0000-0002-5113-3554 About
Dr. Randhir Singh Baghel holds a Ph.D. in Applied Mathematics from Jiwaji University, Gwalior. His doctoral research focused on the modelling and analysis of chaos and pattern formation in biological systems. His research interests include mathematical modelling, nonlinear dynamical systems, mathematical ecology, predator–prey and food-web systems, spatiotemporal dynamics, bifurcation and chaos theory, fractional-order systems, delay differential equations, epidemiological modelling, and computational mathematics. His recent research has also extended to stochastic modelling, artificial intelligence, machine learning, climate-driven biological systems, and interdisciplinary applications of mathematical modelling. He has published numerous research articles in international journals indexed in SCIE and Scopus and has supervised Ph.D. scholars in various areas of applied mathematics. He has also authored international book chapters and books covering engineering mathematics, machine learning, artificial intelligence, data science, and spatiotemporal population modelling, along with several patent applications related to mathematical and computational modelling. His research focuses on developing rigorous mathematical and computational approaches for understanding complex biological, ecological, environmental, and interdisciplinary systems.
Employment
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Poornima University Professor
Education
Education history is unavailable.
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Publications (64)
- Artificial Intelligence: Utilizing a Cost-Safety Matrix to Assess Risk in Material Handling Systems Save
- A fractional-order hepatitis B transmission model with Atangana--Baleanu derivative incorporating sociobehavioral and governmental interventions Save
- Trust, altruism, and ease of participation in a fully remote, decentralized COVID-19 clinical trial Save
- Spatio-temporal dynamics of a food chain model featuring Allect effect on prey’s growth and sexually reproducing top predators Save
- Risk Management Optimization Using Artificial Intelligence: Transforming Risk Analysis Through Intelligent Systems Save
- Optimization of Inventory Management Using Artificial Neural Networks and K-Means Clustering for Cost Reduction and Improved Customer Service Save
- Memory-driven bifurcation analysis of a multi-order fractional biological system with coupled genetic-epigenetic regulation Save
- Leveraging Secure Federated Learning for Data-Driven Business Decisions Save
- Hybrid deterministic--stochastic modeling of epidemic spread: Analytical insights and numerical evidence from an Ornstein--Uhlenbeck SEIR framework Save
- Human-AI collaboration: Balancing trust, emotion, and productivity Save