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Sample records for fuzzy support vector

  1. Single Object Tracking With Fuzzy Least Squares Support Vector Machine.

    PubMed

    Zhang, Shunli; Zhao, Sicong; Sui, Yao; Zhang, Li

    2015-12-01

    Single object tracking, in which a target is often initialized manually in the first frame and then is tracked and located automatically in the subsequent frames, is a hot topic in computer vision. The traditional tracking-by-detection framework, which often formulates tracking as a binary classification problem, has been widely applied and achieved great success in single object tracking. However, there are some potential issues in this formulation. For instance, the boundary between the positive and negative training samples is fuzzy, and the objectives of tracking and classification are inconsistent. In this paper, we attempt to address the above issues from the fuzzy system perspective and propose a novel tracking method by formulating tracking as a fuzzy classification problem. First, we introduce the fuzzy strategy into tracking and propose a novel fuzzy tracking framework, which can measure the importance of the training samples by assigning different memberships to them and offer more strict spatial constraints. Second, we develop a fuzzy least squares support vector machine (FLS-SVM) approach and employ it to implement a concrete tracker. In particular, the primal form, dual form, and kernel form of FLS-SVM are analyzed and the corresponding closed-form solutions are derived for efficient realizations. Besides, a least squares regression model is built to control the update adaptively, retaining the robustness of the appearance model. The experimental results demonstrate that our method can achieve comparable or superior performance to many state-of-the-art methods. PMID:26441419

  2. Prediction of Pork Quality by Fuzzy Support Vector Machine Classifier

    NASA Astrophysics Data System (ADS)

    Zhang, Jianxi; Yu, Huaizhi; Wang, Jiamin

    Existing objective methods to evaluate pork quality in general do not yield satisfactory results and their applications in meat industry are limited. In this study, fuzzy support vector machine (FSVM) method was developed to evaluate and predict pork quality rapidly and nondestructively. Firstly, the discrete wavelet transform (DWT) was used to eliminate the noise component in original spectrum and the new spectrum was reconstructed. Then, considering the characteristic variables still exist correlation and contain some redundant information, principal component analysis (PCA) was carried out. Lastly, FSVM was developed to differentiate and classify pork samples into different quality grades using the features from PCA. Jackknife tests on the working datasets indicated that the prediction accuracies were higher than other methods.

  3. Anticipatory Monitoring and Control of Complex Systems using a Fuzzy based Fusion of Support Vector Regressors

    SciTech Connect

    Miltiadis Alamaniotis; Vivek Agarwal

    2014-10-01

    This paper places itself in the realm of anticipatory systems and envisions monitoring and control methods being capable of making predictions over system critical parameters. Anticipatory systems allow intelligent control of complex systems by predicting their future state. In the current work, an intelligent model aimed at implementing anticipatory monitoring and control in energy industry is presented and tested. More particularly, a set of support vector regressors (SVRs) are trained using both historical and observed data. The trained SVRs are used to predict the future value of the system based on current operational system parameter. The predicted values are then inputted to a fuzzy logic based module where the values are fused to obtain a single value, i.e., final system output prediction. The methodology is tested on real turbine degradation datasets. The outcome of the approach presented in this paper highlights the superiority over single support vector regressors. In addition, it is shown that appropriate selection of fuzzy sets and fuzzy rules plays an important role in improving system performance.

  4. [Automatic classification method of star spectra data based on manifold fuzzy twin support vector machine].

    PubMed

    Liu, Zhong-bao; Gao, Yan-yun; Wang, Jian-zhen

    2015-01-01

    Support vector machine (SVM) with good leaning ability and generalization is widely used in the star spectra data classification. But when the scale of data becomes larger, the shortages of SVM appear: the calculation amount is quite large and the classification speed is too slow. In order to solve the above problems, twin support vector machine (TWSVM) was proposed by Jayadeva. The advantage of TSVM is that the time cost is reduced to 1/4 of that of SVM. While all the methods mentioned above only focus on the global characteristics and neglect the local characteristics. In view of this, an automatic classification method of star spectra data based on manifold fuzzy twin support vector machine (MF-TSVM) is proposed in this paper. In MF-TSVM, manifold-based discriminant analysis (MDA) is used to obtain the global and local characteristics of the input data and the fuzzy membership is introduced to reduce the influences of noise and singular data on the classification results. Comparative experiments with current classification methods, such as C-SVM and KNN, on the SDSS star spectra datasets verify the effectiveness of the proposed method. PMID:25993861

  5. Fuzzy nonlinear proximal support vector machine for land extraction based on remote sensing image.

    PubMed

    Zhong, Xiaomei; Li, Jianping; Dou, Huacheng; Deng, Shijun; Wang, Guofei; Jiang, Yu; Wang, Yongjie; Zhou, Zebing; Wang, Li; Yan, Fei

    2013-01-01

    Currently, remote sensing technologies were widely employed in the dynamic monitoring of the land. This paper presented an algorithm named fuzzy nonlinear proximal support vector machine (FNPSVM) by basing on ETM(+) remote sensing image. This algorithm is applied to extract various types of lands of the city Da'an in northern China. Two multi-category strategies, namely "one-against-one" and "one-against-rest" for this algorithm were described in detail and then compared. A fuzzy membership function was presented to reduce the effects of noises or outliers on the data samples. The approaches of feature extraction, feature selection, and several key parameter settings were also given. Numerous experiments were carried out to evaluate its performances including various accuracies (overall accuracies and kappa coefficient), stability, training speed, and classification speed. The FNPSVM classifier was compared to the other three classifiers including the maximum likelihood classifier (MLC), back propagation neural network (BPN), and the proximal support vector machine (PSVM) under different training conditions. The impacts of the selection of training samples, testing samples and features on the four classifiers were also evaluated in these experiments. PMID:23936016

  6. Fuzzy Nonlinear Proximal Support Vector Machine for Land Extraction Based on Remote Sensing Image

    PubMed Central

    Zhong, Xiaomei; Li, Jianping; Dou, Huacheng; Deng, Shijun; Wang, Guofei; Jiang, Yu; Wang, Yongjie; Zhou, Zebing; Wang, Li; Yan, Fei

    2013-01-01

    Currently, remote sensing technologies were widely employed in the dynamic monitoring of the land. This paper presented an algorithm named fuzzy nonlinear proximal support vector machine (FNPSVM) by basing on ETM+ remote sensing image. This algorithm is applied to extract various types of lands of the city Da’an in northern China. Two multi-category strategies, namely “one-against-one” and “one-against-rest” for this algorithm were described in detail and then compared. A fuzzy membership function was presented to reduce the effects of noises or outliers on the data samples. The approaches of feature extraction, feature selection, and several key parameter settings were also given. Numerous experiments were carried out to evaluate its performances including various accuracies (overall accuracies and kappa coefficient), stability, training speed, and classification speed. The FNPSVM classifier was compared to the other three classifiers including the maximum likelihood classifier (MLC), back propagation neural network (BPN), and the proximal support vector machine (PSVM) under different training conditions. The impacts of the selection of training samples, testing samples and features on the four classifiers were also evaluated in these experiments. PMID:23936016

  7. A novel approach for recognition of control chart patterns: Type-2 fuzzy clustering optimized support vector machine.

    PubMed

    Khormali, Aminollah; Addeh, Jalil

    2016-07-01

    Unnatural patterns in the control charts can be associated with a specific set of assignable causes for process variation. Hence, pattern recognition is very useful in identifying the process problems. In this study, a multiclass SVM (SVM) based classifier is proposed because of the promising generalization capability of support vector machines. In the proposed method type-2 fuzzy c-means (T2FCM) clustering algorithm is used to make a SVM system more effective. The fuzzy support vector machine classifier suggested in this paper is composed of three main sub-networks: fuzzy classifier sub-network, SVM sub-network and optimization sub-network. In SVM training, the hyper-parameters plays a very important role in its recognition accuracy. Therefore, cuckoo optimization algorithm (COA) is proposed for selecting appropriate parameters of the classifier. Simulation results showed that the proposed system has very high recognition accuracy. PMID:27101724

  8. Physical activities recognition from ambulatory ECG signals using neuro-fuzzy classifiers and support vector machines.

    PubMed

    Kher, Rahul; Pawar, Tanmay; Thakar, Vishvjit; Shah, Hitesh

    2015-02-01

    The use of wearable recorders for long-term monitoring of physiological parameters has increased in the last few years. The ambulatory electrocardiogram (A-ECG) signals of five healthy subjects with four body movements or physical activities (PA)-left arm up down, right arm up down, waist twisting and walking-have been recorded using a wearable ECG recorder. The classification of these four PAs has been performed using neuro-fuzzy classifier (NFC) and support vector machines (SVM). The PA classification is based on the distinct, time-frequency features of the extracted motion artifacts contained in recorded A-ECG signals. The motion artifacts in A-ECG signals have been separated first by the discrete wavelet transform (DWT) and the time-frequency features of these motion artifacts have then been extracted using the Gabor transform. The Gabor energy feature vectors have been fed to the NFC and SVM classifiers. Both the classifiers have achieved a PA classification accuracy of over 95% for all subjects. PMID:25641014

  9. Fuzzy classifier based support vector regression framework for Poisson ratio determination

    NASA Astrophysics Data System (ADS)

    Asoodeh, Mojtaba; Bagheripour, Parisa

    2013-09-01

    Poisson ratio is considered as one of the most important rock mechanical properties of hydrocarbon reservoirs. Determination of this parameter through laboratory measurement is time, cost, and labor intensive. Furthermore, laboratory measurements do not provide continuous data along the reservoir intervals. Hence, a fast, accurate, and inexpensive way of determining Poisson ratio which produces continuous data over the whole reservoir interval is desirable. For this purpose, support vector regression (SVR) method based on statistical learning theory (SLT) was employed as a supervised learning algorithm to estimate Poisson ratio from conventional well log data. SVR is capable of accurately extracting the implicit knowledge contained in conventional well logs and converting the gained knowledge into Poisson ratio data. Structural risk minimization (SRM) principle which is embedded in the SVR structure in addition to empirical risk minimization (EMR) principle provides a robust model for finding quantitative formulation between conventional well log data and Poisson ratio. Although satisfying results were obtained from an individual SVR model, it had flaws of overestimation in low Poisson ratios and underestimation in high Poisson ratios. These errors were eliminated through implementation of fuzzy classifier based SVR (FCBSVR). The FCBSVR significantly improved accuracy of the final prediction. This strategy was successfully applied to data from carbonate reservoir rocks of an Iranian Oil Field. Results indicated that SVR predicted Poisson ratio values are in good agreement with measured values.

  10. Classification of urban vegetation patterns from hyperspectral imagery: hybrid algorithm based on genetic algorithm tuned fuzzy support vector machine

    NASA Astrophysics Data System (ADS)

    Zhou, Mandi; Shu, Jiong; Chen, Zhigang; Ji, Minhe

    2012-11-01

    Hyperspectral imagery has been widely used in terrain classification for its high resolution. Urban vegetation, known as an essential part of the urban ecosystem, can be difficult to discern due to high similarity of spectral signatures among some land-cover classes. In this paper, we investigate a hybrid approach of the genetic-algorithm tuned fuzzy support vector machine (GA-FSVM) technique and apply it to urban vegetation classification from aerial hyperspectral urban imagery. The approach adopts the genetic algorithm to optimize parameters of support vector machine, and employs the K-nearest neighbor algorithm to calculate the membership function for each fuzzy parameter, aiming to reduce the effects of the isolated and noisy samples. Test data come from push-broom hyperspectral imager (PHI) hyperspectral remote sensing image which partially covers a corner of the Shanghai World Exposition Park, while PHI is a hyper-spectral sensor developed by Shanghai Institute of Technical Physics. Experimental results show the GA-FSVM model generates overall accuracy of 71.2%, outperforming the maximum likelihood classifier with 49.4% accuracy and the artificial neural network method with 60.8% accuracy. It indicates GA-FSVM is a promising model for vegetation classification from hyperspectral urban data, and has good advantage in the application of classification involving abundant mixed pixels and small samples problem.

  11. Enhanced land use/cover classification using support vector machines and fuzzy k-means clustering algorithms

    NASA Astrophysics Data System (ADS)

    He, Tao; Sun, Yu-Jun; Xu, Ji-De; Wang, Xue-Jun; Hu, Chang-Ru

    2014-01-01

    Land use/cover (LUC) classification plays an important role in remote sensing and land change science. Because of the complexity of ground covers, LUC classification is still regarded as a difficult task. This study proposed a fusion algorithm, which uses support vector machines (SVM) and fuzzy k-means (FKM) clustering algorithms. The main scheme was divided into two steps. First, a clustering map was obtained from the original remote sensing image using FKM; simultaneously, a normalized difference vegetation index layer was extracted from the original image. Then, the classification map was generated by using an SVM classifier. Three different classification algorithms were compared, tested, and verified-parametric (maximum likelihood), nonparametric (SVM), and hybrid (unsupervised-supervised, fusion of SVM and FKM) classifiers, respectively. The proposed algorithm obtained the highest overall accuracy in our experiments.

  12. Detection of Hard Exudates in Colour Fundus Images Using Fuzzy Support Vector Machine-Based Expert System.

    PubMed

    Jaya, T; Dheeba, J; Singh, N Albert

    2015-12-01

    Diabetic retinopathy is a major cause of vision loss in diabetic patients. Currently, there is a need for making decisions using intelligent computer algorithms when screening a large volume of data. This paper presents an expert decision-making system designed using a fuzzy support vector machine (FSVM) classifier to detect hard exudates in fundus images. The optic discs in the colour fundus images are segmented to avoid false alarms using morphological operations and based on circular Hough transform. To discriminate between the exudates and the non-exudates pixels, colour and texture features are extracted from the images. These features are given as input to the FSVM classifier. The classifier analysed 200 retinal images collected from diabetic retinopathy screening programmes. The tests made on the retinal images show that the proposed detection system has better discriminating power than the conventional support vector machine. With the best combination of FSVM and features sets, the area under the receiver operating characteristic curve reached 0.9606, which corresponds to a sensitivity of 94.1% with a specificity of 90.0%. The results suggest that detecting hard exudates using FSVM contribute to computer-assisted detection of diabetic retinopathy and as a decision support system for ophthalmologists. PMID:25822397

  13. PSOFuzzySVM-TMH: identification of transmembrane helix segments using ensemble feature space by incorporated fuzzy support vector machine.

    PubMed

    Hayat, Maqsood; Tahir, Muhammad

    2015-08-01

    Membrane protein is a central component of the cell that manages intra and extracellular processes. Membrane proteins execute a diversity of functions that are vital for the survival of organisms. The topology of transmembrane proteins describes the number of transmembrane (TM) helix segments and its orientation. However, owing to the lack of its recognized structures, the identification of TM helix and its topology through experimental methods is laborious with low throughput. In order to identify TM helix segments reliably, accurately, and effectively from topogenic sequences, we propose the PSOFuzzySVM-TMH model. In this model, evolutionary based information position specific scoring matrix and discrete based information 6-letter exchange group are used to formulate transmembrane protein sequences. The noisy and extraneous attributes are eradicated using an optimization selection technique, particle swarm optimization, from both feature spaces. Finally, the selected feature spaces are combined in order to form ensemble feature space. Fuzzy-support vector Machine is utilized as a classification algorithm. Two benchmark datasets, including low and high resolution datasets, are used. At various levels, the performance of the PSOFuzzySVM-TMH model is assessed through 10-fold cross validation test. The empirical results reveal that the proposed framework PSOFuzzySVM-TMH outperforms in terms of classification performance in the examined datasets. It is ascertained that the proposed model might be a useful and high throughput tool for academia and research community for further structure and functional studies on transmembrane proteins. PMID:26054033

  14. Support vector machines

    NASA Technical Reports Server (NTRS)

    Garay, Michael J.; Mazzoni, Dominic; Davies, Roger; Wagstaff, Kiri

    2004-01-01

    Support Vector Machines (SVMs) are a type of supervised learning algorith,, other examples of which are Artificial Neural Networks (ANNs), Decision Trees, and Naive Bayesian Classifiers. Supervised learning algorithms are used to classify objects labled by a 'supervisor' - typically a human 'expert.'.

  15. A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS

    NASA Astrophysics Data System (ADS)

    Pradhan, Biswajeet

    2013-02-01

    The purpose of the present study is to compare the prediction performances of three different approaches such as decision tree (DT), support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) for landslide susceptibility mapping at Penang Hill area, Malaysia. The necessary input parameters for the landslide susceptibility assessments were obtained from various sources. At first, landslide locations were identified by aerial photographs and field surveys and a total of 113 landslide locations were constructed. The study area contains 340,608 pixels while total 8403 pixels include landslides. The landslide inventory was randomly partitioned into two subsets: (1) part 1 that contains 50% (4000 landslide grid cells) was used in the training phase of the models; (2) part 2 is a validation dataset 50% (4000 landslide grid cells) for validation of three models and to confirm its accuracy. The digitally processed images of input parameters were combined in GIS. Finally, landslide susceptibility maps were produced, and the performances were assessed and discussed. Total fifteen landslide susceptibility maps were produced using DT, SVM and ANFIS based models, and the resultant maps were validated using the landslide locations. Prediction performances of these maps were checked by receiver operating characteristics (ROC) by using both success rate curve and prediction rate curve. The validation results showed that, area under the ROC curve for the fifteen models produced using DT, SVM and ANFIS varied from 0.8204 to 0.9421 for success rate curve and 0.7580 to 0.8307 for prediction rate curves, respectively. Moreover, the prediction curves revealed that model 5 of DT has slightly higher prediction performance (83.07), whereas the success rate showed that model 5 of ANFIS has better prediction (94.21) capability among all models. The results of this study showed that landslide susceptibility mapping in the Penang Hill area using the three approaches (e

  16. Isomap based supporting vector machine

    NASA Astrophysics Data System (ADS)

    Liang, W. N.

    2015-12-01

    This research presents a new isomap based supporting vector machine method. Isomap is a dimension reduction method which is able to analyze nonlinear relationship of data on manifolds. Accordingly, support vector machine is established on the isomap manifold to classify given and predict unknown data. A case study of the isomap based supporting vector machine for environmental planning problems is conducted.

  17. Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation

    SciTech Connect

    Keller, Brad M.; Nathan, Diane L.; Wang Yan; Zheng Yuanjie; Gee, James C.; Conant, Emily F.; Kontos, Despina

    2012-08-15

    Purpose: The amount of fibroglandular tissue content in the breast as estimated mammographically, commonly referred to as breast percent density (PD%), is one of the most significant risk factors for developing breast cancer. Approaches to quantify breast density commonly focus on either semiautomated methods or visual assessment, both of which are highly subjective. Furthermore, most studies published to date investigating computer-aided assessment of breast PD% have been performed using digitized screen-film mammograms, while digital mammography is increasingly replacing screen-film mammography in breast cancer screening protocols. Digital mammography imaging generates two types of images for analysis, raw (i.e., 'FOR PROCESSING') and vendor postprocessed (i.e., 'FOR PRESENTATION'), of which postprocessed images are commonly used in clinical practice. Development of an algorithm which effectively estimates breast PD% in both raw and postprocessed digital mammography images would be beneficial in terms of direct clinical application and retrospective analysis. Methods: This work proposes a new algorithm for fully automated quantification of breast PD% based on adaptive multiclass fuzzy c-means (FCM) clustering and support vector machine (SVM) classification, optimized for the imaging characteristics of both raw and processed digital mammography images as well as for individual patient and image characteristics. Our algorithm first delineates the breast region within the mammogram via an automated thresholding scheme to identify background air followed by a straight line Hough transform to extract the pectoral muscle region. The algorithm then applies adaptive FCM clustering based on an optimal number of clusters derived from image properties of the specific mammogram to subdivide the breast into regions of similar gray-level intensity. Finally, a SVM classifier is trained to identify which clusters within the breast tissue are likely fibroglandular, which are then

  18. Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation

    PubMed Central

    Keller, Brad M.; Nathan, Diane L.; Wang, Yan; Zheng, Yuanjie; Gee, James C.; Conant, Emily F.; Kontos, Despina

    2012-01-01

    Purpose: The amount of fibroglandular tissue content in the breast as estimated mammographically, commonly referred to as breast percent density (PD%), is one of the most significant risk factors for developing breast cancer. Approaches to quantify breast density commonly focus on either semiautomated methods or visual assessment, both of which are highly subjective. Furthermore, most studies published to date investigating computer-aided assessment of breast PD% have been performed using digitized screen-film mammograms, while digital mammography is increasingly replacing screen-film mammography in breast cancer screening protocols. Digital mammography imaging generates two types of images for analysis, raw (i.e., “FOR PROCESSING”) and vendor postprocessed (i.e., “FOR PRESENTATION”), of which postprocessed images are commonly used in clinical practice. Development of an algorithm which effectively estimates breast PD% in both raw and postprocessed digital mammography images would be beneficial in terms of direct clinical application and retrospective analysis. Methods: This work proposes a new algorithm for fully automated quantification of breast PD% based on adaptive multiclass fuzzy c-means (FCM) clustering and support vector machine (SVM) classification, optimized for the imaging characteristics of both raw and processed digital mammography images as well as for individual patient and image characteristics. Our algorithm first delineates the breast region within the mammogram via an automated thresholding scheme to identify background air followed by a straight line Hough transform to extract the pectoral muscle region. The algorithm then applies adaptive FCM clustering based on an optimal number of clusters derived from image properties of the specific mammogram to subdivide the breast into regions of similar gray-level intensity. Finally, a SVM classifier is trained to identify which clusters within the breast tissue are likely fibroglandular, which

  19. Multiscale hierarchical support vector clustering

    NASA Astrophysics Data System (ADS)

    Hansen, Michael Saas; Holm, David Alberg; Sjöstrand, Karl; Ley, Carsten Dan; Rowland, Ian John; Larsen, Rasmus

    2008-03-01

    Clustering is the preferred choice of method in many applications, and support vector clustering (SVC) has proven efficient for clustering noisy and high-dimensional data sets. A method for multiscale support vector clustering is demonstrated, using the recently emerged method for fast calculation of the entire regularization path of the support vector domain description. The method is illustrated on artificially generated examples, and applied for detecting blood vessels from high resolution time series of magnetic resonance imaging data. The obtained results are robust while the need for parameter estimation is reduced, compared to support vector clustering.

  20. A neural support vector machine.

    PubMed

    Jändel, Magnus

    2010-06-01

    Support vector machines are state-of-the-art pattern recognition algorithms that are well founded in optimization and generalization theory but not obviously applicable to the brain. This paper presents Bio-SVM, a biologically feasible support vector machine. An unstable associative memory oscillates between support vectors and interacts with a feed-forward classification pathway. Kernel neurons blend support vectors and sensory input. Downstream temporal integration generates the classification. Instant learning of surprising events and off-line tuning of support vector weights trains the system. Emotion-based learning, forgetting trivia, sleep and brain oscillations are phenomena that agree with the Bio-SVM model. A mapping to the olfactory system is suggested. PMID:20092978

  1. Neighborhood Supported Model Level Fuzzy Aggregation for Moving Object Segmentation.

    PubMed

    Chiranjeevi, Pojala; Sengupta, Somnath

    2014-02-01

    We propose a new algorithm for moving object detection in the presence of challenging dynamic background conditions. We use a set of fuzzy aggregated multifeature similarity measures applied on multiple models corresponding to multimodal backgrounds. The algorithm is enriched with a neighborhood-supported model initialization strategy for faster convergence. A model level fuzzy aggregation measure driven background model maintenance ensures more robustness. Similarity functions are evaluated between the corresponding elements of the current feature vector and the model feature vectors. Concepts from Sugeno and Choquet integrals are incorporated in our algorithm to compute fuzzy similarities from the ordered similarity function values for each model. Model updating and the foreground/background classification decision is based on the set of fuzzy integrals. Our proposed algorithm is shown to outperform other multi-model background subtraction algorithms. The proposed approach completely avoids explicit offline training to initialize background model and can be initialized with moving objects also. The feature space uses a combination of intensity and statistical texture features for better object localization and robustness. Our qualitative and quantitative studies illustrate the mitigation of varieties of challenging situations by our approach. PMID:24235250

  2. Novel Applications of Intuitionistic Fuzzy Digraphs in Decision Support Systems

    PubMed Central

    Sarwar, Mansoor

    2014-01-01

    Many problems of practical interest can be modeled and solved by using graph algorithms. In general, graph theory has a wide range of applications in diverse fields. In this paper, the intuitionistic fuzzy organizational and neural network models, intuitionistic fuzzy neurons in medical diagnosis, intuitionistic fuzzy digraphs in vulnerability assessment of gas pipeline networks, and intuitionistic fuzzy digraphs in travel time are presented as examples of intuitionistic fuzzy digraphs in decision support system. We have also designed and implemented the algorithms for these decision support systems. PMID:25045752

  3. GAPS IN SUPPORT VECTOR OPTIMIZATION

    SciTech Connect

    STEINWART, INGO; HUSH, DON; SCOVEL, CLINT; LIST, NICOLAS

    2007-01-29

    We show that the stopping criteria used in many support vector machine (SVM) algorithms working on the dual can be interpreted as primal optimality bounds which in turn are known to be important for the statistical analysis of SVMs. To this end we revisit the duality theory underlying the derivation of the dual and show that in many interesting cases primal optimality bounds are the same as known dual optimality bounds.

  4. Type-II Fuzzy Decision Support System for Fertilizer

    PubMed Central

    Ashraf, Ather; Sarwar, Mansoor

    2014-01-01

    Type-II fuzzy sets are used to convey the uncertainties in the membership function of type-I fuzzy sets. Linguistic information in expert rules does not give any information about the geometry of the membership functions. These membership functions are mostly constructed through numerical data or range of classes. But there exists an uncertainty about the shape of the membership, that is, whether to go for a triangle membership function or a trapezoidal membership function. In this paper we use a type-II fuzzy set to overcome this uncertainty, and develop a fuzzy decision support system of fertilizers based on a type-II fuzzy set. This type-II fuzzy system takes cropping time and soil nutrients in the form of spatial surfaces as input, fuzzifies it using a type-II fuzzy membership function, and implies fuzzy rules on it in the fuzzy inference engine. The output of the fuzzy inference engine, which is in the form of interval value type-II fuzzy sets, reduced to an interval type-I fuzzy set, defuzzifies it to a crisp value and generates a spatial surface of fertilizers. This spatial surface shows the spatial trend of the required amount of fertilizer needed to cultivate a specific crop. The complexity of our algorithm is O(mnr), where m is the height of the raster, n is the width of the raster, and r is the number of expert rules. PMID:24892071

  5. Vector control of wind turbine on the basis of the fuzzy selective neural net*

    NASA Astrophysics Data System (ADS)

    Engel, E. A.; Kovalev, I. V.; Engel, N. E.

    2016-04-01

    An article describes vector control of wind turbine based on fuzzy selective neural net. Based on the wind turbine system’s state, the fuzzy selective neural net tracks an maximum power point under random perturbations. Numerical simulations are accomplished to clarify the applicability and advantages of the proposed vector wind turbine’s control on the basis of the fuzzy selective neuronet. The simulation results show that the proposed intelligent control of wind turbine achieves real-time control speed and competitive performance, as compared to a classical control model with PID controllers based on traditional maximum torque control strategy.

  6. Natural evolution of neural support vector machines.

    PubMed

    Jändel, Magnus

    2011-01-01

    Two different neural implementations of support vector machines are described and applied to one-shot trainable pattern recognition. The first model is based on oscillating associative memory and is mapped to the olfactory system. The second model is founded on competitive queuing memory originally employed for generating motor action sequences in the brain. Both models include forward pathways where a stream of support vectors is evoked from memory and merges with sensory input to produce support vector machine classifications. Misclassified events are imprinted as new support vector candidates. Support vector machine weights are tuned by virtual experimentation in sleep. Recalled training examples masquerade as sensor input and feedback from the classification process drives a learning process where support vector weights are optimized. For both support vector machine models it is demonstrated that there is a plausible evolutionary path from a simple hard-wired pattern recognizer to a full implementation of a biological kernel machine. Simple and individually beneficial modifications are accumulated in each step along this path. Neural support vector machines can apparently emerge by natural processes. PMID:21744220

  7. TWSVR: Regression via Twin Support Vector Machine.

    PubMed

    Khemchandani, Reshma; Goyal, Keshav; Chandra, Suresh

    2016-02-01

    Taking motivation from Twin Support Vector Machine (TWSVM) formulation, Peng (2010) attempted to propose Twin Support Vector Regression (TSVR) where the regressor is obtained via solving a pair of quadratic programming problems (QPPs). In this paper we argue that TSVR formulation is not in the true spirit of TWSVM. Further, taking motivation from Bi and Bennett (2003), we propose an alternative approach to find a formulation for Twin Support Vector Regression (TWSVR) which is in the true spirit of TWSVM. We show that our proposed TWSVR can be derived from TWSVM for an appropriately constructed classification problem. To check the efficacy of our proposed TWSVR we compare its performance with TSVR and classical Support Vector Regression(SVR) on various regression datasets. PMID:26624223

  8. Neuro-Fuzzy Support of Knowledge Management in Social Regulation

    NASA Astrophysics Data System (ADS)

    Petrovic-Lazarevic, Sonja; Coghill, Ken; Abraham, Ajith

    2002-09-01

    The aim of the paper is to demonstrate the neuro-fuzzy support of knowledge management in social regulation. Knowledge could be understood for social regulation purposes as explicit and tacit. Explicit knowledge relates to the community culture indicating how things work in the community based on social policies and procedures. Tacit knowledge is ethics and norms of the community. The former could be codified, stored and transferable in order to support decision making, while the latter being based on personal knowledge, experience and judgments is difficult to codify and store. Tacit knowledge expressed through linguistic information can be stored and used to support knowledge management in social regulation through the application of fuzzy and neuro-fuzzy logic.

  9. An intelligent fuzzy decision support system for production management

    SciTech Connect

    Vojdani, N.

    1996-11-01

    In the near future the optimizing of production processes in terms of reducing order lead times, delivery times, inventory stocks and production costs, while increasing the flexibility, productivity and quality of all operations will become a matter of survival for many manufacturing enterprises. This paper presents an application using an intelligent fuzzy decision support system in production management. It is based on a goal oriented logistics index numbers system and ensures the competitive capacity of manufacturing enterprises by indicating the means to achieve management goals. The aim is to demonstrate the main properties of the fuzzy decision support system PROMAN.

  10. Multiclass Reduced-Set Support Vector Machines

    NASA Technical Reports Server (NTRS)

    Tang, Benyang; Mazzoni, Dominic

    2006-01-01

    There are well-established methods for reducing the number of support vectors in a trained binary support vector machine, often with minimal impact on accuracy. We show how reduced-set methods can be applied to multiclass SVMs made up of several binary SVMs, with significantly better results than reducing each binary SVM independently. Our approach is based on Burges' approach that constructs each reduced-set vector as the pre-image of a vector in kernel space, but we extend this by recomputing the SVM weights and bias optimally using the original SVM objective function. This leads to greater accuracy for a binary reduced-set SVM, and also allows vectors to be 'shared' between multiple binary SVMs for greater multiclass accuracy with fewer reduced-set vectors. We also propose computing pre-images using differential evolution, which we have found to be more robust than gradient descent alone. We show experimental results on a variety of problems and find that this new approach is consistently better than previous multiclass reduced-set methods, sometimes with a dramatic difference.

  11. Improved image retrieval based on fuzzy colour feature vector

    NASA Astrophysics Data System (ADS)

    Ben-Ahmeida, Ahlam M.; Ben Sasi, Ahmed Y.

    2013-03-01

    One of Image indexing techniques is the Content-Based Image Retrieval which is an efficient way for retrieving images from the image database automatically based on their visual contents such as colour, texture, and shape. In this paper will be discuss how using content-based image retrieval (CBIR) method by colour feature extraction and similarity checking. By dividing the query image and all images in the database into pieces and extract the features of each part separately and comparing the corresponding portions in order to increase the accuracy in the retrieval. The proposed approach is based on the use of fuzzy sets, to overcome the problem of curse of dimensionality. The contribution of colour of each pixel is associated to all the bins in the histogram using fuzzy-set membership functions. As a result, the Fuzzy Colour Histogram (FCH), outperformed the Conventional Colour Histogram (CCH) in image retrieving, due to its speedy results, where were images represented as signatures that took less size of memory, depending on the number of divisions. The results also showed that FCH is less sensitive and more robust to brightness changes than the CCH with better retrieval recall values.

  12. [Rule induction algorithm for brain glioma using support vector machine].

    PubMed

    Li, Guozheng; Yang, Jie; Wang, Jiaju; Geng, Daoying

    2006-04-01

    A new proposed data mining technique, support vector machine (SVM), is used to predict the degree of malignancy in brain glioma. Based on statistical learning theory, SVM realizes the principle of data dependent structure risk minimization, so it can depress the overfitting with better generalization performance, since the prediction in medical diagnosis often deals with a small sample. SVM based rule induction algorithm is implemented in comparison with other data mining techniques such as artificial neural networks, rule induction algorithm and fuzzy rule extraction algorithm based on fuzzy max-min neural networks (FRE-FMMNN) proposed recently. Computation results by 10 fold cross validation method show that SVM can get higher prediction accuracy than artificial neural networks and FRE-FMMNN, which implies SVM can get higher accuracy and more reliability. On the whole data sets, SVM gets one rule with the classification accuracy of 89.29%, while FRE-FMMNN gets two rules of 84. 64%, in which the rule got by SVM is of quantity relation and contains more information than the two rules by FRE-FMMNN. All the above show SVM is a potential algorithm for the medical diagnosis such as the prediction of the degree of malignancy in brain glioma. PMID:16706378

  13. Automatic control of pressure support mechanical ventilation using fuzzy logic.

    PubMed

    Nemoto, T; Hatzakis, G E; Thorpe, C W; Olivenstein, R; Dial, S; Bates, J H

    1999-08-01

    There is currently no universally accepted approach to weaning patients from mechanical ventilation, but there is clearly a feeling within the medical community that it may be possible to formulate the weaning process algorithmically in some manner. Fuzzy logic seems suited this task because of the way it so naturally represents the subjective human notions employed in much of medical decision-making. The purpose of the present study was to develop a fuzzy logic algorithm for controlling pressure support ventilation in patients in the intensive care unit, utilizing measurements of heart rate, tidal volume, breathing frequency, and arterial oxygen saturation. In this report we describe the fuzzy logic algorithm, and demonstrate its use retrospectively in 13 patients with severe chronic obstructive pulmonary disease, by comparing the decisions made by the algorithm with what actually transpired. The fuzzy logic recommendations agreed with the status quo to within 2 cm H(2)O an average of 76% of the time, and to within 4 cm H(2)O an average of 88% of the time (although in most of these instances no medical decisions were taken as to whether or not to change the level of ventilatory support). We also compared the predictions of our algorithm with those cases in which changes in pressure support level were actually made by an attending physician, and found that the physicians tended to reduce the support level somewhat more aggressively than the algorithm did. We conclude that our fuzzy algorithm has the potential to control the level of pressure support ventilation from ongoing measurements of a patient's vital signs. PMID:10430727

  14. Flood Classification Using Support Vector Machines

    NASA Astrophysics Data System (ADS)

    Melsen, Lieke A.; Torfs, Paul J. J.; Brauer, Claudia C.

    2013-04-01

    Lowland floods are in general considered to be less extreme than mountainous floods. In order to investigate this, seven lowland floods in the Netherlands were selected and compared to mountainous floods from the study of Marchi et al. (2010). Both a 2D and 3D approach of the statistical two-group classification method support vector machines (Cortes and Vapnik, 1995) were used to find a statistical difference between the two flood types. Support vector machines were able to draw a decision plane between the two flood types, misclassifying one out of seven lowland floods, and one out of 67 mountainous floods. The main difference between the two flood types can be found in the runoff coefficient (with lowland floods having a lower runoff coefficient than mountainous floods), the cumulative precipitation causing the flood (which was lower for lowland floods), and, obviously, the relief ratio. Support vector machines have proved to be useful for flood classification and might be applicable in future classification studies. References Cortes, C., and V. Vapnik. "Support-Vector Networks." Machine Learning 20: (1995) 273-297. Marchi, L., M. Borga, E. Preciso, and E. Gaume. "Characterisation of selected extreme flash floods in Europe and implications for flood risk management." Journal of Hydrology 394: (2010) 118-133.

  15. Support vector machine for automatic pain recognition

    NASA Astrophysics Data System (ADS)

    Monwar, Md Maruf; Rezaei, Siamak

    2009-02-01

    Facial expressions are a key index of emotion and the interpretation of such expressions of emotion is critical to everyday social functioning. In this paper, we present an efficient video analysis technique for recognition of a specific expression, pain, from human faces. We employ an automatic face detector which detects face from the stored video frame using skin color modeling technique. For pain recognition, location and shape features of the detected faces are computed. These features are then used as inputs to a support vector machine (SVM) for classification. We compare the results with neural network based and eigenimage based automatic pain recognition systems. The experiment results indicate that using support vector machine as classifier can certainly improve the performance of automatic pain recognition system.

  16. Support Vector Machine-Based Endmember Extraction

    SciTech Connect

    Filippi, Anthony M; Archibald, Richard K

    2009-01-01

    Introduced in this paper is the utilization of Support Vector Machines (SVMs) to automatically perform endmember extraction from hyperspectral data. The strengths of SVM are exploited to provide a fast and accurate calculated representation of high-dimensional data sets that may consist of multiple distributions. Once this representation is computed, the number of distributions can be determined without prior knowledge. For each distribution, an optimal transform can be determined that preserves informational content while reducing the data dimensionality, and hence, the computational cost. Finally, endmember extraction for the whole data set is accomplished. Results indicate that this Support Vector Machine-Based Endmember Extraction (SVM-BEE) algorithm has the capability of autonomously determining endmembers from multiple clusters with computational speed and accuracy, while maintaining a robust tolerance to noise.

  17. Automated image segmentation using support vector machines

    NASA Astrophysics Data System (ADS)

    Powell, Stephanie; Magnotta, Vincent A.; Andreasen, Nancy C.

    2007-03-01

    Neurodegenerative and neurodevelopmental diseases demonstrate problems associated with brain maturation and aging. Automated methods to delineate brain structures of interest are required to analyze large amounts of imaging data like that being collected in several on going multi-center studies. We have previously reported on using artificial neural networks (ANN) to define subcortical brain structures including the thalamus (0.88), caudate (0.85) and the putamen (0.81). In this work, apriori probability information was generated using Thirion's demons registration algorithm. The input vector consisted of apriori probability, spherical coordinates, and an iris of surrounding signal intensity values. We have applied the support vector machine (SVM) machine learning algorithm to automatically segment subcortical and cerebellar regions using the same input vector information. SVM architecture was derived from the ANN framework. Training was completed using a radial-basis function kernel with gamma equal to 5.5. Training was performed using 15,000 vectors collected from 15 training images in approximately 10 minutes. The resulting support vectors were applied to delineate 10 images not part of the training set. Relative overlap calculated for the subcortical structures was 0.87 for the thalamus, 0.84 for the caudate, 0.84 for the putamen, and 0.72 for the hippocampus. Relative overlap for the cerebellar lobes ranged from 0.76 to 0.86. The reliability of the SVM based algorithm was similar to the inter-rater reliability between manual raters and can be achieved without rater intervention.

  18. Progressive Classification Using Support Vector Machines

    NASA Technical Reports Server (NTRS)

    Wagstaff, Kiri; Kocurek, Michael

    2009-01-01

    An algorithm for progressive classification of data, analogous to progressive rendering of images, makes it possible to compromise between speed and accuracy. This algorithm uses support vector machines (SVMs) to classify data. An SVM is a machine learning algorithm that builds a mathematical model of the desired classification concept by identifying the critical data points, called support vectors. Coarse approximations to the concept require only a few support vectors, while precise, highly accurate models require far more support vectors. Once the model has been constructed, the SVM can be applied to new observations. The cost of classifying a new observation is proportional to the number of support vectors in the model. When computational resources are limited, an SVM of the appropriate complexity can be produced. However, if the constraints are not known when the model is constructed, or if they can change over time, a method for adaptively responding to the current resource constraints is required. This capability is particularly relevant for spacecraft (or any other real-time systems) that perform onboard data analysis. The new algorithm enables the fast, interactive application of an SVM classifier to a new set of data. The classification process achieved by this algorithm is characterized as progressive because a coarse approximation to the true classification is generated rapidly and thereafter iteratively refined. The algorithm uses two SVMs: (1) a fast, approximate one and (2) slow, highly accurate one. New data are initially classified by the fast SVM, producing a baseline approximate classification. For each classified data point, the algorithm calculates a confidence index that indicates the likelihood that it was classified correctly in the first pass. Next, the data points are sorted by their confidence indices and progressively reclassified by the slower, more accurate SVM, starting with the items most likely to be incorrectly classified. The user

  19. Treatment of Geodetic Survey Data as Fuzzy Vectors

    NASA Astrophysics Data System (ADS)

    Navratil, G.; Heer, E.; Hahn, J.

    2015-08-01

    Geodetic survey data are typically analysed using the assumption that measurement errors can be modelled as noise. The least squares method models noise with the normal distribution and is based on the assumption that it selects measurements with the highest probability value (Ghilani, 2010, p. 179f). There are environment situations where no clear maximum for a measurement can be detected. This can happen, for example, if surveys take place in foggy conditions causing diffusion of light signals. This presents a problem for automated systems because the standard assumption of the least squares method does not hold. A measurement system trying to return a crisp value will produce an arbitrary value that lies within the area of maximum value. However repeating the measurement is unlikely to create a value following a normal distribution, which happens if measurement errors can be modelled as noise. In this article we describe a laboratory experiment that reproduces conditions similar to a foggy situation and present measurement data gathered from this setup. Furthermore we propose methods based on fuzzy set theory to evaluate the data from our measurement.

  20. Incorporating uncertanity into Markov random field classification with the combine use of optical and SAR images and aduptive fuzzy mean vector

    NASA Astrophysics Data System (ADS)

    Welikanna, D. R.; Tamura, M.; Susaki, J.

    2014-09-01

    A Markov Random Field (MRF) model accounting for the classification uncertainty using multisource satellite images and an adaptive fuzzy class mean vector is proposed in this study. The work also highlights the initialization of the class values for an MRF based classification for synthetic aperture radar (SAR) images using optical data. The model uses the contextual information from the optical image pixels and the SAR pixel intensity with corresponding fuzzy grade of memberships respectively, in the classification mechanism. Sub pixel class fractions estimated using Singular Value Decomposition (SVD) from the optical image initializes the class arrangement for the MRF process. Pair-site interactions of the pixels are used to model the prior energy from the initial class arrangement. Fuzzy class mean vector from the SAR intensity pixels is calculated using Fuzzy C-means (FCM) partitioning. Conditional probability for each class was determined by a Gamma distribution for the SAR image. Simulated annealing (SA) to minimize the global energy was executed using a logarithmic and power-law combined annealing schedule. Proposed technique was tested using an Advanced Land Observation Satellite (ALOS) phased array type L-band SAR (PALSAR) and Advanced Visible and Near-Infrared Radiometer-2 (AVNIR-2) data set over a disaster effected urban region in Japan. Proposed method and the conventional MRF results were evaluated with neural network (NN) and support vector machine (SVM) based classifications. The results suggest the possible integration of an adaptive fuzzy class mean vector and multisource data is promising for imprecise class discrimination using a MRF based classification.

  1. Support vector machines for spam categorization.

    PubMed

    Drucker, H; Wu, D; Vapnik, V N

    1999-01-01

    We study the use of support vector machines (SVM's) in classifying e-mail as spam or nonspam by comparing it to three other classification algorithms: Ripper, Rocchio, and boosting decision trees. These four algorithms were tested on two different data sets: one data set where the number of features were constrained to the 1000 best features and another data set where the dimensionality was over 7000. SVM's performed best when using binary features. For both data sets, boosting trees and SVM's had acceptable test performance in terms of accuracy and speed. However, SVM's had significantly less training time. PMID:18252607

  2. Quantum optimization for training support vector machines.

    PubMed

    Anguita, Davide; Ridella, Sandro; Rivieccio, Fabio; Zunino, Rodolfo

    2003-01-01

    Refined concepts, such as Rademacher estimates of model complexity and nonlinear criteria for weighting empirical classification errors, represent recent and promising approaches to characterize the generalization ability of Support Vector Machines (SVMs). The advantages of those techniques lie in both improving the SVM representation ability and yielding tighter generalization bounds. On the other hand, they often make Quadratic-Programming algorithms no longer applicable, and SVM training cannot benefit from efficient, specialized optimization techniques. The paper considers the application of Quantum Computing to solve the problem of effective SVM training, especially in the case of digital implementations. The presented research compares the behavioral aspects of conventional and enhanced SVMs; experiments in both a synthetic and real-world problems support the theoretical analysis. At the same time, the related differences between Quadratic-Programming and Quantum-based optimization techniques are considered. PMID:12850032

  3. Support vector machine in machine condition monitoring and fault diagnosis

    NASA Astrophysics Data System (ADS)

    Widodo, Achmad; Yang, Bo-Suk

    2007-08-01

    Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and fault diagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in machine condition monitoring and fault diagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and fault diagnosis using SVM will be future works.

  4. When do support vector machines work fast?

    SciTech Connect

    Steinwart, I.; Scovel, James C.

    2004-01-01

    The authors establish learning rates to the Bayes risk for support vector machines (SVM's) with hinge loss. Since a theorem of Devroyte states that no learning algorithm can learn with a uniform rate to the Bayes risk for all probability distributions they have to restrict the class of considered distributions: in order to obtain fast rates they assume a noise condition recently proposed by Tsybakov and an approximation condition in terms of the distribution and the reproducing kernel Hilbert space used by the SVM. for Gaussian RBF kernels with varying widths they propose a geometric noise assumption on the distribution which ensures the approximation condition. This geometric assumption is not in terms of smoothness but describes the concentration of the marginal distribution near the decision boundary. In particular they are able to describe nontrivial classes of distributions for which SVM's using a Gaussian kernel can learn with almost linear rate.

  5. Supernova Recognition using Support Vector Machines

    SciTech Connect

    Romano, Raquel A.; Aragon, Cecilia R.; Ding, Chris

    2006-10-01

    We introduce a novel application of Support Vector Machines(SVMs) to the problem of identifying potential supernovae usingphotometric and geometric features computed from astronomical imagery.The challenges of this supervised learning application are significant:1) noisy and corrupt imagery resulting in high levels of featureuncertainty,2) features with heavy-tailed, peaked distributions,3)extremely imbalanced and overlapping positiveand negative data sets, and4) the need to reach high positive classification rates, i.e. to find allpotential supernovae, while reducing the burdensome workload of manuallyexamining false positives. High accuracy is achieved viaasign-preserving, shifted log transform applied to features with peaked,heavy-tailed distributions. The imbalanced data problem is handled byoversampling positive examples,selectively sampling misclassifiednegative examples,and iteratively training multiple SVMs for improvedsupernovarecognition on unseen test data. We present crossvalidationresults and demonstrate the impact on a largescale supernova survey thatcurrently uses the SVM decision value to rank-order 600,000 potentialsupernovae each night.

  6. Privacy preserving RBF kernel support vector machine.

    PubMed

    Li, Haoran; Xiong, Li; Ohno-Machado, Lucila; Jiang, Xiaoqian

    2014-01-01

    Data sharing is challenging but important for healthcare research. Methods for privacy-preserving data dissemination based on the rigorous differential privacy standard have been developed but they did not consider the characteristics of biomedical data and make full use of the available information. This often results in too much noise in the final outputs. We hypothesized that this situation can be alleviated by leveraging a small portion of open-consented data to improve utility without sacrificing privacy. We developed a hybrid privacy-preserving differentially private support vector machine (SVM) model that uses public data and private data together. Our model leverages the RBF kernel and can handle nonlinearly separable cases. Experiments showed that this approach outperforms two baselines: (1) SVMs that only use public data, and (2) differentially private SVMs that are built from private data. Our method demonstrated very close performance metrics compared to nonprivate SVMs trained on the private data. PMID:25013805

  7. Support Vector Training of Protein Alignment Models

    PubMed Central

    Joachims, Thorsten; Elber, Ron; Pillardy, Jaroslaw

    2008-01-01

    Abstract Sequence to structure alignment is an important step in homology modeling of protein structures. Incorporation of features such as secondary structure, solvent accessibility, or evolutionary information improve sequence to structure alignment accuracy, but conventional generative estimation techniques for alignment models impose independence assumptions that make these features difficult to include in a principled way. In this paper, we overcome this problem using a Support Vector Machine (SVM) method that provides a well-founded way of estimating complex alignment models with hundred of thousands of parameters. Furthermore, we show that the method can be trained using a variety of loss functions. In a rigorous empirical evaluation, the SVM algorithm outperforms the generative alignment method SSALN, a highly accurate generative alignment model that incorporates structural information. The alignment model learned by the SVM aligns 50% of the residues correctly and aligns over 70% of the residues within a shift of four positions. PMID:18707536

  8. Rule-based fuzzy vector median filters for 3D phase contrast MRI segmentation

    NASA Astrophysics Data System (ADS)

    Sundareswaran, Kartik S.; Frakes, David H.; Yoganathan, Ajit P.

    2008-02-01

    Recent technological advances have contributed to the advent of phase contrast magnetic resonance imaging (PCMRI) as standard practice in clinical environments. In particular, decreased scan times have made using the modality more feasible. PCMRI is now a common tool for flow quantification, and for more complex vector field analyses that target the early detection of problematic flow conditions. Segmentation is one component of this type of application that can impact the accuracy of the final product dramatically. Vascular segmentation, in general, is a long-standing problem that has received significant attention. Segmentation in the context of PCMRI data, however, has been explored less and can benefit from object-based image processing techniques that incorporate fluids specific information. Here we present a fuzzy rule-based adaptive vector median filtering (FAVMF) algorithm that in combination with active contour modeling facilitates high-quality PCMRI segmentation while mitigating the effects of noise. The FAVMF technique was tested on 111 synthetically generated PC MRI slices and on 15 patients with congenital heart disease. The results were compared to other multi-dimensional filters namely the adaptive vector median filter, the adaptive vector directional filter, and the scalar low pass filter commonly used in PC MRI applications. FAVMF significantly outperformed the standard filtering methods (p < 0.0001). Two conclusions can be drawn from these results: a) Filtering should be performed after vessel segmentation of PC MRI; b) Vector based filtering methods should be used instead of scalar techniques.

  9. Developing a Software for Fuzzy Group Decision Support System: A Case Study

    ERIC Educational Resources Information Center

    Baba, A. Fevzi; Kuscu, Dincer; Han, Kerem

    2009-01-01

    The complex nature and uncertain information in social problems required the emergence of fuzzy decision support systems in social areas. In this paper, we developed user-friendly Fuzzy Group Decision Support Systems (FGDSS) software. The software can be used for multi-purpose decision making processes. It helps the users determine the main and…

  10. Support vector machines for geophysical inversion

    NASA Astrophysics Data System (ADS)

    Kuzma, Heidi Anderson

    This thesis explores what it means to replace classical non-linear geophysical inversion with computer learning via Support Vector Machines. Geophysical inverse problems are almost always ill-posed which means that many different models (i.e. descriptions of the earth) can be found to explain a given noisy or incomplete data set. Regularization and constraints encourage inversions to find physically realistic models. The set of preferred models needs to be defined a priori using as much geologic knowledge as is available. In inversion, it is assumed that data and a forward modeling process is known. The goal is to solve for a model. In the SVM paradigm, a series of models and associated data are known. The goal is to solve for a reverse modeling process. Starting with a series of initial models assembled using all available geologic information, synthetic data is created using the most realistic forward modeling program available. With the synthetic data as inputs and the known models as outputs, a Support Vector Machine is trained to approximate a local inverse to the forward modeling program. The advantages of this approach are that it is honest about the need to establish, a priori, the kinds of models that are reasonable in a particular field situation. There is no need to adjust the forward process to accommodate inversion, because SVMs can be easily modified to capture complicated, non-linear relationships. SVMs are transparent and require very little programming. If an SVM is trained using model/data pairs that are drawn from the same probability distribution that is implicit in the regularization of an inversion, then it will get very similar results to the inversion. Because SVMs can interpret as much data as desired so long as the conditions of an experiment do not change, they can be used to perform otherwise computationally expensive procedures. The SVMs in this paper are trained to emulate linear and non-linear seismic Amplitude Variation with Offset

  11. Fuzzy Relational Compression Applied on Feature Vectors for Infant Cry Recognition

    NASA Astrophysics Data System (ADS)

    Reyes-Galaviz, Orion Fausto; Reyes-García, Carlos Alberto

    Data compression is always advisable when it comes to handling and processing information quickly and efficiently. There are two main problems that need to be solved when it comes to handling data; store information in smaller spaces and processes it in the shortest possible time. When it comes to infant cry analysis (ICA), there is always the need to construct large sound repositories from crying babies. Samples that have to be analyzed and be used to train and test pattern recognition algorithms; making this a time consuming task when working with uncompressed feature vectors. In this work, we show a simple, but efficient, method that uses Fuzzy Relational Product (FRP) to compresses the information inside a feature vector, building with this a compressed matrix that will help us recognize two kinds of pathologies in infants; Asphyxia and Deafness. We describe the sound analysis, which consists on the extraction of Mel Frequency Cepstral Coefficients that generate vectors which will later be compressed by using FRP. There is also a description of the infant cry database used in this work, along with the training and testing of a Time Delay Neural Network with the compressed features, which shows a performance of 96.44% with our proposed feature vector compression.

  12. Justification of Fuzzy Declustering Vector Quantization Modeling in Classification of Genotype-Image Phenotypes

    NASA Astrophysics Data System (ADS)

    Ng, Theam Foo; Pham, Tuan D.; Zhou, Xiaobo

    2010-01-01

    With the fast development of multi-dimensional data compression and pattern classification techniques, vector quantization (VQ) has become a system that allows large reduction of data storage and computational effort. One of the most recent VQ techniques that handle the poor estimation of vector centroids due to biased data from undersampling is to use fuzzy declustering-based vector quantization (FDVQ) technique. Therefore, in this paper, we are motivated to propose a justification of FDVQ based hidden Markov model (HMM) for investigating its effectiveness and efficiency in classification of genotype-image phenotypes. The performance evaluation and comparison of the recognition accuracy between a proposed FDVQ based HMM (FDVQ-HMM) and a well-known LBG (Linde, Buzo, Gray) vector quantization based HMM (LBG-HMM) will be carried out. The experimental results show that the performances of both FDVQ-HMM and LBG-HMM are almost similar. Finally, we have justified the competitiveness of FDVQ-HMM in classification of cellular phenotype image database by using hypotheses t-test. As a result, we have validated that the FDVQ algorithm is a robust and an efficient classification technique in the application of RNAi genome-wide screening image data.

  13. Data selection using support vector regression

    NASA Astrophysics Data System (ADS)

    Richman, Michael B.; Leslie, Lance M.; Trafalis, Theodore B.; Mansouri, Hicham

    2015-03-01

    Geophysical data sets are growing at an ever-increasing rate, requiring computationally efficient data selection (thinning) methods to preserve essential information. Satellites, such as WindSat, provide large data sets for assessing the accuracy and computational efficiency of data selection techniques. A new data thinning technique, based on support vector regression (SVR), is developed and tested. To manage large on-line satellite data streams, observations from WindSat are formed into subsets by Voronoi tessellation and then each is thinned by SVR (TSVR). Three experiments are performed. The first confirms the viability of TSVR for a relatively small sample, comparing it to several commonly used data thinning methods (random selection, averaging and Barnes filtering), producing a 10% thinning rate (90% data reduction), low mean absolute errors (MAE) and large correlations with the original data. A second experiment, using a larger dataset, shows TSVR retrievals with MAE < 1 m s-1 and correlations ⩽ 0.98. TSVR was an order of magnitude faster than the commonly used thinning methods. A third experiment applies a two-stage pipeline to TSVR, to accommodate online data. The pipeline subsets reconstruct the wind field with the same accuracy as the second experiment, is an order of magnitude faster than the nonpipeline TSVR. Therefore, pipeline TSVR is two orders of magnitude faster than commonly used thinning methods that ingest the entire data set. This study demonstrates that TSVR pipeline thinning is an accurate and computationally efficient alternative to commonly used data selection techniques.

  14. Classification SAR targets with support vector machine

    NASA Astrophysics Data System (ADS)

    Cao, Lanying

    2007-02-01

    With the development of Synthetic Aperture Radar (SAR) technology, automatic target recognition (ATR) is becoming increasingly important. In this paper, we proposed a 3-class target classification system in SAR images. The system is based on invariant wavelet moments and support vector machine (SVM) algorithm. It is a two-stage approach. The first stage is to extract and select a small set of wavelet invariant moment features to indicate target images. The wavelet invariant moments take both advantages of the wavelet inherent property of multi-resolution analysis and moment invariants quality of invariant to translation, scaling changes and rotation. The second stage is classification of targets with SVM algorithm. SVM is based on the principle of structural risk minimization (SRM), which has been shown better than the principle of empirical risk minimization (ERM) which is used by many conventional networks. To test the performance and efficiency of the proposed method, we performed experiments on invariant wavelet moments, different kernel functions, 2-class identification, and 3-class identification. Test results show that wavelet invariant moments indicate the target effectively; linear kernel function achieves better results than other kernel functions, and SVM classification approach performs better than conventional nearest distance approach.

  15. Distributed semi-supervised support vector machines.

    PubMed

    Scardapane, Simone; Fierimonte, Roberto; Di Lorenzo, Paolo; Panella, Massimo; Uncini, Aurelio

    2016-08-01

    The semi-supervised support vector machine (S(3)VM) is a well-known algorithm for performing semi-supervised inference under the large margin principle. In this paper, we are interested in the problem of training a S(3)VM when the labeled and unlabeled samples are distributed over a network of interconnected agents. In particular, the aim is to design a distributed training protocol over networks, where communication is restricted only to neighboring agents and no coordinating authority is present. Using a standard relaxation of the original S(3)VM, we formulate the training problem as the distributed minimization of a non-convex social cost function. To find a (stationary) solution in a distributed manner, we employ two different strategies: (i) a distributed gradient descent algorithm; (ii) a recently developed framework for In-Network Nonconvex Optimization (NEXT), which is based on successive convexifications of the original problem, interleaved by state diffusion steps. Our experimental results show that the proposed distributed algorithms have comparable performance with respect to a centralized implementation, while highlighting the pros and cons of the proposed solutions. To the date, this is the first work that paves the way toward the broad field of distributed semi-supervised learning over networks. PMID:27179615

  16. Oilseed Rape Planting Area Extraction by Support Vector Machine Using Landsat TM Data

    NASA Astrophysics Data System (ADS)

    Wang, Yuan; Huang, Jingfeng; Wang, Xiuzhen; Wang, Fumin; Liu, Zhanyu; Xu, Junfeng

    One parametric classify (Maximum likelihood classify, MLC for short) and two non-parametric classifiers (Adaptive resonance theory mappings and Support vector machines, ARTMAP and SVM for short) were presented in this study. Base on the confusion matrix and the pixels fuzzy analysis, the nonparametric classifier may be a more preferable approach than the parametric classifier for some remote sensing applications and deserves further investigation. The ARTMAP classify represent much best than the rest of classify, especially for the grade of pure pixel (90-100% pureness), Kappa coefficients and overall accuracy were nearly 100%. The higher pureness the pixels were, the better classification accuracy was got.

  17. Product demand forecasts using wavelet kernel support vector machine and particle swarm optimization in manufacture system

    NASA Astrophysics Data System (ADS)

    Wu, Qi

    2010-03-01

    Demand forecasts play a crucial role in supply chain management. The future demand for a certain product is the basis for the respective replenishment systems. Aiming at demand series with small samples, seasonal character, nonlinearity, randomicity and fuzziness, the existing support vector kernel does not approach the random curve of the sales time series in the space (quadratic continuous integral space). In this paper, we present a hybrid intelligent system combining the wavelet kernel support vector machine and particle swarm optimization for demand forecasting. The results of application in car sale series forecasting show that the forecasting approach based on the hybrid PSOWv-SVM model is effective and feasible, the comparison between the method proposed in this paper and other ones is also given, which proves that this method is, for the discussed example, better than hybrid PSOv-SVM and other traditional methods.

  18. Support vector machines for automated gait classification.

    PubMed

    Begg, Rezaul K; Palaniswami, Marimuthu; Owen, Brendan

    2005-05-01

    Ageing influences gait patterns causing constant threats to control of locomotor balance. Automated recognition of gait changes has many advantages including, early identification of at-risk gait and monitoring the progress of treatment outcomes. In this paper, we apply an artificial intelligence technique [support vector machines (SVM)] for the automatic recognition of young-old gait types from their respective gait-patterns. Minimum foot clearance (MFC) data of 30 young and 28 elderly participants were analyzed using a PEAK-2D motion analysis system during a 20-min continuous walk on a treadmill at self-selected walking speed. Gait features extracted from individual MFC histogram-plot and Poincaré-plot images were used to train the SVM. Cross-validation test results indicate that the generalization performance of the SVM was on average 83.3% (+/-2.9) to recognize young and elderly gait patterns, compared to a neural network's accuracy of 75.0+/-5.0%. A "hill-climbing" feature selection algorithm demonstrated that a small subset (3-5) of gait features extracted from MFC plots could differentiate the gait patterns with 90% accuracy. Performance of the gait classifier was evaluated using areas under the receiver operating characteristic plots. Improved performance of the classifier was evident when trained with reduced number of selected good features and with radial basis function kernel. These results suggest that SVMs can function as an efficient gait classifier for recognition of young and elderly gait patterns, and has the potential for wider applications in gait identification for falls-risk minimization in the elderly. PMID:15887532

  19. Support Vector Machine Implementations for Classification & Clustering

    PubMed Central

    Winters-Hilt, Stephen; Yelundur, Anil; McChesney, Charlie; Landry, Matthew

    2006-01-01

    Background We describe Support Vector Machine (SVM) applications to classification and clustering of channel current data. SVMs are variational-calculus based methods that are constrained to have structural risk minimization (SRM), i.e., they provide noise tolerant solutions for pattern recognition. The SVM approach encapsulates a significant amount of model-fitting information in the choice of its kernel. In work thus far, novel, information-theoretic, kernels have been successfully employed for notably better performance over standard kernels. Currently there are two approaches for implementing multiclass SVMs. One is called external multi-class that arranges several binary classifiers as a decision tree such that they perform a single-class decision making function, with each leaf corresponding to a unique class. The second approach, namely internal-multiclass, involves solving a single optimization problem corresponding to the entire data set (with multiple hyperplanes). Results Each SVM approach encapsulates a significant amount of model-fitting information in its choice of kernel. In work thus far, novel, information-theoretic, kernels were successfully employed for notably better performance over standard kernels. Two SVM approaches to multiclass discrimination are described: (1) internal multiclass (with a single optimization), and (2) external multiclass (using an optimized decision tree). We describe benefits of the internal-SVM approach, along with further refinements to the internal-multiclass SVM algorithms that offer significant improvement in training time without sacrificing accuracy. In situations where the data isn't clearly separable, making for poor discrimination, signal clustering is used to provide robust and useful information – to this end, novel, SVM-based clustering methods are also described. As with the classification, there are Internal and External SVM Clustering algorithms, both of which are briefly described. PMID:17118147

  20. [The Identification of Lettuce Varieties by Using Unsupervised Possibilistic Fuzzy Learning Vector Quantization and Near Infrared Spectroscopy].

    PubMed

    Wu, Xiao-hong; Cai, Pei-qiang; Wu, Bin; Sun, Jun; Ji, Gang

    2016-03-01

    To solve the noisy sensitivity problem of fuzzy learning vector quantization (FLVQ), unsupervised possibilistic fuzzy learning vector quantization (UPFLVQ) was proposed based on unsupervised possibilistic fuzzy clustering (UPFC). UPFLVQ aimed to use fuzzy membership values and typicality values of UPFC to update the learning rate of learning vector quantization network and cluster centers. UPFLVQ is an unsupervised machine learning algorithm and it can be applied to classify without learning samples. UPFLVQ was used in the identification of lettuce varieties by near infrared spectroscopy (NIS). Short wave and long wave near infrared spectra of three types of lettuces were collected by FieldSpec@3 portable spectrometer in the wave-length range of 350-2 500 nm. When the near infrared spectra were compressed by principal component analysis (PCA), the first three principal components explained 97.50% of the total variance in near infrared spectra. After fuzzy c-means (FCM). clustering was performed for its cluster centers as the initial cluster centers of UPFLVQ, UPFLVQ could classify lettuce varieties with the terminal fuzzy membership values and typicality values. The experimental results showed that UPFLVQ together with NIS provided an effective method of identification of lettuce varieties with advantages such as fast testing, high accuracy rate and non-destructive characteristics. UPFLVQ is a clustering algorithm by combining UPFC and FLVQ, and it need not prepare any learning samples for the identification of lettuce varieties by NIS. UPFLVQ is suitable for linear separable data clustering and it provides a novel method for fast and nondestructive identification of lettuce varieties. PMID:27400511

  1. Vector fuzzy control iterative algorithm for the design of sub-wavelength diffractive optical elements for beam shaping

    NASA Astrophysics Data System (ADS)

    Lin, Yong; Hu, Jiasheng; Wu, Kenan

    2009-08-01

    The vector fuzzy control iterative algorithm (VFCIA) is proposed for the design of phase-only sub-wavelength diffractive optical elements (SWDOEs) for beam shaping. The vector diffraction model put forward by Mansuripur is applied to relate the field distributions between the SWDOE plane and the output plane. Fuzzy control theory is used to decide the constraint method for each iterative process of the algorithm. We have designed a SWDOE that transforms a circular flat-top beam to a square irradiance pattern. Computer design results show that the SWDOE designed by the VFCIA can produce better results than the vector iterative algorithm (VIA). And the finite difference time-domain method (FDTD), a rigorous electromagnetic analysis technique, is used to analyze the designed SWDOE for further confirming the validity of the proposed method.

  2. Fuzzy Neural Networks for Decision Support in Negotiation

    SciTech Connect

    Sakas, D. P.; Vlachos, D. S.; Simos, T. E.

    2008-11-06

    There is a large number of parameters which one can take into account when building a negotiation model. These parameters in general are uncertain, thus leading to models which represents them with fuzzy sets. On the other hand, the nature of these parameters makes them very difficult to model them with precise values. During negotiation, these parameters play an important role by altering the outcomes or changing the state of the negotiators. One reasonable way to model this procedure is to accept fuzzy relations (from theory or experience). The action of these relations to fuzzy sets, produce new fuzzy sets which describe now the new state of the system or the modified parameters. But, in the majority of these situations, the relations are multidimensional, leading to complicated models and exponentially increasing computational time. In this paper a solution to this problem is presented. The use of fuzzy neural networks is shown that it can substitute the use of fuzzy relations with comparable results. Finally a simple simulation is carried in order to test the new method.

  3. Fuzzy-Arden-Syntax-based, Vendor-agnostic, Scalable Clinical Decision Support and Monitoring Platform.

    PubMed

    Adlassnig, Klaus-Peter; Fehre, Karsten; Rappelsberger, Andrea

    2015-01-01

    This study's objective is to develop and use a scalable genuine technology platform for clinical decision support based on Arden Syntax, which was extended by fuzzy set theory and fuzzy logic. Arden Syntax is a widely recognized formal language for representing clinical and scientific knowledge in an executable format, and is maintained by Health Level Seven (HL7) International and approved by the American National Standards Institute (ANSI). Fuzzy set theory and logic permit the representation of knowledge and automated reasoning under linguistic and propositional uncertainty. These forms of uncertainty are a common feature of patients' medical data, the body of medical knowledge, and deductive clinical reasoning. PMID:26262410

  4. SPIDERz: SuPport vector classification for IDEntifying Redshifts

    NASA Astrophysics Data System (ADS)

    Jones, Evan; Singal, J.

    2016-08-01

    SPIDERz (SuPport vector classification for IDEntifying Redshifts) applies powerful support vector machine (SVM) optimization and statistical learning techniques to custom data sets to obtain accurate photometric redshift (photo-z) estimations. It is written for the IDL environment and can be applied to traditional data sets consisting of photometric band magnitudes, or alternatively to data sets with additional galaxy parameters (such as shape information) to investigate potential correlations between the extra galaxy parameters and redshift.

  5. Classification of input vectors of ANN model into "regular event" and "extreme event" subsets with fuzzy c-means algorithm

    NASA Astrophysics Data System (ADS)

    Kentel, Elcin

    2010-05-01

    Estimating future river flows is essential in water resources planning and management. Artificial neural network (ANN) models have been extensively utilized for rainfall-runoff modeling in the last decade. One of the major weaknesses of artificial neural network models is that they may fail to generate good estimates for extreme events, i.e. events that do not occur at all or often enough in the training set. If reliable estimates can be distinguished from unreliable ones, the former can be used with greater confidence in planning and management of the water resources. A fuzzy c-means algorithm is developed to cluster the estimates of the artificial neural networks into reliable and less-reliable river flow values (Kentel, 2009). The proposed algorithm is only tested for a single case (i.e. Güvenç River, Turkey) and produced promising results. In this study, applicability of the fuzzy c-means algorithm for different catchments in Turkey is tested. Three flow gauging stations are selected at four different catchments in mid and south Turkey. First, an ANN is developed for each gauging station; then fuzzy c-means algorithm is used together with the outputs of ANN models to test the success of the clustering algorithm in identifying input vectors that are susceptible to produce unreliable estimates. Results obtained for 12 gauging stations are used to identify the drawbacks of fuzzy c-means algorithm and to suggest modifications to improve the algorithm. Key words: Future river flow estimation; Artificial Neural Network; fuzzy c-means clustering Kentel, E. (2009) "Estimation of River Flow by Artificial Neural Networks and Identification of Input Vectors Susceptible to Producing Unreliable Flow Estimates," Journal of Hydrology, 375, 481-488.

