A novel study of Morlet neural networks to solve the nonlinear HIV infection system of latently infected cells
Yazarlar (6)
Muhammad Umar Hazara University Pakistan, Pakistan
Zulqurnain Sabir
Hazara University Pakistan, Pakistan
Hazara University Pakistan, Pakistan
Muhammad Asif Zahoor Raja
National Yunlin University Of Science And Technology, Tayvan
National Yunlin University Of Science And Technology, Tayvan
Haci Mehmet Baskonus Harran Üniversitesi, Türkiye
Shao Wen Yao
Henan Polytechnic University, Çin
Henan Polytechnic University, Çin
Doç. Dr. Esin İLHAN Kırşehir Ahi Evran Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Results in Physics (Q1) | ||
| Dergi ISSN | 2211-3797 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2021 |
| Kabul Tarihi | 12-04-2026 | Yayınlanma Tarihi | – |
| Cilt / Sayı / Sayfa | 25 / 1 / 1–13 | DOI | 10.1016/j.rinp.2021.104235 |
| Makale Linki | https://doi.org/10.1016/j.rinp.2021.104235 | ||
| Özet |
| The aim of this study is to provide the numerical outcomes of a nonlinear HIV infection system of latently infected CD4+ T cells exists in bioinformatics using Morlet wavelet (MW) artificial neural networks (ANNs) optimized initially with global search of genetic algorithms (GAs) hybridized for speedy local search of sequential quadratic programming (SQP), i.e., MW-ANN-GA-SQP. The design of an error function is presented by designing the MW-ANN models for the differential equations along with the initial conditions that represent the HIV infection system involving latently infected CD4+ T cells. The precision and persistence of the presented approach MW-ANN-GA-SQP are recognized through comparative studies from the results of the Runge-Kutta numerical scheme for solving the HIV infection spread system in case of single and multiple trails of the MW-ANN-GA-SQP. Statistical estimates with ‘Theil’s inequality … |
| Anahtar Kelimeler |
| Bioinformatics | Genetic algorithms | HIV infection models | Morlet wavelets | Neural networks | Sequential quadratic programming |
Science Direct
A novel study of Morlet neural networks to solve the nonlinear HIV infection system of latently infected cells
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 89 |
| Scopus | 26 |
| Web of Science | 73 |