Search results for: Attendance in classes

Authors: A. Bocevska, G. Todorov, T. Neshkov

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New developed approach for Functional Cost Analysis (FCA) based on virtual prototyping (VP) models in CAD/CAE environment, applicable and necessary in developing new products is presented. It is instrument for improving the value of the product while maintaining costs and/or reducing the costs of the product without reducing value. Five broad classes of VP methods are identified. Efficient use of prototypes in FCA is a vital activity that can make the difference between successful and unsuccessful entry of new products into the competitive word market. Successful realization of this approach is illustrated for a specific example using press joint power tool.

Keywords: CAD/CAE environment, Functional Cost Analysis (FCA), Virtual prototyping (VP) models.

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Authors: George E. Tsekouras, Evi Sampanikou

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We compare three categorical data clustering algorithms with respect to the problem of classifying cultural data related to the aesthetic judgment of comics artists. Such a classification is very important in Comics Art theory since the determination of any classes of similarities in such kind of data will provide to art-historians very fruitful information of Comics Art-s evolution. To establish this, we use a categorical data set and we study it by employing three categorical data clustering algorithms. The performances of these algorithms are compared each other, while interpretations of the clustering results are also given.

Keywords: Aesthetic judgment, comics artists, cluster analysis, categorical data.

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Authors: Ayşe Onat, Ferruh Yildiz, Mesut Gündüz

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In this study, an OCR system for segmentation, feature extraction and recognition of Ottoman Scripts has been developed using handwritten characters. Detection of handwritten characters written by humans is a difficult process. Segmentation and feature extraction stages are based on geometrical feature analysis, followed by the chain code transformation of the main strokes of each character. The output of segmentation is well-defined segments that can be fed into any classification approach. The classes of main strokes are identified through left-right Hidden Markov Model (HMM).

Keywords: Chain Code, HMM, Ottoman Script Recognition, OCR

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Authors: Nebi Gedik

Abstract:

Electroencephalography (EEG) investigations of the brain computer interfaces are based on the electrical signals resulting from neural activities in the brain. In this paper, it is offered a method for classifying motor imagery EEG signals. The suggested method classifies EEG signals into two classes using the wave atom transform, and the transform coefficients are assessed, creating the feature set. Classification is done with SVM and k-NN algorithms with and without feature selection. For feature selection t-test approaches are utilized. A test of the approach is performed on the BCI competition III dataset IIIa.

Keywords: motor imagery, EEG, wave atom transform, SVM, k-NN, t-test

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Authors: Asia Majeed

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This paper will investigate a form of learning mathematics by integrating technology in mathematics specifically for the university first-year calculus class to support students’ engagement in learning which influences students' conceptual and procedural understanding of the calculus content in a better way. The students with good grades in high school calculus generally struggle in first-year university calculus classes in learning mathematical analysis concepts. This problem has to be addressed. If this problem is not resolved, then most likely students with less ability to do mathematics might not able to complete their degrees. In this work, MATLAB is used to help students in learning and in improving calculus concepts.

Keywords: Calculus, first-year university students, teaching strategies, MATLAB.

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Authors: Lianglin Xiong, Shouming Zhong, Mao Ye

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This paper addresses the stabilization issues for a class of uncertain switched neutral systems with nonlinear perturbations. Based on new classes of piecewise Lyapunov functionals, the stability assumption on all the main operators or the convex combination of coefficient matrices is avoid, and a new switching rule is introduced to stabilize the neutral systems. The switching rule is designed from the solution of the so-called Lyapunov-Metzler linear matrix inequalities. Finally, three simulation examples are given to demonstrate the significant improvements over the existing results.

Keywords: Switched neutral system, piecewise Lyapunov functional, nonlinear perturbation, Lyapunov-Metzler linear matrix inequality.

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Authors: V.P.Subramanyam Rallabandi, S.K.Sett

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An unsupervised classification algorithm is derived by modeling observed data as a mixture of several mutually exclusive classes that are each described by linear combinations of independent non-Gaussian densities. The algorithm estimates the data density in each class by using parametric nonlinear functions that fit to the non-Gaussian structure of the data. This improves classification accuracy compared with standard Gaussian mixture models. When applied to textures, the algorithm can learn basis functions for images that capture the statistically significant structure intrinsic in the images. We apply this technique to the problem of unsupervised texture classification and segmentation.

Keywords: Gaussian Mixture Model, Independent Component Analysis, Segmentation, Unsupervised Classification.

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Authors: Sammani Danwawu Abdullahi

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Vertex Enumeration Algorithms explore the methods and procedures of generating the vertices of general polyhedra formed by system of equations or inequalities. These problems of enumerating the extreme points (vertices) of general polyhedra are shown to be NP-Hard. This lead to exploring how to count the vertices of general polyhedra without listing them. This is also shown to be #P-Complete. Some fully polynomial randomized approximation schemes (fpras) of counting the vertices of some special classes of polyhedra associated with Down-Sets, Independent Sets, 2-Knapsack problems and 2 x n transportation problems are presented together with some discovered open problems.

Keywords: Approximation, counting with uncertainties, mathematical programming, optimization, vertex enumeration.

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Authors: R. Ebrahimpour, M. Abbasnezhad Arabi, H. Babamiri Moghaddam

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Combining classifiers is a useful method for solving complex problems in machine learning. The ECOC (Error Correcting Output Codes) method has been widely used for designing combining classifiers with an emphasis on the diversity of classifiers. In this paper, in contrast to the standard ECOC approach in which individual classifiers are chosen homogeneously, classifiers are selected according to the complexity of the corresponding binary problem. We use SATIMAGE database (containing 6 classes) for our experiments. The recognition error rate in our proposed method is %10.37 which indicates a considerable improvement in comparison with the conventional ECOC and stack generalization methods.

Keywords: Error correcting output code, combining classifiers, neural networks.

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Authors: Olugbade Damola, Adekomi Adebimbo, Sofowora Olaniyi Alaba

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One of the major problems facing education in Nigeria is the provision of quality Science and Technology education. Inadequate teaching facilities, non-usage of innovative teaching strategies, ineffective classroom management, lack of students’ motivation and poor integration of ICT has resulted in the increase in percentage of students who failed Basic Science and Technology in Junior Secondary Certification Examination for National Examination Council in Nigeria. To address these challenges, the Federal Government came up with a road map on education. This was with a view of enhancing quality education through integration of modern technology into teaching and learning, enhancing quality assurance through proper monitoring and introduction of innovative methods of teaching. This led the researcher to investigate how MOODLE LMS could be used to enhance students’ learning outcomes in BST. A sample of 120 students was purposively selected from four secondary schools in Ogbomoso. The experimental group was taught using MOODLE LMS, while the control group was taught using the conventional method. Data obtained were analyzed using mean, standard deviation and t-test. The result showed that MOODLE LMS was an effective learning platform in teaching BST in junior secondary schools (t=4.953, P<0.05). Students’ attitudes towards BST was also enhanced through MOODLE LMS (t=15.632, P<0.05). The use of MOODLE LMS significantly enhanced students’ retention (t=6.640, P<0.05). In conclusion, the Federal Government efforts at enhancing quality assurance through integration of modern technology and e-learning in Secondary schools proved to have yielded good result has students found MOODLE LMS to be motivating and interactive. Attendance was improved.

Keywords: MOODLE, learning management system, quality assurance, basic science and technology.

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Authors: Sim Hui Tee, Rodziah Atan, Abdul Azim Abd Ghani

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Inner class is a specialized class that defined within a regular outer class. It is used in some programming languages such as Java to carry out the task which is related to its outer class. The functional relatedness between inner class and outer class is always the main concern of defining an inner class. However, excessive use of inner class could sabotage the class cohesiveness. In addition, excessive inner class leads to the difficulty of software maintenance and comprehension. Our research aims at determining the minimum threshold for the functional relatedness of inner-outer class. Such minimum threshold is a guideline for removing or relocating the excessive inner class. Our research provides a feasible way for software developers to define inner classes which are functionally related to the outer class.

