The generalization ability of online SVM classification based on Markov sampling.

Science.gov (United States)

Xu, Jie; Yan Tang, Yuan; Zou, Bin; Xu, Zongben; Li, Luoqing; Lu, Yang

2015-03-01

In this paper, we consider online support vector machine (SVM) classification learning algorithms with uniformly ergodic Markov chain (u.e.M.c.) samples. We establish the bound on the misclassification error of an online SVM classification algorithm with u.e.M.c. samples based on reproducing kernel Hilbert spaces and obtain a satisfactory convergence rate. We also introduce a novel online SVM classification algorithm based on Markov sampling, and present the numerical studies on the learning ability of online SVM classification based on Markov sampling for benchmark repository. The numerical studies show that the learning performance of the online SVM classification algorithm based on Markov sampling is better than that of classical online SVM classification based on random sampling as the size of training samples is larger.

Functional assignment to JEV proteins using SVM.

Science.gov (United States)

Sahoo, Ganesh Chandra; Dikhit, Manas Ranjan; Das, Pradeep

2008-01-01

Identification of different protein functions facilitates a mechanistic understanding of Japanese encephalitis virus (JEV) infection and opens novel means for drug development. Support vector machines (SVM), useful for predicting the functional class of distantly related proteins, is employed to ascribe a possible functional class to Japanese encephalitis virus protein. Our study from SVMProt and available JE virus sequences suggests that structural and nonstructural proteins of JEV genome possibly belong to diverse protein functions, are expected to occur in the life cycle of JE virus. Protein functions common to both structural and non-structural proteins are iron-binding, metal-binding, lipid-binding, copper-binding, transmembrane, outer membrane, channels/Pores - Pore-forming toxins (proteins and peptides) group of proteins. Non-structural proteins perform functions like actin binding, zinc-binding, calcium-binding, hydrolases, Carbon-Oxygen Lyases, P-type ATPase, proteins belonging to major facilitator family (MFS), secreting main terminal branch (MTB) family, phosphotransfer-driven group translocators and ATP-binding cassette (ABC) family group of proteins. Whereas structural proteins besides belonging to same structural group of proteins (capsid, structural, envelope), they also perform functions like nuclear receptor, antibiotic resistance, RNA-binding, DNA-binding, magnesium-binding, isomerase (intra-molecular), oxidoreductase and participate in type II (general) secretory pathway (IISP).

SVM-RFE based feature selection and Taguchi parameters optimization for multiclass SVM classifier.

Science.gov (United States)

Huang, Mei-Ling; Hung, Yung-Hsiang; Lee, W M; Li, R K; Jiang, Bo-Ru

2014-01-01

Recently, support vector machine (SVM) has excellent performance on classification and prediction and is widely used on disease diagnosis or medical assistance. However, SVM only functions well on two-group classification problems. This study combines feature selection and SVM recursive feature elimination (SVM-RFE) to investigate the classification accuracy of multiclass problems for Dermatology and Zoo databases. Dermatology dataset contains 33 feature variables, 1 class variable, and 366 testing instances; and the Zoo dataset contains 16 feature variables, 1 class variable, and 101 testing instances. The feature variables in the two datasets were sorted in descending order by explanatory power, and different feature sets were selected by SVM-RFE to explore classification accuracy. Meanwhile, Taguchi method was jointly combined with SVM classifier in order to optimize parameters C and γ to increase classification accuracy for multiclass classification. The experimental results show that the classification accuracy can be more than 95% after SVM-RFE feature selection and Taguchi parameter optimization for Dermatology and Zoo databases.

Signal peptide discrimination and cleavage site identification using SVM and NN.

Science.gov (United States)

Kazemian, H B; Yusuf, S A; White, K

2014-02-01

About 15% of all proteins in a genome contain a signal peptide (SP) sequence, at the N-terminus, that targets the protein to intracellular secretory pathways. Once the protein is targeted correctly in the cell, the SP is cleaved, releasing the mature protein. Accurate prediction of the presence of these short amino-acid SP chains is crucial for modelling the topology of membrane proteins, since SP sequences can be confused with transmembrane domains due to similar composition of hydrophobic amino acids. This paper presents a cascaded Support Vector Machine (SVM)-Neural Network (NN) classification methodology for SP discrimination and cleavage site identification. The proposed method utilises a dual phase classification approach using SVM as a primary classifier to discriminate SP sequences from Non-SP. The methodology further employs NNs to predict the most suitable cleavage site candidates. In phase one, a SVM classification utilises hydrophobic propensities as a primary feature vector extraction using symmetric sliding window amino-acid sequence analysis for discrimination of SP and Non-SP. In phase two, a NN classification uses asymmetric sliding window sequence analysis for prediction of cleavage site identification. The proposed SVM-NN method was tested using Uni-Prot non-redundant datasets of eukaryotic and prokaryotic proteins with SP and Non-SP N-termini. Computer simulation results demonstrate an overall accuracy of 0.90 for SP and Non-SP discrimination based on Matthews Correlation Coefficient (MCC) tests using SVM. For SP cleavage site prediction, the overall accuracy is 91.5% based on cross-validation tests using the novel SVM-NN model. © 2013 Published by Elsevier Ltd.

CompareSVM: supervised, Support Vector Machine (SVM) inference of gene regularity networks.

Science.gov (United States)

Gillani, Zeeshan; Akash, Muhammad Sajid Hamid; Rahaman, M D Matiur; Chen, Ming

2014-11-30

Predication of gene regularity network (GRN) from expression data is a challenging task. There are many methods that have been developed to address this challenge ranging from supervised to unsupervised methods. Most promising methods are based on support vector machine (SVM). There is a need for comprehensive analysis on prediction accuracy of supervised method SVM using different kernels on different biological experimental conditions and network size. We developed a tool (CompareSVM) based on SVM to compare different kernel methods for inference of GRN. Using CompareSVM, we investigated and evaluated different SVM kernel methods on simulated datasets of microarray of different sizes in detail. The results obtained from CompareSVM showed that accuracy of inference method depends upon the nature of experimental condition and size of the network. For network with nodes (SVM Gaussian kernel outperform on knockout, knockdown, and multifactorial datasets compared to all the other inference methods. For network with large number of nodes (~500), choice of inference method depend upon nature of experimental condition. CompareSVM is available at http://bis.zju.edu.cn/CompareSVM/ .

SVM-dependent pairwise HMM: an application to protein pairwise alignments.

Science.gov (United States)

Orlando, Gabriele; Raimondi, Daniele; Khan, Taushif; Lenaerts, Tom; Vranken, Wim F

2017-12-15

Methods able to provide reliable protein alignments are crucial for many bioinformatics applications. In the last years many different algorithms have been developed and various kinds of information, from sequence conservation to secondary structure, have been used to improve the alignment performances. This is especially relevant for proteins with highly divergent sequences. However, recent works suggest that different features may have different importance in diverse protein classes and it would be an advantage to have more customizable approaches, capable to deal with different alignment definitions. Here we present Rigapollo, a highly flexible pairwise alignment method based on a pairwise HMM-SVM that can use any type of information to build alignments. Rigapollo lets the user decide the optimal features to align their protein class of interest. It outperforms current state of the art methods on two well-known benchmark datasets when aligning highly divergent sequences. A Python implementation of the algorithm is available at http://ibsquare.be/rigapollo. wim.vranken@vub.be. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Protein Sub-Nuclear Localization Prediction Using SVM and Pfam Domain Information

Science.gov (United States)

Kumar, Ravindra; Jain, Sohni; Kumari, Bandana; Kumar, Manish

2014-01-01

The nucleus is the largest and the highly organized organelle of eukaryotic cells. Within nucleus exist a number of pseudo-compartments, which are not separated by any membrane, yet each of them contains only a specific set of proteins. Understanding protein sub-nuclear localization can hence be an important step towards understanding biological functions of the nucleus. Here we have described a method, SubNucPred developed by us for predicting the sub-nuclear localization of proteins. This method predicts protein localization for 10 different sub-nuclear locations sequentially by combining presence or absence of unique Pfam domain and amino acid composition based SVM model. The prediction accuracy during leave-one-out cross-validation for centromeric proteins was 85.05%, for chromosomal proteins 76.85%, for nuclear speckle proteins 81.27%, for nucleolar proteins 81.79%, for nuclear envelope proteins 79.37%, for nuclear matrix proteins 77.78%, for nucleoplasm proteins 76.98%, for nuclear pore complex proteins 88.89%, for PML body proteins 75.40% and for telomeric proteins it was 83.33%. Comparison with other reported methods showed that SubNucPred performs better than existing methods. A web-server for predicting protein sub-nuclear localization named SubNucPred has been established at http://14.139.227.92/mkumar/subnucpred/. Standalone version of SubNucPred can also be downloaded from the web-server. PMID:24897370

A SVM bases AI design for interactive gaming

OpenAIRE

Jiang, Yang; Jiang, Jianmin; Palmer, Ian

2008-01-01

Interactive gaming requires automatic processing on large volume of random data produced by players on spot, such as shooting, football kicking, boxing etc. In this paper, we describe an artificial intelligence approach in processing such random data for interactive gaming by using a one-class support vector machine (OC-SVM). In comparison with existing techniques, our OC-SVM based interactive gaming design has the features of: (i): high speed processing, providing instant response to the pla...

Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations.

Science.gov (United States)

Zhang, Yi; Ren, Jinchang; Jiang, Jianmin

2015-01-01

Maximum likelihood classifier (MLC) and support vector machines (SVM) are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions.

Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations

Directory of Open Access Journals (Sweden)

Yi Zhang

2015-01-01

Full Text Available Maximum likelihood classifier (MLC and support vector machines (SVM are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions.

Accurate Multisteps Traffic Flow Prediction Based on SVM

Directory of Open Access Journals (Sweden)

Zhang Mingheng

2013-01-01

Full Text Available Accurate traffic flow prediction is prerequisite and important for realizing intelligent traffic control and guidance, and it is also the objective requirement for intelligent traffic management. Due to the strong nonlinear, stochastic, time-varying characteristics of urban transport system, artificial intelligence methods such as support vector machine (SVM are now receiving more and more attentions in this research field. Compared with the traditional single-step prediction method, the multisteps prediction has the ability that can predict the traffic state trends over a certain period in the future. From the perspective of dynamic decision, it is far important than the current traffic condition obtained. Thus, in this paper, an accurate multi-steps traffic flow prediction model based on SVM was proposed. In which, the input vectors were comprised of actual traffic volume and four different types of input vectors were compared to verify their prediction performance with each other. Finally, the model was verified with actual data in the empirical analysis phase and the test results showed that the proposed SVM model had a good ability for traffic flow prediction and the SVM-HPT model outperformed the other three models for prediction.

Identification of eggs from different production systems based on hyperspectra and CS-SVM.

Science.gov (United States)

Sun, J; Cong, S L; Mao, H P; Zhou, X; Wu, X H; Zhang, X D

2017-06-01

1. To identify the origin of table eggs more accurately, a method based on hyperspectral imaging technology was studied. 2. The hyperspectral data of 200 samples of intensive and extensive eggs were collected. Standard normalised variables combined with a Savitzky-Golay were used to eliminate noise, then stepwise regression (SWR) was used for feature selection. Grid search algorithm (GS), genetic search algorithm (GA), particle swarm optimisation algorithm (PSO) and cuckoo search algorithm (CS) were applied by support vector machine (SVM) methods to establish an SVM identification model with the optimal parameters. The full spectrum data and the data after feature selection were the input of the model, while egg category was the output. 3. The SWR-CS-SVM model performed better than the other models, including SWR-GS-SVM, SWR-GA-SVM, SWR-PSO-SVM and others based on full spectral data. The training and test classification accuracy of the SWR-CS-SVM model were respectively 99.3% and 96%. 4. SWR-CS-SVM proved effective for identifying egg varieties and could also be useful for the non-destructive identification of other types of egg.

A method of neighbor classes based SVM classification for optical printed Chinese character recognition.

Science.gov (United States)

Zhang, Jie; Wu, Xiaohong; Yu, Yanmei; Luo, Daisheng

2013-01-01

In optical printed Chinese character recognition (OPCCR), many classifiers have been proposed for the recognition. Among the classifiers, support vector machine (SVM) might be the best classifier. However, SVM is a classifier for two classes. When it is used for multi-classes in OPCCR, its computation is time-consuming. Thus, we propose a neighbor classes based SVM (NC-SVM) to reduce the computation consumption of SVM. Experiments of NC-SVM classification for OPCCR have been done. The results of the experiments have shown that the NC-SVM we proposed can effectively reduce the computation time in OPCCR.

A support vector machine (SVM) based voltage stability classifier

Energy Technology Data Exchange (ETDEWEB)

Dosano, R.D.; Song, H. [Kunsan National Univ., Kunsan, Jeonbuk (Korea, Republic of); Lee, B. [Korea Univ., Seoul (Korea, Republic of)

2007-07-01

Power system stability has become even more complex and critical with the advent of deregulated energy markets and the growing desire to completely employ existing transmission and infrastructure. The economic pressure on electricity markets forces the operation of power systems and components to their limit of capacity and performance. System conditions can be more exposed to instability due to greater uncertainty in day to day system operations and increase in the number of potential components for system disturbances potentially resulting in voltage stability. This paper proposed a support vector machine (SVM) based power system voltage stability classifier using local measurements of voltage and active power of load. It described the procedure for fast classification of long-term voltage stability using the SVM algorithm. The application of the SVM based voltage stability classifier was presented with reference to the choice of input parameters; input data preconditioning; moving window for feature vector; determination of learning samples; and other considerations in SVM applications. The paper presented a case study with numerical examples of an 11-bus test system. The test results for the feasibility study demonstrated that the classifier could offer an excellent performance in classification with time-series measurements in terms of long-term voltage stability. 9 refs., 14 figs.

Predicting the Types of Ion Channel-Targeted Conotoxins Based on AVC-SVM Model.

Science.gov (United States)

Xianfang, Wang; Junmei, Wang; Xiaolei, Wang; Yue, Zhang

2017-01-01

The conotoxin proteins are disulfide-rich small peptides. Predicting the types of ion channel-targeted conotoxins has great value in the treatment of chronic diseases, epilepsy, and cardiovascular diseases. To solve the problem of information redundancy existing when using current methods, a new model is presented to predict the types of ion channel-targeted conotoxins based on AVC (Analysis of Variance and Correlation) and SVM (Support Vector Machine). First, the F value is used to measure the significance level of the feature for the result, and the attribute with smaller F value is filtered by rough selection. Secondly, redundancy degree is calculated by Pearson Correlation Coefficient. And the threshold is set to filter attributes with weak independence to get the result of the refinement. Finally, SVM is used to predict the types of ion channel-targeted conotoxins. The experimental results show the proposed AVC-SVM model reaches an overall accuracy of 91.98%, an average accuracy of 92.17%, and the total number of parameters of 68. The proposed model provides highly useful information for further experimental research. The prediction model will be accessed free of charge at our web server.

Feature selection based on SVM significance maps for classification of dementia

NARCIS (Netherlands)

E.E. Bron (Esther); M. Smits (Marion); J.C. van Swieten (John); W.J. Niessen (Wiro); S. Klein (Stefan)

2014-01-01

textabstractSupport vector machine significance maps (SVM p-maps) previously showed clusters of significantly different voxels in dementiarelated brain regions. We propose a novel feature selection method for classification of dementia based on these p-maps. In our approach, the SVM p-maps are

Research on Classification of Chinese Text Data Based on SVM

Science.gov (United States)

Lin, Yuan; Yu, Hongzhi; Wan, Fucheng; Xu, Tao

2017-09-01

Data Mining has important application value in today’s industry and academia. Text classification is a very important technology in data mining. At present, there are many mature algorithms for text classification. KNN, NB, AB, SVM, decision tree and other classification methods all show good classification performance. Support Vector Machine’ (SVM) classification method is a good classifier in machine learning research. This paper will study the classification effect based on the SVM method in the Chinese text data, and use the support vector machine method in the chinese text to achieve the classify chinese text, and to able to combination of academia and practical application.

SVM Based Descriptor Selection and Classification of Neurodegenerative Disease Drugs for Pharmacological Modeling.

Science.gov (United States)

Shahid, Mohammad; Shahzad Cheema, Muhammad; Klenner, Alexander; Younesi, Erfan; Hofmann-Apitius, Martin

2013-03-01

Systems pharmacological modeling of drug mode of action for the next generation of multitarget drugs may open new routes for drug design and discovery. Computational methods are widely used in this context amongst which support vector machines (SVM) have proven successful in addressing the challenge of classifying drugs with similar features. We have applied a variety of such SVM-based approaches, namely SVM-based recursive feature elimination (SVM-RFE). We use the approach to predict the pharmacological properties of drugs widely used against complex neurodegenerative disorders (NDD) and to build an in-silico computational model for the binary classification of NDD drugs from other drugs. Application of an SVM-RFE model to a set of drugs successfully classified NDD drugs from non-NDD drugs and resulted in overall accuracy of ∼80 % with 10 fold cross validation using 40 top ranked molecular descriptors selected out of total 314 descriptors. Moreover, SVM-RFE method outperformed linear discriminant analysis (LDA) based feature selection and classification. The model reduced the multidimensional descriptors space of drugs dramatically and predicted NDD drugs with high accuracy, while avoiding over fitting. Based on these results, NDD-specific focused libraries of drug-like compounds can be designed and existing NDD-specific drugs can be characterized by a well-characterized set of molecular descriptors. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Predication of Crane Condition Parameters Based on SVM and AR

International Nuclear Information System (INIS)

Xu Xiuzhong; Hu Xiong; Zhou Congxiao

2011-01-01

Through statistic analysis of vibration signals of motor on the container crane hoisting mechanism in a port, the feature vectors with vibration are obtained. Through data preprocessing and training data, Training models of condition parameters based on support vector machine (SVM) are established. The testing data of condition monitoring parameters can be predicted by the training models. During training the models, the penalty parameter and kernel function of model are optimized by cross validation. In order to analysis the accurate of SVM model, autoregressive model is used to predict the trend of vibration. The research showed the predicted results of model using SVM are better than the results by autoregressive (AR) modeling.

Lamb Wave Damage Quantification Using GA-Based LS-SVM

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Fuqiang Sun

2017-06-01

Full Text Available Lamb waves have been reported to be an efficient tool for non-destructive evaluations (NDE for various application scenarios. However, accurate and reliable damage quantification using the Lamb wave method is still a practical challenge, due to the complex underlying mechanism of Lamb wave propagation and damage detection. This paper presents a Lamb wave damage quantification method using a least square support vector machine (LS-SVM and a genetic algorithm (GA. Three damage sensitive features, namely, normalized amplitude, phase change, and correlation coefficient, were proposed to describe changes of Lamb wave characteristics caused by damage. In view of commonly used data-driven methods, the GA-based LS-SVM model using the proposed three damage sensitive features was implemented to evaluate the crack size. The GA method was adopted to optimize the model parameters. The results of GA-based LS-SVM were validated using coupon test data and lap joint component test data with naturally developed fatigue cracks. Cases of different loading and manufacturer were also included to further verify the robustness of the proposed method for crack quantification.

Lamb Wave Damage Quantification Using GA-Based LS-SVM.

Science.gov (United States)

Sun, Fuqiang; Wang, Ning; He, Jingjing; Guan, Xuefei; Yang, Jinsong

2017-06-12

Lamb waves have been reported to be an efficient tool for non-destructive evaluations (NDE) for various application scenarios. However, accurate and reliable damage quantification using the Lamb wave method is still a practical challenge, due to the complex underlying mechanism of Lamb wave propagation and damage detection. This paper presents a Lamb wave damage quantification method using a least square support vector machine (LS-SVM) and a genetic algorithm (GA). Three damage sensitive features, namely, normalized amplitude, phase change, and correlation coefficient, were proposed to describe changes of Lamb wave characteristics caused by damage. In view of commonly used data-driven methods, the GA-based LS-SVM model using the proposed three damage sensitive features was implemented to evaluate the crack size. The GA method was adopted to optimize the model parameters. The results of GA-based LS-SVM were validated using coupon test data and lap joint component test data with naturally developed fatigue cracks. Cases of different loading and manufacturer were also included to further verify the robustness of the proposed method for crack quantification.

Laos Organization Name Using Cascaded Model Based on SVM and CRF

Directory of Open Access Journals (Sweden)

Duan Shaopeng

2017-01-01

Full Text Available According to the characteristics of Laos organization name, this paper proposes a two layer model based on conditional random field (CRF and support vector machine (SVM for Laos organization name recognition. A layer of model uses CRF to recognition simple organization name, and the result is used to support the decision of the second level. Based on the driving method, the second layer uses SVM and CRF to recognition the complicated organization name. Finally, the results of the two levels are combined, And by a subsequent treatment to correct results of low confidence recognition. The results show that this approach based on SVM and CRF is efficient in recognizing organization name through open test for real linguistics, and the recalling rate achieve 80. 83％and the precision rate achieves 82. 75％.

PSO-SVM-Based Online Locomotion Mode Identification for Rehabilitation Robotic Exoskeletons.

Science.gov (United States)

Long, Yi; Du, Zhi-Jiang; Wang, Wei-Dong; Zhao, Guang-Yu; Xu, Guo-Qiang; He, Long; Mao, Xi-Wang; Dong, Wei

2016-09-02

Locomotion mode identification is essential for the control of a robotic rehabilitation exoskeletons. This paper proposes an online support vector machine (SVM) optimized by particle swarm optimization (PSO) to identify different locomotion modes to realize a smooth and automatic locomotion transition. A PSO algorithm is used to obtain the optimal parameters of SVM for a better overall performance. Signals measured by the foot pressure sensors integrated in the insoles of wearable shoes and the MEMS-based attitude and heading reference systems (AHRS) attached on the shoes and shanks of leg segments are fused together as the input information of SVM. Based on the chosen window whose size is 200 ms (with sampling frequency of 40 Hz), a three-layer wavelet packet analysis (WPA) is used for feature extraction, after which, the kernel principal component analysis (kPCA) is utilized to reduce the dimension of the feature set to reduce computation cost of the SVM. Since the signals are from two types of different sensors, the normalization is conducted to scale the input into the interval of [0, 1]. Five-fold cross validation is adapted to train the classifier, which prevents the classifier over-fitting. Based on the SVM model obtained offline in MATLAB, an online SVM algorithm is constructed for locomotion mode identification. Experiments are performed for different locomotion modes and experimental results show the effectiveness of the proposed algorithm with an accuracy of 96.00% ± 2.45%. To improve its accuracy, majority vote algorithm (MVA) is used for post-processing, with which the identification accuracy is better than 98.35% ± 1.65%. The proposed algorithm can be extended and employed in the field of robotic rehabilitation and assistance.

PSO-SVM-Based Online Locomotion Mode Identification for Rehabilitation Robotic Exoskeletons

Directory of Open Access Journals (Sweden)

Yi Long

2016-09-01

Full Text Available Locomotion mode identification is essential for the control of a robotic rehabilitation exoskeletons. This paper proposes an online support vector machine (SVM optimized by particle swarm optimization (PSO to identify different locomotion modes to realize a smooth and automatic locomotion transition. A PSO algorithm is used to obtain the optimal parameters of SVM for a better overall performance. Signals measured by the foot pressure sensors integrated in the insoles of wearable shoes and the MEMS-based attitude and heading reference systems (AHRS attached on the shoes and shanks of leg segments are fused together as the input information of SVM. Based on the chosen window whose size is 200 ms (with sampling frequency of 40 Hz, a three-layer wavelet packet analysis (WPA is used for feature extraction, after which, the kernel principal component analysis (kPCA is utilized to reduce the dimension of the feature set to reduce computation cost of the SVM. Since the signals are from two types of different sensors, the normalization is conducted to scale the input into the interval of [0, 1]. Five-fold cross validation is adapted to train the classifier, which prevents the classifier over-fitting. Based on the SVM model obtained offline in MATLAB, an online SVM algorithm is constructed for locomotion mode identification. Experiments are performed for different locomotion modes and experimental results show the effectiveness of the proposed algorithm with an accuracy of 96.00% ± 2.45%. To improve its accuracy, majority vote algorithm (MVA is used for post-processing, with which the identification accuracy is better than 98.35% ± 1.65%. The proposed algorithm can be extended and employed in the field of robotic rehabilitation and assistance.

SVM Classifiers: The Objects Identification on the Base of Their Hyperspectral Features

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Demidova Liliya

2017-01-01

Full Text Available The problem of the objects identification on the base of their hyperspectral features has been considered. It is offered to use the SVM classifiers on the base of the modified PSO algorithm, adapted to specifics of the problem of the objects identification on the base of their hyperspectral features. The results of the objects identification on the base of their hyperspectral features with using of the SVM classifiers have been presented.

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM.

Science.gov (United States)

Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei; Song, Houbing

2018-01-15

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model's performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM's parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models' performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors.

[Measurement of soil organic matter and available K based on SPA-LS-SVM].

Science.gov (United States)

Zhang, Hai-Liang; Liu, Xue-Mei; He, Yong

2014-05-01

Visible and short wave infrared spectroscopy (Vis/SW-NIRS) was investigated in the present study for measurement of soil organic matter (OM) and available potassium (K). Four types of pretreatments including smoothing, SNV, MSC and SG smoothing+first derivative were adopted to eliminate the system noises and external disturbances. Then partial least squares regression (PLSR) and least squares-support vector machine (LS-SVM) models were implemented for calibration models. The LS-SVM model was built by using characteristic wavelength based on successive projections algorithm (SPA). Simultaneously, the performance of LSSVM models was compared with PLSR models. The results indicated that LS-SVM models using characteristic wavelength as inputs based on SPA outperformed PLSR models. The optimal SPA-LS-SVM models were achieved, and the correlation coefficient (r), and RMSEP were 0. 860 2 and 2. 98 for OM and 0. 730 5 and 15. 78 for K, respectively. The results indicated that visible and short wave near infrared spectroscopy (Vis/SW-NIRS) (325 approximately 1 075 nm) combined with LS-SVM based on SPA could be utilized as a precision method for the determination of soil properties.

[Hyperspectral remote sensing image classification based on SVM optimized by clonal selection].

Science.gov (United States)

Liu, Qing-Jie; Jing, Lin-Hai; Wang, Meng-Fei; Lin, Qi-Zhong

2013-03-01

Model selection for support vector machine (SVM) involving kernel and the margin parameter values selection is usually time-consuming, impacts training efficiency of SVM model and final classification accuracies of SVM hyperspectral remote sensing image classifier greatly. Firstly, based on combinatorial optimization theory and cross-validation method, artificial immune clonal selection algorithm is introduced to the optimal selection of SVM (CSSVM) kernel parameter a and margin parameter C to improve the training efficiency of SVM model. Then an experiment of classifying AVIRIS in India Pine site of USA was performed for testing the novel CSSVM, as well as a traditional SVM classifier with general Grid Searching cross-validation method (GSSVM) for comparison. And then, evaluation indexes including SVM model training time, classification overall accuracy (OA) and Kappa index of both CSSVM and GSSVM were all analyzed quantitatively. It is demonstrated that OA of CSSVM on test samples and whole image are 85.1% and 81.58, the differences from that of GSSVM are both within 0.08% respectively; And Kappa indexes reach 0.8213 and 0.7728, the differences from that of GSSVM are both within 0.001; While the ratio of model training time of CSSVM and GSSVM is between 1/6 and 1/10. Therefore, CSSVM is fast and accurate algorithm for hyperspectral image classification and is superior to GSSVM.

Human Walking Pattern Recognition Based on KPCA and SVM with Ground Reflex Pressure Signal

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Zhaoqin Peng

2013-01-01

Full Text Available Algorithms based on the ground reflex pressure (GRF signal obtained from a pair of sensing shoes for human walking pattern recognition were investigated. The dimensionality reduction algorithms based on principal component analysis (PCA and kernel principal component analysis (KPCA for walking pattern data compression were studied in order to obtain higher recognition speed. Classifiers based on support vector machine (SVM, SVM-PCA, and SVM-KPCA were designed, and the classification performances of these three kinds of algorithms were compared using data collected from a person who was wearing the sensing shoes. Experimental results showed that the algorithm fusing SVM and KPCA had better recognition performance than the other two methods. Experimental outcomes also confirmed that the sensing shoes developed in this paper can be employed for automatically recognizing human walking pattern in unlimited environments which demonstrated the potential application in the control of exoskeleton robots.

Absolute cosine-based SVM-RFE feature selection method for prostate histopathological grading.

Science.gov (United States)

Sahran, Shahnorbanun; Albashish, Dheeb; Abdullah, Azizi; Shukor, Nordashima Abd; Hayati Md Pauzi, Suria

2018-04-18

Feature selection (FS) methods are widely used in grading and diagnosing prostate histopathological images. In this context, FS is based on the texture features obtained from the lumen, nuclei, cytoplasm and stroma, all of which are important tissue components. However, it is difficult to represent the high-dimensional textures of these tissue components. To solve this problem, we propose a new FS method that enables the selection of features with minimal redundancy in the tissue components. We categorise tissue images based on the texture of individual tissue components via the construction of a single classifier and also construct an ensemble learning model by merging the values obtained by each classifier. Another issue that arises is overfitting due to the high-dimensional texture of individual tissue components. We propose a new FS method, SVM-RFE(AC), that integrates a Support Vector Machine-Recursive Feature Elimination (SVM-RFE) embedded procedure with an absolute cosine (AC) filter method to prevent redundancy in the selected features of the SV-RFE and an unoptimised classifier in the AC. We conducted experiments on H&E histopathological prostate and colon cancer images with respect to three prostate classifications, namely benign vs. grade 3, benign vs. grade 4 and grade 3 vs. grade 4. The colon benchmark dataset requires a distinction between grades 1 and 2, which are the most difficult cases to distinguish in the colon domain. The results obtained by both the single and ensemble classification models (which uses the product rule as its merging method) confirm that the proposed SVM-RFE(AC) is superior to the other SVM and SVM-RFE-based methods. We developed an FS method based on SVM-RFE and AC and successfully showed that its use enabled the identification of the most crucial texture feature of each tissue component. Thus, it makes possible the distinction between multiple Gleason grades (e.g. grade 3 vs. grade 4) and its performance is far superior to

Diagnosis of Elevator Faults with LS-SVM Based on Optimization by K-CV

Directory of Open Access Journals (Sweden)

Zhou Wan

2015-01-01

Full Text Available Several common elevator malfunctions were diagnosed with a least square support vector machine (LS-SVM. After acquiring vibration signals of various elevator functions, their energy characteristics and time domain indicators were extracted by theoretically analyzing the optimal wavelet packet, in order to construct a feature vector of malfunctions for identifying causes of the malfunctions as input of LS-SVM. Meanwhile, parameters about LS-SVM were optimized by K-fold cross validation (K-CV. After diagnosing deviated elevator guide rail, deviated shape of guide shoe, abnormal running of tractor, erroneous rope groove of traction sheave, deviated guide wheel, and tension of wire rope, the results suggested that the LS-SVM based on K-CV optimization was one of effective methods for diagnosing elevator malfunctions.

Applications of PCA and SVM-PSO Based Real-Time Face Recognition System

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Ming-Yuan Shieh

2014-01-01

Full Text Available This paper incorporates principal component analysis (PCA with support vector machine-particle swarm optimization (SVM-PSO for developing real-time face recognition systems. The integrated scheme aims to adopt the SVM-PSO method to improve the validity of PCA based image recognition systems on dynamically visual perception. The face recognition for most human-robot interaction applications is accomplished by PCA based method because of its dimensionality reduction. However, PCA based systems are only suitable for processing the faces with the same face expressions and/or under the same view directions. Since the facial feature selection process can be considered as a problem of global combinatorial optimization in machine learning, the SVM-PSO is usually used as an optimal classifier of the system. In this paper, the PSO is used to implement a feature selection, and the SVMs serve as fitness functions of the PSO for classification problems. Experimental results demonstrate that the proposed method simplifies features effectively and obtains higher classification accuracy.

Settlement Prediction of Road Soft Foundation Using a Support Vector Machine (SVM Based on Measured Data

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Yu Huiling

2016-01-01

Full Text Available The suppor1t vector machine (SVM is a relatively new artificial intelligence technique which is increasingly being applied to geotechnical problems and is yielding encouraging results. SVM is a new machine learning method based on the statistical learning theory. A case study based on road foundation engineering project shows that the forecast results are in good agreement with the measured data. The SVM model is also compared with BP artificial neural network model and traditional hyperbola method. The prediction results indicate that the SVM model has a better prediction ability than BP neural network model and hyperbola method. Therefore, settlement prediction based on SVM model can reflect actual settlement process more correctly. The results indicate that it is effective and feasible to use this method and the nonlinear mapping relation between foundation settlement and its influence factor can be expressed well. It will provide a new method to predict foundation settlement.

Intelligent Recognition of Lung Nodule Combining Rule-based and C-SVM Classifiers

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Bin Li

2012-02-01

Full Text Available Computer-aided detection(CAD system for lung nodules plays the important role in the diagnosis of lung cancer. In this paper, an improved intelligent recognition method of lung nodule in HRCT combing rule-based and cost-sensitive support vector machine(C-SVM classifiers is proposed for detecting both solid nodules and ground-glass opacity(GGO nodules(part solid and nonsolid. This method consists of several steps. Firstly, segmentation of regions of interest(ROIs, including pulmonary parenchyma and lung nodule candidates, is a difficult task. On one side, the presence of noise lowers the visibility of low-contrast objects. On the other side, different types of nodules, including small nodules, nodules connecting to vasculature or other structures, part-solid or nonsolid nodules, are complex, noisy, weak edge or difficult to define the boundary. In order to overcome the difficulties of obvious boundary-leak and slow evolvement speed problem in segmentatioin of weak edge, an overall segmentation method is proposed, they are: the lung parenchyma is extracted based on threshold and morphologic segmentation method; the image denoising and enhancing is realized by nonlinear anisotropic diffusion filtering(NADF method; candidate pulmonary nodules are segmented by the improved C-V level set method, in which the segmentation result of EM-based fuzzy threshold method is used as the initial contour of active contour model and a constrained energy term is added into the PDE of level set function. Then, lung nodules are classified by using the intelligent classifiers combining rules and C-SVM. Rule-based classification is first used to remove easily dismissible nonnodule objects, then C-SVM classification are used to further classify nodule candidates and reduce the number of false positive(FP objects. In order to increase the efficiency of SVM, an improved training method is used to train SVM, which uses the grid search method to search the optimal

Intelligent Recognition of Lung Nodule Combining Rule-based and C-SVM Classifiers

Directory of Open Access Journals (Sweden)

Bin Li

2011-10-01

Full Text Available Computer-aided detection(CAD system for lung nodules plays the important role in the diagnosis of lung cancer. In this paper, an improved intelligent recognition method of lung nodule in HRCT combing rule-based and costsensitive support vector machine(C-SVM classifiers is proposed for detecting both solid nodules and ground-glass opacity(GGO nodules(part solid and nonsolid. This method consists of several steps. Firstly, segmentation of regions of interest(ROIs, including pulmonary parenchyma and lung nodule candidates, is a difficult task. On one side, the presence of noise lowers the visibility of low-contrast objects. On the other side, different types of nodules, including small nodules, nodules connecting to vasculature or other structures, part-solid or nonsolid nodules, are complex, noisy, weak edge or difficult to define the boundary. In order to overcome the difficulties of obvious boundary-leak and slow evolvement speed problem in segmentatioin of weak edge, an overall segmentation method is proposed, they are: the lung parenchyma is extracted based on threshold and morphologic segmentation method; the image denoising and enhancing is realized by nonlinear anisotropic diffusion filtering(NADF method;candidate pulmonary nodules are segmented by the improved C-V level set method, in which the segmentation result of EM-based fuzzy threshold method is used as the initial contour of active contour model and a constrained energy term is added into the PDE of level set function. Then, lung nodules are classified by using the intelligent classifiers combining rules and C-SVM. Rule-based classification is first used to remove easily dismissible nonnodule objects, then C-SVM classification are used to further classify nodule candidates and reduce the number of false positive(FP objects. In order to increase the efficiency of SVM, an improved training method is used to train SVM, which uses the grid search method to search the optimal parameters

Effective Sequential Classifier Training for SVM-Based Multitemporal Remote Sensing Image Classification

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Guo, Yiqing; Jia, Xiuping; Paull, David

2018-06-01

The explosive availability of remote sensing images has challenged supervised classification algorithms such as Support Vector Machines (SVM), as training samples tend to be highly limited due to the expensive and laborious task of ground truthing. The temporal correlation and spectral similarity between multitemporal images have opened up an opportunity to alleviate this problem. In this study, a SVM-based Sequential Classifier Training (SCT-SVM) approach is proposed for multitemporal remote sensing image classification. The approach leverages the classifiers of previous images to reduce the required number of training samples for the classifier training of an incoming image. For each incoming image, a rough classifier is firstly predicted based on the temporal trend of a set of previous classifiers. The predicted classifier is then fine-tuned into a more accurate position with current training samples. This approach can be applied progressively to sequential image data, with only a small number of training samples being required from each image. Experiments were conducted with Sentinel-2A multitemporal data over an agricultural area in Australia. Results showed that the proposed SCT-SVM achieved better classification accuracies compared with two state-of-the-art model transfer algorithms. When training data are insufficient, the overall classification accuracy of the incoming image was improved from 76.18% to 94.02% with the proposed SCT-SVM, compared with those obtained without the assistance from previous images. These results demonstrate that the leverage of a priori information from previous images can provide advantageous assistance for later images in multitemporal image classification.

The 2nu-SVM: A Cost-Sensitive Extension of the nu-SVM

National Research Council Canada - National Science Library

Davenport, Mark A

2005-01-01

.... In this report we review cost-sensitive extensions of standard support vector machines (SVMs). In particular, we describe cost-sensitive extensions of the C-SVM and the nu-SVM, which we denote the 2C-SVM and 2nu-SVM respectively...

DSP Based Direct Torque Control of Permanent Magnet Synchronous Motor (PMSM) using Space Vector Modulation (DTC-SVM)

DEFF Research Database (Denmark)

Swierczynski, Dariusz; Kazmierkowski, Marian P.; Blaabjerg, Frede

2002-01-01

DSP Based Direct Torque Control of Permanent Magnet Synchronous Motor (PMSM) using Space Vector Modulation (DTC-SVM)......DSP Based Direct Torque Control of Permanent Magnet Synchronous Motor (PMSM) using Space Vector Modulation (DTC-SVM)...

Learning using privileged information: SVM+ and weighted SVM.

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Lapin, Maksim; Hein, Matthias; Schiele, Bernt

2014-05-01

Prior knowledge can be used to improve predictive performance of learning algorithms or reduce the amount of data required for training. The same goal is pursued within the learning using privileged information paradigm which was recently introduced by Vapnik et al. and is aimed at utilizing additional information available only at training time-a framework implemented by SVM+. We relate the privileged information to importance weighting and show that the prior knowledge expressible with privileged features can also be encoded by weights associated with every training example. We show that a weighted SVM can always replicate an SVM+ solution, while the converse is not true and we construct a counterexample highlighting the limitations of SVM+. Finally, we touch on the problem of choosing weights for weighted SVMs when privileged features are not available. Copyright © 2014 Elsevier Ltd. All rights reserved.

Fault diagnosis method based on FFT-RPCA-SVM for Cascaded-Multilevel Inverter.

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Wang, Tianzhen; Qi, Jie; Xu, Hao; Wang, Yide; Liu, Lei; Gao, Diju

2016-01-01

Thanks to reduced switch stress, high quality of load wave, easy packaging and good extensibility, the cascaded H-bridge multilevel inverter is widely used in wind power system. To guarantee stable operation of system, a new fault diagnosis method, based on Fast Fourier Transform (FFT), Relative Principle Component Analysis (RPCA) and Support Vector Machine (SVM), is proposed for H-bridge multilevel inverter. To avoid the influence of load variation on fault diagnosis, the output voltages of the inverter is chosen as the fault characteristic signals. To shorten the time of diagnosis and improve the diagnostic accuracy, the main features of the fault characteristic signals are extracted by FFT. To further reduce the training time of SVM, the feature vector is reduced based on RPCA that can get a lower dimensional feature space. The fault classifier is constructed via SVM. An experimental prototype of the inverter is built to test the proposed method. Compared to other fault diagnosis methods, the experimental results demonstrate the high accuracy and efficiency of the proposed method. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Prediction of N-Methyl-D-Aspartate Receptor GluN1-Ligand Binding Affinity by a Novel SVM-Pose/SVM-Score Combinatorial Ensemble Docking Scheme.

Science.gov (United States)

Leong, Max K; Syu, Ren-Guei; Ding, Yi-Lung; Weng, Ching-Feng

2017-01-06

The glycine-binding site of the N-methyl-D-aspartate receptor (NMDAR) subunit GluN1 is a potential pharmacological target for neurodegenerative disorders. A novel combinatorial ensemble docking scheme using ligand and protein conformation ensembles and customized support vector machine (SVM)-based models to select the docked pose and to predict the docking score was generated for predicting the NMDAR GluN1-ligand binding affinity. The predicted root mean square deviation (RMSD) values in pose by SVM-Pose models were found to be in good agreement with the observed values (n = 30, r 2  = 0.928-0.988,  = 0.894-0.954, RMSE = 0.002-0.412, s = 0.001-0.214), and the predicted pK i values by SVM-Score were found to be in good agreement with the observed values for the training samples (n = 24, r 2  = 0.967,  = 0.899, RMSE = 0.295, s = 0.170) and test samples (n = 13, q 2  = 0.894, RMSE = 0.437, s = 0.202). When subjected to various statistical validations, the developed SVM-Pose and SVM-Score models consistently met the most stringent criteria. A mock test asserted the predictivity of this novel docking scheme. Collectively, this accurate novel combinatorial ensemble docking scheme can be used to predict the NMDAR GluN1-ligand binding affinity for facilitating drug discovery.

In Silico Prediction of Gamma-Aminobutyric Acid Type-A Receptors Using Novel Machine-Learning-Based SVM and GBDT Approaches

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Zhijun Liao

2016-01-01

Full Text Available Gamma-aminobutyric acid type-A receptors (GABAARs belong to multisubunit membrane spanning ligand-gated ion channels (LGICs which act as the principal mediators of rapid inhibitory synaptic transmission in the human brain. Therefore, the category prediction of GABAARs just from the protein amino acid sequence would be very helpful for the recognition and research of novel receptors. Based on the proteins’ physicochemical properties, amino acids composition and position, a GABAAR classifier was first constructed using a 188-dimensional (188D algorithm at 90% cd-hit identity and compared with pseudo-amino acid composition (PseAAC and ProtrWeb web-based algorithms for human GABAAR proteins. Then, four classifiers including gradient boosting decision tree (GBDT, random forest (RF, a library for support vector machine (libSVM, and k-nearest neighbor (k-NN were compared on the dataset at cd-hit 40% low identity. This work obtained the highest correctly classified rate at 96.8% and the highest specificity at 99.29%. But the values of sensitivity, accuracy, and Matthew’s correlation coefficient were a little lower than those of PseAAC and ProtrWeb; GBDT and libSVM can make a little better performance than RF and k-NN at the second dataset. In conclusion, a GABAAR classifier was successfully constructed using only the protein sequence information.

VLSI Design of SVM-Based Seizure Detection System With On-Chip Learning Capability.

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Feng, Lichen; Li, Zunchao; Wang, Yuanfa

2018-02-01

Portable automatic seizure detection system is very convenient for epilepsy patients to carry. In order to make the system on-chip trainable with high efficiency and attain high detection accuracy, this paper presents a very large scale integration (VLSI) design based on the nonlinear support vector machine (SVM). The proposed design mainly consists of a feature extraction (FE) module and an SVM module. The FE module performs the three-level Daubechies discrete wavelet transform to fit the physiological bands of the electroencephalogram (EEG) signal and extracts the time-frequency domain features reflecting the nonstationary signal properties. The SVM module integrates the modified sequential minimal optimization algorithm with the table-driven-based Gaussian kernel to enable efficient on-chip learning. The presented design is verified on an Altera Cyclone II field-programmable gate array and tested using the two publicly available EEG datasets. Experiment results show that the designed VLSI system improves the detection accuracy and training efficiency.

[Identification of varieties of cashmere by Vis/NIR spectroscopy technology based on PCA-SVM].

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Wu, Gui-Fang; He, Yong

2009-06-01

One mixed algorithm was presented to discriminate cashmere varieties with principal component analysis (PCA) and support vector machine (SVM). Cashmere fiber has such characteristics as threadlike, softness, glossiness and high tensile strength. The quality characters and economic value of each breed of cashmere are very different. In order to safeguard the consumer's rights and guarantee the quality of cashmere product, quickly, efficiently and correctly identifying cashmere has significant meaning to the production and transaction of cashmere material. The present research adopts Vis/NIRS spectroscopy diffuse techniques to collect the spectral data of cashmere. The near infrared fingerprint of cashmere was acquired by principal component analysis (PCA), and support vector machine (SVM) methods were used to further identify the cashmere material. The result of PCA indicated that the score map made by the scores of PC1, PC2 and PC3 was used, and 10 principal components (PCs) were selected as the input of support vector machine (SVM) based on the reliabilities of PCs of 99.99%. One hundred cashmere samples were used for calibration and the remaining 75 cashmere samples were used for validation. A one-against-all multi-class SVM model was built, the capabilities of SVM with different kernel function were comparatively analyzed, and the result showed that SVM possessing with the Gaussian kernel function has the best identification capabilities with the accuracy of 100%. This research indicated that the data mining method of PCA-SVM has a good identification effect, and can work as a new method for rapid identification of cashmere material varieties.

Power quality events recognition using a SVM-based method

Energy Technology Data Exchange (ETDEWEB)

Cerqueira, Augusto Santiago; Ferreira, Danton Diego; Ribeiro, Moises Vidal; Duque, Carlos Augusto [Department of Electrical Circuits, Federal University of Juiz de Fora, Campus Universitario, 36036 900, Juiz de Fora MG (Brazil)

2008-09-15

In this paper, a novel SVM-based method for power quality event classification is proposed. A simple approach for feature extraction is introduced, based on the subtraction of the fundamental component from the acquired voltage signal. The resulting signal is presented to a support vector machine for event classification. Results from simulation are presented and compared with two other methods, the OTFR and the LCEC. The proposed method shown an improved performance followed by a reasonable computational cost. (author)

SVM-based glioma grading. Optimization by feature reduction analysis

International Nuclear Information System (INIS)

Zoellner, Frank G.; Schad, Lothar R.; Emblem, Kyrre E.; Harvard Medical School, Boston, MA; Oslo Univ. Hospital

2012-01-01

We investigated the predictive power of feature reduction analysis approaches in support vector machine (SVM)-based classification of glioma grade. In 101 untreated glioma patients, three analytic approaches were evaluated to derive an optimal reduction in features; (i) Pearson's correlation coefficients (PCC), (ii) principal component analysis (PCA) and (iii) independent component analysis (ICA). Tumor grading was performed using a previously reported SVM approach including whole-tumor cerebral blood volume (CBV) histograms and patient age. Best classification accuracy was found using PCA at 85% (sensitivity = 89%, specificity = 84%) when reducing the feature vector from 101 (100-bins rCBV histogram + age) to 3 principal components. In comparison, classification accuracy by PCC was 82% (89%, 77%, 2 dimensions) and 79% by ICA (87%, 75%, 9 dimensions). For improved speed (up to 30%) and simplicity, feature reduction by all three methods provided similar classification accuracy to literature values (∝87%) while reducing the number of features by up to 98%. (orig.)

SVM-based glioma grading. Optimization by feature reduction analysis

Energy Technology Data Exchange (ETDEWEB)

Zoellner, Frank G.; Schad, Lothar R. [University Medical Center Mannheim, Heidelberg Univ., Mannheim (Germany). Computer Assisted Clinical Medicine; Emblem, Kyrre E. [Massachusetts General Hospital, Charlestown, A.A. Martinos Center for Biomedical Imaging, Boston MA (United States). Dept. of Radiology; Harvard Medical School, Boston, MA (United States); Oslo Univ. Hospital (Norway). The Intervention Center

2012-11-01

We investigated the predictive power of feature reduction analysis approaches in support vector machine (SVM)-based classification of glioma grade. In 101 untreated glioma patients, three analytic approaches were evaluated to derive an optimal reduction in features; (i) Pearson's correlation coefficients (PCC), (ii) principal component analysis (PCA) and (iii) independent component analysis (ICA). Tumor grading was performed using a previously reported SVM approach including whole-tumor cerebral blood volume (CBV) histograms and patient age. Best classification accuracy was found using PCA at 85% (sensitivity = 89%, specificity = 84%) when reducing the feature vector from 101 (100-bins rCBV histogram + age) to 3 principal components. In comparison, classification accuracy by PCC was 82% (89%, 77%, 2 dimensions) and 79% by ICA (87%, 75%, 9 dimensions). For improved speed (up to 30%) and simplicity, feature reduction by all three methods provided similar classification accuracy to literature values ({proportional_to}87%) while reducing the number of features by up to 98%. (orig.)

GI-SVM: A sensitive method for predicting genomic islands based on unannotated sequence of a single genome.

Science.gov (United States)

Lu, Bingxin; Leong, Hon Wai

2016-02-01

Genomic islands (GIs) are clusters of functionally related genes acquired by lateral genetic transfer (LGT), and they are present in many bacterial genomes. GIs are extremely important for bacterial research, because they not only promote genome evolution but also contain genes that enhance adaption and enable antibiotic resistance. Many methods have been proposed to predict GI. But most of them rely on either annotations or comparisons with other closely related genomes. Hence these methods cannot be easily applied to new genomes. As the number of newly sequenced bacterial genomes rapidly increases, there is a need for methods to detect GI based solely on sequences of a single genome. In this paper, we propose a novel method, GI-SVM, to predict GIs given only the unannotated genome sequence. GI-SVM is based on one-class support vector machine (SVM), utilizing composition bias in terms of k-mer content. From our evaluations on three real genomes, GI-SVM can achieve higher recall compared with current methods, without much loss of precision. Besides, GI-SVM allows flexible parameter tuning to get optimal results for each genome. In short, GI-SVM provides a more sensitive method for researchers interested in a first-pass detection of GI in newly sequenced genomes.

Abnormal Gait Behavior Detection for Elderly Based on Enhanced Wigner-Ville Analysis and Cloud Incremental SVM Learning

Directory of Open Access Journals (Sweden)

Jian Luo

2016-01-01

Full Text Available A cloud based health care system is proposed in this paper for the elderly by providing abnormal gait behavior detection, classification, online diagnosis, and remote aid service. Intelligent mobile terminals with triaxial acceleration sensor embedded are used to capture the movement and ambulation information of elderly. The collected signals are first enhanced by a Kalman filter. And the magnitude of signal vector features is then extracted and decomposed into a linear combination of enhanced Gabor atoms. The Wigner-Ville analysis method is introduced and the problem is studied by joint time-frequency analysis. In order to solve the large-scale abnormal behavior data lacking problem in training process, a cloud based incremental SVM (CI-SVM learning method is proposed. The original abnormal behavior data are first used to get the initial SVM classifier. And the larger abnormal behavior data of elderly collected by mobile devices are then gathered in cloud platform to conduct incremental training and get the new SVM classifier. By the CI-SVM learning method, the knowledge of SVM classifier could be accumulated due to the dynamic incremental learning. Experimental results demonstrate that the proposed method is feasible and can be applied to aged care, emergency aid, and related fields.

Optimal structural design of the midship of a VLCC based on the strategy integrating SVM and GA

Science.gov (United States)

Sun, Li; Wang, Deyu

2012-03-01

In this paper a hybrid process of modeling and optimization, which integrates a support vector machine (SVM) and genetic algorithm (GA), was introduced to reduce the high time cost in structural optimization of ships. SVM, which is rooted in statistical learning theory and an approximate implementation of the method of structural risk minimization, can provide a good generalization performance in metamodeling the input-output relationship of real problems and consequently cuts down on high time cost in the analysis of real problems, such as FEM analysis. The GA, as a powerful optimization technique, possesses remarkable advantages for the problems that can hardly be optimized with common gradient-based optimization methods, which makes it suitable for optimizing models built by SVM. Based on the SVM-GA strategy, optimization of structural scantlings in the midship of a very large crude carrier (VLCC) ship was carried out according to the direct strength assessment method in common structural rules (CSR), which eventually demonstrates the high efficiency of SVM-GA in optimizing the ship structural scantlings under heavy computational complexity. The time cost of this optimization with SVM-GA has been sharply reduced, many more loops have been processed within a small amount of time and the design has been improved remarkably.

Classification of cardiovascular tissues using LBP based descriptors and a cascade SVM.

Science.gov (United States)

Mazo, Claudia; Alegre, Enrique; Trujillo, Maria

2017-08-01

Histological images have characteristics, such as texture, shape, colour and spatial structure, that permit the differentiation of each fundamental tissue and organ. Texture is one of the most discriminative features. The automatic classification of tissues and organs based on histology images is an open problem, due to the lack of automatic solutions when treating tissues without pathologies. In this paper, we demonstrate that it is possible to automatically classify cardiovascular tissues using texture information and Support Vector Machines (SVM). Additionally, we realised that it is feasible to recognise several cardiovascular organs following the same process. The texture of histological images was described using Local Binary Patterns (LBP), LBP Rotation Invariant (LBPri), Haralick features and different concatenations between them, representing in this way its content. Using a SVM with linear kernel, we selected the more appropriate descriptor that, for this problem, was a concatenation of LBP and LBPri. Due to the small number of the images available, we could not follow an approach based on deep learning, but we selected the classifier who yielded the higher performance by comparing SVM with Random Forest and Linear Discriminant Analysis. Once SVM was selected as the classifier with a higher area under the curve that represents both higher recall and precision, we tuned it evaluating different kernels, finding that a linear SVM allowed us to accurately separate four classes of tissues: (i) cardiac muscle of the heart, (ii) smooth muscle of the muscular artery, (iii) loose connective tissue, and (iv) smooth muscle of the large vein and the elastic artery. The experimental validation was conducted using 3000 blocks of 100 × 100 sized pixels, with 600 blocks per class and the classification was assessed using a 10-fold cross-validation. using LBP as the descriptor, concatenated with LBPri and a SVM with linear kernel, the main four classes of tissues were

A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM

Directory of Open Access Journals (Sweden)

HungLinh Ao

2014-01-01

Full Text Available This study investigates a novel method for roller bearing fault diagnosis based on local characteristic-scale decomposition (LCD energy entropy, together with a support vector machine designed using an Artificial Chemical Reaction Optimisation Algorithm, referred to as an ACROA-SVM. First, the original acceleration vibration signals are decomposed into intrinsic scale components (ISCs. Second, the concept of LCD energy entropy is introduced. Third, the energy features extracted from a number of ISCs that contain the most dominant fault information serve as input vectors for the support vector machine classifier. Finally, the ACROA-SVM classifier is proposed to recognize the faulty roller bearing pattern. The analysis of roller bearing signals with inner-race and outer-race faults shows that the diagnostic approach based on the ACROA-SVM and using LCD to extract the energy levels of the various frequency bands as features can identify roller bearing fault patterns accurately and effectively. The proposed method is superior to approaches based on Empirical Mode Decomposition method and requires less time.

Cross Validation Through Two-Dimensional Solution Surface for Cost-Sensitive SVM.

Science.gov (United States)

Gu, Bin; Sheng, Victor S; Tay, Keng Yeow; Romano, Walter; Li, Shuo

2017-06-01

Model selection plays an important role in cost-sensitive SVM (CS-SVM). It has been proven that the global minimum cross validation (CV) error can be efficiently computed based on the solution path for one parameter learning problems. However, it is a challenge to obtain the global minimum CV error for CS-SVM based on one-dimensional solution path and traditional grid search, because CS-SVM is with two regularization parameters. In this paper, we propose a solution and error surfaces based CV approach (CV-SES). More specifically, we first compute a two-dimensional solution surface for CS-SVM based on a bi-parameter space partition algorithm, which can fit solutions of CS-SVM for all values of both regularization parameters. Then, we compute a two-dimensional validation error surface for each CV fold, which can fit validation errors of CS-SVM for all values of both regularization parameters. Finally, we obtain the CV error surface by superposing K validation error surfaces, which can find the global minimum CV error of CS-SVM. Experiments are conducted on seven datasets for cost sensitive learning and on four datasets for imbalanced learning. Experimental results not only show that our proposed CV-SES has a better generalization ability than CS-SVM with various hybrids between grid search and solution path methods, and than recent proposed cost-sensitive hinge loss SVM with three-dimensional grid search, but also show that CV-SES uses less running time.

Selecting Feature Subsets Based on SVM-RFE and the Overlapping Ratio with Applications in Bioinformatics.

Science.gov (United States)

Lin, Xiaohui; Li, Chao; Zhang, Yanhui; Su, Benzhe; Fan, Meng; Wei, Hai

2017-12-26

Feature selection is an important topic in bioinformatics. Defining informative features from complex high dimensional biological data is critical in disease study, drug development, etc. Support vector machine-recursive feature elimination (SVM-RFE) is an efficient feature selection technique that has shown its power in many applications. It ranks the features according to the recursive feature deletion sequence based on SVM. In this study, we propose a method, SVM-RFE-OA, which combines the classification accuracy rate and the average overlapping ratio of the samples to determine the number of features to be selected from the feature rank of SVM-RFE. Meanwhile, to measure the feature weights more accurately, we propose a modified SVM-RFE-OA (M-SVM-RFE-OA) algorithm that temporally screens out the samples lying in a heavy overlapping area in each iteration. The experiments on the eight public biological datasets show that the discriminative ability of the feature subset could be measured more accurately by combining the classification accuracy rate with the average overlapping degree of the samples compared with using the classification accuracy rate alone, and shielding the samples in the overlapping area made the calculation of the feature weights more stable and accurate. The methods proposed in this study can also be used with other RFE techniques to define potential biomarkers from big biological data.

