Thammano, Arit

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Thammano, Arit
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Thammano, A.
Thummano, Arit
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arit.th@kmitl.ac.th

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    Item type:Publication,
    Modification of Sand Cat Swarm Optimization for Classification Problems
    (2024-01-01)
    Pravesjit, Sakkayaphop
    ;
    Kantawong, Krittika
    ;
    Hunta, Sathien
    ;
    Jitkongchuen, Duangjai
    ;
    Thammano, Arit
    The proposed system represents an enhanced version in search of food of Sand Cat based on Levy distribution and Firework algorithm for the image classification of grape leaf diseases. In the preprocessing step, the proposed system utilizes convolution kernels to transform images into input data within the range of (0,1). Successively, the Levy distribution and Firework algorithm are incorporated into the SCSO model as an exploration search mechanism. The study employed a grape leaf dataset sourced from the Plant Village project (www.plantvillage.org), comprising 4062 labeled images measuring 256 by 256 pixels and categorized into 4 distinct classes: healthy, Black Rot, Black Measles, and Isariopsis leaf spot, which was utilized to evaluate the efficacy of the proposed system. The experimental findings demonstrate that the proposed system outperforms the analyses of VGG16, GLCM with SVM, Low contrast haze reduction-neighborhood component analysis with SVM, and SCSO.
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    Item type:Publication,
    An Improvement on Exploration Step of Whale Optimization Algorithm with Levy Distribution for Classification Problems
    (2024-01-01)
    Pravesjit, Sakkayaphop
    ;
    Kantawong, Krittika
    ;
    Kamkhad, Natdanai
    ;
    Sabaiporn, Saksit
    ;
    Monchanuan, Jantawan
    The proposed system represents an enhanced movement in search of food of Whale Optimization Algorithm (WOA), based on Levy distribution for image classification of grape leaf disease. In the preprocessing, the proposed system uses convolution kernels to transform images into input data within the range of (0,1). Thereafter, the Levy distribution is incorporated into the WOA model as an exploration search mechanism. A grape leaf dataset from the Plant Village project (www.plantvillage.org), consisting of 4062 labeled images with dimensions of 256 by 256 pixels and divided into four classes -healthy, Black Rot, Black Measles, and Isariopsis leaf spot -is used to evaluate the performance of the proposed system. Experimental results show that the proposed system is better than Visual Geometry Group (VGG16), Gray Level Co-occurrence Matrix (GLCM) with SVM, Low contrast haze reduction-neighborhood component analysis with SVM, and whale optimization algorithm (WOA).