Keywords: Cohesion, functional relatedness of inner-outer class, inner class.

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Authors: Z. Shaaban

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This paper presents a new approach to tackle the problem of recognizing machine-printed Arabic texts. Because of the difficulty of recognizing cursive Arabic words, the text has to be normalized and segmented to be ready for the recognition stage. The new scheme for recognizing Arabic characters depends on multiple parallel neural networks classifier. The classifier has two phases. The first phase categories the input character into one of eight groups. The second phase classifies the character into one of the Arabic character classes in the group. The system achieved high recognition rate.

Keywords: Neural Networks, character recognition, feature extraction, multiple networks, Arabic text.

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Authors: Jehad Al Dallal

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Class cohesion is an important object-oriented software quality attribute. It indicates how much the members in a class are related. Assessing the class cohesion and improving the class quality accordingly during the object-oriented design phase allows for cheaper management of the later phases. In this paper, the notion of distance between pairs of methods and pairs of attribute types in a class is introduced and used as a basis for introducing a novel class cohesion metric. The metric considers the methodmethod, attribute-attribute, and attribute-method direct interactions. It is shown that the metric gives more sensitive values than other well-known design-based class cohesion metrics.

Keywords: Object-oriented software quality, object-orienteddesign, class cohesion.

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Authors: P. H. Tee, S. M. Zamri, K. M. Kasim, S. K. Tiew

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This study evaluates the management of osteoporosis in a tertiary care government hospital in Malaysia. As the number of admitted patients having osteoporotic fractures is on the rise, osteoporotic medications are an increasing financial burden to government hospitals because they account for half of the orthopedic budget and expenditure. Comprehensive knowledge among practitioners is important to detect early and avoid this preventable disease and its serious complications. The purpose of this study is to evaluate the awareness, knowledge, and practices in managing osteoporosis among practitioners in Hospital Tengku Ampuan Rahimah (HTAR), Klang. A questionnaire from an overseas study in managing osteoporosis among primary care physicians is adapted to Malaysia’s Clinical Practice Guideline of Osteoporosis 2012 (revised 2015) and international guidelines were distributed to all orthopedic practitioners in HTAR Klang (including surgeons, orthopedic medical officers), endocrinologists, rheumatologists and geriatricians. The participants were evaluated on their expertise in the diagnosis, prevention, treatment decision and medications for osteoporosis. Collected data were analyzed for all descriptive and statistical analyses as appropriate. All 45 participants responded to the questionnaire. Participants scored highest on expertise in prevention, followed by diagnosis, treatment decision and lastly, medication. Most practitioners stated that own-initiated continuing professional education from articles and books was the most effective way to update their knowledge, followed by attendance in conferences on osteoporosis. This study confirms the importance of comprehensive training and education regarding osteoporosis among tertiary care physicians and surgeons, predominantly in pharmacotherapy, to deliver wholesome care for osteoporotic patients.

Keywords: Awareness, knowledge, osteoporosis, practices.

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Authors: Ágnes Bogárdi-Mészöly, Takeshi Hashimoto, Shohei Yokoyama, Hiroshi Ishikawa

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The spread of Web 2.0 has caused user-generated content explosion. Users can tag resources to describe and organize them. Tag clouds provide rough impression of relative importance of each tag within overall cloud in order to facilitate browsing among numerous tags and resources. The goal of our paper is to enrich visualization of tag clouds. A font distribution algorithm has been proposed to calculate a novel metric based on frequency and content, and to classify among classes from this metric based on power law distribution and percentages. The suggested algorithm has been validated and verified on the tag cloud of a real-world thesis portal.

Keywords: Tag cloud, font distribution algorithm, frequency-based, content-based, power law.

