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Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on

Date Nov. 29 2010-Dec. 1 2010

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Displaying Results 1 - 25 of 258
  • [Front matter]

    Page(s): i - xiv
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    Freely Available from IEEE
  • A simple evaluation of face detection algorithms using unpublished static images

    Page(s): 1 - 5
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    In this paper, we document the face detection competition that we have organized in conjunction with the ISDA 2010 conference. The objective was to compare different face detection engines performance on new unpublished datasets. We believe researchers can benefit from this competition by identifying strong and weak areas in their algorithms relative to others. We have also identified, based on the results, the common areas of improvement necessary for real life scenario applications. View full abstract»

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  • GADF — Genetic Algorithms for distribution fitting

    Page(s): 6 - 11
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    Distribution fitting is a widely recurring problem in different fields such as telecommunication, finance and economics, sociology, physics, etc. Standard methods often require solving difficult equations systems or investments in specialized software. The paper presents a new approach to distribution fitting that exploits Genetic Algorithms in order to simultaneously identify the distribution type and tune its parameters by exploiting a dataset sampled from the observed random variable and a set of distribution families. The strength of this approach lies in the easiness of the expansion of this set: in fact distributions are simply described by means of their probability density functions and cumulative distribution functions, which are well-known. This approach employs two different score metrics, the Mean Absolute Error and the Kolmogorov-Smirnov test, that are linearly combined in order to find the best fitting distribution. The results obtained in an industrial application are presented and discussed. View full abstract»

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  • Image reconstruction of a metal fill industrial process using Genetic Programming

    Page(s): 12 - 17
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    Electrical Capacitance Tomography (ECT) is one of the most attractive technique for industrial process imaging because of its low construction cost, safety, non-invasiveness, non-intrusiveness, fast data acquisition, simple structure, wide application field and suitability for most kinds of flask and vessels. However, image reconstruction based ECT suffers many limitations. They include the Soft-field and Ill-condition characteristic of ECT. The basic idea of the ECT for image reconstruction for a metal fill problem is to model the image pixels as a function of the capacitance measurements. Developing this relationship represents a challenge for systems engineering community. In this paper, we presents our innovative idea on solving the non-linear inverse problem for conductive materials of the ECT using Genetic Programming (GP). GP found to be a very efficient algorithm in producing a mathematical model of image pixels in the form of Lisp expression. The reported results are promising. View full abstract»

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  • Optimal product line design workflows using a service oriented architecture

    Page(s): 18 - 23
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    The optimal product line design problem consists of predicting the acceptance of new products in the market, prior of the actual production phase. It requires a unique blend of techniques and resources as it deals with multiple parameters and hard optimization problems. This paper proposes a service oriented architecture which outlines the basic features that a marketing decision support system should provide in order to address this problem. Each feature corresponds to a distinct service while the overall functionality is achieved though service orchestration. The proposed breakdown of the problem into distinct services fits intrinsically the way that marketing managers anticipate the optimal product line process, while it allows them the opportunity to get a solution without getting obstructed of the various, complex optimization parameters and needing to coordinate the whole process. View full abstract»

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  • An event driven software framework for enabling enterprise integration and control of enterprise processes

    Page(s): 24 - 30
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    In recent years to sustain competitive advantages, manufacturing enterprises strive for improvements in their monitoring and control of enterprise processes. Hence in the current contribution, an event driven software framework for enabling enterprise integration (EI) and enhancing control of enterprise processes is presented. The proposed framework is composed of four main components: First, a data collection engine is used for physical resource integration. Second, data aggregation engine integrates data from different enterprise levels, thus enabling EI. Third, control of enterprise processes is performed using a complex event processing engine. Finally, real-time resource and control data, and historical data can be visualized through process visualization clients. The framework has been validated in an industrial scenario. View full abstract»

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  • Adaptive resource allocation for preemptable jobs in cloud systems

    Page(s): 31 - 36
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    In cloud computing, computational resources are provided to remote users in the form of leases. For a cloud user, he/she can request multiple cloud services simultaneously. In this case, parallel processing in the cloud system can improve the performance. When applying parallel processing in cloud computing, it is necessary to implement a mechanism to allocate resource and schedule the tasks execution order. Furthermore, a resource allocation mechanism with preemptable task execution can increase the utilization of clouds. In this paper, we propose an adaptive resource allocation algorithm for the cloud system with preemptable tasks. Our algorithms adjust the resource allocation adaptively based on the updated of the actual task executions. And the experimental results show that our algorithms works significantly in the situation where resource contention is fierce. View full abstract»

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  • Street navigation using visual information on mobile phones

