Search results for: Vector norms

Authors: Yanting Cao, Kazumitsu Nawata

Abstract:

As smartphones are continually upgrading, their software and hardware are getting smarter, so the smartphone-based human activity recognition will be described more refined, complex and detailed. In this context, we analyzed a set of experimental data, obtained by observing and measuring 30 volunteers with six activities of daily living (ADL). Due to the large sample size, especially a 561-feature vector with time and frequency domain variables, cleaning these intractable features and training a proper model become extremely challenging. After a series of feature selection and parameters adjustments, a well-performed SVM classifier has been trained. 

Keywords: smart sensors, human activity recognition, artificial intelligence, SVM

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Authors: Pushpa B. Patil, Manesh B. Kokare

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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: B. Gangloff, N. Malleh

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Well-being at work has many positive aspects. Our general hypothesis is that employees who feel well-being at work will be positively valued by their superiors, and that this positive value, which evokes the concept of social norms, allows us to assign to well-being at work a normative status. Three populations (line managers, students destined to become human resource managers, and employees) responded to a well-being questionnaire. Managers had to indicate, for each item, if they appreciated (or not) an employee feeling the well-being presented in the item; students had to indicate which items an employee should check if s/he wants to be positively (versus negatively) appreciated by his/her superior; and employees had to indicate to what degree each item corresponded to the well-being they used to feel. Three hypotheses are developed and confirmed: Managers positively value employees feeling some sense of well-being; students are aware of this positivity; spontaneously employees show a state of well-being, which means, knowing that spontaneous self-presentation is often produced by social desirability, that employees are aware of the well-being positivity. These data are discussed under a conceptual and applied angle.

Keywords: Normativity, well-being at work, organization, evaluation.

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Authors: Pavel Adámek

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The concept of corporate social responsibility (CSR) in the Czech Republic has evolved notably during the last few years and an issue that started as an interest- and motive-based activity for businesses is becoming more commonplace. Governments have a role to play in ensuring that corporations behave according to the rules and norms of society and can legislate, foster, collaborate with businesses and endorse good practice in order to facilitate the development of CSR. The purpose of this paper is to examine the opportunities and options of CSR in government policy and research its relevance to a business sector. An increasing number of companies is engaging in responsible activities, the public awareness of CSR is rising, and customers are giving higher importance to CSR of companies in their choice. By drawing on existing CSR approach in Czech and understanding of CSR are demonstrated. The paper provides an overview, more detailed government approach of CSR.

Keywords: Approach, corporate social responsibility, government policy, instruments.

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Authors: Ketevan Kokrashvili, Rusudan Kutateladze, Nino Pailodze

Abstract:

Importance of management of legal entities under private law of which especially corporate management, as well as looking for ways of its improvement and perfection has become especially relevant in the twenty-first century, which was greatly contributed to by the global economic crisis. Some states have adopted Corporate Governance Codes; the European Union has set to work on a series of directives the main purpose of which is an improvement of corporate governance, provision of greater transparency and implementation of an effective control mechanism. This process is not yet completed, and various problematic issues associated with management of legal persons are still being debated among practitioner experts and scholars. Georgia is not an exception in this regard. The article discusses the legislative gaps, and in some cases, discrepancies having arisen in legal relationships under private law and having caused many practical problems. This especially applies to the management of capital companies.

Keywords: Business entities, corporate management, public capital management, collective norms, existing problems, legal discrepancies.

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Authors: E. Ahvar, M. Fathy

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Ad hoc networks are characterized by multihop wireless connectivity, frequently changing network topology and the need for efficient dynamic routing protocols. We compare the performance of three routing protocols for mobile ad hoc networks: Dynamic Source Routing (DSR) , Ad Hoc On-Demand Distance Vector Routing (AODV), location-aided routing(LAR1).The performance differentials are analyzed using varying network load, mobility, and network size. We simulate protocols with GLOMOSIM simulator. Based on the observations, we make recommendations about when the performance of either protocol can be best.

