Generalized approach for non-square plants (NSP) control based on multi agent system (MAS) is proposed. The control system provides relevant reconfiguration and adaptation in case of plant changes, due to manipulated variables (MV) saturation, filature of input and/or output instrumentation, close proximity to ill-conditioning. A big flexibility is reached when objective function, operation properties and constraints are dynamically changed. Simulation results are presented from MAS realized on JADE platform for two inputs two outputs (TITO) plant using stigmergy as an optimizing method. Comparative analysis of synchronous and asynchronous MAS is carried out
{"title":"Intelligent Agents Based Non -- Square Plants Control","authors":"M. Hadjiski, V. Sgurev, V. Boishina","doi":"10.1109/IS.2006.348455","DOIUrl":"https://doi.org/10.1109/IS.2006.348455","url":null,"abstract":"Generalized approach for non-square plants (NSP) control based on multi agent system (MAS) is proposed. The control system provides relevant reconfiguration and adaptation in case of plant changes, due to manipulated variables (MV) saturation, filature of input and/or output instrumentation, close proximity to ill-conditioning. A big flexibility is reached when objective function, operation properties and constraints are dynamically changed. Simulation results are presented from MAS realized on JADE platform for two inputs two outputs (TITO) plant using stigmergy as an optimizing method. Comparative analysis of synchronous and asynchronous MAS is carried out","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133098183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Geo-ontologies have a key role to play in the development of the geo-semantic Web, with regard to facilitating the search for geographical information and resources. They normally hold large amounts of geographic information and undergo a continuous process of revision and update to ensure their currency. Hence, means of ensuring their integrity are crucial and needed to allow them to serve their purpose. This paper proposes the use of qualitative spatial reasoning as a tool to support the development of a geo-ontology management system. Spatial integrity rules based on uniform and hybrid spatial reasoning are proposed for the automatic derivation of spatial relationships and for maintaining the spatial consistency of the geographic data. A framework for the representation of and reasoning over geo-ontologies is presented using the Web ontology language OWL and its associated reasoning tools. Spatial reasoning and integrity rules are represented using a spatial rule engine extension to the reasoning tools associated with OWL. To demonstrate the proposed approach, a case study showing a prototype geo-ontology and the implementation of the spatial reasoning engine is presented. This work is a step towards the realisation of a complete geo-ontology management system for the semantic Web
{"title":"Towards the Practical Use of Qualitative Spatial Reasoning in Geographic Information Retrieval","authors":"A. Abdelmoty, P. Smart, B. El-Geresy","doi":"10.1109/IS.2006.348396","DOIUrl":"https://doi.org/10.1109/IS.2006.348396","url":null,"abstract":"Geo-ontologies have a key role to play in the development of the geo-semantic Web, with regard to facilitating the search for geographical information and resources. They normally hold large amounts of geographic information and undergo a continuous process of revision and update to ensure their currency. Hence, means of ensuring their integrity are crucial and needed to allow them to serve their purpose. This paper proposes the use of qualitative spatial reasoning as a tool to support the development of a geo-ontology management system. Spatial integrity rules based on uniform and hybrid spatial reasoning are proposed for the automatic derivation of spatial relationships and for maintaining the spatial consistency of the geographic data. A framework for the representation of and reasoning over geo-ontologies is presented using the Web ontology language OWL and its associated reasoning tools. Spatial reasoning and integrity rules are represented using a spatial rule engine extension to the reasoning tools associated with OWL. To demonstrate the proposed approach, a case study showing a prototype geo-ontology and the implementation of the spatial reasoning engine is presented. This work is a step towards the realisation of a complete geo-ontology management system for the semantic Web","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130274264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Stoean, C. Stoean, M. Preuss, E. El-Darzi, D. Dumitrescu
The aim of this paper is to validate the new paradigm of evolutionary support vector machines (ESVMs) for binary classification also through an application to a real-world problem, i.e. the diagnosis of diabetes mellitus. ESVMs were developed through hybridization between the strong learning paradigm of support vector machines (SVMs) and the optimization power of evolutionary computation. Hybridization is achieved at the level of solving the constrained optimization problem within the SVMs, which is a difficult task to perform in its standard manner. ESVMs have been so far applied to the binary classification of two-dimensional points. In this paper, experiments are conducted on the benchmark problem concerning diabetes of the UCI repository of machine learning data sets. Obtained results proved the correctness and promise of the new hybridized learning technique and demonstrated its ability to solve any case of binary standard classification
