidya%20Rattan"> Vidya Rattan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Age estimation is a crucial step to build the identity of a person, both in case of deceased and alive. In adults, age can be estimated on the basis of six regressive (Attrition, Secondary dentine, Dentine transparency, Root resorption, Cementum apposition and Periodontal Disease) changes in teeth qualitatively using scoring system and quantitatively by micrometric method. The present research was designed to establish the reliability of qualitative (method 1) and quantitative (method 2) of age estimation among North Indians and to compare the efficacy of these two methods. Method: 250 single-rooted extracted teeth (18-75 yrs.) were collected from Department of Oral Health Sciences, PGIMER, Chandigarh. Before extraction, periodontal score of each tooth was noted. Labiolingual sections were prepared and examined under light microscope for regressive changes. Each parameter was scored using Gustafson’s 0-3 point score system (qualitative), and total score was calculated. For quantitative method, each regressive change was measured quantitatively in form of 18 micrometric parameters under microscope with the help of measuring eyepiece. Age was estimated using linear and multiple regression analysis in Gustafson’s method and Kedici’s method respectively. Estimated age was compared with actual age on the basis of absolute mean error. Results: In pooled data, by Gustafson’s method, significant correlation (r= 0.8) was observed between total score and actual age. Total score generated an absolute mean error of ±7.8 years. Whereas, for Kedici’s method, a value of correlation coefficient of r=0.5 (p<0.01) was observed between all the eighteen micrometric parameters and known age. Using multiple regression equation, age was estimated, and an absolute mean error of age was found to be ±12.18 years. Conclusion: Gustafson’s (qualitative) method was found to be a better predictor for age estimation among North Indians. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=forensic%20odontology" title="forensic odontology">forensic odontology</a>, <a href="https://publications.waset.org/abstracts/search?q=age%20estimation" title=" age estimation"> age estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=North%20India" title=" North India"> North India</a>, <a href="https://publications.waset.org/abstracts/search?q=teeth" title=" teeth"> teeth</a> </p> <a href="https://publications.waset.org/abstracts/56110/age-estimation-from-teeth-among-north-indian-population-comparison-and-reliability-of-qualitative-and-quantitative-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56110.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">242</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">2299</span> The Theory of Number "0"</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Iryna%20Shevchenko">Iryna Shevchenko</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The science of mathematics was originated at the order of count of objects and subsequently for the measurement of size and quality of objects using the logical or abstract means. The laws of mathematics are based on the study of absolute values. The number 0 or "nothing" is the purely logical (as the opposite to absolute) value as the "nothing" should always assume the space for the something that had existed there; otherwise the "something" would never come to existence. In this work we are going to prove that the number "0" is the abstract (logical) and not an absolute number and it has the absolute value of “∞” (infinity). Therefore, the number "0" might not stand in the row of numbers that symbolically represents the absolute values, as it would be the mathematically incorrect. The symbolical value of number "0" in the row of numbers could be represented with symbol "∞" (infinity). As a result, we have the mathematical row of numbers: epsilon, ...4, 3, 2, 1, ∞. As the conclusions of the theory of number “0” we presented the statements: multiplication and division by fractions of numbers is illegal operation and the mathematical division by number “0” is allowed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=illegal%20operation%20of%20division%20and%20multiplication%20by%20fractions%20of%20number" title="illegal operation of division and multiplication by fractions of number">illegal operation of division and multiplication by fractions of number</a>, <a href="https://publications.waset.org/abstracts/search?q=infinity" title=" infinity"> infinity</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%20row%20of%20numbers" title=" mathematical row of numbers"> mathematical row of numbers</a>, <a href="https://publications.waset.org/abstracts/search?q=theory%20of%20number%20%E2%80%9C0%E2%80%9D" title=" theory of number “0”"> theory of number “0”</a> </p> <a href="https://publications.waset.org/abstracts/27872/the-theory-of-number-0" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27872.