Selecting Feature Subsets Based on SVM-RFE and the Overlapping Ratio with Applications in Bioinformatics

Directory of Open Access Journals (Sweden)

Xiaohui Lin

2017-12-01

Full Text Available Feature selection is an important topic in bioinformatics. Defining informative features from complex high dimensional biological data is critical in disease study, drug development, etc. Support vector machine-recursive feature elimination (SVM-RFE is an efficient feature selection technique that has shown its power in many applications. It ranks the features according to the recursive feature deletion sequence based on SVM. In this study, we propose a method, SVM-RFE-OA, which combines the classification accuracy rate and the average overlapping ratio of the samples to determine the number of features to be selected from the feature rank of SVM-RFE. Meanwhile, to measure the feature weights more accurately, we propose a modified SVM-RFE-OA (M-SVM-RFE-OA algorithm that temporally screens out the samples lying in a heavy overlapping area in each iteration. The experiments on the eight public biological datasets show that the discriminative ability of the feature subset could be measured more accurately by combining the classification accuracy rate with the average overlapping degree of the samples compared with using the classification accuracy rate alone, and shielding the samples in the overlapping area made the calculation of the feature weights more stable and accurate. The methods proposed in this study can also be used with other RFE techniques to define potential biomarkers from big biological data.

Classification of Multiple Chinese Liquors by Means of a QCM-based E-Nose and MDS-SVM Classifier.

Science.gov (United States)

Li, Qiang; Gu, Yu; Jia, Jing

2017-01-30

Chinese liquors are internationally well-known fermentative alcoholic beverages. They have unique flavors attributable to the use of various bacteria and fungi, raw materials, and production processes. Developing a novel, rapid, and reliable method to identify multiple Chinese liquors is of positive significance. This paper presents a pattern recognition system for classifying ten brands of Chinese liquors based on multidimensional scaling (MDS) and support vector machine (SVM) algorithms in a quartz crystal microbalance (QCM)-based electronic nose (e-nose) we designed. We evaluated the comprehensive performance of the MDS-SVM classifier that predicted all ten brands of Chinese liquors individually. The prediction accuracy (98.3%) showed superior performance of the MDS-SVM classifier over the back-propagation artificial neural network (BP-ANN) classifier (93.3%) and moving average-linear discriminant analysis (MA-LDA) classifier (87.6%). The MDS-SVM classifier has reasonable reliability, good fitting and prediction (generalization) performance in classification of the Chinese liquors. Taking both application of the e-nose and validation of the MDS-SVM classifier into account, we have thus created a useful method for the classification of multiple Chinese liquors.

Classification of Multiple Chinese Liquors by Means of a QCM-based E-Nose and MDS-SVM Classifier

Directory of Open Access Journals (Sweden)

Qiang Li

2017-01-01

Full Text Available Chinese liquors are internationally well-known fermentative alcoholic beverages. They have unique flavors attributable to the use of various bacteria and fungi, raw materials, and production processes. Developing a novel, rapid, and reliable method to identify multiple Chinese liquors is of positive significance. This paper presents a pattern recognition system for classifying ten brands of Chinese liquors based on multidimensional scaling (MDS and support vector machine (SVM algorithms in a quartz crystal microbalance (QCM-based electronic nose (e-nose we designed. We evaluated the comprehensive performance of the MDS-SVM classifier that predicted all ten brands of Chinese liquors individually. The prediction accuracy (98.3% showed superior performance of the MDS-SVM classifier over the back-propagation artificial neural network (BP-ANN classifier (93.3% and moving average-linear discriminant analysis (MA-LDA classifier (87.6%. The MDS-SVM classifier has reasonable reliability, good fitting and prediction (generalization performance in classification of the Chinese liquors. Taking both application of the e-nose and validation of the MDS-SVM classifier into account, we have thus created a useful method for the classification of multiple Chinese liquors.

Universum Learning for Multiclass SVM

OpenAIRE

Dhar, Sauptik; Ramakrishnan, Naveen; Cherkassky, Vladimir; Shah, Mohak

2016-01-01

We introduce Universum learning for multiclass problems and propose a novel formulation for multiclass universum SVM (MU-SVM). We also propose a span bound for MU-SVM that can be used for model selection thereby avoiding resampling. Empirical results demonstrate the effectiveness of MU-SVM and the proposed bound.

Adaptive SVM for Data Stream Classification

Directory of Open Access Journals (Sweden)

Isah A. Lawal

2017-07-01

Full Text Available In this paper, we address the problem of learning an adaptive classifier for the classification of continuous streams of data. We present a solution based on incremental extensions of the Support Vector Machine (SVM learning paradigm that updates an existing SVM whenever new training data are acquired. To ensure that the SVM effectiveness is guaranteed while exploiting the newly gathered data, we introduce an on-line model selection approach in the incremental learning process. We evaluated the proposed method on real world applications including on-line spam email filtering and human action classification from videos. Experimental results show the effectiveness and the potential of the proposed approach.

New KF-PP-SVM classification method for EEG in brain-computer interfaces.

Science.gov (United States)

Yang, Banghua; Han, Zhijun; Zan, Peng; Wang, Qian

2014-01-01

Classification methods are a crucial direction in the current study of brain-computer interfaces (BCIs). To improve the classification accuracy for electroencephalogram (EEG) signals, a novel KF-PP-SVM (kernel fisher, posterior probability, and support vector machine) classification method is developed. Its detailed process entails the use of common spatial patterns to obtain features, based on which the within-class scatter is calculated. Then the scatter is added into the kernel function of a radial basis function to construct a new kernel function. This new kernel is integrated into the SVM to obtain a new classification model. Finally, the output of SVM is calculated based on posterior probability and the final recognition result is obtained. To evaluate the effectiveness of the proposed KF-PP-SVM method, EEG data collected from laboratory are processed with four different classification schemes (KF-PP-SVM, KF-SVM, PP-SVM, and SVM). The results showed that the overall average improvements arising from the use of the KF-PP-SVM scheme as opposed to KF-SVM, PP-SVM and SVM schemes are 2.49%, 5.83 % and 6.49 % respectively.

A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images

Science.gov (United States)

Xu, Yongzheng; Yu, Guizhen; Wang, Yunpeng; Wu, Xinkai; Ma, Yalong

2016-01-01

A new hybrid vehicle detection scheme which integrates the Viola-Jones (V-J) and linear SVM classifier with HOG feature (HOG + SVM) methods is proposed for vehicle detection from low-altitude unmanned aerial vehicle (UAV) images. As both V-J and HOG + SVM are sensitive to on-road vehicles’ in-plane rotation, the proposed scheme first adopts a roadway orientation adjustment method, which rotates each UAV image to align the roads with the horizontal direction so the original V-J or HOG + SVM method can be directly applied to achieve fast detection and high accuracy. To address the issue of descending detection speed for V-J and HOG + SVM, the proposed scheme further develops an adaptive switching strategy which sophistically integrates V-J and HOG + SVM methods based on their different descending trends of detection speed to improve detection efficiency. A comprehensive evaluation shows that the switching strategy, combined with the road orientation adjustment method, can significantly improve the efficiency and effectiveness of the vehicle detection from UAV images. The results also show that the proposed vehicle detection method is competitive compared with other existing vehicle detection methods. Furthermore, since the proposed vehicle detection method can be performed on videos captured from moving UAV platforms without the need of image registration or additional road database, it has great potentials of field applications. Future research will be focusing on expanding the current method for detecting other transportation modes such as buses, trucks, motors, bicycles, and pedestrians. PMID:27548179

A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images.

Science.gov (United States)

Xu, Yongzheng; Yu, Guizhen; Wang, Yunpeng; Wu, Xinkai; Ma, Yalong

2016-08-19

A new hybrid vehicle detection scheme which integrates the Viola-Jones (V-J) and linear SVM classifier with HOG feature (HOG + SVM) methods is proposed for vehicle detection from low-altitude unmanned aerial vehicle (UAV) images. As both V-J and HOG + SVM are sensitive to on-road vehicles' in-plane rotation, the proposed scheme first adopts a roadway orientation adjustment method, which rotates each UAV image to align the roads with the horizontal direction so the original V-J or HOG + SVM method can be directly applied to achieve fast detection and high accuracy. To address the issue of descending detection speed for V-J and HOG + SVM, the proposed scheme further develops an adaptive switching strategy which sophistically integrates V-J and HOG + SVM methods based on their different descending trends of detection speed to improve detection efficiency. A comprehensive evaluation shows that the switching strategy, combined with the road orientation adjustment method, can significantly improve the efficiency and effectiveness of the vehicle detection from UAV images. The results also show that the proposed vehicle detection method is competitive compared with other existing vehicle detection methods. Furthermore, since the proposed vehicle detection method can be performed on videos captured from moving UAV platforms without the need of image registration or additional road database, it has great potentials of field applications. Future research will be focusing on expanding the current method for detecting other transportation modes such as buses, trucks, motors, bicycles, and pedestrians.

SVM classification model in depression recognition based on mutation PSO parameter optimization

Directory of Open Access Journals (Sweden)

Zhang Ming

2017-01-01

Full Text Available At present, the clinical diagnosis of depression is mainly through structured interviews by psychiatrists, which is lack of objective diagnostic methods, so it causes the higher rate of misdiagnosis. In this paper, a method of depression recognition based on SVM and particle swarm optimization algorithm mutation is proposed. To address on the problem that particle swarm optimization (PSO algorithm easily trap in local optima, we propose a feedback mutation PSO algorithm (FBPSO to balance the local search and global exploration ability, so that the parameters of the classification model is optimal. We compared different PSO mutation algorithms about classification accuracy for depression, and found the classification accuracy of support vector machine (SVM classifier based on feedback mutation PSO algorithm is the highest. Our study promotes important reference value for establishing auxiliary diagnostic used in depression recognition of clinical diagnosis.

Prediction of the strength of concrete radiation shielding based on LS-SVM

International Nuclear Information System (INIS)

Juncai, Xu; Qingwen, Ren; Zhenzhong, Shen

2015-01-01

Highlights: • LS-SVM was introduced for prediction of the strength of RSC. • A model for prediction of the strength of RSC was implemented. • The grid search algorithm was used to optimize the parameters of the LS-SVM. • The performance of LS-SVM in predicting the strength of RSC was evaluated. - Abstract: Radiation-shielding concrete (RSC) and conventional concrete differ in strength because of their distinct constituents. Predicting the strength of RSC with different constituents plays a vital role in radiation shielding (RS) engineering design. In this study, a model to predict the strength of RSC is established using a least squares-support vector machine (LS-SVM) through grid search algorithm. The algorithm is used to optimize the parameters of the LS-SVM on the basis of traditional prediction methods for conventional concrete. The predicted results of the LS-SVM model are compared with the experimental data. The results of the prediction are stable and consistent with the experimental results. In addition, the studied parameters exhibit significant effects on the simulation results. Therefore, the proposed method can be applied in predicting the strength of RSC, and the predicted results can be adopted as an important reference for RS engineering design

In-Vivo Imaging of Cell Migration Using Contrast Enhanced MRI and SVM Based Post-Processing.

Science.gov (United States)

Weis, Christian; Hess, Andreas; Budinsky, Lubos; Fabry, Ben

2015-01-01

The migration of cells within a living organism can be observed with magnetic resonance imaging (MRI) in combination with iron oxide nanoparticles as an intracellular contrast agent. This method, however, suffers from low sensitivity and specificty. Here, we developed a quantitative non-invasive in-vivo cell localization method using contrast enhanced multiparametric MRI and support vector machines (SVM) based post-processing. Imaging phantoms consisting of agarose with compartments containing different concentrations of cancer cells labeled with iron oxide nanoparticles were used to train and evaluate the SVM for cell localization. From the magnitude and phase data acquired with a series of T2*-weighted gradient-echo scans at different echo-times, we extracted features that are characteristic for the presence of superparamagnetic nanoparticles, in particular hyper- and hypointensities, relaxation rates, short-range phase perturbations, and perturbation dynamics. High detection quality was achieved by SVM analysis of the multiparametric feature-space. The in-vivo applicability was validated in animal studies. The SVM detected the presence of iron oxide nanoparticles in the imaging phantoms with high specificity and sensitivity with a detection limit of 30 labeled cells per mm3, corresponding to 19 μM of iron oxide. As proof-of-concept, we applied the method to follow the migration of labeled cancer cells injected in rats. The combination of iron oxide labeled cells, multiparametric MRI and a SVM based post processing provides high spatial resolution, specificity, and sensitivity, and is therefore suitable for non-invasive in-vivo cell detection and cell migration studies over prolonged time periods.

Fault diagnosis of nuclear-powered equipment based on HMM and SVM

International Nuclear Information System (INIS)

Yue Xia; Zhang Chunliang; Zhu Houyao; Quan Yanming

2012-01-01

For the complexity and the small fault samples of nuclear-powered equipment, a hybrid HMM/SVM method was introduced in fault diagnosis. The hybrid method has two steps: first, HMM is utilized for primary diagnosis, in which the range of possible failure is reduced and the state trends can be observed; then faults can be recognized taking the advantage of the generalization ability of SVM. Experiments on the main pump failure simulator show that the HMM/SVM system has a high recognition rate and can be used in the fault diagnosis of nuclear-powered equipment. (authors)

A Power Transformers Fault Diagnosis Model Based on Three DGA Ratios and PSO Optimization SVM

Science.gov (United States)

Ma, Hongzhe; Zhang, Wei; Wu, Rongrong; Yang, Chunyan

2018-03-01

In order to make up for the shortcomings of existing transformer fault diagnosis methods in dissolved gas-in-oil analysis (DGA) feature selection and parameter optimization, a transformer fault diagnosis model based on the three DGA ratios and particle swarm optimization (PSO) optimize support vector machine (SVM) is proposed. Using transforming support vector machine to the nonlinear and multi-classification SVM, establishing the particle swarm optimization to optimize the SVM multi classification model, and conducting transformer fault diagnosis combined with the cross validation principle. The fault diagnosis results show that the average accuracy of test method is better than the standard support vector machine and genetic algorithm support vector machine, and the proposed method can effectively improve the accuracy of transformer fault diagnosis is proved.

SVM and SVM Ensembles in Breast Cancer Prediction

OpenAIRE

Huang, Min-Wei; Chen, Chih-Wen; Lin, Wei-Chao; Ke, Shih-Wen; Tsai, Chih-Fong

2017-01-01

Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction per...

Learning SVM in Kreĭn Spaces.

Science.gov (United States)

Loosli, Gaelle; Canu, Stephane; Ong, Cheng Soon

2016-06-01

This paper presents a theoretical foundation for an SVM solver in Kreĭn spaces. Up to now, all methods are based either on the matrix correction, or on non-convex minimization, or on feature-space embedding. Here we justify and evaluate a solution that uses the original (indefinite) similarity measure, in the original Kreĭn space. This solution is the result of a stabilization procedure. We establish the correspondence between the stabilization problem (which has to be solved) and a classical SVM based on minimization (which is easy to solve). We provide simple equations to go from one to the other (in both directions). This link between stabilization and minimization problems is the key to obtain a solution in the original Kreĭn space. Using KSVM, one can solve SVM with usually troublesome kernels (large negative eigenvalues or large numbers of negative eigenvalues). We show experiments showing that our algorithm KSVM outperforms all previously proposed approaches to deal with indefinite matrices in SVM-like kernel methods.

Hybrid PSO–SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability

International Nuclear Information System (INIS)

García Nieto, P.J.; García-Gonzalo, E.; Sánchez Lasheras, F.; Cos Juez, F.J. de

2015-01-01

The present paper describes a hybrid PSO–SVM-based model for the prediction of the remaining useful life of aircraft engines. The proposed hybrid model combines support vector machines (SVMs), which have been successfully adopted for regression problems, with the particle swarm optimization (PSO) technique. This optimization technique involves kernel parameter setting in the SVM training procedure, which significantly influences the regression accuracy. However, its use in reliability applications has not been yet widely explored. Bearing this in mind, remaining useful life values have been predicted here by using the hybrid PSO–SVM-based model from the remaining measured parameters (input variables) for aircraft engines with success. A coefficient of determination equal to 0.9034 was obtained when this hybrid PSO–RBF–SVM-based model was applied to experimental data. The agreement of this model with experimental data confirmed its good performance. One of the main advantages of this predictive model is that it does not require information about the previous operation states of the engine. Finally, the main conclusions of this study are exposed. - Highlights: • A hybrid PSO–SVM-based model is built as a predictive model of the RUL values for aircraft engines. • The remaining physical–chemical variables in this process are studied in depth. • The obtained regression accuracy of our method is about 95%. • The results show that PSO–SVM-based model can assist in the diagnosis of the RUL values with accuracy

Research on gesture recognition of augmented reality maintenance guiding system based on improved SVM

Science.gov (United States)

Zhao, Shouwei; Zhang, Yong; Zhou, Bin; Ma, Dongxi

2014-09-01

Interaction is one of the key techniques of augmented reality (AR) maintenance guiding system. Because of the complexity of the maintenance guiding system's image background and the high dimensionality of gesture characteristics, the whole process of gesture recognition can be divided into three stages which are gesture segmentation, gesture characteristic feature modeling and trick recognition. In segmentation stage, for solving the misrecognition of skin-like region, a segmentation algorithm combing background mode and skin color to preclude some skin-like regions is adopted. In gesture characteristic feature modeling of image attributes stage, plenty of characteristic features are analyzed and acquired, such as structure characteristics, Hu invariant moments features and Fourier descriptor. In trick recognition stage, a classifier based on Support Vector Machine (SVM) is introduced into the augmented reality maintenance guiding process. SVM is a novel learning method based on statistical learning theory, processing academic foundation and excellent learning ability, having a lot of issues in machine learning area and special advantages in dealing with small samples, non-linear pattern recognition at high dimension. The gesture recognition of augmented reality maintenance guiding system is realized by SVM after the granulation of all the characteristic features. The experimental results of the simulation of number gesture recognition and its application in augmented reality maintenance guiding system show that the real-time performance and robustness of gesture recognition of AR maintenance guiding system can be greatly enhanced by improved SVM.

Predicting enhancer activity and variant impact using gkm-SVM.

Science.gov (United States)

Beer, Michael A

2017-09-01

We participated in the Critical Assessment of Genome Interpretation eQTL challenge to further test computational models of regulatory variant impact and their association with human disease. Our prediction model is based on a discriminative gapped-kmer SVM (gkm-SVM) trained on genome-wide chromatin accessibility data in the cell type of interest. The comparisons with massively parallel reporter assays (MPRA) in lymphoblasts show that gkm-SVM is among the most accurate prediction models even though all other models used the MPRA data for model training, and gkm-SVM did not. In addition, we compare gkm-SVM with other MPRA datasets and show that gkm-SVM is a reliable predictor of expression and that deltaSVM is a reliable predictor of variant impact in K562 cells and mouse retina. We further show that DHS (DNase-I hypersensitive sites) and ATAC-seq (assay for transposase-accessible chromatin using sequencing) data are equally predictive substrates for training gkm-SVM, and that DHS regions flanked by H3K27Ac and H3K4me1 marks are more predictive than DHS regions alone. © 2017 Wiley Periodicals, Inc.

Constructing and Validating High-Performance MIEC-SVM Models in Virtual Screening for Kinases: A Better Way for Actives Discovery.

Science.gov (United States)

Sun, Huiyong; Pan, Peichen; Tian, Sheng; Xu, Lei; Kong, Xiaotian; Li, Youyong; Dan Li; Hou, Tingjun

2016-04-22

The MIEC-SVM approach, which combines molecular interaction energy components (MIEC) derived from free energy decomposition and support vector machine (SVM), has been found effective in capturing the energetic patterns of protein-peptide recognition. However, the performance of this approach in identifying small molecule inhibitors of drug targets has not been well assessed and validated by experiments. Thereafter, by combining different model construction protocols, the issues related to developing best MIEC-SVM models were firstly discussed upon three kinase targets (ABL, ALK, and BRAF). As for the investigated targets, the optimized MIEC-SVM models performed much better than the models based on the default SVM parameters and Autodock for the tested datasets. Then, the proposed strategy was utilized to screen the Specs database for discovering potential inhibitors of the ALK kinase. The experimental results showed that the optimized MIEC-SVM model, which identified 7 actives with IC50 < 10 μM from 50 purchased compounds (namely hit rate of 14%, and 4 in nM level) and performed much better than Autodock (3 actives with IC50 < 10 μM from 50 purchased compounds, namely hit rate of 6%, and 2 in nM level), suggesting that the proposed strategy is a powerful tool in structure-based virtual screening.

sw-SVM: sensor weighting support vector machines for EEG-based brain-computer interfaces.

Science.gov (United States)

Jrad, N; Congedo, M; Phlypo, R; Rousseau, S; Flamary, R; Yger, F; Rakotomamonjy, A

2011-10-01

In many machine learning applications, like brain-computer interfaces (BCI), high-dimensional sensor array data are available. Sensor measurements are often highly correlated and signal-to-noise ratio is not homogeneously spread across sensors. Thus, collected data are highly variable and discrimination tasks are challenging. In this work, we focus on sensor weighting as an efficient tool to improve the classification procedure. We present an approach integrating sensor weighting in the classification framework. Sensor weights are considered as hyper-parameters to be learned by a support vector machine (SVM). The resulting sensor weighting SVM (sw-SVM) is designed to satisfy a margin criterion, that is, the generalization error. Experimental studies on two data sets are presented, a P300 data set and an error-related potential (ErrP) data set. For the P300 data set (BCI competition III), for which a large number of trials is available, the sw-SVM proves to perform equivalently with respect to the ensemble SVM strategy that won the competition. For the ErrP data set, for which a small number of trials are available, the sw-SVM shows superior performances as compared to three state-of-the art approaches. Results suggest that the sw-SVM promises to be useful in event-related potentials classification, even with a small number of training trials.

F-SVM: Combination of Feature Transformation and SVM Learning via Convex Relaxation

OpenAIRE

Wu, Xiaohe; Zuo, Wangmeng; Zhu, Yuanyuan; Lin, Liang

2015-01-01

The generalization error bound of support vector machine (SVM) depends on the ratio of radius and margin, while standard SVM only considers the maximization of the margin but ignores the minimization of the radius. Several approaches have been proposed to integrate radius and margin for joint learning of feature transformation and SVM classifier. However, most of them either require the form of the transformation matrix to be diagonal, or are non-convex and computationally expensive. In this ...

Cerebral 18F-FDG PET in macrophagic myofasciitis: An individual SVM-based approach.

Science.gov (United States)

Blanc-Durand, Paul; Van Der Gucht, Axel; Guedj, Eric; Abulizi, Mukedaisi; Aoun-Sebaiti, Mehdi; Lerman, Lionel; Verger, Antoine; Authier, François-Jérôme; Itti, Emmanuel

2017-01-01

Macrophagic myofasciitis (MMF) is an emerging condition with highly specific myopathological alterations. A peculiar spatial pattern of a cerebral glucose hypometabolism involving occipito-temporal cortex and cerebellum have been reported in patients with MMF; however, the full pattern is not systematically present in routine interpretation of scans, and with varying degrees of severity depending on the cognitive profile of patients. Aim was to generate and evaluate a support vector machine (SVM) procedure to classify patients between healthy or MMF 18F-FDG brain profiles. 18F-FDG PET brain images of 119 patients with MMF and 64 healthy subjects were retrospectively analyzed. The whole-population was divided into two groups; a training set (100 MMF, 44 healthy subjects) and a testing set (19 MMF, 20 healthy subjects). Dimensionality reduction was performed using a t-map from statistical parametric mapping (SPM) and a SVM with a linear kernel was trained on the training set. To evaluate the performance of the SVM classifier, values of sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV) and accuracy (Acc) were calculated. The SPM12 analysis on the training set exhibited the already reported hypometabolism pattern involving occipito-temporal and fronto-parietal cortices, limbic system and cerebellum. The SVM procedure, based on the t-test mask generated from the training set, correctly classified MMF patients of the testing set with following Se, Sp, PPV, NPV and Acc: 89%, 85%, 85%, 89%, and 87%. We developed an original and individual approach including a SVM to classify patients between healthy or MMF metabolic brain profiles using 18F-FDG-PET. Machine learning algorithms are promising for computer-aided diagnosis but will need further validation in prospective cohorts.

SVM models for analysing the headstreams of mine water inrush

Energy Technology Data Exchange (ETDEWEB)

Yan Zhi-gang; Du Pei-jun; Guo Da-zhi [China University of Science and Technology, Xuzhou (China). School of Environmental Science and Spatial Informatics

2007-08-15

The support vector machine (SVM) model was introduced to analyse the headstrean of water inrush in a coal mine. The SVM model, based on a hydrogeochemical method, was constructed for recognising two kinds of headstreams and the H-SVMs model was constructed for recognising multi- headstreams. The SVM method was applied to analyse the conditions of two mixed headstreams and the value of the SVM decision function was investigated as a means of denoting the hydrogeochemical abnormality. The experimental results show that the SVM is based on a strict mathematical theory, has a simple structure and a good overall performance. Moreover the parameter W in the decision function can describe the weights of discrimination indices of the headstream of water inrush. The value of the decision function can denote hydrogeochemistry abnormality, which is significant in the prevention of water inrush in a coal mine. 9 refs., 1 fig., 7 tabs.

A Mass Spectrometric Analysis Method Based on PPCA and SVM for Early Detection of Ovarian Cancer.

Science.gov (United States)

Wu, Jiang; Ji, Yanju; Zhao, Ling; Ji, Mengying; Ye, Zhuang; Li, Suyi

2016-01-01

Background. Surfaced-enhanced laser desorption-ionization-time of flight mass spectrometry (SELDI-TOF-MS) technology plays an important role in the early diagnosis of ovarian cancer. However, the raw MS data is highly dimensional and redundant. Therefore, it is necessary to study rapid and accurate detection methods from the massive MS data. Methods. The clinical data set used in the experiments for early cancer detection consisted of 216 SELDI-TOF-MS samples. An MS analysis method based on probabilistic principal components analysis (PPCA) and support vector machine (SVM) was proposed and applied to the ovarian cancer early classification in the data set. Additionally, by the same data set, we also established a traditional PCA-SVM model. Finally we compared the two models in detection accuracy, specificity, and sensitivity. Results. Using independent training and testing experiments 10 times to evaluate the ovarian cancer detection models, the average prediction accuracy, sensitivity, and specificity of the PCA-SVM model were 83.34%, 82.70%, and 83.88%, respectively. In contrast, those of the PPCA-SVM model were 90.80%, 92.98%, and 88.97%, respectively. Conclusions. The PPCA-SVM model had better detection performance. And the model combined with the SELDI-TOF-MS technology had a prospect in early clinical detection and diagnosis of ovarian cancer.

A method of distributed avionics data processing based on SVM classifier

Science.gov (United States)

Guo, Hangyu; Wang, Jinyan; Kang, Minyang; Xu, Guojing

2018-03-01

Under the environment of system combat, in order to solve the problem on management and analysis of the massive heterogeneous data on multi-platform avionics system, this paper proposes a management solution which called avionics "resource cloud" based on big data technology, and designs an aided decision classifier based on SVM algorithm. We design an experiment with STK simulation, the result shows that this method has a high accuracy and a broad application prospect.

A Statistical Parameter Analysis and SVM Based Fault Diagnosis Strategy for Dynamically Tuned Gyroscopes

Institute of Scientific and Technical Information of China (English)

无

2007-01-01

Gyro's fault diagnosis plays a critical role in inertia navigation systems for higher reliability and precision. A new fault diagnosis strategy based on the statistical parameter analysis (SPA) and support vector machine(SVM) classification model was proposed for dynamically tuned gyroscopes (DTG). The SPA, a kind of time domain analysis approach, was introduced to compute a set of statistical parameters of vibration signal as the state features of DTG, with which the SVM model, a novel learning machine based on statistical learning theory (SLT), was applied and constructed to train and identify the working state of DTG. The experimental results verify that the proposed diagnostic strategy can simply and effectively extract the state features of DTG, and it outperforms the radial-basis function (RBF) neural network based diagnostic method and can more reliably and accurately diagnose the working state of DTG.

SVM-Based Spectral Analysis for Heart Rate from Multi-Channel WPPG Sensor Signals.

Science.gov (United States)

Xiong, Jiping; Cai, Lisang; Wang, Fei; He, Xiaowei

2017-03-03

Although wrist-type photoplethysmographic (hereafter referred to as WPPG) sensor signals can measure heart rate quite conveniently, the subjects' hand movements can cause strong motion artifacts, and then the motion artifacts will heavily contaminate WPPG signals. Hence, it is challenging for us to accurately estimate heart rate from WPPG signals during intense physical activities. The WWPG method has attracted more attention thanks to the popularity of wrist-worn wearable devices. In this paper, a mixed approach called Mix-SVM is proposed, it can use multi-channel WPPG sensor signals and simultaneous acceleration signals to measurement heart rate. Firstly, we combine the principle component analysis and adaptive filter to remove a part of the motion artifacts. Due to the strong relativity between motion artifacts and acceleration signals, the further denoising problem is regarded as a sparse signals reconstruction problem. Then, we use a spectrum subtraction method to eliminate motion artifacts effectively. Finally, the spectral peak corresponding to heart rate is sought by an SVM-based spectral analysis method. Through the public PPG database in the 2015 IEEE Signal Processing Cup, we acquire the experimental results, i.e., the average absolute error was 1.01 beat per minute, and the Pearson correlation was 0.9972. These results also confirm that the proposed Mix-SVM approach has potential for multi-channel WPPG-based heart rate estimation in the presence of intense physical exercise.

An SVM Based Approach for the Analysis Of Mammography Images

Science.gov (United States)

Gan, X.; Kapsokalivas, L.; Skaliotis, A.; Steinhöfel, K.; Tangaro, S.

2007-09-01

Mammography is among the most popular imaging techniques used in the diagnosis of breast cancer. Nevertheless distinguishing between healthy and ill images is hard even for an experienced radiologist, because a single image usually includes several regions of interest (ROIs). The hardness of this classification problem along with the substantial amount of data, gathered from patients' medical history, motivates the use of a machine learning approach as part of a CAD (Computer Aided Detection) tool, aiming to assist radiologists in the characterization of mammography images. Specifically, our approach involves: i) the ROI extraction, ii) the Feature Vector extraction, iii) the Support Vector Machine (SVM) classification of ROIs and iv) the characterization of the whole image. We evaluate the performance of our approach in terms of the SVM's training and testing error and in terms of ROI specificity—sensitivity. The results show a relation between the number of features used and the SVM's performance.

An SVM Based Approach for the Analysis Of Mammography Images

International Nuclear Information System (INIS)

Gan, X.; Kapsokalivas, L.; Skaliotis, A.; Steinhoefel, K.; Tangaro, S.

2007-01-01

Mammography is among the most popular imaging techniques used in the diagnosis of breast cancer. Nevertheless distinguishing between healthy and ill images is hard even for an experienced radiologist, because a single image usually includes several regions of interest (ROIs). The hardness of this classification problem along with the substantial amount of data, gathered from patients' medical history, motivates the use of a machine learning approach as part of a CAD (Computer Aided Detection) tool, aiming to assist radiologists in the characterization of mammography images. Specifically, our approach involves: i) the ROI extraction, ii) the Feature Vector extraction, iii) the Support Vector Machine (SVM) classification of ROIs and iv) the characterization of the whole image. We evaluate the performance of our approach in terms of the SVM's training and testing error and in terms of ROI specificity - sensitivity. The results show a relation between the number of features used and the SVM's performance

Automatic epileptic seizure detection in EEGs using MF-DFA, SVM based on cloud computing.

Science.gov (United States)

Zhang, Zhongnan; Wen, Tingxi; Huang, Wei; Wang, Meihong; Li, Chunfeng

2017-01-01

Epilepsy is a chronic disease with transient brain dysfunction that results from the sudden abnormal discharge of neurons in the brain. Since electroencephalogram (EEG) is a harmless and noninvasive detection method, it plays an important role in the detection of neurological diseases. However, the process of analyzing EEG to detect neurological diseases is often difficult because the brain electrical signals are random, non-stationary and nonlinear. In order to overcome such difficulty, this study aims to develop a new computer-aided scheme for automatic epileptic seizure detection in EEGs based on multi-fractal detrended fluctuation analysis (MF-DFA) and support vector machine (SVM). New scheme first extracts features from EEG by MF-DFA during the first stage. Then, the scheme applies a genetic algorithm (GA) to calculate parameters used in SVM and classify the training data according to the selected features using SVM. Finally, the trained SVM classifier is exploited to detect neurological diseases. The algorithm utilizes MLlib from library of SPARK and runs on cloud platform. Applying to a public dataset for experiment, the study results show that the new feature extraction method and scheme can detect signals with less features and the accuracy of the classification reached up to 99%. MF-DFA is a promising approach to extract features for analyzing EEG, because of its simple algorithm procedure and less parameters. The features obtained by MF-DFA can represent samples as well as traditional wavelet transform and Lyapunov exponents. GA can always find useful parameters for SVM with enough execution time. The results illustrate that the classification model can achieve comparable accuracy, which means that it is effective in epileptic seizure detection.

Optimised Selection of Stroke Biomarker Based on Svm and Information Theory

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Wang Xiang

2017-01-01

Full Text Available With the development of molecular biology and gene-engineering technology, gene diagnosis has been an emerging approach for modern life sciences. Biological marker, recognized as the hot topic in the molecular and gene fields, has important values in early diagnosis, malignant tumor stage, treatment and therapeutic efficacy evaluation. So far, the researcher has not found any effective way to predict and distinguish different type of stroke. In this paper, we aim to optimize stroke biomarker and figure out effective stroke detection index based on SVM (support vector machine and information theory. Through mutual information analysis and principal component analysis to complete the selection of biomarkers and then we use SVM to verify our model. According to the testing data of patients provided by Xuanwu Hospital, we explore the significant markers of the stroke through data analysis. Our model can predict stroke well. Then discuss the effects of each biomarker on the incidence of stroke.

COMSAT: Residue contact prediction of transmembrane proteins based on support vector machines and mixed integer linear programming.

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Zhang, Huiling; Huang, Qingsheng; Bei, Zhendong; Wei, Yanjie; Floudas, Christodoulos A

2016-03-01

In this article, we present COMSAT, a hybrid framework for residue contact prediction of transmembrane (TM) proteins, integrating a support vector machine (SVM) method and a mixed integer linear programming (MILP) method. COMSAT consists of two modules: COMSAT_SVM which is trained mainly on position-specific scoring matrix features, and COMSAT_MILP which is an ab initio method based on optimization models. Contacts predicted by the SVM model are ranked by SVM confidence scores, and a threshold is trained to improve the reliability of the predicted contacts. For TM proteins with no contacts above the threshold, COMSAT_MILP is used. The proposed hybrid contact prediction scheme was tested on two independent TM protein sets based on the contact definition of 14 Å between Cα-Cα atoms. First, using a rigorous leave-one-protein-out cross validation on the training set of 90 TM proteins, an accuracy of 66.8%, a coverage of 12.3%, a specificity of 99.3% and a Matthews' correlation coefficient (MCC) of 0.184 were obtained for residue pairs that are at least six amino acids apart. Second, when tested on a test set of 87 TM proteins, the proposed method showed a prediction accuracy of 64.5%, a coverage of 5.3%, a specificity of 99.4% and a MCC of 0.106. COMSAT shows satisfactory results when compared with 12 other state-of-the-art predictors, and is more robust in terms of prediction accuracy as the length and complexity of TM protein increase. COMSAT is freely accessible at http://hpcc.siat.ac.cn/COMSAT/. © 2016 Wiley Periodicals, Inc.

Linear SVM-Based Android Malware Detection for Reliable IoT Services

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Hyo-Sik Ham

2014-01-01

Full Text Available Current many Internet of Things (IoT services are monitored and controlled through smartphone applications. By combining IoT with smartphones, many convenient IoT services have been provided to users. However, there are adverse underlying effects in such services including invasion of privacy and information leakage. In most cases, mobile devices have become cluttered with important personal user information as various services and contents are provided through them. Accordingly, attackers are expanding the scope of their attacks beyond the existing PC and Internet environment into mobile devices. In this paper, we apply a linear support vector machine (SVM to detect Android malware and compare the malware detection performance of SVM with that of other machine learning classifiers. Through experimental validation, we show that the SVM outperforms other machine learning classifiers.

A structural SVM approach for reference parsing.

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Zhang, Xiaoli; Zou, Jie; Le, Daniel X; Thoma, George R

2011-06-09

Automated extraction of bibliographic data, such as article titles, author names, abstracts, and references is essential to the affordable creation of large citation databases. References, typically appearing at the end of journal articles, can also provide valuable information for extracting other bibliographic data. Therefore, parsing individual reference to extract author, title, journal, year, etc. is sometimes a necessary preprocessing step in building citation-indexing systems. The regular structure in references enables us to consider reference parsing a sequence learning problem and to study structural Support Vector Machine (structural SVM), a newly developed structured learning algorithm on parsing references. In this study, we implemented structural SVM and used two types of contextual features to compare structural SVM with conventional SVM. Both methods achieve above 98% token classification accuracy and above 95% overall chunk-level accuracy for reference parsing. We also compared SVM and structural SVM to Conditional Random Field (CRF). The experimental results show that structural SVM and CRF achieve similar accuracies at token- and chunk-levels. When only basic observation features are used for each token, structural SVM achieves higher performance compared to SVM since it utilizes the contextual label features. However, when the contextual observation features from neighboring tokens are combined, SVM performance improves greatly, and is close to that of structural SVM after adding the second order contextual observation features. The comparison of these two methods with CRF using the same set of binary features show that both structural SVM and CRF perform better than SVM, indicating their stronger sequence learning ability in reference parsing.

An Efficient Normalized Rank Based SVM for Room Level Indoor WiFi Localization with Diverse Devices

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Yasmine Rezgui

2017-01-01

Full Text Available This paper proposes an efficient and effective WiFi fingerprinting-based indoor localization algorithm, which uses the Received Signal Strength Indicator (RSSI of WiFi signals. In practical harsh indoor environments, RSSI variation and hardware variance can significantly degrade the performance of fingerprinting-based localization methods. To address the problem of hardware variance and signal fluctuation in WiFi fingerprinting-based localization, we propose a novel normalized rank based Support Vector Machine classifier (NR-SVM. Moving from RSSI value based analysis to the normalized rank transformation based analysis, the principal features are prioritized and the dimensionalities of signature vectors are taken into account. The proposed method has been tested using sixteen different devices in a shopping mall with 88 shops. The experimental results demonstrate its robustness with no less than 98.75% correct estimation in 93.75% of the tested cases and 100% correct rate in 56.25% of cases. In the experiments, the new method shows better performance over the KNN, Naïve Bayes, Random Forest, and Neural Network algorithms. Furthermore, we have compared the proposed approach with three popular calibration-free transformation based methods, including difference method (DIFF, Signal Strength Difference (SSD, and the Hyperbolic Location Fingerprinting (HLF based SVM. The results show that the NR-SVM outperforms these popular methods.

Grouped fuzzy SVM with EM-based partition of sample space for clustered microcalcification detection.

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Wang, Huiya; Feng, Jun; Wang, Hongyu

2017-07-20

Detection of clustered microcalcification (MC) from mammograms plays essential roles in computer-aided diagnosis for early stage breast cancer. To tackle problems associated with the diversity of data structures of MC lesions and the variability of normal breast tissues, multi-pattern sample space learning is required. In this paper, a novel grouped fuzzy Support Vector Machine (SVM) algorithm with sample space partition based on Expectation-Maximization (EM) (called G-FSVM) is proposed for clustered MC detection. The diversified pattern of training data is partitioned into several groups based on EM algorithm. Then a series of fuzzy SVM are integrated for classification with each group of samples from the MC lesions and normal breast tissues. From DDSM database, a total of 1,064 suspicious regions are selected from 239 mammography, and the measurement of Accuracy, True Positive Rate (TPR), False Positive Rate (FPR) and EVL = TPR* 1-FPR are 0.82, 0.78, 0.14 and 0.72, respectively. The proposed method incorporates the merits of fuzzy SVM and multi-pattern sample space learning, decomposing the MC detection problem into serial simple two-class classification. Experimental results from synthetic data and DDSM database demonstrate that our integrated classification framework reduces the false positive rate significantly while maintaining the true positive rate.

Evaluation of Effectiveness of Wavelet Based Denoising Schemes Using ANN and SVM for Bearing Condition Classification

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Vijay G. S.

2012-01-01

Full Text Available The wavelet based denoising has proven its ability to denoise the bearing vibration signals by improving the signal-to-noise ratio (SNR and reducing the root-mean-square error (RMSE. In this paper seven wavelet based denoising schemes have been evaluated based on the performance of the Artificial Neural Network (ANN and the Support Vector Machine (SVM, for the bearing condition classification. The work consists of two parts, the first part in which a synthetic signal simulating the defective bearing vibration signal with Gaussian noise was subjected to these denoising schemes. The best scheme based on the SNR and the RMSE was identified. In the second part, the vibration signals collected from a customized Rolling Element Bearing (REB test rig for four bearing conditions were subjected to these denoising schemes. Several time and frequency domain features were extracted from the denoised signals, out of which a few sensitive features were selected using the Fisher’s Criterion (FC. Extracted features were used to train and test the ANN and the SVM. The best denoising scheme identified, based on the classification performances of the ANN and the SVM, was found to be the same as the one obtained using the synthetic signal.

Elucidation of Metallic Plume and Spatter Characteristics Based on SVM During High-Power Disk Laser Welding

International Nuclear Information System (INIS)

Gao Xiangdong; Liu Guiqian

2015-01-01

During deep penetration laser welding, there exist plume (weak plasma) and spatters, which are the results of weld material ejection due to strong laser heating. The characteristics of plume and spatters are related to welding stability and quality. Characteristics of metallic plume and spatters were investigated during high-power disk laser bead-on-plate welding of Type 304 austenitic stainless steel plates at a continuous wave laser power of 10 kW. An ultraviolet and visible sensitive high-speed camera was used to capture the metallic plume and spatter images. Plume area, laser beam path through the plume, swing angle, distance between laser beam focus and plume image centroid, abscissa of plume centroid and spatter numbers are defined as eigenvalues, and the weld bead width was used as a characteristic parameter that reflected welding stability. Welding status was distinguished by SVM (support vector machine) after data normalization and characteristic analysis. Also, PCA (principal components analysis) feature extraction was used to reduce the dimensions of feature space, and PSO (particle swarm optimization) was used to optimize the parameters of SVM. Finally a classification model based on SVM was established to estimate the weld bead width and welding stability. Experimental results show that the established algorithm based on SVM could effectively distinguish the variation of weld bead width, thus providing an experimental example of monitoring high-power disk laser welding quality. (plasma technology)

Fault detection of Tennessee Eastman process based on topological features and SVM

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Zhao, Huiyang; Hu, Yanzhu; Ai, Xinbo; Hu, Yu; Meng, Zhen

2018-03-01

Fault detection in industrial process is a popular research topic. Although the distributed control system(DCS) has been introduced to monitor the state of industrial process, it still cannot satisfy all the requirements for fault detection of all the industrial systems. In this paper, we proposed a novel method based on topological features and support vector machine(SVM), for fault detection of industrial process. The proposed method takes global information of measured variables into account by complex network model and predicts whether a system has generated some faults or not by SVM. The proposed method can be divided into four steps, i.e. network construction, network analysis, model training and model testing respectively. Finally, we apply the model to Tennessee Eastman process(TEP). The results show that this method works well and can be a useful supplement for fault detection of industrial process.

Construction of Pancreatic Cancer Classifier Based on SVM Optimized by Improved FOA

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Ma, Xiaoqi

2015-01-01

A novel method is proposed to establish the pancreatic cancer classifier. Firstly, the concept of quantum and fruit fly optimal algorithm (FOA) are introduced, respectively. Then FOA is improved by quantum coding and quantum operation, and a new smell concentration determination function is defined. Finally, the improved FOA is used to optimize the parameters of support vector machine (SVM) and the classifier is established by optimized SVM. In order to verify the effectiveness of the proposed method, SVM and other classification methods have been chosen as the comparing methods. The experimental results show that the proposed method can improve the classifier performance and cost less time. PMID:26543867

[Rapid determination of COD in aquaculture water based on LS-SVM with ultraviolet/visible spectroscopy].

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Liu, Xue-Mei; Zhang, Hai-Liang

2014-10-01

Ultraviolet/visible (UV/Vis) spectroscopy was studied for the rapid determination of chemical oxygen demand (COD), which was an indicator to measure the concentration of organic matter in aquaculture water. In order to reduce the influence of the absolute noises of the spectra, the extracted 135 absorbance spectra were preprocessed by Savitzky-Golay smoothing (SG), EMD, and wavelet transform (WT) methods. The preprocessed spectra were then used to select latent variables (LVs) by partial least squares (PLS) methods. Partial least squares (PLS) was used to build models with the full spectra, and back- propagation neural network (BPNN) and least square support vector machine (LS-SVM) were applied to build models with the selected LVs. The overall results showed that BPNN and LS-SVM models performed better than PLS models, and the LS-SVM models with LVs based on WT preprocessed spectra obtained the best results with the determination coefficient (r2) and RMSE being 0. 83 and 14. 78 mg · L(-1) for calibration set, and 0.82 and 14.82 mg · L(-1) for the prediction set respectively. The method showed the best performance in LS-SVM model. The results indicated that it was feasible to use UV/Vis with LVs which were obtained by PLS method, combined with LS-SVM calibration could be applied to the rapid and accurate determination of COD in aquaculture water. Moreover, this study laid the foundation for further implementation of online analysis of aquaculture water and rapid determination of other water quality parameters.

Comparing SVM and ANN based Machine Learning Methods for Species Identification of Food Contaminating Beetles.

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Bisgin, Halil; Bera, Tanmay; Ding, Hongjian; Semey, Howard G; Wu, Leihong; Liu, Zhichao; Barnes, Amy E; Langley, Darryl A; Pava-Ripoll, Monica; Vyas, Himansu J; Tong, Weida; Xu, Joshua

2018-04-25

Insect pests, such as pantry beetles, are often associated with food contaminations and public health risks. Machine learning has the potential to provide a more accurate and efficient solution in detecting their presence in food products, which is currently done manually. In our previous research, we demonstrated such feasibility where Artificial Neural Network (ANN) based pattern recognition techniques could be implemented for species identification in the context of food safety. In this study, we present a Support Vector Machine (SVM) model which improved the average accuracy up to 85%. Contrary to this, the ANN method yielded ~80% accuracy after extensive parameter optimization. Both methods showed excellent genus level identification, but SVM showed slightly better accuracy  for most species. Highly accurate species level identification remains a challenge, especially in distinguishing between species from the same genus which may require improvements in both imaging and machine learning techniques. In summary, our work does illustrate a new SVM based technique and provides a good comparison with the ANN model in our context. We believe such insights will pave better way forward for the application of machine learning towards species identification and food safety.

SVM-Based Spectral Analysis for Heart Rate from Multi-Channel WPPG Sensor Signals

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Jiping Xiong

2017-03-01

Full Text Available Although wrist-type photoplethysmographic (hereafter referred to as WPPG sensor signals can measure heart rate quite conveniently, the subjects’ hand movements can cause strong motion artifacts, and then the motion artifacts will heavily contaminate WPPG signals. Hence, it is challenging for us to accurately estimate heart rate from WPPG signals during intense physical activities. The WWPG method has attracted more attention thanks to the popularity of wrist-worn wearable devices. In this paper, a mixed approach called Mix-SVM is proposed, it can use multi-channel WPPG sensor signals and simultaneous acceleration signals to measurement heart rate. Firstly, we combine the principle component analysis and adaptive filter to remove a part of the motion artifacts. Due to the strong relativity between motion artifacts and acceleration signals, the further denoising problem is regarded as a sparse signals reconstruction problem. Then, we use a spectrum subtraction method to eliminate motion artifacts effectively. Finally, the spectral peak corresponding to heart rate is sought by an SVM-based spectral analysis method. Through the public PPG database in the 2015 IEEE Signal Processing Cup, we acquire the experimental results, i.e., the average absolute error was 1.01 beat per minute, and the Pearson correlation was 0.9972. These results also confirm that the proposed Mix-SVM approach has potential for multi-channel WPPG-based heart rate estimation in the presence of intense physical exercise.

Classification of different kinds of pesticide residues on lettuce based on fluorescence spectra and WT-BCC-SVM algorithm

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Zhou, Xin; Jun, Sun; Zhang, Bing; Jun, Wu

2017-07-01

In order to improve the reliability of the spectrum feature extracted by wavelet transform, a method combining wavelet transform (WT) with bacterial colony chemotaxis algorithm and support vector machine (BCC-SVM) algorithm (WT-BCC-SVM) was proposed in this paper. Besides, we aimed to identify different kinds of pesticide residues on lettuce leaves in a novel and rapid non-destructive way by using fluorescence spectra technology. The fluorescence spectral data of 150 lettuce leaf samples of five different kinds of pesticide residues on the surface of lettuce were obtained using Cary Eclipse fluorescence spectrometer. Standard normalized variable detrending (SNV detrending), Savitzky-Golay coupled with Standard normalized variable detrending (SG-SNV detrending) were used to preprocess the raw spectra, respectively. Bacterial colony chemotaxis combined with support vector machine (BCC-SVM) and support vector machine (SVM) classification models were established based on full spectra (FS) and wavelet transform characteristics (WTC), respectively. Moreover, WTC were selected by WT. The results showed that the accuracy of training set, calibration set and the prediction set of the best optimal classification model (SG-SNV detrending-WT-BCC-SVM) were 100%, 98% and 93.33%, respectively. In addition, the results indicated that it was feasible to use WT-BCC-SVM to establish diagnostic model of different kinds of pesticide residues on lettuce leaves.

SVM classifier on chip for melanoma detection.

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Afifi, Shereen; GholamHosseini, Hamid; Sinha, Roopak

2017-07-01

Support Vector Machine (SVM) is a common classifier used for efficient classification with high accuracy. SVM shows high accuracy for classifying melanoma (skin cancer) clinical images within computer-aided diagnosis systems used by skin cancer specialists to detect melanoma early and save lives. We aim to develop a medical low-cost handheld device that runs a real-time embedded SVM-based diagnosis system for use in primary care for early detection of melanoma. In this paper, an optimized SVM classifier is implemented onto a recent FPGA platform using the latest design methodology to be embedded into the proposed device for realizing online efficient melanoma detection on a single system on chip/device. The hardware implementation results demonstrate a high classification accuracy of 97.9% and a significant acceleration factor of 26 from equivalent software implementation on an embedded processor, with 34% of resources utilization and 2 watts for power consumption. Consequently, the implemented system meets crucial embedded systems constraints of high performance and low cost, resources utilization and power consumption, while achieving high classification accuracy.

Image Interpolation Scheme based on SVM and Improved PSO

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Jia, X. F.; Zhao, B. T.; Liu, X. X.; Song, H. P.

2018-01-01

In order to obtain visually pleasing images, a support vector machines (SVM) based interpolation scheme is proposed, in which the improved particle swarm optimization is applied to support vector machine parameters optimization. Training samples are constructed by the pixels around the pixel to be interpolated. Then the support vector machine with optimal parameters is trained using training samples. After the training, we can get the interpolation model, which can be employed to estimate the unknown pixel. Experimental result show that the interpolated images get improvement PNSR compared with traditional interpolation methods, which is agrees with the subjective quality.

pDHS-SVM: A prediction method for plant DNase I hypersensitive sites based on support vector machine.

Science.gov (United States)

Zhang, Shanxin; Zhou, Zhiping; Chen, Xinmeng; Hu, Yong; Yang, Lindong

2017-08-07

DNase I hypersensitive sites (DHSs) are accessible chromatin regions hypersensitive to cleavages by DNase I endonucleases. DHSs are indicative of cis-regulatory DNA elements (CREs), all of which play important roles in global gene expression regulation. It is helpful for discovering CREs by recognition of DHSs in genome. To accelerate the investigation, it is an important complement to develop cost-effective computational methods to identify DHSs. However, there is a lack of tools used for identifying DHSs in plant genome. Here we presented pDHS-SVM, a computational predictor to identify plant DHSs. To integrate the global sequence-order information and local DNA properties, reverse complement kmer and dinucleotide-based auto covariance of DNA sequences were applied to construct the feature space. In this work, fifteen physical-chemical properties of dinucleotides were used and Support Vector Machine (SVM) was employed. To further improve the performance of the predictor and extract an optimized subset of nucleotide physical-chemical properties positive for the DHSs, a heuristic nucleotide physical-chemical property selection algorithm was introduced. With the optimized subset of properties, experimental results of Arabidopsis thaliana and rice (Oryza sativa) showed that pDHS-SVM could achieve accuracies up to 87.00%, and 85.79%, respectively. The results indicated the effectiveness of proposed method for predicting DHSs. Furthermore, pDHS-SVM could provide a helpful complement for predicting CREs in plant genome. Our implementation of the novel proposed method pDHS-SVM is freely available as source code, at https://github.com/shanxinzhang/pDHS-SVM. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Method for Aileron Actuator Fault Diagnosis Based on PCA and PGC-SVM

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Wei-Li Qin

2016-01-01

Full Text Available Aileron actuators are pivotal components for aircraft flight control system. Thus, the fault diagnosis of aileron actuators is vital in the enhancement of the reliability and fault tolerant capability. This paper presents an aileron actuator fault diagnosis approach combining principal component analysis (PCA, grid search (GS, 10-fold cross validation (CV, and one-versus-one support vector machine (SVM. This method is referred to as PGC-SVM and utilizes the direct drive valve input, force motor current, and displacement feedback signal to realize fault detection and location. First, several common faults of aileron actuators, which include force motor coil break, sensor coil break, cylinder leakage, and amplifier gain reduction, are extracted from the fault quadrantal diagram; the corresponding fault mechanisms are analyzed. Second, the data feature extraction is performed with dimension reduction using PCA. Finally, the GS and CV algorithms are employed to train a one-versus-one SVM for fault classification, thus obtaining the optimal model parameters and assuring the generalization of the trained SVM, respectively. To verify the effectiveness of the proposed approach, four types of faults are introduced into the simulation model established by AMESim and Simulink. The results demonstrate its desirable diagnostic performance which outperforms that of the traditional SVM by comparison.

Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data

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Harris Lyndsay N

2006-04-01

Full Text Available Abstract Background Like microarray-based investigations, high-throughput proteomics techniques require machine learning algorithms to identify biomarkers that are informative for biological classification problems. Feature selection and classification algorithms need to be robust to noise and outliers in the data. Results We developed a recursive support vector machine (R-SVM algorithm to select important genes/biomarkers for the classification of noisy data. We compared its performance to a similar, state-of-the-art method (SVM recursive feature elimination or SVM-RFE, paying special attention to the ability of recovering the true informative genes/biomarkers and the robustness to outliers in the data. Simulation experiments show that a 5 %-~20 % improvement over SVM-RFE can be achieved regard to these properties. The SVM-based methods are also compared with a conventional univariate method and their respective strengths and weaknesses are discussed. R-SVM was applied to two sets of SELDI-TOF-MS proteomics data, one from a human breast cancer study and the other from a study on rat liver cirrhosis. Important biomarkers found by the algorithm were validated by follow-up biological experiments. Conclusion The proposed R-SVM method is suitable for analyzing noisy high-throughput proteomics and microarray data and it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features. The multivariate SVM-based method outperforms the univariate method in the classification performance, but univariate methods can reveal more of the differentially expressed features especially when there are correlations between the features.

lncRScan-SVM: A Tool for Predicting Long Non-Coding RNAs Using Support Vector Machine.

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Sun, Lei; Liu, Hui; Zhang, Lin; Meng, Jia

2015-01-01

Functional long non-coding RNAs (lncRNAs) have been bringing novel insight into biological study, however it is still not trivial to accurately distinguish the lncRNA transcripts (LNCTs) from the protein coding ones (PCTs). As various information and data about lncRNAs are preserved by previous studies, it is appealing to develop novel methods to identify the lncRNAs more accurately. Our method lncRScan-SVM aims at classifying PCTs and LNCTs using support vector machine (SVM). The gold-standard datasets for lncRScan-SVM model training, lncRNA prediction and method comparison were constructed according to the GENCODE gene annotations of human and mouse respectively. By integrating features derived from gene structure, transcript sequence, potential codon sequence and conservation, lncRScan-SVM outperforms other approaches, which is evaluated by several criteria such as sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC) and area under curve (AUC). In addition, several known human lncRNA datasets were assessed using lncRScan-SVM. LncRScan-SVM is an efficient tool for predicting the lncRNAs, and it is quite useful for current lncRNA study.

Modulation transfer function (MTF) measurement method based on support vector machine (SVM)

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Zhang, Zheng; Chen, Yueting; Feng, Huajun; Xu, Zhihai; Li, Qi

2016-03-01

An imaging system's spatial quality can be expressed by the system's modulation spread function (MTF) as a function of spatial frequency in terms of the linear response theory. Methods have been proposed to assess the MTF of an imaging system using point, slit or edge techniques. The edge method is widely used for the low requirement of targets. However, the traditional edge methods are limited by the edge angle. Besides, image noise will impair the measurement accuracy, making the measurement result unstable. In this paper, a novel measurement method based on the support vector machine (SVM) is proposed. Image patches with different edge angles and MTF levels are generated as the training set. Parameters related with MTF and image structure are extracted from the edge images. Trained with image parameters and the corresponding MTF, the SVM classifier can assess the MTF of any edge image. The result shows that the proposed method has an excellent performance on measuring accuracy and stability.

Efficient HIK SVM learning for image classification.

Science.gov (United States)

Wu, Jianxin

2012-10-01

Histograms are used in almost every aspect of image processing and computer vision, from visual descriptors to image representations. Histogram intersection kernel (HIK) and support vector machine (SVM) classifiers are shown to be very effective in dealing with histograms. This paper presents contributions concerning HIK SVM for image classification. First, we propose intersection coordinate descent (ICD), a deterministic and scalable HIK SVM solver. ICD is much faster than, and has similar accuracies to, general purpose SVM solvers and other fast HIK SVM training methods. We also extend ICD to the efficient training of a broader family of kernels. Second, we show an important empirical observation that ICD is not sensitive to the C parameter in SVM, and we provide some theoretical analyses to explain this observation. ICD achieves high accuracies in many problems, using its default parameters. This is an attractive property for practitioners, because many image processing tasks are too large to choose SVM parameters using cross-validation.

