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Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on

Date 28-30 Nov. 2005

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Displaying Results 1 - 25 of 195
  • International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce - Cover

    Publication Year: 2005 , Page(s): c1
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  • International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce - Title

    Publication Year: 2005 , Page(s): i - iii
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  • International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce - Copyright

    Publication Year: 2005 , Page(s): iv
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  • International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce - Table of contents

    Publication Year: 2005 , Page(s): v - xix
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  • Message from the Program and Organizing Chair of CIMCA

    Publication Year: 2005 , Page(s): xx
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  • Message from the Program and Organizing Chair of IAWTIC

    Publication Year: 2005 , Page(s): xxi
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  • International Program Committee and Liaisons of CIMCA

    Publication Year: 2005 , Page(s): xxii
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  • International Program Committee and Liaisons of IAWTIC

    Publication Year: 2005 , Page(s): xxv
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  • Abstracts

    Publication Year: 2005 , Page(s): xxix
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (82 KB) |  | HTML iconHTML  

    Presents abstracts of papers presented at the conference proceedings. View full abstract»

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  • Intelligent Text Extraction from PDF Documents

    Publication Year: 2005 , Page(s): 2 - 6
    Cited by:  Papers (2)  |  Patents (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1716 KB) |  | HTML iconHTML  

    In recent years, PDF has become the de-facto standard for the exchange of print-oriented documents on the Web. This includes many business documents such as financial reports, newsletters and patent applications, and there are many commercial applications that require data to be extracted from these documents and processed by computer systems. A number of products currently exist on the market that navigate, extract and transform data from HTML pages; a process known as wrapping. One such methodology is Lixto, a product of research at our institute. However, none of these products are currently able to work with PDF files. We are investigating this possibility as part of the NEX-TWRAP project. This paper describes our work in progress, and details some of the low-level page segmentation techniques that we have investigated View full abstract»

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  • Building an Adaptive Hierarchy of Clusters for Text Data

    Publication Year: 2005 , Page(s): 7 - 12
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (274 KB) |  | HTML iconHTML  

    Text clustering has been recognized as an important component in Web-based applications. Clustering data on a hierarchical structure enables exploring data on different levels of granularity, providing a more intuitive view that is close to the way humans view the world. Self-organizing map (SOM) based models have been found to have certain advantages for clustering sizeable text data. However, current existing approaches lack in providing an adaptive hierarchical structure within in a single model. This paper proposes an unsupervised hierarchical clustering approach based on the growing self-organizing map (GSOM). By utilizing GSOM's spread factor, our approach offers an adaptive architecture with the capability of detecting necessary layers to form a hierarchy, avoiding a number of issues that a traditional top-down or bottom-up hierarchical clustering approach often encounter. Experiment has shown that this approach has the potential for efficiently clustering heterogeneous text data View full abstract»

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  • Web Document Clustering using Semantic Link Analysis

    Publication Year: 2005 , Page(s): 13 - 18
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (203 KB) |  | HTML iconHTML  

    Searching and discovering the relevant information on the Web have always been challenging research areas. Web document clustering is a promising technique in preparing a huge collection of Web documents suitable for Web search engines. This paper proposes a semantic document clustering approach to categorize Web documents in a semantic manner. First, the formal methods and algorithms are introduced as techniques for document extraction and clustering. The approach incorporates WordNet and ontology knowledge as the assistant mechanisms such that the resulting set of concepts are thus utilized as formal representation for extracted documents. As a consequence, the semantic-based clusters are finally determined the cluster scores. Next, the semantic-based link analysis method is also proposed for clustering Web documents into semantic clusters that are scored based on the notion of semantic-based concepts and documents. Finally, these document scores are subsequently used for evaluating the semantic document similarity and document quality. As such, the precision criterion is employed for efficient evaluations by comparing with keywords-based search method. The experimental results reported that the proposed method was able to outperform the TF/IDF method up to 9% on average View full abstract»

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  • Using Cross-Language Information Retrieval Methods for Bilingual Search of the Web

    Publication Year: 2005 , Page(s): 19 - 24
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (155 KB) |  | HTML iconHTML  

    English content dominates the Web, while the number of Web users speaking other languages is increasing rapidly. This paper describes a system that uses various cross-language information retrieval methods to provide search engines with capability of automatic search in a local language as well as English even when the user makes search queries only in the local language. The system would be useful for Web users, expanding the international scope of the Web View full abstract»

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  • Genetic Algorithms for Minimal Fuel Consumption of Electric Propulsion Space Vehicles

    Publication Year: 2005 , Page(s): 25 - 30
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (268 KB) |  | HTML iconHTML  

    This paper demonstrates the utility of genetic algorithms (GAs) to determine a near optimal control strategy for electric propulsion systems. The various strategies implemented are simple GA, simple GA with elitism and micro GA. The accuracy and performance of the control strategy obtained using these methods are discussed along with their detailed description. This work inherently validates the use of ionic thrust for deep space missions View full abstract»

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  • Efficient Finite Word Length Determination For Neural Networks Implementation

    Publication Year: 2005 , Page(s): 31 - 35
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (185 KB) |  | HTML iconHTML  

    Most of the artificial neural networks (ANN) based applications are implemented on FPGAs using fixed-point arithmetic. The problem is to achieve a balance between the need for numerical precision, which is important for network accuracy, and the cost of logic areas, i.e. FPGA resources. In this paper we propose a genetic algorithm based methodology permitting the optimization of the FPGA resources needed for the implementation of a pipelined recurrent neural network (PRNN) while respecting the precision constraints. The quality of our methodology would be evaluated through experiment on a PRNN based WCDMA receiver. Our methodology is not restricted to this class of ANNs and can be used for any complex with variable dimensions system View full abstract»

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  • A Heuristic Immune-Genetic Algorithm for Multimodal Function Optimization

    Publication Year: 2005 , Page(s): 36 - 40
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (206 KB) |  | HTML iconHTML  

    To avoid premature convergence and guarantee the diversity of the population, a heuristic immune-genetic algorithm (HIGA) is proposed. Rapid immune response (secondary response), adaptive mutation and density operators in the HIGA are emphatically designed to improve the searching ability, greatly increase the converging speed, and avoid locating the local maxima due to the premature convergence. The simulation results show that HIGA converges rapidly, guarantees the diversity, stability and good searching ability View full abstract»

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  • Multi-Valued Function Chains in Evolutionary Algorithm

    Publication Year: 2005 , Page(s): 41 - 46
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (207 KB) |  | HTML iconHTML  

    The choice of function modelling methods for the computer aided engineering/computer aided design tools is based on numerous criterions. The possibility to use evolutionary algorithms to optimisation in design electronic circuits arouses the large interest. Multi-valued function chains (MFC) as the representation of individuals in evolutionary algorithm is presented in the paper. The MFC gives possibility to describe circuit on different abstraction levels in CAD/E tools. It is especially good for the register transfer level. The connection of MFC with the evolutionary algorithm gives more extensive possibilities View full abstract»

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  • Genetic Network Programming with Acquisition Mechanisms of Association Rules in Dense Database

    Publication Year: 2005 , Page(s): 47 - 54
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (380 KB) |  | HTML iconHTML  

    A method of association rule mining using genetic network programming (GNP) is proposed to improve the performance of association rule extraction from dense database. Rule extraction is done without identifying frequent itemsets used in a priori-like methods. Association rules are represented as the connections of nodes in GNP. The proposed mechanisms calculate measurements of association rules directly from a database using GNP, and measure the significance of the association via the chi-squared test. The proposed system evolves itself by an evolutionary method and obtains candidates of association rules by genetic operations. Extracted association rules are stored in a pool all together through generations and reflected in genetic operators as acquired information. In this paper, we describe an algorithm capable of finding important association rules using GNP with sophisticated rule acquisition mechanisms and present some experimental results View full abstract»

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  • Constructing university timetable using constraint satisfaction programming approach

    Publication Year: 2005 , Page(s): 55 - 60
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (180 KB) |  | HTML iconHTML  

    The timetabling problem consists of a set of subjects to be scheduled in different timeslots, a set of rooms in which the subjects can take place, a set of students who attend the subjects, and a set of subjects satisfied by rooms and required by timeslots. The heart of the problem is the constraints that exist as regulations within each resource and between resources. There are various solution approaches to solve the timetabling problem. This paper focuses on developing a constraint satisfaction problem model for a university timetabling problem. A solution of a constraint satisfaction problem is a consistent assignment of all variables to values in such a way that all constraints are satisfied. A sample case study problem is investigated and a constraint satisfaction programming approach is implemented using ILOG scheduler and ILOG solver. We use various goals in ILOG to investigate the performance of the CSP approach View full abstract»

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  • M-GA: A Genetic Algorithm to Search for the Best Conditional Gaussian Bayesian Network

    Publication Year: 2005 , Page(s): 61 - 67
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (244 KB) |  | HTML iconHTML  

    The search of optimal Bayesian network from a database of observations is NP-hard. Nevertheless, several heuristic search strategies have been found to be effective. We present a new population-based algorithm to learn the structure of Bayesian networks without assuming any ordering of nodes and allowing for the presence of both discrete and continuous random variables. Numerical performances of our mixed-genetic algorithm, (M-GA), are investigated on a case study taken from the literature and compared with greedy search View full abstract»

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  • Can Ensemble Method Convert a 'Weak' Evolutionary Algorithm to a 'Strong' One?

