Pub Date : 2015-11-01DOI: 10.1109/IWCIA.2015.7449471
M. Nagao, H. Seki
In multi-relational data mining (MRDM), there have been proposed many methods for searching for patterns that involve multiple tables (relations) from a relational database. In this paper, we consider closed pattern mining from a multi-relational database (MRDB). Closed patterns, a.k.a. concept intents, give the condensed representations of frequent patterns, without losing any information, and they would be of help to discover information on hidden relationship among a given database. Since the computation of MRDM is costly compared with the conventional itemset mining, we propose a parallel algorithm for computing closed patterns on multi-core processors. In particular, we present a new load-balancing strategy which tries to fully exploit the task-parallelism intrinsic in the search process of the problem, and give some experimental results, which show the effectiveness of the proposed method.
{"title":"Towards parallel mining of closed patterns from multi-relational data","authors":"M. Nagao, H. Seki","doi":"10.1109/IWCIA.2015.7449471","DOIUrl":"https://doi.org/10.1109/IWCIA.2015.7449471","url":null,"abstract":"In multi-relational data mining (MRDM), there have been proposed many methods for searching for patterns that involve multiple tables (relations) from a relational database. In this paper, we consider closed pattern mining from a multi-relational database (MRDB). Closed patterns, a.k.a. concept intents, give the condensed representations of frequent patterns, without losing any information, and they would be of help to discover information on hidden relationship among a given database. Since the computation of MRDM is costly compared with the conventional itemset mining, we propose a parallel algorithm for computing closed patterns on multi-core processors. In particular, we present a new load-balancing strategy which tries to fully exploit the task-parallelism intrinsic in the search process of the problem, and give some experimental results, which show the effectiveness of the proposed method.","PeriodicalId":298756,"journal":{"name":"2015 IEEE 8th International Workshop on Computational Intelligence and Applications (IWCIA)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129880592","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 : 2015-11-01DOI: 10.1109/IWCIA.2015.7449458
F. Yasir, P. Prasad, A. Alsadoon, A. Elchouemi
This paper presents a SIFT-based geometrically computational approach to vigorously recognize Bangla sign language (BdSL). Gaussian distribution and grayscaling techniques are applied for image processing and normalizing the sign image. After this pre-processing, features are extracted from the sign image by implementing scale invariant feature transform. Acquiring all descriptors from the sign image, k-means clustering is executed on all the descriptors which are previously computed by SIFT. Based on the sample training set, each of the cluster denotes as a visual word. Considering the histograms of the clustering descriptors, Bag of words model is introduced on this hybrid approach which develops a set of visual vocabulary. Finally for each of sign word, a binary linear support vector machine (SVM) classifier is trained with a respective training data set. Considering these binary classifiers, we obtained a respective recognition rate on both Bangla signs of expressions and alphabets.
{"title":"SIFT based approach on Bangla sign language recognition","authors":"F. Yasir, P. Prasad, A. Alsadoon, A. Elchouemi","doi":"10.1109/IWCIA.2015.7449458","DOIUrl":"https://doi.org/10.1109/IWCIA.2015.7449458","url":null,"abstract":"This paper presents a SIFT-based geometrically computational approach to vigorously recognize Bangla sign language (BdSL). Gaussian distribution and grayscaling techniques are applied for image processing and normalizing the sign image. After this pre-processing, features are extracted from the sign image by implementing scale invariant feature transform. Acquiring all descriptors from the sign image, k-means clustering is executed on all the descriptors which are previously computed by SIFT. Based on the sample training set, each of the cluster denotes as a visual word. Considering the histograms of the clustering descriptors, Bag of words model is introduced on this hybrid approach which develops a set of visual vocabulary. Finally for each of sign word, a binary linear support vector machine (SVM) classifier is trained with a respective training data set. Considering these binary classifiers, we obtained a respective recognition rate on both Bangla signs of expressions and alphabets.","PeriodicalId":298756,"journal":{"name":"2015 IEEE 8th International Workshop on Computational Intelligence and Applications (IWCIA)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116466123","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 : 2015-11-01DOI: 10.1109/IWCIA.2015.7449468
A. Gupta, P. Prasad, A. Alsadoon, Kamini Bajaj
The Gait recognition is the 2nd generation of biometric identification technology which aims to identify people at a distance by the way they walk. Due to the fact that there has been increasing research interest in the identification of an individual in access controlled environments such as the airports, banks and car parks, it has been observed that the effective human gait recognition plays a very important role in such video surveillance based applications. This paper proposes an effective Gait recognition method for automatic person recognition using SVM and Bayesian Network. In this method frames of videos are used as an input, these videos are live and are from the CASIA dataset. The background subtraction is done using Gait Pal and Pal Entropy and a Median Filter is also used to remove noise from the background. Feature selection is done using the Hanavan's model to reduce the computational cost during training and recognition. Support Vector Machine (SVM) and Bayesian Network are used for training and testing purpose. The experimental results show that the proposed approach has a very effective Correct Classification rate (CCR).
