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Updated: Jun 28, 2025

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Bio inspired heuristic computing scheme for the human liver nonlinear model.

Zulqurnain Sabir1, Salem Ben Said2, Qasem Al-Mdallal2

  • 1Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon.

Heliyon
|April 15, 2024
PubMed
Summary
This summary is machine-generated.

A novel bio-inspired computing method effectively models the human liver's nonlinear dynamics. This artificial neural network (ANN) approach, optimized with genetic algorithm (GA) and interior-point (IP) methods, offers accurate and reliable solutions.

Keywords:
Genetic algorithmHeuristicInterior pointLiver modelSimulationsTransfer function

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Area of Science:

  • Computational biology
  • Bio-inspired computing
  • Biomedical modeling

Background:

  • The human liver exhibits complex nonlinear behavior crucial for physiological understanding.
  • Existing models may lack the precision to capture these intricate dynamics.

Purpose of the Study:

  • To develop a bio-inspired heuristic computing approach for solving the nonlinear human liver model.
  • To validate the accuracy and reliability of the proposed computational method.

Main Methods:

  • Utilizing stochastic computation based on artificial neural networks (ANN).
  • Employing a hybrid optimization scheme combining genetic algorithm (GA) and interior-point (IP) methods (GAIP).
  • Designing a fitness function from the differential form of the liver model and optimizing it with GAIP.

Main Results:

  • The proposed GAIP solver demonstrated high accuracy, evidenced by close agreement with the reference Adams scheme solutions.
  • Low absolute error values confirm the efficacy and worth of the developed computational solver.
  • Statistical analyses further validated the reliability of the approach for nonlinear liver modeling.

Conclusions:

  • The bio-inspired GAIP approach provides an accurate and reliable method for solving nonlinear human liver models.
  • This computational strategy, utilizing ANNs and hybrid optimization, holds promise for advancing biomedical simulations.