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vector machines application</title> <meta name="description" content="Search results for: support vector machines application"> <meta name="keywords" content="support vector machines application"> <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 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method="get" 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 machines application"> <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> 15634</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: support vector machines application</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">15634</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">15633</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> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">15632</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">15631</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">15630</span> Application of Support Vector Machines in Forecasting Non-Residential</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wiwat%20Kittinaraporn">Wiwat Kittinaraporn</a>, <a href="https://publications.waset.org/abstracts/search?q=Napat%20Harnpornchai"> Napat Harnpornchai</a>, <a href="https://publications.waset.org/abstracts/search?q=Sutja%20Boonyachut"> Sutja Boonyachut</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with the application of a novel neural network technique, so-called Support Vector Machine (SVM). The objective of this study is to explore the variable and parameter of forecasting factors in the construction industry to build up forecasting model for construction quantity in Thailand. The scope of the research is to study the non-residential construction quantity in Thailand. There are 44 sets of yearly data available, ranging from 1965 to 2009. The correlation between economic indicators and construction demand with the lag of one year was developed by Apichat Buakla. The selected variables are used to develop SVM models to forecast the non-residential construction quantity in Thailand. The parameters are selected by using ten-fold cross-validation method. The results are indicated in term of Mean Absolute Percentage Error (MAPE). The MAPE value for the non-residential construction quantity predicted by Epsilon-SVR in corporation with Radial Basis Function (RBF) of kernel function type is 5.90. Analysis of the experimental results show that the support vector machine modelling technique can be applied to forecast construction quantity time series which is useful for decision planning and management purpose. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=forecasting" title="forecasting">forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=non-residential" title=" non-residential"> non-residential</a>, <a href="https://publications.waset.org/abstracts/search?q=construction" title=" construction"> construction</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/16280/application-of-support-vector-machines-in-forecasting-non-residential" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16280.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">434</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">15629</span> Using New Machine Algorithms to Classify Iranian Musical Instruments According to Temporal, Spectral and Coefficient Features</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ronak%20Khosravi">Ronak Khosravi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahmood%20Abbasi%20Layegh"> Mahmood Abbasi Layegh</a>, <a href="https://publications.waset.org/abstracts/search?q=Siamak%20Haghipour"> Siamak Haghipour</a>, <a href="https://publications.waset.org/abstracts/search?q=Avin%20Esmaili"> Avin Esmaili</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a study on classification of musical woodwind instruments using a small set of features selected from a broad range of extracted ones by the sequential forward selection method was carried out. Firstly, we extract 42 features for each record in the music database of 402 sound files belonging to five different groups of Flutes (end blown and internal duct), Single –reed, Double –reed (exposed and capped), Triple reed and Quadruple reed. Then, the sequential forward selection method is adopted to choose the best feature set in order to achieve very high classification accuracy. Two different classification techniques of support vector machines and relevance vector machines have been tested out and an accuracy of up to 96% can be achieved by using 21 time, frequency and coefficient features and relevance vector machine with the Gaussian kernel function. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=coefficient%20features" title="coefficient features">coefficient features</a>, <a href="https://publications.waset.org/abstracts/search?q=relevance%20vector%20machines" title=" relevance vector machines"> relevance vector machines</a>, <a href="https://publications.waset.org/abstracts/search?q=spectral%20features" title=" spectral features"> spectral features</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=temporal%20features" title=" temporal features"> temporal features</a> </p> <a href="https://publications.waset.org/abstracts/54321/using-new-machine-algorithms-to-classify-iranian-musical-instruments-according-to-temporal-spectral-and-coefficient-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54321.