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JCSIS

Biosensors for Virus Detection Optimization Using IoT-Based Machine Learning Algorithms and Techniques

Sam M. K.Lima HongouSofia Arkhstan
Volume 4

Abstract

In the era of global health challenges, the demand for rapid, accurate, and scalable virus detection systems has grown significantly. This study explores the integration of biosensor technologies with Internet of Things (IoT) infrastructure and machine learning (ML) algorithms to optimize virus detection in real-time. The proposed framework leverages advanced ML techniques—such as decision trees, support vector machines (SVM), and deep learning models—to analyze biosensor data transmitted through IoT networks. These methods enhance the detection accuracy, reduce false positives, and enable timely responses to viral outbreaks. Additionally, edge computing is employed to minimize latency and ensure efficient data processing in resource-constrained environments. The system's performance is evaluated across various biosensor platforms and virus datasets, demonstrating high sensitivity, specificity, and adaptability. The integration of intelligent biosensing and IoT-based ML optimization offers a promising direction for next-generation public health monitoring and epidemic management systems

Keywords

biosensors, virus detection, Internet of Things, machine learning, smart healthcare, real-time monitoring

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