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An Efficient GNSS Coordinate Classification Strategy with an Adaptive KNN Algorithm for Epidemic Management

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Listed:
  • Jong-Shin Chen

    (Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung 413310, Taiwan)

  • Chun-Ming Kuo

    (Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung 413310, Taiwan)

Abstract

In times of widespread epidemics, numerous individuals are at risk of contracting viruses, such as COVID-19, monkeypox, and pneumonia, leading to a ripple effect of impacts on others. Consequently, the Centers for Disease Control (CDC) typically devises strategies to manage the situation by monitoring and tracing the infected individuals and their areas. For convenience, “targets” and “areas” represent the following individuals and areas. A global navigation satellite system (GNSS) can assist in evaluating the located areas of the targets with pointing-in-polygon (PIP) related technology. When there are many targets and areas, relying solely on PIP technology for classification from targets to areas could be more efficient. The classification technique of k-nearest neighbors (KNN) classification is widely utilized across various domains, offering reliable classification accuracy. However, KNN classification requires a certain quantity of targets with areas (training dataset) for execution, and the size of the training dataset and classification time often exhibit an exponential relationship. This study presents a strategy for applying KNN technology to classify targets into areas. Additionally, within the strategy, we propose an adaptive KNN algorithm to enhance the efficiency of the classification procedure.

Suggested Citation

  • Jong-Shin Chen & Chun-Ming Kuo, 2024. "An Efficient GNSS Coordinate Classification Strategy with an Adaptive KNN Algorithm for Epidemic Management," Mathematics, MDPI, vol. 12(4), pages 1-20, February.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:4:p:536-:d:1336183
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    References listed on IDEAS

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    1. Sotiropoulou, Kalliopi F. & Vavatsikos, Athanasios P. & Botsaris, Pantelis N., 2024. "A hybrid AHP-PROMETHEE II onshore wind farms multicriteria suitability analysis using kNN and SVM regression models in northeastern Greece," Renewable Energy, Elsevier, vol. 221(C).
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    Cited by:

    1. Jong-Shin Chen & Ruo-Wei Hung & Cheng-Ying Yang, 2025. "An Efficient Target-to-Area Classification Strategy with a PIP-Based KNN Algorithm for Epidemic Management," Mathematics, MDPI, vol. 13(4), pages 1-25, February.

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