Search results for: motivated support vector regression

Authors: A. Ouanane, A. Serir

Abstract:

In this paper, a novel algorithm based on Ridgelet Transform and support vector machine is proposed for human action recognition. The Ridgelet transform is a directional multi-resolution transform and it is more suitable for describing the human action by performing its directional information to form spatial features vectors. The dynamic transition between the spatial features is carried out using both the Principal Component Analysis and clustering algorithm K-means. First, the Principal Component Analysis is used to reduce the dimensionality of the obtained vectors. Then, the kmeans algorithm is then used to perform the obtained vectors to form the spatio-temporal pattern, called set-of-labels, according to given periodicity of human action. Finally, a Support Machine classifier is used to discriminate between the different human actions. Different tests are conducted on popular Datasets, such as Weizmann and KTH. The obtained results show that the proposed method provides more significant accuracy rate and it drives more robustness in very challenging situations such as lighting changes, scaling and dynamic environment

Keywords: Human action, Ridgelet Transform, PCA, K-means, SVM.

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Authors: L. Lakhdari, B. Bouchiba, M. Bechar

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The multi-level inverters present an important novelty in the field of energy control with high voltage and power. The major advantage of all multi-level inverters is the improvement and spectral quality of its generated output signals. In recent years, various pulse width modulation techniques have been developed. From these technics we have: Sinusoidal Pulse Width Modulation (SPWM) and Space Vector Pulse Width Modulation (SVPWM). This work presents a detailed analysis of the comparative advantage of space vector pulse width modulation (SVPWM) and the standard SPWM technique for Three Level Diode Clamped Inverter fed Induction Motor. The comparison is based on the evaluation of harmonic distortion THD.

Keywords: Induction motor, multi-level inverters, NPC inverter, sinusoidal pulse width modulation, space vector pulse width modulation.

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Authors: Josu Arteche, Jesus Orbe

Abstract:

The log periodogram regression is widely used in empirical applications because of its simplicity, since only a least squares regression is required to estimate the memory parameter, d, its good asymptotic properties and its robustness to misspecification of the short term behavior of the series. However, the asymptotic distribution is a poor approximation of the (unknown) finite sample distribution if the sample size is small. Here the finite sample performance of different nonparametric residual bootstrap procedures is analyzed when applied to construct confidence intervals. In particular, in addition to the basic residual bootstrap, the local and block bootstrap that might adequately replicate the structure that may arise in the errors of the regression are considered when the series shows weak dependence in addition to the long memory component. Bias correcting bootstrap to adjust the bias caused by that structure is also considered. Finally, the performance of the bootstrap in log periodogram regression based confidence intervals is assessed in different type of models and how its performance changes as sample size increases.

Keywords: bootstrap, confidence interval, log periodogram regression, long memory.

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Authors: Ling-fei Xu, Qi Zhao, Yan-ru Chen, Mu-chun Zhou, Meng Zhang, Shi-xue Xu

Abstract:

SVM ( Support Vector Machine ) is a new method in the artificial neural network ( ANN ). In the steel making, how to use computer to predict the end point of BOF accuracy is a great problem. A lot of method and theory have been claimed, but most of the results is not satisfied. Now the hot topic in the BOF end point predicting is to use optical way the predict the end point in the BOF. And we found that there exist some regular in the characteristic curve of the flame from the mouse of pudding. And we can use SVM to predict end point of the BOF, just single spectrum intensity should be required as the input parameter. Moreover, its compatibility for the input space is better than the BP network.

Keywords: SVM, predict, BOF, single spectrum intensity.

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Authors: Vinod Kumar, Navin Kumar, Surjit Angra, Prince Sharma

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This paper presents the design analysis of saddle support of a horizontal pressure vessel. Since saddle have the vital role to support the pressure vessel and to maintain its stability, it should be designed in such a way that it can afford the vessel load and internal pressure of the vessel due to liquid contained in the vessel. A model of horizontal pressure vessel and saddle support is created in ANSYS. Stresses are calculated using mathematical approach and ANSYS software. The analysis reveals the zone of high localized stress at the junction part of the pressure vessel and saddle support due to operating conditions. The results obtained by both the methods are compared with allowable stress value for safe designing.