{"title":"Evolutionary Support Vector Machines for Diabetes Mellitus Diagnosis","authors":"R. Stoean, C. Stoean, M. Preuss, E. El-Darzi, D. Dumitrescu","doi":"10.1109/IS.2006.348414","DOIUrl":"https://doi.org/10.1109/IS.2006.348414","url":null,"abstract":"The aim of this paper is to validate the new paradigm of evolutionary support vector machines (ESVMs) for binary classification also through an application to a real-world problem, i.e. the diagnosis of diabetes mellitus. ESVMs were developed through hybridization between the strong learning paradigm of support vector machines (SVMs) and the optimization power of evolutionary computation. Hybridization is achieved at the level of solving the constrained optimization problem within the SVMs, which is a difficult task to perform in its standard manner. ESVMs have been so far applied to the binary classification of two-dimensional points. In this paper, experiments are conducted on the benchmark problem concerning diabetes of the UCI repository of machine learning data sets. Obtained results proved the correctness and promise of the new hybridized learning technique and demonstrated its ability to solve any case of binary standard classification","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114226680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
State failure has been traditionally defined as the collapse of national authority, which may be reflected in disasters such as wars and disruptive regime transitions. The availability of comprehensive datasets and the limitations exhibited by previous forecasting analyses led us to integrate different predictive resources and models through statistical analysis and machine learning. Here we demonstrate the predictive ability of unsupervised and supervised learning approaches to detecting meaningful relationships between country cases, encoded by several socio-economic indicators, and the emergence of violent conflicts. Two clustering-based analyses (Kohonen maps and a network-based approach) provided the basis for exploratory analyses that confirmed hypotheses about the relevance of the data and the differences between state failure types. We also illustrate the potential of a novel network-based clustering approach for sub-class discovery in the area of political instability analysis. Furthermore, we show significant relationships between the emergence of violent conflicts and a dataset of quantitative indicators of good governance, which allows the design of effective supervised and unsupervised classifiers. This study contributes to the development of intelligent data analysis techniques for supporting hypothesis generation and testing in international conflict analyses
{"title":"Integrative Data Mining for Assessing International Conflict Events","authors":"F. Azuaje, Haiying Wang, Huiru Zheng, Chang Liu, Hui Wang, Ruth Rios-Morales","doi":"10.1109/IS.2006.348464","DOIUrl":"https://doi.org/10.1109/IS.2006.348464","url":null,"abstract":"State failure has been traditionally defined as the collapse of national authority, which may be reflected in disasters such as wars and disruptive regime transitions. The availability of comprehensive datasets and the limitations exhibited by previous forecasting analyses led us to integrate different predictive resources and models through statistical analysis and machine learning. Here we demonstrate the predictive ability of unsupervised and supervised learning approaches to detecting meaningful relationships between country cases, encoded by several socio-economic indicators, and the emergence of violent conflicts. Two clustering-based analyses (Kohonen maps and a network-based approach) provided the basis for exploratory analyses that confirmed hypotheses about the relevance of the data and the differences between state failure types. We also illustrate the potential of a novel network-based clustering approach for sub-class discovery in the area of political instability analysis. Furthermore, we show significant relationships between the emergence of violent conflicts and a dataset of quantitative indicators of good governance, which allows the design of effective supervised and unsupervised classifiers. This study contributes to the development of intelligent data analysis techniques for supporting hypothesis generation and testing in international conflict analyses","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133247976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sequential pattern mining algorithms have been developed which mine the set of frequent subsequences satisfying a minimum support constraint in a sequence database. However, previous sequential mining algorithms treat sequential patterns uniformly while sequential patterns have different importance. Another main problem in most of the sequence mining algorithms is that they still generate an exponentially large number of sequential patterns when a minimum support is lowered and they do not provide alternative ways to adjust the number of sequential patterns other than increasing the minimum support. In this paper, we propose a weighted sequential pattern mining algorithm called WSpan. Our main approach is to push the weight constraints into the sequential pattern growth approach while maintaining the downward closure property. A weight range is defined to maintain the downward closure property and items are given different weights within the weight range. In scanning a sequence database, a maximum weight in the sequence database is used to prune weighted infrequent sequential patterns and in the mining step, maximum weights of projected sequence databases are used. By doing so, the downward closure property can be maintained. WSpan generates fewer but important weighted sequential patterns in large databases, particularly dense databases with a low minimum support, by adjusting a weight range
