Pub Date : 2005-12-15DOI: 10.1109/CIMA.2005.1662361
J.Y. Yang, M. Qu Yang, Zuojie Luo, O. Ersoy
In our attempts to construct methods for automated structural and functional annotation of proteins, the prediction of intrinsically unstructured/disordered protein (IUP) regions, i.e. those with a lack of stable secondary or tertiary structure, has recently gained importance. We developed a software tool for identifying IUP and structured protein regions. The predictor uses both supervised and unsupervised learning techniques and both structural and motional information of amino acids. We demonstrate the effectiveness of our IUP predictor which utilizes feature selection, bootstrapping aggregation, boosting and consensus networking algorithms
{"title":"Automated Identifying Intrinsic Unstructured Regions in Proteins - A Software Tool","authors":"J.Y. Yang, M. Qu Yang, Zuojie Luo, O. Ersoy","doi":"10.1109/CIMA.2005.1662361","DOIUrl":"https://doi.org/10.1109/CIMA.2005.1662361","url":null,"abstract":"In our attempts to construct methods for automated structural and functional annotation of proteins, the prediction of intrinsically unstructured/disordered protein (IUP) regions, i.e. those with a lack of stable secondary or tertiary structure, has recently gained importance. We developed a software tool for identifying IUP and structured protein regions. The predictor uses both supervised and unsupervised learning techniques and both structural and motional information of amino acids. We demonstrate the effectiveness of our IUP predictor which utilizes feature selection, bootstrapping aggregation, boosting and consensus networking algorithms","PeriodicalId":306045,"journal":{"name":"2005 ICSC Congress on Computational Intelligence Methods and Applications","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133132978","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}
Pub Date : 2005-12-15DOI: 10.1109/CIMA.2005.1662297
Bao Liu, Hua Han, Yongsheng Ding
Since the conventional decoupling algorithms are complicated and difficult to be implemented, we present a bio-imitated decoupling controller based on the bi-regulation principle of growth hormone (GH), and provide its decoupling algorithm. It has two or more control units. Every control unit consists of a control module, a decoupling distributing module, and an output module. These units communicate with each other to exchange control information, and then adjust the actuators harmoniously. The decoupling algorithm is simpler than any other decoupling algorithm, and can be implemented more easily. As a result, the process can be controlled stably, and the coupling influences among the various loops can be removed. Simulation results demonstrate that the decoupling scheme can completely eliminate the coupling influence, and has better control performance
{"title":"A decoupling control based on the bi-regulation principle of growth hormone","authors":"Bao Liu, Hua Han, Yongsheng Ding","doi":"10.1109/CIMA.2005.1662297","DOIUrl":"https://doi.org/10.1109/CIMA.2005.1662297","url":null,"abstract":"Since the conventional decoupling algorithms are complicated and difficult to be implemented, we present a bio-imitated decoupling controller based on the bi-regulation principle of growth hormone (GH), and provide its decoupling algorithm. It has two or more control units. Every control unit consists of a control module, a decoupling distributing module, and an output module. These units communicate with each other to exchange control information, and then adjust the actuators harmoniously. The decoupling algorithm is simpler than any other decoupling algorithm, and can be implemented more easily. As a result, the process can be controlled stably, and the coupling influences among the various loops can be removed. Simulation results demonstrate that the decoupling scheme can completely eliminate the coupling influence, and has better control performance","PeriodicalId":306045,"journal":{"name":"2005 ICSC Congress on Computational Intelligence Methods and Applications","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122980685","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}
Pub Date : 2005-12-15DOI: 10.1109/CIMA.2005.1662316
S. Selouani, Mustapha Kardouchi, É. Hervet, D. Roy
A template-based technique for automatic recognition of birdsong syllables is presented. This technique combines time delay neural networks (TDNNs) with an autoregressive (AR) version of the backpropagation algorithm in order to improve the accuracy of bird species identification. The proposed neural network structure (AR-TDNN) has the advantage of dealing with a pattern classification of syllable alphabet and also of capturing the temporal structure of birdsong. We choose to carry out trials on song patterns obtained from sixteen species living in New Brunswick province of Canada. The results show that the proposed AR-TDNN system achieves a highly recognition rate compared to the baseline backpropagation-based system
