Modification of Sand Cat Swarm Optimization for Classification Problems
1
Issued Date
2024-01-01
Resource Type
Scopus ID
2-s2.0-85206568431
Journal Title
2024 5th International Conference on Big Data Analytics and Practices, IBDAP 2024
Start Page
113
End Page
117
Rights Holder(s)
SCOPUS
Bibliographic Citation
2024 5th International Conference on Big Data Analytics and Practices, IBDAP 2024 (2024) , 113-117
Suggested Citation
Pravesjit S., Kantawong K., Hunta S., Jitkongchuen D., Thammano A., Longpradit P. Modification of Sand Cat Swarm Optimization for Classification Problems. 2024 5th International Conference on Big Data Analytics and Practices, IBDAP 2024 (2024) , 113-117. 117. doi:10.1109/IBDAP62940.2024.10689683 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/101724
Title
Modification of Sand Cat Swarm Optimization for Classification Problems
Corresponding Author(s)
Other Contributor(s)
Abstract
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.