Search for: support-vector-machines
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    Parental control based on speaker class verification

    , Article IEEE Transactions on Consumer Electronics ; Volume 54, Issue 3 , 2008 , Pages 1244-1251 ; 00983063 (ISSN) Shirali-Shahreza, S ; Sameti, H ; Shirali Shahreza, M ; Sharif University of Technology
    Restricting children access to materials unsuitable for them such as violence scenes is very important for parents. So there is a feature named Parental Control in devices such as televisions and computers to define the contents children can access. The parental control setting must be protected from children and is usually done by a password. In this paper, we propose a new method for distinguishing between adult users and child users based on human speech. In our proposed method, the user must say a word and the adult users are identified by processing the speech. Our current implementation has 92.5% accuracy for distinguishing adult users from children. © 2008 IEEE  

    Robust airborne target recognition based on recurrence plot quantification of micro-Doppler radar signatures

    , Article Proceedings International Radar Symposium, 10 May 2016 through 12 May 2016 ; Volume 2016-June , 2016 ; 21555753 (ISSN) ; 9781509025183 (ISBN) Johari, M. M ; Nayebi, M. M ; Sharif University of Technology
    IEEE Computer Society  2016
    A robust target recognition method proposed based on recurrence plot and recurrence quantification analysis (RQA) to generate robust features against noise, target velocity and aspect angle from micro-Doppler (m-D) signatures. The proposed method is tested on simulated data of three different targets using multiclass support vector machine (MSVM) and classification rate of about 95 % is achieved. Also, effect of noise and coherent processing time (CPT) on classification rate is investigated  

    Combining Supervised and Semi-Supervised Learning in the Design of a New Identifier for NPPs Transients

    , Article IEEE Transactions on Nuclear Science ; Volume 63, Issue 3 , 2016 , Pages 1882-1888 ; 00189499 (ISSN) Moshkbar Bakhshayesh, K ; Ghofrani, M. B ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2016
    This study introduces a new identifier for nuclear power plants (NPPs) transients. The proposed identifier performs its function in two steps. First, the transient is identified by the previously developed supervised classifier combining ARIMA model and EBP algorithm. In the second step, the patterns of unknown transients are fed to the identifier based on the semi-supervised learning (SSL). The transductive support vector machine (TSVM) as a semi-supervised algorithm is trained by the labeled data of transients to predict some unlabeled data. The labeled and newly predicted data is then used to train the TSVM for another portion of unlabeled data. Training and prediction is continued until... 

    Classifications of disturbances using wavelet transform and support vector machine

    , Article Turkish Journal of Electrical Engineering and Computer Sciences ; Volume 25, Issue 2 , 2017 , Pages 832-843 ; 13000632 (ISSN) Hajibandeh, N ; Faghihi, F ; Ranjbar, H ; Kazari, H ; Sharif University of Technology
    Turkiye Klinikleri Journal of Medical Sciences  2017
    This paper proposes a new method to detect and classify all kinds of faults, capacitor switching, and load switching in a power system network based on wavelet transform and support vector machines (SVMs). In this regard, a sample of a power system is simulated via MATLAB/Simulink, and by reading the voltage of the point of common coupling and using the wavelet transform, the differences of the outputs of the wavelet transform are investigated. The SVM approach is employed to distinguish the type of the transient (capacitor switching, fault, and/or load switching) in use for the high level outputs of the wavelet transform. Similar to neural networks, this method, which is based on learning,... 

    BSS: Boosted steganography scheme with cover image preprocessing

    , Article Expert Systems with Applications ; Volume 37, Issue 12 , December , 2010 , Pages 7703-7710 ; 09574174 (ISSN) Sajedi, H ; Jamzad, M ; Sharif University of Technology
    The existing powerful steganalyzers can find out the presence of secret information in images with high accuracy. Increasing the embedding capacity of cover images reduces the detection risk of stego images. In this respect, we introduce boosted steganography scheme (BSS) that has a preprocessing stage before applying steganography methods. The goal of BSS is increasing the undetectability of stego images. Due to the dependence of embedding capacity of images to their content, we proposed an ensemble steganalyzer to estimate the embedding capacity of each cover image. Since the content of cover images has less significance in steganography, therefore to have more security, the steganographer... 