{"title":"Automatic birdsong recognition based on autoregressive time-delay neural networks","authors":"S. Selouani, Mustapha Kardouchi, É. Hervet, D. Roy","doi":"10.1109/CIMA.2005.1662316","DOIUrl":"https://doi.org/10.1109/CIMA.2005.1662316","url":null,"abstract":"A template-based technique for automatic recognition of birdsong syllables is presented. This technique combines time delay neural networks (TDNNs) with an autoregressive (AR) version of the backpropagation algorithm in order to improve the accuracy of bird species identification. The proposed neural network structure (AR-TDNN) has the advantage of dealing with a pattern classification of syllable alphabet and also of capturing the temporal structure of birdsong. We choose to carry out trials on song patterns obtained from sixteen species living in New Brunswick province of Canada. The results show that the proposed AR-TDNN system achieves a highly recognition rate compared to the baseline backpropagation-based system","PeriodicalId":306045,"journal":{"name":"2005 ICSC Congress on Computational Intelligence Methods and Applications","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116431204","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}
Pub Date : 2005-12-15DOI: 10.1109/CIMA.2005.1662350
Hsuan-Shih Lee
A key objective of procurement is to purchase the right product from right supplier at right price in due time. In this paper, we are going to propose a fuzzy multi-criteria decision making model to address the problem of identifying right supplier in purchasing process. Fuzzy numbers are introduced to enable evaluators encompass vagueness in the evaluation process of suppliers. With the proposed model, evaluators may give ratings in fuzzy numbers to different suppliers under consideration against postulated criteria, and two indices and a aggregated index would be generated for each supplier so that suppliers can be ranked accordingly
{"title":"A fuzzy multi-criteria decision making model for supplier rating","authors":"Hsuan-Shih Lee","doi":"10.1109/CIMA.2005.1662350","DOIUrl":"https://doi.org/10.1109/CIMA.2005.1662350","url":null,"abstract":"A key objective of procurement is to purchase the right product from right supplier at right price in due time. In this paper, we are going to propose a fuzzy multi-criteria decision making model to address the problem of identifying right supplier in purchasing process. Fuzzy numbers are introduced to enable evaluators encompass vagueness in the evaluation process of suppliers. With the proposed model, evaluators may give ratings in fuzzy numbers to different suppliers under consideration against postulated criteria, and two indices and a aggregated index would be generated for each supplier so that suppliers can be ranked accordingly","PeriodicalId":306045,"journal":{"name":"2005 ICSC Congress on Computational Intelligence Methods and Applications","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125333029","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}
Pub Date : 2005-12-15DOI: 10.1109/CIMA.2005.1662362
S. Arafat, M. Dohrmann, M. Skubic
This paper discusses the use of combined uncertainty methods in the diagnosis of coronary artery disease using ECG stress signals. Combined uncertainty computes a composite of two types of uncertainties, fuzzy and probabilistic. First, we introduce basic definitions for fuzzy and probabilistic uncertainty types. Next, the ECG analysis problem is discussed in the context of classifying ECG signals using traditional methods. Three examples of models that compute fuzzy, probabilistic, and combined uncertainty models are introduced in the next section. Our experimental results show that models developed by combined uncertainty produce better results, in terms of ECG signals correct classification percentage, compared to those computed using only fuzzy or probabilistic uncertainty
{"title":"Classification of coronary artery disease stress ECGs using uncertainty modeling","authors":"S. Arafat, M. Dohrmann, M. Skubic","doi":"10.1109/CIMA.2005.1662362","DOIUrl":"https://doi.org/10.1109/CIMA.2005.1662362","url":null,"abstract":"This paper discusses the use of combined uncertainty methods in the diagnosis of coronary artery disease using ECG stress signals. Combined uncertainty computes a composite of two types of uncertainties, fuzzy and probabilistic. First, we introduce basic definitions for fuzzy and probabilistic uncertainty types. Next, the ECG analysis problem is discussed in the context of classifying ECG signals using traditional methods. Three examples of models that compute fuzzy, probabilistic, and combined uncertainty models are introduced in the next section. Our experimental results show that models developed by combined uncertainty produce better results, in terms of ECG signals correct classification percentage, compared to those computed using only fuzzy or probabilistic uncertainty","PeriodicalId":306045,"journal":{"name":"2005 ICSC Congress on Computational Intelligence Methods and Applications","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125552412","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}
Pub Date : 2005-12-15DOI: 10.1109/CIMA.2005.1662331
Ehsan Salari, K. Eshghi
Ant colony optimization (ACO) is a well-known metaheuristic in which a colony of artificial ants cooperate in exploring good solutions to a combinatorial optimization problem. In this paper, an ACO algorithm is presented for the graph coloring problem. This ACO algorithm conforms to max-min ant system structure and exploits a local search heuristic to improve its performance. Experimental results on DIMACS test instances show improvements over existing ACO algorithms of the graph coloring problem