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">321</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">15628</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">15627</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">15626</span> Hindi Speech Synthesis by Concatenation of Recognized Hand Written Devnagri Script Using Support Vector Machines Classifier</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saurabh%20Farkya">Saurabh Farkya</a>, <a href="https://publications.waset.org/abstracts/search?q=Govinda%20Surampudi"> Govinda Surampudi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Optical Character Recognition is one of the current major research areas. This paper is focussed on recognition of Devanagari script and its sound generation. This Paper consists of two parts. First, Optical Character Recognition of Devnagari handwritten Script. Second, speech synthesis of the recognized text. This paper shows an implementation of support vector machines for the purpose of Devnagari Script recognition. The Support Vector Machines was trained with Multi Domain features; Transform Domain and Spatial Domain or Structural Domain feature. Transform Domain includes the wavelet feature of the character. Structural Domain consists of Distance Profile feature and Gradient feature. The Segmentation of the text document has been done in 3 levels-Line Segmentation, Word Segmentation, and Character Segmentation. The pre-processing of the characters has been done with the help of various Morphological operations-Otsu's Algorithm, Erosion, Dilation, Filtration and Thinning techniques. The Algorithm was tested on the self-prepared database, a collection of various handwriting. Further, Unicode was used to convert recognized Devnagari text into understandable computer document. The document so obtained is an array of codes which was used to generate digitized text and to synthesize Hindi speech. Phonemes from the self-prepared database were used to generate the speech of the scanned document using concatenation technique. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Character%20Recognition%20%28OCR%29" title="Character Recognition (OCR)">Character Recognition (OCR)</a>, <a href="https://publications.waset.org/abstracts/search?q=Text%20to%20Speech%20%28TTS%29" title=" Text to Speech (TTS)"> Text to Speech (TTS)</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=Library%20of%20Support%20Vector%20Machines%20%28LIBSVM%29" title=" Library of Support Vector Machines (LIBSVM)"> Library of Support Vector Machines (LIBSVM)</a> </p> <a href="https://publications.waset.org/abstracts/19232/hindi-speech-synthesis-by-concatenation-of-recognized-hand-written-devnagri-script-using-support-vector-machines-classifier" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19232.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">499</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">15625</span> Anomaly Detection with ANN and SVM for Telemedicine Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Edward%20Guill%C3%A9n">Edward Guillén</a>, <a href="https://publications.waset.org/abstracts/search?q=Jeisson%20S%C3%A1nchez"> Jeisson Sánchez</a>, <a href="https://publications.waset.org/abstracts/search?q=Carlos%20Omar%20Ramos"> Carlos Omar Ramos</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, a wide variety of applications are developed with Support Vector Machines -SVM- methods and Artificial Neural Networks -ANN-. In general, these methods depend on intrusion knowledge databases such as KDD99, ISCX, and CAIDA among others. New classes of detectors are generated by machine learning techniques, trained and tested over network databases. Thereafter, detectors are employed to detect anomalies in network communication scenarios according to user’s connections behavior. The first detector based on training dataset is deployed in different real-world networks with mobile and non-mobile devices to analyze the performance and accuracy over static detection. The vulnerabilities are based on previous work in telemedicine apps that were developed on the research group. This paper presents the differences on detections results between some network scenarios by applying traditional detectors deployed with artificial neural networks and support vector machines. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=anomaly%20detection" title="anomaly detection">anomaly detection</a>, <a href="https://publications.waset.org/abstracts/search?q=back-propagation%20neural%20networks" title=" back-propagation neural networks"> back-propagation neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20intrusion%20detection%20systems" title=" network intrusion detection systems"> network intrusion detection systems</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/42120/anomaly-detection-with-ann-and-svm-for-telemedicine-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42120.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">357</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">15624</span> The Use Support Vector Machine and Back Propagation Neural Network for Prediction of Daily Tidal Levels Along The Jeddah Coast, Saudi Arabia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=E.%20A.%20Mlybari">E. A. Mlybari</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20S.%20Elbisy"> M. S. Elbisy</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20H.%20Alshahri"> A. H. Alshahri</a>, <a href="https://publications.waset.org/abstracts/search?q=O.%20M.%20Albarakati"> O. M. Albarakati</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sea level rise threatens to increase the impact of future storms and hurricanes on coastal communities. Accurate sea level change prediction and supplement is an important task in determining constructions and human activities in coastal and oceanic areas. In this study, support vector machines (SVM) is proposed to predict daily tidal levels along the Jeddah Coast, Saudi Arabia. The optimal parameter values of kernel function are determined using a genetic algorithm. The SVM results are compared with the field data and with back propagation (BP). Among the models, the SVM is superior to BPNN and has better generalization performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=tides" title="tides">tides</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</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%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=back-propagation%20neural%20network" title=" back-propagation neural network"> back-propagation neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=risk" title=" risk"> risk</a>, <a href="https://publications.waset.org/abstracts/search?q=hazards" title=" hazards"> hazards</a> </p> <a href="https://publications.waset.org/abstracts/4437/the-use-support-vector-machine-and-back-propagation-neural-network-for-prediction-of-daily-tidal-levels-along-the-jeddah-coast-saudi-arabia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4437.