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2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)最新文献

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Algorithm for Detection with Localization of Multi-targets in Wireless Acoustic Sensor Networks 无线声传感器网络中多目标定位检测算法
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.28
Jaechan Lim, Jinseok Lee, Sangjin Hong, P. Park
{"title":"Algorithm for Detection with Localization of Multi-targets in Wireless Acoustic Sensor Networks","authors":"Jaechan Lim, Jinseok Lee, Sangjin Hong, P. Park","doi":"10.1109/ICTAI.2006.28","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.28","url":null,"abstract":"In most multitarget tracking approaches based on joint probabilistic data association (JPDA), it is difficult to apply the solutions to problems (due to the dimensionality curse of heavy complexity) where the number of target varies dramatically. In this paper, we introduce an algorithm for detection of multitargets in wireless acoustic sensor networks (ADMAN); we localize detected targets by particle filtering after ADMAN. The purpose of ADMAN is detecting any number of targets (We know the approximate locations of targets during the detection algorithm.) in the field of interest. The advantage of ADMAN is its ability to cope with varying number of targets in time. ADMAN does not have any restrictions on the varying pattern of the target number","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126604642","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
A Multi-HMM Approach to ECG Segmentation 一种多hmm心电分割方法
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.17
Julien Thomas, C. Rose, F. Charpillet
{"title":"A Multi-HMM Approach to ECG Segmentation","authors":"Julien Thomas, C. Rose, F. Charpillet","doi":"10.1109/ICTAI.2006.17","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.17","url":null,"abstract":"Pharmaceutic studies require to analyze thousands of ECGs in order to evaluate the side effects of a new drug. In this paper we present a new approach to automatic ECG segmentation based on hierarchic continuous density hidden Markov models. We applied a wavelet transform to the signals in order to highlight the discontinuities in the modeled ECGs. A training base of standard 12-lead ECGs segmented by cardiologists was used to evaluate the performance of our method. We used a Bayesian HMM clustering algorithm to partition the training base, and we improved the method by using a multi-model approach. We present a statistical analysis of the results where we compare different automatic methods to the segmentation of the cardiologist","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114616575","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: 26
A Greedy Search Approach to Co-clustering Sparse Binary Matrices 稀疏二值矩阵共聚类的贪婪搜索方法
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.10
F. Angiulli, Eugenio Cesario, C. Pizzuti
{"title":"A Greedy Search Approach to Co-clustering Sparse Binary Matrices","authors":"F. Angiulli, Eugenio Cesario, C. Pizzuti","doi":"10.1109/ICTAI.2006.10","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.10","url":null,"abstract":"A co-clustering algorithm for large sparse binary data matrices, based on a greedy technique and enriched with a local search strategy to escape poor local maxima, is proposed. The algorithm starts with an initial random solution and searches for a locally optimal solution by successive transformations that improve a quality function which combines row and column means together with the size of the co-cluster. Experimental results on synthetic and real data sets show that the method is able to find significant co-clusters","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115687647","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
An Approximation to Mean-Shift via Swarm Intelligence 基于群体智能的均值漂移近似
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.30
M. Thomas, C. Kambhamettu
{"title":"An Approximation to Mean-Shift via Swarm Intelligence","authors":"M. Thomas, C. Kambhamettu","doi":"10.1109/ICTAI.2006.30","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.30","url":null,"abstract":"Mean shift based feature space analysis has been shown to be an elegant, accurate and robust technique. The elegance in this non-parametric algorithm is mainly due to its simplicity in performing gradient ascent to estimate the modes in a multidimensional data. One characteristic aspect of mean shift is that the mode estimation is performed at each data point. Since it is important to describe the data in as succinct manner as possible, it is important to focus on modal points in the data instead of every data point. In this paper, we attempt to tackle the mean shift problem through a \"mode centric\" approach using swarm intelligence. Here, the mode estimation is cast as a problem of goal seeking for the swarm as it moves through the multidimensional data space. Local maxima/minima and plateaus are avoided through information exchange between each member of the swarm, thereby converging at the mode values efficiently","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121536205","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
Face Recognition Using Multiple Classifiers 基于多分类器的人脸识别
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.59
P. Parveen, B. Thuraisingham
{"title":"Face Recognition Using Multiple Classifiers","authors":"P. Parveen, B. Thuraisingham","doi":"10.1109/ICTAI.2006.59","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.59","url":null,"abstract":"In this paper, we propose a near real-time effective face recognition system for consumer applications. Since the nature of application domain requires real time result and better accuracy, it poses a serious challenge. To address this challenge, we study various classification techniques, namely, support vector machine (SVM), linear discriminant analysis (LDA) and K nearest neighbor (KNN). We observe that although KNN is as effective as SVM but KNN prohibits its usage due to high response time when data is high dimensional. To speed up KNN retrieval, we propose a feature reduction technique using principle component analysis (PCA) to facilitate near real time face recognition along with better accuracy. We apply KNN after we reduce the number of features by PCA. Hence, we test various classification approaches, namely, SVM, KNN, KNN with PCA, LDA, and LDA with PCA on a benchmark dataset and demonstrate the effectiveness of KNN with PCA over SVM and LDA","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122744606","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: 70
Sequence Mining Without Sequences: A New Way for Privacy Preserving 无序列序列挖掘:一种新的隐私保护方法
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.103
Stéphanie Jacquemont, F. Jacquenet, M. Sebban
