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- W2898020995 abstract "The ability to identify urban locations with a high risk of diseases infections is a central aspect of public policies aiming at controlling these diseases. In this paper, we investigate the use of street-level images, such as those from Google Street View, along with Convolutional Neural Networks to predict Dengue Fever (DF) and Dengue Hemorrhagic Fever (DHF) rates in urban locations. We conduct a case study in the city of Rio De Janeiro, Brazil, using the proposed methodology and DF/DHF data between the years of 2010 to 2014. We compare two Siamesebased CNNs, yielding an overall accuracy of 67% for 20,400 different locations. We conclude that street-level images are useful for the problem." @default.
- W2898020995 created "2018-10-26" @default.
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- W2898020995 date "2018-07-01" @default.
- W2898020995 modified "2023-09-30" @default.
- W2898020995 title "Towards Predicting Dengue Fever Rates Using Convolutional Neural Networks and Street-Level Images" @default.
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- W2898020995 doi "https://doi.org/10.1109/ijcnn.2018.8489567" @default.
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