{"title":"Hybrid method for Gait recognition using SVM and Baysian Network","authors":"A. Gupta, P. Prasad, A. Alsadoon, Kamini Bajaj","doi":"10.1109/IWCIA.2015.7449468","DOIUrl":"https://doi.org/10.1109/IWCIA.2015.7449468","url":null,"abstract":"The Gait recognition is the 2nd generation of biometric identification technology which aims to identify people at a distance by the way they walk. Due to the fact that there has been increasing research interest in the identification of an individual in access controlled environments such as the airports, banks and car parks, it has been observed that the effective human gait recognition plays a very important role in such video surveillance based applications. This paper proposes an effective Gait recognition method for automatic person recognition using SVM and Bayesian Network. In this method frames of videos are used as an input, these videos are live and are from the CASIA dataset. The background subtraction is done using Gait Pal and Pal Entropy and a Median Filter is also used to remove noise from the background. Feature selection is done using the Hanavan's model to reduce the computational cost during training and recognition. Support Vector Machine (SVM) and Bayesian Network are used for training and testing purpose. The experimental results show that the proposed approach has a very effective Correct Classification rate (CCR).","PeriodicalId":298756,"journal":{"name":"2015 IEEE 8th International Workshop on Computational Intelligence and Applications (IWCIA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121627017","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 : 2015-11-01DOI: 10.1109/IWCIA.2015.7449483
Gulpi Qorik Oktagalu Pratamasunu, Zhencheng Hu, A. Arifin, A. Yuniarti, D. A. Navastara, Arya Yudhi Wijaya, Wijayanti Nurul Khotimah, A. Asano
In this paper, we propose an automatic image thresholding method based on an index of fuzziness and a fuzzy similarity measure. This work aims at overcoming the limitation of the existing method which is semi-supervised. Using an index of fuzziness, two initial regions of gray levels located at the boundaries of the histogram are defined based on the fuzzy region. Then the threshold point is found by using a fuzzy similarity measure. No prior knowledge of the image is required. Experiments on practical images illustrate the effectiveness of the proposed method.
{"title":"Image thresholding based on index of fuzziness and fuzzy similarity measure","authors":"Gulpi Qorik Oktagalu Pratamasunu, Zhencheng Hu, A. Arifin, A. Yuniarti, D. A. Navastara, Arya Yudhi Wijaya, Wijayanti Nurul Khotimah, A. Asano","doi":"10.1109/IWCIA.2015.7449483","DOIUrl":"https://doi.org/10.1109/IWCIA.2015.7449483","url":null,"abstract":"In this paper, we propose an automatic image thresholding method based on an index of fuzziness and a fuzzy similarity measure. This work aims at overcoming the limitation of the existing method which is semi-supervised. Using an index of fuzziness, two initial regions of gray levels located at the boundaries of the histogram are defined based on the fuzzy region. Then the threshold point is found by using a fuzzy similarity measure. No prior knowledge of the image is required. Experiments on practical images illustrate the effectiveness of the proposed method.","PeriodicalId":298756,"journal":{"name":"2015 IEEE 8th International Workshop on Computational Intelligence and Applications (IWCIA)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132805350","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 : 2015-11-01DOI: 10.1109/IWCIA.2015.7449474
Usman S. Sanusi, D. Corne
There is increasing demand for accurate short-term forecasting of weather conditions at specified locations. This demand arises partly from the growing numbers of renewable energy facilities. In order successfully to integrate renewable energy supplies with grid sources, the short term (e.g. next 24 hrs) output profile of the renewable system needs to be forecast as accurately as possible, to avoid over-reliance on fossil fuels at times when renewables are available, and to avoid deficit in supply when they aren't. In particular, the inherent variability in wind-speed poses an additional challenge. Several approaches for wind-speed forecasting have previously been developed, ranging from simple time series analysis to the use of a combination of global weather forecasting, computational fluid dynamics and machine learning methods. For localized forecasting, statistical methods that rely on historical location data come to the forefront. Recent such work (building localized forecast models with multivariate linear regression) has found that accuracy can gain significantly by learning from multiple types of local weather features. Here, we build on that work by investigating the potential benefits of simple additional `derived' features, such as the gradient in wind-speed or other variables. Following extensive experimentation using data from sites in Nigeria (primarily), Scotland and Italy, we conclude that the ideal forecasting model for a given location will use a judicious combination of direct and derived features.