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">552</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">2298</span> Effects of Manufacture and Assembly Errors on the Output Error of Globoidal Cam Mechanisms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shuting%20Ji">Shuting Ji</a>, <a href="https://publications.waset.org/abstracts/search?q=Yueming%20Zhang"> Yueming Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jing%20Zhao"> Jing Zhao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The output error of the globoidal cam mechanism can be considered as a relevant indicator of mechanism performance, because it determines kinematic and dynamical behavior of mechanical transmission. Based on the differential geometry and the rigid body transformations, the mathematical model of surface geometry of the globoidal cam is established. Then we present the analytical expression of the output error (including the transmission error and the displacement error along the output axis) by considering different manufacture and assembly errors. The effects of the center distance error, the perpendicular error between input and output axes and the rotational angle error of the globoidal cam on the output error are systematically analyzed. A globoidal cam mechanism which is widely used in automatic tool changer of CNC machines is applied for illustration. Our results show that the perpendicular error and the rotational angle error have little effects on the transmission error but have great effects on the displacement error along the output axis. This study plays an important role in the design, manufacture and assembly of the globoidal cam mechanism. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=globoidal%20cam%20mechanism" title="globoidal cam mechanism">globoidal cam mechanism</a>, <a href="https://publications.waset.org/abstracts/search?q=manufacture%20error" title=" manufacture error"> manufacture error</a>, <a href="https://publications.waset.org/abstracts/search?q=transmission%20error" title=" transmission error"> transmission error</a>, <a href="https://publications.waset.org/abstracts/search?q=automatic%20tool%20changer" title=" automatic tool changer"> automatic tool changer</a> </p> <a href="https://publications.waset.org/abstracts/33472/effects-of-manufacture-and-assembly-errors-on-the-output-error-of-globoidal-cam-mechanisms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33472.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">574</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">2297</span> Support Vector Regression with Weighted Least Absolute Deviations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kang-Mo%20Jung">Kang-Mo Jung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Least squares support vector machine (LS-SVM) is a penalized regression which considers both fitting and generalization ability of a model. However, the squared loss function is very sensitive to even single outlier. We proposed a weighted absolute deviation loss function for the robustness of the estimates in least absolute deviation support vector machine. The proposed estimates can be obtained by a quadratic programming algorithm. Numerical experiments on simulated datasets show that the proposed algorithm is competitive in view of robustness to outliers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=least%20absolute%20deviation" title="least absolute deviation">least absolute deviation</a>, <a href="https://publications.waset.org/abstracts/search?q=quadratic%20programming" title=" quadratic programming"> quadratic programming</a>, <a href="https://publications.waset.org/abstracts/search?q=robustness" title=" robustness"> robustness</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a>, <a href="https://publications.waset.org/abstracts/search?q=weight" title=" weight"> weight</a> </p> <a href="https://publications.waset.org/abstracts/23674/support-vector-regression-with-weighted-least-absolute-deviations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23674.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">527</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">2296</span> Groundwater Flow Assessment Based on Numerical Simulation at Omdurman Area, Khartoum State, Sudan</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adil%20Balla%20Elkrail">Adil Balla Elkrail</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Visual MODFLOW computer codes were selected to simulate head distribution, calculate the groundwater budgets of the area, and evaluate the effect of external stresses on the groundwater head and to demonstrate how the groundwater model can be used as a comparative technique in order to optimize utilization of the groundwater resource. A conceptual model of the study area, aquifer parameters, boundary, and initial conditions were used to simulate the flow model. The trial-and-error technique was used to calibrate the model. The most important criteria used to check the calibrated model were Root Mean Square error (RMS), Mean Absolute error (AM), Normalized Root Mean Square error (NRMS) and mass balance. The maps of the simulated heads elaborated acceptable model calibration compared to observed heads map. A time length of eight years and the observed heads of the year 2004 were used for model prediction. The predictive simulation showed that the continuation of pumping will cause relatively high changes in head distribution and components of groundwater budget whereas, the low deficit computed (7122 m3/d) between inflows and outflows cannot create a significant drawdown of the potentiometric level. Hence, the area under consideration may represent a high permeability and productive zone and strongly recommended for further groundwater development. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aquifers" title="aquifers">aquifers</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20simulation" title=" model simulation"> model simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=groundwater" title=" groundwater"> groundwater</a>, <a href="https://publications.waset.org/abstracts/search?q=calibrations" title=" calibrations"> calibrations</a>, <a href="https://publications.waset.org/abstracts/search?q=trail-and-%20error" title=" trail-and- error"> trail-and- error</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a> </p> <a href="https://publications.waset.org/abstracts/45571/groundwater-flow-assessment-based-on-numerical-simulation-at-omdurman-area-khartoum-state-sudan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45571.