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迈向基于机器学习的用于大流行管理的语义物联网：COVID-19 使能技术综述

Towards an ML-based semantic IoT for pandemic management: A survey of enabling technologies for COVID-19.

作者信息

Zgheib Rita, Chahbandarian Ghazar, Kamalov Firuz, Messiry Haythem El, Al-Gindy Ahmed

机构信息

Department of Computer Engineering, Canadian University Dubai, Dubai, United Arab Emirates.

Institut de recherche en informatique de Toulouse, Toulouse, France.

出版信息

Neurocomputing (Amst). 2023 Apr 1;528:160-177. doi: 10.1016/j.neucom.2023.01.007. Epub 2023 Jan 12.

DOI:10.1016/j.neucom.2023.01.007
PMID:36647510
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9833856/
Abstract

The connection between humans and digital technologies has been documented extensively in the past decades but needs to be evaluated through the current global pandemic. Artificial Intelligence(AI), with its two strands, Machine Learning (ML) and Semantic Reasoning, has proven to be a great solution to provide efficient ways to prevent, diagnose and limit the spread of COVID-19. IoT solutions have been widely proposed for COVID-19 disease monitoring, infection geolocation, and social applications. In this paper, we investigate the usage of the three technologies for handling the COVID-19 pandemic. For this purpose, we surveyed the existing ML applications and algorithms proposed during the pandemic to detect COVID-19 disease using symptom factors and image processing. The survey includes existing approaches including semantic technologies and IoT systems for COVID-19. Based on the survey result, we classified the main challenges and the solutions that could solve them. The study proposes a conceptual framework for pandemic management and discusses challenges and trends for future research.

摘要

在过去几十年里，人类与数字技术之间的联系已有大量记载，但需要通过当前的全球大流行来进行评估。人工智能（AI）及其机器学习（ML）和语义推理这两个分支，已被证明是提供有效方法来预防、诊断和限制新冠病毒传播的绝佳解决方案。物联网解决方案已被广泛用于新冠病毒疾病监测、感染地理位置追踪及社交应用。在本文中，我们研究这三种技术在应对新冠疫情中的应用。为此，我们调查了疫情期间提出的利用症状因素和图像处理来检测新冠病毒疾病的现有机器学习应用和算法。该调查涵盖了包括用于新冠疫情的语义技术和物联网系统在内的现有方法。基于调查结果，我们对主要挑战以及能够解决这些挑战的解决方案进行了分类。本研究提出了一个疫情管理的概念框架，并讨论了未来研究的挑战和趋势。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b41/9833856/5b4b143f9808/gr10_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b41/9833856/a581ee8b6103/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b41/9833856/e818e8dca8f3/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b41/9833856/cc8491042013/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b41/9833856/77c0a4ea2311/gr4_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b41/9833856/d3f86b235612/gr5_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b41/9833856/77960956ed5e/gr6_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b41/9833856/6155fa71843b/gr7_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b41/9833856/196012a345e0/gr8_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b41/9833856/524200ac3cfc/gr9_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b41/9833856/5b4b143f9808/gr10_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b41/9833856/a581ee8b6103/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b41/9833856/e818e8dca8f3/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b41/9833856/cc8491042013/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b41/9833856/77c0a4ea2311/gr4_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b41/9833856/d3f86b235612/gr5_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b41/9833856/77960956ed5e/gr6_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b41/9833856/6155fa71843b/gr7_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b41/9833856/196012a345e0/gr8_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b41/9833856/524200ac3cfc/gr9_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b41/9833856/5b4b143f9808/gr10_lrg.jpg

相似文献

[1]
Towards an ML-based semantic IoT for pandemic management: A survey of enabling technologies for COVID-19.

Neurocomputing (Amst). 2023-4-1

[2]
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[3]
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[4]
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[5]
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[6]
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[7]
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[8]
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[9]
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[10]
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引用本文的文献

[1]
Non-Invasive Sensors Integration for NCDs with AIoT Based Telemedicine System.

Sensors (Basel). 2024-7-9

[2]
Early detection of cardiorespiratory complications and training monitoring using wearable ECG sensors and CNN.

BMC Med Inform Decis Mak. 2024-7-16

[3]
Design and Development of a Non-Contact ECG-Based Human Emotion Recognition System Using SVM and RF Classifiers.

Diagnostics (Basel). 2023-6-16

本文引用的文献

[1]
Coordinating virus research: The Virus Infectious Disease Ontology.

PLoS One. 2024

[2]
Neural Collaborative Filtering with Ontologies for Integrated Recommendation Systems.

Sensors (Basel). 2022-1-17

[3]
Characterizing Long COVID: Deep Phenotype of a Complex Condition.

EBioMedicine. 2021-12

[4]
Blockchain-Empowered Multi-Robot Collaboration to Fight COVID-19 and Future Pandemics.

IEEE Access. 2020-10-26

[5]
IoT in the Wake of COVID-19: A Survey on Contributions, Challenges and Evolution.

IEEE Access. 2020-10-12

[6]
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network.

Appl Intell (Dordr). 2021

[7]
AANet: Adaptive Attention Network for COVID-19 Detection From Chest X-Ray Images.

IEEE Trans Neural Netw Learn Syst. 2021-11

[8]
The Role of Digital Technology in Responding to COVID-19 Pandemic: Saudi Arabia's Experience.

Risk Manag Healthc Policy. 2021-9-21

[9]
The Infectious Disease Ontology in the age of COVID-19.

J Biomed Semantics. 2021-7-18

[10]
Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning Based Approach.

Biocybern Biomed Eng. 2021

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