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vector regression</title> <meta name="description" content="Search results for: support vector regression"> <meta name="keywords" content="support vector regression"> <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 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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="support vector regression"> <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> 10525</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: support vector regression</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10525</span> An Epsilon Hierarchical Fuzzy Twin Support Vector Regression </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arindam%20Chaudhuri">Arindam Chaudhuri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The research presents epsilon- hierarchical fuzzy twin support vector regression (epsilon-HFTSVR) based on epsilon-fuzzy twin support vector regression (epsilon-FTSVR) and epsilon-twin support vector regression (epsilon-TSVR). Epsilon-FTSVR is achieved by incorporating trapezoidal fuzzy numbers to epsilon-TSVR which takes care of uncertainty existing in forecasting problems. Epsilon-FTSVR determines a pair of epsilon-insensitive proximal functions by solving two related quadratic programming problems. The structural risk minimization principle is implemented by introducing regularization term in primal problems of epsilon-FTSVR. This yields dual stable positive definite problems which improves regression performance. Epsilon-FTSVR is then reformulated as epsilon-HFTSVR consisting of a set of hierarchical layers each containing epsilon-FTSVR. Experimental results on both synthetic and real datasets reveal that epsilon-HFTSVR has remarkable generalization performance with minimum training time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=regression" title="regression">regression</a>, <a href="https://publications.waset.org/abstracts/search?q=epsilon-TSVR" title=" epsilon-TSVR"> epsilon-TSVR</a>, <a href="https://publications.waset.org/abstracts/search?q=epsilon-FTSVR" title=" epsilon-FTSVR"> epsilon-FTSVR</a>, <a href="https://publications.waset.org/abstracts/search?q=epsilon-HFTSVR" title=" epsilon-HFTSVR"> epsilon-HFTSVR</a> </p> <a href="https://publications.waset.org/abstracts/20236/an-epsilon-hierarchical-fuzzy-twin-support-vector-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20236.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">375</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">10524</span> Optimization of Machine Learning Regression Results: An Application on Health Expenditures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Songul%20Cinaroglu">Songul Cinaroglu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine learning regression methods are recommended as an alternative to classical regression methods in the existence of variables which are difficult to model. Data for health expenditure is typically non-normal and have a heavily skewed distribution. This study aims to compare machine learning regression methods by hyperparameter tuning to predict health expenditure per capita. A multiple regression model was conducted and performance results of Lasso Regression, Random Forest Regression and Support Vector Machine Regression recorded when different hyperparameters are assigned. Lambda (λ) value for Lasso Regression, number of trees for Random Forest Regression, epsilon (ε) value for Support Vector Regression was determined as hyperparameters. Study results performed by using 'k' fold cross validation changed from 5 to 50, indicate the difference between machine learning regression results in terms of R², RMSE and MAE values that are statistically significant (p < 0.001). Study results reveal that Random Forest Regression (R² ˃ 0.7500, RMSE ≤ 0.6000 ve MAE ≤ 0.4000) outperforms other machine learning regression methods. It is highly advisable to use machine learning regression methods for modelling health expenditures. <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=lasso%20regression" title=" lasso regression"> lasso regression</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest%20regression" title=" random forest regression"> random forest regression</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20regression" title=" support vector regression"> support vector regression</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperparameter%20tuning" title=" hyperparameter tuning"> hyperparameter tuning</a>, <a href="https://publications.waset.org/abstracts/search?q=health%20expenditure" title=" health expenditure"> health expenditure</a> </p> <a href="https://publications.waset.org/abstracts/97629/optimization-of-machine-learning-regression-results-an-application-on-health-expenditures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97629.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">226</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">10523</span> Forecasting of Grape Juice Flavor by Using Support Vector Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ren-Jieh%20Kuo">Ren-Jieh Kuo</a>, <a href="https://publications.waset.org/abstracts/search?q=Chun-Shou%20Huang"> Chun-Shou Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The research of juice flavor forecasting has become more important in China. Due to the fast economic growth in China, many different kinds of juices have been introduced to the market. If a beverage company can understand their customers’ preference well, the juice can be served more attractively. Thus, this study intends to introduce the basic theory and computing process of grapes juice flavor forecasting based on support vector regression (SVR). Applying SVR, BPN and LR to forecast the flavor of grapes juice in real data, the result shows that SVR is more suitable and effective at predicting performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=flavor%20forecasting" title="flavor forecasting">flavor forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title=" artificial neural networks"> artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=Support%20Vector%20Regression" title=" Support Vector Regression"> Support Vector Regression</a>, <a href="https://publications.waset.org/abstracts/search?q=China" title=" China"> China</a> </p> <a href="https://publications.waset.org/abstracts/21311/forecasting-of-grape-juice-flavor-by-using-support-vector-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21311.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">492</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">10522</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">10521</span> SVM-Based Modeling of Mass Transfer Potential of Multiple Plunging Jets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Surinder%20Deswal">Surinder Deswal</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahesh%20Pal"> Mahesh Pal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper investigates the potential of support vector machines based regression approach to model the mass transfer capacity of multiple plunging jets, both vertical (θ = 90°) and inclined (θ = 60°). The data set used in this study consists of four input parameters with a total of eighty eight cases. For testing, tenfold cross validation was used. Correlation coefficient values of 0.971 and 0.981 (root mean square error values of 0.0025 and 0.0020) were achieved by using polynomial and radial basis kernel functions based support vector regression respectively. Results suggest an improved performance by radial basis function in comparison to polynomial kernel based support vector machines. The estimated overall mass transfer coefficient, by both the kernel functions, is in good agreement with actual experimental values (within a scatter of ±15 %); thereby suggesting the utility of support vector machines based regression approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mass%20transfer" title="mass transfer">mass transfer</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20plunging%20jets" title=" multiple plunging jets"> multiple plunging jets</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machines" title=" support vector machines"> support vector machines</a>, <a href="https://publications.waset.org/abstracts/search?q=ecological%20sciences" title=" ecological sciences"> ecological sciences</a> </p> <a href="https://publications.waset.org/abstracts/9906/svm-based-modeling-of-mass-transfer-potential-of-multiple-plunging-jets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9906.