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">468</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">15623</span> A Hybrid System for Boreholes Soil Sample</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Ulvi%20Uzer">Ali Ulvi Uzer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data reduction is an important topic in the field of pattern recognition applications. The basic concept is the reduction of multitudinous amounts of data down to the meaningful parts. The Principal Component Analysis (PCA) method is frequently used for data reduction. The Support Vector Machine (SVM) method is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data, the algorithm outputs an optimal hyperplane which categorizes new examples. This study offers a hybrid approach that uses the PCA for data reduction and Support Vector Machines (SVM) for classification. In order to detect the accuracy of the suggested system, two boreholes taken from the soil sample was used. The classification accuracies for this dataset were obtained through using ten-fold cross-validation method. As the results suggest, this system, which is performed through size reduction, is a feasible system for faster recognition of dataset so our study result appears to be very promising. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title="feature selection">feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=sequential%20forward%20selection" title=" sequential forward selection"> sequential forward selection</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=soil%20sample" title=" soil sample"> soil sample</a> </p> <a href="https://publications.waset.org/abstracts/11096/a-hybrid-system-for-boreholes-soil-sample" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11096.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">455</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">15622</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">15621</span> Change Detection Analysis on Support Vector Machine Classifier of Land Use and Land Cover Changes: Case Study on Yangon</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khin%20Mar%20Yee">Khin Mar Yee</a>, <a href="https://publications.waset.org/abstracts/search?q=Mu%20Mu%20Than"> Mu Mu Than</a>, <a href="https://publications.waset.org/abstracts/search?q=Kyi%20Lint"> Kyi Lint</a>, <a href="https://publications.waset.org/abstracts/search?q=Aye%20Aye%20Oo"> Aye Aye Oo</a>, <a href="https://publications.waset.org/abstracts/search?q=Chan%20Mya%20Hmway"> Chan Mya Hmway</a>, <a href="https://publications.waset.org/abstracts/search?q=Khin%20Zar%20Chi%20Winn"> Khin Zar Chi Winn</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The dynamic changes of Land Use and Land Cover (LULC) changes in Yangon have generally resulted the improvement of human welfare and economic development since the last twenty years. Making map of LULC is crucially important for the sustainable development of the environment. However, the exactly data on how environmental factors influence the LULC situation at the various scales because the nature of the natural environment is naturally composed of non-homogeneous surface features, so the features in the satellite data also have the mixed pixels. The main objective of this study is to the calculation of accuracy based on change detection of LULC changes by Support Vector Machines (SVMs). For this research work, the main data was satellite images of 1996, 2006 and 2015. Computing change detection statistics use change detection statistics to compile a detailed tabulation of changes between two classification images and Support Vector Machines (SVMs) process was applied with a soft approach at allocation as well as at a testing stage and to higher accuracy. The results of this paper showed that vegetation and cultivated area were decreased (average total 29 % from 1996 to 2015) because of conversion to the replacing over double of the built up area (average total 30 % from 1996 to 2015). The error matrix and confidence limits led to the validation of the result for LULC mapping. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=land%20use%20and%20land%20cover%20change" title="land use and land cover change">land use and land cover change</a>, <a href="https://publications.waset.org/abstracts/search?q=change%20detection" title=" change detection"> change detection</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</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/105016/change-detection-analysis-on-support-vector-machine-classifier-of-land-use-and-land-cover-changes-case-study-on-yangon" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/105016.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">139</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">15620</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">15619</span> Multiclass Support Vector Machines with Simultaneous Multi-Factors Optimization for Corporate Credit Ratings</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hyunchul%20Ahn">Hyunchul Ahn</a>, <a href="https://publications.waset.org/abstracts/search?q=William%20X.%20S.%20Wong"> William X. S. Wong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Corporate credit rating prediction is one of the most important topics, which has been studied by researchers in the last decade. Over the last decade, researchers are pushing the limit to enhance the exactness of the corporate credit rating prediction model by applying several data-driven tools including statistical and artificial intelligence methods. Among them, multiclass support vector machine (MSVM) has been widely applied due to its good predictability. However, heuristics, for example, parameters of a kernel function, appropriate feature and instance subset, has become the main reason for the critics on MSVM, as they have dictate the MSVM architectural variables. This study presents a hybrid MSVM model that is intended to optimize all the parameter such as feature selection, instance selection, and kernel parameter. Our model adopts genetic algorithm (GA) to simultaneously optimize multiple heterogeneous design factors of MSVM. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=corporate%20credit%20rating%20prediction" title="corporate credit rating prediction">corporate credit rating prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=Feature%20selection" title=" Feature selection"> Feature selection</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=instance%20selection" title=" instance selection"> instance selection</a>, <a href="https://publications.waset.org/abstracts/search?q=multiclass%20support%20vector%20machines" title=" multiclass support vector machines"> multiclass support vector machines</a> </p> <a href="https://publications.waset.org/abstracts/44856/multiclass-support-vector-machines-with-simultaneous-multi-factors-optimization-for-corporate-credit-ratings" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44856.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">294</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">15618</span> Tracking and Classifying Client Interactions with Personal Coaches</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kartik%20Thakore">Kartik Thakore</a>, <a href="https://publications.waset.org/abstracts/search?q=Anna-Roza%20Tamas"> Anna-Roza Tamas</a>, <a href="https://publications.waset.org/abstracts/search?q=Adam%20Cole"> Adam Cole</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The world health organization (WHO) reports that by 2030 more than 23.7 million deaths annually will be caused by Cardiovascular Diseases (CVDs); with a 2008 economic impact of $3.76 T. Metabolic syndrome is a disorder of multiple metabolic risk factors strongly indicated in the development of cardiovascular diseases. Guided lifestyle intervention driven by live coaching has been shown to have a positive impact on metabolic risk factors. Individuals’ path to improved (decreased) metabolic risk factors are driven by personal motivation and personalized messages delivered by coaches and augmented by technology. Using interactions captured between 400 individuals and 3 coaches over a program period of 500 days, a preliminary model was designed. A novel real time event tracking system was created to track and classify clients based on their genetic profile, baseline questionnaires and usage of a mobile application with live coaching sessions. Classification of clients and coaches was done using a support vector machines application build on Apache Spark, Stanford Natural Language Processing Library (SNLPL) and decision-modeling. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=guided%20lifestyle%20intervention" title="guided lifestyle intervention">guided lifestyle intervention</a>, <a href="https://publications.waset.org/abstracts/search?q=metabolic%20risk%20factors" title=" metabolic risk factors"> metabolic risk factors</a>, <a href="https://publications.waset.org/abstracts/search?q=personal%20coaching" title=" personal coaching"> personal coaching</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machines%20application" title=" support vector machines application"> support vector machines application</a>, <a href="https://publications.waset.org/abstracts/search?q=Apache%20Spark" title=" Apache Spark"> Apache Spark</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a> </p> <a href="https://publications.waset.org/abstracts/25701/tracking-and-classifying-client-interactions-with-personal-coaches" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25701.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">433</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">15617</span> Fusion Models for Cyber Threat Defense: Integrating Clustering, Random Forests, and Support Vector Machines to Against Windows Malware</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Azita%20Ramezani">Azita Ramezani</a>, <a href="https://publications.waset.org/abstracts/search?q=Atousa%20Ramezani"> Atousa Ramezani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the ever-escalating landscape of windows malware the necessity for pioneering defense strategies turns into undeniable this study introduces an avant-garde approach fusing the capabilities of clustering random forests and support vector machines SVM to combat the intricate web of cyber threats our fusion model triumphs with a staggering accuracy of 98.67 and an equally formidable f1 score of 98.68 a testament to its effectiveness in the realm of windows malware defense by deciphering the intricate patterns within malicious code our model not only raises the bar for detection precision but also redefines the paradigm of cybersecurity preparedness this breakthrough underscores the potential embedded in the fusion of diverse analytical methodologies and signals a paradigm shift in fortifying against the relentless evolution of windows malicious threats as we traverse through the dynamic cybersecurity terrain this research serves as a beacon illuminating the path toward a resilient future where innovative fusion models stand at the forefront of cyber threat defense. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fusion%20models" title="fusion models">fusion models</a>, <a href="https://publications.waset.org/abstracts/search?q=cyber%20threat%20defense" title=" cyber threat defense"> cyber threat defense</a>, <a href="https://publications.waset.org/abstracts/search?q=windows%20malware" title=" windows malware"> windows malware</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forests" title=" random forests"> random forests</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=accuracy" title=" accuracy"> accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=f1-score" title=" f1-score"> f1-score</a>, <a href="https://publications.waset.org/abstracts/search?q=cybersecurity" title=" cybersecurity"> cybersecurity</a>, <a href="https://publications.waset.org/abstracts/search?q=malicious%20code%20detection" title=" malicious code detection"> malicious code detection</a> </p> <a href="https://publications.waset.org/abstracts/179650/fusion-models-for-cyber-threat-defense-integrating-clustering-random-forests-and-support-vector-machines-to-against-windows-malware" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/179650.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">71</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">15616</span> Vector Control of Two Five Phase PMSM Connected in Series Powered by Matrix Converter Application to the Rail Traction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Meguenni">S. Meguenni</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Djahbar"> A. Djahbar</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Tounsi"> K. Tounsi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Electric railway traction systems are complex; they have electrical couplings, magnetic and solid mechanics. These couplings impose several constraints that complicate the modeling and analysis of these systems. An example of drive systems, which combine the advantages of the use of multiphase machines, power electronics and computing means, is mono convert isseur multi-machine system which can control a fully decoupled so many machines whose electric windings are connected in series. In this approach, our attention especially on modeling and independent control of two five phase synchronous machine with permanent magnet connected in series and fed by a matrix converter application to the rail traction (bogie of a locomotive BB 36000). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=synchronous%20machine" title="synchronous machine">synchronous machine</a>, <a href="https://publications.waset.org/abstracts/search?q=vector%20control%20Multi-machine%2F%20Multi-inverter" title=" vector control Multi-machine/ Multi-inverter"> vector control Multi-machine/ Multi-inverter</a>, <a href="https://publications.waset.org/abstracts/search?q=matrix%20inverter" title=" matrix inverter"> matrix inverter</a>, <a href="https://publications.waset.org/abstracts/search?q=Railway%20traction" title=" Railway traction"> Railway traction</a> </p> <a href="https://publications.waset.org/abstracts/49131/vector-control-of-two-five-phase-pmsm-connected-in-series-powered-by-matrix-converter-application-to-the-rail-traction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49131.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">372</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">15615</span> A Comparative Study of Series-Connected Two-Motor Drive Fed by a Single Inverter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Djahbar">A. Djahbar</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Bounadja"> E. Bounadja</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Zegaoui"> A. Zegaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Allouache"> H. Allouache</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, vector control of a series-connected two-machine drive system fed by a single inverter (CSI/VSI) is presented. The two stator windings of both machines are connected in series while the rotors may be connected to different loads, are called series-connected two-machine drive. Appropriate phase transposition is introduced while connecting the series stator winding to obtain decoupled control the two-machines. The dynamic decoupling of each machine from the group is obtained using the vector control algorithm. The independent control is demonstrated by analyzing the characteristics of torque and speed of each machine obtained via simulation under vector control scheme. The viability of the control techniques is proved using analytically and simulation approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=drives" title="drives">drives</a>, <a href="https://publications.waset.org/abstracts/search?q=inverter" title=" inverter"> inverter</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-phase%20induction%20machine" title=" multi-phase induction machine"> multi-phase induction machine</a>, <a href="https://publications.waset.org/abstracts/search?q=vector%20control" title=" vector control"> vector control</a> </p> <a href="https://publications.waset.org/abstracts/42943/a-comparative-study-of-series-connected-two-motor-drive-fed-by-a-single-inverter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42943.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">15614</span> Protein Remote Homology Detection by Using Profile-Based Matrix Transformation Approaches</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bin%20Liu">Bin Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As one of the most important tasks in protein sequence analysis, protein remote homology detection has been studied for decades. Currently, the profile-based methods show state-of-the-art performance. Position-Specific Frequency Matrix (PSFM) is widely used profile. However, there exists noise information in the profiles introduced by the amino acids with low frequencies. In this study, we propose a method to remove the noise information in the PSFM by removing the amino acids with low frequencies called Top frequency profile (TFP). Three new matrix transformation methods, including Autocross covariance (ACC) transformation, Tri-gram, and K-separated bigram (KSB), are performed on these profiles to convert them into fixed length feature vectors. Combined with Support Vector Machines (SVMs), the predictors are constructed. Evaluated on two benchmark datasets, and experimental results show that these proposed methods outperform other state-of-the-art predictors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=protein%20remote%20homology%20detection" title="protein remote homology detection">protein remote homology detection</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20fold%20recognition" title=" protein fold recognition"> protein fold recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=top%20frequency%20profile" title=" top frequency profile"> top frequency profile</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/103989/protein-remote-homology-detection-by-using-profile-based-matrix-transformation-approaches" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103989.