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This paper is concerned with a multi-resolution tool for screening paper formation variations in various frequency regions on production line. A paper web is illuminated by two red diode lasers and the reflected light recorded as two time series of high resolution measurements constitute the input signal to the papermaking process monitoring system. The time series are divided into blocks and each block is analyzed separately. The task is treated as kernel based novelty detection applied to a multi-resolution time series representation obtained from the band-pass filtering of the Fourier power spectrum of the series. The frequency content of each frequency region is characterized by a feature vector, which is transformed using the canonical correlation analysis and then categorized into the inlier or outlier class by the novelty detector. The ratio of outlying data points, significantly exceeding the predetermined value, indicates abnormalities in the paper formation. The tools developed are used for online paper formation monitoring in a paper mill. [ABSTRACT FROM AUTHOR]

This paper presents the advantage that knowledge-intensive activities, such as Scientific Conference Management, can take by the exploitation of expert components in the key tasks. Typically, in this domain the task of scheduling the activities and resources or the assignment of reviewers to papers is performed manually leading therefore to time-consuming procedures with high degree of inconsistency due to many parameters and constraints to be considered. The proposed systems, evaluated on real conference datasets, show good results compared to manual scheduling and assignment, in terms of both accuracy and reduction of runtime. [ABSTRACT FROM AUTHOR]

The aim of this paper is to give theoretical and experimental tools for measuring the driving force in evolving complex networks. First a discrete-time stochastic model framework is introduced to state the question of how the dynamics of these networks depend on the properties of the parts of the system. Then a method is presented to determine this dependence in the possession of the required data about the system. This measurement method is applied to the citation network of high energy physics papers to extract the in-degree and age dependence of the dynamics. It is shown that the method yields close to "optimal" results. [ABSTRACT FROM AUTHOR]

TSP(Traveling Salesman Problem) used widely for solving the optimization is the problem to find out the shortest distance out of possible courses where one starts a certain city, visits every city among N cities and turns back to a staring city. At this time, the condition is to visit N cities exactly only once. TSP is defined easily, but as the number of visiting cities increases, the calculation rate increases geometrically. This is why TSP is classified into NP-Hard Problem. Genetic Algorithm is used representatively to solve the TSP. Various operators have been developed and studied until now for solving the TSP more effectively. This paper applied the new Population Initialization Method (using the Random Initialization method and Induced Initialization method simultaneously), solved TSP more effectively, and proved the improvement of capability by comparing this new method with existing methods. Keywords: Genetic Algorithm, GA, Optimization, Initialization. [ABSTRACT FROM AUTHOR]

Accurate cancer classification is very important to cancer diagnosis and treatment. As molecular information is increasing for the cancer classification, a lot of techniques have been proposed and utilized to classify and predict the cancers from gene expression profiles. In this paper, we propose a method based on speciated evolution for the cancer classification. The optimal combination among several feature-classifier pairs from the various features and classifiers is evolutionarily searched using the deterministic crowding genetic algorithm. Experimental results demonstrate that the proposed method is more effective than the standard genetic algorithm and the fitness sharing genetic algorithm as well as the best single classifier to search the optimal ensembles for the cancer classification. [ABSTRACT FROM AUTHOR]

We consider some problems about the computation time of evolutionary algorithms in this paper. First, some exact analytic expressions of the mean first hitting times of general evolutionary algorithms in finite search spaces are obtained theoretically by using the properties of Markov chain associated with evolutionary algorithms considered here. Then, by introducing drift analysis and applying Dynkin's Formula, the general upper and lower bounds of the mean first hitting times of evolutionary algorithms are estimated rigorously under some mild conditions listed in the paper. Those results in this paper are commonly useful. Also, the analytic techniques adopted in the paper are widely instructive for analyzing the computation time of evolutionary algorithms in a given search space as long as some specific mathematical arts are introduced accordingly. [ABSTRACT FROM AUTHOR]

This paper presents the integration of linguistic knowledge in learning semantic user profiles able to represent user interests in a more effective way with respect to classical keyword-based profilesThis research was partially funded by the EC under the 6th FP IST IP VIKEF No. 507173, Priority 2.3.1.7 Semantic Based Knowledge Systems (www.vikef.net) and under the DELOS NoE on Digital Libraries, Priority: Technology-enhanced Learning and Access to Cultural Heritage - Contract n. G038-507618 (www.delos.info). The work was supported also by the research project "Modelli avanzati di personalizzazione per biblioteche elettroniche", year 2005, funded by the University of Bari.. Semantic profiles are obtained by integrating a naïve Bayes approach for text categorization with a word sense disambiguation strategy based on the WordNet lexical database (Section 2). Semantic profiles are exploited by the "conference participant advisor" service in order to suggest papers to be read and talks to be attended by a conference participant. Experiments on a real dataset show the effectiveness of the service (Section 3). [ABSTRACT FROM AUTHOR]

