This paper introduces Support Vector Machines (SVM) in the particular field of decision support systems for consulting engineering companies and studies the differences and particularities of the corresponding solutions. A detailed analysis has been performed in order to assess the suitability and adaptability of these methods for the particular task taking into account the risk/benefit tradeoff.
{"title":"Profit Forecasting Using Support Vector Regression for Consulting Engineering Firms","authors":"V. Yepes, E. Pellicer, F. Ferri","doi":"10.1109/ISDA.2009.43","DOIUrl":"https://doi.org/10.1109/ISDA.2009.43","url":null,"abstract":"This paper introduces Support Vector Machines (SVM) in the particular field of decision support systems for consulting engineering companies and studies the differences and particularities of the corresponding solutions. A detailed analysis has been performed in order to assess the suitability and adaptability of these methods for the particular task taking into account the risk/benefit tradeoff.","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121392846","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}
We proceed from a method for protein structure comparison in which information about the geometry and physico-chemical properties of such structures are represented in the form of labeled point clouds, that is, a set of labeled points in three-dimensional Euclidean space. Two point clouds are then compared by computing an optimal spatial superposition. This approach has recently been introduced in the literature and was shown to produce very good similarity scores. It does not, however, establish an alignment in the sense of a one-to-one correspondence between the basic units of two or more protein structures. From a biological point of view, alignments of this kind are of great interest, as they offer important information about evolution, heredity, and the mutual correspondence between molecular constituents. In this paper, we therefore developed a method for computing pairwise or multiple alignments of protein structures on the basis of labeled point cloud superpositions.
{"title":"Efficient Construction of Multiple Geometrical Alignments for the Comparison of Protein Binding Sites","authors":"T. Fober, G. Klebe, E. Hüllermeier","doi":"10.1109/ISDA.2009.210","DOIUrl":"https://doi.org/10.1109/ISDA.2009.210","url":null,"abstract":"We proceed from a method for protein structure comparison in which information about the geometry and physico-chemical properties of such structures are represented in the form of labeled point clouds, that is, a set of labeled points in three-dimensional Euclidean space. Two point clouds are then compared by computing an optimal spatial superposition. This approach has recently been introduced in the literature and was shown to produce very good similarity scores. It does not, however, establish an alignment in the sense of a one-to-one correspondence between the basic units of two or more protein structures. From a biological point of view, alignments of this kind are of great interest, as they offer important information about evolution, heredity, and the mutual correspondence between molecular constituents. In this paper, we therefore developed a method for computing pairwise or multiple alignments of protein structures on the basis of labeled point cloud superpositions.","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123035664","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 field-programmable gate array (FPGA)-based recurrent wavelet neural network (RWNN) control system is proposed to control the mover position of a linear ultrasonic motor (LUSM) in this study. First, the structure and operating principles of the LUSM are introduced. Since the dynamic characteristics and motor parameters of the LUSM are nonlinear and time-varying, an RWNN controller is designed to improve the control performance for the precision tracking of various reference trajectories. The network structure and its on-line learning algorithm using delta adaptation law of the RWNN are described in detail. Moreover, an FPGA chip is adopted to implement the developed control algorithm for possible low-cost and high-performance industrial applications. Finally, the effectiveness of the proposed control system is verified by some experimental results.
{"title":"FPGA-Based Recurrent Wavelet Neural Network Control System for Linear Ultrasonic Motor","authors":"Ying-Chih Hung, F. Lin","doi":"10.1109/ISDA.2009.61","DOIUrl":"https://doi.org/10.1109/ISDA.2009.61","url":null,"abstract":"A field-programmable gate array (FPGA)-based recurrent wavelet neural network (RWNN) control system is proposed to control the mover position of a linear ultrasonic motor (LUSM) in this study. First, the structure and operating principles of the LUSM are introduced. Since the dynamic characteristics and motor parameters of the LUSM are nonlinear and time-varying, an RWNN controller is designed to improve the control performance for the precision tracking of various reference trajectories. The network structure and its on-line learning algorithm using delta adaptation law of the RWNN are described in detail. Moreover, an FPGA chip is adopted to implement the developed control algorithm for possible low-cost and high-performance industrial applications. Finally, the effectiveness of the proposed control system is verified by some experimental results.","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122209503","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}
In this paper we introduce a new procedure for comparing and ordering social welfare situations by considering location, dispersion, consensus and welfare measures generated by exponential means. These measures satisfy interesting properties and generalize some measures used in welfare economics.
