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A COMPARISON BETWEEN SYNTHETIC OVER- SAMPLING EQUILIBRIUM AND OBSERVED SUBSETS OF DATA FOR EPIDEMIC VECTOR CLASSIFICATION

Fusco, Terence, Bi, Yaxin, Nugent, Chris D and Wu, Shingli (2015) A COMPARISON BETWEEN SYNTHETIC OVER- SAMPLING EQUILIBRIUM AND OBSERVED SUBSETS OF DATA FOR EPIDEMIC VECTOR CLASSIFICATION. In: Dragon 3 symposium. ESA Communications.. ESA SP-724. 1 pp. [Conference contribution]

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Abstract

In this work we provide results of data mining and machine learning techniques, which form the basis of our prediction model for snail density classification in relation to the Schistosomiasis epidemic disease. All experiments to date are cognitive components in the development of our prediction model for the epidemic disease Schistosomiasis. This disease is detrimental to the health of the communities of affected areas as well as the crop and cattle life. If detected for early warning of the disease, the local communities can be better prepared to deal with any consequences of a breakout. This report gives an insight into the relationship between using a snapshot sample of environment data for epidemic disease vector classification, as opposed to the construction of an increased synthetic dataset.

Item Type:Conference contribution (Poster)
Keywords:Earth observation, Vector born disease, Epidemic vector classification
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute > Smart Environments
Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:38992
Deposited By: Dr Yaxin Bi
Deposited On:15 Nov 2017 15:10
Last Modified:15 Nov 2017 15:10

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