Intelligent computing knacks for infected media and time delay impacts on dynamical behaviors and control measures of rumor-spreading model

Muhammad Asif Zahoor Raja; Adeeba Haider; Kottakkaran Sooppy Nisar; Muhammad Shoaib; Muhammad Asif Zahoor Raja; Adeeba Haider; Kottakkaran Sooppy Nisar; Muhammad Shoaib

doi:10.3934/biophy.2024001

AIMS Biophysics

2024, Volume 11, Issue 1: 1-17. doi: 10.3934/biophy.2024001

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Research article

Intelligent computing knacks for infected media and time delay impacts on dynamical behaviors and control measures of rumor-spreading model

1.
Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section .3, Douliou, Yunlin 64002, Taiwan, R.O.C
2.
Department of Mathematics, COMSATS University Islamabad, Attock Campus, Pakistan
3.
Department of Mathematics, College of Science and Humanities in Alkharj, Prince Sattam bin Abdulaziz University, Al Kharj, Saudi Arabia
4.
Saveetha School of Engineering, SIMATS, Chennai, India
5.
Yuan Ze University, AI Centre, Taoyuan 320, Taiwan

Received: 24 July 2023 Revised: 27 November 2023 Accepted: 04 December 2023 Published: 02 January 2024

Artificial neural networks (ANNs) have transformed machine learning and computational intelligence by providing unprecedented powers in modeling complicated data and addressing a wide range of challenges. In the field of ANNs, back propagation is a key approach for training neural networks. However, obtaining optimum network efficiency while tackling over fitting and controlling uncertainty is a difficult task. The present study employs the Bayesian Regularization Method with Neural Network Backpropagation (BRM-NNB) technique to investigate the rumors spreading delay model. With their rapid spread, rumors have the potential to cause fear and even financial loss. Thus, we must take decisive actions to stop the rumor from spreading. Nowadays, rumors can spread through instant messaging, emails, or publishing, thanks to the development of the internet. In this research, an XY-SIR rumors spreading delay model (XY-SIR-RS-DM) is investigated in relation to the novel spreading pattern. Media networks can be categorized into susceptible and infected media, while friendship networks can be categorized into three groups: spreaders (S, I, and R), who actively disseminate rumors, those who are ignorant and those who have no desire to do so. To estimate the solution of the suggested model, the Bayesian regularization method with neural network back propagation (BRM-NNB) is used. The data set is generated by applying the explicit Runge-Kutta method. The computing BRM-NNB strategy is implemented for three different performances, where the training, testing, and verification data are reported as 80%, 15%, and 5%, respectively, with 10 hidden neurons. To verify the validity of the developed artificial intelligence (AI) approach represented by the BRM-NNB, outcome comparisons are presented. The result is compatible with obtaining a minimal absolute error that is nearly equal to zero, thereby proving the efficacy of the proposed method.
- Bayesian regularization method,
- regression,
- neural networks backpropagation,
- rumors spread model
Citation: Muhammad Asif Zahoor Raja, Adeeba Haider, Kottakkaran Sooppy Nisar, Muhammad Shoaib. Intelligent computing knacks for infected media and time delay impacts on dynamical behaviors and control measures of rumor-spreading model[J]. AIMS Biophysics, 2024, 11(1): 1-17. doi: 10.3934/biophy.2024001

Related Papers:

Abstract

Artificial neural networks (ANNs) have transformed machine learning and computational intelligence by providing unprecedented powers in modeling complicated data and addressing a wide range of challenges. In the field of ANNs, back propagation is a key approach for training neural networks. However, obtaining optimum network efficiency while tackling over fitting and controlling uncertainty is a difficult task. The present study employs the Bayesian Regularization Method with Neural Network Backpropagation (BRM-NNB) technique to investigate the rumors spreading delay model. With their rapid spread, rumors have the potential to cause fear and even financial loss. Thus, we must take decisive actions to stop the rumor from spreading. Nowadays, rumors can spread through instant messaging, emails, or publishing, thanks to the development of the internet. In this research, an XY-SIR rumors spreading delay model (XY-SIR-RS-DM) is investigated in relation to the novel spreading pattern. Media networks can be categorized into susceptible and infected media, while friendship networks can be categorized into three groups: spreaders (S, I, and R), who actively disseminate rumors, those who are ignorant and those who have no desire to do so. To estimate the solution of the suggested model, the Bayesian regularization method with neural network back propagation (BRM-NNB) is used. The data set is generated by applying the explicit Runge-Kutta method. The computing BRM-NNB strategy is implemented for three different performances, where the training, testing, and verification data are reported as 80%, 15%, and 5%, respectively, with 10 hidden neurons. To verify the validity of the developed artificial intelligence (AI) approach represented by the BRM-NNB, outcome comparisons are presented. The result is compatible with obtaining a minimal absolute error that is nearly equal to zero, thereby proving the efficacy of the proposed method.

Nomenclature

BRM-NNB

Bayesian Regularization Method with Neural Networks Backpropagation

MSE

Mean Square Error

X(t)

susceptible media that isn't rumor-infected

Y(t)

infected media that is rumor-infected

S(t)

spreaders; who know and actively disseminate rumors

I(t)

those who are ignorant and have never heard of rumors

R(t)

those who know but have no desire in spreading rumors

τ_i

time delays

A₁

susceptible media's probability

infected media's specific probability

ϵ₁

probability of leaving system by individuals

the media action rate

A₂

friendship network layer's probability

probability of changing the ignorant person to spreader

ϵ₂

the migration rate

Probability of changing the spreader into the stifler

EHs

Error Histograms

Artificial Intelligence

Acknowledgments

“This study is supported via funding from Prince Sattam bin Abdulaziz University project number (PSAU/2023/R/1444)”.

Conflict of interest

The authors declare no conflict of interest.

Author contributions

Conceptualization: KSN, MS; Formal analysis: MAZR; Investigation: MAZR, AH, KSN, MS; Methodology: MAZR; Software: AH, KSN, MS; Validation: MAZR, MS; Visualization: AH, MS; Writing - original draft: MAZR, AH, KSN, MS; Writing - review editing: KSN, MS.

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