The weight in TOPSIS approach (technique for order preference by similarity ideal solution -TOPSIS ) is given by experts or decision makers. The value of weight would be influenced by experts' subjective judgments. A slight difference in value of weight may result in diversity of order of alternatives. In this paper, a Bayesian method for decision of weight for MADM model with interval data is introduced. The value of weight is decided by prior information (other experts' knowledge, or numerical simulation etc.) and experts' knowledge (or decision makers' experience/ preference). This method effectively takes advantage of experts' knowledge and avoids the problem with experts' subjectivity. An illustrative example is showed to explore the applications of proposed method. The method is valuable for field of multi-attribute decisionmaking with interval data.
Internet congestion control can be respected as a nonlinear feedback system with uncertainties and communication delay. According to this characteristic, a self-configuring Active Queue Management (AQM) controller is proposed utilizing the predominance of fuzzy theory in dealing with the uncertain event and its stability is analyzed using Lyapunov Direct method, and the performance is analyzed using NS-2 simulation system. Compared with other traditional method, the algorithm is more reasonable, stable and robust. This electronic document is a "live" template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.
In general, Intelligent Tutoring Systems (ITS) fail to take account of the emotional and cognitive states of the students who use them. This paper explores the relationship between emotion and cognition when students learn via the medium of video lectures. A cognitive emotional model was constructed to determine the student's cognitive and emotional state while watching an instructional video. This model was a Bayesian belief network (BBN) model. With the method of ten times 10-fold cross-validation, evaluation results showed that the Bayesian network classifies the emotion state with 60% accuracy and classifies both the emotion and cognitive state with 48.82% accuracy. This model provides an emotional and cognitive states recognition solution for video lecture learners in a non-intrusive way with low cost.
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