• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

一个用于预测和预防新冠肺炎的集成雾计算与人工智能智能健康框架。

An integrated fog and Artificial Intelligence smart health framework to predict and prevent COVID-19.

作者信息

Singh Prabhdeep, Kaur Rajbir

机构信息

Department of Computer Science & Engineering, Punjabi University, Patiala, IN, India.

Department of Electronics & Communication Engineering, Punjabi University, Patiala, IN, India.

出版信息

Glob Transit. 2020;2:283-292. doi: 10.1016/j.glt.2020.11.002. Epub 2020 Nov 12.

DOI:10.1016/j.glt.2020.11.002
PMID:33205037
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7659515/
Abstract

Nowadays, COVID-19 is spreading at a rapid rate in almost all the continents of the world. It has already affected many people who are further spreading it day by day. Hence, it is the most essential to alert nearby people to be aware of it due to its communicable behavior. Till May 2020, no vaccine is available for the treatment of this COVID-19, but the existing technologies can be used to minimize its effect. Cloud/fog computing could be used to monitor and control this rapidly spreading infection in a cost-effective and time-saving manner. To strengthen COVID-19 patient prediction, Artificial Intelligence(AI) can be integrated with cloud/fog computing for practical solutions. In this paper, fog assisted the internet of things based quality of service framework is presented to prevent and protect from COVID-19. It provides real-time processing of users' health data to predict the COVID-19 infection by observing their symptoms and immediately generates an emergency alert, medical reports, and significant precautions to the user, their guardian as well as doctors/experts. It collects sensitive information from the hospitals/quarantine shelters through the patient IoT devices for taking necessary actions/decisions. Further, it generates an alert message to the government health agencies for controlling the outbreak of chronic illness and for tanking quick and timely actions.

摘要

如今,新冠病毒在世界几乎所有大洲都在迅速传播。它已经感染了许多人,而且这些人还在日复一日地进一步传播病毒。因此,鉴于其传染性,提醒附近的人对此有所警惕至关重要。到2020年5月,尚无治疗新冠病毒的疫苗,但可利用现有技术将其影响降至最低。云计算/雾计算可用于以经济高效且节省时间的方式监测和控制这种迅速传播的感染。为了加强对新冠病毒患者的预测,可将人工智能(AI)与云计算/雾计算集成以提供实际解决方案。本文提出了一种基于雾辅助物联网的服务质量框架,以预防和抵御新冠病毒。它对用户的健康数据进行实时处理,通过观察症状来预测新冠病毒感染,并立即向用户、其监护人以及医生/专家生成紧急警报、医疗报告和重要预防措施。它通过患者物联网设备从医院/隔离庇护所收集敏感信息,以便采取必要行动/做出决策。此外,它向政府卫生机构生成警报信息,以控制慢性病的爆发并迅速及时采取行动。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda4/7659515/c5154dd4c062/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda4/7659515/033a2c0e1c6c/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda4/7659515/b135c1dd3500/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda4/7659515/3368d94fd665/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda4/7659515/188cdea466aa/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda4/7659515/077e4355aa38/fx2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda4/7659515/9d751c0aa4c0/fx3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda4/7659515/e44c849a3ca1/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda4/7659515/490e61da1c22/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda4/7659515/cf744832f7c4/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda4/7659515/c5154dd4c062/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda4/7659515/033a2c0e1c6c/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda4/7659515/b135c1dd3500/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda4/7659515/3368d94fd665/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda4/7659515/188cdea466aa/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda4/7659515/077e4355aa38/fx2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda4/7659515/9d751c0aa4c0/fx3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda4/7659515/e44c849a3ca1/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda4/7659515/490e61da1c22/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda4/7659515/cf744832f7c4/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda4/7659515/c5154dd4c062/gr7.jpg

