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2010 3rd International Conference on Advanced Computer Theory and engineering, ICACTE 2010

作者： Zhou, Chunbao Wan, Liming Liang, Yanchun College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun 130012 China Research Institute on General Development and Evaluation of Equipment EAAF of PLA Beijing 100076 China

ISBN: (纸本)9781424465408

Classification and prediction of different cancers based on gene expression profiles are important for cancer diagnosis, cancer treatment and medication discovery. The k nearest neighbor algorithm (k-NN) is one easy and efficient machine learning method for cancer classification and the parameter k is crucial. In this paper, we integrate minimum spanning tree (MST) and k nearest neighbor algorithm (k-NN) for cancer classification. The MST is designed for the selection of parameter k and the nearest neighbors for k-NN. Firstly we build a minimum spanning tree (MST) based on Euclidean distance between each two samples for gene expression data only including one unknown class sample. Secondly for unknown class sample in the gene expression data, we find the connected samples and then apply majority vote principle. Thirdly if there are tied votes then we expend the connected samples with the nearest neighbors for unknown class sample until all the samples are expended or the class for unknown sample is obtained. This hybrid algorithm is referred to as MSTNN. The hybrid algorithm MSTNN is compared with kNN and other 3 existing classification algorithms on CNS dataset, Colon dataset and Lung dataset, 3 binary class gene expression datasets and 3 multi-class gene expression datasets: Leukemial, Leukemia2, and Leukemia3 *** human cancers. The MSTNN algorithm improves 5.65% better than k-NN on average LOOCV accuracy and 13.80% better than kNN on testing datasets classification average accuracy, and achieves the best performance in all the 5 algorithms. The results demonstrate that the proposed MSTNN algorithm is feasible to classify human cancers. © 2010 IEEE.

关键词： Classification (of information)

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Semi-automatic hardware design using ontologies

Semi-automatic hardware design using ontologies

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8th International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Hu, He Liu, Da-You Du, Xiao-Yong School of Information Renmin University of China Beijin 100872 China Open Symbol Computation and Knowledge Engineering Laboratory College of Computer Science and Technology Jilin University Changchun 130012 China

ISBN: (纸本)0780386531

Hardware description language (HDL) is widely used to model the structure and behavior of digital systems ranging from simple hardware building blocks to complete systems in today's hardware design. To tackle with ever-increasing complexity of process models, higher level of abstraction needs to be exploited. We propose an ontology-based architecture in the process of hardware design using HDLs. The architecture combines the traditional HDL design processes with ontology models and offers a semi-automatic process utilizing the ontology reasoning abilities of OWL (Web Ontology Language) Based on such ontology assistant architecture, more flexible and automatic processes can be devised, which are of crucial importance to meet current market demands. © 2004 IEEE.

关键词： Computer hardware description languages

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Simulating the Folding Pathway of RNA Secondary Structure Using the Modified Ant Colony Algorithm

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Journal of Bionic engineering 2010年第4期7卷 382-389页

作者： Jun Yu~(1,2),Changhai Zhang~(1,2),Yuanning Liu~(1,2),Xin Li~(1,2) 1.College of Computer Science and Technology,Jilin University,Changchun 130012,P.R.China 2.key laboratory of symbolic computation and knowledge engineering (Ministry of Education,China) Jilin University,Changchun 130012,P.R.China [a] College of Computer Science Technology Jilin University Changchun 130012 P. R. China [b] Key Laboratory of Symbolic Computation Knowledge Engineering (Ministry of Education China) Jilin University Changchun 130012 P. R. China

A new method for simulating the folding pathway of RNA secondary structure using the modified ant colony algorithmis *** a given RNA sequence,the set of all possible stems is obtained and the energy of each stem iscalculated and stored at the initial ***,a more realistic formula is used to compute the energy ofmulti-branch loop in the following *** a folding pathway is simulated,including such processes as constructionof the heuristic information,the rule of initializing the pheromone,the mechanism of choosing the initial andnext stem and the strategy of updating the pheromone between two different *** by testing RNA sequences withknown secondary structures from the public databases,we analyze the experimental data to select appropriate values *** measure indexes show that our procedure is more consistent with phylogenetically proven structures thansoftware RNAstructure sometimes and more effective than the standard Genetic Algorithm.