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">464</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">10520</span> Support Vector Regression for Retrieval of Soil Moisture Using Bistatic Scatterometer Data at X-Band</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dileep%20Kumar%20Gupta">Dileep Kumar Gupta</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajendra%20Prasad"> Rajendra Prasad</a>, <a href="https://publications.waset.org/abstracts/search?q=Pradeep%20Kumar"> Pradeep Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Varun%20Narayan%20Mishra"> Varun Narayan Mishra</a>, <a href="https://publications.waset.org/abstracts/search?q=Ajeet%20Kumar%20Vishwakarma"> Ajeet Kumar Vishwakarma</a>, <a href="https://publications.waset.org/abstracts/search?q=Prashant%20K.%20Srivastava"> Prashant K. Srivastava</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An approach was evaluated for the retrieval of soil moisture of bare soil surface using bistatic scatterometer data in the angular range of 200 to 700 at VV- and HH- polarization. The microwave data was acquired by specially designed X-band (10 GHz) bistatic scatterometer. The linear regression analysis was done between scattering coefficients and soil moisture content to select the suitable incidence angle for retrieval of soil moisture content. The 250 incidence angle was found more suitable. The support vector regression analysis was used to approximate the function described by the input-output relationship between the scattering coefficient and corresponding measured values of the soil moisture content. The performance of support vector regression algorithm was evaluated by comparing the observed and the estimated soil moisture content by statistical performance indices %Bias, root mean squared error (RMSE) and Nash-Sutcliffe Efficiency (NSE). The values of %Bias, root mean squared error (RMSE) and Nash-Sutcliffe Efficiency (NSE) were found 2.9451, 1.0986, and 0.9214, respectively at HH-polarization. At VV- polarization, the values of %Bias, root mean squared error (RMSE) and Nash-Sutcliffe Efficiency (NSE) were found 3.6186, 0.9373, and 0.9428, respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bistatic%20scatterometer" title="bistatic scatterometer">bistatic scatterometer</a>, <a href="https://publications.waset.org/abstracts/search?q=soil%20moisture" title=" soil moisture"> soil moisture</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20regression" title=" support vector regression"> support vector regression</a>, <a href="https://publications.waset.org/abstracts/search?q=RMSE" title=" RMSE"> RMSE</a>, <a href="https://publications.waset.org/abstracts/search?q=%25Bias" title=" %Bias"> %Bias</a>, <a href="https://publications.waset.org/abstracts/search?q=NSE" title=" NSE"> NSE</a> </p> <a href="https://publications.waset.org/abstracts/34385/support-vector-regression-for-retrieval-of-soil-moisture-using-bistatic-scatterometer-data-at-x-band" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34385.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">428</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">10519</span> Support Vector Regression Combined with Different Optimization Algorithms to Predict Global Solar Radiation on Horizontal Surfaces in Algeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Laidi%20Maamar">Laidi Maamar</a>, <a href="https://publications.waset.org/abstracts/search?q=Achwak%20Madani"> Achwak Madani</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdellah%20El%20Ahdj%20Abdellah"> Abdellah El Ahdj Abdellah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this work is to use Support Vector regression (SVR) combined with dragonfly, firefly, Bee Colony and particle swarm Optimization algorithm to predict global solar radiation on horizontal surfaces in some cities in Algeria. Combining these optimization algorithms with SVR aims principally to enhance accuracy by fine-tuning the parameters, speeding up the convergence of the SVR model, and exploring a larger search space efficiently; these parameters are the regularization parameter (C), kernel parameters, and epsilon parameter. By doing so, the aim is to improve the generalization and predictive accuracy of the SVR model. Overall, the aim is to leverage the strengths of both SVR and optimization algorithms to create a more powerful and effective regression model for various cities and under different climate conditions. Results demonstrate close agreement between predicted and measured data in terms of different metrics. In summary, SVM has proven to be a valuable tool in modeling global solar radiation, offering accurate predictions and demonstrating versatility when combined with other algorithms or used in hybrid forecasting models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20regression%20%28SVR%29" title="support vector regression (SVR)">support vector regression (SVR)</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20algorithms" title=" optimization algorithms"> optimization algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20solar%20radiation%20prediction" title=" global solar radiation prediction"> global solar radiation prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20forecasting%20models" title=" hybrid forecasting models"> hybrid forecasting models</a> </p> <a href="https://publications.waset.org/abstracts/186719/support-vector-regression-combined-with-different-optimization-algorithms-to-predict-global-solar-radiation-on-horizontal-surfaces-in-algeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186719.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">35</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">10518</span> Using Support Vector Machines for Measuring Democracy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tommy%20Krieger">Tommy Krieger</a>, <a href="https://publications.waset.org/abstracts/search?q=Klaus%20Gruendler"> Klaus Gruendler </a> </p> <p class="card-text"><strong>Abstract:</strong></p> We present a novel approach for measuring democracy, which enables a very detailed and sensitive index. This method is based on Support Vector Machines, a mathematical algorithm for pattern recognition. Our implementation evaluates 188 countries in the period between 1981 and 2011. The Support Vector Machines Democracy Index (SVMDI) is continuously on the 0-1-Interval and robust to variations in the numerical process parameters. The algorithm introduced here can be used for every concept of democracy without additional adjustments, and due to its flexibility it is also a valuable tool for comparison studies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=democracy" title="democracy">democracy</a>, <a href="https://publications.waset.org/abstracts/search?q=democracy%20index" title=" democracy index"> democracy index</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=support%20vector%20machines" title=" support vector machines"> support vector machines</a> </p> <a href="https://publications.waset.org/abstracts/31697/using-support-vector-machines-for-measuring-democracy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31697.