  6. Bayesian clustering of fuzzy feature vectors using a quasi-likelihood approach.

    PubMed

    Marttinen, Pekka; Tang, Jing; De Baets, Bernard; Dawyndt, Peter; Corander, Jukka

    2009-01-01

    Bayesian model-based classifiers, both unsupervised and supervised, have been studied extensively and their value and versatility have been demonstrated on a wide spectrum of applications within science and engineering. A majority of the classifiers are built on the assumption of intrinsic discreteness of the considered data features or on the discretization of them prior to the modeling. On the other hand, Gaussian mixture classifiers have also been utilized to a large extent for continuous features in the Bayesian framework. Often the primary reason for discretization in the classification context is the simplification of the analytical and numerical properties of the models. However, the discretization can be problematic due to its \\textit{ad hoc} nature and the decreased statistical power to detect the correct classes in the resulting procedure. We introduce an unsupervised classification approach for fuzzy feature vectors that utilizes a discrete model structure while preserving the continuous characteristics of data. This is achieved by replacing the ordinary likelihood by a binomial quasi-likelihood to yield an analytical expression for the posterior probability of a given clustering solution. The resulting model can be justified from an information-theoretic perspective. Our method is shown to yield highly accurate clusterings for challenging synthetic and empirical data sets. PMID:19029547

  7. Surrogate-based Reliability Analysis Using Support Vector Machine

    NASA Astrophysics Data System (ADS)

    Li, Gang; Liu, Zhiqiang

    2010-05-01

    An approach of surrogate-based reliability analysis by support vector machine with Monte-Carlo simulation is proposed. The efficient sampling techniques, such as uniform design and Latin Hypercube sampling, are used, and the SVM is trained with the sample pairs of input and output data obtained by the finite element analysis. The trained SVM model, as a solver-surrogate model, is intended to approximate the real performance function. Considering the selection of parameters for SVM affects the learning performance of SVM strongly, the Genetic Algorithm (GA) is integrated to the construction of the SVM, by optimizing the relevant parameters. The influence of the parameters on SVM is discussed and a methodology is proposed for selecting the SVM model. Support Vector Classification (SVC) based and Support Vector Regression (SVR) based reliability analyses are studied. Some numerical examples demonstrate the efficiency and applicability of the proposed method.

  8. Subvoxel segmentation and representation of brain cortex using fuzzy clustering and gradient vector diffusion

    NASA Astrophysics Data System (ADS)

    Chang, Ming-Ching; Tao, Xiaodong

    2010-03-01

    Segmentation and representation of human brain cortex from Magnetic Resonance (MR) images is an important step for visualization and analysis in many neuro imaging applications. In this paper, we propose an automatic and fast algorithm to segment the brain cortex and to represent it as a geometric surface on which analysis can be carried out. The algorithm works on T1 weighted MR brain images with extracranial tissue removed. A fuzzy clustering algorithm with a parametric bias field model is applied to assign membership values of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) to each voxel. The cortical boundaries, namely the WM-GM and GM-CSF boundary surfaces, are extracted as iso-surfaces of functions derived from these membership functions. The central surface (CS), which traces the peak values (or ridges) of the GM membership function, is then extracted using gradient vector diffusion. Our main contribution is to provide a generic, accurate, fast, yet fully-automatic approach to (i) produce a soft segmentation of the MR brain image with intensity field correction, (ii) extract both the boundary and the center of the cortex in a surface form, where the topology and geometry can be explicitly examined, and (iii) use the extracted surfaces to model the curvy, folding cortical volume, which allows an intuitive measurement of the thickness. As a demonstration, we compute cortical thickness from the surfaces and compare the results with what has been reported in the literature. The entire process from raw MR image to cortical surface reconstruction takes on average between five to ten minutes.

  9. Identifying saltcedar with hyperspectral data and support vector machines

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Saltcedar (Tamarix spp.) are a group of dense phreatophytic shrubs and trees that are invasive to riparian areas throughout the United States. This study determined the feasibility of using hyperspectral data and a support vector machine (SVM) classifier to discriminate saltcedar from other cover t...

  10. Support Vector Machines: Relevance Feedback and Information Retrieval.

    ERIC Educational Resources Information Center

    Drucker, Harris; Shahrary, Behzad; Gibbon, David C.

    2002-01-01

    Compares support vector machines (SVMs) to Rocchio, Ide regular and Ide dec-hi algorithms in information retrieval (IR) of text documents using relevancy feedback. If the preliminary search is so poor that one has to search through many documents to find at least one relevant document, then SVM is preferred. Includes nine tables. (Contains 24…

  11. Support vector machines classifiers of physical activities in preschoolers

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The goal of this study is to develop, test, and compare multinomial logistic regression (MLR) and support vector machines (SVM) in classifying preschool-aged children physical activity data acquired from an accelerometer. In this study, 69 children aged 3-5 years old were asked to participate in a s...

  12. Using of support vector machines for link spam detection

    NASA Astrophysics Data System (ADS)

    Sharapov, Ruslan V.; Sharapova, Ekaterina V.

    2011-10-01

    In this article we described methods of link spam detection with using of machine learning. We analyzed main factors of link spam, which helps to find them. There is algorithm of link spam detection, based on support vector machines. The methods of link spam detection shows good results

  13. Diagnosis of Acute Coronary Syndrome with a Support Vector Machine.

    PubMed

    Berikol, Göksu Bozdereli; Yildiz, Oktay; Özcan, I Türkay

    2016-04-01

    Acute coronary syndrome (ACS) is a serious condition arising from an imbalance of supply and demand to meet myocardium's metabolic needs. Patients typically present with retrosternal chest pain radiating to neck and left arm. Electrocardiography (ECG) and laboratory tests are used indiagnosis. However in emergency departments, there are some difficulties for physicians to decide whether hospitalizing, following up or discharging the patient. The aim of the study is to diagnose ACS and helping the physician with his decisionto discharge or to hospitalizevia machine learning techniques such as support vector machine (SVM) by using patient data including age, sex, risk factors, and cardiac enzymes (CK-MB, Troponin I) of patients presenting to emergency department with chest pain. Clinical, laboratory, and imaging data of 228 patients presenting to emergency department with chest pain were reviewedand the performance of support vector machine. Four different methods (Support vector machine (SVM), Artificial neural network (ANN), Naïve Bayes and Logistic Regression) were tested and the results of SVM which has the highest accuracy is reported. Among 228 patients aged 19 to 91 years who were included in the study, 99 (43.4 %) were qualified as ACS, while 129 (56.5 %) had no ACS. The classification model using SVM attained a 99.13 % classification success. The present study showed a 99.13 % classification success for ACS diagnosis attained by Support Vector Machine. This study showed that machine learning techniques may help emergency department staff make decisions by rapidly producing relevant data. PMID:26815338

  14. Feature clustering in direct eigen-vector data reduction using support vector machines

    NASA Astrophysics Data System (ADS)

    Riasati, Vahid R.; Gao, Wenhue

    2012-06-01

    Principal Component Analysis (PCA) has been used in a variety of applications like feature extraction for classification, data compression and dimensionality reduction. Often, a small set of principal components are sufficient to capture the largest variations in the data. As a result, the eigen-values of the data covariance matrix with the lowest magnitude are ignored (along with their corresponding eigen-vectors) and the remaining eigenvectors are used for a 'coarse' representation of the data. It is well known that this process of choosing a few principal components naturally induces a loss in information from a signal reconstruction standpoint. We propose a new technique to represent the data in terms of a new set of basis vectors where the high-frequency detail is preserved, at the expense of a 'feature-scale blurring'. In other words, the 'blurring' that occurs due to possible colinearities in the bases vectors is relative to the eigen-features' scales; this is inherently different from a systematic blurring function. Instead of thresholding the eigen-values, we retain all eigen-values, and apply thresholds on the components of each eigen-vector separately. The resulting basis vectors can no longer be interpreted as eigenvectors and will, in general, lose their orthogonality properties, but offer benefits in terms of preserving detail that is crucial for classification tasks. We test the merits of this new basis representation for magnitude synthetic aperture radar (SAR) Automatic Target Recognition (ATR). A feature vector is obtained by projecting a SAR image onto the aforementioned basis. Decision engines such as support vector machines (SVMs) are trained on example feature vectors per class and ultimately used to recognize the target class in real-time. Experimental validation are performed on the MSTAR database and involve comparisons against a PCA based ATR algorithm.

  15. Fast support vector data descriptions for novelty detection.

    PubMed

    Liu, Yi-Hung; Liu, Yan-Chen; Chen, Yen-Jen

    2010-08-01

    Support vector data description (SVDD) has become a very attractive kernel method due to its good results in many novelty detection problems. However, the decision function of SVDD is expressed in terms of the kernel expansion, which results in a run-time complexity linear in the number of support vectors. For applications where fast real-time response is needed, how to speed up the decision function is crucial. This paper aims at dealing with the issue of reducing the testing time complexity of SVDD. A method called fast SVDD (F-SVDD) is proposed. Unlike the traditional methods which all try to compress a kernel expansion into one with fewer terms, the proposed F-SVDD directly finds the preimage of a feature vector, and then uses a simple relationship between this feature vector and the SVDD sphere center to re-express the center with a single vector. The decision function of F-SVDD contains only one kernel term, and thus the decision boundary of F-SVDD is only spherical in the original space. Hence, the run-time complexity of the F-SVDD decision function is no longer linear in the support vectors, but is a constant, no matter how large the training set size is. In this paper, we also propose a novel direct preimage-finding method, which is noniterative and involves no free parameters. The unique preimage can be obtained in real time by the proposed direct method without taking trial-and-error. For demonstration, several real-world data sets and a large-scale data set, the extended MIT face data set, are used in experiments. In addition, a practical industry example regarding liquid crystal display micro-defect inspection is also used to compare the applicability of SVDD and our proposed F-SVDD when faced with mass data input. The results are very encouraging. PMID:20639178

  16. Quantum Support Vector Machine for Big Data Classification

    NASA Astrophysics Data System (ADS)

    Rebentrost, Patrick; Mohseni, Masoud; Lloyd, Seth

    2014-09-01

    Supervised machine learning is the classification of new data based on already classified training examples. In this work, we show that the support vector machine, an optimized binary classifier, can be implemented on a quantum computer, with complexity logarithmic in the size of the vectors and the number of training examples. In cases where classical sampling algorithms require polynomial time, an exponential speedup is obtained. At the core of this quantum big data algorithm is a nonsparse matrix exponentiation technique for efficiently performing a matrix inversion of the training data inner-product (kernel) matrix.

  17. Fuzzy Logic-Supported Detection of Complex Geospatial Features in a Web Service Environment

    NASA Astrophysics Data System (ADS)

    He, L. L.; Di, L. P.; Yue, P.; Zhang, M. D.

    2013-10-01

    Spatial relations among simple features can be used to characterize complex geospatial features. These spatial relations are often represented using linguistic terms such as near, which have inherent vagueness and imprecision. Fuzzy logic can be used to modeling fuzziness of the terms. Once simple features are extracted from remote sensing imagery, degree of satisfaction of spatial relations among these simple features can be derived to detect complex features. The derivation process can be performed in a distributed service environment, which benefits Earth science society in the last decade. Workflow-based service can provide ondemand uncertainty-aware discovery of complex features in a distributed environment. A use case on the complex facility detection illustrates the applicability of the fuzzy logic-supported service-oriented approach.

  18. Clinical diagnosis support system based on case based fuzzy cognitive maps and semantic web.

    PubMed

    Douali, Nassim; De Roo, Jos; Jaulent, Marie-Christine

    2012-01-01

    Incorrect or improper diagnostic tests uses have important implications for health outcomes and costs. Clinical Decision Support Systems purports to optimize the use of diagnostic tests in clinical practice. The computerized medical reasoning should not only focus on existing medical knowledge but also on physician's previous experiences and new knowledge. Such medical knowledge is vague and defines uncertain relationships between facts and diagnosis, in this paper, Case Based Fuzzy Cognitive Maps (CBFCM) are proposed as an evolution of Fuzzy Cognitive Maps. They allow more complete representation of knowledge since case-based fuzzy rules are introduced to improve diagnosis decision. We have developed a framework for interacting with patient's data and formalizing knowledge from Guidelines in the domain of Urinary Tract Infection. The conducted study allowed us to test cognitive approaches for implementing Guidelines with Semantic Web tools. The advantage of this approach is to enable the sharing and reuse of knowledge from Guidelines, physicians experiences and simplify maintenance. PMID:22874199

  19. Fuzzy-supported estimator for HPO-AS process

    SciTech Connect

    Yin, M.T.; Yuan, W.; Stenstrom, M.K.

    1999-02-01

    Lack of real-time measurements is a major problem in the operation and control of the high-purity oxygen activated-sludge (HPO-AS) process. A real-time estimator using dissolved oxygen measurements in each stage and liquid flow rates was developed to estimate biomass and substrate concentrations, biomass growth, and decay rates. A fuzzy algorithm was used to estimate unmeasured variables, such as influent substrate and recycle biomass concentrations. The convergence of the algorithms used for the estimator is fast and stable, even with a large range of initial inputs and noisy dissolved oxygen measurements. The estimated results compare well with both plant data and synthetic results produced by a process model and corrupted with white noise. The difference in the predictions of single substrate and structure models is demonstrated. The estimator was tested successfully for certain types of process upsets, such as shock hydraulic loading and high diluted sludge volume index.

  20. Adaptive support vector regression for UAV flight control.

    PubMed

    Shin, Jongho; Jin Kim, H; Kim, Youdan

    2011-01-01

    This paper explores an application of support vector regression for adaptive control of an unmanned aerial vehicle (UAV). Unlike neural networks, support vector regression (SVR) generates global solutions, because SVR basically solves quadratic programming (QP) problems. With this advantage, the input-output feedback-linearized inverse dynamic model and the compensation term for the inversion error are identified off-line, which we call I-SVR (inversion SVR) and C-SVR (compensation SVR), respectively. In order to compensate for the inversion error and the unexpected uncertainty, an online adaptation algorithm for the C-SVR is proposed. Then, the stability of the overall error dynamics is analyzed by the uniformly ultimately bounded property in the nonlinear system theory. In order to validate the effectiveness of the proposed adaptive controller, numerical simulations are performed on the UAV model. PMID:20970303

  1. Evolutionary Support Vector Machines for Transient Stability Monitoring

    NASA Astrophysics Data System (ADS)

    Dora Arul Selvi, B.; Kamaraj, N.

    2012-03-01

    Currently, power systems are in the need of fast and reliable contingency monitoring systems for the purpose of maintaining stability in the presence of deregulated and open market environment. In this paper, a quick and unfailing transient stability monitoring algorithm that considers both the symmetrical and unsymmetrical faults is presented. support vector machines (SVMs) are employed as pattern classifiers so as to construct fast relation mappings between the transient stability results and the selected input attributes using mutual information. The type of fault is recognized by a SVM classifier and the critical clearing time of the fault is estimated by a support vector regression machine. The SVM parameters are tuned by an elitist multi-objective non-dominated sorting genetic algorithm in such a manner that the best classification and regression performance are accomplished. To demonstrate the good potential of the scheme, IEEE 3 generator system and a South Indian Grid are utilized.

  2. A novel support vector machine with globality-locality preserving.

    PubMed

    Ma, Cheng-Long; Yuan, Yu-Bo

    2014-01-01

    Support vector machine (SVM) is regarded as a powerful method for pattern classification. However, the solution of the primal optimal model of SVM is susceptible for class distribution and may result in a nonrobust solution. In order to overcome this shortcoming, an improved model, support vector machine with globality-locality preserving (GLPSVM), is proposed. It introduces globality-locality preserving into the standard SVM, which can preserve the manifold structure of the data space. We complete rich experiments on the UCI machine learning data sets. The results validate the effectiveness of the proposed model, especially on the Wine and Iris databases; the recognition rate is above 97% and outperforms all the algorithms that were developed from SVM. PMID:25045750

  3. Mathematical Modeling of spatial disease variables by Spatial Fuzzy Logic for Spatial Decision Support Systems

    NASA Astrophysics Data System (ADS)

    Platz, M.; Rapp, J.; Groessler, M.; Niehaus, E.; Babu, A.; Soman, B.

    2014-11-01

    A Spatial Decision Support System (SDSS) provides support for decision makers and should not be viewed as replacing human intelligence with machines. Therefore it is reasonable that decision makers are able to use a feature to analyze the provided spatial decision support in detail to crosscheck the digital support of the SDSS with their own expertise. Spatial decision support is based on risk and resource maps in a Geographic Information System (GIS) with relevant layers e.g. environmental, health and socio-economic data. Spatial fuzzy logic allows the representation of spatial properties with a value of truth in the range between 0 and 1. Decision makers can refer to the visualization of the spatial truth of single risk variables of a disease. Spatial fuzzy logic rules that support the allocation of limited resources according to risk can be evaluated with measure theory on topological spaces, which allows to visualize the applicability of this rules as well in a map. Our paper is based on the concept of a spatial fuzzy logic on topological spaces that contributes to the development of an adaptive Early Warning And Response System (EWARS) providing decision support for the current or future spatial distribution of a disease. It supports the decision maker in testing interventions based on available resources and apply risk mitigation strategies and provide guidance tailored to the geo-location of the user via mobile devices. The software component of the system would be based on open source software and the software developed during this project will also be in the open source domain, so that an open community can build on the results and tailor further work to regional or international requirements and constraints. A freely available EWARS Spatial Fuzzy Logic Demo was developed wich enables a user to visualize risk and resource maps based on individual data in several data formats.

  4. Classifier based on support vector machine for JET plasma configurationsa)

    NASA Astrophysics Data System (ADS)

    Dormido-Canto, S.; Farias, G.; Vega, J.; Dormido, R.; Sánchez, J.; Duro, N.; Vargas, H.; Murari, A.; Jet-Efda Contributors

    2008-10-01

    The last flux surface can be used to identify the plasma configuration of discharges. For automated recognition of JET configurations, a learning system based on support vector machines has been developed. Each configuration is described by 12 geometrical parameters. A multiclass system has been developed by means of the one-versus-the-rest approach. Results with eight simultaneous classes (plasma configurations) show a success rate close to 100%.

  5. Feed-forward support vector machine without multipliers.

    PubMed

    Anguita, Davide; Pischiutta, Stefano; Ridella, Sandro; Sterpi, Dario

    2006-09-01

    In this letter, we propose a coordinate rotation digital computer (CORDIC)-like algorithm for computing the feed-forward phase of a support vector machine (SVM) in fixed-point arithmetic, using only shift and add operations and avoiding resource-consuming multiplications. This result is obtained thanks to a hardware-friendly kernel, which greatly simplifies the SVM feed-forward phase computation and, at the same time, maintains good classification performance respect to the conventional Gaussian kernel. PMID:17001991

  6. An unsupervised support vector method for change detection

    NASA Astrophysics Data System (ADS)

    Bovolo, F.; Camps-Valls, G.; Bruzzone, L.

    2007-10-01

    This paper formulates the problem of distinguishing changed from unchanged pixels in remote sensing images as a minimum enclosing ball (MEB) problem with changed pixels as target class. The definition of the sphere shaped decision boundary with minimal volume that embraces changed pixels is approached in the context the support vector formalism adopting a support vector domain description (SVDD) one-class classifier. The SVDD maps the data into a high dimensional feature space where the spherical support of the high dimensional distribution of changed pixels is computed. The proposed formulation of the SVDD uses both target and outlier samples for defining the MEB, and is included here in an unsupervised system for change detection. For this purpose, nearly certain examples for the classes of both targets (i.e., changed pixels) and outliers (i.e., unchanged pixels) for training are identified based on thresholding the magnitude of spectral change vectors. Experimental results obtained on two different multitemporal and multispectral remote sensing images pointed out the effectiveness of the proposed method.

  7. Optimized support vector regression for drilling rate of penetration estimation

    NASA Astrophysics Data System (ADS)

    Bodaghi, Asadollah; Ansari, Hamid Reza; Gholami, Mahsa

    2015-12-01

    In the petroleum industry, drilling optimization involves the selection of operating conditions for achieving the desired depth with the minimum expenditure while requirements of personal safety, environment protection, adequate information of penetrated formations and productivity are fulfilled. Since drilling optimization is highly dependent on the rate of penetration (ROP), estimation of this parameter is of great importance during well planning. In this research, a novel approach called `optimized support vector regression' is employed for making a formulation between input variables and ROP. Algorithms used for optimizing the support vector regression are the genetic algorithm (GA) and the cuckoo search algorithm (CS). Optimization implementation improved the support vector regression performance by virtue of selecting proper values for its parameters. In order to evaluate the ability of optimization algorithms in enhancing SVR performance, their results were compared to the hybrid of pattern search and grid search (HPG) which is conventionally employed for optimizing SVR. The results demonstrated that the CS algorithm achieved further improvement on prediction accuracy of SVR compared to the GA and HPG as well. Moreover, the predictive model derived from back propagation neural network (BPNN), which is the traditional approach for estimating ROP, is selected for comparisons with CSSVR. The comparative results revealed the superiority of CSSVR. This study inferred that CSSVR is a viable option for precise estimation of ROP.

  8. Weighted K-means support vector machine for cancer prediction.

    PubMed

    Kim, SungHwan

    2016-01-01

    To date, the support vector machine (SVM) has been widely applied to diverse bio-medical fields to address disease subtype identification and pathogenicity of genetic variants. In this paper, I propose the weighted K-means support vector machine (wKM-SVM) and weighted support vector machine (wSVM), for which I allow the SVM to impose weights to the loss term. Besides, I demonstrate the numerical relations between the objective function of the SVM and weights. Motivated by general ensemble techniques, which are known to improve accuracy, I directly adopt the boosting algorithm to the newly proposed weighted KM-SVM (and wSVM). For predictive performance, a range of simulation studies demonstrate that the weighted KM-SVM (and wSVM) with boosting outperforms the standard KM-SVM (and SVM) including but not limited to many popular classification rules. I applied the proposed methods to simulated data and two large-scale real applications in the TCGA pan-cancer methylation data of breast and kidney cancer. In conclusion, the weighted KM-SVM (and wSVM) increases accuracy of the classification model, and will facilitate disease diagnosis and clinical treatment decisions to benefit patients. A software package (wSVM) is publicly available at the R-project webpage (https://www.r-project.org). PMID:27512621

  9. An improved method of fuzzy support degree based on uncertainty analysis

    NASA Astrophysics Data System (ADS)

    Huang, Yuan; Wu, Jing; Wu, Lihua; Sheng, Weidong

    2015-10-01

    Most multisensor association algorithms based on fuzzy set theory forms the opinion of fuzzy proposition using a simple triangular function. It does not take the randomness of measurements into account. Otherwise, the variance of sensors supposed to be known in the triangular function, but in fact the exact variance is difficult to acquire. This paper discuss about two situations with known and unknown variance of sensors. First, with known variance and known mean. This paper proposes a method, which use the probability ratio to calculate the fuzzy support degree. The interaction between the two objects is considered. Second, with unknown variance and known mean value, we replace the sample mean in the gray auto correlation function with the real sensor mean value to analysis the uncertainty which is the correlation coefficient between targets and measurements actually. In this way, it can deal with the case of small sample. Finally, form the opinion about the fuzzy proposition in terms of weighting the opinion of all the sensors based on the result of uncertainty analysis. Sufficient simulations on some typical scenarios are performed, and the results indicate that the method presented is efficient.

  10. Scorebox extraction from mobile sports videos using Support Vector Machines

    NASA Astrophysics Data System (ADS)

    Kim, Wonjun; Park, Jimin; Kim, Changick

    2008-08-01

    Scorebox plays an important role in understanding contents of sports videos. However, the tiny scorebox may give the small-display-viewers uncomfortable experience in grasping the game situation. In this paper, we propose a novel framework to extract the scorebox from sports video frames. We first extract candidates by using accumulated intensity and edge information after short learning period. Since there are various types of scoreboxes inserted in sports videos, multiple attributes need to be used for efficient extraction. Based on those attributes, the optimal information gain is computed and top three ranked attributes in terms of information gain are selected as a three-dimensional feature vector for Support Vector Machines (SVM) to distinguish the scorebox from other candidates, such as logos and advertisement boards. The proposed method is tested on various videos of sports games and experimental results show the efficiency and robustness of our proposed method.

  11. Neural cell image segmentation method based on support vector machine

    NASA Astrophysics Data System (ADS)

    Niu, Shiwei; Ren, Kan

    2015-10-01

    In the analysis of neural cell images gained by optical microscope, accurate and rapid segmentation is the foundation of nerve cell detection system. In this paper, a modified image segmentation method based on Support Vector Machine (SVM) is proposed to reduce the adverse impact caused by low contrast ratio between objects and background, adherent and clustered cells' interference etc. Firstly, Morphological Filtering and OTSU Method are applied to preprocess images for extracting the neural cells roughly. Secondly, the Stellate Vector, Circularity and Histogram of Oriented Gradient (HOG) features are computed to train SVM model. Finally, the incremental learning SVM classifier is used to classify the preprocessed images, and the initial recognition areas identified by the SVM classifier are added to the library as the positive samples for training SVM model. Experiment results show that the proposed algorithm can achieve much better segmented results than the classic segmentation algorithms.

  12. Fuzzy associative memories

    NASA Technical Reports Server (NTRS)

    Kosko, Bart

    1991-01-01

    Mappings between fuzzy cubes are discussed. This level of abstraction provides a surprising and fruitful alternative to the propositional and predicate-calculas reasoning techniques used in expert systems. It allows one to reason with sets instead of propositions. Discussed here are fuzzy and neural function estimators, neural vs. fuzzy representation of structured knowledge, fuzzy vector-matrix multiplication, and fuzzy associative memory (FAM) system architecture.

  13. Improvements on ν-Twin Support Vector Machine.

    PubMed

    Khemchandani, Reshma; Saigal, Pooja; Chandra, Suresh

    2016-07-01

    In this paper, we propose two novel binary classifiers termed as "Improvements on ν-Twin Support Vector Machine: Iν-TWSVM and Iν-TWSVM (Fast)" that are motivated by ν-Twin Support Vector Machine (ν-TWSVM). Similar to ν-TWSVM, Iν-TWSVM determines two nonparallel hyperplanes such that they are closer to their respective classes and are at least ρ distance away from the other class. The significant advantage of Iν-TWSVM over ν-TWSVM is that Iν-TWSVM solves one smaller-sized Quadratic Programming Problem (QPP) and one Unconstrained Minimization Problem (UMP); as compared to solving two related QPPs in ν-TWSVM. Further, Iν-TWSVM (Fast) avoids solving a smaller sized QPP and transforms it as a unimodal function, which can be solved using line search methods and similar to Iν-TWSVM, the other problem is solved as a UMP. Due to their novel formulation, the proposed classifiers are faster than ν-TWSVM and have comparable generalization ability. Iν-TWSVM also implements structural risk minimization (SRM) principle by introducing a regularization term, along with minimizing the empirical risk. The other properties of Iν-TWSVM, related to support vectors (SVs), are similar to that of ν-TWSVM. To test the efficacy of the proposed method, experiments have been conducted on a wide range of UCI and a skewed variation of NDC datasets. We have also given the application of Iν-TWSVM as a binary classifier for pixel classification of color images. PMID:27136663

  14. Tea category classification using morphological characteristics and support vector machines

    NASA Astrophysics Data System (ADS)

    Li, X. L.; He, Y.; Qiu, Z. J.; Bao, Y. D.

    2008-11-01

    Tea categories classification is an importance task for quality inspection. And traditional way for doing this by human is time-consuming, requirement of too much manual labor. This study proposed a method for discriminating green tea categories based on multi-spectral images technique. Four tea categories were selected for this study, and total of 243 multi-spectral images were collected using a common-aperture multi-spectral charged coupled device camera with three channels (550, 660 and 800 nm). A compound image which has the clearest outline of samples was process by combination of the three monochrome images (550, 660 and 800 nm). After image preprocessing, 18 morphometry parameters were obtained for each samples. The 18 parameters used including area, perimeter, centroid and eccentricity et al. To better understanding these parameters, principal component analysis was conducted on them, and score plot of the first three independent components was obtained. The first three components accounted for 99.02% of the variation of original 18 parameters. It can be found that the four tea categories were distributed in dense clusters respectively in score plot. But the boundaries among them were not clear, so a further discrimination must be developed. Three algorithms including support vector machines, artificial neural network and linear discriminant analysis were adopted for developed classification models based on the optimized 9 features. Wonderful result was obtained by support vector machines model with accuracy of 93.75% for prediction unknown samples in testing set. It can be concluded that it is an effective method to classification tea categories based on computer vision, and support vector machines is very specialized for development of classification model.

  15. Gene classification using codon usage and support vector machines.

    PubMed

    Ma, Jianmin; Nguyen, Minh N; Rajapakse, Jagath C

    2009-01-01

    A novel approach for gene classification, which adopts codon usage bias as input feature vector for classification by support vector machines (SVM) is proposed. The DNA sequence is first converted to a 59-dimensional feature vector where each element corresponds to the relative synonymous usage frequency of a codon. As the input to the classifier is independent of sequence length and variance, our approach is useful when the sequences to be classified are of different lengths, a condition that homology-based methods tend to fail. The method is demonstrated by using 1,841 Human Leukocyte Antigen (HLA) sequences which are classified into two major classes: HLA-I and HLA-II; each major class is further subdivided into sub-groups of HLA-I and HLA-II molecules. Using codon usage frequencies, binary SVM achieved accuracy rate of 99.3% for HLA major class classification and multi-class SVM achieved accuracy rates of 99.73% and 98.38% for sub-class classification of HLA-I and HLA-II molecules, respectively. The results show that gene classification based on codon usage bias is consistent with the molecular structures and biological functions of HLA molecules. PMID:19179707

  16. Objective image quality assessment based on support vector regression.

    PubMed

    Narwaria, Manish; Lin, Weisi

    2010-03-01

    Objective image quality estimation is useful in many visual processing systems, and is difficult to perform in line with the human perception. The challenge lies in formulating effective features and fusing them into a single number to predict the quality score. In this brief, we propose a new approach to address the problem, with the use of singular vectors out of singular value decomposition (SVD) as features for quantifying major structural information in images and then support vector regression (SVR) for automatic prediction of image quality. The feature selection with singular vectors is novel and general for gauging structural changes in images as a good representative of visual quality variations. The use of SVR exploits the advantages of machine learning with the ability to learn complex data patterns for an effective and generalized mapping of features into a desired score, in contrast with the oft-utilized feature pooling process in the existing image quality estimators; this is to overcome the difficulty of model parameter determination for such a system to emulate the related, complex human visual system (HVS) characteristics. Experiments conducted with three independent databases confirm the effectiveness of the proposed system in predicting image quality with better alignment with the HVS's perception than the relevant existing work. The tests with untrained distortions and databases further demonstrate the robustness of the system and the importance of the feature selection. PMID:20100674

  17. Applications of Support Vector Machines In Chemo And Bioinformatics

    NASA Astrophysics Data System (ADS)

    Jayaraman, V. K.; Sundararajan, V.

    2010-10-01

    Conventional linear & nonlinear tools for classification, regression & data driven modeling are being replaced on a rapid scale by newer techniques & tools based on artificial intelligence and machine learning. While the linear techniques are not applicable for inherently nonlinear problems, newer methods serve as attractive alternatives for solving real life problems. Support Vector Machine (SVM) classifiers are a set of universal feed-forward network based classification algorithms that have been formulated from statistical learning theory and structural risk minimization principle. SVM regression closely follows the classification methodology. In this work recent applications of SVM in Chemo & Bioinformatics will be described with suitable illustrative examples.

  18. Optimization of Support Vector Machine (SVM) for Object Classification

    NASA Technical Reports Server (NTRS)

    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.

  19. Support vector machine classifiers for large data sets.

    SciTech Connect

    Gertz, E. M.; Griffin, J. D.

    2006-01-31

    This report concerns the generation of support vector machine classifiers for solving the pattern recognition problem in machine learning. Several methods are proposed based on interior point methods for convex quadratic programming. Software implementations are developed by adapting the object-oriented packaging OOQP to the problem structure and by using the software package PETSc to perform time-intensive computations in a distributed setting. Linear systems arising from classification problems with moderately large numbers of features are solved by using two techniques--one a parallel direct solver, the other a Krylov-subspace method incorporating novel preconditioning strategies. Numerical results are provided, and computational experience is discussed.

  20. Reinforced adaboost face detector using support vector machine

    NASA Astrophysics Data System (ADS)

    Jang, Jaeyoon; Yunkoo, C.; Jaehong, K.; Yoon, Hosub

    2014-08-01

    We propose a novel face detection algorithm in order to improve higher detection rate of face-detector than conventional haar - adaboost face detector. Our purposed method not only improves detection rate of a face but decreases the number of false-positive component. In order to get improved detection ability, we merged two classifiers: adaboost and support vector machine. Because SVM and Adaboost use different feature, they are complementary each other. We can get 2~4% improved performance using proposed method than previous our detector that is not applied proposed method. This method makes improved detector that shows better performance without algorithm replacement.

  1. Cardiovascular Response Identification Based on Nonlinear Support Vector Regression

    NASA Astrophysics Data System (ADS)

    Wang, Lu; Su, Steven W.; Chan, Gregory S. H.; Celler, Branko G.; Cheng, Teddy M.; Savkin, Andrey V.

    This study experimentally investigates the relationships between central cardiovascular variables and oxygen uptake based on nonlinear analysis and modeling. Ten healthy subjects were studied using cycle-ergometry exercise tests with constant workloads ranging from 25 Watt to 125 Watt. Breath by breath gas exchange, heart rate, cardiac output, stroke volume and blood pressure were measured at each stage. The modeling results proved that the nonlinear modeling method (Support Vector Regression) outperforms traditional regression method (reducing Estimation Error between 59% and 80%, reducing Testing Error between 53% and 72%) and is the ideal approach in the modeling of physiological data, especially with small training data set.

  2. Vector control and fuzzy logic control of doubly fed variable speed drives with DSP implementation

    SciTech Connect

    Tang, Y.; Xu, L.

    1995-12-01

    Field orientation control and fuzzy logic control are designed for variable speed drive systems with a doubly fed machine in slip power recovery configuration. Laboratory implementation with a general purpose DSP (digital signal processing) system is described and experimental results are given. High performance potential of a slip power recovery system is realized with these advanced controls, while flexible reactive power control becomes possible, and compared to the ordinary variable speed drives with singly fed induction machine, power converter rating is reduced.

  3. Surgical planning for horizontal strabismus using Support Vector Regression.

    PubMed

    Almeida, João Dallyson Sousa de; Silva, Aristófanes Corrêa; Teixeira, Jorge Antonio Meireles; Paiva, Anselmo Cardoso; Gattass, Marcelo

    2015-08-01

    Strabismus is a pathology which affects about 4% of the population, causing esthetic problems (reversible at any age) and irreversible sensory disorders, altering the vision mechanism. Many techniques can be applied to settle the muscular balance, thus eliminating strabismus. However, when the conservative treatment is not enough, the surgical treatment is adopted, applying recoils or resections to the ocular muscles affected. The factors involved in the surgical strategy in cases of strabismus are complex, demanding both theoretical knowledge and experience from the surgeon. So, the present work proposes a methodology based on Support Vector Regression to help the physician with decision related to horizontal strabismus surgeries. The efficiency of the method at the indication of the surgical plan was evaluated through the average difference between the values that it provided and the values indicated by the specialists. In the planning of medial rectus muscles surgeries, the average error was 0.5mm for recoil and 0.7 for resection. For lateral rectus muscles, the mean error was 0.6 for recoil and 0.8 for resection. The results are promising and prove the feasibility of the use of Support Vector Regression in the indication of strabismus surgeries. PMID:26093785

  4. Biologically relevant neural network architectures for support vector machines.

    PubMed

    Jändel, Magnus

    2014-01-01

    Neural network architectures that implement support vector machines (SVM) are investigated for the purpose of modeling perceptual one-shot learning in biological organisms. A family of SVM algorithms including variants of maximum margin, 1-norm, 2-norm and ν-SVM is considered. SVM training rules adapted for neural computation are derived. It is found that competitive queuing memory (CQM) is ideal for storing and retrieving support vectors. Several different CQM-based neural architectures are examined for each SVM algorithm. Although most of the sixty-four scanned architectures are unconvincing for biological modeling four feasible candidates are found. The seemingly complex learning rule of a full ν-SVM implementation finds a particularly simple and natural implementation in bisymmetric architectures. Since CQM-like neural structures are thought to encode skilled action sequences and bisymmetry is ubiquitous in motor systems it is speculated that trainable pattern recognition in low-level perception has evolved as an internalized motor programme. PMID:24126252

  5. Clifford support vector machines for classification, regression, and recurrence.

    PubMed

    Bayro-Corrochano, Eduardo Jose; Arana-Daniel, Nancy

    2010-11-01

    This paper introduces the Clifford support vector machines (CSVM) as a generalization of the real and complex-valued support vector machines using the Clifford geometric algebra. In this framework, we handle the design of kernels involving the Clifford or geometric product. In this approach, one redefines the optimization variables as multivectors. This allows us to have a multivector as output. Therefore, we can represent multiple classes according to the dimension of the geometric algebra in which we work. We show that one can apply CSVM for classification and regression and also to build a recurrent CSVM. The CSVM is an attractive approach for the multiple input multiple output processing of high-dimensional geometric entities. We carried out comparisons between CSVM and the current approaches to solve multiclass classification and regression. We also study the performance of the recurrent CSVM with experiments involving time series. The authors believe that this paper can be of great use for researchers and practitioners interested in multiclass hypercomplex computing, particularly for applications in complex and quaternion signal and image processing, satellite control, neurocomputation, pattern recognition, computer vision, augmented virtual reality, robotics, and humanoids. PMID:20876017

  6. Support Vector Machine Diagnosis of Acute Abdominal Pain

    NASA Astrophysics Data System (ADS)

    Björnsdotter, Malin; Nalin, Kajsa; Hansson, Lars-Erik; Malmgren, Helge

    This study explores the feasibility of a decision-support system for patients seeking care for acute abdominal pain, and, specifically the diagnosis of acute diverticulitis. We used a linear support vector machine (SVM) to separate diverticulitis from all other reported cases of abdominal pain and from the important differential diagnosis non-specific abdominal pain (NSAP). On a database containing 3337 patients, the SVM obtained results comparable to those of the doctors in separating diverticulitis or NSAP from the remaining diseases. The distinction between diverticulitis and NSAP was, however, substantially improved by the SVM. For this patient group, the doctors achieved a sensitivity of 0.714 and a specificity of 0.963. When adjusted to the physicians' results, the SVM sensitivity/specificity was higher at 0.714/0.985 and 0.786/0.963 respectively. Age was found as the most important discriminative variable, closely followed by C-reactive protein level and lower left side pain.

  7. Automated identification of biomedical article type using support Vector machines

    NASA Astrophysics Data System (ADS)

    Kim, In Cheol; Le, Daniel X.; Thoma, George R.

    2011-01-01

    Authors of short papers such as letters or editorials often express complementary opinions, and sometimes contradictory ones, on related work in previously published articles. The MEDLINE® citations for such short papers are required to list bibliographic data on these "commented on" articles in a "CON" field. The challenge is to automatically identify the CON articles referred to by the author of the short paper (called "Comment-in" or CIN paper). Our approach is to use support vector machines (SVM) to first classify a paper as either a CIN or a regular full-length article (which is exempt from this requirement), and then to extract from the CIN paper the bibliographic data of the CON articles. A solution to the first part of the problem, identifying CIN articles, is addressed here. We implement and compare the performance of two types of SVM, one with a linear kernel function and the other with a radial basis kernel function (RBF). Input feature vectors for the SVMs are created by combining four types of features based on statistics of words in the article title, words that suggest the article type (letter, correspondence, editorial), size of body text, and cue phrases. Experiments conducted on a set of online biomedical articles show that the SVM with a linear kernel function yields a significantly lower false negative error rate than the one with an RBF. Our experiments also show that the SVM with a linear kernel function achieves a significantly higher level of accuracy, and lower false positive and false negative error rates by using input feature vectors created by combining all four types of features rather than any single type.

  8. Hybrid wavelet-support vector machine approach for modelling rainfall-runoff process.

    PubMed

    Komasi, Mehdi; Sharghi, Soroush

    2016-01-01

    Because of the importance of water resources management, the need for accurate modeling of the rainfall-runoff process has rapidly grown in the past decades. Recently, the support vector machine (SVM) approach has been used by hydrologists for rainfall-runoff modeling and the other fields of hydrology. Similar to the other artificial intelligence models, such as artificial neural network (ANN) and adaptive neural fuzzy inference system, the SVM model is based on the autoregressive properties. In this paper, the wavelet analysis was linked to the SVM model concept for modeling the rainfall-runoff process of Aghchai and Eel River watersheds. In this way, the main time series of two variables, rainfall and runoff, were decomposed to multiple frequent time series by wavelet theory; then, these time series were imposed as input data on the SVM model in order to predict the runoff discharge one day ahead. The obtained results show that the wavelet SVM model can predict both short- and long-term runoff discharges by considering the seasonality effects. Also, the proposed hybrid model is relatively more appropriate than classical autoregressive ones such as ANN and SVM because it uses the multi-scale time series of rainfall and runoff data in the modeling process. PMID:27120649

  9. Predictive based monitoring of nuclear plant component degradation using support vector regression

    SciTech Connect

    Agarwal, Vivek; Alamaniotis, Miltiadis; Tsoukalas, Lefteri H.

    2015-02-01

    Nuclear power plants (NPPs) are large installations comprised of many active and passive assets. Degradation monitoring of all these assets is expensive (labor cost) and highly demanding task. In this paper a framework based on Support Vector Regression (SVR) for online surveillance of critical parameter degradation of NPP components is proposed. In this case, on time replacement or maintenance of components will prevent potential plant malfunctions, and reduce the overall operational cost. In the current work, we apply SVR equipped with a Gaussian kernel function to monitor components. Monitoring includes the one-step-ahead prediction of the component’s respective operational quantity using the SVR model, while the SVR model is trained using a set of previous recorded degradation histories of similar components. Predictive capability of the model is evaluated upon arrival of a sensor measurement, which is compared to the component failure threshold. A maintenance decision is based on a fuzzy inference system that utilizes three parameters: (i) prediction evaluation in the previous steps, (ii) predicted value of the current step, (iii) and difference of current predicted value with components failure thresholds. The proposed framework will be tested on turbine blade degradation data.

  10. An Adaptive Supervisory Sliding Fuzzy Cerebellar Model Articulation Controller for Sensorless Vector-Controlled Induction Motor Drive Systems

    PubMed Central

    Wang, Shun-Yuan; Tseng, Chwan-Lu; Lin, Shou-Chuang; Chiu, Chun-Jung; Chou, Jen-Hsiang

    2015-01-01

    This paper presents the implementation of an adaptive supervisory sliding fuzzy cerebellar model articulation controller (FCMAC) in the speed sensorless vector control of an induction motor (IM) drive system. The proposed adaptive supervisory sliding FCMAC comprised a supervisory controller, integral sliding surface, and an adaptive FCMAC. The integral sliding surface was employed to eliminate steady-state errors and enhance the responsiveness of the system. The adaptive FCMAC incorporated an FCMAC with a compensating controller to perform a desired control action. The proposed controller was derived using the Lyapunov approach, which guarantees learning-error convergence. The implementation of three intelligent control schemes—the adaptive supervisory sliding FCMAC, adaptive sliding FCMAC, and adaptive sliding CMAC—were experimentally investigated under various conditions in a realistic sensorless vector-controlled IM drive system. The root mean square error (RMSE) was used as a performance index to evaluate the experimental results of each control scheme. The analysis results indicated that the proposed adaptive supervisory sliding FCMAC substantially improved the system performance compared with the other control schemes. PMID:25815450

  11. Support Vector Machines for Non-linear Geophysical Inversion

    NASA Astrophysics Data System (ADS)

    Kuzma, H. A.; Rector, J. W.

    2004-12-01

    Classical non-linear geophysical inversion can be simulated using computer learning via Support Vector Machines. Geophysical inverse problems are almost always ill-posed which means that many different models (i.e. descriptions of the earth) can be found to explain a given noisy or incomplete data set. Regularization and constraints encourage inversions to find physically realistic models. The set of preferred models needs to be defined a priori using as much geologic knowledge as is available. In inversion, it is assumed that data and a forward modeling process is known. The goal is to solve for a model. In the SVM paradigm, a series of models and associated data are known. The goal is to solve for a reverse modeling process. Starting with a series of initial models assembled using all available geologic information, synthetic data is created using the most realistic forward modeling program available. With the synthetic data as inputs and the known models as outputs, a Support Vector Machine is trained to approximate a local inverse to the forward modeling program. The advantages of this approach are that it is honest about the need to establish, a priori, the kinds of models that are reasonable in a particular field situation. There is no need to adjust the forward process to accommodate inversion, because SVMs can be easily modified to capture complicated, non-linear relationships. SVMs are transparent and require very little programming. If an SVM is trained using model/data pairs that are drawn from the same probability distribution that is implicit in the regularization of an inversion, then it will get very similar results to the inversion. Because SVMs can interpret as much data as desired so long as the conditions of an experiment do not change, they can be used to perform otherwise computationally expensive procedures. Support Vector Machines are trained to emulate non-linear seismic Amplitude Variation with Offset (AVO) inversions, gravity inversions

  12. Using support vector machines for anomalous change detonation

    SciTech Connect

    Theiler, James P; Steinwart, Ingo; Llamocca, Daniel

    2010-01-01

    We cast anomalous change detection as a binary classification problem, and use a support vector machine (SVM) to build a detector that does not depend on assumptions about the underlying data distribution. To speed up the computation, our SVM is implemented, in part, on a graphical processing unit. Results on real and simulated anomalous changes are used to compare performance to algorithms which effectively assume a Gaussian distribution. In this paper, we investigate the use of support vector machines (SVMs) with radial basis kernels for finding anomalous changes. Compared to typical applications of SVMs, we are operating in a regime of very low false alarm rate. This means that even for relatively large training sets, the data are quite meager in the regime of operational interest. This drives us to use larger training sets, which in turn places more of a computational burden on the SVM. We initially considered three different approaches to to address the need to work in the very low false alarm rate regime. The first is a standard SVM which is trained at one threshold (where more reliable estimates of false alarm rates are possible) and then re-thresholded for the low false alarm rate regime. The second uses the same thresholding approach, but employs a so-called least squares SVM; here a quadratic (instead of a hinge-based) loss function is employed, and for this model, there are good theoretical arguments in favor of adjusting the threshold in a straightforward manner. The third approach employs a weighted support vector machine, where the weights for the two types of errors (false alarm and missed detection) are automatically adjusted to achieve the desired false alarm rate. We have found in previous experiments (not shown here) that the first two types can in some cases work well, while in other cases they do not. This renders both approaches unreliable for automated change detection. By contrast, the third approach reliably produces good results, but at

  13. A Longitudinal Support Vector Regression for Prediction of ALS Score

    PubMed Central

    Du, Wei; Cheung, Huey; Goldberg, Ilya; Thambisetty, Madhav; Becker, Kevin; Johnson, Calvin A.

    2015-01-01

    Longitudinal studies play a key role in various fields, including epidemiology, clinical research, and genomic analysis. Currently, the most popular methods in longitudinal data analysis are model-driven regression approaches, which impose strong prior assumptions and are unable to scale to large problems in the manner of machine learning algorithms. In this work, we propose a novel longitudinal support vector regression (LSVR) algorithm that not only takes the advantage of one of the most popular machine learning methods, but also is able to model the temporal nature of longitudinal data by taking into account observational dependence within subjects. We test LSVR on publicly available data from the DREAM-Phil Bowen ALS Prediction Prize4Life challenge. Results suggest that LSVR is at a minimum competitive with favored machine learning methods and is able to outperform those methods in predicting ALS score one month in advance. PMID:27042700

  14. Extended robust support vector machine based on financial risk minimization.

    PubMed

    Takeda, Akiko; Fujiwara, Shuhei; Kanamori, Takafumi

    2014-11-01

    Financial risk measures have been used recently in machine learning. For example, ν-support vector machine ν-SVM) minimizes the conditional value at risk (CVaR) of margin distribution. The measure is popular in finance because of the subadditivity property, but it is very sensitive to a few outliers in the tail of the distribution. We propose a new classification method, extended robust SVM (ER-SVM), which minimizes an intermediate risk measure between the CVaR and value at risk (VaR) by expecting that the resulting model becomes less sensitive than ν-SVM to outliers. We can regard ER-SVM as an extension of robust SVM, which uses a truncated hinge loss. Numerical experiments imply the ER-SVM's possibility of achieving a better prediction performance with proper parameter setting. PMID:25058701

  15. Improved Online Support Vector Machines Spam Filtering Using String Kernels

    NASA Astrophysics Data System (ADS)

    Amayri, Ola; Bouguila, Nizar

    A major bottleneck in electronic communications is the enormous dissemination of spam emails. Developing of suitable filters that can adequately capture those emails and achieve high performance rate become a main concern. Support vector machines (SVMs) have made a large contribution to the development of spam email filtering. Based on SVMs, the crucial problems in email classification are feature mapping of input emails and the choice of the kernels. In this paper, we present thorough investigation of several distance-based kernels and propose the use of string kernels and prove its efficiency in blocking spam emails. We detail a feature mapping variants in text classification (TC) that yield improved performance for the standard SVMs in filtering task. Furthermore, to cope for realtime scenarios we propose an online active framework for spam filtering.

  16. Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms

    PubMed Central

    Hu, Zhongyi; Xiong, Tao

    2013-01-01

    Electricity load forecasting is an important issue that is widely explored and examined in power systems operation literature and commercial transactions in electricity markets literature as well. Among the existing forecasting models, support vector regression (SVR) has gained much attention. Considering the performance of SVR highly depends on its parameters; this study proposed a firefly algorithm (FA) based memetic algorithm (FA-MA) to appropriately determine the parameters of SVR forecasting model. In the proposed FA-MA algorithm, the FA algorithm is applied to explore the solution space, and the pattern search is used to conduct individual learning and thus enhance the exploitation of FA. Experimental results confirm that the proposed FA-MA based SVR model can not only yield more accurate forecasting results than the other four evolutionary algorithms based SVR models and three well-known forecasting models but also outperform the hybrid algorithms in the related existing literature. PMID:24459425

  17. Support vector regression for real-time flood stage forecasting

    NASA Astrophysics Data System (ADS)

    Yu, Pao-Shan; Chen, Shien-Tsung; Chang, I.-Fan

    2006-09-01

    SummaryFlood forecasting is an important non-structural approach for flood mitigation. The flood stage is chosen as the variable to be forecasted because it is practically useful in flood forecasting. The support vector machine, a novel artificial intelligence-based method developed from statistical learning theory, is adopted herein to establish a real-time stage forecasting model. The lags associated with the input variables are determined by applying the hydrological concept of the time of response, and a two-step grid search method is applied to find the optimal parameters, and thus overcome the difficulties in constructing the learning machine. Two structures of models used to perform multiple-hour-ahead stage forecasts are developed. Validation results from flood events in Lan-Yang River, Taiwan, revealed that the proposed models can effectively predict the flood stage forecasts one-to-six-hours ahead. Moreover, a sensitivity analysis was conducted on the lags associated with the input variables.

  18. Intelligent Quality Prediction Using Weighted Least Square Support Vector Regression

    NASA Astrophysics Data System (ADS)

    Yu, Yaojun

    A novel quality prediction method with mobile time window is proposed for small-batch producing process based on weighted least squares support vector regression (LS-SVR). The design steps and learning algorithm are also addressed. In the method, weighted LS-SVR is taken as the intelligent kernel, with which the small-batch learning is solved well and the nearer sample is set a larger weight, while the farther is set the smaller weight in the history data. A typical machining process of cutting bearing outer race is carried out and the real measured data are used to contrast experiment. The experimental results demonstrate that the prediction accuracy of the weighted LS-SVR based model is only 20%-30% that of the standard LS-SVR based one in the same condition. It provides a better candidate for quality prediction of small-batch producing process.

  19. Aero-Engine Condition Monitoring Based on Support Vector Machine

    NASA Astrophysics Data System (ADS)

    Zhang, Chunxiao; Wang, Nan

    The maintenance and management of civil aero-engine require advanced monitor approaches to estimate aero-engine performance and health in order to increase life of aero-engine and reduce maintenance costs. In this paper, we adopted support vector machine (SVM) regression approach to monitor an aero-engine health and condition by building monitoring models of main aero-engine performance parameters(EGT, N1, N2 and FF). The accuracy of nonlinear baseline models of performance parameters is tested and the maximum relative error does not exceed ±0.3%, which meets the engineering requirements. The results show that SVM nonlinear regression is an effective method in aero-engine monitoring.

  20. Support Vector Machines for Hyperspectral Remote Sensing Classification

    NASA Technical Reports Server (NTRS)

    Gualtieri, J. Anthony; Cromp, R. F.

    1998-01-01

    The Support Vector Machine provides a new way to design classification algorithms which learn from examples (supervised learning) and generalize when applied to new data. We demonstrate its success on a difficult classification problem from hyperspectral remote sensing, where we obtain performances of 96%, and 87% correct for a 4 class problem, and a 16 class problem respectively. These results are somewhat better than other recent results on the same data. A key feature of this classifier is its ability to use high-dimensional data without the usual recourse to a feature selection step to reduce the dimensionality of the data. For this application, this is important, as hyperspectral data consists of several hundred contiguous spectral channels for each exemplar. We provide an introduction to this new approach, and demonstrate its application to classification of an agriculture scene.

  1. Support Vector Machine with Ensemble Tree Kernel for Relation Extraction.

    PubMed

    Liu, Xiaoyong; Fu, Hui; Du, Zhiguo

    2016-01-01

    Relation extraction is one of the important research topics in the field of information extraction research. To solve the problem of semantic variation in traditional semisupervised relation extraction algorithm, this paper proposes a novel semisupervised relation extraction algorithm based on ensemble learning (LXRE). The new algorithm mainly uses two kinds of support vector machine classifiers based on tree kernel for integration and integrates the strategy of constrained extension seed set. The new algorithm can weaken the inaccuracy of relation extraction, which is caused by the phenomenon of semantic variation. The numerical experimental research based on two benchmark data sets (PropBank and AIMed) shows that the LXRE algorithm proposed in the paper is superior to other two common relation extraction methods in four evaluation indexes (Precision, Recall, F-measure, and Accuracy). It indicates that the new algorithm has good relation extraction ability compared with others. PMID:27118966

  2. Image replica detection based on support vector classifier

    NASA Astrophysics Data System (ADS)

    Maret, Y.; Dufaux, F.; Ebrahimi, T.

    2005-08-01

    In this paper, we propose a technique for image replica detection. By replica, we mean equivalent versions of a given reference image, e.g. after it has undergone operations such as compression, filtering or resizing. Applications of this technique include discovery of copyright infringement or detection of illicit content. The technique is based on the extraction of multiple features from an image, namely texture, color, and spatial distribution of colors. Similar features are then grouped into groups and the similarity between two images is given by several partial distances. The decision function to decide whether a test image is a replica of a given reference image is finally derived using Support Vector Classifier (SVC). In this paper, we show that this technique achieves good results on a large database of images. For instance, for a false negative rate of 5 % the system yields a false positive rate of only 6 " 10-5.

  3. Support Vector Machine with Ensemble Tree Kernel for Relation Extraction

    PubMed Central

    Fu, Hui; Du, Zhiguo

    2016-01-01

    Relation extraction is one of the important research topics in the field of information extraction research. To solve the problem of semantic variation in traditional semisupervised relation extraction algorithm, this paper proposes a novel semisupervised relation extraction algorithm based on ensemble learning (LXRE). The new algorithm mainly uses two kinds of support vector machine classifiers based on tree kernel for integration and integrates the strategy of constrained extension seed set. The new algorithm can weaken the inaccuracy of relation extraction, which is caused by the phenomenon of semantic variation. The numerical experimental research based on two benchmark data sets (PropBank and AIMed) shows that the LXRE algorithm proposed in the paper is superior to other two common relation extraction methods in four evaluation indexes (Precision, Recall, F-measure, and Accuracy). It indicates that the new algorithm has good relation extraction ability compared with others. PMID:27118966

  4. Bullet Image Classification using Support Vector Machine (SVM)

    NASA Astrophysics Data System (ADS)

    Ratna S, Dwi; Setyono, Budi; Herdha, Tyara

    2016-02-01

    All Quality control of the bullet is usually done manually. Manual inspection process has some disadvantages for instance human error factor and the time inspection are relatively longer. In this study, defect detection and classification of bullets by using Support Vector Machine (SVM)has been done. The stages of classification of scheme bullets in this study include pre-processing, feature extraction with Principal Component Analysis (PCA) and classification of bullets by using SVM normalized. Performance of the proposed classification of scheme was tested using 80 images of bullets in which 40 images free of defects and 40 images with defect. The results show that the scheme can classify images of bullets with a 90% accuracy rate with test data in the form of 10 images free-defect and 10 image defects.

  5. An assessment of support vector machines for land cover classification

    USGS Publications Warehouse

    Huang, C.; Davis, L.S.; Townshend, J.R.G.

    2002-01-01

    The support vector machine (SVM) is a group of theoretically superior machine learning algorithms. It was found competitive with the best available machine learning algorithms in classifying high-dimensional data sets. This paper gives an introduction to the theoretical development of the SVM and an experimental evaluation of its accuracy, stability and training speed in deriving land cover classifications from satellite images. The SVM was compared to three other popular classifiers, including the maximum likelihood classifier (MLC), neural network classifiers (NNC) and decision tree classifiers (DTC). The impacts of kernel configuration on the performance of the SVM and of the selection of training data and input variables on the four classifiers were also evaluated in this experiment.

  6. Finger vein image quality evaluation using support vector machines

    NASA Astrophysics Data System (ADS)

    Yang, Lu; Yang, Gongping; Yin, Yilong; Xiao, Rongyang

    2013-02-01

    In an automatic finger-vein recognition system, finger-vein image quality is significant for segmentation, enhancement, and matching processes. In this paper, we propose a finger-vein image quality evaluation method using support vector machines (SVMs). We extract three features including the gradient, image contrast, and information capacity from the input image. An SVM model is built on the training images with annotated quality labels (i.e., high/low) and then applied to unseen images for quality evaluation. To resolve the class-imbalance problem in the training data, we perform oversampling for the minority class with random-synthetic minority oversampling technique. Cross-validation is also employed to verify the reliability and stability of the learned model. Our experimental results show the effectiveness of our method in evaluating the quality of finger-vein images, and by discarding low-quality images detected by our method, the overall finger-vein recognition performance is considerably improved.

  7. Material characterization via least squares support vector machines

    NASA Astrophysics Data System (ADS)

    Swaddiwudhipong, S.; Tho, K. K.; Liu, Z. S.; Hua, J.; Ooi, N. S. B.

    2005-09-01

    Analytical methods to interpret the load indentation curves are difficult to formulate and execute directly due to material and geometric nonlinearities as well as complex contact interactions. In the present study, a new approach based on the least squares support vector machines (LS-SVMs) is adopted in the characterization of materials obeying power law strain-hardening. The input data for training and verification of the LS-SVM model are obtained from 1000 large strain-large deformation finite element analyses which were carried out earlier to simulate indentation tests. The proposed LS-SVM model relates the characteristics of the indentation load-displacement curve directly to the elasto-plastic material properties without resorting to any iterative schemes. The tuned LS-SVM model is able to accurately predict the material properties when presented with new sets of load-indentation curves which were not used in the training and verification of the model.

  8. HYBRID NEURAL NETWORK AND SUPPORT VECTOR MACHINE METHOD FOR OPTIMIZATION

    NASA Technical Reports Server (NTRS)

    Rai, Man Mohan (Inventor)

    2005-01-01

    System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

  9. Hybrid Neural Network and Support Vector Machine Method for Optimization

    NASA Technical Reports Server (NTRS)

    Rai, Man Mohan (Inventor)

    2007-01-01

    System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

  10. Segmentation of mosaicism in cervicographic images using support vector machines

    NASA Astrophysics Data System (ADS)

    Xue, Zhiyun; Long, L. Rodney; Antani, Sameer; Jeronimo, Jose; Thoma, George R.

    2009-02-01

    The National Library of Medicine (NLM), in collaboration with the National Cancer Institute (NCI), is creating a large digital repository of cervicographic images for the study of uterine cervix cancer prevention. One of the research goals is to automatically detect diagnostic bio-markers in these images. Reliable bio-marker segmentation in large biomedical image collections is a challenging task due to the large variation in image appearance. Methods described in this paper focus on segmenting mosaicism, which is an important vascular feature used to visually assess the degree of cervical intraepithelial neoplasia. The proposed approach uses support vector machines (SVM) trained on a ground truth dataset annotated by medical experts (which circumvents the need for vascular structure extraction). We have evaluated the performance of the proposed algorithm and experimentally demonstrated its feasibility.

  11. Application of Support Vector Machine to Forex Monitoring

    NASA Astrophysics Data System (ADS)

    Kamruzzaman, Joarder; Sarker, Ruhul A.

    Previous studies have demonstrated superior performance of artificial neural network (ANN) based forex forecasting models over traditional regression models. This paper applies support vector machines to build a forecasting model from the historical data using six simple technical indicators and presents a comparison with an ANN based model trained by scaled conjugate gradient (SCG) learning algorithm. The models are evaluated and compared on the basis of five commonly used performance metrics that measure closeness of prediction as well as correctness in directional change. Forecasting results of six different currencies against Australian dollar reveal superior performance of SVM model using simple linear kernel over ANN-SCG model in terms of all the evaluation metrics. The effect of SVM parameter selection on prediction performance is also investigated and analyzed.

  12. 3D ultrasound image segmentation using wavelet support vector machines

    PubMed Central

    Akbari, Hamed; Fei, Baowei

    2012-01-01

    Purpose: Transrectal ultrasound (TRUS) imaging is clinically used in prostate biopsy and therapy. Segmentation of the prostate on TRUS images has many applications. In this study, a three-dimensional (3D) segmentation method for TRUS images of the prostate is presented for 3D ultrasound-guided biopsy. Methods: This segmentation method utilizes a statistical shape, texture information, and intensity profiles. A set of wavelet support vector machines (W-SVMs) is applied to the images at various subregions of the prostate. The W-SVMs are trained to adaptively capture the features of the ultrasound images in order to differentiate the prostate and nonprostate tissue. This method consists of a set of wavelet transforms for extraction of prostate texture features and a kernel-based support vector machine to classify the textures. The voxels around the surface of the prostate are labeled in sagittal, coronal, and transverse planes. The weight functions are defined for each labeled voxel on each plane and on the model at each region. In the 3D segmentation procedure, the intensity profiles around the boundary between the tentatively labeled prostate and nonprostate tissue are compared to the prostate model. Consequently, the surfaces are modified based on the model intensity profiles. The segmented prostate is updated and compared to the shape model. These two steps are repeated until they converge. Manual segmentation of the prostate serves as the gold standard and a variety of methods are used to evaluate the performance of the segmentation method. Results: The results from 40 TRUS image volumes of 20 patients show that the Dice overlap ratio is 90.3% ± 2.3% and that the sensitivity is 87.7% ± 4.9%. Conclusions: The proposed method provides a useful tool in our 3D ultrasound image-guided prostate biopsy and can also be applied to other applications in the prostate. PMID:22755682

  13. Complex support vector machines for regression and quaternary classification.

    PubMed

    Bouboulis, Pantelis; Theodoridis, Sergios; Mavroforakis, Charalampos; Evaggelatou-Dalla, Leoni

    2015-06-01

    The paper presents a new framework for complex support vector regression (SVR) as well as Support Vector Machines (SVM) for quaternary classification. The method exploits the notion of widely linear estimation to model the input-out relation for complex-valued data and considers two cases: 1) the complex data are split into their real and imaginary parts and a typical real kernel is employed to map the complex data to a complexified feature space and 2) a pure complex kernel is used to directly map the data to the induced complex feature space. The recently developed Wirtinger's calculus on complex reproducing kernel Hilbert spaces is employed to compute the Lagrangian and derive the dual optimization problem. As one of our major results, we prove that any complex SVM/SVR task is equivalent with solving two real SVM/SVR tasks exploiting a specific real kernel, which is generated by the chosen complex kernel. In particular, the case of pure complex kernels leads to the generation of new kernels, which have not been considered before. In the classification case, the proposed framework inherently splits the complex space into four parts. This leads naturally to solving the four class-task (quaternary classification), instead of the typical two classes of the real SVM. In turn, this rationale can be used in a multiclass problem as a split-class scenario based on four classes, as opposed to the one-versus-all method; this can lead to significant computational savings. Experiments demonstrate the effectiveness of the proposed framework for regression and classification tasks that involve complex data. PMID:25095266

  14. Emergency response nurse scheduling with medical support robot by multi-agent and fuzzy technique.

    PubMed

    Kono, Shinya; Kitamura, Akira

    2015-08-01

    In this paper, a new co-operative re-scheduling method corresponding the medical support tasks that the time of occurrence can not be predicted is described, assuming robot can co-operate medical activities with the nurse. Here, Multi-Agent-System (MAS) is used for the co-operative re-scheduling, in which Fuzzy-Contract-Net (FCN) is applied to the robots task assignment for the emergency tasks. As the simulation results, it is confirmed that the re-scheduling results by the proposed method can keep the patients satisfaction and decrease the work load of the nurse. PMID:26738190

  15. A Distributed Support Vector Machine Learning Over Wireless Sensor Networks.

    PubMed

    Kim, Woojin; Stanković, Milos S; Johansson, Karl H; Kim, H Jin

    2015-11-01

    This paper is about fully-distributed support vector machine (SVM) learning over wireless sensor networks. With the concept of the geometric SVM, we propose to gossip the set of extreme points of the convex hull of local data set with neighboring nodes. It has the advantages of a simple communication mechanism and finite-time convergence to a common global solution. Furthermore, we analyze the scalability with respect to the amount of exchanged information and convergence time, with a specific emphasis on the small-world phenomenon. First, with the proposed naive convex hull algorithm, the message length remains bounded as the number of nodes increases. Second, by utilizing a small-world network, we have an opportunity to drastically improve the convergence performance with only a small increase in power consumption. These properties offer a great advantage when dealing with a large-scale network. Simulation and experimental results support the feasibility and effectiveness of the proposed gossip-based process and the analysis. PMID:26470063

  16. Effective forecasting of hourly typhoon rainfall using support vector machines

    NASA Astrophysics Data System (ADS)

    Lin, Gwo-Fong; Chen, Guo-Rong; Wu, Ming-Chang; Chou, Yang-Ching

    2009-08-01

    Typhoon rainfall is one of the most difficult elements of the hydrologic cycle to forecast because of the high variability in space and time and the complex physical process. To obtain more effective forecasts of hourly typhoon rainfall, novel models with better ability are desired. On the basis of support vector machines (SVMs), which are a novel kind of neural networks (NNs), effective hourly typhoon rainfall forecasting models are constructed. As compared with backpropagation networks (BPNs), which are the most frequently used conventional NNs, SVMs have three advantages: (1) SVMs have better generalization ability, (2) the architectures and the weights of the SVMs are guaranteed to be unique and globally optimal, and (3) SVM is trained much more rapidly. An application is conducted to clearly demonstrate these three advantages. The results indicate that the proposed SVM-based models are better performed, robust, and efficient than the existing BPN-based models. To further improve the long lead time forecasting, typhoon characteristics are added as key input to the proposed models. The comparison between SVM-based models with and without typhoon characteristics confirms the significant improvement in forecasting performance due to the addition of typhoon characteristics for long lead time forecasting. The proposed SVM-based models are recommended as an alternative to the existing models. The proposed modeling technique is also expected to be useful to support reservoir operation systems and flood, landslide, debris flow, and other disaster warning systems.