{"title":"An ACO algorithm for graph coloring problem","authors":"Ehsan Salari, K. Eshghi","doi":"10.1109/CIMA.2005.1662331","DOIUrl":"https://doi.org/10.1109/CIMA.2005.1662331","url":null,"abstract":"Ant colony optimization (ACO) is a well-known metaheuristic in which a colony of artificial ants cooperate in exploring good solutions to a combinatorial optimization problem. In this paper, an ACO algorithm is presented for the graph coloring problem. This ACO algorithm conforms to max-min ant system structure and exploits a local search heuristic to improve its performance. Experimental results on DIMACS test instances show improvements over existing ACO algorithms of the graph coloring problem","PeriodicalId":306045,"journal":{"name":"2005 ICSC Congress on Computational Intelligence Methods and Applications","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126784436","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}
Pub Date : 2005-12-15DOI: 10.1109/CIMA.2005.1662326
M.Q. Yang, J.Y. Yang, O. Ersoy
In our attempts to construct methods for automated structural prediction and annotation of proteins as well as automated drug design and discovery, the identification of structure and function from the primary structure of a protein is an important, but difficult problem. We extract features using biophysical properties of the different amino acids and using the patterns of poly-peptide sequences. Based on these features we construct different predictors for different tasks. We demonstrate that our classifiers compare favorably to existing classifiers, and we experiment with the use of ensemble methods to enhance our predictors' accuracies and explaining powers. We showed the synergy of approaches from computational intelligence and biophysics is powerful. This work has particular relevance for the study of ion-channels, ligand binding sites, and alternative splicing
{"title":"Computational intelligence - a broad initiative in automated learning from sequences","authors":"M.Q. Yang, J.Y. Yang, O. Ersoy","doi":"10.1109/CIMA.2005.1662326","DOIUrl":"https://doi.org/10.1109/CIMA.2005.1662326","url":null,"abstract":"In our attempts to construct methods for automated structural prediction and annotation of proteins as well as automated drug design and discovery, the identification of structure and function from the primary structure of a protein is an important, but difficult problem. We extract features using biophysical properties of the different amino acids and using the patterns of poly-peptide sequences. Based on these features we construct different predictors for different tasks. We demonstrate that our classifiers compare favorably to existing classifiers, and we experiment with the use of ensemble methods to enhance our predictors' accuracies and explaining powers. We showed the synergy of approaches from computational intelligence and biophysics is powerful. This work has particular relevance for the study of ion-channels, ligand binding sites, and alternative splicing","PeriodicalId":306045,"journal":{"name":"2005 ICSC Congress on Computational Intelligence Methods and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129882241","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}
Pub Date : 2005-12-15DOI: 10.1109/CIMA.2005.1662342
Z. Bingul, H. Ertunc, C. Oysu
An artificial neural network (ANN) using backpropagation algorithm is applied to solve inverse kinematics problems of industrial robot manipulator. 6R robot manipulator with offset wrist was chosen as industrial robot manipulator because geometric feature of this robot does not allow solving inverse kinematics problems analytically. In other words, there is no closed form solution for this problem. In order to define orientation of robot end-effector, three different representations are used here: homogeneous transformation matrix, Euler angles and equivalent angle axis. These representations were compared to obtain inverse kinematics solutions for 6R robot manipulator with offset wrist. Simulation results show that prediction performance from the approximation accuracy point of view is satisfactory with low effective errors based on 10 degrees data resolution
{"title":"Comparison of inverse kinematics solutions using neural network for 6R robot manipulator with offset","authors":"Z. Bingul, H. Ertunc, C. Oysu","doi":"10.1109/CIMA.2005.1662342","DOIUrl":"https://doi.org/10.1109/CIMA.2005.1662342","url":null,"abstract":"An artificial neural network (ANN) using backpropagation algorithm is applied to solve inverse kinematics problems of industrial robot manipulator. 6R robot manipulator with offset wrist was chosen as industrial robot manipulator because geometric feature of this robot does not allow solving inverse kinematics problems analytically. In other words, there is no closed form solution for this problem. In order to define orientation of robot end-effector, three different representations are used here: homogeneous transformation matrix, Euler angles and equivalent angle axis. These representations were compared to obtain inverse kinematics solutions for 6R robot manipulator with offset wrist. Simulation results show that prediction performance from the approximation accuracy point of view is satisfactory with low effective errors based on 10 degrees data resolution","PeriodicalId":306045,"journal":{"name":"2005 ICSC Congress on Computational Intelligence Methods and Applications","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126947252","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}
