PRINCIPAL COMPONENT ANALYSIS COMBINED WITH SECOND ORDER STATISTICAL FEATURE METHOD FOR MALARIA PARASITES CLASSIFICATION

Wahab, Iis Hamsir Ayub and Susanto, Adhi and Santosa, P. Insap and Tjokronegoro, Maesadji (2014) PRINCIPAL COMPONENT ANALYSIS COMBINED WITH SECOND ORDER STATISTICAL FEATURE METHOD FOR MALARIA PARASITES CLASSIFICATION. Journal of Theoretical and Applied Information Technology, 63 (1). pp. 219-225. ISSN 1817-3195

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Abstract

The main challenge in detecting malaria parasites is how to identify the subset of relevant features. The objective of this study was to identify a subset of features that are most predictive of malaria parasites using second-order statistical features and principal component analysis methods. Relevant features will provide the successful implementation of the overall detection modelling, which will reduce the computational and analytical efforts. The results showed that the combination of the principal components of the feature value the correlation to the ASM, and the contrast to the correlation can separate classes of malaria parasites.

Item Type:	Article
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Depositing User:	Dr iis hamsir ayub wahab
Date Deposited:	26 Nov 2022 19:12
Last Modified:	26 Nov 2022 19:12
URI:	http://repository.unkhair.ac.id/id/eprint/151

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PRINCIPAL COMPONENT ANALYSIS COMBINED WITH SECOND ORDER STATISTICAL FEATURE METHOD FOR MALARIA PARASITES CLASSIFICATION. (deposited 26 Nov 2022 19:12) [Currently Displayed]
- PRINCIPAL COMPONENT ANALYSIS COMBINED WITH SECOND ORDER STATISTICAL FEATURE METHOD FOR MALARIA PARASITES CLASSIFICATION. (deposited 16 Jan 2023 06:52)

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