  17. Support vector machines classifiers of physical activities in preschoolers

    PubMed Central

    Zhao, Wei; Adolph, Anne L; Puyau, Maurice R; Vohra, Firoz A; Butte, Nancy F; Zakeri, Issa F

    2013-01-01

    The goal of this study is to develop, test, and compare multinomial logistic regression (MLR) and support vector machines (SVM) in classifying preschool-aged children physical activity data acquired from an accelerometer. In this study, 69 children aged 3–5 years old were asked to participate in a supervised protocol of physical activities while wearing a triaxial accelerometer. Accelerometer counts, steps, and position were obtained from the device. We applied K-means clustering to determine the number of natural groupings presented by the data. We used MLR and SVM to classify the six activity types. Using direct observation as the criterion method, the 10-fold cross-validation (CV) error rate was used to compare MLR and SVM classifiers, with and without sleep. Altogether, 58 classification models based on combinations of the accelerometer output variables were developed. In general, the SVM classifiers have a smaller 10-fold CV error rate than their MLR counterparts. Including sleep, a SVM classifier provided the best performance with a 10-fold CV error rate of 24.70%. Without sleep, a SVM classifier-based triaxial accelerometer counts, vector magnitude, steps, position, and 1- and 2-min lag and lead values achieved a 10-fold CV error rate of 20.16% and an overall classification error rate of 15.56%. SVM supersedes the classical classifier MLR in categorizing physical activities in preschool-aged children. Using accelerometer data, SVM can be used to correctly classify physical activities typical of preschool-aged children with an acceptable classification error rate. PMID:24303099

  18. Application of probabilistic and fuzzy cognitive approaches in semantic web framework for medical decision support.

    PubMed

    Papageorgiou, Elpiniki I; Huszka, Csaba; De Roo, Jos; Douali, Nassim; Jaulent, Marie-Christine; Colaert, Dirk

    2013-12-01

    This study aimed to focus on medical knowledge representation and reasoning using the probabilistic and fuzzy influence processes, implemented in the semantic web, for decision support tasks. Bayesian belief networks (BBNs) and fuzzy cognitive maps (FCMs), as dynamic influence graphs, were applied to handle the task of medical knowledge formalization for decision support. In order to perform reasoning on these knowledge models, a general purpose reasoning engine, EYE, with the necessary plug-ins was developed in the semantic web. The two formal approaches constitute the proposed decision support system (DSS) aiming to recognize the appropriate guidelines of a medical problem, and to propose easily understandable course of actions to guide the practitioners. The urinary tract infection (UTI) problem was selected as the proof-of-concept example to examine the proposed formalization techniques implemented in the semantic web. The medical guidelines for UTI treatment were formalized into BBN and FCM knowledge models. To assess the formal models' performance, 55 patient cases were extracted from a database and analyzed. The results showed that the suggested approaches formalized medical knowledge efficiently in the semantic web, and gave a front-end decision on antibiotics' suggestion for UTI. PMID:23953959

  19. Fuzzy Based Decision Support System for Condition Assessment and Rating of Bridges

    NASA Astrophysics Data System (ADS)

    Srinivas, Voggu; Sasmal, Saptarshi; Karusala, Ramanjaneyulu

    2016-06-01

    In this work, a knowledge based decision support system has been developed to efficiently handle the issues such as distress diagnosis, assessment of damages and condition rating of existing bridges towards developing an exclusive and robust Bridge Management System (BMS) for sustainable bridges. The Knowledge Based Expert System (KBES) diagnoses the distresses and finds the cause of distress in the bridge by processing the data which are heuristic and combined with site inspection results, laboratory test results etc. The coupling of symbolic and numeric type of data has been successfully implemented in the expert system to strengthen its decision making process. Finally, the condition rating of the bridge is carried out using the assessment results obtained from the KBES and the information received from the bridge inspector. A systematic procedure has been developed using fuzzy mathematics for condition rating of bridges by combining the fuzzy weighted average and resolution identity technique. The proposed methodologies and the decision support system will facilitate in developing a robust and exclusive BMS for a network of bridges across the country and allow the bridge engineers and decision makers to carry out maintenance of bridges in a rational and systematic way.

  20. Data filtering with support vector machines in geometric camera calibration.

    PubMed

    Ergun, B; Kavzoglu, T; Colkesen, I; Sahin, C

    2010-02-01

    The use of non-metric digital cameras in close-range photogrammetric applications and machine vision has become a popular research agenda. Being an essential component of photogrammetric evaluation, camera calibration is a crucial stage for non-metric cameras. Therefore, accurate camera calibration and orientation procedures have become prerequisites for the extraction of precise and reliable 3D metric information from images. The lack of accurate inner orientation parameters can lead to unreliable results in the photogrammetric process. A camera can be well defined with its principal distance, principal point offset and lens distortion parameters. Different camera models have been formulated and used in close-range photogrammetry, but generally sensor orientation and calibration is performed with a perspective geometrical model by means of the bundle adjustment. In this study, support vector machines (SVMs) using radial basis function kernel is employed to model the distortions measured for Olympus Aspherical Zoom lens Olympus E10 camera system that are later used in the geometric calibration process. It is intended to introduce an alternative approach for the on-the-job photogrammetric calibration stage. Experimental results for DSLR camera with three focal length settings (9, 18 and 36 mm) were estimated using bundle adjustment with additional parameters, and analyses were conducted based on object point discrepancies and standard errors. Results show the robustness of the SVMs approach on the correction of image coordinates by modelling total distortions on-the-job calibration process using limited number of images. PMID:20174021

  1. River stage prediction based on a distributed support vector regression

    NASA Astrophysics Data System (ADS)

    Wu, C. L.; Chau, K. W.; Li, Y. S.

    2008-08-01

    SummaryAn accurate and timely prediction of river flow flooding can provide time for the authorities to take pertinent flood protection measures such as evacuation. Various data-derived models including LR (linear regression), NNM (the nearest-neighbor method) ANN (artificial neural network) and SVR (support vector regression), have been successfully applied to water level prediction. Of them, SVR is particularly highly valued, because it has the advantage over many data-derived models in overcoming overfitting of training data. However, SVR is computationally time-consuming when used to solve large-size problems. In the context of river flow prediction, equipped with LR model as a benchmark and genetic algorithm-based ANN (ANN-GA) and NNM as counterparts, a novel distributed SVR (D-SVR) model is proposed in this study. It implements a local approximation to training data because partitioned original training data are independently fitted by each local SVR model. ANN-GA and LR models are also used to help determine input variables. A two-step GA algorithm is employed to find the optimal triplets ( C, ɛ, σ) for D-SVR model. The validation results reveal that the proposed D-SVR model can carry out the river flow prediction better in comparison with others, and dramatically reduce the training time compared with the conventional SVR model. The pivotal factor contributing to the performance of D-SVR may be that it implements a local approximation method and the principle of structural risk minimization.

  2. Support Vector Machine algorithm for regression and classification

    SciTech Connect

    Yu, Chenggang; Zavaljevski, Nela

    2001-08-01

    The software is an implementation of the Support Vector Machine (SVM) algorithm that was invented and developed by Vladimir Vapnik and his co-workers at AT&T Bell Laboratories. The specific implementation reported here is an Active Set method for solving a quadratic optimization problem that forms the major part of any SVM program. The implementation is tuned to specific constraints generated in the SVM learning. Thus, it is more efficient than general-purpose quadratic optimization programs. A decomposition method has been implemented in the software that enables processing large data sets. The size of the learning data is virtually unlimited by the capacity of the computer physical memory. The software is flexible and extensible. Two upper bounds are implemented to regulate the SVM learning for classification, which allow users to adjust the false positive and false negative rates. The software can be used either as a standalone, general-purpose SVM regression or classification program, or be embedded into a larger software system.

  3. Support vector machines for nuclear reactor state estimation

    SciTech Connect

    Zavaljevski, N.; Gross, K. C.

    2000-02-14

    Validation of nuclear power reactor signals is often performed by comparing signal prototypes with the actual reactor signals. The signal prototypes are often computed based on empirical data. The implementation of an estimation algorithm which can make predictions on limited data is an important issue. A new machine learning algorithm called support vector machines (SVMS) recently developed by Vladimir Vapnik and his coworkers enables a high level of generalization with finite high-dimensional data. The improved generalization in comparison with standard methods like neural networks is due mainly to the following characteristics of the method. The input data space is transformed into a high-dimensional feature space using a kernel function, and the learning problem is formulated as a convex quadratic programming problem with a unique solution. In this paper the authors have applied the SVM method for data-based state estimation in nuclear power reactors. In particular, they implemented and tested kernels developed at Argonne National Laboratory for the Multivariate State Estimation Technique (MSET), a nonlinear, nonparametric estimation technique with a wide range of applications in nuclear reactors. The methodology has been applied to three data sets from experimental and commercial nuclear power reactor applications. The results are promising. The combination of MSET kernels with the SVM method has better noise reduction and generalization properties than the standard MSET algorithm.

  4. Predicting Computer System Failures Using Support Vector Machines

    SciTech Connect

    Fulp, Errin W.; Fink, Glenn A.; Haack, Jereme N.

    2008-12-07

    Mitigating the impact of computer failure is possible if accurate failure predictions are provided. Resources, applications, and services can be scheduled around predicted failure and limit the impact. Such strategies are especially important for multi-computer systems, such as compute clusters, that experience a higher rate failure due to the large number of components. However providing accurate predictions with sufficient lead time remains a challenging problem. This paper describes a new spectrum-kernel Support Vector Machine (SVM) approach to predict failure events based on system log files. These files contain messages that represent a change of system state. While a single message in the file may not be sufficient for predicting failure, a sequence or pattern of messages may be. The approach described in this paper will use a sliding window (sub-sequence) of messages to predict the likelihood of failure. The frequency representation of the message sub-sequences observed are then used as input to the SVM that associates the messages to a class of failed or non-failed system. Experimental results using actual system log files from a Linux-based compute cluster indicate the proposed SVM approach can predict hard disk failure with an accuracy of 76% one day in advance.

  5. Using support vector machines in the multivariate state estimation technique

    SciTech Connect

    Zavaljevski, N.; Gross, K.C.

    1999-07-01

    One approach to validate nuclear power plant (NPP) signals makes use of pattern recognition techniques. This approach often assumes that there is a set of signal prototypes that are continuously compared with the actual sensor signals. These signal prototypes are often computed based on empirical models with little or no knowledge about physical processes. A common problem of all data-based models is their limited ability to make predictions on the basis of available training data. Another problem is related to suboptimal training algorithms. Both of these potential shortcomings with conventional approaches to signal validation and sensor operability validation are successfully resolved by adopting a recently proposed learning paradigm called the support vector machine (SVM). The work presented here is a novel application of SVM for data-based modeling of system state variables in an NPP, integrated with a nonlinear, nonparametric technique called the multivariate state estimation technique (MSET), an algorithm developed at Argonne National Laboratory for a wide range of nuclear plant applications.

  6. A support vector machine approach for detection of microcalcifications.

    PubMed

    El-Naqa, Issam; Yang, Yongyi; Wernick, Miles N; Galatsanos, Nikolas P; Nishikawa, Robert M

    2002-12-01

    In this paper, we investigate an approach based on support vector machines (SVMs) for detection of microcalcification (MC) clusters in digital mammograms, and propose a successive enhancement learning scheme for improved performance. SVM is a machine-learning method, based on the principle of structural risk minimization, which performs well when applied to data outside the training set. We formulate MC detection as a supervised-learning problem and apply SVM to develop the detection algorithm. We use the SVM to detect at each location in the image whether an MC is present or not. We tested the proposed method using a database of 76 clinical mammograms containing 1120 MCs. We use free-response receiver operating characteristic curves to evaluate detection performance, and compare the proposed algorithm with several existing methods. In our experiments, the proposed SVM framework outperformed all the other methods tested. In particular, a sensitivity as high as 94% was achieved by the SVM method at an error rate of one false-positive cluster per image. The ability of SVM to out perform several well-known methods developed for the widely studied problem of MC detection suggests that SVM is a promising technique for object detection in a medical imaging application. PMID:12588039

  7. Semisupervised Support Vector Machines With Tangent Space Intrinsic Manifold Regularization.

    PubMed

    Sun, Shiliang; Xie, Xijiong

    2016-09-01

    Semisupervised learning has been an active research topic in machine learning and data mining. One main reason is that labeling examples is expensive and time-consuming, while there are large numbers of unlabeled examples available in many practical problems. So far, Laplacian regularization has been widely used in semisupervised learning. In this paper, we propose a new regularization method called tangent space intrinsic manifold regularization. It is intrinsic to data manifold and favors linear functions on the manifold. Fundamental elements involved in the formulation of the regularization are local tangent space representations, which are estimated by local principal component analysis, and the connections that relate adjacent tangent spaces. Simultaneously, we explore its application to semisupervised classification and propose two new learning algorithms called tangent space intrinsic manifold regularized support vector machines (TiSVMs) and tangent space intrinsic manifold regularized twin SVMs (TiTSVMs). They effectively integrate the tangent space intrinsic manifold regularization consideration. The optimization of TiSVMs can be solved by a standard quadratic programming, while the optimization of TiTSVMs can be solved by a pair of standard quadratic programmings. The experimental results of semisupervised classification problems show the effectiveness of the proposed semisupervised learning algorithms. PMID:26277005

  8. Support vector classifiers via gradient systems with discontinuous righthand sides.

    PubMed

    Ferreira, Leonardo V; Kaszkurewicz, Eugenius; Bhaya, Amit

    2006-12-01

    Gradient dynamical systems with discontinuous righthand sides are designed using Persidskii-type nonsmooth Lyapunov functions to work as support vector machines (SVMs) for the discrimination of nonseparable classes. The gradient systems are obtained from an exact penalty method applied to the constrained quadratic optimization problems, which are formulations of two well known SVMs. Global convergence of the trajectories of the gradient dynamical systems to the solution of the corresponding constrained problems is shown to be independent of the penalty parameters and of the parameters of the SVMs. The proposed gradient systems can be implemented as simple analog circuits as well as using standard software for integration of ODEs, and in order to use efficient integration methods with adaptive stepsize selection, the discontinuous terms are smoothed around a neighborhood of the discontinuity surface by means of the boundary layer technique. The scalability of the proposed gradient systems is also shown by means of an implementation using parallel computers, resulting in smaller processing times when compared with traditional SVM packages. PMID:17011165

  9. Predicting full thickness skin sensitization using a support vector machine

    PubMed Central

    Lee, Serom; Dong, David Xu; Jindal, Rohit; Maguire, Tim; Mitra, Bhaskar; Schloss, Rene; Yarmush, Martin

    2015-01-01

    To assess the public’s propensity for allergic contact dermatitis (ACD), many alternatives to in vivo chemical screening have been developed which generally incorporate a small panel of cell surface and secreted dendritic cell biomarkers. However, given the underlying complexity of ACD, one cell type and limited cellular metrics may be insufficient to predict contact sensitizers accurately. To identify a molecular signature that can further characterize sensitization, we developed a novel system using RealSkin, a full thickness skin equivalent, in co-culture with MUTZ-3 derived Langerhan’s cells. This system was used to distinguish a model moderate pro-hapten isoeugenol (IE) and a model strong pre-hapten p-phenylenediamine (PPD) from irritant, salicylic acid (SA). Commonly evaluated metrics such as CD86, CD54, and IL-8 secretion were assessed, in concert with a 27-cytokine multi-plex screen and a functional chemotaxis assay. Data were analyzed with feature selection methods using ANOVA, hierarchical cluster analysis, and a support vector machine to identify the best molecular signature for sensitization. A panel consisting of IL-12, IL-9, VEGF, and IFN-γ predicted sensitization with over 90% accuracy using this co-culture system analysis. Thus, a multi-metric approach that has the potential to identify a molecular signature may be more predictive of contact sensitization. PMID:25025180

  10. Fast and Accurate Support Vector Machines on Large Scale Systems

    SciTech Connect

    Vishnu, Abhinav; Narasimhan, Jayenthi; Holder, Larry; Kerbyson, Darren J.; Hoisie, Adolfy

    2015-09-08

    Support Vector Machines (SVM) is a supervised Machine Learning and Data Mining (MLDM) algorithm, which has become ubiquitous largely due to its high accuracy and obliviousness to dimensionality. The objective of SVM is to find an optimal boundary --- also known as hyperplane --- which separates the samples (examples in a dataset) of different classes by a maximum margin. Usually, very few samples contribute to the definition of the boundary. However, existing parallel algorithms use the entire dataset for finding the boundary, which is sub-optimal for performance reasons. In this paper, we propose a novel distributed memory algorithm to eliminate the samples which do not contribute to the boundary definition in SVM. We propose several heuristics, which range from early (aggressive) to late (conservative) elimination of the samples, such that the overall time for generating the boundary is reduced considerably. In a few cases, a sample may be eliminated (shrunk) pre-emptively --- potentially resulting in an incorrect boundary. We propose a scalable approach to synchronize the necessary data structures such that the proposed algorithm maintains its accuracy. We consider the necessary trade-offs of single/multiple synchronization using in-depth time-space complexity analysis. We implement the proposed algorithm using MPI and compare it with libsvm--- de facto sequential SVM software --- which we enhance with OpenMP for multi-core/many-core parallelism. Our proposed approach shows excellent efficiency using up to 4096 processes on several large datasets such as UCI HIGGS Boson dataset and Offending URL dataset.

  11. Detection of Splice Sites Using Support Vector Machine

    NASA Astrophysics Data System (ADS)

    Varadwaj, Pritish; Purohit, Neetesh; Arora, Bhumika

    Automatic identification and annotation of exon and intron region of gene, from DNA sequences has been an important research area in field of computational biology. Several approaches viz. Hidden Markov Model (HMM), Artificial Intelligence (AI) based machine learning and Digital Signal Processing (DSP) techniques have extensively and independently been used by various researchers to cater this challenging task. In this work, we propose a Support Vector Machine based kernel learning approach for detection of splice sites (the exon-intron boundary) in a gene. Electron-Ion Interaction Potential (EIIP) values of nucleotides have been used for mapping character sequences to corresponding numeric sequences. Radial Basis Function (RBF) SVM kernel is trained using EIIP numeric sequences. Furthermore this was tested on test gene dataset for detection of splice site by window (of 12 residues) shifting. Optimum values of window size, various important parameters of SVM kernel have been optimized for a better accuracy. Receiver Operating Characteristic (ROC) curves have been utilized for displaying the sensitivity rate of the classifier and results showed 94.82% accuracy for splice site detection on test dataset.

  12. Segmentation of multiple sclerosis lesions using support vector machines

    NASA Astrophysics Data System (ADS)

    Ferrari, Ricardo J.; Wei, Xingchang; Zhang, Yunyan; Scott, James N.; Mitchell, J. R.

    2003-05-01

    In this paper we present preliminary results to automatically segment multiple sclerosis (MS) lesions in multispectral magnetic resonance datasets using support vector machines (SVM). A total of eighteen studies (each composed of T1-, T2-weighted and FLAIR images) acquired from a 3T GE Signa scanner was analyzed. A neuroradiologist used a computer-assisted technique to identify all MS lesions in each study. These results were used later in the training and testing stages of the SVM classifier. A preprocessing stage including anisotropic diffusion filtering, non-uniformity intensity correction, and intensity tissue normalization was applied to the images. The SVM kernel used in this study was the radial basis function (RBF). The kernel parameter (γ) and the penalty value for the errors were determined by using a very loose stopping criterion for the SVM decomposition. Overall, a 5-fold cross-validation accuracy rate of 80% was achieved in the automatic classification of MS lesion voxels using the proposed SVM-RBF classifier.

  13. End-user display calibration via support vector regression

    NASA Astrophysics Data System (ADS)

    Bastani, Behnam; Funt, Brian; Xiong, Weihua

    2006-01-01

    The technique of support vector regression (SVR) is applied to the color display calibration problem. Given a set of training data, SVR estimates a continuous-valued function encoding the fundamental interrelation between a given input and its corresponding output. This mapping can then be used to find an output value for a given input value not in the training data set. Here, SVR is applied directly to the display's non-linearized RGB digital input values to predict output CIELAB values. There are several different linear methods for calibrating different display technologies (GOG, Masking and Wyble). An advantage of using SVR for color calibration is that the end-user does not need to apply a different calibration model for each different display technology. We show that the same model can be used to calibrate CRT, LCD and DLP displays accurately. We also show that the accuracy of the model is comparable to that of the optimal linear transformation introduced by Funt et al.

  14. An Equivalence Between Sparse Approximation and Support Vector Machines.

    PubMed

    Girosi

    1998-07-28

    This article shows a relationship between two different approximation techniques: the support vector machines (SVM), proposed by V. Vapnik (1995) and a sparse approximation scheme that resembles the basis pursuit denoising algorithm (Chen, 1995; Chen, Donoho, and Saunders, 1995). SVM is a technique that can be derived from the structural risk minimization principle (Vapnik, 1982) and can be used to estimate the parameters of several different approximation schemes, including radial basis functions, algebraic and trigonometric polynomials, B-splines, and some forms of multilayer perceptrons. Basis pursuit denoising is a sparse approximation technique in which a function is reconstructed by using a small number of basis functions chosen from a large set (the dictionary). We show that if the data are noiseless, the modified version of basis pursuit denoising proposed in this article is equivalent to SVM in the following sense: if applied to the same data set, the two techniques give the same solution, which is obtained by solving the same quadratic programming problem. In the appendix, we present a derivation of the SVM technique in one framework of regularization theory, rather than statistical learning theory, establishing a connection between SVM, sparse approximation, and regularization theory. PMID:9698353

  15. Support Vector Machine algorithm for regression and classification

    Energy Science and Technology Software Center (ESTSC)

    2001-08-01

    The software is an implementation of the Support Vector Machine (SVM) algorithm that was invented and developed by Vladimir Vapnik and his co-workers at AT&T Bell Laboratories. The specific implementation reported here is an Active Set method for solving a quadratic optimization problem that forms the major part of any SVM program. The implementation is tuned to specific constraints generated in the SVM learning. Thus, it is more efficient than general-purpose quadratic optimization programs. Amore » decomposition method has been implemented in the software that enables processing large data sets. The size of the learning data is virtually unlimited by the capacity of the computer physical memory. The software is flexible and extensible. Two upper bounds are implemented to regulate the SVM learning for classification, which allow users to adjust the false positive and false negative rates. The software can be used either as a standalone, general-purpose SVM regression or classification program, or be embedded into a larger software system.« less

  16. A duct mapping method using least squares support vector machines

    NASA Astrophysics Data System (ADS)

    Douvenot, RéMi; Fabbro, Vincent; Gerstoft, Peter; Bourlier, Christophe; Saillard, Joseph

    2008-12-01

    This paper introduces a "refractivity from clutter" (RFC) approach with an inversion method based on a pregenerated database. The RFC method exploits the information contained in the radar sea clutter return to estimate the refractive index profile. Whereas initial efforts are based on algorithms giving a good accuracy involving high computational needs, the present method is based on a learning machine algorithm in order to obtain a real-time system. This paper shows the feasibility of a RFC technique based on the least squares support vector machine inversion method by comparing it to a genetic algorithm on simulated and noise-free data, at 1 and 5 GHz. These data are simulated in the presence of ideal trilinear surface-based ducts. The learning machine is based on a pregenerated database computed using Latin hypercube sampling to improve the efficiency of the learning. The results show that little accuracy is lost compared to a genetic algorithm approach. The computational time of a genetic algorithm is very high, whereas the learning machine approach is real time. The advantage of a real-time RFC system is that it could work on several azimuths in near real time.

  17. Automatic inspection of textured surfaces by support vector machines

    NASA Astrophysics Data System (ADS)

    Jahanbin, Sina; Bovik, Alan C.; Pérez, Eduardo; Nair, Dinesh

    2009-08-01

    Automatic inspection of manufactured products with natural looking textures is a challenging task. Products such as tiles, textile, leather, and lumber project image textures that cannot be modeled as periodic or otherwise regular; therefore, a stochastic modeling of local intensity distribution is required. An inspection system to replace human inspectors should be flexible in detecting flaws such as scratches, cracks, and stains occurring in various shapes and sizes that have never been seen before. A computer vision algorithm is proposed in this paper that extracts local statistical features from grey-level texture images decomposed with wavelet frames into subbands of various orientations and scales. The local features extracted are second order statistics derived from grey-level co-occurrence matrices. Subsequently, a support vector machine (SVM) classifier is trained to learn a general description of normal texture from defect-free samples. This algorithm is implemented in LabVIEW and is capable of processing natural texture images in real-time.

  18. Analysis of metabolomic data using support vector machines.

    PubMed

    Mahadevan, Sankar; Shah, Sirish L; Marrie, Thomas J; Slupsky, Carolyn M

    2008-10-01

    Metabolomics is an emerging field providing insight into physiological processes. It is an effective tool to investigate disease diagnosis or conduct toxicological studies by observing changes in metabolite concentrations in various biofluids. Multivariate statistical analysis is generally employed with nuclear magnetic resonance (NMR) or mass spectrometry (MS) data to determine differences between groups (for instance diseased vs healthy). Characteristic predictive models may be built based on a set of training data, and these models are subsequently used to predict whether new test data falls under a specific class. In this study, metabolomic data is obtained by doing a (1)H NMR spectroscopy on urine samples obtained from healthy subjects (male and female) and patients suffering from Streptococcus pneumoniae. We compare the performance of traditional PLS-DA multivariate analysis to support vector machines (SVMs), a technique widely used in genome studies on two case studies: (1) a case where nearly complete distinction may be seen (healthy versus pneumonia) and (2) a case where distinction is more ambiguous (male versus female). We show that SVMs are superior to PLS-DA in both cases in terms of predictive accuracy with the least number of features. With fewer number of features, SVMs are able to give better predictive model when compared to that of PLS-DA. PMID:18767870

  19. Clustering by Fuzzy Neural Gas and Evaluation of Fuzzy Clusters

    PubMed Central

    Geweniger, Tina; Fischer, Lydia; Kaden, Marika; Lange, Mandy; Villmann, Thomas

    2013-01-01

    We consider some modifications of the neural gas algorithm. First, fuzzy assignments as known from fuzzy c-means and neighborhood cooperativeness as known from self-organizing maps and neural gas are combined to obtain a basic Fuzzy Neural Gas. Further, a kernel variant and a simulated annealing approach are derived. Finally, we introduce a fuzzy extension of the ConnIndex to obtain an evaluation measure for clusterings based on fuzzy vector quantization. PMID:24396342

  20. Ensemble Feature Learning of Genomic Data Using Support Vector Machine

    PubMed Central

    Anaissi, Ali; Goyal, Madhu; Catchpoole, Daniel R.; Braytee, Ali; Kennedy, Paul J.

    2016-01-01

    The identification of a subset of genes having the ability to capture the necessary information to distinguish classes of patients is crucial in bioinformatics applications. Ensemble and bagging methods have been shown to work effectively in the process of gene selection and classification. Testament to that is random forest which combines random decision trees with bagging to improve overall feature selection and classification accuracy. Surprisingly, the adoption of these methods in support vector machines has only recently received attention but mostly on classification not gene selection. This paper introduces an ensemble SVM-Recursive Feature Elimination (ESVM-RFE) for gene selection that follows the concepts of ensemble and bagging used in random forest but adopts the backward elimination strategy which is the rationale of RFE algorithm. The rationale behind this is, building ensemble SVM models using randomly drawn bootstrap samples from the training set, will produce different feature rankings which will be subsequently aggregated as one feature ranking. As a result, the decision for elimination of features is based upon the ranking of multiple SVM models instead of choosing one particular model. Moreover, this approach will address the problem of imbalanced datasets by constructing a nearly balanced bootstrap sample. Our experiments show that ESVM-RFE for gene selection substantially increased the classification performance on five microarray datasets compared to state-of-the-art methods. Experiments on the childhood leukaemia dataset show that an average 9% better accuracy is achieved by ESVM-RFE over SVM-RFE, and 5% over random forest based approach. The selected genes by the ESVM-RFE algorithm were further explored with Singular Value Decomposition (SVD) which reveals significant clusters with the selected data. PMID:27304923

  1. Support Vector Machines for Petrophysical Modelling and Lithoclassification

    NASA Astrophysics Data System (ADS)

    Al-Anazi, Ammal Fannoush Khalifah

    2011-12-01

    Given increasing challenges of oil and gas production from partially depleted conventional or unconventional reservoirs, reservoir characterization is a key element of the reservoir development workflow. Reservoir characterization impacts well placement, injection and production strategies, and field management. Reservoir characterization projects point and line data to a large three-dimensional volume. The relationship between variables, e.g. porosity and permeability, is often established by regression yet the complexities between measured variables often lead to poor correlation coefficients between the regressed variables. Recent advances in machine learning methods have provided attractive alternatives for constructing interpretation models of rock properties in heterogeneous reservoirs. Here, Support Vector Machines (SVMs), a class of a learning machine that is formulated to output regression models and classifiers of competitive generalization capability, has been explored to determine its capabilities for determining the relationship, both in regression and in classification, between reservoir rock properties. This thesis documents research on the capability of SVMs to model petrophysical and elastic properties in heterogeneous sandstone and carbonate reservoirs. Specifically, the capabilities of SVM regression and classification has been examined and compared to neural network-based methods, namely multilayered neural networks, radial basis function neural networks, general regression neural networks, probabilistic neural networks, and linear discriminant analysis. The petrophysical properties that have been evaluated include porosity, permeability, Poisson's ratio and Young's modulus. Statistical error analysis reveals that the SVM method yields comparable or superior predictions of petrophysical and elastic rock properties and classification of the lithology compared to neural networks. The SVM method also shows uniform prediction capability under the

  2. Support vector machine for day ahead electricity price forecasting

    NASA Astrophysics Data System (ADS)

    Razak, Intan Azmira binti Wan Abdul; Abidin, Izham bin Zainal; Siah, Yap Keem; Rahman, Titik Khawa binti Abdul; Lada, M. Y.; Ramani, Anis Niza binti; Nasir, M. N. M.; Ahmad, Arfah binti

    2015-05-01

    Electricity price forecasting has become an important part of power system operation and planning. In a pool- based electric energy market, producers submit selling bids consisting in energy blocks and their corresponding minimum selling prices to the market operator. Meanwhile, consumers submit buying bids consisting in energy blocks and their corresponding maximum buying prices to the market operator. Hence, both producers and consumers use day ahead price forecasts to derive their respective bidding strategies to the electricity market yet reduce the cost of electricity. However, forecasting electricity prices is a complex task because price series is a non-stationary and highly volatile series. Many factors cause for price spikes such as volatility in load and fuel price as well as power import to and export from outside the market through long term contract. This paper introduces an approach of machine learning algorithm for day ahead electricity price forecasting with Least Square Support Vector Machine (LS-SVM). Previous day data of Hourly Ontario Electricity Price (HOEP), generation's price and demand from Ontario power market are used as the inputs for training data. The simulation is held using LSSVMlab in Matlab with the training and testing data of 2004. SVM that widely used for classification and regression has great generalization ability with structured risk minimization principle rather than empirical risk minimization. Moreover, same parameter settings in trained SVM give same results that absolutely reduce simulation process compared to other techniques such as neural network and time series. The mean absolute percentage error (MAPE) for the proposed model shows that SVM performs well compared to neural network.

  3. Noninvasive extraction of fetal electrocardiogram based on Support Vector Machine

    NASA Astrophysics Data System (ADS)

    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.

  4. Quantifying Changes in Intrinsic Molecular Motion Using Support Vector Machines.

    PubMed

    Leighty, Ralph E; Varma, Sameer

    2013-02-12

    The ensemble of three-dimensional (3-D) configurations exhibited by a molecule, that is, its intrinsic motion, can be altered by several environmental factors, and also by the binding of other molecules. Quantification of such induced changes in intrinsic motion is important because it provides a basis for relating thermodynamic changes to changes in molecular motion. This task is, however, challenging because it requires comparing two high-dimensional data sets. Traditionally, when analyzing molecular simulations, this problem is circumvented by first reducing the dimensions of the two ensembles separately, and then comparing summary statistics from the two ensembles against each other. However, since dimensionality reduction is carried out prior to ensemble comparison, such strategies are susceptible to artifactual biases from information loss. Here, we introduce a method based on support vector machines that yields a normalized quantitative estimate for the difference between two ensembles after comparing them directly against one another. While this method can be applied to any molecular system, including nonbiological molecules and crystals, here, we show how it can be applied to identify the specific regions of a paramyxovirus G protein that are affected by the binding of its preferred human receptor, Ephrin B2. This protein-protein interaction initiates the fusion of the virus with the host cell. Specifically, for every residue in the G protein, we obtain separately a quantitative difference between the ensemble of configurations they sample in the presence and in the absence of Ephrin B2. These ensembles were generated using molecular dynamics simulations. Rank-ordering and then mapping the residues that undergo the greatest change in motion onto the 3-D structure of the G protein reveals that they are clustered primarily on a single contiguous facet of the protein and include the set that is known experimentally to play a vital role in regulating viral

  5. Prediction of cell penetrating peptides by support vector machines.

    PubMed

    Sanders, William S; Johnston, C Ian; Bridges, Susan M; Burgess, Shane C; Willeford, Kenneth O

    2011-07-01

    Cell penetrating peptides (CPPs) are those peptides that can transverse cell membranes to enter cells. Once inside the cell, different CPPs can localize to different cellular components and perform different roles. Some generate pore-forming complexes resulting in the destruction of cells while others localize to various organelles. Use of machine learning methods to predict potential new CPPs will enable more rapid screening for applications such as drug delivery. We have investigated the influence of the composition of training datasets on the ability to classify peptides as cell penetrating using support vector machines (SVMs). We identified 111 known CPPs and 34 known non-penetrating peptides from the literature and commercial vendors and used several approaches to build training data sets for the classifiers. Features were calculated from the datasets using a set of basic biochemical properties combined with features from the literature determined to be relevant in the prediction of CPPs. Our results using different training datasets confirm the importance of a balanced training set with approximately equal number of positive and negative examples. The SVM based classifiers have greater classification accuracy than previously reported methods for the prediction of CPPs, and because they use primary biochemical properties of the peptides as features, these classifiers provide insight into the properties needed for cell-penetration. To confirm our SVM classifications, a subset of peptides classified as either penetrating or non-penetrating was selected for synthesis and experimental validation. Of the synthesized peptides predicted to be CPPs, 100% of these peptides were shown to be penetrating. PMID:21779156

  6. Cloud Detection of Optical Satellite Images Using Support Vector Machine

    NASA Astrophysics Data System (ADS)

    Lee, Kuan-Yi; Lin, Chao-Hung

    2016-06-01

    Cloud covers are generally present in optical remote-sensing images, which limit the usage of acquired images and increase the difficulty of data analysis, such as image compositing, correction of atmosphere effects, calculations of vegetation induces, land cover classification, and land cover change detection. In previous studies, thresholding is a common and useful method in cloud detection. However, a selected threshold is usually suitable for certain cases or local study areas, and it may be failed in other cases. In other words, thresholding-based methods are data-sensitive. Besides, there are many exceptions to control, and the environment is changed dynamically. Using the same threshold value on various data is not effective. In this study, a threshold-free method based on Support Vector Machine (SVM) is proposed, which can avoid the abovementioned problems. A statistical model is adopted to detect clouds instead of a subjective thresholding-based method, which is the main idea of this study. The features used in a classifier is the key to a successful classification. As a result, Automatic Cloud Cover Assessment (ACCA) algorithm, which is based on physical characteristics of clouds, is used to distinguish the clouds and other objects. In the same way, the algorithm called Fmask (Zhu et al., 2012) uses a lot of thresholds and criteria to screen clouds, cloud shadows, and snow. Therefore, the algorithm of feature extraction is based on the ACCA algorithm and Fmask. Spatial and temporal information are also important for satellite images. Consequently, co-occurrence matrix and temporal variance with uniformity of the major principal axis are used in proposed method. We aim to classify images into three groups: cloud, non-cloud and the others. In experiments, images acquired by the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and images containing the landscapes of agriculture, snow area, and island are tested. Experiment results demonstrate the detection

  7. Fuzzy Content-Based Retrieval in Image Databases.

    ERIC Educational Resources Information Center

    Wu, Jian Kang; Narasimhalu, A. Desai

    1998-01-01

    Proposes a fuzzy-image database model and a concept of fuzzy space; describes fuzzy-query processing in fuzzy space and fuzzy indexing on complete fuzzy vectors; and uses an example image database, the computer-aided facial-image inference and retrieval system (CAFIIR), for explanation throughout. (Author/LRW)

  8. Classification of Regional Ionospheric Disturbances Based on Support Vector Machines

    NASA Astrophysics Data System (ADS)

    Begüm Terzi, Merve; Arikan, Feza; Arikan, Orhan; Karatay, Secil

    2016-07-01

    Ionosphere is an anisotropic, inhomogeneous, time varying and spatio-temporally dispersive medium whose parameters can be estimated almost always by using indirect measurements. Geomagnetic, gravitational, solar or seismic activities cause variations of ionosphere at various spatial and temporal scales. This complex spatio-temporal variability is challenging to be identified due to extensive scales in period, duration, amplitude and frequency of disturbances. Since geomagnetic and solar indices such as Disturbance storm time (Dst), F10.7 solar flux, Sun Spot Number (SSN), Auroral Electrojet (AE), Kp and W-index provide information about variability on a global scale, identification and classification of regional disturbances poses a challenge. The main aim of this study is to classify the regional effects of global geomagnetic storms and classify them according to their risk levels. For this purpose, Total Electron Content (TEC) estimated from GPS receivers, which is one of the major parameters of ionosphere, will be used to model the regional and local variability that differs from global activity along with solar and geomagnetic indices. In this work, for the automated classification of the regional disturbances, a classification technique based on a robust machine learning technique that have found wide spread use, Support Vector Machine (SVM) is proposed. SVM is a supervised learning model used for classification with associated learning algorithm that analyze the data and recognize patterns. In addition to performing linear classification, SVM can efficiently perform nonlinear classification by embedding data into higher dimensional feature spaces. Performance of the developed classification technique is demonstrated for midlatitude ionosphere over Anatolia using TEC estimates generated from the GPS data provided by Turkish National Permanent GPS Network (TNPGN-Active) for solar maximum year of 2011. As a result of implementing the developed classification

  9. A speech recognition system based on hybrid wavelet network including a fuzzy decision support system

    NASA Astrophysics Data System (ADS)

    Jemai, Olfa; Ejbali, Ridha; Zaied, Mourad; Ben Amar, Chokri

    2015-02-01

    This paper aims at developing a novel approach for speech recognition based on wavelet network learnt by fast wavelet transform (FWN) including a fuzzy decision support system (FDSS). Our contributions reside in, first, proposing a novel learning algorithm for speech recognition based on the fast wavelet transform (FWT) which has many advantages compared to other algorithms and in which major problems of the previous works to compute connection weights were solved. They were determined by a direct solution which requires computing matrix inversion, which may be intensive. However, the new algorithm was realized by the iterative application of FWT to compute connection weights. Second, proposing a new classification way for this speech recognition system. It operated a human reasoning mode employing a FDSS to compute similarity degrees between test and training signals. Extensive empirical experiments were conducted to compare the proposed approach with other approaches. Obtained results show that the new speech recognition system has a better performance than previously established ones.

  10. Supporting capacity sharing in the cloud manufacturing environment based on game theory and fuzzy logic

    NASA Astrophysics Data System (ADS)

    Argoneto, Pierluigi; Renna, Paolo

    2016-02-01

    This paper proposes a Framework for Capacity Sharing in Cloud Manufacturing (FCSCM) able to support the capacity sharing issue among independent firms. The success of geographical distributed plants depends strongly on the use of opportune tools to integrate their resources and demand forecast in order to gather a specific production objective. The framework proposed is based on two different tools: a cooperative game algorithm, based on the Gale-Shapley model, and a fuzzy engine. The capacity allocation policy takes into account the utility functions of the involved firms. It is shown how the capacity allocation policy proposed induces all firms to report truthfully their information about their requirements. A discrete event simulation environment has been developed to test the proposed FCSCM. The numerical results show the drastic reduction of unsatisfied capacity obtained by the model of cooperation implemented in this work.

  11. The Virtual Learning Commons: Supporting the Fuzzy Front End of Scientific Research with Emerging Technologies

    NASA Astrophysics Data System (ADS)

    Pennington, D. D.; Gandara, A.; Gris, I.

    2012-12-01

    The Virtual Learning Commons (VLC), funded by the National Science Foundation Office of Cyberinfrastructure CI-Team Program, is a combination of Semantic Web, mash up, and social networking tools that supports knowledge sharing and innovation across scientific disciplines in research and education communities and networks. The explosion of scientific resources (data, models, algorithms, tools, and cyberinfrastructure) challenges the ability of researchers to be aware of resources that might benefit them. Even when aware, it can be difficult to understand enough about those resources to become potential adopters or re-users. Often scientific data and emerging technologies have little documentation, especially about the context of their use. The VLC tackles this challenge by providing mechanisms for individuals and groups of researchers to organize Web resources into virtual collections, and engage each other around those collections in order to a) learn about potentially relevant resources that are available; b) design research that leverages those resources; and c) develop initial work plans. The VLC aims to support the "fuzzy front end" of innovation, where novel ideas emerge and there is the greatest potential for impact on research design. It is during the fuzzy front end that conceptual collisions across disciplines and exposure to diverse perspectives provide opportunity for creative thinking that can lead to inventive outcomes. The VLC integrates Semantic Web functionality for structuring distributed information, mash up functionality for retrieving and displaying information, and social media for discussing/rating information. We are working to provide three views of information that support researchers in different ways: 1. Innovation Marketplace: supports users as they try to understand what research is being conducted, who is conducting it, where they are located, and who they collaborate with; 2. Conceptual Mapper: supports users as they organize their

  12. Novel Architecture for supporting medical decision making of different data types based on Fuzzy Cognitive Map Framework.

    PubMed

    Papageorgiou, Elpiniki; Stylios, Chrysostomos; Groumpos, Peter

    2007-01-01

    Medical problems involve different types of variables and data, which have to be processed, analyzed and synthesized in order to reach a decision and/or conclude to a diagnosis. Usually, information and data set are both symbolic and numeric but most of the well-known data analysis methods deal with only one kind of data. Even when fuzzy approaches are considered, which are not depended on the scales of variables, usually only numeric data is considered. The medical decision support methods usually are accessed in only one type of available data. Thus, sophisticated methods have been proposed such as integrated hybrid learning approaches to process symbolic and numeric data for the decision support tasks. Fuzzy Cognitive Maps (FCM) is an efficient modelling method, which is based on human knowledge and experience and it can handle with uncertainty and it is constructed by extracted knowledge in the form of fuzzy rules. The FCM model can be enhanced if a fuzzy rule base (IF-THEN rules) is available. This rule base could be derived by a number of machine learning and knowledge extraction methods. Here it is introduced a hybrid attempt to handle situations with different types of available medical and/or clinical data and with difficulty to handle them for decision support tasks using soft computing techniques. PMID:18002176

  13. Using a Geographical-Information-System-Based Decision Support to Enhance Malaria Vector Control in Zambia

    PubMed Central

    Chanda, Emmanuel; Mukonka, Victor Munyongwe; Mthembu, David; Kamuliwo, Mulakwa; Coetzer, Sarel; Shinondo, Cecilia Jill

    2012-01-01

    Geographic information systems (GISs) with emerging technologies are being harnessed for studying spatial patterns in vector-borne diseases to reduce transmission. To implement effective vector control, increased knowledge on interactions of epidemiological and entomological malaria transmission determinants in the assessment of impact of interventions is critical. This requires availability of relevant spatial and attribute data to support malaria surveillance, monitoring, and evaluation. Monitoring the impact of vector control through a GIS-based decision support (DSS) has revealed spatial relative change in prevalence of infection and vector susceptibility to insecticides and has enabled measurement of spatial heterogeneity of trend or impact. The revealed trends and interrelationships have allowed the identification of areas with reduced parasitaemia and increased insecticide resistance thus demonstrating the impact of resistance on vector control. The GIS-based DSS provides opportunity for rational policy formulation and cost-effective utilization of limited resources for enhanced malaria vector control. PMID:22548086

  14. Insect cell transformation vectors that support high level expression and promoter assessment in insect cell culture

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A somatic transformation vector, pDP9, was constructed that provides a simplified means of producing permanently transformed cultured insect cells that support high levels of protein expression of foreign genes. The pDP9 plasmid vector incorporates DNA sequences from the Junonia coenia densovirus th...

  15. A Two-Layer Least Squares Support Vector Machine Approach to Credit Risk Assessment

    NASA Astrophysics Data System (ADS)

    Liu, Jingli; Li, Jianping; Xu, Weixuan; Shi, Yong

    Least squares support vector machine (LS-SVM) is a revised version of support vector machine (SVM) and has been proved to be a useful tool for pattern recognition. LS-SVM had excellent generalization performance and low computational cost. In this paper, we propose a new method called two-layer least squares support vector machine which combines kernel principle component analysis (KPCA) and linear programming form of least square support vector machine. With this method sparseness and robustness is obtained while solving large dimensional and large scale database. A U.S. commercial credit card database is used to test the efficiency of our method and the result proved to be a satisfactory one.

  16. Supporting meningitis diagnosis amongst infants and children through the use of fuzzy cognitive mapping

    PubMed Central

    2012-01-01

    Background Meningitis is characterized by an inflammation of the meninges, or the membranes surrounding the brain and spinal cord. Early diagnosis and treatment is crucial for a positive outcome, yet identifying meningitis is a complex process involving an array of signs and symptoms and multiple causal factors which require novel solutions to support clinical decision-making. In this work, we explore the potential of fuzzy cognitive map to assist in the modeling of meningitis, as a support tool for physicians in the accurate diagnosis and treatment of the condition. Methods Fuzzy cognitive mapping (FCM) is a method for analysing and depicting human perception of a given system. FCM facilitates the development of a conceptual model which is not limited by exact values and measurements and thus is well suited to representing relatively unstructured knowledge and associations expressed in imprecise terms. A team of doctors (physicians), comprising four paediatricians, was formed to define the multifarious signs and symptoms associated with meningitis and to identify risk factors integral to its causality, as indicators used by clinicians to identify the presence or absence of meningitis in patients. The FCM model, consisting of 20 concept nodes, has been designed by the team of paediatricians in collaborative dialogue with the research team. Results The paediatricians were supplied with a form containing various input parameters to be completed at the time of diagnosing meningitis among infants and children. The paediatricians provided information on a total of 56 patient cases amongst children whose age ranged from 2 months to 7 years. The physicians’ decision to diagnose meningitis was available for each individual case which was used as the outcome measure for evaluating the model. The FCM was trained using 40 cases with an accuracy of 95%, and later 16 test cases were used to analyze the accuracy and reliability of the model. The system produced the results

  17. Design of Clinical Support Systems Using Integrated Genetic Algorithm and Support Vector Machine

    NASA Astrophysics Data System (ADS)

    Chen, Yung-Fu; Huang, Yung-Fa; Jiang, Xiaoyi; Hsu, Yuan-Nian; Lin, Hsuan-Hung

    Clinical decision support system (CDSS) provides knowledge and specific information for clinicians to enhance diagnostic efficiency and improving healthcare quality. An appropriate CDSS can highly elevate patient safety, improve healthcare quality, and increase cost-effectiveness. Support vector machine (SVM) is believed to be superior to traditional statistical and neural network classifiers. However, it is critical to determine suitable combination of SVM parameters regarding classification performance. Genetic algorithm (GA) can find optimal solution within an acceptable time, and is faster than greedy algorithm with exhaustive searching strategy. By taking the advantage of GA in quickly selecting the salient features and adjusting SVM parameters, a method using integrated GA and SVM (IGS), which is different from the traditional method with GA used for feature selection and SVM for classification, was used to design CDSSs for prediction of successful ventilation weaning, diagnosis of patients with severe obstructive sleep apnea, and discrimination of different cell types form Pap smear. The results show that IGS is better than methods using SVM alone or linear discriminator.

  18. A Decision Support System for Do Predictionbased on Fuzzy Model and Neural Network

    NASA Astrophysics Data System (ADS)

    Wang, Ruimei; Liu, Qigen; He, Youyuan; Fu, Zetian

    Dissolved oxygen (DO) concentration plays a very important role in fish life and aquaculture, but DO prediction is very difficult. So a decision support system for DO prediction based on fuzzy model and neural network was attempted. The paper was based on vast monitored data, every day detecting for two years, in aquaculture pond in North China for two years. This is a preliminary attempt towards a wider use of Artificial Neural Networks in the management of aquaculture water quality. It proposes a model to be used effectively in prediction of DO concentration in aquaculture. This is really a crucial task, especially during the long dry summer months. The prediction of potential risk due to low DO is also very important. This data volume was divided in the training subset comprising of 106 cases and in the testing subset containing 26 cases. The input parameters are sunlight, wind speed, temperature, water temperature, air pressure, pH value and NH-NH3. Consequently three structural and seven dynamic factors are considered. After several and extended training-testing efforts a Modular Artificial Neural Network was determined to be the optimal one.

  19. Efficient defect structure analysis in semi-insulating materials by support vector machine and relevance vector machine

    NASA Astrophysics Data System (ADS)

    Jankowski, Stanisław; Będkowski, Janusz; Danilewicz, Przemysław; Szymański, Zbigniew

    2008-01-01

    We propose new approach for defect centers parameters extraction in semi-insulating GaAs. The experimental data is obtained by high-resolution photoinduced transient spectroscopy (HR-PITS). Two algorithms have been introduced: support vector machine - sequential minimal optimization (SVM-SMO) and relevance vector machine (RVM). Those methods perform the approximation of the Laplace surface. The advantages of proposed methods are: good accuracy of approximation, low complexity, excellent generalization. We developed SVM-RVM-PITS system, which enables graphical representation of Laplace surface, defining local area for defect parameter extraction, choosing the SVM or RVM method for approximation, calculation of the Arrhenius line factors and finally the parameters of the defect centers.

  20. Applications of fuzzy logic

    SciTech Connect

    Zargham, M.R.

    1995-06-01

    Recently, fuzzy logic has been applied to many areas, such as process control, image understanding, robots, expert systems, and decision support systems. This paper will explain the basic concepts of fuzzy logic and its application in different fields. The steps to design a control system will be explained in detail. Fuzzy control is the first successful industrial application of fuzzy logic. A fuzzy controller is able to control systems which previously could only be controlled by skilled operators. In recent years Japan has achieved significant progress in this area and has applied it to variety of products such as cruise control for cars, video cameras, rice cookers, washing machines, etc.

  1. Building Ultra-Low False Alarm Rate Support Vector Classifier Ensembles Using Random Subspaces

    SciTech Connect

    Chen, B Y; Lemmond, T D; Hanley, W G

    2008-10-06

    This paper presents the Cost-Sensitive Random Subspace Support Vector Classifier (CS-RS-SVC), a new learning algorithm that combines random subspace sampling and bagging with Cost-Sensitive Support Vector Classifiers to more effectively address detection applications burdened by unequal misclassification requirements. When compared to its conventional, non-cost-sensitive counterpart on a two-class signal detection application, random subspace sampling is shown to very effectively leverage the additional flexibility offered by the Cost-Sensitive Support Vector Classifier, yielding a more than four-fold increase in the detection rate at a false alarm rate (FAR) of zero. Moreover, the CS-RS-SVC is shown to be fairly robust to constraints on the feature subspace dimensionality, enabling reductions in computation time of up to 82% with minimal performance degradation.

  2. EVALUATION FOR JUDGMENT CRITERIA OF REPAIR ON CIVIL ENGINEERING STRUCTURE BY SUPPORT VECTOR MACHINE

    NASA Astrophysics Data System (ADS)

    Yuki, Kazunori; Kobayashi, Hiroki; Ohishi, Hiroyuki; Sugimoto, Hiroyuki; Iida, Takeshi; Furukawa, Kohei

    In this study, setting method for judgment criteria for repair of civil engineering structures is analyzed by the use of inspection and repair record of expansion joint of bridges with the Support Vector Machine. The Support Vector Machine is a technique used to apply for setting of risk degree of disasters on natural slopes. However it is needed that effective exclusion method of noise data has to be considered to apply for the analysis of the setting method. Therefore the noise data is excluded objectively in order that high confidence data can be extracted from the record. In this way setting the method can be developed. As a result of this study, it can be shown that the setting method by Support Vector Machine is effective as a tool for maintenance management plan of civil engineering structures since the method has a high integrity with evaluation by professional engineer.

  3. Testing of the Support Vector Machine for Binary-Class Classification

    NASA Technical Reports Server (NTRS)

    Scholten, Matthew

    2011-01-01

    The Support Vector Machine is a powerful algorithm, useful in classifying data in to species. The Support Vector Machines implemented in this research were used as classifiers for the final stage in a Multistage Autonomous Target Recognition system. A single kernel SVM known as SVMlight, and a modified version known as a Support Vector Machine 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 SMV as a method for classification. From trial to trial, SVM produces consistent results

  4. Automated Classification of Epiphyses in the Distal Radius and Ulna using a Support Vector Machine.

    PubMed

    Wang, Ya-hui; Liu, Tai-ang; Wei, Hua; Wan, Lei; Ying, Chong-liang; Zhu, Guang-you

    2016-03-01

    The aim of this study was to automatically classify epiphyses in the distal radius and ulna using a support vector machine (SVM) and to examine the accuracy of the epiphyseal growth grades generated by the support vector machine. X-ray images of distal radii and ulnae were collected from 140 Chinese teenagers aged between 11.0 and 19.0 years. Epiphyseal growth of the two elements was classified into five grades. Features of each element were extracted using a histogram of oriented gradient (HOG), and models were established using support vector classification (SVC). The prediction results and the validity of the models were evaluated with a cross-validation test and independent test for accuracy (PA ). Our findings suggest that this new technique for epiphyseal classification was successful and that an automated technique using an SVM is reliable and feasible, with a relative high accuracy for the models. PMID:27404614

  5. Bayesian data assimilation provides rapid decision support for vector-borne diseases.

    PubMed

    Jewell, Chris P; Brown, Richard G

    2015-07-01

    Predicting the spread of vector-borne diseases in response to incursions requires knowledge of both host and vector demographics in advance of an outbreak. Although host population data are typically available, for novel disease introductions there is a high chance of the pathogen using a vector for which data are unavailable. This presents a barrier to estimating the parameters of dynamical models representing host-vector-pathogen interaction, and hence limits their ability to provide quantitative risk forecasts. The Theileria orientalis (Ikeda) outbreak in New Zealand cattle demonstrates this problem: even though the vector has received extensive laboratory study, a high degree of uncertainty persists over its national demographic distribution. Addressing this, we develop a Bayesian data assimilation approach whereby indirect observations of vector activity inform a seasonal spatio-temporal risk surface within a stochastic epidemic model. We provide quantitative predictions for the future spread of the epidemic, quantifying uncertainty in the model parameters, case infection times and the disease status of undetected infections. Importantly, we demonstrate how our model learns sequentially as the epidemic unfolds and provide evidence for changing epidemic dynamics through time. Our approach therefore provides a significant advance in rapid decision support for novel vector-borne disease outbreaks. PMID:26136225

  6. Support Vector Regression Algorithms in the Forecasting of Daily Maximums of Tropospheric Ozone Concentration in Madrid

    NASA Astrophysics Data System (ADS)

    Ortiz-García, E. G.; Salcedo-Sanz, S.; Pérez-Bellido, A. M.; Gascón-Moreno, J.; Portilla-Figueras, A.

    In this paper we present the application of a support vector regression algorithm to a real problem of maximum daily tropospheric ozone forecast. The support vector regression approach proposed is hybridized with an heuristic for optimal selection of hyper-parameters. The prediction of maximum daily ozone is carried out in all the station of the air quality monitoring network of Madrid. In the paper we analyze how the ozone prediction depends on meteorological variables such as solar radiation and temperature, and also we perform a comparison against the results obtained using a multi-layer perceptron neural network in the same prediction problem.

  7. Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization.

    PubMed

    Ma, Yuliang; Ding, Xiaohui; She, Qingshan; Luo, Zhizeng; Potter, Thomas; Zhang, Yingchun

    2016-01-01

    Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to improve the classification performance of support vector machines. The performance of the optimized classifier was evaluated with motor imagery EEG signals in terms of both classification and prediction. Results show that the optimized classifier can significantly improve the classification accuracy of motor imagery EEG signals. PMID:27313656

  8. Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization

    PubMed Central

    Ma, Yuliang; Ding, Xiaohui; She, Qingshan; Luo, Zhizeng; Potter, Thomas; Zhang, Yingchun

    2016-01-01

    Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to improve the classification performance of support vector machines. The performance of the optimized classifier was evaluated with motor imagery EEG signals in terms of both classification and prediction. Results show that the optimized classifier can significantly improve the classification accuracy of motor imagery EEG signals. PMID:27313656

  9. Automatic vehicle detection based on automatic histogram-based fuzzy C-means algorithm and perceptual grouping using very high-resolution aerial imagery and road vector data

    NASA Astrophysics Data System (ADS)

    Ghaffarian, Saman; Gökaşar, Ilgın

    2016-01-01

    This study presents an approach for the automatic detection of vehicles using very high-resolution images and road vector data. Initially, road vector data and aerial images are integrated to extract road regions. Then, the extracted road/street region is clustered using an automatic histogram-based fuzzy C-means algorithm, and edge pixels are detected using the Canny edge detector. In order to automatically detect vehicles, we developed a local perceptual grouping approach based on fusion of edge detection and clustering outputs. To provide the locality, an ellipse is generated using characteristics of the candidate clusters individually. Then, ratio of edge pixels to nonedge pixels in the corresponding ellipse is computed to distinguish the vehicles. Finally, a point-merging rule is conducted to merge the points that satisfy a predefined threshold and are supposed to denote the same vehicles. The experimental validation of the proposed method was carried out on six very high-resolution aerial images that illustrate two highways, two shadowed roads, a crowded narrow street, and a street in a dense urban area with crowded parked vehicles. The evaluation of the results shows that our proposed method performed 86% and 83% in overall correctness and completeness, respectively.

  10. Bayesian data assimilation provides rapid decision support for vector-borne diseases

    PubMed Central

    Jewell, Chris P.; Brown, Richard G.

    2015-01-01

    Predicting the spread of vector-borne diseases in response to incursions requires knowledge of both host and vector demographics in advance of an outbreak. Although host population data are typically available, for novel disease introductions there is a high chance of the pathogen using a vector for which data are unavailable. This presents a barrier to estimating the parameters of dynamical models representing host–vector–pathogen interaction, and hence limits their ability to provide quantitative risk forecasts. The Theileria orientalis (Ikeda) outbreak in New Zealand cattle demonstrates this problem: even though the vector has received extensive laboratory study, a high degree of uncertainty persists over its national demographic distribution. Addressing this, we develop a Bayesian data assimilation approach whereby indirect observations of vector activity inform a seasonal spatio-temporal risk surface within a stochastic epidemic model. We provide quantitative predictions for the future spread of the epidemic, quantifying uncertainty in the model parameters, case infection times and the disease status of undetected infections. Importantly, we demonstrate how our model learns sequentially as the epidemic unfolds and provide evidence for changing epidemic dynamics through time. Our approach therefore provides a significant advance in rapid decision support for novel vector-borne disease outbreaks. PMID:26136225

  11. A divide-and-combine method for large scale nonparallel support vector machines.

    PubMed

    Tian, Yingjie; Ju, Xuchan; Shi, Yong

    2016-03-01

    Nonparallel Support Vector Machine (NPSVM) which is more flexible and has better generalization than typical SVM is widely used for classification. Although some methods and toolboxes like SMO and libsvm for NPSVM are used, NPSVM is hard to scale up when facing millions of samples. In this paper, we propose a divide-and-combine method for large scale nonparallel support vector machine (DCNPSVM). In the division step, DCNPSVM divide samples into smaller sub-samples aiming at solving smaller subproblems independently. We theoretically and experimentally prove that the objective function value, solutions, and support vectors solved by DCNPSVM are close to the objective function value, solutions, and support vectors of the whole NPSVM problem. In the combination step, the sub-solutions combined as initial iteration points are used to solve the whole problem by global coordinate descent which converges quickly. In order to balance the accuracy and efficiency, we adopt a multi-level structure which outperforms state-of-the-art methods. Moreover, our DCNPSVM can tackle unbalance problems efficiently by tuning the parameters. Experimental results on lots of large data sets show the effectiveness of our method in memory usage, classification accuracy and time consuming. PMID:26690682

  12. Applying spectral unmixing and support vector machine to airborne hyperspectral imagery for detecting giant reed

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This study evaluated linear spectral unmixing (LSU), mixture tuned matched filtering (MTMF) and support vector machine (SVM) techniques for detecting and mapping giant reed (Arundo donax L.), an invasive weed that presents a severe threat to agroecosystems and riparian areas throughout the southern ...

  13. Fabric wrinkle characterization and classification using modified wavelet coefficients and optimized support-vector-machine classifier

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This paper presents a novel wrinkle evaluation method that uses modified wavelet coefficients and an optimized support-vector-machine (SVM) classification scheme to characterize and classify wrinkle appearance of fabric. Fabric images were decomposed with the wavelet transform (WT), and five parame...

  14. Support Vector Machine applied to predict the zoonotic potential of E. coli O157 cattle isolates

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Methods based on sequence data analysis facilitate the tracking of disease outbreaks, allow relationships between strains to be reconstructed and virulence factors to be identified. However, these methods are used postfactum after an outbreak has happened. Here, we show that support vector machine a...

  15. Computer-aided diagnosis of Alzheimer's disease using support vector machines and classification trees

    NASA Astrophysics Data System (ADS)

    Salas-Gonzalez, D.; Górriz, J. M.; Ramírez, J.; López, M.; Álvarez, I.; Segovia, F.; Chaves, R.; Puntonet, C. G.

    2010-05-01

    This paper presents a computer-aided diagnosis technique for improving the accuracy of early diagnosis of Alzheimer-type dementia. The proposed methodology is based on the selection of voxels which present Welch's t-test between both classes, normal and Alzheimer images, greater than a given threshold. The mean and standard deviation of intensity values are calculated for selected voxels. They are chosen as feature vectors for two different classifiers: support vector machines with linear kernel and classification trees. The proposed methodology reaches greater than 95% accuracy in the classification task.

  16. Using a Support Vector Machine (SVM) to Improve Generalization Ability of Load Model Parameters

    SciTech Connect

    Ma, Jian; Dong, Zhao Yang; Zhang, Pei

    2009-04-24

    Load modeling plays an important role in power system stability analysis and planning studies. The parameters of load models may experience variations in different application situations. Choosing appropriate parameters is critical for dynamic simulation and stability studies in power system. This paper presents a method to select the parameters with good generalization ability based on a given large number of available parameters that have been identified from dynamic simulation data in different scenarios. Principal component analysis is used to extract the major features of the given parameter sets. Reduced feature vectors are obtained by mapping the given parameter sets into principal component space. Then support vectors are found by implementing a classification problem. Load model parameters based on the obtained support vectors are built to reflect the dynamic property of the load. All of the given parameter sets were identified from simulation data based on the New England 10-machine 39-bus system, by taking into account different situations, such as load types, fault locations, fault types, and fault clearing time. The parameters obtained by support vector machine have good generalization capability, and can represent the load more accurately in most situations.

  17. Application of higher order spectral features and support vector machines for bearing faults classification.

    PubMed

    Saidi, Lotfi; Ben Ali, Jaouher; Fnaiech, Farhat

    2015-01-01

    Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals. PMID:25282095

  18. Application of support vector machines and relevance vector machines in predicting uniaxial compressive strength of volcanic rocks

    NASA Astrophysics Data System (ADS)

    Ceryan, Nurcihan

    2014-12-01

    The uniaxial compressive strength (UCS) of intact rocks is an important and pertinent property for characterizing a rock mass. It is known that standard UCS tests are destructive, expensive and time-consuming task, which is particularly true for thinly bedded, highly fractured, foliated, highly porous and weak rocks. Consequently, prediction models have become an attractive alternative for engineering geologists. In the last several years, a new, alternative kernel-based technique, support vector machines (SVMs), has been popular in modeling studies. Despite superior SVM performance, this technique has certain significant, practical drawbacks. Hence, the relevance vector machines (RVMs) approach has been proposed to recast the main ideas underlying SVMs in a Bayesian context. The primary purpose of this study is to examine the applicability and capability of RVM and SVM models for predicting the UCS of volcanic rocks from NE Turkey and comparing its performance with ANN models. In these models, the porosity and P-durability index representing microstructural variables are the input parameters. The study results indicate that these methods can successfully predict the UCS for the volcanic rocks. The SVM and RVM performed better than the ANN model. When these kernel based models are considered, RVM model found successful in terms of statistical performance criterions (e.g., performance index, PI values for training and testing data are computed as 1.579 and 1.449). These values for SVM are 1.509 and 1.307. Although SVM and RVM models are powerful techniques, the RVM run time was considerably faster, and it yielded the highest accuracy.

  19. Viral vector tropism for supporting cells in the developing murine cochlea.

    PubMed

    Sheffield, Abraham M; Gubbels, Samuel P; Hildebrand, Michael S; Newton, Stephen S; Chiorini, John A; Di Pasquale, Giovanni; Smith, Richard J H

    2011-07-01

    Gene-based therapeutics are being developed as novel treatments for genetic hearing loss. One roadblock to effective gene therapy is the identification of vectors which will safely deliver therapeutics to targeted cells. The cellular heterogeneity that exists within the cochlea makes viral tropism a vital consideration for effective inner ear gene therapy. There are compelling reasons to identify a viral vector with tropism for organ of Corti supporting cells. Supporting cells are the primary expression site of connexin 26 gap junction proteins that are mutated in the most common form of congenital genetic deafness (DFNB1). Supporting cells are also primary targets for inducing hair cell regeneration. Since many genetic forms of deafness are congenital it is necessary to administer gene transfer-based therapeutics prior to the onset of significant hearing loss. We have used transuterine microinjection of the fetal murine otocyst to investigate viral tropism in the developing inner ear. For the first time we have characterized viral tropism for supporting cells following in utero delivery to their progenitors. We report the inner ear tropism and potential ototoxicity of three previously untested vectors: early-generation adenovirus (Ad5.CMV.GFP), advanced-generation adenovirus (Adf.11D) and bovine adeno-associated virus (BAAV.CMV.GFP). Adenovirus showed robust tropism for organ of Corti supporting cells throughout the cochlea but induced increased ABR thresholds indicating ototoxicity. BAAV also showed tropism for organ of Corti supporting cells, with preferential transduction toward the cochlear apex. Additionally, BAAV readily transduced spiral ganglion neurons. Importantly, the BAAV-injected ears exhibited normal hearing at 5 weeks of age when compared to non-injected ears. Our results support the use of BAAV for safe and efficient targeting of supporting cell progenitors in the developing murine inner ear. PMID:21530627

  20. Color Image Watermarking Scheme Based on Efficient Preprocessing and Support Vector Machines

    NASA Astrophysics Data System (ADS)

    Fındık, Oğuz; Bayrak, Mehmet; Babaoğlu, Ismail; Çomak, Emre

    This paper suggests a new block based watermarking technique utilizing preprocessing and support vector machine (PPSVMW) to protect color image's intellectual property rights. Binary test set is employed here to train support vector machine (SVM). Before adding binary data into the original image, blocks have been separated into two parts to train SVM for better accuracy. Watermark's 1 valued bits were randomly added into the first block part and 0 into the second block part. Watermark is embedded by modifying the blue channel pixel value in the middle of each block so that watermarked image could be composed. SVM was trained with set-bits and three other features which are averages of the differences of pixels in three distinct shapes extracted from each block, and hence without the need of original image, it could be extracted. The results of PPSVMW technique proposed in this study were compared with those of the Tsai's technique. Our technique was proved to be more efficient.

  1. Surface-enhanced Raman spectroscopy + support vector machine: a new noninvasive method for prostate cancer screening?

    PubMed

    Li, Shaoxin; Guo, Zhouyi; Liu, Zhiming

    2015-01-01

    Prostate cancer is one of the most common malignancies of the older males worldwide. Early diagnosis and treatment are important to improve the survival of patients. Recently, we developed a new method for prostate cancer screening: by measuring the serum surface-enhanced Raman spectroscopy of prostate cancer patients and normal subjects, combining with classification algorithms of support vector machines, the measured surface-enhanced Raman spectroscopy spectra are successfully classified with accuracy of 98.1%. Although the practical application faces several difficulties, we believe that this label-free serum surface-enhanced Raman spectroscopy analysis technique combined with support vector machine diagnostic algorithms will become a powerful tool for noninvasive prostate cancer screening in the future. PMID:25525666

  2. Metabolic changes in rat urine after acute paraquat poisoning and discriminated by support vector machine.

    PubMed

    Wen, Congcong; Wang, Zhiyi; Zhang, Meiling; Wang, Shuanghu; Geng, Peiwu; Sun, Fa; Chen, Mengchun; Lin, Guanyang; Hu, Lufeng; Ma, Jianshe; Wang, Xianqin

    2016-01-01

    Paraquat is quick-acting and non-selective, killing green plant tissue on contact; it is also toxic to human beings and animals. In this study, we developed a urine metabonomic method by gas chromatography-mass spectrometry to evaluate the effect of acute paraquat poisoning on rats. Pattern recognition analysis, including both partial least squares discriminate analysis and principal component analysis revealed that acute paraquat poisoning induced metabolic perturbations. Compared with the control group, the levels of benzeneacetic acid and hexadecanoic acid of the acute paraquat poisoning group (intragastric administration 36 mg/kg) increased, while the levels of butanedioic acid, pentanedioic acid, altronic acid decreased. Based on these urinary metabolomics data, support vector machine was applied to discriminate the metabolomic change of paraquat groups from the control group, which achieved 100% classification accuracy. In conclusion, metabonomic method combined with support vector machine can be used as a useful diagnostic tool in paraquat-poisoned rats. PMID:26419410

  3. nu-Anomica: A Fast Support Vector Based Novelty Detection Technique

    NASA Technical Reports Server (NTRS)

    Das, Santanu; Bhaduri, Kanishka; Oza, Nikunj C.; Srivastava, Ashok N.

    2009-01-01

    In this paper we propose nu-Anomica, a novel anomaly detection technique that can be trained on huge data sets with much reduced running time compared to the benchmark one-class Support Vector Machines algorithm. In -Anomica, the idea is to train the machine such that it can provide a close approximation to the exact decision plane using fewer training points and without losing much of the generalization performance of the classical approach. We have tested the proposed algorithm on a variety of continuous data sets under different conditions. We show that under all test conditions the developed procedure closely preserves the accuracy of standard one-class Support Vector Machines while reducing both the training time and the test time by 5 - 20 times.

  4. Support vector machine based IS/OS disruption detection from SD-OCT images

    NASA Astrophysics Data System (ADS)

    Wang, Liyun; Zhu, Weifang; Liao, Jianping; Xiang, Dehui; Jin, Chao; Chen, Haoyu; Chen, Xinjian

    2014-03-01

    In this paper, we sought to find a method to detect the Inner Segment /Outer Segment (IS/OS)disruption region automatically. A novel support vector machine (SVM) based method was proposed for IS/OS disruption detection. The method includes two parts: training and testing. During the training phase, 7 features from the region around the fovea are calculated. Support vector machine (SVM) is utilized as the classification method. In the testing phase, the training model derived is utilized to classify the disruption and non-disruption region of the IS/OS, and calculate the accuracy separately. The proposed method was tested on 9 patients' SD-OCT images using leave-one-out strategy. The preliminary results demonstrated the feasibility and efficiency of the proposed method.

  5. Identification of comment-on sentences in online biomedical documents using support vector machines

    NASA Astrophysics Data System (ADS)

    Kim, In Cheol; Le, Daniel X.; Thoma, George R.