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">378</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">10517</span> Modeling Aeration of Sharp Crested Weirs by Using Support Vector Machines</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arun%20Goel">Arun Goel </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present paper attempts to investigate the prediction of air entrainment rate and aeration efficiency of a free over-fall jets issuing from a triangular sharp crested weir by using regression based modelling. The empirical equations, support vector machine (polynomial and radial basis function) models and the linear regression techniques were applied on the triangular sharp crested weirs relating the air entrainment rate and the aeration efficiency to the input parameters namely drop height, discharge, and vertex angle. It was observed that there exists a good agreement between the measured values and the values obtained using empirical equations, support vector machine (Polynomial and rbf) models, and the linear regression techniques. The test results demonstrated that the SVM based (Poly & rbf) model also provided acceptable prediction of the measured values with reasonable accuracy along with empirical equations and linear regression techniques in modelling the air entrainment rate and the aeration efficiency of a free over-fall jets issuing from triangular sharp crested weir. Further sensitivity analysis has also been performed to study the impact of input parameter on the output in terms of air entrainment rate and aeration efficiency. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=air%20entrainment%20rate" title="air entrainment rate">air entrainment rate</a>, <a href="https://publications.waset.org/abstracts/search?q=dissolved%20oxygen" title=" dissolved oxygen"> dissolved oxygen</a>, <a href="https://publications.waset.org/abstracts/search?q=weir" title=" weir"> weir</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM" title=" SVM"> SVM</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</a> </p> <a href="https://publications.waset.org/abstracts/3752/modeling-aeration-of-sharp-crested-weirs-by-using-support-vector-machines" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3752.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">436</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">10516</span> Machine Vision System for Measuring the Quality of Bulk Sun-dried Organic Raisins</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Navab%20Karimi">Navab Karimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Tohid%20Alizadeh"> Tohid Alizadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An intelligent vision-based system was designed to measure the quality and purity of raisins. A machine vision setup was utilized to capture the images of bulk raisins in ranges of 5-50% mixed pure-impure berries. The textural features of bulk raisins were extracted using Grey-level Histograms, Co-occurrence Matrix, and Local Binary Pattern (a total of 108 features). Genetic Algorithm and neural network regression were used for selecting and ranking the best features (21 features). As a result, the GLCM features set was found to have the highest accuracy (92.4%) among the other sets. Followingly, multiple feature combinations of the previous stage were fed into the second regression (linear regression) to increase accuracy, wherein a combination of 16 features was found to be the optimum. Finally, a Support Vector Machine (SVM) classifier was used to differentiate the mixtures, producing the best efficiency and accuracy of 96.2% and 97.35%, respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sun-dried%20organic%20raisin" title="sun-dried organic raisin">sun-dried organic raisin</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=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=ann%20regression" title=" ann regression"> ann regression</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20regression" title=" linear regression"> linear regression</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=south%20azerbaijan." title=" south azerbaijan."> south azerbaijan.</a> </p> <a href="https://publications.waset.org/abstracts/172004/machine-vision-system-for-measuring-the-quality-of-bulk-sun-dried-organic-raisins" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/172004.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">73</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">10515</span> Image Compression Based on Regression SVM and Biorthogonal Wavelets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zikiou%20Nadia">Zikiou Nadia</a>, <a href="https://publications.waset.org/abstracts/search?q=Lahdir%20Mourad"> Lahdir Mourad</a>, <a href="https://publications.waset.org/abstracts/search?q=Ameur%20Soltane"> Ameur Soltane</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose an effective method for image compression based on SVM Regression (SVR), with three different kernels, and biorthogonal 2D Discrete Wavelet Transform. SVM regression could learn dependency from training data and compressed using fewer training points (support vectors) to represent the original data and eliminate the redundancy. Biorthogonal wavelet has been used to transform the image and the coefficients acquired are then trained with different kernels SVM (Gaussian, Polynomial, and Linear). Run-length and Arithmetic coders are used to encode the support vectors and its corresponding weights, obtained from the SVM regression. The peak signal noise ratio (PSNR) and their compression ratios of several test images, compressed with our algorithm, with different kernels are presented. Compared with other kernels, Gaussian kernel achieves better image quality. Experimental results show that the compression performance of our method gains much improvement. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20compression" title="image compression">image compression</a>, <a href="https://publications.waset.org/abstracts/search?q=2D%20discrete%20wavelet%20transform%20%28DWT-2D%29" title=" 2D discrete wavelet transform (DWT-2D)"> 2D discrete wavelet transform (DWT-2D)</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20regression%20%28SVR%29" title=" support vector regression (SVR)"> support vector regression (SVR)</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM%20Kernels" title=" SVM Kernels"> SVM Kernels</a>, <a href="https://publications.waset.org/abstracts/search?q=run-length" title=" run-length"> run-length</a>, <a href="https://publications.waset.org/abstracts/search?q=arithmetic%20coding" title=" arithmetic coding"> arithmetic coding</a> </p> <a href="https://publications.waset.org/abstracts/17954/image-compression-based-on-regression-svm-and-biorthogonal-wavelets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17954.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">382</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">10514</span> Competition between Regression Technique and Statistical Learning Models for Predicting Credit Risk Management</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chokri%20Slim">Chokri Slim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The objective of this research is attempting to respond to this question: Is there a significant difference between the regression model and statistical learning models in predicting credit risk management? A Multiple Linear Regression (MLR) model was compared with neural networks including Multi-Layer Perceptron (MLP), and a Support vector regression (SVR). The population of this study includes 50 listed Banks in Tunis Stock Exchange (TSE) market from 2000 to 2016. Firstly, we show the factors that have significant effect on the quality of loan portfolios of banks in Tunisia. Secondly, it attempts to establish that the systematic use of objective techniques and methods designed to apprehend and assess risk when considering applications for granting credit, has a positive effect on the quality of loan portfolios of banks and their future collectability. Finally, we will try to show that the bank governance has an impact on the choice of methods and techniques for analyzing and measuring the risks inherent in the banking business, including the risk of non-repayment. The results of empirical tests confirm our claims. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=credit%20risk%20management" title="credit risk management">credit risk management</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20linear%20regression" title=" multiple linear regression"> multiple linear regression</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20components%20analysis" title=" principal components analysis"> principal components analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title=" artificial neural networks"> artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machines" title=" support vector machines"> support vector machines</a> </p> <a href="https://publications.waset.org/abstracts/103512/competition-between-regression-technique-and-statistical-learning-models-for-predicting-credit-risk-management" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103512.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">150</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10513</span> Annual Water Level Simulation Using Support Vector Machine</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maryam%20Khalilzadeh%20Poshtegal">Maryam Khalilzadeh Poshtegal</a>, <a href="https://publications.waset.org/abstracts/search?q=Seyed%20Ahmad%20Mirbagheri"> Seyed Ahmad Mirbagheri</a>, <a href="https://publications.waset.org/abstracts/search?q=Mojtaba%20Noury"> Mojtaba Noury</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, by application of the input yearly data of rainfall, temperature and flow to the Urmia Lake, the simulation of water level fluctuation were applied by means of three models. According to the climate change investigation the fluctuation of lakes water level are of high interest. This study investigate data-driven models, support vector machines (SVM), SVM method which is a new regression procedure in water resources are applied to the yearly level data of Lake Urmia that is the biggest and the hyper saline lake in Iran. The evaluated lake levels are found to be in good correlation with the observed values. The results of SVM simulation show better accuracy and implementation. The mean square errors, mean absolute relative errors and determination coefficient statistics are used as comparison criteria. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=simulation" title="simulation">simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20level%20fluctuation" title=" water level fluctuation"> water level fluctuation</a>, <a href="https://publications.waset.org/abstracts/search?q=urmia%20lake" title=" urmia lake"> urmia lake</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a> </p> <a href="https://publications.waset.org/abstracts/44229/annual-water-level-simulation-using-support-vector-machine" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44229.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">367</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">10512</span> An Analysis of Classification of Imbalanced Datasets by Using Synthetic Minority Over-Sampling Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ghada%20A.%20Alfattni">Ghada A. Alfattni </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Analysing unbalanced datasets is one of the challenges that practitioners in machine learning field face. However, many researches have been carried out to determine the effectiveness of the use of the synthetic minority over-sampling technique (SMOTE) to address this issue. The aim of this study was therefore to compare the effectiveness of the SMOTE over different models on unbalanced datasets. Three classification models (Logistic Regression, Support Vector Machine and Nearest Neighbour) were tested with multiple datasets, then the same datasets were oversampled by using SMOTE and applied again to the three models to compare the differences in the performances. Results of experiments show that the highest number of nearest neighbours gives lower values of error rates.  <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=imbalanced%20datasets" title="imbalanced datasets">imbalanced datasets</a>, <a href="https://publications.waset.org/abstracts/search?q=SMOTE" title=" SMOTE"> SMOTE</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=logistic%20regression" title=" logistic regression"> logistic regression</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=nearest%20neighbour" title=" nearest neighbour"> nearest neighbour</a> </p> <a href="https://publications.waset.org/abstracts/50056/an-analysis-of-classification-of-imbalanced-datasets-by-using-synthetic-minority-over-sampling-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50056.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">10511</span> The Predictors of Student Engagement: Instructional Support vs Emotional Support</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tahani%20Salman%20Alangari">Tahani Salman Alangari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Student success can be impacted by internal factors such as their emotional well-being and external factors such as organizational support and instructional support in the classroom. This study is to identify at least one factor that forecasts student engagement. It is a cross-sectional, conducted on 6206 teachers and encompassed three years of data collection and observations of math instruction in approximately 50 schools and 300 classrooms. A multiple linear regression revealed that a model predicting student engagement from emotional support, classroom organization, and instructional support was significant. Four linear regression models were tested using hierarchical regression to examine the effects of independent variables: emotional support was the highest predictor of student engagement while instructional support was the lowest. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=student%20engagement" title="student engagement">student engagement</a>, <a href="https://publications.waset.org/abstracts/search?q=emotional%20support" title=" emotional support"> emotional support</a>, <a href="https://publications.waset.org/abstracts/search?q=organizational%20support" title=" organizational support"> organizational support</a>, <a href="https://publications.waset.org/abstracts/search?q=instructional%20support" title=" instructional support"> instructional support</a>, <a href="https://publications.waset.org/abstracts/search?q=well-being" title=" well-being"> well-being</a> </p> <a href="https://publications.waset.org/abstracts/170199/the-predictors-of-student-engagement-instructional-support-vs-emotional-support" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170199.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">81</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">10510</span> Modelling the Indonesian Goverment Securities Yield Curve Using Nelson-Siegel-Svensson and Support Vector Regression </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jamilatuzzahro">Jamilatuzzahro</a>, <a href="https://publications.waset.org/abstracts/search?q=Rezzy%20Eko%20Caraka"> Rezzy Eko Caraka</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The yield curve is the plot of the yield to maturity of zero-coupon bonds against maturity. In practice, the yield curve is not observed but must be extracted from observed bond prices for a set of (usually) incomplete maturities. There exist many methodologies and theory to analyze of yield curve. We use two methods (the Nelson-Siegel Method, the Svensson Method, and the SVR method) in order to construct and compare our zero-coupon yield curves. The objectives of this research were: (i) to study the adequacy of NSS model and SVR to Indonesian government bonds data, (ii) to choose the best optimization or estimation method for NSS model and SVR. To obtain that objective, this research was done by the following steps: data preparation, cleaning or filtering data, modeling, and model evaluation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20regression" title="support vector regression">support vector regression</a>, <a href="https://publications.waset.org/abstracts/search?q=Nelson-Siegel-Svensson" title=" Nelson-Siegel-Svensson"> Nelson-Siegel-Svensson</a>, <a href="https://publications.waset.org/abstracts/search?q=yield%20curve" title=" yield curve"> yield curve</a>, <a href="https://publications.waset.org/abstracts/search?q=Indonesian%20government" title=" Indonesian government"> Indonesian government</a> </p> <a href="https://publications.waset.org/abstracts/63081/modelling-the-indonesian-goverment-securities-yield-curve-using-nelson-siegel-svensson-and-support-vector-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63081.