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A radial basis scale conjugate gradient neural network process for the Zika model with human movement and reservoirs

Author

Listed:
  • Sabir, Zulqurnain
  • Souayeh, Basma
  • Umar, Muhammad
  • Salahshour, Soheil
  • Alfannakh, Huda
  • Suresh Kumar Raju, S.

Abstract

The purpose of current research is to find the numerical solutions of the nonlinear Zika model with human movement and reservoirs (ZMHMR) by designing a novel radial basis scale conjugate gradient neural network (RB-SCGNN). This nonlinear model contains ten different groups, and the numerical solutions are presented by the stochastic RB-SCGNN process. A design of dataset is presented through the Runge-Kutta scheme to lessen the values of the mean square error by splitting the data into training as 72 %, while 14 %, 14 % for both verification and testing. Fifteen neurons in the hidden layers, single input, and radial basis activation function are used to solve the ZMHMR. The accuracy of the proposed scheme is judged through the overlapping of the outputs, whereas smaller values of the absolute error indicate the exactness of the RB-SCGNN. Additionally, the statistical representations using different operators validate the approach's trustworthiness.

Suggested Citation

  • Sabir, Zulqurnain & Souayeh, Basma & Umar, Muhammad & Salahshour, Soheil & Alfannakh, Huda & Suresh Kumar Raju, S., 2025. "A radial basis scale conjugate gradient neural network process for the Zika model with human movement and reservoirs," Chaos, Solitons & Fractals, Elsevier, vol. 199(P1).
    • Handle: RePEc:eee:chsofr:v:199:y:2025:i:p1:s0960077925007246
    DOI: 10.1016/j.chaos.2025.116711
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    References listed on IDEAS

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    1. Dawn M. Dudley & Matthew T. Aliota & Emma L. Mohr & Andrea M. Weiler & Gabrielle Lehrer-Brey & Kim L. Weisgrau & Mariel S. Mohns & Meghan E. Breitbach & Mustafa N. Rasheed & Christina M. Newman & Dane, 2016. "A rhesus macaque model of Asian-lineage Zika virus infection," Nature Communications, Nature, vol. 7(1), pages 1-9, November.
    2. Vinushi Amaratunga & Lasini Wickramasinghe & Anushka Perera & Jeevani Jayasinghe & Upaka Rathnayake, 2020. "Artificial Neural Network to Estimate the Paddy Yield Prediction Using Climatic Data," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-11, July.
    3. Patrícia Brasil & Zilton Vasconcelos & Tara Kerin & Claudia Raja Gabaglia & Ieda P. Ribeiro & Myrna C. Bonaldo & Luana Damasceno & Marcos V. Pone & Sheila Pone & Andrea Zin & Irena Tsui & Kristina Ada, 2020. "Zika virus vertical transmission in children with confirmed antenatal exposure," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
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    1. Suhaib, Basit & Awan, Saeed Ehsan & Awais, Muhammad & Sabir, Zulqurnain & Maqsood, Sahar & Malik, M.Y. & Alqahtani, A.S. & Khan, Zuhaib Ashfaq, 2026. "Peristaltic rheology of nanomaterial in a porous channel under thermal radiation and variable heat source: An artificial neural network framework," Chaos, Solitons & Fractals, Elsevier, vol. 203(C).

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