Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Detecting Aedes Aegypti Mosquitoes through Audio Classification with
  Convolutional Neural Networks   The incidence of mosquito-borne diseases is significant in under-developed
regions, mostly due to the lack of resources to implement aggressive control
measurements against mosquito proliferation. A potential strategy to raise
community awareness regarding mosquito proliferation is building a live map of
mosquito incidences using smartphone apps and crowdsourcing. In this paper, we
explore the possibility of identifying Aedes aegypti mosquitoes using machine
learning techniques and audio analysis captured from commercially available
smartphones. In summary, we downsampled Aedes aegypti wingbeat recordings and
used them to train a convolutional neural network (CNN) through supervised
learning. As a feature, we used the recording spectrogram to represent the
mosquito wingbeat frequency over time visually. We trained and compared three
classifiers: a binary, a multiclass, and an ensemble of binary classifiers. In
our evaluation, the binary and ensemble models achieved accuracy of 97.65%
($\pm$ 0.55) and 94.56% ($\pm$ 0.77), respectively, whereas the multiclass had
an accuracy of 78.12% ($\pm$ 2.09). The best sensitivity was observed in the
ensemble approach (96.82% $\pm$ 1.62), followed by the multiclass for the
particular case of Aedes aegypti (90.23% $\pm$ 3.83) and the binary (88.49%
$\pm$ 6.68). The binary classifier and the multiclass classifier presented the
best balance between precision and recall, with F1-measure close to 90%.
Although the ensemble classifier achieved the lowest precision, thus impairing
its F1-measure (79.95% $\pm$ 2.13), it was the most powerful classifier to
detect Aedes aegypti in our dataset.

30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas