In this paper, we introduce some new signature schemes and secret sharing schemes from the known Gröbner basis cryptosystems. Then, we discuss the issues related to the security of these schemes. Finally, we draw...
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The increase of microarray experiments brings a fresh challenge to analyze microarray data across datasets. Several methods have been developed and implemented. But the current tools were either complicated software w...
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In this paper, a bionic optimization algorithm based dimension reduction method named Ant Colony Optimization -Selection （ACO-S） is proposed for high-dimensional datasets. Because microarray datasets comprise tens o...
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In this paper, a bionic optimization algorithm based dimension reduction method named Ant Colony Optimization -Selection （ACO-S） is proposed for high-dimensional datasets. Because microarray datasets comprise tens of thousands of features （genes）, they are usually used to test the dimension reduction techniques. ACO-S consists of two stages in which two well-known ACO algorithms, namely ant system and ant colony system, are utilized to seek for genes, respectively. In the first stage, a modified ant system is used to filter the nonsignificant genes from high-dimensional space, and a number of promising genes are reserved in the next step. In the second stage, an improved ant colony system is applied to gene selection. In order to enhance the search ability of ACOs, we propose a method for calculating priori available heuristic information and design a fuzzy logic controller to dynamically adjust the number of ants in ant colony system. Furthermore, we devise another fuzzy logic controller to tune the parameter （q0） in ant colony system. We evaluate the performance of ACO-S on five microarray datasets, which have dimensions varying from 7129 to 12000. We also compare the performance of ACO-S with the results obtained from four existing well-known bionic optimization algorithms. The comparison results show that ACO-S has a notable ability to＂ generate a gene subset with the smallest size and salient features while yielding high classification accuracy. The comparative results generated by ACO-S adopting different classifiers are also given. The proposed method is shown to be a promising and effective tool for mining high-dimension data and mobile robot navigation.
In this paper, we propose a novel image retrieval method based on mutual information descriptors (MIDs). Under the physiological property of human eyes and human visual perception theory, MIDs are extracted to encode ...
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Most researches on co-authorship network analyze the author's information globally according to the overall network topology structure, instead of analyzing the author's local network. Therefore, this paper pr...
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Most researches on co-authorship network analyze the author's information globally according to the overall network topology structure, instead of analyzing the author's local network. Therefore, this paper presents a community mining algorithm and divides big co-authorship network into small communities, in which entities' relationship is closer. Then we mine central authors in community by three different centrality standards including closeness centrality, eigenvector centrality and a new proposed measure termed extensity degree centrality. We choose the SIGMOD data as datasets and measure the centrality from different views. And experiments in co-authorship network achieve many interesting results, which indicate our technique is efficient and feasible, and also have reference value for scientific evaluation.
In this paper we use an improved Particle Swarm Optimization algorithm to solve Multiple Sequence Alignment (MSA). MSA is a key problem in bioinformatics. The thesis starts with the theory of Particle swarm optimizati...
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A text mining algorithm named HMM-TFM (Hidden Markov Model based transcription factor name mining) is presented. The proposed algorithm does not need a dictionary of transcription factor names. A small verb set is def...
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In this paper, a Loop Tightness Algorithm (LTA) is proposed. First, it finds the network loops and calculates it tightness value quickly. Then, it obtains the communities of the networks based on the tightness values....
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In this paper, a Loop Tightness Algorithm (LTA) is proposed. First, it finds the network loops and calculates it tightness value quickly. Then, it obtains the communities of the networks based on the tightness values. Finally, it reveals the relationship between the network loops and the community structure. The LTA is tested and validated by means of synthetic networks and real networks.
We tackle the problem of improving the helpful actions technique for contingent planning. A revision of the heuristic computation that rearranges the relaxed planning graph is proposed, where the unreasonable conforma...
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In this paper, on the basis of principal component analysis, we use least squares support vector machine (LS-SVM) to predict tRNA. Appearance frequencies of single nucleotide, 2-nucleotides, (G-C)% and (A-T)% were cho...
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