{"title":"Sequence Mining Without Sequences: A New Way for Privacy Preserving","authors":"Stéphanie Jacquemont, F. Jacquenet, M. Sebban","doi":"10.1109/ICTAI.2006.103","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.103","url":null,"abstract":"During the last decade, sequential pattern mining has been the core of numerous researches. It is now possible to efficiently discover users' behavior in various domains such as purchases in supermarkets, Web site visits, etc. Nevertheless, classical algorithms do not respect individual's privacy, exploiting personal information (name, IP address, etc.). We provide an original solution to privacy preserving by using a probabilistic automaton instead of the original data. An application in car flow modeling is presented, showing the ability of our algorithm to discover frequent routes without any individual information. A comparison with SPAM is done showing that even if we sample from the automaton, our approach is more efficient","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124126964","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
Mining Maximum Length Frequent Itemsets: A Summary of Results 挖掘最大长度频繁项集:结果摘要
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.84
Tianming Hu, Qian Fu, Xiaonan Wang, S. Sung
{"title":"Mining Maximum Length Frequent Itemsets: A Summary of Results","authors":"Tianming Hu, Qian Fu, Xiaonan Wang, S. Sung","doi":"10.1109/ICTAI.2006.84","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.84","url":null,"abstract":"The use of frequent itemsets has been limited by the high computational cost as well as the large number of resulting itemsets. In many real-world scenarios, however, it is often sufficient to mine a small representative subset of frequent itemsets with low computational cost. To that end, in this paper, we define a new problem of finding the frequent itemsets with a maximum length and present a novel algorithm to solve this problem. Indeed, maximum length frequent itemsets can be efficiently identified in very large data sets and are useful in many application domains. Our algorithm generates the maximum length frequent itemsets by adapting a pattern fragment growth methodology based on the FP-tree structure. Also, a number of optimization techniques have been exploited to prune the search space. Our experimental results show that our algorithm is very efficient and scalable","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126303122","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
Intelligent Software Measurement System for Automating the Goal-Question-Metrics Process 自动化目标-问题-度量过程的智能软件测量系统
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.70
Junling Huang, B. Far
{"title":"Intelligent Software Measurement System for Automating the Goal-Question-Metrics Process","authors":"Junling Huang, B. Far","doi":"10.1109/ICTAI.2006.70","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.70","url":null,"abstract":"Intelligent software measurement system (ISMS) has been developed to generate a software measurement plan towards a user's initial business goal. The ISMS is based on adapting two methodologies: (1) the normalized 10-step goal-driven software measurement process within the goal/question/metrics (GQM) paradigm; and (2) the software measurement knowledge base building based on the ISMS ontology. In order to take full advantages of these two methodologies, ISMS is designed to be a multiagent system (MAS). In this paper, the goal-driven software measurement process, the design and implementation of the ISMS knowledge base and the design and implementation of the MAS for ISMS are discussed","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126683796","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
Using Correlation to Improve Boosting Technique: An Application for Time Series Forecasting 利用相关改进Boosting技术:在时间序列预测中的应用
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.118
L. V. D. Souza, A. Pozo, Anselmo Chaves Neto
{"title":"Using Correlation to Improve Boosting Technique: An Application for Time Series Forecasting","authors":"L. V. D. Souza, A. Pozo, Anselmo Chaves Neto","doi":"10.1109/ICTAI.2006.118","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.118","url":null,"abstract":"Time series forecasting has been widely used to support decision making, in this context a highly accurate prediction is essential to ensure the quality of the decisions. Ensembles of machines currently receive a lot of attention; they combine predictions from different forecasting methods as a procedure to improve the accuracy. This paper explores genetic programming and boosting technique to obtain an ensemble of regressors and proposes a new formula for the final hypothesis. This new formula is based on the correlation coefficient instead of the geometric median used by the boosting algorithm. To validate this method, experiments were performed, the mean squared error (MSE) has been used to compare the accuracy of the proposed method against the results obtained by GP, GP using a boosting technique and the traditional statistical methodology (ARMA). The results show advantages in the use of the proposed approach","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131734075","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
Models and Methods for Discovering Automatable Activity Segments in a Service-Oriented Environment 在面向服务的环境中发现可自动化活动段的模型和方法
2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) Pub Date : 2006-11-13 DOI: 10.1109/ICTAI.2006.87
K. Laskey, Peter Brown
{"title":"Models and Methods for Discovering Automatable Activity Segments in a Service-Oriented Environment","authors":"K. Laskey, Peter Brown","doi":"10.1109/ICTAI.2006.87","DOIUrl":"https://doi.org/10.1109/ICTAI.2006.87","url":null,"abstract":"Although computers were invented in 1945, barely fifty years ago, their enormous value and impact on society has relegated much of computing discipline, fortunately or unfortunately, into a commodity status already. Not only are semiconductor-based computers inherently fast, the unique use of bootstrapping to use computers to design even faster computers has brought us to the precipitous edge of unprecedented computing performance and exceptionally powerful systems. As unintended consequences, while the pedagogical theories of computing have failed to keep pace with the rapid technological advances, of immense importance, a systematic approach to achieving reliable systems design despite complexity compounding on virtually daily basis, is sorely lacking. As a result, systems design is spinning out of control and urgently warrants a new approach to rein it in so that they may serve humanity in a safe and reliable manner. A quick analysis of the evolution of IBM Corporation is highly revealing of the challenges faced by the industry. IBM began its journey as a giant in computer hardware design including the legendary IBM 7044 and then quickly turned into a turn-key systems company with its proprietary operating system in the 1970s. By the late 1970s, the dawn of the VLSI age relegated computer hardware to the level of a commodity and IBM's desire to maintain a high profit margin forced it to turn to software as its core competency. The value of data and its manipulation became a dominant force in the marketplace and IBM emerged as a specialist in database-oriented products. Stiff competition from Oracle and other database vendors, and with the introduction of Microsoft's Access have steadily eroded IBM's cutting edge to the point where IBM is presently being forced to evolve into an entirely new company and a major player in the service industry","PeriodicalId":169424,"journal":{"name":"2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131515496","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: 0
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