Keywords: Ad hoc Network , Glomosim , routing protocols.

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Authors: Lily Ingsrisawang, Supawadee Ingsriswang, Saisuda Somchit, Prasert Aungsuratana, Warawut Khantiyanan

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This paper presents the methodology from machine learning approaches for short-term rain forecasting system. Decision Tree, Artificial Neural Network (ANN), and Support Vector Machine (SVM) were applied to develop classification and prediction models for rainfall forecasts. The goals of this presentation are to demonstrate (1) how feature selection can be used to identify the relationships between rainfall occurrences and other weather conditions and (2) what models can be developed and deployed for predicting the accurate rainfall estimates to support the decisions to launch the cloud seeding operations in the northeastern part of Thailand. Datasets collected during 2004-2006 from the Chalermprakiat Royal Rain Making Research Center at Hua Hin, Prachuap Khiri khan, the Chalermprakiat Royal Rain Making Research Center at Pimai, Nakhon Ratchasima and Thai Meteorological Department (TMD). A total of 179 records with 57 features was merged and matched by unique date. There are three main parts in this work. Firstly, a decision tree induction algorithm (C4.5) was used to classify the rain status into either rain or no-rain. The overall accuracy of classification tree achieves 94.41% with the five-fold cross validation. The C4.5 algorithm was also used to classify the rain amount into three classes as no-rain (0-0.1 mm.), few-rain (0.1- 10 mm.), and moderate-rain (>10 mm.) and the overall accuracy of classification tree achieves 62.57%. Secondly, an ANN was applied to predict the rainfall amount and the root mean square error (RMSE) were used to measure the training and testing errors of the ANN. It is found that the ANN yields a lower RMSE at 0.171 for daily rainfall estimates, when compared to next-day and next-2-day estimation. Thirdly, the ANN and SVM techniques were also used to classify the rain amount into three classes as no-rain, few-rain, and moderate-rain as above. The results achieved in 68.15% and 69.10% of overall accuracy of same-day prediction for the ANN and SVM models, respectively. The obtained results illustrated the comparison of the predictive power of different methods for rainfall estimation.

Keywords: Machine learning, decision tree, artificial neural network, support vector machine, root mean square error.

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Authors: Insaf Ajili, Malik Mallem, Jean-Yves Didier

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Motion recognition from videos is actually a very complex task due to the high variability of motions. This paper describes the challenges of human motion recognition, especially motion representation step with relevant features. Our descriptor vector is inspired from Laban Movement Analysis method. We propose discriminative features using the Random Forest algorithm in order to remove redundant features and make learning algorithms operate faster and more effectively. We validate our method on MSRC-12 and UTKinect datasets.

Keywords: Human motion recognition, Discriminative LMA features, random forest, features reduction.

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Authors: Cheima Ben Soltane, Ittansa Yonas Kelbesa

Abstract:

Speaker Identification (SI) is the task of establishing identity of an individual based on his/her voice characteristics. The SI task is typically achieved by two-stage signal processing: training and testing. The training process calculates speaker specific feature parameters from the speech and generates speaker models accordingly. In the testing phase, speech samples from unknown speakers are compared with the models and classified. Even though performance of speaker identification systems has improved due to recent advances in speech processing techniques, there is still need of improvement. In this paper, a Closed-Set Tex-Independent Speaker Identification System (CISI) based on a Multiple Classifier System (MCS) is proposed, using Mel Frequency Cepstrum Coefficient (MFCC) as feature extraction and suitable combination of vector quantization (VQ) and Gaussian Mixture Model (GMM) together with Expectation Maximization algorithm (EM) for speaker modeling. The use of Voice Activity Detector (VAD) with a hybrid approach based on Short Time Energy (STE) and Statistical Modeling of Background Noise in the pre-processing step of the feature extraction yields a better and more robust automatic speaker identification system. Also investigation of Linde-Buzo-Gray (LBG) clustering algorithm for initialization of GMM, for estimating the underlying parameters, in the EM step improved the convergence rate and systems performance. It also uses relative index as confidence measures in case of contradiction in identification process by GMM and VQ as well. Simulation results carried out on voxforge.org speech database using MATLAB highlight the efficacy of the proposed method compared to earlier work.