The inherent nonlinear of switched reluctance motor (SRM) makes it hard to get a good performance by using the conventional PID controller to the speed control of SRM. This paper develops a radial basis function (RBF) artificial neural network (ANN) nonlinear prediction model based adaptive PID controller for SRM. ANN, under certain condition, can approximate any nonlinear function with arbitrary precision. It also has a strong ability of adaptive, self-learning and self-organization. So, combining it with the conventional PID controller, a neural network based adaptive PID controller can be developed. Appling it to the speed control of SRM, a good control performance can be gotten. At the same time, the nonlinear mapping property and high parallel operation ability of ANN make it suitable to be applied to establish nonlinear prediction model performing parameter prediction. In this paper, two ANN - NNC and NNI are employed. The former is a back propagation (BP) ANN with sigmoid activation function. The later is an ANN using RBF as activation function. The former is used to adaptively adjust the parameters of the PID controller on line. The later is used to establish nonlinear prediction model performing parameter prediction. Compared with BP ANN with sigmoid activation function, the RBF ANN has a more fast convergence speed and can avoid getting stuck in a local optimum. Through parameter prediction, response speed of the system can be improved. To increase the convergence speed of ANN, an adaptive learning algorithm is adopted in this paper that is to adjust the learning rate according to the error. This can increase the convergence speed of ANN and make the system response quick. The experimental results demonstrate that a high control performance is achieved. The system responds quickly with little overshoot. The steady state error is zero. The system shows robust performance to the load torque disturbance. [ABSTRACT FROM AUTHOR]

This paper suggests an automated method for document classification using an ontology, which expresses terminology information and vocabulary contained in Web documents by way of a hierarchical structure. Ontologybased document classification involves determining document features that represent the Web documents most accurately, and classifying them into the most appropriate categories after analyzing their contents by using at least two pre-defined categories per given document features. In this paper, Web documents are classified in real time not with experimental data or a learning process, but by similar calculations between the terminology information extracted from Web texts and ontology categories. This results in a more accurate document classification since the meanings and relationships unique to each document are determined. Keywords: Document classification, Ontology, Web Page classification. [ABSTRACT FROM AUTHOR]

The automatic relevance determination (ARD) shows good performance in many applications. Recently, it has been applied to brain current estimation with the variational method. Although people who use the ARD tend to pay attention to one benefit of the ARD, sparsity, we, in this paper, focus on another benefit, generalization. In this paper, we clarify the generalization error of the ARD in the case that a class of prior distributions is used, and show that good generalization is caused by singularities of the ARD. Sparsity is not observed in that case, however, the mechanism that the singularities provide good generalization implies the mechanism that they also provide sparsity. [ABSTRACT FROM AUTHOR]

This paper presents iris recognition for personal identification using neural networks. Iris recognition system consists of localization of the iris region and generation of data set of iris images and then iris pattern recognition. One of the problems in iris recognition is fast and accurate localization of the iris image. In this paper, fast algorithm is used for the localization of the inner and outer boundaries of the iris region. Located iris is extracted from an eye image, and, after normalization and enhancement it is represented by a data set. Using this data set a neural network is applied for the classification of iris patterns. Results of simulations illustrate the effectiveness of the neural system in personal identification. [ABSTRACT FROM AUTHOR]

The www is a dynamic environment and it is difficult to capture the user preferences and interests without interfering with the normal activity of the user. Hence to improve user searching in the World Wide Web, we have proposed a personalized search system that supports user searches by learning about user preferences and by observing responses to prior search experiences aided by a new index called the User Conceptual Index (UCI). This paper models every user's search behavior as a User's Search Behavior (USB) Graph. The main focus of this paper is the analysis of the USB graph and the redesign of the UCI using the results arrived from the analysis. [ABSTRACT FROM AUTHOR]

