Sistema de apoio ao diagnóstico da Doença de Chagas baseado em escalogramas e redes neurais auto-organizáveis

Neto, João Souza; Carvalho, João L. A.; Rocha, Adson Ferreira da; Nascimento, Francisco Assis de Oliveira

doi:10.4322/rbeb.2013.027

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…The ANN-SOM has been used in several activities that require the creation of clusters -such as in those for the identification of ecoregions (Soares et al, 2014), exploratory analysis of multivariate geospatial data (Leal et al, 2015), regionalization of socioeconomic units, and territorial management (Silva et al, 2014a) -and in many others, from medical (Souza Neto et al, 2013) to economic sciences (Summa & Macrini, 2014). However, the used database should be without topological errors, overlaps or gaps, following a pattern already established by Infraestrutura Nacional de Dados Espaciais (INDE, 2014).…”

Section: Classes 11 and 12mentioning

confidence: 99%

Artificial neural network for ecological-economic zoning as a tool for spatial planning

Sadeck

Lima

Adami

2017

Pesq. agropec. bras.

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-The objective of this work was to analyze social and environmental information through an artificial neural network-self-organizing map (ANN-SOM), in order to provide subsidy to ecologicaleconomic zoning (EEZ) as a tool to reduce the subjectivity of the process. The study area comprises 16 municipalities in the northeast of the state of Pará, Brazil, representative of the agricultural development in the state. Data processing involved three steps: preparation of the data in a geographic information system (GIS) environment; mathematical processing (ANN-SOM) of the data; and visualization and interpretation of the processing results, allowing the spatial planning of northeastern Pará. The results comprised 13 classes, regrouped according to behavioral similarity criteria into four categories, which represent the main areas of sustainability proposed for the state of Pará, according to existing EEZ. The proposed methodology allows individualizing areas in the region that EEZ had not defined, mainly due to the greater possibility of combining and integrating a large number of physical, social, and economic variables through the SOM.Index terms: Amazon, regional planning, regionalization, self-organizing maps. Rede neural artificial para o zoneamento ecológico-econômico como instrumento de ordenamento territorialResumo -O objetivo deste trabalho foi analisar informações socioambientais por meio de rede neural artificial-mapa auto-organizável (RNA-SOM), para fornecer subsídio ao zoneamento ecológico econômico (ZEE) como instrumento para diminuir a subjetividade do processo. A área de estudo compreende 16 municípios do Nordeste Paraense, expressivos no desenvolvimento agropecuário do estado. O tratamento dos dados envolveu três etapas: preparação dos dados em ambiente de sistema de informação geográfica (SIG); processamento matemático (RNA-SOM) dos dados; e visualização e interpretação dos resultados dos processamentos, o que permitiu o ordenamento territorial do Nordeste Paraense. Os resultados compreenderam 13 classes, reagrupadas de acordo com critérios de similaridade de comportamento em quatro categorias, que representam os principais eixos de sustentabilidade propostos para o Estado do Pará, a partir do ZEE existente. A metodologia proposta permite individualizar zonas na região que o ZEE não havia definido, principalmente em razão da maior possibilidade de conjugar e integrar um grande número de variáveis físicas, sociais e econômicas por meio do SOM.Termos para indexação: Amazônia, planejamento regional, regionalização, mapas auto-organizáveis.

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Section: Classes 11 and 12mentioning

confidence: 99%

Artificial neural network for ecological-economic zoning as a tool for spatial planning

Sadeck

Lima

Adami

2017

Pesq. agropec. bras.

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Artificial Neural Network Methods Applied to Drug Discovery for Neglected Diseases

Scotti

Ishiki²,

Júnior³

et al. 2015

CCHTS

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Among the chemometric tools used in rational drug design, we find artificial neural network methods (ANNs), a statistical learning algorithm similar to the human brain, to be quite powerful. Some ANN applications use biological and molecular data of the training series that are inserted to ensure the machine learning, and to generate robust and predictive models. In drug discovery, researchers use this methodology, looking to find new chemotherapeutic agents for various diseases. The neglected diseases are a group of tropical parasitic diseases that primarily affect poor countries in Africa, Asia, and South America. Current drugs against these diseases cause side effects, are ineffective during the chronic stages of the disease, and are often not available to the needy population, have relative high toxicity, and face developing resistance. Faced with so many problems, new chemotherapeutic agents to treat these infections are much needed. The present review reports on neural network research, which studies new ligands against Chagas' disease, sleeping sickness, malaria, tuberculosis, and leishmaniasis; a few of the neglected diseases.

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Sistema de apoio ao diagnóstico da Doença de Chagas baseado em escalogramas e redes neurais auto-organizáveis

Cited by 2 publications

References 24 publications

Artificial neural network for ecological-economic zoning as a tool for spatial planning

Artificial neural network for ecological-economic zoning as a tool for spatial planning

Artificial Neural Network Methods Applied to Drug Discovery for Neglected Diseases

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