Keywords: Feature Extraction, Speaker Modeling, Feature Matching, Mel Frequency Cepstrum Coefficient (MFCC), Gaussian mixture model (GMM), Vector Quantization (VQ), Linde-Buzo-Gray (LBG), Expectation Maximization (EM), pre-processing, Voice Activity Detection (VAD), Short Time Energy (STE), Background Noise Statistical Modeling, Closed-Set Tex-Independent Speaker Identification System (CISI).

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Authors: Dmitri Men, Byong-soon Chun, Soon-ok Myong

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The article deals with the problems of political and economic processes in Kazakhstan since independence in the context of globalization. It analyzes the geopolitical situation and selfpositioning processes in the world after the end of the "cold war". It examines the problems of internal economization of the Republic for 20 years of independence. The authors argue that the reforms proceeded in the economic sphere have brought ambiguous and tangible results. Despite the difficult economic and political conditions facing a world economical crisis the country has undergone fundamental and radical transformations in the whole socio-economic system

Keywords: Globalization, Kazakhstan, integration, economic processes, financial crisis, multi-vector.

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Authors: A. Ourici

Abstract:

This paper proposes a direct power control for doubly-fed induction machine for variable speed wind power generation. It provides decoupled regulation of the primary side active and reactive power and it is suitable for both electric energy generation and drive applications. In order to control the power flowing between the stator of the DFIG and the network, a decoupled control of active and reactive power is synthesized using PI controllers.The obtained simulation results show the feasibility and the effectiveness of the suggested method

Keywords: Doubly fed induction machine , decoupled power control , vector control , active and reactive power, PWM inverter

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Authors: Z. Baimagambetova, Zh. Sairambaeva

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This article discusses the prospects of participation of the Republic of Kazakhstan in Hague Conference on Private International Law on the unification of collision law in the international trade. The article analyzes some conventions on international trade. The appropriate conclusions based on the opinions of scientists and experts in this field have been made. First, all issues presented in the form of gaps or spaces in conventions should be the subject to direct negotiations in the course of the activities of Hague Conference, and have a comprehensive feature, be transparent and taken under simplified procedure. Secondly, one should not underestimate the value of conventions that do not become active due to various reasons and having a positive impact on the development and improvement of national legislation and practice in the field of private international law. Thirdly, Kazakhstan has to reconsider its attitude to Hague Conference, having become its full member and aiming at providing constructive and fruitful cooperation with both the organization itself and its member states.

Keywords: Hague Conference on Private International Law, Hague Conventions, unification, collision norms, international trade, international private law, integration.

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Authors: Daniel I. Morariu, Lucian N. Vintan, Volker Tresp

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Text categorization is the problem of classifying text documents into a set of predefined classes. In this paper, we investigated three approaches to build a meta-classifier in order to increase the classification accuracy. The basic idea is to learn a metaclassifier to optimally select the best component classifier for each data point. The experimental results show that combining classifiers can significantly improve the accuracy of classification and that our meta-classification strategy gives better results than each individual classifier. For 7083 Reuters text documents we obtained a classification accuracies up to 92.04%.

Keywords: Meta-classification, Learning with Kernels, Support Vector Machine, and Performance Evaluation.

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Authors: Nicolay Zarutskiy, Roman Bulkin

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The paper deals with the existing methods of force-torque sensor calibration with a number of components from one to six, analyzed their advantages and disadvantages, the necessity of introduction of a calibration method. Calibration method and its constructive realization are also described here. A calibration method allows performing automated force-torque sensor calibration both with selected components of the main vector of forces and moments and with complex loading. Thus, two main advantages of the proposed calibration method are achieved: the automation of the calibration process and universality.

Keywords: Automation, calibration, calibration device, calibration method, force-torque sensors.

