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2009 International Conference of Soft Computing and Pattern Recognition最新文献

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Mining Up-to-Date Knowledge Based on Tree Structures 基于树形结构的最新知识挖掘
2009 International Conference of Soft Computing and Pattern Recognition Pub Date : 2009-12-04 DOI: 10.1109/SoCPaR.2009.36
Chun-Wei Lin, T. Hong, Wen-Hsiang Lu
{"title":"Mining Up-to-Date Knowledge Based on Tree Structures","authors":"Chun-Wei Lin, T. Hong, Wen-Hsiang Lu","doi":"10.1109/SoCPaR.2009.36","DOIUrl":"https://doi.org/10.1109/SoCPaR.2009.36","url":null,"abstract":"In the past, the up-to-date patterns is proposed to mine the frequent itemsets within its corresponding lifetime. This hybrid method is based on the Apriori-like approach, which requests high computational cost and memory requirement. In this paper, the up-to-date pattern tree (UDP tree) is proposed to keep the up-to-date patterns in a tree structure. The experimental results show that the proposed approach has a better performance than the level-wise up-to-date algorithm.","PeriodicalId":284743,"journal":{"name":"2009 International Conference of Soft Computing and Pattern Recognition","volume":"317 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124484770","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}
引用次数: 2
The Application of the Locally Linear Model Tree on Customer Churn Prediction 局部线性模型树在客户流失预测中的应用
2009 International Conference of Soft Computing and Pattern Recognition Pub Date : 2009-12-04 DOI: 10.1109/SoCPaR.2009.97
A. Ghorbani, F. Taghiyareh, C. Lucas
{"title":"The Application of the Locally Linear Model Tree on Customer Churn Prediction","authors":"A. Ghorbani, F. Taghiyareh, C. Lucas","doi":"10.1109/SoCPaR.2009.97","DOIUrl":"https://doi.org/10.1109/SoCPaR.2009.97","url":null,"abstract":"Acquiring new customers in any business is much more expensive than trying to keep the existing ones. Thus many prediction models are presented to detect churning customers. The objective of this paper was to improve the predictive accuracy and interpretability of churn detection. For this purpose, the application of the locally linear model tree (LOLIMOT) algorithm, which integrates the advantage of neural networks, tree model and fuzzy modeling, was experimented. Applied to the data of a major telecommunication company, the method is found to improve prediction accuracy significantly compared to other algorithms, such as artificial neural networks, decision trees, and logistic regression. The results also indicate that LOLIMOT can have accurate outcome in extremely unbalanced datasets.","PeriodicalId":284743,"journal":{"name":"2009 International Conference of Soft Computing and Pattern Recognition","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121525391","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}
引用次数: 17
Feature Extraction and Image Matching of 3D Lung Cancer Cell Image 肺癌细胞三维图像特征提取与图像匹配
2009 International Conference of Soft Computing and Pattern Recognition Pub Date : 2009-12-04 DOI: 10.1109/SoCPaR.2009.103
H. Madzin, R. Zainuddin
{"title":"Feature Extraction and Image Matching of 3D Lung Cancer Cell Image","authors":"H. Madzin, R. Zainuddin","doi":"10.1109/SoCPaR.2009.103","DOIUrl":"https://doi.org/10.1109/SoCPaR.2009.103","url":null,"abstract":"The demand for automation in medical analysis is continuously growing with large number of application in biotechnology and medical research. Feature extraction and image matching are important steps in analyzing medical cells. In this research paper, we are concentrating on extracting and matching features from a full 3D volume data of lung cancer cell that was recorded with a confocal laser scanning microscopy (LSM) at a voxel size of about (0.3μm)3. In order to apply feature extraction on 3D cell image, the image is slices into ten different viewpoints of 2D images with thickness of each slice are about 0.1μm. An experiment has been done based on local invariant features methods which are HarrisLaplace method to extract features of each slices and SIFT matching method to find and match same features in each slices. The experiment shows that these methods can extract the same features although in different viewpoints. This research paper application can be served as preliminary step for further research study in analyzing 3D structure of cancer cell image.","PeriodicalId":284743,"journal":{"name":"2009 International Conference of Soft Computing and Pattern Recognition","volume":"620 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123321952","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}
