DNA hybridization arrays simultaneously measure the expression level for thousands of genes. A great challenge in the bioinformatics field is to discover gene interactions from such measurements and estimate gene networks. In this paper, we exploit data mining techniques for discovering interactions among genes based on multiple expression measurements. We present an application of the Apriori algorithm to extract temporal association rules from gene expression data. Furthermore, we address the problem of real value discretization by using both fixed thresholds and clustering techniques. Finally, we estimate the value of each rule by means of an appropriate quality index. Preliminary experimental results on Saccharomyces cerevisiae cell cycle gene expression data show the effectiveness of the proposed method.
{"title":"Temporal association rules for gene regulatory networks","authors":"Elena Baralis, G. Bruno, E. Ficarra","doi":"10.1109/IS.2008.4670511","DOIUrl":"https://doi.org/10.1109/IS.2008.4670511","url":null,"abstract":"DNA hybridization arrays simultaneously measure the expression level for thousands of genes. A great challenge in the bioinformatics field is to discover gene interactions from such measurements and estimate gene networks. In this paper, we exploit data mining techniques for discovering interactions among genes based on multiple expression measurements. We present an application of the Apriori algorithm to extract temporal association rules from gene expression data. Furthermore, we address the problem of real value discretization by using both fixed thresholds and clustering techniques. Finally, we estimate the value of each rule by means of an appropriate quality index. Preliminary experimental results on Saccharomyces cerevisiae cell cycle gene expression data show the effectiveness of the proposed method.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131985941","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}
D. Peneva, V. Tasseva, E. Popov, I. Petrounias, V. Kodogiannis, F. Anwar, A. Shannon
In this paper is represented one idea for an extension to the existing software for generalized net (GN) models simulation. Given solution for storing of the characteristics of the tokens during their movements through the GN-models improves the existing GN-interpreter. The software tool is designed to be the independent central component of GN enabled applications. In this paper the idea for adding a new functionality is presented and the bases for storing tokens characteristics development are laid.
{"title":"Accumulation, storage and obtainment of generalized net tokens characteristics history","authors":"D. Peneva, V. Tasseva, E. Popov, I. Petrounias, V. Kodogiannis, F. Anwar, A. Shannon","doi":"10.1109/IS.2008.4670543","DOIUrl":"https://doi.org/10.1109/IS.2008.4670543","url":null,"abstract":"In this paper is represented one idea for an extension to the existing software for generalized net (GN) models simulation. Given solution for storing of the characteristics of the tokens during their movements through the GN-models improves the existing GN-interpreter. The software tool is designed to be the independent central component of GN enabled applications. In this paper the idea for adding a new functionality is presented and the bases for storing tokens characteristics development are laid.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129550321","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 paper presents a bilevel optimization problem, solved by a noniterative coordination method founded on the approximation of inexplicit upper level optimization problem to well defined analytical problem. The coordination problem founds on ldquoproposition - correctionrdquo operational algorithm which allows to perform limited data transfer between the optimization levels. This coordination benefits the real time operation in multilevel system. Example is provided for the solution of nonlinear steady state optimization control problem.
{"title":"Bilevel noniterative interconnected optimization","authors":"T. Stoilov, K. Stoilova","doi":"10.1109/IS.2008.4670532","DOIUrl":"https://doi.org/10.1109/IS.2008.4670532","url":null,"abstract":"The paper presents a bilevel optimization problem, solved by a noniterative coordination method founded on the approximation of inexplicit upper level optimization problem to well defined analytical problem. The coordination problem founds on ldquoproposition - correctionrdquo operational algorithm which allows to perform limited data transfer between the optimization levels. This coordination benefits the real time operation in multilevel system. Example is provided for the solution of nonlinear steady state optimization control problem.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130552863","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}
S. Shirali-Shahreza, S. Yeganeh, H. Abolhassani, J. Habibi
One of the important problems in knowledge discovery from data is clustering. Clustering is the problem of partitioning a set of data using unsupervised techniques. An important characteristic of a clustering technique is the shape of the cluster it can find. Clustering methods which are capable to find simple cluster shapes are usually fast but inaccurate for complex data sets. Ones capable to find complex cluster shapes are usually not fast but accurate. In this paper, we propose a simple clustering technique named circlusters. Circlusters are circles partitioned into different radius sectors. Circlusters can be used to create hybrid approaches with density based or partitioning based methods. We also propose a naive clustering method that is capable to find complex clusters in O(n). This method operates in two phases. In the first phase, circlusters are created to approximate the shape of the data set. In the second phase, connected circlusters are found to form the final clusters.