{"title":"WSpan: Weighted Sequential pattern mining in large sequence databases","authors":"Unil Yun, J. Leggett","doi":"10.1109/IS.2006.348472","DOIUrl":"https://doi.org/10.1109/IS.2006.348472","url":null,"abstract":"Sequential pattern mining algorithms have been developed which mine the set of frequent subsequences satisfying a minimum support constraint in a sequence database. However, previous sequential mining algorithms treat sequential patterns uniformly while sequential patterns have different importance. Another main problem in most of the sequence mining algorithms is that they still generate an exponentially large number of sequential patterns when a minimum support is lowered and they do not provide alternative ways to adjust the number of sequential patterns other than increasing the minimum support. In this paper, we propose a weighted sequential pattern mining algorithm called WSpan. Our main approach is to push the weight constraints into the sequential pattern growth approach while maintaining the downward closure property. A weight range is defined to maintain the downward closure property and items are given different weights within the weight range. In scanning a sequence database, a maximum weight in the sequence database is used to prune weighted infrequent sequential patterns and in the mining step, maximum weights of projected sequence databases are used. By doing so, the downward closure property can be maintained. WSpan generates fewer but important weighted sequential patterns in large databases, particularly dense databases with a low minimum support, by adjusting a weight range","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133372620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A novel methodology for decision-making under uncertainty in environmental assessment of urban mobility is proposed. The problem treated is complex with insufficient, fuzzy and uncertain data. Hence, we propose to use belief theory (Dempster-Shafer theory) in order to combine the opinions of experts to evaluate the environmental impact of an ameliorative action to be carried out in the sector of transportation. First, we present urban mobility and its environmental impact. Secondly, we propose an approach and an algorithm for decision making under uncertainty in order to assess projects related to transportation to improve urban mobility. Finally a study is used to validate the proposed approach
{"title":"An Approach for Environmental impacts Assessment using Belief Theory","authors":"H. Omrani, L. Ion-Boussier, P. Trigano","doi":"10.1109/IS.2006.348462","DOIUrl":"https://doi.org/10.1109/IS.2006.348462","url":null,"abstract":"A novel methodology for decision-making under uncertainty in environmental assessment of urban mobility is proposed. The problem treated is complex with insufficient, fuzzy and uncertain data. Hence, we propose to use belief theory (Dempster-Shafer theory) in order to combine the opinions of experts to evaluate the environmental impact of an ameliorative action to be carried out in the sector of transportation. First, we present urban mobility and its environmental impact. Secondly, we propose an approach and an algorithm for decision making under uncertainty in order to assess projects related to transportation to improve urban mobility. Finally a study is used to validate the proposed approach","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133544872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Based on rough set theory, the paper proposed a clustering algorithm to deal with the quality and efficiency of clustering algorithm. By use of the consistency of condition attributes and decision attributes in the information table, the algorithm introduced a formula of attributes importance to reduce the redundant attributes. According to the data super-cube and entropy, the algorithm discretized the information table from global angle to local angle. Due to the set feature vector and set dissimilarity, the algorithm can cluster data just by scanning the information table only one time. The result of experiment indicates that the algorithm is efficient and effective
{"title":"An Clustering Algorithm Based on Rough Set","authors":"E. Xu, Gao Xuedong, Wu Sen, Yu Bin","doi":"10.1109/IS.2006.348465","DOIUrl":"https://doi.org/10.1109/IS.2006.348465","url":null,"abstract":"Based on rough set theory, the paper proposed a clustering algorithm to deal with the quality and efficiency of clustering algorithm. By use of the consistency of condition attributes and decision attributes in the information table, the algorithm introduced a formula of attributes importance to reduce the redundant attributes. According to the data super-cube and entropy, the algorithm discretized the information table from global angle to local angle. Due to the set feature vector and set dissimilarity, the algorithm can cluster data just by scanning the information table only one time. The result of experiment indicates that the algorithm is efficient and effective","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121879038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The simplest vague selection criterion in database queries is often expressed by a gradual property, or in other words, a certain qualification required to the targeted objects. Complex criteria often combine two or more simple and independent criteria, through an aggregation operation that means a multi-qualification of the searched objects. But there are also cases when in a complex criterion, one of the qualification is applicable in the context created from another attribute values, that means relative qualification. The relative qualification to another gradual property and the relative qualification to another crisp attribute are presented in the paper. In order to complete a real database querying problem, adequate procedures to dynamical defining linguistic values on database attributes are also proposed
