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- Title
Smart COVIDNet: designing an IoT-based COVID-19 disease prediction framework using attentive and adaptive-derived ensemble deep learning.
- Authors
Karthikeyan, D.; Baskaran, P.; Somasundaram, S. K.; Sathya, K.; Srithar, S.
- 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.
- Subjects
COVID-19; DEEP learning; CONVOLUTIONAL neural networks; COVID-19 pandemic; VACCINE development; TIME-varying networks
- Publication
Knowledge & Information Systems, 2024, Vol 66, Issue 4, p2269
- ISSN
0219-1377
- Publication type
Article
- DOI
10.1007/s10115-023-02007-0