    Rigorous modeling of gypsum solubility in Na-Ca-Mg-Fe-Al-H-Cl-H2O system at elevated temperatures

    , Article Neural Computing and Applications ; Volume 25, Issue 3 , September , 2014 , pp 955-965 ; ISSN: 09410643 Safari, H ; Gharagheizi, F ; Lemraski, A. S ; Jamialahmadi, M ; Mohammadi, A. H ; Ebrahimi, M ; Sharif University of Technology
    Precipitation and scaling of calcium sulfate have been known as major problems facing process industries and oilfield operations. Most scale prediction models are based on aqueous thermodynamics and solubility behavior of salts in aqueous electrolyte solutions. There is yet a huge interest in developing reliable, simple, and accurate solubility prediction models. In this study, a comprehensive model based on least-squares support vector machine (LS-SVM) is presented, which is mainly devoted to calcium sulfate dihydrate (or gypsum) solubility in aqueous solutions of mixed electrolytes covering wide temperature ranges. In this respect, an aggregate of 880 experimental data were gathered from... 

    Prediction of natural gas flow through chokes using support vector machine algorithm

    , Article Journal of Natural Gas Science and Engineering ; Vol. 18, issue , 2014 , pp. 155-163 ; ISSN: 18755100 Nejatian, I ; Kanani, M ; Arabloo, M ; Bahadori, A ; Zendehboudi, S ; Sharif University of Technology
    In oil and gas fields, it is a common practice to flow liquid and gas mixtures through choke valves. In general, different types of primary valves are employed to control pressure and flow rate when the producing well directs the natural gas to the processing equipment. In this case, the valve normally is affected by elevated levels of flow (or velocity) as well as solid materials suspended in the gas phase (e.g., fine sand and other debris). Both surface and subsurface chokes may be installed to regulate flow rates and to protect the porous medium and surface facilities from unusual pressure instabilities.In this study a reliable, novel, computer based predictive model using Least-Squares... 

    A clinical decision support system based on support vector machine and binary particle swarm optimisation for cardiovascular disease diagnosis

    , Article International Journal of Data Mining and Bioinformatics ; Volume 15, Issue 4 , 2016 , Pages 312-327 ; 17485673 (ISSN) Sali, R ; Shavandi, H ; Sadeghi, M ; Sharif University of Technology
    Inderscience Enterprises Ltd  2016
    Cardiovascular diseases have been known as one of the main reasons of mortality all around the world. Nevertheless, this disease is preventable if it can be diagnosed in an early stage. Therefore, it is crucial to develop Clinical Decision Support Systems (CDSSs) that are able to help physicians diagnose the disease and its related risks. This study focuses on cardiovascular disease diagnosis in an Iranian community by developing a CDSS, based on Support Vector Machine (SVM) combined with Binary Particle Swarm Optimisation (BPSO). We used SVM as the classifier and benefited enormously from optimisation capabilities of BPSO in model development as well as feature selection. Finally,... 

    Prediction of CO2 equilibrium moisture content using least squares support vector machines algorithm

    , Article Petroleum and Coal ; Volume 58, Issue 1 , 2016 , Pages 27-46 ; 13377027 (ISSN) Ghiasi, M.M ; Abdi, J ; Bahadori, M ; Lee, M ; Bahadori, A ; Sharif University of Technology
    Slovnaft VURUP a.s  2016
    The burning of fossil fuels such as gasoline, coal, oil, natural gas in combustion reactions results in the production of carbon dioxide. The phase behavior of the carbon dioxide + water system is complex topic. Unlike methane, CO2 exhibits a minimum in the water content. These minima cannot be predicted by existing methods accurately. In this communication, two mathematical-based procedures have been proposed for accurate computation of CO2 water content for tempe-ratures between 273.15 and 348.15 K and the pressure range between 0.5 and 21 MPa. The first is based on least squares support vector machine (LSSVM) algorithm and the second applies multilayer perceptron (MLP) artificial neural... 

    Modeling the permeability of heterogeneous oil reservoirs using a robust method

    , Article Geosciences Journal ; Volume 20, Issue 2 , 2016 , Pages 259-271 ; 12264806 (ISSN) Kamari, A ; Moeini, F ; Shamsoddini Moghadam, M. J ; Hosseini, S. A ; Mohammadi, A. H ; Hemmati Sarapardeh, A ; Sharif University of Technology
    Korean Association of Geoscience Societies  2016
    Permeability as a fundamental reservoir property plays a key role in reserve estimation, numerical reservoir simulation, reservoir engineering calculations, drilling planning, and mapping reservoir quality. In heterogeneous reservoir, due to complexity, natural heterogeneity, non-uniformity, and non-linearity in parameters, prediction of permeability is not straightforward. To ease this problem, a novel mathematical robust model has been proposed to predict the permeability in heterogeneous carbonate reservoirs. To this end, a fairly new soft computing method, namely least square support vector machine (LSSVM) modeling optimized with coupled simulated annealing (CSA) optimization technique... 

