Heuristic computing with sequential quadratic programming for solving a nonlinear hepatitis B virus model
This current study presents a nonlinear system of the hepatitis B virus infection by using the strength of artificial neural network (ANN) together with the competences of global and local search efficiencies of genetic algorithm (GA) and sequential quadratic programming scheme (SQPS), i.e., ANN-GA-...
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| Vydáno v: | Mathematics and computers in simulation Ročník 212; s. 234 - 248 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
01.10.2023
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| Témata: | |
| ISSN: | 0378-4754, 1872-7166 |
| On-line přístup: | Získat plný text |
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| Abstract | This current study presents a nonlinear system of the hepatitis B virus infection by using the strength of artificial neural network (ANN) together with the competences of global and local search efficiencies of genetic algorithm (GA) and sequential quadratic programming scheme (SQPS), i.e., ANN-GA-SQPS. An error function is designed to use the mathematical form of the hepatitis B virus differential model and its initial conditions. The optimization of the error function is performed by using the hybridization efficiency of the GA-SQPS for the hepatitis B virus infection disease model. For the competence of ANN-GA-SQPS, the matching of obtained and the Adams numerical solutions has been observed. The absolute error is found in good measures for solving a nonlinear hepatitis B virus infection disease model. Moreover, statistical measures using different indices for 50 independent executions and 30 variables have been performed to check constancy, reliability, and effectiveness of the ANN-GA-SQPS. |
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| AbstractList | This current study presents a nonlinear system of the hepatitis B virus infection by using the strength of artificial neural network (ANN) together with the competences of global and local search efficiencies of genetic algorithm (GA) and sequential quadratic programming scheme (SQPS), i.e., ANN-GA-SQPS. An error function is designed to use the mathematical form of the hepatitis B virus differential model and its initial conditions. The optimization of the error function is performed by using the hybridization efficiency of the GA-SQPS for the hepatitis B virus infection disease model. For the competence of ANN-GA-SQPS, the matching of obtained and the Adams numerical solutions has been observed. The absolute error is found in good measures for solving a nonlinear hepatitis B virus infection disease model. Moreover, statistical measures using different indices for 50 independent executions and 30 variables have been performed to check constancy, reliability, and effectiveness of the ANN-GA-SQPS. |
| Author | Sabir, Zulqurnain Shah, Nehad Ali Raja, Muhammad Asif Zahoor Baskonus, Haci Mehmet Umar, Muhammad Ali, Mohamed R. |
| Author_xml | – sequence: 1 givenname: Muhammad surname: Umar fullname: Umar, Muhammad email: umar_maths@hu.edu.pk organization: Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan – sequence: 2 givenname: Zulqurnain surname: Sabir fullname: Sabir, Zulqurnain email: zulqurnain_maths@hu.edu.pk organization: Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan – sequence: 3 givenname: Muhammad Asif Zahoor surname: Raja fullname: Raja, Muhammad Asif Zahoor email: rajamaz@yuntect.edu.tw organization: Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan, ROC – sequence: 4 givenname: Haci Mehmet surname: Baskonus fullname: Baskonus, Haci Mehmet email: hmbaskonus@gmail.com organization: Faculty of Education, Harran University, Sanliurfa, Turkey – sequence: 5 givenname: Mohamed R. orcidid: 0000-0002-0795-0709 surname: Ali fullname: Ali, Mohamed R. email: mohamed.reda@bhit.bu.edu.eg organization: Department of Mathematics, Benha Faculty of Engineering, Benha University, Egypt – sequence: 6 givenname: Nehad Ali surname: Shah fullname: Shah, Nehad Ali email: nehadali199@sejong.ac.kr organization: Department of Mechanical Engineering, Sejong University, Seoul 05006, Republic of Korea |
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| Keywords | Infection disease Sequential quadratic algorithm scheme Statistical analysis Hepatitis B virus system Artificial neural networks |
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