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An enhanced segmentation technique and improved support vector machine classifier for facial image recognition

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International Journal of Intelligent Computing and Cybernetics 2022年第2期15卷 302-317页

作者： Rangayya Virupakshappa Nagabhushan Patil Sharnbasva University KalaburagiIndia Poojya Doddappa Appa College of Engineering GulbargaIndia

Purpose-One of the challenging issues in computer vision and pattern recognition is face image *** studies based on face recognition were introduced in the past decades,but it has few classification issues in terms of poor ***,the authors proposed a novel model for face ***/methodology/approach-The proposed method consists of four major sections such as data acquisition,segmentation,feature extraction and ***,the images are transferred into grayscale images,and they pose issues that are eliminated by resizing the input *** contrast limited adaptive histogram equalization(CLAHE)utilizes the image preprocessing step,thereby eliminating unwanted noise and improving the image contrast ***,the active contour and level set-based segmentation(ALS)with neural network(NN)or ALS with NN algorithm is used for facial image ***,the four major kinds of feature descriptors are dominant color structure descriptors,scale-invariant feature transform descriptors,improved center-symmetric local binary patterns(ICSLBP)and histograms of gradients(HOG)are based on clour and texture ***,the support vector machine(SVM)with modified random forest(MRF)model for facial image ***-Experimentally,the proposed method performance is evaluated using different kinds of evaluation criterions such as accuracy,similarity index,dice similarity coefficient,precision,recall and F-score ***,the proposed method offers superior recognition performances than other state-of-art *** face recognition was analyzed with the metrics such as accuracy,precision,recall and F-score and attained 99.2,96,98 and 96%,***/value-The good facial recognition method is proposed in this research work to overcome threat to privacy,violation of rights and provide better security of data.

关键词： Face recognition Active contour and Level set-based segmentation Neural network algorithm support vector machine Modified random forest classifier

support vector machine based machine learning method for GS 8QAM constellation classification in seamless integrated fiber and visible light communication system

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Science China(Information Sciences) 2020年第10期63卷 182-193页

作者： Wenqing NIU Yinaer HA Nan CHI Key Laboratory for Information Science of Electromagnetic Waves (MoE) Fudan University

Visible light communication(VLC) network over optical fiber has become a potential candidate in ultra-high speed indoor wireless communication. To mitigate signal distortion accumulated in optical fiber and VLC channel, we present to utilize support vector machine(SVM) for constellation classification in two kinds of geometrically-shaped 8 QAM(quadrature amplitude modulation) seamless integrated fiber and VLC system. We introduce 4 sub-bands to simulate multi-user. Experimental results show that system performance can be significantly improved, and transmission at-2.5 dB m input optical power under 7%forward error correction(FEC) threshold can be realized employing Circular(7, 1) geometrically-shaped8 QAM and SVM. At overall capacity of 960 Mbps, Q-factor increases by up to 11.5 dB.

关键词： visible light communication VLC support vector machine SVM geometrically shaping GS VLC network constellation classification

support vector machine Assisted GPS Navigation in Limited Satellite Visibility

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Computers, Materials & Continua 2021年第10期69卷 555-574页

作者： Dah-Jing Jwo Jia-Chyi Wu Kuan-Lin Ho Department of Communications Navigation and Control EngineeringTaiwan Ocean UniversityKeelung202301TaiwanChina

This paper investigates performance improvement via the incorporation of the support vector machine(SVM)into the vector tracking loop(VTL)for the Global Positioning System(GPS)in limited satellite *** the traditional scalar tracking loop(STL),the tracking and navigation modules in the VTL are not independent anymore since the user’s position can be determined by using the information from other satellites and can be predicted on the basis of the states of the *** method proposed in this paper makes use of the SVM to bridge the GPS signal and prevent the error growth due to signal *** to the neural network,the SVM is motivated by its ability to approximate an unknown nonlinear input-output mapping through supervised *** SVM is employed for predicting adequate numerical control oscillator(NCO)inputs,i.e.,providing better prediction of residuals for the Doppler frequency and code phase in order to maintain regular operation of the navigation *** the navigation processing is in good condition,the SVM is at the training stage,and the output information from the discriminator and navigation filter is adopted as the *** machine learning(ML)algorithms such as the radial basis function neural network(RBFNN)and the Adaptive Network-Based Fuzzy Inference System(ANFIS)are employed for *** evaluation for the SVM assisted architecture as compared to the RBFNNand ANFIS-assisted methods and the un-assisted VTL will be carried out and the performance evaluation during GPS signal outage will be *** proposed design is very useful for navigation during the environment of limited satellite visibility to effectively overcome the problem in the environment of GPS outage.