{"title":"Circluster: Storing cluster shapes for clustering","authors":"S. Shirali-Shahreza, S. Yeganeh, H. Abolhassani, J. Habibi","doi":"10.1109/IS.2008.4670502","DOIUrl":"https://doi.org/10.1109/IS.2008.4670502","url":null,"abstract":"One of the important problems in knowledge discovery from data is clustering. Clustering is the problem of partitioning a set of data using unsupervised techniques. An important characteristic of a clustering technique is the shape of the cluster it can find. Clustering methods which are capable to find simple cluster shapes are usually fast but inaccurate for complex data sets. Ones capable to find complex cluster shapes are usually not fast but accurate. In this paper, we propose a simple clustering technique named circlusters. Circlusters are circles partitioned into different radius sectors. Circlusters can be used to create hybrid approaches with density based or partitioning based methods. We also propose a naive clustering method that is capable to find complex clusters in O(n). This method operates in two phases. In the first phase, circlusters are created to approximate the shape of the data set. In the second phase, connected circlusters are found to form the final clusters.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114932966","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 present a general framework and results on factorization of systems of fuzzy sets by similarity. The result of such factorization can be regarded as a simplified version of the original system in which we deliberately do not distinguish elements which are highly similar. We assume that the fuzzy sets are fixed points of some closure operator. Examples of such systems are fuzzy concept lattices, fuzzy sets in a given universe, and complete residuated lattices. The similarity relation we consider is an a-cut of a particular fuzzy equivalence relation with a being a similarity threshold supplied by a user which controls the meaning of ldquohighly similarrdquo. We present results describing the factorization including an efficient way to compute the factor structure. In addition, we describe a-central points of a given collection of fixed points of a closure operator, i.e. points which are similar to every point in the collection to degree at least a.
{"title":"Factor structures and central points by similarity","authors":"R. Belohlávek, M. Krupka","doi":"10.1109/IS.2008.4670531","DOIUrl":"https://doi.org/10.1109/IS.2008.4670531","url":null,"abstract":"We present a general framework and results on factorization of systems of fuzzy sets by similarity. The result of such factorization can be regarded as a simplified version of the original system in which we deliberately do not distinguish elements which are highly similar. We assume that the fuzzy sets are fixed points of some closure operator. Examples of such systems are fuzzy concept lattices, fuzzy sets in a given universe, and complete residuated lattices. The similarity relation we consider is an a-cut of a particular fuzzy equivalence relation with a being a similarity threshold supplied by a user which controls the meaning of ldquohighly similarrdquo. We present results describing the factorization including an efficient way to compute the factor structure. In addition, we describe a-central points of a given collection of fixed points of a closure operator, i.e. points which are similar to every point in the collection to degree at least a.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114971508","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 advanced research in robotics scientific discipline has produced a paradigm shift from the classical robot manipulator used only in industrial environment to the modern mobile robot viewed as an every day life ldquopartnerrdquo. This first objective of this paper is to offer a short but comprehensive history of the paradigm shifts that triggered the industrial robotics research advancements. The short list of paradigm shifts that are to be discussed include: computer integrated manufacturing and engineering systems (C.I.M.E. - 1976), flexible manufacturing systems (FMS - psila80), lean manufacturing (psila90), agile manufacturing, fractal and/or holonic manufacturing systems (psila90), intelligent manufacturing systems (2000). The second objective is to discuss generic design principles and architecture guidelines for the development of a complex robotic system. These include collaborative robotics, artificial intelligence, human-robot interaction, multi-agent systems, service oriented architecture.
{"title":"From industrial robotics towards intelligent robotic systems","authors":"A. Stanescu, A. Nita, M. Moisescu, I. Sacala","doi":"10.1109/IS.2008.4670441","DOIUrl":"https://doi.org/10.1109/IS.2008.4670441","url":null,"abstract":"The advanced research in robotics scientific discipline has produced a paradigm shift from the classical robot manipulator used only in industrial environment to the modern mobile robot viewed as an every day life ldquopartnerrdquo. This first objective of this paper is to offer a short but comprehensive history of the paradigm shifts that triggered the industrial robotics research advancements. The short list of paradigm shifts that are to be discussed include: computer integrated manufacturing and engineering systems (C.I.M.E. - 1976), flexible manufacturing systems (FMS - psila80), lean manufacturing (psila90), agile manufacturing, fractal and/or holonic manufacturing systems (psila90), intelligent manufacturing systems (2000). The second objective is to discuss generic design principles and architecture guidelines for the development of a complex robotic system. These include collaborative robotics, artificial intelligence, human-robot interaction, multi-agent systems, service oriented architecture.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122390774","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 ability of understanding humanpsilas behavior is a required component for many applications. This understanding includes, among other tasks, automatically generating and maintaining models of human actions, goals and plans. This paper presents a system to infer the actions that people perform in order to accomplish activities of daily living starting from sensory inputs. Our approach is based on using relational learning to infer predictions about which action has just been executed. We learn a model for recognizing executed actions based on the state changes detected from sensor readings. Each change has been produced by a performed action, while a sequence of these actions forms a plan to accomplish a high-level action or to achieve a goal. Using a relational learning tool, Tilde, we obtain classifiers to map changes in the states to actions performed by a user. We have performed some experiments using an environment simulator feeded by data gathered from real human behaviour. The results show that we can obtain a good accuracy even in presence of noise.
