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Big Data Value Calculation Method Based on Particle Swarm Optimization Algorithm

SI is a relatively recent technology that was inspired by observations of natural social insects and artificial systems. This system comprises multiple individual agents who rely on collective behavior in decentralized and self-organized networks. One of the biggest difficulties for existing compute...

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Detalles Bibliográficos
Autores principales: Ma, Wensheng, Hou, Xilin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9270169/
https://www.ncbi.nlm.nih.gov/pubmed/35814581
http://dx.doi.org/10.1155/2022/5356164
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author Ma, Wensheng
Hou, Xilin
author_facet Ma, Wensheng
Hou, Xilin
author_sort Ma, Wensheng
collection PubMed
description SI is a relatively recent technology that was inspired by observations of natural social insects and artificial systems. This system comprises multiple individual agents who rely on collective behavior in decentralized and self-organized networks. One of the biggest difficulties for existing computer techniques is learning from such large datasets, which is addressed utilizing big data. Big data-based categorization refers to the challenge of determining which set of classifications a new discovery belongs to. This classification is based on a training set of data that comprises observations that have been assigned to a certain category. In this paper, CIN-big data value calculation based on particle swarm optimization (BD-PSO) algorithm is proposed by operating in local optima and to improve the operating efficiency. The convergence speed of the particle swarm optimization (PSO), which operates in the local optima, is improved by big data-based particle swarm optimization (BD-PSO). It improves computing efficiency by improving the method, resulting in a reduction in calculation time. The performance of the BD-PSO is tested on four benchmark dataset, which is taken from the UCI. The datasets used for evaluation are wine, iris, blood transfusion, and zoo. SVM and CG-CNB are the two existing methods used for the comparison of BD-PSO. It achieves 92% of accuracy, 92% of precision, 92% of recall, and 1.34 of F1 measure, and time taken for execution is 149 ms, which in turn outperforms the existing approaches. It achieves robust solutions and identifies appropriate intelligent technique related to the optimization problem.
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spelling pubmed-92701692022-07-09 Big Data Value Calculation Method Based on Particle Swarm Optimization Algorithm Ma, Wensheng Hou, Xilin Comput Intell Neurosci Research Article SI is a relatively recent technology that was inspired by observations of natural social insects and artificial systems. This system comprises multiple individual agents who rely on collective behavior in decentralized and self-organized networks. One of the biggest difficulties for existing computer techniques is learning from such large datasets, which is addressed utilizing big data. Big data-based categorization refers to the challenge of determining which set of classifications a new discovery belongs to. This classification is based on a training set of data that comprises observations that have been assigned to a certain category. In this paper, CIN-big data value calculation based on particle swarm optimization (BD-PSO) algorithm is proposed by operating in local optima and to improve the operating efficiency. The convergence speed of the particle swarm optimization (PSO), which operates in the local optima, is improved by big data-based particle swarm optimization (BD-PSO). It improves computing efficiency by improving the method, resulting in a reduction in calculation time. The performance of the BD-PSO is tested on four benchmark dataset, which is taken from the UCI. The datasets used for evaluation are wine, iris, blood transfusion, and zoo. SVM and CG-CNB are the two existing methods used for the comparison of BD-PSO. It achieves 92% of accuracy, 92% of precision, 92% of recall, and 1.34 of F1 measure, and time taken for execution is 149 ms, which in turn outperforms the existing approaches. It achieves robust solutions and identifies appropriate intelligent technique related to the optimization problem. Hindawi 2022-07-01 /pmc/articles/PMC9270169/ /pubmed/35814581 http://dx.doi.org/10.1155/2022/5356164 Text en Copyright © 2022 Wensheng Ma and Xilin Hou. 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
Ma, Wensheng
Hou, Xilin
Big Data Value Calculation Method Based on Particle Swarm Optimization Algorithm
title Big Data Value Calculation Method Based on Particle Swarm Optimization Algorithm
title_full Big Data Value Calculation Method Based on Particle Swarm Optimization Algorithm
title_fullStr Big Data Value Calculation Method Based on Particle Swarm Optimization Algorithm
title_full_unstemmed Big Data Value Calculation Method Based on Particle Swarm Optimization Algorithm
title_short Big Data Value Calculation Method Based on Particle Swarm Optimization Algorithm
title_sort big data value calculation method based on particle swarm optimization algorithm
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9270169/
https://www.ncbi.nlm.nih.gov/pubmed/35814581
http://dx.doi.org/10.1155/2022/5356164
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