{"title":"Priority ordered direction basis function neural networks and the application for object recognition","authors":"Wenming Cao, Fei Lu, Shoujue Wang","doi":"10.1109/GRC.2006.1635788","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635788","url":null,"abstract":"A brand new architecture of neural networks has been introduced, In this architecture, outputs of direction basis function neurons[1] are with different priorities. It has been discussed that the Priority Ordered Direction Basis Function Neural Network (PODBFNN). The Priority Ordered Direction Basis Function Nets (PODBFN) for object recognition has been analyzed. The experiment shows that the learning speed of the PODBFN are much faster than that of the multilayered feedforward neural networks with existing BP algorithms.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126633802","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}
{"title":"A graduate seminar on foundations of data mining","authors":"Zhengxin Chen","doi":"10.1109/GRC.2006.1635816","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635816","url":null,"abstract":"Data mining, the key step in the knowledge discovery in database (KDD) process, is aimed to find implicit, previously unknown and interesting knowledge patterns which can be used to guide our future activities. In order to conduct data mining in a more systematic way, recently the issue of foundations of data mining (FDM) has attracted attentions from researchers. Approaches to FDM such as those based on granular computing (GrC) have been developed. We believe exploring the FDM is an important aspect of DM education. For this purpose, we have developed a graduate seminar course at University of Nebraska at Omaha, which was offered first time in Fall 2005. In this paper, we describe our motivation of developing such a course, report activities conducted in the course, student reading lists, as well as outcome of this course.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"258 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124239114","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}
{"title":"Research for Hopf bifurcation of an inertial two-neuron system with time delay","authors":"Qun Liu, X. Liao, Guoyin Wang, Yuehua Wu","doi":"10.1109/GRC.2006.1635830","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635830","url":null,"abstract":"The inertia can be considered a useful tool that is added to help in the generation of chaos in neural systems. So it can be added to the standard Hopfield equation. This paper is concerned with a study of the influence of a time delay occurring in a two-inertial neuron system .It is found that as the time delay increases beyond a critical value, the equilibrium position of the inertial two-neuron system becomes unstable and may have Hopf bifurcation. Using the time delay as a bifurcation parameter, Hopf bifurcation is studied by using theory of retarded functional differential equations, then necessary and sufficient conditions for Hopf bifurcation to occur are derived.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121733768","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}
{"title":"Error awareness data mining","authors":"Xingquan Zhu, Xindong Wu","doi":"10.1109/GRC.2006.1635795","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635795","url":null,"abstract":"Real-world data mining applications often deal with low-quality information sources where data collection inaccuracy, device limitations, data transmission and discretization errors, or man-made perturbations frequently result in imprecise or vague data. Two common practices are to adopt either data cleansing to enhance data consistency or simply take noisy data as quality sources and feed them into the data mining algorithms. Either way may substantially sacrifice the mining performances. In this paper, we consider an error awareness data mining framework, which takes advantage of statistical error information (such as noise level and noise distribution) to improve data mining results. We assume such noise knowledge is available in advance, and propose a solution to incorporate it into the mining process. More specifically, we use noise knowledge to restore original data distributions, and then use the restored information to modify the model built from noise corrupted data. We present an Error Awareness Naive Bayes (EA_NB) classification algorithm, and provide extensive experimental comparisons to demonstrate the effectiveness of this effort.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123473824","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}
{"title":"Quotient space approachability basing on measure space and fusion model of weighted graphical structure","authors":"Hongbin Fang, Ling Zhang","doi":"10.1109/GRC.2006.1635856","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635856","url":null,"abstract":"In this paper, a fusion model of weighted semi-order spaces is presented as well as the quotient space approachability theorem basing on measure space is proved. Fusion theory is not only concerned with information fusion, but with space structure that topology relation among elements on the space exists. We consider inductive reasoning basing on granularity analysis is also a kind of fusion in light of the quotient space approachability theorem. While qualitative thing is uncertain to the corresponding quantitative thing basing on the theory of quotient space, the theory in the paper can be used to integrate qualitative probabilistic networks, as qualitative analogues to an original Bayesian networks, abstracting from the numerical detail.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115336391","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}
P. Ghosh, Samik Ghosh, K. Basu, Sajal K. Das, S. Daefler