    2007-01-01

    MEDLINE (R) is the premier bibliographic online database of the National Library of Medicine, containing approximately 14 million citations and abstracts from over 4,800 biomedical journals. This paper presents an automated method based on support vector machines to identify a "comment-on" list, which is a field in a MEDLINE citation denoting previously published articles commented on by a given article. For comparative study, we also introduce another method based on scoring functions that estimate the significance of each sentence in a given article. Preliminary experiments conducted on HTML-formatted online biomedical documents collected from 24 different journal titles show that the support vector machine with polynomial kernel function performs best in terms of recall and F-measure rates.

  6. Interpreting linear support vector machine models with heat map molecule coloring

    PubMed Central

    2011-01-01

    Background Model-based virtual screening plays an important role in the early drug discovery stage. The outcomes of high-throughput screenings are a valuable source for machine learning algorithms to infer such models. Besides a strong performance, the interpretability of a machine learning model is a desired property to guide the optimization of a compound in later drug discovery stages. Linear support vector machines showed to have a convincing performance on large-scale data sets. The goal of this study is to present a heat map molecule coloring technique to interpret linear support vector machine models. Based on the weights of a linear model, the visualization approach colors each atom and bond of a compound according to its importance for activity. Results We evaluated our approach on a toxicity data set, a chromosome aberration data set, and the maximum unbiased validation data sets. The experiments show that our method sensibly visualizes structure-property and structure-activity relationships of a linear support vector machine model. The coloring of ligands in the binding pocket of several crystal structures of a maximum unbiased validation data set target indicates that our approach assists to determine the correct ligand orientation in the binding pocket. Additionally, the heat map coloring enables the identification of substructures important for the binding of an inhibitor. Conclusions In combination with heat map coloring, linear support vector machine models can help to guide the modification of a compound in later stages of drug discovery. Particularly substructures identified as important by our method might be a starting point for optimization of a lead compound. The heat map coloring should be considered as complementary to structure based modeling approaches. As such, it helps to get a better understanding of the binding mode of an inhibitor. PMID:21439031

  7. Performance evaluation of a FPGA implementation of a digital rotation support vector machine

    NASA Astrophysics Data System (ADS)

    Lamela, Horacio; Gimeno, Jesús; Jiménez, Matías; Ruiz, Marta

    2008-04-01

    In this paper we provide a simple and fast hardware implementation for a Support Vector Machine (SVM). By using the CORDIC algorithm and implementing a 2-based exponential kernel that allows us to simplify operations, we overcome the problems caused by too many internal multiplications found in the classification process, both while applying the Kernel formula and later on multiplying by the weights. We show a simple example of classification with the algorithm and analyze the classification speed and accuracy.

  8. Optical diagnosis of colon and cervical cancer by support vector machine

    NASA Astrophysics Data System (ADS)

    Mukhopadhyay, Sabyasachi; Kurmi, Indrajit; Dey, Rajib; Das, Nandan K.; Pradhan, Sanjay; Pradhan, Asima; Ghosh, Nirmalya; Panigrahi, Prasanta K.; Mohanty, Samarendra

    2016-05-01

    A probabilistic robust diagnostic algorithm is very much essential for successful cancer diagnosis by optical spectroscopy. We report here support vector machine (SVM) classification to better discriminate the colon and cervical cancer tissues from normal tissues based on elastic scattering spectroscopy. The efficacy of SVM based classification with different kernel has been tested on multifractal parameters like Hurst exponent, singularity spectrum width in order to classify the cancer tissues.

  9. Inversion of a radiative transfer model for estimation of rice chlorophyll content using support vector machine

    NASA Astrophysics Data System (ADS)

    Lv, Jie; Yan, Zhenguo; Wei, Jingyi

    2014-11-01

    Accurate retrieval of crop chlorophyll content is of great importance for crop growth monitoring, crop stress situations, and the crop yield estimation. This study focused on retrieval of rice chlorophyll content from data through radiative transfer model inversion. A field campaign was carried out in September 2009 in the farmland of ChangChun, Jinlin province, China. A different set of 10 sites of the same species were used in 2009 for validation of methodologies. Reflectance of rice was collected using ASD field spectrometer for the solar reflective wavelengths (350-2500 nm), chlorophyll content of rice was measured by SPAD-502 chlorophyll meter. Each sample sites was recorded with a Global Position System (GPS).Firstly, the PROSPECT radiative transfer model was inverted using support vector machine in order to link rice spectrum and the corresponding chlorophyll content. Secondly, genetic algorithms were adopted to select parameters of support vector machine, then support vector machine was trained the training data set, in order to establish leaf chlorophyll content estimation model. Thirdly, a validation data set was established based on hyperspectral data, and the leaf chlorophyll content estimation model was applied to the validation data set to estimate leaf chlorophyll content of rice in the research area. Finally, the outcome of the inversion was evaluated using the calculated R2 and RMSE values with the field measurements. The results of the study highlight the significance of support vector machine in estimating leaf chlorophyll content of rice. Future research will concentrated on the view of the definition of satellite images and the selection of the best measurement configuration for accurate estimation of rice characteristics.

  10. Prediction of Spirometric Forced Expiratory Volume (FEV1) Data Using Support Vector Regression

    NASA Astrophysics Data System (ADS)

    Kavitha, A.; Sujatha, C. M.; Ramakrishnan, S.

    2010-01-01

    In this work, prediction of forced expiratory volume in 1 second (FEV1) in pulmonary function test is carried out using the spirometer and support vector regression analysis. Pulmonary function data are measured with flow volume spirometer from volunteers (N=175) using a standard data acquisition protocol. The acquired data are then used to predict FEV1. Support vector machines with polynomial kernel function with four different orders were employed to predict the values of FEV1. The performance is evaluated by computing the average prediction accuracy for normal and abnormal cases. Results show that support vector machines are capable of predicting FEV1 in both normal and abnormal cases and the average prediction accuracy for normal subjects was higher than that of abnormal subjects. Accuracy in prediction was found to be high for a regularization constant of C=10. Since FEV1 is the most significant parameter in the analysis of spirometric data, it appears that this method of assessment is useful in diagnosing the pulmonary abnormalities with incomplete data and data with poor recording.

  11. Robust Support Vector Machines for Classification with Nonconvex and Smooth Losses.

    PubMed

    Feng, Yunlong; Yang, Yuning; Huang, Xiaolin; Mehrkanoon, Siamak; Suykens, Johan A K

    2016-06-01

    This letter addresses the robustness problem when learning a large margin classifier in the presence of label noise. In our study, we achieve this purpose by proposing robustified large margin support vector machines. The robustness of the proposed robust support vector classifiers (RSVC), which is interpreted from a weighted viewpoint in this work, is due to the use of nonconvex classification losses. Besides the robustness, we also show that the proposed RSCV is simultaneously smooth, which again benefits from using smooth classification losses. The idea of proposing RSVC comes from M-estimation in statistics since the proposed robust and smooth classification losses can be taken as one-sided cost functions in robust statistics. Its Fisher consistency property and generalization ability are also investigated. Besides the robustness and smoothness, another nice property of RSVC lies in the fact that its solution can be obtained by solving weighted squared hinge loss-based support vector machine problems iteratively. We further show that in each iteration, it is a quadratic programming problem in its dual space and can be solved by using state-of-the-art methods. We thus propose an iteratively reweighted type algorithm and provide a constructive proof of its convergence to a stationary point. Effectiveness of the proposed classifiers is verified on both artificial and real data sets. PMID:27137357

  12. Feature weighting algorithms for classification of hyperspectral images using a support vector machine.

    PubMed

    Qi, Bin; Zhao, Chunhui; Yin, Guisheng

    2014-05-01

    The support vector machine (SVM) is a widely used approach for high-dimensional data classification. Traditionally, SVMs use features from the spectral bands of hyperspectral images with each feature contributing equally to the classification. In practical applications, although affected by noise, slight contributions can also be obtained from deteriorated bands. Thus, compared with feature reduction or equal assignment of weights to all the features, feature weighting is a trade-off choice. In this study, we examined two approaches to assigning weights to SVM features to increase the overall classification accuracy: (1) "CSC-SVM" refers to a support vector machine with compactness and a separation coefficient feature weighting algorithm, and (2) "SE-SVM" refers to a support vector machine with a similarity entropy feature weighting algorithm. Analyses were conducted on a public data set with nine selected land-cover classes. In comparison with traditional SVMs and other classical feature weighting algorithms, the proposed weighting algorithms increase the overall classification accuracy, and even better results could be obtained with few training samples. PMID:24921869

  13. Semi-supervised least squares support vector machine algorithm: application to offshore oil reservoir

    NASA Astrophysics Data System (ADS)

    Luo, Wei-Ping; Li, Hong-Qi; Shi, Ning

    2016-06-01

    At the early stages of deep-water oil exploration and development, fewer and further apart wells are drilled than in onshore oilfields. Supervised least squares support vector machine algorithms are used to predict the reservoir parameters but the prediction accuracy is low. We combined the least squares support vector machine (LSSVM) algorithm with semi-supervised learning and established a semi-supervised regression model, which we call the semi-supervised least squares support vector machine (SLSSVM) model. The iterative matrix inversion is also introduced to improve the training ability and training time of the model. We use the UCI data to test the generalization of a semi-supervised and a supervised LSSVM models. The test results suggest that the generalization performance of the LSSVM model greatly improves and with decreasing training samples the generalization performance is better. Moreover, for small-sample models, the SLSSVM method has higher precision than the semi-supervised K-nearest neighbor (SKNN) method. The new semisupervised LSSVM algorithm was used to predict the distribution of porosity and sandstone in the Jingzhou study area.

  14. Estimation of Reservoir Porosity and Water Saturation Based on Seismic Attributes Using Support Vector Regression Approach

    NASA Astrophysics Data System (ADS)

    Na'imi, S. R.; Shadizadeh, S. R.; Riahi, M. A.; Mirzakhanian, M.

    2014-08-01

    Porosity and fluid saturation distributions are crucial properties of hydrocarbon reservoirs and are involved in almost all calculations related to reservoir and production. True measurements of these parameters derived from laboratory measurements, are only available at the isolated localities of a reservoir and also are expensive and time-consuming. Therefore, employing other methodologies which have stiffness, simplicity, and cheapness is needful. Support Vector Regression approach is a moderately novel method for doing functional estimation in regression problems. Contrary to conventional neural networks which minimize the error on the training data by the use of usual Empirical Risk Minimization principle, Support Vector Regression minimizes an upper bound on the anticipated risk by means of the Structural Risk Minimization principle. This difference which is the destination in statistical learning causes greater ability of this approach for generalization tasks. In this study, first, appropriate seismic attributes which have an underlying dependency with reservoir porosity and water saturation are extracted. Subsequently, a non-linear support vector regression algorithm is utilized to obtain quantitative formulation between porosity and water saturation parameters and selected seismic attributes. For an undrilled reservoir, in which there are no sufficient core and log data, it is moderately possible to characterize hydrocarbon bearing formation by means of this method.

  15. Online Support Vector Regression with Varying Parameters for Time-Dependent Data

    SciTech Connect

    Omitaomu, Olufemi A; Jeong, Myong K; Badiru, Adedeji B

    2011-01-01

    Support vector regression (SVR) is a machine learning technique that continues to receive interest in several domains including manufacturing, engineering, and medicine. In order to extend its application to problems in which datasets arrive constantly and in which batch processing of the datasets is infeasible or expensive, an accurate online support vector regression (AOSVR) technique was proposed. The AOSVR technique efficiently updates a trained SVR function whenever a sample is added to or removed from the training set without retraining the entire training data. However, the AOSVR technique assumes that the new samples and the training samples are of the same characteristics; hence, the same value of SVR parameters is used for training and prediction. This assumption is not applicable to data samples that are inherently noisy and non-stationary such as sensor data. As a result, we propose Accurate On-line Support Vector Regression with Varying Parameters (AOSVR-VP) that uses varying SVR parameters rather than fixed SVR parameters, and hence accounts for the variability that may exist in the samples. To accomplish this objective, we also propose a generalized weight function to automatically update the weights of SVR parameters in on-line monitoring applications. The proposed function allows for lower and upper bounds for SVR parameters. We tested our proposed approach and compared results with the conventional AOSVR approach using two benchmark time series data and sensor data from nuclear power plant. The results show that using varying SVR parameters is more applicable to time dependent data.

  16. Insect cell transformation vectors that support high level expression and promoter assessment in insect cell culture.

    PubMed

    Shirk, Paul D; Furlong, Richard B

    2016-01-01

    A somatic transformation vector, pDP9, was constructed that provides a simplified means of producing permanently transformed cultured insect cells that support high levels of protein expression of foreign genes. The pDP9 plasmid vector incorporates DNA sequences from the Junonia coenia densovirus that are involved in integration of the densovirus in insect cell chromosomes and a promoter/enhancer system that results in high levels of expression. The plasmid also contains two markers that permit selection of transformed insect cells by antibiotic resistance or by cell-sorting for fluorescent protein expression. Transformation of Bombyx mori Bm5 or Spodoptera frugiperda Sf9 cultured cells with the pDP9 vectors results in the integration of the pDP9 plasmid into genomic DNA of Bm5 and Sf9 cells. pDP9 contains a multiple cloning site (MCS) 3' of the densoviral P9 promoter and insertion of a protein coding sequence within the MCS results in high level expression by pDP9 transformed cells. P9 driven transcription in the pDP9 transformed Sf9 cells produced foreign gene transcript levels that were 30 fold higher than actin 3 driven transgenes and equivalent to hr5IE1 driven transgenes. The pDP9 vector transformation results in the efficient selection of clones for assessment of promoter activity. PMID:26794473

  17. A Fuzzy Logic Enhanced Environmental Protection Education Model for Policies Decision Support in Green Community Development

    PubMed Central

    2013-01-01

    This study proposes the promotion of environmental protection education among communities as a solution to the serious problems of high energy consumption and carbon emissions around the world. Environmental protection education has direct and lasting influences on everyone in society; therefore, it is helpful in our fight against many serious problems caused by high energy consumption. In this study, the Delphi method and the fuzzy logic theory are used to develop a quantizing assessment model based on qualitative analysis. This model can be used to assess the results and influences of community residents' participation in environmental protection education on green community development. In addition, it can be used to provide references for governing authorities in their decision making of green community development policies. PMID:24363614

  18. Interpreting support vector machine models for multivariate group wise analysis in neuroimaging

    PubMed Central

    Gaonkar, Bilwaj; Shinohara, Russell T; Davatzikos, Christos

    2015-01-01

    Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that contribute significantly to classifier decisions remains an open problem. This is an issue of critical importance in imaging studies seeking to determine which anatomical or physiological imaging features contribute to the classifier’s decision, thereby allowing users to critically evaluate the findings of such machine learning methods and to understand disease mechanisms. The majority of published work addresses the question of statistical inference for support vector classification using permutation tests based on SVM weight vectors. Such permutation testing ignores the SVM margin, which is critical in SVM theory. In this work we emphasize the use of a statistic that explicitly accounts for the SVM margin and show that the null distributions associated with this statistic are asymptotically normal. Further, our experiments show that this statistic is a lot less conservative as compared to weight based permutation tests and yet specific enough to tease out multivariate patterns in the data. Thus, we can better understand the multivariate patterns that the SVM uses for neuroimaging based classification. PMID:26210913

  19. Interpreting support vector machine models for multivariate group wise analysis in neuroimaging.

    PubMed

    Gaonkar, Bilwaj; T Shinohara, Russell; Davatzikos, Christos

    2015-08-01

    Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that contribute significantly to classifier decisions remains an open problem. This is an issue of critical importance in imaging studies seeking to determine which anatomical or physiological imaging features contribute to the classifier's decision, thereby allowing users to critically evaluate the findings of such machine learning methods and to understand disease mechanisms. The majority of published work addresses the question of statistical inference for support vector classification using permutation tests based on SVM weight vectors. Such permutation testing ignores the SVM margin, which is critical in SVM theory. In this work we emphasize the use of a statistic that explicitly accounts for the SVM margin and show that the null distributions associated with this statistic are asymptotically normal. Further, our experiments show that this statistic is a lot less conservative as compared to weight based permutation tests and yet specific enough to tease out multivariate patterns in the data. Thus, we can better understand the multivariate patterns that the SVM uses for neuroimaging based classification. PMID:26210913

  20. A Novel Empirical Mode Decomposition With Support Vector Regression for Wind Speed Forecasting.

    PubMed

    Ren, Ye; Suganthan, Ponnuthurai Nagaratnam; Srikanth, Narasimalu

    2016-08-01

    Wind energy is a clean and an abundant renewable energy source. Accurate wind speed forecasting is essential for power dispatch planning, unit commitment decision, maintenance scheduling, and regulation. However, wind is intermittent and wind speed is difficult to predict. This brief proposes a novel wind speed forecasting method by integrating empirical mode decomposition (EMD) and support vector regression (SVR) methods. The EMD is used to decompose the wind speed time series into several intrinsic mode functions (IMFs) and a residue. Subsequently, a vector combining one historical data from each IMF and the residue is generated to train the SVR. The proposed EMD-SVR model is evaluated with a wind speed data set. The proposed EMD-SVR model outperforms several recently reported methods with respect to accuracy or computational complexity. PMID:25222957

  1. Neutron-gamma discrimination based on the support vector machine method

    NASA Astrophysics Data System (ADS)

    Yu, Xunzhen; Zhu, Jingjun; Lin, ShinTed; Wang, Li; Xing, Haoyang; Zhang, Caixun; Xia, Yuxi; Liu, Shukui; Yue, Qian; Wei, Weiwei; Du, Qiang; Tang, Changjian

    2015-03-01

    In this study, the combination of the support vector machine (SVM) method with the moment analysis method (MAM) is proposed and utilized to perform neutron/gamma (n/γ) discrimination of the pulses from an organic liquid scintillator (OLS). Neutron and gamma events, which can be firmly separated on the scatter plot drawn by the charge comparison method (CCM), are detected to form the training data set and the test data set for the SVM, and the MAM is used to create the feature vectors for individual events in the data sets. Compared to the traditional methods, such as CCM, the proposed method can not only discriminate the neutron and gamma signals, even at lower energy levels, but also provide the corresponding classification accuracy for each event, which is useful in validating the discrimination. Meanwhile, the proposed method can also offer a predication of the classification for the under-energy-limit events.

  2. Gastroesophageal Reflux Disease Diagnosis Using Hierarchical Heterogeneous Descriptor Fusion Support Vector Machine.

    PubMed

    Huang, Chun-Rong; Chen, Yan-Ting; Chen, Wei-Ying; Cheng, Hsiu-Chi; Sheu, Bor-Shyang

    2016-03-01

    A new computer-aided diagnosis method is proposed to diagnose the gastroesophageal reflux disease (GERD) from endoscopic images of the esophageal-gastric junction. To avoid the interferences of different endoscope devices and automatic camera white balance adjustment, heterogeneous descriptors computed from heterogeneous color models are used to represent endoscopic images. Instead of concatenating these descriptors to a super vector, a hierarchical heterogeneous descriptor fusion support vector machine (HHDF-SVM) framework is proposed to simultaneously apply heterogeneous descriptors for GERD diagnosis and overcome the curse of dimensionality problem. During validation, heterogeneous descriptors are extracted from test endoscopic images at first. The classification result is obtained by using HHDF-SVM with heterogeneous descriptors. As shown in the experiments, our method can automatically diagnose GERD without any manual selection of region of interest and achieve better accuracy compared to states-of-the-art methods. PMID:26276981

  3. Fuzzy logic particle tracking velocimetry

    NASA Technical Reports Server (NTRS)

    Wernet, Mark P.

    1993-01-01

    Fuzzy logic has proven to be a simple and robust method for process control. Instead of requiring a complex model of the system, a user defined rule base is used to control the process. In this paper the principles of fuzzy logic control are applied to Particle Tracking Velocimetry (PTV). Two frames of digitally recorded, single exposure particle imagery are used as input. The fuzzy processor uses the local particle displacement information to determine the correct particle tracks. Fuzzy PTV is an improvement over traditional PTV techniques which typically require a sequence (greater than 2) of image frames for accurately tracking particles. The fuzzy processor executes in software on a PC without the use of specialized array or fuzzy logic processors. A pair of sample input images with roughly 300 particle images each, results in more than 200 velocity vectors in under 8 seconds of processing time.

  4. Seismic interpretation using Support Vector Machines implemented on Graphics Processing Units

    SciTech Connect

    Kuzma, H A; Rector, J W; Bremer, D

    2006-06-22

    Support Vector Machines (SVMs) estimate lithologic properties of rock formations from seismic data by interpolating between known models using synthetically generated model/data pairs. SVMs are related to kriging and radial basis function neural networks. In our study, we train an SVM to approximate an inverse to the Zoeppritz equations. Training models are sampled from distributions constructed from well-log statistics. Training data is computed via a physically realistic forward modeling algorithm. In our experiments, each training data vector is a set of seismic traces similar to a 2-d image. The SVM returns a model given by a weighted comparison of the new data to each training data vector. The method of comparison is given by a kernel function which implicitly transforms data into a high-dimensional feature space and performs a dot-product. The feature space of a Gaussian kernel is made up of sines and cosines and so is appropriate for band-limited seismic problems. Training an SVM involves estimating a set of weights from the training model/data pairs. It is designed to be an easy problem; at worst it is a quadratic programming problem on the order of the size of the training set. By implementing the slowest part of our SVM algorithm on a graphics processing unit (GPU), we improve the speed of the algorithm by two orders of magnitude. Our SVM/GPU combination achieves results that are similar to those of conventional iterative inversion in fractions of the time.

  5. Support Vector Machines for Improved Peptide Identification from Tandem Mass Spectrometry Database Search

    SciTech Connect

    Webb-Robertson, Bobbie-Jo M.

    2009-05-06

    Accurate identification of peptides is a current challenge in mass spectrometry (MS) based proteomics. The standard approach uses a search routine to compare tandem mass spectra to a database of peptides associated with the target organism. These database search routines yield multiple metrics associated with the quality of the mapping of the experimental spectrum to the theoretical spectrum of a peptide. The structure of these results make separating correct from false identifications difficult and has created a false identification problem. Statistical confidence scores are an approach to battle this false positive problem that has led to significant improvements in peptide identification. We have shown that machine learning, specifically support vector machine (SVM), is an effective approach to separating true peptide identifications from false ones. The SVM-based peptide statistical scoring method transforms a peptide into a vector representation based on database search metrics to train and validate the SVM. In practice, following the database search routine, a peptides is denoted in its vector representation and the SVM generates a single statistical score that is then used to classify presence or absence in the sample

  6. Protein Kinase Classification with 2866 Hidden Markov Models and One Support Vector Machine

    NASA Technical Reports Server (NTRS)

    Weber, Ryan; New, Michael H.; Fonda, Mark (Technical Monitor)

    2002-01-01

    The main application considered in this paper is predicting true kinases from randomly permuted kinases that share the same length and amino acid distributions as the true kinases. Numerous methods already exist for this classification task, such as HMMs, motif-matchers, and sequence comparison algorithms. We build on some of these efforts by creating a vector from the output of thousands of structurally based HMMs, created offline with Pfam-A seed alignments using SAM-T99, which then must be combined into an overall classification for the protein. Then we use a Support Vector Machine for classifying this large ensemble Pfam-Vector, with a polynomial and chisquared kernel. In particular, the chi-squared kernel SVM performs better than the HMMs and better than the BLAST pairwise comparisons, when predicting true from false kinases in some respects, but no one algorithm is best for all purposes or in all instances so we consider the particular strengths and weaknesses of each.

  7. Spacebased Estimation of Moisture Transport in Marine Atmosphere Using Support Vector Regression

    NASA Technical Reports Server (NTRS)

    Xie, Xiaosu; Liu, W. Timothy; Tang, Benyang

    2007-01-01

    An improved algorithm is developed based on support vector regression (SVR) to estimate horizonal water vapor transport integrated through the depth of the atmosphere ((Theta)) over the global ocean from observations of surface wind-stress vector by QuikSCAT, cloud drift wind vector derived from the Multi-angle Imaging SpectroRadiometer (MISR) and geostationary satellites, and precipitable water from the Special Sensor Microwave/Imager (SSM/I). The statistical relation is established between the input parameters (the surface wind stress, the 850 mb wind, the precipitable water, time and location) and the target data ((Theta) calculated from rawinsondes and reanalysis of numerical weather prediction model). The results are validated with independent daily rawinsonde observations, monthly mean reanalysis data, and through regional water balance. This study clearly demonstrates the improvement of (Theta) derived from satellite data using SVR over previous data sets based on linear regression and neural network. The SVR methodology reduces both mean bias and standard deviation comparedwith rawinsonde observations. It agrees better with observations from synoptic to seasonal time scales, and compare more favorably with the reanalysis data on seasonal variations. Only the SVR result can achieve the water balance over South America. The rationale of the advantage by SVR method and the impact of adding the upper level wind will also be discussed.

  8. A Nonlinear Adaptive Beamforming Algorithm Based on Least Squares Support Vector Regression

    PubMed Central

    Wang, Lutao; Jin, Gang; Li, Zhengzhou; Xu, Hongbin

    2012-01-01

    To overcome the performance degradation in the presence of steering vector mismatches, strict restrictions on the number of available snapshots, and numerous interferences, a novel beamforming approach based on nonlinear least-square support vector regression machine (LS-SVR) is derived in this paper. In this approach, the conventional linearly constrained minimum variance cost function used by minimum variance distortionless response (MVDR) beamformer is replaced by a squared-loss function to increase robustness in complex scenarios and provide additional control over the sidelobe level. Gaussian kernels are also used to obtain better generalization capacity. This novel approach has two highlights, one is a recursive regression procedure to estimate the weight vectors on real-time, the other is a sparse model with novelty criterion to reduce the final size of the beamformer. The analysis and simulation tests show that the proposed approach offers better noise suppression capability and achieve near optimal signal-to-interference-and-noise ratio (SINR) with a low computational burden, as compared to other recently proposed robust beamforming techniques.

  9. Least squares support vector machines for direction of arrival estimation with error control and validation.

    SciTech Connect

    Christodoulou, Christos George (University of New Mexico, Albuquerque, NM); Abdallah, Chaouki T. (University of New Mexico, Albuquerque, NM); Rohwer, Judd Andrew

    2003-02-01

    The paper presents a multiclass, multilabel implementation of least squares support vector machines (LS-SVM) for direction of arrival (DOA) estimation in a CDMA system. For any estimation or classification system, the algorithm's capabilities and performance must be evaluated. Specifically, for classification algorithms, a high confidence level must exist along with a technique to tag misclassifications automatically. The presented learning algorithm includes error control and validation steps for generating statistics on the multiclass evaluation path and the signal subspace dimension. The error statistics provide a confidence level for the classification accuracy.

  10. Application of Multi-Sensor Information Fusion Method Based on Rough Sets and Support Vector Machine

    NASA Astrophysics Data System (ADS)

    Xue, Jinxue; Wang, Guohu; Wang, Xiaoqiang; Cui, Fengkui

    In order to improve the precision and date processing speed of multi-sensor information fusion, a kind of multi-sensor data fusion process algorithm has been studied in this paper. First, based on rough set theory (RS) to attribute reduction the parameter set, we use the advantages of rough set theory in dealing with large amount of data to eliminate redundant information. Then, the data can be trained and classified by Support Vector Machine (SYM). Experimental results showed that this method can improve the speed and accuracy of multi-sensor fusion system.

  11. Land cover classification by support vector machine: training set size and selection issues.

    NASA Astrophysics Data System (ADS)

    Mathur, A.; Foody, G.

    The accuracy of land cover maps derived via supervised classification is often insufficient for operational applications. One of the important reasons for this is associated with the inputs to supervised classification analyses, especially the training data. The aim of this poster paper is to highlight the effect of variation in training set size on classification accuracy with respect to a series of supervised classifiers with particular regard to identifying an approach to allow accurate classification from small training sets. The classifiers investigated are the widely used maximum-likelihood classifier (MLC), feedforward artificial neural networks (ANN), decision trees (DT) and support vector machines (SVM). An advanced pattern recognition technique that has recently attracted the attention of the remote sensing community is support vector machines (SVM). A key attraction of the SVM based approach to classification is that it seeks to fit an optimal hyperplane between the classes and since it uses only the training samples that lie at the edge of the class distributions in feature space it may require only a small training sample. The potential of SVM classification was demonstrated from a series of analyses that classified land cover from imagery of Feltwell, Norfolk, U.K. All four classifiers were able to classify land cover accurately, each > 90% correct. However, for training sets ranging in size from 15 to 100 samples per-class, the highest accuracy was derived from the SVM. Moreover, inspection of the number of support vectors used in each SVM classification indicated a potential to reduce training set size without any negative impact on classification accuracy. This requires a means to intelligently identify training samples and the second part of this poster focuses on one such approach, based on the use of ancillary data on soil type to direct training site acquisition. It is shown that with information on soil type, training sample acquisition can be

  12. Support vector regression for porosity prediction in a heterogeneous reservoir: A comparative study

    NASA Astrophysics Data System (ADS)

    Al-Anazi, A. F.; Gates, I. D.

    2010-12-01

    In wells with limited log and core data, porosity, a fundamental and essential property to characterize reservoirs, is challenging to estimate by conventional statistical methods from offset well log and core data in heterogeneous formations. Beyond simple regression, neural networks have been used to develop more accurate porosity correlations. Unfortunately, neural network-based correlations have limited generalization ability and global correlations for a field are usually less accurate compared to local correlations for a sub-region of the reservoir. In this paper, support vector machines are explored as an intelligent technique to correlate porosity to well log data. Recently, support vector regression (SVR), based on the statistical learning theory, have been proposed as a new intelligence technique for both prediction and classification tasks. The underlying formulation of support vector machines embodies the structural risk minimization (SRM) principle which has been shown to be superior to the traditional empirical risk minimization (ERM) principle employed by conventional neural networks and classical statistical methods. This new formulation uses margin-based loss functions to control model complexity independently of the dimensionality of the input space, and kernel functions to project the estimation problem to a higher dimensional space, which enables the solution of more complex nonlinear problem optimization methods to exist for a globally optimal solution. SRM minimizes an upper bound on the expected risk using a margin-based loss function ( ɛ-insensitivity loss function for regression) in contrast to ERM which minimizes the error on the training data. Unlike classical learning methods, SRM, indexed by margin-based loss function, can also control model complexity independent of dimensionality. The SRM inductive principle is designed for statistical estimation with finite data where the ERM inductive principle provides the optimal solution (the

  13. Application of multi-output support vector regression on EMGs to decode hand continuous movement trajectory.

    PubMed

    Tian, Pan; Hu, Jie; Qi, Jin; Xia, Peng; Peng, Ying-Hong

    2015-01-01

    Applications of neural machine interfaces have received increased attention during the last decades. It is crucial to realize the continuous control of prosthetic devices based on biological signals. In order to deal with the highly nonlinear relationship between the Electromyography (EMG) signals and motion, this study presents a novel decoding approach which employs multi-output support vector regression (M-SVR). The proposed M-SVR is compared with other popular regression techniques and the experimental results demonstrate the effectiveness of M-SVR in hand continuous movement trajectory reconstruction. PMID:26406051

  14. Automatic classification of 3D segmented CT data using data fusion and support vector machine

    NASA Astrophysics Data System (ADS)

    Osman, Ahmad; Kaftandjian, Valérie; Hassler, Ulf

    2011-07-01

    The three dimensional X-ray computed tomography (3D-CT) has proved its successful usage as inspection method in non destructive testing. The generated 3D volume using high efficiency reconstruction algorithms contains all the inner structures of the inspected part. Segmentation of this volume reveals suspicious regions which need to be classified into defects or false alarms. This paper deals with the classification step using data fusion theory and support vector machine. Results achieved are very promising and prove the effectiveness of the data fusion theory as a method to build stronger classifier.

  15. Bidding strategy with forecast technology based on support vector machine in the electricity market

    NASA Astrophysics Data System (ADS)

    Gao, Ciwei; Bompard, Ettore; Napoli, Roberto; Wan, Qiulan; Zhou, Jian

    2008-06-01

    The participants in the electricity market are concerned very much with the market price evolution. Various technologies have been developed for price forecasting. The SVM (Support Vector Machine) has shown its good performance in market price forecasting. Two approaches for forming the market bidding strategies based on SVM are proposed. One is based on the price forecasting accuracy, with which the rejection risk is defined. The other takes into account the impact of the producer’s own bid. The risks associated with the bidding are controlled by the parameter settings. The proposed approaches have been tested on a numerical example.

  16. Material grain size characterization method based on energy attenuation coefficient spectrum and support vector regression.

    PubMed

    Li, Min; Zhou, Tong; Song, Yanan

    2016-07-01

    A grain size characterization method based on energy attenuation coefficient spectrum and support vector regression (SVR) is proposed. First, the spectra of the first and second back-wall echoes are cut into several frequency bands to calculate the energy attenuation coefficient spectrum. Second, the frequency band that is sensitive to grain size variation is determined. Finally, a statistical model between the energy attenuation coefficient in the sensitive frequency band and average grain size is established through SVR. Experimental verification is conducted on austenitic stainless steel. The average relative error of the predicted grain size is 5.65%, which is better than that of conventional methods. PMID:26995732

  17. Eyebrows Identity Authentication Based on Wavelet Transform and Support Vector Machines

    NASA Astrophysics Data System (ADS)

    Jun-bin, CAO; Haitao, Yang; Lili, Ding

    In order to study the novel biometric of eyebrow,,this paper presents an Eyebrows identity authentication based on wavelet transform and support vector machines. The features of the eyebrows image are extracted by wavelet transform, and then classifies them based on SVM. Verification results of the experiment on an eyebrow database taken from 100 of self-built personal demonstrate the effectiveness of the system. The system has a lower FAR 0.22%and FRR 28% Therefore, eyebrow recongnition may possibly apply to personal identification.

  18. Segmentation of Doppler optical coherence tomography signatures using a support-vector machine

    PubMed Central

    Singh, Amardeep S. G.; Schmoll, Tilman; Leitgeb, Rainer A.

    2011-01-01

    When processing Doppler optical coherence tomography images, there is a need to segment the Doppler signatures of the vessels. This can be used for visualization, for finding the center point of the flow areas or to facilitate the quantitative analysis of the vessel flow. We propose the use of a support-vector machine classifier in order to segment the flow. It uses the phase values of the Doppler image as well as texture information. We show that superior results compared to conventional simple threshold-based methods can be achieved in conditions of significant phase noise, which inhibit the use of a simple threshold of the phase values. PMID:21559144

  19. A Radio Visibility Data Auto-Flag Method Based on Support Vector Machine

    NASA Astrophysics Data System (ADS)

    Dai, H. M.; Mei, Y.; Wang, W.; Deng, H.; Wang, F.

    2016-01-01

    The Mingantu Ultrawide Spectral Radioheliograph (MUSER) has entered the trial observation stage. After the construction of data acquisition and real-time storage system, it is urgent to automatically flag and eliminate abnormal visibility data so as to improve the image quality. In this paper, according to the observational records, we create a credible visibility set, and further obtain a corresponding model by using support vector machine (SVM) technology. The results show that the SVM is a robust approach to flag the MUSER visibility data, and could reach the accuracy of about 86%. Meanwhile, the approach would not be affected by solar activities such as flare eruptions.

  20. Enhanced land use/cover classification of heterogeneous tropical landscapes using support vector machines and textural homogeneity

    NASA Astrophysics Data System (ADS)

    Paneque-Gálvez, Jaime; Mas, Jean-François; Moré, Gerard; Cristóbal, Jordi; Orta-Martínez, Martí; Luz, Ana Catarina; Guèze, Maximilien; Macía, Manuel J.; Reyes-García, Victoria

    2013-08-01

    Land use/cover classification is a key research field in remote sensing and land change science as thematic maps derived from remotely sensed data have become the basis for analyzing many socio-ecological issues. However, land use/cover classification remains a difficult task and it is especially challenging in heterogeneous tropical landscapes where nonetheless such maps are of great importance. The present study aims at establishing an efficient classification approach to accurately map all broad land use/cover classes in a large, heterogeneous tropical area, as a basis for further studies (e.g., land use/cover change, deforestation and forest degradation). Specifically, we first compare the performance of parametric (maximum likelihood), non-parametric (k-nearest neighbor and four different support vector machines - SVM), and hybrid (unsupervised-supervised) classifiers, using hard and soft (fuzzy) accuracy assessments. We then assess, using the maximum likelihood algorithm, what textural indices from the gray-level co-occurrence matrix lead to greater classification improvements at the spatial resolution of Landsat imagery (30 m), and rank them accordingly. Finally, we use the textural index that provides the most accurate classification results to evaluate whether its usefulness varies significantly with the classifier used. We classified imagery corresponding to dry and wet seasons and found that SVM classifiers outperformed all the rest. We also found that the use of some textural indices, but particularly homogeneity and entropy, can significantly improve classifications. We focused on the use of the homogeneity index, which has so far been neglected in land use/cover classification efforts, and found that this index along with reflectance bands significantly increased the overall accuracy of all the classifiers, but particularly of SVM. We observed that improvements in producer's and user's accuracies through the inclusion of homogeneity were different

  1. Climate Change, Public Health, and Decision Support: The New Threat of Vector-borne Disease

    NASA Astrophysics Data System (ADS)

    Grant, F.; Kumar, S.

    2011-12-01

    Climate change and vector-borne diseases constitute a massive threat to human development. It will not be enough to cut emissions of greenhouse gases-the tide of the future has already been established. Climate change and vector-borne diseases are already undermining the world's efforts to reduce extreme poverty. It is in the best interests of the world leaders to think in terms of concerted global actions, but adaptation and mitigation must be accomplished within the context of local community conditions, resources, and needs. Failure to act will continue to consign developed countries to completely avoidable health risks and significant expense. Failure to act will also reduce poorest of the world's population-some 2.6 billion people-to a future of diminished opportunity. Northrop Grumman has taken significant steps forward to develop the tools needed to assess climate change impacts on public health, collect relevant data for decision making, model projections at regional and local levels; and, deliver information and knowledge to local and regional stakeholders. Supporting these tools is an advanced enterprise architecture consisting of high performance computing, GIS visualization, and standards-based architecture. To address current deficiencies in local planning and decision making with respect to regional climate change and its effect on human health, our research is focused on performing a dynamical downscaling with the Weather Research and Forecasting (WRF) model to develop decision aids that translate the regional climate data into actionable information for users. For the present climate WRF was forced with the Max Planck Institute European Center/Hamburg Model version 5 (ECHAM5) General Circulation Model 20th century simulation. For the 21th century climate, we used an ECHAM5 simulation with the Special Report on Emissions (SRES) A1B emissions scenario. WRF was run in nested mode at spatial resolution of 108 km, 36 km and 12 km and 28 vertical levels

  2. Fuzzy cognitive mapping in support of integrated ecosystem assessments: Developing a shared conceptual model among stakeholders.

    PubMed

    Vasslides, James M; Jensen, Olaf P

    2016-01-15

    Ecosystem-based approaches, including integrated ecosystem assessments, are a popular methodology being used to holistically address management issues in social-ecological systems worldwide. In this study we utilized fuzzy logic cognitive mapping to develop conceptual models of a complex estuarine system among four stakeholder groups. The average number of categories in an individual map was not significantly different among groups, and there were no significant differences between the groups in the average complexity or density indices of the individual maps. When ordered by their complexity scores, eight categories contributed to the top four rankings of the stakeholder groups, with six of the categories shared by at least half of the groups. While non-metric multidimensional scaling (nMDS) analysis displayed a high degree of overlap between the individual models across groups, there was also diversity within each stakeholder group. These findings suggest that while all of the stakeholders interviewed perceive the subject ecosystem as a complex series of social and ecological interconnections, there are a core set of components that are present in most of the groups' models that are crucial in managing the system towards some desired outcome. However, the variability in the connections between these core components and the rest of the categories influences the exact nature of these outcomes. Understanding the reasons behind these differences will be critical to developing a shared conceptual model that will be acceptable to all stakeholder groups and can serve as the basis for an integrated ecosystem assessment. PMID:26520042

  3. Characterization of complexity in the electroencephalograph activity of Alzheimer's disease based on fuzzy entropy

    NASA Astrophysics Data System (ADS)

    Cao, Yuzhen; Cai, Lihui; Wang, Jiang; Wang, Ruofan; Yu, Haitao; Cao, Yibin; Liu, Jing

    2015-08-01

    In this paper, experimental neurophysiologic recording and statistical analysis are combined to investigate the nonlinear characteristic and the cognitive function of the brain. Fuzzy approximate entropy and fuzzy sample entropy are applied to characterize the model-based simulated series and electroencephalograph (EEG) series of Alzheimer's disease (AD). The effectiveness and advantages of these two kinds of fuzzy entropy are first verified through the simulated EEG series generated by the alpha rhythm model, including stronger relative consistency and robustness. Furthermore, in order to detect the abnormality of irregularity and chaotic behavior in the AD brain, the complexity features based on these two fuzzy entropies are extracted in the delta, theta, alpha, and beta bands. It is demonstrated that, due to the introduction of fuzzy set theory, the fuzzy entropies could better distinguish EEG signals of AD from that of the normal than the approximate entropy and sample entropy. Moreover, the entropy values of AD are significantly decreased in the alpha band, particularly in the temporal brain region, such as electrode T3 and T4. In addition, fuzzy sample entropy could achieve higher group differences in different brain regions and higher average classification accuracy of 88.1% by support vector machine classifier. The obtained results prove that fuzzy sample entropy may be a powerful tool to characterize the complexity abnormalities of AD, which could be helpful in further understanding of the disease.

  4. Characterization of complexity in the electroencephalograph activity of Alzheimer's disease based on fuzzy entropy.

    PubMed

    Cao, Yuzhen; Cai, Lihui; Wang, Jiang; Wang, Ruofan; Yu, Haitao; Cao, Yibin; Liu, Jing

    2015-08-01

    In this paper, experimental neurophysiologic recording and statistical analysis are combined to investigate the nonlinear characteristic and the cognitive function of the brain. Fuzzy approximate entropy and fuzzy sample entropy are applied to characterize the model-based simulated series and electroencephalograph (EEG) series of Alzheimer's disease (AD). The effectiveness and advantages of these two kinds of fuzzy entropy are first verified through the simulated EEG series generated by the alpha rhythm model, including stronger relative consistency and robustness. Furthermore, in order to detect the abnormality of irregularity and chaotic behavior in the AD brain, the complexity features based on these two fuzzy entropies are extracted in the delta, theta, alpha, and beta bands. It is demonstrated that, due to the introduction of fuzzy set theory, the fuzzy entropies could better distinguish EEG signals of AD from that of the normal than the approximate entropy and sample entropy. Moreover, the entropy values of AD are significantly decreased in the alpha band, particularly in the temporal brain region, such as electrode T3 and T4. In addition, fuzzy sample entropy could achieve higher group differences in different brain regions and higher average classification accuracy of 88.1% by support vector machine classifier. The obtained results prove that fuzzy sample entropy may be a powerful tool to characterize the complexity abnormalities of AD, which could be helpful in further understanding of the disease. PMID:26328567

  5. A Multi-Label Learning Based Kernel Automatic Recommendation Method for Support Vector Machine

    PubMed Central

    Zhang, Xueying; Song, Qinbao

    2015-01-01

    Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance. PMID:25893896

  6. Development of a Fuzzy Decision Support System to Determine the Severity of Obstructive Pulmonary in Chemical Injured Victims

    PubMed Central

    Samad-Soltani, Taha; Ghanei, Mostafa; Langarizadeh, Mostafa

    2015-01-01

    Background: Chronic Obstructive Pulmonary Disease (COPD) is the most common known complication of exposure to mustard gas. Thus, all clinical guidelines have provided some recommendation for diagnosis, clinical management and treatment of this disease. Decision support systems are used to increase the acceptance of clinical guidelines. The purpose of this research is to develop a CDSS to determine the severity of COPD in chemical injured victims. Objectives: Development of a decision support system to determine the severity of COPD. Patients and Methods: First, the variables influencing to determining the severity of the disease was classified through studying the clinical guidelines. Then, the fuzzy model was implemented. To testing the system, the data from 50 patients were used. Results: the overall accuracy in determining the severity of the injury is equal to 92%, these indicators reflect the proper functioning of the system to assist the physician regarding the diagnosis of chronic obstructive pulmonary disease and determining its severity. Conclusions: The CDSS has efficient results and satisfactory performance. Although, the medical expert systems cannot be expected to provide 100 percent correct responses, however, they can be useful in the areas of patient management, diagnosis and treatment planning. PMID:26236078

  7. Support-vector-machines-based multidimensional signal classification for fetal activity characterization

    NASA Astrophysics Data System (ADS)

    Ribes, S.; Voicu, I.; Girault, J. M.; Fournier, M.; Perrotin, F.; Tranquart, F.; Kouamé, D.

    2011-03-01

    Electronic fetal monitoring may be required during the whole pregnancy to closely monitor specific fetal and maternal disorders. Currently used methods suffer from many limitations and are not sufficient to evaluate fetal asphyxia. Fetal activity parameters such as movements, heart rate and associated parameters are essential indicators of the fetus well being, and no current device gives a simultaneous and sufficient estimation of all these parameters to evaluate the fetus well-being. We built for this purpose, a multi-transducer-multi-gate Doppler system and developed dedicated signal processing techniques for fetal activity parameter extraction in order to investigate fetus's asphyxia or well-being through fetal activity parameters. To reach this goal, this paper shows preliminary feasibility of separating normal and compromised fetuses using our system. To do so, data set consisting of two groups of fetal signals (normal and compromised) has been established and provided by physicians. From estimated parameters an instantaneous Manning-like score, referred to as ultrasonic score was introduced and was used together with movements, heart rate and associated parameters in a classification process using Support Vector Machines (SVM) method. The influence of the fetal activity parameters and the performance of the SVM were evaluated using the computation of sensibility, specificity, percentage of support vectors and total classification accuracy. We showed our ability to separate the data into two sets : normal fetuses and compromised fetuses and obtained an excellent matching with the clinical classification performed by physician.

  8. Prediction of water quality index in constructed wetlands using support vector machine.

    PubMed

    Mohammadpour, Reza; Shaharuddin, Syafiq; Chang, Chun Kiat; Zakaria, Nor Azazi; Ab Ghani, Aminuddin; Chan, Ngai Weng

    2015-04-01

    Poor water quality is a serious problem in the world which threatens human health, ecosystems, and plant/animal life. Prediction of surface water quality is a main concern in water resource and environmental systems. In this research, the support vector machine and two methods of artificial neural networks (ANNs), namely feed forward back propagation (FFBP) and radial basis function (RBF), were used to predict the water quality index (WQI) in a free constructed wetland. Seventeen points of the wetland were monitored twice a month over a period of 14 months, and an extensive dataset was collected for 11 water quality variables. A detailed comparison of the overall performance showed that prediction of the support vector machine (SVM) model with coefficient of correlation (R(2)) = 0.9984 and mean absolute error (MAE) = 0.0052 was either better or comparable with neural networks. This research highlights that the SVM and FFBP can be successfully employed for the prediction of water quality in a free surface constructed wetland environment. These methods simplify the calculation of the WQI and reduce substantial efforts and time by optimizing the computations. PMID:25408070

  9. Agricultural mapping using Support Vector Machine-Based Endmember Extraction (SVM-BEE)

    SciTech Connect

    Archibald, Richard K; Filippi, Anthony M; Bhaduri, Budhendra L; Bright, Eddie A

    2009-01-01

    Extracting endmembers from remotely sensed images of vegetated areas can present difficulties. In this research, we applied a recently developed endmember-extraction algorithm based on Support Vector Machines (SVMs) to the problem of semi-autonomous estimation of vegetation endmembers from a hyperspectral image. This algorithm, referred to as Support Vector Machine-Based Endmember Extraction (SVM-BEE), accurately and rapidly yields a computed representation of hyperspectral data that can accommodate multiple distributions. The number of distributions is identified without prior knowledge, based upon this representation. Prior work established that SVM-BEE is robustly noise-tolerant and can semi-automatically and effectively estimate endmembers; synthetic data and a geologic scene were previously analyzed. Here we compared the efficacies of the SVM-BEE and N-FINDR algorithms in extracting endmembers from a predominantly agricultural scene. SVM-BEE was able to estimate vegetation and other endmembers for all classes in the image, which N-FINDR failed to do. Classifications based on SVM-BEE endmembers were markedly more accurate compared with those based on N-FINDR endmembers.

  10. Research on multi-class classification of support vector data description

    NASA Astrophysics Data System (ADS)

    Shen, Minghua; Xiao, Huaitie; Fu, Qiang

    2007-11-01

    Support Vector Data Description (SVDD) is a one-class classification method developed in recent years. It has been used in many fields because of its good performance and high executive efficiency when there are only one-class training samples. It has been proven that SVDD has less support vector numbers, less optimization time and faster testing speed than those of two-class classifier such as SVM. At present, researches and acquirable literatures about SVDD multi-class classification are little, which restricts the SVDD application. One SVDD multi-class classification algorithm is proposed in the paper. Based on minimum distance classification rule, the misclassification in multi-class classification is well solved and by applying the threshold strategy the rejection in multi-class classification is greatly alleviated. Finally, by classifying range profiles of three targets, the effect of kernel function parameter and SNR on the proposed algorithm is investigated and the effectiveness of the algorithm is testified by quantities of experiments.

  11. Near-Bayesian Support Vector Machines for imbalanced data classification with equal or unequal misclassification costs.

    PubMed

    Datta, Shounak; Das, Swagatam

    2015-10-01

    Support Vector Machines (SVMs) form a family of popular classifier algorithms originally developed to solve two-class classification problems. However, SVMs are likely to perform poorly in situations with data imbalance between the classes, particularly when the target class is under-represented. This paper proposes a Near-Bayesian Support Vector Machine (NBSVM) for such imbalanced classification problems, by combining the philosophies of decision boundary shift and unequal regularization costs. Based on certain assumptions which hold true for most real-world datasets, we use the fractions of representation from each of the classes, to achieve the boundary shift as well as the asymmetric regularization costs. The proposed approach is extended to the multi-class scenario and also adapted for cases with unequal misclassification costs for the different classes. Extensive comparison with standard SVM and some state-of-the-art methods is furnished as a proof of the ability of the proposed approach to perform competitively on imbalanced datasets. A modified Sequential Minimal Optimization (SMO) algorithm is also presented to solve the NBSVM optimization problem in a computationally efficient manner. PMID:26210983

  12. Using support vector machines to improve elemental ion identification in macromolecular crystal structures

    SciTech Connect

    Morshed, Nader; Echols, Nathaniel; Adams, Paul D.

    2015-05-01

    A method to automatically identify possible elemental ions in X-ray crystal structures has been extended to use support vector machine (SVM) classifiers trained on selected structures in the PDB, with significantly improved sensitivity over manually encoded heuristics. In the process of macromolecular model building, crystallographers must examine electron density for isolated atoms and differentiate sites containing structured solvent molecules from those containing elemental ions. This task requires specific knowledge of metal-binding chemistry and scattering properties and is prone to error. A method has previously been described to identify ions based on manually chosen criteria for a number of elements. Here, the use of support vector machines (SVMs) to automatically classify isolated atoms as either solvent or one of various ions is described. Two data sets of protein crystal structures, one containing manually curated structures deposited with anomalous diffraction data and another with automatically filtered, high-resolution structures, were constructed. On the manually curated data set, an SVM classifier was able to distinguish calcium from manganese, zinc, iron and nickel, as well as all five of these ions from water molecules, with a high degree of accuracy. Additionally, SVMs trained on the automatically curated set of high-resolution structures were able to successfully classify most common elemental ions in an independent validation test set. This method is readily extensible to other elemental ions and can also be used in conjunction with previous methods based on a priori expectations of the chemical environment and X-ray scattering.

  13. Automatic Generation Of Training Data For Hyperspectral Image Classification Using Support Vector Machine

    NASA Astrophysics Data System (ADS)

    Abbasi, B.; Arefi, H.; Bigdeli, B.; Roessner, S.

    2015-04-01

    An image classification method based on Support Vector Machine (SVM) is proposed on hyperspectral and 3K DSM data. To obtain training data we applied an automatic method relating to four classes namely; building, grass, tree, and ground pixels. First, some initial segments regarding to building, tree, grass, and ground pixels are produced using different feature descriptors. The feature descriptors are generated using optical (hyperspectral) as well as range (3K DSM) images. The initial building regions are created using DSM segmentation. Fusion of NDVI and elevation information assist us to provide initial segments regarding to the grass and tree areas. Also, we created initial segment regarding to ground pixel after geodesic based filtering of DSM and elimination of the non-ground pixels. To improve classification accuracy, the hyperspectral image and 3K DSM were utilized simultaneously to perform image classification. For obtaining testing data, labelled pixels was divide into two parts: test and training. Experimental result shows a final classification accuracy of about 90% using Support Vector Machine. In the process of satellite image classification; provided by 3K camera. Both datasets correspond to Munich area in Germany.

  14. A tool for urban soundscape evaluation applying Support Vector Machines for developing a soundscape classification model.

    PubMed

    Torija, Antonio J; Ruiz, Diego P; Ramos-Ridao, Angel F

    2014-06-01

    To ensure appropriate soundscape management in urban environments, the urban-planning authorities need a range of tools that enable such a task to be performed. An essential step during the management of urban areas from a sound standpoint should be the evaluation of the soundscape in such an area. In this sense, it has been widely acknowledged that a subjective and acoustical categorization of a soundscape is the first step to evaluate it, providing a basis for designing or adapting it to match people's expectations as well. In this sense, this work proposes a model for automatic classification of urban soundscapes. This model is intended for the automatic classification of urban soundscapes based on underlying acoustical and perceptual criteria. Thus, this classification model is proposed to be used as a tool for a comprehensive urban soundscape evaluation. Because of the great complexity associated with the problem, two machine learning techniques, Support Vector Machines (SVM) and Support Vector Machines trained with Sequential Minimal Optimization (SMO), are implemented in developing model classification. The results indicate that the SMO model outperforms the SVM model in the specific task of soundscape classification. With the implementation of the SMO algorithm, the classification model achieves an outstanding performance (91.3% of instances correctly classified). PMID:24007752

  15. Predicting the biomechanical strength of proximal femur specimens with Minkowski functionals and support vector regression

    NASA Astrophysics Data System (ADS)

    Yang, Chien-Chun; Nagarajan, Mahesh B.; Huber, Markus B.; Carballido-Gamio, Julio; Bauer, Jan S.; Baum, Thomas; Eckstein, Felix; Lochmüller, Eva-Maria; Link, Thomas M.; Wismüller, Axel

    2014-03-01

    Regional trabecular bone quality estimation for purposes of femoral bone strength prediction is important for improving the clinical assessment of osteoporotic fracture risk. In this study, we explore the ability of 3D Minkowski Functionals derived from multi-detector computed tomography (MDCT) images of proximal femur specimens in predicting their corresponding biomechanical strength. MDCT scans were acquired for 50 proximal femur specimens harvested from human cadavers. An automated volume of interest (VOI)-fitting algorithm was used to define a consistent volume in the femoral head of each specimen. In these VOIs, the trabecular bone micro-architecture was characterized by statistical moments of its BMD distribution and by topological features derived from Minkowski Functionals. A linear multiregression analysis and a support vector regression (SVR) algorithm with a linear kernel were used to predict the failure load (FL) from the feature sets; the predicted FL was compared to the true FL determined through biomechanical testing. The prediction performance was measured by the root mean square error (RMSE) for each feature set. The best prediction result was obtained from the Minkowski Functional surface used in combination with SVR, which had the lowest prediction error (RMSE = 0.939 ± 0.345) and which was significantly lower than mean BMD (RMSE = 1.075 ± 0.279, p<0.005). Our results indicate that the biomechanical strength prediction can be significantly improved in proximal femur specimens with Minkowski Functionals extracted from on MDCT images used in conjunction with support vector regression.

  16. Using support vector regression to predict PM10 and PM2.5

    NASA Astrophysics Data System (ADS)

    Weizhen, Hou; Zhengqiang, Li; Yuhuan, Zhang; Hua, Xu; Ying, Zhang; Kaitao, Li; Donghui, Li; Peng, Wei; Yan, Ma

    2014-03-01

    Support vector machine (SVM), as a novel and powerful machine learning tool, can be used for the prediction of PM10 and PM2.5 (particulate matter less or equal than 10 and 2.5 micrometer) in the atmosphere. This paper describes the development of a successive over relaxation support vector regress (SOR-SVR) model for the PM10 and PM2.5 prediction, based on the daily average aerosol optical depth (AOD) and meteorological parameters (atmospheric pressure, relative humidity, air temperature, wind speed), which were all measured in Beijing during the year of 2010-2012. The Gaussian kernel function, as well as the k-fold crosses validation and grid search method, are used in SVR model to obtain the optimal parameters to get a better generalization capability. The result shows that predicted values by the SOR-SVR model agree well with the actual data and have a good generalization ability to predict PM10 and PM2.5. In addition, AOD plays an important role in predicting particulate matter with SVR model, which should be included in the prediction model. If only considering the meteorological parameters and eliminating AOD from the SVR model, the prediction results of predict particulate matter will be not satisfying.

  17. Identification of Peptide Inhibitors of Enveloped Viruses Using Support Vector Machine

    PubMed Central

    Xu, Yongtao; Yu, Shui; Zou, Jian-Wei; Hu, Guixiang; Rahman, Noorsaadah A. B. D.; Othman, Rozana Binti; Tao, Xia; Huang, Meilan

    2015-01-01

    The peptides derived from envelope proteins have been shown to inhibit the protein-protein interactions in the virus membrane fusion process and thus have a great potential to be developed into effective antiviral therapies. There are three types of envelope proteins each exhibiting distinct structure folds. Although the exact fusion mechanism remains elusive, it was suggested that the three classes of viral fusion proteins share a similar mechanism of membrane fusion. The common mechanism of action makes it possible to correlate the properties of self-derived peptide inhibitors with their activities. Here we developed a support vector machine model using sequence-based statistical scores of self-derived peptide inhibitors as input features to correlate with their activities. The model displayed 92% prediction accuracy with the Matthew’s correlation coefficient of 0.84, obviously superior to those using physicochemical properties and amino acid decomposition as input. The predictive support vector machine model for self- derived peptides of envelope proteins would be useful in development of antiviral peptide inhibitors targeting the virus fusion process. PMID:26636321

  18. Support vector machine based classification of fast Fourier transform spectroscopy of proteins

    NASA Astrophysics Data System (ADS)

    Lazarevic, Aleksandar; Pokrajac, Dragoljub; Marcano, Aristides; Melikechi, Noureddine

    2009-02-01

    Fast Fourier transform spectroscopy has proved to be a powerful method for study of the secondary structure of proteins since peak positions and their relative amplitude are affected by the number of hydrogen bridges that sustain this secondary structure. However, to our best knowledge, the method has not been used yet for identification of proteins within a complex matrix like a blood sample. The principal reason is the apparent similarity of protein infrared spectra with actual differences usually masked by the solvent contribution and other interactions. In this paper, we propose a novel machine learning based method that uses protein spectra for classification and identification of such proteins within a given sample. The proposed method uses principal component analysis (PCA) to identify most important linear combinations of original spectral components and then employs support vector machine (SVM) classification model applied on such identified combinations to categorize proteins into one of given groups. Our experiments have been performed on the set of four different proteins, namely: Bovine Serum Albumin, Leptin, Insulin-like Growth Factor 2 and Osteopontin. Our proposed method of applying principal component analysis along with support vector machines exhibits excellent classification accuracy when identifying proteins using their infrared spectra.

  19. Development of fault diagnosis system for transformer based on multi-class support vector machines

    NASA Astrophysics Data System (ADS)

    Cao, Jian; Qian, Suxiang; Hu, Hongsheng; Yan, Gongbiao

    2007-12-01

    The support vector machine (SVM) is an algorithm based on structure risk minimizing principle and having high generalization ability. It is strong to solve the problem with small sample, nonlinear and high dimension. The fundamental theory of DGA (Dissolved Gas Analysis, DGA) and fault characteristic of transformer is firstly researched in this paper, and then the disadvantages of traditional method of transformer fault diagnosis are analyzed, finally, a new fault diagnosis method using multi-class support vector machines (M-SVMs) based on DGA theory for transformer is put forward. Then the fault diagnosis model based on M-SVMs for transformer is established. At the same time, the fault diagnosis system based on M-SVMs for transformer is developed. The system can realize the acquisition of the dissolving gas in the transformer oil and data timely and low cost transmission by GPRS (General Packet Radio Service, GPRS). And it can identify out the transformer running state according to the acquisition data. The test results show that the method proposed has an excellent performance on correct ratio. And it can overcome the disadvantage of the traditional three-ratio method which lacks of fault coding and no fault types in the existent coding. Combining the wireless communication technology with the monitoring technology, the designed and developed system can greatly improve the real-time and continuity for the transformer' condition monitoring and fault diagnosis.

  20. Human action recognition with group lasso regularized-support vector machine

    NASA Astrophysics Data System (ADS)

    Luo, Huiwu; Lu, Huanzhang; Wu, Yabei; Zhao, Fei

    2016-05-01

    The bag-of-visual-words (BOVW) and Fisher kernel are two popular models in human action recognition, and support vector machine (SVM) is the most commonly used classifier for the two models. We show two kinds of group structures in the feature representation constructed by BOVW and Fisher kernel, respectively, since the structural information of feature representation can be seen as a prior for the classifier and can improve the performance of the classifier, which has been verified in several areas. However, the standard SVM employs L2-norm regularization in its learning procedure, which penalizes each variable individually and cannot express the structural information of feature representation. We replace the L2-norm regularization with group lasso regularization in standard SVM, and a group lasso regularized-support vector machine (GLRSVM) is proposed. Then, we embed the group structural information of feature representation into GLRSVM. Finally, we introduce an algorithm to solve the optimization problem of GLRSVM by alternating directions method of multipliers. The experiments evaluated on KTH, YouTube, and Hollywood2 datasets show that our method achieves promising results and improves the state-of-the-art methods on KTH and YouTube datasets.

  1. Rule extraction from support vector machines using ensemble learning approach: an application for diagnosis of diabetes.

    PubMed

    Han, Longfei; Luo, Senlin; Yu, Jianmin; Pan, Limin; Chen, Songjing

    2015-03-01

    Diabetes mellitus is a chronic disease and a worldwide public health challenge. It has been shown that 50-80% proportion of T2DM is undiagnosed. In this paper, support vector machines are utilized to screen diabetes, and an ensemble learning module is added, which turns the "black box" of SVM decisions into comprehensible and transparent rules, and it is also useful for solving imbalance problem. Results on China Health and Nutrition Survey data show that the proposed ensemble learning method generates rule sets with weighted average precision 94.2% and weighted average recall 93.9% for all classes. Furthermore, the hybrid system can provide a tool for diagnosis of diabetes, and it supports a second opinion for lay users. PMID:24860043

  2. Support-vector-machine tree-based domain knowledge learning toward automated sports video classification

    NASA Astrophysics Data System (ADS)

    Xiao, Guoqiang; Jiang, Yang; Song, Gang; Jiang, Jianmin

    2010-12-01

    We propose a support-vector-machine (SVM) tree to hierarchically learn from domain knowledge represented by low-level features toward automatic classification of sports videos. The proposed SVM tree adopts a binary tree structure to exploit the nature of SVM's binary classification, where each internal node is a single SVM learning unit, and each external node represents the classified output type. Such a SVM tree presents a number of advantages, which include: 1. low computing cost; 2. integrated learning and classification while preserving individual SVM's learning strength; and 3. flexibility in both structure and learning modules, where different numbers of nodes and features can be added to address specific learning requirements, and various learning models can be added as individual nodes, such as neural networks, AdaBoost, hidden Markov models, dynamic Bayesian networks, etc. Experiments support that the proposed SVM tree achieves good performances in sports video classifications.

  3. Magnetic properties prediction of NdFeB magnets by using support vector regression

    NASA Astrophysics Data System (ADS)

    Cheng, Wende

    2014-09-01

    A novel model using support vector regression (SVR) combined with particle swarm optimization (PSO) was employed to construct mathematical model for prediction of the magnetic properties of the NdFeB magnets. The leave-one-out cross-validation (LOOCV) test results strongly supports that the generalization ability of SVR is high enough. Predicted results show that the mean absolute percentage error for magnetic remanence Br, coercivity Hcj and maximum magnetic energy product (BH)max are 0.53%, 3.90%, 1.73%, and the correlation coefficient (R2) is as high as 0.839, 0.967 and 0.940, respectively. This investigation suggests that the PSO-SVR is not only an effective and practical method to simulate the properties of NdFeB, but also a powerful tool to optimatize designing or controlling the experimental process.

  4. Bone age assessment in young children using automatic carpal bone feature extraction and support vector regression.

    PubMed

    Somkantha, Krit; Theera-Umpon, Nipon; Auephanwiriyakul, Sansanee

    2011-12-01

    Boundary extraction of carpal bone images is a critical operation of the automatic bone age assessment system, since the contrast between the bony structure and soft tissue are very poor. In this paper, we present an edge following technique for boundary extraction in carpal bone images and apply it to assess bone age in young children. Our proposed technique can detect the boundaries of carpal bones in X-ray images by using the information from the vector image model and the edge map. Feature analysis of the carpal bones can reveal the important information for bone age assessment. Five features for bone age assessment are calculated from the boundary extraction result of each carpal bone. All features are taken as input into the support vector regression (SVR) that assesses the bone age. We compare the SVR with the neural network regression (NNR). We use 180 images of carpal bone from a digital hand atlas to assess the bone age of young children from 0 to 6 years old. Leave-one-out cross validation is used for testing the efficiency of the techniques. The opinions of the skilled radiologists provided in the atlas are used as the ground truth in bone age assessment. The SVR is able to provide more accurate bone age assessment results than the NNR. The experimental results from SVR are very close to the bone age assessment by skilled radiologists. PMID:21347746

  5. An Improved Systematic Approach to Predicting Transcription Factor Target Genes Using Support Vector Machine

    PubMed Central

    Cui, Song; Youn, Eunseog; Lee, Joohyun; Maas, Stephan J.

    2014-01-01

    Biological prediction of transcription factor binding sites and their corresponding transcription factor target genes (TFTGs) makes great contribution to understanding the gene regulatory networks. However, these approaches are based on laborious and time-consuming biological experiments. Numerous computational approaches have shown great potential to circumvent laborious biological methods. However, the majority of these algorithms provide limited performances and fail to consider the structural property of the datasets. We proposed a refined systematic computational approach for predicting TFTGs. Based on previous work done on identifying auxin response factor target genes from Arabidopsis thaliana co-expression data, we adopted a novel reverse-complementary distance-sensitive n-gram profile algorithm. This algorithm converts each upstream sub-sequence into a high-dimensional vector data point and transforms the prediction task into a classification problem using support vector machine-based classifier. Our approach showed significant improvement compared to other computational methods based on the area under curve value of the receiver operating characteristic curve using 10-fold cross validation. In addition, in the light of the highly skewed structure of the dataset, we also evaluated other metrics and their associated curves, such as precision-recall curves and cost curves, which provided highly satisfactory results. PMID:24743548

  6. An adaptive online learning approach for Support Vector Regression: Online-SVR-FID

    NASA Astrophysics Data System (ADS)

    Liu, Jie; Zio, Enrico

    2016-08-01

    Support Vector Regression (SVR) is a popular supervised data-driven approach for building empirical models from available data. Like all data-driven methods, under non-stationary environmental and operational conditions it needs to be provided with adaptive learning capabilities, which might become computationally burdensome with large datasets cumulating dynamically. In this paper, a cost-efficient online adaptive learning approach is proposed for SVR by combining Feature Vector Selection (FVS) and Incremental and Decremental Learning. The proposed approach adaptively modifies the model only when different pattern drifts are detected according to proposed criteria. Two tolerance parameters are introduced in the approach to control the computational complexity, reduce the influence of the intrinsic noise in the data and avoid the overfitting problem of SVR. Comparisons of the prediction results is made with other online learning approaches e.g. NORMA, SOGA, KRLS, Incremental Learning, on several artificial datasets and a real case study concerning time series prediction based on data recorded on a component of a nuclear power generation system. The performance indicators MSE and MARE computed on the test dataset demonstrate the efficiency of the proposed online learning method.

  7. Fall Detection with the Support Vector Machine during Scripted and Continuous Unscripted Activities

    PubMed Central

    Liu, Shing-Hong; Cheng, Wen-Chang

    2012-01-01

    In recent years, the number of proposed fall-detection systems that have been developed has increased dramatically. A threshold-based algorithm utilizing an accelerometer has been used to detect low-complexity falling activities. In this study, we defined activities in which the body's center of gravity quickly declines as falling activities of daily life (ADLs). In the non-falling ADLs, we also focused on the body's center of gravity. A hyperplane of the support vector machine (SVM) was used as the separating plane to replace the traditional threshold method for the detection of falling ADLs. The scripted and continuous unscripted activities were performed by two groups of young volunteers (20 subjects) and one group of elderly volunteers (five subjects). The results showed that the four parameters of the input vector had the best accuracy with 99.1% and 98.4% in the training and testing, respectively. For the continuous unscripted test of one hour, there were two and one false positive events among young volunteers and elderly volunteers, respectively. PMID:23112713

  8. Condition classification of small reciprocating compressor for refrigerators using artificial neural networks and support vector machines

    NASA Astrophysics Data System (ADS)

    Yang, Bo-Suk; Hwang, Won-Woo; Kim, Dong-Jo; Chit Tan, Andy

    2005-03-01

    The need to increase machine reliability and decrease production loss due to faulty products in highly automated line requires accurate and reliable fault classification technique. Wavelet transform and statistical method are used to extract salient features from raw noise and vibration signals. The wavelet transform decomposes the raw time-waveform signals into two respective parts in the time space and frequency domain. With wavelet transform prominent features can be obtained easily than from time-waveform analysis. This paper focuses on the development of an advanced signal classifier for small reciprocating refrigerator compressors using noise and vibration signals. Three classifiers, self-organising feature map, learning vector quantisation and support vector machine (SVM) are applied in training and testing for feature extraction and the classification accuracies of the techniques are compared to determine the optimum fault classifier. The classification technique selected for detecting faulty reciprocating refrigerator compressors involves artificial neural networks and SVMs. The results confirm that the classification technique can differentiate faulty compressors from healthy ones and with high flexibility and reliability.