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">242</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">2295</span> Prediction of Formation Pressure Using Artificial Intelligence Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdulmalek%20Ahmed">Abdulmalek Ahmed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Formation pressure is the main function that affects drilling operation economically and efficiently. Knowing the pore pressure and the parameters that affect it will help to reduce the cost of drilling process. Many empirical models reported in the literature were used to calculate the formation pressure based on different parameters. Some of these models used only drilling parameters to estimate pore pressure. Other models predicted the formation pressure based on log data. All of these models required different trends such as normal or abnormal to predict the pore pressure. Few researchers applied artificial intelligence (AI) techniques to predict the formation pressure by only one method or a maximum of two methods of AI. The objective of this research is to predict the pore pressure based on both drilling parameters and log data namely; weight on bit, rotary speed, rate of penetration, mud weight, bulk density, porosity and delta sonic time. A real field data is used to predict the formation pressure using five different artificial intelligence (AI) methods such as; artificial neural networks (ANN), radial basis function (RBF), fuzzy logic (FL), support vector machine (SVM) and functional networks (FN). All AI tools were compared with different empirical models. AI methods estimated the formation pressure by a high accuracy (high correlation coefficient and low average absolute percentage error) and outperformed all previous. The advantage of the new technique is its simplicity, which represented from its estimation of pore pressure without the need of different trends as compared to other models which require a two different trend (normal or abnormal pressure). Moreover, by comparing the AI tools with each other, the results indicate that SVM has the advantage of pore pressure prediction by its fast processing speed and high performance (a high correlation coefficient of 0.997 and a low average absolute percentage error of 0.14%). In the end, a new empirical correlation for formation pressure was developed using ANN method that can estimate pore pressure with a high precision (correlation coefficient of 0.998 and average absolute percentage error of 0.17%). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Artificial%20Intelligence%20%28AI%29" title="Artificial Intelligence (AI)">Artificial Intelligence (AI)</a>, <a href="https://publications.waset.org/abstracts/search?q=Formation%20pressure" title=" Formation pressure"> Formation pressure</a>, <a href="https://publications.waset.org/abstracts/search?q=Artificial%20Neural%20Networks%20%28ANN%29" title=" Artificial Neural Networks (ANN)"> Artificial Neural Networks (ANN)</a>, <a href="https://publications.waset.org/abstracts/search?q=Fuzzy%20Logic%20%28FL%29" title=" Fuzzy Logic (FL)"> Fuzzy Logic (FL)</a>, <a href="https://publications.waset.org/abstracts/search?q=Support%20Vector%20Machine%20%28SVM%29" title=" Support Vector Machine (SVM)"> Support Vector Machine (SVM)</a>, <a href="https://publications.waset.org/abstracts/search?q=Functional%20Networks%20%28FN%29" title=" Functional Networks (FN)"> Functional Networks (FN)</a>, <a href="https://publications.waset.org/abstracts/search?q=Radial%20Basis%20Function%20%28RBF%29" title=" Radial Basis Function (RBF)"> Radial Basis Function (RBF)</a> </p> <a href="https://publications.waset.org/abstracts/95931/prediction-of-formation-pressure-using-artificial-intelligence-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95931.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">149</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">2294</span> Artificial Intelligence Based Predictive Models for Short Term Global Horizontal Irradiation Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kudzanayi%20Chiteka">Kudzanayi Chiteka</a>, <a href="https://publications.waset.org/abstracts/search?q=Wellington%20Makondo"> Wellington Makondo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The whole world is on the drive to go green owing to the negative effects of burning fossil fuels. Therefore, there is immediate need to identify and utilise alternative renewable energy sources. Among these energy sources solar energy is one of the most dominant in Zimbabwe. Solar power plants used to generate electricity are entirely dependent on solar radiation. For planning purposes, solar radiation values should be known in advance to make necessary arrangements to minimise the negative effects of the absence of solar radiation due to cloud cover and other naturally occurring phenomena. This research focused on the prediction of Global Horizontal Irradiation values for the sixth day given values for the past five days. Artificial intelligence techniques were used in this research. Three models were developed based on Support Vector Machines, Radial Basis Function, and Feed Forward Back-Propagation Artificial neural network. Results revealed that Support Vector Machines gives the best results compared to the other two with a mean absolute percentage error (MAPE) of 2%, Mean Absolute Error (MAE) of 0.05kWh/m²/day root mean square (RMS) error of 0.15kWh/m²/day and a coefficient of determination of 0.990. The other predictive models had prediction accuracies of MAPEs of 4.5% and 6% respectively for Radial Basis Function and Feed Forward Back-propagation Artificial neural network. These two models also had coefficients of determination of 0.975 and 0.970 respectively. It was found that prediction of GHI values for the future days is possible using artificial intelligence-based predictive models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=solar%20energy" title="solar energy">solar energy</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20horizontal%20irradiation" title=" global horizontal irradiation"> global horizontal irradiation</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20models" title=" predictive models"> predictive models</a> </p> <a href="https://publications.waset.org/abstracts/65891/artificial-intelligence-based-predictive-models-for-short-term-global-horizontal-irradiation-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65891.