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">125</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">15613</span> Comparison of Different Artificial Intelligence-Based Protein Secondary Structure Prediction Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jamerson%20Felipe%20Pereira%20Lima">Jamerson Felipe Pereira Lima</a>, <a href="https://publications.waset.org/abstracts/search?q=Jeane%20Cec%C3%ADlia%20Bezerra%20de%20Melo"> Jeane Cecília Bezerra de Melo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The difficulty and cost related to obtaining of protein tertiary structure information through experimental methods, such as X-ray crystallography or NMR spectroscopy, helped raising the development of computational methods to do so. An approach used in these last is prediction of tridimensional structure based in the residue chain, however, this has been proved an NP-hard problem, due to the complexity of this process, explained by the Levinthal paradox. An alternative solution is the prediction of intermediary structures, such as the secondary structure of the protein. Artificial Intelligence methods, such as Bayesian statistics, artificial neural networks (ANN), support vector machines (SVM), among others, were used to predict protein secondary structure. Due to its good results, artificial neural networks have been used as a standard method to predict protein secondary structure. Recent published methods that use this technique, in general, achieved a Q3 accuracy between 75% and 83%, whereas the theoretical accuracy limit for protein prediction is 88%. Alternatively, to achieve better results, support vector machines prediction methods have been developed. The statistical evaluation of methods that use different AI techniques, such as ANNs and SVMs, for example, is not a trivial problem, since different training sets, validation techniques, as well as other variables can influence the behavior of a prediction method. In this study, we propose a prediction method based on artificial neural networks, which is then compared with a selected SVM method. The chosen SVM protein secondary structure prediction method is the one proposed by Huang in his work Extracting Physico chemical Features to Predict Protein Secondary Structure (2013). The developed ANN method has the same training and testing process that was used by Huang to validate his method, which comprises the use of the CB513 protein data set and three-fold cross-validation, so that the comparative analysis of the results can be made comparing directly the statistical results of each method. <p class="card-text"><strong>Keywords:</strong> <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=protein%20secondary%20structure" title=" protein secondary structure"> protein secondary structure</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20structure%20prediction" title=" protein structure prediction"> protein structure prediction</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/22850/comparison-of-different-artificial-intelligence-based-protein-secondary-structure-prediction-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22850.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">621</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">15612</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">15611</span> A Comprehensive Review of Axial Flux Machines and Its Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shahbaz%20Amin">Shahbaz Amin</a>, <a href="https://publications.waset.org/abstracts/search?q=Sabir%20Hussain%20Shah"> Sabir Hussain Shah</a>, <a href="https://publications.waset.org/abstracts/search?q=Sahib%20Khan"> Sahib Khan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a thorough review concerning the design types of axial flux permanent magnet machines (AFPM) in terms of different features such as construction, design, materials, and manufacturing. Particular emphasis is given on the design and performance analysis of AFPM machines. A comparison among different permanent magnet machines is also provided. First of all, early and modern axial flux machines are mentioned. Secondly, rotor construction of different axial flux machines is described, then different stator constructions are mentioned depending upon the presence of slots and stator back iron. Then according to the arrangement of the rotor stator structure the machines are classified into single, double and multi-stack arrangements. Advantages, disadvantages and applications of each type of rotor and stator are pointed out. Finally on the basis of the reviewed literature merits, demerits, features and application of different axial flux machines structures are explained and clarified. Thus, this paper provides connection between the machines that are currently being used in industry and the developments of AFPM throughout the years. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=axial%20flux%20machines" title="axial flux machines">axial flux machines</a>, <a href="https://publications.waset.org/abstracts/search?q=axial%20flux%20applications" title=" axial flux applications"> axial flux applications</a>, <a href="https://publications.waset.org/abstracts/search?q=coreless%20machines" title=" coreless machines"> coreless machines</a>, <a href="https://publications.waset.org/abstracts/search?q=PM%20machines" title=" PM machines"> PM machines</a> </p> <a href="https://publications.waset.org/abstracts/95500/a-comprehensive-review-of-axial-flux-machines-and-its-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95500.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">217</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">15610</span> Machine Learning-Driven Prediction of Cardiovascular Diseases: A Supervised Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thota%20Sai%20Prakash">Thota Sai Prakash</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Yaswanth"> B. Yaswanth</a>, <a href="https://publications.waset.org/abstracts/search?q=Jhade%20Bhuvaneswar"> Jhade Bhuvaneswar</a>, <a href="https://publications.waset.org/abstracts/search?q=Marreddy%20Divakar%20Reddy"> Marreddy Divakar Reddy</a>, <a href="https://publications.waset.org/abstracts/search?q=Shyam%20Ji%20Gupta"> Shyam Ji Gupta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Across the globe, there are a lot of chronic diseases, and heart disease stands out as one of the most perilous. Sadly, many lives are lost to this condition, even though early intervention could prevent such tragedies. However, identifying heart disease in its initial stages is not easy. To address this challenge, we propose an automated system aimed at predicting the presence of heart disease using advanced techniques. By doing so, we hope to empower individuals with the knowledge needed to take proactive measures against this potentially fatal illness. Our approach towards this problem involves meticulous data preprocessing and the development of predictive models utilizing classification algorithms such as Support Vector Machines (SVM), Decision Tree, and Random Forest. We assess the efficiency of every model based on metrics like accuracy, ensuring that we select the most reliable option. Additionally, we conduct thorough data analysis to reveal the importance of different attributes. Among the models considered, Random Forest emerges as the standout performer with an accuracy rate of 96.04% in our study. <p class="card-text"><strong>Keywords:</strong> <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=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a> </p> <a href="https://publications.waset.org/abstracts/185653/machine-learning-driven-prediction-of-cardiovascular-diseases-a-supervised-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185653.