关键词： RNA secondary structure folding pathway ant colony algorithm

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A novel method to measure graph similarity 12

A novel method to measure graph similarity

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12th IEEE International Conference on E-Business engineering, ICEBE 2015

作者： Wang, Xu Ouyang, Jihong College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

ISBN: (纸本)9781467380027

A novel method to measure the graph similarity is proposed, where the labels, in-degrees, and out-degrees of the vertices in the graph are comprehensively considered in order to conquer the high complexity and information loss in the measurement process. In this proposal, the graph is decomposed into a generalized tree and its similarity can be measured by computing the cost of the node sequence triples in the generalized tree. The proposed method decreases the computational complexity to O(n2) in comparison with the other available graph similarity methods. An example is provided to illustrate that the method is feasible and effective. © 2015 IEEE.

关键词： Forestry

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Interval-valued fuzzy description logic IFALCN - Preliminary results

Interval-valued fuzzy description logic IFALCN - Preliminary...

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2010 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2010

作者： Huo, Linlin Ouyang, Jihong Liu, Dayou Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9781424465835

To represent and reason with interval-value information of applications in description logic, based on interval-fuzzy set the classical des cription logic ALCN is extended to the fuzzy description logic IFALCN. Its' syntax, semantics and fuzzy tableau algorithm are presented in detail. Our work enhances the expressiveness and reasoning ability of ALCN. IFALCN is the generalization of fuzzy ALCN based on single value and more expressive than the latter and can conform to human cognition better. ©2010 IEEE.

关键词： Fuzzy sets

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A novel method for RNA secondary structure prediction

A novel method for RNA secondary structure prediction

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2011 7th International Conference on Natural computation, ICNC 2011

作者： Xing, Chong Ji, Zhaohua Wang, Gang Wang, Yao Shen, Wei Liang, Yanchun Key Laboratory for Symbol Computation and Knowledge Engineering of National Education MinistryCollege of Computer Science and TechnologyJilin University Changchun China College of Information Engineering Guanghua College of Changchun University Changchun China College of Communications Engineering Jilin University Changchun China

ISBN: (纸本)9781424499533

In this paper, we propose PSOfold, a particle swarm optimization for RNA secondary structure prediction. PSOfold is based on the recently published IPSO. We present two strategies to improve the performance of IPSO. Firstly, in order to boost the competence in searching an optimal solution, fuzzy logic control is used to adaptively adjust the parameters in PSO. Accordingly, three fuzzy logic controls are designed by which the inertia weight, learning factors and the number of ants are tuned respectively. Secondly, to further settle the stem permutation problem, we put forward a solution conversion strategy (SCS), which can transform discrete values of stems into an ordered stem combination, thereby supplying an enhanced solution to evaluation of objective function. An evaluation of the performance of PSOfold in terms of prediction accuracy is made via comparison with one dynamic programming algorithm mfold and four metaheuristics, IPSO, ACRNA, RnaPredict, SARNA-Predict and mfold for ten individual known structures. PSOfold is able to predict structures with higher prediction accuracy than the other metaheuristic based methods on certain sequences, and has comparable performance compared with mfold. © 2011 IEEE.

关键词： Particle swarm optimization (PSO)

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Support vector machine regression algorithm based on chunking incremental learning

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ICCS 2006: 6th International Conference on computational Science

作者： Jingqing, Jiang Chuyi, Song Chunguo, Wu Maurizio, Marchese Yangchun, Liang Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China College of Mathematics and Computer Science Inner Mongolia University for Nationalities Tongliao 028043 China Key Laboratory of Information Science and Engineering Beijing Jiaotong University Railway Ministry Beijing 100044 China Italy

ISBN: (纸本)3540343792

On the basis of least squares support vector machine regression (LSSVR), an adaptive and iterative support vector machine regression algorithm based on chunking incremental learning (CISVR) is presented in this paper. CISVR is an iterative algorithm and the samples are added to the working set in batches. The inverse of the matrix of coefficients from previous iteration is used to calculate the regression parameters. Therefore, the proposed approach permits to avoid the calculation of the inverse of a large-scale matrix and improves the learning speed of the algorithm. Support vectors are selected adaptively in the iteration to maintain the sparseness. Experimental results show that the learning speed of CISVR is improved greatly compared with LSSVR for the similar training accuracy. At the same time the number of the support vectors obtained by the presented algorithm is less than that obtained by LSSVR greatly. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Learning systems

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An improved ant colony optimization algorithm based on route optimization and its applications in travelling salesman problem

An improved ant colony optimization algorithm based on route...

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7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE

作者： Zhang, Yi Pei, Zhi-Li Yang, Jin-Hui Liang, Yan-Chun College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Department of Computer Science Jilin Business and Technology College College of Mathematics and Computer Science Inner Mongolia University for Nationalities

ISBN: (纸本)1424415098

In this paper, we introduce two improvements on Ant Colony Optimization (ACO) algorithm: route optimization and individual variation. The first is an optimized implementation of ACO, by which the running time of ants routing is largely reduced. The results of the simulated experiments show that the improved algorithm not only reduces the number of routing in the ACO but also surpasses existing algorithms in performance in solving large-scale TSP problems. In the second improvement, we introduce individual variation to ACO, by which the ants have different routing strategies. Simulation results show that the speed of convergence of ACO algorithm could be enhanced greatly. ©2007 IEEE.

关键词： Ant colony optimization

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Multiple Auxiliary Information Based Deep Model for Collaborative Filtering

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Journal of Computer Science & Technology 2018年第4期33卷 668-681页

作者： Lin Yue Xiao-Xin Sun Wen-Zhu Gao Guo-Zhong Feng Bang-Zuo Zhang School of Information Science and Technology Northeast Normal University Changchun 130117 China Key Laboratory of Applied Statistics of Ministry of Education Northeast Normal University Changchun 130024 China School of Environment Northeast Normal University Changchun 130117 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China

With the ever-growing dynamicity, complexity, technique is proposed and becomes one of the most effective and volume of information resources, the recommendation techniques for solving the so-called problem of information overload. Traditional recommendation algorithms, such as collaborative filtering based on the user or item, only measure the degree of similarity between users or items with single criterion, i.e., ratings. According to the experience of previous studies, single criterion cannot accurately measure the similarity between user preferences or items. In recent years, the application of deep learning techniques has gained significant momentum in recommender systems for better understanding of user preferences, item characteristics, and historical interactions. In this work, we integrate plot information as auxiliary information into the denoising autoencoder （DAE）, called SemRe-DCF, which aims at learning semantic representations of item descriptions and succeeds in capturing fine-grained semantic regularities by using vector arithmetic to get better rating prediction. The results manifest that the proposed method can effectively improve the accuracy of prediction and solve the cold start problem.

关键词： semantic representation plot information denoising autoencoder collaborative filtering auxiliary information

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RMADV-Hop: An improved DV-Hop localization algorithm

RMADV-Hop: An improved DV-Hop localization algorithm

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7th International Conference on Information Technology - New Generations, ITNG 2010

作者： Yi, Tingting Fang, Zhiyi Li, Ruixue College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun 130012 China

ISBN: (纸本)9780769539843

In recent years, with the development of the wireless sensor networks, the localization method receives the attention of many researchers. However, due to the network cost and characteristics of sensor nodes, most of the localization algorithms are not well suitable for the wireless sensor network, positioning error is relatively large. Based on the regular moving anchor node and RSSI ranging technique assisting positioning, in this paper, we proposed the RMADV-Hop (Regular Moving Anchor DV-Hop) localization algorithm to improve the DV-Hop algorithm. In the same network condition, we simulated these two localization algorithms using the omnet++ simulation software. Simulation results show that RMADV-Hop algorithm uses less anchor nodes than the DV-Hop algorithm, reduces the cost of network hardware, approximately achieves the evenly distribution of anchor nodes, and can get higher positioning accuracy than the DV-Hop algorithm in the randomly distributed network. © 2010 IEEE.

关键词： Sensor nodes

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