{"title":"Feature selection for accurate short-term forecasting of local wind-speed","authors":"Usman S. Sanusi, D. Corne","doi":"10.1109/IWCIA.2015.7449474","DOIUrl":"https://doi.org/10.1109/IWCIA.2015.7449474","url":null,"abstract":"There is increasing demand for accurate short-term forecasting of weather conditions at specified locations. This demand arises partly from the growing numbers of renewable energy facilities. In order successfully to integrate renewable energy supplies with grid sources, the short term (e.g. next 24 hrs) output profile of the renewable system needs to be forecast as accurately as possible, to avoid over-reliance on fossil fuels at times when renewables are available, and to avoid deficit in supply when they aren't. In particular, the inherent variability in wind-speed poses an additional challenge. Several approaches for wind-speed forecasting have previously been developed, ranging from simple time series analysis to the use of a combination of global weather forecasting, computational fluid dynamics and machine learning methods. For localized forecasting, statistical methods that rely on historical location data come to the forefront. Recent such work (building localized forecast models with multivariate linear regression) has found that accuracy can gain significantly by learning from multiple types of local weather features. Here, we build on that work by investigating the potential benefits of simple additional `derived' features, such as the gradient in wind-speed or other variables. Following extensive experimentation using data from sites in Nigeria (primarily), Scotland and Italy, we conclude that the ideal forecasting model for a given location will use a judicious combination of direct and derived features.","PeriodicalId":298756,"journal":{"name":"2015 IEEE 8th International Workshop on Computational Intelligence and Applications (IWCIA)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123862597","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 : 2015-11-01DOI: 10.1109/IWCIA.2015.7449461
Yunmei Fang, Dan Wu, J. Fei
In this paper, a novel adaptive control scheme that incorporates fully tuned radial basis function (RBF) neural network (NN) is proposed for the control of MEMS gyroscope with respect to external disturbances and model uncertainties. An adaptive fully tuned RBF neural network controller is used to compensate the external disturbances and model uncertainties, thus improving the dynamic characteristics and robustness of the MEMS gyroscope. The fully tuned RBF neural network compensating controller and the adaptive nominal controller are combined in the unified Lynapunov framework to ensure the stability of the control system. By using the proposed scheme, not only the effect of model uncertainties and external disturbances can be eliminated, but also satisfactory dynamic characteristics and strong robustness can be obtained. Simulation studies are implemented to verify the effectiveness of the proposed scheme and demonstrate that the fully tuned RBF network control has better robustness and dynamic characteristics than traditional RBF network control.
