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Leveraging Blockchain with Optimal Deep Learning-Based Drug Supply Chain Management for Pharmaceutical Industries

Shanthi Perumalsamy, Venkatesh Kaliyamurthy*

Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603 203, India

* Corresponding Author: Venkatesh Kaliyamurthy. Email: email

Computers, Materials & Continua 2023, 77(2), 2341-2357. https://doi.org/10.32604/cmc.2023.040269

Abstract

Due to its complexity and involvement of numerous stakeholders, the pharmaceutical supply chain presents many challenges that companies must overcome to deliver necessary medications to patients efficiently. The pharmaceutical supply chain poses different challenging issues, encompasses supply chain visibility, cold-chain shipping, drug counterfeiting, and rising prescription drug prices, which can considerably surge out-of-pocket patient costs. Blockchain (BC) offers the technical base for such a scheme, as it could track legitimate drugs and avoid fake circulation. The designers presented the procedure of BC with fabric for creating a secured drug supply-chain management (DSCM) method. With this motivation, the study presents a new blockchain with optimal deep learning-enabled DSCM and recommendation scheme (BCODL-DSCMRS) for Pharmaceutical Industries. Firstly, Hyperledger fabric is used for DSC management, enabling effective tracking processes in the smart pharmaceutical industry. In addition, a hybrid deep belief network (HDBN) model is used to suggest the best or top-rated medicines to healthcare providers and consumers. The spotted hyena optimizer (SHO) algorithm is used to optimize the performance of the HDBN model. The design of the HSO algorithm for tuning the HDBN model demonstrates the novelty of the work. The presented model is tested on the UCI repository’s open-access drug reviews database.

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Cite This Article

S. Perumalsamy and V. Kaliyamurthy, "Leveraging blockchain with optimal deep learning-based drug supply chain management for pharmaceutical industries," Computers, Materials & Continua, vol. 77, no.2, pp. 2341–2357, 2023.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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