This paper is the first in a series which address the development of a temporal planner using timed Petri net (TPN) models of concurrent temporal plans. Unlike previous work which uses Petri net unfoldings to develop partially ordered plans (in the untimed case), the approach presented here is purely algebraic, and is based on a (min,max,+) discrete event dynamical system description of a TPN. This paper focuses primarily on policy evaluation, with subsequent work detailing how to use this approach for efficient optimal temporal planning. [ABSTRACT FROM AUTHOR]

In this paper, we utilize a combination of SWEBOK and text categorization to categorize software engineering knowledge. SWEBOK serves as a backbone taxonomy while text categorization provides a collection of algorithms including knowledge representation, feature enrichment and machine learning. Firstly, fundamental knowledge types in software engineering are carefully analyzed as well as their characteristics. Then, incorporated with SWEBOK, we propose a knowledge categorization methodology as well as its implementing algorithms. Finally, we conduct experiments to evaluate the proposed method. The experimental results demonstrate that our methodology can serve as an effective solution for the categorization of software engineering knowledge. [ABSTRACT FROM AUTHOR]

Transposition tables have long been a viable tool in the pruning mechanisms of game-tree search algorithms. In such applications, a transposition table can reduce a game-tree to a game-graph with unique board positions at the nodes. This paper proposes a transposition table extension, called a template matching table, where templates that prove winning positions are used to map features of board positions to board values. This paper demonstrates that a game-tree search for the game of Hex can have a more effective pruning mechanism using a template matching table than it does using a transposition table. [ABSTRACT FROM AUTHOR]

A dual-population genetic algorithm (DPGA) is a new multi-population genetic algorithm that solves problems using two populations with different evolutionary objectives. The main population is similar to that of an ordinary genetic algorithm, and it evolves in order to obtain suitable solutions. The reserve population evolves to maintain and offer diversity to the main population. The two populations exchange genetic materials using interpopulation crossbreeding. This paper proposes a new fitness function of the reserve population based on the distance to the main populations. The experimental results have shown that the performance of DPGA is highly related to the distance between the populations and that the best distance differs for each problem. Generally, it is difficult to decide the best distance between the populations without prior knowledge about the problem. Therefore, this paper also proposes a method to dynamically adjust the distance between the populations using the distance between good parents, i.e., the parents that generated good offspring. [ABSTRACT FROM AUTHOR]

There are numerous families of Neural Networks (NN) used in the study and development of the field of Artificial Intelligence (AI). One of the more recent NNs involves the Bursting neuron, pioneered by Rulkov. The latter has the desirable property that it can also be used to model a system (for example, the "brain") which switches between modes in which the activity is excessive ("bursty"), to the alternate case when the system is "dormant". This paper, which we believe is a of pioneering sort, derives some of the analytic properties of the Bursting neuron, and the associated NN. To be more specific, the model proposed by Rulkov [11] explains the so-called "bursting" phenomenon in the system (brain), in which a low frequency pulse output serves as an envelope of high frequency spikes. Although various models for bursting have been proposed, Rulkov's model seems to be the one that is both analytically tractable and experimentally meaningful. In this paper, we show that a "small" scale network consisting of Bursting neurons rapidly converges to a synchronized behavior implying that increasing the number of neurons does not contribute significantly to the synchronization of the individual Bursting neurons. The consequences of such a conclusion lead to a phenomenon that we call "behavioral synchronization". [ABSTRACT FROM AUTHOR]

Radio Frequency Identification (RFID) has come under the spotlight as technology supporting ubiquitous society. But now, we face several security problems and challenges in RFID systems. Recent papers have reported that RFID systems have to achieve the following requirements: (1) Indistinguishability, (2) Forward Security, (3) Replay Attack resistance, (4) Tag Killing resistance, and (5) Ownership Transferability. We have to design RFID system that achieves the above-mentioned requirements. The previous security methods achieve only some of them individually, and no RFID system has been constructed that achieves all requirements. In this paper, we propose an RFID security method that achieves all requirements based on a hash function and a symmetric key cryptosystem. In addition, our proposed method provides not only high-security but also high-efficiency. [ABSTRACT FROM AUTHOR]

Recently, a local-search heuristic algorithm called Extremal Optimization (EO) has been successfully applied in some combinatorial optimization problems. However, there are only limited papers studying on the mechanism of EO applied to the numerical optimization problems so far. This paper presents the studies on the applications of EO to numerical constrained optimization problems with a set of popular benchmark problems. To enhance and improve the search performance and efficiency of EO, we developed a novel EO strategy with population based search. The newly developed EO algorithm is named population-based EO (PEO). Additionally, we adopted the adaptive Lévy mutation, which is more likely to generate an offspring that is farther away from its parent than the commonly employed Gaussian mutation. Compared with three state-of-the-art stochastic search methods with six popular benchmark problems, it has been shown that our approach is a good choice to deal with the numerical constrained optimization problems. [ABSTRACT FROM AUTHOR]

