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Lookup NU author(s): Professor Paolo Missier
This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by Springer, 2018.
For re-use rights please refer to the publisher's terms and conditions.
Zika and Dengue are viral diseases transmitted by infected mosquitoes (Aedes aegypti) found in warm, humid environments. Mining data from social networks helps to find locations with highest density of reported cases. Differently from approaches that analyze text from social networks, we present a new strategy analyzing Instagram images. We use two customized Deep Neural Networks. The first detects objects commonly used for mosquito reproduction with 85% precision. The second differentiates mosquitoes as Culex or Aedes aegypti with 82.5% accuracy. Results indicate that both networks can improve the effectiveness of current social network mining strategies such as the VazaZika project.
Author(s): Barros HP, Lima BGC, Crispim FC, Vieira T, Missier P, Fonseca B
Editor(s): Campilho A; Karray F: ter Haar Romeny B
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 15th International Conference on Image Analysis and Recognition
Year of Conference: 2018
Print publication date: 31/07/2018
Online publication date: 06/06/2018
Acceptance date: 02/04/2018
Date deposited: 08/07/2018
Library holdings: Search Newcastle University Library for this item
Series Title: Lecture Notes in Computer Science