Name
Title | ||
|---|---|---|
Neuro-swarm intelligent computing paradigm for nonlinear HIV infection model with CD4+ T-cells |
Abstract | ||
|---|---|---|
In the investigations presented here, an efficient computing approach is applied to solve Human Immunodeficiency Virus (HIV) infection spread. This approach involves CD4+ T-cells by feed-forward artificial neural networks (FF-ANNs) trained with particle swarm optimization (PSO) and interior point method (IPM), i.e., FF-ANN-PSO-IPM. In the proposed solver FF-ANN-PSO-IPM, the FF-ANN models of differential equations are used to develop the fitness functions for an infection model of T-cells. The training of networks through minimization problem are proficiently conducted by integrated heuristic capability of PSO-IPM. The reliability, stability and exactness of the proposed FF-ANN-PSO-IPM are established through comparison with outcomes of standard numerical procedure with Adams method for both single and multiple autonomous trials with precision of order 4 to 8 decimal places of accuracy. The statistical measures are effectively used to validate the outcomes of the proposed FF-ANN-PSO-IPM. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.matcom.2021.04.008 | Mathematics and Computers in Simulation |
Keywords | DocType | Volume |
HIV infection model,Particle swarm optimization,Artificial neural networks,Interior-point method,Statistical analysis | Journal | 188 |
ISSN | Citations | PageRank |
0378-4754 | 2 | 0.36 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Muhammad Umar | 1 | 9 | 3.24 |
| Zulqurnain Sabir | 2 | 13 | 5.71 |
| Muhammad Asif Zahoor Raja | 3 | 2 | 0.36 |
| J.F. Gómez Aguilar | 4 | 2 | 0.36 |
| Fazli Amin | 5 | 35 | 5.26 |
| Muhammad Shoaib | 6 | 1263 | 88.37 |