This paper discusses the asymptotic convergence of evolutionary algorithms based on finite search space by using the properties of Markov chains and Perron-Frobenius Theorem. First, some convergence results of general square matrices are given. Then, some useful properties of homogeneous Markov chains with finite states are investigated. Finally, the geometric convergence rates of the transition operators, which is determined by the revised spectral of the corresponding transition matrix of a Markov chain associated with the EA considered here, are estimated by combining the acquired results in this paper. [ABSTRACT FROM AUTHOR]

The paper proposes the use of the multilayer perceptron model to the problem of detecting ham and spam e-mail patterns. It also proposes an intensive use of data pre-processing and feature selection methods to simplify the task of the multilayer perceptron in classifying ham and spam e-mails. The multilayer perceptron is trained and assessed on patterns extracted from the SpamAssassin Public Corpus. It is required to classify novel types of ham and spam patterns. The results are presented and evaluated in the paper. [ABSTRACT FROM AUTHOR]

In singular models, the Bayes estimation, commonly, has the advantage of the generalization performance over the maximum likelihood estimation, however, its accurate approximation using Markov chain Monte Carlo methods requires huge computational costs. The variational Bayes (VB) approach, a tractable alternative, has recently shown good performance in the automatic relevance determination model (ARD), a kind of hierarchical Bayesian learning, in brain current estimation from magnetoencephalography (MEG) data, an ill-posed linear inverse problem. On the other hand, it has been proved that, in three-layer linear neural networks (LNNs), the VB approach is asymptotically equivalent to a positive-part James-Stein type shrinkage estimation. In this paper, noting the similarity between the ARD in a linear problem and an LNN, we analyze a simplified version of the VB approach in the ARD. We discuss its relation to the shrinkage estimation and how ill-posedness affects learning. We also propose the algorithm that requires simpler computation than, and will provide similar performance to, the VB approach. [ABSTRACT FROM AUTHOR]

Effective automatic summarization usually requires simulating human reasoning such as abstraction or relevance reasoning. In this paper we describe a solution for this type of reasoning in the particular case of surveillance of the behavior of a dynamic system using sensor data. The paper first presents the approach describing the required type of knowledge with a possible representation. This includes knowledge about the system structure, behavior, interpretation and saliency. Then, the paper shows the inference algorithm to produce a summarization tree based on the exploitation of the physical characteristics of the system. The paper illustrates how the method is used in the context of automatic generation of summaries of behavior in an application for basin surveillance in the presence of river floods. [ABSTRACT FROM AUTHOR]

This paper reports a snapshot of our on-going experiments in which a common target we call a-tiny-island-in-a-huge-lake is explored with different methods ranging from a data-mining technique to an artificial immune system. Our implicit interest is a network intrusion detection, and we assume data floating in the huge lake are normal while ones found on the tiny island are abnormal. Our goal here is twofold. One is to know (i) whether or not it is possible to train a system using just normal data alone. The other is to study (ii) a limit of the size of the detectable area, when we decrease the size of the island eventually shrinking to zero, equivalently so-called a-needle-in-a-haystack which is still an open and worth while tuckling problem. To learn these two issues, a fuzzy rule extraction system - one with fixed triangle/trapezoid membership functions for our first goal, and for the second goal with Gaussian membership functions whose shape is adaptively determined for the second goal, are exploited in this paper. [ABSTRACT FROM AUTHOR]

Neural networks (NNs) have been widely used in the power industry for applications such as fault classification, protection, fault diagnosis, relaying schemes, load forecasting, power generation and optimal power flow etc. Most of NNs are built upon the environment of real numbers. However, it is well known that in computations related to electric power systems, such as load-flow analysis and fault level estimation etc., complex numbers are extensively involved. The reactive power drawn from a substation, the impedance, busbar voltages and currents are all expressed in complex numbers. Hence, NNs in the complex domain must be adopted for these applications. This paper proposes the complex-valued neural network (CVNN) and a new fuzzy clustering complex-valued neural network (FC-CVNN) to estimate busbar voltages in a load-flow problem. The aim of this paper is to present a comparative study of estimation busbar voltages in load-flow analysis using the conventional neural network (real-valued neural network, RVNN), the CVNN and the new FC-CVNN. The results suggest that a new proposed FC-CVNN and CVNN architecture can generalize better than ordinary RVNN and the FC-CVNN is also learn faster. [ABSTRACT FROM AUTHOR]

