322,974 research outputs found

    Rough sets theory for travel demand analysis in Malaysia

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    This study integrates the rough sets theory into tourism demand analysis. Originated from the area of Artificial Intelligence, the rough sets theory was introduced to disclose important structures and to classify objects. The Rough Sets methodology provides definitions and methods for finding which attributes separates one class or classification from another. Based on this theory can propose a formal framework for the automated transformation of data into knowledge. This makes the rough sets approach a useful classification and pattern recognition technique. This study introduces a new rough sets approach for deriving rules from information table of tourist in Malaysia. The induced rules were able to forecast change in demand with certain accuracy

    Log-canonical pairs and Gorenstein stable surfaces with KX2=1K_X^2=1

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    We classify log-canonical pairs (X,Δ)(X, \Delta) of dimension two with KX+ΔK_X+\Delta an ample Cartier divisor with (KX+Δ)2=1(K_X+\Delta)^2=1, giving some applications to stable surfaces with K2=1K^2=1. A rough classification is also given in the case Δ=0\Delta=0

    A note on a separating system of rational invariants for finite dimensional generic algebras

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    The paper deals with a construction of a separating system of rational invariants for finite dimensional generic algebras. In the process of dealing an approach to a rough classification of finite dimensional algebras is offered by attaching them some quadratic forms

    A breast cancer diagnosis system: a combined approach using rough sets and probabilistic neural networks

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    In this paper, we present a medical decision support system based on a hybrid approach utilising rough sets and a probabilistic neural network. We utilised the ability of rough sets to perform dimensionality reduction to eliminate redundant attributes from a biomedical dataset. We then utilised a probabilistic neural network to perform supervised classification. Our results indicate that rough sets was able to reduce the number of attributes in the dataset by 67% without sacrificing classification accuracy. Our classification accuracy results yielded results on the order of 93%
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