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September 5, 2025

A Hybrid Feature Optimized Framework for Enhanced COVID-19 Detection with IoT Sensor Data

Puntos clave

Enhanced COVID-19 detection achieved through machine learning algorithms and IoT sensor data, leading to improved accuracy.
The study utilizes a classification model that efficiently identifies SC2 and other virus categories from IoT-generated data.
Feature optimization addresses the limitations of traditional algorithms in handling complex datasets for virus detection.
This framework supports advancements in public health technology by enabling real-time and precise illness detection.

Resumen

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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Biswajit Patra (Fri,) studied this question.

synapsesocial.com/papers/68bb3edf2b87ece8dc956f13 https://doi.org/https://doi.org/10.48047/nxz91k56