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">10509</span> Modelling Conceptual Quantities Using Support Vector Machines</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ka%20C.%20Lam">Ka C. Lam</a>, <a href="https://publications.waset.org/abstracts/search?q=Oluwafunmibi%20S.%20Idowu"> Oluwafunmibi S. Idowu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Uncertainty in cost is a major factor affecting performance of construction projects. To our knowledge, several conceptual cost models have been developed with varying degrees of accuracy. Incorporating conceptual quantities into conceptual cost models could improve the accuracy of early predesign cost estimates. Hence, the development of quantity models for estimating conceptual quantities of framed reinforced concrete structures using supervised machine learning is the aim of the current research. Using measured quantities of structural elements and design variables such as live loads and soil bearing pressures, response and predictor variables were defined and used for constructing conceptual quantities models. Twenty-four models were developed for comparison using a combination of non-parametric support vector regression, linear regression, and bootstrap resampling techniques. R programming language was used for data analysis and model implementation. Gross soil bearing pressure and gross floor loading were discovered to have a major influence on the quantities of concrete and reinforcement used for foundations. Building footprint and gross floor loading had a similar influence on beams and slabs. Future research could explore the modelling of other conceptual quantities for walls, finishes, and services using machine learning techniques. Estimation of conceptual quantities would assist construction planners in early resource planning and enable detailed performance evaluation of early cost predictions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bootstrapping" title="bootstrapping">bootstrapping</a>, <a href="https://publications.waset.org/abstracts/search?q=conceptual%20quantities" title=" conceptual quantities"> conceptual quantities</a>, <a href="https://publications.waset.org/abstracts/search?q=modelling" title=" modelling"> modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=reinforced%20concrete" title=" reinforced concrete"> reinforced concrete</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20regression" title=" support vector regression"> support vector regression</a> </p> <a href="https://publications.waset.org/abstracts/75356/modelling-conceptual-quantities-using-support-vector-machines" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75356.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">206</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">10508</span> New Approach for Load Modeling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Slim%20Chokri">Slim Chokri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Load forecasting is one of the central functions in power systems operations. Electricity cannot be stored, which means that for electric utility, the estimate of the future demand is necessary in managing the production and purchasing in an economically reasonable way. A majority of the recently reported approaches are based on neural network. The attraction of the methods lies in the assumption that neural networks are able to learn properties of the load. However, the development of the methods is not finished, and the lack of comparative results on different model variations is a problem. This paper presents a new approach in order to predict the Tunisia daily peak load. The proposed method employs a computational intelligence scheme based on the Fuzzy neural network (FNN) and support vector regression (SVR). Experimental results obtained indicate that our proposed FNN-SVR technique gives significantly good prediction accuracy compared to some classical techniques. <p class="card-text"><strong>Keywords:</strong> <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=load%20forecasting" title=" load forecasting"> load forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20inference" title=" fuzzy inference"> fuzzy inference</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=fuzzy%20modeling%20and%20rule%20extraction" title=" fuzzy modeling and rule extraction"> fuzzy modeling and rule extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20regression" title=" support vector regression"> support vector regression</a> </p> <a href="https://publications.waset.org/abstracts/21065/new-approach-for-load-modeling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21065.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">435</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">10507</span> Heart Ailment Prediction Using Machine Learning Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abhigyan%20Hedau">Abhigyan Hedau</a>, <a href="https://publications.waset.org/abstracts/search?q=Priya%20Shelke"> Priya Shelke</a>, <a href="https://publications.waset.org/abstracts/search?q=Riddhi%20Mirajkar"> Riddhi Mirajkar</a>, <a href="https://publications.waset.org/abstracts/search?q=Shreyash%20Chaple"> Shreyash Chaple</a>, <a href="https://publications.waset.org/abstracts/search?q=Mrunali%20Gadekar"> Mrunali Gadekar</a>, <a href="https://publications.waset.org/abstracts/search?q=Himanshu%20Akula"> Himanshu Akula</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The heart is the coordinating centre of the major endocrine glandular structure of the body, which produces hormones that profoundly affect the operations of the body, and diagnosing cardiovascular disease is a difficult but critical task. By extracting knowledge and information about the disease from patient data, data mining is a more practical technique to help doctors detect disorders. We use a variety of machine learning methods here, including logistic regression and support vector classifiers (SVC), K-nearest neighbours Classifiers (KNN), Decision Tree Classifiers, Random Forest classifiers and Gradient Boosting classifiers. These algorithms are applied to patient data containing 13 different factors to build a system that predicts heart disease in less time with more accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression" title="logistic regression">logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20classifier" title=" support vector classifier"> support vector classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=k-nearest%20neighbour" title=" k-nearest neighbour"> k-nearest neighbour</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest%20and%20gradient%20boosting" title=" random forest and gradient boosting"> random forest and gradient boosting</a> </p> <a href="https://publications.waset.org/abstracts/184353/heart-ailment-prediction-using-machine-learning-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184353.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">51</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">10506</span> Adaptive Neuro Fuzzy Inference System Model Based on Support Vector Regression for Stock Time Series Forecasting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anita%20Setianingrum">Anita Setianingrum</a>, <a href="https://publications.waset.org/abstracts/search?q=Oki%20S.