    Page(s): 37 - 42
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    Applications with street navigation have been recently introduced on mobile phone devices. A major part of existing systems use integrated GPS as input for indicating the location. However, these systems often fail or make abrupt shifts in urban environment due to occlusion of satellites. Furthermore, they only give the position of a person and not the object of his attention, which is just as important for localization based services. In this paper we introduce a system using mobile phones built-in cameras for navigation and localization using visual information in accordance with the way we as humans navigate. The introduced method uses local features for extraction of natural feature points from images which are compared to a database for localization. The system is tested and evaluated in a real urban environment and the result shows very high success rate. View full abstract»

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  • A relational fuzzy c-means clustering algorithm based on multiple dissimilarity matrices

    Page(s): 43 - 48
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    This paper introduces a relational fuzzy c-means clustering algorithm that is able to partition objects taking into account simultaneously several dissimilarity matrices. The aim is to obtain a collaborative role of the different dissimilarity matrices in order to obtain a final consensus partition. These matrices could have been obtained using different sets of variables and dissimilarity functions. This algorithm is designed to give a fuzzy partition and a prototype for each cluster as well as to learn a relevance weight for each dissimilarity matrix by optimizing an objective function. These relevance weights change at each algorithm's iteration and are different from one cluster to another. Experiments with datasets from UCI machine learning repository show the usefulness of the proposed algorithm. View full abstract»

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  • Proximity fuzzy clustering and its application to time series clustering and prediction

    Page(s): 49 - 54
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    A new time series prediction architecture is introduced using a fuzzy inference system (FIS) and a new framework for fuzzy relational clustering of time series. The FIS is used to predict future samples in a time series where recurrent neural networks comprise the consequents of the rules. The antecedents come in the form of fuzzy relations; however, previous approaches such as FCM build these antecedents in a Euclidean feature space which is very limiting and not well suited to the problem of clustering time series. Our approach to learning the antecedents of the rules involves clustering time series using proximity values, indicative of closeness. A variant of the classical correlation is used to measure proximity. Our objective is to investigate and evaluate the application of proximity fuzzy clustering in the domain of time series prediction by comparing its performance against several commonly used time series prediction models. View full abstract»

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  • A three-part input-output clustering-based approach to fuzzy system identification

    Page(s): 55 - 60
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (187 KB) |  | HTML iconHTML  

    This article presents a clustering-based approach to fuzzy system identification. In order to construct an effective initial fuzzy model, this article tries to present a modular method to identify fuzzy systems based on a hybrid clustering-based technique. Moreover, the determination of the proper number of clusters and the appropriate location of clusters are one of primary considerations on constructing an effective initial fuzzy model. Due to the above reasons, a hybrid clustering algorithm concerning input, output, generalization and specialization has hence been introduced in this article. Further, the proposed clustering technique, three-part input-output clustering algorithm, integrates a variety of clustering features simultaneously, including the advantages of input clustering, output clustering, flat clustering, and hierarchical clustering, to effectively perform the identification of clustering problem. View full abstract»

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  • An Interactive Genetic Algorithm with c-Means clustering for the Unequal Area Facility Layout Problem

    Page(s): 61 - 66
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    Unequal Area Facility Layout Problem (UA-FLP) has been addressed by several methods. However, UA-FLP has only been solved regarding quantitative criteria. Our approach includes subjective features to UA-FLP, which are difficult to take into account with a classical heuristic optimization. For that, an Interactive Genetic Algorithm (IGA) is proposed that allows an interaction between the algorithm and the Decision Maker (DM). Involving the DM knowledge into the approach guides the search process, adjusting it to the DM's preferences at every iteration of the algorithm. The whole population is evaluated through the DM subjective evaluations of the representative solutions, which are different enough and are chosen by means of c-Means clustering method. The empirical test results show that the proposed IGA is capable of capturing DM preferences and that it can progress towards a good solution in a reasonable number of iterations. View full abstract»

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  • Triangular kernel nearest neighbor based clustering for pattern extraction in spatio-temporal database

    Page(s): 67 - 73
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (348 KB) |  | HTML iconHTML  

    To date, various fields of applications have utilized spatio-temporal databases not only to store data, but to support decision making. For example, in traffic accident analysis; it is required to have knowledge on the pattern of accidents resulting in death. Thus, in such analysis, clustering technique is desired to implement pattern extraction. This paper presents clustering of spatio-temporal database using kernel nearest neighbor approach. It is chosen due to its ability to determine the number of clusters automatically. There are various types of kernel functions exist in the literatures, but the issue of concern is how to determine an appropriate kernel function for this application. In this study, two commonly used kernel functions, namely Gaussian and triangular, are investigated. From various experiments conducted, both functions produce reasonable clusters, but the triangular kernel nearest neighbor based clustering (TKNN) provides better performance with smaller number of iteration compared to Gaussian kernel nearest neighbor based clustering (ILGC) and K-means. Thus, TKNN is good option in clustering spatio-temporal database. View full abstract»

