基于超参数调优的深度神经网络在疑似Covid-19症状识别早期检测中的应用

IF 0.6 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
D. Djuniadi, N. Iksan, Alfa Faridh Suni, A. F. Hastawan
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引用次数: 0

摘要

世卫组织表示,冠状病毒疫情(COVID-19)仍是全世界关注的问题。虽然这种病毒已得到控制,但仍在开展预防工作。预防该病毒可以通过识别有发热、呼吸窘迫和喉咙痛等症状的患者来完成。本研究旨在利用热像仪传感器结合超参数调谐的深度神经网络,通过对COVID-19感染症状的识别,开发一种早期检测系统。结果表明,DNN算法可以作为检测COVID-19疑似患者的正确算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Neural Network with Hyperparameter Tuning on Early Detection for Symptom Recognition in Suspected Covid-19
The Coronavirus outbreak (COVID-19) is still a concern for the world according to WHO. Although this virus has been controlled, prevention efforts are still being carried out. Prevention of the virus can be done by identifying patients with symptoms such as fever, respiratory distress, and sore throat. This research aims to develop an early detection system through the recognition of symptoms of COVID-19 infection using thermal camera sensors combined with the DNN using Hyperparameter Tuning. The result is the DNN algorithm can be proposed as the right algorithm to detect someone suspected of COVID-19.
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来源期刊
TEM Journal-Technology Education Management Informatics
TEM Journal-Technology Education Management Informatics COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
2.20
自引率
14.30%
发文量
176
审稿时长
8 weeks
期刊介绍: TEM JOURNAL - Technology, Education, Management, Informatics Is a an Open Access, Double-blind peer reviewed journal that publishes articles of interdisciplinary sciences: • Technology, • Computer and informatics sciences, • Education, • Management
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