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Authors: Marc Diesse, Hochschule Heilbronn

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We address the question of identifying the configuration space singularities of linkages, i.e., points where the configuration space is not locally a submanifold of Euclidean space. Because the configuration space cannot be smoothly parameterized at such points, these singularity types have a significantly negative impact on the kinematics of the linkage. It is known that Jacobian methods do not provide sufficient conditions for the existence of CS-singularities. Herein, we present several additional algebraic criteria that provide the sufficient conditions. Further, we use those criteria to analyze certain classes of planar linkages. These examples will also show how the presented criteria can be checked using algorithmic methods.

Keywords: Linkages, configuration space singularities, real algebraic geometry, analytic geometry, computer algebra.

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Authors: Mehran Garmehi, Yasser Mansouri

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There are two paradigms proposed to provide QoS for Internet applications: Integrated service (IntServ) and Differentiated service (DiffServ).Intserv is not appropriate for large network like Internet. Because is very complex. Therefore, to reduce the complexity of QoS management, DiffServ was introduced to provide QoS within a domain using aggregation of flow and per- class service. In theses networks QoS between classes is constant and it allows low priority traffic to be effected from high priority traffic, which is not suitable. In this paper, we proposed a fuzzy controller, which reduced the effect of low priority class on higher priority ones. Our simulations shows that, our approach reduces the latency dependency of low priority class on higher priority ones, in an effective manner.

Keywords: QoS, Differentiated Service (DiffServ), FuzzyController, Delay.

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Authors: Sanmohan Krüger, Volker Krüger

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Imitation learning is considered to be an effective way of teaching humanoid robots and action recognition is the key step to imitation learning. In this paper an online algorithm to recognize parametric actions with object context is presented. Objects are key instruments in understanding an action when there is uncertainty. Ambiguities arising in similar actions can be resolved with objectn context. We classify actions according to the changes they make to the object space. Actions that produce the same state change in the object movement space are classified to belong to the same class. This allow us to define several classes of actions where members of each class are connected with a semantic interpretation.

Keywords: Parametric actions, Action primitives, Hand gesture recognition, Imitation learning

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Authors: Myunghoun Jang

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A survey of small-size contractors in Jeju was conducted to investigate college graduate's computer-aided design (CAD) competence. Most of small-size contractors use CAD software to review and update drawings submitted from an architect. This research analyzed the curriculum of the architectural engineering in several national universities. The CAD classes have 4 or 6 hours per week and use AutoCAD primarily. This paper proposes that a CAD class needs 6 hours per week, 2D drawing is the main theme in the curriculum, and exercises to make 3D models are also included in the CAD class. An improved method, for example Internet cafe and real time feedbacks using smartphones, to evaluate the reports and exercise results is necessary.

Keywords: Computer-aided design, CAD education, education improvement, small-size contractor.

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Authors: Makram Ben Jeddou

Abstract:

ABC classification is widely used by managers for inventory control. The classical ABC classification is based on Pareto principle and according to the criterion of the annual use value only. Single criterion classification is often insufficient for a closely inventory control. Multi-criteria inventory classification models have been proposed by researchers in order to consider other important criteria. From these models, we will consider a specific model in order to make a sensitive analysis on the composite score calculated for each item. In fact, this score, based on a normalized average between a good and a bad optimized index, can affect the ABC-item classification. We will focus on items differently assigned to classes and then propose a classification compromise.

Keywords: ABC classification, Multi criteria inventory classification models, ZF-model.

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Authors: R. C. Joshi, Shashikala Tapaswi

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The paper proposes an approach using genetic algorithm for computing the region based image similarity. The image is denoted using a set of segmented regions reflecting color and texture properties of an image. An image is associated with a family of image features corresponding to the regions. The resemblance of two images is then defined as the overall similarity between two families of image features, and quantified by a similarity measure, which integrates properties of all the regions in the images. A genetic algorithm is applied to decide the most plausible matching. The performance of the proposed method is illustrated using examples from an image database of general-purpose images, and is shown to produce good results.

Keywords: Image Features, color descriptor, segmented classes, texture descriptors, genetic algorithm.

