UCSY's Research Repository

Browsing International Conference on Computer Applications (ICCA) by Author "Khaing, Myo"

Browsing International Conference on Computer Applications (ICCA) by Author "Khaing, Myo"

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  • A Detection and Prevention Technique on SQL Injection Attacks 
    Hlaing, Zar Chi Su Su; Khaing, Myo (Proceedings of the Eighteenth International Conference On Computer Applications (ICCA 2020), 2020-02-28)
    With the web advancements are rapidly developing, the greater part of individuals makes their transactions on web, for example, searching through data, banking, shopping, managing, overseeing and controlling dam ...
  • Effective Feature Selection for Preprocessing Step of Classification Using Modified-MCA 
    Khaing, Myo; Kham, Nang Saing Moon (Tenth International Conference On Computer Applications (ICCA 2012), 2012-02-28)
    A novel metric that integrates the correlation and reliability information between each feature and each class obtained from Multiple Correspondence Analysis (MCA) is currently the popular solution to score the features ...
  • Modified-MCA Based Feature Selection Model for Classification 
    Khaing, Myo; Kham, Nang Saing Moon (Ninth International Conference On Computer Applications (ICCA 2011), 2011-05-05)
    A central problem in machine learning is identifying a representative set of features from which to construct a classification model for a particular task. A good feature set that contains highly correlated features ...
  • Modified-MCA: An Effective Feature Selection 
    Khaing, Myo; Kham, Nang Saing Moon (Eleventh International Conference On Computer Applications (ICCA 2013), 2013-02-26)
    Feature selection is often an essential data processing step prior to applying a learning algorithm. The removal of irrelevant and redundant information improves the performance of machine learning algorithms. In this ...
  • Reducing the Size of Feature Set by Using Modified MCA 
    Khaing, Myo (Fourteenth International Conference On Computer Applications (ICCA 2016), 2016-02-25)
    Reducing the size of a feature set, without altering the original representation, is an essential data processing step prior to applying a learning algorithm. The removal of irrelevant and redundant information improves ...

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