{"title":"Relative Qualification in Database Flexible Queries","authors":"C. Tudorie, S. Bumbaru, L. Dumitriu","doi":"10.1109/IS.2006.348398","DOIUrl":"https://doi.org/10.1109/IS.2006.348398","url":null,"abstract":"The simplest vague selection criterion in database queries is often expressed by a gradual property, or in other words, a certain qualification required to the targeted objects. Complex criteria often combine two or more simple and independent criteria, through an aggregation operation that means a multi-qualification of the searched objects. But there are also cases when in a complex criterion, one of the qualification is applicable in the context created from another attribute values, that means relative qualification. The relative qualification to another gradual property and the relative qualification to another crisp attribute are presented in the paper. In order to complete a real database querying problem, adequate procedures to dynamical defining linguistic values on database attributes are also proposed","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123603018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Decision support systems have been utilised since 1960, providing physicians with fast and accurate means towards more accurate diagnoses, increased tolerance when handling missing or incomplete data. In this paper an integrated intelligent framework has been developed for the analysis/diagnosis of wireless capsule endoscopic images. The proposed system extracts texture features from the texture spectra in the chromatic and achromatic domains for a selected region of interest from each colour component histogram of images and utilises an advanced neural network in a multiple classifier scheme. The preliminary test results support the feasibility of the proposed methodology
{"title":"A Computerised Diagnostic Decision Support System in Wireless-Capsule Endoscopy","authors":"V. Kodogiannis, J. Lygouras","doi":"10.1109/IS.2006.348494","DOIUrl":"https://doi.org/10.1109/IS.2006.348494","url":null,"abstract":"Decision support systems have been utilised since 1960, providing physicians with fast and accurate means towards more accurate diagnoses, increased tolerance when handling missing or incomplete data. In this paper an integrated intelligent framework has been developed for the analysis/diagnosis of wireless capsule endoscopic images. The proposed system extracts texture features from the texture spectra in the chromatic and achromatic domains for a selected region of interest from each colour component histogram of images and utilises an advanced neural network in a multiple classifier scheme. The preliminary test results support the feasibility of the proposed methodology","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125108110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A simulation platform in MATLAB was developed to investigate dynamics of level in-queue flights under different control influence. Insight is given to a supplement of this system, which optimizes the work regime in terms of flight level, speed and order. The objectives of the optimization is to achieve economy of the flight through minimization of fuel loss, while providing control possibilities over the speed and flight level of the queue. The optimal flight parameters will not change during the queue flight, till new events trigger new optimization procedure. The effect of each flight regime over the queue is described as a multidimensional observation vector. A hierarchical additive value function is constructed under strict certainty and mutual preferential independence of the coordinates, which reflects the preferences of a rational decision maker over the observation vectors. An algorithm to find the optimal flight parameters is proposed, and the possibilities of the software that executes it are described. A numerical experiment is conducted, which illustrates the necessity of situation optimization
{"title":"Optimizing In-Queue Flight Regimes","authors":"D. Dimitrakiev, N. Nikolova, K. Tenekedjiev","doi":"10.1109/IS.2006.348408","DOIUrl":"https://doi.org/10.1109/IS.2006.348408","url":null,"abstract":"A simulation platform in MATLAB was developed to investigate dynamics of level in-queue flights under different control influence. Insight is given to a supplement of this system, which optimizes the work regime in terms of flight level, speed and order. The objectives of the optimization is to achieve economy of the flight through minimization of fuel loss, while providing control possibilities over the speed and flight level of the queue. The optimal flight parameters will not change during the queue flight, till new events trigger new optimization procedure. The effect of each flight regime over the queue is described as a multidimensional observation vector. A hierarchical additive value function is constructed under strict certainty and mutual preferential independence of the coordinates, which reflects the preferences of a rational decision maker over the observation vectors. An algorithm to find the optimal flight parameters is proposed, and the possibilities of the software that executes it are described. A numerical experiment is conducted, which illustrates the necessity of situation optimization","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128068957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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