引用次数: 6
Investigations of Factors Affecting the Genetic Algorithm for Shortest Driving Time 影响最短驾驶时间遗传算法的因素研究
2009 International Conference of Soft Computing and Pattern Recognition Pub Date : 2009-12-04 DOI: 10.1109/SoCPaR.2009.32
Chu-Hsing Lin, Chen-Yu Lee, Jung-Chun Liu, Hao Zuo
{"title":"Investigations of Factors Affecting the Genetic Algorithm for Shortest Driving Time","authors":"Chu-Hsing Lin, Chen-Yu Lee, Jung-Chun Liu, Hao Zuo","doi":"10.1109/SoCPaR.2009.32","DOIUrl":"https://doi.org/10.1109/SoCPaR.2009.32","url":null,"abstract":"In this paper we investigate the influences on the genetic algorithm for the shortest driving time problem due to factors such as nodes on a map, the population size, the mutation rate, the crossover rate, and the converging rate. When the nodes on the map increase, more execution time is needed and much difference between the approximate solution and the exact solution appear on running genetic algorithms. Also, from the view point of the population initialization, restart type and reback type affect the precision of approximate solutions and the execution time. The characteristics of the factors we find in the paper provide us insight how to improve the genetic algorithm for the shortest driving time problem.","PeriodicalId":284743,"journal":{"name":"2009 International Conference of Soft Computing and Pattern Recognition","volume":"9 25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124682842","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}
引用次数: 3
Accelerating Multi-scale Image Fusion Algorithms Using CUDA 使用CUDA加速多尺度图像融合算法
2009 International Conference of Soft Computing and Pattern Recognition Pub Date : 2009-12-04 DOI: 10.1109/SoCPaR.2009.63
Seung-Hun Yoo, Jin-Hyung Park, Chang-Sung Jeong
{"title":"Accelerating Multi-scale Image Fusion Algorithms Using CUDA","authors":"Seung-Hun Yoo, Jin-Hyung Park, Chang-Sung Jeong","doi":"10.1109/SoCPaR.2009.63","DOIUrl":"https://doi.org/10.1109/SoCPaR.2009.63","url":null,"abstract":"Recently, fusion speed has emerged as an important factor in the image fusion and a substantial amount of memory and computing power are required for a high-speed fusion. This paper shows approaches to accelerate multi-scale image fusion speed on GPU (Graphics Processing Unit) using CUDA (Compute Unified Device Architecture). The GPU has evolved into a very powerful and flexible streaming processor, which provides a good computational power and memory bandwidth. We implement the multi-scale image fusion algorithms using CUDA software platform of the latest version of GPU and theirs fusion speeds are compared and evaluated with implementation in Core2 Quad processor with 2.4GHz. The GPU version achieved a speedup of 6x-8x over the CPU version.","PeriodicalId":284743,"journal":{"name":"2009 International Conference of Soft Computing and Pattern Recognition","volume":"49 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123144888","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}
引用次数: 14
On Fuzzy Inference System Based Failure Mode and Effect Analysis (FMEA) Methodology 基于模糊推理系统的失效模式与影响分析(FMEA)方法
2009 International Conference of Soft Computing and Pattern Recognition Pub Date : 2009-12-04 DOI: 10.1109/SoCPaR.2009.72
K. Tay