{"title":"Measuring Social Welfare through Location and Consensus Measures","authors":"J. García-Lapresta, R. M. Pereira","doi":"10.1109/ISDA.2009.182","DOIUrl":"https://doi.org/10.1109/ISDA.2009.182","url":null,"abstract":"In this paper we introduce a new procedure for comparing and ordering social welfare situations by considering location, dispersion, consensus and welfare measures generated by exponential means. These measures satisfy interesting properties and generalize some measures used in welfare economics.","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"400 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120932770","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}
This paper introduces a robust prediction method for symbolic interval data based on the simple linear regression methodology. Each example of the data set is described by feature vector, for which each feature is an interval. Two classic robust regression models are fitted, respectively for range and mid-points of the interval values assumed by the variables in the data set. The prediction of the lower and upper bounds of the new intervals is performed from these fits. To validate this model, experiments with a synthetic interval data set and an application with a cardiology interval-valued data set are considered. The fit and prediction qualities are assessed by a pooled root mean square error measure calculated from learning and test data sets, respectively.
{"title":"A Robust Prediction Method for Interval Symbolic Data","authors":"Roberta Fagundes, R. Souza, F. Cysneiros","doi":"10.1109/ISDA.2009.36","DOIUrl":"https://doi.org/10.1109/ISDA.2009.36","url":null,"abstract":"This paper introduces a robust prediction method for symbolic interval data based on the simple linear regression methodology. Each example of the data set is described by feature vector, for which each feature is an interval. Two classic robust regression models are fitted, respectively for range and mid-points of the interval values assumed by the variables in the data set. The prediction of the lower and upper bounds of the new intervals is performed from these fits. To validate this model, experiments with a synthetic interval data set and an application with a cardiology interval-valued data set are considered. The fit and prediction qualities are assessed by a pooled root mean square error measure calculated from learning and test data sets, respectively.","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121650458","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 keyword which describes in the most effective way Twenty-first century Higher education is suggested by Daniel et al. (2009)[1] and it is “expansion”. More and more people will invest in their own education and in this process Higher Education is on the front line. The need for education and training, though, cannot be generic, what nowadays society wants cannot be identified in static knowledge. At the end of the learning path students should be able to employ the outcomes of learning in order to generate new learning. That is why new solutions and methods must be acquired and adopted. Being those the conditions, and having at disposal the undeniable chances that the Internet offers, a possible direction to be undertaken could be the one that drives us directly to the potentialities of distance education performed in adaptive environments. The present contribution aims at analyzing Higher Education present needs, offers a general highlight of adaptive and intelligent web based education systems and concentrates on examples which better respond to the needs previously highlighted.
{"title":"Higher Education in the Twenty-First Century. The Chance of Adaptive Learning Environments","authors":"A. Poce","doi":"10.1109/ISDA.2009.141","DOIUrl":"https://doi.org/10.1109/ISDA.2009.141","url":null,"abstract":"The keyword which describes in the most effective way Twenty-first century Higher education is suggested by Daniel et al. (2009)[1] and it is “expansion”. More and more people will invest in their own education and in this process Higher Education is on the front line. The need for education and training, though, cannot be generic, what nowadays society wants cannot be identified in static knowledge. At the end of the learning path students should be able to employ the outcomes of learning in order to generate new learning. That is why new solutions and methods must be acquired and adopted. Being those the conditions, and having at disposal the undeniable chances that the Internet offers, a possible direction to be undertaken could be the one that drives us directly to the potentialities of distance education performed in adaptive environments. The present contribution aims at analyzing Higher Education present needs, offers a general highlight of adaptive and intelligent web based education systems and concentrates on examples which better respond to the needs previously highlighted.","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122421028","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}
Maria Letizia Guerra, Laerte Sorini, Luciano Stefanini
Real options are a typical framework in economics that involves uncertainty. The definition of the value function of real options can take advantage of a model of uncertainty that includes stochastic processes and fuzzy numbers; to perform the complete analysis with american type real options, we need to compute the fuzzy extension of the value function for A special version of the multiple population differential evolution algorithm is designed to compute the level-cuts of the fuzzy extension of the multidimensional real valued function of fuzzy numbers in the resulting optimization problems. We perform some computational experiments connected with the option to defer investment, that is an American call option on the present value of the completed expected cash flows with the exercise price equal to the required outlay.
