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Design of intelligent mosquito nets based on deep learning algorithms

Liu, Y, Wang, X, She, X, Yi, M, Li, Y and Jiang, F 2021, Design of intelligent mosquito nets based on deep learning algorithms, Computers, Materials and Continua, vol. 69, no. 2, pp. 2261-2276, doi: 10.32604/cmc.2021.015501.

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Title Design of intelligent mosquito nets based on deep learning algorithms
Author(s) Liu, Y
Wang, X
She, X
Yi, M
Li, Y
Jiang, F
Journal name Computers, Materials and Continua
Volume number 69
Issue number 2
Start page 2261
End page 2276
Total pages 15
Publisher Tech Science Press
Place of publication Encino, Calif.
Publication date 2021
ISSN 1546-2218
1546-2226
Keyword(s) cultural heritage
mapping
surveying
indigenous place values
colonisation
Michel de Certeau
urban morphology
lost landscapes
design reparation
Science & Technology
Technology
Computer Science, Information Systems
Materials Science, Multidisciplinary
Computer Science
Materials Science
Internet of things
smart home
ZigBee protocol
internet of medical things
deep learning
Summary An intelligent mosquito net employing deep learning has been one of the hotspots in the field of Internet of Things as it can reduce significantly the spread of pathogens carried by mosquitoes, and help people live well in mosquito-infested areas. In this study, we propose an intelligent mosquito net that can produce and transmit data through the Internet of Medical Things. In our method, decision-making is controlled by a deep learning model, and the proposed method uses infrared sensors and an array of pressure sensors to collect data.Moreover the ZigBee protocol is used to transmit the pressure map which is formed by pressure sensors with the deep learning perception model, determining automatically the intention of the user to open or close the mosquito net.We used optical flow to extract pressure map features, and they were fed to a 3-dimensional convolutional neural network (3D-CNN) classification model subsequently. We achieved the expected results using a nested cross-validation method to evaluate our model. Deep learning has better adaptability than the traditionalmethods and also has better anti-interference by the different bodies of users. This research has the potential to be used in intelligent medical protection and large-scale sensor array perception of the environment.
Language eng
DOI 10.32604/cmc.2021.015501
Field of Research 0103 Numerical and Computational Mathematics
0912 Materials Engineering
0915 Interdisciplinary Engineering
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30154122

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.