Enhanced network flow methods have been proposed in the logical operations frame aiming at efficient methods and algorithms in the analysis and synthesis of various logical structures. Quantitative truth maintenance methods have been combined or replaced by qualitative optimization calculations. The method is widely applicable in intelligent systems.
{"title":"Network flow interpretation of logical structures in decision support systems","authors":"V. Sgurev, M. Hadjiski, V. Jotsov","doi":"10.1109/IS.2008.4670448","DOIUrl":"https://doi.org/10.1109/IS.2008.4670448","url":null,"abstract":"Enhanced network flow methods have been proposed in the logical operations frame aiming at efficient methods and algorithms in the analysis and synthesis of various logical structures. Quantitative truth maintenance methods have been combined or replaced by qualitative optimization calculations. The method is widely applicable in intelligent systems.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"127 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":"122498623","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 new complete procedure for the selection of pruning threshold in MIMO (multiple input multiple output) feedforward artificial neural networks (FANN) is presented. It is based on the evaluation of a local sensitivity index calculated with respect of any single output of the network. Special emphasis is given to a particular class of neural networks with multiple heterogeneous outputs. It will be shown how to take into account of the non-homogeneous nature of the outputs by deriving an ldquoimportance indexrdquo from the nonlinear correlation of data. An example of the proposed method will be shown by the development of a neural architecture devoted to a specific multi-output inversion system.
{"title":"A pruning method for multiple heterogeneous output neural networks","authors":"F. Grasso, A. Luchetta, S. Manetti","doi":"10.1109/IS.2008.4670442","DOIUrl":"https://doi.org/10.1109/IS.2008.4670442","url":null,"abstract":"A new complete procedure for the selection of pruning threshold in MIMO (multiple input multiple output) feedforward artificial neural networks (FANN) is presented. It is based on the evaluation of a local sensitivity index calculated with respect of any single output of the network. Special emphasis is given to a particular class of neural networks with multiple heterogeneous outputs. It will be shown how to take into account of the non-homogeneous nature of the outputs by deriving an ldquoimportance indexrdquo from the nonlinear correlation of data. An example of the proposed method will be shown by the development of a neural architecture devoted to a specific multi-output inversion system.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"106 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":"128157385","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}
Many statistical algorithms have been proposed for software quality prediction of fault-prone and non fault-prone program modules. The main goal of these algorithms is the improvement of software development processes. In this paper, we introduce a new software prediction algorithm. Our approach is purely Bayesian and is based on finite Dirichlet mixture models. The implementation of the Bayesian approach is done through the use of the Gibbs sampler. Experimental results are presented using simulated data, and a real application for software modules classification is also included.
{"title":"A Bayesian approach for software quality prediction","authors":"N. Bouguila, J. Wang, A. Ben Hamza","doi":"10.1109/IS.2008.4670508","DOIUrl":"https://doi.org/10.1109/IS.2008.4670508","url":null,"abstract":"Many statistical algorithms have been proposed for software quality prediction of fault-prone and non fault-prone program modules. The main goal of these algorithms is the improvement of software development processes. In this paper, we introduce a new software prediction algorithm. Our approach is purely Bayesian and is based on finite Dirichlet mixture models. The implementation of the Bayesian approach is done through the use of the Gibbs sampler. Experimental results are presented using simulated data, and a real application for software modules classification is also included.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"11 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":"130691966","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}
Neural networks have become increasingly important in areas such as medical diagnosis, bio-informatics, intrusion detection, and homeland security. In most of these applications, one major issue is preserving privacy of individualpsilas private information and sensitive data. In this paper, we propose two secure protocols for perceptron learning algorithm when input data is horizontally and vertically partitioned among the parties. These protocols can be applied in both linearly separable and non-separable datasets, while not only data belonging to each party remains private, but the final learning model is also securely shared among those parties. Parties then can jointly and securely apply the constructed model to predict the output corresponding to their target data. Also, these protocols can be used incrementally, i.e. they process new coming data, adjusting the previously constructed network.
{"title":"Privacy-preserving protocols for perceptron learning algorithm in neural networks","authors":"Saeed Samet, Ali Miri","doi":"10.1109/IS.2008.4670499","DOIUrl":"https://doi.org/10.1109/IS.2008.4670499","url":null,"abstract":"Neural networks have become increasingly important in areas such as medical diagnosis, bio-informatics, intrusion detection, and homeland security. In most of these applications, one major issue is preserving privacy of individualpsilas private information and sensitive data. In this paper, we propose two secure protocols for perceptron learning algorithm when input data is horizontally and vertically partitioned among the parties. These protocols can be applied in both linearly separable and non-separable datasets, while not only data belonging to each party remains private, but the final learning model is also securely shared among those parties. Parties then can jointly and securely apply the constructed model to predict the output corresponding to their target data. Also, these protocols can be used incrementally, i.e. they process new coming data, adjusting the previously constructed network.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"1 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":"130252091","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}
Lyophilization plants are widely used by pharmaceutical industries to produce stable dried medications and important preparations. Since, a Lyophilization cycle involves a high energy demands it is needed to be used an improved control strategy in order to minimize the operating costs. This paper describes a method for designing a nonlinear model predictive controller to be used in a Lyophilization plant. The controller is based on a truncated fuzzy-neural Volterra predictive model and a simplified gradient optimization algorithm. The proposed approach is studied to control the product temperature in a Lyophilization plant. The efficiency of the proposed approach is tested and proved by simulation experiments.
{"title":"Volterra model predictive control of a lyophilization plant","authors":"Yancho V. Todorov, Tsvetan D. Tsvetkov","doi":"10.1109/IS.2008.4670467","DOIUrl":"https://doi.org/10.1109/IS.2008.4670467","url":null,"abstract":"Lyophilization plants are widely used by pharmaceutical industries to produce stable dried medications and important preparations. Since, a Lyophilization cycle involves a high energy demands it is needed to be used an improved control strategy in order to minimize the operating costs. This paper describes a method for designing a nonlinear model predictive controller to be used in a Lyophilization plant. The controller is based on a truncated fuzzy-neural Volterra predictive model and a simplified gradient optimization algorithm. The proposed approach is studied to control the product temperature in a Lyophilization plant. The efficiency of the proposed approach is tested and proved by simulation experiments.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"1 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":"130370794","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 a new hybrid method for training fuzzy cognitive maps is presented. FCMs are based on the knowledge of human experts and may not be accurate enough because of probable mistakes of experts. Thus, some learning methods have been investigated to train FCMs, so that these probable mistakes are covered. Two learning methods, PSO and NHL, and a new hybrid of them are introduced and implemented and tested for a chemical control problem.
{"title":"A hybrid method using PSO and NHL algorithms to train Fuzzy Cognitive Maps","authors":"M. N. Yazdi, C. Lucas","doi":"10.1109/IS.2008.4670458","DOIUrl":"https://doi.org/10.1109/IS.2008.4670458","url":null,"abstract":"In this paper a new hybrid method for training fuzzy cognitive maps is presented. FCMs are based on the knowledge of human experts and may not be accurate enough because of probable mistakes of experts. Thus, some learning methods have been investigated to train FCMs, so that these probable mistakes are covered. Two learning methods, PSO and NHL, and a new hybrid of them are introduced and implemented and tested for a chemical control problem.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"7 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":"130232282","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}
Collaboration among enterprises in a dynamic environment makes the actors to concentrate on their respective core competences and allow provision and sharing of expertise, resources, and skills for taking advantages and better respond to business opportunities. The coming together of such organizations, usually enhanced by computer network, is referred to as virtual enterprise (VE) or task group. This partnership is only possible if the systems in the various associated organizations can process the data in one another. A major challenge in the enterprise collaborative system that has attracted many research efforts in the recent past is semantic interoperability. This collaborative-based market place requires among others common conceptualization and meaningful data exchange. Effective collaboration among VE members is a major key to the accomplishment of this noble objective. For this interoperability to be effective, we propose in this paper an ontology-based middleware framework ldquoOntology Gatewayrdquo used by players in such situation to exchange information needed to carry out the process. The middleware not only assists in the formation of VE by interested members, but also facilitates semantic interpretability among task groups.
{"title":"Ontology based semantic interoperability facilitator among task group","authors":"M. Pasha, Mukaila Rahman, H. Ahmad","doi":"10.1109/IS.2008.4670476","DOIUrl":"https://doi.org/10.1109/IS.2008.4670476","url":null,"abstract":"Collaboration among enterprises in a dynamic environment makes the actors to concentrate on their respective core competences and allow provision and sharing of expertise, resources, and skills for taking advantages and better respond to business opportunities. The coming together of such organizations, usually enhanced by computer network, is referred to as virtual enterprise (VE) or task group. This partnership is only possible if the systems in the various associated organizations can process the data in one another. A major challenge in the enterprise collaborative system that has attracted many research efforts in the recent past is semantic interoperability. This collaborative-based market place requires among others common conceptualization and meaningful data exchange. Effective collaboration among VE members is a major key to the accomplishment of this noble objective. For this interoperability to be effective, we propose in this paper an ontology-based middleware framework ldquoOntology Gatewayrdquo used by players in such situation to exchange information needed to carry out the process. The middleware not only assists in the formation of VE by interested members, but also facilitates semantic interpretability among task groups.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"1 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":"131215068","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}
Fuzzy linear regression model with crisp input, fuzzy output is investigated. A least absolute deviation approach, by introducing and applying a new metric on the space of fuzzy numbers, is developed. In addition, a new capability index is proposed to evaluate the proposed model.
{"title":"Fuzzy least absolutes regression","authors":"S. M. Taheri, M. Kelkinnama","doi":"10.1109/IS.2008.4670509","DOIUrl":"https://doi.org/10.1109/IS.2008.4670509","url":null,"abstract":"Fuzzy linear regression model with crisp input, fuzzy output is investigated. A least absolute deviation approach, by introducing and applying a new metric on the space of fuzzy numbers, is developed. In addition, a new capability index is proposed to evaluate the proposed model.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"04 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":"128559729","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. Fetais, D. Al-Abdulla, A. Alatawnah, S. Alkhulaifi, T. El-Fouly
The rapid improvement in latest technologies motivates individuals to progress in their societies. Fast and worthy can be significant titles for the demands of this modern life. And because of technology improvements, there arenpsilat any excuses for slow processing of jobs and duties. Everyone at any position wants things to be done in the shortest time. Therefore, attempts to apply instant, fast and convenient processing methods became a common aim. Taking an example of administrative services illustrates the need of such attempts. In order to get an authenticated application or document, this consumes a lot of effort in reviewing many employees and getting many administratorspsila approvals. Also, it might need transactions from one place to place, not to mention that a person may need more than one application. In fact, it becomes a common situation to hear complaints from applicants, which leads to thinking of an effective solution. Saving time, minimizing customerpsilas transactions, and digitizing life are all core targets in such condition. Moreover, these targets are the significant requirements for modern life evolution, and this work is considered as the first step for approaching this goal.
{"title":"Digital government service machine DGSM","authors":"A. Fetais, D. Al-Abdulla, A. Alatawnah, S. Alkhulaifi, T. El-Fouly","doi":"10.1109/IS.2008.4670524","DOIUrl":"https://doi.org/10.1109/IS.2008.4670524","url":null,"abstract":"The rapid improvement in latest technologies motivates individuals to progress in their societies. Fast and worthy can be significant titles for the demands of this modern life. And because of technology improvements, there arenpsilat any excuses for slow processing of jobs and duties. Everyone at any position wants things to be done in the shortest time. Therefore, attempts to apply instant, fast and convenient processing methods became a common aim. Taking an example of administrative services illustrates the need of such attempts. In order to get an authenticated application or document, this consumes a lot of effort in reviewing many employees and getting many administratorspsila approvals. Also, it might need transactions from one place to place, not to mention that a person may need more than one application. In fact, it becomes a common situation to hear complaints from applicants, which leads to thinking of an effective solution. Saving time, minimizing customerpsilas transactions, and digitizing life are all core targets in such condition. Moreover, these targets are the significant requirements for modern life evolution, and this work is considered as the first step for approaching this goal.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"54 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":"128194219","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}
Recognition of specific functionally-important DNA sequence fragments is considered one of the most important problems in bioinformatics. One type of such fragments are promoters, i.e., short regulatory DNA sequences located upstream of a gene. Detection of promoters in DNA sequences is important for successful gene prediction. In this paper, a machine learning method, called support vector machine (SVM), is used for classification of DNA sequences and promoter recognition. For optimal classification, 11 rules for mapping of DNA sequences into binary SVM feature space are analyzed. Classification is performed using a power series kernel function. Kernel parameters are optimized using a modification of the Nelder-Mead (downhill simplex) optimization method. The results of classification for drosophila and human sequence datasets are presented.
{"title":"Analysis of binary feature mapping rules for promoter recognition in imbalanced DNA sequence datasets using Support Vector Machine","authors":"Robertas Damaševičius","doi":"10.1109/IS.2008.4670503","DOIUrl":"https://doi.org/10.1109/IS.2008.4670503","url":null,"abstract":"Recognition of specific functionally-important DNA sequence fragments is considered one of the most important problems in bioinformatics. One type of such fragments are promoters, i.e., short regulatory DNA sequences located upstream of a gene. Detection of promoters in DNA sequences is important for successful gene prediction. In this paper, a machine learning method, called support vector machine (SVM), is used for classification of DNA sequences and promoter recognition. For optimal classification, 11 rules for mapping of DNA sequences into binary SVM feature space are analyzed. Classification is performed using a power series kernel function. Kernel parameters are optimized using a modification of the Nelder-Mead (downhill simplex) optimization method. The results of classification for drosophila and human sequence datasets are presented.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"29 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":"121929513","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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