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">273</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">2293</span> Comparison between Some of Robust Regression Methods with OLS Method with Application</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sizar%20Abed%20Mohammed">Sizar Abed Mohammed</a>, <a href="https://publications.waset.org/abstracts/search?q=Zahraa%20Ghazi%20Sadeeq"> Zahraa Ghazi Sadeeq </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of the classic method, least squares (OLS) to estimate the linear regression parameters, when they are available assumptions, and capabilities that have good characteristics, such as impartiality, minimum variance, consistency, and so on. The development of alternative statistical techniques to estimate the parameters, when the data are contaminated with outliers. These are powerful methods (or resistance). In this paper, three of robust methods are studied, which are: Maximum likelihood type estimate M-estimator, Modified Maximum likelihood type estimate MM-estimator and Least Trimmed Squares LTS-estimator, and their results are compared with OLS method. These methods applied to real data taken from Duhok company for manufacturing furniture, the obtained results compared by using the criteria: Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE) and Mean Sum of Absolute Error (MSAE). Important conclusions that this study came up with are: a number of typical values detected by using four methods in the furniture line and very close to the data. This refers to the fact that close to the normal distribution of standard errors, but typical values in the doors line data, using OLS less than that detected by the powerful ways. This means that the standard errors of the distribution are far from normal departure. Another important conclusion is that the estimated values of the parameters by using the lifeline is very far from the estimated values using powerful methods for line doors, gave LTS- destined better results using standard MSE, and gave the M- estimator better results using standard MAPE. Moreover, we noticed that using standard MSAE, and MM- estimator is better. The programs S-plus (version 8.0, professional 2007), Minitab (version 13.2) and SPSS (version 17) are used to analyze the data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Robest" title="Robest">Robest</a>, <a href="https://publications.waset.org/abstracts/search?q=LTS" title=" LTS"> LTS</a>, <a href="https://publications.waset.org/abstracts/search?q=M%20estimate" title=" M estimate"> M estimate</a>, <a href="https://publications.waset.org/abstracts/search?q=MSE" title=" MSE"> MSE</a> </p> <a href="https://publications.waset.org/abstracts/53677/comparison-between-some-of-robust-regression-methods-with-ols-method-with-application" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53677.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">232</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">2292</span> Shoulder Range of Motion Measurements using Computer Vision Compared to Hand-Held Goniometric Measurements</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lakshmi%20Sujeesh">Lakshmi Sujeesh</a>, <a href="https://publications.waset.org/abstracts/search?q=Aaron%20Ramzeen"> Aaron Ramzeen</a>, <a href="https://publications.waset.org/abstracts/search?q=Ricky%20Ziming%20Guo"> Ricky Ziming Guo</a>, <a href="https://publications.waset.org/abstracts/search?q=Abhishek%20Agrawal"> Abhishek Agrawal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Range of motion (ROM) is often measured by physiotherapists using hand-held goniometer as part of mobility assessment for diagnosis. Due to the nature of hand-held goniometer measurement procedure, readings often tend to have some variations depending on the physical therapist taking the measurements (Riddle et al.). This study aims to validate computer vision software readings against goniometric measurements for quick and consistent ROM measurements to be taken by clinicians. The use of this computer vision software hopes to improve the future of musculoskeletal space with more efficient diagnosis from recording of patient’s ROM with minimal human error across different physical therapists. Methods: Using the hand-held long arm goniometer measurements as the “gold-standard”, healthy study participants (n = 20) were made to perform 4 exercises: Front elevation, Abduction, Internal Rotation, and External Rotation, using both arms. Assessment of active ROM using computer vision software at different angles set by goniometer for each exercise was done. Interclass Correlation Coefficient (ICC) using 2-way random effects model, Box-Whisker plots, and Root Mean Square error (RMSE) were used to find the degree of correlation and absolute error measured between set and recorded angles across the repeated trials by the same rater. Results: ICC (2,1) values for all 4 exercises are above 0.9, indicating excellent reliability. Lowest overall RMSE was for external rotation (5.67°) and highest for front elevation (8.00°). Box-whisker plots showed have showed that there is a potential zero error in the measurements done by the computer vision software for abduction, where absolute error for measurements taken at 0 degree are shifted away from the ideal 0 line, with its lowest recorded error being 8°. Conclusion: Our results indicate that the use of computer vision software is valid and reliable to use in clinical settings by physiotherapists for measuring shoulder ROM. Overall, computer vision helps improve accessibility to quality care provided for individual patients, with the ability to assess ROM for their condition at home throughout a full cycle of musculoskeletal care (American Academy of Orthopaedic Surgeons) without the need for a trained therapist. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=physiotherapy" title="physiotherapy">physiotherapy</a>, <a href="https://publications.waset.org/abstracts/search?q=frozen%20shoulder" title=" frozen shoulder"> frozen shoulder</a>, <a href="https://publications.waset.org/abstracts/search?q=joint%20range%20of%20motion" title=" joint range of motion"> joint range of motion</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a> </p> <a href="https://publications.waset.org/abstracts/164545/shoulder-range-of-motion-measurements-using-computer-vision-compared-to-hand-held-goniometric-measurements" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164545.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">107</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">2291</span> The Relationship between Self Concept Clarity and Need for Absolute Truth and Problem Solving and Symptoms of Stress in Homosexual Male</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gizem%20Akcan">Gizem Akcan</a>, <a href="https://publications.waset.org/abstracts/search?q=Erdinc%20Ozturk"> Erdinc Ozturk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> When it is examined as historically, it has caught attention that homosexual people try to behave as heterosexual or come out to have a place in community. Homosexual people have identity confusion during identity development, they have high levels of need for absolute truth and their psychological well being is affected negatively because of high levels of need for absolute truth and they have problems about self concept clarity. People who have problems about self concept clarity have problems on problem solving and show lots of symptoms of stress. People who have clear self concept use healthier coping strategies to solve problems. The purpose of this study is to show whether need for absolute truth predicts problem solving and symptoms of stress via mediator effect of self concept clarity or not on homosexual men. The participants of this study were 200 homosexual men. The ages of participants were 20-50. In addition, Demographic Information Form, Self Concept Clarity Scale, Need for Absolute Truth Scale, Stres Self-Assessment Checklist and Problem Solving Inventory were applied to the participants. The assessment of the data was made with confirmatory factor analysis and structural equation modeling analysis. According to the results of this study, need for absolute truth predicts problem solving and symptoms of stress via mediator effect of self concept clarity on homosexual men. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=need%20for%20absolute%20truth" title="need for absolute truth">need for absolute truth</a>, <a href="https://publications.waset.org/abstracts/search?q=self%20concept%20clarity" title=" self concept clarity"> self concept clarity</a>, <a href="https://publications.waset.org/abstracts/search?q=symptoms%20of%20stress" title=" symptoms of stress"> symptoms of stress</a>, <a href="https://publications.waset.org/abstracts/search?q=problem%20solving" title=" problem solving "> problem solving </a> </p> <a href="https://publications.waset.org/abstracts/85897/the-relationship-between-self-concept-clarity-and-need-for-absolute-truth-and-problem-solving-and-symptoms-of-stress-in-homosexual-male" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85897.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">224</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">2290</span> Co-Integration Model for Predicting Inflation Movement in Nigeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Salako%20Rotimi">Salako Rotimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Oshungade%20Stephen"> Oshungade Stephen</a>, <a href="https://publications.waset.org/abstracts/search?q=Ojewoye%20Opeyemi"> Ojewoye Opeyemi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The maintenance of price stability is one of the macroeconomic challenges facing Nigeria as a nation. This paper attempts to build a co-integration multivariate time series model for inflation movement in Nigeria using data extracted from the abstract of statistics of the Central Bank of Nigeria (CBN) from 2008 to 2017. The Johansen cointegration test suggests at least one co-integration vector describing the long run relationship between Consumer Price Index (CPI), Food Price Index (FPI) and Non-Food Price Index (NFPI). All three series show increasing pattern, which indicates a sign of non-stationary in each of the series. Furthermore, model predictability was established with root-mean-square-error, mean absolute error, mean average percentage error, and Theil’s unbiased statistics for n-step forecasting. The result depicts that the long run coefficient of a consumer price index (CPI) has a positive long-run relationship with the food price index (FPI) and non-food price index (NFPI). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=economic" title="economic">economic</a>, <a href="https://publications.waset.org/abstracts/search?q=inflation" title=" inflation"> inflation</a>, <a href="https://publications.waset.org/abstracts/search?q=model" title=" model"> model</a>, <a href="https://publications.waset.org/abstracts/search?q=series" title=" series"> series</a> </p> <a href="https://publications.waset.org/abstracts/109868/co-integration-model-for-predicting-inflation-movement-in-nigeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/109868.