Incremental class learning - Hidden Markov models (ICL-HMM) system combines two different approaches adopted in pattern recognition area to form a new, robust solution. Our system is composed of two parts - ICL feature extractor and HMM sequence recognizer. The former, ICL, is an artificial neural network capable of incrementally learning to recognize features of patterns from a narrow window sliding over them. HMMs simulate systems that transfer from one hidden state to another. In every state the system generates some observations. In our system we train one HMM for every class of patterns by presenting to it the sequences of observations generated by ICL for patterns belonging to its class. In the testing phase, every HMM checks how well it models the sequence of observations generated for an unknown pattern. We present promising results of applying ICL-HMM system to printed Latin character recognition task.
{"title":"Pattern classification with incremental class learning and Hidden Markov models","authors":"Filip Lukaszewski, K. Nagorko","doi":"10.1109/ISDA.2005.76","DOIUrl":"https://doi.org/10.1109/ISDA.2005.76","url":null,"abstract":"Incremental class learning - Hidden Markov models (ICL-HMM) system combines two different approaches adopted in pattern recognition area to form a new, robust solution. Our system is composed of two parts - ICL feature extractor and HMM sequence recognizer. The former, ICL, is an artificial neural network capable of incrementally learning to recognize features of patterns from a narrow window sliding over them. HMMs simulate systems that transfer from one hidden state to another. In every state the system generates some observations. In our system we train one HMM for every class of patterns by presenting to it the sequences of observations generated by ICL for patterns belonging to its class. In the testing phase, every HMM checks how well it models the sequence of observations generated for an unknown pattern. We present promising results of applying ICL-HMM system to printed Latin character recognition task.","PeriodicalId":345842,"journal":{"name":"5th International Conference on Intelligent Systems Design and Applications (ISDA'05)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121270650","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 is focused on the development of intelligent decision-making model which is based on the application of artificial neural networks (ANN) and swarm intelligence algorithm. The proposed model generates one-step forward investment decisions. The ANN are used to make the analysis of historical stock returns and to calculate one day forward possible profit, which could be get while following the model proposed decisions concerning the purchase of the stocks. Subsequently the particle swarm optimization (PSO) algorithm is applied for training of ANN. The training of ANN is made through the adjustment of all ANN towards the weights of "global best" ANN. The experimental investigations were made considering different forms of decision-making model: different structure ANN, input variables etc. The paper introduces experimental investigation for the evaluation of decision-making model. The experimental results show that the application of the proposed decision-making model lets to achieve better results than the average of the market.
{"title":"Adapting particle swarm optimization to stock markets","authors":"J. Nenortaite, R. Simutis","doi":"10.1109/ISDA.2005.17","DOIUrl":"https://doi.org/10.1109/ISDA.2005.17","url":null,"abstract":"The paper is focused on the development of intelligent decision-making model which is based on the application of artificial neural networks (ANN) and swarm intelligence algorithm. The proposed model generates one-step forward investment decisions. The ANN are used to make the analysis of historical stock returns and to calculate one day forward possible profit, which could be get while following the model proposed decisions concerning the purchase of the stocks. Subsequently the particle swarm optimization (PSO) algorithm is applied for training of ANN. The training of ANN is made through the adjustment of all ANN towards the weights of \"global best\" ANN. The experimental investigations were made considering different forms of decision-making model: different structure ANN, input variables etc. The paper introduces experimental investigation for the evaluation of decision-making model. The experimental results show that the application of the proposed decision-making model lets to achieve better results than the average of the market.","PeriodicalId":345842,"journal":{"name":"5th International Conference on Intelligent Systems Design and Applications (ISDA'05)","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115564305","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 are proposing a robotic wheelchair that enables a wheelchair bound person to climb over steps up to about 8 cm in height without assistance from others. By using the proposed robotic wheelchair, a user can maintain inverse pendulum control after raising its front wheels. Then, a user can move forward to the step maintaining the inverse pendulum control, and can climb over the step using motor force of a rear wheel shaft. This paper described the control system designs and simulations of the inverse pendulum control. An observer based optimal control (LQG or H/sub 2/) with an integral action is discussed in order to obtain better control performances for the inverse pendulum control.
