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International Journal of Multimedia and Ubiquitous engineering 2014年第12期9卷 375-382页

作者： Liu, Yubao Qin, Guihe College of Computer Science and Technology Jilin University Changchun China College of Computer Science and Technology Changchun University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

A new algorithm combined Eagle Strategy with PSO is proposed. The new algorithm performs by two phases: First Eagle Strategy is used to do global search;Second PSO algorithm is used to do fast local search around a promising solution. The balance of global search and local search is considered simultaneously. It is not only help to jump out of local optimum but also accelerate local convergence. Experimental results on three benchmark problems illustrate that the presented approach is effective, efficient and accurate for solving reliability redundancy optimization problems. Copyright © 2014 SERSC

关键词： Nonlinear programming

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Comparative Study on Metaheuristic-Based Feature Selection for Cotton Foreign Fibers Recognition

Comparative Study on Metaheuristic-Based Feature Selection f...

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The 9th International Conference on Computer and Computing Technologies in Agriculture (CCTA 2015)

作者： Xuehua Zhao Xueyan Liu Daoliang Li Huiling Chen Shuangyin Liu Xinbin Yang Shaobin Zhan Wenyong Zhao School of Digital Media Shenzhen Institute of Information Technology Key Laboratory of Symbolic Computation and Knowledge Engineer (Jilin University) Ministry of Education College of Information and Electrical Engineering China Agricultural University College of Physics and Electronic Information Wenzhou University College of Information Guangdong Ocean University

ISBN: (纸本)9783319483566

The excellent feature set or feature combination of cotton foreign fibers is great significant to improve the performance of machine-vision-based recognition system of cotton foreign fibers. To find the excellent feature sets offoreign fibers, in this paper presents three metaheuristic-based feature selection approaches for cotton foreign fibers recognition, which are particle swarm optimization, ant colony optimization and genetic algorithm, respectively. The k-nearest neighbor classifier and support vector machine classifier with k-fold cross validation are used to evaluate the quality offeature subset and identify the cotton foreign fibers. The results show that the metaheuristic-based feature selection methods can efficiently find the optimal feature sets consisting of a few features. It is highly significant to improve the performance of recognition system for cotton foreign fibers.

关键词： Metaheuristic Feature selection Foreign fibers Recognition system Feature extraction recognition system Fiber Fibrous materials Cotton Support Vector Network

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A new image fusion approach using IHS and wavelet transform for CBF and CT images

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Journal of computational Information Systems 2014年第17期10卷 7571-7578页

作者： Meng, Qingming Du, Qinsheng Li, Xiongfei Zhang, Xiaoli College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Computer Science and Technology Changchun University Changchun China

The paper aims at tackling the problem of image fusion for panchromatic CT and multispectral CBF images. We proposed a fusion algorithm based on Intensity-hue-saturation (IHS) transform and Discrete Wavelet Transform (DWT) in the paper. We use different fusion rules for different parts of the images. The area energy is adopted to fuse the high frequency parts of the original images. While, for the low frequency parts, weighted averaging is applied. Experimental results show that the proposed algorithm is not only competent for retaining the spatial resolution of the panchromatic image, but also solves the problem of spectral distortions.

关键词： Discrete wavelet transforms

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Clustering dynamic XML based on conspicuous frequent substructure

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Journal of computational Information Systems 2014年第18期10卷 8067-8074页

作者： Ding, Tienan Li, Xiongfei Li, Wei Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China School of Economics and Management Changchun University of Science and Technology Changchun China

XML document is dynamic and dynamic XML document collection cluster analysis is a hot research topic. This paper proposed a data model named TDOM to record the dynamic changing process of XML document, then proposed a method to discover all the conspicuous frequent substructures from TDOM dataset, finally, proposed a method to cluster the XML documents by the conspicuous frequent substructures. The experiment runs on the synthetic dataset, the experimental result shows that our method is efficiency and scalable.

关键词： Data mining

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Difference transform and its application to multi-focus image fusion

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Journal of computational Information Systems 2014年第10期10卷 4245-4252页

作者： Zhang, Xiaoli Li, Xiongfei Feng, Yuncong Zhao, Haoyu Liu, Zhaojun Key Laboratory of Symbolic Computation and Knowledge Engineering for Ministry of Education Jilin University Changchun 130012 China College of Computer Science and Technology Jilin University Changchun 130012 China College of Software Jilin University Changchun 130012 China

We propose a new multi-focus image fusion algorithm which is based on Difference Transform (DT). In this paper, we investigate the use of Difference Transform in image focus detection in Laplacian Pyramid and Discrete Wavelet domain. The interests of the transform involve easy implementation and intuition. The multi-focus image fusion algorithm based on DT includes three steps: decomposition, coefficients combination and reconstruction. DT is adopted in the second step to select coefficients in high frequency sub-bands. We test the fusion algorithms on a group of widely used testing images, and the results indicate that DT-based image fusion algorithms are good alternatives. 1553-9105/Copyright © 2014 Binary Information Press.

