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Lightweight Artificial Intelligence for Secure Data Communication in Energy-Constrained Healthcare Devices
Logistics is the transfer of goods from one place to another, mostly from the production house to the customers. A logistics network is a set of operations that involve designing, production, and marketing the goods. Cold-chain logistics are those that needed to be transported in a cold refrigeratio...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9451988/ https://www.ncbi.nlm.nih.gov/pubmed/36093495 http://dx.doi.org/10.1155/2022/7934582 |
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author | Pande, Suyog Vinayak Nattarkannan, K. Rama Bai, M. Kapila, Dhiraj Anandan, P. Mishra, Nilamadhab Abera, Worku |
author_facet | Pande, Suyog Vinayak Nattarkannan, K. Rama Bai, M. Kapila, Dhiraj Anandan, P. Mishra, Nilamadhab Abera, Worku |
author_sort | Pande, Suyog Vinayak |
collection | PubMed |
description | Logistics is the transfer of goods from one place to another, mostly from the production house to the customers. A logistics network is a set of operations that involve designing, production, and marketing the goods. Cold-chain logistics are those that needed to be transported in a cold refrigeration right from the production house to the customer. A secured networking model is essential to handle the logistics networks. In this article, we are going to see an intelligent secured networking model to identify the optimal path for cold-chain logistics to hospitals. The optimal pathfinder is used to find the path between point A to point B, which is short and best. It also considers the road traffic and cost of transport. The cold-chain logistics to the hospitals include medicines and vaccines, which are to be stored at a particular temperature. Thus, path optimization is more essential in cold-chain logistics to hospitals than other types of logistics. In this research, the bee-ant optimization algorithm (BAOA) is proposed to perform the intelligent transportation to the hospitals. The proposed algorithm is compared with the existing ant colony optimization (ACO), bee colony optimization (BCO), and neural network model. From the results, it can be observed that the proposed algorithm shows 98.83% for the accurate delivery of logistics to the hospitals. |
format | Online Article Text |
id | pubmed-9451988 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-94519882022-09-08 Lightweight Artificial Intelligence for Secure Data Communication in Energy-Constrained Healthcare Devices Pande, Suyog Vinayak Nattarkannan, K. Rama Bai, M. Kapila, Dhiraj Anandan, P. Mishra, Nilamadhab Abera, Worku Comput Intell Neurosci Research Article Logistics is the transfer of goods from one place to another, mostly from the production house to the customers. A logistics network is a set of operations that involve designing, production, and marketing the goods. Cold-chain logistics are those that needed to be transported in a cold refrigeration right from the production house to the customer. A secured networking model is essential to handle the logistics networks. In this article, we are going to see an intelligent secured networking model to identify the optimal path for cold-chain logistics to hospitals. The optimal pathfinder is used to find the path between point A to point B, which is short and best. It also considers the road traffic and cost of transport. The cold-chain logistics to the hospitals include medicines and vaccines, which are to be stored at a particular temperature. Thus, path optimization is more essential in cold-chain logistics to hospitals than other types of logistics. In this research, the bee-ant optimization algorithm (BAOA) is proposed to perform the intelligent transportation to the hospitals. The proposed algorithm is compared with the existing ant colony optimization (ACO), bee colony optimization (BCO), and neural network model. From the results, it can be observed that the proposed algorithm shows 98.83% for the accurate delivery of logistics to the hospitals. Hindawi 2022-08-31 /pmc/articles/PMC9451988/ /pubmed/36093495 http://dx.doi.org/10.1155/2022/7934582 Text en Copyright © 2022 Suyog Vinayak Pande et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Pande, Suyog Vinayak Nattarkannan, K. Rama Bai, M. Kapila, Dhiraj Anandan, P. Mishra, Nilamadhab Abera, Worku Lightweight Artificial Intelligence for Secure Data Communication in Energy-Constrained Healthcare Devices |
title | Lightweight Artificial Intelligence for Secure Data Communication in Energy-Constrained Healthcare Devices |
title_full | Lightweight Artificial Intelligence for Secure Data Communication in Energy-Constrained Healthcare Devices |
title_fullStr | Lightweight Artificial Intelligence for Secure Data Communication in Energy-Constrained Healthcare Devices |
title_full_unstemmed | Lightweight Artificial Intelligence for Secure Data Communication in Energy-Constrained Healthcare Devices |
title_short | Lightweight Artificial Intelligence for Secure Data Communication in Energy-Constrained Healthcare Devices |
title_sort | lightweight artificial intelligence for secure data communication in energy-constrained healthcare devices |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9451988/ https://www.ncbi.nlm.nih.gov/pubmed/36093495 http://dx.doi.org/10.1155/2022/7934582 |
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