Modeling the milling tool wear by using an evolutionary SVM-based model from milling runs experimental data

Science.gov (United States)

Nieto, Paulino José García; García-Gonzalo, Esperanza; Vilán, José Antonio Vilán; Robleda, Abraham Segade

2015-12-01

The main aim of this research work is to build a new practical hybrid regression model to predict the milling tool wear in a regular cut as well as entry cut and exit cut of a milling tool. The model was based on Particle Swarm Optimization (PSO) in combination with support vector machines (SVMs). This optimization mechanism involved kernel parameter setting in the SVM training procedure, which significantly influences the regression accuracy. Bearing this in mind, a PSO-SVM-based model, which is based on the statistical learning theory, was successfully used here to predict the milling tool flank wear (output variable) as a function of the following input variables: the time duration of experiment, depth of cut, feed, type of material, etc. To accomplish the objective of this study, the experimental dataset represents experiments from runs on a milling machine under various operating conditions. In this way, data sampled by three different types of sensors (acoustic emission sensor, vibration sensor and current sensor) were acquired at several positions. A second aim is to determine the factors with the greatest bearing on the milling tool flank wear with a view to proposing milling machine's improvements. Firstly, this hybrid PSO-SVM-based regression model captures the main perception of statistical learning theory in order to obtain a good prediction of the dependence among the flank wear (output variable) and input variables (time, depth of cut, feed, etc.). Indeed, regression with optimal hyperparameters was performed and a determination coefficient of 0.95 was obtained. The agreement of this model with experimental data confirmed its good performance. Secondly, the main advantages of this PSO-SVM-based model are its capacity to produce a simple, easy-to-interpret model, its ability to estimate the contributions of the input variables, and its computational efficiency. Finally, the main conclusions of this study are exposed.

Application of SVM classifier in thermographic image classification for early detection of breast cancer

Science.gov (United States)

Oleszkiewicz, Witold; Cichosz, Paweł; Jagodziński, Dariusz; Matysiewicz, Mateusz; Neumann, Łukasz; Nowak, Robert M.; Okuniewski, Rafał

2016-09-01

This article presents the application of machine learning algorithms for early detection of breast cancer on the basis of thermographic images. Supervised learning model: Support vector machine (SVM) and Sequential Minimal Optimization algorithm (SMO) for the training of SVM classifier were implemented. The SVM classifier was included in a client-server application which enables to create a training set of examinations and to apply classifiers (including SVM) for the diagnosis and early detection of the breast cancer. The sensitivity and specificity of SVM classifier were calculated based on the thermographic images from studies. Furthermore, the heuristic method for SVM's parameters tuning was proposed.

Accurate Fluid Level Measurement in Dynamic Environment Using Ultrasonic Sensor and ν-SVM

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Jenny TERZIC

2009-10-01

Full Text Available A fluid level measurement system based on a single Ultrasonic Sensor and Support Vector Machines (SVM based signal processing and classification system has been developed to determine the fluid level in automotive fuel tanks. The novel approach based on the ν-SVM classification method uses the Radial Basis Function (RBF to compensate for the measurement error induced by the sloshing effects in the tank caused by vehicle motion. A broad investigation on selected pre-processing filters, namely, Moving Mean, Moving Median, and Wavelet filter, has also been presented. Field drive trials were performed under normal driving conditions at various fuel volumes ranging from 5 L to 50 L to acquire sample data from the ultrasonic sensor for the training of SVM model. Further drive trials were conducted to obtain data to verify the SVM results. A comparison of the accuracy of the predicted fluid level obtained using SVM and the pre-processing filters is provided. It is demonstrated that the ν-SVM model using the RBF kernel function and the Moving Median filter has produced the most accurate outcome compared with the other signal filtration methods in terms of fluid level measurement.

SPECTRAL RECONSTRUCTION BASED ON SVM FOR CROSS CALIBRATION

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H. Gao

2017-05-01

Full Text Available Chinese HY-1C/1D satellites will use a 5nm/10nm-resolutional visible-near infrared(VNIR hyperspectral sensor with the solar calibrator to cross-calibrate with other sensors. The hyperspectral radiance data are composed of average radiance in the sensor’s passbands and bear a spectral smoothing effect, a transform from the hyperspectral radiance data to the 1-nm-resolution apparent spectral radiance by spectral reconstruction need to be implemented. In order to solve the problem of noise cumulation and deterioration after several times of iteration by the iterative algorithm, a novel regression method based on SVM is proposed, which can approach arbitrary complex non-linear relationship closely and provide with better generalization capability by learning. In the opinion of system, the relationship between the apparent radiance and equivalent radiance is nonlinear mapping introduced by spectral response function(SRF, SVM transform the low-dimensional non-linear question into high-dimensional linear question though kernel function, obtaining global optimal solution by virtue of quadratic form. The experiment is performed using 6S-simulated spectrums considering the SRF and SNR of the hyperspectral sensor, measured reflectance spectrums of water body and different atmosphere conditions. The contrastive result shows: firstly, the proposed method is with more reconstructed accuracy especially to the high-frequency signal; secondly, while the spectral resolution of the hyperspectral sensor reduces, the proposed method performs better than the iterative method; finally, the root mean square relative error(RMSRE which is used to evaluate the difference of the reconstructed spectrum and the real spectrum over the whole spectral range is calculated, it decreses by one time at least by proposed method.

LMethyR-SVM: Predict Human Enhancers Using Low Methylated Regions based on Weighted Support Vector Machines.

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Xu, Jingting; Hu, Hong; Dai, Yang

The identification of enhancers is a challenging task. Various types of epigenetic information including histone modification have been utilized in the construction of enhancer prediction models based on a diverse panel of machine learning schemes. However, DNA methylation profiles generated from the whole genome bisulfite sequencing (WGBS) have not been fully explored for their potential in enhancer prediction despite the fact that low methylated regions (LMRs) have been implied to be distal active regulatory regions. In this work, we propose a prediction framework, LMethyR-SVM, using LMRs identified from cell-type-specific WGBS DNA methylation profiles and a weighted support vector machine learning framework. In LMethyR-SVM, the set of cell-type-specific LMRs is further divided into three sets: reliable positive, like positive and likely negative, according to their resemblance to a small set of experimentally validated enhancers in the VISTA database based on an estimated non-parametric density distribution. Then, the prediction model is obtained by solving a weighted support vector machine. We demonstrate the performance of LMethyR-SVM by using the WGBS DNA methylation profiles derived from the human embryonic stem cell type (H1) and the fetal lung fibroblast cell type (IMR90). The predicted enhancers are highly conserved with a reasonable validation rate based on a set of commonly used positive markers including transcription factors, p300 binding and DNase-I hypersensitive sites. In addition, we show evidence that the large fraction of the LMethyR-SVM predicted enhancers are not predicted by ChromHMM in H1 cell type and they are more enriched for the FANTOM5 enhancers. Our work suggests that low methylated regions detected from the WGBS data are useful as complementary resources to histone modification marks in developing models for the prediction of cell-type-specific enhancers.

Area Determination of Diabetic Foot Ulcer Images Using a Cascaded Two-Stage SVM-Based Classification.

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Wang, Lei; Pedersen, Peder C; Agu, Emmanuel; Strong, Diane M; Tulu, Bengisu

2017-09-01

The standard chronic wound assessment method based on visual examination is potentially inaccurate and also represents a significant clinical workload. Hence, computer-based systems providing quantitative wound assessment may be valuable for accurately monitoring wound healing status, with the wound area the best suited for automated analysis. Here, we present a novel approach, using support vector machines (SVM) to determine the wound boundaries on foot ulcer images captured with an image capture box, which provides controlled lighting and range. After superpixel segmentation, a cascaded two-stage classifier operates as follows: in the first stage, a set of k binary SVM classifiers are trained and applied to different subsets of the entire training images dataset, and incorrectly classified instances are collected. In the second stage, another binary SVM classifier is trained on the incorrectly classified set. We extracted various color and texture descriptors from superpixels that are used as input for each stage in the classifier training. Specifically, color and bag-of-word representations of local dense scale invariant feature transformation features are descriptors for ruling out irrelevant regions, and color and wavelet-based features are descriptors for distinguishing healthy tissue from wound regions. Finally, the detected wound boundary is refined by applying the conditional random field method. We have implemented the wound classification on a Nexus 5 smartphone platform, except for training which was done offline. Results are compared with other classifiers and show that our approach provides high global performance rates (average sensitivity = 73.3%, specificity = 94.6%) and is sufficiently efficient for a smartphone-based image analysis.

Multi-view L2-SVM and its multi-view core vector machine.

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Huang, Chengquan; Chung, Fu-lai; Wang, Shitong

2016-03-01

In this paper, a novel L2-SVM based classifier Multi-view L2-SVM is proposed to address multi-view classification tasks. The proposed Multi-view L2-SVM classifier does not have any bias in its objective function and hence has the flexibility like μ-SVC in the sense that the number of the yielded support vectors can be controlled by a pre-specified parameter. The proposed Multi-view L2-SVM classifier can make full use of the coherence and the difference of different views through imposing the consensus among multiple views to improve the overall classification performance. Besides, based on the generalized core vector machine GCVM, the proposed Multi-view L2-SVM classifier is extended into its GCVM version MvCVM which can realize its fast training on large scale multi-view datasets, with its asymptotic linear time complexity with the sample size and its space complexity independent of the sample size. Our experimental results demonstrated the effectiveness of the proposed Multi-view L2-SVM classifier for small scale multi-view datasets and the proposed MvCVM classifier for large scale multi-view datasets. Copyright © 2015 Elsevier Ltd. All rights reserved.

Short Term Prediction of Freeway Exiting Volume Based on SVM and KNN

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Xiang Wang

2015-09-01

The model results indicate that the proposed algorithm is feasible and accurate. The Mean Absolute Percentage Error is under 10%. When comparing with the results of single KNN or SVM method, the results show that the combination of KNN and SVM can improve the reliability of the prediction significantly. The proposed method can be implemented in the on-line application of exiting volume prediction, which is able to consider different vehicle types.

An SVM-based solution for fault detection in wind turbines.

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Santos, Pedro; Villa, Luisa F; Reñones, Aníbal; Bustillo, Andres; Maudes, Jesús

2015-03-09

Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which vibration signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs) are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two fault typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs) shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets.

An SVM-Based Solution for Fault Detection in Wind Turbines

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Pedro Santos

2015-03-01

Full Text Available Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which vibration signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two fault typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets.

Prediction of interactions between viral and host proteins using supervised machine learning methods.

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Ranjan Kumar Barman

Full Text Available BACKGROUND: Viral-host protein-protein interaction plays a vital role in pathogenesis, since it defines viral infection of the host and regulation of the host proteins. Identification of key viral-host protein-protein interactions (PPIs has great implication for therapeutics. METHODS: In this study, a systematic attempt has been made to predict viral-host PPIs by integrating different features, including domain-domain association, network topology and sequence information using viral-host PPIs from VirusMINT. The three well-known supervised machine learning methods, such as SVM, Naïve Bayes and Random Forest, which are commonly used in the prediction of PPIs, were employed to evaluate the performance measure based on five-fold cross validation techniques. RESULTS: Out of 44 descriptors, best features were found to be domain-domain association and methionine, serine and valine amino acid composition of viral proteins. In this study, SVM-based method achieved better sensitivity of 67% over Naïve Bayes (37.49% and Random Forest (55.66%. However the specificity of Naïve Bayes was the highest (99.52% as compared with SVM (74% and Random Forest (89.08%. Overall, the SVM and Random Forest achieved accuracy of 71% and 72.41%, respectively. The proposed SVM-based method was evaluated on blind dataset and attained a sensitivity of 64%, specificity of 83%, and accuracy of 74%. In addition, unknown potential targets of hepatitis B virus-human and hepatitis E virus-human PPIs have been predicted through proposed SVM model and validated by gene ontology enrichment analysis. Our proposed model shows that, hepatitis B virus "C protein" binds to membrane docking protein, while "X protein" and "P protein" interacts with cell-killing and metabolic process proteins, respectively. CONCLUSION: The proposed method can predict large scale interspecies viral-human PPIs. The nature and function of unknown viral proteins (HBV and HEV, interacting partners of host

[Non-destructive detection research for hollow heart of potato based on semi-transmission hyperspectral imaging and SVM].

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Huang, Tao; Li, Xiao-yu; Xu, Meng-ling; Jin, Rui; Ku, Jing; Xu, Sen-miao; Wu, Zhen-zhong

2015-01-01

The quality of potato is directly related to their edible value and industrial value. Hollow heart of potato, as a physiological disease occurred inside the tuber, is difficult to be detected. This paper put forward a non-destructive detection method by using semi-transmission hyperspectral imaging with support vector machine (SVM) to detect hollow heart of potato. Compared to reflection and transmission hyperspectral image, semi-transmission hyperspectral image can get clearer image which contains the internal quality information of agricultural products. In this study, 224 potato samples (149 normal samples and 75 hollow samples) were selected as the research object, and semi-transmission hyperspectral image acquisition system was constructed to acquire the hyperspectral images (390-1 040 nn) of the potato samples, and then the average spectrum of region of interest were extracted for spectral characteristics analysis. Normalize was used to preprocess the original spectrum, and prediction model were developed based on SVM using all wave bands, the accurate recognition rate of test set is only 87. 5%. In order to simplify the model competitive.adaptive reweighed sampling algorithm (CARS) and successive projection algorithm (SPA) were utilized to select important variables from the all 520 spectral variables and 8 variables were selected (454, 601, 639, 664, 748, 827, 874 and 936 nm). 94. 64% of the accurate recognition rate of test set was obtained by using the 8 variables to develop SVM model. Parameter optimization algorithms, including artificial fish swarm algorithm (AFSA), genetic algorithm (GA) and grid search algorithm, were used to optimize the SVM model parameters: penalty parameter c and kernel parameter g. After comparative analysis, AFSA, a new bionic optimization algorithm based on the foraging behavior of fish swarm, was proved to get the optimal model parameter (c=10. 659 1, g=0. 349 7), and the recognition accuracy of 10% were obtained for the AFSA-SVM

[Study on application of SVM in prediction of coronary heart disease].

Science.gov (United States)

Zhu, Yue; Wu, Jianghua; Fang, Ying

2013-12-01

Base on the data of blood pressure, plasma lipid, Glu and UA by physical test, Support Vector Machine (SVM) was applied to identify coronary heart disease (CHD) in patients and non-CHD individuals in south China population for guide of further prevention and treatment of the disease. Firstly, the SVM classifier was built using radial basis kernel function, liner kernel function and polynomial kernel function, respectively. Secondly, the SVM penalty factor C and kernel parameter sigma were optimized by particle swarm optimization (PSO) and then employed to diagnose and predict the CHD. By comparison with those from artificial neural network with the back propagation (BP) model, linear discriminant analysis, logistic regression method and non-optimized SVM, the overall results of our calculation demonstrated that the classification performance of optimized RBF-SVM model could be superior to other classifier algorithm with higher accuracy rate, sensitivity and specificity, which were 94.51%, 92.31% and 96.67%, respectively. So, it is well concluded that SVM could be used as a valid method for assisting diagnosis of CHD.

Quality-Oriented Classification of Aircraft Material Based on SVM

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Hongxia Cai

2014-01-01

Full Text Available The existing material classification is proposed to improve the inventory management. However, different materials have the different quality-related attributes, especially in the aircraft industry. In order to reduce the cost without sacrificing the quality, we propose a quality-oriented material classification system considering the material quality character, Quality cost, and Quality influence. Analytic Hierarchy Process helps to make feature selection and classification decision. We use the improved Kraljic Portfolio Matrix to establish the three-dimensional classification model. The aircraft materials can be divided into eight types, including general type, key type, risk type, and leveraged type. Aiming to improve the classification accuracy of various materials, the algorithm of Support Vector Machine is introduced. Finally, we compare the SVM and BP neural network in the application. The results prove that the SVM algorithm is more efficient and accurate and the quality-oriented material classification is valuable.

Face Verification using MLP and SVM

OpenAIRE

Cardinaux, Fabien; Marcel, Sébastien

2002-01-01

The performance of machine learning algorithms has steadily improved over the past few years, such as MLP or more recently SVM. In this paper, we compare two successful discriminant machine learning algorithms apply to the problem of face verification: MLP and SVM. These two algorithms are tested on a benchmark database, namely XM2VTS. Results show that a MLP is better than a SVM on this particular task.

LMD Based Features for the Automatic Seizure Detection of EEG Signals Using SVM.

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Zhang, Tao; Chen, Wanzhong

2017-08-01

Achieving the goal of detecting seizure activity automatically using electroencephalogram (EEG) signals is of great importance and significance for the treatment of epileptic seizures. To realize this aim, a newly-developed time-frequency analytical algorithm, namely local mean decomposition (LMD), is employed in the presented study. LMD is able to decompose an arbitrary signal into a series of product functions (PFs). Primarily, the raw EEG signal is decomposed into several PFs, and then the temporal statistical and non-linear features of the first five PFs are calculated. The features of each PF are fed into five classifiers, including back propagation neural network (BPNN), K-nearest neighbor (KNN), linear discriminant analysis (LDA), un-optimized support vector machine (SVM) and SVM optimized by genetic algorithm (GA-SVM), for five classification cases, respectively. Confluent features of all PFs and raw EEG are further passed into the high-performance GA-SVM for the same classification tasks. Experimental results on the international public Bonn epilepsy EEG dataset show that the average classification accuracy of the presented approach are equal to or higher than 98.10% in all the five cases, and this indicates the effectiveness of the proposed approach for automated seizure detection.

LaSVM-based big data learning system for dynamic prediction of air pollution in Tehran.

Science.gov (United States)

Ghaemi, Z; Alimohammadi, A; Farnaghi, M

2018-04-20

Due to critical impacts of air pollution, prediction and monitoring of air quality in urban areas are important tasks. However, because of the dynamic nature and high spatio-temporal variability, prediction of the air pollutant concentrations is a complex spatio-temporal problem. Distribution of pollutant concentration is influenced by various factors such as the historical pollution data and weather conditions. Conventional methods such as the support vector machine (SVM) or artificial neural networks (ANN) show some deficiencies when huge amount of streaming data have to be analyzed for urban air pollution prediction. In order to overcome the limitations of the conventional methods and improve the performance of urban air pollution prediction in Tehran, a spatio-temporal system is designed using a LaSVM-based online algorithm. Pollutant concentration and meteorological data along with geographical parameters are continually fed to the developed online forecasting system. Performance of the system is evaluated by comparing the prediction results of the Air Quality Index (AQI) with those of a traditional SVM algorithm. Results show an outstanding increase of speed by the online algorithm while preserving the accuracy of the SVM classifier. Comparison of the hourly predictions for next coming 24 h, with those of the measured pollution data in Tehran pollution monitoring stations shows an overall accuracy of 0.71, root mean square error of 0.54 and coefficient of determination of 0.81. These results are indicators of the practical usefulness of the online algorithm for real-time spatial and temporal prediction of the urban air quality.

A Fault Diagnosis Approach for Gears Based on IMF AR Model and SVM

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Yu Yang

2008-05-01

Full Text Available An accurate autoregressive (AR model can reflect the characteristics of a dynamic system based on which the fault feature of gear vibration signal can be extracted without constructing mathematical model and studying the fault mechanism of gear vibration system, which are experienced by the time-frequency analysis methods. However, AR model can only be applied to stationary signals, while the gear fault vibration signals usually present nonstationary characteristics. Therefore, empirical mode decomposition (EMD, which can decompose the vibration signal into a finite number of intrinsic mode functions (IMFs, is introduced into feature extraction of gear vibration signals as a preprocessor before AR models are generated. On the other hand, by targeting the difficulties of obtaining sufficient fault samples in practice, support vector machine (SVM is introduced into gear fault pattern recognition. In the proposed method in this paper, firstly, vibration signals are decomposed into a finite number of intrinsic mode functions, then the AR model of each IMF component is established; finally, the corresponding autoregressive parameters and the variance of remnant are regarded as the fault characteristic vectors and used as input parameters of SVM classifier to classify the working condition of gears. The experimental analysis results show that the proposed approach, in which IMF AR model and SVM are combined, can identify working condition of gears with a success rate of 100% even in the case of smaller number of samples.

A SVM-based quantitative fMRI method for resting-state functional network detection.

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Song, Xiaomu; Chen, Nan-kuei

2014-09-01

Resting-state functional magnetic resonance imaging (fMRI) aims to measure baseline neuronal connectivity independent of specific functional tasks and to capture changes in the connectivity due to neurological diseases. Most existing network detection methods rely on a fixed threshold to identify functionally connected voxels under the resting state. Due to fMRI non-stationarity, the threshold cannot adapt to variation of data characteristics across sessions and subjects, and generates unreliable mapping results. In this study, a new method is presented for resting-state fMRI data analysis. Specifically, the resting-state network mapping is formulated as an outlier detection process that is implemented using one-class support vector machine (SVM). The results are refined by using a spatial-feature domain prototype selection method and two-class SVM reclassification. The final decision on each voxel is made by comparing its probabilities of functionally connected and unconnected instead of a threshold. Multiple features for resting-state analysis were extracted and examined using an SVM-based feature selection method, and the most representative features were identified. The proposed method was evaluated using synthetic and experimental fMRI data. A comparison study was also performed with independent component analysis (ICA) and correlation analysis. The experimental results show that the proposed method can provide comparable or better network detection performance than ICA and correlation analysis. The method is potentially applicable to various resting-state quantitative fMRI studies. Copyright © 2014 Elsevier Inc. All rights reserved.

SVM Classifier - a comprehensive java interface for support vector machine classification of microarray data.

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Pirooznia, Mehdi; Deng, Youping

2006-12-12

Graphical user interface (GUI) software promotes novelty by allowing users to extend the functionality. SVM Classifier is a cross-platform graphical application that handles very large datasets well. The purpose of this study is to create a GUI application that allows SVM users to perform SVM training, classification and prediction. The GUI provides user-friendly access to state-of-the-art SVM methods embodied in the LIBSVM implementation of Support Vector Machine. We implemented the java interface using standard swing libraries. We used a sample data from a breast cancer study for testing classification accuracy. We achieved 100% accuracy in classification among the BRCA1-BRCA2 samples with RBF kernel of SVM. We have developed a java GUI application that allows SVM users to perform SVM training, classification and prediction. We have demonstrated that support vector machines can accurately classify genes into functional categories based upon expression data from DNA microarray hybridization experiments. Among the different kernel functions that we examined, the SVM that uses a radial basis kernel function provides the best performance. The SVM Classifier is available at http://mfgn.usm.edu/ebl/svm/.

A Study on SVM Based on the Weighted Elitist Teaching-Learning-Based Optimization and Application in the Fault Diagnosis of Chemical Process

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Cao Junxiang

2015-01-01

Full Text Available Teaching-Learning-Based Optimization (TLBO is a new swarm intelligence optimization algorithm that simulates the class learning process. According to such problems of the traditional TLBO as low optimizing efficiency and poor stability, this paper proposes an improved TLBO algorithm mainly by introducing the elite thought in TLBO and adopting different inertia weight decreasing strategies for elite and ordinary individuals of the teacher stage and the student stage. In this paper, the validity of the improved TLBO is verified by the optimizations of several typical test functions and the SVM optimized by the weighted elitist TLBO is used in the diagnosis and classification of common failure data of the TE chemical process. Compared with the SVM combining other traditional optimizing methods, the SVM optimized by the weighted elitist TLBO has a certain improvement in the accuracy of fault diagnosis and classification.

Quantitative analysis of glycated albumin in serum based on ATR-FTIR spectrum combined with SiPLS and SVM.

Science.gov (United States)

Li, Yuanpeng; Li, Fucui; Yang, Xinhao; Guo, Liu; Huang, Furong; Chen, Zhenqiang; Chen, Xingdan; Zheng, Shifu

2018-08-05

A rapid quantitative analysis model for determining the glycated albumin (GA) content based on Attenuated total reflectance (ATR)-Fourier transform infrared spectroscopy (FTIR) combining with linear SiPLS and nonlinear SVM has been developed. Firstly, the real GA content in human serum was determined by GA enzymatic method, meanwhile, the ATR-FTIR spectra of serum samples from the population of health examination were obtained. The spectral data of the whole spectra mid-infrared region (4000-600 cm -1 ) and GA's characteristic region (1800-800 cm -1 ) were used as the research object of quantitative analysis. Secondly, several preprocessing steps including first derivative, second derivative, variable standardization and spectral normalization, were performed. Lastly, quantitative analysis regression models were established by using SiPLS and SVM respectively. The SiPLS modeling results are as follows: root mean square error of cross validation (RMSECV T ) = 0.523 g/L, calibration coefficient (R C ) = 0.937, Root Mean Square Error of Prediction (RMSEP T ) = 0.787 g/L, and prediction coefficient (R P ) = 0.938. The SVM modeling results are as follows: RMSECV T  = 0.0048 g/L, R C  = 0.998, RMSEP T  = 0.442 g/L, and R p  = 0.916. The results indicated that the model performance was improved significantly after preprocessing and optimization of characteristic regions. While modeling performance of nonlinear SVM was considerably better than that of linear SiPLS. Hence, the quantitative analysis model for GA in human serum based on ATR-FTIR combined with SiPLS and SVM is effective. And it does not need sample preprocessing while being characterized by simple operations and high time efficiency, providing a rapid and accurate method for GA content determination. Copyright © 2018 Elsevier B.V. All rights reserved.

Soft-sensing Modeling Based on MLS-SVM Inversion for L-lysine Fermentation Processes

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Bo Wang

2015-06-01

Full Text Available A modeling approach 63 based on multiple output variables least squares support vector machine (MLS-SVM inversion is presented by a combination of inverse system and support vector machine theory. Firstly, a dynamic system model is developed based on material balance relation of a fed-batch fermentation process, with which it is analyzed whether an inverse system exists or not, and into which characteristic information of a fermentation process is introduced to set up an extended inversion model. Secondly, an initial extended inversion model is developed off-line by the use of the fitting capacity of MLS-SVM; on-line correction is made by the use of a differential evolution (DE algorithm on the basis of deviation information. Finally, a combined pseudo-linear system is formed by means of a serial connection of a corrected extended inversion model behind the L-lysine fermentation processes; thereby crucial biochemical parameters of a fermentation process could be predicted on-line. The simulation experiment shows that this soft-sensing modeling method features very high prediction precision and can predict crucial biochemical parameters of L-lysine fermentation process very well.

Determination of the carmine content based on spectrum fluorescence spectral and PSO-SVM

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Wang, Shu-tao; Peng, Tao; Cheng, Qi; Wang, Gui-chuan; Kong, De-ming; Wang, Yu-tian

2018-03-01

Carmine is a widely used food pigment in various food and beverage additives. Excessive consumption of synthetic pigment shall do harm to body seriously. The food is generally associated with a variety of colors. Under the simulation context of various food pigments' coexistence, we adopted the technology of fluorescence spectroscopy, together with the PSO-SVM algorithm, so that to establish a method for the determination of carmine content in mixed solution. After analyzing the prediction results of PSO-SVM, we collected a bunch of data: the carmine average recovery rate was 100.84%, the root mean square error of prediction (RMSEP) for 1.03e-04, 0.999 for the correlation coefficient between the model output and the real value of the forecast. Compared with the prediction results of reverse transmission, the correlation coefficient of PSO-SVM was 2.7% higher, the average recovery rate for 0.6%, and the root mean square error was nearly one order of magnitude lower. According to the analysis results, it can effectively avoid the interference caused by pigment with the combination of the fluorescence spectrum technique and PSO-SVM, accurately determining the content of carmine in mixed solution with an effect better than that of BP.

Optimization of Support Vector Machine (SVM) for Object Classification

Science.gov (United States)

Scholten, Matthew; Dhingra, Neil; Lu, Thomas T.; Chao, Tien-Hsin

2012-01-01

The Support Vector Machine (SVM) is a powerful algorithm, useful in classifying data into species. The SVMs implemented in this research were used as classifiers for the final stage in a Multistage Automatic Target Recognition (ATR) system. A single kernel SVM known as SVMlight, and a modified version known as a SVM with K-Means Clustering were used. These SVM algorithms were tested as classifiers under varying conditions. Image noise levels varied, and the orientation of the targets changed. The classifiers were then optimized to demonstrate their maximum potential as classifiers. Results demonstrate the reliability of SVM as a method for classification. From trial to trial, SVM produces consistent results.

A hybrid particle swarm optimization-SVM classification for automatic cardiac auscultation

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Prasertsak Charoen

2017-04-01

Full Text Available Cardiac auscultation is a method for a doctor to listen to heart sounds, using a stethoscope, for examining the condition of the heart. Automatic cardiac auscultation with machine learning is a promising technique to classify heart conditions without need of doctors or expertise. In this paper, we develop a classification model based on support vector machine (SVM and particle swarm optimization (PSO for an automatic cardiac auscultation system. The model consists of two parts: heart sound signal processing part and a proposed PSO for weighted SVM (WSVM classifier part. In this method, the PSO takes into account the degree of importance for each feature extracted from wavelet packet (WP decomposition. Then, by using principle component analysis (PCA, the features can be selected. The PSO technique is used to assign diverse weights to different features for the WSVM classifier. Experimental results show that both continuous and binary PSO-WSVM models achieve better classification accuracy on the heart sound samples, by reducing system false negatives (FNs, compared to traditional SVM and genetic algorithm (GA based SVM.

Solution Path for Pin-SVM Classifiers With Positive and Negative $\\tau $ Values.

Science.gov (United States)

Huang, Xiaolin; Shi, Lei; Suykens, Johan A K

2017-07-01

Applying the pinball loss in a support vector machine (SVM) classifier results in pin-SVM. The pinball loss is characterized by a parameter τ . Its value is related to the quantile level and different τ values are suitable for different problems. In this paper, we establish an algorithm to find the entire solution path for pin-SVM with different τ values. This algorithm is based on the fact that the optimal solution to pin-SVM is continuous and piecewise linear with respect to τ . We also show that the nonnegativity constraint on τ is not necessary, i.e., τ can be extended to negative values. First, in some applications, a negative τ leads to better accuracy. Second, τ = -1 corresponds to a simple solution that links SVM and the classical kernel rule. The solution for τ = -1 can be obtained directly and then be used as a starting point of the solution path. The proposed method efficiently traverses τ values through the solution path, and then achieves good performance by a suitable τ . In particular, τ = 0 corresponds to C-SVM, meaning that the traversal algorithm can output a result at least as good as C-SVM with respect to validation error.

GenSVM: a generalized multiclass support vector machine

NARCIS (Netherlands)

G.J.J. van den Burg (Gertjan); P.J.F. Groenen (Patrick)

2016-01-01

textabstractTraditional extensions of the binary support vector machine (SVM) to multiclass problems are either heuristics or require solving a large dual optimization problem. Here, a generalized multiclass SVM is proposed called GenSVM. In this method classification boundaries for a K-class

Tuning to optimize SVM approach for assisting ovarian cancer diagnosis with photoacoustic imaging.

Science.gov (United States)

Wang, Rui; Li, Rui; Lei, Yanyan; Zhu, Quing

2015-01-01

Support vector machine (SVM) is one of the most effective classification methods for cancer detection. The efficiency and quality of a SVM classifier depends strongly on several important features and a set of proper parameters. Here, a series of classification analyses, with one set of photoacoustic data from ovarian tissues ex vivo and a widely used breast cancer dataset- the Wisconsin Diagnostic Breast Cancer (WDBC), revealed the different accuracy of a SVM classification in terms of the number of features used and the parameters selected. A pattern recognition system is proposed by means of SVM-Recursive Feature Elimination (RFE) with the Radial Basis Function (RBF) kernel. To improve the effectiveness and robustness of the system, an optimized tuning ensemble algorithm called as SVM-RFE(C) with correlation filter was implemented to quantify feature and parameter information based on cross validation. The proposed algorithm is first demonstrated outperforming SVM-RFE on WDBC. Then the best accuracy of 94.643% and sensitivity of 94.595% were achieved when using SVM-RFE(C) to test 57 new PAT data from 19 patients. The experiment results show that the classifier constructed with SVM-RFE(C) algorithm is able to learn additional information from new data and has significant potential in ovarian cancer diagnosis.

COMPARISON OF SVM AND FUZZY CLASSIFIER FOR AN INDIAN SCRIPT

Directory of Open Access Journals (Sweden)

M. J. Baheti

2012-01-01

Full Text Available With the advent of technological era, conversion of scanned document (handwritten or printed into machine editable format has attracted many researchers. This paper deals with the problem of recognition of Gujarati handwritten numerals. Gujarati numeral recognition requires performing some specific steps as a part of preprocessing. For preprocessing digitization, segmentation, normalization and thinning are done with considering that the image have almost no noise. Further affine invariant moments based model is used for feature extraction and finally Support Vector Machine (SVM and Fuzzy classifiers are used for numeral classification. . The comparison of SVM and Fuzzy classifier is made and it can be seen that SVM procured better results as compared to Fuzzy Classifier.

An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications.

Science.gov (United States)

Ye, Fei; Lou, Xin Yuan; Sun, Lin Fu

2017-01-01

This paper proposes a new support vector machine (SVM) optimization scheme based on an improved chaotic fly optimization algorithm (FOA) with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. In the improved FOA, the chaotic particle initializes the fruit fly swarm location and replaces the expression of distance for the fruit fly to find the food source. However, the proposed mutation strategy uses two distinct generative mechanisms for new food sources at the osphresis phase, allowing the algorithm procedure to search for the optimal solution in both the whole solution space and within the local solution space containing the fruit fly swarm location. In an evaluation based on a group of ten benchmark problems, the proposed algorithm's performance is compared with that of other well-known algorithms, and the results support the superiority of the proposed algorithm. Moreover, this algorithm is successfully applied in a SVM to perform both parameter setting turning for the SVM and feature selection to solve real-world classification problems. This method is called chaotic fruit fly optimization algorithm (CIFOA)-SVM and has been shown to be a more robust and effective optimization method than other well-known methods, particularly in terms of solving the medical diagnosis problem and the credit card problem.

AllerHunter: a SVM-pairwise system for assessment of allergenicity and allergic cross-reactivity in proteins.

Directory of Open Access Journals (Sweden)

Hon Cheng Muh

Full Text Available Allergy is a major health problem in industrialized countries. The number of transgenic food crops is growing rapidly creating the need for allergenicity assessment before they are introduced into human food chain. While existing bioinformatic methods have achieved good accuracies for highly conserved sequences, the discrimination of allergens and non-allergens from allergen-like non-allergen sequences remains difficult. We describe AllerHunter, a web-based computational system for the assessment of potential allergenicity and allergic cross-reactivity in proteins. It combines an iterative pairwise sequence similarity encoding scheme with SVM as the discriminating engine. The pairwise vectorization framework allows the system to model essential features in allergens that are involved in cross-reactivity, but not limited to distinct sets of physicochemical properties. The system was rigorously trained and tested using 1,356 known allergen and 13,449 putative non-allergen sequences. Extensive testing was performed for validation of the prediction models. The system is effective for distinguishing allergens and non-allergens from allergen-like non-allergen sequences. Testing results showed that AllerHunter, with a sensitivity of 83.4% and specificity of 96.4% (accuracy = 95.3%, area under the receiver operating characteristic curve AROC = 0.928+/-0.004 and Matthew's correlation coefficient MCC = 0.738, performs significantly better than a number of existing methods using an independent dataset of 1443 protein sequences. AllerHunter is available at (http://tiger.dbs.nus.edu.sg/AllerHunter.

Introducing instrumental variables in the LS-SVM based identification framework

NARCIS (Netherlands)

Laurain, V.; Zheng, W-X.; Toth, R.

2011-01-01

Least-Squares Support Vector Machines (LS-SVM) represent a promising approach to identify nonlinear systems via nonparametric estimation of the nonlinearities in a computationally and stochastically attractive way. All the methods dedicated to the solution of this problem rely on the minimization of

Nonlinear Time Series Prediction Using LS-SVM with Chaotic Mutation Evolutionary Programming for Parameter Optimization

International Nuclear Information System (INIS)

Xu Ruirui; Chen Tianlun; Gao Chengfeng

2006-01-01

Nonlinear time series prediction is studied by using an improved least squares support vector machine (LS-SVM) regression based on chaotic mutation evolutionary programming (CMEP) approach for parameter optimization. We analyze how the prediction error varies with different parameters (σ, γ) in LS-SVM. In order to select appropriate parameters for the prediction model, we employ CMEP algorithm. Finally, Nasdaq stock data are predicted by using this LS-SVM regression based on CMEP, and satisfactory results are obtained.

SVM Classifier – a comprehensive java interface for support vector machine classification of microarray data

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Pirooznia, Mehdi; Deng, Youping

2006-01-01

Motivation Graphical user interface (GUI) software promotes novelty by allowing users to extend the functionality. SVM Classifier is a cross-platform graphical application that handles very large datasets well. The purpose of this study is to create a GUI application that allows SVM users to perform SVM training, classification and prediction. Results The GUI provides user-friendly access to state-of-the-art SVM methods embodied in the LIBSVM implementation of Support Vector Machine. We implemented the java interface using standard swing libraries. We used a sample data from a breast cancer study for testing classification accuracy. We achieved 100% accuracy in classification among the BRCA1–BRCA2 samples with RBF kernel of SVM. Conclusion We have developed a java GUI application that allows SVM users to perform SVM training, classification and prediction. We have demonstrated that support vector machines can accurately classify genes into functional categories based upon expression data from DNA microarray hybridization experiments. Among the different kernel functions that we examined, the SVM that uses a radial basis kernel function provides the best performance. The SVM Classifier is available at . PMID:17217518

Geographical traceability of wild Boletus edulis based on data fusion of FT-MIR and ICP-AES coupled with data mining methods (SVM)

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Li, Yun; Zhang, Ji; Li, Tao; Liu, Honggao; Li, Jieqing; Wang, Yuanzhong

2017-04-01

In this work, the data fusion strategy of Fourier transform mid infrared (FT-MIR) spectroscopy and inductively coupled plasma-atomic emission spectrometry (ICP-AES) was used in combination with Support Vector Machine (SVM) to determine the geographic origin of Boletus edulis collected from nine regions of Yunnan Province in China. Firstly, competitive adaptive reweighted sampling (CARS) was used for selecting an optimal combination of key wavenumbers of second derivative FT-MIR spectra, and thirteen elements were sorted with variable importance in projection (VIP) scores. Secondly, thirteen subsets of multi-elements with the best VIP score were generated and each subset was used to fuse with FT-MIR. Finally, the classification models were established by SVM, and the combination of parameter C and γ (gamma) of SVM models was calculated by the approaches of grid search (GS) and genetic algorithm (GA). The results showed that both GS-SVM and GA-SVM models achieved good performances based on the #9 subset and the prediction accuracy in calibration and validation sets of the two models were 81.40% and 90.91%, correspondingly. In conclusion, it indicated that the data fusion strategy of FT-MIR and ICP-AES coupled with the algorithm of SVM can be used as a reliable tool for accurate identification of B. edulis, and it can provide a useful way of thinking for the quality control of edible mushrooms.

A SVM-based method for sentiment analysis in Persian language

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Hajmohammadi, Mohammad Sadegh; Ibrahim, Roliana

2013-03-01

Persian language is the official language of Iran, Tajikistan and Afghanistan. Local online users often represent their opinions and experiences on the web with written Persian. Although the information in those reviews is valuable to potential consumers and sellers, the huge amount of web reviews make it difficult to give an unbiased evaluation to a product. In this paper, standard machine learning techniques SVM and naive Bayes are incorporated into the domain of online Persian Movie reviews to automatically classify user reviews as positive or negative and performance of these two classifiers is compared with each other in this language. The effects of feature presentations on classification performance are discussed. We find that accuracy is influenced by interaction between the classification models and the feature options. The SVM classifier achieves as well as or better accuracy than naive Bayes in Persian movie. Unigrams are proved better features than bigrams and trigrams in capturing Persian sentiment orientation.

An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications

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Lou, Xin Yuan; Sun, Lin Fu

2017-01-01

This paper proposes a new support vector machine (SVM) optimization scheme based on an improved chaotic fly optimization algorithm (FOA) with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. In the improved FOA, the chaotic particle initializes the fruit fly swarm location and replaces the expression of distance for the fruit fly to find the food source. However, the proposed mutation strategy uses two distinct generative mechanisms for new food sources at the osphresis phase, allowing the algorithm procedure to search for the optimal solution in both the whole solution space and within the local solution space containing the fruit fly swarm location. In an evaluation based on a group of ten benchmark problems, the proposed algorithm’s performance is compared with that of other well-known algorithms, and the results support the superiority of the proposed algorithm. Moreover, this algorithm is successfully applied in a SVM to perform both parameter setting turning for the SVM and feature selection to solve real-world classification problems. This method is called chaotic fruit fly optimization algorithm (CIFOA)-SVM and has been shown to be a more robust and effective optimization method than other well-known methods, particularly in terms of solving the medical diagnosis problem and the credit card problem. PMID:28369096

[Hyperspectral Estimation of Apple Tree Canopy LAI Based on SVM and RF Regression].

Science.gov (United States)

Han, Zhao-ying; Zhu, Xi-cun; Fang, Xian-yi; Wang, Zhuo-yuan; Wang, Ling; Zhao, Geng-Xing; Jiang, Yuan-mao

2016-03-01

Leaf area index (LAI) is the dynamic index of crop population size. Hyperspectral technology can be used to estimate apple canopy LAI rapidly and nondestructively. It can be provide a reference for monitoring the tree growing and yield estimation. The Red Fuji apple trees of full bearing fruit are the researching objects. Ninety apple trees canopies spectral reflectance and LAI values were measured by the ASD Fieldspec3 spectrometer and LAI-2200 in thirty orchards in constant two years in Qixia research area of Shandong Province. The optimal vegetation indices were selected by the method of correlation analysis of the original spectral reflectance and vegetation indices. The models of predicting the LAI were built with the multivariate regression analysis method of support vector machine (SVM) and random forest (RF). The new vegetation indices, GNDVI527, ND-VI676, RVI682, FD-NVI656 and GRVI517 and the previous two main vegetation indices, NDVI670 and NDVI705, are in accordance with LAI. In the RF regression model, the calibration set decision coefficient C-R2 of 0.920 and validation set decision coefficient V-R2 of 0.889 are higher than the SVM regression model by 0.045 and 0.033 respectively. The root mean square error of calibration set C-RMSE of 0.249, the root mean square error validation set V-RMSE of 0.236 are lower than that of the SVM regression model by 0.054 and 0.058 respectively. Relative analysis of calibrating error C-RPD and relative analysis of validation set V-RPD reached 3.363 and 2.520, 0.598 and 0.262, respectively, which were higher than the SVM regression model. The measured and predicted the scatterplot trend line slope of the calibration set and validation set C-S and V-S are close to 1. The estimation result of RF regression model is better than that of the SVM. RF regression model can be used to estimate the LAI of red Fuji apple trees in full fruit period.

HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features.

Science.gov (United States)

Zaman, Rianon; Chowdhury, Shahana Yasmin; Rashid, Mahmood A; Sharma, Alok; Dehzangi, Abdollah; Shatabda, Swakkhar

2017-01-01

DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM) as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. We experimentally show that our method outperforms the state-of-the-art methods found in the literature.

HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features

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Rianon Zaman

2017-01-01

Full Text Available DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. We experimentally show that our method outperforms the state-of-the-art methods found in the literature.

Simultaneous data pre-processing and SVM classification model selection based on a parallel genetic algorithm applied to spectroscopic data of olive oils.

Science.gov (United States)

Devos, Olivier; Downey, Gerard; Duponchel, Ludovic

2014-04-01

Classification is an important task in chemometrics. For several years now, support vector machines (SVMs) have proven to be powerful for infrared spectral data classification. However such methods require optimisation of parameters in order to control the risk of overfitting and the complexity of the boundary. Furthermore, it is established that the prediction ability of classification models can be improved using pre-processing in order to remove unwanted variance in the spectra. In this paper we propose a new methodology based on genetic algorithm (GA) for the simultaneous optimisation of SVM parameters and pre-processing (GENOPT-SVM). The method has been tested for the discrimination of the geographical origin of Italian olive oil (Ligurian and non-Ligurian) on the basis of near infrared (NIR) or mid infrared (FTIR) spectra. Different classification models (PLS-DA, SVM with mean centre data, GENOPT-SVM) have been tested and statistically compared using McNemar's statistical test. For the two datasets, SVM with optimised pre-processing give models with higher accuracy than the one obtained with PLS-DA on pre-processed data. In the case of the NIR dataset, most of this accuracy improvement (86.3% compared with 82.8% for PLS-DA) occurred using only a single pre-processing step. For the FTIR dataset, three optimised pre-processing steps are required to obtain SVM model with significant accuracy improvement (82.2%) compared to the one obtained with PLS-DA (78.6%). Furthermore, this study demonstrates that even SVM models have to be developed on the basis of well-corrected spectral data in order to obtain higher classification rates. Copyright © 2013 Elsevier Ltd. All rights reserved.

Geographical traceability of wild Boletus edulis based on data fusion of FT-MIR and ICP-AES coupled with data mining methods (SVM).

Science.gov (United States)

Li, Yun; Zhang, Ji; Li, Tao; Liu, Honggao; Li, Jieqing; Wang, Yuanzhong

2017-04-15

In this work, the data fusion strategy of Fourier transform mid infrared (FT-MIR) spectroscopy and inductively coupled plasma-atomic emission spectrometry (ICP-AES) was used in combination with Support Vector Machine (SVM) to determine the geographic origin of Boletus edulis collected from nine regions of Yunnan Province in China. Firstly, competitive adaptive reweighted sampling (CARS) was used for selecting an optimal combination of key wavenumbers of second derivative FT-MIR spectra, and thirteen elements were sorted with variable importance in projection (VIP) scores. Secondly, thirteen subsets of multi-elements with the best VIP score were generated and each subset was used to fuse with FT-MIR. Finally, the classification models were established by SVM, and the combination of parameter C and γ (gamma) of SVM models was calculated by the approaches of grid search (GS) and genetic algorithm (GA). The results showed that both GS-SVM and GA-SVM models achieved good performances based on the #9 subset and the prediction accuracy in calibration and validation sets of the two models were 81.40% and 90.91%, correspondingly. In conclusion, it indicated that the data fusion strategy of FT-MIR and ICP-AES coupled with the algorithm of SVM can be used as a reliable tool for accurate identification of B. edulis, and it can provide a useful way of thinking for the quality control of edible mushrooms. Copyright © 2017. Published by Elsevier B.V.

A novel application of wavelet based SVM to transient phenomena identification of power transformers

International Nuclear Information System (INIS)

Jazebi, S.; Vahidi, B.; Jannati, M.

2011-01-01

A novel differential protection approach is introduced in the present paper. The proposed scheme is a combination of Support Vector Machine (SVM) and wavelet transform theories. Two common transients such as magnetizing inrush current and internal fault are considered. A new wavelet feature is extracted which reduces the computational cost and enhances the discrimination accuracy of SVM. Particle swarm optimization technique (PSO) has been applied to tune SVM parameters. The suitable performance of this method is demonstrated by simulation of different faults and switching conditions on a power transformer in PSCAD/EMTDC software. The method has the advantages of high accuracy and low computational burden (less than a quarter of a cycle). The other advantage is that the method is not dependent on a specific threshold. Sympathetic and recovery inrush currents also have been simulated and investigated. Results show that the proposed method could remain stable even in noisy environments.

Sales Growth Rate Forecasting Using Improved PSO and SVM

Directory of Open Access Journals (Sweden)

Xibin Wang

2014-01-01

Full Text Available Accurate forecast of the sales growth rate plays a decisive role in determining the amount of advertising investment. In this study, we present a preclassification and later regression based method optimized by improved particle swarm optimization (IPSO for sales growth rate forecasting. We use support vector machine (SVM as a classification model. The nonlinear relationship in sales growth rate forecasting is efficiently represented by SVM, while IPSO is optimizing the training parameters of SVM. IPSO addresses issues of traditional PSO, such as relapsing into local optimum, slow convergence speed, and low convergence precision in the later evolution. We performed two experiments; firstly, three classic benchmark functions are used to verify the validity of the IPSO algorithm against PSO. Having shown IPSO outperform PSO in convergence speed, precision, and escaping local optima, in our second experiment, we apply IPSO to the proposed model. The sales growth rate forecasting cases are used to testify the forecasting performance of proposed model. According to the requirements and industry knowledge, the sample data was first classified to obtain types of the test samples. Next, the values of the test samples were forecast using the SVM regression algorithm. The experimental results demonstrate that the proposed model has good forecasting performance.

A Method of Particle Swarm Optimized SVM Hyper-spectral Remote Sensing Image Classification

International Nuclear Information System (INIS)

Liu, Q J; Jing, L H; Wang, L M; Lin, Q Z

2014-01-01

Support Vector Machine (SVM) has been proved to be suitable for classification of remote sensing image and proposed to overcome the Hughes phenomenon. Hyper-spectral sensors are intrinsically designed to discriminate among a broad range of land cover classes which may lead to high computational time in SVM mutil-class algorithms. Model selection for SVM involving kernel and the margin parameter values selection which is usually time-consuming, impacts training efficiency of SVM model and final classification accuracies of SVM hyper-spectral remote sensing image classifier greatly. Firstly, based on combinatorial optimization theory and cross-validation method, particle swarm algorithm is introduced to the optimal selection of SVM (PSSVM) kernel parameter σ and margin parameter C to improve the modelling efficiency of SVM model. Then an experiment of classifying AVIRIS in India Pine site of USA was performed for evaluating the novel PSSVM, as well as traditional SVM classifier with general Grid-Search cross-validation method (GSSVM). And then, evaluation indexes including SVM model training time, classification Overall Accuracy (OA) and Kappa index of both PSSVM and GSSVM are all analyzed quantitatively. It is demonstrated that OA of PSSVM on test samples and whole image are 85% and 82%, the differences with that of GSSVM are both within 0.08% respectively. And Kappa indexes reach 0.82 and 0.77, the differences with that of GSSVM are both within 0.001. While the modelling time of PSSVM can be only 1/10 of that of GSSVM, and the modelling. Therefore, PSSVM is an fast and accurate algorithm for hyper-spectral image classification and is superior to GSSVM

A DWT and SVM based method for rolling element bearing fault diagnosis and its comparison with Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Sunil Tyagi

2017-04-01

Full Text Available A classification technique using Support Vector Machine (SVM classifier for detection of rolling element bearing fault is presented here.  The SVM was fed from features that were extracted from of vibration signals obtained from experimental setup consisting of rotating driveline that was mounted on rolling element bearings which were run in normal and with artificially faults induced conditions. The time-domain vibration signals were divided into 40 segments and simple features such as peaks in time domain and spectrum along with statistical features such as standard deviation, skewness, kurtosis etc. were extracted. Effectiveness of SVM classifier was compared with the performance of Artificial Neural Network (ANN classifier and it was found that the performance of SVM classifier is superior to that of ANN. The effect of pre-processing of the vibration signal by Discreet Wavelet Transform (DWT prior to feature extraction is also studied and it is shown that pre-processing of vibration signal with DWT enhances the effectiveness of both ANN and SVM classifiers. It has been demonstrated from experiment results that performance of SVM classifier is better than ANN in detection of bearing condition and pre-processing the vibration signal with DWT improves the performance of SVM classifier.

An intelligent framework for medical image retrieval using MDCT and multi SVM.

Science.gov (United States)

Balan, J A Alex Rajju; Rajan, S Edward

2014-01-01

Volumes of medical images are rapidly generated in medical field and to manage them effectively has become a great challenge. This paper studies the development of innovative medical image retrieval based on texture features and accuracy. The objective of the paper is to analyze the image retrieval based on diagnosis of healthcare management systems. This paper traces the development of innovative medical image retrieval to estimate both the image texture features and accuracy. The texture features of medical images are extracted using MDCT and multi SVM. Both the theoretical approach and the simulation results revealed interesting observations and they were corroborated using MDCT coefficients and SVM methodology. All attempts to extract the data about the image in response to the query has been computed successfully and perfect image retrieval performance has been obtained. Experimental results on a database of 100 trademark medical images show that an integrated texture feature representation results in 98% of the images being retrieved using MDCT and multi SVM. Thus we have studied a multiclassification technique based on SVM which is prior suitable for medical images. The results show the retrieval accuracy of 98%, 99% for different sets of medical images with respect to the class of image.

DCS-SVM: a novel semi-automated method for human brain MR image segmentation.

Science.gov (United States)

Ahmadvand, Ali; Daliri, Mohammad Reza; Hajiali, Mohammadtaghi

2017-11-27

In this paper, a novel method is proposed which appropriately segments magnetic resonance (MR) brain images into three main tissues. This paper proposes an extension of our previous work in which we suggested a combination of multiple classifiers (CMC)-based methods named dynamic classifier selection-dynamic local training local Tanimoto index (DCS-DLTLTI) for MR brain image segmentation into three main cerebral tissues. This idea is used here and a novel method is developed that tries to use more complex and accurate classifiers like support vector machine (SVM) in the ensemble. This work is challenging because the CMC-based methods are time consuming, especially on huge datasets like three-dimensional (3D) brain MR images. Moreover, SVM is a powerful method that is used for modeling datasets with complex feature space, but it also has huge computational cost for big datasets, especially those with strong interclass variability problems and with more than two classes such as 3D brain images; therefore, we cannot use SVM in DCS-DLTLTI. Therefore, we propose a novel approach named "DCS-SVM" to use SVM in DCS-DLTLTI to improve the accuracy of segmentation results. The proposed method is applied on well-known datasets of the Internet Brain Segmentation Repository (IBSR) and promising results are obtained.

Throughput Maximization Using an SVM for Multi-Class Hypothesis-Based Spectrum Sensing in Cognitive Radio

Directory of Open Access Journals (Sweden)

Sana Ullah Jan

2018-03-01

Full Text Available A framework of spectrum sensing with a multi-class hypothesis is proposed to maximize the achievable throughput in cognitive radio networks. The energy range of a sensing signal under the hypothesis that the primary user is absent (in a conventional two-class hypothesis is further divided into quantized regions, whereas the hypothesis that the primary user is present is conserved. The non-radio frequency energy harvesting-equiped secondary user transmits, when the primary user is absent, with transmission power based on the hypothesis result (the energy level of the sensed signal and the residual energy in the battery: the lower the energy of the received signal, the higher the transmission power, and vice versa. Conversely, the lower is the residual energy in the node, the lower is the transmission power. This technique increases the throughput of a secondary link by providing a higher number of transmission events, compared to the conventional two-class hypothesis. Furthermore, transmission with low power for higher energy levels in the sensed signal reduces the probability of interference with primary users if, for instance, detection was missed. The familiar machine learning algorithm known as a support vector machine (SVM is used in a one-versus-rest approach to classify the input signal into predefined classes. The input signal to the SVM is composed of three statistical features extracted from the sensed signal and a number ranging from 0 to 100 representing the percentage of residual energy in the node’s battery. To increase the generalization of the classifier, k-fold cross-validation is utilized in the training phase. The experimental results show that an SVM with the given features performs satisfactorily for all kernels, but an SVM with a polynomial kernel outperforms linear and radial-basis function kernels in terms of accuracy. Furthermore, the proposed multi-class hypothesis achieves higher throughput compared to the

a Comparison Study of Different Kernel Functions for Svm-Based Classification of Multi-Temporal Polarimetry SAR Data

Science.gov (United States)

Yekkehkhany, B.; Safari, A.; Homayouni, S.; Hasanlou, M.

2014-10-01

In this paper, a framework is developed based on Support Vector Machines (SVM) for crop classification using polarimetric features extracted from multi-temporal Synthetic Aperture Radar (SAR) imageries. The multi-temporal integration of data not only improves the overall retrieval accuracy but also provides more reliable estimates with respect to single-date data. Several kernel functions are employed and compared in this study for mapping the input space to higher Hilbert dimension space. These kernel functions include linear, polynomials and Radial Based Function (RBF). The method is applied to several UAVSAR L-band SAR images acquired over an agricultural area near Winnipeg, Manitoba, Canada. In this research, the temporal alpha features of H/A/α decomposition method are used in classification. The experimental tests show an SVM classifier with RBF kernel for three dates of data increases the Overall Accuracy (OA) to up to 3% in comparison to using linear kernel function, and up to 1% in comparison to a 3rd degree polynomial kernel function.

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

Directory of Open Access Journals (Sweden)

Fei Wang

2017-12-01

Full Text Available Accurate solar photovoltaic (PV power forecasting is an essential tool for mitigating the negative effects caused by the uncertainty of PV output power in systems with high penetration levels of solar PV generation. Weather classification based modeling is an effective way to increase the accuracy of day-ahead short-term (DAST solar PV power forecasting because PV output power is strongly dependent on the specific weather conditions in a given time period. However, the accuracy of daily weather classification relies on both the applied classifiers and the training data. This paper aims to reveal how these two factors impact the classification performance and to delineate the relation between classification accuracy and sample dataset scale. Two commonly used classification methods, K-nearest neighbors (KNN and support vector machines (SVM are applied to classify the daily local weather types for DAST solar PV power forecasting using the operation data from a grid-connected PV plant in Hohhot, Inner Mongolia, China. We assessed the performance of SVM and KNN approaches, and then investigated the influences of sample scale, the number of categories, and the data distribution in different categories on the daily weather classification results. The simulation results illustrate that SVM performs well with small sample scale, while KNN is more sensitive to the length of the training dataset and can achieve higher accuracy than SVM with sufficient samples.

