Titlebook: Intelligent and Evolutionary Systems; The 19th Asia Pacific Kittichai Lavangnananda,Somnuk Phon-Amnuaisuk,Jona Conference proceedings 2016 T

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A Logical Model of Communication Channelsns arise about the nature of such a statement as well as about the attitude of the agent herself in doing these announcements. Does the agent know whether the statement is true? Is this agent announcing that statement or its opposite in any other channel? Extensions to Dynamic Epistemic Logics have

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Recursive Binary Tube Partitioning for Classificationcision tree built using a recursive partitioning algorithm. This paper applies the recursive partitioning technique based on the series of tubes. A tube is identified from three information; 1) a core vector, 2) a tube length and 3) a tube radius. The first component is the core vector generated by

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Extreme-Centroid Tree for Outlier Detection In the real-world datasets, the majority data points normally line up into patterns that can be captured by some models. In this paper, we propose the new outlier detection algorithm based on the dynamically updated tree model. It composes of two-step processes (1) constructing the extreme-centroid

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Gaussian Fuzzy Integral Based Classificationjection is along with a group of linear lines. In reality, data distribution is not regular, so the straight line for projection is too limited. Gaussian function is applied to natural science widely. It is close to normal distribution and can cover more data. In this article, a new generalization o

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Prediction of Student Dropout Using Personal Profile and Data Mining Approachedicting dropout occurences with the first-year students and determine the factors behind these cases. Despite several classification techniques being made available in the literature, the current study focuses on using decision trees and rule induction models to discover knowledge from data of stud

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The Optimization of Parallel DBN Based on Sparkneck problem when training DBN in a single computational node. And traditional parallel full-batch gradient descent exists the problem that the speed of convergence is slow when we use it to train DBN. To solve this problem, the article proposes a parallel mini-batch gradient descent algorithm based