  9. A Support Vector Machine Blind Equalization Algorithm Based on Immune Clone Algorithm

    NASA Astrophysics Data System (ADS)

    Yecai, Guo; Rui, Ding

    Aiming at affecting of the parameter selection method of support vector machine(SVM) on its application in blind equalization algorithm, a SVM constant modulus blind equalization algorithm based on immune clone selection algorithm(CSA-SVM-CMA) is proposed. In this proposed algorithm, the immune clone algorithm is used to optimize the parameters of the SVM on the basis advantages of its preventing evolutionary precocious, avoiding local optimum, and fast convergence. The proposed algorithm can improve the parameter selection efficiency of SVM constant modulus blind equalization algorithm(SVM-CMA) and overcome the defect of the artificial setting parameters. Accordingly, the CSA-SVM-CMA has faster convergence rate and smaller mean square error than the SVM-CMA. Computer simulations in underwater acoustic channels have proved the validity of the algorithm.

  10. Experimental study on light induced influence model to mice using support vector machine

    NASA Astrophysics Data System (ADS)

    Ji, Lei; Zhao, Zhimin; Yu, Yinshan; Zhu, Xingyue

    2014-08-01

    Previous researchers have made studies on different influences created by light irradiation to animals, including retinal damage, changes of inner index and so on. However, the model of light induced damage to animals using physiological indicators as features in machine learning method is never founded. This study was designed to evaluate the changes in micro vascular diameter, the serum absorption spectrum and the blood flow influenced by light irradiation of different wavelengths, powers and exposure time with support vector machine (SVM). The micro images of the mice auricle were recorded and the vessel diameters were calculated by computer program. The serum absorption spectrums were analyzed. The result shows that training sample rate 20% and 50% have almost the same correct recognition rate. Better performance and accuracy was achieved by third-order polynomial kernel SVM quadratic optimization method and it worked suitably for predicting the light induced damage to organisms.

  11. Prediction of biochar yield from cattle manure pyrolysis via least squares support vector machine intelligent approach.

    PubMed

    Cao, Hongliang; Xin, Ya; Yuan, Qiaoxia

    2016-02-01

    To predict conveniently the biochar yield from cattle manure pyrolysis, intelligent modeling approach was introduced in this research. A traditional artificial neural networks (ANN) model and a novel least squares support vector machine (LS-SVM) model were developed. For the identification and prediction evaluation of the models, a data set with 33 experimental data was used, which were obtained using a laboratory-scale fixed bed reaction system. The results demonstrated that the intelligent modeling approach is greatly convenient and effective for the prediction of the biochar yield. In particular, the novel LS-SVM model has a more satisfying predicting performance and its robustness is better than the traditional ANN model. The introduction and application of the LS-SVM modeling method gives a successful example, which is a good reference for the modeling study of cattle manure pyrolysis process, even other similar processes. PMID:26708483

  12. Ice breakup forecast in the reach of the Yellow River: the support vector machines approach

    NASA Astrophysics Data System (ADS)

    Zhou, H.; Li, W.; Zhang, C.; Liu, J.

    2009-04-01

    Accurate lead-time forecast of ice breakup is one of the key aspects for ice flood prevention and reducing losses. In this paper, a new data-driven model based on the Statistical Learning Theory was employed for ice breakup prediction. The model, known as Support Vector Machine (SVM), follows the principle that aims at minimizing the structural risk rather than the empirical risk. In order to estimate the appropriate parameters of the SVM, Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM-UA) algorithm is performed through exponential transformation. A case study was conducted in the reach of the Yellow River. Results from the proposed model showed a promising performance compared with that from artificial neural network, so the model can be considered as an alternative and practical tool for ice breakup forecast.

  13. Using support vector machine and dynamic parameter encoding to enhance global optimization

    NASA Astrophysics Data System (ADS)

    Zheng, Z.; Chen, X.; Liu, C.; Huang, K.

    2016-05-01

    This study presents an approach which combines support vector machine (SVM) and dynamic parameter encoding (DPE) to enhance the run-time performance of global optimization with time-consuming fitness function evaluations. SVMs are used as surrogate models to partly substitute for fitness evaluations. To reduce the computation time and guarantee correct convergence, this work proposes a novel strategy to adaptively adjust the number of fitness evaluations needed according to the approximate error of the surrogate model. Meanwhile, DPE is employed to compress the solution space, so that it not only accelerates the convergence but also decreases the approximate error. Numerical results of optimizing a few benchmark functions and an antenna in a practical application are presented, which verify the feasibility, efficiency and robustness of the proposed approach.

  14. Exploiting uncertainty measures in compounds activity prediction using support vector machines.

    PubMed

    Smusz, Sabina; Czarnecki, Wojciech Marian; Warszycki, Dawid; Bojarski, Andrzej J

    2015-01-01

    The great majority of molecular modeling tasks require the construction of a model that is then used to evaluate new compounds. Although various types of these models exist, at some stage, they all use knowledge about the activity of a given group of compounds, and the performance of the models is dependent on the quality of these data. Biological experiments verifying the activity of chemical compounds are often not reproducible; hence, databases containing these results often possess various activity records for a given molecule. In this study, we developed a method that incorporates the uncertainty of biological tests in machine-learning-based experiments using the Support Vector Machine as a classification model. We show that the developed methodology improves the classification effectiveness in the tested conditions. PMID:25466199

  15. Support vector machine-based feature extractor for L/H transitions in JETa)

    NASA Astrophysics Data System (ADS)

    González, S.; Vega, J.; Murari, A.; Pereira, A.; Ramírez, J. M.; Dormido-Canto, S.; Jet-Efda Contributors

    2010-10-01

    Support vector machines (SVM) are machine learning tools originally developed in the field of artificial intelligence to perform both classification and regression. In this paper, we show how SVM can be used to determine the most relevant quantities to characterize the confinement transition from low to high confinement regimes in tokamak plasmas. A set of 27 signals is used as starting point. The signals are discarded one by one until an optimal number of relevant waveforms is reached, which is the best tradeoff between keeping a limited number of quantities and not loosing essential information. The method has been applied to a database of 749 JET discharges and an additional database of 150 JET discharges has been used to test the results obtained.

  16. Identification of oil spills by near-infrared spectroscopy (NIR) and support vector machine (SVM)

    NASA Astrophysics Data System (ADS)

    Bi, Weihong; Tan, Ailing; Zhao, Yong; Gao, Meijing

    2009-11-01

    The identification of the spilled oil is an essential and important part in the investigation and handling of oil spill accidents. The combination of near-infrared spectroscopy (NIR) and chemometrics is ideal for such a situation. NIR spectroscopy is a powerful and effective technique and qualitative information can be obtained with classification models. Support vector machines (SVM) have been introduced recently in chemometrics and have proven to be powerful in NIR spectra classification tasks, such as material identification and food discrimination. In this work, the SVM is utilized to classify near infrared spectroscopy of simulated spilled oils of gasoline, diesel fuel and kerosene on the marine. A good classification performance is obtained :the identification rate were 100%, 96% and 98% on the test sets respectively.

  17. Prediction of banana quality indices from color features using support vector regression.

    PubMed

    Sanaeifar, Alireza; Bakhshipour, Adel; de la Guardia, Miguel

    2016-02-01

    Banana undergoes significant quality indices and color transformations during shelf-life process, which in turn affect important chemical and physical characteristics for the organoleptic quality of banana. A computer vision system was implemented in order to evaluate color of banana in RGB, L*a*b* and HSV color spaces, and changes in color features of banana during shelf-life were employed for the quantitative prediction of quality indices. The radial basis function (RBF) was applied as the kernel function of support vector regression (SVR) and the color features, in different color spaces, were selected as the inputs of the model, being determined total soluble solids, pH, titratable acidity and firmness as the output. Experimental results provided an improvement in predictive accuracy as compared with those obtained by using artificial neural network (ANN). PMID:26653423

  18. [Modelling a penicillin fed-batch fermentation using least squares support vector machines].

    PubMed

    Liu, Yi; Wang, Hai-Qing

    2006-01-01

    The biochemical processes are usually characterized as seriously time varying and nonlinear dynamic systems. Building their first-principle models are very costly and difficult due to the absence of inherent mechanism and efficient on-line sensors. Furthermore, these detailed and complicated models do not necessary guarantee a good performance in practice. An approach via least squares support vector machines (LS-SVM) based on Pensim simulator is proposed for modelling the penicillin fed-batch fermentation process, and the adjustment strategy for parameters of LS-SVM is presented. Based on the proposed modelling method, the predictive models of penicillin concentration, biomass concentration and substrate concentration are obtained by using very limited on-line measurements. The results show that the models established are more accurate and efficient, and suffice for the requirements of control and optimization for biochemical processes. PMID:16572855

  19. Credit Risk Evaluation Using a C-Variable Least Squares Support Vector Classification Model

    NASA Astrophysics Data System (ADS)

    Yu, Lean; Wang, Shouyang; Lai, K. K.

    Credit risk evaluation is one of the most important issues in financial risk management. In this paper, a C-variable least squares support vector classification (C-VLSSVC) model is proposed for credit risk analysis. The main idea of this model is based on the prior knowledge that different classes may have different importance for modeling and more weights should be given to those classes with more importance. The C-VLSSVC model can be constructed by a simple modification of the regularization parameter in LSSVC, whereby more weights are given to the lease squares classification errors with important classes than the lease squares classification errors with unimportant classes while keeping the regularized terms in its original form. For illustration purpose, a real-world credit dataset is used to test the effectiveness of the C-VLSSVC model.

  20. Remote sensing image classification based on support vector machine with the multi-scale segmentation

    NASA Astrophysics Data System (ADS)

    Bao, Wenxing; Feng, Wei; Ma, Ruishi

    2015-12-01

    In this paper, we proposed a new classification method based on support vector machine (SVM) combined with multi-scale segmentation. The proposed method obtains satisfactory segmentation results which are based on both the spectral characteristics and the shape parameters of segments. SVM method is used to label all these regions after multiscale segmentation. It can effectively improve the classification results. Firstly, the homogeneity of the object spectra, texture and shape are calculated from the input image. Secondly, multi-scale segmentation method is applied to the RS image. Combining graph theory based optimization with the multi-scale image segmentations, the resulting segments are merged regarding the heterogeneity criteria. Finally, based on the segmentation result, the model of SVM combined with spectrum texture classification is constructed and applied. The results show that the proposed method can effectively improve the remote sensing image classification accuracy and classification efficiency.

  1. Probability-based least square support vector regression metamodeling technique for crashworthiness optimization problems

    NASA Astrophysics Data System (ADS)

    Wang, Hu; Li, Enying; Li, G. Y.

    2011-03-01

    This paper presents a crashworthiness design optimization method based on a metamodeling technique. The crashworthiness optimization is a highly nonlinear and large scale problem, which is composed various nonlinearities, such as geometry, material and contact and needs a large number expensive evaluations. In order to obtain a robust approximation efficiently, a probability-based least square support vector regression is suggested to construct metamodels by considering structure risk minimization. Further, to save the computational cost, an intelligent sampling strategy is applied to generate sample points at the stage of design of experiment (DOE). In this paper, a cylinder, a full vehicle frontal collision is involved. The results demonstrate that the proposed metamodel-based optimization is efficient and effective in solving crashworthiness, design optimization problems.

  2. Cervical cancer survival prediction using hybrid of SMOTE, CART and smooth support vector machine

    NASA Astrophysics Data System (ADS)

    Purnami, S. W.; Khasanah, P. M.; Sumartini, S. H.; Chosuvivatwong, V.; Sriplung, H.

    2016-04-01

    According to the WHO, every two minutes there is one patient who died from cervical cancer. The high mortality rate is due to the lack of awareness of women for early detection. There are several factors that supposedly influence the survival of cervical cancer patients, including age, anemia status, stage, type of treatment, complications and secondary disease. This study wants to classify/predict cervical cancer survival based on those factors. Various classifications methods: classification and regression tree (CART), smooth support vector machine (SSVM), three order spline SSVM (TSSVM) were used. Since the data of cervical cancer are imbalanced, synthetic minority oversampling technique (SMOTE) is used for handling imbalanced dataset. Performances of these methods are evaluated using accuracy, sensitivity and specificity. Results of this study show that balancing data using SMOTE as preprocessing can improve performance of classification. The SMOTE-SSVM method provided better result than SMOTE-TSSVM and SMOTE-CART.

  3. Using Support Vector Machine (SVM) for Classification of Selectivity of H1N1 Neuraminidase Inhibitors.

    PubMed

    Li, Yang; Kong, Yue; Zhang, Mengdi; Yan, Aixia; Liu, Zhenming

    2016-04-01

    Inhibition of the neuraminidase is one of the most promising strategies for preventing influenza virus spreading. 479 neuraminidase inhibitors are collected for dataset 1 and 208 neuraminidase inhibitors for A/P/8/34 are collected for dataset 2. Using support vector machine (SVM), four computational models were built to predict whether a compound is an active or weakly active inhibitor of neuraminidase. Each compound is represented by MASSC fingerprints and ADRIANA.Code descriptors. The predication accuracies for the test sets of all the models are over 78 %. Model 2B, which is the best model, obtains a prediction accuracy and a Matthews Correlation Coefficient (MCC) of 89.71 % and 0.81 on test set, respectively. The molecular polarizability, molecular shape, molecular size and hydrogen bonding are related to the activities of neuraminidase inhibitors. The models can be obtained from the authors. PMID:27491921

  4. Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines.

    PubMed

    Derya Ubeyli, Elif

    2008-01-01

    A new approach based on the implementation of multiclass support vector machine (SVM) with the error correcting output codes (ECOC) is presented for classification of electroencephalogram (EEG) signals. In practical applications of pattern recognition, there are often diverse features extracted from raw data which needs recognizing. Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the classifiers trained on the extracted features. The aim of the study is classification of the EEG signals by the combination of eigenvector methods and multiclass SVM. The purpose is to determine an optimum classification scheme for this problem and also to infer clues about the extracted features. The present research demonstrated that the eigenvector methods are the features which well represent the EEG signals and the multiclass SVM trained on these features achieved high classification accuracies. PMID:17651716

  5. Combining eigenvector methods and support vector machines for detecting variability of Doppler ultrasound signals.

    PubMed

    Ubeyli, Elif Derya

    2007-05-01

    In this paper, the multiclass support vector machines (SVMs) with the error correcting output codes (ECOC) were presented for detecting variabilities of the multiclass Doppler ultrasound signals. The ophthalmic arterial (OA) Doppler signals were recorded from healthy subjects, subjects suffering from OA stenosis, subjects suffering from ocular Behcet disease. The internal carotid arterial (ICA) Doppler signals were recorded from healthy subjects, subjects suffering from ICA stenosis, subjects suffering from ICA occlusion. Methods of combining multiple classifiers with diverse features are viewed as a general problem in various application areas of pattern recognition. Because of the importance of making the right decision, better classification procedures for Doppler ultrasound signals are searched. Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the SVMs trained on the extracted features. The research demonstrated that the multiclass SVMs trained on extracted features achieved high accuracy rates. PMID:17289211

  6. Analysis of dengue infection based on Raman spectroscopy and support vector machine (SVM)

    PubMed Central

    Khan, Saranjam; Ullah, Rahat; Khan, Asifullah; Wahab, Noorul; Bilal, Muhammad; Ahmed, Mushtaq

    2016-01-01

    The current study presents the use of Raman spectroscopy combined with support vector machine (SVM) for the classification of dengue suspected human blood sera. Raman spectra for 84 clinically dengue suspected patients acquired from Holy Family Hospital, Rawalpindi, Pakistan, have been used in this study.The spectral differences between dengue positive and normal sera have been exploited by using effective machine learning techniques. In this regard, SVM models built on the basis of three different kernel functions including Gaussian radial basis function (RBF), polynomial function and linear functionhave been employed to classify the human blood sera based on features obtained from Raman Spectra.The classification model have been evaluated with the 10-fold cross validation method. In the present study, the best performance has been achieved for the polynomial kernel of order 1. A diagnostic accuracy of about 85% with the precision of 90%, sensitivity of 73% and specificity of 93% has been achieved under these conditions. PMID:27375941

  7. A Numerical Comparison of Rule Ensemble Methods and Support Vector Machines

    SciTech Connect

    Meza, Juan C.; Woods, Mark

    2009-12-18

    Machine or statistical learning is a growing field that encompasses many scientific problems including estimating parameters from data, identifying risk factors in health studies, image recognition, and finding clusters within datasets, to name just a few examples. Statistical learning can be described as 'learning from data' , with the goal of making a prediction of some outcome of interest. This prediction is usually made on the basis of a computer model that is built using data where the outcomes and a set of features have been previously matched. The computer model is called a learner, hence the name machine learning. In this paper, we present two such algorithms, a support vector machine method and a rule ensemble method. We compared their predictive power on three supernova type 1a data sets provided by the Nearby Supernova Factory and found that while both methods give accuracies of approximately 95%, the rule ensemble method gives much lower false negative rates.

  8. Improving the performance of physiologic hot flash measures with support vector machines.

    PubMed

    Thurston, Rebecca C; Matthews, Karen A; Hernandez, Javier; De La Torre, Fernando

    2009-03-01

    Hot flashes are experienced by over 70% of menopausal women. Criteria to classify hot flashes from physiologic signals show variable performance. The primary aim was to compare conventional criteria to Support Vector Machines (SVMs), an advanced machine learning method, to classify hot flashes from sternal skin conductance. Thirty women with > or =4 hot flashes/day underwent laboratory hot flash testing with skin conductance measurement. Hot flashes were quantified with conventional (> or =2 micromho, 30 s) and SVM methods. Conventional methods had poor sensitivity (sensitivity=0.41, specificity=1, positive predictive value (PPV)=0.94, negative predictive value (NPV)=0.85) in classifying hot flashes, with poorest performance among women with high body mass index or anxiety. SVM models showed improved performance (sensitivity=0.89, specificity=0.96, PPV=0.85, NPV=0.96). SVM may improve the performance of skin conductance measures of hot flashes. PMID:19170952

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

    PubMed

    Malvoni, M; De Giorgi, M G; Congedo, P M

    2016-12-01

    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. PMID:27622206

  10. Automatic pathology classification using a single feature machine learning support - vector machines

    NASA Astrophysics Data System (ADS)

    Yepes-Calderon, Fernando; Pedregosa, Fabian; Thirion, Bertrand; Wang, Yalin; Lepore, Natasha

    2014-03-01

    Magnetic Resonance Imaging (MRI) has been gaining popularity in the clinic in recent years as a safe in-vivo imaging technique. As a result, large troves of data are being gathered and stored daily that may be used as clinical training sets in hospitals. While numerous machine learning (ML) algorithms have been implemented for Alzheimer's disease classification, their outputs are usually difficult to interpret in the clinical setting. Here, we propose a simple method of rapid diagnostic classification for the clinic using Support Vector Machines (SVM)1 and easy to obtain geometrical measurements that, together with a cortical and sub-cortical brain parcellation, create a robust framework capable of automatic diagnosis with high accuracy. On a significantly large imaging dataset consisting of over 800 subjects taken from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, classification-success indexes of up to 99.2% are reached with a single measurement.

  11. Fault diagnosis based on signed directed graph and support vector machine

    NASA Astrophysics Data System (ADS)

    Han, Xiaoming; Lv, Qing; Xie, Gang; Zheng, Jianxia

    2011-12-01

    Support Vector Machine (SVM) based on Structural Risk Minimization (SRM) of Statistical Learning Theory has excellent performance in fault diagnosis. However, its training speed and diagnosis speed are relatively slow. Signed Directed Graph (SDG) based on deep knowledge model has better completeness that is knowledge representation ability. However, much quantitative information is not utilized in qualitative SDG model which often produces a false solution. In order to speed up the training and diagnosis of SVM and improve the diagnostic resolution of SDG, SDG and SVM are combined in this paper. Training samples' dimension of SVM is reduced to improve training speed and diagnosis speed by the consistent path of SDG; the resolution of SDG is improved by good classification performance of SVM. The Matlab simulation by Tennessee-Eastman Process (TEP) simulation system demonstrates the feasibility of the fault diagnosis algorithm proposed in this paper.

  12. Fault diagnosis based on signed directed graph and support vector machine

    NASA Astrophysics Data System (ADS)

    Han, Xiaoming; Lv, Qing; Xie, Gang; Zheng, Jianxia

    2012-01-01

    Support Vector Machine (SVM) based on Structural Risk Minimization (SRM) of Statistical Learning Theory has excellent performance in fault diagnosis. However, its training speed and diagnosis speed are relatively slow. Signed Directed Graph (SDG) based on deep knowledge model has better completeness that is knowledge representation ability. However, much quantitative information is not utilized in qualitative SDG model which often produces a false solution. In order to speed up the training and diagnosis of SVM and improve the diagnostic resolution of SDG, SDG and SVM are combined in this paper. Training samples' dimension of SVM is reduced to improve training speed and diagnosis speed by the consistent path of SDG; the resolution of SDG is improved by good classification performance of SVM. The Matlab simulation by Tennessee-Eastman Process (TEP) simulation system demonstrates the feasibility of the fault diagnosis algorithm proposed in this paper.

  13. The research and application of visual saliency and adaptive support vector machine in target tracking field.

    PubMed

    Chen, Yuantao; Xu, Weihong; Kuang, Fangjun; Gao, Shangbing

    2013-01-01

    The efficient target tracking algorithm researches have become current research focus of intelligent robots. The main problems of target tracking process in mobile robot face environmental uncertainty. They are very difficult to estimate the target states, illumination change, target shape changes, complex backgrounds, and other factors and all affect the occlusion in tracking robustness. To further improve the target tracking's accuracy and reliability, we present a novel target tracking algorithm to use visual saliency and adaptive support vector machine (ASVM). Furthermore, the paper's algorithm has been based on the mixture saliency of image features. These features include color, brightness, and sport feature. The execution process used visual saliency features and those common characteristics have been expressed as the target's saliency. Numerous experiments demonstrate the effectiveness and timeliness of the proposed target tracking algorithm in video sequences where the target objects undergo large changes in pose, scale, and illumination. PMID:24363779

  14. Sensorless tension control of shuttleless loom system based on support vector regression

    NASA Astrophysics Data System (ADS)

    Han, Dong Chang; Back, Woon Jae; Lee, Yoon Chul; Lee, Sang Hwa; Lee, Hyuk Jin; Noh, Seok Hong; Kim, Han Kil; Park, Jae Yong; Lee, Suk Gyu; Chun, Du Hwan

    2005-12-01

    Tension control of loom system are usually achieved by using loadcell sensor and powder clutch, which require additional mounting space, reduce the reliability in harsh environments and increase the cost of a loom system. Moreover, the physical properties of textile fabrics are very sensitive to several factors(temperature, humidity, radius change of warp beam etc.) which result in tension change. In this paper, a novel sensorless tension control of a shuttleless loom system based on SVR(Support Vector Regression) is presented. The sensorless tension algorithm of shuttleless loom system driven by servo motor which is robust to disturbance and tension variation. First, the modeling and dynamic behaviors of a shuttleless loom system is described. Then, different tension control strategies are analyzed and discussed. And finally, the validity and the usefulness of proposed algorithm are thoroughly verified through numerical simulation.

  15. Prediction of the solar radiation on the Earth using support vector regression technique

    NASA Astrophysics Data System (ADS)

    Piri, Jamshid; Shamshirband, Shahaboddin; Petković, Dalibor; Tong, Chong Wen; Rehman, Muhammad Habib ur

    2015-01-01

    The solar rays on the surface of Earth is one of the major factor in water resources, environmental and agricultural modeling. The main environmental factors influencing plants growth are temperature, moisture, and solar radiation. Solar radiation is rarely obtained in weather stations; as a result, many empirical approaches have been applied to estimate it by using other parameters. In this study, a soft computing technique, named support vector regression (SVR) has been used to estimate the solar radiation. The data was collected from two synoptic stations with different climate conditions (Zahedan and Bojnurd) during the period of 5 and 7 years, respectively. These data contain sunshine hours, maximum temperature, minimum temperature, average relative humidity and daily solar radiation. In this study, the polynomial and radial basis functions (RBF) are applied as the SVR kernel function to estimate solar radiation. The performance of the proposed estimators is confirmed with the simulation results.

  16. Blind multiuser detector for chaos-based CDMA using support vector machine.

    PubMed

    Kao, Johnny Wei-Hsun; Berber, Stevan Mirko; Kecman, Vojislav

    2010-08-01

    The algorithm and the results of a blind multiuser detector using a machine learning technique called support vector machine (SVM) on a chaos-based code division multiple access system is presented in this paper. Simulation results showed that the performance achieved by using SVM is comparable to existing minimum mean square error (MMSE) detector under both additive white Gaussian noise (AWGN) and Rayleigh fading conditions. However, unlike the MMSE detector, the SVM detector does not require the knowledge of spreading codes of other users in the system or the estimate of the channel noise variance. The optimization of this algorithm is considered in this paper and its complexity is compared with the MMSE detector. This detector is much more suitable to work in the forward link than MMSE. In addition, original theoretical bit-error rate expressions for the SVM detector under both AWGN and Rayleigh fading are derived to verify the simulation results. PMID:20570769

  17. A new approach to simulate characterization of particulate matter employing support vector machines.

    PubMed

    Mogireddy, K; Devabhaktuni, V; Kumar, A; Aggarwal, P; Bhattacharya, P

    2011-02-28

    This paper, for the first time, applies the support vector machines (SVMs) paradigm to identify the optimal segmentation algorithm for physical characterization of particulate matter. Size of the particles is an essential component of physical characterization as larger particles get filtered through nose and throat while smaller particles have detrimental effect on human health. Typical particulate characterization processes involve image reading, preprocessing, segmentation, feature extraction, and representation. Of these various steps, knowledge based selection of optimal image segmentation algorithm (from existing segmentation algorithms) is the key for accurately analyzing the captured images of fine particulate matter. Motivated by the emerging machine-learning concepts, we present a new framework for automating the selection of optimal image segmentation algorithm employing SVMs trained and validated with image feature data. Results show that the SVM method accurately predicts the best segmentation algorithm. As well, an image processing algorithm based on Sobel edge detection is developed and illustrated. PMID:21185646

  18. Assessment Method of Harmonic Emission Level Based on the Improved Weighted Support Vector Machine Regression

    NASA Astrophysics Data System (ADS)

    Jiang, Wei-Zhong; Su, Ning; Ding, Li-Ping; Qiu, Si-Yu

    This paper presents a new method to estimate the system harmonic impedance and the harmonic emission level based on the improved weighted support vector machine (WSVM) regression. According to the differences of harmonic measurement data at the point of common coupling, the WSVM can be obtained by correcting the error requirement of SVM by Euclidean distance as a weighted index and determining the weighted coefficient of penalty parameter by linear interpolation, then the system harmonic impedance and the harmonic emission level can be calculated. Based on analyzing the simulation of the circuit and the practical application of field data, it proves that the proposed method can effectively restrain the influence caused by the fluctuation of background harmonic on estimation results. Compared with other methods, the estimate result of the proposed method is more reasonable.

  19. A newborn screening system based on service-oriented architecture embedded support vector machine.

    PubMed

    Hsu, Kai-Ping; Hsieh, Sung-Huai; Hsieh, Sheau-Ling; Cheng, Po-Hsun; Weng, Yung-Ching; Wu, Jang-Hung; Lai, Feipei

    2010-10-01

    The clinical symptoms of metabolic disorders are rarely apparent during the neonatal period, and if they are not treated earlier, irreversible damages, such as mental retardation or even death, may occur. Therefore, the practice of newborn screening is essential to prevent permanent disabilities in newborns. In the paper, we design, implement a newborn screening system using Support Vector Machine (SVM) classifications. By evaluating metabolic substances data collected from tandem mass spectrometry (MS/MS), we can interpret and determine whether a newborn has a metabolic disorder. In addition, National Taiwan University Hospital Information System (NTUHIS) has been developed and implemented to integrate heterogeneous platforms, protocols, databases as well as applications. To expedite adapting the diversities, we deploy Service-Oriented Architecture (SOA) concepts to the newborn screening system based on web services. The system can be embedded seamlessly into NTUHIS. PMID:20703618

  20. Support-vector-based emergent self-organising approach for emotional understanding

    NASA Astrophysics Data System (ADS)

    Nguwi, Yok-Yen; Cho, Siu-Yeung

    2010-12-01

    This study discusses the computational analysis of general emotion understanding from questionnaires methodology. The questionnaires method approaches the subject by investigating the real experience that accompanied the emotions, whereas the other laboratory approaches are generally associated with exaggerated elements. We adopted a connectionist model called support-vector-based emergent self-organising map (SVESOM) to analyse the emotion profiling from the questionnaires method. The SVESOM first identifies the important variables by giving discriminative features with high ranking. The classifier then performs the classification based on the selected features. Experimental results show that the top rank features are in line with the work of Scherer and Wallbott [(1994), 'Evidence for Universality and Cultural Variation of Differential Emotion Response Patterning', Journal of Personality and Social Psychology, 66, 310-328], which approached the emotions physiologically. While the performance measures show that using the full features for classifications can degrade the performance, the selected features provide superior results in terms of accuracy and generalisation.

  1. Analysis of dengue infection based on Raman spectroscopy and support vector machine (SVM).

    PubMed

    Khan, Saranjam; Ullah, Rahat; Khan, Asifullah; Wahab, Noorul; Bilal, Muhammad; Ahmed, Mushtaq

    2016-06-01

    The current study presents the use of Raman spectroscopy combined with support vector machine (SVM) for the classification of dengue suspected human blood sera. Raman spectra for 84 clinically dengue suspected patients acquired from Holy Family Hospital, Rawalpindi, Pakistan, have been used in this study.The spectral differences between dengue positive and normal sera have been exploited by using effective machine learning techniques. In this regard, SVM models built on the basis of three different kernel functions including Gaussian radial basis function (RBF), polynomial function and linear functionhave been employed to classify the human blood sera based on features obtained from Raman Spectra.The classification model have been evaluated with the 10-fold cross validation method. In the present study, the best performance has been achieved for the polynomial kernel of order 1. A diagnostic accuracy of about 85% with the precision of 90%, sensitivity of 73% and specificity of 93% has been achieved under these conditions. PMID:27375941

  2. Morphological analysis of dendrites and spines by hybridization of ridge detection with twin support vector machine.

    PubMed

    Wang, Shuihua; Chen, Mengmeng; Li, Yang; Shao, Ying; Zhang, Yudong; Du, Sidan; Wu, Jane

    2016-01-01

    Dendritic spines are described as neuronal protrusions. The morphology of dendritic spines and dendrites has a strong relationship to its function, as well as playing an important role in understanding brain function. Quantitative analysis of dendrites and dendritic spines is essential to an understanding of the formation and function of the nervous system. However, highly efficient tools for the quantitative analysis of dendrites and dendritic spines are currently undeveloped. In this paper we propose a novel three-step cascaded algorithm-RTSVM- which is composed of ridge detection as the curvature structure identifier for backbone extraction, boundary location based on differences in density, the Hu moment as features and Twin Support Vector Machine (TSVM) classifiers for spine classification. Our data demonstrates that this newly developed algorithm has performed better than other available techniques used to detect accuracy and false alarm rates. This algorithm will be used effectively in neuroscience research. PMID:27547530

  3. Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines

    PubMed Central

    del Val, Lara; Izquierdo-Fuente, Alberto; Villacorta, Juan J.; Raboso, Mariano

    2015-01-01

    Drawing on the results of an acoustic biometric system based on a MSE classifier, a new biometric system has been implemented. This new system preprocesses acoustic images, extracts several parameters and finally classifies them, based on Support Vector Machine (SVM). The preprocessing techniques used are spatial filtering, segmentation—based on a Gaussian Mixture Model (GMM) to separate the person from the background, masking—to reduce the dimensions of images—and binarization—to reduce the size of each image. An analysis of classification error and a study of the sensitivity of the error versus the computational burden of each implemented algorithm are presented. This allows the selection of the most relevant algorithms, according to the benefits required by the system. A significant improvement of the biometric system has been achieved by reducing the classification error, the computational burden and the storage requirements. PMID:26091392

  4. On combining support vector machines and simulated annealing in stereovision matching.

    PubMed

    Pajares, Gonzalo; de la Cruz, Jesús M

    2004-08-01

    This paper outlines a method for solving the stereovision matching problem using edge segments as the primitives. In stereovision matching, the following constraints are commonly used: epipolar, similarity, smoothness, ordering, and uniqueness. We propose a new strategy in which such constraints are sequentially combined. The goal is to achieve high performance in terms of correct matches by combining several strategies. The contributions of this paper are reflected in the development of a similarity measure through a support vector machines classification approach; the transformation of the smoothness, ordering and epipolar constraints into the form of an energy function, through an optimization simulated annealing approach, whose minimum value corresponds to a good matching solution and by introducing specific conditions to overcome the violation of the smoothness and ordering constraints. The performance of the proposed method is illustrated by comparative analysis against some recent global matching methods. PMID:15462432

  5. [Optimization of extraction process of compound Clematidis Radix spray by support vector machine].

    PubMed

    Zhao, Li; Li, Hui; Liu, Yi-fan; Fu, Yan; Liu, Yu-ling; Zhang, Xiao-li

    2015-04-01

    L9 (3(4)) orthogonal experiment was used to design the extraction technology of compound Clematidis Radix spray. Weight coefficients of active ingredients and dry extract rate were solved by information entropy. Support vector machine (SVM) was established and the model parameters were optimized through the genetic algorithm. Grid search algorithm was used for optimization of extraction technology of Clematidis Radix spray. The optimal extraction technology was to extract Clematidis Radix spray in water with 6 times the weight of herbal medicine for 3 times, with 2 h once. Bias of value between real and predicted by SVM was 1.23%. SVM was compared with traditional intuitive analysis of orthogonal design. It indicates that the new method used to optimize the extraction parameters of compound Clematidis Radix spray is more accurate and reliable. PMID:26281549

  6. Use of support vector machines and fabry-perot interferometry to classify states of a laser

    NASA Astrophysics Data System (ADS)

    McKinnon, John

    This thesis develops an algorithm that can determine if a laser is functioning correctly over a long period of time. A Fourier fit is created to model fringe profiles from a Fabry-Perot interferometer, and singular value decomposition is used to reduce noise in each signal. Levenberg-Marquardt gradient descent is performed to correctly locate the center of each image and to optimize each fit with respect to the spatial frequency. The Fourier fit is used to extract important information from each image to be used for separating the image types from one another. Principal component analysis is used to reduce the dimensionality of the data set and to plot a projection of the data using its first two principal components. It is determined that the image data are not linearly separable and require a non-linear support vector network to complete the classification of each image type.

  7. Application of the Support Vector Machine to Predict Subclinical Mastitis in Dairy Cattle

    PubMed Central

    Mammadova, Nazira

    2013-01-01

    This study presented a potentially useful alternative approach to ascertain the presence of subclinical and clinical mastitis in dairy cows using support vector machine (SVM) techniques. The proposed method detected mastitis in a cross-sectional representative sample of Holstein dairy cattle milked using an automatic milking system. The study used such suspected indicators of mastitis as lactation rank, milk yield, electrical conductivity, average milking duration, and control season as input data. The output variable was somatic cell counts obtained from milk samples collected monthly throughout the 15 months of the control period. Cattle were judged to be healthy or infected based on those somatic cell counts. This study undertook a detailed scrutiny of the SVM methodology, constructing and examining a model which showed 89% sensitivity, 92% specificity, and 50% error in mastitis detection. PMID:24574862

  8. Automatic road sign detecion and classification based on support vector machines and HOG descriptos

    NASA Astrophysics Data System (ADS)

    Adam, A.; Ioannidis, C.

    2014-05-01

    This paper examines the detection and classification of road signs in color-images acquired by a low cost camera mounted on a moving vehicle. A new method for the detection and classification of road signs is proposed based on color based detection, in order to locate regions of interest. Then, a circular Hough transform is applied to complete detection taking advantage of the shape properties of the road signs. The regions of interest are finally represented using HOG descriptors and are fed into trained Support Vector Machines (SVMs) in order to be recognized. For the training procedure, a database with several training examples depicting Greek road sings has been developed. Many experiments have been conducted and are presented, to measure the efficiency of the proposed methodology especially under adverse weather conditions and poor illumination. For the experiments training datasets consisting of different number of examples were used and the results are presented, along with some possible extensions of this work.

  9. Application of the support vector machine to predict subclinical mastitis in dairy cattle.

    PubMed

    Mammadova, Nazira; Keskin, Ismail

    2013-01-01

    This study presented a potentially useful alternative approach to ascertain the presence of subclinical and clinical mastitis in dairy cows using support vector machine (SVM) techniques. The proposed method detected mastitis in a cross-sectional representative sample of Holstein dairy cattle milked using an automatic milking system. The study used such suspected indicators of mastitis as lactation rank, milk yield, electrical conductivity, average milking duration, and control season as input data. The output variable was somatic cell counts obtained from milk samples collected monthly throughout the 15 months of the control period. Cattle were judged to be healthy or infected based on those somatic cell counts. This study undertook a detailed scrutiny of the SVM methodology, constructing and examining a model which showed 89% sensitivity, 92% specificity, and 50% error in mastitis detection. PMID:24574862

  10. Adaboost and Support Vector Machines for White Matter Lesion Segmentation in MR Images.

    PubMed

    Quddus, Azhar; Fieguth, Paul; Basir, Otman

    2005-01-01

    The use of two powerful classification techniques (boosting and SVM) is explored for the segmentation of white-matter lesions in the MRI scans of human brain. Simple features are generated from Proton Density (PD) scans. Radial Basis Function (RBF) based Adaboost technique and Support Vector Machines (SVM) are employed for this task. The classifiers are trained on severe, moderate and mild cases. The segmentation is performed in T1 acquisition space rather than standard space (with more slices). Hence, the proposed approach requires less time for manual verification. The results indicate that the proposed approach can handle MR field inhomogeneities quite well and is completely independent from manual selection process so that it can be run under batch mode. Segmentation performance comparison with manual detection is also provided. PMID:17282216

  11. Particulate matter characterization by gray level co-occurrence matrix based support vector machines.

    PubMed

    Manivannan, K; Aggarwal, P; Devabhaktuni, V; Kumar, A; Nims, D; Bhattacharya, P

    2012-07-15

    An efficient and highly reliable automatic selection of optimal segmentation algorithm for characterizing particulate matter is presented in this paper. Support vector machines (SVMs) are used as a new self-regulating classifier trained by gray level co-occurrence matrix (GLCM) of the image. This matrix is calculated at various angles and the texture features are evaluated for classifying the images. Results show that the performance of GLCM-based SVMs is drastically improved over the previous histogram-based SVMs. Our proposed GLCM-based approach of training SVM predicts a robust and more accurate segmentation algorithm than the standard histogram technique, as additional information based on the spatial relationship between pixels is incorporated for image classification. Further, the GLCM-based SVM classifiers were more accurate and required less training data when compared to the artificial neural network (ANN) classifiers. PMID:22595545

  12. A Bayesian least squares support vector machines based framework for fault diagnosis and failure prognosis

    NASA Astrophysics Data System (ADS)

    Khawaja, Taimoor Saleem

    A high-belief low-overhead Prognostics and Health Management (PHM) system is desired for online real-time monitoring of complex non-linear systems operating in a complex (possibly non-Gaussian) noise environment. This thesis presents a Bayesian Least Squares Support Vector Machine (LS-SVM) based framework for fault diagnosis and failure prognosis in nonlinear non-Gaussian systems. The methodology assumes the availability of real-time process measurements, definition of a set of fault indicators and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. An efficient yet powerful Least Squares Support Vector Machine (LS-SVM) algorithm, set within a Bayesian Inference framework, not only allows for the development of real-time algorithms for diagnosis and prognosis but also provides a solid theoretical framework to address key concepts related to classification for diagnosis and regression modeling for prognosis. SVM machines are founded on the principle of Structural Risk Minimization (SRM) which tends to find a good trade-off between low empirical risk and small capacity. The key features in SVM are the use of non-linear kernels, the absence of local minima, the sparseness of the solution and the capacity control obtained by optimizing the margin. The Bayesian Inference framework linked with LS-SVMs allows a probabilistic interpretation of the results for diagnosis and prognosis. Additional levels of inference provide the much coveted features of adaptability and tunability of the modeling parameters. The two main modules considered in this research are fault diagnosis and failure prognosis. With the goal of designing an efficient and reliable fault diagnosis scheme, a novel Anomaly Detector is suggested based on the LS-SVM machines. The proposed scheme uses only baseline data to construct a 1-class LS-SVM machine which, when presented with online data is able to distinguish between normal behavior

  13. Morphological analysis of dendrites and spines by hybridization of ridge detection with twin support vector machine

    PubMed Central

    Wang, Shuihua; Chen, Mengmeng; Li, Yang; Shao, Ying; Zhang, Yudong

    2016-01-01

    Dendritic spines are described as neuronal protrusions. The morphology of dendritic spines and dendrites has a strong relationship to its function, as well as playing an important role in understanding brain function. Quantitative analysis of dendrites and dendritic spines is essential to an understanding of the formation and function of the nervous system. However, highly efficient tools for the quantitative analysis of dendrites and dendritic spines are currently undeveloped. In this paper we propose a novel three-step cascaded algorithm–RTSVM— which is composed of ridge detection as the curvature structure identifier for backbone extraction, boundary location based on differences in density, the Hu moment as features and Twin Support Vector Machine (TSVM) classifiers for spine classification. Our data demonstrates that this newly developed algorithm has performed better than other available techniques used to detect accuracy and false alarm rates. This algorithm will be used effectively in neuroscience research. PMID:27547530

  14. Support vector machine-based feature extractor for L/H transitions in JET

    SciTech Connect

    Gonzalez, S.; Vega, J.; Pereira, A.; Ramirez, J. M.; Dormido-Canto, S.; Collaboration: JET-EFDA Contributors

    2010-10-15

    Support vector machines (SVM) are machine learning tools originally developed in the field of artificial intelligence to perform both classification and regression. In this paper, we show how SVM can be used to determine the most relevant quantities to characterize the confinement transition from low to high confinement regimes in tokamak plasmas. A set of 27 signals is used as starting point. The signals are discarded one by one until an optimal number of relevant waveforms is reached, which is the best tradeoff between keeping a limited number of quantities and not loosing essential information. The method has been applied to a database of 749 JET discharges and an additional database of 150 JET discharges has been used to test the results obtained.

  15. PSO-based support vector machine with cuckoo search technique for clinical disease diagnoses.

    PubMed

    Liu, Xiaoyong; Fu, Hui

    2014-01-01

    Disease diagnosis is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM), particle swarm optimization (PSO), and cuckoo search (CS). The new method consists of two stages: firstly, a CS based approach for parameter optimization of SVM is developed to find the better initial parameters of kernel function, and then PSO is applied to continue SVM training and find the best parameters of SVM. Experimental results indicate that the proposed CS-PSO-SVM model achieves better classification accuracy and F-measure than PSO-SVM and GA-SVM. Therefore, we can conclude that our proposed method is very efficient compared to the previously reported algorithms. PMID:24971382

  16. Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines.

    PubMed

    del Val, Lara; Izquierdo-Fuente, Alberto; Villacorta, Juan J; Raboso, Mariano

    2015-01-01

    Drawing on the results of an acoustic biometric system based on a MSE classifier, a new biometric system has been implemented. This new system preprocesses acoustic images, extracts several parameters and finally classifies them, based on Support Vector Machine (SVM). The preprocessing techniques used are spatial filtering, segmentation-based on a Gaussian Mixture Model (GMM) to separate the person from the background, masking-to reduce the dimensions of images-and binarization-to reduce the size of each image. An analysis of classification error and a study of the sensitivity of the error versus the computational burden of each implemented algorithm are presented. This allows the selection of the most relevant algorithms, according to the benefits required by the system. A significant improvement of the biometric system has been achieved by reducing the classification error, the computational burden and the storage requirements. PMID:26091392

  17. Feature Selection and Classification of Hyperspectral Images with Support Vector Machines

    SciTech Connect

    Archibald, Richard K; Fann, George I

    2007-01-01

    Hyperspectral images consist of large number of bands which require sophisticated analysis to extract. One approach to reduce computational cost, information representation, and accelerate knowledge discovery is to eliminate bands that do not add value to the classification and analysis method which is being applied. In particular, algorithms that perform band elimination should be designed to take advantage of the structure of the classification method used. This letter introduces an embedded-feature-selection (EFS) algorithm that is tailored to operate with support vector machines (SVMs) to perform band selection and classification simultaneously. We have successfully applied this algorithm to determine a reasonable subset of bands without any user-defined stopping criteria on some sample AVIRIS images; a problem occurs in benchmarking recursive-feature-elimination methods for the SVMs.

  18. Fruit fly optimization based least square support vector regression for blind image restoration

    NASA Astrophysics Data System (ADS)

    Zhang, Jiao; Wang, Rui; Li, Junshan; Yang, Yawei

    2014-11-01

    The goal of image restoration is to reconstruct the original scene from a degraded observation. It is a critical and challenging task in image processing. Classical restorations require explicit knowledge of the point spread function and a description of the noise as priors. However, it is not practical for many real image processing. The recovery processing needs to be a blind image restoration scenario. Since blind deconvolution is an ill-posed problem, many blind restoration methods need to make additional assumptions to construct restrictions. Due to the differences of PSF and noise energy, blurring images can be quite different. It is difficult to achieve a good balance between proper assumption and high restoration quality in blind deconvolution. Recently, machine learning techniques have been applied to blind image restoration. The least square support vector regression (LSSVR) has been proven to offer strong potential in estimating and forecasting issues. Therefore, this paper proposes a LSSVR-based image restoration method. However, selecting the optimal parameters for support vector machine is essential to the training result. As a novel meta-heuristic algorithm, the fruit fly optimization algorithm (FOA) can be used to handle optimization problems, and has the advantages of fast convergence to the global optimal solution. In the proposed method, the training samples are created from a neighborhood in the degraded image to the central pixel in the original image. The mapping between the degraded image and the original image is learned by training LSSVR. The two parameters of LSSVR are optimized though FOA. The fitness function of FOA is calculated by the restoration error function. With the acquired mapping, the degraded image can be recovered. Experimental results show the proposed method can obtain satisfactory restoration effect. Compared with BP neural network regression, SVR method and Lucy-Richardson algorithm, it speeds up the restoration rate and

  19. Prediction of extreme rainfall event using weather pattern recognition and support vector machine classifier

    NASA Astrophysics Data System (ADS)

    Nayak, Munir Ahmad; Ghosh, Subimal

    2013-11-01

    A major component of flood alert broadcasting is the short-term prediction of extreme rainfall events, which remains a challenging task, even with the improvements of numerical weather prediction models. Such prediction is a high priority research challenge, specifically in highly urbanized areas like Mumbai, India, which is extremely prone to urban flooding. Here, we attempt to develop an algorithm based on a machine learning technique, support vector machine (SVM), to predict extreme rainfall with a lead time of 6-48 h in Mumbai, using mesoscale (20-200 km) and synoptic scale (200-2,000 km) weather patterns. The underlying hypothesis behind this algorithm is that the weather patterns before (6-48 h) extreme events are significantly different from those of normal weather days. The present algorithm attempts to identify those specific patterns for extreme events and applies SVM-based classifiers for extreme rainfall classification and prediction. Here, we develop the anomaly frequency method (AFM), where the predictors (and their patterns) for SVM are identified with the frequency of high anomaly values of weather variables at different pressure levels, which are present before extreme events, but absent for non-extreme conditions. We observe that weather patterns before the extreme rainfall events during nighttime (1800 to 0600Z) is different from those during daytime (0600 to 1800Z) and, accordingly, we develop a two-phase support vector classifier for extreme prediction. Though there are false alarms associated with this prediction method, the model predicts all the extreme events well in advance. The performance is compared with the state-of-the-art statistical technique fingerprinting approach and is observed to be better in terms of false alarm and prediction.

  20. SNPs selection using support vector regression and genetic algorithms in GWAS

    PubMed Central

    2014-01-01

    Introduction This paper proposes a new methodology to simultaneously select the most relevant SNPs markers for the characterization of any measurable phenotype described by a continuous variable using Support Vector Regression with Pearson Universal kernel as fitness function of a binary genetic algorithm. The proposed methodology is multi-attribute towards considering several markers simultaneously to explain the phenotype and is based jointly on statistical tools, machine learning and computational intelligence. Results The suggested method has shown potential in the simulated database 1, with additive effects only, and real database. In this simulated database, with a total of 1,000 markers, and 7 with major effect on the phenotype and the other 993 SNPs representing the noise, the method identified 21 markers. Of this total, 5 are relevant SNPs between the 7 but 16 are false positives. In real database, initially with 50,752 SNPs, we have reduced to 3,073 markers, increasing the accuracy of the model. In the simulated database 2, with additive effects and interactions (epistasis), the proposed method matched to the methodology most commonly used in GWAS. Conclusions The method suggested in this paper demonstrates the effectiveness in explaining the real phenotype (PTA for milk), because with the application of the wrapper based on genetic algorithm and Support Vector Regression with Pearson Universal, many redundant markers were eliminated, increasing the prediction and accuracy of the model on the real database without quality control filters. The PUK demonstrated that it can replicate the performance of linear and RBF kernels. PMID:25573332

  1. Monthly evaporation forecasting using artificial neural networks and support vector machines

    NASA Astrophysics Data System (ADS)

    Tezel, Gulay; Buyukyildiz, Meral

    2016-04-01

    Evaporation is one of the most important components of the hydrological cycle, but is relatively difficult to estimate, due to its complexity, as it can be influenced by numerous factors. Estimation of evaporation is important for the design of reservoirs, especially in arid and semi-arid areas. Artificial neural network methods and support vector machines (SVM) are frequently utilized to estimate evaporation and other hydrological variables. In this study, usability of artificial neural networks (ANNs) (multilayer perceptron (MLP) and radial basis function network (RBFN)) and ɛ-support vector regression (SVR) artificial intelligence methods was investigated to estimate monthly pan evaporation. For this aim, temperature, relative humidity, wind speed, and precipitation data for the period 1972 to 2005 from Beysehir meteorology station were used as input variables while pan evaporation values were used as output. The Romanenko and Meyer method was also considered for the comparison. The results were compared with observed class A pan evaporation data. In MLP method, four different training algorithms, gradient descent with momentum and adaptive learning rule backpropagation (GDX), Levenberg-Marquardt (LVM), scaled conjugate gradient (SCG), and resilient backpropagation (RBP), were used. Also, ɛ-SVR model was used as SVR model. The models were designed via 10-fold cross-validation (CV); algorithm performance was assessed via mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R 2). According to the performance criteria, the ANN algorithms and ɛ-SVR had similar results. The ANNs and ɛ-SVR methods were found to perform better than the Romanenko and Meyer methods. Consequently, the best performance using the test data was obtained using SCG(4,2,2,1) with R 2 = 0.905.

  2. Feature-matching Pattern-based Support Vector Machines for Robust Peptide Mass Fingerprinting*

    PubMed Central

    Li, Youyuan; Hao, Pei; Zhang, Siliang; Li, Yixue

    2011-01-01

    Peptide mass fingerprinting, regardless of becoming complementary to tandem mass spectrometry for protein identification, is still the subject of in-depth study because of its higher sample throughput, higher level of specificity for single peptides and lower level of sensitivity to unexpected post-translational modifications compared with tandem mass spectrometry. In this study, we propose, implement and evaluate a uniform approach using support vector machines to incorporate individual concepts and conclusions for accurate PMF. We focus on the inherent attributes and critical issues of the theoretical spectrum (peptides), the experimental spectrum (peaks) and spectrum (masses) alignment. Eighty-one feature-matching patterns derived from cleavage type, uniqueness and variable masses of theoretical peptides together with the intensity rank of experimental peaks were proposed to characterize the matching profile of the peptide mass fingerprinting procedure. We developed a new strategy including the participation of matched peak intensity redistribution to handle shared peak intensities and 440 parameters were generated to digitalize each feature-matching pattern. A high performance for an evaluation data set of 137 items was finally achieved by the optimal multi-criteria support vector machines approach, with 491 final features out of a feature vector of 35,640 normalized features through cross training and validating a publicly available “gold standard” peptide mass fingerprinting data set of 1733 items. Compared with the Mascot, MS-Fit, ProFound and Aldente algorithms commonly used for MS-based protein identification, the feature-matching patterns algorithm has a greater ability to clearly separate correct identifications and random matches with the highest values for sensitivity (82%), precision (97%) and F1-measure (89%) of protein identification. Several conclusions reached via this research make general contributions to MS-based protein identification

  3. Sensitivity Analysis of a Spatio-Temporal Avalanche Forecasting Model Based on Support Vector Machines

    NASA Astrophysics Data System (ADS)

    Matasci, G.; Pozdnoukhov, A.; Kanevski, M.

    2009-04-01

    The recent progress in environmental monitoring technologies allows capturing extensive amount of data that can be used to assist in avalanche forecasting. While it is not straightforward to directly obtain the stability factors with the available technologies, the snow-pack profiles and especially meteorological parameters are becoming more and more available at finer spatial and temporal scales. Being very useful for improving physical modelling, these data are also of particular interest regarding their use involving the contemporary data-driven techniques of machine learning. Such, the use of support vector machine classifier opens ways to discriminate the ``safe'' and ``dangerous'' conditions in the feature space of factors related to avalanche activity based on historical observations. The input space of factors is constructed from the number of direct and indirect snowpack and weather observations pre-processed with heuristic and physical models into a high-dimensional spatially varying vector of input parameters. The particular system presented in this work is implemented for the avalanche-prone site of Ben Nevis, Lochaber region in Scotland. A data-driven model for spatio-temporal avalanche danger forecasting provides an avalanche danger map for this local (5x5 km) region at the resolution of 10m based on weather and avalanche observations made by forecasters on a daily basis at the site. We present the further work aimed at overcoming the ``black-box'' type modelling, a disadvantage the machine learning methods are often criticized for. It explores what the data-driven method of support vector machine has to offer to improve the interpretability of the forecast, uncovers the properties of the developed system with respect to highlighting which are the important features that led to the particular prediction (both in time and space), and presents the analysis of sensitivity of the prediction with respect to the varying input parameters. The purpose of the

  4. Large-scale ligand-based predictive modelling using support vector machines.

    PubMed

    Alvarsson, Jonathan; Lampa, Samuel; Schaal, Wesley; Andersson, Claes; Wikberg, Jarl E S; Spjuth, Ola

    2016-01-01

    The increasing size of datasets in drug discovery makes it challenging to build robust and accurate predictive models within a reasonable amount of time. In order to investigate the effect of dataset sizes on predictive performance and modelling time, ligand-based regression models were trained on open datasets of varying sizes of up to 1.2 million chemical structures. For modelling, two implementations of support vector machines (SVM) were used. Chemical structures were described by the signatures molecular descriptor. Results showed that for the larger datasets, the LIBLINEAR SVM implementation performed on par with the well-established libsvm with a radial basis function kernel, but with dramatically less time for model building even on modest computer resources. Using a non-linear kernel proved to be infeasible for large data sizes, even with substantial computational resources on a computer cluster. To deploy the resulting models, we extended the Bioclipse decision support framework to support models from LIBLINEAR and made our models of logD and solubility available from within Bioclipse. PMID:27516811

  5. Knowledge-based systems as decision support tools in an ecosystem approach to fisheries: Comparing a fuzzy-logic and a rule-based approach

    NASA Astrophysics Data System (ADS)

    Jarre, Astrid; Paterson, Barbara; Moloney, Coleen L.; Miller, David C. M.; Field, John G.; Starfield, Anthony M.

    2008-10-01

    In an ecosystem approach to fisheries (EAF), management must draw on information of widely different types, and information addressing various scales. Knowledge-based systems assist in the decision-making process by summarising this information in a logical, transparent and reproducible way. Both rule-based Boolean and fuzzy-logic models have been used successfully as knowledge-based decision support tools. This study compares two such systems relevant to fisheries management in an EAF developed for the southern Benguela. The first is a rule-based system for the prediction of anchovy recruitment and the second is a fuzzy-logic tool to monitor implementation of an EAF in the sardine fishery. We construct a fuzzy-logic counterpart to the rule-based model, and a rule-based counterpart to the fuzzy-logic model, compare their results, and include feedback from potential users of these two decision support tools in our evaluation of the two approaches. With respect to the model objectives, no method clearly outperformed the other. The advantages of numerically processing continuous variables, and interpreting the final output, as in fuzzy-logic models, can be weighed up against the advantages of using a few, qualitative, easy-to-understand categories as in rule-based models. The natural language used in rule-based implementations is easily understood by, and communicated among, users of these systems. Users unfamiliar with fuzzy-set theory must “trust” the logic of the model. Graphical visualization of intermediate and end results is an important advantage of any system. Applying the two approaches in parallel improved our understanding of the model as well as of the underlying problems. Even for complex problems, small knowledge-based systems such as the ones explored here are worth developing and using. Their strengths lie in (i) synthesis of the problem in a logical and transparent framework, (ii) helping scientists to deliberate how to apply their science to

  6. Discovering cis-regulatory RNAs in Shewanella genomes by Support Vector Machines.

    PubMed

    Xu, Xing; Ji, Yongmei; Stormo, Gary D

    2009-04-01

    An increasing number of cis-regulatory RNA elements have been found to regulate gene expression post-transcriptionally in various biological processes in bacterial systems. Effective computational tools for large-scale identification of novel regulatory RNAs are strongly desired to facilitate our exploration of gene regulation mechanisms and regulatory networks. We present a new computational program named RSSVM (RNA Sampler+Support Vector Machine), which employs Support Vector Machines (SVMs) for efficient identification of functional RNA motifs from random RNA secondary structures. RSSVM uses a set of distinctive features to represent the common RNA secondary structure and structural alignment predicted by RNA Sampler, a tool for accurate common RNA secondary structure prediction, and is trained with functional RNAs from a variety of bacterial RNA motif/gene families covering a wide range of sequence identities. When tested on a large number of known and random RNA motifs, RSSVM shows a significantly higher sensitivity than other leading RNA identification programs while maintaining the same false positive rate. RSSVM performs particularly well on sets with low sequence identities. The combination of RNA Sampler and RSSVM provides a new, fast, and efficient pipeline for large-scale discovery of regulatory RNA motifs. We applied RSSVM to multiple Shewanella genomes and identified putative regulatory RNA motifs in the 5' untranslated regions (UTRs) in S. oneidensis, an important bacterial organism with extraordinary respiratory and metal reducing abilities and great potential for bioremediation and alternative energy generation. From 1002 sets of 5'-UTRs of orthologous operons, we identified 166 putative regulatory RNA motifs, including 17 of the 19 known RNA motifs from Rfam, an additional 21 RNA motifs that are supported by literature evidence, 72 RNA motifs overlapping predicted transcription terminators or attenuators, and other candidate regulatory RNA motifs

  7. Supervised Change Detection in VHR Images Using Support Vector Machines and Contextual Information

    NASA Astrophysics Data System (ADS)

    Volpi, Michele; Kanevski, Mikhail

    2010-05-01

    One of the recent challenges in environmental studies is how to include and exploit multitemporal information from multispectral very high resolution (VHR) images. This problem is also known as change detection (CD). Nowadays, many approaches, both supervised and unsupervised, are known and the selection of the method depends strongly on the application, the scope of the study and on available time. In the present research an accurate multiclass supervised method based on Support Vector Machines (SVM) for multitemporal remotely sensed image classification is proposed. SVM is a method issued from the statistical learning theory, known for its good generalization abilities and its performance when dealing with high dimensional spaces. Moreover, its sparse solution provides a final model depending only on a few patterns with an associated nonzero weights (support vectors), and resulting in an optimal regularized complexity. The final decision is obtained with a linear separation of data in an induced kernel feature space, corresponding to a nonlinear classification in the input space. When dealing with CD in VHR imagery, misclassified patterns are often caused by the high variance of the information at pixel level, caused by noise and by the influence of the high spatial resolution. Considering a precise coregistration, the variance at object level is high both in space and in time. The usefulness of adding such information is in smoothing, following an object based or a texture based criteria, the interclass variance and increasing the intraclass variance. By adding such information the classifier can better perform when predicting the class of pixels, because of the neighborhood information that was intrinsically extrapolated by the filtering. In the proposed approach, the behavior of mathematical morphology and morphological profiles obtained with different parameters are studied in a CD setting. The series of features are extracted both on the multispectral images

  8. A Support Vector Machine Classification of Thyroid Bioptic Specimens Using MALDI-MSI Data

    PubMed Central

    De Sio, Gabriele; Chinello, Clizia; Pagni, Fabio

    2016-01-01

    Biomarkers able to characterise and predict multifactorial diseases are still one of the most important targets for all the “omics” investigations. In this context, Matrix-Assisted Laser Desorption/Ionisation-Mass Spectrometry Imaging (MALDI-MSI) has gained considerable attention in recent years, but it also led to a huge amount of complex data to be elaborated and interpreted. For this reason, computational and machine learning procedures for biomarker discovery are important tools to consider, both to reduce data dimension and to provide predictive markers for specific diseases. For instance, the availability of protein and genetic markers to support thyroid lesion diagnoses would impact deeply on society due to the high presence of undetermined reports (THY3) that are generally treated as malignant patients. In this paper we show how an accurate classification of thyroid bioptic specimens can be obtained through the application of a state-of-the-art machine learning approach (i.e., Support Vector Machines) on MALDI-MSI data, together with a particular wrapper feature selection algorithm (i.e., recursive feature elimination). The model is able to provide an accurate discriminatory capability using only 20 out of 144 features, resulting in an increase of the model performances, reliability, and computational efficiency. Finally, tissue areas rather than average proteomic profiles are classified, highlighting potential discriminating areas of clinical interest. PMID:27293431

  9. Forest encroachment mapping in Baratang Island, India, using maximum likelihood and support vector machine classifiers

    NASA Astrophysics Data System (ADS)

    Tiwari, Laxmi Kant; Sinha, Satish K.; Saran, Sameer; Tolpekin, Valentyn A.; Raju, Penumetcha L. N.

    2016-01-01

    Maximum likelihood classifier (MLC) and support vector machines (SVMs) are commonly used supervised classification methods in remote sensing applications. MLC is a parametric method, whereas SVM is a nonparametric method. In an environmental application, a hybrid scheme is designed to identify forest encroachment (FE) pockets by classifying medium-resolution remote sensing images with SVM, incorporating knowledge-base and GPS readings in the geographical information system. The classification scheme has enabled us to identify small scattered noncontiguous FE pockets supported by ground truthing. On Baratang Island, the detected FE area from the classified thematic map for the year 2003 was ˜202 ha, and for the year 2013, the encroachment was ˜206 ha. While some of the older FE pockets were vacated, new FE pockets appeared in the area. Furthermore, comparisons of different classification results in terms of Z-statistics indicate that linear SVM is superior to MLC, whereas linear and nonlinear SVM are not significantly different. Accuracy assessment shows that SVM-based classification results have higher accuracy than MLC-based results. Statistical accuracy in terms of kappa values achieved for the linear SVM-classified thematic maps for the years 2003 and 2013 is 0.98 and 1.0, respectively.

  10. Estimation of Electrically-Evoked Knee Torque from Mechanomyography Using Support Vector Regression.

    PubMed

    Ibitoye, Morufu Olusola; Hamzaid, Nur Azah; Abdul Wahab, Ahmad Khairi; Hasnan, Nazirah; Olatunji, Sunday Olusanya; Davis, Glen M

    2016-01-01

    The difficulty of real-time muscle force or joint torque estimation during neuromuscular electrical stimulation (NMES) in physical therapy and exercise science has motivated recent research interest in torque estimation from other muscle characteristics. This study investigated the accuracy of a computational intelligence technique for estimating NMES-evoked knee extension torque based on the Mechanomyographic signals (MMG) of contracting muscles that were recorded from eight healthy males. Simulation of the knee torque was modelled via Support Vector Regression (SVR) due to its good generalization ability in related fields. Inputs to the proposed model were MMG amplitude characteristics, the level of electrical stimulation or contraction intensity, and knee angle. Gaussian kernel function, as well as its optimal parameters were identified with the best performance measure and were applied as the SVR kernel function to build an effective knee torque estimation model. To train and test the model, the data were partitioned into training (70%) and testing (30%) subsets, respectively. The SVR estimation accuracy, based on the coefficient of determination (R²) between the actual and the estimated torque values was up to 94% and 89% during the training and testing cases, with root mean square errors (RMSE) of 9.48 and 12.95, respectively. The knee torque estimations obtained using SVR modelling agreed well with the experimental data from an isokinetic dynamometer. These findings support the realization of a closed-loop NMES system for functional tasks using MMG as the feedback signal source and an SVR algorithm for joint torque estimation. PMID:27447638

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

    PubMed

    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. PMID:15852509

  12. Model for noise-induced hearing loss using support vector machine

    NASA Astrophysics Data System (ADS)

    Qiu, Wei; Ye, Jun; Liu-White, Xiaohong; Hamernik, Roger P.

    2005-09-01

    Contemporary noise standards are based on the assumption that an energy metric such as the equivalent noise level is sufficient for estimating the potential of a noise stimulus to cause noise-induced hearing loss (NIHL). Available data, from laboratory-based experiments (Lei et al., 1994; Hamernik and Qiu, 2001) indicate that while an energy metric may be necessary, it is not sufficient for the prediction of NIHL. A support vector machine (SVM) NIHL prediction model was constructed, based on a 550-subject (noise-exposed chinchillas) database. Training of the model used data from 367 noise-exposed subjects. The model was tested using the remaining 183 subjects. Input variables for the model included acoustic, audiometric, and biological variables, while output variables were PTS and cell loss. The results show that an energy parameter is not sufficient to predict NIHL, especially in complex noise environments. With the kurtosis and other noise and biological parameters included as additional inputs, the performance of SVM prediction model was significantly improved. The SVM prediction model has the potential to reliably predict noise-induced hearing loss. [Work supported by NIOSH.

  13. A Support Vector Machine Classification of Thyroid Bioptic Specimens Using MALDI-MSI Data.

    PubMed

    Galli, Manuel; Zoppis, Italo; De Sio, Gabriele; Chinello, Clizia; Pagni, Fabio; Magni, Fulvio; Mauri, Giancarlo

    2016-01-01

    Biomarkers able to characterise and predict multifactorial diseases are still one of the most important targets for all the "omics" investigations. In this context, Matrix-Assisted Laser Desorption/Ionisation-Mass Spectrometry Imaging (MALDI-MSI) has gained considerable attention in recent years, but it also led to a huge amount of complex data to be elaborated and interpreted. For this reason, computational and machine learning procedures for biomarker discovery are important tools to consider, both to reduce data dimension and to provide predictive markers for specific diseases. For instance, the availability of protein and genetic markers to support thyroid lesion diagnoses would impact deeply on society due to the high presence of undetermined reports (THY3) that are generally treated as malignant patients. In this paper we show how an accurate classification of thyroid bioptic specimens can be obtained through the application of a state-of-the-art machine learning approach (i.e., Support Vector Machines) on MALDI-MSI data, together with a particular wrapper feature selection algorithm (i.e., recursive feature elimination). The model is able to provide an accurate discriminatory capability using only 20 out of 144 features, resulting in an increase of the model performances, reliability, and computational efficiency. Finally, tissue areas rather than average proteomic profiles are classified, highlighting potential discriminating areas of clinical interest. PMID:27293431

  14. Classification of optical tomographic images of rheumatoid finger joints with support vector machines

    NASA Astrophysics Data System (ADS)

    Balasubramanyam, Vivek; Hielscher, Andreas H.

    2005-04-01

    Over the last years we have developed a sagittal laser optical tomographic (SLOT) imaging system for the diagnosis and monitoring of inflammatory processes in proximal interphalangeal (PIP) joint of patients with rheumatoid arthritis (RA). While cross sectional images of the distribution of optical properties can now be generated easily, clinical interpretation of these images remains a challenge. In first clinical studies involving 78 finger joints, we compared optical tomographs to ultrasound images and clinical analyses. Receiver-operator curves (ROC) were generated using various image parameters, such as minimum and maximum scattering or absorption coefficients. These studies resulted in specificities and sensitivities in the range of 0.7 to 0.76. Recently, we have trained support vector machines (SVMs) to classify images of healthy and diseased joints. By eliminating redundancy using feature selection, we are achieving sensitivities of 0.72 and specificities up to 1.0. Studies with larger patient groups are necessary to validate these findings; but these initial results support the expectation that SVMs and other machine learning techniques can considerably improve image interpretation analysis in optical tomography.

  15. Mechanical properties prediction for carbon nanotubes/epoxy composites by using support vector regression

    NASA Astrophysics Data System (ADS)

    Cheng, W. D.; Cai, C. Z.; Luo, Y.; Li, Y. H.; Zhao, C. J.