相似文献

1
An integrated fog and Artificial Intelligence smart health framework to predict and prevent COVID-19.一个用于预测和预防新冠肺炎的集成雾计算与人工智能智能健康框架。
Glob Transit. 2020;2:283-292. doi: 10.1016/j.glt.2020.11.002. Epub 2020 Nov 12.
2
Wearable IoT sensor based healthcare system for identifying and controlling chikungunya virus.基于可穿戴物联网传感器的用于识别和控制基孔肯雅病毒的医疗保健系统。
Comput Ind. 2017 Oct;91:33-44. doi: 10.1016/j.compind.2017.05.006. Epub 2017 Jun 10.
3
STROVE: spatial data infrastructure enabled cloud-fog-edge computing framework for combating COVID-19 pandemic.STROVE:用于抗击新冠疫情的支持空间数据基础设施的云-雾-边缘计算框架
Innov Syst Softw Eng. 2022 Jun 2:1-17. doi: 10.1007/s11334-022-00458-2.
4
An Overview of Fog Data Analytics for IoT Applications.物联网应用中的雾数据分析概述。
Sensors (Basel). 2022 Dec 24;23(1):199. doi: 10.3390/s23010199.
5
Fog-cloud architecture-driven Internet of Medical Things framework for healthcare monitoring.面向医疗保健监测的雾云体系结构驱动的医疗物联网框架。
Med Biol Eng Comput. 2023 May;61(5):1133-1147. doi: 10.1007/s11517-023-02776-4. Epub 2023 Jan 21.
6
A Smart Home Energy Management System Using Two-Stage Non-Intrusive Appliance Load Monitoring over Fog-Cloud Analytics Based on Tridium's Niagara Framework for Residential Demand-Side Management.基于 Tridium 的 Niagara 框架的用于住宅需求侧管理的雾-云分析的两级非侵入式家电负载监测的智能家居能源管理系统。
Sensors (Basel). 2021 Apr 20;21(8):2883. doi: 10.3390/s21082883.
7
Fog-based deep learning framework for real-time pandemic screening in smart cities from multi-site tomographies.基于雾计算的深度学习框架,用于从多站点层析成像进行智慧城市的实时大流行病筛查。
BMC Med Imaging. 2024 May 27;24(1):123. doi: 10.1186/s12880-024-01302-8.
8
Fog-Based Smart Cardiovascular Disease Prediction System Powered by Modified Gated Recurrent Unit.基于雾计算的智能心血管疾病预测系统:由改进门控循环单元驱动
Diagnostics (Basel). 2023 Jun 15;13(12):2071. doi: 10.3390/diagnostics13122071.
9
Artificial intelligence-inspired comprehensive framework for Covid-19 outbreak control.人工智能启发的新冠疫情综合防控框架。
Artif Intell Med. 2022 May;127:102288. doi: 10.1016/j.artmed.2022.102288. Epub 2022 Mar 26.
10
Imtidad: A Reference Architecture and a Case Study on Developing Distributed AI Services for Skin Disease Diagnosis over Cloud, Fog and Edge.Imtidad:用于在云、雾和边缘上开发皮肤病诊断分布式 AI 服务的参考架构和案例研究。
Sensors (Basel). 2022 Feb 26;22(5):1854. doi: 10.3390/s22051854.

引用本文的文献

1
Applications of Fog Computing in Healthcare.雾计算在医疗保健中的应用。
Cureus. 2024 Jul 10;16(7):e64263. doi: 10.7759/cureus.64263. eCollection 2024 Jul.
2
Innovative applications of artificial intelligence during the COVID-19 pandemic.人工智能在新冠疫情期间的创新应用。
Infect Med (Beijing). 2024 Feb 21;3(1):100095. doi: 10.1016/j.imj.2024.100095. eCollection 2024 Mar.
3
A Novel Social Distancing Approach for Limiting the Number of Vehicles in Smart Buildings Using LiFi Hybrid-Network.利用 LiFi 混合网络限制智能建筑中车辆数量的新型社交隔离方法。

