A Hybrid Feature Optimized Framework for Enhanced COVID-19 Detection with IoT Sensor Data
DOI:
https://doi.org/10.48047/nxz91k56Keywords:
Internet of Things, COVID-19 Classification, Random Forest Infused Black Widow Optimization, Linear Logistic Regression-based Genetic Optimization, Custom Convolution Neural NetworkAbstract
The COVID-19 pandemic has profoundly affected global public health, requiring the creation of precise and effective illness detection tools. The expansion of the healthcare sector has been significantly enhanced by the Internet of Things (IoT), which supports various applications such as telemedicine and direct consultations. COVID-19 can be readily identified by employing artificial intelligence algorithms on users' IoT data. Nevertheless, conventional artificial intelligence algorithms were inadequate in extracting and selecting features from the dataset. This study employed the machine learning optimized COVID-19 classification model to identify SC2, other, and no virus categories from IoT data
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References
Kavitha, K. S., and Megha P. Arakeri. "Computer Vision and Machine Learning-Based Techniques for Detecting the Safety Violations of COVID-19 Scenarios: A Review." Computational Vision and BioInspired Computing: Proceedings of ICCVBIC 2021 (2022): 239-251.
Magazzino, Cosimo, Marco Mele, and Mario Coccia. "A machine learning algorithm to analyse the effects of vaccination on COVID-19 mortality." Epidemiology & Infection 150 (2022): e168.
Absar, Nurul, et al. "The efficacy of deep learning based LSTM model in forecasting the outbreak of contagious diseases." Infectious Disease Modelling 7.1 (2022): 170-183.
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