Although many works have been done in recent years for the designing Fuzzy-Neural Networks (FNN) from input-output data, the results concerning how to analyze the performance of some methods from a rigorous mathematical point of view are somewhat few. In this paper, the approximation bound for the Table Look-up Scheme with the Bell Membership Function is established. The detailed formulas of the error bound between the nonlinear function to be approximated and the FNN system designed based on the input-output data are derived. [ABSTRACT FROM AUTHOR]

The accurate rotor position information is very important for high performance operation of switched reluctance motor (SRM). Traditionally, there is a mechanical rotor position sensor. But this reduces the reliability and increases cost and size of SRM. In order to overcome the disadvantage of mechanical rotor position sensor, a sensorless operation method of SRM based on adaptive network based fuzzy inference system (ANFIS) is developed in this paper. The rotor position can be estimated by using the unique relationship between rotor position, flux linkage and phase current. In this paper, the ANFIS is used to map this relationship. Among the sensorless position estimation method, approach based on fuzzy neural network (FNN) is one of the promising methods. By combining the benefits of artificial neural network (ANN) and fuzzy inference system (FIS) in a single model, the ANFIS shows characteristics of fast and accurate learning, the ability of using both linguistic information and data information and good generalization capability. For its antecedents are fuzzy sets, the noise in the input signals can be restrained. This approach shows a characteristic of robustness. For its consequent is in linear function of input variables, it has a simple structure and low computation complexity. So, it is well suited to be applied on-line. Applying it to the rotor position estimation, a high accuracy and robust sensorless rotor position estimator is presented. The experimental results proved the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, the kurtosis-based method for the classification of mental activities is proposed. The EEG signals were recorded during imagination of left or right hand movement. The kurtosis of EEG and its dynamic properties with respect to time are analyzed. The experiment results show that the kurtosis can reflect the EEG pattern changes of different motor imageries. According to the analysis and experiment results, a kurtosis based classifier for the classification of left and right movement imagination is designed. This classifier can achieves near 90% correct rate. As the kurtosis is computationally less demanding and can also be estimated in on-line way, so the new method proposed in this paper has the practicability in the application of brain-computer interface. Keywords: EEG; kurtosis; motor imaginary; classification. [ABSTRACT FROM AUTHOR]

This paper proposes a swarm optimization model for energy minimization problem of early vision, which is based on a multi-colony ant scheme. Swarm optimization is a new artificial intelligence field, which has been proved suitable to solve various combinatorial optimization problems. Compared with general optimization problems, energy minimization of early vision has its unique characteristics, such as higher dimensions, more complicate structure of solution space, and dynamic constrain conditions. In this paper, the vision energy functions are optimized by repeatedly minimizing a certain number of sub-problems according to divide-and-conquer principle, and each colony is allocated to optimize one sub-problem independently. Then an appropriate information exchange strategy between neighboring colonies, and an adaptive method for dynamic problem are applied to implement global optimization. As a typical example, stereo correspondence will be solved using the proposed swarm optimization model. Experiments show this method can achieve good results. [ABSTRACT FROM AUTHOR]

How to exploit current information techniques for rapidly and accurately building a fittest neural network becomes increasingly significant for flood peak forecasting. This paper firstly designs a distributed computing architecture and builds a computing environment based on Grid technologies. Then a distributed computing service for neural networks based on a genetic algorithm and a modified BP algorithm is designed and developed to rapidly and accurately building a fittest neural network for flood peak forecasting. Finally, a distributed computing prototype system is developed and implemented on a case study of the flood prevention in Shenzhen city, China. The experiment result shows that the scheme addressed in the paper is efficient and feasible. [ABSTRACT FROM AUTHOR]

Protein mass spectrometry (MS) pattern recognition has recently emerged as a new method for cancer diagnosis. Unfortunately, classification performance may degrade owing to the enormously high dimensionality of the data. This paper investigates the use of Random Projection in protein MS data dimensionality reduction. The effectiveness of Random Projection (RP) is analyzed and compared against Principal Component Analysis (PCA) by using three classification algorithms, namely Support Vector Machine, Feed-forward Neural Networks and K-Nearest Neighbour. Three real-world cancer data sets are employed to evaluate the performances of RP and PCA. Through the investigations, RP method demonstrated better or at least comparable classification performance as PCA if the dimensionality of the projection matrix is sufficiently large. This paper also explores the use of RP as a pre-processing step prior to PCA. The results show that without sacrificing classification accuracy, performing RP prior to PCA significantly improves the computational time. [ABSTRACT FROM AUTHOR]