{"title":"On Fuzzy Inference System Based Failure Mode and Effect Analysis (FMEA) Methodology","authors":"K. Tay","doi":"10.1109/SoCPaR.2009.72","DOIUrl":"https://doi.org/10.1109/SoCPaR.2009.72","url":null,"abstract":"Failure Mode and Effect Analysis (FMEA) is a popular problem prevention methodology. It utilizes a Risk Priority Number (RPN) model to evaluate the risk associated to each failure mode. The conventional RPN model is simple, but, its accuracy is argued. A fuzzy RPN model is proposed as an alternative to the conventional RPN. The fuzzy RPN model allows the relation between the RPN score and Severity, Occurrence and Detect ratings to be of non-linear relationship, and it maybe a more realistic representation. In this paper, the efficiency of the fuzzy RPN model in order to allow valid and meaningful comparisons among different failure modes in FMEA to be made is investigated. It is suggested that the fuzzy RPN should be subjected to certain theoretical properties of a length function e.g. monotonicity, sub-additivity and etc. In this paper, focus is on the monotonicity property. The monotonicity property for the fuzzy RPN is firstly defined, and a sufficient condition for a FIS to be monotone is applied to the fuzzy RPN model. This is an easy and reliable guideline to construct the fuzzy RPN in practice. Case studies relating to semiconductor industry are then presented.","PeriodicalId":284743,"journal":{"name":"2009 International Conference of Soft Computing and Pattern Recognition","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129726588","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}
引用次数: 10
OntRel: An Ontology Indexer to Store OWL-DL Ontologies and Its Instances OntRel:存储OWL-DL本体及其实例的本体索引器
2009 International Conference of Soft Computing and Pattern Recognition Pub Date : 2009-12-04 DOI: 10.1109/SoCPaR.2009.98
Adnan Khalid, Syed Adnan Hussain Shah, M. Qadir
{"title":"OntRel: An Ontology Indexer to Store OWL-DL Ontologies and Its Instances","authors":"Adnan Khalid, Syed Adnan Hussain Shah, M. Qadir","doi":"10.1109/SoCPaR.2009.98","DOIUrl":"https://doi.org/10.1109/SoCPaR.2009.98","url":null,"abstract":"Ontology is one of the most important components of a semantic based information system which provides context to the document. Ontology enables us to capture the semantics of documents. To interpret information and perform reasoning we require storing Ontologies in a way that is correct, consistent, scalable and efficient to retrieve. RDBMS (Relational Database Management Systems) is the most efficient and reliable Data Structure in terms of storage and retrieval. One of the ways is to store Ontologies in RDBMS. OWL is used to represent ontologies. To store OWL documents in RDBMS multiple techniques have been proposed, but they either deal with single ontology or they do not store complete semantics expressed in OWL ontologies which compromise the correct retrieval of the data. Some of the techniques are not really scalable, as the ontology is dynamic and extensible where as the RDBMS schema is not dynamically extensible. So, there is a need to preserve the dynamic OWL documents in the Relational structure in such a way that no data or relationship is lost and advantages of RDBMS are also gained. This paper addresses the issue of storing multiple OWL ontologies in an RDBMS in a way that is correct, consistent, scalable and efficient to retrieve. A relational schema, with the name of OntRel, has been proposed along with a set of rules to populate database from OWL documents. OntRel has been implemented and tested and available as on the web for open use. Finally, we have compared the Load time, Query performance and completeness of OntRel with other indexing techniques using Lehigh University Benchmark (LUBM) and University Ontology Benchmark (UOBM).","PeriodicalId":284743,"journal":{"name":"2009 International Conference of Soft Computing and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130992768","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}
引用次数: 12
Computing the Autopilot Control Algorithm Using Predictive Functional Control for Unstable Model 基于预测函数控制的不稳定模型自动驾驶仪控制算法计算
2009 International Conference of Soft Computing and Pattern Recognition Pub Date : 2009-12-04 DOI: 10.1109/SoCPaR.2009.43
H. Kasdirin, J. Rossiter
