Artificial Intelligence Applications and Innovations 12th IFIP WG 12.5 International Conference and Workshops, AIAI 2016 Thessaloniki, Greece, September 16–18, 2016
1University of Ulster (University of Ulster Cromore Road Coleraine Co. Londonderry BT52 1SA - Ireland)
Abstract : The purpose of this paper is to propose a framework of building classification models to deal with the problem in predicting Schistosomiasis vector density. We aim to resolve this problem using remotely sensed satellite image extraction of environment feature values, in conjunction with data mining and machine learning approaches. In this paper we assert that there exists an intrinsic link between the density and distribution of the Schistosomiasis disease vector and the rate of infection of the disease in any given community; it is this link that the paper is focused to investigate. Using machine learning techniques, we want to accumulate the most significant amount of data possible to help with training the machine to classify snail density (SD) levels. We propose to use a novel cumulative training approach (CTA) as a way of increasing the accuracy when building our classification and prediction model.
https://hal.inria.fr/hal-01557630
Contributor : Hal Ifip
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Submitted on : Thursday, July 6, 2017 - 1:55:28 PM
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Terence Fusco, Yaxin Bi. A Cumulative Training Approach to Schistosomiasis Vector Density Prediction. 12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2016, Thessaloniki, Greece. pp.3-13, ⟨10.1007/978-3-319-44944-9_1⟩. ⟨hal-01557630⟩
