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Shing-Chung Ngan

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6 papers
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6

EAAI Journal 2013 Journal Article

Correlation coefficient of linguistic variables and its applications to quantifying relations in imprecise management data

  • Shing-Chung Ngan

We frequently use the standard correlation coefficient to quantify linear relation between two given variables of interest in crisp industrial data. On the other hand, in many real world applications involving the opinions of experts, the domain of a variable of interest, e. g. the rating of the innovativeness of a new product idea, is oftentimes composed of subjective linguistic concepts such as very poor, poor, average, good and excellent. In this article, we extend the standard correlation coefficient to the subjective, linguistic setting, so as to quantify relations in imprecise industrial and management data. Unlike the correlation measures for fuzzy variables proposed in the literature, the present approach allows one to develop a correlation coefficient for linguistic variables that can account for and reflect the conditional dependence assumptions underlying a given data set. We apply the proposed method to quantify the degree of correlation between technology and management achievements of 15 large-scale machinery firms in Taiwan. It is shown that the flexibility of the present framework in allowing for the incorporation of appropriate conditional dependence assumptions to derive a correlation measure for linguistic variables can be essential in approximate reasoning applications.

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AIIM Journal 2011 Journal Article

Investigating the enhancement of template-free activation detection of event-related fMRI data using wavelet shrinkage and figures of merit

  • Shing-Chung Ngan
  • Xiaoping Hu
  • Pek-Lan Khong

Objective We propose a method for preprocessing event-related functional magnetic resonance imaging (fMRI) data that can lead to enhancement of template-free activation detection. The method is based on using a figure of merit to guide the wavelet shrinkage of a given fMRI data set. Background Several previous studies have demonstrated that in the root-mean-square error setting, wavelet shrinkage can improve the signal-to-noise ratio of fMRI time courses. However, preprocessing fMRI data in the root-mean-square error setting does not necessarily lead to enhancement of template-free activation detection. Motivated by this observation, in this paper, we move to the detection setting and investigate the possibility of using wavelet shrinkage to enhance template-free activation detection of fMRI data. Methodology The main ingredients of our method are (i) forward wavelet transform of the voxel time courses, (ii) shrinking the resulting wavelet coefficients as directed by an appropriate figure of merit, (iii) inverse wavelet transform of the shrunk data, and (iv) submitting these preprocessed time courses to a given activation detection algorithm. Two figures of merit are developed in the paper, and two other figures of merit adapted from the literature are described. Results Receiver-operating characteristic analyses with simulated fMRI data showed quantitative evidence that data preprocessing as guided by the figures of merit developed in the paper can yield improved detectability of the template-free measures. We also demonstrate the application of our methodology on an experimental fMRI data set. Conclusions The proposed method is useful for enhancing template-free activation detection in event-related fMRI data. It is of significant interest to extend the present framework to produce comprehensive, adaptive and fully automated preprocessing of fMRI data optimally suited for subsequent data analysis steps.

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AIIM Journal 2002 Journal Article

Node merging in Kohonen’s self-organizing mapping of fMRI data

  • Shing-Chung Ngan
  • Essa S. Yacoub
  • William F. Auffermann
  • Xiaoping Hu

In this paper, Kohonen’s self-organizing mapping (SOM) is used as a data-driven technique for analyzing functional magnetic resonance imaging (fMRI) data. Upon the completion of an SOM analysis, a cluster merging technique, based on examining the reproducibility of the fMRI data across epochs, is utilized to merge SOM nodes whose feature vectors are sufficiently similar to one another. The resulting ‘super nodes’ give time course templates of potential interest. These templates can be subsequently used in traditional template-based analysis methods, such as cross-correlation analysis, yielding statistical maps and activation patterns. This technique has been demonstrated on two fMRI datasets obtained from a visually-guided motor paradigm and a visual paradigm, respectively, showing satisfactory results.

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