Keywords: ANSYS, Pressure Vessel, Saddle, Support.

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Authors: Fatemeh Valizadeh Gamchi, Edward L. Boone

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This research focuses on Lyme disease, a widespread infectious condition in the United States caused by the bacterium Borrelia burgdorferi sensu stricto. It is critical to identify environmental and economic elements that are contributing to the spread of the disease. This study examined data from Virginia to identify a subset of explanatory variables significant for Lyme disease case numbers. To identify relevant variables and avoid overfitting, linear poisson, and regularization regression methods such as ridge, lasso, and elastic net penalty were employed. Cross-validation was performed to acquire tuning parameters. The methods proposed can automatically identify relevant disease count covariates. The efficacy of the techniques was assessed using four criteria on three simulated datasets. Finally, using the Virginia Department of Health’s Lyme disease dataset, the study successfully identified key factors, and the results were consistent with previous studies.

Keywords: Lyme disease, Poisson generalized linear model, Ridge regression, Lasso Regression, elastic net regression.

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Authors: Nebi Gedik, Ayten Atasoy

Abstract:

This paper investigates successful sub-bands of wave atom transform via classification of mammograms, when the coefficients of sub-bands are used as features. A computer-aided diagnosis system is constructed by using wave atom transform, support vector machine and k-nearest neighbor classifiers. Two-class classification is studied in detail using two data sets, separately. The successful sub-bands are determined according to the accuracy rates, coefficient numbers, and sensitivity rates.

Keywords: Breast cancer, wave atom transform, SVM, k-NN.

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Authors: Okolo Chimaobi Valentine

Abstract:

Commercial banks in Nigeria adopted many strategies to attract fresh deposits including the use of high deposit rate. However, pricing of banking services moved in favor of the banks at the expense of customers, resulting in their seeking other investment alternatives rather than saving their money in the bank. Both deposit and lending rates were greatly influenced by the Central Bank of Nigeria (CBN) decision on interest rate. Therefore, commercial bank effort to attract deposits via manipulation of her rates was greatly limited, otherwise the banks will be giving out more than it earned. The study aimed at examining the relationship between interest rate and fixed fund deposit of commercial banks, how policy-controlled interest rate affected commercial bank’s fixed fund deposit The researcher employed ordinary least square technique, using, multiple linear regression, unrestricted vector auto-regression, correlation matrix test, granger causality and impulse response graph in the analysis. Commercial bank’s interest rates affected commercial bank’s fixed fund deposit significantly while policy-controlled interest rate did not significantly transmit through the commercial bank’s interest rates to affect fixed fund deposit. While commercial banks seek creative ways to expand their fixed fund deposit, policy authorities in Nigeria should better coordinate interest rate fluctuation and induce competition in the entire financial sector.

Keywords: Commercial bank, fixed fund deposit, fluctuation effects, interest rate.

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Authors: Yan Wang, Yasushi Asami, Yukio Sadahiro

Abstract:

In urban area, several landmarks may affect housing price and rents, and hedonic analysis should employ distance variables corresponding to each landmarks. Unfortunately, the effects of distances to landmarks on housing prices are generally not consistent with the true price. These distance variables may cause magnitude error in regression, pointing a problem of spatial multicollinearity. In this paper, we provided some approaches for getting the samples with less bias and method on locating the specific sampling area to avoid the multicollinerity problem in two specific landmarks case.

Keywords: Landmarks, hedonic regression, distance variables, collinearity, multicollinerity.

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Authors: P. V. Pramila, V. Mahesh

Abstract:

Pulmonary Function Tests are important non-invasive diagnostic tests to assess respiratory impairments and provides quantifiable measures of lung function. Spirometry is the most frequently used measure of lung function and plays an essential role in the diagnosis and management of pulmonary diseases. However, the test requires considerable patient effort and cooperation, markedly related to the age of patients resulting in incomplete data sets. This paper presents, a nonlinear model built using Multivariate adaptive regression splines and Random forest regression model to predict the missing spirometric features. Random forest based feature selection is used to enhance both the generalization capability and the model interpretability. In the present study, flow-volume data are recorded for N= 198 subjects. The ranked order of feature importance index calculated by the random forests model shows that the spirometric features FVC, FEF25, PEF, FEF25-75, FEF50 and the demographic parameter height are the important descriptors. A comparison of performance assessment of both models prove that, the prediction ability of MARS with the `top two ranked features namely the FVC and FEF25 is higher, yielding a model fit of R2= 0.96 and R2= 0.99 for normal and abnormal subjects. The Root Mean Square Error analysis of the RF model and the MARS model also shows that the latter is capable of predicting the missing values of FEV1 with a notably lower error value of 0.0191 (normal subjects) and 0.0106 (abnormal subjects) with the aforementioned input features. It is concluded that combining feature selection with a prediction model provides a minimum subset of predominant features to train the model, as well as yielding better prediction performance. This analysis can assist clinicians with a intelligence support system in the medical diagnosis and improvement of clinical care.

Keywords: FEV1, Multivariate Adaptive Regression Splines Pulmonary Function Test, Random Forest.

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Authors: Nikolaos Georgantzis, Efi Vasileiou

Abstract:

We present results from experimental price-setting oligopolies in which green firms undertake different levels of energy-saving investments motivated by public subsidies and demand-side advantages. We find that consumers reveal higher willingness to pay for greener sellers’ products. This observation in conjunction to the fact that greener sellers set higher prices is compatible with the use and interpretation of energy-saving behaviour as a differentiation strategy. However, sellers do not exploit the resulting advantage through sufficiently high price-cost margins, because they seem trapped into “run to stay still” competition. Regarding the use of public subsidies to energy-saving sellers we uncover an undesirable crowding-out effect of consumers’ intrinsic tendency to support green manufacturers. Namely, consumers may be less willing to support a green seller whose energy-saving strategy entails a direct financial benefit. Finally, we disentangle two alternative motivations for consumer’s attractions to pro-social firms; first, the self-interested recognition of the firm’s contribution to the public and private welfare and, second, the need to compensate a firm for the cost entailed in each pro-social action. Our results show the prevalence of the former over the latter.

Keywords: Corporate social responsibility, energy savings, public good, experiments, vertical differentiation, altruism.

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Authors: Sun K. Yoo, Ki-Chang Nam, Hyun-Young Shin, Ho-Seong Moon, Hee Cheol Kang

Abstract:

Automated intelligent, clinical decision support systems generally promote to help or to assist physicians and patients regarding to prevention of diseases or treatment of illnesses using computer represented knowledge and information. In this paper, assessment factors affecting the proper design of clinical decision support system were investigated. The required procedure steps for gathering the data from clinical trial and extracting the information from large volume of healthcare repositories were listed, which are necessary for validation and verification of evidence-based implementation of clinical decision support system. The goal of this paper is to extract useful evaluation factors affecting the quality of the clinical decision support system in the design, development, and implementation of a computer-based decision support system.

Keywords: Evaluation, Clinical Decision Support System.

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Authors: R. Behmanesh, I. Rahimi

Abstract:

recurrent neural network (RNN) is an efficient tool for modeling production control process as well as modeling services. In this paper one RNN was combined with regression model and were employed in order to be checked whether the obtained data by the model in comparison with actual data, are valid for variable process control chart. Therefore, one maintenance process in workshop of Esfahan Oil Refining Co. (EORC) was taken for illustration of models. First, the regression was made for predicting the response time of process based upon determined factors, and then the error between actual and predicted response time as output and also the same factors as input were used in RNN. Finally, according to predicted data from combined model, it is scrutinized for test values in statistical process control whether forecasting efficiency is acceptable. Meanwhile, in training process of RNN, design of experiments was set so as to optimize the RNN.

Keywords: RNN, DOE, regression, control chart.

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Authors: A. Bekbaev, M. Dzhamanbaev, R. Abitaeva, A. Karbozova, G. Nabyeva

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In this paper, the regression dependence of dancing intensity from wind speed and length of span was established due to the statistic data obtained from multi-year observations on line wires dancing accumulated by power systems of Kazakhstan and the Russian Federation. The lower and upper limitations of the equations parameters were estimated, as well as the adequacy of the regression model. The constructed model will be used in research of dancing phenomena for the development of methods and means of protection against dancing and for zoning plan of the territories of line wire dancing.