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Authors: Mohib ur Rehman, Imran Ihsan, Mobin Uddin Ahmed, Nadeem Iftikhar, Muhammad Abdul Qadir

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Multimedia, as it stands now is perhaps the most diverse and rich culture around the globe. One of the major needs of Multimedia is to have a single system that enables people to efficiently search through their multimedia catalogues. Many Domain Specific Systems and architectures have been proposed but up till now no generic and complete architecture is proposed. In this paper, we have suggested a generic architecture for Multimedia Database. The main strengths of our architecture besides being generic are Semantic Libraries to reduce semantic gap, levels of feature extraction for more specific and detailed feature extraction according to classes defined by prior level, and merging of two types of queries i.e. text and QBE (Query by Example) for more accurate yet detailed results.

Keywords: Multimedia Database Architecture, Semantics, Feature Extraction, Ontology.

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Authors: I. Lucas, N. Schomberg, F-A. Couturier

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Most empirical studies have analyzed how liquidity risks faced by individual institutions turn into systemic risk. Recent banking crisis has highlighted the importance of grasping and controlling the systemic risk, and the acceptance by Central Banks to ease their monetary policies for saving default or illiquid banks. This last point shows that banks would pay less attention to liquidity risk which, in turn, can become a new important channel of loss. The financial regulation focuses on the most important and “systemic” banks in the global network. However, to quantify the expected loss associated with liquidity risk, it is worth to analyze sensitivity to this channel for the various elements of the global bank network. A small bank is not considered as potentially systemic; however the interaction of small banks all together can become a systemic element. This paper analyzes the impact of medium and small banks interaction on a set of banks which is considered as the core of the network. The proposed method uses the structure of agent-based model in a two-class environment. In first class, the data from actual balance sheets of 22 large and systemic banks (such as BNP Paribas or Barclays) are collected. In second one, to model a network as closely as possible to actual interbank market, 578 fictitious banks smaller than the ones belonging to first class have been split into two groups of small and medium ones. All banks are active on the European interbank network and have deposit and market activity. A simulation of 12 three month periods representing a midterm time interval three years is projected. In each period, there is a set of behavioral descriptions: repayment of matured loans, liquidation of deposits, income from securities, collection of new deposits, new demands of credit, and securities sale. The last two actions are part of refunding process developed in this paper. To strengthen reliability of proposed model, random parameters dynamics are managed with stochastic equations as rates the variations of which are generated by Vasicek model. The Central Bank is considered as the lender of last resort which allows banks to borrow at REPO rate and some ejection conditions of banks from the system are introduced.

Liquidity crunch due to exogenous crisis is simulated in the first class and the loss impact on other bank classes is analyzed though aggregate values representing the aggregate of loans and/or the aggregate of borrowing between classes. It is mainly shown that the three groups of European interbank network do not have the same response, and that intermediate banks are the most sensitive to liquidity risk.

Keywords: Systemic Risk, Financial Contagion, Liquidity Risk, Interbank Market, Network Model.

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Authors: Mitko Veta, Tomislav Kartalov, Zoran Ivanovski

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Organization of video databases is becoming difficult task as the amount of video content increases. Video classification based on the content of videos can significantly increase the speed of tasks such as browsing and searching for a particular video in a database. In this paper, a content-based videos classification system for the classes indoor and outdoor is presented. The system is intended to be used on a mobile platform with modest resources. The algorithm makes use of the temporal redundancy in videos, which allows using an uncomplicated classification model while still achieving reasonable accuracy. The training and evaluation was done on a video database of 443 videos downloaded from a video sharing service. A total accuracy of 87.36% was achieved.

Keywords: Indoor/outdoor, video classification, imageclassification

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Authors: L. Dzierzbicka-Głowacka, M. Janecki

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Seasonal variability of nutrients concentration in the Baltic Sea using the 3D ecosystem numerical model 3D-CEMBS has been investigated. Additionally this study shows horizontal and vertical distribution of nutrients in the Baltic Sea. Model domain is an extended Baltic Sea area divided into 600x640 horizontal grid cells. Aside from standard hydrodynamic parameters 3D-CEMBS produces modeled ecological variables such as: three types of phytoplankton, two detrital classes, dissolved oxygen and the nutrients (nitrate, ammonium, phosphate and silicate). The presented model allows prediction of parameters that describe distribution of nutrients concentration and phytoplankton biomass. 3D-CEMBS can be used to study the effect of different hydrodynamic and biogeochemical processes on distributions of these variables in a larger scale.