{"title":"Computing the Autopilot Control Algorithm Using Predictive Functional Control for Unstable Model","authors":"H. Kasdirin, J. Rossiter","doi":"10.1109/SoCPaR.2009.43","DOIUrl":"https://doi.org/10.1109/SoCPaR.2009.43","url":null,"abstract":"This paper discusses the computing development of a control algorithm using Predictive Functional Control (PFC) for model-based that having one or more unstable poles. One basic Ballistic Missile model [10] is used as an unstable model to formulate the control law algorithm using PFC. PFC algorithm development is computationally simple as a controller and it is not very complicated as the function of a missile will explode as it reaches the target. Furthermore, the analysis and issues of the implementation relating linear discrete-time unstable process are also being discussed. Hence, designed PFC algorithm need to find the suitable tuning parameters as its play an important part of the designing the autopilot controller. Thus, the tuning of the desired time constant, Ψ and small coincidence horizon n1 in a single coincidence point shows that the PFC control law is built better in the dynamic pole of the unstable missile mode. As a result, by using a trajectory set-point, some positive results is presented and discussed as the missile follow its reference trajectory via some simulation using MATLAB 7.0.","PeriodicalId":284743,"journal":{"name":"2009 International Conference of Soft Computing and Pattern Recognition","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116509368","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}
引用次数: 3
Motif Discovery Using Evolutionary Algorithms 基于进化算法的Motif发现
2009 International Conference of Soft Computing and Pattern Recognition Pub Date : 2009-12-04 DOI: 10.1109/SoCPaR.2009.88
Linlin Shao, Yuehui Chen, A. Abraham
{"title":"Motif Discovery Using Evolutionary Algorithms","authors":"Linlin Shao, Yuehui Chen, A. Abraham","doi":"10.1109/SoCPaR.2009.88","DOIUrl":"https://doi.org/10.1109/SoCPaR.2009.88","url":null,"abstract":"The bacterial foraging optimization (BFO) algorithm is a nature and biologically inspired computing method. We propose an alternative solution integrating bacterial foraging optimization algorithm and tabu search (TS) algorithm namely TS-BFO. We modify the original BFO via established a self-control multi-length chemotactic step mechanism, and introduce rao metric. We utilize it to solve motif discovery problem and compare the experimental result with existing famous DE/EDA algorithm which combines global information extracted by estimation of distribution algorithm (EDA) with differential information obtained by Differential evolution (DE) to search promising solutions. The experiments on real data set selected from TRANSFAC and SCPD database have predicted meaningful motif which demonstrated that TS-BFO and DE/EDA are promising approaches for finding motif and enrich the technique of motif discovery.","PeriodicalId":284743,"journal":{"name":"2009 International Conference of Soft Computing and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131968093","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}
引用次数: 18
CBGP: Classification Based on Gradual Patterns CBGP:基于渐进模式的分类
2009 International Conference of Soft Computing and Pattern Recognition Pub Date : 2009-12-04 DOI: 10.1109/SoCPaR.2009.15
Y. W. Choong, Lisa Di-Jorio, Anne Laurent, D. Laurent, M. Teisseire
{"title":"CBGP: Classification Based on Gradual Patterns","authors":"Y. W. Choong, Lisa Di-Jorio, Anne Laurent, D. Laurent, M. Teisseire","doi":"10.1109/SoCPaR.2009.15","DOIUrl":"https://doi.org/10.1109/SoCPaR.2009.15","url":null,"abstract":"In this paper, we address the issue of mining gradual classification rules. In general, gradual patterns refer to regularities such as ``The older a person, the higher his salary''. Such patterns are extensively and successfully used in command-based systems, especially in fuzzy command applications. However, in such applications, gradual patterns are supposed to be known and/or provided by an expert, which is not always realistic in practice. In this work, we aim at mining from a given training dataset such gradual patterns for the generation of gradual classification rules. Gradual classification rules thus refer to rules where the antecedent is a gradual pattern and the conclusion is a class value.","PeriodicalId":284743,"journal":{"name":"2009 International Conference of Soft Computing and Pattern Recognition","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128316112","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}
引用次数: 4
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