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">244</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">2289</span> On Differential Growth Equation to Stochastic Growth Model Using Hyperbolic Sine Function in Height/Diameter Modeling of Pines</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20O.%20Oyamakin">S. O. Oyamakin</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20U.%20Chukwu"> A. U. Chukwu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Richard's growth equation being a generalized logistic growth equation was improved upon by introducing an allometric parameter using the hyperbolic sine function. The integral solution to this was called hyperbolic Richard's growth model having transformed the solution from deterministic to a stochastic growth model. Its ability in model prediction was compared with the classical Richard's growth model an approach which mimicked the natural variability of heights/diameter increment with respect to age and therefore provides a more realistic height/diameter predictions using the coefficient of determination (R2), Mean Absolute Error (MAE) and Mean Square Error (MSE) results. The Kolmogorov-Smirnov test and Shapiro-Wilk test was also used to test the behavior of the error term for possible violations. The mean function of top height/Dbh over age using the two models under study predicted closely the observed values of top height/Dbh in the hyperbolic Richard's nonlinear growth models better than the classical Richard's growth model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=height" title="height">height</a>, <a href="https://publications.waset.org/abstracts/search?q=Dbh" title=" Dbh"> Dbh</a>, <a href="https://publications.waset.org/abstracts/search?q=forest" title=" forest"> forest</a>, <a href="https://publications.waset.org/abstracts/search?q=Pinus%20caribaea" title=" Pinus caribaea"> Pinus caribaea</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperbolic" title=" hyperbolic"> hyperbolic</a>, <a href="https://publications.waset.org/abstracts/search?q=Richard%27s" title=" Richard's"> Richard's</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic" title=" stochastic"> stochastic</a> </p> <a href="https://publications.waset.org/abstracts/17738/on-differential-growth-equation-to-stochastic-growth-model-using-hyperbolic-sine-function-in-heightdiameter-modeling-of-pines" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17738.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">480</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2288</span> An Alternative Richards’ Growth Model Based on Hyperbolic Sine Function</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Samuel%20Oluwafemi%20Oyamakin">Samuel Oluwafemi Oyamakin</a>, <a href="https://publications.waset.org/abstracts/search?q=Angela%20Unna%20Chukwu"> Angela Unna Chukwu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Richrads growth equation being a generalized logistic growth equation was improved upon by introducing an allometric parameter using the hyperbolic sine function. The integral solution to this was called hyperbolic Richards growth model having transformed the solution from deterministic to a stochastic growth model. Its ability in model prediction was compared with the classical Richards growth model an approach which mimicked the natural variability of heights/diameter increment with respect to age and therefore provides a more realistic height/diameter predictions using the coefficient of determination (R2), Mean Absolute Error (MAE) and Mean Square Error (MSE) results. The Kolmogorov-Smirnov test and Shapiro-Wilk test was also used to test the behavior of the error term for possible violations. The mean function of top height/Dbh over age using the two models under study predicted closely the observed values of top height/Dbh in the hyperbolic Richards nonlinear growth models better than the classical Richards growth model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=height" title="height">height</a>, <a href="https://publications.waset.org/abstracts/search?q=diameter%20at%20breast%20height" title=" diameter at breast height"> diameter at breast height</a>, <a href="https://publications.waset.org/abstracts/search?q=DBH" title=" DBH"> DBH</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperbolic%20sine%20function" title=" hyperbolic sine function"> hyperbolic sine function</a>, <a href="https://publications.waset.org/abstracts/search?q=Pinus%20caribaea" title=" Pinus caribaea"> Pinus caribaea</a>, <a href="https://publications.waset.org/abstracts/search?q=Richards%27%20growth%20model" title=" Richards' growth model"> Richards' growth model</a> </p> <a href="https://publications.waset.org/abstracts/66329/an-alternative-richards-growth-model-based-on-hyperbolic-sine-function" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66329.