Keywords: Power lines, line wire dancing, dancing intensity, regression equation, dancing area intensity.

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Authors: Ahmed Abdolkhalig

Abstract:

This paper examines many mathematical methods for molding the hourly price forward curve (HPFC); the model will be constructed by numerous regression methods, like polynomial regression, radial basic function neural networks & a furrier series. Examination the models goodness of fit will be done by means of statistical & graphical tools. The criteria for choosing the model will depend on minimize the Root Mean Squared Error (RMSE), using the correlation analysis approach for the regression analysis the optimal model will be distinct, which are robust against model misspecification. Learning & supervision technique employed to determine the form of the optimal parameters corresponding to each measure of overall loss. By using all the numerical methods that mentioned previously; the explicit expressions for the optimal model derived and the optimal designs will be implemented.

Keywords: Forward curve, furrier series, regression, radial basic function neural networks.

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Authors: K. S. Chia, H. Abdul Rahim, R. Abdul Rahim

Abstract:

The Principal component regression (PCR) is a combination of principal component analysis (PCA) and multiple linear regression (MLR). The objective of this paper is to revise the use of PCR in shortwave near infrared (SWNIR) (750-1000nm) spectral analysis. The idea of PCR was explained mathematically and implemented in the non-destructive assessment of the soluble solid content (SSC) of pineapple based on SWNIR spectral data. PCR achieved satisfactory results in this application with root mean squared error of calibration (RMSEC) of 0.7611 Brix°, coefficient of determination (R2) of 0.5865 and root mean squared error of crossvalidation (RMSECV) of 0.8323 Brix° with principal components (PCs) of 14.

Keywords: Pineapple, Shortwave near infrared, Principal component regression, Non-invasive measurement; Soluble solids content

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Authors: Nikhil, Ari Visa, Chin-Chao Chen, Chiu-Yue Lin, Jaakko A. Puhakka, Olli Yli-Harja

Abstract:

A predictive clustering hybrid regression (pCHR) approach was developed and evaluated using dataset from H2- producing sucrose-based bioreactor operated for 15 months. The aim was to model and predict the H2-production rate using information available about envirome and metabolome of the bioprocess. Selforganizing maps (SOM) and Sammon map were used to visualize the dataset and to identify main metabolic patterns and clusters in bioprocess data. Three metabolic clusters: acetate coupled with other metabolites, butyrate only, and transition phases were detected. The developed pCHR model combines principles of k-means clustering, kNN classification and regression techniques. The model performed well in modeling and predicting the H2-production rate with mean square error values of 0.0014 and 0.0032, respectively.

Keywords: Biohydrogen, bioprocess modeling, clusteringhybrid regression.

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Authors: Ε. Giovanis

Abstract:

The purpose of this paper is to present two different approaches of financial distress pre-warning models appropriate for risk supervisors, investors and policy makers. We examine a sample of the financial institutions and electronic companies of Taiwan Security Exchange (TSE) market from 2002 through 2008. We present a binary logistic regression with paned data analysis. With the pooled binary logistic regression we build a model including more variables in the regression than with random effects, while the in-sample and out-sample forecasting performance is higher in random effects estimation than in pooled regression. On the other hand we estimate an Adaptive Neuro-Fuzzy Inference System (ANFIS) with Gaussian and Generalized Bell (Gbell) functions and we find that ANFIS outperforms significant Logit regressions in both in-sample and out-of-sample periods, indicating that ANFIS is a more appropriate tool for financial risk managers and for the economic policy makers in central banks and national statistical services.

Keywords: ANFIS, Binary logistic regression, Financialdistress, Panel data

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Authors: Liubov Evstigneeva

Abstract:

In this paper an attempt is made to establish causal nexuses between innovation and international trade in Russia. The topicality of this issue is determined by the necessity of choosing policy instruments for economic modernization and transition to innovative development. The vector auto regression (VAR) model and Granger test are applied for the Russian monthly data from 2005 until the second quartile of 2015. Both lagged import and export at the national level cause innovation, the latter starts to stimulate foreign trade since it is a remote lag. In comparison to aggregate data, the results by patent’s categories are more diverse. Importing technologies from foreign countries stimulates patent activity, while innovations created in Russia are only Granger causality for import to Commonwealth of Independent States.