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Authors: Yang Zhang, Yuncai Liu

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Accurately predicting non-peak traffic is crucial to daily traffic for all forecasting models. In the paper, least squares support vector machines (LS-SVMs) are investigated to solve such a practical problem. It is the first time to apply the approach and analyze the forecast performance in the domain. For comparison purpose, two parametric and two non-parametric techniques are selected because of their effectiveness proved in past research. Having good generalization ability and guaranteeing global minima, LS-SVMs perform better than the others. Providing sufficient improvement in stability and robustness reveals that the approach is practically promising.

Keywords: Parametric and Nonparametric Techniques, Non-peak Traffic Forecasting

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Authors: R. Kongnuy, P. Pongsumpun

Abstract:

Dengue, a disease found in most tropical and subtropical areas of the world. It has become the most common arboviral disease of humans. This disease is caused by any of four serotypes of dengue virus (DEN1-DEN4). In many endemic countries, the average age of getting dengue infection is shifting upwards, dengue in pregnancy and infancy are likely to be encountered more frequently. The dynamics of the disease is studied by a compartmental model involving ordinary differential equations for the pregnant, infant human and the vector populations. The stability of each equilibrium point is given. The epidemic dynamic is discussed. Moreover, the numerical results are shown for difference values of dengue antibody.

Keywords: Dengue antibody, infant, pregnant human, mathematical model.

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Authors: Troy L. Story

Abstract:

A mathematical model for the Dynamics of Economic Profit is constructed by proposing a characteristic differential oneform for this dynamics (analogous to the action in Hamiltonian dynamics). After processing this form with exterior calculus, a pair of characteristic differential equations is generated and solved for the rate of change of profit P as a function of revenue R (t) and cost C (t). By contracting the characteristic differential one-form with a vortex vector, the Lagrangian is obtained for the Dynamics of Economic Profit.

Keywords: Differential geometry, exterior calculus, Hamiltonian geometry, mathematical economics, economic functions, and dynamics

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Authors: Amir Hossein Ghaffari Nejad, Forouhar Ferdowsi, Reza Mashhadi

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In the interest-free banking system of Iran, the savings of society are in the form of bank deposits, and banks using the Islamic contracts, allocate the resources to applicants for obtaining facilities and credit. In the meantime, the central bank, with the aim of introducing monetary policy, determines the maximum interest rate on bank deposits in terms of macroeconomic requirements. But in recent years, the country's economic constraints with the stagflation and the consequence of the institutional weaknesses of the financial market of Iran have resulted in massive disturbances in the balance sheet of the banking system, resulting in a period of mismatch maturity in the banks' assets and liabilities and the implementation of a Ponzi game. This issue caused determination of the interest rate in long-term bank deposit contracts to be associated with non-observance of the maximum rate set by the central bank. The result of this condition was in the allocation of new sources of equipment to meet past commitments towards the old depositors and, as a result, a significant part of the supply of equipment was leaked out of the facilitating cycle and credit crunch emerged. The purpose of this study is to identify the most important factors affecting the occurrence of non-confirmatory financial banking behavior using data from 19 public and private banks of Iran. For this purpose, the causes of this non-confirmatory behavior of banks have been investigated using the panel vector autoregression method (PVAR) for the period of 2007-2015. Granger's causality test results suggest that the return of parallel markets for bank deposits, non-performing loans and the high share of the ratio of facilities to banks' deposits are all a cause of the formation of non-confirmatory behavior. Also, according to the results of impulse response functions and variance decomposition, NPL and the ratio of facilities to deposits have the highest long-term effect and also have a high contribution to explaining the changes in banks' non-confirmatory behavior in determining the interest rate on deposits.

Keywords: Non-conformity behavior, Ponzi game, panel vector autoregression, nonperforming loans.