{"title":"Adaptive fully tuned RBF neural control of MEMS gyroscope","authors":"Yunmei Fang, Dan Wu, J. Fei","doi":"10.1109/IWCIA.2015.7449461","DOIUrl":"https://doi.org/10.1109/IWCIA.2015.7449461","url":null,"abstract":"In this paper, a novel adaptive control scheme that incorporates fully tuned radial basis function (RBF) neural network (NN) is proposed for the control of MEMS gyroscope with respect to external disturbances and model uncertainties. An adaptive fully tuned RBF neural network controller is used to compensate the external disturbances and model uncertainties, thus improving the dynamic characteristics and robustness of the MEMS gyroscope. The fully tuned RBF neural network compensating controller and the adaptive nominal controller are combined in the unified Lynapunov framework to ensure the stability of the control system. By using the proposed scheme, not only the effect of model uncertainties and external disturbances can be eliminated, but also satisfactory dynamic characteristics and strong robustness can be obtained. Simulation studies are implemented to verify the effectiveness of the proposed scheme and demonstrate that the fully tuned RBF network control has better robustness and dynamic characteristics than traditional RBF network control.","PeriodicalId":298756,"journal":{"name":"2015 IEEE 8th International Workshop on Computational Intelligence and Applications (IWCIA)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124839448","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 : 2015-11-01DOI: 10.1109/IWCIA.2015.7449477
Junko Hashimoto, Tetsuya Shigeyasu
By improvements of ICT (Information and Communications Technologies), a lot of advanced high speed communication standards such as 3G (3rd generation mobile telecommunications), LTE (Long Term Evolution) and High speed WLAN (Wireless Local Area Network) have been developed. Recently, most devices destined for consumer use, equip more than one communication interface based on the above high speed communication standards. For exploiting the advantages of devices employing multiple communication interfaces, several transport protocols using simultaneously multiple communication channels for one transmission purpose have been proposed. However, there are few number of evaluations relating to such multi-path transport protocols. In this paper, for clarifying the characteristics of multi-path transport protocols, we will report the evaluation results of MPTPC under heterogeneous communication characteristics.
{"title":"Performance evaluation of MPTCP under heterogeneous channel characteristics","authors":"Junko Hashimoto, Tetsuya Shigeyasu","doi":"10.1109/IWCIA.2015.7449477","DOIUrl":"https://doi.org/10.1109/IWCIA.2015.7449477","url":null,"abstract":"By improvements of ICT (Information and Communications Technologies), a lot of advanced high speed communication standards such as 3G (3rd generation mobile telecommunications), LTE (Long Term Evolution) and High speed WLAN (Wireless Local Area Network) have been developed. Recently, most devices destined for consumer use, equip more than one communication interface based on the above high speed communication standards. For exploiting the advantages of devices employing multiple communication interfaces, several transport protocols using simultaneously multiple communication channels for one transmission purpose have been proposed. However, there are few number of evaluations relating to such multi-path transport protocols. In this paper, for clarifying the characteristics of multi-path transport protocols, we will report the evaluation results of MPTPC under heterogeneous communication characteristics.","PeriodicalId":298756,"journal":{"name":"2015 IEEE 8th International Workshop on Computational Intelligence and Applications (IWCIA)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128949432","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 : 2015-11-01DOI: 10.1109/IWCIA.2015.7449476
Toshiki Nishio, J. Kushida, Akira Hara, T. Takahama
The paper presents adaptive particle swarm optimization with multi-dimensional mutation (MM-APSO), which can perform move efficient search than the conventional adaptive particle swarm optimization (APSO). In particular, it can solve non-separable fitness functions such as banana functions with high accuracy and rapid convergence. MM-APSO consists of APSO and additional two methods. One is multi-dimensional mutation, which uses movement vector of population. The other is reinitializing velocity to 0 when mutation occurs. Experiments were conducted on 10 unimodal and multimodal benchmark functions. The experimental results show that MM-APSO substantially enhances the performance of the APSO in terms of convergence speed and solution accuracy.
{"title":"Adaptive particle swarm optimization with multi-dimensional mutation","authors":"Toshiki Nishio, J. Kushida, Akira Hara, T. Takahama","doi":"10.1109/IWCIA.2015.7449476","DOIUrl":"https://doi.org/10.1109/IWCIA.2015.7449476","url":null,"abstract":"The paper presents adaptive particle swarm optimization with multi-dimensional mutation (MM-APSO), which can perform move efficient search than the conventional adaptive particle swarm optimization (APSO). In particular, it can solve non-separable fitness functions such as banana functions with high accuracy and rapid convergence. MM-APSO consists of APSO and additional two methods. One is multi-dimensional mutation, which uses movement vector of population. The other is reinitializing velocity to 0 when mutation occurs. Experiments were conducted on 10 unimodal and multimodal benchmark functions. The experimental results show that MM-APSO substantially enhances the performance of the APSO in terms of convergence speed and solution accuracy.","PeriodicalId":298756,"journal":{"name":"2015 IEEE 8th International Workshop on Computational Intelligence and Applications (IWCIA)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126541500","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：