<<

References

[1] Goodfellow I, Bengio Y, Courville A. Deep Learning [Internet]. Cambridge, Massachusetts, USA: MIT Press;

2016. Available from: http://www. deeplearningbook.org

[2] Yoav S, Perrault R, Brynjolfsson E, Jack C, Legassick C.

Artificial Intelligence Index, 2017 Annual Report [Internet]. 2017. Available from: http:// aiindex.org/2017-report.pdf

[3] Kajaree D, Behera R. A survey on machine learning: Concept, algorithms and applications. International Journal of Electronics Communication and Computer Engineering. 2017;5: 1302-1309. DOI: 10.15680/ IJIRCCE.2017

[4] De Souza VMA, Silva DF, Batista GEAPA. Classification of data streams applied to insect recognition: Initial results. In: Proc—2013 Brazilian Conf Intell Syst, BRACIS 2013.

2013. pp. 76-81. DOI: 10.1109/ BRACIS.2013.21

[5] Silva DF, De Souza VMA, Batista GEAPA, Keogh E, Ellis DPW. Applying machine learning and audio analysis techniques to insect recognition in intelligent traps. In: Proceedings—2013 12th International Conference on Machine Learning and Applications, ICMLA 2013. 2013. pp. 99-104. DOI: 10.1109/ ICMLA.2013.24

[6] Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems. 2012;25(2):1-9. DOI: 10.1016/j.protcy.2014.09.007

[7] WHO. Global Vector Control Response 2017-2030—Background Document to Inform Deliberations during the 70th Session of the World Health Assembly. WHO. 2017. p. 47

[8] Park SI, Bisgin H, Ding H, Semey HG, Langley DA, Tong W, et al. Species identification of food contaminating beetles by recognizing patterns in microscopic images of elytra fragments. PLoS One. 2016;11:1-22. DOI: 10.1371/ journal.pone.0157940

[9] Pombi M, Guelbeogo WM, Calzetta M, Sagnon N, Petrarca V, La Gioia V,

et al. Evaluation of a protocol for remote identification of mosquito vector species reveals BG-sentinel trap as an efficient tool for Anopheles gambiae outdoor collection in Burkina Faso.

Malaria Journal. 2015;14:161. DOI: 10.1186/ s12936-015-0674-7

[10] Ouyang TH, Yang EC, Jiang JA, Lin TT. Mosquito vector monitoring system based on optical wingbeat classification. Computers and Electronics in Agriculture. 2015;118:47-55. DOI: 10.1016/j.compag.2015.08.021

[11] Utsugi J, Toshihide K, Motomi ITO. Current progress in DNA barcoding and future implications for entomology. Entomological Science. 2011;14:107-124. DOI: 10.1111/j.1479-8298.2011.00449.x

[12] Karthika P, Vadivalagan C, Thirumurugan D, Kumar RR, Murugan K, Canale A, et al. DNA barcoding of five Japanese encephalitis mosquito vectors (Culexfuscocephala, Culex gelidus, Culex Iritaeniorhynchus, Culex pseudovishnui and Culex vishnui). Acta Tropica. 2018;183:84-91. DOI: 10.1016/j. actatropica.2018.04.006

[13] Consoli RAGB, de Oliveira RL. Principais mosquitos de importancia sanitaria no Brasil. Rio de Janeiro, Brasil: Fundaςao Oswaldo Cruz; 1998. DOI: 10.1590/S0102-311X1995000100027

[14] Schaper S, Hernandez-Chavarria F. Scanning electron microscopy of the four larval instars of the Dengue

fever vector Aedes aegypti (Diptera: Culicidae). Revista de Biologia Tropical. 2006;54:847-852

[15] Kumar NP, Rajavel AR, Natarajan R, Jambulingam P. DNA barcodes can distinguish species of Indian mosquitoes (Diptera: Culicidae). Journal of Medical Entomology. 2007;44:1-7. DOI:

10.1603/0022-2585(2007)44[1:DBCDSO ]2.0.CO;2

[16] Lorenz C, Sergio A, Suesdek L. Artificial neural network applied as a methodology of mosquito species identification. Acta Tropica. 2015;152:165-169. DOI: 10.1016/j. actatropica.2015.09.011

[17] Reyes AMMDL, Reyes ACA, Torres

JL, Padilla DA, Villaverde J. Detection of Aedes Aegypti mosquito by digital image processing techniques and support vector machine. In: 2016 IEEE Region 10 Conference (TENCON). 2016. pp. 2342-2345. DOI: 10.1109/ TENCON.2016.7848448

[18] Sanchez-Ortiz A, Fierro-Radilla A, Arista-Jalife A, Cedillo-Hernandez M, Nakano-Miyatake M, Robles-Camarillo D, et al. Mosquito larva classification method based on convolutional neural networks. In: 2017 International Conference on Electronics, Communications and Computers (CONIELECOMP). 2017. pp. 1-6. DOI: 10.1109/CONIELECOMP.2017.7891835

<< |
Source: Savic Sara (ed.). Vectors and Vector-Borne Zoonotic Diseases. ITexLi,2019. — 110 p. 2019

More on the topic References: