Artificial intelligent solvers for the HIV-1 system including AIDS based on the cancer cells

Zulqurnain Sabir, Salem Ben Said, Qasem Al-Mdallal

Research output: Contribution to journalArticlepeer-review


The present investigations are related to provide the numerical performances of the HIV-1 dynamical infection model in patients with cancer (HIV-DIMC) by applying the artificial intelligence (AI) scheme based on Levenberg-Marquardt backpropagation neural networks (LBMBP-NNs). The current biological system is presented into three dynamics including cells of cancer population (T), healthy (H), and infected HIV (I). The substantiations, training and testing measures are used as sample statics to solve the HIV-DIMC. These performances with statistical ratios have been chosen as 75% training, substantiations 13% and testing 12% in order to solve the dynamical model. The correctness of achieved performances based on the HIV-DIMC is observed by using the assessment of the obtained and reference results. The absolute error is performed around 10−06 to 10−07 describe the efficiency of the scheme. The achieved measures of the dynamical system are stated to reduce the mean square error in interval 10−11-10−13. To perceive the effectiveness, credibility and aptitude of AI based LBMBP-NNs, the computing performances are proficient to analyze the convergence based on the histogram diagrams and correlation catalogue.

Original languageEnglish
Article number200309
JournalIntelligent Systems with Applications
Publication statusPublished - Mar 2024


  • Artificial intelligence
  • Cancer
  • Dynamical model
  • Levenberg-Marquardt Backpropagation
  • Numerical results

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Artificial Intelligence


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