Lim Eng Aik
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Lim Eng Aik
Official Name
Lim, Eng Aik
Alternative Name
Aik, Limeng
Lim, E. A.
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Scopus Author ID
23491376300
Researcher ID
EKL-8415-2022

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  • Publication
    K-means Algorithm Based on Flower Pollination Algorithm and Calinski-Harabasz Index
    ( 2023-01-01)
    Lim Eng Aik
    ;
    Tan Wee Choon
    ;
    Mohd Syafarudy Abu
    Aiming at the problems that the Flower Pollination (FP) algorithm is easy to fall into the local optimum, the searchability is weak, and the k-means algorithm is easily affected by the selection of the initial clustering centre, a k-means algorithm based on the FP algorithm is proposed. Six benchmark functions test the improved FP algorithm. The effectiveness of the k-means algorithm based on the improved FP algorithm was tested and verified with UCI machine learning and artificial datasets. The verification results showed that the improved FP algorithm improved based on ensuring a faster convergence speed. Compared with other algorithms, the performance of this algorithm has been significantly improved in all aspects.
  • Publication
    The SIR model of Zika virus disease outbreak in Brazil at year 2015
    ( 2017-05-19)
    Lim Eng Aik
    ;
    Lam Chee Kiang
    ;
    Tan Wei Hong
    ;
    Mohd Syafarudy Abu
    This research study demonstrates a numerical model intended for comprehension the spread of the year 2015 Zika virus disease utilizing the standard SIR framework. In modeling virulent disease dynamics, it is important to explore whether the illness spread could accomplish a pandemic level or it could be eradicated. Information from the year 2015 Zika virus disease event is utilized and Brazil where the event began is considered in this research study. A three dimensional nonlinear differential equation is formulated and solved numerically utilizing the Euler's method in MS excel. It is appeared from the research study that, with health intercessions of public, the viable regenerative number can be decreased making it feasible for the event to cease to exist. It is additionally indicated numerically that the pandemic can just cease to exist when there are no new infected people in the populace.