本文引用的文献

1
Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing.利用机器学习和云计算预测新冠疫情的发展与趋势。
Internet Things (Amst). 2020 Sep;11:100222. doi: 10.1016/j.iot.2020.100222. Epub 2020 May 12.
2
Fractional-Order SEIQRDP Model for Simulating the Dynamics of COVID-19 Epidemic.用于模拟COVID-19疫情动态的分数阶SEIQRDP模型
IEEE Open J Eng Med Biol. 2020 Aug 26;1:249-256. doi: 10.1109/OJEMB.2020.3019758. eCollection 2020.
3
DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive Surveillance of COVID-19 Using Heterogeneous Features and Their Interactions.
Int J Environ Res Public Health. 2023 Feb 15;20(4):3438. doi: 10.3390/ijerph20043438.
4
At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives.在基于物联网应用的人工智能和边缘计算的融合:综述与新视角。
Sensors (Basel). 2023 Feb 2;23(3):1639. doi: 10.3390/s23031639.
5
Adopting effective hierarchal IoMTs computing with K-efficient clustering to control and forecast COVID-19 cases.采用具有K-高效聚类的有效分层物联网计算来控制和预测新冠肺炎病例。
Comput Electr Eng. 2022 Dec;104:108472. doi: 10.1016/j.compeleceng.2022.108472. Epub 2022 Nov 10.
6
Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey.医疗管理系统新兴技术的最新进展:一项调查
Healthcare (Basel). 2022 Oct 3;10(10):1940. doi: 10.3390/healthcare10101940.
7
IoT-Enabled smart mask to detect COVID19 outbreak.用于检测新冠疫情爆发的物联网智能口罩。
Health Technol (Berl). 2022;12(5):1025-1036. doi: 10.1007/s12553-022-00695-2. Epub 2022 Aug 19.
8
Smart Healthcare System for Severity Prediction and Critical Tasks Management of COVID-19 Patients in IoT-Fog Computing Environments.物联网-雾计算环境中 COVID-19 患者严重程度预测和关键任务管理的智能医疗保健系统。
Comput Intell Neurosci. 2022 Jul 19;2022:5012962. doi: 10.1155/2022/5012962. eCollection 2022.
9
A Survey on Machine Learning and Internet of Medical Things-Based Approaches for Handling COVID-19: Meta-Analysis.基于机器学习和医疗物联网的 COVID-19 处理方法研究综述:荟萃分析。
Front Public Health. 2022 Jun 23;10:869238. doi: 10.3389/fpubh.2022.869238. eCollection 2022.
10
Detection of Dental Diseases through X-Ray Images Using Neural Search Architecture Network.基于神经搜索架构网络的 X 射线图像牙齿疾病检测。
Comput Intell Neurosci. 2022 Apr 30;2022:3500552. doi: 10.1155/2022/3500552. eCollection 2022.
深度新冠病毒网络:一种利用异构特征及其相互作用对新冠病毒进行预测监测的可解释深度学习模型。
IEEE Access. 2020 Aug 28;8:159915-159930. doi: 10.1109/ACCESS.2020.3019989. eCollection 2020.
4
Detecting Regions At Risk for Spreading COVID-19 Using Existing Cellular Wireless Network Functionalities.利用现有蜂窝无线网络功能检测新冠病毒传播风险区域
IEEE Open J Eng Med Biol. 2020 Jun 15;1:187-189. doi: 10.1109/OJEMB.2020.3002447. eCollection 2020.
5
CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection.CovidGAN:使用辅助分类器生成对抗网络进行数据增强以改进新冠病毒检测
IEEE Access. 2020 May 14;8:91916-91923. doi: 10.1109/ACCESS.2020.2994762. eCollection 2020.
6
Quantifying COVID-19 Content in the Online Health Opinion War Using Machine Learning.利用机器学习量化在线健康舆论战中的新冠疫情相关内容
IEEE Access. 2020 May 11;8:91886-91893. doi: 10.1109/ACCESS.2020.2993967. eCollection 2020.
7
A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT.一种基于弱监督的 COVID-19 分类和胸部 CT 病变定位框架。
IEEE Trans Med Imaging. 2020 Aug;39(8):2615-2625. doi: 10.1109/TMI.2020.2995965.
8
A drone-based networked system and methods for combating coronavirus disease (COVID-19) pandemic.一种用于抗击冠状病毒病(COVID-19)大流行的基于无人机的网络系统和方法。
Future Gener Comput Syst. 2021 Feb;115:1-19. doi: 10.1016/j.future.2020.08.046. Epub 2020 Sep 3.
9
Deep Transfer Learning Based Classification Model for COVID-19 Disease.基于深度迁移学习的新冠肺炎疾病分类模型
Ing Rech Biomed. 2022 Apr;43(2):87-92. doi: 10.1016/j.irbm.2020.05.003. Epub 2020 May 20.
10
Extent of Prior Lung Irradiation and Mortality in COVID-19 Patients With a Cancer History.有癌症病史的COVID-19患者先前肺部放疗范围与死亡率
Adv Radiat Oncol. 2020 May 20;5(4):707-710. doi: 10.1016/j.adro.2020.04.028. eCollection 2020 Jul-Aug.