Keywords: Export, import, innovation, patents.

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Authors: R. Usubamatov

Abstract:

The velocity of a moving point in a general path is the vector quantity, which has both magnitude and direction. The magnitude or the direction of the velocity vector can change over time as a result of acceleration that the time rate of velocity changes. Acceleration analysis is important because inertial forces and inertial torques are proportional to rectilinear and angular accelerations accordingly. The loads must be determined in advance to ensure that a machine is adequately designed to handle these dynamic loads. For planar motion, the vector direction of acceleration is commonly separated into two elements: tangential and centripetal or radial components of a point on a rotating body. All textbooks in physics, kinematics and dynamics of machinery consider the magnitude of a radial acceleration at condition when a point rotates with a constant angular velocity and it means without acceleration. The magnitude of the tangential acceleration considered on a basis of acceleration for a rotating point. Such condition of presentation of magnitudes for two components of acceleration logically and mathematically is not correct and may cause further confusion in calculation. This paper presents new analytical expressions of the radial and absolute accelerations of a rotating point with acceleration and covers the gap in theoretical study of acceleration analysis.

Keywords: acceleration analysis, kinematics of mechanisms.

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Authors: Tudor Barbu

Abstract:

We provide a supervised speech-independent voice recognition technique in this paper. In the feature extraction stage we propose a mel-cepstral based approach. Our feature vector classification method uses a special nonlinear metric, derived from the Hausdorff distance for sets, and a minimum mean distance classifier.

Keywords: Text-independent speaker recognition, mel cepstral analysis, speech feature vector, Hausdorff-based metric, supervised classification.

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Authors: Mehdi Karbalaye Zadeh, Ehsan M. Siavashi

Abstract:

The controllable electrical loss which consists of the copper loss and iron loss can be minimized by the optimal control of the armature current vector. The control algorithm of current vector minimizing the electrical loss is proposed and the optimal current vector can be decided according to the operating speed and the load conditions. The proposed control algorithm is applied to the experimental PM motor drive system and this paper presents a modern approach of speed control for permanent magnet synchronous motor (PMSM) applied for Electric Vehicle using a nonlinear control. The regulation algorithms are based on the feedback linearization technique. The direct component of the current is controlled to be zero which insures the maximum torque operation. The near unity power factor operation is also achieved. More over, among EV-s motor electric propulsion features, the energy efficiency is a basic characteristic that is influenced by vehicle dynamics and system architecture. For this reason, the EV dynamics are taken into account.

Keywords: PMSM, Electric Vehicle, Optimal control, Traction.

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Authors: Md. Ashikur Rahman Khan, M. M. Rahman

Abstract:

Electrical discharge machining (EDM) is a relatively modern machining process having distinct advantages over other machining processes and can machine Ti-alloys effectively. The present study emphasizes the features of the development of regression equation based on response surface methodology (RSM) for correlating the interactive and higher-order influences of machining parameters on surface finish of Titanium alloy Ti-6Al-4V. The process parameters selected in this study are discharge current, pulse on time, pulse off time and servo voltage. Machining has been accomplished using negative polarity of Graphite electrode. Analysis of variance is employed to ascertain the adequacy of the developed regression model. Experiments based on central composite of response surface method are carried out. Scanning electron microscopy (SEM) analysis was performed to investigate the surface topography of the EDMed job. The results evidence that the proposed regression equation can predict the surface roughness effectively. The lower ampere and short pulse on time yield better surface finish.

Keywords: Graphite electrode, regression model, response surface methodology, surface roughness.

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Authors: A. Sopharak, B. Uyyanonvara, S. Barman

Abstract:

Diabetic retinopathy is characterized by the development of retinal microaneurysms. The damage can be prevented if disease is treated in its early stages. In this paper, we are comparing Support Vector Machine (SVM) and Naïve Bayes (NB) classifiers for automatic microaneurysm detection in images acquired through non-dilated pupils. The Nearest Neighbor classifier is used as a baseline for comparison. Detected microaneurysms are validated with expert ophthalmologists’ hand-drawn ground-truths. The sensitivity, specificity, precision and accuracy of each method are also compared.