    2015-02-01

    Studies have shown there are several process/geometry parameters affecting the mechanical properties of the carbon nanotubes/epoxy composites. The relationship between the response and process/geometry parameters is highly nonlinear and quite complicated. It is very valuable to have an accurate model to estimate the response under different process/geometry parameters. In this paper, the support vector regression (SVR) combined with particle swarm optimization (PSO) for its parameter optimization was employed to construct mathematical models for prediction of mechanical properties of the carbon nanotubes/epoxy composites according to an experimental data set. The leave-one-out cross-validation (LOOCV) test results by SVR models support that the generalization ability of SVR model is high enough. The statistical mean absolute percentage error for tensile strength, elongation and elastic modulus are 3.96%, 3.14% and 2.62%, the correlation coefficients (R2) achieve as high as 0.991, 0.990 and 0.997, respectively. This study suggests that the established SVR model can be used to accurately foresee the mechanical properties of carbon nanotubes/epoxy composites and can be used to optimize designing or controlling of the experimental process/geometry in practice.

  16. Discrimination of Maize Haploid Seeds from Hybrid Seeds Using Vis Spectroscopy and Support Vector Machine Method.

    PubMed

    Liu, Jin; Guo, Ting-ting; Li, Hao-chuan; Jia, Shi-qiang; Yan, Yan-lu; An, Dong; Zhang, Yao; Chen, Shao-jiang

    2015-11-01

    Doubled haploid (DH) lines are routinely applied in the hybrid maize breeding programs of many institutes and companies for their advantages of complete homozygosity and short breeding cycle length. A key issue in this approach is an efficient screening system to identify haploid kernels from the hybrid kernels crossed with the inducer. At present, haploid kernel selection is carried out manually using the"red-crown" kernel trait (the haploid kernel has a non-pigmented embryo and pigmented endosperm) controlled by the R1-nj gene. Manual selection is time-consuming and unreliable. Furthermore, the color of the kernel embryo is concealed by the pericarp. Here, we establish a novel approach for identifying maize haploid kernels based on visible (Vis) spectroscopy and support vector machine (SVM) pattern recognition technology. The diffuse transmittance spectra of individual kernels (141 haploid kernels and 141 hybrid kernels from 9 genotypes) were collected using a portable UV-Vis spectrometer and integrating sphere. The raw spectral data were preprocessed using smoothing and vector normalization methods. The desired feature wavelengths were selected based on the results of the Kolmogorov-Smirnov test. The wavelengths with p values above 0. 05 were eliminated because the distributions of absorbance data in these wavelengths show no significant difference between haploid and hybrid kernels. Principal component analysis was then performed to reduce the number of variables. The SVM model was evaluated by 9-fold cross-validation. In each round, samples of one genotype were used as the testing set, while those of other genotypes were used as the training set. The mean rate of correct discrimination was 92.06%. This result demonstrates the feasibility of using Vis spectroscopy to identify haploid maize kernels. The method would help develop a rapid and accurate automated screening-system for haploid kernels. PMID:26978947

  17. TJ-II wave forms analysis with wavelets and support vector machines

    SciTech Connect

    Dormido-Canto, S.; Farias, G.; Dormido, R.; Vega, J.; Sanchez, J.; Santos, M.

    2004-10-01

    Since the fusion plasma experiment generates hundreds of signals, it is essential to have automatic mechanisms for searching similarities and retrieving of specific data in the wave form database. Wavelet transform (WT) is a transformation that allows one to map signals to spaces of lower dimensionality. Support vector machine (SVM) is a very effective method for general purpose pattern recognition. Given a set of input vectors which belong to two different classes, the SVM maps the inputs into a high-dimensional feature space through some nonlinear mapping, where an optimal separating hyperplane is constructed. In this work, the combined use of WT and SVM is proposed for searching and retrieving similar wave forms in the TJ-II database. In a first stage, plasma signals will be preprocessed by WT to reduce their dimensionality and to extract their main features. In the next stage, and using the smoothed signals produced by the WT, SVM will be applied to show up the efficiency of the proposed method to deal with the problem of sorting out thousands of fusion plasma signals.From observation of several experiments, our WT+SVM method is very viable, and the results seems promising. However, we have further work to do. We have to finish the development of a Matlab toolbox for WT+SVM processing and to include new relevant features in the SVM inputs to improve the technique. We have also to make a better preprocessing of the input signals and to study the performance of other generic and self custom kernels. To reach it, and since the preprocessing stages are very time consuming, we are going to study the viability of using DSPs, RPGAs or parallel programming techniques to reduce the execution time.

  18. A support vector machine to search for metal-poor galaxies

    NASA Astrophysics Data System (ADS)

    Shi, Fei; Liu, Yu-Yan; Kong, Xu; Chen, Yang; Li, Zhong-Hua; Zhi, Shu-Teng

    2014-10-01

    To develop a fast and reliable method for selecting metal-poor galaxies (MPGs), especially in large surveys and huge data bases, a support vector machine (SVM) supervized learning algorithms is applied to a sample of star-forming galaxies from the Sloan Digital Sky Survey data release 9 provided by the Max Planck Institute and the Johns Hopkins University (http://www.sdss3.org/dr9/spectro/spectroaccess.php). A two-step approach is adopted: (i) the SVM must be trained with a subset of objects that are known to be either MPGs or metal-rich galaxies (MRGs), treating the strong emission line flux measurements as input feature vectors in n-dimensional space, where n is the number of strong emission line flux ratios. (ii) After training on a sample of star-forming galaxies, the remaining galaxies are classified in the automatic test analysis as either MPGs or MRGs using a 10-fold cross-validation technique. For target selection, we have achieved an acquisition accuracy for MPGs of ˜96 and ˜95 per cent for an MPG threshold of 12 + log(O/H) = 8.00 and 12 + log(O/H) = 8.39, respectively. Running the code takes minutes in most cases under the MATLAB 2013a software environment. The code in the Letter is available on the web (http://fshi5388.blog.163.com). The SVM method can easily be extended to any MPGs target selection task and can be regarded as an efficient classification method particularly suitable for modern large surveys.

  19. Stellar Spectral Classification with Minimum Within-Class and Maximum Between-Class Scatter Support Vector Machine

    NASA Astrophysics Data System (ADS)

    Zhong-Bao, Liu

    2016-06-01

    Support Vector Machine (SVM) is one of the important stellar spectral classification methods, and it is widely used in practice. But its classification efficiencies cannot be greatly improved because it does not take the class distribution into consideration. In view of this, a modified SVM named Minimum within-class and Maximum between-class scatter Support Vector Machine (MMSVM) is constructed to deal with the above problem. MMSVM merges the advantages of Fisher's Discriminant Analysis (FDA) and SVM, and the comparative experiments on the Sloan Digital Sky Survey (SDSS) show that MMSVM performs better than SVM.

  20. Support vector machine based decision for mechanical fault condition monitoring in induction motor using an advanced Hilbert-Park transform.

    PubMed

    Ben Salem, Samira; Bacha, Khmais; Chaari, Abdelkader

    2012-09-01

    In this work we suggest an original fault signature based on an improved combination of Hilbert and Park transforms. Starting from this combination we can create two fault signatures: Hilbert modulus current space vector (HMCSV) and Hilbert phase current space vector (HPCSV). These two fault signatures are subsequently analysed using the classical fast Fourier transform (FFT). The effects of mechanical faults on the HMCSV and HPCSV spectrums are described, and the related frequencies are determined. The magnitudes of spectral components, relative to the studied faults (air-gap eccentricity and outer raceway ball bearing defect), are extracted in order to develop the input vector necessary for learning and testing the support vector machine with an aim of classifying automatically the various states of the induction motor. PMID:22742760

  1. Site selection for drinking-water pumping boreholes using a fuzzy spatial decision support system in the Korinthia prefecture, SE Greece

    NASA Astrophysics Data System (ADS)

    Antonakos, Andreas K.; Voudouris, Konstantinos S.; Lambrakis, Nikolaos I.

    2014-12-01

    The implementation of a geographic information system (GIS)/fuzzy spatial decision support system in the selection of sites for drinking-water pumping boreholes is described. Groundwater is the main source of domestic supply and irrigation in Korinthia prefecture, south-eastern Greece. Water demand has increased considerably over the last 30 years and is mainly met by groundwater abstracted via numerous wells and boreholes. The definition of the most "suitable" site for the drilling of new boreholes is a major issue in this area. A method of allocating suitable locations has been developed based on multicriteria analysis and fuzzy logic. Twelve parameters were finally involved in the model, prearranged into three categories: borehole yield, groundwater quality, and economic and technical constraints. GIS was used to create a classification map of the research area, based on the suitability of each point for the placement of new borehole fields. The coastal part of the study area is completely unsuitable, whereas high values of suitability are recorded in the south-western part. The study demonstrated that the method of multicriteria analysis in combination with fuzzy logic is a useful tool for selecting the best sites for new borehole drilling on a regional scale. The results could be used by local authorities and decision-makers for integrated groundwater resources management.

  2. Support Vector Machine Model for Automatic Detection and Classification of Seismic Events

    NASA Astrophysics Data System (ADS)

    Barros, Vesna; Barros, Lucas

    2016-04-01

    The automated processing of multiple seismic signals to detect, localize and classify seismic events is a central tool in both natural hazards monitoring and nuclear treaty verification. However, false detections and missed detections caused by station noise and incorrect classification of arrivals are still an issue and the events are often unclassified or poorly classified. Thus, machine learning techniques can be used in automatic processing for classifying the huge database of seismic recordings and provide more confidence in the final output. Applied in the context of the International Monitoring System (IMS) - a global sensor network developed for the Comprehensive Nuclear-Test-Ban Treaty (CTBT) - we propose a fully automatic method for seismic event detection and classification based on a supervised pattern recognition technique called the Support Vector Machine (SVM). According to Kortström et al., 2015, the advantages of using SVM are handleability of large number of features and effectiveness in high dimensional spaces. Our objective is to detect seismic events from one IMS seismic station located in an area of high seismicity and mining activity and classify them as earthquakes or quarry blasts. It is expected to create a flexible and easily adjustable SVM method that can be applied in different regions and datasets. Taken a step further, accurate results for seismic stations could lead to a modification of the model and its parameters to make it applicable to other waveform technologies used to monitor nuclear explosions such as infrasound and hydroacoustic waveforms. As an authorized user, we have direct access to all IMS data and bulletins through a secure signatory account. A set of significant seismic waveforms containing different types of events (e.g. earthquake, quarry blasts) and noise is being analysed to train the model and learn the typical pattern of the signal from these events. Moreover, comparing the performance of the support-vector

  3. Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features

    PubMed Central

    2013-01-01

    Background Named entity recognition (NER) is an important task in clinical natural language processing (NLP) research. Machine learning (ML) based NER methods have shown good performance in recognizing entities in clinical text. Algorithms and features are two important factors that largely affect the performance of ML-based NER systems. Conditional Random Fields (CRFs), a sequential labelling algorithm, and Support Vector Machines (SVMs), which is based on large margin theory, are two typical machine learning algorithms that have been widely applied to clinical NER tasks. For features, syntactic and semantic information of context words has often been used in clinical NER systems. However, Structural Support Vector Machines (SSVMs), an algorithm that combines the advantages of both CRFs and SVMs, and word representation features, which contain word-level back-off information over large unlabelled corpus by unsupervised algorithms, have not been extensively investigated for clinical text processing. Therefore, the primary goal of this study is to evaluate the use of SSVMs and word representation features in clinical NER tasks. Methods In this study, we developed SSVMs-based NER systems to recognize clinical entities in hospital discharge summaries, using the data set from the concept extration task in the 2010 i2b2 NLP challenge. We compared the performance of CRFs and SSVMs-based NER classifiers with the same feature sets. Furthermore, we extracted two different types of word representation features (clustering-based representation features and distributional representation features) and integrated them with the SSVMs-based clinical NER system. We then reported the performance of SSVM-based NER systems with different types of word representation features. Results and discussion Using the same training (N = 27,837) and test (N = 45,009) sets in the challenge, our evaluation showed that the SSVMs-based NER systems achieved better performance than the CRFs

  4. Flood damage assessment performed based on Support Vector Machines combined with Landsat TM imagery and GIS

    NASA Astrophysics Data System (ADS)

    Alouene, Y.; Petropoulos, G. P.; Kalogrias, A.; Papanikolaou, F.

    2012-04-01

    Floods are a water-related natural disaster affecting and often threatening different aspects of human life, such as property damage, economic degradation, and in some instances even loss of precious human lives. Being able to provide accurately and cost-effectively assessment of damage from floods is essential to both scientists and policy makers in many aspects ranging from mitigating to assessing damage extent as well as in rehabilitation of affected areas. Remote Sensing often combined with Geographical Information Systems (GIS) has generally shown a very promising potential in performing rapidly and cost-effectively flooding damage assessment, particularly so in remote, otherwise inaccessible locations. The progress in remote sensing during the last twenty years or so has resulted to the development of a large number of image processing techniques suitable for use with a range of remote sensing data in performing flooding damage assessment. Supervised image classification is regarded as one of the most widely used approaches employed for this purpose. Yet, the use of recently developed image classification algorithms such as of machine learning-based Support Vector Machines (SVMs) classifier has not been adequately investigated for this purpose. The objective of our work had been to quantitatively evaluate the ability of SVMs combined with Landsat TM multispectral imagery in performing a damage assessment of a flood occurred in a Mediterranean region. A further objective has been to examine if the inclusion of additional spectral information apart from the original TM bands in SVMs can improve flooded area extraction accuracy. As a case study is used the case of a river Evros flooding of 2010 located in the north of Greece, in which TM imagery before and shortly after the flooding was available. Assessment of the flooded area is performed in a GIS environment on the basis of classification accuracy assessment metrics as well as comparisons versus a vector

  5. Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System

    NASA Astrophysics Data System (ADS)

    Akhavan, P.; Karimi, M.; Pahlavani, P.

    2014-10-01

    Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL) created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.

  6. Prediction of protein subcellular localization by support vector machines using multi-scale energy and pseudo amino acid composition.

    PubMed

    Shi, J-Y; Zhang, S-W; Pan, Q; Cheng, Y-M; Xie, J

    2007-07-01

    As more and more genomes have been discovered in recent years, there is an urgent need to develop a reliable method to predict the subcellular localization for the explosion of newly found proteins. However, many well-known prediction methods based on amino acid composition have problems utilizing the sequence-order information. Here, based on the concept of Chou's pseudo amino acid composition (PseAA), a new feature extraction method, the multi-scale energy (MSE) approach, is introduced to incorporate the sequence-order information. First, a protein sequence was mapped to a digital signal using the amino acid index. Then, by wavelet transform, the mapped signal was broken down into several scales in which the energy factors were calculated and further formed into an MSE feature vector. Following this, combining this MSE feature vector with amino acid composition (AA), we constructed a series of MSEPseAA feature vectors to represent the protein subcellular localization sequences. Finally, according to a new kind of normalization approach, the MSEPseAA feature vectors were normalized to form the improved MSEPseAA vectors, named as IEPseAA. Using the technique of IEPseAA, C-support vector machine (C-SVM) and three multi-class SVMs strategies, quite promising results were obtained, indicating that MSE is quite effective in reflecting the sequence-order effects and might become a useful tool for predicting the other attributes of proteins as well. PMID:17235454

  7. Genetic ancestry inference using support vector machines, and the active emergence of a unique American population.

    PubMed

    Haasl, Ryan J; McCarty, Catherine A; Payseur, Bret A

    2013-05-01

    We use genotype data from the Marshfield Clinical Research Foundation Personalized Medicine Research Project to investigate genetic similarity and divergence between Europeans and the sampled population of European Americans in Central Wisconsin, USA. To infer recent genetic ancestry of the sampled Wisconsinites, we train support vector machines (SVMs) on the positions of Europeans along top principal components (PCs). Our SVM models partition continent-wide European genetic variance into eight regional classes, which is an improvement over the geographically broader categories of recent ancestry reported by personal genomics companies. After correcting for misclassification error associated with the SVMs (<10%, in all cases), we observe a >14% discrepancy between insular ancestries reported by Wisconsinites and those inferred by SVM. Values of FST as well as Mantel tests for correlation between genetic and European geographic distances indicate minimal divergence between Europe and the local Wisconsin population. However, we find that individuals from the Wisconsin sample show greater dispersion along higher-order PCs than individuals from Europe. Hypothesizing that this pattern is characteristic of nascent divergence, we run computer simulations that mimic the recent peopling of Wisconsin. Simulations corroborate the pattern in higher-order PCs, demonstrate its transient nature, and show that admixture accelerates the rate of divergence between the admixed population and its parental sources relative to drift alone. Together, empirical and simulation results suggest that genetic divergence between European source populations and European Americans in Central Wisconsin is subtle but already under way. PMID:23211701

  8. Time-frequency atoms-driven support vector machine method for bearings incipient fault diagnosis

    NASA Astrophysics Data System (ADS)

    Liu, Ruonan; Yang, Boyuan; Zhang, Xiaoli; Wang, Shibin; Chen, Xuefeng

    2016-06-01

    Bearing plays an essential role in the performance of mechanical system and fault diagnosis of mechanical system is inseparably related to the diagnosis of the bearings. However, it is a challenge to detect weak fault from the complex and non-stationary vibration signals with a large amount of noise, especially at the early stage. To improve the anti-noise ability and detect incipient fault, a novel fault detection method based on a short-time matching method and Support Vector Machine (SVM) is proposed. In this paper, the mechanism of roller bearing is discussed and the impact time frequency dictionary is constructed targeting the multi-component characteristics and fault feature of roller bearing fault vibration signals. Then, a short-time matching method is described and the simulation results show the excellent feature extraction effects in extremely low signal-to-noise ratio (SNR). After extracting the most relevance atoms as features, SVM was trained for fault recognition. Finally, the practical bearing experiments indicate that the proposed method is more effective and efficient than the traditional methods in weak impact signal oscillatory characters extraction and incipient fault diagnosis.

  9. Detection of abnormal living patterns for elderly living alone using support vector data description.

    PubMed

    Shin, Jae Hyuk; Lee, Boreom; Park, Kwang Suk

    2011-05-01

    In this study, we developed an automated behavior analysis system using infrared (IR) motion sensors to assist the independent living of the elderly who live alone and to improve the efficiency of their healthcare. An IR motion-sensor-based activity-monitoring system was installed in the houses of the elderly subjects to collect motion signals and three different feature values, activity level, mobility level, and nonresponse interval (NRI). These factors were calculated from the measured motion signals. The support vector data description (SVDD) method was used to classify normal behavior patterns and to detect abnormal behavioral patterns based on the aforementioned three feature values. The simulation data and real data were used to verify the proposed method in the individual analysis. A robust scheme is presented in this paper for optimally selecting the values of different parameters especially that of the scale parameter of the Gaussian kernel function involving in the training of the SVDD window length, T of the circadian rhythmic approach with the aim of applying the SVDD to the daily behavior patterns calculated over 24 h. Accuracies by positive predictive value (PPV) were 95.8% and 90.5% for the simulation and real data, respectively. The results suggest that the monitoring system utilizing the IR motion sensors and abnormal-behavior-pattern detection with SVDD are effective methods for home healthcare of elderly people living alone. PMID:21317086

  10. Texture discrimination of green tea categories based on least squares support vector machine (LSSVM) classifier

    NASA Astrophysics Data System (ADS)

    Li, Xiaoli; He, Yong; Qiu, Zhengjun; Wu, Di

    2008-03-01

    This research aimed for development multi-spectral imaging technique for green tea categories discrimination based on texture analysis. Three key wavelengths of 550, 650 and 800 nm were implemented in a common-aperture multi-spectral charged coupled device camera, and images were acquired for 190 unique images in a four different kinds of green tea data set. An image data set consisting of 15 texture features for each image was generated based on texture analysis techniques including grey level co-occurrence method (GLCM) and texture filtering. For optimization the texture features, 5 features that weren't correlated with the category of tea were eliminated. Unsupervised cluster analysis was conducted using the optimized texture features based on principal component analysis. The cluster analysis showed that the four kinds of green tea could be separated in the first two principal components space, however there was overlapping phenomenon among the different kinds of green tea. To enhance the performance of discrimination, least squares support vector machine (LSSVM) classifier was developed based on the optimized texture features. The excellent discrimination performance for sample in prediction set was obtained with 100%, 100%, 75% and 100% for four kinds of green tea respectively. It can be concluded that texture discrimination of green tea categories based on multi-spectral image technology is feasible.

  11. Estimating stellar atmospheric parameters based on LASSO and support-vector regression

    NASA Astrophysics Data System (ADS)

    Lu, Yu; Li, Xiangru

    2015-09-01

    A scheme for estimating atmospheric parameters Teff, log g and [Fe/H] is proposed on the basis of the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and Haar wavelet. The proposed scheme consists of three processes. A spectrum is decomposed using the Haar wavelet transform and low-frequency components at the fourth level are considered as candidate features. Then, spectral features from the candidate features are detected using the LASSO algorithm to estimate the atmospheric parameters. Finally, atmospheric parameters are estimated from the extracted spectral features using the support-vector regression (SVR) method. The proposed scheme was evaluated using three sets of stellar spectra from the Sloan Digital Sky Survey (SDSS), Large Sky Area Multi-object Fibre Spectroscopic Telescope (LAMOST) and Kurucz's model, respectively. The mean absolute errors are as follows: for the 40 000 SDSS spectra, 0.0062 dex for log Teff (85.83 K for Teff), 0.2035 dex for log g and 0.1512 dex for [Fe/H]; for the 23 963 LAMOST spectra, 0.0074 dex for log Teff (95.37 K for Teff), 0.1528 dex for log g and 0.1146 dex for [Fe/H]; for the 10 469 synthetic spectra, 0.0010 dex for log Teff (14.42K for Teff), 0.0123 dex for log g and 0.0125 dex for [Fe/H].

  12. Knowledge-based analysis of microarray gene expression data by using support vector machines

    SciTech Connect

    William Grundy; Manuel Ares, Jr.; David Haussler

    2001-06-18

    The authors introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. They test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, they use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.

  13. Using Support Vector Machines to Characterize Runoff-triggering in Small Watersheds

    NASA Astrophysics Data System (ADS)

    Misra, D.; Oommen, T.; Radatz, T.; Thompson, A.

    2009-12-01

    Runoff is one of the most complex hydrological phenomena to comprehend due to the tremendous spatial variability of catchment characteristics and precipitation patterns. However, the determination of runoff is critical for flood protection works, effective water storage and release, and protection of agricultural lands. The quantity of runoff depends on parameters such as rainfall intensity, duration, initial soil moisture, land use, and catchment geomorphology or relief. One common approach to estimate runoff is to develop physical models validated with measured data that relate the variables (input - output relationship) in the system. Conversely, this extraction of knowledge from the data requires large datasets, sophisticated modeling techniques as well as human intuition and experience. Additionally, the exact conditions that trigger runoff are difficult to predict because of their dependency on a combination of rainfall intensity, antecedent soil moisture conditions, and physical soil properties. Currently, pattern-learning algorithms based on artificial intelligence have shown promise in developing non-parametric models involving complex processes using few input parameters due to their ability to learn and recognize trends in the data. In this study, we explore the applicability of a sparse pattern-learning algorithm called Support Vector Machines (SVM) for modeling runoff from small watersheds. Results indicate that these methods can be an effective alternative to physical models for identifying runoff generation characteristics. Once identified, characteristics that trigger runoff from catchments, such as rainfall intensity and antecedent soil moisture, may be successfully used for large scale monitoring of watersheds using remote methods such as satellite sensors.

  14. Setting up the critical rainfall line for debris flows via support vector machines

    NASA Astrophysics Data System (ADS)

    Tsai, Y. F.; Chan, C. H.; Chang, C. H.

    2015-10-01

    The Chi-Chi earthquake in 1999 caused tremendous landslides which triggered many debris flows and resulted in significant loss of public lives and property. To prevent the disaster of debris flow, setting a critical rainfall line for each debris-flow stream is necessary. Firstly, 8 predisposing factors of debris flow were used to cluster 377 streams which have similar rainfall lines into 7 groups via the genetic algorithm. Then, support vector machines (SVM) were applied to setup the critical rainfall line for debris flows. SVM is a machine learning approach proposed based on statistical learning theory and has been widely used on pattern recognition and regression. This theory raises the generalized ability of learning mechanisms according to the minimum structural risk. Therefore, the advantage of using SVM can obtain results of minimized error rates without many training samples. Finally, the experimental results confirm that SVM method performs well in setting a critical rainfall line for each group of debris-flow streams.

  15. Remotely Sensed Percent Tree Cover Mapping Using Support Vector Machine Combined with Autonomous Endmember Extraction

    NASA Astrophysics Data System (ADS)

    Bai, Liming; Lin, Hui; Sun, Hua; Zang, Zhuo; Mo, Dengkui

    Remotely sensed forest mapping has become an important way to meet the increasing needs for forest-cover-associated data. However, accuracy for such products varies with the condition of forest ecosystem. In this paper, a support vector machine (SVM) classifier combined with autonomous endmember extraction technique was performed to improve the performance of machine learning in satellite land cover classification and percent tree cover mapping. For the study area, Pingnan County, Guangxi Zhuang Autonomous Region, China, that featured as a complex and fragmented subtropical forest habitat, the TM imagery was first processed with SMACC endmember extraction to find spectral endmembers of expected land cover classes. Secondly, the refined endmembers were input into SVM instead of conventional visual selection of training ROIs. The percent tree cover for the county is 53.6%, underestimated by 1.3% when compared with the National Continuous Forest Inventory 2004 statistics, suggesting a fair agreement with ground truth. The approach also shows a robust performance with an overall RMSE of 10.1.

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

    NASA Astrophysics Data System (ADS)

    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.

  17. Influence of stimuli colour in SSVEP-based BCI wheelchair control using support vector machines.

    PubMed

    Singla, Rajesh; Khosla, Arun; Jha, Rameshwar

    2014-04-01

    This study aims to develop a Steady State Visual Evoked Potential (SSVEP)-based Brain Computer Interface (BCI) system to control a wheelchair, with improving accuracy as the major goal. The developed wheelchair can move in forward, backward, left, right and stop positions. Four different flickering frequencies in the low frequency region were used to elicit the SSVEPs and were displayed on a Liquid Crystal Display (LCD) monitor using LabVIEW. Four colours (green, red, blue and violet) were included in the study to investigate the colour influence in SSVEPs. The Electroencephalogram (EEG) signals recorded from the occipital region were first segmented into 1 s windows and features were extracted by using Fast Fourier Transform (FFT). Three different classifiers, two based on Artificial Neural Network (ANN) and one based on Support Vector Machine (SVM), were compared to yield better accuracy. Twenty subjects participated in the experiment and the accuracy was calculated by considering the number of correct detections produced while performing a pre-defined movement sequence. SSVEP with violet colour showed higher performance than green and red. The One-Against-All (OAA) based multi-class SVM classifier showed better accuracy than the ANN classifiers. The classification accuracy over 20 subjects varies between 75-100%, while information transfer rates (ITR) varies from 12.13-27 bpm for BCI wheelchair control with SSVEPs elicited by violet colour stimuli and classified using OAA-SVM. PMID:24533888

  18. Evaluation models for soil nutrient based on support vector machine and artificial neural networks.

    PubMed

    Li, Hao; Leng, Weijia; Zhou, Yibing; Chen, Fudi; Xiu, Zhilong; Yang, Dazuo

    2014-01-01

    Soil nutrient is an important aspect that contributes to the soil fertility and environmental effects. Traditional evaluation approaches of soil nutrient are quite hard to operate, making great difficulties in practical applications. In this paper, we present a series of comprehensive evaluation models for soil nutrient by using support vector machine (SVM), multiple linear regression (MLR), and artificial neural networks (ANNs), respectively. We took the content of organic matter, total nitrogen, alkali-hydrolysable nitrogen, rapidly available phosphorus, and rapidly available potassium as independent variables, while the evaluation level of soil nutrient content was taken as dependent variable. Results show that the average prediction accuracies of SVM models are 77.87% and 83.00%, respectively, while the general regression neural network (GRNN) model's average prediction accuracy is 92.86%, indicating that SVM and GRNN models can be used effectively to assess the levels of soil nutrient with suitable dependent variables. In practical applications, both SVM and GRNN models can be used for determining the levels of soil nutrient. PMID:25548781

  19. Recurrence quantification analysis and support vector machines for golf handicap and low back pain EMG classification.

    PubMed

    Silva, Luís; Vaz, João Rocha; Castro, Maria António; Serranho, Pedro; Cabri, Jan; Pezarat-Correia, Pedro

    2015-08-01

    The quantification of non-linear characteristics of electromyography (EMG) must contain information allowing to discriminate neuromuscular strategies during dynamic skills. There are a lack of studies about muscle coordination under motor constrains during dynamic contractions. In golf, both handicap (Hc) and low back pain (LBP) are the main factors associated with the occurrence of injuries. The aim of this study was to analyze the accuracy of support vector machines SVM on EMG-based classification to discriminate Hc (low and high handicap) and LBP (with and without LPB) in the main phases of golf swing. For this purpose recurrence quantification analysis (RQA) features of the trunk and the lower limb muscles were used to feed a SVM classifier. Recurrence rate (RR) and the ratio between determinism (DET) and RR showed a high discriminant power. The Hc accuracy for the swing, backswing, and downswing were 94.4±2.7%, 97.1±2.3%, and 95.3±2.6%, respectively. For LBP, the accuracy was 96.9±3.8% for the swing, and 99.7±0.4% in the backswing. External oblique (EO), biceps femoris (BF), semitendinosus (ST) and rectus femoris (RF) showed high accuracy depending on the laterality within the phase. RQA features and SVM showed a high muscle discriminant capacity within swing phases by Hc and by LBP. Low back pain golfers showed different neuromuscular coordination strategies when compared with asymptomatic. PMID:26027794

  20. Improved mapping of information distribution across the cortical surface with the Support Vector Machine

    PubMed Central

    Xiao, Youping; Rao, Ravi; Cecchi, Guillermo; Kaplan, Ehud

    2008-01-01

    The early visual cortices represent information of several stimulus attributes, such as orientation and color. To understand the coding mechanisms of these attributes in the brain, and the functional organization of the early visual cortices, it is necessary to determine whether different attributes are represented by different compartments within each cortex. Previous studies addressing this question have focused on the information encoded by the response amplitude of individual neurons or cortical columns, and have reached conflicting conclusions. Given the correlated variability in response amplitude across neighboring columns, it is likely that the spatial pattern of responses across these columns encodes the attribute information more reliably than the response amplitude does. Here we present a new method of mapping the spatial distribution of information that is encoded by both the response amplitude and spatial pattern. This new method is based on a statistical learning approach, the Support Vector Machine (SVM). Application of this new method to our optical imaging data suggests that information about stimulus orientation and color are distributed differently in the striate cortex, and this observation is consistent with the hypothesis of segregated representations of orientation and color in this area. We also demonstrate that SVM can be used to extract ‘single-condition’ activation maps from noisy images of intrinsic optical signals. PMID:18249089

  1. Landslides Identification Using Airborne Laser Scanning Data Derived Topographic Terrain Attributes and Support Vector Machine Classification

    NASA Astrophysics Data System (ADS)

    Pawłuszek, Kamila; Borkowski, Andrzej

    2016-06-01

    Since the availability of high-resolution Airborne Laser Scanning (ALS) data, substantial progress in geomorphological research, especially in landslide analysis, has been carried out. First and second order derivatives of Digital Terrain Model (DTM) have become a popular and powerful tool in landslide inventory mapping. Nevertheless, an automatic landslide mapping based on sophisticated classifiers including Support Vector Machine (SVM), Artificial Neural Network or Random Forests is often computationally time consuming. The objective of this research is to deeply explore topographic information provided by ALS data and overcome computational time limitation. For this reason, an extended set of topographic features and the Principal Component Analysis (PCA) were used to reduce redundant information. The proposed novel approach was tested on a susceptible area affected by more than 50 landslides located on Rożnów Lake in Carpathian Mountains, Poland. The initial seven PCA components with 90% of the total variability in the original topographic attributes were used for SVM classification. Comparing results with landslide inventory map, the average user's accuracy (UA), producer's accuracy (PA), and overall accuracy (OA) were calculated for two models according to the classification results. Thereby, for the PCA-feature-reduced model UA, PA, and OA were found to be 72%, 76%, and 72%, respectively. Similarly, UA, PA, and OA in the non-reduced original topographic model, was 74%, 77% and 74%, respectively. Using the initial seven PCA components instead of the twenty original topographic attributes does not significantly change identification accuracy but reduce computational time.

  2. The impact of functional connectivity changes on support vector machines mapping of fMRI data.

    PubMed

    Sato, João Ricardo; Mourão-Miranda, Janaina; Morais Martin, Maria da Graça; Amaro, Edson; Morettin, Pedro Alberto; Brammer, Michael John

    2008-07-15

    Functional magnetic resonance imaging (fMRI) is currently one of the most widely used methods for studying human brain function in vivo. Although many different approaches to fMRI analysis are available, the most widely used methods employ so called "mass-univariate" modeling of responses in a voxel-by-voxel fashion to construct activation maps. However, it is well known that many brain processes involve networks of interacting regions and for this reason multivariate analyses might seem to be attractive alternatives to univariate approaches. The current paper focuses on one multivariate application of statistical learning theory: the statistical discrimination maps (SDM) based on support vector machine, and seeks to establish some possible interpretations when the results differ from univariate approaches. In fact, when there are changes not only on the activation level of two conditions but also on functional connectivity, SDM seems more informative. We addressed this question using both simulations and applications to real data. We have shown that the combined use of univariate approaches and SDM yields significant new insights into brain activations not available using univariate methods alone. In the application to a visual working memory fMRI data, we demonstrated that the interaction among brain regions play a role in SDM's power to detect discriminative voxels. PMID:18499266

  3. Nondestructive detection of pork comprehensive quality based on spectroscopy and support vector machine

    NASA Astrophysics Data System (ADS)

    Liu, Yuanyuan; Peng, Yankun; Zhang, Leilei; Dhakal, Sagar; Wang, Caiping

    2014-05-01

    Pork is one of the highly consumed meat item in the world. With growing improvement of living standard, concerned stakeholders including consumers and regulatory body pay more attention to comprehensive quality of fresh pork. Different analytical-laboratory based technologies exist to determine quality attributes of pork. However, none of the technologies are able to meet industrial desire of rapid and non-destructive technological development. Current study used optical instrument as a rapid and non-destructive tool to classify 24 h-aged pork longissimus dorsi samples into three kinds of meat (PSE, Normal and DFD), on the basis of color L* and pH24. Total of 66 samples were used in the experiment. Optical system based on Vis/NIR spectral acquisition system (300-1100 nm) was self- developed in laboratory to acquire spectral signal of pork samples. Median smoothing filter (M-filter) and multiplication scatter correction (MSC) was used to remove spectral noise and signal drift. Support vector machine (SVM) prediction model was developed to classify the samples based on their comprehensive qualities. The results showed that the classification model is highly correlated with the actual quality parameters with classification accuracy more than 85%. The system developed in this study being simple and easy to use, results being promising, the system can be used in meat processing industry for real time, non-destructive and rapid detection of pork qualities in future.

  4. Comparison of support vector machine-based processing chains for hyperspectral image classification

    NASA Astrophysics Data System (ADS)

    Rojas, Marta; Dópido, Inmaculada; Plaza, Antonio; Gamba, Paolo

    2010-08-01

    Many different approaches have been proposed in recent years for remotely sensed hyperspectral image classification. Despite the variety of techniques designed to tackle the aforementioned problem, the definition of standardized processing chains for hyperspectral image classification is a difficult objective, which may ultimately depend on the application being addressed. Generally speaking, a hyperspectral image classification chain may be defined from two perspectives: 1) the provider's viewpoint, and 2) the user's viewpoint, where the first part of the chain comprises activities such as data calibration and geo-correction aspects, while the second part of the chain comprises information extraction processes from the collected data. The modules in the second part of the chain (which constitutes our main focus in this paper) should be ideally flexible enough to be accommodated not only to different application scenarios, but also to different hyperspectral imaging instruments with varying characteristics, and spatial and spectral resolutions. In this paper, we evaluate the performance of different processing chains resulting from combinations of modules for dimensionality reduction, feature extraction/ selection, image classification, and spatial post-processing. The support vector machine (SVM) classifier is adopted as a baseline due to its ability to classify hyperspectral data sets using limited training samples. A specific classification scenario is investigated, using a reference hyperspectral data set collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the Indian Pines region in Indiana, USA.

  5. [Application of support vector machine approach in studying nephron toxicity of Chinese medicinal materials].

    PubMed

    Zhang, Jing-fang; Jiang, Lu-di; Zhang, Yan-ling

    2015-03-01

    On the basis of web databases, 111 compounds with nephrotoxicity and 90 compounds without nephrotoxicity were collected as data set of nephrotoxicity discrimination model, 39 compounds with tubular necrosis and 39 compounds without tubular necrosis were collected as data set of tubular necrosis discrimination model. The 6 122 molecular descriptors, including physicochemical, charge distribution and geometrical descriptors were calculated to characterize the molecular structure of the above-mentioned compounds. CfsSubsetEval valuation method and BestFirst-D1-N5 searching method were used to select molecular descriptors. Two models with high accuracy were built based on the support vector machine (SVM) approach, respectively. Accuracy, sensitivity, specificity and matthew's correlation coefficient of the two models were all above 70%. By using 22 nephrotoxicity compounds of Chinese medicine, the nephrotoxicity discrimination model was further verified with an accuracy of 72.73%. Using the tubular necrosis discrimination model, 10 potential compounds which can cause tubular necrosis were screened from the positive results of nephrotoxicity discrimination model, 6 of them have been verified by literatures. The results demonstrated that the discrimination models can be applied to screen nephrotoxic compounds from Chinese medicinal materials, and they also offer a new research idea for the further studies on the mechanism of nephrotoxicity. PMID:26226759

  6. Prediction of hydrogen and carbon chemical shifts from RNA using database mining and support vector regression.

    PubMed

    Brown, Joshua D; Summers, Michael F; Johnson, Bruce A

    2015-09-01

    The Biological Magnetic Resonance Data Bank (BMRB) contains NMR chemical shift depositions for over 200 RNAs and RNA-containing complexes. We have analyzed the (1)H NMR and (13)C chemical shifts reported for non-exchangeable protons of 187 of these RNAs. Software was developed that downloads BMRB datasets and corresponding PDB structure files, and then generates residue-specific attributes based on the calculated secondary structure. Attributes represent properties present in each sequential stretch of five adjacent residues and include variables such as nucleotide type, base-pair presence and type, and tetraloop types. Attributes and (1)H and (13)C NMR chemical shifts of the central nucleotide are then used as input to train a predictive model using support vector regression. These models can then be used to predict shifts for new sequences. The new software tools, available as stand-alone scripts or integrated into the NMR visualization and analysis program NMRViewJ, should facilitate NMR assignment and/or validation of RNA (1)H and (13)C chemical shifts. In addition, our findings enabled the re-calibration a ring-current shift model using published NMR chemical shifts and high-resolution X-ray structural data as guides. PMID:26141454

  7. A Novel Support Vector Machine-Based Approach for Rare Variant Detection

    PubMed Central

    Fang, Yao-Hwei; Chiu, Yen-Feng

    2013-01-01

    Advances in next-generation sequencing technologies have enabled the identification of multiple rare single nucleotide polymorphisms involved in diseases or traits. Several strategies for identifying rare variants that contribute to disease susceptibility have recently been proposed. An important feature of many of these statistical methods is the pooling or collapsing of multiple rare single nucleotide variants to achieve a reasonably high frequency and effect. However, if the pooled rare variants are associated with the trait in different directions, then the pooling may weaken the signal, thereby reducing its statistical power. In the present paper, we propose a backward support vector machine (BSVM)-based variant selection procedure to identify informative disease-associated rare variants. In the selection procedure, the rare variants are weighted and collapsed according to their positive or negative associations with the disease, which may be associated with common variants and rare variants with protective, deleterious, or neutral effects. This nonparametric variant selection procedure is able to account for confounding factors and can also be adopted in other regression frameworks. The results of a simulation study and a data example show that the proposed BSVM approach is more powerful than four other approaches under the considered scenarios, while maintaining valid type I errors. PMID:23940698

  8. Automatic retinal vessel classification using a Least Square-Support Vector Machine in VAMPIRE.

    PubMed

    Relan, D; MacGillivray, T; Ballerini, L; Trucco, E

    2014-01-01

    It is important to classify retinal blood vessels into arterioles and venules for computerised analysis of the vasculature and to aid discovery of disease biomarkers. For instance, zone B is the standardised region of a retinal image utilised for the measurement of the arteriole to venule width ratio (AVR), a parameter indicative of microvascular health and systemic disease. We introduce a Least Square-Support Vector Machine (LS-SVM) classifier for the first time (to the best of our knowledge) to label automatically arterioles and venules. We use only 4 image features and consider vessels inside zone B (802 vessels from 70 fundus camera images) and in an extended zone (1,207 vessels, 70 fundus camera images). We achieve an accuracy of 94.88% and 93.96% in zone B and the extended zone, respectively, with a training set of 10 images and a testing set of 60 images. With a smaller training set of only 5 images and the same testing set we achieve an accuracy of 94.16% and 93.95%, respectively. This experiment was repeated five times by randomly choosing 10 and 5 images for the training set. Mean classification accuracy are close to the above mentioned result. We conclude that the performance of our system is very promising and outperforms most recently reported systems. Our approach requires smaller training data sets compared to others but still results in a similar or higher classification rate. PMID:25569917

  9. Detection and classification of organophosphate nerve agent simulants using support vector machines with multiarray sensors.

    PubMed

    Sadik, Omowunmi; Land, Walker H; Wanekaya, Adam K; Uematsu, Michiko; Embrechts, Mark J; Wong, Lut; Leibensperger, Dale; Volykin, Alex

    2004-01-01

    The need for rapid and accurate detection systems is expanding and the utilization of cross-reactive sensor arrays to detect chemical warfare agents in conjunction with novel computational techniques may prove to be a potential solution to this challenge. We have investigated the detection, prediction, and classification of various organophosphate (OP) nerve agent simulants using sensor arrays with a novel learning scheme known as support vector machines (SVMs). The OPs tested include parathion, malathion, dichlorvos, trichlorfon, paraoxon, and diazinon. A new data reduction software program was written in MATLAB V. 6.1 to extract steady-state and kinetic data from the sensor arrays. The program also creates training sets by mixing and randomly sorting any combination of data categories into both positive and negative cases. The resulting signals were fed into SVM software for "pairwise" and "one" vs all classification. Experimental results for this new paradigm show a significant increase in classification accuracy when compared to artificial neural networks (ANNs). Three kernels, the S2000, the polynomial, and the Gaussian radial basis function (RBF), were tested and compared to the ANN. The following measures of performance were considered in the pairwise classification: receiver operating curve (ROC) Az indices, specificities, and positive predictive values (PPVs). The ROC Az) values, specifities, and PPVs increases ranged from 5% to 25%, 108% to 204%, and 13% to 54%, respectively, in all OP pairs studied when compared to the ANN baseline. Dichlorvos, trichlorfon, and paraoxon were perfectly predicted. Positive prediction for malathion was 95%. PMID:15032529

  10. Using support vector machines to improve elemental ion identification in macromolecular crystal structures

    DOE PAGESBeta

    Morshed, Nader; Echols, Nathaniel; Adams, Paul D.

    2015-04-25

    In the process of macromolecular model building, crystallographers must examine electron density for isolated atoms and differentiate sites containing structured solvent molecules from those containing elemental ions. This task requires specific knowledge of metal-binding chemistry and scattering properties and is prone to error. A method has previously been described to identify ions based on manually chosen criteria for a number of elements. Here, the use of support vector machines (SVMs) to automatically classify isolated atoms as either solvent or one of various ions is described. Two data sets of protein crystal structures, one containing manually curated structures deposited with anomalousmore » diffraction data and another with automatically filtered, high-resolution structures, were constructed. On the manually curated data set, an SVM classifier was able to distinguish calcium from manganese, zinc, iron and nickel, as well as all five of these ions from water molecules, with a high degree of accuracy. Additionally, SVMs trained on the automatically curated set of high-resolution structures were able to successfully classify most common elemental ions in an independent validation test set. This method is readily extensible to other elemental ions and can also be used in conjunction with previous methods based on a priori expectations of the chemical environment and X-ray scattering.« less

  11. LOCUSTRA: accurate prediction of local protein structure using a two-layer support vector machine approach.

    PubMed

    Zimmermann, Olav; Hansmann, Ulrich H E

    2008-09-01

    Constraint generation for 3d structure prediction and structure-based database searches benefit from fine-grained prediction of local structure. In this work, we present LOCUSTRA, a novel scheme for the multiclass prediction of local structure that uses two layers of support vector machines (SVM). Using a 16-letter structural alphabet from de Brevern et al. (Proteins: Struct., Funct., Bioinf. 2000, 41, 271-287), we assess its prediction ability for an independent test set of 222 proteins and compare our method to three-class secondary structure prediction and direct prediction of dihedral angles. The prediction accuracy is Q16=61.0% for the 16 classes of the structural alphabet and Q3=79.2% for a simple mapping to the three secondary classes helix, sheet, and coil. We achieve a mean phi(psi) error of 24.74 degrees (38.35 degrees) and a median RMSDA (root-mean-square deviation of the (dihedral) angles) per protein chain of 52.1 degrees. These results compare favorably with related approaches. The LOCUSTRA web server is freely available to researchers at http://www.fz-juelich.de/nic/cbb/service/service.php. PMID:18763837

  12. Automatic defect detection for TFT-LCD array process using quasiconformal kernel support vector data description.

    PubMed

    Liu, Yi-Hung; Chen, Yan-Jen

    2011-01-01

    Defect detection has been considered an efficient way to increase the yield rate of panels in thin film transistor liquid crystal display (TFT-LCD) manufacturing. In this study we focus on the array process since it is the first and key process in TFT-LCD manufacturing. Various defects occur in the array process, and some of them could cause great damage to the LCD panels. Thus, how to design a method that can robustly detect defects from the images captured from the surface of LCD panels has become crucial. Previously, support vector data description (SVDD) has been successfully applied to LCD defect detection. However, its generalization performance is limited. In this paper, we propose a novel one-class machine learning method, called quasiconformal kernel SVDD (QK-SVDD) to address this issue. The QK-SVDD can significantly improve generalization performance of the traditional SVDD by introducing the quasiconformal transformation into a predefined kernel. Experimental results, carried out on real LCD images provided by an LCD manufacturer in Taiwan, indicate that the proposed QK-SVDD not only obtains a high defect detection rate of 96%, but also greatly improves generalization performance of SVDD. The improvement has shown to be over 30%. In addition, results also show that the QK-SVDD defect detector is able to accomplish the task of defect detection on an LCD image within 60 ms. PMID:22016625

  13. Multispectral MRI segmentation of age related white matter changes using a cascade of support vector machines.

    PubMed

    Damangir, Soheil; Manzouri, Amirhossein; Oppedal, Ketil; Carlsson, Stefan; Firbank, Michael J; Sonnesyn, Hogne; Tysnes, Ole-Bjørn; O'Brien, John T; Beyer, Mona K; Westman, Eric; Aarsland, Dag; Wahlund, Lars-Olof; Spulber, Gabriela

    2012-11-15

    White matter changes (WMC) are the focus of intensive research and have been linked to cognitive impairment and depression in the elderly. Cumbersome manual outlining procedures make research on WMC labor intensive and prone to subjective bias. We present a fast, fully automated method for WMC segmentation using a cascade of reduced support vector machines (SVMs) with active learning. Data of 102 subjects was used in this study. Two MRI sequences (T1-weighted and FLAIR) and masks of manually outlined WMC from each subject were used for the image analysis. The segmentation framework comprises pre-processing, classification (training and core segmentation) and post-processing. After pre-processing, the model was trained on two subjects and tested on the remaining 100 subjects. The effectiveness and robustness of the classification was assessed using the receiver operating curve technique. The cascade of SVMs segmentation framework outputted accurate results with high sensitivity (90%) and specificity (99.5%) values, with the manually outlined WMC as reference. An algorithm for the segmentation of WMC is proposed. This is a completely competitive and fast automatic segmentation framework, capable of using different input sequences, without changes or restrictions of the image analysis algorithm. PMID:22921728

  14. Support vector machines for detecting age-related changes in running kinematics.

    PubMed

    Fukuchi, Reginaldo K; Eskofier, Bjoern M; Duarte, Marcos; Ferber, Reed

    2011-02-01

    Age-related changes in running kinematics have been reported in the literature using classical inferential statistics. However, this approach has been hampered by the increased number of biomechanical gait variables reported and subsequently the lack of differences presented in these studies. Data mining techniques have been applied in recent biomedical studies to solve this problem using a more general approach. In the present work, we re-analyzed lower extremity running kinematic data of 17 young and 17 elderly male runners using the Support Vector Machine (SVM) classification approach. In total, 31 kinematic variables were extracted to train the classification algorithm and test the generalized performance. The results revealed different accuracy rates across three different kernel methods adopted in the classifier, with the linear kernel performing the best. A subsequent forward feature selection algorithm demonstrated that with only six features, the linear kernel SVM achieved 100% classification performance rate, showing that these features provided powerful combined information to distinguish age groups. The results of the present work demonstrate potential in applying this approach to improve knowledge about the age-related differences in running gait biomechanics and encourages the use of the SVM in other clinical contexts. PMID:20980005

  15. Embedded Hardware-Efficient Real-Time Classification With Cascade Support Vector Machines.

    PubMed

    Kyrkou, Christos; Bouganis, Christos-Savvas; Theocharides, Theocharis; Polycarpou, Marios M

    2016-01-01

    Cascade support vector machines (SVMs) are optimized to efficiently handle problems, where the majority of the data belong to one of the two classes, such as image object classification, and hence can provide speedups over monolithic (single) SVM classifiers. However, SVM classification is a computationally demanding task and existing hardware architectures for SVMs only consider monolithic classifiers. This paper proposes the acceleration of cascade SVMs through a hybrid processing hardware architecture optimized for the cascade SVM classification flow, accompanied by a method to reduce the required hardware resources for its implementation, and a method to improve the classification speed utilizing cascade information to further discard data samples. The proposed SVM cascade architecture is implemented on a Spartan-6 field-programmable gate array (FPGA) platform and evaluated for object detection on 800×600 (Super Video Graphics Array) resolution images. The proposed architecture, boosted by a neural network that processes cascade information, achieves a real-time processing rate of 40 frames/s for the benchmark face detection application. Furthermore, the hardware-reduction method results in the utilization of 25% less FPGA custom-logic resources and 20% peak power reduction compared with a baseline implementation. PMID:26011869

  16. Stellar Spectral Classification with Locality Preserving Projections and Support Vector Machine

    NASA Astrophysics Data System (ADS)

    Zhong-bao, Liu

    2016-06-01

    With the help of computer tools and algorithms, automatic stellar spectral classification has become an area of current interest. The process of stellar spectral classification mainly includes two steps: dimension reduction and classification. As a popular dimensionality reduction technique, Principal Component Analysis (PCA) is widely used in stellar spectra classification. Another dimensionality reduction technique, Locality Preserving Projections (LPP) has not been widely used in astronomy. The advantage of LPP is that it can preserve the local structure of the data after dimensionality reduction. In view of this, we investigate how to apply LPP+SVM in classifying the stellar spectral subclasses. In the comparative experiment, the performance of LPP is compared with PCA. The stellar spectral classification process is composed of the following steps. Firstly, PCA and LPP are respectively applied to reduce the dimension of spectra data. Then, Support Vector Machine (SVM) is used to classify the 4 subclasses of K-type and 3 subclasses of F-type spectra from Sloan Digital Sky Survey (SDSS). Lastly, the performance of LPP+SVM is compared with that of PCA+SVM in stellar spectral classification, and we found that LPP does better than PCA.

  17. Prediction of hourly O 3 concentrations using support vector regression algorithms

    NASA Astrophysics Data System (ADS)

    Ortiz-García, E. G.; Salcedo-Sanz, S.; Pérez-Bellido, Á. M.; Portilla-Figueras, J. A.; Prieto, L.

    2010-11-01

    In this paper we present an application of the Support Vector Regression algorithm (SVMr) to the prediction of hourly ozone values in Madrid urban area. In order to improve the training capacity of SVMrs, we have used a recently proposed approach, based on reductions of the SVMr hyper-parameters search space. Using the modified SVMr, we study different influences which may modify the ozone prediction, such as previous ozone measurements in a given station, measurements in neighbors stations, and the influence of meteorologic variables. We use statistical tests to verify the significance of incorporating different variables into the SVMr. A comparison with the results obtained using a neural network (multi-layer perceptron) is also carried out. This study has been carried out in 5 different stations of the air pollution monitoring network of Madrid, so the conclusions raised are backed by real data. The final result of the work is a robust and powerful software for tropospheric ozone prediction in Madrid. Also, the prediction tool based on SVMr is flexible enough to incorporate any other prediction variable, such as city models, or traffic patters, which may improve the prediction obtained with the SVMr.

  18. A fast gene selection method for multi-cancer classification using multiple support vector data description.

    PubMed

    Cao, Jin; Zhang, Li; Wang, Bangjun; Li, Fanzhang; Yang, Jiwen

    2015-02-01

    For cancer classification problems based on gene expression, the data usually has only a few dozen sizes but has thousands to tens of thousands of genes which could contain a large number of irrelevant genes. A robust feature selection algorithm is required to remove irrelevant genes and choose the informative ones. Support vector data description (SVDD) has been applied to gene selection for many years. However, SVDD cannot address the problems with multiple classes since it only considers the target class. In addition, it is time-consuming when applying SVDD to gene selection. This paper proposes a novel fast feature selection method based on multiple SVDD and applies it to multi-class microarray data. A recursive feature elimination (RFE) scheme is introduced to iteratively remove irrelevant features, so the proposed method is called multiple SVDD-RFE (MSVDD-RFE). To make full use of all classes for a given task, MSVDD-RFE independently selects a relevant gene subset for each class. The final selected gene subset is the union of these relevant gene subsets. The effectiveness and accuracy of MSVDD-RFE are validated by experiments on five publicly available microarray datasets. Our proposed method is faster and more effective than other methods. PMID:25549938

  19. Estimation of diffusion coefficients from voltammetric signals by support vector and gaussian process regression

    PubMed Central

    2014-01-01

    Background Support vector regression (SVR) and Gaussian process regression (GPR) were used for the analysis of electroanalytical experimental data to estimate diffusion coefficients. Results For simulated cyclic voltammograms based on the EC, Eqr, and EqrC mechanisms these regression algorithms in combination with nonlinear kernel/covariance functions yielded diffusion coefficients with higher accuracy as compared to the standard approach of calculating diffusion coefficients relying on the Nicholson-Shain equation. The level of accuracy achieved by SVR and GPR is virtually independent of the rate constants governing the respective reaction steps. Further, the reduction of high-dimensional voltammetric signals by manual selection of typical voltammetric peak features decreased the performance of both regression algorithms compared to a reduction by downsampling or principal component analysis. After training on simulated data sets, diffusion coefficients were estimated by the regression algorithms for experimental data comprising voltammetric signals for three organometallic complexes. Conclusions Estimated diffusion coefficients closely matched the values determined by the parameter fitting method, but reduced the required computational time considerably for one of the reaction mechanisms. The automated processing of voltammograms according to the regression algorithms yields better results than the conventional analysis of peak-related data. PMID:24987463

  20. Biomarkers of Eating Disorders Using Support Vector Machine Analysis of Structural Neuroimaging Data: Preliminary Results

    PubMed Central

    Cerasa, Antonio; Castiglioni, Isabella; Salvatore, Christian; Funaro, Angela; Martino, Iolanda; Alfano, Stefania; Donzuso, Giulia; Perrotta, Paolo; Gioia, Maria Cecilia; Gilardi, Maria Carla; Quattrone, Aldo

    2015-01-01

    Presently, there are no valid biomarkers to identify individuals with eating disorders (ED). The aim of this work was to assess the feasibility of a machine learning method for extracting reliable neuroimaging features allowing individual categorization of patients with ED. Support Vector Machine (SVM) technique, combined with a pattern recognition method, was employed utilizing structural magnetic resonance images. Seventeen females with ED (six with diagnosis of anorexia nervosa and 11 with bulimia nervosa) were compared against 17 body mass index-matched healthy controls (HC). Machine learning allowed individual diagnosis of ED versus HC with an Accuracy ≥ 0.80. Voxel-based pattern recognition analysis demonstrated that voxels influencing the classification Accuracy involved the occipital cortex, the posterior cerebellar lobule, precuneus, sensorimotor/premotor cortices, and the medial prefrontal cortex, all critical regions known to be strongly involved in the pathophysiological mechanisms of ED. Although these findings should be considered preliminary given the small size investigated, SVM analysis highlights the role of well-known brain regions as possible biomarkers to distinguish ED from HC at an individual level, thus encouraging the translational implementation of this new multivariate approach in the clinical practice. PMID:26648660

  1. BacHbpred: Support Vector Machine Methods for the Prediction of Bacterial Hemoglobin-Like Proteins

    PubMed Central

    Selvaraj, MuthuKrishnan; Puri, Munish; Dikshit, Kanak L.; Lefevre, Christophe

    2016-01-01

    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. PMID:27034664

  2. Support vector machines for broad-area feature classification in remotely sensed images

    NASA Astrophysics Data System (ADS)

    Perkins, Simon J.; Harvey, Neal R.; Brumby, Steven P.; Lacker, Kevin

    2001-08-01

    Classification of broad area features in satellite imagery is one of the most important applications of remote sensing. It is often difficult and time-consuming to develop classifiers by hand, so many researchers have turned to techniques from the fields of statistics and machine learning to automatically generate classifiers. Common techniques include Maximum Likelihood classifiers, neural networks and genetic algorithms. We present a new system called Afreet, which uses a recently developed machine learning paradigm called Support Vector Machines (SVMs). In contrast to other techniques, SVMs offer a solid mathematical foundation that provides a probabalistic guarantee on how well the classifier will generalize to unseen data. In addition the SVM training algorithm is guaranteed to converge to the globally optimal SVM classifier, can learn highly non-linear discrimination functions, copes extremely well with high-dimensional feature spaces (such as hyperspectral data), and scales well to large problem sizes. Afreet combines an SVM with a sophisticated spatio-spectral feature construction mechanism that allows it to classify spectrally ambiguous pixels. We demonstrate the effectiveness of the system by applying Afreet to several broad area classification problems in remote sensing, and provide a comparison with conventional Maximum Likelihood classification.

  3. Monitoring of cigarette smoking using wearable sensors and support vector machines.

    PubMed

    Lopez-Meyer, Paulo; Tiffany, Stephen; Patil, Yogendra; Sazonov, Edward

    2013-07-01

    Cigarette smoking is a serious risk factor for cancer, cardiovascular, and pulmonary diseases. Current methods of monitoring of cigarette smoking habits rely on various forms of self-report that are prone to errors and under reporting. This paper presents a first step in the development of a methodology for accurate and objective assessment of smoking using noninvasive wearable sensors (Personal Automatic Cigarette Tracker-PACT) by demonstrating feasibility of automatic recognition of smoke inhalations from signals arising from continuous monitoring of breathing and hand-to-mouth gestures by support vector machine classifiers. The performance of subject-dependent (individually calibrated) models was compared to performance of subject-independent (group) classification models. The models were trained and validated on a dataset collected from 20 subjects performing 12 different activities representative of everyday living (total duration 19.5 h or 21,411 breath cycles). Precision and recall were used as the accuracy metrics. Group models obtained 87% and 80% of average precision and recall, respectively. Individual models resulted in 90% of average precision and recall, indicating a significant presence of individual traits in signal patterns. These results suggest the feasibility of monitoring cigarette smoking by means of a wearable and noninvasive sensor system in free living conditions. PMID:23372073

  4. SNP Selection in Genome-Wide Association Studies via Penalized Support Vector Machine with MAX Test

    PubMed Central

    Kim, Jinseog; Kim, Dennis (Dong Hwan); Jung, Sin-Ho

    2013-01-01

    One of main objectives of a genome-wide association study (GWAS) is to develop a prediction model for a binary clinical outcome using single-nucleotide polymorphisms (SNPs) which can be used for diagnostic and prognostic purposes and for better understanding of the relationship between the disease and SNPs. Penalized support vector machine (SVM) methods have been widely used toward this end. However, since investigators often ignore the genetic models of SNPs, a final model results in a loss of efficiency in prediction of the clinical outcome. In order to overcome this problem, we propose a two-stage method such that the the genetic models of each SNP are identified using the MAX test and then a prediction model is fitted using a penalized SVM method. We apply the proposed method to various penalized SVMs and compare the performance of SVMs using various penalty functions. The results from simulations and real GWAS data analysis show that the proposed method performs better than the prediction methods ignoring the genetic models in terms of prediction power and selectivity. PMID:24174989

  5. Support Vector Machine combined with Distance Correlation learning for Dst forecasting during intense geomagnetic storms

    NASA Astrophysics Data System (ADS)

    Lu, J. Y.; Peng, Y. X.; Wang, M.; Gu, S. J.; Zhao, M. X.

    2016-01-01

    In this study we apply the Support Vector Machine (SVM) combined together with Distance Correlation (DC) to the forecasting of Dst index by using 80 intense geomagnetic storms (Dst ≤ - 100 nT) from 1995 to 2014. We also train the Neural Network (NN) and the Linear Machine (LM) to verify the effectiveness of SVM. The purpose for us to introduce DC is to make feature screening in input datasets that can effectively improve the forecasting performance of the SVM. For comparison, we estimate the correlation coefficients (CC), the RMS errors, the absolute value of difference in minimum Dst (ΔDstmin) and the absolute value of difference in minimum time (ΔtDst) between observed Dst and predicted one. K-fold Cross Validation is used to improve the reliability of the results. It is shown that DC-SVM model exhibits the best forecasting performance for all parameters when all 80 events are considered. The CC, the RMS error, the ΔDstmin, and the ΔtDst of DC-SVM are 0.95, 16.8 nT, 9.7 nT and 1.7 h, respectively. For further comparison, we divide the 80 storm events into two groups depending on minimum value of Dst. It is also found that the DC-SVM is better than other models in the two groups.

  6. An Enhanced MEMS Error Modeling Approach Based on Nu-Support Vector Regression

    PubMed Central

    Bhatt, Deepak; Aggarwal, Priyanka; Bhattacharya, Prabir; Devabhaktuni, Vijay

    2012-01-01

    Micro Electro Mechanical System (MEMS)-based inertial sensors have made possible the development of a civilian land vehicle navigation system by offering a low-cost solution. However, the accurate modeling of the MEMS sensor errors is one of the most challenging tasks in the design of low-cost navigation systems. These sensors exhibit significant errors like biases, drift, noises; which are negligible for higher grade units. Different conventional techniques utilizing the Gauss Markov model and neural network method have been previously utilized to model the errors. However, Gauss Markov model works unsatisfactorily in the case of MEMS units due to the presence of high inherent sensor errors. On the other hand, modeling the random drift utilizing Neural Network (NN) is time consuming, thereby affecting its real-time implementation. We overcome these existing drawbacks by developing an enhanced Support Vector Machine (SVM) based error model. Unlike NN, SVMs do not suffer from local minimisation or over-fitting problems and delivers a reliable global solution. Experimental results proved that the proposed SVM approach reduced the noise standard deviation by 10–35% for gyroscopes and 61–76% for accelerometers. Further, positional error drifts under static conditions improved by 41% and 80% in comparison to NN and GM approaches. PMID:23012552

  7. Automated Cell Selection Using Support Vector Machine for Application to Spectral Nanocytology

    PubMed Central

    Miao, Qin; Derbas, Justin; Eid, Aya; Subramanian, Hariharan; Backman, Vadim

    2016-01-01

    Partial wave spectroscopy (PWS) enables quantification of the statistical properties of cell structures at the nanoscale, which has been used to identify patients harboring premalignant tumors by interrogating easily accessible sites distant from location of the lesion. Due to its high sensitivity, cells that are well preserved need to be selected from the smear images for further analysis. To date, such cell selection has been done manually. This is time-consuming, is labor-intensive, is vulnerable to bias, and has considerable inter- and intraoperator variability. In this study, we developed a classification scheme to identify and remove the corrupted cells or debris that are of no diagnostic value from raw smear images. The slide of smear sample is digitized by acquiring and stitching low-magnification transmission. Objects are then extracted from these images through segmentation algorithms. A training-set is created by manually classifying objects as suitable or unsuitable. A feature-set is created by quantifying a large number of features for each object. The training-set and feature-set are used to train a selection algorithm using Support Vector Machine (SVM) classifiers. We show that the selection algorithm achieves an error rate of 93% with a sensitivity of 95%. PMID:26904682

  8. A support vector machine to identify irrigated crop types using time-series Landsat NDVI data

    NASA Astrophysics Data System (ADS)

    Zheng, Baojuan; Myint, Soe W.; Thenkabail, Prasad S.; Aggarwal, Rimjhim M.

    2015-02-01

    Site-specific information of crop types is required for many agro-environmental assessments. The study investigated the potential of support vector machines (SVMs) in discriminating various crop types in a complex cropping system in the Phoenix Active Management Area. We applied SVMs to Landsat time-series Normalized Difference Vegetation Index (NDVI) data using training datasets selected by two different approaches: stratified random approach and intelligent selection approach using local knowledge. The SVM models effectively classified nine major crop types with overall accuracies of >86% for both training datasets. Our results showed that the intelligent selection approach was able to reduce the training set size and achieved higher overall classification accuracy than the stratified random approach. The intelligent selection approach is particularly useful when the availability of reference data is limited and unbalanced among different classes. The study demonstrated the potential of utilizing multi-temporal Landsat imagery to systematically monitor crop types and cropping patterns over time in arid and semi-arid regions.

  9. Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines

    NASA Astrophysics Data System (ADS)

    Jegadeeshwaran, R.; Sugumaran, V.

    2015-02-01

    Hydraulic brakes in automobiles are important components for the safety of passengers; therefore, the brakes are a good subject for condition monitoring. The condition of the brake components can be monitored by using the vibration characteristics. On-line condition monitoring by using machine learning approach is proposed in this paper as a possible solution to such problems. The vibration signals for both good as well as faulty conditions of brakes were acquired from a hydraulic brake test setup with the help of a piezoelectric transducer and a data acquisition system. Descriptive statistical features were extracted from the acquired vibration signals and the feature selection was carried out using the C4.5 decision tree algorithm. There is no specific method to find the right number of features required for classification for a given problem. Hence an extensive study is needed to find the optimum number of features. The effect of the number of features was also studied, by using the decision tree as well as Support Vector Machines (SVM). The selected features were classified using the C-SVM and Nu-SVM with different kernel functions. The results are discussed and the conclusion of the study is presented.

  10. Detection of segments with fetal QRS complex from abdominal maternal ECG recordings using support vector machine

    NASA Astrophysics Data System (ADS)

    Delgado, Juan A.; Altuve, Miguel; Nabhan Homsi, Masun

    2015-12-01

    This paper introduces a robust method based on the Support Vector Machine (SVM) algorithm to detect the presence of Fetal QRS (fQRS) complexes in electrocardiogram (ECG) recordings provided by the PhysioNet/CinC challenge 2013. ECG signals are first segmented into contiguous frames of 250 ms duration and then labeled in six classes. Fetal segments are tagged according to the position of fQRS complex within each one. Next, segment features extraction and dimensionality reduction are obtained by applying principal component analysis on Haar-wavelet transform. After that, two sub-datasets are generated to separate representative segments from atypical ones. Imbalanced class problem is dealt by applying sampling without replacement on each sub-dataset. Finally, two SVMs are trained and cross-validated using the two balanced sub-datasets separately. Experimental results show that the proposed approach achieves high performance rates in fetal heartbeats detection that reach up to 90.95% of accuracy, 92.16% of sensitivity, 88.51% of specificity, 94.13% of positive predictive value and 84.96% of negative predictive value. A comparative study is also carried out to show the performance of other two machine learning algorithms for fQRS complex estimation, which are K-nearest neighborhood and Bayesian network.

  11. An Irregularity Measurement Based Cardiac Status Recognition Using Support Vector Machine

    PubMed Central

    Banerjee, Poulami

    2015-01-01

    An automated robust feature extraction technique is proposed in this paper based on inherent structural distribution of heart sound to analyze the phonocardiogram signal in presence of environmental noise and interference of lung sound signal. The structural complexity of the heart sound signal is estimated in terms of sample entropy using a nonlinear signal processing framework. The effectiveness of the feature is evaluated using a support vector machine under two different circumstances which include Gaussian noise and pulmonary perturbation. The analysis framework has been executed on a composite data set of 60 healthy and 60 pathological individuals for different SNR levels (−5 to 10 dB) and the performance accuracy is close to that of the clean signal. In addition, a comparative study has been done with conventional approaches which includes waveform analysis, spectral domain inspection, and spectrogram evaluation. The experimental results show that sample entropy based classification method gives an accuracy of 96.67% for clean data and 91.66% for noisy data of SNR 10 dB. The result suggests that the proposed method performs significantly well over the visual and audio test. PMID:27019845

  12. A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction

    PubMed Central

    Zhang, Daqing; Xiao, Jianfeng; Zhou, Nannan; Zheng, Mingyue; Luo, Xiaomin; Jiang, Hualiang; Chen, Kaixian

    2015-01-01

    Blood-brain barrier (BBB) is a highly complex physical barrier determining what substances are allowed to enter the brain. Support vector machine (SVM) is a kernel-based machine learning method that is widely used in QSAR study. For a successful SVM model, the kernel parameters for SVM and feature subset selection are the most important factors affecting prediction accuracy. In most studies, they are treated as two independent problems, but it has been proven that they could affect each other. We designed and implemented genetic algorithm (GA) to optimize kernel parameters and feature subset selection for SVM regression and applied it to the BBB penetration prediction. The results show that our GA/SVM model is more accurate than other currently available log BB models. Therefore, to optimize both SVM parameters and feature subset simultaneously with genetic algorithm is a better approach than other methods that treat the two problems separately. Analysis of our log BB model suggests that carboxylic acid group, polar surface area (PSA)/hydrogen-bonding ability, lipophilicity, and molecular charge play important role in BBB penetration. Among those properties relevant to BBB penetration, lipophilicity could enhance the BBB penetration while all the others are negatively correlated with BBB penetration. PMID:26504797

  13. Hadoop-Based Distributed System for Online Prediction of Air Pollution Based on Support Vector Machine

    NASA Astrophysics Data System (ADS)

    Ghaemi, Z.; Farnaghi, M.; Alimohammadi, A.