The accuracy of 3D measurements of objects is highly affected by the errors originated from camera calibration. Therefore, camera calibration has been one of the most challenging research fields in the computer vision and photogrammetry recently. In this paper, an Artificial Neural Network Based Camera Calibration Method, NBM, is proposed. The NBM is especially useful for back-projection in the applications that do not require internal and external camera calibration parameters in addition to the expert knowledge. The NBM offers solutions to various camera calibration problems such as calibrating cameras with automated active lenses that are often encountered in computer vision applications. The difference of the NBM from the other artificial neural network based back-projection algorithms used in intelligent photogrammetry (photogrammetron) is its ability to support the multiple view geometry. In this paper, a comparison of the proposed method has been made with the Bundle Block Adjustment based back-projection algorithm, BBA. The performance of accuracy and validity of the NBM have been tested and verified over real images by extensive simulations. [ABSTRACT FROM AUTHOR]

Kohonen neural network is capable of self-organizing and recognizingclustering center, which is used in many artificial intelligence (AI) fields. One electronic support measures (ESM) system must sort the received radar pulses to cells with same features by pulse parameters, such as radio frequency (RF), angle of arrival (AOA), pulse width (PW), Pulse Repetition Interval(PRI), etc. Kohonen SOFM algorithm is one valid method for clustering, which can be used to accomplish such radar pulses sorting. Considering the variety character of pulses parameters which is the character of modern radar system, a new definition of "distance" in the SOFM neural net is proposed in this paper, which decreases the effect of large variety range of special parameter among them. This paper employs the "distance" to improve the clustering capability in such special environments. The computer simulation shows the validity of these improvements. [ABSTRACT FROM AUTHOR]

This paper studies fault-tolerance problem of feedforward neural networks implemented in pattern recognition. Based on dynamical system theory, two concepts of pseudo-attractor and its region of attraction are introduced. A method estimating fault tolerance of feedforward neural networks has been developed. This paper also presents definitions of terminologies and detailed derivations of the methodology. Some preliminary results of case studies using the proposed method are shown, the proposed method has provided a framework and an efficient way for direct evaluation of fault-tolerance in feedforward neural networks. [ABSTRACT FROM AUTHOR]

This paper introduces a morphological neural network approach to extract vehicle targets from high resolution panchromatic satellite imagery. In the approach, the morphological shared-weight neural network (MSNN) is used to classify image pixels on roads into vehicle targets and non-vehicle targets, and a morphological preprocessing algorithm is developed to identify candidate vehicle pixels. Experiments on 0.6 meter resolution QuickBird panchromatic data are reported in this paper. The experimental results show that the MSNN has a good detection performance. [ABSTRACT FROM AUTHOR]

There is a famous saying "The consumer is God". So exactly analyzing consumers' purchasing behavior is the key factor to success for assuring the profit of the whole e-supply chain. Firstly, this paper proposes and compares three kinds of research modes to analyze consumers' purchasing behavior, and then points out that the research mode driven by data is the most scientific one. Secondly, under the guidance of the idea of the research mode driven by data, this paper takes Self-Organizing Feature Map Neural Network (SOFM NN) as a tool to analyze consumers' purchasing behavior in e-supply chain of book. Lastly, based on the result of analyzing consumers' purchasing behavior, this paper gives some corresponding cyber-marketing strategies that can help to increase the loyalty of consumers and maximize the profit of the whole e-supply chain. Because consumer's purchasing behavior analysis based on SOFM NN is a comparatively novel method, the result of research in this paper is just for reference. Keywords: SOFM; behavior; consumer; neural network. [ABSTRACT FROM AUTHOR]

