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A novel study of Morlet neural networks to solve the nonlinear HIV infection system of latently infected cells     
Yazarlar
Muhammad Umar
Sabir Zulqurnain
Muhammad Asif Zahoor Raja
Hacı Mehmet Başkonuş
Harran Üniversitesi, Türkiye
Yaod Shao-Wen
 Esin İLHAN Esin İLHAN
Kırşehir Ahi Evran Üniversitesi, Türkiye
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 coefficient' and 'root mean square error' based indices further validate the sustainability and applicability of proposed MW-ANN-GA-SQP solver.
Anahtar Kelimeler
Morlet wavelets | HIV infection models | Genetic algorithms | Neural networks | Sequential quadratic programming | Bioinformatics
Makale Türü Özgün Makale
Makale Alt Türü SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale
Dergi Adı RESULTS IN PHYSICS
Dergi ISSN 2211-3797
Dergi Tarandığı Indeksler SCI-Expanded
Dergi Grubu Q1
Makale Dili İngilizce
Basım Tarihi 06-2021
Cilt No 25
Sayı 1
Sayfalar 1 / 13
Doi Numarası 10.1016/j.rinp.2021.104235
Makale Linki http://dx.doi.org/10.1016/j.rinp.2021.104235