Abstract:
Because terrorist organizations often operate in network forms where individual terrorists collaborate with each other to carry out attacks, we could gain valuable knowledge about the terrorist organizations by studying structural properties of such terrorist networks. However, previous studies of terrorist network structure have generated little actionable results. This is due to the difficulty in collecting and accessing reliable data and the lack of advanced network analysis methodologies in the field. To address these problems, we introduced the Web structural mining technique into the terrorist network analysis field which, to the best our knowledge, has never been done before. We employed the proposed technique on a Global Salafi Jihad network dataset collected through a large scale empirical study. Results from our analysis not only provide insights for terrorism research community but also support decision making in law-reinforcement, intelligence, and security domains to make our nation safer.
Abstract:
Prediction of gene functions is a major challenge to biologists in the post-genomic era. Interactions between genes and their products compose networks and can be used to infer gene functions. Most previous studies used heuristic approaches based on either local or global information of gene interaction networks to assign unknown gene functions. In this study, we propose a graph kernel-based method that can capture the structure of gene interaction networks to predict gene functions. We conducted an experimental study on a test-bed of P53-related genes. The experimental results demonstrated better performance for our proposed method as compared with baseline methods. © 2007 IEEE.
PMID: 7657730;Abstract:
This research examined the applicability of using a neural network approach to the estimation of aqueous activity coefficients of aromatic organic compounds from fragmented structural information. A set of 95 compounds was used to train the neural network, and the trained network was tested on a set of 31 compounds. A comparison was made between the results and those obtained using multiple linear regression analysis. With the proper selection of neural network parameters, the backpropagation network provided a more accurate prediction of the aqueous activity coefficients for testing data than did regression analysis. This research indicates that neural networks have the potential to become a useful analytical technique for quantitative prediction of structure-activity relationships. © 1995 American Chemical Society.
Abstract:
Over the past decade the development of digital library activities within Asia Pacific has been steadily increasing. Through a meta-analysis of the publications and content within International Conference on Asian Digital Libraries (ICADL) and other major regional digital library conferences over the past few years, we see an increase in the level of activity in Asian digital library research. This reflects high continuous interest among digital library researchers and practitioners internationally. Digital library research in the Asia Pacific is uniquely positioned to help develop digital libraries of significant cultural heritage and indigenous knowledge and advance cross-cultural and cross-lingual digital library research. © Springer-Verlag Berlin Heidelberg 2005.