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">40</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">15609</span> Comparison of Different Machine Learning Algorithms for Solubility Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammet%20Baldan">Muhammet Baldan</a>, <a href="https://publications.waset.org/abstracts/search?q=Emel%20Timu%C3%A7in"> Emel Timuçin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Molecular solubility prediction plays a crucial role in various fields, such as drug discovery, environmental science, and material science. In this study, we compare the performance of five machine learning algorithms—linear regression, support vector machines (SVM), random forests, gradient boosting machines (GBM), and neural networks—for predicting molecular solubility using the AqSolDB dataset. The dataset consists of 9981 data points with their corresponding solubility values. MACCS keys (166 bits), RDKit properties (20 properties), and structural properties(3) features are extracted for every smile representation in the dataset. A total of 189 features were used for training and testing for every molecule. Each algorithm is trained on a subset of the dataset and evaluated using metrics accuracy scores. Additionally, computational time for training and testing is recorded to assess the efficiency of each algorithm. Our results demonstrate that random forest model outperformed other algorithms in terms of predictive accuracy, achieving an 0.93 accuracy score. Gradient boosting machines and neural networks also exhibit strong performance, closely followed by support vector machines. Linear regression, while simpler in nature, demonstrates competitive performance but with slightly higher errors compared to ensemble methods. Overall, this study provides valuable insights into the performance of machine learning algorithms for molecular solubility prediction, highlighting the importance of algorithm selection in achieving accurate and efficient predictions in practical applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title="random forest">random forest</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=comparison" title=" comparison"> comparison</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a> </p> <a href="https://publications.waset.org/abstracts/186745/comparison-of-different-machine-learning-algorithms-for-solubility-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186745.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">40</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">15608</span> On the Network Packet Loss Tolerance of SVM Based Activity Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gamze%20Uslu">Gamze Uslu</a>, <a href="https://publications.waset.org/abstracts/search?q=Sebnem%20Baydere"> Sebnem Baydere</a>, <a href="https://publications.waset.org/abstracts/search?q=Alper%20K.%20Demir"> Alper K. Demir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, data loss tolerance of Support Vector Machines (SVM) based activity recognition model and multi activity classification performance when data are received over a lossy wireless sensor network is examined. Initially, the classification algorithm we use is evaluated in terms of resilience to random data loss with 3D acceleration sensor data for sitting, lying, walking and standing actions. The results show that the proposed classification method can recognize these activities successfully despite high data loss. Secondly, the effect of differentiated quality of service performance on activity recognition success is measured with activity data acquired from a multi hop wireless sensor network, which introduces high data loss. The effect of number of nodes on the reliability and multi activity classification success is demonstrated in simulation environment. To the best of our knowledge, the effect of data loss in a wireless sensor network on activity detection success rate of an SVM based classification algorithm has not been studied before. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=activity%20recognition" title="activity recognition">activity recognition</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=acceleration%20sensor" title=" acceleration sensor"> acceleration sensor</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20sensor%20networks" title=" wireless sensor networks"> wireless sensor networks</a>, <a href="https://publications.waset.org/abstracts/search?q=packet%20loss" title=" packet loss"> packet loss</a> </p> <a href="https://publications.waset.org/abstracts/14201/on-the-network-packet-loss-tolerance-of-svm-based-activity-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14201.