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In this paper, we present a novel technique of building hybrid decision support systems which integrates traditional decision support systems with agent based models for use in breast cancer analysis for better prediction and recommendation. Our system is based on using queries from data (converted to a standardized electronic template) to provide for simulation variables in an agent-based model....
Unsupervised data mining techniques require human intervention for understanding and analysis of the clustering results. This becomes an issue in dynamic users/applications and there is a need for real-time decision making and interpretation. In this paper we will present an approach to automate the annotation of results obtained from data stream clustering to facilitate interpreting that whether...
In this paper, the precisions of the logistic regression, naive-Bayes and linear data classification methods, with regard to the area under curve (AUC) metric have been compared. The effect of parameters including size of the dataset, kind of the independent attributes, number of the discrete attributes, and their values have been investigated. From the results, it can be concluded that in datasets...
Research and development (R&D) project selection is a complex decision-making process. Risks and uncertainties are also associated with the investments and returns of R&D projects. This research paper illustrates an application of ANP (analytic network process). The analytic network process (ANP) is presented as a potentially valuable method to support the selection of projects in a research...
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