%20Jaya"> Oki S. Jaya</a>, <a href="https://publications.waset.org/abstracts/search?q=Zuherman%20Rustam"> Zuherman Rustam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Forecasting stock price is a challenging task due to the complex time series of the data. The complexity arises from many variables that affect the stock market. Many time series models have been proposed before, but those previous models still have some problems: 1) put the subjectivity of choosing the technical indicators, and 2) rely upon some assumptions about the variables, so it is limited to be applied to all datasets. Therefore, this paper studied a novel Adaptive Neuro-Fuzzy Inference System (ANFIS) time series model based on Support Vector Regression (SVR) for forecasting the stock market. In order to evaluate the performance of proposed models, stock market transaction data of TAIEX and HIS from January to December 2015 is collected as experimental datasets. As a result, the method has outperformed its counterparts in terms of accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ANFIS" title="ANFIS">ANFIS</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20time%20series" title=" fuzzy time series"> fuzzy time series</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20forecasting" title=" stock forecasting"> stock forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=SVR" title=" SVR"> SVR</a> </p> <a href="https://publications.waset.org/abstracts/62703/adaptive-neuro-fuzzy-inference-system-model-based-on-support-vector-regression-for-stock-time-series-forecasting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62703.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">247</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">10505</span> Life Prediction Method of Lithium-Ion Battery Based on Grey Support Vector Machines</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiaogang%20Li">Xiaogang Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Jieqiong%20Miao"> Jieqiong Miao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As for the problem of the grey forecasting model prediction accuracy is low, an improved grey prediction model is put forward. Firstly, use trigonometric function transform the original data sequence in order to improve the smoothness of data , this model called SGM( smoothness of grey prediction model), then combine the improved grey model with support vector machine , and put forward the grey support vector machine model (SGM - SVM).Before the establishment of the model, we use trigonometric functions and accumulation generation operation preprocessing data in order to enhance the smoothness of the data and weaken the randomness of the data, then use support vector machine (SVM) to establish a prediction model for pre-processed data and select model parameters using genetic algorithms to obtain the optimum value of the global search. Finally, restore data through the "regressive generate" operation to get forecasting data. In order to prove that the SGM-SVM model is superior to other models, we select the battery life data from calce. The presented model is used to predict life of battery and the predicted result was compared with that of grey model and support vector machines．For a more intuitive comparison of the three models, this paper presents root mean square error of this three different models .The results show that the effect of grey support vector machine (SGM-SVM) to predict life is optimal, and the root mean square error is only 3.18%. Keywords: grey forecasting model, trigonometric function, support vector machine, genetic algorithms, root mean square error <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Grey%20prediction%20model" title="Grey prediction model">Grey prediction model</a>, <a href="https://publications.waset.org/abstracts/search?q=trigonometric%20functions" title=" trigonometric functions"> trigonometric functions</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machines" title=" support vector machines"> support vector machines</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithms" title=" genetic algorithms"> genetic algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=root%20mean%20square%20error" title=" root mean square error"> root mean square error</a> </p> <a href="https://publications.waset.org/abstracts/29370/life-prediction-method-of-lithium-ion-battery-based-on-grey-support-vector-machines" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29370.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">461</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10504</span> Voltage Problem Location Classification Using Performance of Least Squares Support Vector Machine LS-SVM and Learning Vector Quantization LVQ</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Khaled%20Abduesslam">M. Khaled Abduesslam</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Ali"> Mohammed Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Basher%20H.%20Alsdai"> Basher H. Alsdai</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Nizam%20Inayati"> Muhammad Nizam Inayati</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the voltage problem location classification using performance of Least Squares Support Vector Machine (LS-SVM) and Learning Vector Quantization (LVQ) in electrical power system for proper voltage problem location implemented by IEEE 39 bus New-England. The data was collected from the time domain simulation by using Power System Analysis Toolbox (PSAT). Outputs from simulation data such as voltage, phase angle, real power and reactive power were taken as input to estimate voltage stability at particular buses based on Power Transfer Stability Index (PTSI).The simulation data was carried out on the IEEE 39 bus test system by considering load bus increased on the system. To verify of the proposed LS-SVM its performance was compared to Learning Vector Quantization (LVQ). The results showed that LS-SVM is faster and better as compared to LVQ. The results also demonstrated that the LS-SVM was estimated by 0% misclassification whereas LVQ had 7.69% misclassification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=IEEE%2039%20bus" title="IEEE 39 bus">IEEE 39 bus</a>, <a href="https://publications.waset.org/abstracts/search?q=least%20squares%20support%20vector%20machine" title=" least squares support vector machine"> least squares support vector machine</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20vector%20quantization" title=" learning vector quantization"> learning vector quantization</a>, <a href="https://publications.waset.org/abstracts/search?q=voltage%20collapse" title=" voltage collapse"> voltage collapse</a> </p> <a href="https://publications.waset.org/abstracts/11211/voltage-problem-location-classification-using-performance-of-least-squares-support-vector-machine-ls-svm-and-learning-vector-quantization-lvq" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11211.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">441</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">10503</span> A Hierarchical Method for Multi-Class Probabilistic Classification Vector Machines</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20Byrnes">P. Byrnes</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20A.