Título :	Semi-supervised text classification framework : an overview of Dengue landscape factors and satellite earth observation
Autor :	Li, Zhichao Gurgel, Helen da Costa Dessay, Nadine Hu, Luojia Xu, Lei Gong, Peng
metadata.dc.identifier.orcid:	https://orcid.org/ 0000-0002-4250-6742 https://orcid.org/ 0000-0003-2566-2118 https://orcid.org/ 0000-0003-1513-3765
Assunto::	Dengue Paisagem Observação terrestre por satélite Aprendizagem ativa profunda Processamento de linguagem natural
Fecha de publicación :	2020
Editorial :	MDPI
Citación :	LI, Zhichao et al. Semi-supervised text classification framework : an overview of Dengue landscape factors and satellite earth observation. International Journal of Environmental Research and Public Health, [S.l.], v. 17, n. 12, 4509, 2020. DOI: https://doi.org/10.3390/ijerph17124509. Disponível em: https://www.mdpi.com/1660-4601/17/12/4509. Acesso em: 13 jan. 2022.
Abstract:	In recent years there has been an increasing use of satellite Earth observation (EO) data in dengue research, in particular the identification of landscape factors affecting dengue transmission. Summarizing landscape factors and satellite EO data sources, and making the information public are helpful for guiding future research and improving health decision-making. In this case, a review of the literature would appear to be an appropriate tool. However, this is not an easy-to-use tool. The review process mainly includes defining the topic, searching, screening at both title/abstract and full-text levels and data extraction that needs consistent knowledge from experts and is time-consuming and labor intensive. In this context, this study integrates the review process, text scoring, active learning (AL) mechanism, and bidirectional long short-term memory (BiLSTM) networks, and proposes a semi-supervised text classification framework that enables the efficient and accurate selection of the relevant articles. Specifically, text scoring and BiLSTM-based active learning were used to replace the title/abstract screening and full-text screening, respectively, which greatly reduces the human workload. In this study, 101 relevant articles were selected from 4 bibliographic databases, and a catalogue of essential dengue landscape factors was identified and divided into four categories: land use (LU), land cover (LC), topography and continuous land surface features. Moreover, various satellite EO sensors and products used for identifying landscape factors were tabulated. Finally, possible future directions of applying satellite EO data in dengue research in terms of landscape patterns, satellite sensors and deep learning were proposed. The proposed semi-supervised text classification framework was successfully applied in research evidence synthesis that could be easily applied to other topics, particularly in an interdisciplinary context.
Licença::	(CC BY) © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
DOI:	https://doi.org/10.3390/ijerph17124509
Aparece en las colecciones:	Artigos publicados em periódicos e afins

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