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Intelligent and Secure Evolved Framework for Vaccine Supply Chain Management Using Machine Learning and Blockchain

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Abstract

Supply chain management (SCM) ensures that fragile goods are delivered from their origin to their destination without suffering any damage. The COVID-19 pandemic’s widespread has exposed systemic weaknesses in SCM across a variety of sectors, particularly healthcare. This paper aims to tackle issues related to vaccine expiration and fraudulent vaccine records by facilitating vaccine traceability and innovative contract features. The proposed model suggests utilizing an intelligent and secure framework that merges machine learning intelligent techniques with blockchain technology. The proposed model utilizes the Ethereum blockchain’s characteristics to establish novel smart contracts between diverse participants in the supply chain. In addition, the proposed model presents two modules to assist in predicting the demand for vaccines and analyzing the sentiment of vaccine reviews to improve the quality of vaccines. Therefore, an Improved Honey Badger Algorithm (IHBA) is presented to enhance the performance of the Long Short-Term Memory (LSTM) algorithm in predicting vaccine demand besides the credibility evaluation of reviews. For the vaccine demand prediction module, a real dataset obtained from the centers for Disease Control and Prevention (CDC) is collected and the proposed model is applied obtaining the minimum RMSE of 1.3828 for the prediction results compared with other approaches. For the review analysis module, a publicly available dataset from the University of California, Irvine (UCI) is used, and the proposed model achieved a high accuracy of user reviews of 90.6% compared with other related approaches. The proposed model is compared with other related metaheuristic algorithms and the standard deep learning optimizers. The proposed new algorithm outperforms other competitors for tuning the hyperparameters of deep learning models. Finally, the proposed system presents implications for sustainable supply chain management, enterprise operations, and public policy.

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Acknowledgements

All authors approved the final version of the manuscript.

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Authors and Affiliations

  1. Faculty of Computers and Information Science, Mansoura University, Mansoura, Egypt

    Mahmoud Abdel-salam, Mohamed Elhoseny & Ibrahim M. El-hasnony

  2. College of Computing and Informatics, University of Sharjah, Sharjah, UAE

    Mohamed Elhoseny

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  1. Mahmoud Abdel-salam
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Contributions

Mohamed Elhoseny: supervision, software, methodology, conceptualization, formal analysis, investigation, visualization, writing—review and editing. Mahmoud Abdel-salam: methodology, software, conceptualization, data curation, validation, writing—review and editing. Ibrahim M. El-hasnony: conceptualization, formal analysis, resources, data curation, writing—original draft, writing—review and editing. All authors read and approved the final paper.

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Correspondence to Mahmoud Abdel-salam.

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Abdel-salam, M., Elhoseny, M. & El-hasnony, I.M. Intelligent and Secure Evolved Framework for Vaccine Supply Chain Management Using Machine Learning and Blockchain. SN COMPUT. SCI. 6, 121 (2025). https://doi.org/10.1007/s42979-024-03609-3

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