{"title":"Modern control approach for robotic wheelchair with inverse pendulum control","authors":"Yoshihiko Takahashi, O. Tsubouchi","doi":"10.1109/ISDA.2005.67","DOIUrl":"https://doi.org/10.1109/ISDA.2005.67","url":null,"abstract":"We are proposing a robotic wheelchair that enables a wheelchair bound person to climb over steps up to about 8 cm in height without assistance from others. By using the proposed robotic wheelchair, a user can maintain inverse pendulum control after raising its front wheels. Then, a user can move forward to the step maintaining the inverse pendulum control, and can climb over the step using motor force of a rear wheel shaft. This paper described the control system designs and simulations of the inverse pendulum control. An observer based optimal control (LQG or H/sub 2/) with an integral action is discussed in order to obtain better control performances for the inverse pendulum control.","PeriodicalId":345842,"journal":{"name":"5th International Conference on Intelligent Systems Design and Applications (ISDA'05)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123399385","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 examines the usefulness of the browser cache for studying the properties of Web objects. The cache contains detailed, personalized data on a user interaction with the Web during the period covering a few previous weeks. The data could be used for a variety of purposes including adaptive systems but the paper concentrates on the objects' changeability. Precise knowledge about the scope and nature of the changeability enables us to estimate the upper limit of caching efficiency, no matter what algorithms are used. Caching reduces both the volume of the traffic and the perceived latency. The paper discusses the results of an experiment and suggests other areas in which the data extracted from the local Internet buffer may be useful.
{"title":"Changeability of Web objects - browser perspective","authors":"A. Sieminski","doi":"10.1109/ISDA.2005.32","DOIUrl":"https://doi.org/10.1109/ISDA.2005.32","url":null,"abstract":"The paper examines the usefulness of the browser cache for studying the properties of Web objects. The cache contains detailed, personalized data on a user interaction with the Web during the period covering a few previous weeks. The data could be used for a variety of purposes including adaptive systems but the paper concentrates on the objects' changeability. Precise knowledge about the scope and nature of the changeability enables us to estimate the upper limit of caching efficiency, no matter what algorithms are used. Caching reduces both the volume of the traffic and the perceived latency. The paper discusses the results of an experiment and suggests other areas in which the data extracted from the local Internet buffer may be useful.","PeriodicalId":345842,"journal":{"name":"5th International Conference on Intelligent Systems Design and Applications (ISDA'05)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115646552","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}
Lukasz Jastrzebski, Maciej Piasecki, Grzegorz Strzelecki
Our objective is to provide a flexible, scalable, distributed architecture that assures a high performance for information extraction (IE) systems working in Internet. The architecture is based on both the general paradigm of the service-oriented architecture, client-server approach and strong separation of concerns between storage and processing components. An experimental IE system, named Semanta, utilising the proposed architecture is also presented. In the following document, we describe five main Semanta services, which are Web user interface (WebUI), Web crawler service (WCS), parsing service (PS), IE service and manager
{"title":"Distributed service-oriented architecture for information extraction system \"Semanta\"","authors":"Lukasz Jastrzebski, Maciej Piasecki, Grzegorz Strzelecki","doi":"10.1109/ISDA.2005.39","DOIUrl":"https://doi.org/10.1109/ISDA.2005.39","url":null,"abstract":"Our objective is to provide a flexible, scalable, distributed architecture that assures a high performance for information extraction (IE) systems working in Internet. The architecture is based on both the general paradigm of the service-oriented architecture, client-server approach and strong separation of concerns between storage and processing components. An experimental IE system, named Semanta, utilising the proposed architecture is also presented. In the following document, we describe five main Semanta services, which are Web user interface (WebUI), Web crawler service (WCS), parsing service (PS), IE service and manager","PeriodicalId":345842,"journal":{"name":"5th International Conference on Intelligent Systems Design and Applications (ISDA'05)","volume":"29 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114050453","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 the paper problems related to the classification of singing voice quality are presented. For this purpose a database consisting of singers' sample recordings is constructed and parameters are extracted from recorded voice of trained and untrained singers. The parameterization process is based on both voice source and formant analysis of a singing voice. These parameters are explained as to their physical interpretation and analyzed statistically in order to diminish their number. The statistical analysis is based on the Fisher statistic. In such a way a feature vector of a singing voice is formed. Decision systems based on neutral networks and rough sets are utilized in the context of the voice type and voice quality classification. Results obtained in the automatic classification performed by both decision systems are compared. A possibility to classify automatically type/quality of voice is judged. The methodology proposed provides means for discerning trained and untrained singers.
