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Swarming morlet wavelet neural network procedures for the mathematical robot system
The task of this work is to present the solutions of the mathematical robot system (MRS) to examine the positive coronavirus cases through the artificial intelligence (AI) based Morlet wavelet neural network (MWNN). The MRS is divided into two classes, infected [Formula: see text] and Robots [Formul...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Authors. Published by Elsevier Ltd.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9507784/ https://www.ncbi.nlm.nih.gov/pubmed/36185733 http://dx.doi.org/10.1016/j.imu.2022.101081 |
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author | Singkibud, Peerapongpat Sabir, Zulqurnain Fathurrochman, Irwan Alhazmi, Sharifah E. Ali, Mohamed R. |
author_facet | Singkibud, Peerapongpat Sabir, Zulqurnain Fathurrochman, Irwan Alhazmi, Sharifah E. Ali, Mohamed R. |
author_sort | Singkibud, Peerapongpat |
collection | PubMed |
description | The task of this work is to present the solutions of the mathematical robot system (MRS) to examine the positive coronavirus cases through the artificial intelligence (AI) based Morlet wavelet neural network (MWNN). The MRS is divided into two classes, infected [Formula: see text] and Robots [Formula: see text]. The design of the fitness function is presented by using the differential MRS and then optimized by the hybrid of the global swarming computational particle swarm optimization (PSO) and local active set procedure (ASP). For the exactness of the AI based MWNN-PSOIPS, the comparison of the results is presented by using the proposed and reference solutions. The reliability of the MWNN-PSOASP is authenticated by extending the data into 20 trials to check the performance of the scheme by using the statistical operators with 10 hidden numbers of neurons to solve the MRS. |
format | Online Article Text |
id | pubmed-9507784 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Authors. Published by Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95077842022-09-26 Swarming morlet wavelet neural network procedures for the mathematical robot system Singkibud, Peerapongpat Sabir, Zulqurnain Fathurrochman, Irwan Alhazmi, Sharifah E. Ali, Mohamed R. Inform Med Unlocked Article The task of this work is to present the solutions of the mathematical robot system (MRS) to examine the positive coronavirus cases through the artificial intelligence (AI) based Morlet wavelet neural network (MWNN). The MRS is divided into two classes, infected [Formula: see text] and Robots [Formula: see text]. The design of the fitness function is presented by using the differential MRS and then optimized by the hybrid of the global swarming computational particle swarm optimization (PSO) and local active set procedure (ASP). For the exactness of the AI based MWNN-PSOIPS, the comparison of the results is presented by using the proposed and reference solutions. The reliability of the MWNN-PSOASP is authenticated by extending the data into 20 trials to check the performance of the scheme by using the statistical operators with 10 hidden numbers of neurons to solve the MRS. The Authors. Published by Elsevier Ltd. 2022 2022-09-24 /pmc/articles/PMC9507784/ /pubmed/36185733 http://dx.doi.org/10.1016/j.imu.2022.101081 Text en © 2022 The Authors Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Singkibud, Peerapongpat Sabir, Zulqurnain Fathurrochman, Irwan Alhazmi, Sharifah E. Ali, Mohamed R. Swarming morlet wavelet neural network procedures for the mathematical robot system |
title | Swarming morlet wavelet neural network procedures for the mathematical robot system |
title_full | Swarming morlet wavelet neural network procedures for the mathematical robot system |
title_fullStr | Swarming morlet wavelet neural network procedures for the mathematical robot system |
title_full_unstemmed | Swarming morlet wavelet neural network procedures for the mathematical robot system |
title_short | Swarming morlet wavelet neural network procedures for the mathematical robot system |
title_sort | swarming morlet wavelet neural network procedures for the mathematical robot system |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9507784/ https://www.ncbi.nlm.nih.gov/pubmed/36185733 http://dx.doi.org/10.1016/j.imu.2022.101081 |
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