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2000
Volume 3, Issue 1
  • ISSN: 2950-3752
  • E-ISSN: 2950-3760

Abstract

Introduction

Ensuring the integrity of Global Navigation Satellite System (GNSS) signals is critical for the timely and accurate delivery of pharmaceuticals within smart medical supply chain logistics (SMSCL).

Method

In this study, we propose a novel deep learning (DL) framework that integrates a Bidirectional Long Short-Term Memory (BiLSTM)-Attention model with Principal Component Analysis (PCA) and Bayesian Optimization (BO) for feature selection. This approach enhances GNSS signal reliability by accurately detecting anomalies, especially in environments prone to interference. The PCA-BO feature selection process optimizes relevant features like signal strength and Doppler shifts, improving model performance while reducing overfitting.

Results and Discussion

Our results demonstrate that the proposed model significantly outperforms conventional methods, enhancing the precision of pharmaceutical deliveries in critical healthcare settings.

Conclusion

This work represents a key advancement in using DL to ensure GNSS signal integrity for SMSCL, contributing to more efficient and secure logistics operations.

© 2025 Bentham Science Publishers
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2025-01-01
2025-09-29

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Enhancing GNSS Signal Integrity in Medical Logistics: A Deep Learning Solution
Curr Arti Intell 3, e29503752352719 (2025); https://doi.org/10.2174/0129503752352719250214061245
/content/journals/cai/10.2174/0129503752352719250214061245
/content/journals/cai/10.2174/0129503752352719250214061245
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  • Article Type:     Research Article
Keyword(s): BiLSTM-attention; deep learning; GNSS signal integrity; PCA-BO feature selection; satellite monitoring and control; Smart medical supply chain logistics

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