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">392</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">2287</span> Universe at Zero Second and the Creation Process of the First Particle from the Absolute Void</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shivan%20Sirdy">Shivan Sirdy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, we discuss the properties of absolute void space or the universe at zero seconds, and how these properties play a vital role in creating a mechanism in which the very first particle gets created simultaneously everywhere. We find the limit in which when the absolute void volume reaches will lead to the collapse that leads to the creation of the first particle. This discussion is made following the elementary dimensions theory study that was peer-reviewed at the end of 2020; everything in the universe is made from four elementary dimensions, these dimensions are the three spatial dimensions (X, Y, and Z) and the Void resistance as the factor of change among the four. Time itself was not considered as the fourth dimension. Rather time corresponds to a factor of change, and during the research, it was found out that the Void resistance is the factor of change in the absolute Void space, where time is a hypothetical concept that represents changes during certain events compared to a constant change rate event. Therefore, time does exist, but as a factor of change as the Void resistance: Time= factor of change= Void resistance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=elementary%20dimensions" title="elementary dimensions">elementary dimensions</a>, <a href="https://publications.waset.org/abstracts/search?q=absolute%20void" title=" absolute void"> absolute void</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20alternative" title=" time alternative"> time alternative</a>, <a href="https://publications.waset.org/abstracts/search?q=early%20universe" title=" early universe"> early universe</a>, <a href="https://publications.waset.org/abstracts/search?q=universe%20at%20zero%20second" title=" universe at zero second"> universe at zero second</a>, <a href="https://publications.waset.org/abstracts/search?q=Void%20resistant" title=" Void resistant"> Void resistant</a>, <a href="https://publications.waset.org/abstracts/search?q=Hydrogen%20atom" title=" Hydrogen atom"> Hydrogen atom</a>, <a href="https://publications.waset.org/abstracts/search?q=Hadron%20field" title=" Hadron field"> Hadron field</a>, <a href="https://publications.waset.org/abstracts/search?q=Lepton%20field" title=" Lepton field"> Lepton field</a> </p> <a href="https://publications.waset.org/abstracts/140791/universe-at-zero-second-and-the-creation-process-of-the-first-particle-from-the-absolute-void" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/140791.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">2286</span> Cellular Traffic Prediction through Multi-Layer Hybrid Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Supriya%20H.%20S.">Supriya H. S.</a>, <a href="https://publications.waset.org/abstracts/search?q=Chandrakala%20B.%20M."> Chandrakala B. M.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Deep learning based models have been recently successful adoption for network traffic prediction. However, training a deep learning model for various prediction tasks is considered one of the critical tasks due to various reasons. This research work develops Multi-Layer Hybrid Network (MLHN) for network traffic prediction and analysis; MLHN comprises the three distinctive networks for handling the different inputs for custom feature extraction. Furthermore, an optimized and efficient parameter-tuning algorithm is introduced to enhance parameter learning. MLHN is evaluated considering the “Big Data Challenge” dataset considering the Mean Absolute Error, Root Mean Square Error and R^2as metrics; furthermore, MLHN efficiency is proved through comparison with a state-of-art approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MLHN" title="MLHN">MLHN</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20traffic%20prediction" title=" network traffic prediction"> network traffic prediction</a> </p> <a href="https://publications.waset.org/abstracts/154887/cellular-traffic-prediction-through-multi-layer-hybrid-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/154887.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">88</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2285</span> The Prognostic Value of Dynamic Changes of Hematological Indices in Oropharyngeal Cancer Patients Treated with Radiotherapy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yao%20Song">Yao Song</a>, <a href="https://publications.waset.org/abstracts/search?q=Danni%20Cheng"> Danni Cheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Jianjun%20Ren"> Jianjun Ren</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Objectives: We aimed to explore the prognostic effects of absolute values and dynamic changes of common hematological indices on oropharynx squamous cell carcinoma (OPSCC) patients treated with radiation. Methods and materials: The absolute values of white blood cell (WBC), absolute neutrophil count (ANC), absolute lymphocyte count (ALC), hemoglobin (Hb), platelet (Plt), albumin (Alb), neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) at baseline (within 45 days before radiation), 1-, 3-, 6- and 12-months after the start of radiotherapy were retrospectively collected. Locally-estimated smoothing scatterplots were used to describe the smooth trajectory of each index. A mixed-effect model with a random slope was fitted to describe the changing rate and trend of indices over time. Cox proportional hazard analysis was conducted to assess the correlation between hematological indices and treatment outcomes. Results: Of the enrolled 85 OPSCC patients, inflammatory indices, such as WBC and ALC, dropped rapidly during acute treatment and gradually recovered, while NLR and PLR increased at first three months and subsequently declined within 3-12 months. Higher absolute value or increasing trend of nutritional indices (Alb and Hb) was associated with better prognosis (all p<0.05). In contrast, patients with higher absolute value or upward trend of inflammatory indices (WBC, ANC, Plt, PLR and NLR) had worse survival (all p<0.05). Conclusions: The absolute values and dynamic changes of hematological indices were valuable prognostic factors for OPSCC patients who underwent radiotherapy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hematological%20indices" title="hematological indices">hematological indices</a>, <a href="https://publications.waset.org/abstracts/search?q=oropharyngeal%20cancer" title=" oropharyngeal cancer"> oropharyngeal cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=radiotherapy" title=" radiotherapy"> radiotherapy</a>, <a href="https://publications.waset.org/abstracts/search?q=NLR" title=" NLR"> NLR</a>, <a href="https://publications.waset.org/abstracts/search?q=PLR" title=" PLR"> PLR</a> </p> <a href="https://publications.waset.org/abstracts/145304/the-prognostic-value-of-dynamic-changes-of-hematological-indices-in-oropharyngeal-cancer-patients-treated-with-radiotherapy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145304.