Assessment of ANN and SVM models for estimating normal direct irradiation (H_b)

International Nuclear Information System (INIS)

Santos, Cícero Manoel dos; Escobedo, João Francisco; Teramoto, Érico Tadao; Modenese Gorla da Silva, Silvia Helena

2016-01-01

Highlights: • The performance of SVM and ANN in estimating Normal Direct Irradiation (H_b) was evaluated. • 12 models using different input variables are developed (hourly and daily partitions). • The most relevant input variables for DNI are kt, H_s_c and insolation ratio (r′ = n/N). • Support Vector Machine (SVM) provides accurate estimates and outperforms the Artificial Neural Network (ANN). - Abstract: This study evaluates the estimation of hourly and daily normal direct irradiation (H_b) using machine learning techniques (ML): Artificial Neural Network (ANN) and Support Vector Machine (SVM). Time series of different meteorological variables measured over thirteen years in Botucatu were used for training and validating ANN and SVM. Seven different sets of input variables were tested and evaluated, which were chosen based on statistical models reported in the literature. Relative Mean Bias Error (rMBE), Relative Root Mean Square Error (rRMSE), determination coefficient (R"2) and “d” Willmott index were used to evaluate ANN and SVM models. When compared to statistical models which use the same set of input variables (R"2 between 0.22 and 0.78), ANN and SVM show higher values of R"2 (hourly models between 0.52 and 0.88; daily models between 0.42 and 0.91). Considering the input variables, atmospheric transmissivity of global radiation (kt), integrated solar constant (H_s_c) and insolation ratio (n/N, n is sunshine duration and N is photoperiod) were the most relevant in ANN and SVM models. The rMBE and rRMSE values in the two time partitions of SVM models are lower than those obtained with ANN. Hourly ANN and SVM models have higher rRMSE values than daily models. Optimal performance with hourly models was obtained with ANN4"h (rMBE = 12.24%, rRMSE = 23.99% and “d” = 0.96) and SVM4"h (rMBE = 1.75%, rRMSE = 20.10% and “d” = 0.96). Optimal performance with daily models was obtained with ANN2"d (rMBE = −3.09%, rRMSE = 18.95% and “d” = 0

CNN-SVM for Microvascular Morphological Type Recognition with Data Augmentation.

Science.gov (United States)

Xue, Di-Xiu; Zhang, Rong; Feng, Hui; Wang, Ya-Lei

2016-01-01

This paper focuses on the problem of feature extraction and the classification of microvascular morphological types to aid esophageal cancer detection. We present a patch-based system with a hybrid SVM model with data augmentation for intraepithelial papillary capillary loop recognition. A greedy patch-generating algorithm and a specialized CNN named NBI-Net are designed to extract hierarchical features from patches. We investigate a series of data augmentation techniques to progressively improve the prediction invariance of image scaling and rotation. For classifier boosting, SVM is used as an alternative to softmax to enhance generalization ability. The effectiveness of CNN feature representation ability is discussed for a set of widely used CNN models, including AlexNet, VGG-16, and GoogLeNet. Experiments are conducted on the NBI-ME dataset. The recognition rate is up to 92.74% on the patch level with data augmentation and classifier boosting. The results show that the combined CNN-SVM model beats models of traditional features with SVM as well as the original CNN with softmax. The synthesis results indicate that our system is able to assist clinical diagnosis to a certain extent.

Feature genes in metastatic breast cancer identified by MetaDE and SVM classifier methods.

Science.gov (United States)

Tuo, Youlin; An, Ning; Zhang, Ming

2018-03-01

The aim of the present study was to investigate the feature genes in metastatic breast cancer samples. A total of 5 expression profiles of metastatic breast cancer samples were downloaded from the Gene Expression Omnibus database, which were then analyzed using the MetaQC and MetaDE packages in R language. The feature genes between metastasis and non‑metastasis samples were screened under the threshold of PSVM) classifier training and verification. The accuracy of the SVM classifier was then evaluated using another independent dataset from The Cancer Genome Atlas database. Finally, function and pathway enrichment analyses for genes in the SVM classifier were performed. A total of 541 feature genes were identified between metastatic and non‑metastatic samples. The top 10 genes with the highest betweenness centrality values in the PPI network of feature genes were Nuclear RNA Export Factor 1, cyclin‑dependent kinase 2 (CDK2), myelocytomatosis proto‑oncogene protein (MYC), Cullin 5, SHC Adaptor Protein 1, Clathrin heavy chain, Nucleolin, WD repeat domain 1, proteasome 26S subunit non‑ATPase 2 and telomeric repeat binding factor 2. The cyclin‑dependent kinase inhibitor 1A (CDKN1A), E2F transcription factor 1 (E2F1), and MYC interacted with CDK2. The SVM classifier constructed by the top 30 feature genes was able to distinguish metastatic samples from non‑metastatic samples [correct rate, specificity, positive predictive value and negative predictive value >0.89; sensitivity >0.84; area under the receiver operating characteristic curve (AUROC) >0.96]. The verification of the SVM classifier in an independent dataset (35 metastatic samples and 143 non‑metastatic samples) revealed an accuracy of 94.38% and AUROC of 0.958. Cell cycle associated functions and pathways were the most significant terms of the 30 feature genes. A SVM classifier was constructed to assess the possibility of breast cancer metastasis, which presented high accuracy in several

SVM Pixel Classification on Colour Image Segmentation

Science.gov (United States)

Barui, Subhrajit; Latha, S.; Samiappan, Dhanalakshmi; Muthu, P.

2018-04-01

The aim of image segmentation is to simplify the representation of an image with the help of cluster pixels into something meaningful to analyze. Segmentation is typically used to locate boundaries and curves in an image, precisely to label every pixel in an image to give each pixel an independent identity. SVM pixel classification on colour image segmentation is the topic highlighted in this paper. It holds useful application in the field of concept based image retrieval, machine vision, medical imaging and object detection. The process is accomplished step by step. At first we need to recognize the type of colour and the texture used as an input to the SVM classifier. These inputs are extracted via local spatial similarity measure model and Steerable filter also known as Gabon Filter. It is then trained by using FCM (Fuzzy C-Means). Both the pixel level information of the image and the ability of the SVM Classifier undergoes some sophisticated algorithm to form the final image. The method has a well developed segmented image and efficiency with respect to increased quality and faster processing of the segmented image compared with the other segmentation methods proposed earlier. One of the latest application result is the Light L16 camera.

Intelligent Agent-Based Intrusion Detection System Using Enhanced Multiclass SVM

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Ganapathy, S.; Yogesh, P.; Kannan, A.

2012-01-01

Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set. PMID:23056036

Protein-protein interaction inference based on semantic similarity of Gene Ontology terms.

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Zhang, Shu-Bo; Tang, Qiang-Rong

2016-07-21

Identifying protein-protein interactions is important in molecular biology. Experimental methods to this issue have their limitations, and computational approaches have attracted more and more attentions from the biological community. The semantic similarity derived from the Gene Ontology (GO) annotation has been regarded as one of the most powerful indicators for protein interaction. However, conventional methods based on GO similarity fail to take advantage of the specificity of GO terms in the ontology graph. We proposed a GO-based method to predict protein-protein interaction by integrating different kinds of similarity measures derived from the intrinsic structure of GO graph. We extended five existing methods to derive the semantic similarity measures from the descending part of two GO terms in the GO graph, then adopted a feature integration strategy to combines both the ascending and the descending similarity scores derived from the three sub-ontologies to construct various kinds of features to characterize each protein pair. Support vector machines (SVM) were employed as discriminate classifiers, and five-fold cross validation experiments were conducted on both human and yeast protein-protein interaction datasets to evaluate the performance of different kinds of integrated features, the experimental results suggest the best performance of the feature that combines information from both the ascending and the descending parts of the three ontologies. Our method is appealing for effective prediction of protein-protein interaction. Copyright © 2016 Elsevier Ltd. All rights reserved.

Comparative study of SVM methods combined with voxel selection for object category classification on fMRI data.

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Song, Sutao; Zhan, Zhichao; Long, Zhiying; Zhang, Jiacai; Yao, Li

2011-02-16

Support vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional studies which focused either merely on the evaluation of different types of SVM or the voxel selection methods, we aimed to investigate the overall performance of linear and RBF SVM for fMRI classification together with voxel selection schemes on classification accuracy and time-consuming. Six different voxel selection methods were employed to decide which voxels of fMRI data would be included in SVM classifiers with linear and RBF kernels in classifying 4-category objects. Then the overall performances of voxel selection and classification methods were compared. Results showed that: (1) Voxel selection had an important impact on the classification accuracy of the classifiers: in a relative low dimensional feature space, RBF SVM outperformed linear SVM significantly; in a relative high dimensional space, linear SVM performed better than its counterpart; (2) Considering the classification accuracy and time-consuming holistically, linear SVM with relative more voxels as features and RBF SVM with small set of voxels (after PCA) could achieve the better accuracy and cost shorter time. The present work provides the first empirical result of linear and RBF SVM in classification of fMRI data, combined with voxel selection methods. Based on the findings, if only classification accuracy was concerned, RBF SVM with appropriate small voxels and linear SVM with relative more voxels were two suggested solutions; if users concerned more about the computational time, RBF SVM with relative small set of voxels when part of the principal components were kept as features was a better choice.

Time Reversal Reconstruction Algorithm Based on PSO Optimized SVM Interpolation for Photoacoustic Imaging

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Mingjian Sun

2015-01-01

Full Text Available Photoacoustic imaging is an innovative imaging technique to image biomedical tissues. The time reversal reconstruction algorithm in which a numerical model of the acoustic forward problem is run backwards in time is widely used. In the paper, a time reversal reconstruction algorithm based on particle swarm optimization (PSO optimized support vector machine (SVM interpolation method is proposed for photoacoustics imaging. Numerical results show that the reconstructed images of the proposed algorithm are more accurate than those of the nearest neighbor interpolation, linear interpolation, and cubic convolution interpolation based time reversal algorithm, which can provide higher imaging quality by using significantly fewer measurement positions or scanning times.

Novel Hybrid of LS-SVM and Kalman Filter for GPS/INS Integration

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Xu, Zhenkai; Li, Yong; Rizos, Chris; Xu, Xiaosu

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) technologies can overcome the drawbacks of the individual systems. One of the advantages is that the integrated solution can provide continuous navigation capability even during GPS outages. However, bridging the GPS outages is still a challenge when Micro-Electro-Mechanical System (MEMS) inertial sensors are used. Methods being currently explored by the research community include applying vehicle motion constraints, optimal smoother, and artificial intelligence (AI) techniques. In the research area of AI, the neural network (NN) approach has been extensively utilised up to the present. In an NN-based integrated system, a Kalman filter (KF) estimates position, velocity and attitude errors, as well as the inertial sensor errors, to output navigation solutions while GPS signals are available. At the same time, an NN is trained to map the vehicle dynamics with corresponding KF states, and to correct INS measurements when GPS measurements are unavailable. To achieve good performance it is critical to select suitable quality and an optimal number of samples for the NN. This is sometimes too rigorous a requirement which limits real world application of NN-based methods.The support vector machine (SVM) approach is based on the structural risk minimisation principle, instead of the minimised empirical error principle that is commonly implemented in an NN. The SVM can avoid local minimisation and over-fitting problems in an NN, and therefore potentially can achieve a higher level of global performance. This paper focuses on the least squares support vector machine (LS-SVM), which can solve highly nonlinear and noisy black-box modelling problems. This paper explores the application of the LS-SVM to aid the GPS/INS integrated system, especially during GPS outages. The paper describes the principles of the LS-SVM and of the KF hybrid method, and introduces the LS-SVM regression algorithm. Field

COMPARISON OF PERFORMANCES OF DIFFERENT SVM IMPLEMENTATIONS WHEN USED FOR AUTOMATED EVALUATION OF DESCRIPTIVE ANSWERS

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C. Sunil Kumar

2015-04-01

Full Text Available In this paper, we studied the performances of models built using various SVM implementations during the multiclass classification task of automated evaluation of descriptive answers. The performances were evaluated on five datasets each with 900 samples and with each of the datasets treated using symmetric uncertainty feature selection filter. We quantitatively analyzed the best SVM implementation technique from amongst the 17 different SVM implementation combinations derived by using various SVM classifier libraries, SVM types and Kernel methods. Accuracy, F Score, Kappa and Area under ROC curve are used as model evaluation metrics in order to evaluate the models and rank them according to their performances. Based on the results, we derived the conclusion that SMO classifier when used with Polynomial kernel is the overall best performing classifier applicable for auto evaluation of descriptive answers.

Training set extension for SVM ensemble in P300-speller with familiar face paradigm.

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Li, Qi; Shi, Kaiyang; Gao, Ning; Li, Jian; Bai, Ou

2018-03-27

P300-spellers are brain-computer interface (BCI)-based character input systems. Support vector machine (SVM) ensembles are trained with large-scale training sets and used as classifiers in these systems. However, the required large-scale training data necessitate a prolonged collection time for each subject, which results in data collected toward the end of the period being contaminated by the subject's fatigue. This study aimed to develop a method for acquiring more training data based on a collected small training set. A new method was developed in which two corresponding training datasets in two sequences are superposed and averaged to extend the training set. The proposed method was tested offline on a P300-speller with the familiar face paradigm. The SVM ensemble with extended training set achieved 85% classification accuracy for the averaged results of four sequences, and 100% for 11 sequences in the P300-speller. In contrast, the conventional SVM ensemble with non-extended training set achieved only 65% accuracy for four sequences, and 92% for 11 sequences. The SVM ensemble with extended training set achieves higher classification accuracies than the conventional SVM ensemble, which verifies that the proposed method effectively improves the classification performance of BCI P300-spellers, thus enhancing their practicality.

A linear-RBF multikernel SVM to classify big text corpora.

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Romero, R; Iglesias, E L; Borrajo, L

2015-01-01

Support vector machine (SVM) is a powerful technique for classification. However, SVM is not suitable for classification of large datasets or text corpora, because the training complexity of SVMs is highly dependent on the input size. Recent developments in the literature on the SVM and other kernel methods emphasize the need to consider multiple kernels or parameterizations of kernels because they provide greater flexibility. This paper shows a multikernel SVM to manage highly dimensional data, providing an automatic parameterization with low computational cost and improving results against SVMs parameterized under a brute-force search. The model consists in spreading the dataset into cohesive term slices (clusters) to construct a defined structure (multikernel). The new approach is tested on different text corpora. Experimental results show that the new classifier has good accuracy compared with the classic SVM, while the training is significantly faster than several other SVM classifiers.

Analisis Perbandingan KNN dengan SVM untuk Klasifikasi Penyakit Diabetes Retinopati berdasarkan Citra Eksudat dan Mikroaneurisma

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SUCI AULIA

2015-01-01

Full Text Available ABSTRAK Penelitian mengenai pengklasifikasian tingkat keparahan penyakit Diabetes Retinopati berbasis image processing masih hangat dibicarakan, citra yang biasa digunakan untuk mendeteksi jenis penyakit ini adalah citra optik disk, mikroaneurisma, eksudat, dan hemorrhages yang berasal dari citra fundus. Pada penelitian ini telah dilakukan perbandingan algoritma SVM dengan KNN untuk klasifikasi penyakit diabetes retinopati (mild, moderate, severe berdasarkan citra eksudat dan microaneurisma. Untuk proses ekstraksi ciri digunakan metode wavelet  pada masing-masing kedua metode tersebut. Pada penelitian ini digunakan 160 data uji, masing-masing 40 citra untuk kelas normal, kelas mild, kelas moderate, kelas saviere. Tingkat akurasi yang diperoleh dengan menggunakan metode KNN lebih tinggi dibandingkan SVM, yaitu 65 % dan 62%. Klasifikasi dengan algoritma KNN diperoleh hasil terbaik dengan parameter K=9 cityblock. Sedangkan klasifikasi dengan metode SVM diperoleh hasil terbaik dengan parameter One Agains All. Kata kunci: Diabetic Retinopathy, KNN , SVM, Wavelet.   ABSTRACT Research based on severity classification of the disease diabetic retinopathy by using image processing method is still hotly debated, the image is used to detect the type of this disease is an optical image of the disk, microaneurysm, exudates, and bleeding of the image of the fundus. This study was performed to compare SVM method with KNN method for classification of diabetic retinopathy disease (mild, moderate, severe based on exudate and microaneurysm image. For feature extraction uses wavelet method, and each of the two methods. This study made use of 160 test data, each of 40 images for normal class, mild class, moderate class, severe class. The accuracy obtained by KNN higher than SVM, with 65% and 62%. KNN classification method achieved the best results with the parameters K = 9, cityblock. While the classification with SVM method obtained the best results with

Sensitivity Analysis Based SVM Application on Automatic Incident Detection of Rural Road in China

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Xingliang Liu

2018-01-01

Full Text Available Traditional automatic incident detection methods such as artificial neural networks, backpropagation neural network, and Markov chains are not suitable for addressing the incident detection problem of rural roads in China which have a relatively high accident rate and a low reaction speed caused by the character of small traffic volume. This study applies the support vector machine (SVM and parameter sensitivity analysis methods to build an accident detection algorithm in a rural road condition, based on real-time data collected in a field experiment. The sensitivity of four parameters (speed, front distance, vehicle group time interval, and free driving ratio is analyzed, and the data sets of two parameters with a significant sensitivity are chosen to form the traffic state feature vector. The SVM and k-fold cross validation (K-CV methods are used to build the accident detection algorithm, which shows an excellent performance in detection accuracy (98.15% of the training data set and 87.5% of the testing data set. Therefore, the problem of low incident reaction speed of rural roads in China could be solved to some extent.

Detecting microcalcifications in mammograms by using SVM method for the diagnostics of breast cancer

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Wan, Baikun; Wang, Ruiping; Qi, Hongzhi; Cao, Xuchen

2005-01-01

Support vector machine (SVM) is a new statistical learning method. Compared with the classical machine learning methods, SVM learning discipline is to minimize the structural risk instead of the empirical risk of the classical methods, and it gives better generative performance. Because SVM algorithm is a convex quadratic optimization problem, the local optimal solution is certainly the global optimal one. In this paper a SVM algorithm is applied to detect the micro-calcifications (MCCs) in mammograms for the diagnostics of breast cancer that has not been reported yet. It had been tested with 10 mammograms and the results show that the algorithm can achieve a higher true positive in comparison with artificial neural network (ANN) based on the empirical risk minimization, and is valuable for further study and application in the clinical engineering.

Hybrid NN/SVM Computational System for Optimizing Designs

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Rai, Man Mohan

2009-01-01

A computational method and system based on a hybrid of an artificial neural network (NN) and a support vector machine (SVM) (see figure) has been conceived as a means of maximizing or minimizing an objective function, optionally subject to one or more constraints. Such maximization or minimization could be performed, for example, to optimize solve a data-regression or data-classification problem or to optimize a design associated with a response function. A response function can be considered as a subset of a response surface, which is a surface in a vector space of design and performance parameters. A typical example of a design problem that the method and system can be used to solve is that of an airfoil, for which a response function could be the spatial distribution of pressure over the airfoil. In this example, the response surface would describe the pressure distribution as a function of the operating conditions and the geometric parameters of the airfoil. The use of NNs to analyze physical objects in order to optimize their responses under specified physical conditions is well known. NN analysis is suitable for multidimensional interpolation of data that lack structure and enables the representation and optimization of a succession of numerical solutions of increasing complexity or increasing fidelity to the real world. NN analysis is especially useful in helping to satisfy multiple design objectives. Feedforward NNs can be used to make estimates based on nonlinear mathematical models. One difficulty associated with use of a feedforward NN arises from the need for nonlinear optimization to determine connection weights among input, intermediate, and output variables. It can be very expensive to train an NN in cases in which it is necessary to model large amounts of information. Less widely known (in comparison with NNs) are support vector machines (SVMs), which were originally applied in statistical learning theory. In terms that are necessarily

An SVM model with hybrid kernels for hydrological time series

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Wang, C.; Wang, H.; Zhao, X.; Xie, Q.

2017-12-01

Support Vector Machine (SVM) models have been widely applied to the forecast of climate/weather and its impact on other environmental variables such as hydrologic response to climate/weather. When using SVM, the choice of the kernel function plays the key role. Conventional SVM models mostly use one single type of kernel function, e.g., radial basis kernel function. Provided that there are several featured kernel functions available, each having its own advantages and drawbacks, a combination of these kernel functions may give more flexibility and robustness to SVM approach, making it suitable for a wide range of application scenarios. This paper presents such a linear combination of radial basis kernel and polynomial kernel for the forecast of monthly flowrate in two gaging stations using SVM approach. The results indicate significant improvement in the accuracy of predicted series compared to the approach with either individual kernel function, thus demonstrating the feasibility and advantages of such hybrid kernel approach for SVM applications.

Extraction of prostatic lumina and automated recognition for prostatic calculus image using PCA-SVM.

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Wang, Zhuocai; Xu, Xiangmin; Ding, Xiaojun; Xiao, Hui; Huang, Yusheng; Liu, Jian; Xing, Xiaofen; Wang, Hua; Liao, D Joshua

2011-01-01

Identification of prostatic calculi is an important basis for determining the tissue origin. Computation-assistant diagnosis of prostatic calculi may have promising potential but is currently still less studied. We studied the extraction of prostatic lumina and automated recognition for calculus images. Extraction of lumina from prostate histology images was based on local entropy and Otsu threshold recognition using PCA-SVM and based on the texture features of prostatic calculus. The SVM classifier showed an average time 0.1432 second, an average training accuracy of 100%, an average test accuracy of 93.12%, a sensitivity of 87.74%, and a specificity of 94.82%. We concluded that the algorithm, based on texture features and PCA-SVM, can recognize the concentric structure and visualized features easily. Therefore, this method is effective for the automated recognition of prostatic calculi.

Extraction of Prostatic Lumina and Automated Recognition for Prostatic Calculus Image Using PCA-SVM

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Wang, Zhuocai; Xu, Xiangmin; Ding, Xiaojun; Xiao, Hui; Huang, Yusheng; Liu, Jian; Xing, Xiaofen; Wang, Hua; Liao, D. Joshua

2011-01-01

Identification of prostatic calculi is an important basis for determining the tissue origin. Computation-assistant diagnosis of prostatic calculi may have promising potential but is currently still less studied. We studied the extraction of prostatic lumina and automated recognition for calculus images. Extraction of lumina from prostate histology images was based on local entropy and Otsu threshold recognition using PCA-SVM and based on the texture features of prostatic calculus. The SVM classifier showed an average time 0.1432 second, an average training accuracy of 100%, an average test accuracy of 93.12%, a sensitivity of 87.74%, and a specificity of 94.82%. We concluded that the algorithm, based on texture features and PCA-SVM, can recognize the concentric structure and visualized features easily. Therefore, this method is effective for the automated recognition of prostatic calculi. PMID:21461364

SVM-based Partial Discharge Pattern Classification for GIS

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Ling, Yin; Bai, Demeng; Wang, Menglin; Gong, Xiaojin; Gu, Chao

2018-01-01

Partial discharges (PD) occur when there are localized dielectric breakdowns in small regions of gas insulated substations (GIS). It is of high importance to recognize the PD patterns, through which we can diagnose the defects caused by different sources so that predictive maintenance can be conducted to prevent from unplanned power outage. In this paper, we propose an approach to perform partial discharge pattern classification. It first recovers the PRPD matrices from the PRPD2D images; then statistical features are extracted from the recovered PRPD matrix and fed into SVM for classification. Experiments conducted on a dataset containing thousands of images demonstrates the high effectiveness of the method.

A Matter of Time: Faster Percolator Analysis via Efficient SVM Learning for Large-Scale Proteomics.

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Halloran, John T; Rocke, David M

2018-05-04

Percolator is an important tool for greatly improving the results of a database search and subsequent downstream analysis. Using support vector machines (SVMs), Percolator recalibrates peptide-spectrum matches based on the learned decision boundary between targets and decoys. To improve analysis time for large-scale data sets, we update Percolator's SVM learning engine through software and algorithmic optimizations rather than heuristic approaches that necessitate the careful study of their impact on learned parameters across different search settings and data sets. We show that by optimizing Percolator's original learning algorithm, l 2 -SVM-MFN, large-scale SVM learning requires nearly only a third of the original runtime. Furthermore, we show that by employing the widely used Trust Region Newton (TRON) algorithm instead of l 2 -SVM-MFN, large-scale Percolator SVM learning is reduced to nearly only a fifth of the original runtime. Importantly, these speedups only affect the speed at which Percolator converges to a global solution and do not alter recalibration performance. The upgraded versions of both l 2 -SVM-MFN and TRON are optimized within the Percolator codebase for multithreaded and single-thread use and are available under Apache license at bitbucket.org/jthalloran/percolator_upgrade .

Atterberg Limits Prediction Comparing SVM with ANFIS Model

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Mohammad Murtaza Sherzoy

2017-03-01

Full Text Available Support Vector Machine (SVM and Adaptive Neuro-Fuzzy inference Systems (ANFIS both analytical methods are used to predict the values of Atterberg limits, such as the liquid limit, plastic limit and plasticity index. The main objective of this study is to make a comparison between both forecasts (SVM & ANFIS methods. All data of 54 soil samples are used and taken from the area of Peninsular Malaysian and tested for different parameters containing liquid limit, plastic limit, plasticity index and grain size distribution and were. The input parameter used in for this case are the fraction of grain size distribution which are the percentage of silt, clay and sand. The actual and predicted values of Atterberg limit which obtained from the SVM and ANFIS models are compared by using the correlation coefficient R2 and root mean squared error (RMSE value.  The outcome of the study show that the ANFIS model shows higher accuracy than SVM model for the liquid limit (R2 = 0.987, plastic limit (R2 = 0.949 and plastic index (R2 = 0966. RMSE value that obtained for both methods have shown that the ANFIS model has represent the best performance than SVM model to predict the Atterberg Limits as a whole.

Prediction of protein subcellular localization using support vector machine with the choice of proper kernel

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Al Mehedi Hasan

2017-07-01

Full Text Available The prediction of subcellular locations of proteins can provide useful hints for revealing their functions as well as for understanding the mechanisms of some diseases and, finally, for developing novel drugs. As the number of newly discovered proteins has been growing exponentially, laboratory-based experiments to determine the location of an uncharacterized protein in a living cell have become both expensive and time-consuming. Consequently, to tackle these challenges, computational methods are being developed as an alternative to help biologists in selecting target proteins and designing related experiments. However, the success of protein subcellular localization prediction is still a complicated and challenging problem, particularly when query proteins may have multi-label characteristics, i.e. their simultaneous existence in more than one subcellular location, or if they move between two or more different subcellular locations as well. At this point, to get rid of this problem, several types of subcellular localization prediction methods with different levels of accuracy have been proposed. The support vector machine (SVM has been employed to provide potential solutions for problems connected with the prediction of protein subcellular localization. However, the practicability of SVM is affected by difficulties in selecting its appropriate kernel as well as in selecting the parameters of that selected kernel. The literature survey has shown that most researchers apply the radial basis function (RBF kernel to build a SVM based subcellular localization prediction system. Surprisingly, there are still many other kernel functions which have not yet been applied in the prediction of protein subcellular localization. However, the nature of this classification problem requires the application of different kernels for SVM to ensure an optimal result. From this viewpoint, this paper presents the work to apply different kernels for SVM in protein

A prediction model of drug-induced ototoxicity developed by an optimal support vector machine (SVM) method.

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Zhou, Shu; Li, Guo-Bo; Huang, Lu-Yi; Xie, Huan-Zhang; Zhao, Ying-Lan; Chen, Yu-Zong; Li, Lin-Li; Yang, Sheng-Yong

2014-08-01

Drug-induced ototoxicity, as a toxic side effect, is an important issue needed to be considered in drug discovery. Nevertheless, current experimental methods used to evaluate drug-induced ototoxicity are often time-consuming and expensive, indicating that they are not suitable for a large-scale evaluation of drug-induced ototoxicity in the early stage of drug discovery. We thus, in this investigation, established an effective computational prediction model of drug-induced ototoxicity using an optimal support vector machine (SVM) method, GA-CG-SVM. Three GA-CG-SVM models were developed based on three training sets containing agents bearing different risk levels of drug-induced ototoxicity. For comparison, models based on naïve Bayesian (NB) and recursive partitioning (RP) methods were also used on the same training sets. Among all the prediction models, the GA-CG-SVM model II showed the best performance, which offered prediction accuracies of 85.33% and 83.05% for two independent test sets, respectively. Overall, the good performance of the GA-CG-SVM model II indicates that it could be used for the prediction of drug-induced ototoxicity in the early stage of drug discovery. Copyright © 2014 Elsevier Ltd. All rights reserved.

Comparison of SVM RBF-NN and DT for crop and weed identification based on spectral measurement over corn fields

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It is important to find an appropriate pattern-recognition method for in-field plant identification based on spectral measurement in order to classify the crop and weeds accurately. In this study, the method of Support Vector Machine (SVM) was evaluated and compared with two other methods, Decision ...

Comparison of hand-craft feature based SVM and CNN based deep learning framework for automatic polyp classification.

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Younghak Shin; Balasingham, Ilangko

2017-07-01

Colonoscopy is a standard method for screening polyps by highly trained physicians. Miss-detected polyps in colonoscopy are potential risk factor for colorectal cancer. In this study, we investigate an automatic polyp classification framework. We aim to compare two different approaches named hand-craft feature method and convolutional neural network (CNN) based deep learning method. Combined shape and color features are used for hand craft feature extraction and support vector machine (SVM) method is adopted for classification. For CNN approach, three convolution and pooling based deep learning framework is used for classification purpose. The proposed framework is evaluated using three public polyp databases. From the experimental results, we have shown that the CNN based deep learning framework shows better classification performance than the hand-craft feature based methods. It achieves over 90% of classification accuracy, sensitivity, specificity and precision.

Age and gender estimation using Region-SIFT and multi-layered SVM

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Kim, Hyunduk; Lee, Sang-Heon; Sohn, Myoung-Kyu; Hwang, Byunghun

2018-04-01

In this paper, we propose an age and gender estimation framework using the region-SIFT feature and multi-layered SVM classifier. The suggested framework entails three processes. The first step is landmark based face alignment. The second step is the feature extraction step. In this step, we introduce the region-SIFT feature extraction method based on facial landmarks. First, we define sub-regions of the face. We then extract SIFT features from each sub-region. In order to reduce the dimensions of features we employ a Principal Component Analysis (PCA) and a Linear Discriminant Analysis (LDA). Finally, we classify age and gender using a multi-layered Support Vector Machines (SVM) for efficient classification. Rather than performing gender estimation and age estimation independently, the use of the multi-layered SVM can improve the classification rate by constructing a classifier that estimate the age according to gender. Moreover, we collect a dataset of face images, called by DGIST_C, from the internet. A performance evaluation of proposed method was performed with the FERET database, CACD database, and DGIST_C database. The experimental results demonstrate that the proposed approach classifies age and performs gender estimation very efficiently and accurately.

Damage Detection of Structures for Ambient Loading Based on Cross Correlation Function Amplitude and SVM

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Lin-sheng Huo

2016-01-01

Full Text Available An effective method for the damage detection of skeletal structures which combines the cross correlation function amplitude (CCFA with the support vector machine (SVM is presented in this paper. The proposed method consists of two stages. Firstly, the data features are extracted from the CCFA, which, calculated from dynamic responses and as a representation of the modal shapes of the structure, changes when damage occurs on the structure. The data features are then input into the SVM with the one-against-one (OAO algorithm to classify the damage status of the structure. The simulation data of IASC-ASCE benchmark model and a vibration experiment of truss structure are adopted to verify the feasibility of proposed method. The results show that the proposed method is suitable for the damage identification of skeletal structures with the limited sensors subjected to ambient excitation. As the CCFA based data features are sensitive to damage, the proposed method demonstrates its reliability in the diagnosis of structures with damage, especially for those with minor damage. In addition, the proposed method shows better noise robustness and is more suitable for noisy environments.

Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification

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C. Fernandez-Lozano

2013-01-01

Full Text Available Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM. Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA, the most representative variables for a specific classification problem can be selected.

Lex-SVM: exploring the potential of exon expression profiling for disease classification.

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Yuan, Xiongying; Zhao, Yi; Liu, Changning; Bu, Dongbo

2011-04-01

Exon expression profiling technologies, including exon arrays and RNA-Seq, measure the abundance of every exon in a gene. Compared with gene expression profiling technologies like 3' array, exon expression profiling technologies could detect alterations in both transcription and alternative splicing, therefore they are expected to be more sensitive in diagnosis. However, exon expression profiling also brings higher dimension, more redundancy, and significant correlation among features. Ignoring the correlation structure among exons of a gene, a popular classification method like L1-SVM selects exons individually from each gene and thus is vulnerable to noise. To overcome this limitation, we present in this paper a new variant of SVM named Lex-SVM to incorporate correlation structure among exons and known splicing patterns to promote classification performance. Specifically, we construct a new norm, ex-norm, including our prior knowledge on exon correlation structure to regularize the coefficients of a linear SVM. Lex-SVM can be solved efficiently using standard linear programming techniques. The advantage of Lex-SVM is that it can select features group-wisely, force features in a subgroup to take equal weihts and exclude the features that contradict the majority in the subgroup. Experimental results suggest that on exon expression profile, Lex-SVM is more accurate than existing methods. Lex-SVM also generates a more compact model and selects genes more consistently in cross-validation. Unlike L1-SVM selecting only one exon in a gene, Lex-SVM assigns equal weights to as many exons in a gene as possible, lending itself easier for further interpretation.

Multiclass Posterior Probability Twin SVM for Motor Imagery EEG Classification.

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She, Qingshan; Ma, Yuliang; Meng, Ming; Luo, Zhizeng

2015-01-01

Motor imagery electroencephalography is widely used in the brain-computer interface systems. Due to inherent characteristics of electroencephalography signals, accurate and real-time multiclass classification is always challenging. In order to solve this problem, a multiclass posterior probability solution for twin SVM is proposed by the ranking continuous output and pairwise coupling in this paper. First, two-class posterior probability model is constructed to approximate the posterior probability by the ranking continuous output techniques and Platt's estimating method. Secondly, a solution of multiclass probabilistic outputs for twin SVM is provided by combining every pair of class probabilities according to the method of pairwise coupling. Finally, the proposed method is compared with multiclass SVM and twin SVM via voting, and multiclass posterior probability SVM using different coupling approaches. The efficacy on the classification accuracy and time complexity of the proposed method has been demonstrated by both the UCI benchmark datasets and real world EEG data from BCI Competition IV Dataset 2a, respectively.

Steady Modeling for an Ammonia Synthesis Reactor Based on a Novel CDEAS-LS-SVM Model

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Zhuoqian Liu

2014-01-01

Full Text Available A steady-state mathematical model is built in order to represent plant behavior under stationary operating conditions. A novel modeling using LS-SVR based on Cultural Differential Evolution with Ant Search is proposed. LS-SVM is adopted to establish the model of the net value of ammonia. The modeling method has fast convergence speed and good global adaptability for identification of the ammonia synthesis process. The LS-SVR model was established using the above-mentioned method. Simulation results verify the validity of the method.

基于信息熵的SVM入侵检测技术%Exploring SVM-based intrusion detection through information entropy theory

Institute of Scientific and Technical Information of China (English)

朱文杰; 王强; 翟献军

2013-01-01

在传统基于SVM的入侵检测中,核函数构造和特征选择采用先验知识,普遍存在准确度不高、效率低下的问题.通过信息熵理论与SVM算法相结合的方法改进为基于信息熵的SVM入侵检测算法,可以提高入侵检测的准确性,提升入侵检测的效率.基于信息熵的SVM入侵检测算法包括两个方面:一方面,根据样本包含的用户信息熵和方差,将样本特征统一,以特征是否属于置信区间来度量.将得到的样本特征置信向量作为SVM核函数的构造参数,既可保证训练样本集与最优分类面之间的对应关系,又可得到入侵检测需要的最大分类间隔；另一方面,将样本包含的用户信息量作为度量大幅度约简样本特征子集,不但降低了样本计算规模,而且提高了分类器的训练速度.实验表明,该算法在入侵检测系统中的应用优于传统的SVM算法.%In traditional SVM based intrusion detection approaches,both core function construction and feature selection use prior knowdege.Due to this,they are not only inefficient but also inaccurate.It is observed that integrating information entropy theory into SVM-based intrusion detection can enhance both the precision and the speed.Concludely speaking,SVM-based entropy intrusion detection algorithms are made up of two aspects:on one hand,setting sample confidence vector as core function's constructor of SVM algorithm can guarantee the mapping relationship between training sample and optimization classification plane.Also,the intrusion detection's maximum interval can be acquired.On the other hand,simplifying feature subset with samples's entropy as metric standard can not only shrink the computing scale but also improve the speed.Experiments prove that the SVM based entropy intrusion detection algoritm outperfomrs other tradional algorithms.

Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders.

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Subasi, Abdulhamit

2013-06-01

Support vector machine (SVM) is an extensively used machine learning method with many biomedical signal classification applications. In this study, a novel PSO-SVM model has been proposed that hybridized the particle swarm optimization (PSO) and SVM to improve the EMG signal classification accuracy. This optimization mechanism involves kernel parameter setting in the SVM training procedure, which significantly influences the classification accuracy. The experiments were conducted on the basis of EMG signal to classify into normal, neurogenic or myopathic. In the proposed method the EMG signals were decomposed into the frequency sub-bands using discrete wavelet transform (DWT) and a set of statistical features were extracted from these sub-bands to represent the distribution of wavelet coefficients. The obtained results obviously validate the superiority of the SVM method compared to conventional machine learning methods, and suggest that further significant enhancements in terms of classification accuracy can be achieved by the proposed PSO-SVM classification system. The PSO-SVM yielded an overall accuracy of 97.41% on 1200 EMG signals selected from 27 subject records against 96.75%, 95.17% and 94.08% for the SVM, the k-NN and the RBF classifiers, respectively. PSO-SVM is developed as an efficient tool so that various SVMs can be used conveniently as the core of PSO-SVM for diagnosis of neuromuscular disorders. Copyright © 2013 Elsevier Ltd. All rights reserved.

Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems.

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Cho, Ming-Yuan; Hoang, Thi Thom

2017-01-01

Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO) based support vector machine (SVM) classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR) method with a pseudorandom binary sequence (PRBS) stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.

Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems

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Ming-Yuan Cho

2017-01-01

Full Text Available Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO based support vector machine (SVM classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR method with a pseudorandom binary sequence (PRBS stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.

A Realistic Seizure Prediction Study Based on Multiclass SVM.

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Direito, Bruno; Teixeira, César A; Sales, Francisco; Castelo-Branco, Miguel; Dourado, António

2017-05-01

A patient-specific algorithm, for epileptic seizure prediction, based on multiclass support-vector machines (SVM) and using multi-channel high-dimensional feature sets, is presented. The feature sets, combined with multiclass classification and post-processing schemes aim at the generation of alarms and reduced influence of false positives. This study considers 216 patients from the European Epilepsy Database, and includes 185 patients with scalp EEG recordings and 31 with intracranial data. The strategy was tested over a total of 16,729.80[Formula: see text]h of inter-ictal data, including 1206 seizures. We found an overall sensitivity of 38.47% and a false positive rate per hour of 0.20. The performance of the method achieved statistical significance in 24 patients (11% of the patients). Despite the encouraging results previously reported in specific datasets, the prospective demonstration on long-term EEG recording has been limited. Our study presents a prospective analysis of a large heterogeneous, multicentric dataset. The statistical framework based on conservative assumptions, reflects a realistic approach compared to constrained datasets, and/or in-sample evaluations. The improvement of these results, with the definition of an appropriate set of features able to improve the distinction between the pre-ictal and nonpre-ictal states, hence minimizing the effect of confounding variables, remains a key aspect.

Recursive SVM biomarker selection for early detection of breast cancer in peripheral blood.

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Zhang, Fan; Kaufman, Howard L; Deng, Youping; Drabier, Renee

2013-01-01

Breast cancer is worldwide the second most common type of cancer after lung cancer. Traditional mammography and Tissue Microarray has been studied for early cancer detection and cancer prediction. However, there is a need for more reliable diagnostic tools for early detection of breast cancer. This can be a challenge due to a number of factors and logistics. First, obtaining tissue biopsies can be difficult. Second, mammography may not detect small tumors, and is often unsatisfactory for younger women who typically have dense breast tissue. Lastly, breast cancer is not a single homogeneous disease but consists of multiple disease states, each arising from a distinct molecular mechanism and having a distinct clinical progression path which makes the disease difficult to detect and predict in early stages. In the paper, we present a Support Vector Machine based on Recursive Feature Elimination and Cross Validation (SVM-RFE-CV) algorithm for early detection of breast cancer in peripheral blood and show how to use SVM-RFE-CV to model the classification and prediction problem of early detection of breast cancer in peripheral blood.The training set which consists of 32 health and 33 cancer samples and the testing set consisting of 31 health and 34 cancer samples were randomly separated from a dataset of peripheral blood of breast cancer that is downloaded from Gene Express Omnibus. First, we identified the 42 differentially expressed biomarkers between "normal" and "cancer". Then, with the SVM-RFE-CV we extracted 15 biomarkers that yield zero cross validation score. Lastly, we compared the classification and prediction performance of SVM-RFE-CV with that of SVM and SVM Recursive Feature Elimination (SVM-RFE). We found that 1) the SVM-RFE-CV is suitable for analyzing noisy high-throughput microarray data, 2) it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features, and 3) it can improve the prediction performance (Area Under

Customer and performance rating in QFD using SVM classification

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Dzulkifli, Syarizul Amri; Salleh, Mohd Najib Mohd; Leman, A. M.

2017-09-01

In a classification problem, where each input is associated to one output. Training data is used to create a model which predicts values to the true function. SVM is a popular method for binary classification due to their theoretical foundation and good generalization performance. However, when trained with noisy data, the decision hyperplane might deviate from optimal position because of the sum of misclassification errors in the objective function. In this paper, we introduce fuzzy in weighted learning approach for improving the accuracy of Support Vector Machine (SVM) classification. The main aim of this work is to determine appropriate weighted for SVM to adjust the parameters of learning method from a given set of noisy input to output data. The performance and customer rating in Quality Function Deployment (QFD) is used as our case study to determine implementing fuzzy SVM is highly scalable for very large data sets and generating high classification accuracy.

Hadamard Kernel SVM with applications for breast cancer outcome predictions.

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Jiang, Hao; Ching, Wai-Ki; Cheung, Wai-Shun; Hou, Wenpin; Yin, Hong

2017-12-21

Breast cancer is one of the leading causes of deaths for women. It is of great necessity to develop effective methods for breast cancer detection and diagnosis. Recent studies have focused on gene-based signatures for outcome predictions. Kernel SVM for its discriminative power in dealing with small sample pattern recognition problems has attracted a lot attention. But how to select or construct an appropriate kernel for a specified problem still needs further investigation. Here we propose a novel kernel (Hadamard Kernel) in conjunction with Support Vector Machines (SVMs) to address the problem of breast cancer outcome prediction using gene expression data. Hadamard Kernel outperform the classical kernels and correlation kernel in terms of Area under the ROC Curve (AUC) values where a number of real-world data sets are adopted to test the performance of different methods. Hadamard Kernel SVM is effective for breast cancer predictions, either in terms of prognosis or diagnosis. It may benefit patients by guiding therapeutic options. Apart from that, it would be a valuable addition to the current SVM kernel families. We hope it will contribute to the wider biology and related communities.

Application of EMD-Based SVD and SVM to Coal-Gangue Interface Detection

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Wei Liu

2014-01-01

Full Text Available Coal-gangue interface detection during top-coal caving mining is a challenging problem. This paper proposes a new vibration signal analysis approach to detecting the coal-gangue interface based on singular value decomposition (SVD techniques and support vector machines (SVMs. Due to the nonstationary characteristics in vibration signals of the tail boom support of the longwall mining machine in this complicated environment, the empirical mode decomposition (EMD is used to decompose the raw vibration signals into a number of intrinsic mode functions (IMFs by which the initial feature vector matrices can be formed automatically. By applying the SVD algorithm to the initial feature vector matrices, the singular values of matrices can be obtained and used as the input feature vectors of SVMs classifier. The analysis results of vibration signals from the tail boom support of a longwall mining machine show that the method based on EMD, SVD, and SVM is effective for coal-gangue interface detection even when the number of samples is small.

SVM-Based Control System for a Robot Manipulator

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Foudil Abdessemed

2012-12-01

Full Text Available Real systems are usually non-linear, ill-defined, have variable parameters and are subject to external disturbances. Modelling these systems is often an approximation of the physical phenomena involved. However, it is from this approximate system of representation that we propose - in this paper - to build a robust control, in the sense that it must ensure low sensitivity towards parameters, uncertainties, variations and external disturbances. The computed torque method is a well-established robot control technique which takes account of the dynamic coupling between the robot links. However, its main disadvantage lies on the assumption of an exactly known dynamic model which is not realizable in practice. To overcome this issue, we propose the estimation of the dynamics model of the nonlinear system with a machine learning regression method. The output of this regressor is used in conjunction with a PD controller to achieve the tracking trajectory task of a robot manipulator. In cases where some of the parameters of the plant undergo a change in their values, poor performance may result. To cope with this drawback, a fuzzy precompensator is inserted to reinforce the SVM computed torque-based controller and avoid any deterioration. The theory is developed and the simulation results are carried out on a two-degree of freedom robot manipulator to demonstrate the validity of the proposed approach.

Prediction and analysis of beta-turns in proteins by support vector machine.

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Pham, Tho Hoan; Satou, Kenji; Ho, Tu Bao

2003-01-01

Tight turn has long been recognized as one of the three important features of proteins after the alpha-helix and beta-sheet. Tight turns play an important role in globular proteins from both the structural and functional points of view. More than 90% tight turns are beta-turns. Analysis and prediction of beta-turns in particular and tight turns in general are very useful for the design of new molecules such as drugs, pesticides, and antigens. In this paper, we introduce a support vector machine (SVM) approach to prediction and analysis of beta-turns. We have investigated two aspects of applying SVM to the prediction and analysis of beta-turns. First, we developed a new SVM method, called BTSVM, which predicts beta-turns of a protein from its sequence. The prediction results on the dataset of 426 non-homologous protein chains by sevenfold cross-validation technique showed that our method is superior to the other previous methods. Second, we analyzed how amino acid positions support (or prevent) the formation of beta-turns based on the "multivariable" classification model of a linear SVM. This model is more general than the other ones of previous statistical methods. Our analysis results are more comprehensive and easier to use than previously published analysis results.

Prediction of Protein-Protein Interaction By Metasample-Based Sparse Representation

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Xiuquan Du

2015-01-01

Full Text Available Protein-protein interactions (PPIs play key roles in many cellular processes such as transcription regulation, cell metabolism, and endocrine function. Understanding these interactions takes a great promotion to the pathogenesis and treatment of various diseases. A large amount of data has been generated by experimental techniques; however, most of these data are usually incomplete or noisy, and the current biological experimental techniques are always very time-consuming and expensive. In this paper, we proposed a novel method (metasample-based sparse representation classification, MSRC for PPIs prediction. A group of metasamples are extracted from the original training samples and then use the l1-regularized least square method to express a new testing sample as the linear combination of these metasamples. PPIs prediction is achieved by using a discrimination function defined in the representation coefficients. The MSRC is applied to PPIs dataset; it achieves 84.9% sensitivity, and 94.55% specificity, which is slightly lower than support vector machine (SVM and much higher than naive Bayes (NB, neural networks (NN, and k-nearest neighbor (KNN. The result shows that the MSRC is efficient for PPIs prediction.

An Improved TA-SVM Method Without Matrix Inversion and Its Fast Implementation for Nonstationary Datasets.

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Shi, Yingzhong; Chung, Fu-Lai; Wang, Shitong

2015-09-01

Recently, a time-adaptive support vector machine (TA-SVM) is proposed for handling nonstationary datasets. While attractive performance has been reported and the new classifier is distinctive in simultaneously solving several SVM subclassifiers locally and globally by using an elegant SVM formulation in an alternative kernel space, the coupling of subclassifiers brings in the computation of matrix inversion, thus resulting to suffer from high computational burden in large nonstationary dataset applications. To overcome this shortcoming, an improved TA-SVM (ITA-SVM) is proposed using a common vector shared by all the SVM subclassifiers involved. ITA-SVM not only keeps an SVM formulation, but also avoids the computation of matrix inversion. Thus, we can realize its fast version, that is, improved time-adaptive core vector machine (ITA-CVM) for large nonstationary datasets by using the CVM technique. ITA-CVM has the merit of asymptotic linear time complexity for large nonstationary datasets as well as inherits the advantage of TA-SVM. The effectiveness of the proposed classifiers ITA-SVM and ITA-CVM is also experimentally confirmed.

Hardware realization of an SVM algorithm implemented in FPGAs

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Wiśniewski, Remigiusz; Bazydło, Grzegorz; Szcześniak, Paweł

2017-08-01

The paper proposes a technique of hardware realization of a space vector modulation (SVM) of state function switching in matrix converter (MC), oriented on the implementation in a single field programmable gate array (FPGA). In MC the SVM method is based on the instantaneous space-vector representation of input currents and output voltages. The traditional computation algorithms usually involve digital signal processors (DSPs) which consumes the large number of power transistors (18 transistors and 18 independent PWM outputs) and "non-standard positions of control pulses" during the switching sequence. Recently, hardware implementations become popular since computed operations may be executed much faster and efficient due to nature of the digital devices (especially concurrency). In the paper, we propose a hardware algorithm of SVM computation. In opposite to the existing techniques, the presented solution applies COordinate Rotation DIgital Computer (CORDIC) method to solve the trigonometric operations. Furthermore, adequate arithmetic modules (that is, sub-devices) used for intermediate calculations, such as code converters or proper sectors selectors (for output voltages and input current) are presented in detail. The proposed technique has been implemented as a design described with the use of Verilog hardware description language. The preliminary results of logic implementation oriented on the Xilinx FPGA (particularly, low-cost device from Artix-7 family from Xilinx was used) are also presented.

Using the Relevance Vector Machine Model Combined with Local Phase Quantization to Predict Protein-Protein Interactions from Protein Sequences

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Ji-Yong An

2016-01-01

Full Text Available We propose a novel computational method known as RVM-LPQ that combines the Relevance Vector Machine (RVM model and Local Phase Quantization (LPQ to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the LPQ feature representation on a Position Specific Scoring Matrix (PSSM, reducing the influence of noise using a Principal Component Analysis (PCA, and using a Relevance Vector Machine (RVM based classifier. We perform 5-fold cross-validation experiments on Yeast and Human datasets, and we achieve very high accuracies of 92.65% and 97.62%, respectively, which is significantly better than previous works. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM classifier on the Yeast dataset. The experimental results demonstrate that our RVM-LPQ method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool for future proteomics research.

PCA-MLP SVM distinction of salivary Raman spectra of dengue fever infection.

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Radzol, A R M; Lee, Khuan Y; Mansor, W; Wong, P S; Looi, I

2017-07-01

Dengue fever (DF) is a disease of major concern caused by flavivirus infection. Delayed diagnosis leads to severe stages, which could be deadly. Of recent, non-structural protein (NS1) has been acknowledged as a biomarker, alternative to immunoglobulins for early detection of dengue in blood. Further, non-invasive detection of NS1 in saliva makes the approach more appealing. However, since its concentration in saliva is less than blood, a sensitive and specific technique, Surface Enhanced Raman Spectroscopy (SERS), is employed. Our work here intends to define an optimal PCA-SVM (Principal Component Analysis-Support Vector Machine) with Multilayer Layer Perceptron (MLP) kernel model to distinct between positive and negative NS1 infected samples from salivary SERS spectra, which, to the best of our knowledge, has never been explored. Salivary samples of DF positive and negative subjects were collected, pre-processed and analyzed. PCA and SVM classifier were then used to differentiate the SERS analyzed spectra. Since performance of the model depends on the PCA criterion and MLP parameters, both are examined in tandem. Its performance is also compared to our previous works on simulated NS1 salivary samples. It is found that the best PCA-SVM (MLP) model can be defined by 95 PCs from CPV criterion with P1 and P2 values of 0.01 and -0.2 respectively. A classification performance of [76.88%, 85.92%, 67.83%] is achieved.

Combined Forecasting Method of Landslide Deformation Based on MEEMD, Approximate Entropy, and WLS-SVM

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Shaofeng Xie

2017-01-01

Full Text Available Given the chaotic characteristics of the time series of landslides, a new method based on modified ensemble empirical mode decomposition (MEEMD, approximate entropy and the weighted least square support vector machine (WLS-SVM was proposed. The method mainly started from the chaotic sequence of time-frequency analysis and improved the model performance as follows: first a deformation time series was decomposed into a series of subsequences with significantly different complexity using MEEMD. Then the approximate entropy method was used to generate a new subsequence for the combination of subsequences with similar complexity, which could effectively concentrate the component feature information and reduce the computational scale. Finally the WLS-SVM prediction model was established for each new subsequence. At the same time, phase space reconstruction theory and the grid search method were used to select the input dimension and the optimal parameters of the model, and then the superposition of each predicted value was the final forecasting result. Taking the landslide deformation data of Danba as an example, the experiments were carried out and compared with wavelet neural network, support vector machine, least square support vector machine and various combination schemes. The experimental results show that the algorithm has high prediction accuracy. It can ensure a better prediction effect even in landslide deformation periods of rapid fluctuation, and it can also better control the residual value and effectively reduce the error interval.

Robust LS-SVM-based adaptive constrained control for a class of uncertain nonlinear systems with time-varying predefined performance

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Luo, Jianjun; Wei, Caisheng; Dai, Honghua; Yuan, Jianping

2018-03-01

This paper focuses on robust adaptive control for a class of uncertain nonlinear systems subject to input saturation and external disturbance with guaranteed predefined tracking performance. To reduce the limitations of classical predefined performance control method in the presence of unknown initial tracking errors, a novel predefined performance function with time-varying design parameters is first proposed. Then, aiming at reducing the complexity of nonlinear approximations, only two least-square-support-vector-machine-based (LS-SVM-based) approximators with two design parameters are required through norm form transformation of the original system. Further, a novel LS-SVM-based adaptive constrained control scheme is developed under the time-vary predefined performance using backstepping technique. Wherein, to avoid the tedious analysis and repeated differentiations of virtual control laws in the backstepping technique, a simple and robust finite-time-convergent differentiator is devised to only extract its first-order derivative at each step in the presence of external disturbance. In this sense, the inherent demerit of backstepping technique-;explosion of terms; brought by the recursive virtual controller design is conquered. Moreover, an auxiliary system is designed to compensate the control saturation. Finally, three groups of numerical simulations are employed to validate the effectiveness of the newly developed differentiator and the proposed adaptive constrained control scheme.

Multiclass Classification of Cardiac Arrhythmia Using Improved Feature Selection and SVM Invariants.

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Mustaqeem, Anam; Anwar, Syed Muhammad; Majid, Muahammad

2018-01-01

Arrhythmia is considered a life-threatening disease causing serious health issues in patients, when left untreated. An early diagnosis of arrhythmias would be helpful in saving lives. This study is conducted to classify patients into one of the sixteen subclasses, among which one class represents absence of disease and the other fifteen classes represent electrocardiogram records of various subtypes of arrhythmias. The research is carried out on the dataset taken from the University of California at Irvine Machine Learning Data Repository. The dataset contains a large volume of feature dimensions which are reduced using wrapper based feature selection technique. For multiclass classification, support vector machine (SVM) based approaches including one-against-one (OAO), one-against-all (OAA), and error-correction code (ECC) are employed to detect the presence and absence of arrhythmias. The SVM method results are compared with other standard machine learning classifiers using varying parameters and the performance of the classifiers is evaluated using accuracy, kappa statistics, and root mean square error. The results show that OAO method of SVM outperforms all other classifiers by achieving an accuracy rate of 81.11% when used with 80/20 data split and 92.07% using 90/10 data split option.

A Multi-Classification Method of Improved SVM-based Information Fusion for Traffic Parameters Forecasting

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Hongzhuan Zhao

2016-04-01

Full Text Available With the enrichment of perception methods, modern transportation system has many physical objects whose states are influenced by many information factors so that it is a typical Cyber-Physical System (CPS. Thus, the traffic information is generally multi-sourced, heterogeneous and hierarchical. Existing research results show that the multisourced traffic information through accurate classification in the process of information fusion can achieve better parameters forecasting performance. For solving the problem of traffic information accurate classification, via analysing the characteristics of the multi-sourced traffic information and using redefined binary tree to overcome the shortcomings of the original Support Vector Machine (SVM classification in information fusion, a multi-classification method using improved SVM in information fusion for traffic parameters forecasting is proposed. The experiment was conducted to examine the performance of the proposed scheme, and the results reveal that the method can get more accurate and practical outcomes.

SVM-Based Dynamic Reconfiguration CPS for Manufacturing System in Industry 4.0

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Hyun-Jun Shin

2018-01-01

Full Text Available CPS is potential application in various fields, such as medical, healthcare, energy, transportation, and defense, as well as Industry 4.0 in Germany. Although studies on the equipment aging and prediction of problem have been done by combining CPS with Industry 4.0, such studies were based on small numbers and majority of the papers focused primarily on CPS methodology. Therefore, it is necessary to study active self-protection to enable self-management functions, such as self-healing by applying CPS in shop-floor. In this paper, we have proposed modeling of shop-floor and a dynamic reconfigurable CPS scheme that can predict the occurrence of anomalies and self-protection in the model. For this purpose, SVM was used as a machine learning technology and it was possible to restrain overloading in manufacturing process. In addition, we design CPS framework based on machine learning for Industry 4.0, simulate it, and perform. Simulation results show the simulation model autonomously detects the abnormal situation and it is dynamically reconfigured through self-healing.

SVM-based feature extraction and classification of aflatoxin contaminated corn using fluorescence hyperspectral data

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Support Vector Machine (SVM) was used in the Genetic Algorithms (GA) process to select and classify a subset of hyperspectral image bands. The method was applied to fluorescence hyperspectral data for the detection of aflatoxin contamination in Aspergillus flavus infected single corn kernels. In the...

Comparison between SARS CoV and MERS CoV Using Apriori Algorithm, Decision Tree, SVM

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Jang Seongpil

2016-01-01

Full Text Available MERS (Middle East Respiratory Syndrome is a worldwide disease these days. The number of infected people is 1038(08/03/2015 in Saudi Arabia and 186(08/03/2015 in South Korea. MERS is all over the world including Europe and the fatality rate is 38.8%, East Asia and the Middle East. The MERS is also known as a cousin of SARS (Severe Acute Respiratory Syndrome because both diseases show similar symptoms such as high fever and difficulty in breathing. This is why we compared MERS with SARS. We used data of the spike glycoprotein from NCBI. As a way of analyzing the protein, apriori algorithm, decision tree, SVM were used, and particularly SVM was iterated by normal, polynomial, and sigmoid. The result came out that the MERS and the SARS are alike but also different in some way.

