Blockchain Based Epsilon Greedy and Hadamard Gradient Deep Secured Information Sharing for Pharma Supply Chain

P. Anitha () and C. Srimathi ()
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P. Anitha: VIT University
C. Srimathi: VIT University

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 1, No 33, 367-381

Abstract: Abstract Pharmaceutical establishments are surfacing problems in tracking their medical products during the supply chain process, conceding that counterfeiters or the fake persons add their fake medicines into the market. The existing authentication techniques are in high demand for unauthorized access to sensitive drug information. The blockchain has the full capability to control and track the supply chain process very significantly. A deep learning-based pharmaceutical supply chain method called Epsilon Greedy Consensus-based Hadamard Deep Authentication (EGC-HDA) is proposed. The pharmaceutical supply chain management is deployed using Epsilon Greedy Consensus Block Validation which is capable of continuously monitoring and validating each block. Then, the Hadamard Gradient LSTM Authentication scheme is employed for authenticating blocks or the users (i.e., manufacturers, distributors, and resellers) in the deep learning module to do robust authentication. Security analysis of the proposed method is robust which attains performance in authentication time, latency, as well as true positive rate. In the experimental analysis, the results reveal that the EGC-HDA technique performs better with a 6% improvement in latency and true positive rate by 24%, and 7%, faster authentication time by 42% for the pharmaceutical supply chain compared to existing works.

Keywords: Pharmaceutical Supply Chain; Blockchain; Deep Learning; Epsilon Greedy; Consensus; Block Validation; Hadamard Gradient; Long Short-Term Memory (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13198-022-01746-7

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