The Extensible Authentication protocol (EAP), is the main component of the standard AAA (Authentication Authorization and Accounting) framework for network access control. AAA frameworks support cross-domain authentication that enables a certain access network to authenticate a roaming client that belongs to a remote network. The cross-domain authentication requires message exchange between the AAA server of the visited network and the AAA server of the client's home network. Because these inter-domain exchanges occur over the Internet, they are subject to degradations such as packet loss and network delays thus increasing the overall authentication time. When the client changes of access point, the same authentication procedure takes place again, disrupting the user traffic at each hand-off. In this paper, we examine the performance of inter-domain EAP authentication in terms of authentication delay using an emulated environment. Moreover, in an effort to understand the performance of Inter-domain authentication over the Internet, we model the authentication time using mathematical approach and we define basis of future research items focusing on the estimation of inter-domain EAP authentication delays. [ABSTRACT FROM AUTHOR]

The performance of a Radial Basis Functions network (RBF) can be increased with the use of an ensemble of RBF networks because the RBF networks are successfully applied to solve classification problems and they can be trained by gradient descent algorithms. Reviewing the bibliography we can see that the performance of ensembles of Multilayer Feedforward (MF) networks can be improved by the use of the two combination methods based on Stacked Generalization described in [1]. We think that we could get a better classification system if we applied these combiners to an RBF ensemble. In this paper we satisfactory apply these two new methods, Stacked and Stacked+, on ensembles of RBF networks. Increasing the number of networks used in the combination module is also successfully proposed in this paper. The results show that training 3 MF networks to combine an RBF ensemble is the best alternative. [ABSTRACT FROM AUTHOR]

In this paper, we attempt to implement classical conditioning with spiking neurons instead of connectionist neural networks. The neuron model used is a leaky linear integrate-and-fire model with a learning algorithm combining spike-time dependent Hebbian learning and spike-time dependent anti-Hebbian learning. Experimental results show that the major phenomena of classical conditioning, including Pavlovian conditioning, extinction, partial conditioning, blocking, inhibitory conditioning, overshadow and secondary conditioning, can be implemented by the spiking neuron model proposed here and further indicate that spiking neuron models are well suited to implementing classical conditioning. [ABSTRACT FROM AUTHOR]

Classifier Combination has been investigated as an alternative to obtain improvements in design and/or accuracy for difficult pattern recognition problems. In the literature, many combination methods and algorithms have been developed, including methods based on computational Intelligence, such as: fuzzy sets, neural networks and fuzzy neural networks. This paper presents an evaluation of how different levels of diversity reached by the choice of the components can affect the accuracy of some combination methods. The aim of this analysis is to investigate whether or not fuzzy, neural and fuzzy-neural combination methods are affected by the choice of the ensemble members. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach for directly tuning the gaussian kernel matrix for one class learning. The popular gaussian kernel includes a free parameter, σ, that requires tuning typically performed through validation. The value of this parameter impacts model performance significantly. This paper explores an automated method for tuning this kernel based upon a hill climbing optimization of statistics obtained from the kernel matrix. [ABSTRACT FROM AUTHOR]

This paper discusses an alternative approach to parameter optimization of prototype-based learning algorithms that aim to minimize an objective function based on gradient search. The proposed approach is a stochastic optimization method called the Cross Entropy (CE) method. The CE method is used to tackle the initialization sensitiveness problem associated with the original generalized Learning Vector Quantization (GLVQ) algorithm and its variants and to locate the globally optimal solutions. We will focus our study on a variant which deals with a weighted norm instead of the Euclidean norm in order to select the most relevant features. The results in this paper indicate that the CE method can successfully be applied to this kind of problems and efficiently generate high quality solutions. Also, highly competitive numerical results on real world data sets are reported. [ABSTRACT FROM AUTHOR]

Multidimensional Scaling algorithms (MDS) are useful tools that help to discover high dimensional object relationships. They have been applied to a wide range of practical problems and particularly to the visualization of the semantic relations among documents or terms in textual databases. The MDS algorithms proposed in the literature often suffer from a low discriminant power due to its unsupervised nature and to the ‘curse of dimensionality'. Fortunately, textual databases provide frequently a manually created classification for a subset of documents that may help to overcome this problem. In this paper we propose a semi-supervised version of the Torgerson MDS algorithm that takes advantage of this document classification to improve the discriminant power of the word maps generated. The algorithm has been applied to the visualization of term relationships. The experimental results show that the model proposed outperforms well known unsupervised alternatives. [ABSTRACT FROM AUTHOR]