    Publication Year: 2005 , Page(s): 68 - 74
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (205 KB) |  | HTML iconHTML  

    The contribution of the paper is bringing ensemble method to the field of evolutionary computation. The conceptive model of evolutionary algorithm ensemble is introduced, in which a collection of evolutionary algorithms are designed to solve the same problem and each interact with others. Two implementation methods are invented: data-based ensemble and model-based ensemble. In data-based ensemble, componential evolutionary algorithm shares a common data pool with others, and population of each algorithm is sampled from the pool using bagging method. In model-based ensemble, there are a collection of models describing the evolution status, and they cooperate by the way of information interaction. As examples, simple genetic algorithm and PBIL (population based incremental learning) are used to implement the ideas respectively. Experiments on combinatorial optimization problems show that ensemble method improves the performance of evolutionary algorithm. It can be concluded ensemble method can convert a `weak' evolutionary algorithm to a `strong' one View full abstract»

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  • Neural System for in silico Drug-Drug Interaction Screening

    Publication Year: 2005 , Page(s): 75 - 80
    Cited by:  Papers (97)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (232 KB) |  | HTML iconHTML  

    Drug usage is always associated with risk drugs interactions are considered to be one of the potential sources of undesirable action of drugs. Such a situation enforced U.S. Food and Drug Administration (FDA) as well as European Medicines Agency (EMEA) to issue a guidance for industry and researchers for in vivo and in vitro drug interactions studies. The authors proposed neural networks based in silico system for potential drug-drug interactions screening with use of simple physico-chemical data describing each chemical substance particle. Initial results where 77% of classification rate in generalization test was found suggest that computational intelligence based systems could be effective in this area View full abstract»

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  • Neural Networks and Spectral Feature Selection for Retrieval of Hot Gases Temperature Profiles

    Publication Year: 2005 , Page(s): 81 - 86
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (396 KB) |  | HTML iconHTML  

    Neural networks appear to be a promising tool to solve the so-called inverse problems focused to obtain a retrieval of certain physical properties related to the radiative transference of energy. In this paper the capability of neural networks to retrieve the temperature profile in a combustion environment is proposed. Temperature profile retrieval will be obtained from the measurement of the spectral distribution of energy radiated by the hot gases (combustion products) at wavelengths corresponding to the infrared region. High spectral resolution is usually needed to gain a certain accuracy in the retrieval process. However, this great amount of information makes mandatory a reduction of the dimensionality of the problem. In this sense a careful selection of wavelengths in the spectrum must be performed. With this purpose principal component analysis technique is used to automatically determine those wavelengths in the spectrum that carry relevant information on temperature distribution. A multilayer perceptron will be trained with the different energies associated to the selected wavelengths. The results presented show that multilayer perceptron combined with principal component analysis is a suitable alternative in this field View full abstract»

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  • Detectioning of an Asynergy Using the Neural Network

    Publication Year: 2005 , Page(s): 87 - 91
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (536 KB) |  | HTML iconHTML  

    Recently in medical fields, various imaging diagnostic technologies have been studied and used in practical. It is necessary to develop an automatic diagnosing processing system for detecting and diagnosing the internal organs. By the way, cardiac disease is one of the most common cause of death. Therefore, it is necessary to measure cardiac function quantitatively. The processing images are X-ray photograms of the left ventricle by cardiac catheterization. In this paper, we propose the detection system of asynergy in the left ventricle by using a neural network. Furthermore, in order to demonstrate the effectiveness of the proposed method, we show the simulation example by using the real data View full abstract»

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  • Prediction of Delays in Public Transportation using Neural Networks

    Publication Year: 2005 , Page(s): 92 - 97
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (662 KB) |  | HTML iconHTML  

    The project the authors of this paper are involved in is titled "system for intelligent realtime timetable optimization and monitoring". The objective is to develop a system being able to use delay-predictions for real-time-delay-monitoring, and in the long term, for a timetable-optimization in the range of train networks. The presented paper deals with the part of the system responsible for processing existing delays in the network to generate delay-predictions for depending trains in the near future. Therefore a rule-based system was developed, processing a set of predefined rules with the input of a specific delay in a deterministic manner, delivering a resulting delay-scenario as output. This rule-based system was used as a comparison to the specially developed neural network in order to evaluate the accuracy and faculty of abstraction of such an artificially intelligent component. An excerpt of the real train network of the Deutsche Bahn was the basis for this research, for simulation purposes we used the SNNS (Stuttgart neural network simulator). At the end of this paper we can draw a conclusion in favour of the neural network, which is able to abstract from known delay constellations View full abstract»

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