关键词： Global positioning system support vector machine machine learning vector tracking loop signal outage

support vector machine FOR STRUCTURAL RELIABILITY ANALYSIS

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Applied Mathematics and Mechanics(English Edition) 2006年第10期27卷 1295-1303页

作者：李洪双吕震宙岳珠峰 School of Aeronautics Northwestern Polytechnical UniversityXi'an 710072P.R.China

support vector machine （SVM） was introduced to analyze the reliability of the implicit performance function, which is difficult to implement by the classical methods such as the first order reliability method （FORM） and the Monte Carlo simulation （MCS）. As a classification method where the underlying structural risk minimization inference rule is employed, SVM possesses excellent learning capacity with a small amount of information and good capability of generalization over the complete data. Hence, two approaches, i.e., SVM-based FORM and SVM-based MCS, were presented for the structural reliability analysis of the implicit limit state function. Compared to the conventional response surface method （RSM） and the artificial neural network （ANN）, which are widely used to replace the implicit state function for alleviating the computation cost, the more important advantages of SVM are that it can approximate the implicit function with higher precision and better generalization under the small amount of information and avoid the ＂curse of dimensionality＂. The SVM-based reliability approaches can approximate the actual performance function over the complete sampling data with the decreased number of the implicit performance function analysis （usually finite element analysis）, and the computational precision can satisfy the engineering requirement, which are demonstrated by illustrations.

关键词： structural reliability implicit performance function support vector machine

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support vector machine for SAR/QSAR of phenethyl-amines

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Acta Pharmacologica Sinica 2007年第7期28卷 1075-1086页

作者： Bing NIU~2,Wen-cong LU~(2,3,6),Shan-sheng YANG~2,Yu-dong CAI~(3,4,6),Guo-zheng LI~5 ~2 College of Material Science and Engineering,Shanghai University,Shanghai 200444,China ~3 Laboratory of Chemical Data Mining,Department of Chemistry,College of Sciences,Shanghai University,Shanghai 200444,China ~4 Department of Combinatorics and Geometry,CAS-MPG Partner Institute for Computational Biology,Shanghai Institutes for Biological Sciences,Chinese Academy of Sciences,Shanghai200031,China ~5 School of Computer Science and Engineering,Shanghai University,Shanghai 200072,China College of Material Science and Engineering Shanghai University Shanghai China Department of Chemistry Laboratory of Chemical Data Mining College of Sciences Shanghai University Shanghai China Department of Combinatorics and Geometry CAS-MPG Partner Institute for Computational Biology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai China School of Computer Science and Engineering Shanghai Univeristy Shanghai China

Aim:To discriminate 32 phenethyl-amines between antagonists and agonists,and predict the activities of these ***:The support vectormachine (SVM) is employed to investigate the structure-activity relationship(SAR)/quantitative structure-activity relationship (QSAR) of phenethyl-aminesbased on molecular ***:By using the leave-one-out cross-vali-dation (LOOCV) test,1 optimal SAR and 2 optimal QSAR models for agonists andantagonists were *** accuracy of prediction for the classification ofphenethyl-amines by using the LOOCV test is 91.67%,and the accuracy of predic-tion for the classification of phenethyl-amines by using the independent test is100%;the results are better than those of the Fisher,the artificial neural network(ANN),and the K-nearest neighbor models for this real world *** RMSE(root mean square error)of antagonists' QSAR model is 0.5881,and the RMSE ofagonists' QSAR model is 0.4779,which are better than those of the multiple linearregression,partial least squares,and ANN models for this real world ***:The SVM can be used to investigate the SAR and QSAR of phenethyl-amines and could be a promising tool in the field of SAR/QSAR research.