Protein-Ligand Empirical Interaction Components for Virtual Screening.

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Yan, Yuna; Wang, Weijun; Sun, Zhaoxi; Zhang, John Z H; Ji, Changge

2017-08-28

A major shortcoming of empirical scoring functions is that they often fail to predict binding affinity properly. Removing false positives of docking results is one of the most challenging works in structure-based virtual screening. Postdocking filters, making use of all kinds of experimental structure and activity information, may help in solving the issue. We describe a new method based on detailed protein-ligand interaction decomposition and machine learning. Protein-ligand empirical interaction components (PLEIC) are used as descriptors for support vector machine learning to develop a classification model (PLEIC-SVM) to discriminate false positives from true positives. Experimentally derived activity information is used for model training. An extensive benchmark study on 36 diverse data sets from the DUD-E database has been performed to evaluate the performance of the new method. The results show that the new method performs much better than standard empirical scoring functions in structure-based virtual screening. The trained PLEIC-SVM model is able to capture important interaction patterns between ligand and protein residues for one specific target, which is helpful in discarding false positives in postdocking filtering.

Extended SVM algorithms for multilevel trans-Z-source inverter

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Aida Baghbany Oskouei

2016-03-01

Full Text Available This paper suggests extended algorithms for multilevel trans-Z-source inverter. These algorithms are based on space vector modulation (SVM, which works with high switching frequency and does not generate the mean value of the desired load voltage in every switching interval. In this topology the output voltage is not limited to dc voltage source similar to traditional cascaded multilevel inverter and can be increased with trans-Z-network shoot-through state control. Besides, it is more reliable against short circuit, and due to several number of dc sources in each phase of this topology, it is possible to use it in hybrid renewable energy. Proposed SVM algorithms include the following: Combined modulation algorithm (SVPWM and shoot-through implementation in dwell times of voltage vectors algorithm. These algorithms are compared from viewpoint of simplicity, accuracy, number of switching, and THD. Simulation and experimental results are presented to demonstrate the expected representations.

A novel transmission line protection using DOST and SVM

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M. Jaya Bharata Reddy

2016-06-01

Full Text Available This paper proposes a smart fault detection, classification and location (SFDCL methodology for transmission systems with multi-generators using discrete orthogonal Stockwell transform (DOST. The methodology is based on synchronized current measurements from remote telemetry units (RTUs installed at both ends of the transmission line. The energy coefficients extracted from the transient current signals due to occurrence of different types of faults using DOST are being utilized for real-time fault detection and classification. Support vector machine (SVM has been deployed for locating the fault distance using the extracted coefficients. A comparative study is performed for establishing the superiority of SVM over other popular computational intelligence methods, such as adaptive neuro-fuzzy inference system (ANFIS and artificial neural network (ANN, for more precise and reliable estimation of fault distance. The results corroborate the effectiveness of the suggested SFDCL algorithm for real-time transmission line fault detection, classification and localization.

SVM-based multisensor data fusion for phase concentration measurement in biomass-coal co-combustion

Science.gov (United States)

Wang, Xiaoxin; Hu, Hongli; Jia, Huiqin; Tang, Kaihao

2018-05-01

In this paper, the electrical method combines the electrostatic sensor and capacitance sensor to measure the phase concentration of pulverized coal/biomass/air three-phase flow through data fusion technology. In order to eliminate the effects of flow regimes and improve the accuracy of the phase concentration measurement, the mel frequency cepstrum coefficient features extracted from electrostatic signals are used to train the Continuous Gaussian Mixture Hidden Markov Model (CGHMM) for flow regime identification. Support Vector Machine (SVM) is introduced to establish the concentration information fusion model under identified flow regimes. The CGHMM models and SVM models are transplanted on digital signal processing (DSP) to realize on-line accurate measurement. The DSP flow regime identification time is 1.4 ms, and the concentration predict time is 164 μs, which can fully meet the real-time requirement. The average absolute value of the relative error of the pulverized coal is about 1.5% and that of the biomass is about 2.2%.

Biometric identification based on feature fusion with PCA and SVM

Science.gov (United States)

Lefkovits, László; Lefkovits, Szidónia; Emerich, Simina

2018-04-01

Biometric identification is gaining ground compared to traditional identification methods. Many biometric measurements may be used for secure human identification. The most reliable among them is the iris pattern because of its uniqueness, stability, unforgeability and inalterability over time. The approach presented in this paper is a fusion of different feature descriptor methods such as HOG, LIOP, LBP, used for extracting iris texture information. The classifiers obtained through the SVM and PCA methods demonstrate the effectiveness of our system applied to one and both irises. The performances measured are highly accurate and foreshadow a fusion system with a rate of identification approaching 100% on the UPOL database.

Elastic SCAD as a novel penalization method for SVM classification tasks in high-dimensional data.

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Becker, Natalia; Toedt, Grischa; Lichter, Peter; Benner, Axel

2011-05-09

Classification and variable selection play an important role in knowledge discovery in high-dimensional data. Although Support Vector Machine (SVM) algorithms are among the most powerful classification and prediction methods with a wide range of scientific applications, the SVM does not include automatic feature selection and therefore a number of feature selection procedures have been developed. Regularisation approaches extend SVM to a feature selection method in a flexible way using penalty functions like LASSO, SCAD and Elastic Net.We propose a novel penalty function for SVM classification tasks, Elastic SCAD, a combination of SCAD and ridge penalties which overcomes the limitations of each penalty alone.Since SVM models are extremely sensitive to the choice of tuning parameters, we adopted an interval search algorithm, which in comparison to a fixed grid search finds rapidly and more precisely a global optimal solution. Feature selection methods with combined penalties (Elastic Net and Elastic SCAD SVMs) are more robust to a change of the model complexity than methods using single penalties. Our simulation study showed that Elastic SCAD SVM outperformed LASSO (L1) and SCAD SVMs. Moreover, Elastic SCAD SVM provided sparser classifiers in terms of median number of features selected than Elastic Net SVM and often better predicted than Elastic Net in terms of misclassification error.Finally, we applied the penalization methods described above on four publicly available breast cancer data sets. Elastic SCAD SVM was the only method providing robust classifiers in sparse and non-sparse situations. The proposed Elastic SCAD SVM algorithm provides the advantages of the SCAD penalty and at the same time avoids sparsity limitations for non-sparse data. We were first to demonstrate that the integration of the interval search algorithm and penalized SVM classification techniques provides fast solutions on the optimization of tuning parameters.The penalized SVM

Supervised learning methods for pathological arterial pulse wave differentiation: A SVM and neural networks approach.

Science.gov (United States)

Paiva, Joana S; Cardoso, João; Pereira, Tânia

2018-01-01

The main goal of this study was to develop an automatic method based on supervised learning methods, able to distinguish healthy from pathologic arterial pulse wave (APW), and those two from noisy waveforms (non-relevant segments of the signal), from the data acquired during a clinical examination with a novel optical system. The APW dataset analysed was composed by signals acquired in a clinical environment from a total of 213 subjects, including healthy volunteers and non-healthy patients. The signals were parameterised by means of 39pulse features: morphologic, time domain statistics, cross-correlation features, wavelet features. Multiclass Support Vector Machine Recursive Feature Elimination (SVM RFE) method was used to select the most relevant features. A comparative study was performed in order to evaluate the performance of the two classifiers: Support Vector Machine (SVM) and Artificial Neural Network (ANN). SVM achieved a statistically significant better performance for this problem with an average accuracy of 0.9917±0.0024 and a F-Measure of 0.9925±0.0019, in comparison with ANN, which reached the values of 0.9847±0.0032 and 0.9852±0.0031 for Accuracy and F-Measure, respectively. A significant difference was observed between the performances obtained with SVM classifier using a different number of features from the original set available. The comparison between SVM and NN allowed reassert the higher performance of SVM. The results obtained in this study showed the potential of the proposed method to differentiate those three important signal outcomes (healthy, pathologic and noise) and to reduce bias associated with clinical diagnosis of cardiovascular disease using APW. Copyright © 2017 Elsevier B.V. All rights reserved.

Insights into Protein Sequence and Structure-Derived Features Mediating 3D Domain Swapping Mechanism using Support Vector Machine Based Approach

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Khader Shameer

2010-06-01

Full Text Available 3-dimensional domain swapping is a mechanism where two or more protein molecules form higher order oligomers by exchanging identical or similar subunits. Recently, this phenomenon has received much attention in the context of prions and neuro-degenerative diseases, due to its role in the functional regulation, formation of higher oligomers, protein misfolding, aggregation etc. While 3-dimensional domain swap mechanism can be detected from three-dimensional structures, it remains a formidable challenge to derive common sequence or structural patterns from proteins involved in swapping. We have developed a SVM-based classifier to predict domain swapping events using a set of features derived from sequence and structural data. The SVM classifier was trained on features derived from 150 proteins reported to be involved in 3D domain swapping and 150 proteins not known to be involved in swapped conformation or related to proteins involved in swapping phenomenon. The testing was performed using 63 proteins from the positive dataset and 63 proteins from the negative dataset. We obtained 76.33% accuracy from training and 73.81% accuracy from testing. Due to high diversity in the sequence, structure and functions of proteins involved in domain swapping, availability of such an algorithm to predict swapping events from sequence and structure-derived features will be an initial step towards identification of more putative proteins that may be involved in swapping or proteins involved in deposition disease. Further, the top features emerging in our feature selection method may be analysed further to understand their roles in the mechanism of domain swapping.

SVM and ANFIS Models for precipitaton Modeling (Case Study: GonbadKavouse

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N. Zabet Pishkhani

2016-10-01

Full Text Available Introduction: In recent years, according to the intelligent models increased as new techniques and tools in hydrological processes such as precipitation forecasting. ANFIS model has good ability in train, construction and classification, and also has the advantage that allows the extraction of fuzzy rules from numerical information or knowledge. Another intelligent technique in recent years has been used in various areas is support vector machine (SVM. In this paper the ability of artificial intelligence methods including support vector machine (SVM and adaptive neuro fuzzy inference system (ANFIS were analyzed in monthly precipitation prediction. Materials and Methods: The study area was the city of Gonbad in Golestan Province. The city has a temperate climate in the southern highlands and southern plains, mountains and temperate humid, semi-arid and semi-arid in the north of Gorganroud river. In total, the city's climate is temperate and humid. In the present study, monthly precipitation was modeled in Gonbad using ANFIS and SVM and two different database structures were designed. The first structure: input layer consisted of mean temperature, relative humidity, pressure and wind speed at Gonbad station. The second structure: According to Pearson coefficient, the monthly precipitation data were used from four stations: Arazkoose, Bahalke, Tamar and Aqqala which had a higher correlation with Gonbad station precipitation. In this study precipitation data was used from 1995 to 2012. 80% data were used for model training and the remaining 20% of data for validation. SVM was developed from support vector machines in the 1990s by Vapnik. SVM has been widely recognized as a powerful tool to deal with function fitting problems. An Adaptive Neuro-Fuzzy Inference System (ANFIS refers, in general, to an adaptive network which performs the function of a fuzzy inference system. The most commonly used fuzzy system in ANFIS architectures is the Sugeno model

Prediction of residue-residue contact matrix for protein-protein interaction with Fisher score features and deep learning.

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Du, Tianchuan; Liao, Li; Wu, Cathy H; Sun, Bilin

2016-11-01

Protein-protein interactions play essential roles in many biological processes. Acquiring knowledge of the residue-residue contact information of two interacting proteins is not only helpful in annotating functions for proteins, but also critical for structure-based drug design. The prediction of the protein residue-residue contact matrix of the interfacial regions is challenging. In this work, we introduced deep learning techniques (specifically, stacked autoencoders) to build deep neural network models to tackled the residue-residue contact prediction problem. In tandem with interaction profile Hidden Markov Models, which was used first to extract Fisher score features from protein sequences, stacked autoencoders were deployed to extract and learn hidden abstract features. The deep learning model showed significant improvement over the traditional machine learning model, Support Vector Machines (SVM), with the overall accuracy increased by 15% from 65.40% to 80.82%. We showed that the stacked autoencoders could extract novel features, which can be utilized by deep neural networks and other classifiers to enhance learning, out of the Fisher score features. It is further shown that deep neural networks have significant advantages over SVM in making use of the newly extracted features. Copyright © 2016. Published by Elsevier Inc.

A Novel Approach for Protein-Named Entity Recognition and Protein-Protein Interaction Extraction

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Meijing Li

2015-01-01

Full Text Available Many researchers focus on developing protein-named entity recognition (Protein-NER or PPI extraction systems. However, the studies about these two topics cannot be merged well; then existing PPI extraction systems’ Protein-NER still needs to improve. In this paper, we developed the protein-protein interaction extraction system named PPIMiner based on Support Vector Machine (SVM and parsing tree. PPIMiner consists of three main models: natural language processing (NLP model, Protein-NER model, and PPI discovery model. The Protein-NER model, which is named ProNER, identifies the protein names based on two methods: dictionary-based method and machine learning-based method. ProNER is capable of identifying more proteins than dictionary-based Protein-NER model in other existing systems. The final discovered PPIs extracted via PPI discovery model are represented in detail because we showed the protein interaction types and the occurrence frequency through two different methods. In the experiments, the result shows that the performances achieved by our ProNER and PPI discovery model are better than other existing tools. PPIMiner applied this protein-named entity recognition approach and parsing tree based PPI extraction method to improve the performance of PPI extraction. We also provide an easy-to-use interface to access PPIs database and an online system for PPIs extraction and Protein-NER.

An SVM classifier to separate false signals from microcalcifications in digital mammograms

Energy Technology Data Exchange (ETDEWEB)

Bazzani, Armando; Bollini, Dante; Brancaccio, Rosa; Campanini, Renato; Riccardi, Alessandro; Romani, Davide [Department of Physics, University of Bologna (Italy); INFN, Bologna (Italy); Lanconelli, Nico [Department of Physics, University of Bologna, and INFN, Bologna (Italy). E-mail: nico.lanconelli@bo.infn.it; Bevilacqua, Alessandro [Department of Electronics, Computer Science and Systems, University of Bologna, and INFN, Bologna (Italy)

2001-06-01

In this paper we investigate the feasibility of using an SVM (support vector machine) classifier in our automatic system for the detection of clustered microcalcifications in digital mammograms. SVM is a technique for pattern recognition which relies on the statistical learning theory. It minimizes a function of two terms: the number of misclassified vectors of the training set and a term regarding the generalization classifier capability. We compare the SVM classifier with an MLP (multi-layer perceptron) in the false-positive reduction phase of our detection scheme: a detected signal is considered either microcalcification or false signal, according to the value of a set of its features. The SVM classifier gets slightly better results than the MLP one (Az value of 0.963 against 0.958) in the presence of a high number of training data; the improvement becomes much more evident (Az value of 0.952 against 0.918) in training sets of reduced size. Finally, the setting of the SVM classifier is much easier than the MLP one. (author)

Fault Diagnosis of Complex Industrial Process Using KICA and Sparse SVM

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Jie Xu

2013-01-01

Full Text Available New approaches are proposed for complex industrial process monitoring and fault diagnosis based on kernel independent component analysis (KICA and sparse support vector machine (SVM. The KICA method is a two-phase algorithm: whitened kernel principal component analysis (KPCA. The data are firstly mapped into high-dimensional feature subspace. Then, the ICA algorithm seeks the projection directions in the KPCA whitened space. Performance monitoring is implemented through constructing the statistical index and control limit in the feature space. If the statistical indexes exceed the predefined control limit, a fault may have occurred. Then, the nonlinear score vectors are calculated and fed into the sparse SVM to identify the faults. The proposed method is applied to the simulation of Tennessee Eastman (TE chemical process. The simulation results show that the proposed method can identify various types of faults accurately and rapidly.

An improved conjugate gradient scheme to the solution of least squares SVM.

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Chu, Wei; Ong, Chong Jin; Keerthi, S Sathiya

2005-03-01

The least square support vector machines (LS-SVM) formulation corresponds to the solution of a linear system of equations. Several approaches to its numerical solutions have been proposed in the literature. In this letter, we propose an improved method to the numerical solution of LS-SVM and show that the problem can be solved using one reduced system of linear equations. Compared with the existing algorithm for LS-SVM, the approach used in this letter is about twice as efficient. Numerical results using the proposed method are provided for comparisons with other existing algorithms.

SVM-based prediction of propeptide cleavage sites in spider toxins identifies toxin innovation in an Australian tarantula.

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Emily S W Wong

Full Text Available Spider neurotoxins are commonly used as pharmacological tools and are a popular source of novel compounds with therapeutic and agrochemical potential. Since venom peptides are inherently toxic, the host spider must employ strategies to avoid adverse effects prior to venom use. It is partly for this reason that most spider toxins encode a protective proregion that upon enzymatic cleavage is excised from the mature peptide. In order to identify the mature toxin sequence directly from toxin transcripts, without resorting to protein sequencing, the propeptide cleavage site in the toxin precursor must be predicted bioinformatically. We evaluated different machine learning strategies (support vector machines, hidden Markov model and decision tree and developed an algorithm (SpiderP for prediction of propeptide cleavage sites in spider toxins. Our strategy uses a support vector machine (SVM framework that combines both local and global sequence information. Our method is superior or comparable to current tools for prediction of propeptide sequences in spider toxins. Evaluation of the SVM method on an independent test set of known toxin sequences yielded 96% sensitivity and 100% specificity. Furthermore, we sequenced five novel peptides (not used to train the final predictor from the venom of the Australian tarantula Selenotypus plumipes to test the accuracy of the predictor and found 80% sensitivity and 99.6% 8-mer specificity. Finally, we used the predictor together with homology information to predict and characterize seven groups of novel toxins from the deeply sequenced venom gland transcriptome of S. plumipes, which revealed structural complexity and innovations in the evolution of the toxins. The precursor prediction tool (SpiderP is freely available on ArachnoServer (http://www.arachnoserver.org/spiderP.html, a web portal to a comprehensive relational database of spider toxins. All training data, test data, and scripts used are available from

Parameters Optimization and Application to Glutamate Fermentation Model Using SVM

OpenAIRE

Zhang, Xiangsheng; Pan, Feng

2015-01-01

Aimed at the parameters optimization in support vector machine (SVM) for glutamate fermentation modelling, a new method is developed. It optimizes the SVM parameters via an improved particle swarm optimization (IPSO) algorithm which has better global searching ability. The algorithm includes detecting and handling the local convergence and exhibits strong ability to avoid being trapped in local minima. The material step of the method was shown. Simulation experiments demonstrate the effective...

Multiplex protein pattern unmixing using a non-linear variable-weighted support vector machine as optimized by a particle swarm optimization algorithm.

Science.gov (United States)

Yang, Qin; Zou, Hong-Yan; Zhang, Yan; Tang, Li-Juan; Shen, Guo-Li; Jiang, Jian-Hui; Yu, Ru-Qin

2016-01-15

Most of the proteins locate more than one organelle in a cell. Unmixing the localization patterns of proteins is critical for understanding the protein functions and other vital cellular processes. Herein, non-linear machine learning technique is proposed for the first time upon protein pattern unmixing. Variable-weighted support vector machine (VW-SVM) is a demonstrated robust modeling technique with flexible and rational variable selection. As optimized by a global stochastic optimization technique, particle swarm optimization (PSO) algorithm, it makes VW-SVM to be an adaptive parameter-free method for automated unmixing of protein subcellular patterns. Results obtained by pattern unmixing of a set of fluorescence microscope images of cells indicate VW-SVM as optimized by PSO is able to extract useful pattern features by optimally rescaling each variable for non-linear SVM modeling, consequently leading to improved performances in multiplex protein pattern unmixing compared with conventional SVM and other exiting pattern unmixing methods. Copyright © 2015 Elsevier B.V. All rights reserved.

Support vector machine regression (LS-SVM)--an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data?

Science.gov (United States)

Balabin, Roman M; Lomakina, Ekaterina I

2011-06-28

A multilayer feed-forward artificial neural network (MLP-ANN) with a single, hidden layer that contains a finite number of neurons can be regarded as a universal non-linear approximator. Today, the ANN method and linear regression (MLR) model are widely used for quantum chemistry (QC) data analysis (e.g., thermochemistry) to improve their accuracy (e.g., Gaussian G2-G4, B3LYP/B3-LYP, X1, or W1 theoretical methods). In this study, an alternative approach based on support vector machines (SVMs) is used, the least squares support vector machine (LS-SVM) regression. It has been applied to ab initio (first principle) and density functional theory (DFT) quantum chemistry data. So, QC + SVM methodology is an alternative to QC + ANN one. The task of the study was to estimate the Møller-Plesset (MPn) or DFT (B3LYP, BLYP, BMK) energies calculated with large basis sets (e.g., 6-311G(3df,3pd)) using smaller ones (6-311G, 6-311G*, 6-311G**) plus molecular descriptors. A molecular set (BRM-208) containing a total of 208 organic molecules was constructed and used for the LS-SVM training, cross-validation, and testing. MP2, MP3, MP4(DQ), MP4(SDQ), and MP4/MP4(SDTQ) ab initio methods were tested. Hartree-Fock (HF/SCF) results were also reported for comparison. Furthermore, constitutional (CD: total number of atoms and mole fractions of different atoms) and quantum-chemical (QD: HOMO-LUMO gap, dipole moment, average polarizability, and quadrupole moment) molecular descriptors were used for the building of the LS-SVM calibration model. Prediction accuracies (MADs) of 1.62 ± 0.51 and 0.85 ± 0.24 kcal mol(-1) (1 kcal mol(-1) = 4.184 kJ mol(-1)) were reached for SVM-based approximations of ab initio and DFT energies, respectively. The LS-SVM model was more accurate than the MLR model. A comparison with the artificial neural network approach shows that the accuracy of the LS-SVM method is similar to the accuracy of ANN. The extrapolation and interpolation results show that LS-SVM is

"Active Flux" DTFC-SVM Sensorless Control of IPMSM

DEFF Research Database (Denmark)

Boldea, Ion; Codruta Paicu, Mihaela; Gheorghe-Daniel, Andreescu,

2009-01-01

This paper proposes an implementation of a motionsensorless control system in wide speed range based on "active flux" observer, and direct torque and flux control with space vector modulation (DTFC-SVM) for the interior permanent magnet synchronous motor (IPMSM), without signal injection....... The concept of "active flux" (or "torque producing flux") turns all the rotor salient-pole ac machines into fully nonsalient-pole ones. A new function for Lq inductance depending on torque is introduced to model the magnetic saturation. Notable simplification in the rotor position and speed estimation...

Prediction of Protein Structural Class Based on Gapped-Dipeptides and a Recursive Feature Selection Approach

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Taigang Liu

2015-12-01

Full Text Available The prior knowledge of protein structural class may offer useful clues on understanding its functionality as well as its tertiary structure. Though various significant efforts have been made to find a fast and effective computational approach to address this problem, it is still a challenging topic in the field of bioinformatics. The position-specific score matrix (PSSM profile has been shown to provide a useful source of information for improving the prediction performance of protein structural class. However, this information has not been adequately explored. To this end, in this study, we present a feature extraction technique which is based on gapped-dipeptides composition computed directly from PSSM. Then, a careful feature selection technique is performed based on support vector machine-recursive feature elimination (SVM-RFE. These optimal features are selected to construct a final predictor. The results of jackknife tests on four working datasets show that our method obtains satisfactory prediction accuracies by extracting features solely based on PSSM and could serve as a very promising tool to predict protein structural class.

Comparison of two Classification methods (MLC and SVM) to extract land use and land cover in Johor Malaysia

Science.gov (United States)

Rokni Deilmai, B.; Ahmad, B. Bin; Zabihi, H.

2014-06-01

Mapping is essential for the analysis of the land use and land cover, which influence many environmental processes and properties. For the purpose of the creation of land cover maps, it is important to minimize error. These errors will propagate into later analyses based on these land cover maps. The reliability of land cover maps derived from remotely sensed data depends on an accurate classification. In this study, we have analyzed multispectral data using two different classifiers including Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM). To pursue this aim, Landsat Thematic Mapper data and identical field-based training sample datasets in Johor Malaysia used for each classification method, which results indicate in five land cover classes forest, oil palm, urban area, water, rubber. Classification results indicate that SVM was more accurate than MLC. With demonstrated capability to produce reliable cover results, the SVM methods should be especially useful for land cover classification.

Comparison of two Classification methods (MLC and SVM) to extract land use and land cover in Johor Malaysia

International Nuclear Information System (INIS)

Deilmai, B Rokni; Ahmad, B Bin; Zabihi, H

2014-01-01

Mapping is essential for the analysis of the land use and land cover, which influence many environmental processes and properties. For the purpose of the creation of land cover maps, it is important to minimize error. These errors will propagate into later analyses based on these land cover maps. The reliability of land cover maps derived from remotely sensed data depends on an accurate classification. In this study, we have analyzed multispectral data using two different classifiers including Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM). To pursue this aim, Landsat Thematic Mapper data and identical field-based training sample datasets in Johor Malaysia used for each classification method, which results indicate in five land cover classes forest, oil palm, urban area, water, rubber. Classification results indicate that SVM was more accurate than MLC. With demonstrated capability to produce reliable cover results, the SVM methods should be especially useful for land cover classification

[Application of optimized parameters SVM based on photoacoustic spectroscopy method in fault diagnosis of power transformer].

Science.gov (United States)

Zhang, Yu-xin; Cheng, Zhi-feng; Xu, Zheng-ping; Bai, Jing

2015-01-01

In order to solve the problems such as complex operation, consumption for the carrier gas and long test period in traditional power transformer fault diagnosis approach based on dissolved gas analysis (DGA), this paper proposes a new method which is detecting 5 types of characteristic gas content in transformer oil such as CH4, C2H2, C2H4, C2H6 and H2 based on photoacoustic Spectroscopy and C2H2/C2H4, CH4/H2, C2H4/C2H6 three-ratios data are calculated. The support vector machine model was constructed using cross validation method under five support vector machine functions and four kernel functions, heuristic algorithms were used in parameter optimization for penalty factor c and g, which to establish the best SVM model for the highest fault diagnosis accuracy and the fast computing speed. Particles swarm optimization and genetic algorithm two types of heuristic algorithms were comparative studied in this paper for accuracy and speed in optimization. The simulation result shows that SVM model composed of C-SVC, RBF kernel functions and genetic algorithm obtain 97. 5% accuracy in test sample set and 98. 333 3% accuracy in train sample set, and genetic algorithm was about two times faster than particles swarm optimization in computing speed. The methods described in this paper has many advantages such as simple operation, non-contact measurement, no consumption for the carrier gas, long test period, high stability and sensitivity, the result shows that the methods described in this paper can instead of the traditional transformer fault diagnosis by gas chromatography and meets the actual project needs in transformer fault diagnosis.

A DDoS Attack Detection Method Based on SVM in Software Defined Network

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Jin Ye

2018-01-01

Full Text Available The detection of DDoS attacks is an important topic in the field of network security. The occurrence of software defined network (SDN (Zhang et al., 2018 brings up some novel methods to this topic in which some deep learning algorithm is adopted to model the attack behavior based on collecting from the SDN controller. However, the existing methods such as neural network algorithm are not practical enough to be applied. In this paper, the SDN environment by mininet and floodlight (Ning et al., 2014 simulation platform is constructed, 6-tuple characteristic values of the switch flow table is extracted, and then DDoS attack model is built by combining the SVM classification algorithms. The experiments show that average accuracy rate of our method is 95.24% with a small amount of flow collecting. Our work is of good value for the detection of DDoS attack in SDN.

DISEÑO Y EVALUACIÓN DE UN CLASIFICADOR DE TEXTURAS BASADO EN LS-SVM

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Beitmantt Cárdenas Quintero

2013-07-01

Full Text Available Evaluar el desempeño y el costo computacional de diferentes arquitecturas y metodologías Least Square Support Vector Machine (LS-SVM ante la segmentación de imágenes por textura y a partir de dichos resultados postular un modelo de un clasificador de texturas LS-SVM.  Metodología: Ante un problema de clasificación binaria representado por la segmentación  de 32 imágenes, organizadas en 4 grupos y formadas por pares de texturas típicas (granito/corteza, ladrillo/tapicería, madera/mármol, tejido/pelaje, se mide y compara el desempeño y el costo computacional de dos tipos de núcleo (Radial / Polinomial, dos funciones de optimización (mínimo local / búsqueda exhaustiva y dos funciones de costo (validación cruzada aleatoria / Validación cruzada dejando al menos uno en una LS-SVM que toma como entrada los pixeles que conforman la vecindad cruz del pixel a evaluar (no se hace extracción de características. Resultados: LS-SVM como clasificador de texturas, presenta mejor desempeño y exige menor costo computacional cuando utiliza un kernel de base radial y una función de optimización basada en un algoritmo de búsqueda de mínimos locales acompañado de una función de costo que use validación cruzada aleatoria.

Weighted Feature Gaussian Kernel SVM for Emotion Recognition.

Science.gov (United States)

Wei, Wei; Jia, Qingxuan

2016-01-01

Emotion recognition with weighted feature based on facial expression is a challenging research topic and has attracted great attention in the past few years. This paper presents a novel method, utilizing subregion recognition rate to weight kernel function. First, we divide the facial expression image into some uniform subregions and calculate corresponding recognition rate and weight. Then, we get a weighted feature Gaussian kernel function and construct a classifier based on Support Vector Machine (SVM). At last, the experimental results suggest that the approach based on weighted feature Gaussian kernel function has good performance on the correct rate in emotion recognition. The experiments on the extended Cohn-Kanade (CK+) dataset show that our method has achieved encouraging recognition results compared to the state-of-the-art methods.

Predicting beta-turns in proteins using support vector machines with fractional polynomials.

Science.gov (United States)

Elbashir, Murtada; Wang, Jianxin; Wu, Fang-Xiang; Wang, Lusheng

2013-11-07

β-turns are secondary structure type that have essential role in molecular recognition, protein folding, and stability. They are found to be the most common type of non-repetitive structures since 25% of amino acids in protein structures are situated on them. Their prediction is considered to be one of the crucial problems in bioinformatics and molecular biology, which can provide valuable insights and inputs for the fold recognition and drug design. We propose an approach that combines support vector machines (SVMs) and logistic regression (LR) in a hybrid prediction method, which we call (H-SVM-LR) to predict β-turns in proteins. Fractional polynomials are used for LR modeling. We utilize position specific scoring matrices (PSSMs) and predicted secondary structure (PSS) as features. Our simulation studies show that H-SVM-LR achieves Qtotal of 82.87%, 82.84%, and 82.32% on the BT426, BT547, and BT823 datasets respectively. These values are the highest among other β-turns prediction methods that are based on PSSMs and secondary structure information. H-SVM-LR also achieves favorable performance in predicting β-turns as measured by the Matthew's correlation coefficient (MCC) on these datasets. Furthermore, H-SVM-LR shows good performance when considering shape strings as additional features. In this paper, we present a comprehensive approach for β-turns prediction. Experiments show that our proposed approach achieves better performance compared to other competing prediction methods.

Application of Machine Learning Approaches for Protein-protein Interactions Prediction.

Science.gov (United States)

Zhang, Mengying; Su, Qiang; Lu, Yi; Zhao, Manman; Niu, Bing

2017-01-01

Proteomics endeavors to study the structures, functions and interactions of proteins. Information of the protein-protein interactions (PPIs) helps to improve our knowledge of the functions and the 3D structures of proteins. Thus determining the PPIs is essential for the study of the proteomics. In this review, in order to study the application of machine learning in predicting PPI, some machine learning approaches such as support vector machine (SVM), artificial neural networks (ANNs) and random forest (RF) were selected, and the examples of its applications in PPIs were listed. SVM and RF are two commonly used methods. Nowadays, more researchers predict PPIs by combining more than two methods. This review presents the application of machine learning approaches in predicting PPI. Many examples of success in identification and prediction in the area of PPI prediction have been discussed, and the PPIs research is still in progress. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Parameters Optimization and Application to Glutamate Fermentation Model Using SVM

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Xiangsheng Zhang

2015-01-01

Full Text Available Aimed at the parameters optimization in support vector machine (SVM for glutamate fermentation modelling, a new method is developed. It optimizes the SVM parameters via an improved particle swarm optimization (IPSO algorithm which has better global searching ability. The algorithm includes detecting and handling the local convergence and exhibits strong ability to avoid being trapped in local minima. The material step of the method was shown. Simulation experiments demonstrate the effectiveness of the proposed algorithm.

Comparison of two methods forecasting binding rate of plasma protein.

Science.gov (United States)

Hongjiu, Liu; Yanrong, Hu

2014-01-01

By introducing the descriptors calculated from the molecular structure, the binding rates of plasma protein (BRPP) with seventy diverse drugs are modeled by a quantitative structure-activity relationship (QSAR) technique. Two algorithms, heuristic algorithm (HA) and support vector machine (SVM), are used to establish linear and nonlinear models to forecast BRPP. Empirical analysis shows that there are good performances for HA and SVM with cross-validation correlation coefficients Rcv(2) of 0.80 and 0.83. Comparing HA with SVM, it was found that SVM has more stability and more robustness to forecast BRPP.

A Method to Integrate GMM, SVM and DTW for Speaker Recognition

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Ing-Jr Ding

2014-01-01

Full Text Available This paper develops an effective and efficient scheme to integrate Gaussian mixture model (GMM, support vector machine (SVM, and dynamic time wrapping (DTW for automatic speaker recognition. GMM and SVM are two popular classifiers for speaker recognition applications. DTW is a fast and simple template matching method, and it is frequently seen in applications of speech recognition. In this work, DTW does not play a role to perform speech recognition, and it will be employed to be a verifier for verification of valid speakers. The proposed combination scheme of GMM, SVM and DTW, called SVMGMM-DTW, for speaker recognition in this study is a two-phase verification process task including GMM-SVM verification of the first phase and DTW verification of the second phase. By providing a double check to verify the identity of a speaker, it will be difficult for imposters to try to pass the security protection; therefore, the safety degree of speaker recognition systems will be largely increased. A series of experiments designed on door access control applications demonstrated that the superiority of the developed SVMGMM-DTW on speaker recognition accuracy.

Fuzzy Pruning Based LS-SVM Modeling Development for a Fermentation Process

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Weili Xiong

2014-01-01

Full Text Available Due to the complexity and uncertainty of microbial fermentation processes, data coming from the plants often contain some outliers. However, these data may be treated as the normal support vectors, which always deteriorate the performance of soft sensor modeling. Since the outliers also contaminate the correlation structure of the least square support vector machine (LS-SVM, the fuzzy pruning method is provided to deal with the problem. Furthermore, by assigning different fuzzy membership scores to data samples, the sensitivity of the model to the outliers can be reduced greatly. The effectiveness and efficiency of the proposed approach are demonstrated through two numerical examples as well as a simulator case of penicillin fermentation process.

Modeling of SVM Diode Clamping Three-Level Inverter Connected to Grid

DEFF Research Database (Denmark)

Guo, Yougui; Zeng, Ping; Zhu, Jieqiong

2011-01-01

PLECS is used to model the diode clamping three-level inverter connected to grid and good results are obtained. First the output voltage SVM is described for diode clamping three-level inverter with loads connected to Y. Then the output voltage SVM of diode clamping three-level inverter is simply...... analyzed with loads connected to △. But it will be further researched in the future. Third, PLECS is briefly introduced. Fourth, the modeling of diode clamping three-level inverter is briefly presented with PLECS. Finally, a series of simulations are carried out. The simulation results tell us PLECS...... is very powerful tool to real power circuits and it is very easy to simulate them. They have also verified that SVM control strategy is feasible to control the diode clamping three-level inverter....

A comparative QSAR study on the estrogenic activities of persistent organic pollutants by PLS and SVM

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Fei Li

2015-11-01

Full Text Available Quantitative structure-activity relationships (QSARs were determined using partial least square (PLS and support vector machine (SVM. The predicted values by the final QSAR models were in good agreement with the corresponding experimental values. Chemical estrogenic activities are related to atomic properties (atomic Sanderson electronegativities, van der Waals volumes and polarizabilities. Comparison of the results obtained from two models, the SVM method exhibited better overall performances. Besides, three PLS models were constructed for some specific families based on their chemical structures. These predictive models should be useful to rapidly identify potential estrogenic endocrine disrupting chemicals.

Exploring the potential of 3D Zernike descriptors and SVM for protein-protein interface prediction.

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Daberdaku, Sebastian; Ferrari, Carlo

2018-02-06

The correct determination of protein-protein interaction interfaces is important for understanding disease mechanisms and for rational drug design. To date, several computational methods for the prediction of protein interfaces have been developed, but the interface prediction problem is still not fully understood. Experimental evidence suggests that the location of binding sites is imprinted in the protein structure, but there are major differences among the interfaces of the various protein types: the characterising properties can vary a lot depending on the interaction type and function. The selection of an optimal set of features characterising the protein interface and the development of an effective method to represent and capture the complex protein recognition patterns are of paramount importance for this task. In this work we investigate the potential of a novel local surface descriptor based on 3D Zernike moments for the interface prediction task. Descriptors invariant to roto-translations are extracted from circular patches of the protein surface enriched with physico-chemical properties from the HQI8 amino acid index set, and are used as samples for a binary classification problem. Support Vector Machines are used as a classifier to distinguish interface local surface patches from non-interface ones. The proposed method was validated on 16 classes of proteins extracted from the Protein-Protein Docking Benchmark 5.0 and compared to other state-of-the-art protein interface predictors (SPPIDER, PrISE and NPS-HomPPI). The 3D Zernike descriptors are able to capture the similarity among patterns of physico-chemical and biochemical properties mapped on the protein surface arising from the various spatial arrangements of the underlying residues, and their usage can be easily extended to other sets of amino acid properties. The results suggest that the choice of a proper set of features characterising the protein interface is crucial for the interface prediction

Comparison of ANN and SVM for classification of eye movements in EOG signals

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Qi, Lim Jia; Alias, Norma

2018-03-01

Nowadays, electrooculogram is regarded as one of the most important biomedical signal in measuring and analyzing eye movement patterns. Thus, it is helpful in designing EOG-based Human Computer Interface (HCI). In this research, electrooculography (EOG) data was obtained from five volunteers. The (EOG) data was then preprocessed before feature extraction methods were employed to further reduce the dimensionality of data. Three feature extraction approaches were put forward, namely statistical parameters, autoregressive (AR) coefficients using Burg method, and power spectral density (PSD) using Yule-Walker method. These features would then become input to both artificial neural network (ANN) and support vector machine (SVM). The performance of the combination of different feature extraction methods and classifiers was presented and analyzed. It was found that statistical parameters + SVM achieved the highest classification accuracy of 69.75%.

Fast subcellular localization by cascaded fusion of signal-based and homology-based methods

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Wang Wei

2011-10-01

Full Text Available Abstract Background The functions of proteins are closely related to their subcellular locations. In the post-genomics era, the amount of gene and protein data grows exponentially, which necessitates the prediction of subcellular localization by computational means. Results This paper proposes mitigating the computation burden of alignment-based approaches to subcellular localization prediction by a cascaded fusion of cleavage site prediction and profile alignment. Specifically, the informative segments of protein sequences are identified by a cleavage site predictor using the information in their N-terminal shorting signals. Then, the sequences are truncated at the cleavage site positions, and the shortened sequences are passed to PSI-BLAST for computing their profiles. Subcellular localization are subsequently predicted by a profile-to-profile alignment support-vector-machine (SVM classifier. To further reduce the training and recognition time of the classifier, the SVM classifier is replaced by a new kernel method based on the perturbational discriminant analysis (PDA. Conclusions Experimental results on a new dataset based on Swiss-Prot Release 57.5 show that the method can make use of the best property of signal- and homology-based approaches and can attain an accuracy comparable to that achieved by using full-length sequences. Analysis of profile-alignment score matrices suggest that both profile creation time and profile alignment time can be reduced without significant reduction in subcellular localization accuracy. It was found that PDA enjoys a short training time as compared to the conventional SVM. We advocate that the method will be important for biologists to conduct large-scale protein annotation or for bioinformaticians to perform preliminary investigations on new algorithms that involve pairwise alignments.

Study on specificity of colon carcinoma-associated serum markers and establishment of SVM prediction model

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Lu Li

2017-03-01

Full Text Available We aimed to evaluate the specificity of 12 tumor markers related to colon carcinoma and identify the most sensitive index. Logistic regression and Bhattacharyya distance were used to evaluate the index. Then, different index combinations were used to establish a support vector machine (SVM diagnosis model of malignant colon carcinoma. The accuracy of the model was checked. High accuracy was assumed to indicate the high specificity of the index. Through Logistic regression, three indexes, CEA, HSP60 and CA199, were screened out. Using Bhattacharyya distance, four indexes with the largest Bhattacharyya distance were screened out, including CEA, NSE, AFP, and CA724. The specificity of the combination of the above six indexes was higher than that of other combinations, so did the accuracy of the established SVM identification model. Using Logistic regression and Bhattacharyya distance for detection and establishing an SVM model based on different serum marker combinations can increase diagnostic accuracy, providing a theoretical basis for application of mathematical models in cancer diagnosis.

A feature-based approach to modeling protein-protein interaction hot spots.

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Cho, Kyu-il; Kim, Dongsup; Lee, Doheon

2009-05-01

Identifying features that effectively represent the energetic contribution of an individual interface residue to the interactions between proteins remains problematic. Here, we present several new features and show that they are more effective than conventional features. By combining the proposed features with conventional features, we develop a predictive model for interaction hot spots. Initially, 54 multifaceted features, composed of different levels of information including structure, sequence and molecular interaction information, are quantified. Then, to identify the best subset of features for predicting hot spots, feature selection is performed using a decision tree. Based on the selected features, a predictive model for hot spots is created using support vector machine (SVM) and tested on an independent test set. Our model shows better overall predictive accuracy than previous methods such as the alanine scanning methods Robetta and FOLDEF, and the knowledge-based method KFC. Subsequent analysis yields several findings about hot spots. As expected, hot spots have a larger relative surface area burial and are more hydrophobic than other residues. Unexpectedly, however, residue conservation displays a rather complicated tendency depending on the types of protein complexes, indicating that this feature is not good for identifying hot spots. Of the selected features, the weighted atomic packing density, relative surface area burial and weighted hydrophobicity are the top 3, with the weighted atomic packing density proving to be the most effective feature for predicting hot spots. Notably, we find that hot spots are closely related to pi-related interactions, especially pi . . . pi interactions.

Effectively identifying compound-protein interactions by learning from positive and unlabeled examples.

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Cheng, Zhanzhan; Zhou, Shuigeng; Wang, Yang; Liu, Hui; Guan, Jihong; Chen, Yi-Ping Phoebe

2016-05-18

Prediction of compound-protein interactions (CPIs) is to find new compound-protein pairs where a protein is targeted by at least a compound, which is a crucial step in new drug design. Currently, a number of machine learning based methods have been developed to predict new CPIs in the literature. However, as there is not yet any publicly available set of validated negative CPIs, most existing machine learning based approaches use the unknown interactions (not validated CPIs) selected randomly as the negative examples to train classifiers for predicting new CPIs. Obviously, this is not quite reasonable and unavoidably impacts the CPI prediction performance. In this paper, we simply take the unknown CPIs as unlabeled examples, and propose a new method called PUCPI (the abbreviation of PU learning for Compound-Protein Interaction identification) that employs biased-SVM (Support Vector Machine) to predict CPIs using only positive and unlabeled examples. PU learning is a class of learning methods that leans from positive and unlabeled (PU) samples. To the best of our knowledge, this is the first work that identifies CPIs using only positive and unlabeled examples. We first collect known CPIs as positive examples and then randomly select compound-protein pairs not in the positive set as unlabeled examples. For each CPI/compound-protein pair, we extract protein domains as protein features and compound substructures as chemical features, then take the tensor product of the corresponding compound features and protein features as the feature vector of the CPI/compound-protein pair. After that, biased-SVM is employed to train classifiers on different datasets of CPIs and compound-protein pairs. Experiments over various datasets show that our method outperforms six typical classifiers, including random forest, L1- and L2-regularized logistic regression, naive Bayes, SVM and k-nearest neighbor (kNN), and three types of existing CPI prediction models. Source code, datasets and

A RLS-SVM Aided Fusion Methodology for INS during GPS Outages.

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Yao, Yiqing; Xu, Xiaosu

2017-02-24

In order to maintain a relatively high accuracy of navigation performance during global positioning system (GPS) outages, a novel robust least squares support vector machine (LS-SVM)-aided fusion methodology is explored to provide the pseudo-GPS position information for the inertial navigation system (INS). The relationship between the yaw, specific force, velocity, and the position increment is modeled. Rather than share the same weight in the traditional LS-SVM, the proposed algorithm allocates various weights for different data, which makes the system immune to the outliers. Field test data was collected to evaluate the proposed algorithm. The comparison results indicate that the proposed algorithm can effectively provide position corrections for standalone INS during the 300 s GPS outage, which outperforms the traditional LS-SVM method. Historical information is also involved to better represent the vehicle dynamics.

A RLS-SVM Aided Fusion Methodology for INS during GPS Outages

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Yiqing Yao

2017-02-01

Full Text Available In order to maintain a relatively high accuracy of navigation performance during global positioning system (GPS outages, a novel robust least squares support vector machine (LS-SVM-aided fusion methodology is explored to provide the pseudo-GPS position information for the inertial navigation system (INS. The relationship between the yaw, specific force, velocity, and the position increment is modeled. Rather than share the same weight in the traditional LS-SVM, the proposed algorithm allocates various weights for different data, which makes the system immune to the outliers. Field test data was collected to evaluate the proposed algorithm. The comparison results indicate that the proposed algorithm can effectively provide position corrections for standalone INS during the 300 s GPS outage, which outperforms the traditional LS-SVM method. Historical information is also involved to better represent the vehicle dynamics.

SVM-Based System for Prediction of Epileptic Seizures from iEEG Signal

Science.gov (United States)

Cherkassky, Vladimir; Lee, Jieun; Veber, Brandon; Patterson, Edward E.; Brinkmann, Benjamin H.; Worrell, Gregory A.

2017-01-01

Objective This paper describes a data-analytic modeling approach for prediction of epileptic seizures from intracranial electroencephalogram (iEEG) recording of brain activity. Even though it is widely accepted that statistical characteristics of iEEG signal change prior to seizures, robust seizure prediction remains a challenging problem due to subject-specific nature of data-analytic modeling. Methods Our work emphasizes understanding of clinical considerations important for iEEG-based seizure prediction, and proper translation of these clinical considerations into data-analytic modeling assumptions. Several design choices during pre-processing and post-processing are considered and investigated for their effect on seizure prediction accuracy. Results Our empirical results show that the proposed SVM-based seizure prediction system can achieve robust prediction of preictal and interictal iEEG segments from dogs with epilepsy. The sensitivity is about 90–100%, and the false-positive rate is about 0–0.3 times per day. The results also suggest good prediction is subject-specific (dog or human), in agreement with earlier studies. Conclusion Good prediction performance is possible only if the training data contain sufficiently many seizure episodes, i.e., at least 5–7 seizures. Significance The proposed system uses subject-specific modeling and unbalanced training data. This system also utilizes three different time scales during training and testing stages. PMID:27362758

Comparison of SVM, RF and ELM on an Electronic Nose for the Intelligent Evaluation of Paraffin Samples

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Hong Men

2018-01-01

Full Text Available Paraffin odor intensity is an important quality indicator when a paraffin inspection is performed. Currently, paraffin odor level assessment is mainly dependent on an artificial sensory evaluation. In this paper, we developed a paraffin odor analysis system to classify and grade four kinds of paraffin samples. The original feature set was optimized using Principal Component Analysis (PCA and Partial Least Squares (PLS. Support Vector Machine (SVM, Random Forest (RF, and Extreme Learning Machine (ELM were applied to three different feature data sets for classification and level assessment of paraffin. For classification, the model based on SVM, with an accuracy rate of 100%, was superior to that based on RF, with an accuracy rate of 98.33–100%, and ELM, with an accuracy rate of 98.01–100%. For level assessment, the R2 related to the training set was above 0.97 and the R2 related to the test set was above 0.87. Through comprehensive comparison, the generalization of the model based on ELM was superior to those based on SVM and RF. The scoring errors for the three models were 0.0016–0.3494, lower than the error of 0.5–1.0 measured by industry standard experts, meaning these methods have a higher prediction accuracy for scoring paraffin level.

New Milk Protein-Derived Peptides with Potential Antimicrobial Activity: An Approach Based on Bioinformatic Studies

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Bartłomiej Dziuba

2014-08-01

Full Text Available New peptides with potential antimicrobial activity, encrypted in milk protein sequences, were searched for with the use of bioinformatic tools. The major milk proteins were hydrolyzed in silico by 28 enzymes. The obtained peptides were characterized by the following parameters: molecular weight, isoelectric point, composition and number of amino acid residues, net charge at pH 7.0, aliphatic index, instability index, Boman index, and GRAVY index, and compared with those calculated for known 416 antimicrobial peptides including 59 antimicrobial peptides (AMPs from milk proteins listed in the BIOPEP database. A simple analysis of physico-chemical properties and the values of biological activity indicators were insufficient to select potentially antimicrobial peptides released in silico from milk proteins by proteolytic enzymes. The final selection was made based on the results of multidimensional statistical analysis such as support vector machines (SVM, random forest (RF, artificial neural networks (ANN and discriminant analysis (DA available in the Collection of Anti-Microbial Peptides (CAMP database. Eleven new peptides with potential antimicrobial activity were selected from all peptides released during in silico proteolysis of milk proteins.

Cancer Classification Based on Support Vector Machine Optimized by Particle Swarm Optimization and Artificial Bee Colony.

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Gao, Lingyun; Ye, Mingquan; Wu, Changrong

2017-11-29

Intelligent optimization algorithms have advantages in dealing with complex nonlinear problems accompanied by good flexibility and adaptability. In this paper, the FCBF (Fast Correlation-Based Feature selection) method is used to filter irrelevant and redundant features in order to improve the quality of cancer classification. Then, we perform classification based on SVM (Support Vector Machine) optimized by PSO (Particle Swarm Optimization) combined with ABC (Artificial Bee Colony) approaches, which is represented as PA-SVM. The proposed PA-SVM method is applied to nine cancer datasets, including five datasets of outcome prediction and a protein dataset of ovarian cancer. By comparison with other classification methods, the results demonstrate the effectiveness and the robustness of the proposed PA-SVM method in handling various types of data for cancer classification.

PredPPCrys: accurate prediction of sequence cloning, protein production, purification and crystallization propensity from protein sequences using multi-step heterogeneous feature fusion and selection.

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Huilin Wang

Full Text Available X-ray crystallography is the primary approach to solve the three-dimensional structure of a protein. However, a major bottleneck of this method is the failure of multi-step experimental procedures to yield diffraction-quality crystals, including sequence cloning, protein material production, purification, crystallization and ultimately, structural determination. Accordingly, prediction of the propensity of a protein to successfully undergo these experimental procedures based on the protein sequence may help narrow down laborious experimental efforts and facilitate target selection. A number of bioinformatics methods based on protein sequence information have been developed for this purpose. However, our knowledge on the important determinants of propensity for a protein sequence to produce high diffraction-quality crystals remains largely incomplete. In practice, most of the existing methods display poorer performance when evaluated on larger and updated datasets. To address this problem, we constructed an up-to-date dataset as the benchmark, and subsequently developed a new approach termed 'PredPPCrys' using the support vector machine (SVM. Using a comprehensive set of multifaceted sequence-derived features in combination with a novel multi-step feature selection strategy, we identified and characterized the relative importance and contribution of each feature type to the prediction performance of five individual experimental steps required for successful crystallization. The resulting optimal candidate features were used as inputs to build the first-level SVM predictor (PredPPCrys I. Next, prediction outputs of PredPPCrys I were used as the input to build second-level SVM classifiers (PredPPCrys II, which led to significantly enhanced prediction performance. Benchmarking experiments indicated that our PredPPCrys method outperforms most existing procedures on both up-to-date and previous datasets. In addition, the predicted crystallization

Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM and Artificial Neural Network (ANN

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Maria Grazia De Giorgi

2014-08-01

Full Text Available A high penetration of wind energy into the electricity market requires a parallel development of efficient wind power forecasting models. Different hybrid forecasting methods were applied to wind power prediction, using historical data and numerical weather predictions (NWP. A comparative study was carried out for the prediction of the power production of a wind farm located in complex terrain. The performances of Least-Squares Support Vector Machine (LS-SVM with Wavelet Decomposition (WD were evaluated at different time horizons and compared to hybrid Artificial Neural Network (ANN-based methods. It is acknowledged that hybrid methods based on LS-SVM with WD mostly outperform other methods. A decomposition of the commonly known root mean square error was beneficial for a better understanding of the origin of the differences between prediction and measurement and to compare the accuracy of the different models. A sensitivity analysis was also carried out in order to underline the impact that each input had in the network training process for ANN. In the case of ANN with the WD technique, the sensitivity analysis was repeated on each component obtained by the decomposition.

QuaBingo: A Prediction System for Protein Quaternary Structure Attributes Using Block Composition

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Chi-Hua Tung

2016-01-01

Full Text Available Background. Quaternary structures of proteins are closely relevant to gene regulation, signal transduction, and many other biological functions of proteins. In the current study, a new method based on protein-conserved motif composition in block format for feature extraction is proposed, which is termed block composition. Results. The protein quaternary assembly states prediction system which combines blocks with functional domain composition, called QuaBingo, is constructed by three layers of classifiers that can categorize quaternary structural attributes of monomer, homooligomer, and heterooligomer. The building of the first layer classifier uses support vector machines (SVM based on blocks and functional domains of proteins, and the second layer SVM was utilized to process the outputs of the first layer. Finally, the result is determined by the Random Forest of the third layer. We compared the effectiveness of the combination of block composition, functional domain composition, and pseudoamino acid composition of the model. In the 11 kinds of functional protein families, QuaBingo is 23% of Matthews Correlation Coefficient (MCC higher than the existing prediction system. The results also revealed the biological characterization of the top five block compositions. Conclusions. QuaBingo provides better predictive ability for predicting the quaternary structural attributes of proteins.

LS-SVM: uma nova ferramenta quimiométrica para regressão multivariada. Comparação de modelos de regressão LS-SVM e PLS na quantificação de adulterantes em leite em pó empregando NIR LS-SVM: a new chemometric tool for multivariate regression. Comparison of LS-SVM and pls regression for determination of common adulterants in powdered milk by nir spectroscopy

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Marco F. Ferrão

2007-08-01

Full Text Available Least-squares support vector machines (LS-SVM were used as an alternative multivariate calibration method for the simultaneous quantification of some common adulterants found in powdered milk samples, using near-infrared spectroscopy. Excellent models were built using LS-SVM for determining R², RMSECV and RMSEP values. LS-SVMs show superior performance for quantifying starch, whey and sucrose in powdered milk samples in relation to PLSR. This study shows that it is possible to determine precisely the amount of one and two common adulterants simultaneously in powdered milk samples using LS-SVM and NIR spectra.

Methods for discriminating gas-liquid two phase flow patterns based on gray neural networks and SVM

International Nuclear Information System (INIS)

Li Jingjing; Zhou Tao; Duan Jun; Zhang Lei

2013-01-01

Background: The flow patterns of two phase flow will directly influence the heat transfer and mass transfer of the flow. Purpose: By wavelet analysis of the pressure drop experimental data, the wavelet coefficients of different frequency can be obtained. Methods: Get the wavelet energy and then train them in the model of BP neural network to distinguish the flow patterns. Introduced the implant gray neural networks model and use it for the two phase flow for the first time. At the same time, set up the method of training the pressure data and wavelet energy data in the support vector machine. Results: Through treatment of the gray layer, the result of the neural network is more accuracy. It can obviously reduce the effect of data marginalization. The accuracy of the pressure drop Lib-SVM method is 95.2%. Conclusions: The results show that these three methods can make a distinction among the different flow patterns and the Lib-SVM method gets the best result, then the gray neural networks, and at last the BP neural networks. (authors)

PCA criterion for SVM (MLP) classifier for flavivirus biomarker from salivary SERS spectra at febrile stage.

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Radzol, A R M; Lee, Khuan Y; Mansor, W; Omar, I S

2016-08-01

Non-structural protein (NS1) has been conceded as one of the biomarkers for flavivirus that causes diseases with life threatening consequences. NS1 is an antigen that allows detection of the illness at febrile stage, mostly from blood samples currently. Our work here intends to define an optimum model for PCA-SVM with MLP kernel for classification of flavivirus biomarker, NS1 molecule, from SERS spectra of saliva, which to the best of our knowledge has never been explored. Since performance of the model depends on the PCA criterion and MLP parameters, both are examined in tandem. Input vector to classifier determined by each PCA criterion is subjected to brute force tuning of MLP parameters for entirety. Its performance is also compared to our previous works where a Linear and RBF kernel are used. It is found that the best PCA-SVM (MLP) model can be defined by 5 PCs from Cattel's Scree test for PCA, together with P1 and P2 values of 0.1 and -0.2 respectively, with a classification performance of [96.9%, 93.8%, 100.0%].

A fast learning method for large scale and multi-class samples of SVM

Science.gov (United States)

Fan, Yu; Guo, Huiming

2017-06-01

A multi-class classification SVM(Support Vector Machine) fast learning method based on binary tree is presented to solve its low learning efficiency when SVM processing large scale multi-class samples. This paper adopts bottom-up method to set up binary tree hierarchy structure, according to achieved hierarchy structure, sub-classifier learns from corresponding samples of each node. During the learning, several class clusters are generated after the first clustering of the training samples. Firstly, central points are extracted from those class clusters which just have one type of samples. For those which have two types of samples, cluster numbers of their positive and negative samples are set respectively according to their mixture degree, secondary clustering undertaken afterwards, after which, central points are extracted from achieved sub-class clusters. By learning from the reduced samples formed by the integration of extracted central points above, sub-classifiers are obtained. Simulation experiment shows that, this fast learning method, which is based on multi-level clustering, can guarantee higher classification accuracy, greatly reduce sample numbers and effectively improve learning efficiency.

