Swarm Intelligence Optimization: An Exploration and Application of Machine Learning Technology

In the agriculture development and growth, the efficient machinery and equipment plays an important role. Various research studies are involved in the implementation of the research and patents to aid the smart agriculture and authors and reviewers that machine leaning technologies are providing the...

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Autores principales:	Cai Yinying, Sharma Amit
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	swarm intelligence optimization machine learning algorithms v3cfoa v3cfoa-rf model Science Q Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/66c37e9e948d445c9c59cf401e1082f0
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id	oai:doaj.org-article:66c37e9e948d445c9c59cf401e1082f0
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spelling	oai:doaj.org-article:66c37e9e948d445c9c59cf401e1082f02021-12-05T14:10:51ZSwarm Intelligence Optimization: An Exploration and Application of Machine Learning Technology0334-18602191-026X10.1515/jisys-2020-0084https://doaj.org/article/66c37e9e948d445c9c59cf401e1082f02021-01-01T00:00:00Zhttps://doi.org/10.1515/jisys-2020-0084https://doaj.org/toc/0334-1860https://doaj.org/toc/2191-026XIn the agriculture development and growth, the efficient machinery and equipment plays an important role. Various research studies are involved in the implementation of the research and patents to aid the smart agriculture and authors and reviewers that machine leaning technologies are providing the best support for this growth. To explore machine learning technology and machine learning algorithms, the most of the applications are studied based on the swarm intelligence optimization. An optimized V3CFOA-RF model is built through V3CFOA. The algorithm is tested in the data set collected concerning rice pests, later analyzed and compared in detail with other existing algorithms. The research result shows that the model and algorithm proposed are not only more accurate in recognition and prediction, but also solve the time lagging problem to a degree. The model and algorithm helped realize a higher accuracy in crop pest prediction, which ensures a more stable and higher output of rice. Thus they can be employed as an important decision-making instrument in the agricultural production sector.Cai YinyingSharma AmitDe Gruyterarticleswarm intelligence optimizationmachine learning algorithmsv3cfoav3cfoa-rf modelScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 460-469 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	swarm intelligence optimization machine learning algorithms v3cfoa v3cfoa-rf model Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	swarm intelligence optimization machine learning algorithms v3cfoa v3cfoa-rf model Science Q Electronic computers. Computer science QA75.5-76.95 Cai Yinying Sharma Amit Swarm Intelligence Optimization: An Exploration and Application of Machine Learning Technology
description	In the agriculture development and growth, the efficient machinery and equipment plays an important role. Various research studies are involved in the implementation of the research and patents to aid the smart agriculture and authors and reviewers that machine leaning technologies are providing the best support for this growth. To explore machine learning technology and machine learning algorithms, the most of the applications are studied based on the swarm intelligence optimization. An optimized V3CFOA-RF model is built through V3CFOA. The algorithm is tested in the data set collected concerning rice pests, later analyzed and compared in detail with other existing algorithms. The research result shows that the model and algorithm proposed are not only more accurate in recognition and prediction, but also solve the time lagging problem to a degree. The model and algorithm helped realize a higher accuracy in crop pest prediction, which ensures a more stable and higher output of rice. Thus they can be employed as an important decision-making instrument in the agricultural production sector.
format	article
author	Cai Yinying Sharma Amit
author_facet	Cai Yinying Sharma Amit
author_sort	Cai Yinying
title	Swarm Intelligence Optimization: An Exploration and Application of Machine Learning Technology
title_short	Swarm Intelligence Optimization: An Exploration and Application of Machine Learning Technology
title_full	Swarm Intelligence Optimization: An Exploration and Application of Machine Learning Technology
title_fullStr	Swarm Intelligence Optimization: An Exploration and Application of Machine Learning Technology
title_full_unstemmed	Swarm Intelligence Optimization: An Exploration and Application of Machine Learning Technology
title_sort	swarm intelligence optimization: an exploration and application of machine learning technology
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/66c37e9e948d445c9c59cf401e1082f0
work_keys_str_mv	AT caiyinying swarmintelligenceoptimizationanexplorationandapplicationofmachinelearningtechnology AT sharmaamit swarmintelligenceoptimizationanexplorationandapplicationofmachinelearningtechnology
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Swarm Intelligence Optimization: An Exploration and Application of Machine Learning Technology

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