    2015-12-01

    The critical impact of air pollution on human health and environment in one hand and the complexity of pollutant concentration behavior in the other hand lead the scientists to look for advance techniques for monitoring and predicting the urban air quality. Additionally, recent developments in data measurement techniques have led to collection of various types of data about air quality. Such data is extremely voluminous and to be useful it must be processed at high velocity. Due to the complexity of big data analysis especially for dynamic applications, online forecasting of pollutant concentration trends within a reasonable processing time is still an open problem. The purpose of this paper is to present an online forecasting approach based on Support Vector Machine (SVM) to predict the air quality one day in advance. In order to overcome the computational requirements for large-scale data analysis, distributed computing based on the Hadoop platform has been employed to leverage the processing power of multiple processing units. The MapReduce programming model is adopted for massive parallel processing in this study. Based on the online algorithm and Hadoop framework, an online forecasting system is designed to predict the air pollution of Tehran for the next 24 hours. The results have been assessed on the basis of Processing Time and Efficiency. Quite accurate predictions of air pollutant indicator levels within an acceptable processing time prove that the presented approach is very suitable to tackle large scale air pollution prediction problems.

  14. Genetic algorithm-support vector regression for high reliability SHM system based on FBG sensor network

    NASA Astrophysics Data System (ADS)

    Zhang, XiaoLi; Liang, DaKai; Zeng, Jie; Asundi, Anand

    2012-02-01

    Structural Health Monitoring (SHM) based on fiber Bragg grating (FBG) sensor network has attracted considerable attention in recent years. However, FBG sensor network is embedded or glued in the structure simply with series or parallel. In this case, if optic fiber sensors or fiber nodes fail, the fiber sensors cannot be sensed behind the failure point. Therefore, for improving the survivability of the FBG-based sensor system in the SHM, it is necessary to build high reliability FBG sensor network for the SHM engineering application. In this study, a model reconstruction soft computing recognition algorithm based on genetic algorithm-support vector regression (GA-SVR) is proposed to achieve the reliability of the FBG-based sensor system. Furthermore, an 8-point FBG sensor system is experimented in an aircraft wing box. The external loading damage position prediction is an important subject for SHM system; as an example, different failure modes are selected to demonstrate the SHM system's survivability of the FBG-based sensor network. Simultaneously, the results are compared with the non-reconstruct model based on GA-SVR in each failure mode. Results show that the proposed model reconstruction algorithm based on GA-SVR can still keep the predicting precision when partial sensors failure in the SHM system; thus a highly reliable sensor network for the SHM system is facilitated without introducing extra component and noise.

  15. [Support vector data description for finding non-coding RNA gene].

    PubMed

    Zhao, Yingjie; Wang, Zhengzhi

    2010-08-01

    In the field of computational molecule biology, there is still a challenging question of how to detect non-coding RNA gene in lots of unlabeled sequences. Generally, the methods of machine learning and classification are employed to answer this question. However, only a limited number of positive training samples and unlabeled samples are available. The negative samples are difficult to define appropriately, yet they are necessary for usual learning-then-classification method. The common way for most of the existing non-coding RNA gene finding methods is to produce a number of random sequences as negative samples, which may hold some characteristic of positive sample sequences. Consequently, the contrived uncertain factor was introduced and the performance of methods was not good enough. In this paper, Support Vector Data Description (SVDD) is in use for to learning and classification as well as for detecting non-coding RNA gene in lots of unlabeled sequences, and the k-means clustering algorithm is employed before SVDD training to deal with the high flase positive fault in the result of SVDD. The training samples (target samples) are non-coding RNA genes validated by experiment. Moreover, appropriate features were constructed by Principal Component Analysis (PCA). The effectiveness and performance of the method are demonstrated by testing the cases in NONCODE databases and E. coli genome. PMID:20842844

  16. Price forecast in the competitive electricity market by support vector machine

    NASA Astrophysics Data System (ADS)

    Gao, Ciwei; Bompard, Ettore; Napoli, Roberto; Cheng, Haozhong

    2007-08-01

    The electricity market has been widely introduced in many countries all over the world and the study on electricity price forecast technology has drawn a lot of attention. In this paper, with different parameter C i and ε i assigned to each training data, the flexible C i Support Vector Regression (SVR) model is developed in terms of the particularity of the price forecast in electricity market. For Day Ahead Market (DAM) price forecast, the load, time of use index and index of day type are taken as the major factors to characterize the market price, therefore, they are selected as the inputs for the flexible SVR forecast model. For the long-term price forecast, we take the reserve margin Rm, HHI and the fuel price index as the inputs, since they are the major factors that drive the market price variation in long run. For short-term price forecast, besides the detailed analysis with the young Italian electricity market, the new model is tested on the experimental stage of the Spanish market, the New York market and the New England market. The long-term forecast with the SVR model presented is justified by the forecast with the data from the Long Run Market Simulator (LREMS).

  17. Hidden Markov Model and Support Vector Machine based decoding of finger movements using Electrocorticography

    PubMed Central

    Wissel, Tobias; Pfeiffer, Tim; Frysch, Robert; Knight, Robert T.; Chang, Edward F.; Hinrichs, Hermann; Rieger, Jochem W.; Rose, Georg

    2013-01-01

    Objective Support Vector Machines (SVM) have developed into a gold standard for accurate classification in Brain-Computer-Interfaces (BCI). The choice of the most appropriate classifier for a particular application depends on several characteristics in addition to decoding accuracy. Here we investigate the implementation of Hidden Markov Models (HMM)for online BCIs and discuss strategies to improve their performance. Approach We compare the SVM, serving as a reference, and HMMs for classifying discrete finger movements obtained from the Electrocorticograms of four subjects doing a finger tapping experiment. The classifier decisions are based on a subset of low-frequency time domain and high gamma oscillation features. Main results We show that decoding optimization between the two approaches is due to the way features are extracted and selected and less dependent on the classifier. An additional gain in HMM performance of up to 6% was obtained by introducing model constraints. Comparable accuracies of up to 90% were achieved with both SVM and HMM with the high gamma cortical response providing the most important decoding information for both techniques. Significance We discuss technical HMM characteristics and adaptations in the context of the presented data as well as for general BCI applications. Our findings suggest that HMMs and their characteristics are promising for efficient online brain-computer interfaces. PMID:24045504

  18. Biological mechanisms of premature ovarian failure caused by psychological stress based on support vector regression

    PubMed Central

    Wang, Xiu-Feng; Zhang, Lei; Wu, Qing-Hua; Min, Jian-Xin; Ma, Na; Luo, Lai-Cheng

    2015-01-01

    Psychological stress has become a common and important cause of premature ovarian failure (POF). Therefore, it is very important to explore the mechanisms of POF resulting from psychological stress. Sixty SD rats were randomly divided into control and model groups. Biomolecules associated with POF (β-EP, IL-1, NOS, NO, GnRH, CRH, FSH, LH, E2, P, ACTH, and CORT) were measured in the control and psychologically stressed rats. The regulation relationships of the biomolecules were explored in the psychologically stressed state using support vector regression (SVR). The values of β-EP, IL-1, NOS, and GnRH in the hypothalamus decreased significantly, and the value of NO changed slightly, when the values of 3 biomolecules in the hypothalamic-pituitary-adrenal axis decreased. The values of E2 and P in the hypothalamic-pituitary-ovarian axis decreased significantly, while the values of FSH and LH changed slightly, when the values of the biomolecules in the hypothalamus decreased. The values of FSH and LH in the pituitary layer of the hypothalamic-pituitary-ovarian axis changed slightly when the values of E2 and P in the target gland layer of the hypothalamic-pituitary-ovarian axis decreased. An Imbalance in the neuroendocrine-immune bimolecular network, particularly the failure of the feedback action of the target gland layer to pituitary layer in the pituitary-ovarian axis, is possibly one of the pathogenic mechanisms of POF. PMID:26885082

  19. Identification of small molecule aggregators from large compound libraries by support vector machines.

    PubMed

    Rao, Hanbing; Li, Zerong; Li, Xiangyuan; Ma, Xiaohua; Ung, Choongyong; Li, Hu; Liu, Xianghui; Chen, Yuzong

    2010-03-01

    Small molecule aggregators non-specifically inhibit multiple unrelated proteins, rendering them therapeutically useless. They frequently appear as false hits and thus need to be eliminated in high-throughput screening campaigns. Computational methods have been explored for identifying aggregators, which have not been tested in screening large compound libraries. We used 1319 aggregators and 128,325 non-aggregators to develop a support vector machines (SVM) aggregator identification model, which was tested by four methods. The first is five fold cross-validation, which showed comparable aggregator and significantly improved non-aggregator identification rates against earlier studies. The second is the independent test of 17 aggregators discovered independently from the training aggregators, 71% of which were correctly identified. The third is retrospective screening of 13M PUBCHEM and 168K MDDR compounds, which predicted 97.9% and 98.7% of the PUBCHEM and MDDR compounds as non-aggregators. The fourth is retrospective screening of 5527 MDDR compounds similar to the known aggregators, 1.14% of which were predicted as aggregators. SVM showed slightly better overall performance against two other machine learning methods based on five fold cross-validation studies of the same settings. Molecular features of aggregation, extracted by a feature selection method, are consistent with published profiles. SVM showed substantial capability in identifying aggregators from large libraries at low false-hit rates. PMID:19569201

  20. Using support vector machine for improving protein-protein interaction prediction utilizing domain interactions

    SciTech Connect

    Singhal, Mudita; Shah, Anuj R.; Brown, Roslyn N.; Adkins, Joshua N.

    2010-10-02

    Understanding protein interactions is essential to gain insights into the biological processes at the whole cell level. The high-throughput experimental techniques for determining protein-protein interactions (PPI) are error prone and expensive with low overlap amongst them. Although several computational methods have been proposed for predicting protein interactions there is definite room for improvement. Here we present DomainSVM, a predictive method for PPI that uses computationally inferred domain-domain interaction values in a Support Vector Machine framework to predict protein interactions. DomainSVM method utilizes evidence of multiple interacting domains to predict a protein interaction. It outperforms existing methods of PPI prediction by achieving very high explanation ratios, precision, specificity, sensitivity and F-measure values in a 10 fold cross-validation study conducted on the positive and negative PPIs in yeast. A Functional comparison study using GO annotations on the positive and the negative test sets is presented in addition to discussing novel PPI predictions in Salmonella Typhimurium.

  1. The construction of support vector machine classifier using the firefly algorithm.

    PubMed

    Chao, Chih-Feng; Horng, Ming-Huwi

    2015-01-01

    The setting of parameters in the support vector machines (SVMs) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM). This tool is not considered the feature selection, because the SVM, together with feature selection, is not suitable for the application in a multiclass classification, especially for the one-against-all multiclass SVM. In experiments, binary and multiclass classifications are explored. In the experiments on binary classification, ten of the benchmark data sets of the University of California, Irvine (UCI), machine learning repository are used; additionally the firefly-SVM is applied to the multiclass diagnosis of ultrasonic supraspinatus images. The classification performance of firefly-SVM is also compared to the original LIBSVM method associated with the grid search method and the particle swarm optimization based SVM (PSO-SVM). The experimental results advocate the use of firefly-SVM to classify pattern classifications for maximum accuracy. PMID:25802511

  2. Predicting the auto-ignition temperatures of organic compounds from molecular structure using support vector machine.

    PubMed

    Pan, Yong; Jiang, Juncheng; Wang, Rui; Cao, Hongyin; Cui, Yi

    2009-05-30

    A quantitative structure-property relationship (QSPR) study is suggested for the prediction of auto-ignition temperatures (AIT) of organic compounds. Various kinds of molecular descriptors were calculated to represent the molecular structures of compounds, such as topological, charge, and geometric descriptors. The variable selection method of genetic algorithm (GA) was employed to select optimal subset of descriptors that have significant contribution to the overall AIT property from the large pool of calculated descriptors. The novel modeling method of support vector machine (SVM) was then employed to model the possible quantitative relationship existed between these selected descriptors and AIT property. The resulted model showed high prediction ability with the average absolute error being 28.88 degrees C, and the root mean square error being 36.86 for the prediction set, which are within the range of the experimental error of AIT measurements. The proposed method can be successfully used to predict the auto-ignition temperatures of organic compounds with only nine pre-selected theoretical descriptors which can be calculated directly from molecular structure alone. PMID:18952371

  3. Using support vector machines to improve elemental ion identification in macromolecular crystal structures

    SciTech Connect

    Morshed, Nader; Echols, Nathaniel; Adams, Paul D.

    2015-04-25

    In the process of macromolecular model building, crystallographers must examine electron density for isolated atoms and differentiate sites containing structured solvent molecules from those containing elemental ions. This task requires specific knowledge of metal-binding chemistry and scattering properties and is prone to error. A method has previously been described to identify ions based on manually chosen criteria for a number of elements. Here, the use of support vector machines (SVMs) to automatically classify isolated atoms as either solvent or one of various ions is described. Two data sets of protein crystal structures, one containing manually curated structures deposited with anomalous diffraction data and another with automatically filtered, high-resolution structures, were constructed. On the manually curated data set, an SVM classifier was able to distinguish calcium from manganese, zinc, iron and nickel, as well as all five of these ions from water molecules, with a high degree of accuracy. Additionally, SVMs trained on the automatically curated set of high-resolution structures were able to successfully classify most common elemental ions in an independent validation test set. This method is readily extensible to other elemental ions and can also be used in conjunction with previous methods based on a priori expectations of the chemical environment and X-ray scattering.

  4. Identifying organic-rich Marcellus Shale lithofacies by support vector machine classifier in the Appalachian basin

    NASA Astrophysics Data System (ADS)

    Wang, Guochang; Carr, Timothy R.; Ju, Yiwen; Li, Chaofeng

    2014-03-01

    Unconventional shale reservoirs as the result of extremely low matrix permeability, higher potential gas productivity requires not only sufficient gas-in-place, but also a high concentration of brittle minerals (silica and/or carbonate) that is amenable to hydraulic fracturing. Shale lithofacies is primarily defined by mineral composition and organic matter richness, and its representation as a 3-D model has advantages in recognizing productive zones of shale-gas reservoirs, designing horizontal wells and stimulation strategy, and aiding in understanding depositional process of organic-rich shale. A challenging and key step is to effectively recognize shale lithofacies from well conventional logs, where the relationship is very complex and nonlinear. In the recognition of shale lithofacies, the application of support vector machine (SVM), which underlies statistical learning theory and structural risk minimization principle, is superior to the traditional empirical risk minimization principle employed by artificial neural network (ANN). We propose SVM classifier combined with learning algorithms, such as grid searching, genetic algorithm and particle swarm optimization, and various kernel functions the approach to identify Marcellus Shale lithofacies. Compared with ANN classifiers, the experimental results of SVM classifiers showed higher cross-validation accuracy, better stability and less computational time cost. The SVM classifier with radius basis function as kernel worked best as it is trained by particle swarm optimization. The lithofacies predicted using the SVM classifier are used to build a 3-D Marcellus Shale lithofacies model, which assists in identifying higher productive zones, especially with thermal maturity and natural fractures.

  5. Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection

    PubMed Central

    Wang, Tian; Chen, Jie; Zhou, Yi; Snoussi, Hichem

    2013-01-01

    The abnormal event detection problem is an important subject in real-time video surveillance. In this paper, we propose a novel online one-class classification algorithm, online least squares one-class support vector machine (online LS-OC-SVM), combined with its sparsified version (sparse online LS-OC-SVM). LS-OC-SVM extracts a hyperplane as an optimal description of training objects in a regularized least squares sense. The online LS-OC-SVM learns a training set with a limited number of samples to provide a basic normal model, then updates the model through remaining data. In the sparse online scheme, the model complexity is controlled by the coherence criterion. The online LS-OC-SVM is adopted to handle the abnormal event detection problem. Each frame of the video is characterized by the covariance matrix descriptor encoding the moving information, then is classified into a normal or an abnormal frame. Experiments are conducted, on a two-dimensional synthetic distribution dataset and a benchmark video surveillance dataset, to demonstrate the promising results of the proposed online LS-OC-SVM method. PMID:24351629

  6. Monitoring land use/land cover dynamics in northwestern Ethiopia using support vector machine

    NASA Astrophysics Data System (ADS)

    Zewdie, Worku; Csaplovics, E.

    2014-10-01

    Land use/land cover (LULC) change assessment explores a terrestrial ecosystem in relation to the impact of natural processes and anthropogenic activities towards temporal and spatial change. This study explores spatial and quantitative dynamics of land use change in the semi-arid regions of northwestern Ethiopia using Landsat-5 (1984) and Landsat-8 (2014) which provided recent and historical LULC conditions of the region. Supervised classification algorithm using support vector machines (SVM) was used to map and monitor land use transformations. A post-classification change detection assessment was applied to individual image classification outputs of the best performing SVM model in order to identify respective two-date change trajectories. The change detection analysis with an extended transition matrix showed a net quantity change of 44.0% and total change of 53.7% of the study area, with the latter change is due to swap changes. Post-classification comparisons of the classified imagery identified a major woodland transformation to cropland which is attributed to population size and economic activity. The area of cropland has increased significantly (52.8%) in 2014 contributing to the reduction in native vegetation cover. In the study period, 55.6% of woodland lost signifying a significant change in ecosystems. This significant land use transformation is due to accelerated human impact and subsequent agricultural land expansion. The loss in vegetation cover has exposed the surface and it is common to see a haze of cloud in a most semiarid region of NW Ethiopia.

  7. Land Cover Classification from Full-Waveform LIDAR Data Based on Support Vector Machines

    NASA Astrophysics Data System (ADS)

    Zhou, M.; Li, C. R.; Ma, L.; Guan, H. C.

    2016-06-01

    In this study, a land cover classification method based on multi-class Support Vector Machines (SVM) is presented to predict the types of land cover in Miyun area. The obtained backscattered full-waveforms were processed following a workflow of waveform pre-processing, waveform decomposition and feature extraction. The extracted features, which consist of distance, intensity, Full Width at Half Maximum (FWHM) and back scattering cross-section, were corrected and used as attributes for training data to generate the SVM prediction model. The SVM prediction model was applied to predict the types of land cover in Miyun area as ground, trees, buildings and farmland. The classification results of these four types of land covers were obtained based on the ground truth information according to the CCD image data of Miyun area. It showed that the proposed classification algorithm achieved an overall classification accuracy of 90.63%. In order to better explain the SVM classification results, the classification results of SVM method were compared with that of Artificial Neural Networks (ANNs) method and it showed that SVM method could achieve better classification results.

  8. Using Support Vector Machine Ensembles for Target Audience Classification on Twitter

    PubMed Central

    Lo, Siaw Ling; Chiong, Raymond; Cornforth, David

    2015-01-01

    The vast amount and diversity of the content shared on social media can pose a challenge for any business wanting to use it to identify potential customers. In this paper, our aim is to investigate the use of both unsupervised and supervised learning methods for target audience classification on Twitter with minimal annotation efforts. Topic domains were automatically discovered from contents shared by followers of an account owner using Twitter Latent Dirichlet Allocation (LDA). A Support Vector Machine (SVM) ensemble was then trained using contents from different account owners of the various topic domains identified by Twitter LDA. Experimental results show that the methods presented are able to successfully identify a target audience with high accuracy. In addition, we show that using a statistical inference approach such as bootstrapping in over-sampling, instead of using random sampling, to construct training datasets can achieve a better classifier in an SVM ensemble. We conclude that such an ensemble system can take advantage of data diversity, which enables real-world applications for differentiating prospective customers from the general audience, leading to business advantage in the crowded social media space. PMID:25874768

  9. Eddy current characterization of small cracks using least square support vector machine

    NASA Astrophysics Data System (ADS)

    Chelabi, M.; Hacib, T.; Le Bihan, Y.; Ikhlef, N.; Boughedda, H.; Mekideche, M. R.

    2016-04-01

    Eddy current (EC) sensors are used for non-destructive testing since they are able to probe conductive materials. Despite being a conventional technique for defect detection and localization, the main weakness of this technique is that defect characterization, of the exact determination of the shape and dimension, is still a question to be answered. In this work, we demonstrate the capability of small crack sizing using signals acquired from an EC sensor. We report our effort to develop a systematic approach to estimate the size of rectangular and thin defects (length and depth) in a conductive plate. The achieved approach by the novel combination of a finite element method (FEM) with a statistical learning method is called least square support vector machines (LS-SVM). First, we use the FEM to design the forward problem. Next, an algorithm is used to find an adaptive database. Finally, the LS-SVM is used to solve the inverse problems, creating polynomial functions able to approximate the correlation between the crack dimension and the signal picked up from the EC sensor. Several methods are used to find the parameters of the LS-SVM. In this study, the particle swarm optimization (PSO) and genetic algorithm (GA) are proposed for tuning the LS-SVM. The results of the design and the inversions were compared to both simulated and experimental data, with accuracy experimentally verified. These suggested results prove the applicability of the presented approach.

  10. Discriminative analysis of schizophrenia using support vector machine and recursive feature elimination on structural MRI images.

    PubMed

    Lu, Xiaobing; Yang, Yongzhe; Wu, Fengchun; Gao, Minjian; Xu, Yong; Zhang, Yue; Yao, Yongcheng; Du, Xin; Li, Chengwei; Wu, Lei; Zhong, Xiaomei; Zhou, Yanling; Fan, Ni; Zheng, Yingjun; Xiong, Dongsheng; Peng, Hongjun; Escudero, Javier; Huang, Biao; Li, Xiaobo; Ning, Yuping; Wu, Kai

    2016-07-01

    Structural abnormalities in schizophrenia (SZ) patients have been well documented with structural magnetic resonance imaging (MRI) data using voxel-based morphometry (VBM) and region of interest (ROI) analyses. However, these analyses can only detect group-wise differences and thus, have a poor predictive value for individuals. In the present study, we applied a machine learning method that combined support vector machine (SVM) with recursive feature elimination (RFE) to discriminate SZ patients from normal controls (NCs) using their structural MRI data. We first employed both VBM and ROI analyses to compare gray matter volume (GMV) and white matter volume (WMV) between 41 SZ patients and 42 age- and sex-matched NCs. The method of SVM combined with RFE was used to discriminate SZ patients from NCs using significant between-group differences in both GMV and WMV as input features. We found that SZ patients showed GM and WM abnormalities in several brain structures primarily involved in the emotion, memory, and visual systems. An SVM with a RFE classifier using the significant structural abnormalities identified by the VBM analysis as input features achieved the best performance (an accuracy of 88.4%, a sensitivity of 91.9%, and a specificity of 84.4%) in the discriminative analyses of SZ patients. These results suggested that distinct neuroanatomical profiles associated with SZ patients might provide a potential biomarker for disease diagnosis, and machine-learning methods can reveal neurobiological mechanisms in psychiatric diseases. PMID:27472673

  11. Target localization in wireless sensor networks using online semi-supervised support vector regression.

    PubMed

    Yoo, Jaehyun; Kim, H Jin

    2015-01-01

    Machine learning has been successfully used for target localization in wireless sensor networks (WSNs) due to its accurate and robust estimation against highly nonlinear and noisy sensor measurement. For efficient and adaptive learning, this paper introduces online semi-supervised support vector regression (OSS-SVR). The first advantage of the proposed algorithm is that, based on semi-supervised learning framework, it can reduce the requirement on the amount of the labeled training data, maintaining accurate estimation. Second, with an extension to online learning, the proposed OSS-SVR automatically tracks changes of the system to be learned, such as varied noise characteristics. We compare the proposed algorithm with semi-supervised manifold learning, an online Gaussian process and online semi-supervised colocalization. The algorithms are evaluated for estimating the unknown location of a mobile robot in a WSN. The experimental results show that the proposed algorithm is more accurate under the smaller amount of labeled training data and is robust to varying noise. Moreover, the suggested algorithm performs fast computation, maintaining the best localization performance in comparison with the other methods. PMID:26024420

  12. Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor Classification

    PubMed Central

    Rajesh Sharma, R.; Marikkannu, P.

    2015-01-01

    A novel hybrid approach for the identification of brain regions using magnetic resonance images accountable for brain tumor is presented in this paper. Classification of medical images is substantial in both clinical and research areas. Magnetic resonance imaging (MRI) modality outperforms towards diagnosing brain abnormalities like brain tumor, multiple sclerosis, hemorrhage, and many more. The primary objective of this work is to propose a three-dimensional (3D) novel brain tumor classification model using MRI images with both micro- and macroscale textures designed to differentiate the MRI of brain under two classes of lesion, benign and malignant. The design approach was initially preprocessed using 3D Gaussian filter. Based on VOI (volume of interest) of the image, features were extracted using 3D volumetric Square Centroid Lines Gray Level Distribution Method (SCLGM) along with 3D run length and cooccurrence matrix. The optimal features are selected using the proposed refined gravitational search algorithm (RGSA). Support vector machines, over backpropagation network, and k-nearest neighbor are used to evaluate the goodness of classifier approach. The preliminary evaluation of the system is performed using 320 real-time brain MRI images. The system is trained and tested by using a leave-one-case-out method. The performance of the classifier is tested using the receiver operating characteristic curve of 0.986 (±002). The experimental results demonstrate the systematic and efficient feature extraction and feature selection algorithm to the performance of state-of-the-art feature classification methods. PMID:26509188

  13. Comparative evaluation of support vector machine classification for computer aided detection of breast masses in mammography

    NASA Astrophysics Data System (ADS)

    Lesniak, J. M.; Hupse, R.; Blanc, R.; Karssemeijer, N.; Székely, G.

    2012-08-01

    False positive (FP) marks represent an obstacle for effective use of computer-aided detection (CADe) of breast masses in mammography. Typically, the problem can be approached either by developing more discriminative features or by employing different classifier designs. In this paper, the usage of support vector machine (SVM) classification for FP reduction in CADe is investigated, presenting a systematic quantitative evaluation against neural networks, k-nearest neighbor classification, linear discriminant analysis and random forests. A large database of 2516 film mammography examinations and 73 input features was used to train the classifiers and evaluate for their performance on correctly diagnosed exams as well as false negatives. Further, classifier robustness was investigated using varying training data and feature sets as input. The evaluation was based on the mean exam sensitivity in 0.05-1 FPs on normals on the free-response receiver operating characteristic curve (FROC), incorporated into a tenfold cross validation framework. It was found that SVM classification using a Gaussian kernel offered significantly increased detection performance (P = 0.0002) compared to the reference methods. Varying training data and input features, SVMs showed improved exploitation of large feature sets. It is concluded that with the SVM-based CADe a significant reduction of FPs is possible outperforming other state-of-the-art approaches for breast mass CADe.

  14. Knodle: A Support Vector Machines-Based Automatic Perception of Organic Molecules from 3D Coordinates.

    PubMed

    Kadukova, Maria; Grudinin, Sergei

    2016-08-22

    Here we address the problem of the assignment of atom types and bond orders in low molecular weight compounds. For this purpose, we have developed a prediction model based on nonlinear Support Vector Machines (SVM), implemented in a KNOwledge-Driven Ligand Extractor called Knodle, a software library for the recognition of atomic types, hybridization states, and bond orders in the structures of small molecules. We trained the model using an excessive amount of structural data collected from the PDBbindCN database. Accuracy of the results and the running time of our method is comparable with other popular methods, such as NAOMI, fconv, and I-interpret. On the popular Labute's benchmark set consisting of 179 protein-ligand complexes, Knodle makes five to six perception errors, NAOMI makes seven errors, I-interpret makes nine errors, and fconv makes 13 errors. On a larger set of 3,000 protein-ligand structures collected from the PDBBindCN general data set (v2014), Knodle and NAOMI have a comparable accuracy of approximately 3.9% and 4.7% of errors, I-interpret made 6.0% of errors, while fconv produced approximately 12.8% of errors. On a more general set of 332,974 entries collected from the Ligand Expo database, Knodle made 4.5% of errors. Overall, our study demonstrates the efficiency and robustness of nonlinear SVM in structure perception tasks. Knodle is available at https://team.inria.fr/nano-d/software/Knodle . PMID:27405533

  15. Using support vector machines to improve elemental ion identification in macromolecular crystal structures

    PubMed Central

    Morshed, Nader; Echols, Nathaniel; Adams, Paul D.

    2015-01-01

    In the process of macromolecular model building, crystallographers must examine electron density for isolated atoms and differentiate sites containing structured solvent molecules from those containing elemental ions. This task requires specific knowledge of metal-binding chemistry and scattering properties and is prone to error. A method has previously been described to identify ions based on manually chosen criteria for a number of elements. Here, the use of support vector machines (SVMs) to automatically classify isolated atoms as either solvent or one of various ions is described. Two data sets of protein crystal structures, one containing manually curated structures deposited with anomalous diffraction data and another with automatically filtered, high-resolution structures, were constructed. On the manually curated data set, an SVM classifier was able to distinguish calcium from manganese, zinc, iron and nickel, as well as all five of these ions from water molecules, with a high degree of accuracy. Additionally, SVMs trained on the automatically curated set of high-resolution structures were able to successfully classify most common elemental ions in an independent validation test set. This method is readily extensible to other elemental ions and can also be used in conjunction with previous methods based on a priori expectations of the chemical environment and X-ray scattering. PMID:25945580

  16. Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval.

    PubMed

    Tao, Dacheng; Tang, Xiaoou; Li, Xuelong; Wu, Xindong

    2006-07-01

    Relevance feedback schemes based on support vector machines (SVM) have been widely used in content-based image retrieval (CBIR). However, the performance of SVM-based relevance feedback is often poor when the number of labeled positive feedback samples is small. This is mainly due to three reasons: 1) an SVM classifier is unstable on a small-sized training set, 2) SVM's optimal hyperplane may be biased when the positive feedback samples are much less than the negative feedback samples, and 3) overfitting happens because the number of feature dimensions is much higher than the size of the training set. In this paper, we develop a mechanism to overcome these problems. To address the first two problems, we propose an asymmetric bagging-based SVM (AB-SVM). For the third problem, we combine the random subspace method and SVM for relevance feedback, which is named random subspace SVM (RS-SVM). Finally, by integrating AB-SVM and RS-SVM, an asymmetric bagging and random subspace SVM (ABRS-SVM) is built to solve these three problems and further improve the relevance feedback performance. PMID:16792098

  17. A novel strategy for forensic age prediction by DNA methylation and support vector regression model

    PubMed Central

    Xu, Cheng; Qu, Hongzhu; Wang, Guangyu; Xie, Bingbing; Shi, Yi; Yang, Yaran; Zhao, Zhao; Hu, Lan; Fang, Xiangdong; Yan, Jiangwei; Feng, Lei

    2015-01-01

    High deviations resulting from prediction model, gender and population difference have limited age estimation application of DNA methylation markers. Here we identified 2,957 novel age-associated DNA methylation sites (P < 0.01 and R2 > 0.5) in blood of eight pairs of Chinese Han female monozygotic twins. Among them, nine novel sites (false discovery rate < 0.01), along with three other reported sites, were further validated in 49 unrelated female volunteers with ages of 20–80 years by Sequenom Massarray. A total of 95 CpGs were covered in the PCR products and 11 of them were built the age prediction models. After comparing four different models including, multivariate linear regression, multivariate nonlinear regression, back propagation neural network and support vector regression, SVR was identified as the most robust model with the least mean absolute deviation from real chronological age (2.8 years) and an average accuracy of 4.7 years predicted by only six loci from the 11 loci, as well as an less cross-validated error compared with linear regression model. Our novel strategy provides an accurate measurement that is highly useful in estimating the individual age in forensic practice as well as in tracking the aging process in other related applications. PMID:26635134

  18. Fault diagnosis of direct-drive wind turbine based on support vector machine

    NASA Astrophysics Data System (ADS)

    An, X. L.; Jiang, D. X.; Li, S. H.; Chen, J.

    2011-07-01

    A fault diagnosis method of direct-drive wind turbine based on support vector machine (SVM) and feature selection is presented. The time-domain feature parameters of main shaft vibration signal in the horizontal and vertical directions are considered in the method. Firstly, in laboratory scale five experiments of direct-drive wind turbine with normal condition, wind wheel mass imbalance fault, wind wheel aerodynamic imbalance fault, yaw fault and blade airfoil change fault are carried out. The features of five experiments are analyzed. Secondly, the sensitive time-domain feature parameters in the horizontal and vertical directions of vibration signal in the five conditions are selected and used as feature samples. By training, the mapping relation between feature parameters and fault types are established in SVM model. Finally, the performance of the proposed method is verified through experimental data. The results show that the proposed method is effective in identifying the fault of wind turbine. It has good classification ability and robustness to diagnose the fault of direct-drive wind turbine.

  19. Support vector machines classification of hERG liabilities based on atom types.

    PubMed

    Jia, Lei; Sun, Hongmao

    2008-06-01

    Drug-induced long QT syndrome (LQTS) can cause critical cardiovascular side effects and has accounted for the withdrawal of several drugs from the market. Blockade of the potassium ion channel encoded by the human ether-a-go-go-related gene (hERG) has been identified as a major contributor to drug-induced LQTS. Experimental measurement of hERG activity for each compound in development is costly and time-consuming, thus it is beneficial to develop a predictive hERG model. Here, we present a hERG classification model formulated using support vector machines (SVM) as machine learning method and using atom types as molecular descriptors. The training set used in this study was composed of 977 corporate compounds with hERG activities measured under the same conditions. The impact of soft margin and kernel function on the performance of the SVM models was examined. The robustness of SVM was evaluated by comparing the predictive power of the models built with 90%, 50%, and 10% of the training set data. The final SVM model was able to correctly classify 94% of an external testing set containing 66 drug molecules. The most important atom types with respect to discriminative power were extracted and analyzed. PMID:18448342

  20. Investigating driver injury severity patterns in rollover crashes using support vector machine models.

    PubMed

    Chen, Cong; Zhang, Guohui; Qian, Zhen; Tarefder, Rafiqul A; Tian, Zong

    2016-05-01

    Rollover crash is one of the major types of traffic crashes that induce fatal injuries. It is important to investigate the factors that affect rollover crashes and their influence on driver injury severity outcomes. This study employs support vector machine (SVM) models to investigate driver injury severity patterns in rollover crashes based on two-year crash data gathered in New Mexico. The impacts of various explanatory variables are examined in terms of crash and environmental information, vehicle features, and driver demographics and behavior characteristics. A classification and regression tree (CART) model is utilized to identify significant variables and SVM models with polynomial and Gaussian radius basis function (RBF) kernels are used for model performance evaluation. It is shown that the SVM models produce reasonable prediction performance and the polynomial kernel outperforms the Gaussian RBF kernel. Variable impact analysis reveals that factors including comfortable driving environment conditions, driver alcohol or drug involvement, seatbelt use, number of travel lanes, driver demographic features, maximum vehicle damages in crashes, crash time, and crash location are significantly associated with driver incapacitating injuries and fatalities. These findings provide insights for better understanding rollover crash causes and the impacts of various explanatory factors on driver injury severity patterns. PMID:26938584

  1. Symbolic features and classification via support vector machine for predicting death in patients with Chagas disease.

    PubMed

    Sady, Cristina C R; Ribeiro, Antonio Luiz P

    2016-03-01

    This paper introduces a technique for predicting death in patients with Chagas disease using features extracted from symbolic series and time-frequency indices of heart rate variability (HRV). The study included 150 patients: 15 patients who died and 135 who did not. The HRV series were obtained from 24-h Holter monitoring. Sequences of symbols from 5-min epochs from series of RR intervals were generated using symbolic dynamics and ordinal pattern statistics. Fourteen features were extracted from symbolic series and four derived from clinical aspects of patients. For classification, the 18 features from each epoch were used as inputs in a support vector machine (SVM) with a radial basis function (RBF) kernel. The results showed that it is possible to distinguish between the two classes, patients with Chagas disease who did or did not die, with a 95% accuracy rate. Therefore, we suggest that the use of new features based on symbolic series, coupled with classic time-frequency and clinical indices, proves to be a good predictor of death in patients with Chagas disease. PMID:26851730

  2. A Novel Classification Algorithm Based on Incremental Semi-Supervised Support Vector Machine

    PubMed Central

    Gao, Fei; Mei, Jingyuan; Sun, Jinping; Wang, Jun; Yang, Erfu; Hussain, Amir

    2015-01-01

    For current computational intelligence techniques, a major challenge is how to learn new concepts in changing environment. Traditional learning schemes could not adequately address this problem due to a lack of dynamic data selection mechanism. In this paper, inspired by human learning process, a novel classification algorithm based on incremental semi-supervised support vector machine (SVM) is proposed. Through the analysis of prediction confidence of samples and data distribution in a changing environment, a “soft-start” approach, a data selection mechanism and a data cleaning mechanism are designed, which complete the construction of our incremental semi-supervised learning system. Noticeably, with the ingenious design procedure of our proposed algorithm, the computation complexity is reduced effectively. In addition, for the possible appearance of some new labeled samples in the learning process, a detailed analysis is also carried out. The results show that our algorithm does not rely on the model of sample distribution, has an extremely low rate of introducing wrong semi-labeled samples and can effectively make use of the unlabeled samples to enrich the knowledge system of classifier and improve the accuracy rate. Moreover, our method also has outstanding generalization performance and the ability to overcome the concept drift in a changing environment. PMID:26275294

  3. An all-sky support vector machine selection of WISE YSO candidates

    NASA Astrophysics Data System (ADS)

    Marton, G.; Tóth, L. V.; Paladini, R.; Kun, M.; Zahorecz, S.; McGehee, P.; Kiss, Cs.

    2016-06-01

    We explored the AllWISE catalogue of the Wide-field Infrared Survey Explorer (WISE) mission and identified Young Stellar Object (YSO) candidates. Reliable 2MASS and WISE photometric data combined with Planck dust opacity values were used to build our data set and to find the best classification scheme. A sophisticated statistical method, the support vector machine (SVM) is used to analyse the multidimensional data space and to remove source types identified as contaminants (extragalactic sources, main-sequence stars, evolved stars and sources related to the interstellar medium). Objects listed in the SIMBAD data base are used to identify the already known sources and to train our method. A new all-sky selection of 133 980 Class I/II YSO candidates is presented. The estimated contamination was found to be well below 1 per cent based on comparison with our SIMBAD training set. We also compare our results to that of existing methods and catalogues. The SVM selection process successfully identified >90 per cent of the Class I/II YSOs based on comparison with photometric and spectroscopic YSO catalogues. Our conclusion is that by using the SVM, our classification is able to identify more known YSOs of the training sample than other methods based on colour-colour and magnitude-colour selection. The distribution of the YSO candidates well correlates with that of the Planck Galactic Cold Clumps in the Taurus-Auriga-Perseus-California region.

  4. Nonlinear regression in environmental sciences by support vector machines combined with evolutionary strategy

    NASA Astrophysics Data System (ADS)

    Lima, Aranildo R.; Cannon, Alex J.; Hsieh, William W.

    2013-01-01

    A hybrid algorithm combining support vector regression with evolutionary strategy (SVR-ES) is proposed for predictive models in the environmental sciences. SVR-ES uses uncorrelated mutation with p step sizes to find the optimal SVR hyper-parameters. Three environmental forecast datasets used in the WCCI-2006 contest - surface air temperature, precipitation and sulphur dioxide concentration - were tested. We used multiple linear regression (MLR) as benchmark and a variety of machine learning techniques including bootstrap-aggregated ensemble artificial neural network (ANN), SVR-ES, SVR with hyper-parameters given by the Cherkassky-Ma estimate, the M5 regression tree, and random forest (RF). We also tested all techniques using stepwise linear regression (SLR) first to screen out irrelevant predictors. We concluded that SVR-ES is an attractive approach because it tends to outperform the other techniques and can also be implemented in an almost automatic way. The Cherkassky-Ma estimate is a useful approach for minimizing the mean absolute error and saving computational time related to the hyper-parameter search. The ANN and RF are also good options to outperform multiple linear regression (MLR). Finally, the use of SLR for predictor selection can dramatically reduce computational time and often help to enhance accuracy.

  5. Hyperspectral image preprocessing with bilateral filter for improving the classification accuracy of support vector machines

    NASA Astrophysics Data System (ADS)

    Sahadevan, Anand S.; Routray, Aurobinda; Das, Bhabani S.; Ahmad, Saquib

    2016-04-01

    Bilateral filter (BF) theory is applied to integrate spatial contextual information into the spectral domain for improving the accuracy of the support vector machine (SVM) classifier. The proposed classification framework is a two-stage process. First, an edge-preserved smoothing is carried out on a hyperspectral image (HSI). Then, the SVM multiclass classifier is applied on the smoothed HSI. One of the advantages of the BF-based implementation is that it considers the spatial as well as spectral closeness for smoothing the HSI. Therefore, the proposed method provides better smoothing in the homogeneous region and preserves the image details, which in turn improves the separability between the classes. The performance of the proposed method is tested using benchmark HSIs obtained from the airborne-visible-infrared-imaging-spectrometer (AVIRIS) and the reflective-optics-system-imaging-spectrometer (ROSIS) sensors. Experimental results demonstrate the effectiveness of the edge-preserved filtering in the classification of the HSI. Average accuracies (with 10% training samples) of the proposed classification framework are 99.04%, 98.11%, and 96.42% for AVIRIS-Salinas, ROSIS-Pavia University, and AVIRIS-Indian Pines images, respectively. Since the proposed method follows a combination of BF and the SVM formulations, it will be quite simple and practical to implement in real applications.

  6. Empirical mode decomposition coupled with least square support vector machine for river flow forecasting

    NASA Astrophysics Data System (ADS)

    Ismail, Shuhaida; Shabri, Ani; Abadan, Siti Sarah

    2015-02-01

    This paper aims to investigate the ability of Empirical Mode Decompositio n (EMD) coupled with Least Square Support Vector Machine (LSSVM) model in order to improve the accuracy of river flow forecasting. To assess the effectiveness of this model, Bernam monthly river flow data, has served as the case study. The proposed model was set at three important stages which are decomposition, component identification and forecasting stages respectively. The first stage is known as decomposition stage where EMD were employed for decomposing the dataset into several numbers of Intrinsic Mode Functions (IMF) and a residue. During on second stage, the meaningful signals are identified using a statistical measure and the new dataset are obtained in this stage. The final stage applied LSSVM as a forecasting tool to perform the river flow forecasting. The performance of the EMD coupled with LSSVM model is compared with the single LSSVM models using various statistics measures of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), correlation-coefficient (R) and Correlation of Efficiency (CE). The comparison results reveal the proposed model of EMD coupled with LSSVM model serves as a useful tool and a promising new method for river flow forecasting.

  7. Retrieve the evaporation duct height by least-squares support vector machine algorithm

    NASA Astrophysics Data System (ADS)

    Douvenot, Remi; Fabbro, Vincent; Bourlier, Christophe; Saillard, Joseph; Fuchs, Hans-Hellmuth; Essen, Helmut; Foerster, Joerg

    2009-01-01

    The detection and tracking of naval targets, including low Radar Cross Section (RCS) objects like inflatable boats or sea skimming missiles requires a thorough knowledge of the propagation properties of the maritime boundary layer. Models are in existence, which allow a prediction of the propagation factor using the parabolic equation algorithm. As a necessary input, the refractive index has to be known. This index, however, is strongly influenced by the actual atmospheric conditions, characterized mainly by temperature, humidity and air pressure. An approach is initiated to retrieve the vertical profile of the refractive index from the propagation factor measured on an onboard target. The method is based on the LS-SVM (Least-Squares Support Vector Machines) theory. The inversion method is here used to determine refractive index from data measured during the VAMPIRA campaign (Validation Measurement for Propagation in the Infrared and RAdar) conducted as a multinational approach over a transmission path across the Baltic Sea. As a propagation factor has been measured on two reference reflectors mounted onboard a naval vessel at different heights, the inversion method can be tested on both heights. The paper describes the experimental campaign and validates the LS-SVM inversion method for refractivity from propagation factor on simple measured data.

  8. Improve the diagnosis of atrial hypertrophy with the local discriminative support vector machine.

    PubMed

    Ling, Ping; Gao, Dajin; Zhou, Xifeng; Huang, Zhining; Rong, Xiangsheng

    2015-01-01

    Computer-aided diagnosis (CAD) approaches succeed in detecting a number of diseases, however, they are not good at addressing atrial hypertrophy disease due to the lack of training data. Support Vector Machine (SVM) is very popular in few CAD solutions to atrial hypertrophy. Yet the performance of SVM is moderate in atrial hypertrophy detection compared to its success in other classification problems. In this paper we propose a novel CAD algorithm, Local Discriminative SVM (LDSVM), to overcome the above-mentioned difficulty. LDSVM consists of a global SVM that is trained on the training data, and a local kNN that is trained based on the information of SVM and query. When a query arrives, SVM gives the initial decision. If the initial decision is less confident, local kNN begins to modify the initial decision. LDSVM improves the accuracy of SVM through some contributions: the risk tube, the discriminant information derived from SVM hyperplane, the new metric and the self-tuning size of neighborhood. Empirical evidence on real medical datasets show high performance of LDSVM over the peers in addressing atrial hypertrophy problems. PMID:26405952

  9. BacHbpred: Support Vector Machine Methods for the Prediction of Bacterial Hemoglobin-Like Proteins.

    PubMed

    Selvaraj, MuthuKrishnan; Puri, Munish; Dikshit, Kanak L; Lefevre, Christophe

    2016-01-01

    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. PMID:27034664

  10. A Fault Alarm and Diagnosis Method Based on Sensitive Parameters and Support Vector Machine

    NASA Astrophysics Data System (ADS)

    Zhang, Jinjie; Yao, Ziyun; Lv, Zhiquan; Zhu, Qunxiong; Xu, Fengtian; Jiang, Zhinong

    2015-08-01

    Study on the extraction of fault feature and the diagnostic technique of reciprocating compressor is one of the hot research topics in the field of reciprocating machinery fault diagnosis at present. A large number of feature extraction and classification methods have been widely applied in the related research, but the practical fault alarm and the accuracy of diagnosis have not been effectively improved. Developing feature extraction and classification methods to meet the requirements of typical fault alarm and automatic diagnosis in practical engineering is urgent task. The typical mechanical faults of reciprocating compressor are presented in the paper, and the existing data of online monitoring system is used to extract fault feature parameters within 15 types in total; the inner sensitive connection between faults and the feature parameters has been made clear by using the distance evaluation technique, also sensitive characteristic parameters of different faults have been obtained. On this basis, a method based on fault feature parameters and support vector machine (SVM) is developed, which will be applied to practical fault diagnosis. A better ability of early fault warning has been proved by the experiment and the practical fault cases. Automatic classification by using the SVM to the data of fault alarm has obtained better diagnostic accuracy.

  11. A digital architecture for support vector machines: theory, algorithm, and FPGA implementation.

    PubMed

    Anguita, D; Boni, A; Ridella, S

    2003-01-01

    In this paper, we propose a digital architecture for support vector machine (SVM) learning and discuss its implementation on a field programmable gate array (FPGA). We analyze briefly the quantization effects on the performance of the SVM in classification problems to show its robustness, in the feedforward phase, respect to fixed-point math implementations; then, we address the problem of SVM learning. The architecture described here makes use of a new algorithm for SVM learning which is less sensitive to quantization errors respect to the solution appeared so far in the literature. The algorithm is composed of two parts: the first one exploits a recurrent network for finding the parameters of the SVM; the second one uses a bisection process for computing the threshold. The architecture implementing the algorithm is described in detail and mapped on a real current-generation FPGA (Xilinx Virtex II). Its effectiveness is then tested on a channel equalization problem, where real-time performances are of paramount importance. PMID:18244555

  12. Information extraction with object based support vector machines and vegetation indices

    NASA Astrophysics Data System (ADS)

    Ustuner, Mustafa; Abdikan, Saygin; Balik Sanli, Fusun

    2016-07-01

    Information extraction through remote sensing data is important for policy and decision makers as extracted information provide base layers for many application of real world. Classification of remotely sensed data is the one of the most common methods of extracting information however it is still a challenging issue because several factors are affecting the accuracy of the classification. Resolution of the imagery, number and homogeneity of land cover classes, purity of training data and characteristic of adopted classifiers are just some of these challenging factors. Object based image classification has some superiority than pixel based classification for high resolution images since it uses geometry and structure information besides spectral information. Vegetation indices are also commonly used for the classification process since it provides additional spectral information for vegetation, forestry and agricultural areas. In this study, the impacts of the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE) on the classification accuracy of RapidEye imagery were investigated. Object based Support Vector Machines were implemented for the classification of crop types for the study area located in Aegean region of Turkey. Results demonstrated that the incorporation of NDRE increase the classification accuracy from 79,96% to 86,80% as overall accuracy, however NDVI decrease the classification accuracy from 79,96% to 78,90%. Moreover it is proven than object based classification with RapidEye data give promising results for crop type mapping and analysis.

  13. A New Application of Support Vector Machine Method: Condition Monitoring and Analysis of Reactor Coolant Pump

    NASA Astrophysics Data System (ADS)

    Meng, Qinghu; Meng, Qingfeng; Feng, Wuwei

    2012-05-01

    Fukushima nuclear power plant accident caused huge losses and pollution and it showed that the reactor coolant pump is very important in a nuclear power plant. Therefore, to keep the safety and reliability, the condition of the coolant pump needs to be online condition monitored and fault analyzed. In this paper, condition monitoring and analysis based on support vector machine (SVM) is proposed. This method is just to aim at the small sample studies such as reactor coolant pump. Both experiment data and field data are analyzed. In order to eliminate the noise and useless frequency, these data are disposed through a multi-band FIR filter. After that, a fault feature selection method based on principal component analysis is proposed. The related variable quantity is changed into unrelated variable quantity, and the dimension is descended. Then the SVM method is used to separate different fault characteristics. Firstly, this method is used as a two-kind classifier to separate each two different running conditions. Then the SVM is used as a multiple classifier to separate all of the different condition types. The SVM could separate these conditions successfully. After that, software based on SVM was designed for reactor coolant pump condition analysis. This software is installed on the reactor plant control system of Qinshan nuclear power plant in China. It could monitor the online data and find the pump mechanical fault automatically.

  14. Automated Foveola Localization in Retinal 3D-OCT Images Using Structural Support Vector Machine Prediction

    PubMed Central

    Liu, Yu-Ying; Ishikawa, Hiroshi; Chen, Mei; Wollstein, Gadi; Schuman, Joel S.; Rehg, James M.

    2013-01-01

    We develop an automated method to determine the foveola location in macular 3D-OCT images in either healthy or pathological conditions. Structural Support Vector Machine (S-SVM) is trained to directly predict the location of the foveola, such that the score at the ground truth position is higher than that at any other position by a margin scaling with the associated localization loss. This S-SVM formulation directly minimizes the empirical risk of localization error, and makes efficient use of all available training data. It deals with the localization problem in a more principled way compared to the conventional binary classifier learning that uses zero-one loss and random sampling of negative examples. A total of 170 scans were collected for the experiment. Our method localized 95.1% of testing scans within the anatomical area of the foveola. Our experimental results show that the proposed method can effectively identify the location of the foveola, facilitating diagnosis around this important landmark. PMID:23285565

  15. POIMs: positional oligomer importance matrices—understanding support vector machine-based signal detectors

    PubMed Central

    Sonnenburg, Sören; Zien, Alexander; Philips, Petra; Rätsch, Gunnar

    2008-01-01

    Motivation: At the heart of many important bioinformatics problems, such as gene finding and function prediction, is the classification of biological sequences. Frequently the most accurate classifiers are obtained by training support vector machines (SVMs) with complex sequence kernels. However, a cumbersome shortcoming of SVMs is that their learned decision rules are very hard to understand for humans and cannot easily be related to biological facts. Results: To make SVM-based sequence classifiers more accessible and profitable, we introduce the concept of positional oligomer importance matrices (POIMs) and propose an efficient algorithm for their computation. In contrast to the raw SVM feature weighting, POIMs take the underlying correlation structure of k-mer features induced by overlaps of related k-mers into account. POIMs can be seen as a powerful generalization of sequence logos: they allow to capture and visualize sequence patterns that are relevant for the investigated biological phenomena. Availability: All source code, datasets, tables and figures are available at http://www.fml.tuebingen.mpg.de/raetsch/projects/POIM. Contact: Soeren.Sonnenburg@first.fraunhofer.de Supplementary information: Supplementary data are available at Bioinformatics online. PMID:18586746

  16. SUPPORT VECTOR MACHINES FOR BROAD AREA FEATURE CLASSIFICATION IN REMOTELY SENSED IMAGES

    SciTech Connect

    S. PERKINS; N. HARVEY; ET AL

    2001-03-01

    Classification of broad area features in satellite imagery is one of the most important applications of remote sensing. It is often difficult and time-consuming to develop classifiers by hand, so many researchers have turned to techniques from the fields of statistics and machine learning to automatically generate classifiers. Common techniques include maximum likelihood classifiers, neural networks and genetic algorithms. We present a new system called Afreet, which uses a recently developed machine learning paradigm called Support Vector Machines (SVMs). In contrast to other techniques, SVMs offer a solid mathematical foundation that provides a probabilistic guarantee on how well the classifier will generalize to unseen data. In addition the SVM training algorithm is guaranteed to converge to the globally optimal SVM classifier, can learn highly non-linear discrimination functions, copes extremely well with high-dimensional feature spaces (such as hype spectral data), and scales well to large problem sizes. Afreet combines an SVM with a sophisticated spatio-spectral feature construction mechanism that allows it to classify spectrally ambiguous pixels. We demonstrate the effectiveness of the system by applying Afreet to several broad area classification problems in remote sensing, and provide a comparison with conventional maximum likelihood classification.

  17. Terminator Detection by Support Vector Machine Utilizing aStochastic Context-Free Grammar

    SciTech Connect

    Francis-Lyon, Patricia; Cristianini, Nello; Holbrook, Stephen

    2006-12-30

    A 2-stage detector was designed to find rho-independent transcription terminators in the Escherichia coli genome. The detector includes a Stochastic Context Free Grammar (SCFG) component and a Support Vector Machine (SVM) component. To find terminators, the SCFG searches the intergenic regions of nucleotide sequence for local matches to a terminator grammar that was designed and trained utilizing examples of known terminators. The grammar selects sequences that are the best candidates for terminators and assigns them a prefix, stem-loop, suffix structure using the Cocke-Younger-Kasaami (CYK) algorithm, modified to incorporate energy affects of base pairing. The parameters from this inferred structure are passed to the SVM classifier, which distinguishes terminators from non-terminators that score high according to the terminator grammar. The SVM was trained with negative examples drawn from intergenic sequences that include both featureless and RNA gene regions (which were assigned prefix, stem-loop, suffix structure by the SCFG), so that it successfully distinguishes terminators from either of these. The classifier was found to be 96.4% successful during testing.

  18. Features classification using support vector machine for a facial expression recognition system

    NASA Astrophysics Data System (ADS)

    Patil, Rajesh A.; Sahula, Vineet; Mandal, Atanendu S.

    2012-10-01

    A methodology for automatic facial expression recognition in image sequences is proposed, which makes use of the Candide wire frame model and an active appearance algorithm for tracking, and support vector machine (SVM) for classification. A face is detected automatically from the given image sequence and by adapting the Candide wire frame model properly on the first frame of face image sequence, facial features in the subsequent frames are tracked using an active appearance algorithm. The algorithm adapts the Candide wire frame model to the face in each of the frames and then automatically tracks the grid in consecutive video frames over time. We require that first frame of the image sequence corresponds to the neutral facial expression, while the last frame of the image sequence corresponds to greatest intensity of facial expression. The geometrical displacement of Candide wire frame nodes, defined as the difference of the node coordinates between the first and the greatest facial expression intensity frame, is used as an input to the SVM, which classify the facial expression into one of the classes viz happy, surprise, sadness, anger, disgust, and fear.

  19. An intraoperative diagnosis of parotid gland tumors using Raman spectroscopy and support vector machine

    NASA Astrophysics Data System (ADS)

    Yan, Bing; Wen, Zhining; Li, Yi; Li, Longjiang; Xue, Lili

    2014-11-01

    The preoperative and intraoperative diagnosis of parotid gland tumors is difficult, but is important for their surgical management. In order to explore an intraoperative diagnostic method, Raman spectroscopy is applied to detect the normal parotid gland and tumors, including pleomorphic adenoma, Warthin’s tumor and mucoepidermoid carcinoma. In the 600-1800 cm-1 region of the Raman shift, there are numerous spectral differences between the parotid gland and tumors. Compared with Raman spectra of the normal parotid gland, the Raman spectra of parotid tumors show an increase of the peaks assigned to nucleic acids and proteins, but a decrease of the peaks related to lipids. Spectral differences also exist between the spectra of parotid tumors. Based on these differences, a remarkable classification and diagnosis of the parotid gland and tumors are carried out by support vector machine (SVM), with high accuracy (96.7~100%), sensitivity (93.3~100%) and specificity (96.7~100%). Raman spectroscopy combined with SVM has a great potential to aid the intraoperative diagnosis of parotid tumors and could provide an accurate and rapid diagnostic approach.

  20. Identification of Conversion from Normal Elderly Cognition to Alzheimer's Disease using Multimodal Support Vector Machine.

    PubMed

    Zhan, Ye; Chen, Kewei; Wu, Xia; Zhang, Daoqiang; Zhang, Jiacai; Yao, Li; Guo, Xiaojuan

    2015-01-01

    Alzheimer's disease (AD) is one of the most serious progressive neurodegenerative diseases among the elderly, therefore the identification of conversion to AD at the earlier stage has become a crucial issue. In this study, we applied multimodal support vector machine to identify the conversion from normal elderly cognition to mild cognitive impairment (MCI) or AD based on magnetic resonance imaging and positron emission tomography data. The participants included two independent cohorts (Training set: 121 AD patients and 120 normal controls (NC); Testing set: 20 NC converters and 20 NC non-converters) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The multimodal results showed that the accuracy, sensitivity, and specificity of the classification between NC converters and NC non-converters were 67.5% , 73.33% , and 64% , respectively. Furthermore, the classification results with feature selection increased to 70% accuracy, 75% sensitivity, and 66.67% specificity. The classification results using multimodal data are markedly superior to that using a single modality when we identified the conversion from NC to MCI or AD. The model built in this study of identifying the risk of normal elderly converting to MCI or AD will be helpful in clinical diagnosis and pathological research. PMID:26401783

  1. Using support vector machine ensembles for target audience classification on Twitter.

    PubMed

    Lo, Siaw Ling; Chiong, Raymond; Cornforth, David

    2015-01-01

    The vast amount and diversity of the content shared on social media can pose a challenge for any business wanting to use it to identify potential customers. In this paper, our aim is to investigate the use of both unsupervised and supervised learning methods for target audience classification on Twitter with minimal annotation efforts. Topic domains were automatically discovered from contents shared by followers of an account owner using Twitter Latent Dirichlet Allocation (LDA). A Support Vector Machine (SVM) ensemble was then trained using contents from different account owners of the various topic domains identified by Twitter LDA. Experimental results show that the methods presented are able to successfully identify a target audience with high accuracy. In addition, we show that using a statistical inference approach such as bootstrapping in over-sampling, instead of using random sampling, to construct training datasets can achieve a better classifier in an SVM ensemble. We conclude that such an ensemble system can take advantage of data diversity, which enables real-world applications for differentiating prospective customers from the general audience, leading to business advantage in the crowded social media space. PMID:25874768

  2. A comparative analysis of support vector machines and extreme learning machines.

    PubMed

    Liu, Xueyi; Gao, Chuanhou; Li, Ping

    2012-09-01

    The theory of extreme learning machines (ELMs) has recently become increasingly popular. As a new learning algorithm for single-hidden-layer feed-forward neural networks, an ELM offers the advantages of low computational cost, good generalization ability, and ease of implementation. Hence the comparison and model selection between ELMs and other kinds of state-of-the-art machine learning approaches has become significant and has attracted many research efforts. This paper performs a comparative analysis of the basic ELMs and support vector machines (SVMs) from two viewpoints that are different from previous works: one is the Vapnik-Chervonenkis (VC) dimension, and the other is their performance under different training sample sizes. It is shown that the VC dimension of an ELM is equal to the number of hidden nodes of the ELM with probability one. Additionally, their generalization ability and computational complexity are exhibited with changing training sample size. ELMs have weaker generalization ability than SVMs for small sample but can generalize as well as SVMs for large sample. Remarkably, great superiority in computational speed especially for large-scale sample problems is found in ELMs. The results obtained can provide insight into the essential relationship between them, and can also serve as complementary knowledge for their past experimental and theoretical comparisons. PMID:22572469

  3. Structural analysis of online handwritten mathematical symbols based on support vector machines

    NASA Astrophysics Data System (ADS)

    Simistira, Foteini; Papavassiliou, Vassilis; Katsouros, Vassilis; Carayannis, George

    2013-01-01

    Mathematical expression recognition is still a very challenging task for the research community mainly because of the two-dimensional (2d) structure of mathematical expressions (MEs). In this paper, we present a novel approach for the structural analysis between two on-line handwritten mathematical symbols of a ME, based on spatial features of the symbols. We introduce six features to represent the spatial affinity of the symbols and compare two multi-class classification methods that employ support vector machines (SVMs): one based on the "one-against-one" technique and one based on the "one-against-all", in identifying the relation between a pair of symbols (i.e. subscript, numerator, etc). A dataset containing 1906 spatial relations derived from the Competition on Recognition of Online Handwritten Mathematical Expressions (CROHME) 2012 training dataset is constructed to evaluate the classifiers and compare them with the rule-based classifier of the ILSP-1 system participated in the contest. The experimental results give an overall mean error rate of 2.61% for the "one-against-one" SVM approach, 6.57% for the "one-against-all" SVM technique and 12.31% error rate for the ILSP-1 classifier.

  4. The Construction of Support Vector Machine Classifier Using the Firefly Algorithm

    PubMed Central

    Chao, Chih-Feng; Horng, Ming-Huwi

    2015-01-01

    The setting of parameters in the support vector machines (SVMs) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM). This tool is not considered the feature selection, because the SVM, together with feature selection, is not suitable for the application in a multiclass classification, especially for the one-against-all multiclass SVM. In experiments, binary and multiclass classifications are explored. In the experiments on binary classification, ten of the benchmark data sets of the University of California, Irvine (UCI), machine learning repository are used; additionally the firefly-SVM is applied to the multiclass diagnosis of ultrasonic supraspinatus images. The classification performance of firefly-SVM is also compared to the original LIBSVM method associated with the grid search method and the particle swarm optimization based SVM (PSO-SVM). The experimental results advocate the use of firefly-SVM to classify pattern classifications for maximum accuracy. PMID:25802511

  5. An enhanced MEMS error modeling approach based on Nu-Support Vector Regression.

    PubMed

    Bhatt, Deepak; Aggarwal, Priyanka; Bhattacharya, Prabir; Devabhaktuni, Vijay

    2012-01-01

    Micro Electro Mechanical System (MEMS)-based inertial sensors have made possible the development of a civilian land vehicle navigation system by offering a low-cost solution. However, the accurate modeling of the MEMS sensor errors is one of the most challenging tasks in the design of low-cost navigation systems. These sensors exhibit significant errors like biases, drift, noises; which are negligible for higher grade units. Different conventional techniques utilizing the Gauss Markov model and neural network method have been previously utilized to model the errors. However, Gauss Markov model works unsatisfactorily in the case of MEMS units due to the presence of high inherent sensor errors. On the other hand, modeling the random drift utilizing Neural Network (NN) is time consuming, thereby affecting its real-time implementation. We overcome these existing drawbacks by developing an enhanced Support Vector Machine (SVM) based error model. Unlike NN, SVMs do not suffer from local minimisation or over-fitting problems and delivers a reliable global solution. Experimental results proved that the proposed SVM approach reduced the noise standard deviation by 10-35% for gyroscopes and 61-76% for accelerometers. Further, positional error drifts under static conditions improved by 41% and 80% in comparison to NN and GM approaches. PMID:23012552

  6. Real-time support vector classification and feedback of multiple emotional brain states.

    PubMed

    Sitaram, Ranganatha; Lee, Sangkyun; Ruiz, Sergio; Rana, Mohit; Veit, Ralf; Birbaumer, Niels

    2011-05-15

    An important question that confronts current research in affective neuroscience as well as in the treatment of emotional disorders is whether it is possible to determine the emotional state of a person based on the measurement of brain activity alone. Here, we first show that an online support vector machine (SVM) can be built to recognize two discrete emotional states, such as happiness and disgust from fMRI signals, in healthy individuals instructed to recall emotionally salient episodes from their lives. We report the first application of real-time head motion correction, spatial smoothing and feature selection based on a new method called Effect mapping. The classifier also showed robust prediction rates in decoding three discrete emotional states (happiness, disgust and sadness) in an extended group of participants. Subjective reports ascertained that participants performed emotion imagery and that the online classifier decoded emotions and not arbitrary states of the brain. Offline whole brain classification as well as region-of-interest classification in 24 brain areas previously implicated in emotion processing revealed that the frontal cortex was critically involved in emotion induction by imagery. We also demonstrate an fMRI-BCI based on real-time classification of BOLD signals from multiple brain regions, for each repetition time (TR) of scanning, providing visual feedback of emotional states to the participant for potential applications in the clinical treatment of dysfunctional affect. PMID:20692351

  7. Using Support Vector Machine to Forecast Energy Usage of a Manhattan Skyscraper

    NASA Astrophysics Data System (ADS)

    Winter, R.; Boulanger, A.; Anderson, R.; Wu, L.

    2011-12-01

    As our society gains a better understanding of how humans have negatively impacted the environment, research related to reducing carbon emissions and overall energy consumption has become increasingly important. One of the simplest ways to reduce energy usage is by making current buildings less wasteful. By improving energy efficiency, this method of lowering our carbon footprint is particularly worthwhile because it actually reduces energy costs of operating the building, unlike many environmental initiatives that require large monetary investments. In order to improve the efficiency of the heating and air conditioning (HVAC) system of a Manhattan skyscraper, 345 Park Avenue, a predictive computer model was designed to forecast the amount of energy the building will consume. This model uses support vector machine (SVM), a method that builds a regression based purely on historical data of the building, requiring no knowledge of its size, heating and cooling methods, or any other physical properties. This pure dependence on historical data makes the model very easily applicable to different types of buildings with few model adjustments. The SVM model was built to predict a week of future energy usage based on past energy, temperature, and dew point temperature data. The predictive model closely approximated the actual values of energy usage for the spring and less closely for the winter. The prediction may be improved with additional historical data to help the model account for seasonal variability. This model is useful for creating a close approximation of future energy usage and predicting ways to diminish waste.