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">2284</span> EMI Radiation Prediction and Final Measurement Process Optimization by Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hussam%20Elias">Hussam Elias</a>, <a href="https://publications.waset.org/abstracts/search?q=Ninovic%20Perez"> Ninovic Perez</a>, <a href="https://publications.waset.org/abstracts/search?q=Holger%20Hirsch"> Holger Hirsch</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The completion of the EMC regulations worldwide is growing steadily as the usage of electronics in our daily lives is increasing more than ever. In this paper, we introduce a novel method to perform the final phase of Electromagnetic compatibility (EMC) measurement and to reduce the required test time according to the norm EN 55032 by using a developed tool and the conventional neural network(CNN). The neural network was trained using real EMC measurements, which were performed in the Semi Anechoic Chamber (SAC) by CETECOM GmbH in Essen, Germany. To implement our proposed method, we wrote software to perform the radiated electromagnetic interference (EMI) measurements and use the CNN to predict and determine the position of the turntable that meets the maximum radiation value. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=conventional%20neural%20network" title="conventional neural network">conventional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=electromagnetic%20compatibility%20measurement" title=" electromagnetic compatibility measurement"> electromagnetic compatibility measurement</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20absolute%20error" title=" mean absolute error"> mean absolute error</a>, <a href="https://publications.waset.org/abstracts/search?q=position%20error" title=" position error"> position error</a> </p> <a href="https://publications.waset.org/abstracts/143266/emi-radiation-prediction-and-final-measurement-process-optimization-by-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143266.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">200</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">2283</span> Aural Skills Pedagogy for Students with Absolute Pitch</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rika%20Uchida">Rika Uchida</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In teaching sophomore level aural skills, I have dealt with students with absolute pitch do poorly in my courses, particularly in harmonic dictation. They can identify triads; however, identifying quality of seventh chords or chromatic chords poses serious challenges. Most often, they need to spell all the pitches before identifying the chord qualities and Roman Numerals. Growing up in a country where acquiring absolute pitch is considered essential, I started my early music training with fixed do system at age three and learned all my music with solfege. When I was assigned as a TA in aural skills courses at graduate school in US, I had to learn relative pitch quickly. My survival method was listening to music with absolute pitch first, then quickly "translate" to relative pitch. In teaching my courses, I have been using chord progressions (5-8 chords total), in which students are asked to sing chord arpeggiation with movable do solfege. I use same progressions for harmonic dictation; I hoped that students learn to incorporate singing and listening skills by overlapping same materials. This method has proven to be successful for most students; in particular, it has helped students with absolute pitch to hear chord quality and function. Although original progressions are written in C as a tonic, they can identify chords in harmonic dictation in other keys as well. In short, I believe singing chord progression with movable do arpeggiation helps students with absolute pitch to improve hearing function and quality of chords in harmonic dictation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aural%20skills%20pedagogy" title="aural skills pedagogy">aural skills pedagogy</a>, <a href="https://publications.waset.org/abstracts/search?q=music%20theory" title=" music theory"> music theory</a>, <a href="https://publications.waset.org/abstracts/search?q=absolute%20pitch" title=" absolute pitch"> absolute pitch</a>, <a href="https://publications.waset.org/abstracts/search?q=harmonic%20dictation" title=" harmonic dictation"> harmonic dictation</a> </p> <a href="https://publications.waset.org/abstracts/119257/aural-skills-pedagogy-for-students-with-absolute-pitch" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/119257.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">144</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=mean%20absolute%20error&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=mean%20absolute%20error&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=mean%20absolute%20error&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=mean%20absolute%20error&page=5">5</a></li> <li class="page-item"><a class="page-link" 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