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Smart COVIDNet: designing an IoT-based COVID-19 disease prediction framework using attentive and adaptive-derived ensemble deep learning

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Abstract

Since the end of 2019, the world has faced severe issues over Corona Virus Disease of 2019 (COVID-19). So there is a need for some essential precautionary measures until the development of vaccines to battle the COVID-19 pandemic. In addition to that, quarantine and social distancing have become the more significant practice in the world. COVID-19 associated with the virus not only degraded the economy of the world due to the lockdown but also saturated the healthcare system of the people due to its exponential spread. In this case, the Internet of Things (IoT) system offers frequent monitoring facilities to doctors. But, the fight against COVID-19 gets continued until people get vaccinated. Therefore, an IoT-based COVID prediction is designed using deep learning techniques. Firstly, IoT data is collected from online resources. Then, the data is fed to the autoencoder (AE) for attaining the deep features. Further, the deep features are forwarded to the attentive and adaptive ensemble model (AAEM), which includes deep temporal convolution network (DTCN), one-dimensional convolutional neural network (1DCNN), and long short-term memory (LSTM) model for COVID prediction or monitoring. By utilizing the hybrid algorithm fitness position of Eurasian oystercatcher and sewing training (FPEOST), the parameter in the ensemble model is tuned for further improvement in the process. Finally, the COVID-19 disease prediction outcomes are attained on the basis of the high-ranking process. Thus, the developed model achieved an effective prediction rate than conventional approaches over multiple experimental analyses.

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Authors and Affiliations

  1. School of Computer Science and Engineering, VIT University, Vellore, TN, India

    D. Karthikeyan, P. Baskaran, S. K. Somasundaram & K. Sathya

  2. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, 522 302, Andhra Pradesh, India

    S. Srithar

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  1. D. Karthikeyan
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  2. P. Baskaran
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  3. S. K. Somasundaram
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  4. K. Sathya
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All authors have made substantial contributions to the conception and design, revising the manuscript, and the final approval of the version to be published. Also, all authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Correspondence to P. Baskaran.

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Karthikeyan, D., Baskaran, P., Somasundaram, S.K. et al. Smart COVIDNet: designing an IoT-based COVID-19 disease prediction framework using attentive and adaptive-derived ensemble deep learning. Knowl Inf Syst 66, 2269–2305 (2024). https://doi.org/10.1007/s10115-023-02007-0

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  • DOI: https://doi.org/10.1007/s10115-023-02007-0

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