{"title":"Automatic classification of singing voice quality","authors":"B. Kostek, Pawel Zwan","doi":"10.1109/ISDA.2005.28","DOIUrl":"https://doi.org/10.1109/ISDA.2005.28","url":null,"abstract":"In the paper problems related to the classification of singing voice quality are presented. For this purpose a database consisting of singers' sample recordings is constructed and parameters are extracted from recorded voice of trained and untrained singers. The parameterization process is based on both voice source and formant analysis of a singing voice. These parameters are explained as to their physical interpretation and analyzed statistically in order to diminish their number. The statistical analysis is based on the Fisher statistic. In such a way a feature vector of a singing voice is formed. Decision systems based on neutral networks and rough sets are utilized in the context of the voice type and voice quality classification. Results obtained in the automatic classification performed by both decision systems are compared. A possibility to classify automatically type/quality of voice is judged. The methodology proposed provides means for discerning trained and untrained singers.","PeriodicalId":345842,"journal":{"name":"5th International Conference on Intelligent Systems Design and Applications (ISDA'05)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121703449","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}
Self-optimizing neural networks (SONNs) are very effective in solving different classification tasks. They have been successfully used to many different problems. The classical SONN adaptation process has been defined as supervised. This paper introduces a new very interesting SONN feature - the unsupervised clustering ability. The unsupervised SONNs (US-SONNs) are able to find out most differentiating features for some training data and recursively divide them into subgroups. US-SONNs can also characterize the importance of features differentiating these groups. The division of the data is recursively performed till the data in subgroups differ imperceptibly. The SONN clustering proceeds very fast in comparison to other unsupervised clustering methods.
{"title":"Unsupervised clustering using self-optimizing neural networks","authors":"A. Horzyk","doi":"10.1109/ISDA.2005.95","DOIUrl":"https://doi.org/10.1109/ISDA.2005.95","url":null,"abstract":"Self-optimizing neural networks (SONNs) are very effective in solving different classification tasks. They have been successfully used to many different problems. The classical SONN adaptation process has been defined as supervised. This paper introduces a new very interesting SONN feature - the unsupervised clustering ability. The unsupervised SONNs (US-SONNs) are able to find out most differentiating features for some training data and recursively divide them into subgroups. US-SONNs can also characterize the importance of features differentiating these groups. The division of the data is recursively performed till the data in subgroups differ imperceptibly. The SONN clustering proceeds very fast in comparison to other unsupervised clustering methods.","PeriodicalId":345842,"journal":{"name":"5th International Conference on Intelligent Systems Design and Applications (ISDA'05)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116631656","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}
Tourists deciding to explore a destination spontaneously and unprepared will have to walk and search on their own. This kind of investigation can be very uncomfortable as it often ends up in disarrangement. With today's agent technology, tourists can have their own intelligent guide taking care of the whole tour organization and execution in time. This is the main objective of the dynamic tour guide (DTG) - a mobile agent that selects attractions, plans an individual tour, provides navigational guidance and offers location based interpretation. Over all it consistently adapts the tour to a tourist's specific behavior in order to provide any possible support via a mobile device.