In this paper, we propose an evolutionary approach to deal with shortcomings on conventional focused crawling systems in semantic web environment. Thereby, meta-evolution strategy for collaboration among multiple crawlers has to be efficiently carried out. It is based on incremental aggregation of partial semantic structures extracted from web resources, which are in advance annotated with local ontologies. To do this, we employ similarity-based matching algorithm, so that fitness function is formulated by summing all possible semantic similarities. As a result, the best mapping condition (i.e., the fitness is maximized) is obtained for efficiently i) reconciling semantic conflicts between multiple crawlers, and ii) evolving semantic structures of web spaces over time. [ABSTRACT FROM AUTHOR]

In this paper, we examine analysis of clusters of labeled samples to identify their underlying hierarchical structure. The key in this identification is to select a suitable measure of dissimilarity among clusters characterized by subpopulations of the samples. Accordingly, we introduce a dissimilarity measure suitable for measuring a hierarchical structure of subpopulations that fit the mixture model. Glass identification is used as a practical problem for hierarchical cluster analysis, in the experiments in this paper. In the experimental results, we exhibit the effectiveness of the introduced measure, compared to several others. [ABSTRACT FROM AUTHOR]

In this paper we present a well designed method that makes use of edge information to extract textual blocks from gray scale document images. It aims at detecting textual regions on heavy noise infected newspaper images and separate them from graphical regions. The algorithm traces the feature points in different entities and then groups those edge points of textual regions. Finally feature based connected component merging was introduced to gather homogeneous textual regions together within the scope of its bounding rectangles. The proposed method can be used to locate text in-group of newspaper images with multiple page layouts. Initial results are encouraging, then they are experimented with considerable number of newspaper images with different layout structures and promising results were obtained. This finds its major application in digital libraries for OCR where information can be of different quality depending on the age of the scanned paper. [ABSTRACT FROM AUTHOR]

In this paper, we present a HMM based system that is used to recognize ultra low resolution text such as those frequently embedded in images available on the web. We propose a system that takes specifically the challenges of recognizing text in ultra low resolution images into account. In addition to this, we show in this paper that word models can be advantageously built connecting together sub-HMM-character models and inter-character state. Finally we report on the promising performance of the system using HMM topologies which have been improved to take into account the presupposed minimum length of each character. [ABSTRACT FROM AUTHOR]

An automated approach for computation of the frontal plane QRS vector and an important observation of its clinical significance is described in this paper. Frontal plane QRS vector is computed from the six frontal plane leads ( Standard leads I, II, III , AVR, AVL and AVF). The R-R interval of each ECG wave is detected by square derivative technique. The baseline or isoelectric level of every ECG wave is determined. After that the net positive or net negative deflection (NQD) of QRS complex is detected. Net positive or net negative deflection in any lead is obtained by subtracting the smaller deflection (+ve or -ve) from the larger deflection (-ve or +ve). An algorithm is developed for computation of the exact angle,amplitude and direction of the frontal plane QRS vector from maximum and minimum NQD. In the present work, the PTB diagnostic ECG database of normal and Myocardial Infarction (MI) subjects is used for computation of the QRS vector. An interesting clinical observation that, the rotation of QRS axis for MI data may significantly detect the region of the infarcted cardiac wall, is reported in this paper. [ABSTRACT FROM AUTHOR]

Automatic tracking of references involves aggregating and synthesizing references through World Wide Web, thereby introducing greater efficiency and granularity to the task of finding publication information. This paper discusses the design and implementation of crawler-based reference tracking system, which has the advantage of online reference filtering. The system automatically analyses the semantic relevance of the reference article by harvesting keywords and their meanings, from title and abstract of the respective article. Indirectly this attempts to improve the performance of the reference database by reducing the articles that are actually being downloaded thereby improving the performance of the system. The number of levels for recursive downloads of reference articles are specified by the user. According to user's interest the system tracks up the references required for the understanding of the seed article, stores them in the databases and projects the information by threshold based view filtering. [ABSTRACT FROM AUTHOR]

This paper presents a framework for using a case-based reasoning system for stock analysis in financial market. The unique aspect of this paper is the use of a hierarchical structure for case representation. The system further incorporates a multi-criteria decision-making algorithm which furnishes the most suitable solution with respect to the current market scenario. Two important aspects of financial market are addressed in this paper: stock evaluation and investment planning. CBR and multi-criteria when used in conjunction offer an effective tool for evaluating goodness of a particular stock based on certain factors. The system also suggests a suitable investment plan based on the current assets of a particular investor. Stock evaluation maps to a flat case structure, but investment planning offers a scenario more suited for structuring the case into successive detailed layers of information related to different facets. This naturally leads to a hierarchical case structure. [ABSTRACT FROM AUTHOR]