Pub Date : 2005-12-15DOI: 10.1109/CIMA.2005.1662296
I. Guler, E. D. Ubeyli
In this paper, we present the automated diagnostic systems for time-varying biomedical signals classification and determine their accuracies. The combined neural network (CNN) and mixture of experts (ME) were tested and benchmarked for their performance on the classification of the studied time-varying biomedical signals (ophthalmic arterial Doppler signals and electroencephalogram signals). Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the classifiers trained on the extracted features. The purpose was to determine an optimum classification scheme for the problem and also to infer clues about the extracted features. Our research demonstrated that the power levels of power spectral density (PSD) estimations obtained by the eigenvector methods are the valuable features which are representing the time-varying biomedical signals and the CNN and ME trained on these features achieved high classification accuracies
{"title":"Eigenvector methods for automated detection of time-varying biomedical signals","authors":"I. Guler, E. D. Ubeyli","doi":"10.1109/CIMA.2005.1662296","DOIUrl":"https://doi.org/10.1109/CIMA.2005.1662296","url":null,"abstract":"In this paper, we present the automated diagnostic systems for time-varying biomedical signals classification and determine their accuracies. The combined neural network (CNN) and mixture of experts (ME) were tested and benchmarked for their performance on the classification of the studied time-varying biomedical signals (ophthalmic arterial Doppler signals and electroencephalogram signals). Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the classifiers trained on the extracted features. The purpose was to determine an optimum classification scheme for the problem and also to infer clues about the extracted features. Our research demonstrated that the power levels of power spectral density (PSD) estimations obtained by the eigenvector methods are the valuable features which are representing the time-varying biomedical signals and the CNN and ME trained on these features achieved high classification accuracies","PeriodicalId":306045,"journal":{"name":"2005 ICSC Congress on Computational Intelligence Methods and Applications","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114724842","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}
Pub Date : 2005-12-15DOI: 10.1109/CIMA.2005.1662330
J. Kola, K. Revett
The ensuing events subsequent to cerebral ischaemia are complex and multi-faceted, making it difficult to extract causal relationships between the various pathways that are altered during ischaemia. In this study, we analyse a comprehensive DNA microarray dataset of acute experimental ischaemic stroke, in an effort to elucidate key regulatory elements that participate in the triggering of the pathways that lead to tissue damage. The data suggest that genes responsible for immediate early genes, apoptosis, neurotransmitter receptors (principally glutamate), and inflammation are differentially expressed at various time points subsequent to experimental ischaemia. Using unsupervised clustering (self-organising maps) and gene regulatory networks, we were able to establish a framework within which we could place the resultant gene expression changes into. Although not yet complete, the results from this study indicate that even a complicated pathology such as ischaemia can be analysed in a biologically meaningful way using DNA microarray technology
{"title":"Computational modelling of the gene expression profile from acute ischaemic brain injury","authors":"J. Kola, K. Revett","doi":"10.1109/CIMA.2005.1662330","DOIUrl":"https://doi.org/10.1109/CIMA.2005.1662330","url":null,"abstract":"The ensuing events subsequent to cerebral ischaemia are complex and multi-faceted, making it difficult to extract causal relationships between the various pathways that are altered during ischaemia. In this study, we analyse a comprehensive DNA microarray dataset of acute experimental ischaemic stroke, in an effort to elucidate key regulatory elements that participate in the triggering of the pathways that lead to tissue damage. The data suggest that genes responsible for immediate early genes, apoptosis, neurotransmitter receptors (principally glutamate), and inflammation are differentially expressed at various time points subsequent to experimental ischaemia. Using unsupervised clustering (self-organising maps) and gene regulatory networks, we were able to establish a framework within which we could place the resultant gene expression changes into. Although not yet complete, the results from this study indicate that even a complicated pathology such as ischaemia can be analysed in a biologically meaningful way using DNA microarray technology","PeriodicalId":306045,"journal":{"name":"2005 ICSC Congress on Computational Intelligence Methods and Applications","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122158163","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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