Research Article
Diagnosis of abnormal body temperature based on deep neural network
@ARTICLE{10.4108/eetpht.v8i3.660, author={Jinxiang Peng and Li Zhang}, title={Diagnosis of abnormal body temperature based on deep neural network}, journal={EAI Endorsed Transactions on Pervasive Health and Technology}, volume={8}, number={3}, publisher={EAI}, journal_a={PHAT}, year={2022}, month={7}, keywords={deep neural network, human body temperature, abnormal diagnosis, temperature sensor, malicious node}, doi={10.4108/eetpht.v8i3.660} }- Jinxiang Peng
Li Zhang
Year: 2022
Diagnosis of abnormal body temperature based on deep neural network
PHAT
EAI
DOI: 10.4108/eetpht.v8i3.660
Abstract
INTRODUCTION: A method for diagnosing abnormal body temperature based on deep neural network is proposed. OBJECTIVES: To improve the diagnostic accuracy, reduce the false alarm rate, and improve the diagnostic level of abnormal body temperature. METHODS: According to the weight of the temperature sensor node itself and its neighbor nodes, the network trust relationship is established, and the node trust value is output through the combination of decision-making. Use trust value and double threshold to identify and remove malicious nodes, and optimize the network structure. The optimized temperature sensor network is used to collect human body temperature data. RESULTS: A deep neural network is used to construct a diagnosis model of abnormal body temperature, so as to realize the diagnosis of abnormal body temperature. CONCLUSION: The experimental results show that the method in this paper has high diagnostic accuracy, low false positive rate and high diagnostic efficiency, and can improve the diagnostic level of abnormal body temperature.
Copyright © 2022 Jinxiang Peng et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
