Dr. Muhammad Bilal | Editorial Board Member

Dr. Muhammad Bilal | Editorial Board Member

Shanghai University | China

Dr. Muhammad Bilal is an applied mathematics researcher at Shanghai University whose work focuses on nonlinear wave theory, optical solitons, plasma physics, and computational methods for complex dynamical systems. With a strong publication record comprising over 40 documents, more than 1,900 citations, and an h-index of 23, he has established himself as a significant contributor to mathematical physics and nonlinear wave propagation. He completed his advanced education in applied and computational mathematics and has accumulated extensive research experience through collaborative projects in wave dynamics, optical fiber modeling, modulation instability, and analytical methods for nonlinear differential equations. His research interests span nonlinear Schrödinger systems, shallow water wave models, ferromagnetic materials, fractional models, and stability analysis across diverse physical systems. Dr. Bilal has contributed widely cited analytical techniques and exact solution frameworks that have enhanced theoretical understanding and computational modeling in optical communication and fluid dynamics. His work has appeared in reputable international journals such as Mathematical Methods in the Applied Sciences, Results in Physics, Optical and Quantum Electronics, Modern Physics Letters B, and IEEE Access. He has also been recognized for his scientific impact through multiple high-quality publications and his growing influence in applied mathematics research.

Profile : Google Scholar

Featured Publications

Bilal, M. A., Zeeshan, M., Riaz, Q., Shahzad, M. K., Jabeen, H., & Haider, S. A., et al. (2021). Protocol-based deep intrusion detection for DoS and DDoS attacks using UNSW-NB15 and Bot-IoT datasets. IEEE Access, 10, 2269–2283.

Bilal, M., Seadawy, A. R., Younis, M., Rizvi, S. T. R., & Zahed, H. (2021). Dispersive propagation wave solutions to unidirectional shallow water wave Dullin–Gottwald–Holm system and modulation instability analysis. Mathematical Methods in the Applied Sciences, 44(5), 4094–4104.

Bilal, M., Seadawy, A. R., Younis, M., Rizvi, S. T. R., El-Rashidy, K., & Mahmoud, S. F. (2021). Analytical wave structures in plasma physics modelled by the Gilson-Pickering equation using two integration norms. Results in Physics, 23, 103959.

Younis, M., Sulaiman, T. A., Bilal, M., Rehman, S. U., & Younas, U. (2020). Modulation instability analysis and optical solutions to the modified nonlinear Schrödinger equation. Communications in Theoretical Physics, 72(6), 065001.

Younis, M., Younas, U., Rehman, S. U., Bilal, M., & Waheed, A. (2017). Optical bright–dark and Gaussian soliton with third-order dispersion. Optik, 134, 233–238.

Assist. Prof. Dr. Shravan Kumar Rudrabhatla | Best Researcher Award

Assist. Prof. Dr. Shravan Kumar Rudrabhatla | Best Researcher Award

Anurag University | India

Dr. Shravan Kumar Rudrabhatla is an Assistant Professor at Anurag University, Hyderabad, specializing in fluid dynamics and artificial neural networks. He earned his Ph.D. in Applied Mathematics from the National Institute of Technology (NIT), Warangal in 2023 under the supervision of Prof. D. Srinivasacharya, focusing on the artificial neural network treatment of Casson fluid flow over a radially stretching sheet. His research integrates deep learning, computational fluid dynamics, and heat and mass transfer modeling, contributing to the understanding of complex non-Newtonian flows. Dr. Rudrabhatla has authored 6 research articles, accumulated 49 citations from 43 documents, and achieved an h-index of 4, as indexed by Scopus. His recent works include publications in European Journal of Mechanics B/Fluids, Physics of Fluids, Mathematical Models and Computer Simulations, and Journal of Thermal Analysis and Calorimetry. He has participated in numerous faculty development programs, workshops, and GIAN courses focused on machine learning and computational modeling. His academic journey is complemented by strong technical skills in Python, MATLAB, and C++, and a teaching background spanning over a decade. Dr. Rudrabhatla’s work continues to advance the intersection of mathematics, fluid mechanics, and artificial intelligence, contributing significantly to modern computational sciences.

Profiles : Orcid | Google Scholar | Scopus

Featured Publications

Srinivasacharya, D., & Kumar, R. S. (2022). Artificial neural network modeling of the Casson fluid flow over unsteady radially stretching sheet with Soret and Dufour effects. Journal of Thermal Analysis and Calorimetry, 147, 14891–14903. https://doi.org/10.1007/s10973-022-11694-w

Srinivasacharya, D., & Shravan Kumar, R. (2023). Neural network analysis for bioconvection flow of Casson fluid over a vertically extending sheet. International Journal of Applied and Computational Mathematics, 9(5), 80. https://doi.org/10.1007/s40819-023-01556-w

Srinivasacharya, D., & Kumar, R. S. (2023). An artificial neural network solution for the Casson fluid flow past a radially stretching sheet with magnetic and radiation effect. Mathematical Models and Computer Simulations, 15(5), 944–955. https://doi.org/10.1134/S2070048223050101

Nallapu, S., Sneha, G. S., & Kumar, S. R. (2018). Effect of slip on Jeffrey fluid flow through an inclination tube. Journal of Physics: Conference Series, 1000(1), 012041. https://doi.org/10.1088/1742-6596/1000/1/012041

Rudrabhatla, S. K., & Srinivasacharya, D. (2025). Deep learning framework for Casson fluid flow: A PINN approach to heat and mass transfer with chemical reaction and viscous dissipation. European Journal of Mechanics – B/Fluids, 204401. https://doi.org/10.1016/j.euromechflu.2025.204401