关键词： Image fusion

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Recognizing event in short text based on decision tree

Recognizing event in short text based on decision tree

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作者： Lu, Jing Ya Zuo, Wan Li Zhu, Liang College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education of China China

ISBN: (纸本)9783038351399

Mining newsworthy events from a large number of microblogging information is not only the primary problem that several big microblogging websites need to solve, but also a new research field in micro-information age. For now, a lot of study about even recognizing has been made at home and abroad, but relatively rarely contrapose short text(microblogging message). The paper considers newsworthy event recognizing in short text as classification problem, utilizes the decision tree classification algorithm in data mining, sufficiently mines features of event in short text, and then recognizes the newsworthy event in microblogging. In the last, we verify the effect of the model. © (2014) Trans Tech Publications, Switzerland.

关键词： Data mining

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A new model for image fusion using bi-dimensional empirical mode decomposition with directional information

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Journal of Information and computational Science 2014年第10期11卷 3461-3468页

作者： Zhao, Haoyu Zhang, Xiaoli Li, Xiongfei Zang, Xuebai Key Laboratory of Symbolic Computation and Knowledge Engineering for Ministry of Education Jilin University Changchun 130012 China College of Software Jilin University Changchun 130012 China College of Computer Science and Technology Jilin University Changchun 130012 China

Image fusion is a procedure in which two or more images of one scene captured by different sensors are combined into one image. The target of image fusion is to produce images which are more suitable for human visual perception and further machine-processing. Empirical Mode Decomposition (EMD) is a new tool for signal (especially non-stationary signal) analysis. Comparing to other multi-scale transforms, EMD is more suitable for detecting information of images. Therefore, we propose a new model for image fusion based on Bi-dimension EMD (BEMD). In addition, considering that the existing BEMD can hardly capture the directional information which is important for image fusion, directional filter bank will be employed to run on each level coefficients in the step of image decomposition. The proposed method is tested on medical images and compared with some newly-proposed image fusion models. The experimental results show that the proposed method outperforms others in terms of several fusion evaluation metrics. 1548-7741/Copyright © 2014 Binary Information Press.

关键词： Image fusion

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Predicting user influence based on improved linear threshold model in social networks

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Journal of computational Information Systems 2014年第14期10卷 6151-6160页

作者： Tian, Zhongsheng Wang, Ying Zhao, Qiuyue Zuo, Wanli College of Software Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China Key Lab. of Symbolic Computation and Knowledge Engineering Attached to the Ministry of Education Changchun China

With the scale of social networks growing rapidly, the amount of user participating in it increases at astonishing speed. Predicting user influence in social networks is an interesting and useful research direction. There are lots of works on this area, mainly covering the following aspects: Maximize Influence, Influence Diffusion, Predicting Influential Users and Predicting social influence. The common issue of above aspects is that they can't calculate the precise user influence. In this work, we propose a novel method to give each user influence a precise value in social networks. The proposed method is based on Improved Linear Threshold Model (ILTM). To build ILTM, we take a series of measures to overcome the deficiencies brought by Linear Threshold Model, such as assigning random threshold to node, cold start. So we adopt user similarity instead of random threshold and utilize cluster algorithm and link analysis algorithm to surmount cold start. © 2014 by Binary Information Press

关键词： Forecasting

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Implicit knowledge discovery in biomedical ontologies: Computing interesting relatednesses

Implicit knowledge discovery in biomedical ontologies: Compu...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Tian Bai Leiguang Gong Casimir A. Kulikowski Lan Huang Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China Yantai Intelligent Information Technologies Ltd. Yantai China Department of Computer Science Rutgers The State University of New Jersey Piscataway NJ USA

Ontologies, seen as effective representations for sharing and reusing knowledge, have become increasingly important in biomedicine, usually focusing on taxonomic knowledge specific to a subject. Efforts have been made to uncover implicit knowledge within large biomedical ontologies by exploring semantic similarity and relatedness between concepts. However, much less attention has been paid to another potentially helpful approach: discovering implicit knowledge across multiple ontologies of different types, such as disease ontologies, symptom ontologies, and gene ontologies. In this paper, we propose a unified approach to the problem of ontology based implicit knowledge discovery - a Multi-Ontology Relatedness Model (MORM), which includes the formation of multiple related ontologies, a relatedness network and a formal inference mechanism based on set-theoretic operations. Experiments for biomedical applications have been carried out, and preliminary results show the potential value of the proposed approach for biomedical knowledge discovery.

关键词： Biological system modeling Biomedical measurement Ontologies Arthritis Diseases

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Identifying influential nodes in complex networks for network immunization

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Journal of computational Information Systems 2014年第20期10卷 8767-8774页

作者： Zhong, Lu Gao, Chao Zhang, Zili Shi, Ning Huang, Jiajin College of Computer and Information Science Southwest University Chongqing China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun China School of Information Technology Deakin University VIC Australia International WIC Institute Beijing University of Technology Beijing China

Identifying influential nodes is of theoretical significance in network immunization which is one of important methods to prevent virus propagation through protecting the influential nodes in a network. Lots of methods have been proposed to find these influential nodes based on the topological characteristics of a network (e.g., degree, betweenness or K-shell). Whereas due to the diversity of network topologies, these methods are not always effective in identifying influential nodes in any benchmark networks. We combine the advantages of existing methods based on attribute ranking and propose a universal ranking method, namely MAF (Multiple Attribute Fusion), to identify influential nodes from a complex network. We compare the efficiency of our proposed method with existing immunization strategies in different types of networks. Simulation results in the interactive email model show that the immunized nodes selected by MAF can restrain virus propagation effectively.

关键词： Complex networks

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