{"title":"Value Function Computation in Fuzzy Real Options by Differential Evolution","authors":"Maria Letizia Guerra, Laerte Sorini, Luciano Stefanini","doi":"10.1109/ISDA.2009.232","DOIUrl":"https://doi.org/10.1109/ISDA.2009.232","url":null,"abstract":"Real options are a typical framework in economics that involves uncertainty. The definition of the value function of real options can take advantage of a model of uncertainty that includes stochastic processes and fuzzy numbers; to perform the complete analysis with american type real options, we need to compute the fuzzy extension of the value function for A special version of the multiple population differential evolution algorithm is designed to compute the level-cuts of the fuzzy extension of the multidimensional real valued function of fuzzy numbers in the resulting optimization problems. We perform some computational experiments connected with the option to defer investment, that is an American call option on the present value of the completed expected cash flows with the exercise price equal to the required outlay.","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122521816","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}
N. R. D. Matteo, S. Peroni, F. Tamburini, F. Vitali
Semantic search engines rely on the existence of a rich set of semantic connections between the concepts associated to documents and those used for the queries. With folksonomies, this is not always guaranteed. Creating clusters of folksonomic tags around terms of controlled ontological vocabularies is a potentially sophisticated approach, but algorithms abound for this clustering and no clear cut winner exists. In this paper we introduce FolksEngine, a parametric search engine for folksonomies allowing to specify any clustering algorithm as a three step process: the user’s query is expanded according to semantic rules associated to the terms of the query, the new query is then executed on the plain folksonomy search engine, and the results are ranked according to semantic rules associated to the folksonomic tags actually used for the documents.
{"title":"A Parametric Architecture for Tags Clustering in Folksonomic Search Engines","authors":"N. R. D. Matteo, S. Peroni, F. Tamburini, F. Vitali","doi":"10.1109/ISDA.2009.125","DOIUrl":"https://doi.org/10.1109/ISDA.2009.125","url":null,"abstract":"Semantic search engines rely on the existence of a rich set of semantic connections between the concepts associated to documents and those used for the queries. With folksonomies, this is not always guaranteed. Creating clusters of folksonomic tags around terms of controlled ontological vocabularies is a potentially sophisticated approach, but algorithms abound for this clustering and no clear cut winner exists. In this paper we introduce FolksEngine, a parametric search engine for folksonomies allowing to specify any clustering algorithm as a three step process: the user’s query is expanded according to semantic rules associated to the terms of the query, the new query is then executed on the plain folksonomy search engine, and the results are ranked according to semantic rules associated to the folksonomic tags actually used for the documents.","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128018330","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}
This paper describes a technique to control the contrast in images based on the application of ¿ukasiewicz algebra operators. In particular, the technique is based on the bounded-sum and the bounded-product. An interesting feature when applying these operators is that it allows low cost hardware realizations (in terms of resources) and high processing speed. The selection of the control parameters is perform by a fuzzy systems
{"title":"Image Contrast Control Based on Lukasiewicz's Operators and Fuzzy Logic","authors":"N. M. H. Hassan, A. Barriga","doi":"10.1109/ISDA.2009.174","DOIUrl":"https://doi.org/10.1109/ISDA.2009.174","url":null,"abstract":"This paper describes a technique to control the contrast in images based on the application of ¿ukasiewicz algebra operators. In particular, the technique is based on the bounded-sum and the bounded-product. An interesting feature when applying these operators is that it allows low cost hardware realizations (in terms of resources) and high processing speed. The selection of the control parameters is perform by a fuzzy systems","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133470545","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}
This paper describes a multimodal methodology for evolutionary optimization of neural networks. In this approach, we use Differential Evolution with parallel subpopulations to simultaneously train a neural network and find an efficient architecture. The results in three classification problems have shown that the neural network resulting from this method has low complexity and high capability of generalization when compared with other methods found in literature. Furthermore, two regularization techniques, weight decay and weight elimination, are investigated and results are presented.
{"title":"Optimization of Neural Networks Weights and Architecture: A Multimodal Methodology","authors":"Antonio Miguel F. Zarth, Teresa B Ludermir","doi":"10.1109/ISDA.2009.90","DOIUrl":"https://doi.org/10.1109/ISDA.2009.90","url":null,"abstract":"This paper describes a multimodal methodology for evolutionary optimization of neural networks. In this approach, we use Differential Evolution with parallel subpopulations to simultaneously train a neural network and find an efficient architecture. The results in three classification problems have shown that the neural network resulting from this method has low complexity and high capability of generalization when compared with other methods found in literature. Furthermore, two regularization techniques, weight decay and weight elimination, are investigated and results are presented.","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134480759","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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