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Review
. 2022 Mar 2:2022:1987917.
doi: 10.1155/2022/1987917. eCollection 2022.

Development of Advanced Artificial Intelligence and IoT Automation in the Crisis of COVID-19 Detection

Praveen Kumar Kollu  1 , Kailash Kumar  2 , Pravin R Kshirsagar  3 , Saiful Islam  4 , Quadri Noorulhasan Naveed  5 , Mohammad Rashid Hussain  5 , Venkatesa Prabhu Sundramurthy  6
Affiliations
Review

Development of Advanced Artificial Intelligence and IoT Automation in the Crisis of COVID-19 Detection

Praveen Kumar Kollu et al. J Healthc Eng. .

Abstract

Internet of Things (IoT) is a successful area for many industries and academia domains, particularly healthcare is one of the application areas that uses IoT sensors and devices for monitoring. IoT transition replaces contemporary health services with scientific and socioeconomic viewpoints. Since the epidemic began, diverse scientific organizations have been making accelerated efforts to use a wide range of tools to tackle this global challenge and the founders of IoT analytics. Artificial intelligence (AI) plays a key role in measuring, assessing, and diagnosing the risk. It could be used to predict the number of alternate incidents, recovered instances, and casualties, also used for forecasting cases. Within the COVID-19 background, IoT technologies are used to minimize COVID-19 exposure to others by prenatal screening, patient monitoring, and postpatient incident response in specified procedures. In this study, the importance of IoT technology and artificial intelligence in COVID-19 is explored, and the 3 important steps discussed such as the evaluation of networks, implementations, and IoT industries to battle COVID-19, including early detection, quarantine times, and postrecovery activities, are reviewed. In this study, how IoT handles the COVID-19 pandemic at a new level of healthcare is investigated. In this research, the long short-term memory (LSTM) with recurrent neural network (RNN) is used for diagnosis purpose and in particular, its important architecture for the analysis of cough and breathing acoustic characteristics. In comparison with both coughing and respiratory samples, our findings indicate poor accuracy of the voice test.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
AI for COVID-19 pandemic.
Figure 2
Figure 2
IoT tackles the epidemic of COVID-19.
Figure 3
Figure 3
Increase IoT phase map to tackle the pandemic of COVID-19.
Figure 4
Figure 4
Major advantages of IoT for pandemic COVID-19.
Figure 5
Figure 5
(a) Suraksha Kawach device 5. (b) Sensor 5. (c) Smart band.
Figure 6
Figure 6
Use of AI to detect, respond, and recover from COVID-19.
Figure 7
Figure 7
Flow diagram for detection of COVID-19 using IoT and AI.
Figure 8
Figure 8
Recurrent neural network.
Figure 9
Figure 9
COVID-19 effected cases.
Figure 10
Figure 10
Problematic cases.
Figure 11
Figure 11
Nonproblematic cases.
See this image and copyright information in PMC

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