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Authors: K. Oh, J. P. Hwang, E. Kim, H. Lee

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This paper presents an algorithm which extends the rapidly-exploring random tree (RRT) framework to deal with change of the task environments. This algorithm called the Retrieval RRT Strategy (RRS) combines a support vector machine (SVM) and RRT and plans the robot motion in the presence of the change of the surrounding environment. This algorithm consists of two levels. At the first level, the SVM is built and selects a proper path from the bank of RRTs for a given environment. At the second level, a real path is planned by the RRT planners for the given environment. The suggested method is applied to the control of KUKA™,, a commercial 6 DOF robot manipulator, and its feasibility and efficiency are demonstrated via the cosimulatation of MatLab™, and RecurDyn™,.

Keywords: Path planning, RRT, 6 DOF manipulator, SVM.

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Authors: Fatimah Almah Saaid, Darfiana Nur, Robert King

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This paper presents a procedure for estimating VAR using Sequential Discounting VAR (SDVAR) algorithm for online model learning to detect fraudulent acts using the telecommunications call detailed records (CDR). The volatility of the VAR is observed allowing for non-linearity, outliers and change points based on the works of [1]. This paper extends their procedure from univariate to multivariate time series. A simulation and a case study for detecting telecommunications fraud using CDR illustrate the use of the algorithm in the bivariate setting.

Keywords: Telecommunications Fraud, SDVAR Algorithm, Multivariate time series, Vector Autoregressive, Change points.

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Authors: H. El Ayech, A. Trabelsi

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In this article we are going to discuss the improvement of the multi classes- classification problem using multi layer Perceptron. The considered approach consists in breaking down the n-class problem into two-classes- subproblems. The training of each two-class subproblem is made independently; as for the phase of test, we are going to confront a vector that we want to classify to all two classes- models, the elected class will be the strongest one that won-t lose any competition with the other classes. Rates of recognition gotten with the multi class-s approach by two-class-s decomposition are clearly better that those gotten by the simple multi class-s approach.

Keywords: Artificial neural network, letter-recognition, Multi class Classification, Multi Layer Perceptron.

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Authors: Nhu Van NGUYEN, Jean-Marc OGIER, Salvatore TABBONE, Alain BOUCHER

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The state-of-the-art Bag of Words model in Content- Based Image Retrieval has been used for years but the relevance feedback strategies for this model are not fully investigated. Inspired from text retrieval, the Bag of Words model has the ability to use the wealth of knowledge and practices available in text retrieval. We study and experiment the relevance feedback model in text retrieval for adapting it to image retrieval. The experiments show that the techniques from text retrieval give good results for image retrieval and that further improvements is possible.

Keywords: Relevance feedback, bag of words model, probabilistic model, vector space model, image retrieval

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Authors: Yi-Fan Zhu, Wei Zhang, Xuan Zhou, Qun Li, Yong-Lin Lei

Abstract:

The quick training algorithms and accurate solution procedure for incremental learning aim at improving the efficiency of training of SVR, whereas there are some disadvantages for them, i.e. the nonconvergence of the formers for changeable training set and the inefficiency of the latter for a massive dataset. In order to handle the problems, a new training algorithm for a changeable training set, named Approximation Incremental Training Algorithm (AITA), was proposed. This paper explored the reason of nonconvergence theoretically and discussed the realization of AITA, and finally demonstrated the benefits of AITA both on precision and efficiency.

Keywords: support vector regression, incremental learning, changeable training set, quick training algorithm, accurate solutionprocedure

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Authors: J. G. Batista, K. N. Sousa, J. L. Nunes, R. L. S. Sousa, G. A. P. Thé

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This work deals with parameter identification of permanent magnet motors, a class of ac motor which is particularly important in industrial automation due to characteristics like applications high performance, are very attractive for applications with limited space and reducing the need to eliminate because they have reduced size and volume and can operate in a wide speed range, without independent ventilation. By using experimental data and genetic algorithm we have been able to extract values for both the motor inductance and the electromechanical coupling constant, which are then compared to measured and/or expected values.

