Numerical performances based artificial neural networks to deal with the computer viruses spread on the complex networks

Alderremy, A. A.; Gómez-Aguilar, J. F.; Sabir, Zulqurnain; Aly, Shaban; Lavín-Delgado, J. E.; Razo-Hernández, José R.

doi:10.1080/00207160.2024.2326926

International Journal of Computer Mathematics

2024

DOI: 10.1080/00207160.2024.2326926

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Numerical performances based artificial neural networks to deal with the computer viruses spread on the complex networks

A. A. Alderremy,

J. F. Gómez-Aguilar,

Zulqurnain Sabir

et al.

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2024

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Cited by 4 publications

References 59 publications

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Physics‐informed neural networks guided modelling and multiobjective optimization of a mAb production process

Alam,

Anurag,

Gangwar

et al. 2024

Can J Chem Eng

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In this paper, we aim to correlate various process and product quality attributes of a mammalian cell culture process with process parameters. To achieve this, we employed physics‐informed neural networks that solve the governing ordinary differential equations comprising independent variables (inputs‐ time, flow rates, and volume) and dependent variables (outputs‐ viable cell density, dead cell density, glucose concentration, lactate concentration, and monoclonal antibody concentration). The proposed model surpasses the prediction and accuracy capabilities of other commonly used modelling approaches, such as the multilayer perceptron model. It has higher R‐squared (R2), lower root mean square error, and lower mean absolute error than the multilayer perceptron model for all output variables (viable cell density, viability, glucose concentration, lactate concentration, and monoclonal antibody concentration). Furthermore, we incorporate a Bayesian optimization study to maximize viable cell density and monoclonal antibody concentration. Single objective optimization and weighted sum multiobjective optimization were carried out for viable cell density and monoclonal antibody concentration in separate (single objective optimization) and combined (multiobjective optimization) forms. An increment of 13.01% and 18.57% for viable cell density and monoclonal antibody concentration, respectively, were projected under single objective optimization, and 46.32% and 67.86%, respectively, for multiobjective optimization as compared to the base case. This study highlights the potential of the physics‐informed neural networks‐based modelling and optimization of upstream processing of mammalian cell‐based monoclonal antibodies in biopharmaceutical operations.

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Physics‐informed neural networks guided modelling and multiobjective optimization of a mAb production process

Alam,

Anurag,

Gangwar

et al. 2024

Can J Chem Eng

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Neural fractional differential networks for modeling complex dynamical systems

Vellappandi,

Lee

2024

Nonlinear Dyn

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Solitonic solutions for the reduced Maxwell-Bloch equations via the Darboux transformation and artificial neural network in nonlinear wave dynamics

Riaz,

Farooq

2024

Phys. Scr.

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In this article, we explore nonlinear wave dynamics by presenting solitonic solutions for the reduced Maxwell-Bloch equations, a model relevant to both nonlinear optics and Bose-Einstein condensates. First, we derive the Lax pair for the system and apply a Darboux transformation to obtain and analyze the soliton solutions. Then, we introduce a novel approach by integrating the Darboux transformation, a powerful analytical tool, with the Levenberg-Marquardt artificial neural network, a reliable numerical method. This combination enables the identification and validation of soliton solutions, supported by detailed graphical and tabular analyses. The Levenberg-Marquardt artificial neural network effectively demonstrates the uniqueness and convergence of the solutions, with the accuracy of the results validated through comprehensive graphical representations.

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Numerical performances based artificial neural networks to deal with the computer viruses spread on the complex networks

Cited by 4 publications

References 59 publications

Physics‐informed neural networks guided modelling and multiobjective optimization of a mAb production process

Physics‐informed neural networks guided modelling and multiobjective optimization of a mAb production process

Neural fractional differential networks for modeling complex dynamical systems

Solitonic solutions for the reduced Maxwell-Bloch equations via the Darboux transformation and artificial neural network in nonlinear wave dynamics

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