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Sequential Minimal Optimization Algorithm with Support Vector Machine for Mosquito Larvae Identification

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Efficient and fast mosquito larvae identification is of important due to the cost, effort and time taken during manual processes. This paper evaluates the use of the sequential minimal optimization algorithm (SMO) employed with support vector machine (SVM) to improve the identification process that takes into account mosquito larva images. All images of Aedes and Culex are transformed to binary values with the application of pre-processing steps. The comparison results are based on computational experiments of the linear kernel, polynomial kernel, and Gaussian Radial Basis Function (RBF) kernel settings. The findings of RBF offer a better performance compared to linear and polynomial.
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Keywords: Larvae Identification; Linear Kernel; Polynomial Kernel; Sequential Minimal Optimization Algorithm; Support Vector Machine

Document Type: Research Article

Affiliations: 1: Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia 2: Medical Entomology Unit, Infectious Diseases Research Centre, Institute for Medical Research, 50588, Jalan Pahang, Kuala Lumpur, Malaysia

Publication date: May 1, 2017

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  • ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.
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