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Accurate identification of influential nodes facilitates the control of rumor propagation and interrupts the spread of computer viruses. Many classical approaches have been proposed by researchers regarding different aspects. To explore the impact of location information in depth, this paper proposes an improved global structure model to characterize the influence of nodes. The method considers both the node’s self-information and the role of the location information of neighboring nodes. First, degree centrality of each node is calculated, and then degree value of each node is used to represent self-influence, and degree values of the neighbor layer nodes are divided by the power of the path length, which is path attenuation used to represent global influence. Finally, an extended improved global structure model that considers the nearest neighbor information after combining self-influence and global influence is proposed to identify influential nodes. In this paper, the propagation process of a real network is obtained by simulation with the SIR model, and the effectiveness of the proposed method is verified from two aspects of discrimination and accuracy. The experimental results show that the proposed method is more accurate in identifying influential nodes than other comparative methods with multiple networks.

This paper presents a wavelet coding preprocessing algorithm which adopts threshold values based on Bayesian estimation for image compression. The proposed algorithm considers a modification to the quantization stage of set partioning in hierarchical trees (SPIHT) (Said and Pearman, 1996), which achieves better peak signal to noise ratio (PSNR) and visual quality performance than existing embedded wavelet coders and JPEG. The performance of the proposed algorithm is demonstrated with extensive experimental results.

This paper puts forward the use of granularity calculating of quotient space theory dealing with classification problems about machine learning. According to the prior knowledge or the clustering of the training examples, training data are reorganized with granularity to form new instances, and to learn from the new instances. The different sorts of instances, which are combined in granularity, are classified through different layers of classifiers. In this way, the difficulty of the learning is reduced, the capacity of learning from instances is increased, and the classifying accuracy is improved. At the same time, the method can recognize the different sorts of instances, which features are very similar, and improve its generalization of recognition, and reduce the complicacy of calculation. The detailed procedures of the method using covering algorithm and its experimental results are presented. The results show that the method is effective.

In this paper, we compared the advantages and disadvantages of rough set theory (RS) and neighborhood covering algorithm (NCA). A classification method which combines RS with NCA is proposed. Theoretical analysis shows the rationality, feasibility of our method. Experimental results of the recognition of wireless communication signals are given as an example to show the efficiency and practicability of this approach.

This paper discusses Moise cable transmitters' manual dexterity differences using pattern recognition. Firstly, using amplitude and frequency filtering of the frequency sampling voice signals results in the extraction of the characteristics of demodulated base band signal. Secondly, the calculation of the minimum space distance allows recognition of the manual techniques differences of Moise cable transmitters.