  8. Decoding semi-constrained brain activity from FMRI using support vector machines and gaussian processes.

    PubMed

    Schrouff, Jessica; Kussé, Caroline; Wehenkel, Louis; Maquet, Pierre; Phillips, Christophe

    2012-01-01

    Predicting a particular cognitive state from a specific pattern of fMRI voxel values is still a methodological challenge. Decoding brain activity is usually performed in highly controlled experimental paradigms characterized by a series of distinct states induced by a temporally constrained experimental design. In more realistic conditions, the number, sequence and duration of mental states are unpredictably generated by the individual, resulting in complex and imbalanced fMRI data sets. This study tests the classification of brain activity, acquired on 16 volunteers using fMRI, during mental imagery, a condition in which the number and duration of mental events were not externally imposed but self-generated. To deal with these issues, two classification techniques were considered (Support Vector Machines, SVM, and Gaussian Processes, GP), as well as different feature extraction methods (General Linear Model, GLM and SVM). These techniques were combined in order to identify the procedures leading to the highest accuracy measures. Our results showed that 12 data sets out of 16 could be significantly modeled by either SVM or GP. Model accuracies tended to be related to the degree of imbalance between classes and to task performance of the volunteers. We also conclude that the GP technique tends to be more robust than SVM to model unbalanced data sets. PMID:22563410

  9. Application of support vector machine-based ranking strategies to search for target-selective compounds.

    PubMed

    Wassermann, Anne Mai; Geppert, Hanna; Bajorath, Jürgen

    2011-01-01

    Support vector machine (SVM)-based selectivity searching has recently been introduced to identify compounds in virtual screening libraries that are not only active for a target protein, but also selective for this target over a closely related member of the same protein family. In simulated virtual screening calculations, SVM-based strategies termed preference ranking and one-versus-all ranking were successfully applied to rank a database and enrich high-ranking positions with selective compounds while removing nonselective molecules from high ranks. In contrast to the original SVM approach developed for binary classification, these strategies enable learning from more than two classes, considering that distinguishing between selective, promiscuously active, and inactive compounds gives rise to a three-class prediction problem. In this chapter, we describe the extension of the one-versus-all strategy to four training classes. Furthermore, we present an adaptation of the preference ranking strategy that leads to higher recall of selective compounds than previously investigated approaches and is applicable in situations where the removal of nonselective compounds from high-ranking positions is not required. PMID:20838983

  10. An MR brain images classifier system via particle swarm optimization and kernel support vector machine.

    PubMed

    Zhang, Yudong; Wang, Shuihua; Ji, Genlin; Dong, Zhengchao

    2013-01-01

    Automated abnormal brain detection is extremely of importance for clinical diagnosis. Over last decades numerous methods had been presented. In this paper, we proposed a novel hybrid system to classify a given MR brain image as either normal or abnormal. The proposed method first employed digital wavelet transform to extract features then used principal component analysis (PCA) to reduce the feature space. Afterwards, we constructed a kernel support vector machine (KSVM) with RBF kernel, using particle swarm optimization (PSO) to optimize the parameters C and σ . Fivefold cross-validation was utilized to avoid overfitting. In the experimental procedure, we created a 90 images dataset brain downloaded from Harvard Medical School website. The abnormal brain MR images consist of the following diseases: glioma, metastatic adenocarcinoma, metastatic bronchogenic carcinoma, meningioma, sarcoma, Alzheimer, Huntington, motor neuron disease, cerebral calcinosis, Pick's disease, Alzheimer plus visual agnosia, multiple sclerosis, AIDS dementia, Lyme encephalopathy, herpes encephalitis, Creutzfeld-Jakob disease, and cerebral toxoplasmosis. The 5-folded cross-validation classification results showed that our method achieved 97.78% classification accuracy, higher than 86.22% by BP-NN and 91.33% by RBF-NN. For the parameter selection, we compared PSO with those of random selection method. The results showed that the PSO is more effective to build optimal KSVM. PMID:24163610

  11. A Support Vector Machine Approach for Truncated Fingerprint Image Detection from Sweeping Fingerprint Sensors

    PubMed Central

    Chen, Chi-Jim; Pai, Tun-Wen; Cheng, Mox

    2015-01-01

    A sweeping fingerprint sensor converts fingerprints on a row by row basis through image reconstruction techniques. However, a built fingerprint image might appear to be truncated and distorted when the finger was swept across a fingerprint sensor at a non-linear speed. If the truncated fingerprint images were enrolled as reference targets and collected by any automated fingerprint identification system (AFIS), successful prediction rates for fingerprint matching applications would be decreased significantly. In this paper, a novel and effective methodology with low time computational complexity was developed for detecting truncated fingerprints in a real time manner. Several filtering rules were implemented to validate existences of truncated fingerprints. In addition, a machine learning method of supported vector machine (SVM), based on the principle of structural risk minimization, was applied to reject pseudo truncated fingerprints containing similar characteristics of truncated ones. The experimental result has shown that an accuracy rate of 90.7% was achieved by successfully identifying truncated fingerprint images from testing images before AFIS enrollment procedures. The proposed effective and efficient methodology can be extensively applied to all existing fingerprint matching systems as a preliminary quality control prior to construction of fingerprint templates. PMID:25835186

  12. A support vector machine approach for truncated fingerprint image detection from sweeping fingerprint sensors.

    PubMed

    Chen, Chi-Jim; Pai, Tun-Wen; Cheng, Mox

    2015-01-01

    A sweeping fingerprint sensor converts fingerprints on a row by row basis through image reconstruction techniques. However, a built fingerprint image might appear to be truncated and distorted when the finger was swept across a fingerprint sensor at a non-linear speed. If the truncated fingerprint images were enrolled as reference targets and collected by any automated fingerprint identification system (AFIS), successful prediction rates for fingerprint matching applications would be decreased significantly. In this paper, a novel and effective methodology with low time computational complexity was developed for detecting truncated fingerprints in a real time manner. Several filtering rules were implemented to validate existences of truncated fingerprints. In addition, a machine learning method of supported vector machine (SVM), based on the principle of structural risk minimization, was applied to reject pseudo truncated fingerprints containing similar characteristics of truncated ones. The experimental result has shown that an accuracy rate of 90.7% was achieved by successfully identifying truncated fingerprint images from testing images before AFIS enrollment procedures. The proposed effective and efficient methodology can be extensively applied to all existing fingerprint matching systems as a preliminary quality control prior to construction of fingerprint templates. PMID:25835186

  13. Application of support vector machines to breast cancer screening using mammogram and history data

    NASA Astrophysics Data System (ADS)

    Land, Walker H., Jr.; Akanda, Anab; Lo, Joseph Y.; Anderson, Francis; Bryden, Margaret

    2002-05-01

    Support Vector Machines (SVMs) are a new and radically different type of classifiers and learning machines that use a hypothesis space of linear functions in a high dimensional feature space. This relatively new paradigm, based on Statistical Learning Theory (SLT) and Structural Risk Minimization (SRM), has many advantages when compared to traditional neural networks, which are based on Empirical Risk Minimization (ERM). Unlike neural networks, SVM training always finds a global minimum. Furthermore, SVMs have inherent ability to solve pattern classification without incorporating any problem-domain knowledge. In this study, the SVM was employed as a pattern classifier, operating on mammography data used for breast cancer detection. The main focus was to formulate the best learning machine configurations for optimum specificity and positive predictive value at very high sensitivities. Using a mammogram database of 500 biopsy-proven samples, the best performing SVM, on average, was able to achieve (under statistical 5-fold cross-validation) a specificity of 45.0% and a positive predictive value (PPV) of 50.1% at 100% sensitivity. At 97% sensitivity, a specificity of 55.8% and a PPV of 55.2% were obtained.

  14. Estimation of the laser cutting operating cost by support vector regression methodology

    NASA Astrophysics Data System (ADS)

    Jović, Srđan; Radović, Aleksandar; Šarkoćević, Živče; Petković, Dalibor; Alizamir, Meysam

    2016-09-01

    Laser cutting is a popular manufacturing process utilized to cut various types of materials economically. The operating cost is affected by laser power, cutting speed, assist gas pressure, nozzle diameter and focus point position as well as the workpiece material. In this article, the process factors investigated were: laser power, cutting speed, air pressure and focal point position. The aim of this work is to relate the operating cost to the process parameters mentioned above. CO2 laser cutting of stainless steel of medical grade AISI316L has been investigated. The main goal was to analyze the operating cost through the laser power, cutting speed, air pressure, focal point position and material thickness. Since the laser operating cost is a complex, non-linear task, soft computing optimization algorithms can be used. Intelligent soft computing scheme support vector regression (SVR) was implemented. The performance of the proposed estimator was confirmed with the simulation results. The SVR results are then compared with artificial neural network and genetic programing. According to the results, a greater improvement in estimation accuracy can be achieved through the SVR compared to other soft computing methodologies. The new optimization methods benefit from the soft computing capabilities of global optimization and multiobjective optimization rather than choosing a starting point by trial and error and combining multiple criteria into a single criterion.

  15. Detection and localization of damage using empirical mode decomposition and multilevel support vector machine

    NASA Astrophysics Data System (ADS)

    Dushyanth, N. D.; Suma, M. N.; Latte, Mrityanjaya V.

    2016-03-01

    Damage in the structure may raise a significant amount of maintenance cost and serious safety problems. Hence detection of the damage at its early stage is of prime importance. The main contribution pursued in this investigation is to propose a generic optimal methodology to improve the accuracy of positioning of the flaw in a structure. This novel approach involves a two-step process. The first step essentially aims at extracting the damage-sensitive features from the received signal, and these extracted features are often termed the damage index or damage indices, serving as an indicator to know whether the damage is present or not. In particular, a multilevel SVM (support vector machine) plays a vital role in the distinction of faulty and healthy structures. Formerly, when a structure is unveiled as a damaged structure, in the subsequent step, the position of the damage is identified using Hilbert-Huang transform. The proposed algorithm has been evaluated in both simulation and experimental tests on a 6061 aluminum plate with dimensions 300 mm × 300 mm × 5 mm which accordingly yield considerable improvement in the accuracy of estimating the position of the flaw.

  16. Voltammetric Electronic Tongue and Support Vector Machines for Identification of Selected Features in Mexican Coffee

    PubMed Central

    Domínguez, Rocio Berenice; Moreno-Barón, Laura; Muñoz, Roberto; Gutiérrez, Juan Manuel

    2014-01-01

    This paper describes a new method based on a voltammetric electronic tongue (ET) for the recognition of distinctive features in coffee samples. An ET was directly applied to different samples from the main Mexican coffee regions without any pretreatment before the analysis. The resulting electrochemical information was modeled with two different mathematical tools, namely Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). Growing conditions (i.e., organic or non-organic practices and altitude of crops) were considered for a first classification. LDA results showed an average discrimination rate of 88% ± 6.53% while SVM successfully accomplished an overall accuracy of 96.4% ± 3.50% for the same task. A second classification based on geographical origin of samples was carried out. Results showed an overall accuracy of 87.5% ± 7.79% for LDA and a superior performance of 97.5% ± 3.22% for SVM. Given the complexity of coffee samples, the high accuracy percentages achieved by ET coupled with SVM in both classification problems suggested a potential applicability of ET in the assessment of selected coffee features with a simpler and faster methodology along with a null sample pretreatment. In addition, the proposed method can be applied to authentication assessment while improving cost, time and accuracy of the general procedure. PMID:25254303

  17. Modeling of variable speed refrigerated display cabinets based on adaptive support vector machine

    NASA Astrophysics Data System (ADS)

    Cao, Zhikun; Han, Hua; Gu, Bo

    2010-01-01

    In this paper the adaptive support vector machine (ASVM) method is introduced to the field of intelligent modeling of refrigerated display cabinets and used to construct a highly precise mathematical model of their performance. A model for a variable speed open vertical display cabinet was constructed using preprocessing techniques for measured data, including the elimination of outlying data points by the use of an exponential weighted moving average (EWMA). Using dynamic loss coefficient adjustment, the adaptation of the SVM for use in this application was achieved. From there, the object function for energy use per unit of display area total energy consumption (TEC)/total display area (TDA) was constructed and solved using the ASVM method. When compared to the results achieved using a back-propagation neural network (BPNN) model, the ASVM model for the refrigerated display cabinet was characterized by its simple structure, fast convergence speed and high prediction accuracy. The ASVM model also has better noise rejection properties than that of original SVM model. It was revealed by the theoretical analysis and experimental results presented in this paper that it is feasible to model of the display cabinet built using the ASVM method.

  18. Potential of cancer screening with serum surface-enhanced Raman spectroscopy and a support vector machine

    NASA Astrophysics Data System (ADS)

    Li, S. X.; Zhang, Y. J.; Zeng, Q. Y.; Li, L. F.; Guo, Z. Y.; Liu, Z. M.; Xiong, H. L.; Liu, S. H.

    2014-06-01

    Cancer is the most common disease to threaten human health. The ability to screen individuals with malignant tumours with only a blood sample would be greatly advantageous to early diagnosis and intervention. This study explores the possibility of discriminating between cancer patients and normal subjects with serum surface-enhanced Raman spectroscopy (SERS) and a support vector machine (SVM) through a peripheral blood sample. A total of 130 blood samples were obtained from patients with liver cancer, colonic cancer, esophageal cancer, nasopharyngeal cancer, gastric cancer, as well as 113 blood samples from normal volunteers. Several diagnostic models were built with the serum SERS spectra using SVM and principal component analysis (PCA) techniques. The results show that a diagnostic accuracy of 85.5% is acquired with a PCA algorithm, while a diagnostic accuracy of 95.8% is obtained using radial basis function (RBF), PCA-SVM methods. The results prove that a RBF kernel PCA-SVM technique is superior to PCA and conventional SVM (C-SVM) algorithms in classification serum SERS spectra. The study demonstrates that serum SERS, in combination with SVM techniques, has great potential for screening cancerous patients with any solid malignant tumour through a peripheral blood sample.

  19. Reservoir rock permeability prediction using support vector regression in an Iranian oil field

    NASA Astrophysics Data System (ADS)

    Saffarzadeh, Sadegh; Shadizadeh, Seyed Reza

    2012-06-01

    Reservoir permeability is a critical parameter for the evaluation of hydrocarbon reservoirs. It is often measured in the laboratory from reservoir core samples or evaluated from well test data. The prediction of reservoir rock permeability utilizing well log data is important because the core analysis and well test data are usually only available from a few wells in a field and have high coring and laboratory analysis costs. Since most wells are logged, the common practice is to estimate permeability from logs using correlation equations developed from limited core data; however, these correlation formulae are not universally applicable. Recently, support vector machines (SVMs) have been proposed as a new intelligence technique for both regression and classification tasks. The theory has a strong mathematical foundation for dependence estimation and predictive learning from finite data sets. The ultimate test for any technique that bears the claim of permeability prediction from well log data is the accurate and verifiable prediction of permeability for wells where only the well log data are available. The main goal of this paper is to develop the SVM method to obtain reservoir rock permeability based on well log data.

  20. Inferring the location of buried UXO using a support vector machine

    NASA Astrophysics Data System (ADS)

    Fernández, Juan Pablo; Sun, Keli; Barrowes, Benjamin; O'Neill, Kevin; Shamatava, Irma; Shubitidze, Fridon; Paulsen, Keith D.

    2007-04-01

    The identification of unexploded ordnance (UXO) using electromagnetic-induction (EMI) sensors involves two essentially independent steps: Each anomaly detected by the sensor has to be located fairly accurately, and its orientation determined, before one can try to find size/shape/composition properties that identify the object uniquely. The dependence on the latter parameters is linear, and can be solved for efficiently using for example the Normalized Surface Magnetic Charge model. The location and orientation, on the other hand, have a nonlinear effect on the measurable scattered field, making their determination much more time-consuming and thus hampering the ability to carry out discrimination in real time. In particular, it is difficult to resolve for depth when one has measurements taken at only one instrument elevation. In view of the difficulties posed by direct inversion, we propose using a Support Vector Machine (SVM) to infer the location and orientation of buried UXO. SVMs are a method of supervised machine learning: the user can train a computer program by feeding it features of representative examples, and the machine, in turn, can generalize this information by finding underlying patterns and using them to classify or regress unseen instances. In this work we train an SVM using measured-field information, for both synthetic and experimental data, and evaluate its ability to predict the location of different buried objects to reasonable accuracy. We explore various combinations of input data and learning parameters in search of an optimal predictive configuration.

  1. Estimation of Teacher Practices Based on Text Transcripts of Teacher Speech Using a Support Vector Machine Algorithm

    ERIC Educational Resources Information Center

    Araya, Roberto; Plana, Francisco; Dartnell, Pablo; Soto-Andrade, Jorge; Luci, Gina; Salinas, Elena; Araya, Marylen

    2012-01-01

    Teacher practice is normally assessed by observers who watch classes or videos of classes. Here, we analyse an alternative strategy that uses text transcripts and a support vector machine classifier. For each one of the 710 videos of mathematics classes from the 2005 Chilean National Teacher Assessment Programme, a single 4-minute slice was…

  2. Curriculum Assessment Using Artificial Neural Network and Support Vector Machine Modeling Approaches: A Case Study. IR Applications. Volume 29

    ERIC Educational Resources Information Center

    Chen, Chau-Kuang

    2010-01-01

    Artificial Neural Network (ANN) and Support Vector Machine (SVM) approaches have been on the cutting edge of science and technology for pattern recognition and data classification. In the ANN model, classification accuracy can be achieved by using the feed-forward of inputs, back-propagation of errors, and the adjustment of connection weights. In…

  3. Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

    NASA Astrophysics Data System (ADS)

    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.

  4. Support vector machines for automated recognition of obstructive sleep apnea syndrome from ECG recordings.

    PubMed

    Khandoker, Ahsan H; Palaniswami, Marimuthu; Karmakar, Chandan K

    2009-01-01

    Obstructive sleep apnea syndrome (OSAS) is associated with cardiovascular morbidity as well as excessive daytime sleepiness and poor quality of life. In this study, we apply a machine learning technique [support vector machines (SVMs)] for automated recognition of OSAS types from their nocturnal ECG recordings. A total of 125 sets of nocturnal ECG recordings acquired from normal subjects (OSAS - ) and subjects with OSAS (OSAS +), each of approximately 8 h in duration, were analyzed. Features extracted from successive wavelet coefficient levels after wavelet decomposition of signals due to heart rate variability (HRV) from RR intervals and ECG-derived respiration (EDR) from R waves of QRS amplitudes were used as inputs to the SVMs to recognize OSAS +/- subjects. Using leave-one-out technique, the maximum accuracy of classification for 83 training sets was found to be 100% for SVMs using a subset of selected combination of HRV and EDR features. Independent test results on 42 subjects showed that it correctly recognized 24 out of 26 OSAS + subjects and 15 out of 16 OSAS - subjects (accuracy = 92.85%; Cohen's kappa value of 0.85). For estimating the relative severity of OSAS, the posterior probabilities of SVM outputs were calculated and compared with respective apnea/hypopnea index. These results suggest superior performance of SVMs in OSAS recognition supported by wavelet-based features of ECG. The results demonstrate considerable potential in applying SVMs in an ECG-based screening device that can aid a sleep specialist in the initial assessment of patients with suspected OSAS. PMID:19129022

  5. Estimation of Electrically-Evoked Knee Torque from Mechanomyography Using Support Vector Regression

    PubMed Central

    Ibitoye, Morufu Olusola; Hamzaid, Nur Azah; Abdul Wahab, Ahmad Khairi; Hasnan, Nazirah; Olatunji, Sunday Olusanya; Davis, Glen M.

    2016-01-01

    The difficulty of real-time muscle force or joint torque estimation during neuromuscular electrical stimulation (NMES) in physical therapy and exercise science has motivated recent research interest in torque estimation from other muscle characteristics. This study investigated the accuracy of a computational intelligence technique for estimating NMES-evoked knee extension torque based on the Mechanomyographic signals (MMG) of contracting muscles that were recorded from eight healthy males. Simulation of the knee torque was modelled via Support Vector Regression (SVR) due to its good generalization ability in related fields. Inputs to the proposed model were MMG amplitude characteristics, the level of electrical stimulation or contraction intensity, and knee angle. Gaussian kernel function, as well as its optimal parameters were identified with the best performance measure and were applied as the SVR kernel function to build an effective knee torque estimation model. To train and test the model, the data were partitioned into training (70%) and testing (30%) subsets, respectively. The SVR estimation accuracy, based on the coefficient of determination (R2) between the actual and the estimated torque values was up to 94% and 89% during the training and testing cases, with root mean square errors (RMSE) of 9.48 and 12.95, respectively. The knee torque estimations obtained using SVR modelling agreed well with the experimental data from an isokinetic dynamometer. These findings support the realization of a closed-loop NMES system for functional tasks using MMG as the feedback signal source and an SVR algorithm for joint torque estimation. PMID:27447638

  6. Evidence supporting the Egestion-Salivation Hypothesis for inoculation of Xylella fastidiosa by sharpshooter vectors

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Despite more than 70 years of study, the mechanism of inoculation of semipersistent, foregut-borne plant pathogens by their vectors is still unknown. The best model system for these studies is inoculation of the Pierce’s disease bacterium, Xylella fastidiosa (Xf), by vectors such as the glassy-wing...

  7. Prediction of water surface elevation of Great Salt Lake using Support Vector Machine

    NASA Astrophysics Data System (ADS)

    Shrestha, N. K.; Urroz, G.

    2009-12-01

    Record breaking rises of Great Salt Lake (GSL) water levels that were observed in the period 1982-1987 resulted in severe economic impact to the State of Utah. Rising lake levels caused flooding that damaged highways, railways, recreation facilities and industries located in exposed lake bed. Prediction of GSL water levels necessitates the development of a model for accurate predictions of such levels in order to reduce or prevent economic loss due to flooding as happened in the past. A data-driven model, whose intent is to determine the relationship between inputs and outputs without knowing underlying physical process, was used in this project. A data-driven model can bridge the gap between classical regression-based and physically-based hydrological models. A Support Vector Machines (SVM) was used to predict water surface elevation of the GSL. The SVM-based reconstruction was used to develop time series forecast for multiple lead times. The model is able to extract the dynamics of the system by using only a few observed data points for training. The reliability of the algorithm in learning and forecasting the dynamics of the system was tested by changing two parameters: the integer time lag and the dimension (d) of the system. Parameter tau models the delay in which the dynamics unfolds by creating vectors of dimension d out of single measurements. For a given set of parameters tau and d, the discrepancy between observation and prediction is reduced by changing the cost parameter and a parameter called epsilon that controls the width of the SVM insensitive zone. All the data points within the epsilon insensitive zone are neglected in the SVM analysis. The analysis was performed for two time periods. The period of 1982 to 1987 was used to test the model performance in predicting the corresponding dramatic rise of GSL elevation. The period of 1987 to 2008 was used to test the performance of model for the normal water level rise and fall of the GSL. This analysis

  8. Assimilation of PFISR Data Using Support Vector Regression and Ground Based Camera Constraints

    NASA Astrophysics Data System (ADS)

    Clayton, R.; Lynch, K. A.; Nicolls, M. J.; Hampton, D. L.; Michell, R.; Samara, M.; Guinther, J.

    2013-12-01

    In order to best interpret the information gained from multipoint in situ measurements, a Support Vector Regression algorithm is being developed to interpret the data collected from the instruments in the context of ground observations (such as those from camera or radar array). The idea behind SVR is to construct the simplest function that models the data with the least squared error, subject to constraints given by the user. Constraints can be brought into the algorithm from other data sources or from models. As is often the case with data, a perfect solution to such a problem may be impossible, thus 'slack' may be introduced to control how closely the model adheres to the data. The algorithm employs kernels, and chooses radial basis functions as an appropriate kernel. The current SVR code can take input data as one to three dimensional scalars or vectors, and may also include time. External data can be incorporated and assimilated into a model of the environment. Regions of minimal and maximal values are allowed to relax to the sample average (or a user-supplied model) on size and time scales determined by user input, known as feature sizes. These feature sizes can vary for each degree of freedom if the user desires. The user may also select weights for each data point, if it is desirable to weight parts of the data differently. In order to test the algorithm, Poker Flat Incoherent Scatter Radar (PFISR) and MICA sounding rocket data are being used as sample data. The PFISR data consists of many beams, each with multiple ranges. In addition to analyzing the radar data as it stands, the algorithm is being used to simulate data from a localized ionospheric swarm of Cubesats using existing PFISR data. The sample points of the radar at one altitude slice can serve as surrogates for satellites in a cubeswarm. The number of beams of the PFISR radar can then be used to see what the algorithm would output for a swarm of similar size. By using PFISR data in the 15-beam to

  9. Predicting and improving the protein sequence alignment quality by support vector regression

    PubMed Central

    Lee, Minho; Jeong, Chan-seok; Kim, Dongsup

    2007-01-01

    Background For successful protein structure prediction by comparative modeling, in addition to identifying a good template protein with known structure, obtaining an accurate sequence alignment between a query protein and a template protein is critical. It has been known that the alignment accuracy can vary significantly depending on our choice of various alignment parameters such as gap opening penalty and gap extension penalty. Because the accuracy of sequence alignment is typically measured by comparing it with its corresponding structure alignment, there is no good way of evaluating alignment accuracy without knowing the structure of a query protein, which is obviously not available at the time of structure prediction. Moreover, there is no universal alignment parameter option that would always yield the optimal alignment. Results In this work, we develop a method to predict the quality of the alignment between a query and a template. We train the support vector regression (SVR) models to predict the MaxSub scores as a measure of alignment quality. The alignment between a query protein and a template of length n is transformed into a (n + 1)-dimensional feature vector, then it is used as an input to predict the alignment quality by the trained SVR model. Performance of our work is evaluated by various measures including Pearson correlation coefficient between the observed and predicted MaxSub scores. Result shows high correlation coefficient of 0.945. For a pair of query and template, 48 alignments are generated by changing alignment options. Trained SVR models are then applied to predict the MaxSub scores of those and to select the best alignment option which is chosen specifically to the query-template pair. This adaptive selection procedure results in 7.4% improvement of MaxSub scores, compared to those when the single best parameter option is used for all query-template pairs. Conclusion The present work demonstrates that the alignment quality can be

  10. Vector Data Model: A New Model of HDF-EOS to Support GIS Applications in EOS

    NASA Astrophysics Data System (ADS)

    Chi, E.; Edmonds, R d

    2001-05-01

    NASA's Earth Science Data Information System (ESDIS) project has an active program of research and development of systems for the storage and management of Earth science data for Earth Observation System (EOS) mission, a key program of NASA Earth Science Enterprise. EOS has adopted an extension of the Hierarchical Data Format (HDF) as the format of choice for standard product distribution. Three new EOS specific datatypes - point, swath and grid - have been defined within the HDF framework. The enhanced data format is named HDF-EOS. Geographic Information Systems (GIS) are used by Earth scientists in EOS data product generation, visualization, and analysis. There are two major data types in GIS applications, raster and vector. The current HDF-EOS handles only raster type in the swath data model. The vector data model is identified and developed as a new HDFEOS format to meet the requirements of scientists working with EOS data products in vector format. The vector model is designed using a topological data structure, which defines the spatial relationships among points, lines, and polygons. The three major topological concepts that the vector model adopts are: a) lines connect to each other at nodes (connectivity), b) lines that connect to surround an area define a polygon (area definition), and c) lines have direction and left and right sides (contiguity). The vector model is implemented in HDF by mapping the conceptual model to HDF internal data models and structures, viz. Vdata, Vgroup, and their associated attribute structures. The point, line, and polygon geometry and attribute data are stored in similar tables. Further, the vector model utilizes the structure and product metadata, which characterize the HDF-EOS. Both types of metadata are stored as attributes in HDF-EOS files, and are encoded in text format by using Object Description Language (ODL) and stored as global attributes in HDF-EOS files. EOS has developed a series of routines for storing

  11. Connections between inversion, kriging, wiener filters, support vector machines, and neural networks.

    NASA Astrophysics Data System (ADS)

    Kuzma, H. A.; Kappler, K. A.; Rector, J. W.

    2006-12-01

    Kriging, wiener filters, support vector machines (SVMs), neural networks, linear and non-linear inversion are methods for predicting the values of one set of variables given the values of another. They can all be used to estimate a set of model parameters from measured data given that a physical relationship exists between models and data. However, since the methods were developed in different fields, the mathematics used to describe them tend to obscure rather than highlight the links between them. In this poster, we diagram the methods and clarify their connections in hopes that practitioners of one method will be able to understand and learn from the insights developed in another. At the heart of all of the methods are a set of coefficients that must be found by minimizing an objective function. The solution to the objective function can be found either by inverting a matrix, or by searching through a space of possible answers. We distinguish between direct inversion, in which the desired coefficients are those of the model itself, and indirect inversion, in which examples of models and data are used to estimate the coefficients of an inverse process that, once discovered, can be used to compute new models from new data. Kriging is developed from Geostatistics. The model is usually a rock property (such as gold concentration) and the data is a sample location (x,y,z). The desired coefficients are a set of weights which are used to predict the concentration of a sample taken at a new location based on a variogram. The variogram is computed by averaging across a given set of known samples and is manually adjusted to reflect prior knowledge. Wiener filters were developed in signal processing to predict the values of one time-series from measurements of another. A wiener filter can be derived from kriging by replacing variograms with correlation. Support vector machines are an offshoot of statistical learning theory. They can be written as a form of kriging in which

  12. Application of machine learning using support vector machines for crater detection from Martian digital topography data

    NASA Astrophysics Data System (ADS)

    Salamunićcar, Goran; Lončarić, Sven

    In our previous work, in order to extend the GT-57633 catalogue [PSS, 56 (15), 1992-2008] with still uncatalogued impact-craters, the following has been done [GRS, 48 (5), in press, doi:10.1109/TGRS.2009.2037750]: (1) the crater detection algorithm (CDA) based on digital elevation model (DEM) was developed; (2) using 1/128° MOLA data, this CDA proposed 414631 crater-candidates; (3) each crater-candidate was analyzed manually; and (4) 57592 were confirmed as correct detections. The resulting GT-115225 catalog is the significant result of this effort. However, to check such a large number of crater-candidates manually was a demanding task. This was the main motivation for work on improvement of the CDA in order to provide better classification of craters as true and false detections. To achieve this, we extended the CDA with the machine learning capability, using support vector machines (SVM). In the first step, the CDA (re)calculates numerous terrain morphometric attributes from DEM. For this purpose, already existing modules of the CDA from our previous work were reused in order to be capable to prepare these attributes. In addition, new attributes were introduced such as ellipse eccentricity and tilt. For machine learning purpose, the CDA is additionally extended to provide 2-D topography-profile and 3-D shape for each crater-candidate. The latter two are a performance problem because of the large number of crater-candidates in combination with the large number of attributes. As a solution, we developed a CDA architecture wherein it is possible to combine the SVM with a radial basis function (RBF) or any other kernel (for initial set of attributes), with the SVM with linear kernel (for the cases when 2-D and 3-D data are included as well). Another challenge is that, in addition to diversity of possible crater types, there are numerous morphological differences between the smallest (mostly very circular bowl-shaped craters) and the largest (multi-ring) impact

  13. Dimension Reduction via Unsupervised Learning Yields Significant Computational Improvements for Support Vector Machine Based Protein Family Classification.

    SciTech Connect

    Webb-Robertson, Bobbie-Jo M.; Matzke, Melissa M.; Oehmen, Christopher S.

    2009-02-26

    Reducing the dimension of vectors used in training support vector machines (SVMs) results in a proportional speedup in training time. For large-scale problems this can make the difference between tractable and intractable training tasks. However, it is critical that classifiers trained on reduced datasets perform as reliably as their counterparts trained on high-dimensional data. We assessed principal component analysis (PCA) and sequential project pursuit (SPP) as dimension reduction strategies in the biology application of classifying proteins into well-defined functional ‘families’ (SVM-based protein family classification) by their impact on run-time, sensitivity and selectivity. Homology vectors of 4352 elements were reduced to approximately 2% of the original data size without significantly affecting accuracy using PCA and SPP, while leading to approximately a 28-fold speedup in run-time.

  14. Dynamic coupling of support vector machine and K-nearest neighbour for downscaling daily rainfall

    NASA Astrophysics Data System (ADS)

    Devak, Manjula; Dhanya, C. T.; Gosain, A. K.

    2015-06-01

    Climate change impact assessment studies in water resources section demand the simulations of climatic variables at coarser scales from dynamic General Circulation Models (GCMs) to be mapped to even finer scales. Related studies in this area have mostly been relying on statistical techniques for downscaling variables to finer resolution. This demands a careful selection of a suitable downscaling model, to alleviate the downscaling uncertainty. In this study, it is proposed to develop a dynamic framework for downscaling purpose by integrating the frequently used techniques, K-Nearest Neighbour (KNN) and Support Vector Machine (SVM). In order to give flexibility in future predictors-predictand relationships and to account the sensitivity in model parameters, it is also proposed to generate an ensemble of outputs by identifying various plausible model parameter combinations. The performance of this framework for downscaling daily precipitation values at different locations is compared with simple KNN and SVM models. The proposed hybrid model is found to be better in capturing various characteristics of daily precipitation than individual models, especially in simulating the extremes, both in magnitude and duration. The mean ensemble is found to be efficient than single best simulation with optimum parameter combinations. The efficacy of hybrid SVM-KNN ensemble downscaling model is established through detailed investigations. The future downscaled projection for mid-century and late century employing this hybrid model indicates an increased variability in future precipitation, though the intensity varies for various locations. The developed methodology hence ensures lesser downscaling uncertainty and also eliminates the inherent assumption of relationship stationarity considered in many downscaling models.

  15. Support vector regression to predict porosity and permeability: Effect of sample size

    NASA Astrophysics Data System (ADS)

    Al-Anazi, A. F.; Gates, I. D.

    2012-02-01

    Porosity and permeability are key petrophysical parameters obtained from laboratory core analysis. Cores, obtained from drilled wells, are often few in number for most oil and gas fields. Porosity and permeability correlations based on conventional techniques such as linear regression or neural networks trained with core and geophysical logs suffer poor generalization to wells with only geophysical logs. The generalization problem of correlation models often becomes pronounced when the training sample size is small. This is attributed to the underlying assumption that conventional techniques employing the empirical risk minimization (ERM) inductive principle converge asymptotically to the true risk values as the number of samples increases. In small sample size estimation problems, the available training samples must span the complexity of the parameter space so that the model is able both to match the available training samples reasonably well and to generalize to new data. This is achieved using the structural risk minimization (SRM) inductive principle by matching the capability of the model to the available training data. One method that uses SRM is support vector regression (SVR) network. In this research, the capability of SVR to predict porosity and permeability in a heterogeneous sandstone reservoir under the effect of small sample size is evaluated. Particularly, the impact of Vapnik's ɛ-insensitivity loss function and least-modulus loss function on generalization performance was empirically investigated. The results are compared to the multilayer perception (MLP) neural network, a widely used regression method, which operates under the ERM principle. The mean square error and correlation coefficients were used to measure the quality of predictions. The results demonstrate that SVR yields consistently better predictions of the porosity and permeability with small sample size than the MLP method. Also, the performance of SVR depends on both kernel function

  16. Evaluating a coupled discrete wavelet transform and support vector regression for daily and monthly streamflow forecasting

    NASA Astrophysics Data System (ADS)

    Liu, Zhiyong; Zhou, Ping; Chen, Gang; Guo, Ledong

    2014-11-01

    This study investigated the performance and potential of a hybrid model that combined the discrete wavelet transform and support vector regression (the DWT-SVR model) for daily and monthly streamflow forecasting. Three key factors of the wavelet decomposition phase (mother wavelet, decomposition level, and edge effect) were proposed to consider for improving the accuracy of the DWT-SVR model. The performance of DWT-SVR models with different combinations of these three factors was compared with the regular SVR model. The effectiveness of these models was evaluated using the root-mean-squared error (RMSE) and Nash-Sutcliffe model efficiency coefficient (NSE). Daily and monthly streamflow data observed at two stations in Indiana, United States, were used to test the forecasting skill of these models. The results demonstrated that the different hybrid models did not always outperform the SVR model for 1-day and 1-month lead time streamflow forecasting. This suggests that it is crucial to consider and compare the three key factors when using the DWT-SVR model (or other machine learning methods coupled with the wavelet transform), rather than choosing them based on personal preferences. We then combined forecasts from multiple candidate DWT-SVR models using a model averaging technique based upon Akaike's information criterion (AIC). This ensemble prediction was superior to the single best DWT-SVR model and regular SVR model for both 1-day and 1-month ahead predictions. With respect to longer lead times (i.e., 2- and 3-day and 2-month), the ensemble predictions using the AIC averaging technique were consistently better than the best DWT-SVR model and SVR model. Therefore, integrating model averaging techniques with the hybrid DWT-SVR model would be a promising approach for daily and monthly streamflow forecasting. Additionally, we strongly recommend considering these three key factors when using wavelet-based SVR models (or other wavelet-based forecasting models).

  17. Automated classification of bacterial particles in flow by multiangle scatter measurement and support vector machine classifier.

    PubMed

    Rajwa, Bartek; Venkatapathi, Murugesan; Ragheb, Kathy; Banada, Padmapriya P; Hirleman, E Daniel; Lary, Todd; Robinson, J Paul

    2008-04-01

    Biological microparticles, including bacteria, scatter light in all directions when illuminated. The complex scatter pattern is dependent on particle size, shape, refraction index, density, and morphology. Commercial flow cytometers allow measurement of scattered light intensity at forward and perpendicular (side) angles (2 degrees support vector machine-based pattern recognition system. It has been shown that information provided just by five angles of scatter and axial light loss can be sufficient to recognize various

  18. Real-time optical path control method that utilizes multiple support vector machines for traffic prediction

    NASA Astrophysics Data System (ADS)

    Kawase, Hiroshi; Mori, Yojiro; Hasegawa, Hiroshi; Sato, Ken-ichi

    2016-02-01

    An effective solution to the continuous Internet traffic expansion is to offload traffic to lower layers such as the L2 or L1 optical layers. One possible approach is to introduce dynamic optical path operations such as adaptive establishment/tear down according to traffic variation. Path operations cannot be done instantaneously; hence, traffic prediction is essential. Conventional prediction techniques need optimal parameter values to be determined in advance by averaging long-term variations from the past. However, this does not allow adaptation to the ever-changing short-term variations expected to be common in future networks. In this paper, we propose a real-time optical path control method based on a machinelearning technique involving support vector machines (SVMs). A SVM learns the most recent traffic characteristics, and so enables better adaptation to temporal traffic variations than conventional techniques. The difficulty lies in determining how to minimize the time gap between optical path operation and buffer management at the originating points of those paths. The gap makes the required learning data set enormous and the learning process costly. To resolve the problem, we propose the adoption of multiple SVMs running in parallel, trained with non-overlapping subsets of the original data set. The maximum value of the outputs of these SVMs will be the estimated number of necessary paths. Numerical experiments prove that our proposed method outperforms a conventional prediction method, the autoregressive moving average method with optimal parameter values determined by Akaike's information criterion, and reduces the packet-loss ratio by up to 98%.

  19. Using Support Vector Machines to Detect Therapeutically Incorrect Measurements by the MiniMed CGMS®

    PubMed Central

    Bondia, Jorge; Tarín, Cristina; García-Gabin, Winston; Esteve, Eduardo; Fernández-Real, José Manuel; Ricart, Wifredo; Vehí, Josep

    2008-01-01

    Background Current continuous glucose monitors have limited accuracy mainly in the low range of glucose measurements. This lack of accuracy is a limiting factor in their clinical use and in the development of the so-called artificial pancreas. The ability to detect incorrect readings provided by continuous glucose monitors from raw data and other information supplied by the monitor itself is of utmost clinical importance. In this study, support vector machines (SVMs), a powerful statistical learning technique, were used to detect therapeutically incorrect measurements made by the Medtronic MiniMed CGMS®. Methods Twenty patients were monitored for three days (first day at the hospital and two days at home) using the MiniMed CGMS. After the third day, the monitor data were downloaded to the physician's computer. During the first 12 hours, the patients stayed in the hospital, and blood samples were taken every 15 minutes for two hours after meals and every 30 minutes otherwise. Plasma glucose measurements were interpolated using a cubic method for time synchronization with simultaneous MiniMed CGMS measurements every five minutes, obtaining a total of 2281 samples. A Gaussian SVM classifier trained on the monitor's electrical signal and glucose estimation was tuned and validated using multiple runs of k-fold cross-validation. The classes considered were Clarke error grid zones A+B and C+D+E. Results After ten runs of ten-fold cross-validation, an average specificity and sensitivity of 92.74% and 75.49%, respectively, were obtained (see Figure 4). The average correct rate was 91.67%. Conclusions Overall, the SVM performed well, in spite of the somewhat low sensitivity. The classifier was able to detect the time intervals when the monitor's glucose profile could not be trusted due to incorrect measurements. As a result, hypoglycemic episodes missed by the monitor were detected. PMID:19885238

  20. Trajectory classification in circular restricted three-body problem using support vector machine

    NASA Astrophysics Data System (ADS)

    Li, Weipeng; Huang, Hai; Peng, Fujun

    2015-07-01

    In the circular restricted three-body problem (CR3BP), transit orbit is a class of orbit which can pass through the bottleneck region of the zero velocity curve and escapes from the vicinity of the primary or the secondary. This kind of orbit plays a very important role in the design of space exploration missions. A kind of low-energy interplanetary transfer, which is called Interplanetary Superhighway (IPS), can be realized by utilizing transit orbits. To use the transit orbit in actual mission design, a key issue is to find an algorithm which can separate the states corresponding to transit orbits from the states corresponding to other types of orbits rapidly. In fact, the distribution of transit orbit in the phase space has been investigated by numerical method, and a Fourier series approximation method has been introduced to describe the boundary of transit orbits. However, the Fourier series approximation method needs several hundred sets of Fourier series. The coefficients of these Fourier series are neither easy to be computed nor convenient to be stored, which makes the method can hardly be used in actual mission design. In this paper, the support vector machine (SVM) is used to classify the trajectories in the CR3BP. Using the Gaussian kernel, the 6-dimensional states in the CR3BP are mapped into an infinite-dimensional space, and the bound of the transit orbits is described by a hyperplane. A training data generation method is introduced, which reduces the size of training data by generating the states near the hyperplane. The numerical results show that the proposed algorithm gives the good correct rate of classification, and its computing speed is much faster than that of the Fourier series approximation method.

  1. Balanced VS Imbalanced Training Data: Classifying Rapideye Data with Support Vector Machines

    NASA Astrophysics Data System (ADS)

    Ustuner, M.; Sanli, F. B.; Abdikan, S.

    2016-06-01

    The accuracy of supervised image classification is highly dependent upon several factors such as the design of training set (sample selection, composition, purity and size), resolution of input imagery and landscape heterogeneity. The design of training set is still a challenging issue since the sensitivity of classifier algorithm at learning stage is different for the same dataset. In this paper, the classification of RapidEye imagery with balanced and imbalanced training data for mapping the crop types was addressed. Classification with imbalanced training data may result in low accuracy in some scenarios. Support Vector Machines (SVM), Maximum Likelihood (ML) and Artificial Neural Network (ANN) classifications were implemented here to classify the data. For evaluating the influence of the balanced and imbalanced training data on image classification algorithms, three different training datasets were created. Two different balanced datasets which have 70 and 100 pixels for each class of interest and one imbalanced dataset in which each class has different number of pixels were used in classification stage. Results demonstrate that ML and NN classifications are affected by imbalanced training data in resulting a reduction in accuracy (from 90.94% to 85.94% for ML and from 91.56% to 88.44% for NN) while SVM is not affected significantly (from 94.38% to 94.69%) and slightly improved. Our results highlighted that SVM is proven to be a very robust, consistent and effective classifier as it can perform very well under balanced and imbalanced training data situations. Furthermore, the training stage should be precisely and carefully designed for the need of adopted classifier.

  2. Multiangle Remote Sensing of Optically Thin Cirrus Clouds From MISR Using Support Vector Machines

    NASA Astrophysics Data System (ADS)

    Garay, M. J.; Mazzoni, D.; Davies, R.; Wagstaff, K.

    2004-05-01

    Thin cirrus clouds, those with optical depths less than 1, can potentially have large radiative effects on the atmospheric and surface energy budgets in regions where they are prevalent. They also present an impediment to the retrieval of clear sky properties such as aerosol optical depth, temperature profiles, etc. Such clouds, however, are notoriously difficult to detect using standard satellite remote sensing techniques. The unique multiangle sensing capability of the Multiangle Imaging SpectroRadiometer (MISR) on NASA's Terra satellite, in particular the availability of cameras with view angles as large as 70.5 degrees, gives MISR the ability to detect thin cirrus clouds that are invisible to nadir-looking instruments. While MISR has been operational for over four years and many scenes containing thin cirrus have been examined on a per case basis, there remains a need to objectively and automatically identify just the cirrus clouds within any given scene. Based on our previous work applying machine learning technology to develop a more robust MISR cloud mask, we have developed a thin cirrus cloud detector for MISR, using Support Vector Machines (SVMs), and taking advantage of spectral, spatial and angular signature information from MISR's 45.6, 60 and 70.5-degree cameras. For a few representative cases, we will demonstrate the accuracy of the SVM cirrus retrieval, especially in comparison to a traditional nadir-looking retrieval, emphasizing the usefulness of the multiangle approach. We then show how this trained SVM can be used to generate a climatology of thin cirrus clouds.

  3. miRBoost: boosting support vector machines for microRNA precursor classification.

    PubMed

    Tran, Van Du T; Tempel, Sebastien; Zerath, Benjamin; Zehraoui, Farida; Tahi, Fariza

    2015-05-01

    Identification of microRNAs (miRNAs) is an important step toward understanding post-transcriptional gene regulation and miRNA-related pathology. Difficulties in identifying miRNAs through experimental techniques combined with the huge amount of data from new sequencing technologies have made in silico discrimination of bona fide miRNA precursors from non-miRNA hairpin-like structures an important topic in bioinformatics. Among various techniques developed for this classification problem, machine learning approaches have proved to be the most promising. However these approaches require the use of training data, which is problematic due to an imbalance in the number of miRNAs (positive data) and non-miRNAs (negative data), which leads to a degradation of their performance. In order to address this issue, we present an ensemble method that uses a boosting technique with support vector machine components to deal with imbalanced training data. Classification is performed following a feature selection on 187 novel and existing features. The algorithm, miRBoost, performed better in comparison with state-of-the-art methods on imbalanced human and cross-species data. It also showed the highest ability among the tested methods for discovering novel miRNA precursors. In addition, miRBoost was over 1400 times faster than the second most accurate tool tested and was significantly faster than most of the other tools. miRBoost thus provides a good compromise between prediction efficiency and execution time, making it highly suitable for use in genome-wide miRNA precursor prediction. The software miRBoost is available on our web server http://EvryRNA.ibisc.univ-evry.fr. PMID:25795417

  4. A multi-layer soil moisture data assimilation using support vector machines and ensemble particle filter

    NASA Astrophysics Data System (ADS)

    Yu, Zhongbo; Liu, Di; Lü, Haishen; Fu, Xiaolei; Xiang, Long; Zhu, Yonghua

    2012-12-01

    SummaryHybrid data assimilation (DA) is greatly used in recent hydrology and water resources research. In this study, one newly introduced technique, the ensemble particle filter (EnPF), formed by coupling ensemble Kalman filter (EnKF) with particle filter (PF), is applied for a multi-layer soil moisture prediction in the Meilin watershed based on the support vector machines (SVMs). The data used in this paper includes six-layer soil moisture: 0-5 cm, 30 cm, 50 cm, 100 cm, 200 cm and 300 cm and five meteorological parameters: soil temperature at 5 cm and 20 cm, air temperature, relative humidity and solar radiation in the study area. In order to investigate this EnPF approach, another two filters, EnKF and PF are applied as another two data assimilation methods to conduct a comparison. In addition, the SVM model simulated data without updating with data assimilation technique is discussed as well to evaluate the data assimilation technique. Two experimental cases are explored here, one with 200 initial training ensemble members in the SVM training phase while the other with 1000 initial training ensemble members. Three main findings are obtained in this study: (1) the SVMs machine is a statistically sound and robust model for soil moisture prediction in both the surface and root zone layers, and the larger the initial training data ensemble, the more effective the operator derived; (2) data assimilation technique does improve the performance of SVM modeling; (3) EnPF outweighs the performance of other two filters as well as the SVM model; Moreover, the ability of EnPF and PF is not positively related to the resampling ensemble size, when the resampling size exceeds a certain amount, the performance of EnPF and PF would be degraded. Because the EnPF still performs well than EnKF, it can be used as a powerful data assimilation tool in the soil moisture prediction.

  5. Prediction of pore-water pressure response to rainfall using support vector regression

    NASA Astrophysics Data System (ADS)

    Babangida, Nuraddeen Muhammad; Mustafa, Muhammad Raza Ul; Yusuf, Khamaruzaman Wan; Isa, Mohamed Hasnain

    2016-05-01

    Nonlinear complex behavior of pore-water pressure responses to rainfall was modelled using support vector regression (SVR). Pore-water pressure can rise to disturbing levels that may result in slope failure during or after rainfall. Traditionally, monitoring slope pore-water pressure responses to rainfall is tedious and expensive, in that the slope must be instrumented with necessary monitors. Data on rainfall and corresponding responses of pore-water pressure were collected from such a monitoring program at a slope site in Malaysia and used to develop SVR models to predict pore-water pressure fluctuations. Three models, based on their different input configurations, were developed. SVR optimum meta-parameters were obtained using k-fold cross validation and a grid search. Model type 3 was adjudged the best among the models and was used to predict three other points on the slope. For each point, lag intervals of 30 min, 1 h and 2 h were used to make the predictions. The SVR model predictions were compared with predictions made by an artificial neural network model; overall, the SVR model showed slightly better results. Uncertainty quantification analysis was also performed for further model assessment. The uncertainty components were found to be low and tolerable, with d-factor of 0.14 and 74 % of observed data falling within the 95 % confidence bound. The study demonstrated that the SVR model is effective in providing an accurate and quick means of obtaining pore-water pressure response, which may be vital in systems where response information is urgently needed.

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

    PubMed

    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. PMID:26615468

  7. Filtered selection coupled with support vector machines generate a functionally relevant prediction model for colorectal cancer

    PubMed Central

    Gabere, Musa Nur; Hussein, Mohamed Aly; Aziz, Mohammad Azhar

    2016-01-01

    Purpose There has been considerable interest in using whole-genome expression profiles for the classification of colorectal cancer (CRC). The selection of important features is a crucial step before training a classifier. Methods In this study, we built a model that uses support vector machine (SVM) to classify cancer and normal samples using Affymetrix exon microarray data obtained from 90 samples of 48 patients diagnosed with CRC. From the 22,011 genes, we selected the 20, 30, 50, 100, 200, 300, and 500 genes most relevant to CRC using the minimum-redundancy–maximum-relevance (mRMR) technique. With these gene sets, an SVM model was designed using four different kernel types (linear, polynomial, radial basis function [RBF], and sigmoid). Results The best model, which used 30 genes and RBF kernel, outperformed other combinations; it had an accuracy of 84% for both ten fold and leave-one-out cross validations in discriminating the cancer samples from the normal samples. With this 30 genes set from mRMR, six classifiers were trained using random forest (RF), Bayes net (BN), multilayer perceptron (MLP), naïve Bayes (NB), reduced error pruning tree (REPT), and SVM. Two hybrids, mRMR + SVM and mRMR + BN, were the best models when tested on other datasets, and they achieved a prediction accuracy of 95.27% and 91.99%, respectively, compared to other mRMR hybrid models (mRMR + RF, mRMR + NB, mRMR + REPT, and mRMR + MLP). Ingenuity pathway analysis was used to analyze the functions of the 30 genes selected for this model and their potential association with CRC: CDH3, CEACAM7, CLDN1, IL8, IL6R, MMP1, MMP7, and TGFB1 were predicted to be CRC biomarkers. Conclusion This model could be used to further develop a diagnostic tool for predicting CRC based on gene expression data from patient samples. PMID:27330311

  8. Diagnosing Tuberculosis With a Novel Support Vector Machine-Based Artificial Immune Recognition System

    PubMed Central

    Saybani, Mahmoud Reza; Shamshirband, Shahaboddin; Golzari Hormozi, Shahram; Wah, Teh Ying; Aghabozorgi, Saeed; Pourhoseingholi, Mohamad Amin; Olariu, Teodora

    2015-01-01

    Background: Tuberculosis (TB) is a major global health problem, which has been ranked as the second leading cause of death from an infectious disease worldwide. Diagnosis based on cultured specimens is the reference standard, however results take weeks to process. Scientists are looking for early detection strategies, which remain the cornerstone of tuberculosis control. Consequently there is a need to develop an expert system that helps medical professionals to accurately and quickly diagnose the disease. Artificial Immune Recognition System (AIRS) has been used successfully for diagnosing various diseases. However, little effort has been undertaken to improve its classification accuracy. Objectives: In order to increase the classification accuracy of AIRS, this study introduces a new hybrid system that incorporates a support vector machine into AIRS for diagnosing tuberculosis. Patients and Methods: Patient epacris reports obtained from the Pasteur laboratory of Iran were used as the benchmark data set, with the sample size of 175 (114 positive samples for TB and 60 samples in the negative group). The strategy of this study was to ensure representativeness, thus it was important to have an adequate number of instances for both TB and non-TB cases. The classification performance was measured through 10-fold cross-validation, Root Mean Squared Error (RMSE), sensitivity and specificity, Youden’s Index, and Area Under the Curve (AUC). Statistical analysis was done using the Waikato Environment for Knowledge Analysis (WEKA), a machine learning program for windows. Results: With an accuracy of 100%, sensitivity of 100%, specificity of 100%, Youden’s Index of 1, Area Under the Curve of 1, and RMSE of 0, the proposed method was able to successfully classify tuberculosis patients. Conclusions: There have been many researches that aimed at diagnosing tuberculosis faster and more accurately. Our results described a model for diagnosing tuberculosis with 100% sensitivity

  9. Disease Prediction based on Functional Connectomes using a Scalable and Spatially-Informed Support Vector Machine

    PubMed Central

    Watanabe, Takanori; Kessler, Daniel; Scott, Clayton; Angstadt, Michael; Sripada, Chandra

    2014-01-01

    Substantial evidence indicates that major psychiatric disorders are associated with distributed neural dysconnectivity, leading to strong interest in using neuroimaging methods to accurately predict disorder status. In this work, we are specifically interested in a multivariate approach that uses features derived from whole-brain resting state functional connectomes. However, functional connectomes reside in a high dimensional space, which complicates model interpretation and introduces numerous statistical and computational challenges. Traditional feature selection techniques are used to reduce data dimensionality, but are blind to the spatial structure of the connectomes. We propose a regularization framework where the 6-D structure of the functional connectome (defined by pairs of points in 3-D space) is explicitly taken into account via the fused Lasso or the GraphNet regularizer. Our method only restricts the loss function to be convex and margin-based, allowing non-differentiable loss functions such as the hinge-loss to be used. Using the fused Lasso or GraphNet regularizer with the hinge-loss leads to a structured sparse support vector machine (SVM) with embedded feature selection. We introduce a novel efficient optimization algorithm based on the augmented Lagrangian and the classical alternating direction method, which can solve both fused Lasso and GraphNet regularized SVM with very little modification. We also demonstrate that the inner subproblems of the algorithm can be solved efficiently in analytic form by coupling the variable splitting strategy with a data augmentation scheme. Experiments on simulated data and resting state scans from a large schizophrenia dataset show that our proposed approach can identify predictive regions that are spatially contiguous in the 6-D “connectome space,” offering an additional layer of interpretability that could provide new insights about various disease processes. PMID:24704268

  10. Computational Detection of piRNA in Human Using Support Vector Machine

    PubMed Central

    Seyeddokht, Atefeh; Aslaminejad, Ali Asghar; Masoudi-Nejad, Ali; Nassiri, Mohammadreza; Zahiri, Javad; Sadeghi, Balal

    2016-01-01

    Background: Piwi-interacting RNAs (piRNAs) are small non-coding RNAs (ncRNAs), with a length of about 24–32 nucleotides, which have been discovered recently. These ncRNAs play an important role in germline development, transposon silencing, epigenetic regulation, protecting the genome from invasive transposable elements, and the pathophysiology of diseases such as cancer. piRNA identification is challenging due to the lack of conserved piRNA sequences and structural elements. Methods: To detect piRNAs, an appropriate feature set, including 8 diverse feature groups to encode each RNA was applied. In addition, a Support Vector Machine (SVM) classifier was used with optimized parameters for RNA classification. According to the obtained results, the classification performance using the optimized feature subsets was much higher than the one in previously published studies. Results: Our results revealed 98% accuracy, Mathew’ correlation coefficient of 98% and 99% specificity in discriminating piRNAs from the other RNAs. Also, the obtained results show that the proposed method outperforms its competitors. Conclusion: In this paper, a prediction method was proposed to identify piRNA in human. Also, 48 heterogeneous features (sequence and structural features) were used to encode RNAs. To assess the performance of the method, a benchmark dataset containing 515 piRNAs and 1206 types of other RNAs was constructed. Our method reached the accuracy of 99% on the benchmark dataset. Also, our analysis revealed that the structural features are the most contributing features in piRNA prediction. PMID:26855734

  11. A novel approach for accurate prediction of spontaneous passage of ureteral stones: support vector machines.

    PubMed

    Dal Moro, F; Abate, A; Lanckriet, G R G; Arandjelovic, G; Gasparella, P; Bassi, P; Mancini, M; Pagano, F

    2006-01-01

    The objective of this study was to optimally predict the spontaneous passage of ureteral stones in patients with renal colic by applying for the first time support vector machines (SVM), an instance of kernel methods, for classification. After reviewing the results found in the literature, we compared the performances obtained with logistic regression (LR) and accurately trained artificial neural networks (ANN) to those obtained with SVM, that is, the standard SVM, and the linear programming SVM (LP-SVM); the latter techniques show an improved performance. Moreover, we rank the prediction factors according to their importance using Fisher scores and the LP-SVM feature weights. A data set of 1163 patients affected by renal colic has been analyzed and restricted to single out a statistically coherent subset of 402 patients. Nine clinical factors are used as inputs for the classification algorithms, to predict one binary output. The algorithms are cross-validated by training and testing on randomly selected train- and test-set partitions of the data and reporting the average performance on the test sets. The SVM-based approaches obtained a sensitivity of 84.5% and a specificity of 86.9%. The feature ranking based on LP-SVM gives the highest importance to stone size, stone position and symptom duration before check-up. We propose a statistically correct way of employing LR, ANN and SVM for the prediction of spontaneous passage of ureteral stones in patients with renal colic. SVM outperformed ANN, as well as LR. This study will soon be translated into a practical software toolbox for actual clinical usage. PMID:16374437

  12. MSLVP: prediction of multiple subcellular localization of viral proteins using a support vector machine.

    PubMed

    Thakur, Anamika; Rajput, Akanksha; Kumar, Manoj

    2016-07-19

    Knowledge of the subcellular location (SCL) of viral proteins in the host cell is important for understanding their function in depth. Therefore, we have developed "MSLVP", a two-tier prediction algorithm for predicting multiple SCLs of viral proteins. For this study, data sets of comprehensive viral proteins with experimentally validated SCL annotation were collected from UniProt. Non-redundant (90%) data sets of 3480 viral proteins that belonged to single (2715), double (391) and multiple (374) sites were employed. Additionally, 1687 (30% sequence identity) viral proteins were categorised into single (1366), double (167) and multiple (154) sites. Single, double and multiple locations further comprised of eight, four and six categories, respectively. Viral protein locations include the nucleus, cytoplasm, endoplasmic reticulum, extracellular, single-pass membrane, multi-pass membrane, capsid, remaining others and combinations thereof. Support vector machine based models were developed using sequence features like amino acid composition, dipeptide composition, physicochemical properties and their hybrids. We have employed "one-versus-one" as well as "one-versus-other" strategies for multiclass classification. The performance of "one-versus-one" is better than the "one-versus-other" approach during 10-fold cross-validation. For the 90% data set, we achieved an accuracy, a Matthew's correlation coefficient (MCC) and a receiver operating characteristic (ROC) of 99.99%, 1.00, 1.00; 100.00%, 1.00, 1.00 and 99.90%; 1.00, 1.00 for single, double and multiple locations, respectively. Similar results were achieved for a 30% sequence identity data set. Predictive models for each SCL performed equally well on the independent dataset. The MSLVP web server () can predict subcellular locations i.e. single (8; including single and multi-pass membrane), double (4) and multiple (6). This would be helpful for elucidating the functional annotation of viral proteins and potential drug

  13. Advanced signal processing based on support vector regression for lidar applications

    NASA Astrophysics Data System (ADS)

    Gelfusa, M.; Murari, A.; Malizia, A.; Lungaroni, M.; Peluso, E.; Parracino, S.; Talebzadeh, S.; Vega, J.; Gaudio, P.

    2015-10-01

    The LIDAR technique has recently found many applications in atmospheric physics and remote sensing. One of the main issues, in the deployment of systems based on LIDAR, is the filtering of the backscattered signal to alleviate the problems generated by noise. Improvement in the signal to noise ratio is typically achieved by averaging a quite large number (of the order of hundreds) of successive laser pulses. This approach can be effective but presents significant limitations. First of all, it implies a great stress on the laser source, particularly in the case of systems for automatic monitoring of large areas for long periods. Secondly, this solution can become difficult to implement in applications characterised by rapid variations of the atmosphere, for example in the case of pollutant emissions, or by abrupt changes in the noise. In this contribution, a new method for the software filtering and denoising of LIDAR signals is presented. The technique is based on support vector regression. The proposed new method is insensitive to the statistics of the noise and is therefore fully general and quite robust. The developed numerical tool has been systematically compared with the most powerful techniques available, using both synthetic and experimental data. Its performances have been tested for various statistical distributions of the noise and also for other disturbances of the acquired signal such as outliers. The competitive advantages of the proposed method are fully documented. The potential of the proposed approach to widen the capability of the LIDAR technique, particularly in the detection of widespread smoke, is discussed in detail.

  14. Downscaling of Aircraft-, Landsat-, and MODIS-based Land Surface Temperature Images with Support Vector Machines

    NASA Astrophysics Data System (ADS)

    Ha, W.; Gowda, P. H.; Oommen, T.; Howell, T. A.; Hernandez, J. E.

    2010-12-01

    High spatial resolution Land Surface Temperature (LST) images are required to estimate evapotranspiration (ET) at a field scale for irrigation scheduling purposes. Satellite sensors such as Landsat 5 Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS) can offer images at several spectral bandwidths including visible, near-infrared (NIR), shortwave-infrared, and thermal-infrared (TIR). The TIR images usually have coarser spatial resolutions than those from non-thermal infrared bands. Due to this technical constraint of the satellite sensors on these platforms, image downscaling has been proposed in the field of ET remote sensing. This paper explores the potential of the Support Vector Machines (SVM) to perform downscaling of LST images derived from aircraft (4 m spatial resolution), TM (120 m), and MODIS (1000 m) using normalized difference vegetation index images derived from simultaneously acquired high resolution visible and NIR data (1 m for aircraft, 30 m for TM, and 250 m for MODIS). The SVM is a new generation machine learning algorithm that has found a wide application in the field of pattern recognition and time series analysis. The SVM would be ideally suited for downscaling problems due to its generalization ability in capturing non-linear regression relationship between the predictand and the multiple predictors. Remote sensing data acquired over the Texas High Plains during the 2008 summer growing season will be used in this study. Accuracy assessment of the downscaled 1, 30, and 250 m LST images will be made by comparing them with LST data measured with infrared thermometers at a small spatial scale, upscaled 30 m aircraft-based LST images, and upscaled 250 m TM-based LST images, respectively.

  15. A novel concealed information test method based on independent component analysis and support vector machine.

    PubMed

    Gao, Junfeng; Lu, Liang; Yang, Yong; Yu, Gang; Na, Liantao; Rao, NiNi

    2012-01-01

    The concealed information test (CIT) has drawn much attention and has been widely investigated in recent years. In this study, a novel CIT method based on denoised P3 and machine learning was proposed to improve the accuracy of lie detection. Thirty participants were chosen as the guilty and innocent participants to perform the paradigms of 3 types of stimuli. The electroencephalogram (EEG) signals were recorded and separated into many single trials. In order to enhance the signal noise ratio (SNR) of P3 components, the independent component analysis (ICA) method was adopted to separate non-P3 components (i.e., artifacts) from every single trial. In order to automatically identify the P3 independent components (ICs), a new method based on topography template was proposed to automatically identify the P3 ICs. Then the P3 waveforms with high SNR were reconstructed on Pz electrodes. Second, the 3 groups of features based on time,frequency, and wavelets were extracted from the reconstructed P3 waveforms. Finally, 2 classes of feature samples were used to train a support vector machine (SVM) classifier because it has higher performance compared with several other classifiers. Meanwhile, the optimal number of P3 ICs and some other parameter values in the classifiers were determined by the cross-validation procedures. The presented method achieved a balance test accuracy of 84.29% on detecting P3 components for the guilty and innocent participants. The presented method improves the efficiency of CIT in comparison with previous reported methods. PMID:22423552

  16. Support vector machine-based facial-expression recognition method combining shape and appearance

    NASA Astrophysics Data System (ADS)

    Han, Eun Jung; Kang, Byung Jun; Park, Kang Ryoung; Lee, Sangyoun

    2010-11-01

    Facial expression recognition can be widely used for various applications, such as emotion-based human-machine interaction, intelligent robot interfaces, face recognition robust to expression variation, etc. Previous studies have been classified as either shape- or appearance-based recognition. The shape-based method has the disadvantage that the individual variance of facial feature points exists irrespective of similar expressions, which can cause a reduction of the recognition accuracy. The appearance-based method has a limitation in that the textural information of the face is very sensitive to variations in illumination. To overcome these problems, a new facial-expression recognition method is proposed, which combines both shape and appearance information, based on the support vector machine (SVM). This research is novel in the following three ways as compared to previous works. First, the facial feature points are automatically detected by using an active appearance model. From these, the shape-based recognition is performed by using the ratios between the facial feature points based on the facial-action coding system. Second, the SVM, which is trained to recognize the same and different expression classes, is proposed to combine two matching scores obtained from the shape- and appearance-based recognitions. Finally, a single SVM is trained to discriminate four different expressions, such as neutral, a smile, anger, and a scream. By determining the expression of the input facial image whose SVM output is at a minimum, the accuracy of the expression recognition is much enhanced. The experimental results showed that the recognition accuracy of the proposed method was better than previous researches and other fusion methods.

  17. Utilizing support vector machine in real-time crash risk evaluation.

    PubMed

    Yu, Rongjie; Abdel-Aty, Mohamed

    2013-03-01

    Real-time crash risk evaluation models will likely play a key role in Active Traffic Management (ATM). Models have been developed to predict crash occurrence in order to proactively improve traffic safety. Previous real-time crash risk evaluation studies mainly employed logistic regression and neural network models which have a linear functional form and over-fitting drawbacks, respectively. Moreover, these studies mostly focused on estimating the models but barely investigated the models' predictive abilities. In this study, support vector machine (SVM), a recently proposed statistical learning model was introduced to evaluate real-time crash risk. The data has been split into a training dataset (used for developing the models) and scoring datasets (meant for assessing the models' predictive power). Classification and regression tree (CART) model has been developed to select the most important explanatory variables and based on the results, three candidates Bayesian logistic regression models have been estimated with accounting for different levels unobserved heterogeneity. Then SVM models with different kernel functions have been developed and compared to the Bayesian logistic regression model. Model comparisons based on areas under the ROC curve (AUC) demonstrated that the SVM model with Radial-basis kernel function outperformed the others. Moreover, several extension analyses have been conducted to evaluate the effect of sample size on SVM models' predictive capability; the importance of variable selection before developing SVM models; and the effect of the explanatory variables in the SVM models. Results indicate that (1) smaller sample size would enhance the SVM model's classification accuracy, (2) variable selection procedure is needed prior to the SVM model estimation, and (3) explanatory variables have identical effects on crash occurrence for the SVM models and logistic regression models. PMID:23287112

  18. Support vector machine based classification and mapping of atherosclerotic plaques using fluorescence lifetime imaging (Conference Presentation)

    NASA Astrophysics Data System (ADS)

    Fatakdawala, Hussain; Gorpas, Dimitris S.; Bec, Julien; Ma, Dinglong M.; Yankelevich, Diego R.; Bishop, John W.; Marcu, Laura

    2016-02-01

    The progression of atherosclerosis in coronary vessels involves distinct pathological changes in the vessel wall. These changes manifest in the formation of a variety of plaque sub-types. The ability to detect and distinguish these plaques, especially thin-cap fibroatheromas (TCFA) may be relevant for guiding percutaneous coronary intervention as well as investigating new therapeutics. In this work we demonstrate the ability of fluorescence lifetime imaging (FLIm) derived parameters (lifetime values from sub-bands 390/40 nm, 452/45 nm and 542/50 nm respectively) for generating classification maps for identifying eight different atherosclerotic plaque sub-types in ex vivo human coronary vessels. The classification was performed using a support vector machine based classifier that was built from data gathered from sixteen coronary vessels in a previous study. This classifier was validated in the current study using an independent set of FLIm data acquired from four additional coronary vessels with a new rotational FLIm system. Classification maps were compared to co-registered histological data. Results show that the classification maps allow identification of the eight different plaque sub-types despite the fact that new data was gathered with a different FLIm system. Regions with diffuse intimal thickening (n=10), fibrotic tissue (n=2) and thick-cap fibroatheroma (n=1) were correctly identified on the classification map. The ability to identify different plaque types using FLIm data alone may serve as a powerful clinical and research tool for studying atherosclerosis in animal models as well as in humans.

  19. Computer aided detection of breast masses in mammography using support vector machine classification

    NASA Astrophysics Data System (ADS)

    Lesniak, Jan; Hupse, Rianne; Kallenberg, Michiel; Samulski, Maurice; Blanc, Rémi; Karssemeijer, Nico; Székely, Gàbor

    2011-03-01

    The reduction of false positive marks in breast mass CAD is an active area of research. Typically, the problem can be approached either by developing more discriminative features or by employing different classifier designs. Usually one intends to find an optimal combination of classifier configuration and small number of features to ensure high classification performance and a robust model with good generalization capabilities. In this paper, we investigate the potential benefit of relying on a support vector machine (SVM) classifier for the detection of masses. The evaluation is based on a 10-fold cross validation over a large database of screen film mammograms (10397 images). The purpose of this study is twofold: first, we assess the SVM performance compared to neural networks (NNet), k-nearest neighbor classification (k-NN) and linear discriminant analysis (LDA). Second, we study the classifiers' performances when using a set of 30 and a set of 73 region-based features. The CAD performance is quantified by the mean sensitivity in 0.05 to 1 false positives per exam on the free-response receiver operating characteristic curve. The best mean exam sensitivities found were 0.545, 0.636, 0.648, 0.675 for LDA, k-NN, NNet and SVM. K-NN and NNet proved to be stable against variation of the featuresets. Conversely, LDA and SVM exhibited an increase in performance when adding more features. It is concluded that with an SVM a more pronounced reduction of false positives is possible, given that a large number of cases and features are available.

  20. Discovering rules for protein-ligand specificity using support vector inductive logic programming.

    PubMed

    Kelley, Lawrence A; Shrimpton, Paul J; Muggleton, Stephen H; Sternberg, Michael J E

    2009-09-01

    Structural genomics initiatives are rapidly generating vast numbers of protein structures. Comparative modelling is also capable of producing accurate structural models for many protein sequences. However, for many of the known structures, functions are not yet determined, and in many modelling tasks, an accurate structural model does not necessarily tell us about function. Thus, there is a pressing need for high-throughput methods for determining function from structure. The spatial arrangement of key amino acids in a folded protein, on the surface or buried in clefts, is often the determinants of its biological function. A central aim of molecular biology is to understand the relationship between such substructures or surfaces and biological function, leading both to function prediction and to function design. We present a new general method for discovering the features of binding pockets that confer specificity for particular ligands. Using a recently developed machine-learning technique which couples the rule-discovery approach of inductive logic programming with the statistical learning power of support vector machines, we are able to discriminate, with high precision (90%) and recall (86%) between pockets that bind FAD and those that bind NAD on a large benchmark set given only the geometry and composition of the backbone of the binding pocket without the use of docking. In addition, we learn rules governing this specificity which can feed into protein functional design protocols. An analysis of the rules found suggests that key features of the binding pocket may be tied to conformational freedom in the ligand. The representation is sufficiently general to be applicable to any discriminatory binding problem. All programs and data sets are freely available to non-commercial users at http://www.sbg.bio.ic.ac.uk/svilp_ligand/. PMID:19574295