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  • Cluster-based characterization of gene over-expression in cancer sets

    Page(s): 74 - 79
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1757 KB) |  | HTML iconHTML  

    Mining techniques are needed to extract important information from huge high dimensional gene expression sets. Targeting unique expression behavior as over/under-expression is specific to gene expression data and is needed to explore another direction in the relation of genes to tumor conditions. This research proposes criteria for filtering over-expression genes, identifying over-expression related samples and using them to characterize over-expression behaviour in gene clusters and outliers. In return, hypothetical marker genes and functional relations can be provided, ready for approval by the aid of other datasets/results. Experiments are performed on breast cancer expression data. View full abstract»

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  • Data generated type-2 fuzzy logic model for control of wind turbines

    Page(s): 80 - 85
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (294 KB) |  | HTML iconHTML  

    Wind energy is becoming one of the most important and promising areas of renewable energy. During the past few years, wind energy generation underwent strong improvements in several fields including power electronics, mechanics, wind dynamics, etc. However, there is a high need to develop more intelligent control mechanisms that can handle the various sources of uncertainties encountered in wind turbines and allow maximum power to be obtained from wind. The recent years have witnessed the use of type-2 fuzzy logic systems to generate controllers which are able to provide robust control performances in the face of high levels of uncertainty. This paper presents a method to generate a type-2 fuzzy logic model entirely from data to provide a dynamic footprint of uncertainty for the generated fuzzy set. The fuzzy model will be used to predict the wind speed experienced by a wind turbine without the use of sensors. This estimated wind speed is then passed for another fuzzy controller that changes the pitch angles of the wind turbine blades in order to track the maximum power available. View full abstract»

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  • The development of cross-language plagiarism detection tool utilising fuzzy swarm-based summarisation

    Page(s): 86 - 90
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (360 KB) |  | HTML iconHTML  

    This work presents the design and development of a web-based system that supports cross-language similarity analysis and plagiarism detection. A suspicious document dq in a language Lq is to be submitted to the system via a PHP web-based interface. The system will accept the text through either uploading or pasting it directly to a text-area. In order to lighten large texts and provide an ideal set of queries, we introduce the idea of query document reduction via summarisation. Our proposed system utilised a fuzzy swarm-based summarisation tool originally built in Java. Then, the summary is used as a query to find similar web resources in languages Lx other than Lq via a dictionary-based translation. Thereafter, a detailed similarity analysis across the languages Lq and Lx is performed and friendly report of results is produced. Such report has global similarity score on the whole document, which assures high flexibility of utilisation. View full abstract»

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  • Fuzzy logic-based for predicting roughness performance of TiAlN coating

    Page(s): 91 - 96
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (245 KB) |  | HTML iconHTML  

    In this paper a new application of fuzzy logic to predict the performance of Titanium Aluminum Nitride (TiAlN) sputtering coating process is presented. Titanium Aluminum Nitride (TiAlN) coated material is widely used as a cutting tool in machining due to its excellent properties such as hardness, roughness and wear. A fuzzy logic model was proposed to predict the coating roughness with respect to changes in input process parameters, the substrate sputtering power, bias voltage and temperature. Five membership functions are assigned to be associated with each input of the model. The predicted results obtained via fuzzy logic model were compared to the experimental result. The result indicated good agreement between the fuzzy model and experimental results with the 96.39% accuracy. View full abstract»

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  • Detecting anomalies in spatiotemporal data using genetic algorithms with fuzzy community membership

    Page(s): 97 - 102
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    A genetic algorithm is combined with two variants of the modularity (Q) network analysis metric to examine a substantial amount fisheries catch data. The data set produces one of the largest networks evaluated to date by genetic algorithms applied to network community analysis. Rather than using GA to decide community structure that simply maximizes modularity of a network, as is typical, we use two fuzzy community membership functions applied to natural temporal divisions in the network so the GA is used to find interesting areas of the search space through maximization of modularity. The work examines the performance of the genetic algorithm against simulated annealing using both types of fuzzy community membership functions. The algorithms are used in an existing visualization software prototype, where the solutions are evaluated by a fisheries expert. View full abstract»

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  • Decision precising fuzzy technology to evaluate the credit risks of investment projects

    Page(s): 103 - 108
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    This work proposes a decision support technology to minimize risks while choosing among competitive investment projects. The technology combines two fuzzy-statistical methods, providing two stages of investment projects' evaluation. At the first stage preliminary selection of projects with small risks is made on the basis of the expertons method [2],[3]. The second stage makes more precise decisions using the method of possibilistic discrimination analysis. This is a new method that represents a generalization of the fuzzy discrimination analysis [6]. The method is applied to a relatively small number of projects, selected during first stage, to compare and sort out high-quality projects. For the latter, the recommendations to provide credits are made. The article provides calculation examples that explain the work of the offered technology. View full abstract»