{"title":"A city guide agent creating and adapting individual sightseeing tours","authors":"Klaus ten Hagen, Marko Modsching, R. Kramer","doi":"10.1109/ISDA.2005.5","DOIUrl":"https://doi.org/10.1109/ISDA.2005.5","url":null,"abstract":"Tourists deciding to explore a destination spontaneously and unprepared will have to walk and search on their own. This kind of investigation can be very uncomfortable as it often ends up in disarrangement. With today's agent technology, tourists can have their own intelligent guide taking care of the whole tour organization and execution in time. This is the main objective of the dynamic tour guide (DTG) - a mobile agent that selects attractions, plans an individual tour, provides navigational guidance and offers location based interpretation. Over all it consistently adapts the tour to a tourist's specific behavior in order to provide any possible support via a mobile device.","PeriodicalId":345842,"journal":{"name":"5th International Conference on Intelligent Systems Design and Applications (ISDA'05)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121238014","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 propose a new approach for combining item-based collaborative filtering (CF) with case based reasoning (CBR) to pursue personalized information filtering in a knowledge sharing context. Functionally, our personalized information filtering approach allows the use of recommendations by peers with similar interests and domain experts to guide the selection of information deemed relevant to an active user's profile. We apply item-based similarity computation in a CF framework to retrieve N information objects based on the user's interests and recommended by peer. The N information objects are then subjected to a CBR based compositional adaptation method to further select relevant information objects from the N retrieved past cases in order to generate a more fine-grained recommendation.
{"title":"An intelligent knowledge sharing strategy featuring item-based collaborative filtering and case based reasoning","authors":"Zeina Chedrawy, S. Abidi","doi":"10.1109/ISDA.2005.22","DOIUrl":"https://doi.org/10.1109/ISDA.2005.22","url":null,"abstract":"In this paper, we propose a new approach for combining item-based collaborative filtering (CF) with case based reasoning (CBR) to pursue personalized information filtering in a knowledge sharing context. Functionally, our personalized information filtering approach allows the use of recommendations by peers with similar interests and domain experts to guide the selection of information deemed relevant to an active user's profile. We apply item-based similarity computation in a CF framework to retrieve N information objects based on the user's interests and recommended by peer. The N information objects are then subjected to a CBR based compositional adaptation method to further select relevant information objects from the N retrieved past cases in order to generate a more fine-grained recommendation.","PeriodicalId":345842,"journal":{"name":"5th International Conference on Intelligent Systems Design and Applications (ISDA'05)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116152761","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 methods of sequential classification with predefined classes. The classification is based on a sequence, assumed to be probabilistic independent, of feature vectors extracted from signal generated by the object. Each feature vector is a base for calculation of a probability density function for each predefined class. The density functions are estimated by the Gaussian mixture model (GMM) and the t-student mixture model. The model parameters are estimated by algorithms based on the expectation-maximization (EM) method. The estimated densities calculated for a sequence of feature vectors are inputs to analyzed classification rules. These rules are derived from Bayes decision theory with some heuristic modifications. The performance of the proposed rules was tested in an automatic, text independent, speaker identification task. Achieved results are presented.
{"title":"Sequential classification of probabilistic independent feature vectors by mixture models","authors":"T. Walkowiak","doi":"10.1109/ISDA.2005.81","DOIUrl":"https://doi.org/10.1109/ISDA.2005.81","url":null,"abstract":"The paper presents methods of sequential classification with predefined classes. The classification is based on a sequence, assumed to be probabilistic independent, of feature vectors extracted from signal generated by the object. Each feature vector is a base for calculation of a probability density function for each predefined class. The density functions are estimated by the Gaussian mixture model (GMM) and the t-student mixture model. The model parameters are estimated by algorithms based on the expectation-maximization (EM) method. The estimated densities calculated for a sequence of feature vectors are inputs to analyzed classification rules. These rules are derived from Bayes decision theory with some heuristic modifications. The performance of the proposed rules was tested in an automatic, text independent, speaker identification task. Achieved results are presented.","PeriodicalId":345842,"journal":{"name":"5th International Conference on Intelligent Systems Design and Applications (ISDA'05)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121961897","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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