Keywords: ecosystem model, nutrients, Baltic Sea

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Authors: L. Zehtaban, D. Roller

Abstract:

Apart from geometry, functionality is one of the most significant hallmarks of a product. The functionality of a product can be considered as the fundamental justification for a product existence. Therefore a functional analysis including a complete and reliable descriptor has a high potential to improve product development process in various fields especially in knowledge-based design. One of the important applications of the functional analysis and indexing is in retrieval and design reuse concept. More than 75% of design activity for a new product development contains reusing earlier and existing design know-how. Thus, analysis and categorization of product functions concluded by functional indexing, influences directly in design optimization. This paper elucidates and evaluates major classes for functional analysis by discussing their major methods. Moreover it is finalized by presenting a noble hybrid approach for functional analysis.

Keywords: Functional analysis, design reuse, functionalindexing and representation.

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Authors: A. Hossein Sahoolizadeh, B. Zargham Heidari, C. Hamid Dehghani

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In this paper, a new face recognition method based on PCA (principal Component Analysis), LDA (Linear Discriminant Analysis) and neural networks is proposed. This method consists of four steps: i) Preprocessing, ii) Dimension reduction using PCA, iii) feature extraction using LDA and iv) classification using neural network. Combination of PCA and LDA is used for improving the capability of LDA when a few samples of images are available and neural classifier is used to reduce number misclassification caused by not-linearly separable classes. The proposed method was tested on Yale face database. Experimental results on this database demonstrated the effectiveness of the proposed method for face recognition with less misclassification in comparison with previous methods.

Keywords: Face recognition Principal component analysis, Linear discriminant analysis, Neural networks.

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Authors: Mohamed H. S. Mohamed, Yang Xiao-zong, Liu Hong-wei, Wu Zhi-bo

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The reliability of distributed systems and computer networks have been modeled by a probabilistic network or a graph G. Computing the residual connectedness reliability (RCR), denoted by R(G), under the node fault model is very useful, but is an NP-hard problem. Since it may need exponential time of the network size to compute the exact value of R(G), it is important to calculate its tight approximate value, especially its lower bound, at a moderate calculation time. In this paper, we propose an efficient algorithm for reliability lower bound of distributed systems with unreliable nodes. We also applied our algorithm to several typical classes of networks to evaluate the lower bounds and show the effectiveness of our algorithm.

Keywords: Distributed systems, probabilistic network, residual connectedness reliability, lower bound.

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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. After a preprocessing step, the documents are typically represented as large sparse vectors. When training classifiers on large collections of documents, both the time and memory restrictions can be quite prohibitive. This justifies the application of feature selection methods to reduce the dimensionality of the document-representation vector. In this paper, we present three feature selection methods: Information Gain, Support Vector Machine feature selection called (SVM_FS) and Genetic Algorithm with SVM (called GA_SVM). We show that the best results were obtained with GA_SVM method for a relatively small dimension of the feature vector.

Keywords: Feature Selection, Learning with Kernels, Support Vector Machine, Genetic Algorithm, and Classification.

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Authors: Tudor Barbu

Abstract:

An automatic moment-based texture segmentation approach is proposed in this paper. First, we describe the related work in this computer vision domain. Our texture feature extraction, the first part of the texture recognition process, produces a set of moment-based feature vectors. For each image pixel, a texture feature vector is computed as a sequence of area moments. Then, an automatic pixel classification approach is proposed. The feature vectors are clustered using an unsupervised classification algorithm, the optimal number of clusters being determined using a measure based on validation indexes. From the resulted pixel classes one determines easily the desired texture regions of the image.

Keywords: Image segmentation, moment-based texture analysis, automatic classification, validity indexes.

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