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">475</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">15607</span> Short-Term Load Forecasting Based on Variational Mode Decomposition and Least Square Support Vector Machine</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jiangyong%20Liu">Jiangyong Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiangxiang%20Xu"> Xiangxiang Xu</a>, <a href="https://publications.waset.org/abstracts/search?q=Bote%20Luo"> Bote Luo</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaoxue%20Luo"> Xiaoxue Luo</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiang%20Zhu"> Jiang Zhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Lingzhi%20Yi"> Lingzhi Yi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To address the problems of non-linearity and high randomness of the original power load sequence causing the degradation of power load forecasting accuracy, a short-term load forecasting method is proposed. The method is based on the Least Square Support Vector Machine optimized by an Improved Sparrow Search Algorithm combined with the Variational Mode Decomposition proposed in this paper. The application of the variational mode decomposition technique decomposes the raw power load data into a series of Intrinsic Mode Functions components, which can reduce the complexity and instability of the raw data while overcoming modal confounding; the proposed improved sparrow search algorithm can solve the problem of difficult selection of learning parameters in the least Square Support Vector Machine. Finally, through comparison experiments, the results show that the method can effectively improve prediction accuracy. <p class="card-text"><strong>Keywords:</strong> <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=variational%20mode%20decomposition" title=" variational mode decomposition"> variational mode decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=improved%20sparrow%20search%20algorithm" title=" improved sparrow search algorithm"> improved sparrow search algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=least%20square%20support%20vector%20machine" title=" least square support vector machine"> least square support vector machine</a> </p> <a href="https://publications.waset.org/abstracts/170283/short-term-load-forecasting-based-on-variational-mode-decomposition-and-least-square-support-vector-machine" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170283.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">108</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">15606</span> Short Text Classification for Saudi Tweets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Asma%20A.%20Alsufyani">Asma A. Alsufyani</a>, <a href="https://publications.waset.org/abstracts/search?q=Maram%20A.%20Alharthi"> Maram A. Alharthi</a>, <a href="https://publications.waset.org/abstracts/search?q=Maha%20J.%20Althobaiti"> Maha J. Althobaiti</a>, <a href="https://publications.waset.org/abstracts/search?q=Manal%20S.%20Alharthi"> Manal S. Alharthi</a>, <a href="https://publications.waset.org/abstracts/search?q=Huda%20Rizq"> Huda Rizq</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Twitter is one of the most popular microblogging sites that allows users to publish short text messages called 'tweets'. Increasing the number of accounts to follow (followings) increases the number of tweets that will be displayed from different topics in an unclassified manner in the timeline of the user. Therefore, it can be a vital solution for many Twitter users to have their tweets in a timeline classified into general categories to save the user’s time and to provide easy and quick access to tweets based on topics. In this paper, we developed a classifier for timeline tweets trained on a dataset consisting of 3600 tweets in total, which were collected from Saudi Twitter and annotated manually. We experimented with the well-known Bag-of-Words approach to text classification, and we used support vector machines (SVM) in the training process. The trained classifier performed well on a test dataset, with an average F1-measure equal to 92.3%. The classifier has been integrated into an application, which practically proved the classifier’s ability to classify timeline tweets of the user. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=corpus%20creation" title="corpus creation">corpus creation</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=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=short%20text%20classification" title=" short text classification"> short text classification</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20media" title=" social media"> social media</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=Twitter" title=" Twitter"> Twitter</a> </p> <a href="https://publications.waset.org/abstracts/130952/short-text-classification-for-saudi-tweets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130952.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">15605</span> Developed Text-Independent Speaker Verification System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Arif">Mohammed Arif</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdessalam%20Kifouche"> Abdessalam Kifouche</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Speech is a very convenient way of communication between people and machines. It conveys information about the identity of the talker. Since speaker recognition technology is increasingly securing our everyday lives, the objective of this paper is to develop two automatic text-independent speaker verification systems (TI SV) using low-level spectral features and machine learning methods. (i) The first system is based on a support vector machine (SVM), which was widely used in voice signal processing with the aim of speaker recognition involving verifying the identity of the speaker based on its voice characteristics, and (ii) the second is based on Gaussian Mixture Model (GMM) and Universal Background Model (UBM) to combine different functions from different resources to implement the SVM based. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=speaker%20verification" title="speaker verification">speaker verification</a>, <a href="https://publications.waset.org/abstracts/search?q=text-independent" title=" text-independent"> text-independent</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=Gaussian%20mixture%20model" title=" Gaussian mixture model"> Gaussian mixture model</a>, <a href="https://publications.waset.org/abstracts/search?q=cepstral%20analysis" title=" cepstral analysis"> cepstral analysis</a> </p> <a href="https://publications.waset.org/abstracts/183493/developed-text-independent-speaker-verification-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183493.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">58</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%20machines%20application&page=2">2</a></li> <li class="page-item"><a class="page-link" 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