关键词： support vector machine QSAR phenethylamines antagonists agonists

support vector machine method for fore-casting future strong earthquakes in Chinese mainland

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Acta Seismologica Sinica(English Edition) 2006年第1期19卷 30-38页

作者：王炜刘悦李国正吴耿锋马钦忠赵利飞林命週 Earthquake Administration of Shanghai Municipality Shanghai 200062 China School of Computer Engineering and Science Shanghai University Shanghai 200072 China

Statistical learning theory is for small-sample statistics. And support vector machine is a new machine learning method based on the statistical learning theory. The support vector machine not only has solved certain problems in many learning methods, such as small sample, over fitting, high dimension and local minimum, but also has a higher generalization （forecasting） ability than that of artificial neural networks. The strong earthquakes in Chinese mainland are related to a certain extent to the intensive seismicity along the main plate boundaries in the world, however, the relation is nonlinear. In the paper, we have studied this unclear relation by the support vector machine method for the purpose of forecasting strong earthquakes in Chinese mainland.

关键词： statistical learning theory support vector machine artificial neural networks earthquake situation

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support vector machine-Based Nonlinear System Modeling and Control

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Journal of Systems Engineering and Electronics 2003年第3期14卷 53-58页

作者：张浩然韩正之冯瑞于志强 Department of Automation Shanghai Jiaotong University Shanghai 200030 P. R. China

This paper provides an introduction to a support vector machine, a new kernel-based technique introduced in statistical learning theory and structural risk minimization, then presents a modeling-control framework based on SVM. At last a numerical experiment is taken to demonstrate the proposed approach's correctness and effectiveness.

关键词： support vector machine Statistical learning theory Nonlinear systems Modeling and control.

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support vector machine Prediction Modeling for Automobile Ownership

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Journal of Computer and Communications 2022年第6期10卷 37-43页

作者： Ruidong Zhang Xinguang Zhang School of Mechanical and Automotive Engineering Shanghai University of Engineering Science Shanghai China

To address some inherent defects of artificial neural networks, such as insufficient generalization performance, local extremum problem, and dimensional catastrophe problem, a support vector machine was proposed and applied to the modeling of automobile ownership prediction. By analyzing the data on automobile ownership and its influencing factors, the learning sample couples for automobile ownership prediction modeling were constructed, and support vector machine (SVM) was used to regression the nonlinear function relation of automobile ownership prediction model, and the established automobile ownership prediction model was used to predict the automobile ownership in different years. To reduce the impact on the accuracy of automobile ownership prediction caused by the large order of magnitude difference between the data of automobile ownership and its influence factors, the normalization method was used to pre-process the automobile ownership and its influence factors, and the inverse normalization was used to process the automobile ownership prediction results. The comparison between the automobile ownership prediction results and the statistical results shows that the automobile ownership prediction model has good generalization performance, and the support vector machine is an effective method to model the automobile ownership prediction.

关键词： Automobile Ownership Prediction support vector machine

SCADA Data-Based support vector machine for False Alarm Identification for Wind Turbine Management

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Intelligent Automation & Soft Computing 2023年第9期37卷 2595-2608页

作者： Ana María Peco Chacón Isaac Segovia Ramírez Fausto Pedro García Márquez Ingenium Research Group Universidad Castilla-La ManchaCiudad Real13071Spain

Maintenance operations have a critical influence on power gen-eration by wind turbines(WT).Advanced algorithms must analyze large volume of data from condition monitoring systems(CMS)to determine the actual working conditions and avoid false *** paper proposes different support vector machine(SVM)algorithms for the prediction and detection of false alarms.K-Fold cross-validation(CV)is applied to evaluate the classification reliability of these *** Control and Data Acquisition(SCADA)data from an operating WT are applied to test the proposed *** results from the quadratic SVM showed an accuracy rate of 98.6%.Misclassifications from the confusion matrix,alarm log and maintenance records are analyzed to obtain quantitative information and determine if it is a false *** classifier reduces the number of false alarms called misclassifications by 25%.These results demonstrate that the proposed approach presents high reliability and accuracy in false alarm identification.

关键词： machine learning classification support vector machine false alarm wind turbine cross-validation