    AIDSLK: an anomaly based intrusion detection system in linux kernel

    , Article Communications in Computer and Information Science ; Volume 31 , 2009 , Pages 232-243 ; 18650929 (ISSN); 9783642004049 (ISBN) Almassian, N ; Azmi, R ; Berenji, S ; Sharif University of Technology
    The growth of intelligent attacks has prompted the designers to envision the intrusion detection as a built-in process in operating systems. This paper investigates a novel anomaly-based intrusion detection mechanism which utilizes the manner of interactions between users and kernel processes. An adequate feature list has been prepared for distinction between normal and anomalous behavior. The method used is introducing a new component to Linux kernel as a wrapper module with necessary hook function to log initial data for preparing desired features list. SVM neural network was applied to classify and recognize input vectors. The sequence of delayed input vectors of features was appended to... 

    Statistical feature embedding for heart sound classification

    , Article Journal of Electrical Engineering ; Volume 70, Issue 4 , 2019 , Pages 259-272 ; 13353632 (ISSN) Adiban, M ; Babaali, B ; Shehnepoor, S ; Sharif University of Technology
    De Gruyter Open Ltd  2019
    Cardiovascular Disease (CVD) is considered as one of the principal causes of death in the world. Over recent years, this field of study has attracted researchers' attention to investigate heart sounds' patterns for disease diagnostics. In this study, an approach is proposed for normal/abnormal heart sound classification on the Physionet challenge 2016 dataset. For the first time, a fixed length feature vector; called i-vector; is extracted from each heart sound using Mel Frequency Cepstral Coefficient (MFCC) features. Afterwards, Principal Component Analysis (PCA) transform and Variational Autoencoder (VAE) are applied on the i-vector to achieve dimension reduction. Eventually, the reduced... 

    Prediction of shear strength parameters of hydrocarbon contaminated sand based on machine learning methods

    , Article Georisk ; 2020 Rezaee, M ; Mojtahedi, S. F. F ; Taherabadi, E ; Soleymani, K ; Pejman, M ; Sharif University of Technology
    Taylor and Francis Ltd  2020
    The objective of this paper is to predict the effect of hydrocarbon contamination on the shear strength parameters of sand by using various machine learning platforms. Multilayer perceptron, support vector machine, random forest, gradient boosting method, and multi-output support vector machine were methods used to predict the hydrocarbon contamination impacts on the internal friction angle and cohesion of contaminated sand. Random forest exhibited the best results for cohesion, whereas, for the friction angle, the gradient boosting method outperformed other approaches. Moreover, the multi-output support vector machine yielded better results than those pertaining to a single support vector... 

    Comparing performance of metaheuristic algorithms for finding the optimum structure of CNN for face recognition

    , Article International Journal of Nonlinear Analysis and Applications ; Volume 11, Issue 1 , 2020 , Pages 301-319 Rikhtegar, A ; Pooyan, M ; Manzuri, M. T ; Sharif University of Technology
    Semnan University, Center of Excellence in Nonlinear Analysis and Applications  2020
    Local and global based methods are two main trends for face recognition. Local approaches extract salient features by processing different parts of the image whereas global approaches find a general template for face of each person. Unfortunately, most global approaches work under controlled envi-ronments and they are sensitive to changes in the illumination. On the other hand, local approaches are more robust but finding their optimal parameters is a challenging task. This work proposes a new local-based approach that automatically tunes its parameters. The proposed method incorporates different techniques. In the first step, convolutional neural network (CNN) is employed as a trainable... 

    Epileptic seizure detection using neural fuzzy networks

    , Article 2006 IEEE International Conference on Fuzzy Systems, Vancouver, BC, 16 July 2006 through 21 July 2006 ; 2006 , Pages 596-600 ; 10987584 (ISSN); 0780394887 (ISBN); 9780780394889 (ISBN) Sadati, N ; Mohseni, H. R ; Maghsoudi, A ; Sharif University of Technology
    The electroencephalogram (EEG) is a representative signal containing information about the condition of the brain. The shape of the wave may contain useful information about its state. However, the human observer cannot directly monitor these subtle details. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the EEG signal parameters, extracted and analyzed using computers, are highly useful in diagnosis. The aim of this work is to compare the different classifiers when applied to EEG data from normal and epileptic subjects. For this purpose an adaptive neural fuzzy network (ANFN) to classify normal and epileptic EEG signals is... 