Keywords: Diabetic retinopathy, microaneurysm, Naïve Bayes classifier, SVM classifier.

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Authors: Sin Yin Teh, Abdul Rahman Othman, Michael Boon Chong Khoo

Abstract:

In this paper, the concepts of dichotomous logistic regression (DLR) with leave-one-out (L-O-O) were discussed. To illustrate this, the L-O-O was run to determine the importance of the simulation conditions for robust test of spread procedures with good Type I error rates. The resultant model was then evaluated. The discussions included 1) assessment of the accuracy of the model, and 2) parameter estimates. These were presented and illustrated by modeling the relationship between the dichotomous dependent variable (Type I error rates) with a set of independent variables (the simulation conditions). The base SAS software containing PROC LOGISTIC and DATA step functions can be making used to do the DLR analysis.

Keywords: Dichotomous logistic regression, leave-one-out, testof spread.

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Authors: Walid S. Abd El-hamid, Sherif S. El-etriby, Mohiy M. Hadhoud

Abstract:

This paper presents a new methodology to select test cases from regression test suites. The selection strategy is based on analyzing the dynamic behavior of the applications that written in any programming language. Methods based on dynamic analysis are more safe and efficient. We design a technique that combine the code based technique and model based technique, to allow comparing the object oriented of an application that written in any programming language. We have developed a prototype tool that detect changes and select test cases from test suite.

Keywords: Regression testing, Model based testing, Dynamicbehavior.

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Authors: Yung-Gi Wu

Abstract:

With the growing of computer and network, digital data can be spread to anywhere in the world quickly. In addition, digital data can also be copied or tampered easily so that the security issue becomes an important topic in the protection of digital data. Digital watermark is a method to protect the ownership of digital data. Embedding the watermark will influence the quality certainly. In this paper, Vector Quantization (VQ) is used to embed the watermark into the image to fulfill the goal of data hiding. This kind of watermarking is invisible which means that the users will not conscious the existing of embedded watermark even though the embedded image has tiny difference compared to the original image. Meanwhile, VQ needs a lot of computation burden so that we adopt a fast VQ encoding scheme by partial distortion searching (PDS) and mean approximation scheme to speed up the data hiding process. The watermarks we hide to the image could be gray, bi-level and color images. Texts are also can be regarded as watermark to embed. In order to test the robustness of the system, we adopt Photoshop to fulfill sharpen, cropping and altering to check if the extracted watermark is still recognizable. Experimental results demonstrate that the proposed system can resist the above three kinds of tampering in general cases.

Keywords: Data hiding, vector quantization, watermark.

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Authors: Pushpa B. Patil, Manesh B. Kokare

Abstract:

This paper presents content-based image retrieval (CBIR) frameworks with relevance feedback (RF) based on combined learning of support vector machines (SVM) and AdaBoosts. The framework incorporates only most relevant images obtained from both the learning algorithm. To speed up the system, it removes irrelevant images from the database, which are returned from SVM learner. It is the key to achieve the effective retrieval performance in terms of time and accuracy. The experimental results show that this framework had significant improvement in retrieval effectiveness, which can finally improve the retrieval performance.

Keywords: Image retrieval, relevance feedback, wavelet transform.

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Authors: H. B. Kekre, Tanuja K. Sarode

Abstract:

This paper presents a very simple and efficient algorithm for codebook search, which reduces a great deal of computation as compared to the full codebook search. The algorithm is based on sorting and centroid technique for search. The results table shows the effectiveness of the proposed algorithm in terms of computational complexity. In this paper we also introduce a new performance parameter named as Average fractional change in pixel value as we feel that it gives better understanding of the closeness of the image since it is related to the perception. This new performance parameter takes into consideration the average fractional change in each pixel value.

Keywords: Vector Quantization, Data Compression, Encoding, Searching.

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Authors: Alla A. Mityureva

Abstract:

The available data on the cross sections of electronimpact excitation of krypton 5s and 5p configuration levels out of the ground state are represented in convenient and compact form. The results are obtained by regression through all known published data related to this process.

Keywords: Cross section, electron excitation, krypton, regression

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