%20DiazDelaO"> F. A. DiazDelaO</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Support Vector Machine (SVM) has become widely recognised as one of the leading algorithms in machine learning for both regression and binary classification. It expresses predictions in terms of a linear combination of kernel functions, referred to as support vectors. Despite its popularity amongst practitioners, SVM has some limitations, with the most significant being the generation of point prediction as opposed to predictive distributions. Stemming from this issue, a probabilistic model namely, Probabilistic Classification Vector Machines (PCVM), has been proposed which respects the original functional form of SVM whilst also providing a predictive distribution. As physical system designs become more complex, an increasing number of classification tasks involving industrial applications consist of more than two classes. Consequently, this research proposes a framework which allows for the extension of PCVM to a multi class setting. Additionally, the original PCVM framework relies on the use of type II maximum likelihood to provide estimates for both the kernel hyperparameters and model evidence. In a high dimensional multi class setting, however, this approach has been shown to be ineffective due to bad scaling as the number of classes increases. Accordingly, we propose the application of Markov Chain Monte Carlo (MCMC) based methods to provide a posterior distribution over both parameters and hyperparameters. The proposed framework will be validated against current multi class classifiers through synthetic and real life implementations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=probabilistic%20classification%20vector%20machines" title="probabilistic classification vector machines">probabilistic classification vector machines</a>, <a href="https://publications.waset.org/abstracts/search?q=multi%20class%20classification" title=" multi class classification"> multi class classification</a>, <a href="https://publications.waset.org/abstracts/search?q=MCMC" title=" MCMC"> MCMC</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machines" title=" support vector machines"> support vector machines</a> </p> <a href="https://publications.waset.org/abstracts/77928/a-hierarchical-method-for-multi-class-probabilistic-classification-vector-machines" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77928.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">221</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">10502</span> A Machine Learning Approach for Earthquake Prediction in Various Zones Based on Solar Activity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Viacheslav%20Shkuratskyy">Viacheslav Shkuratskyy</a>, <a href="https://publications.waset.org/abstracts/search?q=Aminu%20Bello%20Usman"> Aminu Bello Usman</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20O%E2%80%99Dea"> Michael O’Dea</a>, <a href="https://publications.waset.org/abstracts/search?q=Saifur%20Rahman%20Sabuj"> Saifur Rahman Sabuj</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper examines relationships between solar activity and earthquakes; it applied machine learning techniques: K-nearest neighbour, support vector regression, random forest regression, and long short-term memory network. Data from the SILSO World Data Center, the NOAA National Center, the GOES satellite, NASA OMNIWeb, and the United States Geological Survey were used for the experiment. The 23rd and 24th solar cycles, daily sunspot number, solar wind velocity, proton density, and proton temperature were all included in the dataset. The study also examined sunspots, solar wind, and solar flares, which all reflect solar activity and earthquake frequency distribution by magnitude and depth. The findings showed that the long short-term memory network model predicts earthquakes more correctly than the other models applied in the study, and solar activity is more likely to affect earthquakes of lower magnitude and shallow depth than earthquakes of magnitude 5.5 or larger with intermediate depth and deep depth. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=k-nearest%20neighbour" title="k-nearest neighbour">k-nearest neighbour</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20regression" title=" support vector regression"> support vector regression</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest%20regression" title=" random forest regression"> random forest regression</a>, <a href="https://publications.waset.org/abstracts/search?q=long%20short-term%20memory%20network" title=" long short-term memory network"> long short-term memory network</a>, <a href="https://publications.waset.org/abstracts/search?q=earthquakes" title=" earthquakes"> earthquakes</a>, <a href="https://publications.waset.org/abstracts/search?q=solar%20activity" title=" solar activity"> solar activity</a>, <a href="https://publications.waset.org/abstracts/search?q=sunspot%20number" title=" sunspot number"> sunspot number</a>, <a href="https://publications.waset.org/abstracts/search?q=solar%20wind" title=" solar wind"> solar wind</a>, <a href="https://publications.waset.org/abstracts/search?q=solar%20flares" title=" solar flares"> solar flares</a> </p> <a href="https://publications.waset.org/abstracts/170933/a-machine-learning-approach-for-earthquake-prediction-in-various-zones-based-on-solar-activity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170933.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">73</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">10501</span> A Medical Resource Forecasting Model for Emergency Room Patients with Acute Hepatitis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20J.%20Kuo">R. J. Kuo</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20C.%20Cheng"> W. C. Cheng</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20C.%20Lien"> W. C. Lien</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20J.%20Yang"> T. J. Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Taiwan is a hyper endemic area for the Hepatitis B virus (HBV). The estimated total number of HBsAg carriers in the general population who are more than 20 years old is more than 3 million. Therefore, a case record review is conducted from January 2003 to June 2007 for all patients with a diagnosis of acute hepatitis who were admitted to the Emergency Department (ED) of a well-known teaching hospital. The cost for the use of medical resources is defined as the total medical fee. In this study, principal component analysis (PCA) is firstly employed to reduce the number of dimensions. Support vector regression (SVR) and artificial neural network (ANN) are then used to develop the forecasting model. A total of 117 patients meet the inclusion criteria. 61% patients involved in this study are hepatitis B related. The computational result shows that the proposed PCA-SVR model has superior performance than other compared algorithms. In conclusion, the Child-Pugh score and echogram can both be used to predict the cost of medical resources for patients with acute hepatitis in the ED. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=acute%20hepatitis" title="acute hepatitis">acute hepatitis</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20resource%20cost" title=" medical resource cost"> medical resource cost</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title=" artificial neural network"> artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20regression" title=" support vector regression"> support vector regression</a> </p> <a href="https://publications.waset.org/abstracts/23255/a-medical-resource-forecasting-model-for-emergency-room-patients-with-acute-hepatitis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23255.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">10500</span> Instance Selection for MI-Support Vector Machines</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amy%20M.%20Kwon">Amy M. Kwon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Support vector machine (SVM) is a well-known algorithm in machine learning due to its superior performance, and it also functions well in multiple-instance (MI) problems. Our study proposes a schematic algorithm to select instances based on Hausdorff distance, which can be adapted to SVMs as input vectors under the MI setting. Based on experiments on five benchmark datasets, our strategy for adapting representation outperformed in comparison with original approach. In addition, task execution times (TETs) were reduced by more than 80% based on MissSVM. Hence, it is noteworthy to consider this representation adaptation to SVMs under MI-setting. <p class="card-text"><strong>Keywords:</strong> <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=Margin" title=" Margin"> Margin</a>, <a href="https://publications.waset.org/abstracts/search?q=Hausdorff%20distance" title=" Hausdorff distance"> Hausdorff distance</a>, <a href="https://publications.waset.org/abstracts/search?q=representation%20selection" title=" representation selection"> representation selection</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple-instance%20learning" title=" multiple-instance learning"> multiple-instance learning</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/186528/instance-selection-for-mi-support-vector-machines" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186528.