Diagnosis of abnormal body temperature based on deep neural network

被引：0
作者：
Peng J. [1 ]
Zhang L. [1 ]
机构：
[1] Information Engineering College, Hunan Applied Technology University, Changde
来源：
EAI Endorsed Transactions on Pervasive Health and Technology | 2022年 / 8卷 / 03期
关键词：
Abnormal diagnosis; Deep neural network; Human body temperature; Malicious node; Temperature sensor;
D O I：
10.4108/EETPHT.V8I3.660
中图分类号：
学科分类号：
摘要：
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. © 2022 Jinxiang Peng et al.,.
引用
收藏
相关论文
共 15 条
  • [1] Wang S, Liu X, Liu S, Et al., Human Short-Long Term Cognitive Memory Mechanism for Visual Monitoring in IoT-Assisted Smart Cities, IEEE Internet of Things Journal, 9, 10, pp. 7128-7139, (2022)
  • [2] Wang Q S, Wang W J, Guo X, Et al., Error Correction Method for Distributed Fiber Raman Temperature Sensor[J], Laser & Optoelectronics Progress, 57, 17, pp. 76-84, (2020)
  • [3] Zhang S N, Wu Y S Y, Wang X R, Et al., Development of infant body temperature monitoring clothing based on temperature sensor[J], Journal of Clothing Research, 6, 3, pp. 215-220, (2021)
  • [4] Huang E L, bu X L, Xie W H, Et al., Design of inpatient temperature monitoring system based on wireless technology[J], Journal of Biomedical Engineering Research, 38, 2, pp. 247-251, (2019)
  • [5] Zhang F X, Gao X, Zhong J, Et al., Design of dynamic body temperature monitoring system based on Android and tmp116 digital temperature sensor[J], Industrial Control Computer, 32, 2, pp. 56-57, (2019)
  • [6] Liu S, Liu D, Khan M, Et al., Effective Template Update Mechanism in Visual Tracking with Background Clutter, Neurocomputing, 458, pp. 615-625, (2021)
  • [7] Gu Y Y, Jiang L X, Wang L, Et al., Fault detection of the temperature sensor network in outdoor baseline[J], Electronic Measurement Technology, 42, 19, pp. 187-192, (2019)
  • [8] Swain R R, Khilar P M, Bhoi S K., Underlying and Persistence Fault Diagnosis in Wireless Sensor Networks Using Majority Neighbors Co-ordination Approach[J], Wireless Personal Communications, 111, 12, pp. 763-798, (2020)
  • [9] Jung Y G, Jeong C M., Deep neural network-based automatic unknown protocol classification system using histogram feature[J], The Journal of Supercomputing, 76, 7, pp. 5425-5441, (2020)
  • [10] Zaghari N, Fathy M, Jameii S M, Et al., Improving the learning of self-driving vehicles based on real driving behavior using deep neural network techniques[J], The Journal of Supercomputing, 77, 9, pp. 3752-3794, (2020)
12