Application of SVM on satellite images to detect hotspots in Jharia coal field region of India

Energy Technology Data Exchange (ETDEWEB)

Gautam, R.S.; Singh, D.; Mittal, A.; Sajin, P. [Indian Institute for Technology, Roorkee (India)

2008-07-01

The present paper deals with the application of Support Vector Machine (SVM) and image analysis techniques on NOAA/AVHRR satellite image to detect hotspots on the Jharia coal field region of India. One of the major advantages of using these satellite data is that the data are free with very good temporal resolution; while, one drawback is that these have low spatial resolution (i.e., approximately 1.1 km at nadir). Therefore, it is important to do research by applying some efficient optimization techniques along with the image analysis techniques to rectify these drawbacks and use satellite images for efficient hotspot detection and monitoring. For this purpose, SVM and multi-threshold techniques are explored for hotspot detection. The multi-threshold algorithm is developed to remove the cloud coverage from the land coverage. This algorithm also highlights the hotspots or fire spots in the suspected regions. SVM has the advantage over multi-thresholding technique that it can learn patterns from the examples and therefore is used to optimize the performance by removing the false points which are highlighted in the threshold technique. Both approaches can be used separately or in combination depending on the size of the image. The RBF (Radial Basis Function) kernel is used in training of three sets of inputs: brightness temperature of channel 3, Normalized Difference Vegetation Index (NDVI) and Global Environment Monitoring Index (GEMI), respectively. This makes a classified image in the output that highlights the hotspot and non-hotspot pixels. The performance of the SVM is also compared with the performance obtained from the neural networks and SVM appears to detect hotspots more accurately (greater than 91% classification accuracy) with lesser false alarm rate. The results obtained are found to be in good agreement with the ground based observations of the hotspots.

Application of ANFIS and SVM Systems in Order to Estimate Monthly Reference Crop Evapotranspiration in the Northwest of Iran

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F. Ahmadi

2016-10-01

Full Text Available Introduction Crop evapotranspiration modeling process mainly performs with empirical methods, aerodynamic and energy balance. In these methods, the evapotranspiration is calculated based on the average values of meteorological parameters at different time steps. The linear models didn’t have a good performance in this field due to high variability of evapotranspiration and the researchers have turned to the use of nonlinear and intelligent models. For accurate estimation of this hydrologic variable, it should be spending much time and money to measure many data (19. Materials and Methods Recently the new hybrid methods have been developed by combining some of methods such as artificial neural networks, fuzzy logic and evolutionary computation, that called Soft Computing and Intelligent Systems. These soft techniques are used in various fields of engineering. A fuzzy neurosis is a hybrid system that incorporates the decision ability of fuzzy logic with the computational ability of neural network, which provides a high capability for modeling and estimating. Basically, the Fuzzy part is used to classify the input data set and determines the degree of membership (that each number can be laying between 0 and 1 and decisions for the next activity made based on a set of rules and move to the next stage. Adaptive Neuro-Fuzzy Inference Systems (ANFIS includes some parts of a typical fuzzy expert system which the calculations at each step is performed by the hidden layer neurons and the learning ability of the neural network has been created to increase the system information (9. SVM is a one of supervised learning methods which used for classification and regression affairs. This method was developed by Vapink (15 based on statistical learning theory. The SVM is a method for binary classification in an arbitrary characteristic space, so it is suitable for prediction problems (12. The SVM is originally a two-class Classifier that separates the classes

The system evaluation for report writing skills of summary by HGA-SVM with Ontology: Medical case study in problem based learning

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Yenaeng, Sasikanchana; Saelee, Somkid; Samai, Wirachai

2018-01-01

The system evaluation for report writing skills of summary by Hybrid Genetic Algorithm-Support Vector Machines (HGA-SVM) with Ontology of Medical Case Study in Problem Based Learning (PBL) is a system was developed as a guideline of scoring for the facilitators or medical teacher. The essay answers come from medical student of medical education courses in the nervous system motion and Behavior I and II subject, a third year medical student 20 groups of 9-10 people, the Faculty of Medicine in Prince of Songkla University (PSU). The audit committee have the opinion that the ratings of individual facilitators are inadequate, this system to solve such problems. In this paper proposes a development of the system evaluation for report writing skills of summary by HGA-SVM with Ontology of medical case study in PBL which the mean scores of machine learning score and humans (facilitators) score were not different at the significantly level .05 all 3 essay parts contain problem essay part, hypothesis essay part and learning objective essay part. The result show that, the average score all 3 essay parts that were not significantly different from the rate at the level of significance .05.

Kernel based machine learning algorithm for the efficient prediction of type III polyketide synthase family of proteins

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Mallika V

2010-03-01

Full Text Available Type III Polyketide synthases (PKS are family of proteins considered to have significant role in the biosynthesis of various polyketides in plants, fungi and bacteria. As these proteins show positive effects to human health, more researches are going on regarding this particular protein. Developing a tool to identify the probability of sequence, being a type III polyketide synthase will minimize the time consumption and manpower efforts. In this approach, we have designed and implemented PKSIIIpred, a high performance prediction server for type III PKS where the classifier is Support Vector Machine (SVM. Based on the limited training dataset, the tool efficiently predicts the type III PKS superfamily of proteins with high sensitivity and specificity. PKSIIIpred is available at http://type3pks.in/prediction/. We expect that this tool may serve as a useful resource for type III PKS researchers. Currently work is being progressed for further betterment of prediction accuracy by including more sequence features in the training dataset.

SVM-Maj: a majorization approach to linear support vector machines with different hinge errors

NARCIS (Netherlands)

P.J.F. Groenen (Patrick); G.I. Nalbantov (Georgi); J.C. Bioch (Cor)

2007-01-01

textabstractSupport vector machines (SVM) are becoming increasingly popular for the prediction of a binary dependent variable. SVMs perform very well with respect to competing techniques. Often, the solution of an SVM is obtained by switching to the dual. In this paper, we stick to the primal

Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition.

Science.gov (United States)

Hayat, Maqsood; Khan, Asifullah

2011-02-21

Membrane proteins are vital type of proteins that serve as channels, receptors, and energy transducers in a cell. Prediction of membrane protein types is an important research area in bioinformatics. Knowledge of membrane protein types provides some valuable information for predicting novel example of the membrane protein types. However, classification of membrane protein types can be both time consuming and susceptible to errors due to the inherent similarity of membrane protein types. In this paper, neural networks based membrane protein type prediction system is proposed. Composite protein sequence representation (CPSR) is used to extract the features of a protein sequence, which includes seven feature sets; amino acid composition, sequence length, 2 gram exchange group frequency, hydrophobic group, electronic group, sum of hydrophobicity, and R-group. Principal component analysis is then employed to reduce the dimensionality of the feature vector. The probabilistic neural network (PNN), generalized regression neural network, and support vector machine (SVM) are used as classifiers. A high success rate of 86.01% is obtained using SVM for the jackknife test. In case of independent dataset test, PNN yields the highest accuracy of 95.73%. These classifiers exhibit improved performance using other performance measures such as sensitivity, specificity, Mathew's correlation coefficient, and F-measure. The experimental results show that the prediction performance of the proposed scheme for classifying membrane protein types is the best reported, so far. This performance improvement may largely be credited to the learning capabilities of neural networks and the composite feature extraction strategy, which exploits seven different properties of protein sequences. The proposed Mem-Predictor can be accessed at http://111.68.99.218/Mem-Predictor. Copyright © 2010 Elsevier Ltd. All rights reserved.

Static Voltage Stability Analysis by Using SVM and Neural Network

Directory of Open Access Journals (Sweden)

Mehdi Hajian

2013-01-01

Full Text Available Voltage stability is an important problem in power system networks. In this paper, in terms of static voltage stability, and application of Neural Networks (NN and Supported Vector Machine (SVM for estimating of voltage stability margin (VSM and predicting of voltage collapse has been investigated. This paper considers voltage stability in power system in two parts. The first part calculates static voltage stability margin by Radial Basis Function Neural Network (RBFNN. The advantage of the used method is high accuracy in online detecting the VSM. Whereas the second one, voltage collapse analysis of power system is performed by Probabilistic Neural Network (PNN and SVM. The obtained results in this paper indicate, that time and number of training samples of SVM, are less than NN. In this paper, a new model of training samples for detection system, using the normal distribution load curve at each load feeder, has been used. Voltage stability analysis is estimated by well-know L and VSM indexes. To demonstrate the validity of the proposed methods, IEEE 14 bus grid and the actual network of Yazd Province are used.

A Fast SVM-Based Tongue's Colour Classification Aided by k-Means Clustering Identifiers and Colour Attributes as Computer-Assisted Tool for Tongue Diagnosis

Science.gov (United States)

Ooi, Chia Yee; Kawanabe, Tadaaki; Odaguchi, Hiroshi; Kobayashi, Fuminori

2017-01-01

In tongue diagnosis, colour information of tongue body has kept valuable information regarding the state of disease and its correlation with the internal organs. Qualitatively, practitioners may have difficulty in their judgement due to the instable lighting condition and naked eye's ability to capture the exact colour distribution on the tongue especially the tongue with multicolour substance. To overcome this ambiguity, this paper presents a two-stage tongue's multicolour classification based on a support vector machine (SVM) whose support vectors are reduced by our proposed k-means clustering identifiers and red colour range for precise tongue colour diagnosis. In the first stage, k-means clustering is used to cluster a tongue image into four clusters of image background (black), deep red region, red/light red region, and transitional region. In the second-stage classification, red/light red tongue images are further classified into red tongue or light red tongue based on the red colour range derived in our work. Overall, true rate classification accuracy of the proposed two-stage classification to diagnose red, light red, and deep red tongue colours is 94%. The number of support vectors in SVM is improved by 41.2%, and the execution time for one image is recorded as 48 seconds. PMID:29065640

A Fast SVM-Based Tongue's Colour Classification Aided by k-Means Clustering Identifiers and Colour Attributes as Computer-Assisted Tool for Tongue Diagnosis.

Science.gov (United States)

Kamarudin, Nur Diyana; Ooi, Chia Yee; Kawanabe, Tadaaki; Odaguchi, Hiroshi; Kobayashi, Fuminori

2017-01-01

In tongue diagnosis, colour information of tongue body has kept valuable information regarding the state of disease and its correlation with the internal organs. Qualitatively, practitioners may have difficulty in their judgement due to the instable lighting condition and naked eye's ability to capture the exact colour distribution on the tongue especially the tongue with multicolour substance. To overcome this ambiguity, this paper presents a two-stage tongue's multicolour classification based on a support vector machine (SVM) whose support vectors are reduced by our proposed k -means clustering identifiers and red colour range for precise tongue colour diagnosis. In the first stage, k -means clustering is used to cluster a tongue image into four clusters of image background (black), deep red region, red/light red region, and transitional region. In the second-stage classification, red/light red tongue images are further classified into red tongue or light red tongue based on the red colour range derived in our work. Overall, true rate classification accuracy of the proposed two-stage classification to diagnose red, light red, and deep red tongue colours is 94%. The number of support vectors in SVM is improved by 41.2%, and the execution time for one image is recorded as 48 seconds.

An SVM-Based Classifier for Estimating the State of Various Rotating Components in Agro-Industrial Machinery with a Vibration Signal Acquired from a Single Point on the Machine Chassis

Directory of Open Access Journals (Sweden)

Ruben Ruiz-Gonzalez

2014-11-01

Full Text Available The goal of this article is to assess the feasibility of estimating the state of various rotating components in agro-industrial machinery by employing just one vibration signal acquired from a single point on the machine chassis. To do so, a Support Vector Machine (SVM-based system is employed. Experimental tests evaluated this system by acquiring vibration data from a single point of an agricultural harvester, while varying several of its working conditions. The whole process included two major steps. Initially, the vibration data were preprocessed through twelve feature extraction algorithms, after which the Exhaustive Search method selected the most suitable features. Secondly, the SVM-based system accuracy was evaluated by using Leave-One-Out cross-validation, with the selected features as the input data. The results of this study provide evidence that (i accurate estimation of the status of various rotating components in agro-industrial machinery is possible by processing the vibration signal acquired from a single point on the machine structure; (ii the vibration signal can be acquired with a uniaxial accelerometer, the orientation of which does not significantly affect the classification accuracy; and, (iii when using an SVM classifier, an 85% mean cross-validation accuracy can be reached, which only requires a maximum of seven features as its input, and no significant improvements are noted between the use of either nonlinear or linear kernels.

Dates fruits classification using SVM

Science.gov (United States)

Alzu'bi, Reem; Anushya, A.; Hamed, Ebtisam; Al Sha'ar, Eng. Abdelnour; Vincy, B. S. Angela

2018-04-01

In this paper, we used SVM in classifying various types of dates using their images. Dates have interesting different characteristics that can be valuable to distinguish and determine a particular date type. These characteristics include shape, texture, and color. A system that achieves 100% accuracy was built to classify the dates which can be eatable and cannot be eatable. The built system helps the food industry and customer in classifying dates depending on specific quality measures giving best performance with specific type of dates.

Rancang Bangun Inverter SVM Berbasis Mikrokontroler PIC 18F4431 Untuk Sistem VSD

OpenAIRE

Tarmizi; Muyassar

2013-01-01

Sebuah sistem pengaturan kecepatan motor disebut dengan sistem Variable Speed Drives (VSD). Sistem VSD motor induksi menggunakan inverter untuk mengatur frekuensi suplai motor. Untuk mendapatkan frekuensi suplai motor yang mendekati sinusoidal, inveter perlu di switching dengan metode tertentu. Pada penelitian ini, switching inverter 3 fasa menggunakan metode SVM (Space Vector Modulation) yang dikontrol oleh Mikrokontroler PIC18F4431. Sebelum dilakukan ekperimen, inverter SVM ini lakukan si...

An IPSO-SVM algorithm for security state prediction of mine production logistics system

Science.gov (United States)

Zhang, Yanliang; Lei, Junhui; Ma, Qiuli; Chen, Xin; Bi, Runfang

2017-06-01

A theoretical basis for the regulation of corporate security warning and resources was provided in order to reveal the laws behind the security state in mine production logistics. Considering complex mine production logistics system and the variable is difficult to acquire, a superior security status predicting model of mine production logistics system based on the improved particle swarm optimization and support vector machine (IPSO-SVM) is proposed in this paper. Firstly, through the linear adjustments of inertia weight and learning weights, the convergence speed and search accuracy are enhanced with the aim to deal with situations associated with the changeable complexity and the data acquisition difficulty. The improved particle swarm optimization (IPSO) is then introduced to resolve the problem of parameter settings in traditional support vector machines (SVM). At the same time, security status index system is built to determine the classification standards of safety status. The feasibility and effectiveness of this method is finally verified using the experimental results.

A Research of Speech Emotion Recognition Based on Deep Belief Network and SVM

Directory of Open Access Journals (Sweden)

Chenchen Huang

2014-01-01

Full Text Available Feature extraction is a very important part in speech emotion recognition, and in allusion to feature extraction in speech emotion recognition problems, this paper proposed a new method of feature extraction, using DBNs in DNN to extract emotional features in speech signal automatically. By training a 5 layers depth DBNs, to extract speech emotion feature and incorporate multiple consecutive frames to form a high dimensional feature. The features after training in DBNs were the input of nonlinear SVM classifier, and finally speech emotion recognition multiple classifier system was achieved. The speech emotion recognition rate of the system reached 86.5%, which was 7% higher than the original method.

Application of SVM methods for mid-term load forecasting

Directory of Open Access Journals (Sweden)

Božić Miloš

2011-01-01

Full Text Available This paper presents an approach for the medium-term load forecasting using Support Vector Machines (SVMs. The proposed SVM model was employed to predict the maximum daily load demand for the period of a month. Analyses of available data were performed and the most important features for the construction of SVM model are selected. It was shown that the size and the structure of the training set may significantly affect the accuracy of predictions. The presented model was tested by applying it on real-life load data obtained from distribution company 'ED Jugoistok' for the territory of city Niš and its surroundings. Experimental results show that the proposed approach gives acceptable results for the entire period of prediction, which are in range with other solutions in this area.

Comparison of sensorless FOC and SVM-DTFC of PMSM for low-speed applications

DEFF Research Database (Denmark)

Basar, M. Sertug; Bech, Michael Møller; Andersen, Torben Ole

2013-01-01

This article presents the performance analysis of Field Oriented Control (FOC) and Space Vector Modulation (SVM) Direct Torque and Flux Control (DTFC) of a Non-Salient Permanent Magnet Synchronous Machine (PMSM) under sensorless control within low speed region. The high-frequency alternating...... with a commercially available PMSM machine. Both controllers show satisfactory sensorless performance. FOC provides smoother and more accurate response while SVM-DTFC has the advantage of faster control....

Comparison of sensorless FOC and SVM-DTFC of PMSM for low-speed applications

DEFF Research Database (Denmark)

Basar, Mehmet Sertug

2016-01-01

This article presents the performance analysis of Field Oriented Control (FOC) and Space Vector Modulation (SVM) Direct Torque and Flux Control (DTFC) of a Non-Salient Permanent Magnet Synchronous Machine (PMSM) under sensorless control within low speed region. The high-frequency alternating...... with a commercially available PMSM machine. Both controllers show satisfactory sensorless performance. FOC provides smoother and more accurate response while SVM-DTFC has the advantage of faster control....

A WFS-SVM Model for Soil Salinity Mapping in Keriya Oasis, Northwestern China Using Polarimetric Decomposition and Fully PolSAR Data

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Ilyas Nurmemet

2018-04-01

Full Text Available Timely monitoring and mapping of salt-affected areas are essential for the prevention of land degradation and sustainable soil management in arid and semi-arid regions. The main objective of this study was to develop Synthetic Aperture Radar (SAR polarimetry techniques for improved soil salinity mapping in the Keriya Oasis in the Xinjiang Uyghur Autonomous Region (Xinjiang, China, where salinized soil appears to be a major threat to local agricultural productivity. Multiple polarimetric target decomposition, optimal feature subset selection (wrapper feature selector, WFS, and support vector machine (SVM algorithms were used for optimal soil salinization classification using quad-polarized PALSAR-2 data. A threefold exercise was conducted. First, 16 polarimetric decomposition methods were implemented and a wide range of polarimetric parameters and SAR discriminators were derived in order to mine hidden information in PolSAR data. Second, the optimal polarimetric feature subset that constitutes 19 polarimetric elements was selected adopting the WFS approach; optimum classification parameters were identified, and the optimal SVM classification model was obtained by employing a cross-validation method. Third, the WFS-SVM classification model was constructed, optimized, and implemented based on the optimal match of polarimetric features and optimum classification parameters. Soils with different salinization degrees (i.e., highly, moderately and slightly salinized soils were extracted. Finally, classification results were compared with the Wishart supervised classification and conventional SVM classification to examine the performance of the proposed method for salinity mapping. Detailed field investigations and ground data were used for the validation of the adopted methods. The overall accuracy and kappa coefficient of the proposed WFS-SVM model were 87.57% and 0.85, respectively that were much higher than those obtained by the Wishart supervised

A discriminative method for family-based protein remote homology detection that combines inductive logic programming and propositional models.

Science.gov (United States)

Bernardes, Juliana S; Carbone, Alessandra; Zaverucha, Gerson

2011-03-23

Remote homology detection is a hard computational problem. Most approaches have trained computational models by using either full protein sequences or multiple sequence alignments (MSA), including all positions. However, when we deal with proteins in the "twilight zone" we can observe that only some segments of sequences (motifs) are conserved. We introduce a novel logical representation that allows us to represent physico-chemical properties of sequences, conserved amino acid positions and conserved physico-chemical positions in the MSA. From this, Inductive Logic Programming (ILP) finds the most frequent patterns (motifs) and uses them to train propositional models, such as decision trees and support vector machines (SVM). We use the SCOP database to perform our experiments by evaluating protein recognition within the same superfamily. Our results show that our methodology when using SVM performs significantly better than some of the state of the art methods, and comparable to other. However, our method provides a comprehensible set of logical rules that can help to understand what determines a protein function. The strategy of selecting only the most frequent patterns is effective for the remote homology detection. This is possible through a suitable first-order logical representation of homologous properties, and through a set of frequent patterns, found by an ILP system, that summarizes essential features of protein functions.

The efficacy of support vector machines (SVM)

Indian Academy of Sciences (India)

(2006) by applying an SVM statistical learning machine on the time-scale wavelet decomposition methods. We used the data of 108 events in central Japan with magnitude ranging from 3 to 7.4 recorded at KiK-net network stations, for a source–receiver distance of up to 150 km during the period 1998–2011. We applied a ...

A Fast SVM-Based Tongue’s Colour Classification Aided by k-Means Clustering Identifiers and Colour Attributes as Computer-Assisted Tool for Tongue Diagnosis

Directory of Open Access Journals (Sweden)

Nur Diyana Kamarudin

2017-01-01

Full Text Available In tongue diagnosis, colour information of tongue body has kept valuable information regarding the state of disease and its correlation with the internal organs. Qualitatively, practitioners may have difficulty in their judgement due to the instable lighting condition and naked eye’s ability to capture the exact colour distribution on the tongue especially the tongue with multicolour substance. To overcome this ambiguity, this paper presents a two-stage tongue’s multicolour classification based on a support vector machine (SVM whose support vectors are reduced by our proposed k-means clustering identifiers and red colour range for precise tongue colour diagnosis. In the first stage, k-means clustering is used to cluster a tongue image into four clusters of image background (black, deep red region, red/light red region, and transitional region. In the second-stage classification, red/light red tongue images are further classified into red tongue or light red tongue based on the red colour range derived in our work. Overall, true rate classification accuracy of the proposed two-stage classification to diagnose red, light red, and deep red tongue colours is 94%. The number of support vectors in SVM is improved by 41.2%, and the execution time for one image is recorded as 48 seconds.

Common voltage eliminating of SVM diode clamping three-level inverter connected to grid

DEFF Research Database (Denmark)

Guo, Yougui; Zeng, Ping; Zhu, Jieqiong

2011-01-01

A novel method of common voltage eliminating is put forward for SVM diode clamping three-level inverter connected to grid by calculation of common voltage of its various switching states. PLECS is used to model this three-level inverter connected to grid and good results are obtained. First...... analysis of common mode voltage for switching states of diode clamping 3-level inverter is given in detail. Second the common mode voltage eliminating control strategy of SVM is described for diode clamping three-level inverter. Third, PLECS is briefly introduced. Fourth, the modeling of diode clamping...... three-level inverter is presented with PLECS. Finally, a series of simulations are carried out. The simulation results tell us PLECS is a very powerful tool to real power circuits modeling. They have also verified that proposed common mode voltage eliminating control strategy of SVM is feasible...

Adaptive predictors based on probabilistic SVM for real time disruption mitigation on JET

Science.gov (United States)

Murari, A.; Lungaroni, M.; Peluso, E.; Gaudio, P.; Vega, J.; Dormido-Canto, S.; Baruzzo, M.; Gelfusa, M.; Contributors, JET

2018-05-01

Detecting disruptions with sufficient anticipation time is essential to undertake any form of remedial strategy, mitigation or avoidance. Traditional predictors based on machine learning techniques can be very performing, if properly optimised, but do not provide a natural estimate of the quality of their outputs and they typically age very quickly. In this paper a new set of tools, based on probabilistic extensions of support vector machines (SVM), are introduced and applied for the first time to JET data. The probabilistic output constitutes a natural qualification of the prediction quality and provides additional flexibility. An adaptive training strategy ‘from scratch’ has also been devised, which allows preserving the performance even when the experimental conditions change significantly. Large JET databases of disruptions, covering entire campaigns and thousands of discharges, have been analysed, both for the case of the graphite and the ITER Like Wall. Performance significantly better than any previous predictor using adaptive training has been achieved, satisfying even the requirements of the next generation of devices. The adaptive approach to the training has also provided unique information about the evolution of the operational space. The fact that the developed tools give the probability of disruption improves the interpretability of the results, provides an estimate of the predictor quality and gives new insights into the physics. Moreover, the probabilistic treatment permits to insert more easily these classifiers into general decision support and control systems.

PCVMZM: Using the Probabilistic Classification Vector Machines Model Combined with a Zernike Moments Descriptor to Predict Protein-Protein Interactions from Protein Sequences.

Science.gov (United States)

Wang, Yanbin; You, Zhuhong; Li, Xiao; Chen, Xing; Jiang, Tonghai; Zhang, Jingting

2017-05-11

Protein-protein interactions (PPIs) are essential for most living organisms' process. Thus, detecting PPIs is extremely important to understand the molecular mechanisms of biological systems. Although many PPIs data have been generated by high-throughput technologies for a variety of organisms, the whole interatom is still far from complete. In addition, the high-throughput technologies for detecting PPIs has some unavoidable defects, including time consumption, high cost, and high error rate. In recent years, with the development of machine learning, computational methods have been broadly used to predict PPIs, and can achieve good prediction rate. In this paper, we present here PCVMZM, a computational method based on a Probabilistic Classification Vector Machines (PCVM) model and Zernike moments (ZM) descriptor for predicting the PPIs from protein amino acids sequences. Specifically, a Zernike moments (ZM) descriptor is used to extract protein evolutionary information from Position-Specific Scoring Matrix (PSSM) generated by Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then, PCVM classifier is used to infer the interactions among protein. When performed on PPIs datasets of Yeast and H. Pylori , the proposed method can achieve the average prediction accuracy of 94.48% and 91.25%, respectively. In order to further evaluate the performance of the proposed method, the state-of-the-art support vector machines (SVM) classifier is used and compares with the PCVM model. Experimental results on the Yeast dataset show that the performance of PCVM classifier is better than that of SVM classifier. The experimental results indicate that our proposed method is robust, powerful and feasible, which can be used as a helpful tool for proteomics research.

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics.

Science.gov (United States)

Huang, Shujun; Cai, Nianguang; Pacheco, Pedro Penzuti; Narrandes, Shavira; Wang, Yang; Xu, Wayne

2018-01-01

Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading to the discovery of new biomarkers, new drug targets, and a better understanding of cancer driver genes. Herein we reviewed the recent progress of SVMs in cancer genomic studies. We intend to comprehend the strength of the SVM learning and its future perspective in cancer genomic applications. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Classification of EEG-P300 Signals Extracted from Brain Activities in BCI Systems Using ν-SVM and BLDA Algorithms

Directory of Open Access Journals (Sweden)

Ali MOMENNEZHAD

2014-06-01

Full Text Available In this paper, a linear predictive coding (LPC model is used to improve classification accuracy, convergent speed to maximum accuracy, and maximum bitrates in brain computer interface (BCI system based on extracting EEG-P300 signals. First, EEG signal is filtered in order to eliminate high frequency noise. Then, the parameters of filtered EEG signal are extracted using LPC model. Finally, the samples are reconstructed by LPC coefficients and two classifiers, a Bayesian Linear discriminant analysis (BLDA, and b the υ-support vector machine (υ-SVM are applied in order to classify. The proposed algorithm performance is compared with fisher linear discriminant analysis (FLDA. Results show that the efficiency of our algorithm in improving classification accuracy and convergent speed to maximum accuracy are much better. As example at the proposed algorithms, respectively BLDA with LPC model and υ-SVM with LPC model with8 electrode configuration for subject S1 the total classification accuracy is improved as 9.4% and 1.7%. And also, subject 7 at BLDA and υ-SVM with LPC model algorithms (LPC+BLDA and LPC+ υ-SVM after block 11th converged to maximum accuracy but Fisher Linear Discriminant Analysis (FLDA algorithm did not converge to maximum accuracy (with the same configuration. So, it can be used as a promising tool in designing BCI systems.

Computational Identification of Protein Pupylation Sites by Using Profile-Based Composition of k-Spaced Amino Acid Pairs.

Directory of Open Access Journals (Sweden)

Md Mehedi Hasan

Full Text Available Prokaryotic proteins are regulated by pupylation, a type of post-translational modification that contributes to cellular function in bacterial organisms. In pupylation process, the prokaryotic ubiquitin-like protein (Pup tagging is functionally analogous to ubiquitination in order to tag target proteins for proteasomal degradation. To date, several experimental methods have been developed to identify pupylated proteins and their pupylation sites, but these experimental methods are generally laborious and costly. Therefore, computational methods that can accurately predict potential pupylation sites based on protein sequence information are highly desirable. In this paper, a novel predictor termed as pbPUP has been developed for accurate prediction of pupylation sites. In particular, a sophisticated sequence encoding scheme [i.e. the profile-based composition of k-spaced amino acid pairs (pbCKSAAP] is used to represent the sequence patterns and evolutionary information of the sequence fragments surrounding pupylation sites. Then, a Support Vector Machine (SVM classifier is trained using the pbCKSAAP encoding scheme. The final pbPUP predictor achieves an AUC value of 0.849 in 10-fold cross-validation tests and outperforms other existing predictors on a comprehensive independent test dataset. The proposed method is anticipated to be a helpful computational resource for the prediction of pupylation sites. The web server and curated datasets in this study are freely available at http://protein.cau.edu.cn/pbPUP/.

APPLICATION OF FUSION WITH SAR AND OPTICAL IMAGES IN LAND USE CLASSIFICATION BASED ON SVM

Directory of Open Access Journals (Sweden)

C. Bao

2012-07-01

Full Text Available As the increment of remote sensing data with multi-space resolution, multi-spectral resolution and multi-source, data fusion technologies have been widely used in geological fields. Synthetic Aperture Radar (SAR and optical camera are two most common sensors presently. The multi-spectral optical images express spectral features of ground objects, while SAR images express backscatter information. Accuracy of the image classification could be effectively improved fusing the two kinds of images. In this paper, Terra SAR-X images and ALOS multi-spectral images were fused for land use classification. After preprocess such as geometric rectification, radiometric rectification noise suppression and so on, the two kind images were fused, and then SVM model identification method was used for land use classification. Two different fusion methods were used, one is joining SAR image into multi-spectral images as one band, and the other is direct fusing the two kind images. The former one can raise the resolution and reserve the texture information, and the latter can reserve spectral feature information and improve capability of identifying different features. The experiment results showed that accuracy of classification using fused images is better than only using multi-spectral images. Accuracy of classification about roads, habitation and water bodies was significantly improved. Compared to traditional classification method, the method of this paper for fused images with SVM classifier could achieve better results in identifying complicated land use classes, especially for small pieces ground features.

DisArticle: a web server for SVM-based discrimination of articles on traditional medicine.

Science.gov (United States)

Kim, Sang-Kyun; Nam, SeJin; Kim, SangHyun

2017-01-28

Much research has been done in Northeast Asia to show the efficacy of traditional medicine. While MEDLINE contains many biomedical articles including those on traditional medicine, it does not categorize those articles by specific research area. The aim of this study was to provide a method that searches for articles only on traditional medicine in Northeast Asia, including traditional Chinese medicine, from among the articles in MEDLINE. This research established an SVM-based classifier model to identify articles on traditional medicine. The TAK + HM classifier, trained with the features of title, abstract, keywords, herbal data, and MeSH, has a precision of 0.954 and a recall of 0.902. In particular, the feature of herbal data significantly increased the performance of the classifier. By using the TAK + HM classifier, a total of about 108,000 articles were discriminated as articles on traditional medicine from among all articles in MEDLINE. We also built a web server called DisArticle ( http://informatics.kiom.re.kr/disarticle ), in which users can search for the articles and obtain statistical data. Because much evidence-based research on traditional medicine has been published in recent years, it has become necessary to search for articles on traditional medicine exclusively in literature databases. DisArticle can help users to search for and analyze the research trends in traditional medicine.

Support vector machines for prediction and analysis of beta and gamma-turns in proteins.

Science.gov (United States)

Pham, Tho Hoan; Satou, Kenji; Ho, Tu Bao

2005-04-01

Tight turns have long been recognized as one of the three important features of proteins, together with alpha-helix and beta-sheet. Tight turns play an important role in globular proteins from both the structural and functional points of view. More than 90% tight turns are beta-turns and most of the rest are gamma-turns. Analysis and prediction of beta-turns and gamma-turns is very useful for design of new molecules such as drugs, pesticides, and antigens. In this paper we investigated two aspects of applying support vector machine (SVM), a promising machine learning method for bioinformatics, to prediction and analysis of beta-turns and gamma-turns. First, we developed two SVM-based methods, called BTSVM and GTSVM, which predict beta-turns and gamma-turns in a protein from its sequence. When compared with other methods, BTSVM has a superior performance and GTSVM is competitive. Second, we used SVMs with a linear kernel to estimate the support of amino acids for the formation of beta-turns and gamma-turns depending on their position in a protein. Our analysis results are more comprehensive and easier to use than the previous results in designing turns in proteins.

Exploring QSARs of the interaction of flavonoids with GABA (A) receptor using MLR, ANN and SVM techniques.

Science.gov (United States)

Deeb, Omar; Shaik, Basheerulla; Agrawal, Vijay K

2014-10-01

Quantitative Structure-Activity Relationship (QSAR) models for binding affinity constants (log Ki) of 78 flavonoid ligands towards the benzodiazepine site of GABA (A) receptor complex were calculated using the machine learning methods: artificial neural network (ANN) and support vector machine (SVM) techniques. The models obtained were compared with those obtained using multiple linear regression (MLR) analysis. The descriptor selection and model building were performed with 10-fold cross-validation using the training data set. The SVM and MLR coefficient of determination values are 0.944 and 0.879, respectively, for the training set and are higher than those of ANN models. Though the SVM model shows improvement of training set fitting, the ANN model was superior to SVM and MLR in predicting the test set. Randomization test is employed to check the suitability of the models.

Prediction of flexible/rigid regions from protein sequences using k-spaced amino acid pairs

Directory of Open Access Journals (Sweden)

Ruan Jishou

2007-04-01

Full Text Available Abstract Background Traditionally, it is believed that the native structure of a protein corresponds to a global minimum of its free energy. However, with the growing number of known tertiary (3D protein structures, researchers have discovered that some proteins can alter their structures in response to a change in their surroundings or with the help of other proteins or ligands. Such structural shifts play a crucial role with respect to the protein function. To this end, we propose a machine learning method for the prediction of the flexible/rigid regions of proteins (referred to as FlexRP; the method is based on a novel sequence representation and feature selection. Knowledge of the flexible/rigid regions may provide insights into the protein folding process and the 3D structure prediction. Results The flexible/rigid regions were defined based on a dataset, which includes protein sequences that have multiple experimental structures, and which was previously used to study the structural conservation of proteins. Sequences drawn from this dataset were represented based on feature sets that were proposed in prior research, such as PSI-BLAST profiles, composition vector and binary sequence encoding, and a newly proposed representation based on frequencies of k-spaced amino acid pairs. These representations were processed by feature selection to reduce the dimensionality. Several machine learning methods for the prediction of flexible/rigid regions and two recently proposed methods for the prediction of conformational changes and unstructured regions were compared with the proposed method. The FlexRP method, which applies Logistic Regression and collocation-based representation with 95 features, obtained 79.5% accuracy. The two runner-up methods, which apply the same sequence representation and Support Vector Machines (SVM and Naïve Bayes classifiers, obtained 79.2% and 78.4% accuracy, respectively. The remaining considered methods are

A New Hybrid Model FPA-SVM Considering Cointegration for Particular Matter Concentration Forecasting: A Case Study of Kunming and Yuxi, China.

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Li, Weide; Kong, Demeng; Wu, Jinran

2017-01-01

Air pollution in China is becoming more serious especially for the particular matter (PM) because of rapid economic growth and fast expansion of urbanization. To solve the growing environment problems, daily PM2.5 and PM10 concentration data form January 1, 2015, to August 23, 2016, in Kunming and Yuxi (two important cities in Yunnan Province, China) are used to present a new hybrid model CI-FPA-SVM to forecast air PM2.5 and PM10 concentration in this paper. The proposed model involves two parts. Firstly, due to its deficiency to assess the possible correlation between different variables, the cointegration theory is introduced to get the input-output relationship and then obtain the nonlinear dynamical system with support vector machine (SVM), in which the parameters c and g are optimized by flower pollination algorithm (FPA). Six benchmark models, including FPA-SVM, CI-SVM, CI-GA-SVM, CI-PSO-SVM, CI-FPA-NN, and multiple linear regression model, are considered to verify the superiority of the proposed hybrid model. The empirical study results demonstrate that the proposed model CI-FPA-SVM is remarkably superior to all considered benchmark models for its high prediction accuracy, and the application of the model for forecasting can give effective monitoring and management of further air quality.

Estimation of hydraulic jump characteristics of channels with sudden diverging side walls via SVM.

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Roushangar, Kiyoumars; Valizadeh, Reyhaneh; Ghasempour, Roghayeh

2017-10-01

Sudden diverging channels are one of the energy dissipaters which can dissipate most of the kinetic energy of the flow through a hydraulic jump. An accurate prediction of hydraulic jump characteristics is an important step in designing hydraulic structures. This paper focuses on the capability of the support vector machine (SVM) as a meta-model approach for predicting hydraulic jump characteristics in different sudden diverging stilling basins (i.e. basins with and without appurtenances). In this regard, different models were developed and tested using 1,018 experimental data. The obtained results proved the capability of the SVM technique in predicting hydraulic jump characteristics and it was found that the developed models for a channel with a central block performed more successfully than models for channels without appurtenances or with a negative step. The superior performance for the length of hydraulic jump was obtained for the model with parameters F 1 (Froude number) and (h 2- h 1 )/h 1 (h 1 and h 2 are sequent depth of upstream and downstream respectively). Concerning the relative energy dissipation and sequent depth ratio, the model with parameters F 1 and h 1 /B (B is expansion ratio) led to the best results. According to the outcome of sensitivity analysis, Froude number had the most significant effect on the modeling. Also comparison between SVM and empirical equations indicated the great performance of the SVM.

Forecasting Dry Bulk Freight Index with Improved SVM

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Qianqian Han

2014-01-01

Full Text Available An improved SVM model is presented to forecast dry bulk freight index (BDI in this paper, which is a powerful tool for operators and investors to manage the market trend and avoid price risking shipping industry. The BDI is influenced by many factors, especially the random incidents in dry bulk market, inducing the difficulty in forecasting of BDI. Therefore, to eliminate the impact of random incidents in dry bulk market, wavelet transform is adopted to denoise the BDI data series. Hence, the combined model of wavelet transform and support vector machine is developed to forecast BDI in this paper. Lastly, the BDI data in 2005 to 2012 are presented to test the proposed model. The 84 prior consecutive monthly BDI data are the inputs of the model, and the last 12 monthly BDI data are the outputs of model. The parameters of the model are optimized by genetic algorithm and the final model is conformed through SVM training. This paper compares the forecasting result of proposed method and three other forecasting methods. The result shows that the proposed method has higher accuracy and could be used to forecast the short-term trend of the BDI.

ProDis-ContSHC: learning protein dissimilarity measures and hierarchical context coherently for protein-protein comparison in protein database retrieval.

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Wang, Jingyan; Gao, Xin; Wang, Quanquan; Li, Yongping

2012-05-08

The need to retrieve or classify protein molecules using structure or sequence-based similarity measures underlies a wide range of biomedical applications. Traditional protein search methods rely on a pairwise dissimilarity/similarity measure for comparing a pair of proteins. This kind of pairwise measures suffer from the limitation of neglecting the distribution of other proteins and thus cannot satisfy the need for high accuracy of the retrieval systems. Recent work in the machine learning community has shown that exploiting the global structure of the database and learning the contextual dissimilarity/similarity measures can improve the retrieval performance significantly. However, most existing contextual dissimilarity/similarity learning algorithms work in an unsupervised manner, which does not utilize the information of the known class labels of proteins in the database. In this paper, we propose a novel protein-protein dissimilarity learning algorithm, ProDis-ContSHC. ProDis-ContSHC regularizes an existing dissimilarity measure dij by considering the contextual information of the proteins. The context of a protein is defined by its neighboring proteins. The basic idea is, for a pair of proteins (i, j), if their context N(i) and N(j) is similar to each other, the two proteins should also have a high similarity. We implement this idea by regularizing dij by a factor learned from the context N(i) and N(j).Moreover, we divide the context to hierarchial sub-context and get the contextual dissimilarity vector for each protein pair. Using the class label information of the proteins, we select the relevant (a pair of proteins that has the same class labels) and irrelevant (with different labels) protein pairs, and train an SVM model to distinguish between their contextual dissimilarity vectors. The SVM model is further used to learn a supervised regularizing factor. Finally, with the new Supervised learned Dissimilarity measure, we update the Protein Hierarchial

Using Generalized Entropies and OC-SVM with Mahalanobis Kernel for Detection and Classification of Anomalies in Network Traffic

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Jayro Santiago-Paz

2015-09-01

Full Text Available Network anomaly detection and classification is an important open issue in network security. Several approaches and systems based on different mathematical tools have been studied and developed, among them, the Anomaly-Network Intrusion Detection System (A-NIDS, which monitors network traffic and compares it against an established baseline of a “normal” traffic profile. Then, it is necessary to characterize the “normal” Internet traffic. This paper presents an approach for anomaly detection and classification based on Shannon, Rényi and Tsallis entropies of selected features, and the construction of regions from entropy data employing the Mahalanobis distance (MD, and One Class Support Vector Machine (OC-SVM with different kernels (Radial Basis Function (RBF and Mahalanobis Kernel (MK for “normal” and abnormal traffic. Regular and non-regular regions built from “normal” traffic profiles allow anomaly detection, while the classification is performed under the assumption that regions corresponding to the attack classes have been previously characterized. Although this approach allows the use of as many features as required, only four well-known significant features were selected in our case. In order to evaluate our approach, two different data sets were used: one set of real traffic obtained from an Academic Local Area Network (LAN, and the other a subset of the 1998 MIT-DARPA set. For these data sets, a True positive rate up to 99.35%, a True negative rate up to 99.83% and a False negative rate at about 0.16% were yielded. Experimental results show that certain q-values of the generalized entropies and the use of OC-SVM with RBF kernel improve the detection rate in the detection stage, while the novel inclusion of MK kernel in OC-SVM and k-temporal nearest neighbors improve accuracy in classification. In addition, the results show that using the Box-Cox transformation, the Mahalanobis distance yielded high detection rates with

Estimation of Costs and Durations of Construction of Urban Roads Using ANN and SVM

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Igor Peško

2017-01-01

Full Text Available Offer preparation has always been a specific part of a building process which has significant impact on company business. Due to the fact that income greatly depends on offer’s precision and the balance between planned costs, both direct and overheads, and wished profit, it is necessary to prepare a precise offer within required time and available resources which are always insufficient. The paper presents a research of precision that can be achieved while using artificial intelligence for estimation of cost and duration in construction projects. Both artificial neural networks (ANNs and support vector machines (SVM are analysed and compared. The best SVM has shown higher precision, when estimating costs, with mean absolute percentage error (MAPE of 7.06% compared to the most precise ANNs which has achieved precision of 25.38%. Estimation of works duration has proved to be more difficult. The best MAPEs were 22.77% and 26.26% for SVM and ANN, respectively.

Segmentasi Citra menggunakan Support Vector Machine (SVM dan Ellipsoid Region Search Strategy (ERSS Arimoto Entropy berdasarkan Ciri Warna dan Tekstur

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Lukman Hakim

2016-02-01

Full Text Available Abstrak Segmentasi citra merupakan suatu metode penting dalam pengolahan citra digital yang bertujuan membagi citra menjadi beberapa region yang homogen berdasarkan kriteria kemiripan tertentu. Salah satu syarat utama yang harus dimiliki suatu metode segmentasi citra yaitu menghasilkan citra boundary yang optimal.Untuk memenuhi syarat tersebut suatu metode segmentasi membutuhkan suatu klasifikasi piksel citra yang dapat memisahkan piksel secara linier dan non-linear. Pada penelitian ini, penulis mengusulkan metode segmentasi citra menggunakan SVM dan entropi Arimoto berbasis ERSS sehingga tahan terhadap derau dan mempunyai kompleksitas yang rendah untuk menghasilkan citra boundary yang optimal. Pertama, ekstraksi ciri warna dengan local homogeneity dan ciri tekstur dengan menggunakan Gray Level Co-occurrence Matrix (GLCM yang menghasilkan beberapa fitur. Kedua, pelabelan dengan Arimoto berbasis ERSS yang digunakan sebagai kelas dalam klasifikasi. Ketiga, hasil ekstraksi fitur dan training kemudian diklasifikasi berdasarkan label dengan SVM yang telah di-training. Dari percobaan yang dilakukan menunjukkan hasil segmentasi kurang optimal dengan akurasi 69 %. Reduksi fitur perlu dilakukan untuk menghasilkan citra yang tersegmentasi dengan baik. Kata kunci: segmentasi citra, support vector machine, ERSS Arimoto Entropy, ekstraksi ciri. Abstract Image segmentation is an important tool in image processing that divides an image into homogeneous regions based on certain similarity criteria, which ideally should be meaning-full for a certain purpose. Optimal boundary is one of the main criteria that an image segmentation method should has. A classification method that can partitions pixel linearly or non-linearly is needed by an image segmentation method. We propose a color image segmentation using Support Vector Machine (SVM classification and ERSS Arimoto entropy thresholding to get optimal boundary of segmented image that noise-free and low complexity

A New Hybrid Model FPA-SVM Considering Cointegration for Particular Matter Concentration Forecasting: A Case Study of Kunming and Yuxi, China

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Weide Li

2017-01-01

Full Text Available Air pollution in China is becoming more serious especially for the particular matter (PM because of rapid economic growth and fast expansion of urbanization. To solve the growing environment problems, daily PM2.5 and PM10 concentration data form January 1, 2015, to August 23, 2016, in Kunming and Yuxi (two important cities in Yunnan Province, China are used to present a new hybrid model CI-FPA-SVM to forecast air PM2.5 and PM10 concentration in this paper. The proposed model involves two parts. Firstly, due to its deficiency to assess the possible correlation between different variables, the cointegration theory is introduced to get the input-output relationship and then obtain the nonlinear dynamical system with support vector machine (SVM, in which the parameters c and g are optimized by flower pollination algorithm (FPA. Six benchmark models, including FPA-SVM, CI-SVM, CI-GA-SVM, CI-PSO-SVM, CI-FPA-NN, and multiple linear regression model, are considered to verify the superiority of the proposed hybrid model. The empirical study results demonstrate that the proposed model CI-FPA-SVM is remarkably superior to all considered benchmark models for its high prediction accuracy, and the application of the model for forecasting can give effective monitoring and management of further air quality.

OPTIMALISASI SUPPORT VEKTOR MACHINE (SVM UNTUK KLASIFIKASI TEMA TUGAS AKHIR BERBASIS K-MEANS

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Oman Somantri

2017-01-01

Full Text Available The difficulty in determining the classification of students final project theme often experienced by each college. The purpose of this study is to provide a decision support for policy makers in the study program so that each student can be achieved in accordance with their own competence. From the research that has been done text mining algorithms using Support Vector Machine ( SVM and K -Means as the technology used was produced a better accuracy rate with an accuracy rate of 86.21 % when compared to the SVM without K -Means is 85 , 38 %

Stationary Wavelet Transform and AdaBoost with SVM Based Pathological Brain Detection in MRI Scanning.

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Nayak, Deepak Ranjan; Dash, Ratnakar; Majhi, Banshidhar

2017-01-01

This paper presents an automatic classification system for segregating pathological brain from normal brains in magnetic resonance imaging scanning. The proposed system employs contrast limited adaptive histogram equalization scheme to enhance the diseased region in brain MR images. Two-dimensional stationary wavelet transform is harnessed to extract features from the preprocessed images. The feature vector is constructed using the energy and entropy values, computed from the level- 2 SWT coefficients. Then, the relevant and uncorrelated features are selected using symmetric uncertainty ranking filter. Subsequently, the selected features are given input to the proposed AdaBoost with support vector machine classifier, where SVM is used as the base classifier of AdaBoost algorithm. To validate the proposed system, three standard MR image datasets, Dataset-66, Dataset-160, and Dataset- 255 have been utilized. The 5 runs of k-fold stratified cross validation results indicate the suggested scheme offers better performance than other existing schemes in terms of accuracy and number of features. The proposed system earns ideal classification over Dataset-66 and Dataset-160; whereas, for Dataset- 255, an accuracy of 99.45% is achieved. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

High resolution tempo-spatial ozone prediction with SVM and LSTM

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Gao, D.; Zhang, Y.; Qu, Z.; Sadighi, K.; Coffey, E.; LIU, Q.; Hannigan, M.; Henze, D. K.; Dick, R.; Shang, L.; Lv, Q.

2017-12-01

To investigate and predict the exposure of ozone and other pollutants in urban areas, we utilize data from various infrastructures including EPA, NOAA and RIITS from government of Los Angeles and construct statistical models to conduct ozone concentration prediction in Los Angeles areas at finer spatial and temporal granularity. Our work involves cyber data such as traffic, roads and population data as features for prediction. Two statistical models, Support Vector Machine (SVM) and Long Short-term Memory (LSTM, deep learning method) are used for prediction. . Our experiments show that kernelized SVM gains better prediction performance when taking traffic counts, road density and population density as features, with a prediction RMSE of 7.99 ppb for all-time ozone and 6.92 ppb for peak-value ozone. With simulated NOx from Chemical Transport Model(CTM) as features, SVM generates even better prediction performance, with a prediction RMSE of 6.69ppb. We also build LSTM, which has shown great advantages at dealing with temporal sequences, to predict ozone concentration by treating ozone concentration as spatial-temporal sequences. Trained by ozone concentration measurements from the 13 EPA stations in LA area, the model achieves 4.45 ppb RMSE. Besides, we build a variant of this model which adds spatial dynamics into the model in the form of transition matrix that reveals new knowledge on pollutant transition. The forgetting gate of the trained LSTM is consistent with the delay effect of ozone concentration and the trained transition matrix shows spatial consistency with the common direction of winds in LA area.

Vehicle Detection with Occlusion Handling, Tracking, and OC-SVM Classification: A High Performance Vision-Based System

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Velazquez-Pupo, Roxana; Sierra-Romero, Alberto; Torres-Roman, Deni; Shkvarko, Yuriy V.; Romero-Delgado, Misael

2018-01-01

This paper presents a high performance vision-based system with a single static camera for traffic surveillance, for moving vehicle detection with occlusion handling, tracking, counting, and One Class Support Vector Machine (OC-SVM) classification. In this approach, moving objects are first segmented from the background using the adaptive Gaussian Mixture Model (GMM). After that, several geometric features are extracted, such as vehicle area, height, width, centroid, and bounding box. As occlusion is present, an algorithm was implemented to reduce it. The tracking is performed with adaptive Kalman filter. Finally, the selected geometric features: estimated area, height, and width are used by different classifiers in order to sort vehicles into three classes: small, midsize, and large. Extensive experimental results in eight real traffic videos with more than 4000 ground truth vehicles have shown that the improved system can run in real time under an occlusion index of 0.312 and classify vehicles with a global detection rate or recall, precision, and F-measure of up to 98.190%, and an F-measure of up to 99.051% for midsize vehicles. PMID:29382078

Support vector machine regression (SVR/LS-SVM)--an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data.

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Balabin, Roman M; Lomakina, Ekaterina I

2011-04-21

In this study, we make a general comparison of the accuracy and robustness of five multivariate calibration models: partial least squares (PLS) regression or projection to latent structures, polynomial partial least squares (Poly-PLS) regression, artificial neural networks (ANNs), and two novel techniques based on support vector machines (SVMs) for multivariate data analysis: support vector regression (SVR) and least-squares support vector machines (LS-SVMs). The comparison is based on fourteen (14) different datasets: seven sets of gasoline data (density, benzene content, and fractional composition/boiling points), two sets of ethanol gasoline fuel data (density and ethanol content), one set of diesel fuel data (total sulfur content), three sets of petroleum (crude oil) macromolecules data (weight percentages of asphaltenes, resins, and paraffins), and one set of petroleum resins data (resins content). Vibrational (near-infrared, NIR) spectroscopic data are used to predict the properties and quality coefficients of gasoline, biofuel/biodiesel, diesel fuel, and other samples of interest. The four systems presented here range greatly in composition, properties, strength of intermolecular interactions (e.g., van der Waals forces, H-bonds), colloid structure, and phase behavior. Due to the high diversity of chemical systems studied, general conclusions about SVM regression methods can be made. We try to answer the following question: to what extent can SVM-based techniques replace ANN-based approaches in real-world (industrial/scientific) applications? The results show that both SVR and LS-SVM methods are comparable to ANNs in accuracy. Due to the much higher robustness of the former, the SVM-based approaches are recommended for practical (industrial) application. This has been shown to be especially true for complicated, highly nonlinear objects.

Improving the performance of DomainDiscovery of protein domain boundary assignment using inter-domain linker index

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Zomaya Albert Y

2006-12-01

Full Text Available Abstract Background Knowledge of protein domain boundaries is critical for the characterisation and understanding of protein function. The ability to identify domains without the knowledge of the structure – by using sequence information only – is an essential step in many types of protein analyses. In this present study, we demonstrate that the performance of DomainDiscovery is improved significantly by including the inter-domain linker index value for domain identification from sequence-based information. Improved DomainDiscovery uses a Support Vector Machine (SVM approach and a unique training dataset built on the principle of consensus among experts in defining domains in protein structure. The SVM was trained using a PSSM (Position Specific Scoring Matrix, secondary structure, solvent accessibility information and inter-domain linker index to detect possible domain boundaries for a target sequence. Results Improved DomainDiscovery is compared with other methods by benchmarking against a structurally non-redundant dataset and also CASP5 targets. Improved DomainDiscovery achieves 70% accuracy for domain boundary identification in multi-domains proteins. Conclusion Improved DomainDiscovery compares favourably to the performance of other methods and excels in the identification of domain boundaries for multi-domain proteins as a result of introducing support vector machine with benchmark_2 dataset.

Identification of NAD interacting residues in proteins

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Raghava Gajendra PS

2010-03-01

Full Text Available Abstract Background Small molecular cofactors or ligands play a crucial role in the proper functioning of cells. Accurate annotation of their target proteins and binding sites is required for the complete understanding of reaction mechanisms. Nicotinamide adenine dinucleotide (NAD+ or NAD is one of the most commonly used organic cofactors in living cells, which plays a critical role in cellular metabolism, storage and regulatory processes. In the past, several NAD binding proteins (NADBP have been reported in the literature, which are responsible for a wide-range of activities in the cell. Attempts have been made to derive a rule for the binding of NAD+ to its target proteins. However, so far an efficient model could not be derived due to the time consuming process of structure determination, and limitations of similarity based approaches. Thus a sequence and non-similarity based method is needed to characterize the NAD binding sites to help in the annotation. In this study attempts have been made to predict NAD binding proteins and their interacting residues (NIRs from amino acid sequence using bioinformatics tools. Results We extracted 1556 proteins chains from 555 NAD binding proteins whose structure is available in Protein Data Bank. Then we removed all redundant protein chains and finally obtained 195 non-redundant NAD binding protein chains, where no two chains have more than 40% sequence identity. In this study all models were developed and evaluated using five-fold cross validation technique on the above dataset of 195 NAD binding proteins. While certain type of residues are preferred (e.g. Gly, Tyr, Thr, His in NAD interaction, residues like Ala, Glu, Leu, Lys are not preferred. A support vector machine (SVM based method has been developed using various window lengths of amino acid sequence for predicting NAD interacting residues and obtained maximum Matthew's correlation coefficient (MCC 0.47 with accuracy 74.13% at window length 17

Robust Non-Linear Direct Torque and Flux Control of Adjustable Speed Sensorless PMSM Drive Based on SVM Using a PI Predictive Controller

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F. Naceri

2010-01-01

Full Text Available This paper presents a new sensorless direct torque control method for voltage inverter – fed PMSM. The control methodis used a modified Direct Torque Control scheme with constant inverter switching frequency using Space Vector Modulation(DTC-SVM. The variation of stator and rotor resistance due to changes in temperature or frequency deteriorates theperformance of DTC-SVM controller by introducing errors in the estimated flux linkage and the electromagnetic torque.As a result, this approach will not be suitable for high power drives such as those used in tractions, as they require goodtorque control performance at considerably lower frequency. A novel stator resistance estimator is proposed. The estimationmethod is implemented using the Extended Kalman Filter. Finally extensive simulation results are presented to validate theproposed technique. The system is tested at different speeds and a very satisfactory performance has been achieved.

kmer-SVM: a web server for identifying predictive regulatory sequence features in genomic data sets

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Fletez-Brant, Christopher; Lee, Dongwon; McCallion, Andrew S.; Beer, Michael A.

2013-01-01

Massively parallel sequencing technologies have made the generation of genomic data sets a routine component of many biological investigations. For example, Chromatin immunoprecipitation followed by sequence assays detect genomic regions bound (directly or indirectly) by specific factors, and DNase-seq identifies regions of open chromatin. A major bottleneck in the interpretation of these data is the identification of the underlying DNA sequence code that defines, and ultimately facilitates prediction of, these transcription factor (TF) bound or open chromatin regions. We have recently developed a novel computational methodology, which uses a support vector machine (SVM) with kmer sequence features (kmer-SVM) to identify predictive combinations of short transcription factor-binding sites, which determine the tissue specificity of these genomic assays (Lee, Karchin and Beer, Discriminative prediction of mammalian enhancers from DNA sequence. Genome Res. 2011; 21:2167–80). This regulatory information can (i) give confidence in genomic experiments by recovering previously known binding sites, and (ii) reveal novel sequence features for subsequent experimental testing of cooperative mechanisms. Here, we describe the development and implementation of a web server to allow the broader research community to independently apply our kmer-SVM to analyze and interpret their genomic datasets. We analyze five recently published data sets and demonstrate how this tool identifies accessory factors and repressive sequence elements. kmer-SVM is available at http://kmersvm.beerlab.org. PMID:23771147

Using an Integrated Group Decision Method Based on SVM, TFN-RS-AHP, and TOPSIS-CD for Cloud Service Supplier Selection

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Lian-hui Li

2017-01-01

Full Text Available To solve the cloud service supplier selection problem under the background of cloud computing emergence, an integrated group decision method is proposed. The cloud service supplier selection index framework is built from two perspectives of technology and technology management. Support vector machine- (SVM- based classification model is applied for the preliminary screening to reduce the number of candidate suppliers. A triangular fuzzy number-rough sets-analytic hierarchy process (TFN-RS-AHP method is designed to calculate supplier’s index value by expert’s wisdom and experience. The index weight is determined by criteria importance through intercriteria correlation (CRITIC. The suppliers are evaluated by the improved TOPSIS replacing Euclidean distance with connection distance (TOPSIS-CD. An electric power enterprise’s case is given to illustrate the correctness and feasibility of the proposed method.

An improved PSO-SVM model for online recognition defects in eddy current testing

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Liu, Baoling; Hou, Dibo; Huang, Pingjie; Liu, Banteng; Tang, Huayi; Zhang, Wubo; Chen, Peihua; Zhang, Guangxin

2013-12-01

Accurate and rapid recognition of defects is essential for structural integrity and health monitoring of in-service device using eddy current (EC) non-destructive testing. This paper introduces a novel model-free method that includes three main modules: a signal pre-processing module, a classifier module and an optimisation module. In the signal pre-processing module, a kind of two-stage differential structure is proposed to suppress the lift-off fluctuation that could contaminate the EC signal. In the classifier module, multi-class support vector machine (SVM) based on one-against-one strategy is utilised for its good accuracy. In the optimisation module, the optimal parameters of classifier are obtained by an improved particle swarm optimisation (IPSO) algorithm. The proposed IPSO technique can improve convergence performance of the primary PSO through the following strategies: nonlinear processing of inertia weight, introductions of the black hole and simulated annealing model with extremum disturbance. The good generalisation ability of the IPSO-SVM model has been validated through adding additional specimen into the testing set. Experiments show that the proposed algorithm can achieve higher recognition accuracy and efficiency than other well-known classifiers and the superiorities are more obvious with less training set, which contributes to online application.

Discrimination between Alzheimer's Disease and Mild Cognitive Impairment Using SOM and PSO-SVM

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Shih-Ting Yang

2013-01-01

Full Text Available In this study, an MRI-based classification framework was proposed to distinguish the patients with AD and MCI from normal participants by using multiple features and different classifiers. First, we extracted features (volume and shape from MRI data by using a series of image processing steps. Subsequently, we applied principal component analysis (PCA to convert a set of features of possibly correlated variables into a smaller set of values of linearly uncorrelated variables, decreasing the dimensions of feature space. Finally, we developed a novel data mining framework in combination with support vector machine (SVM and particle swarm optimization (PSO for the AD/MCI classification. In order to compare the hybrid method with traditional classifier, two kinds of classifiers, that is, SVM and a self-organizing map (SOM, were trained for patient classification. With the proposed framework, the classification accuracy is improved up to 82.35% and 77.78% in patients with AD and MCI. The result achieved up to 94.12% and 88.89% in AD and MCI by combining the volumetric features and shape features and using PCA. The present results suggest that novel multivariate methods of pattern matching reach a clinically relevant accuracy for the a priori prediction of the progression from MCI to AD.

ProDis-ContSHC: Learning protein dissimilarity measures and hierarchical context coherently for protein-protein comparison in protein database retrieval

KAUST Repository

Wang, Jim Jing-Yan

2012-05-08

Background: The need to retrieve or classify protein molecules using structure or sequence-based similarity measures underlies a wide range of biomedical applications. Traditional protein search methods rely on a pairwise dissimilarity/similarity measure for comparing a pair of proteins. This kind of pairwise measures suffer from the limitation of neglecting the distribution of other proteins and thus cannot satisfy the need for high accuracy of the retrieval systems. Recent work in the machine learning community has shown that exploiting the global structure of the database and learning the contextual dissimilarity/similarity measures can improve the retrieval performance significantly. However, most existing contextual dissimilarity/similarity learning algorithms work in an unsupervised manner, which does not utilize the information of the known class labels of proteins in the database.Results: In this paper, we propose a novel protein-protein dissimilarity learning algorithm, ProDis-ContSHC. ProDis-ContSHC regularizes an existing dissimilarity measure dij by considering the contextual information of the proteins. The context of a protein is defined by its neighboring proteins. The basic idea is, for a pair of proteins (i, j), if their context N (i) and N (j) is similar to each other, the two proteins should also have a high similarity. We implement this idea by regularizing dij by a factor learned from the context N (i) and N (j). Moreover, we divide the context to hierarchial sub-context and get the contextual dissimilarity vector for each protein pair. Using the class label information of the proteins, we select the relevant (a pair of proteins that has the same class labels) and irrelevant (with different labels) protein pairs, and train an SVM model to distinguish between their contextual dissimilarity vectors. The SVM model is further used to learn a supervised regularizing factor. Finally, with the new Supervised learned Dissimilarity measure, we update

Cardiac arrhythmia beat classification using DOST and PSO tuned SVM.

Science.gov (United States)

Raj, Sandeep; Ray, Kailash Chandra; Shankar, Om

2016-11-01

The increase in the number of deaths due to cardiovascular diseases (CVDs) has gained significant attention from the study of electrocardiogram (ECG) signals. These ECG signals are studied by the experienced cardiologist for accurate and proper diagnosis, but it becomes difficult and time-consuming for long-term recordings. Various signal processing techniques are studied to analyze the ECG signal, but they bear limitations due to the non-stationary behavior of ECG signals. Hence, this study aims to improve the classification accuracy rate and provide an automated diagnostic solution for the detection of cardiac arrhythmias. The proposed methodology consists of four stages, i.e. filtering, R-peak detection, feature extraction and classification stages. In this study, Wavelet based approach is used to filter the raw ECG signal, whereas Pan-Tompkins algorithm is used for detecting the R-peak inside the ECG signal. In the feature extraction stage, discrete orthogonal Stockwell transform (DOST) approach is presented for an efficient time-frequency representation (i.e. morphological descriptors) of a time domain signal and retains the absolute phase information to distinguish the various non-stationary behavior ECG signals. Moreover, these morphological descriptors are further reduced in lower dimensional space by using principal component analysis and combined with the dynamic features (i.e based on RR-interval of the ECG signals) of the input signal. This combination of two different kinds of descriptors represents each feature set of an input signal that is utilized for classification into subsequent categories by employing PSO tuned support vector machines (SVM). The proposed methodology is validated on the baseline MIT-BIH arrhythmia database and evaluated under two assessment schemes, yielding an improved overall accuracy of 99.18% for sixteen classes in the category-based and 89.10% for five classes (mapped according to AAMI standard) in the patient-based

Automated Classification and Removal of EEG Artifacts With SVM and Wavelet-ICA.

Science.gov (United States)

Sai, Chong Yeh; Mokhtar, Norrima; Arof, Hamzah; Cumming, Paul; Iwahashi, Masahiro

2018-05-01

Brain electrical activity recordings by electroencephalography (EEG) are often contaminated with signal artifacts. Procedures for automated removal of EEG artifacts are frequently sought for clinical diagnostics and brain-computer interface applications. In recent years, a combination of independent component analysis (ICA) and discrete wavelet transform has been introduced as standard technique for EEG artifact removal. However, in performing the wavelet-ICA procedure, visual inspection or arbitrary thresholding may be required for identifying artifactual components in the EEG signal. We now propose a novel approach for identifying artifactual components separated by wavelet-ICA using a pretrained support vector machine (SVM). Our method presents a robust and extendable system that enables fully automated identification and removal of artifacts from EEG signals, without applying any arbitrary thresholding. Using test data contaminated by eye blink artifacts, we show that our method performed better in identifying artifactual components than did existing thresholding methods. Furthermore, wavelet-ICA in conjunction with SVM successfully removed target artifacts, while largely retaining the EEG source signals of interest. We propose a set of features including kurtosis, variance, Shannon's entropy, and range of amplitude as training and test data of SVM to identify eye blink artifacts in EEG signals. This combinatorial method is also extendable to accommodate multiple types of artifacts present in multichannel EEG. We envision future research to explore other descriptive features corresponding to other types of artifactual components.

Combination of minimum enclosing balls classifier with SVM in coal-rock recognition

Science.gov (United States)

Song, QingJun; Jiang, HaiYan; Song, Qinghui; Zhao, XieGuang; Wu, Xiaoxuan

2017-01-01

Top-coal caving technology is a productive and efficient method in modern mechanized coal mining, the study of coal-rock recognition is key to realizing automation in comprehensive mechanized coal mining. In this paper we propose a new discriminant analysis framework for coal-rock recognition. In the framework, a data acquisition model with vibration and acoustic signals is designed and the caving dataset with 10 feature variables and three classes is got. And the perfect combination of feature variables can be automatically decided by using the multi-class F-score (MF-Score) feature selection. In terms of nonlinear mapping in real-world optimization problem, an effective minimum enclosing ball (MEB) algorithm plus Support vector machine (SVM) is proposed for rapid detection of coal-rock in the caving process. In particular, we illustrate how to construct MEB-SVM classifier in coal-rock recognition which exhibit inherently complex distribution data. The proposed method is examined on UCI data sets and the caving dataset, and compared with some new excellent SVM classifiers. We conduct experiments with accuracy and Friedman test for comparison of more classifiers over multiple on the UCI data sets. Experimental results demonstrate that the proposed algorithm has good robustness and generalization ability. The results of experiments on the caving dataset show the better performance which leads to a promising feature selection and multi-class recognition in coal-rock recognition. PMID:28937987

Combination of minimum enclosing balls classifier with SVM in coal-rock recognition.

Science.gov (United States)

Song, QingJun; Jiang, HaiYan; Song, Qinghui; Zhao, XieGuang; Wu, Xiaoxuan

2017-01-01

Top-coal caving technology is a productive and efficient method in modern mechanized coal mining, the study of coal-rock recognition is key to realizing automation in comprehensive mechanized coal mining. In this paper we propose a new discriminant analysis framework for coal-rock recognition. In the framework, a data acquisition model with vibration and acoustic signals is designed and the caving dataset with 10 feature variables and three classes is got. And the perfect combination of feature variables can be automatically decided by using the multi-class F-score (MF-Score) feature selection. In terms of nonlinear mapping in real-world optimization problem, an effective minimum enclosing ball (MEB) algorithm plus Support vector machine (SVM) is proposed for rapid detection of coal-rock in the caving process. In particular, we illustrate how to construct MEB-SVM classifier in coal-rock recognition which exhibit inherently complex distribution data. The proposed method is examined on UCI data sets and the caving dataset, and compared with some new excellent SVM classifiers. We conduct experiments with accuracy and Friedman test for comparison of more classifiers over multiple on the UCI data sets. Experimental results demonstrate that the proposed algorithm has good robustness and generalization ability. The results of experiments on the caving dataset show the better performance which leads to a promising feature selection and multi-class recognition in coal-rock recognition.

Integrated Features by Administering the Support Vector Machine (SVM of Translational Initiations Sites in Alternative Polymorphic Contex

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Nurul Arneida Husin

2012-04-01

Full Text Available Many algorithms and methods have been proposed for classification problems in bioinformatics. In this study, the discriminative approach in particular support vector machines (SVM is employed to recognize the studied TIS patterns. The applied discriminative approach is used to learn about some discriminant functions of samples that have been labelled as positive or negative. After learning, the discriminant functions are employed to decide whether a new sample is true or false. In this study, support vector machines (SVM is employed to recognize the patterns for studied translational initiation sites in alternative weak context. The method has been optimized with the best parameters selected; c=100, E=10-6 and ex=2 for non linear kernel function. Results show that with top 5 features and non linear kernel, the best prediction accuracy achieved is 95.8%. J48 algorithm is applied to compare with SVM with top 15 features and the results show a good prediction accuracy of 95.8%. This indicates that the top 5 features selected by the IGR method and that are performed by SVM are sufficient to use in the prediction of TIS in weak contexts.

DISEÑO Y EVALUACIÓN DE UN CLASIFICADOR DE TEXTURAS BASADO EN LS-SVM

OpenAIRE

Beitmantt Cárdenas Quintero; Nelson Enrique Vera Parra; Pablo Emilio Rozo García

2013-01-01

Evaluar el desempeño y el costo computacional de diferentes arquitecturas y metodologías Least Square Support Vector Machine (LS-SVM) ante la segmentación de imágenes por textura y a partir de dichos resultados postular un modelo de un clasificador de texturas LS-SVM.  Metodología: Ante un problema de clasificación binaria representado por la segmentación  de 32 imágenes, organizadas en 4 grupos y formadas por pares de texturas típicas (granito/corteza, ladrillo/tapicería, madera/mármol, teji...

Prediction of size-fractionated airborne particle-bound metals using MLR, BP-ANN and SVM analyses.

Science.gov (United States)

Leng, Xiang'zi; Wang, Jinhua; Ji, Haibo; Wang, Qin'geng; Li, Huiming; Qian, Xin; Li, Fengying; Yang, Meng

2017-08-01

Size-fractionated heavy metal concentrations were observed in airborne particulate matter (PM) samples collected from 2014 to 2015 (spanning all four seasons) from suburban (Xianlin) and industrial (Pukou) areas in Nanjing, a megacity of southeast China. Rapid prediction models of size-fractionated metals were established based on multiple linear regression (MLR), back propagation artificial neural network (BP-ANN) and support vector machine (SVM) by using meteorological factors and PM concentrations as input parameters. About 38% and 77% of PM 2.5 concentrations in Xianlin and Pukou, respectively, were beyond the Chinese National Ambient Air Quality Standard limit of 75 μg/m 3 . Nearly all elements had higher concentrations in industrial areas, and in winter among the four seasons. Anthropogenic elements such as Pb, Zn, Cd and Cu showed larger percentages in the fine fraction (ø≤2.5 μm), whereas the crustal elements including Al, Ba, Fe, Ni, Sr and Ti showed larger percentages in the coarse fraction (ø > 2.5 μm). SVM showed a higher training correlation coefficient (R), and lower mean absolute error (MAE) as well as lower root mean square error (RMSE), than MLR and BP-ANN for most metals. All the three methods showed better prediction results for Ni, Al, V, Cd and As, whereas relatively poor for Cr and Fe. The daily airborne metal concentrations in 2015 were then predicted by the fully trained SVM models and the results showed the heaviest pollution of airborne heavy metals occurred in December and January, whereas the lightest pollution occurred in June and July. Copyright © 2017 Elsevier Ltd. All rights reserved.

Combination of scoring schemes for protein docking

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Schomburg Dietmar

2007-08-01

Full Text Available Abstract Background Docking algorithms are developed to predict in which orientation two proteins are likely to bind under natural conditions. The currently used methods usually consist of a sampling step followed by a scoring step. We developed a weighted geometric correlation based on optimised atom specific weighting factors and combined them with our previously published amino acid specific scoring and with a comprehensive SVM-based scoring function. Results The scoring with the atom specific weighting factors yields better results than the amino acid specific scoring. In combination with SVM-based scoring functions the percentage of complexes for which a near native structure can be predicted within the top 100 ranks increased from 14% with the geometric scoring to 54% with the combination of all scoring functions. Especially for the enzyme-inhibitor complexes the results of the ranking are excellent. For half of these complexes a near-native structure can be predicted within the first 10 proposed structures and for more than 86% of all enzyme-inhibitor complexes within the first 50 predicted structures. Conclusion We were able to develop a combination of different scoring schemes which considers a series of previously described and some new scoring criteria yielding a remarkable improvement of prediction quality.

Effective and efficient Grassfinch kernel for SVM classification and its application to recognition based on image set

International Nuclear Information System (INIS)

Du, Genyuan; Tian, Shengli; Qiu, Yingyu; Xu, Chunyan

2016-01-01

This paper presents an effective and efficient kernel approach to recognize image set which is represented as a point on extended Grassmannian manifold. Several recent studies focus on the applicability of discriminant analysis on Grassmannian manifold and suffer from not obtaining the inherent nonlinear structure of the data itself. Therefore, we propose an extension of Grassmannian manifold to address this issue. Instead of using a linear data embedding with PCA, we develop a non-linear data embedding of such manifold using kernel PCA. This paper mainly consider three folds: 1) introduce a non-linear data embedding of extended Grassmannian manifold, 2) derive a distance metric of Grassmannian manifold, 3) develop an effective and efficient Grassmannian kernel for SVM classification. The extended Grassmannian manifold naturally arises in the application to recognition based on image set, such as face and object recognition. Experiments on several standard databases show better classification accuracy. Furthermore, experimental results indicate that our proposed approach significantly reduces time complexity in comparison to graph embedding discriminant analysis.

Prediction of Flood Warning in Taiwan Using Nonlinear SVM with Simulated Annealing Algorithm

Science.gov (United States)

Lee, C.

2013-12-01

The issue of the floods is important in Taiwan. It is because the narrow and high topography of the island make lots of rivers steep in Taiwan. The tropical depression likes typhoon always causes rivers to flood. Prediction of river flow under the extreme rainfall circumstances is important for government to announce the warning of flood. Every time typhoon passed through Taiwan, there were always floods along some rivers. The warning is classified to three levels according to the warning water levels in Taiwan. The propose of this study is to predict the level of floods warning from the information of precipitation, rainfall duration and slope of riverbed. To classify the level of floods warning by the above-mentioned information and modeling the problems, a machine learning model, nonlinear Support vector machine (SVM), is formulated to classify the level of floods warning. In addition, simulated annealing (SA), a probabilistic heuristic algorithm, is used to determine the optimal parameter of the SVM model. A case study of flooding-trend rivers of different gradients in Taiwan is conducted. The contribution of this SVM model with simulated annealing is capable of making efficient announcement for flood warning and keeping the danger of flood from residents along the rivers.

A feature-based approach to modeling protein–protein interaction hot spots

Science.gov (United States)

Cho, Kyu-il; Kim, Dongsup; Lee, Doheon

2009-01-01

Identifying features that effectively represent the energetic contribution of an individual interface residue to the interactions between proteins remains problematic. Here, we present several new features and show that they are more effective than conventional features. By combining the proposed features with conventional features, we develop a predictive model for interaction hot spots. Initially, 54 multifaceted features, composed of different levels of information including structure, sequence and molecular interaction information, are quantified. Then, to identify the best subset of features for predicting hot spots, feature selection is performed using a decision tree. Based on the selected features, a predictive model for hot spots is created using support vector machine (SVM) and tested on an independent test set. Our model shows better overall predictive accuracy than previous methods such as the alanine scanning methods Robetta and FOLDEF, and the knowledge-based method KFC. Subsequent analysis yields several findings about hot spots. As expected, hot spots have a larger relative surface area burial and are more hydrophobic than other residues. Unexpectedly, however, residue conservation displays a rather complicated tendency depending on the types of protein complexes, indicating that this feature is not good for identifying hot spots. Of the selected features, the weighted atomic packing density, relative surface area burial and weighted hydrophobicity are the top 3, with the weighted atomic packing density proving to be the most effective feature for predicting hot spots. Notably, we find that hot spots are closely related to π–related interactions, especially π · · · π interactions. PMID:19273533

Detection of Cross Site Scripting Attack in Wireless Networks Using n-Gram and SVM

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Jun-Ho Choi

2012-01-01

Full Text Available Large parts of attacks targeting the web are aiming at the weak point of web application. Even though SQL injection, which is the form of XSS (Cross Site Scripting attacks, is not a threat to the system to operate the web site, it is very critical to the places that deal with the important information because sensitive information can be obtained and falsified. In this paper, the method to detect themalicious SQL injection script code which is the typical XSS attack using n-Gram indexing and SVM (Support Vector Machine is proposed. In order to test the proposed method, the test was conducted after classifying each data set as normal code and malicious code, and the malicious script code was detected by applying index term generated by n-Gram and data set generated by code dictionary to SVM classifier. As a result, when the malicious script code detection was conducted using n-Gram index term and SVM, the superior performance could be identified in detecting malicious script and the more improved results than existing methods could be seen in the malicious script code detection recall.

Highly predictive support vector machine (SVM) models for anthrax toxin lethal factor (LF) inhibitors.

Science.gov (United States)

Zhang, Xia; Amin, Elizabeth Ambrose

2016-01-01

Anthrax is a highly lethal, acute infectious disease caused by the rod-shaped, Gram-positive bacterium Bacillus anthracis. The anthrax toxin lethal factor (LF), a zinc metalloprotease secreted by the bacilli, plays a key role in anthrax pathogenesis and is chiefly responsible for anthrax-related toxemia and host death, partly via inactivation of mitogen-activated protein kinase kinase (MAPKK) enzymes and consequent disruption of key cellular signaling pathways. Antibiotics such as fluoroquinolones are capable of clearing the bacilli but have no effect on LF-mediated toxemia; LF itself therefore remains the preferred target for toxin inactivation. However, currently no LF inhibitor is available on the market as a therapeutic, partly due to the insufficiency of existing LF inhibitor scaffolds in terms of efficacy, selectivity, and toxicity. In the current work, we present novel support vector machine (SVM) models with high prediction accuracy that are designed to rapidly identify potential novel, structurally diverse LF inhibitor chemical matter from compound libraries. These SVM models were trained and validated using 508 compounds with published LF biological activity data and 847 inactive compounds deposited in the Pub Chem BioAssay database. One model, M1, demonstrated particularly favorable selectivity toward highly active compounds by correctly predicting 39 (95.12%) out of 41 nanomolar-level LF inhibitors, 46 (93.88%) out of 49 inactives, and 844 (99.65%) out of 847 Pub Chem inactives in external, unbiased test sets. These models are expected to facilitate the prediction of LF inhibitory activity for existing molecules, as well as identification of novel potential LF inhibitors from large datasets. Copyright © 2015 Elsevier Inc. All rights reserved.

AllerTool: a web server for predicting allergenicity and allergic cross-reactivity in proteins.

Science.gov (United States)

Zhang, Zong Hong; Koh, Judice L Y; Zhang, Guang Lan; Choo, Khar Heng; Tammi, Martti T; Tong, Joo Chuan

2007-02-15

Assessment of potential allergenicity and patterns of cross-reactivity is necessary whenever novel proteins are introduced into human food chain. Current bioinformatic methods in allergology focus mainly on the prediction of allergenic proteins, with no information on cross-reactivity patterns among known allergens. In this study, we present AllerTool, a web server with essential tools for the assessment of predicted as well as published cross-reactivity patterns of allergens. The analysis tools include graphical representation of allergen cross-reactivity information; a local sequence comparison tool that displays information of known cross-reactive allergens; a sequence similarity search tool for assessment of cross-reactivity in accordance to FAO/WHO Codex alimentarius guidelines; and a method based on support vector machine (SVM). A 10-fold cross-validation results showed that the area under the receiver operating curve (A(ROC)) of SVM models is 0.90 with 86.00% sensitivity (SE) at specificity (SP) of 86.00%. AllerTool is freely available at http://research.i2r.a-star.edu.sg/AllerTool/.

Combination of minimum enclosing balls classifier with SVM in coal-rock recognition.

Directory of Open Access Journals (Sweden)

QingJun Song

Full Text Available Top-coal caving technology is a productive and efficient method in modern mechanized coal mining, the study of coal-rock recognition is key to realizing automation in comprehensive mechanized coal mining. In this paper we propose a new discriminant analysis framework for coal-rock recognition. In the framework, a data acquisition model with vibration and acoustic signals is designed and the caving dataset with 10 feature variables and three classes is got. And the perfect combination of feature variables can be automatically decided by using the multi-class F-score (MF-Score feature selection. In terms of nonlinear mapping in real-world optimization problem, an effective minimum enclosing ball (MEB algorithm plus Support vector machine (SVM is proposed for rapid detection of coal-rock in the caving process. In particular, we illustrate how to construct MEB-SVM classifier in coal-rock recognition which exhibit inherently complex distribution data. The proposed method is examined on UCI data sets and the caving dataset, and compared with some new excellent SVM classifiers. We conduct experiments with accuracy and Friedman test for comparison of more classifiers over multiple on the UCI data sets. Experimental results demonstrate that the proposed algorithm has good robustness and generalization ability. The results of experiments on the caving dataset show the better performance which leads to a promising feature selection and multi-class recognition in coal-rock recognition.

The more you learn, the less you store : Memory-controlled incremental SVM for visual place recognition

OpenAIRE

Pronobis, Andrzej; Jie, Luo; Caputo, Barbara

2010-01-01

The capability to learn from experience is a key property for autonomous cognitive systems working in realistic settings. To this end, this paper presents an SVM-based algorithm, capable of learning model representations incrementally while keeping under control memory requirements. We combine an incremental extension of SVMs [43] with a method reducing the number of support vectors needed to build the decision function without any loss in performance [15] introducing a parameter which permit...

Real-time detection with AdaBoost-svm combination in various face orientation

Science.gov (United States)

Fhonna, R. P.; Nasution, M. K. M.; Tulus

2018-03-01

Most of the research has used algorithm AdaBoost-SVM for face detection. However, to our knowledge so far there is no research has been facing detection on real-time data with various orientations using the combination of AdaBoost and Support Vector Machine (SVM). Characteristics of complex and diverse face variations and real-time data in various orientations, and with a very complex application will slow down the performance of the face detection system this becomes a challenge in this research. Face orientation performed on the detection system, that is 900, 450, 00, -450, and -900. This combination method is expected to be an effective and efficient solution in various face orientations. The results showed that the highest average detection rate is on the face detection oriented 00 and the lowest detection rate is in the face orientation 900.

Parameter optimization using GA in SVM to predict damage level of non-reshaped berm breakwater.

Digital Repository Service at National Institute of Oceanography (India)

Harish, N.; Lokesha.; Mandal, S.; Rao, S.; Patil, S.G.

tools, such as Artificial Neural Network (ANN), Support Vector Machine (SVM), Adaptive Neuro Fuzzy Inference System (ANFIS), etc., are successfully used in different fields (Kazperkiewiecz et al 1995, Voga and Belchior 2006, Dong et al 2005). Also... Balas C.E., Koc M.L. and Tur R.(2010) ‘’Artificial neural networks based on principal component analysis, fuzzy systems and fuzzy neural networks for preliminary design of rubble mound breakwaters’’, Applied Ocean Research, 32, 425 – 433. Dong B., Cao C...

ESLpred2: improved method for predicting subcellular localization of eukaryotic proteins

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Raghava Gajendra PS

2008-11-01

Full Text Available Abstract Background The expansion of raw protein sequence databases in the post genomic era and availability of fresh annotated sequences for major localizations particularly motivated us to introduce a new improved version of our previously forged eukaryotic subcellular localizations prediction method namely "ESLpred". Since, subcellular localization of a protein offers essential clues about its functioning, hence, availability of localization predictor would definitely aid and expedite the protein deciphering studies. However, robustness of a predictor is highly dependent on the superiority of dataset and extracted protein attributes; hence, it becomes imperative to improve the performance of presently available method using latest dataset and crucial input features. Results Here, we describe augmentation in the prediction performance obtained for our most popular ESLpred method using new crucial features as an input to Support Vector Machine (SVM. In addition, recently available, highly non-redundant dataset encompassing three kingdoms specific protein sequence sets; 1198 fungi sequences, 2597 from animal and 491 plant sequences were also included in the present study. First, using the evolutionary information in the form of profile composition along with whole and N-terminal sequence composition as an input feature vector of 440 dimensions, overall accuracies of 72.7, 75.8 and 74.5% were achieved respectively after five-fold cross-validation. Further, enhancement in performance was observed when similarity search based results were coupled with whole and N-terminal sequence composition along with profile composition by yielding overall accuracies of 75.9, 80.8, 76.6% respectively; best accuracies reported till date on the same datasets. Conclusion These results provide confidence about the reliability and accurate prediction of SVM modules generated in the present study using sequence and profile compositions along with similarity search

Data on Support Vector Machines (SVM model to forecast photovoltaic power

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M. Malvoni

2016-12-01

Full Text Available The data concern the photovoltaic (PV power, forecasted by a hybrid model that considers weather variations and applies a technique to reduce the input data size, as presented in the paper entitled “Photovoltaic forecast based on hybrid pca-lssvm using dimensionality reducted data” (M. Malvoni, M.G. De Giorgi, P.M. Congedo, 2015 [1]. The quadratic Renyi entropy criteria together with the principal component analysis (PCA are applied to the Least Squares Support Vector Machines (LS-SVM to predict the PV power in the day-ahead time frame. The data here shared represent the proposed approach results. Hourly PV power predictions for 1,3,6,12, 24 ahead hours and for different data reduction sizes are provided in Supplementary material.

STUDY COMPARISON OF SVM-, K-NN- AND BACKPROPAGATION-BASED CLASSIFIER FOR IMAGE RETRIEVAL

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Muhammad Athoillah

2015-03-01

Full Text Available Classification is a method for compiling data systematically according to the rules that have been set previously. In recent years classification method has been proven to help many people’s work, such as image classification, medical biology, traffic light, text classification etc. There are many methods to solve classification problem. This variation method makes the researchers find it difficult to determine which method is best for a problem. This framework is aimed to compare the ability of classification methods, such as Support Vector Machine (SVM, K-Nearest Neighbor (K-NN, and Backpropagation, especially in study cases of image retrieval with five category of image dataset. The result shows that K-NN has the best average result in accuracy with 82%. It is also the fastest in average computation time with 17,99 second during retrieve session for all categories class. The Backpropagation, however, is the slowest among three of them. In average it needed 883 second for training session and 41,7 second for retrieve session.

Gradient Evolution-based Support Vector Machine Algorithm for Classification

Science.gov (United States)

Zulvia, Ferani E.; Kuo, R. J.

2018-03-01

This paper proposes a classification algorithm based on a support vector machine (SVM) and gradient evolution (GE) algorithms. SVM algorithm has been widely used in classification. However, its result is significantly influenced by the parameters. Therefore, this paper aims to propose an improvement of SVM algorithm which can find the best SVMs’ parameters automatically. The proposed algorithm employs a GE algorithm to automatically determine the SVMs’ parameters. The GE algorithm takes a role as a global optimizer in finding the best parameter which will be used by SVM algorithm. The proposed GE-SVM algorithm is verified using some benchmark datasets and compared with other metaheuristic-based SVM algorithms. The experimental results show that the proposed GE-SVM algorithm obtains better results than other algorithms tested in this paper.

Prediction of membrane transport proteins and their substrate specificities using primary sequence information.

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Nitish K Mishra

Full Text Available Membrane transport proteins (transporters move hydrophilic substrates across hydrophobic membranes and play vital roles in most cellular functions. Transporters represent a diverse group of proteins that differ in topology, energy coupling mechanism, and substrate specificity as well as sequence similarity. Among the functional annotations of transporters, information about their transporting substrates is especially important. The experimental identification and characterization of transporters is currently costly and time-consuming. The development of robust bioinformatics-based methods for the prediction of membrane transport proteins and their substrate specificities is therefore an important and urgent task.Support vector machine (SVM-based computational models, which comprehensively utilize integrative protein sequence features such as amino acid composition, dipeptide composition, physico-chemical composition, biochemical composition, and position-specific scoring matrices (PSSM, were developed to predict the substrate specificity of seven transporter classes: amino acid, anion, cation, electron, protein/mRNA, sugar, and other transporters. An additional model to differentiate transporters from non-transporters was also developed. Among the developed models, the biochemical composition and PSSM hybrid model outperformed other models and achieved an overall average prediction accuracy of 76.69% with a Mathews correlation coefficient (MCC of 0.49 and a receiver operating characteristic area under the curve (AUC of 0.833 on our main dataset. This model also achieved an overall average prediction accuracy of 78.88% and MCC of 0.41 on an independent dataset.Our analyses suggest that evolutionary information (i.e., the PSSM and the AAIndex are key features for the substrate specificity prediction of transport proteins. In comparison, similarity-based methods such as BLAST, PSI-BLAST, and hidden Markov models do not provide accurate predictions

Development of Health Parameter Model for Risk Prediction of CVD Using SVM

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P. Unnikrishnan

2016-01-01

Full Text Available Current methods of cardiovascular risk assessment are performed using health factors which are often based on the Framingham study. However, these methods have significant limitations due to their poor sensitivity and specificity. We have compared the parameters from the Framingham equation with linear regression analysis to establish the effect of training of the model for the local database. Support vector machine was used to determine the effectiveness of machine learning approach with the Framingham health parameters for risk assessment of cardiovascular disease (CVD. The result shows that while linear model trained using local database was an improvement on Framingham model, SVM based risk assessment model had high sensitivity and specificity of prediction of CVD. This indicates that using the health parameters identified using Framingham study, machine learning approach overcomes the low sensitivity and specificity of Framingham model.

A Hybrid ICA-SVM Approach for Determining the Quality Variables at Fault in a Multivariate Process

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Yuehjen E. Shao

2012-01-01

Full Text Available The monitoring of a multivariate process with the use of multivariate statistical process control (MSPC charts has received considerable attention. However, in practice, the use of MSPC chart typically encounters a difficulty. This difficult involves which quality variable or which set of the quality variables is responsible for the generation of the signal. This study proposes a hybrid scheme which is composed of independent component analysis (ICA and support vector machine (SVM to determine the fault quality variables when a step-change disturbance existed in a multivariate process. The proposed hybrid ICA-SVM scheme initially applies ICA to the Hotelling T2 MSPC chart to generate independent components (ICs. The hidden information of the fault quality variables can be identified in these ICs. The ICs are then served as the input variables of the classifier SVM for performing the classification process. The performance of various process designs is investigated and compared with the typical classification method. Using the proposed approach, the fault quality variables for a multivariate process can be accurately and reliably determined.

DIRProt: a computational approach for discriminating insecticide resistant proteins from non-resistant proteins.

Science.gov (United States)

Meher, Prabina Kumar; Sahu, Tanmaya Kumar; Banchariya, Anjali; Rao, Atmakuri Ramakrishna

2017-03-24

Insecticide resistance is a major challenge for the control program of insect pests in the fields of crop protection, human and animal health etc. Resistance to different insecticides is conferred by the proteins encoded from certain class of genes of the insects. To distinguish the insecticide resistant proteins from non-resistant proteins, no computational tool is available till date. Thus, development of such a computational tool will be helpful in predicting the insecticide resistant proteins, which can be targeted for developing appropriate insecticides. Five different sets of feature viz., amino acid composition (AAC), di-peptide composition (DPC), pseudo amino acid composition (PAAC), composition-transition-distribution (CTD) and auto-correlation function (ACF) were used to map the protein sequences into numeric feature vectors. The encoded numeric vectors were then used as input in support vector machine (SVM) for classification of insecticide resistant and non-resistant proteins. Higher accuracies were obtained under RBF kernel than that of other kernels. Further, accuracies were observed to be higher for DPC feature set as compared to others. The proposed approach achieved an overall accuracy of >90% in discriminating resistant from non-resistant proteins. Further, the two classes of resistant proteins i.e., detoxification-based and target-based were discriminated from non-resistant proteins with >95% accuracy. Besides, >95% accuracy was also observed for discrimination of proteins involved in detoxification- and target-based resistance mechanisms. The proposed approach not only outperformed Blastp, PSI-Blast and Delta-Blast algorithms, but also achieved >92% accuracy while assessed using an independent dataset of 75 insecticide resistant proteins. This paper presents the first computational approach for discriminating the insecticide resistant proteins from non-resistant proteins. Based on the proposed approach, an online prediction server DIRProt has

Metabolic changes in rat serum after administration of suberoylanilide hydroxamic acid and discriminated by SVM.

Science.gov (United States)

Yu, J; Wu, H; Lin, Z; Su, K; Zhang, J; Sun, F; Wang, X; Wen, C; Cao, H; Hu, L

2017-12-01

Suberoylanilide hydroxamic acid (SAHA) exerts marked anticancer effects via promotion of apoptosis, cell cycle arrest, and prevention of oncogene expression. In this study, serum metabolomics and artificial intelligence recognition were used to investigate SAHA toxicity. Forty rats (220 ± 20 g) were randomly divided into control and three SAHA groups (low, medium, and high); the experimental groups were treated with 12.3, 24.5, or 49.0 mg kg -1 SAHA once a day via intragastric administration. After 7 days, blood samples from the four groups were collected and analyzed by gas chromatography-mass spectrometry, and pathological changes in the liver were examined using microscopy. The results showed that increased levels of urea, oleic acid, and glutaconic acid were the most significant indicators of toxicity. Octadecanoic acid, pentadecanoic acid, glycerol, propanoic acid, and uric acid levels were lower in the high SAHA group. Microscopic observation revealed no obvious damage to the liver. Based on these data, a support vector machine (SVM) discrimination model was established that recognized the metabolic changes in the three SAHA groups and the control group with 100% accuracy. In conclusion, the main toxicity caused by SAHA was due to excessive metabolism of saturated fatty acids, which could be recognized by an SVM model.

SVM prediction of ligand-binding sites in bacterial lipoproteins employing shape and physio-chemical descriptors.

Science.gov (United States)

Kadam, Kiran; Prabhakar, Prashant; Jayaraman, V K

2012-11-01

Bacterial lipoproteins play critical roles in various physiological processes including the maintenance of pathogenicity and numbers of them are being considered as potential candidates for generating novel vaccines. In this work, we put forth an algorithm to identify and predict ligand-binding sites in bacterial lipoproteins. The method uses three types of pocket descriptors, namely fpocket descriptors, 3D Zernike descriptors and shell descriptors, and combines them with Support Vector Machine (SVM) method for the classification. The three types of descriptors represent shape-based properties of the pocket as well as its local physio-chemical features. All three types of descriptors, along with their hybrid combinations are evaluated with SVM and to improve classification performance, WEKA-InfoGain feature selection is applied. Results obtained in the study show that the classifier successfully differentiates between ligand-binding and non-binding pockets. For the combination of three types of descriptors, 10 fold cross-validation accuracy of 86.83% is obtained for training while the selected model achieved test Matthews Correlation Coefficient (MCC) of 0.534. Individually or in combination with new and existing methods, our model can be a very useful tool for the prediction of potential ligand-binding sites in bacterial lipoproteins.

Penerapan Support Vector Machine (SVM untuk Pengkategorian Penelitian

Directory of Open Access Journals (Sweden)

Fithri Selva Jumeilah

2017-07-01

Full Text Available Research every college will continue to grow. Research will be stored in softcopy and hardcopy. The preparation of the research should be categorized in order to facilitate the search for people who need reference. To categorize the research, we need a method for text mining, one of them is with the implementation of Support Vector Machines (SVM. The data used to recognize the characteristics of each category then it takes secondary data which is a collection of abstracts of research. The data will be pre-processed with several stages: case folding converts all the letters into lowercase, stop words removal removal of very common words, tokenizing discard punctuation, and stemming searching for root words by removing the prefix and suffix. Further data that has undergone preprocessing will be converted into a numerical form with for the term weighting stage that is the weighting contribution of each word. From the results of term weighting then obtained data that can be used for data training and test data. The training process is done by providing input in the form of text data that is known to the class or category. Then by using the Support Vector Machines algorithm, the input data is transformed into a rule, function, or knowledge model that can be used in the prediction process. From the results of this study obtained that the categorization of research produced by SVM has been very good. This is proven by the results of the test which resulted in an accuracy of 90%.

Damage level prediction of non-reshaped berm breakwater using ANN, SVM and ANFIS models

Digital Repository Service at National Institute of Oceanography (India)

Mandal, S.; SubbaRao; Harish, N.; Lokesha

Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, NITK, Surathkal, India. Soft computing techniques like Artificial Neural Network (ANN), Support Vector Machine (SVM) and Adaptive Neuro Fuzzy Inference system (ANFIS) models...

RVMAB: Using the Relevance Vector Machine Model Combined with Average Blocks to Predict the Interactions of Proteins from Protein Sequences

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Ji-Yong An

2016-05-01

Full Text Available Protein-Protein Interactions (PPIs play essential roles in most cellular processes. Knowledge of PPIs is becoming increasingly more important, which has prompted the development of technologies that are capable of discovering large-scale PPIs. Although many high-throughput biological technologies have been proposed to detect PPIs, there are unavoidable shortcomings, including cost, time intensity, and inherently high false positive and false negative rates. For the sake of these reasons, in silico methods are attracting much attention due to their good performances in predicting PPIs. In this paper, we propose a novel computational method known as RVM-AB that combines the Relevance Vector Machine (RVM model and Average Blocks (AB to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the AB feature representation on a Position Specific Scoring Matrix (PSSM, reducing the influence of noise using a Principal Component Analysis (PCA, and using a Relevance Vector Machine (RVM based classifier. We performed five-fold cross-validation experiments on yeast and Helicobacter pylori datasets, and achieved very high accuracies of 92.98% and 95.58% respectively, which is significantly better than previous works. In addition, we also obtained good prediction accuracies of 88.31%, 89.46%, 91.08%, 91.55%, and 94.81% on other five independent datasets C. elegans, M. musculus, H. sapiens, H. pylori, and E. coli for cross-species prediction. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM classifier on the yeast dataset. The experimental results demonstrate that our RVM-AB method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool. To facilitate extensive studies for future proteomics research, we developed

Weak cation magnetic separation technology and MALDI-TOF-MS in screening serum protein markers in primary type I osteoporosis.

Science.gov (United States)

Shi, X L; Li, C W; Liang, B C; He, K H; Li, X Y

2015-11-30

We investigated weak cation magnetic separation technology and matrix-assisted laser desorption ionization-time of flight-mass spectrometry (MALDI-TOF-MS) in screening serum protein markers of primary type I osteoporosis. We selected 16 postmenopausal women with osteoporosis and nine postmenopausal women as controls to find a new method for screening biomarkers and establishing a diagnostic model for primary type I osteoporosis. Serum samples were obtained from controls and patients. Serum protein was extracted with the WCX protein chip system; protein fingerprints were examined using MALDI-TOF-MS. The preprocessed and model construction data were handled by the ProteinChip system. The diagnostic models were established using a genetic arithmetic model combined with a support vector machine (SVM). The SVM model with the highest Youden index was selected. Combinations with the highest accuracy in distinguishing different groups of data were selected as potential biomarkers. From the two groups of serum proteins, 123 cumulative MS protein peaks were selected. Significant intensity differences in the protein peaks of 16 postmenopausal women with osteoporosis were screened. The difference in Youden index between the four groups of protein peaks showed that the highest peaks had mass-to-charge ratios of 8909.047, 8690.658, 13745.48, and 15114.52. A diagnosis model was established with these four markers as the candidates, and the model specificity and sensitivity were found to be 100%. Two groups of specimens in the SVM results on the scatterplot were distinguishable. We established a diagnosis model, and provided a new serological method for screening and diagnosis of osteoporosis with high sensitivity and specificity.

Shallow water bathymetry mapping using Support Vector Machine (SVM) technique and multispectral imagery

NARCIS (Netherlands)

Misra, Ankita; Vojinovic, Zoran; Ramakrishnan, Balaji; Luijendijk, Arjen; Ranasinghe, Roshanka

2018-01-01

Satellite imagery along with image processing techniques prove to be efficient tools for bathymetry retrieval as they provide time and cost-effective alternatives to traditional methods of water depth estimation. In this article, a nonlinear machine learning technique of Support Vector Machine (SVM)

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics

OpenAIRE

HUANG, SHUJUN; CAI, NIANGUANG; PACHECO, PEDRO PENZUTI; NARANDES, SHAVIRA; WANG, YANG; XU, WAYNE

2017-01-01

Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading to the discovery of new biomarkers, new drug targets, and a better ...

LZW-Kernel: fast kernel utilizing variable length code blocks from LZW compressors for protein sequence classification.

Science.gov (United States)

Filatov, Gleb; Bauwens, Bruno; Kertész-Farkas, Attila

2018-05-07

Bioinformatics studies often rely on similarity measures between sequence pairs, which often pose a bottleneck in large-scale sequence analysis. Here, we present a new convolutional kernel function for protein sequences called the LZW-Kernel. It is based on code words identified with the Lempel-Ziv-Welch (LZW) universal text compressor. The LZW-Kernel is an alignment-free method, it is always symmetric, is positive, always provides 1.0 for self-similarity and it can directly be used with Support Vector Machines (SVMs) in classification problems, contrary to normalized compression distance (NCD), which often violates the distance metric properties in practice and requires further techniques to be used with SVMs. The LZW-Kernel is a one-pass algorithm, which makes it particularly plausible for big data applications. Our experimental studies on remote protein homology detection and protein classification tasks reveal that the LZW-Kernel closely approaches the performance of the Local Alignment Kernel (LAK) and the SVM-pairwise method combined with Smith-Waterman (SW) scoring at a fraction of the time. Moreover, the LZW-Kernel outperforms the SVM-pairwise method when combined with BLAST scores, which indicates that the LZW code words might be a better basis for similarity measures than local alignment approximations found with BLAST. In addition, the LZW-Kernel outperforms n-gram based mismatch kernels, hidden Markov model based SAM and Fisher kernel, and protein family based PSI-BLAST, among others. Further advantages include the LZW-Kernel's reliance on a simple idea, its ease of implementation, and its high speed, three times faster than BLAST and several magnitudes faster than SW or LAK in our tests. LZW-Kernel is implemented as a standalone C code and is a free open-source program distributed under GPLv3 license and can be downloaded from https://github.com/kfattila/LZW-Kernel. akerteszfarkas@hse.ru. Supplementary data are available at Bioinformatics Online.

Improving Accuracy of Intrusion Detection Model Using PCA and optimized SVM

Directory of Open Access Journals (Sweden)

Sumaiya Thaseen Ikram

2016-06-01

Full Text Available Intrusion detection is very essential for providing security to different network domains and is mostly used for locating and tracing the intruders. There are many problems with traditional intrusion detection models (IDS such as low detection capability against unknown network attack, high false alarm rate and insufficient analysis capability. Hence the major scope of the research in this domain is to develop an intrusion detection model with improved accuracy and reduced training time. This paper proposes a hybrid intrusiondetection model by integrating the principal component analysis (PCA and support vector machine (SVM. The novelty of the paper is the optimization of kernel parameters of the SVM classifier using automatic parameter selection technique. This technique optimizes the punishment factor (C and kernel parameter gamma (γ, thereby improving the accuracy of the classifier and reducing the training and testing time. The experimental results obtained on the NSL KDD and gurekddcup dataset show that the proposed technique performs better with higher accuracy, faster convergence speed and better generalization. Minimum resources are consumed as the classifier input requires reduced feature set for optimum classification. A comparative analysis of hybrid models with the proposed model is also performed.

Traffic Flow Prediction Model for Large-Scale Road Network Based on Cloud Computing

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Zhaosheng Yang

2014-01-01

Full Text Available To increase the efficiency and precision of large-scale road network traffic flow prediction, a genetic algorithm-support vector machine (GA-SVM model based on cloud computing is proposed in this paper, which is based on the analysis of the characteristics and defects of genetic algorithm and support vector machine. In cloud computing environment, firstly, SVM parameters are optimized by the parallel genetic algorithm, and then this optimized parallel SVM model is used to predict traffic flow. On the basis of the traffic flow data of Haizhu District in Guangzhou City, the proposed model was verified and compared with the serial GA-SVM model and parallel GA-SVM model based on MPI (message passing interface. The results demonstrate that the parallel GA-SVM model based on cloud computing has higher prediction accuracy, shorter running time, and higher speedup.

BLProt: Prediction of bioluminescent proteins based on support vector machine and relieff feature selection

KAUST Repository

Kandaswamy, Krishna Kumar

2011-08-17

Background: Bioluminescence is a process in which light is emitted by a living organism. Most creatures that emit light are sea creatures, but some insects, plants, fungi etc, also emit light. The biotechnological application of bioluminescence has become routine and is considered essential for many medical and general technological advances. Identification of bioluminescent proteins is more challenging due to their poor similarity in sequence. So far, no specific method has been reported to identify bioluminescent proteins from primary sequence.Results: In this paper, we propose a novel predictive method that uses a Support Vector Machine (SVM) and physicochemical properties to predict bioluminescent proteins. BLProt was trained using a dataset consisting of 300 bioluminescent proteins and 300 non-bioluminescent proteins, and evaluated by an independent set of 141 bioluminescent proteins and 18202 non-bioluminescent proteins. To identify the most prominent features, we carried out feature selection with three different filter approaches, ReliefF, infogain, and mRMR. We selected five different feature subsets by decreasing the number of features, and the performance of each feature subset was evaluated.Conclusion: BLProt achieves 80% accuracy from training (5 fold cross-validations) and 80.06% accuracy from testing. The performance of BLProt was compared with BLAST and HMM. High prediction accuracy and successful prediction of hypothetical proteins suggests that BLProt can be a useful approach to identify bioluminescent proteins from sequence information, irrespective of their sequence similarity. 2011 Kandaswamy et al; licensee BioMed Central Ltd.

BLProt: Prediction of bioluminescent proteins based on support vector machine and relieff feature selection

KAUST Repository

Kandaswamy, Krishna Kumar; Pugalenthi, Ganesan; Hazrati, Mehrnaz Khodam; Kalies, Kai-Uwe; Martinetz, Thomas

2011-01-01

Background: Bioluminescence is a process in which light is emitted by a living organism. Most creatures that emit light are sea creatures, but some insects, plants, fungi etc, also emit light. The biotechnological application of bioluminescence has become routine and is considered essential for many medical and general technological advances. Identification of bioluminescent proteins is more challenging due to their poor similarity in sequence. So far, no specific method has been reported to identify bioluminescent proteins from primary sequence.Results: In this paper, we propose a novel predictive method that uses a Support Vector Machine (SVM) and physicochemical properties to predict bioluminescent proteins. BLProt was trained using a dataset consisting of 300 bioluminescent proteins and 300 non-bioluminescent proteins, and evaluated by an independent set of 141 bioluminescent proteins and 18202 non-bioluminescent proteins. To identify the most prominent features, we carried out feature selection with three different filter approaches, ReliefF, infogain, and mRMR. We selected five different feature subsets by decreasing the number of features, and the performance of each feature subset was evaluated.Conclusion: BLProt achieves 80% accuracy from training (5 fold cross-validations) and 80.06% accuracy from testing. The performance of BLProt was compared with BLAST and HMM. High prediction accuracy and successful prediction of hypothetical proteins suggests that BLProt can be a useful approach to identify bioluminescent proteins from sequence information, irrespective of their sequence similarity. 2011 Kandaswamy et al; licensee BioMed Central Ltd.

KOMPARASI MODEL SUPPORT VECTOR MACHINES (SVM DAN NEURAL NETWORK UNTUK MENGETAHUI TINGKAT AKURASI PREDIKSI TERTINGGI HARGA SAHAM

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R. Hadapiningradja Kusumodestoni

2017-09-01

Full Text Available There are many types of investments to make money, one of which is in the form of shares. Shares is a trading company dealing with securities in the global capital markets. Stock Exchange or also called stock market is actually the activities of private companies in the form of buying and selling investments. To avoid losses in investing, we need a model of predictive analysis with high accuracy and supported by data - lots of data and accurately. The correct techniques in the analysis will be able to reduce the risk for investors in investing. There are many models used in the analysis of stock price movement prediction, in this study the researchers used models of neural networks (NN and a model of support vector machine (SVM. Based on the background of the problems that have been mentioned in the previous description it can be formulated the problem as follows: need an algorithm that can predict stock prices, and need a high accuracy rate by adding a data set on the prediction, two algorithms will be investigated expected results last researchers can deduce where the algorithm accuracy rate predictions are the highest or accurate, then the purpose of this study was to mengkomparasi or compare between the two algorithms are algorithms Neural Network algorithm and Support Vector Machine which later on the end result has an accuracy rate forecast stock prices highest to see the error value RMSEnya. After doing research using the model of neural network and model of support vector machine (SVM to predict the stock using the data value of the shares on the stock index hongkong dated July 20, 2016 at 16:26 pm until the date of 15 September 2016 at 17:40 pm as many as 729 data sets within an interval of 5 minute through a process of training, learning, and then continue the process of testing so the result is that by using a neural network model of the prediction accuracy of 0.503 +/- 0.009 (micro 503 while using the model of support vector machine

Analysis And Voice Recognition In Indonesian Language Using MFCC And SVM Method

Directory of Open Access Journals (Sweden)

Harvianto Harvianto

2016-06-01

Full Text Available Voice recognition technology is one of biometric technology. Sound is a unique part of the human being which made an individual can be easily distinguished one from another. Voice can also provide information such as gender, emotion, and identity of the speaker. This research will record human voices that pronounce digits between 0 and 9 with and without noise. Features of this sound recording will be extracted using Mel Frequency Cepstral Coefficient (MFCC. Mean, standard deviation, max, min, and the combination of them will be used to construct the feature vectors. This feature vectors then will be classified using Support Vector Machine (SVM. There will be two classification models. The first one is based on the speaker and the other one based on the digits pronounced. The classification model then will be validated by performing 10-fold cross-validation.The best average accuracy from two classification model is 91.83%. This result achieved using Mean + Standard deviation + Min + Max as features.

Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM.

Science.gov (United States)

Hojjati, Seyed Hani; Ebrahimzadeh, Ata; Khazaee, Ali; Babajani-Feremi, Abbas

2017-04-15

We investigated identifying patients with mild cognitive impairment (MCI) who progress to Alzheimer's disease (AD), MCI converter (MCI-C), from those with MCI who do not progress to AD, MCI non-converter (MCI-NC), based on resting-state fMRI (rs-fMRI). Graph theory and machine learning approach were utilized to predict progress of patients with MCI to AD using rs-fMRI. Eighteen MCI converts (average age 73.6 years; 11 male) and 62 age-matched MCI non-converters (average age 73.0 years, 28 male) were included in this study. We trained and tested a support vector machine (SVM) to classify MCI-C from MCI-NC using features constructed based on the local and global graph measures. A novel feature selection algorithm was developed and utilized to select an optimal subset of features. Using subset of optimal features in SVM, we classified MCI-C from MCI-NC with an accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve of 91.4%, 83.24%, 90.1%, and 0.95, respectively. Furthermore, results of our statistical analyses were used to identify the affected brain regions in AD. To the best of our knowledge, this is the first study that combines the graph measures (constructed based on rs-fMRI) with machine learning approach and accurately classify MCI-C from MCI-NC. Results of this study demonstrate potential of the proposed approach for early AD diagnosis and demonstrate capability of rs-fMRI to predict conversion from MCI to AD by identifying affected brain regions underlying this conversion. Copyright © 2017 Elsevier B.V. All rights reserved.

Mining Key Skeleton Poses with Latent SVM for Action Recognition

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Xiaoqiang Li

2017-01-01

Full Text Available Human action recognition based on 3D skeleton has become an active research field in recent years with the recently developed commodity depth sensors. Most published methods analyze an entire 3D depth data, construct mid-level part representations, or use trajectory descriptor of spatial-temporal interest point for recognizing human activities. Unlike previous work, a novel and simple action representation is proposed in this paper which models the action as a sequence of inconsecutive and discriminative skeleton poses, named as key skeleton poses. The pairwise relative positions of skeleton joints are used as feature of the skeleton poses which are mined with the aid of the latent support vector machine (latent SVM. The advantage of our method is resisting against intraclass variation such as noise and large nonlinear temporal deformation of human action. We evaluate the proposed approach on three benchmark action datasets captured by Kinect devices: MSR Action 3D dataset, UTKinect Action dataset, and Florence 3D Action dataset. The detailed experimental results demonstrate that the proposed approach achieves superior performance to the state-of-the-art skeleton-based action recognition methods.

Stochastic Optimized Relevance Feedback Particle Swarm Optimization for Content Based Image Retrieval

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Muhammad Imran

2014-01-01

Full Text Available One of the major challenges for the CBIR is to bridge the gap between low level features and high level semantics according to the need of the user. To overcome this gap, relevance feedback (RF coupled with support vector machine (SVM has been applied successfully. However, when the feedback sample is small, the performance of the SVM based RF is often poor. To improve the performance of RF, this paper has proposed a new technique, namely, PSO-SVM-RF, which combines SVM based RF with particle swarm optimization (PSO. The aims of this proposed technique are to enhance the performance of SVM based RF and also to minimize the user interaction with the system by minimizing the RF number. The PSO-SVM-RF was tested on the coral photo gallery containing 10908 images. The results obtained from the experiments showed that the proposed PSO-SVM-RF achieved 100% accuracy in 8 feedback iterations for top 10 retrievals and 80% accuracy in 6 iterations for 100 top retrievals. This implies that with PSO-SVM-RF technique high accuracy rate is achieved at a small number of iterations.

Prediction of Protein-Protein Interactions Related to Protein Complexes Based on Protein Interaction Networks

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Peng Liu

2015-01-01

Full Text Available A method for predicting protein-protein interactions based on detected protein complexes is proposed to repair deficient interactions derived from high-throughput biological experiments. Protein complexes are pruned and decomposed into small parts based on the adaptive k-cores method to predict protein-protein interactions associated with the complexes. The proposed method is adaptive to protein complexes with different structure, number, and size of nodes in a protein-protein interaction network. Based on different complex sets detected by various algorithms, we can obtain different prediction sets of protein-protein interactions. The reliability of the predicted interaction sets is proved by using estimations with statistical tests and direct confirmation of the biological data. In comparison with the approaches which predict the interactions based on the cliques, the overlap of the predictions is small. Similarly, the overlaps among the predicted sets of interactions derived from various complex sets are also small. Thus, every predicted set of interactions may complement and improve the quality of the original network data. Meanwhile, the predictions from the proposed method replenish protein-protein interactions associated with protein complexes using only the network topology.

Intrusion detection model using fusion of chi-square feature selection and multi class SVM

Directory of Open Access Journals (Sweden)

Ikram Sumaiya Thaseen

2017-10-01

Full Text Available Intrusion detection is a promising area of research in the domain of security with the rapid development of internet in everyday life. Many intrusion detection systems (IDS employ a sole classifier algorithm for classifying network traffic as normal or abnormal. Due to the large amount of data, these sole classifier models fail to achieve a high attack detection rate with reduced false alarm rate. However by applying dimensionality reduction, data can be efficiently reduced to an optimal set of attributes without loss of information and then classified accurately using a multi class modeling technique for identifying the different network attacks. In this paper, we propose an intrusion detection model using chi-square feature selection and multi class support vector machine (SVM. A parameter tuning technique is adopted for optimization of Radial Basis Function kernel parameter namely gamma represented by ‘ϒ’ and over fitting constant ‘C’. These are the two important parameters required for the SVM model. The main idea behind this model is to construct a multi class SVM which has not been adopted for IDS so far to decrease the training and testing time and increase the individual classification accuracy of the network attacks. The investigational results on NSL-KDD dataset which is an enhanced version of KDDCup 1999 dataset shows that our proposed approach results in a better detection rate and reduced false alarm rate. An experimentation on the computational time required for training and testing is also carried out for usage in time critical applications.

Optimal parameters of the SVM for temperature prediction

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X. Shi

2015-05-01

Full Text Available This paper established three different optimization models in order to predict the Foping station temperature value. The dimension was reduced to change multivariate climate factors into a few variables by principal component analysis (PCA. And the parameters of support vector machine (SVM were optimized with genetic algorithm (GA, particle swarm optimization (PSO and developed genetic algorithm. The most suitable method was applied for parameter optimization by comparing the results of three different models. The results are as follows: The developed genetic algorithm optimization parameters of the predicted values were closest to the measured value after the analog trend, and it is the most fitting measured value trends, and its homing speed is relatively fast.

Classification of Breast Cancer Resistant Protein (BCRP) Inhibitors and Non-Inhibitors Using Machine Learning Approaches.

Science.gov (United States)

Belekar, Vilas; Lingineni, Karthik; Garg, Prabha

2015-01-01

The breast cancer resistant protein (BCRP) is an important transporter and its inhibitors play an important role in cancer treatment by improving the oral bioavailability as well as blood brain barrier (BBB) permeability of anticancer drugs. In this work, a computational model was developed to predict the compounds as BCRP inhibitors or non-inhibitors. Various machine learning approaches like, support vector machine (SVM), k-nearest neighbor (k-NN) and artificial neural network (ANN) were used to develop the models. The Matthews correlation coefficients (MCC) of developed models using ANN, k-NN and SVM are 0.67, 0.71 and 0.77, and prediction accuracies are 85.2%, 88.3% and 90.8% respectively. The developed models were tested with a test set of 99 compounds and further validated with external set of 98 compounds. Distribution plot analysis and various machine learning models were also developed based on druglikeness descriptors. Applicability domain is used to check the prediction reliability of the new molecules.

A nonlinear QSAR study using oscillating search and SVM as an efficient algorithm to model the inhibition of reverse transcriptase by HEPT derivatives

International Nuclear Information System (INIS)

Ferkous, F.; Saihi, Y.

2018-01-01

Quantitative structure-activity relationships were constructed for 107 inhibitors of HIV-1 reverse transcriptase that are derivatives of 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT). A combination of a support vector machine (SVM) and oscillating search (OS) algorithms for feature selection was adopted to select the most appropriate descriptors. The application was optimized to obtain an SVM model to predict the biological activity EC50 of the HEPT derivatives with a minimum number of descriptors (SpMax4 B h (e) MLOGP MATS5m) and high values of R2 and Q2 (0.8662, 0.8769). The statistical results showed good correlation between the activity and three best descriptors were included in the best SVM model. The values of R2 and Q2 confirmed the stability and good predictive ability of the model. The SVM technique was adequate to produce an effective QSAR model and outperformed those in the literature and the predictive stages for the inhibitory activity of reverse transcriptase by HEPT derivatives. (author)

A robust regression based on weighted LSSVM and penalized trimmed squares

International Nuclear Information System (INIS)

Liu, Jianyong; Wang, Yong; Fu, Chengqun; Guo, Jie; Yu, Qin

2016-01-01

Least squares support vector machine (LS-SVM) for nonlinear regression is sensitive to outliers in the field of machine learning. Weighted LS-SVM (WLS-SVM) overcomes this drawback by adding weight to each training sample. However, as the number of outliers increases, the accuracy of WLS-SVM may decrease. In order to improve the robustness of WLS-SVM, a new robust regression method based on WLS-SVM and penalized trimmed squares (WLSSVM–PTS) has been proposed. The algorithm comprises three main stages. The initial parameters are obtained by least trimmed squares at first. Then, the significant outliers are identified and eliminated by the Fast-PTS algorithm. The remaining samples with little outliers are estimated by WLS-SVM at last. The statistical tests of experimental results carried out on numerical datasets and real-world datasets show that the proposed WLSSVM–PTS is significantly robust than LS-SVM, WLS-SVM and LSSVM–LTS.

Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers

Energy Technology Data Exchange (ETDEWEB)

Salahshoor, Karim [Department of Instrumentation and Automation, Petroleum University of Technology, Tehran (Iran, Islamic Republic of); Kordestani, Mojtaba; Khoshro, Majid S. [Department of Control Engineering, Islamic Azad University South Tehran branch (Iran, Islamic Republic of)

2010-12-15

The subject of FDD (fault detection and diagnosis) has gained widespread industrial interest in machine condition monitoring applications. This is mainly due to the potential advantage to be achieved from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a new FDD scheme for condition machinery of an industrial steam turbine using a data fusion methodology. Fusion of a SVM (support vector machine) classifier with an ANFIS (adaptive neuro-fuzzy inference system) classifier, integrated into a common framework, is utilized to enhance the fault detection and diagnostic tasks. For this purpose, a multi-attribute data is fused into aggregated values of a single attribute by OWA (ordered weighted averaging) operators. The simulation studies indicate that the resulting fusion-based scheme outperforms the individual SVM and ANFIS systems to detect and diagnose incipient steam turbine faults. (author)

Support vector machine-based exergetic modelling of a DI diesel engine running on biodiesel–diesel blends containing expanded polystyrene

International Nuclear Information System (INIS)

Shamshirband, Shahaboddin; Tabatabaei, Meisam; Aghbashlo, Mortaza; Yee, Por Lip; Petković, Dalibor

2016-01-01

Highlights: • SVM-based thermodynamic modelling of a DI diesel engine working with diesel/biodiesel blends containing EPS. • Comparison of SVM-WT, SVM-FFA, SVM-RBF, SVM-QPSO, and ANN approaches for exergetic modelling of the engine. • Satisfactory performance of the SVM-WT for performance modelling of the engine over the other approaches. - Abstract: In the present study, four Support Vector Machine-based (SVM-based) approaches and the standard artificial neural network (ANN) model were designed and compared in modelling the exergetic parameters of a DI diesel engine running on diesel/biodiesel blends containing expanded polystyrene (EPS) wastes. For this aim, the SVM was coupled with discrete wavelet transform (SVM-WT), firefly algorithm (SVM-FFA), radial basis function (SVM-RBF) and quantum particle swarm optimization (SVM-QPSO). The exergetic data were computed using mass, energy, and exergy balance equations for the engine at different speeds and loads as well as various biodiesel and EPS wastes quantities. Three statistical indicators namely root means square error, coefficient of determination and Pearson coefficient were used to access the capability of the developed approaches for exergetic performance modelling of the DI diesel engine. The modelling results indicated that the SVM-WT approach was more efficient in exergetic modelling of the engine than the other three approaches. Moreover, the results obtained confirmed the effectiveness of the SVM-WT model in identifying the most exergy-efficient combustion conditions and the best fuel composition for achieving the most cost-effective and eco-friendly combustion process.

Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization

Science.gov (United States)

Abdulameer, Mohammed Hasan; Othman, Zulaiha Ali

2014-01-01

Existing face recognition methods utilize particle swarm optimizer (PSO) and opposition based particle swarm optimizer (OPSO) to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO) technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM). In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented. PMID:24790584

Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization

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Mohammed Hasan Abdulameer

2014-01-01

Full Text Available Existing face recognition methods utilize particle swarm optimizer (PSO and opposition based particle swarm optimizer (OPSO to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM. In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented.

Fault Diagnosis of Rotating Machinery Based on Multisensor Information Fusion Using SVM and Time-Domain Features

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Ling-li Jiang

2014-01-01

Full Text Available Multisensor information fusion, when applied to fault diagnosis, the time-space scope, and the quantity of information are expanded compared to what could be acquired by a single sensor, so the diagnostic object can be described more comprehensively. This paper presents a methodology of fault diagnosis in rotating machinery using multisensor information fusion that all the features are calculated using vibration data in time domain to constitute fusional vector and the support vector machine (SVM is used for classification. The effectiveness of the presented methodology is tested by three case studies: diagnostic of faulty gear, rolling bearing, and identification of rotor crack. For each case study, the sensibilities of the features are analyzed. The results indicate that the peak factor is the most sensitive feature in the twelve time-domain features for identifying gear defect, and the mean, amplitude square, root mean square, root amplitude, and standard deviation are all sensitive for identifying gear, rolling bearing, and rotor crack defect comparatively.

Ab-initio conformational epitope structure prediction using genetic algorithm and SVM for vaccine design.

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Moghram, Basem Ameen; Nabil, Emad; Badr, Amr

2018-01-01

T-cell epitope structure identification is a significant challenging immunoinformatic problem within epitope-based vaccine design. Epitopes or antigenic peptides are a set of amino acids that bind with the Major Histocompatibility Complex (MHC) molecules. The aim of this process is presented by Antigen Presenting Cells to be inspected by T-cells. MHC-molecule-binding epitopes are responsible for triggering the immune response to antigens. The epitope's three-dimensional (3D) molecular structure (i.e., tertiary structure) reflects its proper function. Therefore, the identification of MHC class-II epitopes structure is a significant step towards epitope-based vaccine design and understanding of the immune system. In this paper, we propose a new technique using a Genetic Algorithm for Predicting the Epitope Structure (GAPES), to predict the structure of MHC class-II epitopes based on their sequence. The proposed Elitist-based genetic algorithm for predicting the epitope's tertiary structure is based on Ab-Initio Empirical Conformational Energy Program for Peptides (ECEPP) Force Field Model. The developed secondary structure prediction technique relies on Ramachandran Plot. We used two alignment algorithms: the ROSS alignment and TM-Score alignment. We applied four different alignment approaches to calculate the similarity scores of the dataset under test. We utilized the support vector machine (SVM) classifier as an evaluation of the prediction performance. The prediction accuracy and the Area Under Receiver Operating Characteristic (ROC) Curve (AUC) were calculated as measures of performance. The calculations are performed on twelve similarity-reduced datasets of the Immune Epitope Data Base (IEDB) and a large dataset of peptide-binding affinities to HLA-DRB1*0101. The results showed that GAPES was reliable and very accurate. We achieved an average prediction accuracy of 93.50% and an average AUC of 0.974 in the IEDB dataset. Also, we achieved an accuracy of 95

Support vector machine based estimation of remaining useful life: current research status and future trends

International Nuclear Information System (INIS)

Huang, Hong Zhong; Wang, Hai Kun; Li, Yan Feng; Zhang, Longlong; Liu, Zhiliang

2015-01-01

Estimation of remaining useful life (RUL) is helpful to manage life cycles of machines and to reduce maintenance cost. Support vector machine (SVM) is a promising algorithm for estimation of RUL because it can easily process small training sets and multi-dimensional data. Many SVM based methods have been proposed to predict RUL of some key components. We did a literature review related to SVM based RUL estimation within a decade. The references reviewed are classified into two categories: improved SVM algorithms and their applications to RUL estimation. The latter category can be further divided into two types: one, to predict the condition state in the future and then build a relationship between state and RUL; two, to establish a direct relationship between current state and RUL. However, SVM is seldom used to track the degradation process and build an accurate relationship between the current health condition state and RUL. Based on the above review and summary, this paper points out that the ability to continually improve SVM, and obtain a novel idea for RUL prediction using SVM will be future works.

Identification and characterization of plastid-type proteins from sequence-attributed features using machine learning

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2013-01-01

Background Plastids are an important component of plant cells, being the site of manufacture and storage of chemical compounds used by the cell, and contain pigments such as those used in photosynthesis, starch synthesis/storage, cell color etc. They are essential organelles of the plant cell, also present in algae. Recent advances in genomic technology and sequencing efforts is generating a huge amount of DNA sequence data every day. The predicted proteome of these genomes needs annotation at a faster pace. In view of this, one such annotation need is to develop an automated system that can distinguish between plastid and non-plastid proteins accurately, and further classify plastid-types based on their functionality. We compared the amino acid compositions of plastid proteins with those of non-plastid ones and found significant differences, which were used as a basis to develop various feature-based prediction models using similarity-search and machine learning. Results In this study, we developed separate Support Vector Machine (SVM) trained classifiers for characterizing the plastids in two steps: first distinguishing the plastid vs. non-plastid proteins, and then classifying the identified plastids into their various types based on their function (chloroplast, chromoplast, etioplast, and amyloplast). Five diverse protein features: amino acid composition, dipeptide composition, the pseudo amino acid composition, Nterminal-Center-Cterminal composition and the protein physicochemical properties are used to develop SVM models. Overall, the dipeptide composition-based module shows the best performance with an accuracy of 86.80% and Matthews Correlation Coefficient (MCC) of 0.74 in phase-I and 78.60% with a MCC of 0.44 in phase-II. On independent test data, this model also performs better with an overall accuracy of 76.58% and 74.97% in phase-I and phase-II, respectively. The similarity-based PSI-BLAST module shows very low performance with about 50% prediction

Noninvasive extraction of fetal electrocardiogram based on Support Vector Machine

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Fu, Yumei; Xiang, Shihan; Chen, Tianyi; Zhou, Ping; Huang, Weiyan

2015-10-01

The fetal electrocardiogram (FECG) signal has important clinical value for diagnosing the fetal heart diseases and choosing suitable therapeutics schemes to doctors. So, the noninvasive extraction of FECG from electrocardiogram (ECG) signals becomes a hot research point. A new method, the Support Vector Machine (SVM) is utilized for the extraction of FECG with limited size of data. Firstly, the theory of the SVM and the principle of the extraction based on the SVM are studied. Secondly, the transformation of maternal electrocardiogram (MECG) component in abdominal composite signal is verified to be nonlinear and fitted with the SVM. Then, the SVM is trained, and the training results are compared with the real data to ensure the effect of the training. Meanwhile, the parameters of the SVM are optimized to achieve the best performance so that the learning machine can be utilized to fit the unknown samples. Finally, the FECG is extracted by removing the optimal estimation of MECG component from the abdominal composite signal. In order to evaluate the performance of FECG extraction based on the SVM, the Signal-to-Noise Ratio (SNR) and the visual test are used. The experimental results show that the FECG with good quality can be extracted, its SNR ratio is significantly increased as high as 9.2349 dB and the time cost is significantly decreased as short as 0.802 seconds. Compared with the traditional method, the noninvasive extraction method based on the SVM has a simple realization, the shorter treatment time and the better extraction quality under the same conditions.

Accurate prediction of subcellular location of apoptosis proteins combining Chou’s PseAAC and PsePSSM based on wavelet denoising

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Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Wang, Ming-Hui; Zhang, Yan

2017-01-01

Apoptosis proteins subcellular localization information are very important for understanding the mechanism of programmed cell death and the development of drugs. The prediction of subcellular localization of an apoptosis protein is still a challenging task because the prediction of apoptosis proteins subcellular localization can help to understand their function and the role of metabolic processes. In this paper, we propose a novel method for protein subcellular localization prediction. Firstly, the features of the protein sequence are extracted by combining Chou's pseudo amino acid composition (PseAAC) and pseudo-position specific scoring matrix (PsePSSM), then the feature information of the extracted is denoised by two-dimensional (2-D) wavelet denoising. Finally, the optimal feature vectors are input to the SVM classifier to predict subcellular location of apoptosis proteins. Quite promising predictions are obtained using the jackknife test on three widely used datasets and compared with other state-of-the-art methods. The results indicate that the method proposed in this paper can remarkably improve the prediction accuracy of apoptosis protein subcellular localization, which will be a supplementary tool for future proteomics research. PMID:29296195

Pre-cancer risk assessment in habitual smokers from DIC images of oral exfoliative cells using active contour and SVM analysis.

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Dey, Susmita; Sarkar, Ripon; Chatterjee, Kabita; Datta, Pallab; Barui, Ananya; Maity, Santi P

2017-04-01

Habitual smokers are known to be at higher risk for developing oral cancer, which is increasing at an alarming rate globally. Conventionally, oral cancer is associated with high mortality rates, although recent reports show the improved survival outcomes by early diagnosis of disease. An effective prediction system which will enable to identify the probability of cancer development amongst the habitual smokers, is thus expected to benefit sizable number of populations. Present work describes a non-invasive, integrated method for early detection of cellular abnormalities based on analysis of different cyto-morphological features of exfoliative oral epithelial cells. Differential interference contrast (DIC) microscopy provides a potential optical tool as this mode provides a pseudo three dimensional (3-D) image with detailed morphological and textural features obtained from noninvasive, label free epithelial cells. For segmentation of DIC images, gradient vector flow snake model active contour process has been adopted. To evaluate cellular abnormalities amongst habitual smokers, the selected morphological and textural features of epithelial cells are compared with the non-smoker (-ve control group) group and clinically diagnosed pre-cancer patients (+ve control group) using support vector machine (SVM) classifier. Accuracy of the developed SVM based classification has been found to be 86% with 80% sensitivity and 89% specificity in classifying the features from the volunteers having smoking habit. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Support Vector Machine-Based Gender Identification Using Speech Signal

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Lee, Kye-Hwan; Kang, Sang-Ick; Kim, Deok-Hwan; Chang, Joon-Hyuk

We propose an effective voice-based gender identification method using a support vector machine (SVM). The SVM is a binary classification algorithm that classifies two groups by finding the voluntary nonlinear boundary in a feature space and is known to yield high classification performance. In the present work, we compare the identification performance of the SVM with that of a Gaussian mixture model (GMM)-based method using the mel frequency cepstral coefficients (MFCC). A novel approach of incorporating a features fusion scheme based on a combination of the MFCC and the fundamental frequency is proposed with the aim of improving the performance of gender identification. Experimental results demonstrate that the gender identification performance using the SVM is significantly better than that of the GMM-based scheme. Moreover, the performance is substantially improved when the proposed features fusion technique is applied.

Estimation of Anti-HIV Activity of HEPT Analogues Using MLR, ANN, and SVM Techniques

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Basheerulla Shaik

2013-01-01

value than those of MLR and SVM techniques. Rm2= metrics and ridge regression analysis indicated that the proposed four-variable model MATS5e, RDF080u, T(O⋯O, and MATS5m as correlating descriptors is the best for estimating the anti-HIV activity (log 1/C present set of compounds.

Sentiment analysis: a comparison of deep learning neural network algorithm with SVM and naϊve Bayes for Indonesian text

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Calvin Frans Mariel, Wahyu; Mariyah, Siti; Pramana, Setia

2018-03-01

Deep learning is a new era of machine learning techniques that essentially imitate the structure and function of the human brain. It is a development of deeper Artificial Neural Network (ANN) that uses more than one hidden layer. Deep Learning Neural Network has a great ability on recognizing patterns from various data types such as picture, audio, text, and many more. In this paper, the authors tries to measure that algorithm’s ability by applying it into the text classification. The classification task herein is done by considering the content of sentiment in a text which is also called as sentiment analysis. By using several combinations of text preprocessing and feature extraction techniques, we aim to compare the precise modelling results of Deep Learning Neural Network with the other two commonly used algorithms, the Naϊve Bayes and Support Vector Machine (SVM). This algorithm comparison uses Indonesian text data with balanced and unbalanced sentiment composition. Based on the experimental simulation, Deep Learning Neural Network clearly outperforms the Naϊve Bayes and SVM and offers a better F-1 Score while for the best feature extraction technique which improves that modelling result is Bigram.

A protein relational database and protein family knowledge bases to facilitate structure-based design analyses.

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Mobilio, Dominick; Walker, Gary; Brooijmans, Natasja; Nilakantan, Ramaswamy; Denny, R Aldrin; Dejoannis, Jason; Feyfant, Eric; Kowticwar, Rupesh K; Mankala, Jyoti; Palli, Satish; Punyamantula, Sairam; Tatipally, Maneesh; John, Reji K; Humblet, Christine

2010-08-01

The Protein Data Bank is the most comprehensive source of experimental macromolecular structures. It can, however, be difficult at times to locate relevant structures with the Protein Data Bank search interface. This is particularly true when searching for complexes containing specific interactions between protein and ligand atoms. Moreover, searching within a family of proteins can be tedious. For example, one cannot search for some conserved residue as residue numbers vary across structures. We describe herein three databases, Protein Relational Database, Kinase Knowledge Base, and Matrix Metalloproteinase Knowledge Base, containing protein structures from the Protein Data Bank. In Protein Relational Database, atom-atom distances between protein and ligand have been precalculated allowing for millisecond retrieval based on atom identity and distance constraints. Ring centroids, centroid-centroid and centroid-atom distances and angles have also been included permitting queries for pi-stacking interactions and other structural motifs involving rings. Other geometric features can be searched through the inclusion of residue pair and triplet distances. In Kinase Knowledge Base and Matrix Metalloproteinase Knowledge Base, the catalytic domains have been aligned into common residue numbering schemes. Thus, by searching across Protein Relational Database and Kinase Knowledge Base, one can easily retrieve structures wherein, for example, a ligand of interest is making contact with the gatekeeper residue.

Classification of Hyperspectral Images by SVM Using a Composite Kernel by Employing Spectral, Spatial and Hierarchical Structure Information

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Yi Wang

2018-03-01

Full Text Available In this paper, we introduce a novel classification framework for hyperspectral images (HSIs by jointly employing spectral, spatial, and hierarchical structure information. In this framework, the three types of information are integrated into the SVM classifier in a way of multiple kernels. Specifically, the spectral kernel is constructed through each pixel’s vector value in the original HSI, and the spatial kernel is modeled by using the extended morphological profile method due to its simplicity and effectiveness. To accurately characterize hierarchical structure features, the techniques of Fish-Markov selector (FMS, marker-based hierarchical segmentation (MHSEG and algebraic multigrid (AMG are combined. First, the FMS algorithm is used on the original HSI for feature selection to produce its spectral subset. Then, the multigrid structure of this subset is constructed using the AMG method. Subsequently, the MHSEG algorithm is exploited to obtain a hierarchy consist of a series of segmentation maps. Finally, the hierarchical structure information is represented by using these segmentation maps. The main contributions of this work is to present an effective composite kernel for HSI classification by utilizing spatial structure information in multiple scales. Experiments were conducted on two hyperspectral remote sensing images to validate that the proposed framework can achieve better classification results than several popular kernel-based classification methods in terms of both qualitative and quantitative analysis. Specifically, the proposed classification framework can achieve 13.46–15.61% in average higher than the standard SVM classifier under different training sets in the terms of overall accuracy.

Comparison of water extraction methods in Tibet based on GF-1 data

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Jia, Lingjun; Shang, Kun; Liu, Jing; Sun, Zhongqing

2018-03-01

In this study, we compared four different water extraction methods with GF-1 data according to different water types in Tibet, including Support Vector Machine (SVM), Principal Component Analysis (PCA), Decision Tree Classifier based on False Normalized Difference Water Index (FNDWI-DTC), and PCA-SVM. The results show that all of the four methods can extract large area water body, but only SVM and PCA-SVM can obtain satisfying extraction results for small size water body. The methods were evaluated by both overall accuracy (OAA) and Kappa coefficient (KC). The OAA of PCA-SVM, SVM, FNDWI-DTC, PCA are 96.68%, 94.23%, 93.99%, 93.01%, and the KCs are 0.9308, 0.8995, 0.8962, 0.8842, respectively, in consistent with visual inspection. In summary, SVM is better for narrow rivers extraction and PCA-SVM is suitable for water extraction of various types. As for dark blue lakes, the methods using PCA can extract more quickly and accurately.

Point Based Emotion Classification Using SVM

OpenAIRE

Swinkels, Wout

2016-01-01

The detection of emotions is a hot topic in the area of computer vision. Emotions are based on subtle changes in the face that are intuitively detected and interpreted by humans. Detecting these subtle changes, based on mathematical models, is a great challenge in the area of computer vision. In this thesis a new method is proposed to achieve state-of-the-art emotion detection performance. This method is based on facial feature points to monitor subtle changes in the face. Therefore the c...

Discrimination of soft tissues using laser-induced breakdown spectroscopy in combination with k nearest neighbors (kNN) and support vector machine (SVM) classifiers

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Li, Xiaohui; Yang, Sibo; Fan, Rongwei; Yu, Xin; Chen, Deying

2018-06-01

In this paper, discrimination of soft tissues using laser-induced breakdown spectroscopy (LIBS) in combination with multivariate statistical methods is presented. Fresh pork fat, skin, ham, loin and tenderloin muscle tissues are manually cut into slices and ablated using a 1064 nm pulsed Nd:YAG laser. Discrimination analyses between fat, skin and muscle tissues, and further between highly similar ham, loin and tenderloin muscle tissues, are performed based on the LIBS spectra in combination with multivariate statistical methods, including principal component analysis (PCA), k nearest neighbors (kNN) classification, and support vector machine (SVM) classification. Performances of the discrimination models, including accuracy, sensitivity and specificity, are evaluated using 10-fold cross validation. The classification models are optimized to achieve best discrimination performances. The fat, skin and muscle tissues can be definitely discriminated using both kNN and SVM classifiers, with accuracy of over 99.83%, sensitivity of over 0.995 and specificity of over 0.998. The highly similar ham, loin and tenderloin muscle tissues can also be discriminated with acceptable performances. The best performances are achieved with SVM classifier using Gaussian kernel function, with accuracy of 76.84%, sensitivity of over 0.742 and specificity of over 0.869. The results show that the LIBS technique assisted with multivariate statistical methods could be a powerful tool for online discrimination of soft tissues, even for tissues of high similarity, such as muscles from different parts of the animal body. This technique could be used for discrimination of tissues suffering minor clinical changes, thus may advance the diagnosis of early lesions and abnormalities.

Exact Heat Kernel on a Hypersphere and Its Applications in Kernel SVM

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Chenchao Zhao

2018-01-01

Full Text Available Many contemporary statistical learning methods assume a Euclidean feature space. This paper presents a method for defining similarity based on hyperspherical geometry and shows that it often improves the performance of support vector machine compared to other competing similarity measures. Specifically, the idea of using heat diffusion on a hypersphere to measure similarity has been previously proposed and tested by Lafferty and Lebanon [1], demonstrating promising results based on a heuristic heat kernel obtained from the zeroth order parametrix expansion; however, how well this heuristic kernel agrees with the exact hyperspherical heat kernel remains unknown. This paper presents a higher order parametrix expansion of the heat kernel on a unit hypersphere and discusses several problems associated with this expansion method. We then compare the heuristic kernel with an exact form of the heat kernel expressed in terms of a uniformly and absolutely convergent series in high-dimensional angular momentum eigenmodes. Being a natural measure of similarity between sample points dwelling on a hypersphere, the exact kernel often shows superior performance in kernel SVM classifications applied to text mining, tumor somatic mutation imputation, and stock market analysis.

Design and implementation of predictive current control of three-phase PWM rectifier using space-vector modulation (SVM)

International Nuclear Information System (INIS)

Bouafia, Abdelouahab; Gaubert, Jean-Paul; Krim, Fateh

2010-01-01

This paper is concerned with the design and implementation of current control of three-phase PWM rectifier based on predictive control strategy. The proposed predictive current control technique operates with constant switching frequency, using space-vector modulation (SVM). The main goal of the designed current control scheme is to maintain the dc-bus voltage at the required level and to achieve the unity power factor (UPF) operation of the converter. For this purpose, two predictive current control algorithms, in the sense of deadbeat control, are developed for direct controlling input current vector of the converter in the stationary α-β and rotating d-q reference frame, respectively. For both predictive current control algorithms, at the beginning of each switching period, the required rectifier average voltage vector allowing the cancellation of both tracking errors of current vector components at the end of the switching period, is computed and applied during a predefined switching period by means of SVM. The main advantages of the proposed predictive current control are that no need to use hysteresis comparators or PI controllers in current control loops, and constant switching frequency. Finally, the developed predictive current control algorithms were tested both in simulations and experimentally, and illustrative results are presented here. Results have proven excellent performance in steady and transient states, and verify the validity of the proposed predictive current control which is compared to other control strategies.

2P2I HUNTER: a tool for filtering orthosteric protein-protein interaction modulators via a dedicated support vector machine.

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Hamon, Véronique; Bourgeas, Raphael; Ducrot, Pierre; Theret, Isabelle; Xuereb, Laura; Basse, Marie Jeanne; Brunel, Jean Michel; Combes, Sebastien; Morelli, Xavier; Roche, Philippe

2014-01-06

Over the last 10 years, protein-protein interactions (PPIs) have shown increasing potential as new therapeutic targets. As a consequence, PPIs are today the most screened target class in high-throughput screening (HTS). The development of broad chemical libraries dedicated to these particular targets is essential; however, the chemical space associated with this 'high-hanging fruit' is still under debate. Here, we analyse the properties of 40 non-redundant small molecules present in the 2P2I database (http://2p2idb.cnrs-mrs.fr/) to define a general profile of orthosteric inhibitors and propose an original protocol to filter general screening libraries using a support vector machine (SVM) with 11 standard Dragon molecular descriptors. The filtering protocol has been validated using external datasets from PubChem BioAssay and results from in-house screening campaigns. This external blind validation demonstrated the ability of the SVM model to reduce the size of the filtered chemical library by eliminating up to 96% of the compounds as well as enhancing the proportion of active compounds by up to a factor of 8. We believe that the resulting chemical space identified in this paper will provide the scientific community with a concrete support to search for PPI inhibitors during HTS campaigns.

Improved Prediction of Blood-Brain Barrier Permeability Through Machine Learning with Combined Use of Molecular Property-Based Descriptors and Fingerprints.

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Yuan, Yaxia; Zheng, Fang; Zhan, Chang-Guo

2018-03-21

Blood-brain barrier (BBB) permeability of a compound determines whether the compound can effectively enter the brain. It is an essential property which must be accounted for in drug discovery with a target in the brain. Several computational methods have been used to predict the BBB permeability. In particular, support vector machine (SVM), which is a kernel-based machine learning method, has been used popularly in this field. For SVM training and prediction, the compounds are characterized by molecular descriptors. Some SVM models were based on the use of molecular property-based descriptors (including 1D, 2D, and 3D descriptors) or fragment-based descriptors (known as the fingerprints of a molecule). The selection of descriptors is critical for the performance of a SVM model. In this study, we aimed to develop a generally applicable new SVM model by combining all of the features of the molecular property-based descriptors and fingerprints to improve the accuracy for the BBB permeability prediction. The results indicate that our SVM model has improved accuracy compared to the currently available models of the BBB permeability prediction.

Performance of svm, k-nn and nbc classifiers for text-independent speaker identification with and without modelling through merging models

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Yussouf Nahayo

2016-04-01

Full Text Available This paper proposes some methods of robust text-independent speaker identification based on Gaussian Mixture Model (GMM. We implemented a combination of GMM model with a set of classifiers such as Support Vector Machine (SVM, K-Nearest Neighbour (K-NN, and Naive Bayes Classifier (NBC. In order to improve the identification rate, we developed a combination of hybrid systems by using validation technique. The experiments were performed on the dialect DR1 of the TIMIT corpus. The results have showed a better performance for the developed technique compared to the individual techniques.

Using support vector machine to predict beta- and gamma-turns in proteins.

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Hu, Xiuzhen; Li, Qianzhong

2008-09-01

By using the composite vector with increment of diversity, position conservation scoring function, and predictive secondary structures to express the information of sequence, a support vector machine (SVM) algorithm for predicting beta- and gamma-turns in the proteins is proposed. The 426 and 320 nonhomologous protein chains described by Guruprasad and Rajkumar (Guruprasad and Rajkumar J. Biosci 2000, 25,143) are used for training and testing the predictive model of the beta- and gamma-turns, respectively. The overall prediction accuracy and the Matthews correlation coefficient in 7-fold cross-validation are 79.8% and 0.47, respectively, for the beta-turns. The overall prediction accuracy in 5-fold cross-validation is 61.0% for the gamma-turns. These results are significantly higher than the other algorithms in the prediction of beta- and gamma-turns using the same datasets. In addition, the 547 and 823 nonhomologous protein chains described by Fuchs and Alix (Fuchs and Alix Proteins: Struct Funct Bioinform 2005, 59, 828) are used for training and testing the predictive model of the beta- and gamma-turns, and better results are obtained. This algorithm may be helpful to improve the performance of protein turns' prediction. To ensure the ability of the SVM method to correctly classify beta-turn and non-beta-turn (gamma-turn and non-gamma-turn), the receiver operating characteristic threshold independent measure curves are provided. (c) 2008 Wiley Periodicals, Inc.

A Novel Feature Extraction Approach Using Window Function Capturing and QPSO-SVM for Enhancing Electronic Nose Performance

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Xiuzhen Guo

2015-06-01

Full Text Available In this paper, a novel feature extraction approach which can be referred to as moving window function capturing (MWFC has been proposed to analyze signals of an electronic nose (E-nose used for detecting types of infectious pathogens in rat wounds. Meanwhile, a quantum-behaved particle swarm optimization (QPSO algorithm is implemented in conjunction with support vector machine (SVM for realizing a synchronization optimization of the sensor array and SVM model parameters. The results prove the efficacy of the proposed method for E-nose feature extraction, which can lead to a higher classification accuracy rate compared to other established techniques. Meanwhile it is interesting to note that different classification results can be obtained by changing the types, widths or positions of windows. By selecting the optimum window function for the sensor response, the performance of an E-nose can be enhanced.

Computational methods using weighed-extreme learning machine to predict protein self-interactions with protein evolutionary information.

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An, Ji-Yong; Zhang, Lei; Zhou, Yong; Zhao, Yu-Jun; Wang, Da-Fu

2017-08-18

Self-interactions Proteins (SIPs) is important for their biological activity owing to the inherent interaction amongst their secondary structures or domains. However, due to the limitations of experimental Self-interactions detection, one major challenge in the study of prediction SIPs is how to exploit computational approaches for SIPs detection based on evolutionary information contained protein sequence. In the work, we presented a novel computational approach named WELM-LAG, which combined the Weighed-Extreme Learning Machine (WELM) classifier with Local Average Group (LAG) to predict SIPs based on protein sequence. The major improvement of our method lies in presenting an effective feature extraction method used to represent candidate Self-interactions proteins by exploring the evolutionary information embedded in PSI-BLAST-constructed position specific scoring matrix (PSSM); and then employing a reliable and robust WELM classifier to carry out classification. In addition, the Principal Component Analysis (PCA) approach is used to reduce the impact of noise. The WELM-LAG method gave very high average accuracies of 92.94 and 96.74% on yeast and human datasets, respectively. Meanwhile, we compared it with the state-of-the-art support vector machine (SVM) classifier and other existing methods on human and yeast datasets, respectively. Comparative results indicated that our approach is very promising and may provide a cost-effective alternative for predicting SIPs. In addition, we developed a freely available web server called WELM-LAG-SIPs to predict SIPs. The web server is available at http://219.219.62.123:8888/WELMLAG/ .

A comparative study of the SVM and K-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signals.

Science.gov (United States)

Palaniappan, Rajkumar; Sundaraj, Kenneth; Sundaraj, Sebastian

2014-06-27

Pulmonary acoustic parameters extracted from recorded respiratory sounds provide valuable information for the detection of respiratory pathologies. The automated analysis of pulmonary acoustic signals can serve as a differential diagnosis tool for medical professionals, a learning tool for medical students, and a self-management tool for patients. In this context, we intend to evaluate and compare the performance of the support vector machine (SVM) and K-nearest neighbour (K-nn) classifiers in diagnosis respiratory pathologies using respiratory sounds from R.A.L.E database. The pulmonary acoustic signals used in this study were obtained from the R.A.L.E lung sound database. The pulmonary acoustic signals were manually categorised into three different groups, namely normal, airway obstruction pathology, and parenchymal pathology. The mel-frequency cepstral coefficient (MFCC) features were extracted from the pre-processed pulmonary acoustic signals. The MFCC features were analysed by one-way ANOVA and then fed separately into the SVM and K-nn classifiers. The performances of the classifiers were analysed using the confusion matrix technique. The statistical analysis of the MFCC features using one-way ANOVA showed that the extracted MFCC features are significantly different (p < 0.001). The classification accuracies of the SVM and K-nn classifiers were found to be 92.19% and 98.26%, respectively. Although the data used to train and test the classifiers are limited, the classification accuracies found are satisfactory. The K-nn classifier was better than the SVM classifier for the discrimination of pulmonary acoustic signals from pathological and normal subjects obtained from the RALE database.

BacHbpred: Support Vector Machine Methods for the Prediction of Bacterial Hemoglobin-Like Proteins

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MuthuKrishnan Selvaraj

2016-01-01

Full Text Available The recent upsurge in microbial genome data has revealed that hemoglobin-like (HbL proteins may be widely distributed among bacteria and that some organisms may carry more than one HbL encoding gene. However, the discovery of HbL proteins has been limited to a small number of bacteria only. This study describes the prediction of HbL proteins and their domain classification using a machine learning approach. Support vector machine (SVM models were developed for predicting HbL proteins based upon amino acid composition (AC, dipeptide composition (DC, hybrid method (AC + DC, and position specific scoring matrix (PSSM. In addition, we introduce for the first time a new prediction method based on max to min amino acid residue (MM profiles. The average accuracy, standard deviation (SD, false positive rate (FPR, confusion matrix, and receiver operating characteristic (ROC were analyzed. We also compared the performance of our proposed models in homology detection databases. The performance of the different approaches was estimated using fivefold cross-validation techniques. Prediction accuracy was further investigated through confusion matrix and ROC curve analysis. All experimental results indicate that the proposed BacHbpred can be a perspective predictor for determination of HbL related proteins. BacHbpred, a web tool, has been developed for HbL prediction.

Comparing the analytical performances of Micro-NIR and FT-NIR spectrometers in the evaluation of acerola fruit quality, using PLS and SVM regression algorithms.

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Malegori, Cristina; Nascimento Marques, Emanuel José; de Freitas, Sergio Tonetto; Pimentel, Maria Fernanda; Pasquini, Celio; Casiraghi, Ernestina

2017-04-01

The main goal of this study was to investigate the analytical performances of a state-of-the-art device, one of the smallest dispersion NIR spectrometers on the market (MicroNIR 1700), making a critical comparison with a benchtop FT-NIR spectrometer in the evaluation of the prediction accuracy. In particular, the aim of this study was to estimate in a non-destructive manner, titratable acidity and ascorbic acid content in acerola fruit during ripening, in a view of direct applicability in field of this new miniaturised handheld device. Acerola (Malpighia emarginata DC.) is a super-fruit characterised by a considerable amount of ascorbic acid, ranging from 1.0% to 4.5%. However, during ripening, acerola colour changes and the fruit may lose as much as half of its ascorbic acid content. Because the variability of chemical parameters followed a non-strictly linear profile, two different regression algorithms were compared: PLS and SVM. Regression models obtained with Micro-NIR spectra give better results using SVM algorithm, for both ascorbic acid and titratable acidity estimation. FT-NIR data give comparable results using both SVM and PLS algorithms, with lower errors for SVM regression. The prediction ability of the two instruments was statistically compared using the Passing-Bablok regression algorithm; the outcomes are critically discussed together with the regression models, showing the suitability of the portable Micro-NIR for in field monitoring of chemical parameters of interest in acerola fruits. Copyright © 2016 Elsevier B.V. All rights reserved.

Comparison of four support-vector based function approximators

NARCIS (Netherlands)

de Kruif, B.J.; de Vries, Theodorus J.A.

2004-01-01

One of the uses of the support vector machine (SVM), as introduced in V.N. Vapnik (2000), is as a function approximator. The SVM and approximators based on it, approximate a relation in data by applying interpolation between so-called support vectors, being a limited number of samples that have been

Improving prediction of heterodimeric protein complexes using combination with pairwise kernel.

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Ruan, Peiying; Hayashida, Morihiro; Akutsu, Tatsuya; Vert, Jean-Philippe

2018-02-19

Since many proteins become functional only after they interact with their partner proteins and form protein complexes, it is essential to identify the sets of proteins that form complexes. Therefore, several computational methods have been proposed to predict complexes from the topology and structure of experimental protein-protein interaction (PPI) network. These methods work well to predict complexes involving at least three proteins, but generally fail at identifying complexes involving only two different proteins, called heterodimeric complexes or heterodimers. There is however an urgent need for efficient methods to predict heterodimers, since the majority of known protein complexes are precisely heterodimers. In this paper, we use three promising kernel functions, Min kernel and two pairwise kernels, which are Metric Learning Pairwise Kernel (MLPK) and Tensor Product Pairwise Kernel (TPPK). We also consider the normalization forms of Min kernel. Then, we combine Min kernel or its normalization form and one of the pairwise kernels by plugging. We applied kernels based on PPI, domain, phylogenetic profile, and subcellular localization properties to predicting heterodimers. Then, we evaluate our method by employing C-Support Vector Classification (C-SVC), carrying out 10-fold cross-validation, and calculating the average F-measures. The results suggest that the combination of normalized-Min-kernel and MLPK leads to the best F-measure and improved the performance of our previous work, which had been the best existing method so far. We propose new methods to predict heterodimers, using a machine learning-based approach. We train a support vector machine (SVM) to discriminate interacting vs non-interacting protein pairs, based on informations extracted from PPI, domain, phylogenetic profiles and subcellular localization. We evaluate in detail new kernel functions to encode these data, and report prediction performance that outperforms the state-of-the-art.

Research on big data risk assessment of major transformer defects and faults fusing power grid, equipment and environment based on SVM

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Guo, Lijuan; Yan, Haijun; Gao, Wensheng; Chen, Yun; Hao, Yongqi

2018-01-01

With the development of power big data, considering the wider power system data, the appropriate large data analysis method can be used to mine the potential law and value of power big data. On the basis of considering all kinds of monitoring data and defects and fault records of main transformer, the paper integrates the power grid, equipment as well as environment data and uses SVM as the main algorithm to evaluate the risk of the main transformer. It gets and compares the evaluation results under different modes, and proves that the risk assessment algorithms and schemes have certain effectiveness. This paper provides a new idea for data fusion of smart grid, and provides a reference for further big data evaluation of power grid equipment.

A real-time neutron-gamma discriminator based on the support vector machine method for the time-of-flight neutron spectrometer

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Wei, ZHANG; Tongyu, WU; Bowen, ZHENG; Shiping, LI; Yipo, ZHANG; Zejie, YIN

2018-04-01

A new neutron-gamma discriminator based on the support vector machine (SVM) method is proposed to improve the performance of the time-of-flight neutron spectrometer. The neutron detector is an EJ-299-33 plastic scintillator with pulse-shape discrimination (PSD) property. The SVM algorithm is implemented in field programmable gate array (FPGA) to carry out the real-time sifting of neutrons in neutron-gamma mixed radiation fields. This study compares the ability of the pulse gradient analysis method and the SVM method. The results show that this SVM discriminator can provide a better discrimination accuracy of 99.1%. The accuracy and performance of the SVM discriminator based on FPGA have been evaluated in the experiments. It can get a figure of merit of 1.30.

Protein-Based Drug-Delivery Materials

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Dave Jao

2017-05-01

Full Text Available There is a pressing need for long-term, controlled drug release for sustained treatment of chronic or persistent medical conditions and diseases. Guided drug delivery is difficult because therapeutic compounds need to survive numerous transport barriers and binding targets throughout the body. Nanoscale protein-based polymers are increasingly used for drug and vaccine delivery to cross these biological barriers and through blood circulation to their molecular site of action. Protein-based polymers compared to synthetic polymers have the advantages of good biocompatibility, biodegradability, environmental sustainability, cost effectiveness and availability. This review addresses the sources of protein-based polymers, compares the similarity and differences, and highlights characteristic properties and functionality of these protein materials for sustained and controlled drug release. Targeted drug delivery using highly functional multicomponent protein composites to guide active drugs to the site of interest will also be discussed. A systematical elucidation of drug-delivery efficiency in the case of molecular weight, particle size, shape, morphology, and porosity of materials will then be demonstrated to achieve increased drug absorption. Finally, several important biomedical applications of protein-based materials with drug-delivery function—including bone healing, antibiotic release, wound healing, and corneal regeneration, as well as diabetes, neuroinflammation and cancer treatments—are summarized at the end of this review.

Support vector machine based battery model for electric vehicles

International Nuclear Information System (INIS)

Wang Junping; Chen Quanshi; Cao Binggang

2006-01-01

The support vector machine (SVM) is a novel type of learning machine based on statistical learning theory that can map a nonlinear function successfully. As a battery is a nonlinear system, it is difficult to establish the relationship between the load voltage and the current under different temperatures and state of charge (SOC). The SVM is used to model the battery nonlinear dynamics in this paper. Tests are performed on an 80Ah Ni/MH battery pack with the Federal Urban Driving Schedule (FUDS) cycle to set up the SVM model. Compared with the Nernst and Shepherd combined model, the SVM model can simulate the battery dynamics better with small amounts of experimental data. The maximum relative error is 3.61%

An effective parameter optimization technique for vibration flow field characterization of PP melts via LS-SVM combined with SALS in an electromagnetism dynamic extruder

Science.gov (United States)

Xian, Guangming

2018-03-01

A method for predicting the optimal vibration field parameters by least square support vector machine (LS-SVM) is presented in this paper. One convenient and commonly used technique for characterizing the the vibration flow field of polymer melts films is small angle light scattering (SALS) in a visualized slit die of the electromagnetism dynamic extruder. The optimal value of vibration vibration frequency, vibration amplitude, and the maximum light intensity projection area can be obtained by using LS-SVM for prediction. For illustrating this method and show its validity, the flowing material is used with polypropylene (PP) and fifteen samples are tested at the rotation speed of screw at 36rpm. This paper first describes the apparatus of SALS to perform the experiments, then gives the theoretical basis of this new method, and detail the experimental results for parameter prediction of vibration flow field. It is demonstrated that it is possible to use the method of SALS and obtain detailed information on optimal parameter of vibration flow field of PP melts by LS-SVM.

Klasifikasi Topik Keluhan Pelanggan Berdasarkan Tweet dengan Menggunakan Penggabungan Feature Hasil Ekstraksi pada Metode Support Vector Machine (SVM

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Enda Esyudha Pratama

2015-12-01

Full Text Available Pemanfaatan twitter sebagai layanan customer serevice perusahaan sudah mulai banyak digunakan, tak terkecuali Speedy. Mekanisme yang ada saat ini untuk proses klasifikasi bentuk dan jenis keluhan serta informasi tentang jumlah keluhan lewat twitter masih dilakukan secara manual. Belum lagi data twitter yang bersifat tidak terstruktur tentunya akan menyulitkan untuk dilakukan analisa dan penggalian informasi dari data tersebut. Berdasarkan permasalahan tersebut, penelitian ini bertujuan untuk memproses data teks dari tweet pengguna twitteryang masuk ke akun @TelkomSpeedy untuk diolah menjadi informasi. Informasi tersebut nantinya digunakan untuk klasifikasi bentuk dan jenis keluhan. Merujuk pada beberapa penelitian terkait, salah satu metode klasifikasi yang paling baik untuk digunakan adalah metode Support Vector Machine (SVM. Konsep dari SVM dapat dijelaskan secara sederhana sebagai usaha mencari hyperplane yang dapat memisahkan dataset sesuai dengan kelasnya. Kelas yang digunakan dalam penelitian kali ini berdasarkan topik keluhan pelanggan yaitu billing, pemasangan/instalasi, putus (disconnect, dan lambat. Faktor penting lainnya dalam hal klasifikasi adalah penentuan feature atau atribut kata yang akan digunakan. Metode feature selection yang digunakan pada penlitian ini adalah term frequency (TF, document frequency (DF, information gain, dan chi-square. Pada penelitian ini juga dilakukan metode penggabungan feature yang telah dihasilkan dari beberapa metode feature selection sebelumnya. Dari hasil penelitian menunjukan bahwa SVM mampu melakukan klasifikasi keluhan dengan baik, hal ini dibuktikan dengan akurasi 82,50% untuk klasifikasi bentuk keluhan dan 86,67% untuk klasifikasi jenis keluhan. Sedangkan untuk kombinasi penggunaan feature dapat meningkatkan akurasi menjadi 83,33% untuk bentuk keluhan dan 89,17% untuk jenis keluhan.   Kata Kunci—customer service, klasifikasi topik keluhan, penggabungan feature, support vector machine

Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

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Yeganeh, B.; Motlagh, M. Shafie Pour; Rashidi, Y.; Kamalan, H.

2012-08-01

Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS-SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS-SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65-85% for hybrid PLS-SVM model respectively. Also it was found that the hybrid PLS-SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS-SVM model.

HybridGO-Loc: mining hybrid features on gene ontology for predicting subcellular localization of multi-location proteins.

Science.gov (United States)

Wan, Shibiao; Mak, Man-Wai; Kung, Sun-Yuan

2014-01-01

Protein subcellular localization prediction, as an essential step to elucidate the functions in vivo of proteins and identify drugs targets, has been extensively studied in previous decades. Instead of only determining subcellular localization of single-label proteins, recent studies have focused on predicting both single- and multi-location proteins. Computational methods based on Gene Ontology (GO) have been demonstrated to be superior to methods based on other features. However, existing GO-based methods focus on the occurrences of GO terms and disregard their relationships. This paper proposes a multi-label subcellular-localization predictor, namely HybridGO-Loc, that leverages not only the GO term occurrences but also the inter-term relationships. This is achieved by hybridizing the GO frequencies of occurrences and the semantic similarity between GO terms. Given a protein, a set of GO terms are retrieved by searching against the gene ontology database, using the accession numbers of homologous proteins obtained via BLAST search as the keys. The frequency of GO occurrences and semantic similarity (SS) between GO terms are used to formulate frequency vectors and semantic similarity vectors, respectively, which are subsequently hybridized to construct fusion vectors. An adaptive-decision based multi-label support vector machine (SVM) classifier is proposed to classify the fusion vectors. Experimental results based on recent benchmark datasets and a new dataset containing novel proteins show that the proposed hybrid-feature predictor significantly outperforms predictors based on individual GO features as well as other state-of-the-art predictors. For readers' convenience, the HybridGO-Loc server, which is for predicting virus or plant proteins, is available online at http://bioinfo.eie.polyu.edu.hk/HybridGoServer/.

MAMMOGRAMS ANALYSIS USING SVM CLASSIFIER IN COMBINED TRANSFORMS DOMAIN

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B.N. Prathibha

2011-02-01

Full Text Available Breast cancer is a primary cause of mortality and morbidity in women. Reports reveal that earlier the detection of abnormalities, better the improvement in survival. Digital mammograms are one of the most effective means for detecting possible breast anomalies at early stages. Digital mammograms supported with Computer Aided Diagnostic (CAD systems help the radiologists in taking reliable decisions. The proposed CAD system extracts wavelet features and spectral features for the better classification of mammograms. The Support Vector Machines classifier is used to analyze 206 mammogram images from Mias database pertaining to the severity of abnormality, i.e., benign and malign. The proposed system gives 93.14% accuracy for discrimination between normal-malign and 87.25% accuracy for normal-benign samples and 89.22% accuracy for benign-malign samples. The study reveals that features extracted in hybrid transform domain with SVM classifier proves to be a promising tool for analysis of mammograms.

Weak cation exchange magnetic beads coupled with matrix-assisted laser desorption ionization-time of flight-mass spectrometry in screening serum protein markers in osteopenia.

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He, Wei-Tao; Liang, Bo-Cheng; Shi, Zhen-Yu; Li, Xu-Yun; Li, Chun-Wen; Shi, Xiao-Lin

2016-01-01

The present study aimed at investigating the weak cation magnetic separation technology and matrix-assisted laser desorption ionization-time of flight-mass spectrometry (MALDI-TOF-MS) in screening serum protein markers of osteopenia from ten postmenopausal women and ten postmenopausal women without osteopenia as control group, to find a new method for screening biomarkers and establishing a diagnostic model for primary type I osteoporosis. Serum samples were collected from postmenopausal women with osteopenia and postmenopausal women with normal bone mass. Proteins were extracted from serum samples by weak cation exchange magnetic beads technology, and mass spectra acquisition was done by MALDI-TOF-MS. The visualization and comparison of data sets, statistical peak evaluation, model recognition, and discovery of biomarker candidates were handled by the proteinchip data analysis system software(ZJU-PDAS). The diagnostic models were established using genetic arithmetic based support vector machine (SVM). The SVM result with the highest Youden Index was selected as the model. Combinatorial Peaks having the highest accuracy in distinguishing different samples were selected as potential biomarker. From the two group serum samples, a total of 133 differential features were selected. Ten features with significant intensity differences were screened. In the pair-wise comparisons, processing of MALDI-TOF spectra resulted in the identification of ten differential features between postmenopausal women with osteopenia and postmenopausal women with normal bone mass. The difference of features by Youden index showed that the highest features had a mass to charge ratio of 1699 and 3038 Da. A diagnosis model was established with these two peaks as the candidate marker, and the specificity of the model is 100 %, the sensitivity was 90 % by leave-one-out cross validation test. The two groups of specimens in SVM results on the scatter plot could be clearly distinguished. The peak

PlantRNA_Sniffer: A SVM-Based Workflow to Predict Long Intergenic Non-Coding RNAs in Plants.

Science.gov (United States)

Vieira, Lucas Maciel; Grativol, Clicia; Thiebaut, Flavia; Carvalho, Thais G; Hardoim, Pablo R; Hemerly, Adriana; Lifschitz, Sergio; Ferreira, Paulo Cavalcanti Gomes; Walter, Maria Emilia M T

2017-03-04

Non-coding RNAs (ncRNAs) constitute an important set of transcripts produced in the cells of organisms. Among them, there is a large amount of a particular class of long ncRNAs that are difficult to predict, the so-called long intergenic ncRNAs (lincRNAs), which might play essential roles in gene regulation and other cellular processes. Despite the importance of these lincRNAs, there is still a lack of biological knowledge and, currently, the few computational methods considered are so specific that they cannot be successfully applied to other species different from those that they have been originally designed to. Prediction of lncRNAs have been performed with machine learning techniques. Particularly, for lincRNA prediction, supervised learning methods have been explored in recent literature. As far as we know, there are no methods nor workflows specially designed to predict lincRNAs in plants. In this context, this work proposes a workflow to predict lincRNAs on plants, considering a workflow that includes known bioinformatics tools together with machine learning techniques, here a support vector machine (SVM). We discuss two case studies that allowed to identify novel lincRNAs, in sugarcane ( Saccharum spp.) and in maize ( Zea mays ). From the results, we also could identify differentially-expressed lincRNAs in sugarcane and maize plants submitted to pathogenic and beneficial microorganisms.

PlantRNA_Sniffer: A SVM-Based Workflow to Predict Long Intergenic Non-Coding RNAs in Plants

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Lucas Maciel Vieira

2017-03-01

Full Text Available Non-coding RNAs (ncRNAs constitute an important set of transcripts produced in the cells of organisms. Among them, there is a large amount of a particular class of long ncRNAs that are difficult to predict, the so-called long intergenic ncRNAs (lincRNAs, which might play essential roles in gene regulation and other cellular processes. Despite the importance of these lincRNAs, there is still a lack of biological knowledge and, currently, the few computational methods considered are so specific that they cannot be successfully applied to other species different from those that they have been originally designed to. Prediction of lncRNAs have been performed with machine learning techniques. Particularly, for lincRNA prediction, supervised learning methods have been explored in recent literature. As far as we know, there are no methods nor workflows specially designed to predict lincRNAs in plants. In this context, this work proposes a workflow to predict lincRNAs on plants, considering a workflow that includes known bioinformatics tools together with machine learning techniques, here a support vector machine (SVM. We discuss two case studies that allowed to identify novel lincRNAs, in sugarcane (Saccharum spp. and in maize (Zea mays. From the results, we also could identify differentially-expressed lincRNAs in sugarcane and maize plants submitted to pathogenic and beneficial microorganisms.

Opinion mining on book review using CNN-L2-SVM algorithm

Science.gov (United States)

Rozi, M. F.; Mukhlash, I.; Soetrisno; Kimura, M.

2018-03-01

Review of a product can represent quality of a product itself. An extraction to that review can be used to know sentiment of that opinion. Process to extract useful information of user review is called Opinion Mining. Review extraction model that is enhancing nowadays is Deep Learning model. This Model has been used by many researchers to obtain excellent performance on Natural Language Processing. In this research, one of deep learning model, Convolutional Neural Network (CNN) is used for feature extraction and L2 Support Vector Machine (SVM) as classifier. These methods are implemented to know the sentiment of book review data. The result of this method shows state-of-the art performance in 83.23% for training phase and 64.6% for testing phase.

Mapping of High Value Crops Through AN Object-Based Svm Model Using LIDAR Data and Orthophoto in Agusan del Norte Philippines

Science.gov (United States)

Candare, Rudolph Joshua; Japitana, Michelle; Cubillas, James Earl; Ramirez, Cherry Bryan

2016-06-01

This research describes the methods involved in the mapping of different high value crops in Agusan del Norte Philippines using LiDAR. This project is part of the Phil-LiDAR 2 Program which aims to conduct a nationwide resource assessment using LiDAR. Because of the high resolution data involved, the methodology described here utilizes object-based image analysis and the use of optimal features from LiDAR data and Orthophoto. Object-based classification was primarily done by developing rule-sets in eCognition. Several features from the LiDAR data and Orthophotos were used in the development of rule-sets for classification. Generally, classes of objects can't be separated by simple thresholds from different features making it difficult to develop a rule-set. To resolve this problem, the image-objects were subjected to Support Vector Machine learning. SVMs have gained popularity because of their ability to generalize well given a limited number of training samples. However, SVMs also suffer from parameter assignment issues that can significantly affect the classification results. More specifically, the regularization parameter C in linear SVM has to be optimized through cross validation to increase the overall accuracy. After performing the segmentation in eCognition, the optimization procedure as well as the extraction of the equations of the hyper-planes was done in Matlab. The learned hyper-planes separating one class from another in the multi-dimensional feature-space can be thought of as super-features which were then used in developing the classifier rule set in eCognition. In this study, we report an overall classification accuracy of greater than 90% in different areas.