A Cumulative Training Approach to Schistosomiasis Vector Density Prediction

Terence Fusco, Yaxin Bi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages3-13
Number of pages11
Volume475
Publication statusPublished - 2 Sep 2016
EventArtificial Intelligence Applications and Innovations - Greece
Duration: 2 Sep 2016 → …

Conference

ConferenceArtificial Intelligence Applications and Innovations
Period2/09/16 → …

Fingerprint

Learning systems
Data mining
Satellites

Keywords

  • Schistomsomiasis
  • Cumulative Training Approach
  • Data Imputation
  • Correlation Co-efficient
  • Co-efficient of Determination

Cite this

Fusco, T., & Bi, Y. (2016). A Cumulative Training Approach to Schistosomiasis Vector Density Prediction. In Unknown Host Publication (Vol. 475, pp. 3-13)
Fusco, Terence ; Bi, Yaxin. / A Cumulative Training Approach to Schistosomiasis Vector Density Prediction. Unknown Host Publication. Vol. 475 2016. pp. 3-13
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title = "A Cumulative Training Approach to Schistosomiasis Vector Density Prediction",
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.",
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author = "Terence Fusco and Yaxin Bi",
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Fusco, T & Bi, Y 2016, A Cumulative Training Approach to Schistosomiasis Vector Density Prediction. in Unknown Host Publication. vol. 475, pp. 3-13, Artificial Intelligence Applications and Innovations, 2/09/16.

A Cumulative Training Approach to Schistosomiasis Vector Density Prediction. / Fusco, Terence; Bi, Yaxin.

Unknown Host Publication. Vol. 475 2016. p. 3-13.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - 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.

AB - 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.

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KW - Data Imputation

KW - Correlation Co-efficient

KW - Co-efficient of Determination

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Fusco T, Bi Y. A Cumulative Training Approach to Schistosomiasis Vector Density Prediction. In Unknown Host Publication. Vol. 475. 2016. p. 3-13