{"title":"An analytical model to estimate the time taken for cytoplasmic reactions for stochastic simulation of complex biological systems","authors":"P. Ghosh, Samik Ghosh, K. Basu, Sajal K. Das, S. Daefler","doi":"10.1109/GRC.2006.1635762","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635762","url":null,"abstract":"The complexity of biological systems motivates the use of a computer or \"in silico\" stochastic event based modeling approach to better identify the dynamic interactions of different processes in the system. This requires the computation of the time taken by different events in the system based on their biological functions and corresponding environment. One such important event is the reactions between the molecules inside the cytoplasm of a cell where the reaction environment is highly chaotic. We present a mathematical formulation for the estimation of the reaction time between two molecules within a cell based on the system state assuming that the reactant molecules enter the system one at a time to initiate reactions. We derive expressions for the average and second moment of the time for reaction to be used by our stochastic event-based simulation. Unlike rate equations, the proposed model does not require the assumption of concentration stability for multiple molecule reactions. The reaction time estimate is considered to be a random variable that suits the stochastic event based simulation method.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124886375","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}
{"title":"Obfuscate arrays by homomorphic functions","authors":"W. Zhu, C. Thomborson, Fei-Yue Wang","doi":"10.1109/GRC.2006.1635914","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635914","url":null,"abstract":"As various computers are connected into a world wide network, software protections becomes a more and more im- portant issue for software users and developers. There are some technical measures for software protections, such as hardware- based protections and software-based techniques, etc. Software obfuscation is one of these measures. It protects software from unauthorized modification by making software more obscure so that it is hard for the potential attacker to understand the obfuscated software. Chow et al. use residue number technique to software obfuscation by encoding variables in the original program to hide the true meaning of these variables (1). There is some discussion about the division of residue numbers in (1), but, in order to lay a sound ground for this technique, we proposed homomorphic functions in (2) to deal with division by several constants in residue numbers. Data structures are important components of programme and they are key clues for people to understand codes. Obfuscating data structures of programme will make it very hard for an enemy to attack them. In this paper, we apply homomorphic functions to obfuscating the data structures of software.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121346157","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}
{"title":"An effective extension to okapi for biomedical text mining","authors":"Ming Zhong, Xiangji Huang","doi":"10.1109/GRC.2006.1635878","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635878","url":null,"abstract":"In biomedical text mining domain, a challenging problem is to identify the biological entity which has multiple forms of name. For this reason, the traditional IR system usually does not have a good performance. We propose an extension to Okapi information retrieval system so that it has the ability to identify the biological entity with multiple lexical variants. This extension integrates the Okapi system, an automatic query expansion algorithm and a new method for transforming a topic written in natural language into a structured query. Experiments on both 2004 and 2005 TREC Genomics data sets show that the proposed extension to Okapi is effective and competitive.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116371797","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}
{"title":"Mining parameters that characterize the communities in web-like networks","authors":"N. Deo, A. Cami","doi":"10.1109/GRC.2006.1635781","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635781","url":null,"abstract":"Community mining in large, complex, real-life networks such as the World Wide Web has emerged as a key data mining problem with important applications. In recent years, several graph theoretic definitions of community, generally motivated by empirical observations and intuitive arguments, have been put forward. However, a formal evaluation of the appropriateness of such definitions has been lacking. We present a new framework developed to address this issue, and then discuss a particular implementation of this framework. Finally, we present a set of experiments aimed at evaluating the effectiveness of two specific graph theoretic structures—alliance and near-clique—in capturing the essential properties of communities.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116562071","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}
{"title":"Generating and exploiting bayesian networks for fault diagnosis in airplane engines","authors":"M. Yavuz, F. Sahin, Z. Arnavut, Önder Uluyol","doi":"10.1109/GRC.2006.1635792","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635792","url":null,"abstract":"Bayesian Networks has been proven to be successful tool for fault diagnosis. There are a variety of approaches for learning the structure of Bayesian Networks from data. This learning problem has been proven to be NP-hard hence none of the approaches are exact when no prior knowledge about the domain of the variables exists. Our approach is based on searching the best network by using particle swarm optimization (PSO) technique. PSO is inherently parallel, works for large domains and does not trap into local maxima. This paper is an application of this technique to a real world problem; fault diagnosis of an airplane engine for oil related variables. It is implemented by our improved software written in C/C++ by using MPI on Linux. Our implementation has the advantages of being general, robust and scalable. Moreover neither expert knowledge, nor node ordering is necessary prior to the optimization. The datasets are generated by preprocessing oil related sensor readings of airplane engines taken during the approach phase of flights. Using this datasets and our software, we constructed Bayesian Networks of the oil related variables in an airplane engine for diagnostics and predictive purposes.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125777511","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}