{"title":"A relational learning approach to activity recognition from sensor readings","authors":"Javier Ortiz","doi":"10.1109/IS.2008.4670462","DOIUrl":"https://doi.org/10.1109/IS.2008.4670462","url":null,"abstract":"The ability of understanding humanpsilas behavior is a required component for many applications. This understanding includes, among other tasks, automatically generating and maintaining models of human actions, goals and plans. This paper presents a system to infer the actions that people perform in order to accomplish activities of daily living starting from sensory inputs. Our approach is based on using relational learning to infer predictions about which action has just been executed. We learn a model for recognizing executed actions based on the state changes detected from sensor readings. Each change has been produced by a performed action, while a sequence of these actions forms a plan to accomplish a high-level action or to achieve a goal. Using a relational learning tool, Tilde, we obtain classifiers to map changes in the states to actions performed by a user. We have performed some experiments using an environment simulator feeded by data gathered from real human behaviour. The results show that we can obtain a good accuracy even in presence of noise.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123325017","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}
During the last decade, mining companies and mobile equipment manufacturers have pursued improved efficiency, productivity, and safety in underground mining operations by automating some of the functions of underground vehicles. The work presented in this paper is the result of an effort to develop new flexible infrastructureless guidance system for autonomous tramming of center-articulated underground mining vehicles.
{"title":"Flexible infrastructure free navigation for vehicles in underground mines","authors":"J. Larsson, M. Broxvall, A. Saffiotti","doi":"10.1109/IS.2008.4670406","DOIUrl":"https://doi.org/10.1109/IS.2008.4670406","url":null,"abstract":"During the last decade, mining companies and mobile equipment manufacturers have pursued improved efficiency, productivity, and safety in underground mining operations by automating some of the functions of underground vehicles. The work presented in this paper is the result of an effort to develop new flexible infrastructureless guidance system for autonomous tramming of center-articulated underground mining vehicles.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125070334","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 paper explains fuzzy critical analysis for the electric generator (EG) protection system. Power system electric generator (EG) is protected to various types of faults and abnormal workings. The protection system (PS) is composed of waiting subsystems, which must properly respond to each kind of dangerous events. An original fuzzy logic- system enables us to analyze the qualitative evaluation of the event-tree, modeling PS behavior. Fuzzy - set logic is used to account for imprecision and uncertainty in data while employing event-tree analysis. The fuzzy event-tree logic allows the use of verbal statement for the probabilities and consequences, such as very high, moderate and low probability.
{"title":"Fuzzy critical analysis for an electric generator protection system","authors":"M. Dumitrescu","doi":"10.1109/IS.2008.4670403","DOIUrl":"https://doi.org/10.1109/IS.2008.4670403","url":null,"abstract":"The paper explains fuzzy critical analysis for the electric generator (EG) protection system. Power system electric generator (EG) is protected to various types of faults and abnormal workings. The protection system (PS) is composed of waiting subsystems, which must properly respond to each kind of dangerous events. An original fuzzy logic- system enables us to analyze the qualitative evaluation of the event-tree, modeling PS behavior. Fuzzy - set logic is used to account for imprecision and uncertainty in data while employing event-tree analysis. The fuzzy event-tree logic allows the use of verbal statement for the probabilities and consequences, such as very high, moderate and low probability.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127512276","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}
Anthony G. Shannon, D. Orozova, E. Sotirova, K. Atanassov, M. Krawczak, P. Melo-Pinto, Roumen Nikolov, S. Sotirov, Taekyung Kim
In a series of research papers, the authors have studied some of the most important models of contemporary universities, such as: the research university, the entrepreneurial university and the digital university and construct their generalized net (GN) models. This paper is based on the case-studies of Sofia University, the Technical University of Munich and the University of Edinburgh. The main focus is to put the analysis of the processes of the functioning of a university which effectively integrates information and communication technologies (ICT) in all university activities. A concrete example based on the process of course administration at University of Edinburgh is considered. This university is in a process of developing an integrated information system covering most of the university activities. The opportunity of using GNs as a tool for modeling such processes is analyzed as well.
{"title":"Towards a model of the digital university: A Generalized Net model for producing course timetables","authors":"Anthony G. Shannon, D. Orozova, E. Sotirova, K. Atanassov, M. Krawczak, P. Melo-Pinto, Roumen Nikolov, S. Sotirov, Taekyung Kim","doi":"10.1109/IS.2008.4670542","DOIUrl":"https://doi.org/10.1109/IS.2008.4670542","url":null,"abstract":"In a series of research papers, the authors have studied some of the most important models of contemporary universities, such as: the research university, the entrepreneurial university and the digital university and construct their generalized net (GN) models. This paper is based on the case-studies of Sofia University, the Technical University of Munich and the University of Edinburgh. The main focus is to put the analysis of the processes of the functioning of a university which effectively integrates information and communication technologies (ICT) in all university activities. A concrete example based on the process of course administration at University of Edinburgh is considered. This university is in a process of developing an integrated information system covering most of the university activities. The opportunity of using GNs as a tool for modeling such processes is analyzed as well.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125964085","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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