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">34</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">10499</span> Regression Model Evaluation on Depth Camera Data for Gaze Estimation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=James%20Purnama">James Purnama</a>, <a href="https://publications.waset.org/abstracts/search?q=Riri%20Fitri%20Sari"> Riri Fitri Sari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We investigate the machine learning algorithm selection problem in the term of a depth image based eye gaze estimation, with respect to its essential difficulty in reducing the number of required training samples and duration time of training. Statistics based prediction accuracy are increasingly used to assess and evaluate prediction or estimation in gaze estimation. This article evaluates Root Mean Squared Error (RMSE) and R-Squared statistical analysis to assess machine learning methods on depth camera data for gaze estimation. There are 4 machines learning methods have been evaluated: Random Forest Regression, Regression Tree, Support Vector Machine (SVM), and Linear Regression. The experiment results show that the Random Forest Regression has the lowest RMSE and the highest R-Squared, which means that it is the best among other methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gaze%20estimation" title="gaze estimation">gaze estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=gaze%20tracking" title=" gaze tracking"> gaze tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=eye%20tracking" title=" eye tracking"> eye tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=kinect" title=" kinect"> kinect</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20model" title=" regression model"> regression model</a>, <a href="https://publications.waset.org/abstracts/search?q=orange%20python" title=" orange python"> orange python</a> </p> <a href="https://publications.waset.org/abstracts/17938/regression-model-evaluation-on-depth-camera-data-for-gaze-estimation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17938.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">538</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">10498</span> Road Accidents Bigdata Mining and Visualization Using Support Vector Machines</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Usha%20Lokala">Usha Lokala</a>, <a href="https://publications.waset.org/abstracts/search?q=Srinivas%20Nowduri"> Srinivas Nowduri</a>, <a href="https://publications.waset.org/abstracts/search?q=Prabhakar%20K.%20Sharma"> Prabhakar K. Sharma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Useful information has been extracted from the road accident data in United Kingdom (UK), using data analytics method, for avoiding possible accidents in rural and urban areas. This analysis make use of several methodologies such as data integration, support vector machines (SVM), correlation machines and multinomial goodness. The entire datasets have been imported from the traffic department of UK with due permission. The information extracted from these huge datasets forms a basis for several predictions, which in turn avoid unnecessary memory lapses. Since data is expected to grow continuously over a period of time, this work primarily proposes a new framework model which can be trained and adapt itself to new data and make accurate predictions. This work also throws some light on use of SVM’s methodology for text classifiers from the obtained traffic data. Finally, it emphasizes the uniqueness and adaptability of SVMs methodology appropriate for this kind of research work. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20mechanism%20%28SVM%29" title="support vector mechanism (SVM)">support vector mechanism (SVM)</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%20%28ML%29" title=" machine learning (ML)"> machine learning (ML)</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machines%20%28SVM%29" title=" support vector machines (SVM)"> support vector machines (SVM)</a>, <a href="https://publications.waset.org/abstracts/search?q=department%20of%20transportation%20%28DFT%29" title=" department of transportation (DFT)"> department of transportation (DFT)</a> </p> <a href="https://publications.waset.org/abstracts/70645/road-accidents-bigdata-mining-and-visualization-using-support-vector-machines" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70645.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">274</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">10497</span> Pyramid Binary Pattern for Age Invariant Face Verification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saroj%20Bijarnia">Saroj Bijarnia</a>, <a href="https://publications.waset.org/abstracts/search?q=Preety%20Singh"> Preety Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose a simple and effective biometrics system based on face verification across aging using a new variant of texture feature, Pyramid Binary Pattern. This employs Local Binary Pattern along with its hierarchical information. Dimension reduction of generated texture feature vector is done using Principal Component Analysis. Support Vector Machine is used for classification. Our proposed method achieves an accuracy of 92:24% and can be used in an automated age-invariant face verification system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biometrics" title="biometrics">biometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=age%20invariant" title=" age invariant"> age invariant</a>, <a href="https://publications.waset.org/abstracts/search?q=verification" title=" verification"> verification</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a> </p> <a href="https://publications.waset.org/abstracts/64435/pyramid-binary-pattern-for-age-invariant-face-verification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/64435.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">353</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10496</span> Application of Support Vector Machines in Fault Detection and Diagnosis of Power Transmission Lines </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=I.%20A.%20Farhat">I. A. Farhat</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Bin%20Hasan"> M. Bin Hasan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A developed approach for the protection of power transmission lines using Support Vector Machines (SVM) technique is presented. In this paper, the SVM technique is utilized for the classification and isolation of faults in power transmission lines. Accurate fault classification and location results are obtained for all possible types of short circuit faults. As in distance protection, the approach utilizes the voltage and current post-fault samples as inputs. The main advantage of the method introduced here is that the method could easily be extended to any power transmission line. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fault%20detection" title="fault detection">fault detection</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=diagnosis" title=" diagnosis"> diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20transmission%20line%20protection" title=" power transmission line protection"> power transmission line protection</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machines%20%28SVM%29" title=" support vector machines (SVM)"> support vector machines (SVM)</a> </p> <a href="https://publications.waset.org/abstracts/13818/application-of-support-vector-machines-in-fault-detection-and-diagnosis-of-power-transmission-lines" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13818.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">559</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=support%20vector%20regression&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=support%20vector%20regression&page=3">3</a></li> <li class="page-item"><a class="page-link" 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