    Evolving application of machine learning in the synthesis of CHA/ZrO2 nanocomposite for the microhardness prediction

    , Article Materials Letters ; Volume 327 , 2022 ; 0167577X (ISSN) Hasani, A ; Shojaei, M. R ; Khayati, G. R ; Sharif University of Technology
    Elsevier B.V  2022
    Nanocomposites containing ZrO2 and HA have been considered in various fields due to their unique mechanical properties. The principal purpose of this paper is to select the models with the maximum accuracy for the prediction of microhardness of CHA/ZrO2 nanocomposite. For this purpose, three models, including gene expression programming (GEP), gray wolf optimization algorithm (GWOA), and least squares support vector machine (LS-SVM), were implemented to predict and optimize the microhardness of the CHA/ZrO2 nanocomposite. Finally, the results showed that the data obtained from the LS-SVM model were closer to the preliminary data than the others. According to the results, the LS-SVM could... 

    Prediction of the aqueous solubility of BaSO4 using pitzer ion interaction model and LSSVM algorithm

    , Article Fluid Phase Equilibria ; Vol. 374, issue , July , 2014 , p. 48-62 ; ISSN: 03783812 Safari, H ; Shokrollahi, A ; Jamialahmadi, M ; Ghazanfari, M. H ; Bahadori, A ; Zendehboudi, S ; Sharif University of Technology
    Deposition of barium sulfate (or BaSO4) has already been recognized as a devastating problem facing process industries and oilfield operations, mainly owing to its low solubility in aqueous solutions. Predicting and also preventing the overall damage caused by BaSO4 precipitation requires a profound knowledge of its solubility under different thermodynamic conditions. The main aim of this study is to develop a solubility prediction model based on a hybrid of least squares support vector nachines (LSSVM) and coupled simulated annealing (CSA) aiming to predict the solubility of barium sulfate over wide ranges of temperature, pressure and ionic compositions. Results indicate that predictions of... 

    Assessment of competitive dye removal using a reliable method

    , Article Journal of Environmental Chemical Engineering ; Vol. 2, issue. 3 , September , 2014 , p. 1672-1683 Abdi, J ; Bastani, D ; Abdi, J ; Mahmoodi, N. M ; Shokrollahi, A ; Mohammadi, A. H ; Sharif University of Technology
    In this study, a reliable and predictive model namely, least-squares support vector machine (LS-SVM) was developed to predict dye removal efficiency. Four LS-SVM models have been developed and tested using more than 630 series of experimental data which were obtained from our previous paper. These data consist of adsorbate type, adsorbent dosage, initial dye concentration, salt, absorbance time and dye removal efficiency. Direct Red 31 (DR31), Direct Green 6 (DG6) and Acid Blue (AB92) were used as a model dyes. The results show that the developed model is more accurate and reliable with the average absolute relative deviation of 0.678%, 0.877%, 0.581% and 0.978% for single systems and... 

    Reservoir oil viscosity determination using a rigorous approach

    , Article Fuel ; Vol. 116, issue , 2014 , p. 39-48 Hemmati-Sarapardeh, A ; Shokrollahi, A ; Tatar, A ; Gharagheizi, F ; Mohammadi, A. H ; Naseri, A ; Sharif University of Technology
    Viscosity of crude oil is a fundamental factor in simulating reservoirs, forecasting production as well as planning thermal enhanced oil recovery methods which make its accurate determination necessary. Experimental determination of reservoir oil viscosity is costly and time consuming. Hence, searching for quick and accurate determination of reservoir oil viscosity is inevitable. The objective of this study is to present a reliable, and predictive model namely, Least-Squares Support Vector Machine (LSSVM) to predict reservoir oil viscosity. To this end, three LSSVM models have been developed for prediction of reservoir oil viscosity in the three regions including, under-saturated, saturated... 

    Asphaltene precipitation due to natural depletion of reservoir: Determination using a SARA fraction based intelligent model

    , Article Fluid Phase Equilibria ; Volume 354 , September , 2013 , Pages 177-184 ; 03783812 (ISSN) Hemmati Sarapardeh, A ; Alipour Yeganeh Marand, R ; Naseri, A ; Safiabadi, A ; Gharagheizi, F ; Ilani Kashkouli, P ; Mohammadi, A. H ; Sharif University of Technology
    Precipitation of asphaltene leads to rigorous problems in petroleum industry such as: wettability alterations, relative permeability reduction, blockage of the flow with additional pressure drop in wellbore tubing, upstream process facilities and surface pipelines. Experimentally determination of the asphaltene precipitation is costly and time consuming. Therefore, searching for some other quick and accurate methods for determination of the asphaltene precipitation is inevitable. The objective of this communication is to present a reliable and predictive model namely, the least - squares support vector machine (LSSVM) to predict the asphaltene precipitation. This model has been developed and...