Keywords: Modeling, AC servomotor, Permanent Magnet Synchronous Motor-PMSM, Genetic Algorithm, Vector Control, Robotic Manipulator, Control.

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Authors: Yury Nikulin

Abstract:

A multicriteria linear programming problem with integer variables and parameterized optimality principle "from lexicographic to Slater" is considered. A situation in which initial coefficients of penalty cost functions are not fixed but may be potentially a subject to variations is studied. For any efficient solution, appropriate measures of the quality are introduced which incorporate information about variations of penalty cost function coefficients. These measures correspond to the so-called stability and accuracy functions defined earlier for efficient solutions of a generic multicriteria combinatorial optimization problem with Pareto and lexicographic optimality principles. Various properties of such functions are studied and maximum norms of perturbations for which an efficient solution preserves the property of being efficient are calculated.

Keywords: Stability and accuracy, multicriteria optimization, lexicographic optimality.

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Authors: Liton Jude Rozario, Mohammad Reduanul Haque, Md. Ziarul Islam, Mohammad Shorif Uddin

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Face recognition is a technique to automatically identify or verify individuals. It receives great attention in identification, authentication, security and many more applications. Diverse methods had been proposed for this purpose and also a lot of comparative studies were performed. However, researchers could not reach unified conclusion. In this paper, we are reporting an extensive quantitative accuracy analysis of four most widely used face recognition algorithms: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) using AT&T, Sheffield and Bangladeshi people face databases under diverse situations such as illumination, alignment and pose variations.

Keywords: PCA, ICA, LDA, SVM, face recognition, noise.

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Authors: Essam Al Daoud

Abstract:

To improve the classification rate of the face recognition, features combination and a novel non-linear kernel are proposed. The feature vector concatenates three different radius of local binary patterns and Gabor wavelet features. Gabor features are the mean, standard deviation and the skew of each scaling and orientation parameter. The aim of the new kernel is to incorporate the power of the kernel methods with the optimal balance between the features. To verify the effectiveness of the proposed method, numerous methods are tested by using four datasets, which are consisting of various emotions, orientations, configuration, expressions and lighting conditions. Empirical results show the superiority of the proposed technique when compared to other methods.

Keywords: Face recognition, Gabor wavelet, LBP, Non-linearkerner

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Authors: Vitalice K. Oduol, C. Ardil

Abstract:

A method is presented for obtaining the error probability for block codes. The method is based on the eigenvalueeigenvector properties of the code correlation matrix. It is found that under a unary transformation and for an additive white Gaussian noise environment, the performance evaluation of a block code becomes a one-dimensional problem in which only one eigenvalue and its corresponding eigenvector are needed in the computation. The obtained error rate results show remarkable agreement between simulations and analysis.

Keywords: bit error rate, block codes, code correlation matrix, eigenstructure, soft-decision decoding, weight vector.

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Authors: Davod Khojasteh Salkuyeh, Sayyed Hasan Azizi

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We study the semiconvergence of Gauss-Seidel iterative methods for the least squares solution of minimal norm of rank deficient linear systems of equations. Necessary and sufficient conditions for the semiconvergence of the Gauss-Seidel iterative method are given. We also show that if the linear system of equations is consistent, then the proposed methods with a zero vector as an initial guess converge in one iteration. Some numerical results are given to illustrate the theoretical results.

Keywords: rank deficient least squares problems, AOR iterativemethod, Gauss-Seidel iterative method, semiconvergence.

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Authors: Yogesh Gupta, Ashish Saini, A.K. Saxena

Abstract:

Information retrieval has become an important field of study and research under computer science due to explosive growth of information available in the form of full text, hypertext, administrative text, directory, numeric or bibliographic text. The research work is going on various aspects of information retrieval systems so as to improve its efficiency and reliability. This paper presents a comprehensive study, which discusses not only emergence and evolution of information retrieval but also includes different information retrieval models and some important aspects such as document representation, similarity measure and query expansion.

Keywords: Information Retrieval, query expansion, similarity measure, query expansion, vector space model.

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