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

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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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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Video Sequence-Based Iris Recognition Inspired by Human Cognition Manner

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Journal of Bionic engineering 2014年第3期11卷 481-489页

作者： Yuanning Liu College of Computer Seience and Technology Jilin University Changchun 130012 P. R. China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 P. R. China College of Software Nanchang HangKong University Nanchang 330063 P. R. China

In video sequence-based iris recognition system, the problem of making full use of relationship and correlation among frames still remains to be solved. A brand new template level multimodal fusion algorithm inspired by human cognition manner is proposed. In that a non-isolated geometrical manifold, named Hyper Sausage Chain due to its sausage shape, is trained using the frames from a pattern class for representing an iris class in feature space. We can classify any input iris by observing which manifold it locates in. This process is closer to the function of human being, which takes ＇matter cognition＇ instead of ＇matter classification＇ as its basic principle. The experiments on self-developed JLUBR-IRIS dataset with several video sequences per person demonstrate the effectiveness and usability of the proposed algorithm for video sequence-based iris recognition. Fur- thermore, the comparative experiments on public CASIA-I and CASIA-V4-Interval datasets show that our method can also achieve improved performance of image-based iris recognition system, provided enough samples are involved in training stage.

关键词： iris recognition video sequences multi-modal fusion bionic recognition hyper sausage neuron

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Generalized Extended tanh-function Method for Traveling Wave Solutions of Nonlinear Physical Equations

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Communications in Mathematical Research 2014年第1期30卷 60-70页

作者： CHANG JING GAO YI-XIAN AND CAI HUA Fundamental Department Aviation University of Air Force College of Mathematics and Statistics Northeast Normal University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University School of Mathematics Jilin University

In this paper, the generalized extended tanh-function method is used for constructing the traveling wave solutions of nonlinear evolution equations. We choose Fisher＇s equation, the nonlinear schr＆#168;odinger equation to illustrate the validity and ad-vantages of the method. Many new and more general traveling wave solutions are obtained. Furthermore, this method can also be applied to other nonlinear equations in physics.

关键词： generalized tanh-function method nonlinear Schrodinger equation,Fisher＇s equation

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computational prediction of human saliva-secreted proteins

Computational prediction of human saliva-secreted proteins

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10th International Symposium on Bioinformatics Research and Applications, ISBRA 2014

作者： Sun, Ying Zhou, Chunguang Wang, Jiaxin Cao, Zhongbo Du, Wei Wang, Yan Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun 130012 China Nari Group Corporation State Grid Electric Power Research Institute Nanjing Jiangsu 211000 China

ISBN: (纸本)9783319081700

Using proteins in saliva as biomarkers has great advantage in early diagnosis and prognosis evaluation of health conditions or diseases. In this article, we present a computational method for predicting secreted proteins in human saliva. Firstly, we collected currently known saliva-secreted proteins and the representatives that deem to be not extracellular secretion into saliva. Secondly, we pruned the negative data concerned the imbalance condition, and then extracted the relevant features from the physicochemical and sequence properties of all remained proteins. After that, a support vector machine classifier was built which got performance of average sensitivity, specificity, precision, accuracy and Matthews correlation coefficient value to 80.67%, 90.56%, 90.09%, 85.53% and 0.7168, respectively. These results indicated that the selected features and the model are effective. Finally, a screening test was implemented to all human proteins in UniProt and acquired 5811 proteins as predicted saliva-secreted proteins which may be used as biomarker candidates for further salivary diagnosis. © 2014 Springer International Publishing Switzerland.

关键词： Biomarkers

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