Web usage mining has become the subject of exhaustive research, as its potential for Webbased personalized services, prediction of user near future intentions, adaptive Web sites, and customer profiling are recognized. Recently, a variety of recommendation systems to predict user future movements through Web usage mining have been proposed. However, the quality of recommendations in the current systems to predict user future requests in a particular Web site is below satisfaction. To effectively provide online prediction, we have developed a recommendation system called WebPUM, an action using Web usage mining system and propose a novel approach online prediction for classifying user navigation patterns to predict users' future intentions. The approach is based on the new graph partitioning algorithm to model user navigation patterns for the navigation patterns mining phase. Furthermore, longest common subsequence algorithm is used for classifying current user activities to predict user next movement. The proposed system has been tested on CTI and MSNBC datasets. The results show an improvement in the quality of recommendations. Furthermore, experiments on scalability prove that the size of dataset and the number of the users in dataset do not significantly contribute to the percentage of accuracy.
In pathologic conditions or poisoning states, iron overload can affect different tissues including liver. In this study, the prophylactic effect of deferoxamine and silymarin was compared in decreasing experimental ironoverload-induced hepatotoxicity in rats. The study was done in six groups of rats, which received drugs q2 days for 2 weeks. The rats in groups 1 to 6 received drugs, respectively: normal saline, iron dextran, iron dextran+deferoxamine (intraperitoneally), iron dextran+silymarin (orally), iron dextran+ silymarin (intraperitoneally), and iron dextran+deferoxamine (intraperitoneally)+silymarin (intraperitoneally). At the end of the study, blood was collected, and serum was separated for laboratory tests. The liver of rats was separated for iron measuring and tissue processing. The serum iron concentration and the serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) activity were determined. The numbers of necrotic hepatocytes were counted as quantity index tissue injury in light microscopic examination. The mean of serum and liver iron in group 2 was significantly greater than group 1. Liver iron was significantly decreased in other groups except group 4. Also serum iron was decreased in groups 3 to 6 compared to group 2 (nearly 400%). ALT activity in group 3 and AST activity in group 5 were significantly lesser than in other groups. The mean of necrotic hepatocytes in group 2 was significantly increased in comparison to group 1. This elevation was significantly prevented by deferoxamine and silymarin. The result of the present study shows that silymarin has a protective effect similar to deferoxamine on iron overload-induced hepatotoxicity.
The Internet is one of the fastest growing areas of intelligence gathering. During their navigation web users leave many records of their activity. This huge amount of data can be a useful source of knowledge. Advanced mining processes are needed for this knowledge to be extracted, understood and used. Web Usage Mining (WUM) systems are specifically designed to carry out this task by analyzing the data representing usage data about a particular Web Site. WUM can model user behavior and, therefore, to forecast their future movements. Online prediction is one web usage mining application. However, the accuracy of the prediction and classification in the current architecture of predicting users' future requests systems can not still satisfy users especially in Huge Web sites. To provide online prediction efficiently, we advance an architecture for online predicting in Web Usage Mining system and propose a novel approach based on LCS algorithm for classifying user navigation patterns for predicting users' future requests. The Excremental results show that the approach can improve accuracy of classification in the architecture.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.