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  • Q(λ)-learning fuzzy logic controller for differential games

    Page(s): 109 - 114
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (276 KB) |  | HTML iconHTML  

    This paper addresses the problem of tuning the input and the output parameters of a fuzzy logic controller. A novel technique that combines Q(λ)-learning with a fuzzy inference system as a function approximation is proposed. The system learns autonomously without supervision or a priori training data. The proposed technique is applied to two different differential games. The proposed technique is compared with the classical control strategy, Q(λ)-learning only, and the technique proposed in [1] in which a neural network is used as a function approximation for Q-learning. Computer simulations show the usefulness of the proposed technique. View full abstract»

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  • Artificial Neural Networks for nonlinear regression and classification

    Page(s): 115 - 120
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    Linear regression and classification techniques are very common in statistical data analysis but they are often able to extract from data only linear models, which can be a limitation in real data context. Aim of this study is to build an innovative procedure to overcome this defect. Initially, a multiple linear regression analysis using the best-subset algorithm was performed to determine the variables for best predicting the dependent variable. Based on the same selected variables, Artificial Neural Networks were employed to improve the prediction of the linear model, taking advantage of their nonlinear modeling capability. Linear and nonlinear models were compared in their classification (ROC curves) and prediction (cross-validation) tasks: nonlinear model resulted to fit better data (36% vs. 10% variance explained for nonlinear and linear, respectively) and provided more reliable parameters for accuracy and misclassification rates (70% and 30% vs. 66% and 34%, respectively). View full abstract»

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  • Predicting acute neurological diseases with Bayesian networks

    Page(s): 121 - 125
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    Emergency physicians in small primary care hospitals seeing patients with acute neurological symptoms have difficulty differentiating ischemic from hemorrhagic strokes and from stroke mimics. Telestroke consults with experienced neurologists supplemented by computerized decision support may aid in this time critical situation. Here we present a Stroke Bayesian Network (SBN) based on a naïve Bayesian classifier to predict the most likely stroke etiology-ischemia, hemorrhage or stroke mimic-in an emergency room (ER) setting. As a proof of concept, this probabilistic network was evaluated in a pilot study on a cohort of 44 acute neurological patients admitted to three primary care hospitals associated with the TASC telestroke network in Saxony-Anhalt. In this cohort, the SBN correctly classified 31 of 36 ischemic stroke patients, and all five stroke mimics, but failed to identify three hemorrhages. For the frequent and significant ischemic stroke type, 97% classification precision and 86% sensitivity were obtained. To properly evaluate the SBN performance, a randomized controlled clinical trial should be conducted on a cohort of patients admitted to the ER. View full abstract»

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  • Sleeping with ants, SVMs, multilayer perceptrons and SOMs

    Page(s): 126 - 131
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    This paper reports the investigations and experimental procedures conducted for designing an automatic sleep classification tool basedconly in the features extracted with wavelets from EEG, EMG and EOG (electro encephalo-mio- and oculo-gram) signals, without any visual aid or context-based evaluation. Real data collected from infants was processed and classified by several traditional and bio-inspired heuristics. Preliminary results show that some methods are able to attain success rates close to 70% when compared to an expert neurologist. Although still not sufficient to implement a reliable sleep classifier, these are promising results that, together with an analysis via Self-Organizing Maps and ant-based clustering, may help to improve the feature extraction and contribute to a better representation of the different classes' characteristics. View full abstract»

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  • A wearable pervasive platform for the intelligent monitoring of muscular fatigue

    Page(s): 132 - 135
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    There are currently approximately 45 million people in Europe who report a long standing health problem or disability; according to the World Health Organization data the total number of persons chronically ill are 860 million at a worldwide level. Within a person centric health management framework, the modern healthcare systems must move away from the 'health care' to 'health management' in order to shift from 'how to treat patients' to 'how to keep people healthy'. Assistive and health monitoring technologies can help to automatically identify and address major deficits. In this work we describe a wearable pervasive platform able to monitor the muscular fatigue during his/her daily life. Our system monitors the performance of a subject through the evaluation of sEMG signals using a wearable device aimed to immediately recognize the onset of muscular fatigue. View full abstract»

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  • A generic framework and runtime environment for development and evaluation of behavioral biometrics solutions

    Page(s): 136 - 141
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    Increasing use of computerized systems in our daily lives creates new adversarial opportunities for which complex mechanisms are exploited to mend the rapid development of new attacks. Behavioral Biometrics appear as one of the promising response to these attacks. But it is a relatively new research area, specific frameworks for evaluation and development of behavioral biometrics solutions could not be found yet. In this paper we present a conception of a generic framework and runtime environment which will enable researchers to develop, evaluate and compare their behavioral biometrics solutions with repeatable experiments under the same conditions with the same data. View full abstract»

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