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Design of Intelligent Mosquito Nets Based on Deep Learning Algorithms

Yuzhen Liu1,3, Xiaoliang Wang1,*, Xinghui She1, Ming Yi1, Yuelong Li1, Frank Jiang2

1 Hunan University of Science and Technology, Xiangtan, 411201, China
2 School of Info Technology, Deakin University, Geelong, 3215, Australia
3 Key Laboratory of Knowledge Processing and Networked Manufacturing, College of Hunan Province, 411201, China

* Corresponding Author: Xiaoliang Wang. Email: email

Computers, Materials & Continua 2021, 69(2), 2261-2276. https://doi.org/10.32604/cmc.2021.015501

Abstract

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 traditional methods 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.

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Cite This Article

Y. Liu, X. Wang, X. She, M. Yi, Y. Li et al., "Design of intelligent mosquito nets based on deep learning algorithms," Computers, Materials & Continua, vol. 69, no.2, pp. 2261–2276, 2021.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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