International Journal of Intelligent Engineering and Systems

8

Authors: Dennis Arturo Ludeña Romaña, Shinichiro Kubota, Kenichi Sugitani, Yasuo Musashi

We carried out an entropy study on the DNS query traffic from the outside of a university campus network to the top domain DNS server when querying about reverse resolution on the PC room terminals through April 1st, 2007 to April 30th, 2008. The following interesting results are given: (1) The total DNS query traffic changes in a mild manner until January 16th, 2008, however it drastically changes after January 17th, 2008. (2) In January 17th, 2008, the DNS query traffic is mainly dominated by several specific IP addresses as their query keywords. (3) We carried out forensic analysis on the PC room terminals in which IP addresses are found in the several specific keywords and it is concluded that the PCs become spam bots when inserting USB based key disk storage.

8

Authors: Qiongshui Wu, Libo Zeng, Ningning Guo

Counting of different classes of white blood cells in bone marrow smears can provide pathologists with valuable information regarding various hematological disorders. For automatic imaging analysis techniques, precise segmentation of White Blood Cells is quite challenging due to the complex contents in bone marrow smears. Far more different from traditional color imaging analysis method, we introduced multispectral imaging techniques. After an image with high quality was acquired, the spectrum of each pixel was directly fed into a trained Support Vector Machine (SVM) for classification, then morphological binary operations were performed to correct the small error-classified regions. Masses of experiments show that our multispectral imaging method is device-independent to a large extent, and the segmentation results are highly satisfactory and inspiring. It proves that the introduction of multispectral imaging analysis techniques into White Blood Cells detection is a success. Multispectral imaging analysis is a promising technique in biomedicine.

10

Authors: Suojun Lu, Jian'an Fang, Qingying Miao

The topology of a network often plays a crucial role in determining its dynamical features. There is increasing interest in trying to understand the relationship between the structural properties of networks and their behaviors. However, this problem has not been fully considered in many neural networks, which mimic certain biological processes. Here, we construct a recurrent network model to simulate the decision-making process of brain. In this model, we examined the effects of network topology on the performance of decision-making by constructing three different topological networks: the regular network, the random network, and the small-world network. We found that the regular network and the small-world network show significant better performance of decision-making than that in the random network when the internal noise of the networks is low. However, following the increase of the internal noise, the random network, instead of two other networks, shows better ability to resist the noise. Finally, to mimic neurodegeneration or neural injury, we introduced two types of neuronal damages: clustered damage and distributed damage. We found that three networks exhibit different network behaviors in the case of neuronal damages. The regular network and the small-world network display severe decrease of the performance in the distributed damage pattern, but not in the clustered damage pattern. The random network shows similarly gradual decrease of the performance in both damage patterns. Furthermore, the small-world network shows the best performance in the high levels of distributed damage. Together, our results indicate that network topology significantly influence the network behaviors in our model of decision-making.