Current Issue : July - September Volume : 2015 Issue Number : 3 Articles : 4 Articles
This paper presents the evaluation method of the effect range from the package insert of medicine with text mining. Most\nof the people who take the over the counter medicines cannot understand the medicinal effects. This is because they have\nlittle knowledge of the medicine. The ingredients of the over the counter medicines are made from prescription products. The\nprescription product shows various laboratory findings to obtain authorization from the Ministry of Health, Labour andWelfare.\nThe medical information is described in the package insert of the medicine, and anyone is available. It is possible to evaluate the\neffect of the ingredient in the over the counter medicines, if we analyzed the medical information described in this package insert\nof the medicine with text mining method. This paper, we focus on both the antipyretic and the antitussive among lots of the over\nthe counter medicines and do intensive research with them. However, variability is observed in the results of the analysis. We\nanalyze the effect of magnification of each experimental configuration. Since the size of this fluctuation range comes from the\nexperimental configuration. Therefore, we summarize the medication type and the experimental data of the medicine in each\nexperimental configuration. As a result, we succeed in the effective range of medicine with text mining, if we extracted the\nexperimental configuration and analyzed above summarization....
Mining web log datasets has been extensively studied using Frequent Pattern Mining (FPM) and its various other forms. Identifying\nfrequent patterns in different sequences can help in analyzing the most common sub-sequences (e.g., the pages visited\ntogether). However, this approach would not be able to identify general structures spanning over multiple sequences. In response\nto understanding general structures, we introduce a new form of sequential pattern mining called super-sequence frequent pattern\nmining (SS-FPM). In contrast to sub-sequences determined by FPM, SS-FPM determines the super-sequences that can contain\nthe common parts from different sequences. This can be useful in understanding the general behavior/flow of users in web usage\nmining, classifying web pages and users, making predictions etc. In essence, finding frequent super-sequence patterns turns\nout to be related to the well-known heaviest (longest) path problem in graphs, which is known to be NP-hard. Accordingly,\nwe transform a given sequential dataset into a sequence graph and formulate the problem as k-hop heaviest path problem. We\nthen propose an efficient heuristic called sequence matrix method using dynamic programming techniques. We compared our\nmethod to the existing Heavypath method. The results show that our method is more efficient especially on large datasets....
Agent-based computational models represent a big challenge in many disciplines. A vital approach receiving much interest is\nagent-based models, which gives a new area providing some ways to tackle some of the restrictions of the analytical models\nin finance. The aim of our research is to contribute to the behavioral finance and agent-based artificial markets by studying\ntheir market-wise implications using computational simulations. We investigate and analyze the behavioral foundations of the\nstylized facts of empirical data such as that characterize real data in financial markets. Our results confirm the existence of most\nthe stylized facts such as leptokurtosis, non-independently distributed, and volatility clustering. From this attention, the artificial\nfinancial market will for all time be evaluated in order to have explication about market dynamics in Tunisian financial market....
Artificial neural network (ANN) theory is emerging as an alternative to conventional statistical methods in modeling nonlinear\nfunctions.The popular Cox proportional hazard model falls short in modeling survival data with nonlinear behaviors. ANN is a\ngood alternative to the Cox PHas the proportionality of the hazard assumption andmodel relaxations are not required. In addition,\nANN possesses a powerful capability of handling complex nonlinear relations within the risk factors associated with survival time.\nIn this study,we present a comprehensive comparison of two different approaches of utilizingANNinmodeling smooth conditional\nhazard probability function.We use real melanoma cancer data to illustrate the usefulness of the proposedANNmethods.We report\nsome significant results in comparing the survival time of male and female melanoma patients....
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