Bibliographic Details
| Title: |
Intelligent computing knacks for infected media and time delay impacts on dynamical behaviors and control measures of rumor-spreading model. |
| Authors: |
Raja, Muhammad Asif Zahoor1 (AUTHOR), Haider, Adeeba2 (AUTHOR), Nisar, Kottakkaran Sooppy3,4 (AUTHOR) n.sooppy@psau.edu.sa, Shoaib, Muhammad5 (AUTHOR) |
| Superior Title: |
AIMS Biophysics. 2024, Vol. 11 Issue 1, p1-17. 17p. |
| Subject Terms: |
*COMPUTATIONAL intelligence, *BACK propagation, *INSTANT messaging, *RUNGE-Kutta formulas, *ARTIFICIAL intelligence, *ARTIFICIAL neural networks |
| 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. [ABSTRACT FROM AUTHOR] |
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| Database: |
Academic Search Premier |
