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About

Zulqurnain Sabir is a rising figure in the intersection of artificial intelligence, applied mathematics, and epidemiology, with a focused expertise in using advanced neural network architectures to model complex dynamical systems. His primary research areas include stochastic computational procedures, fractional-order modeling, and the application of machine learning to biological and robotic systems. Sabir’s major contributions lie in developing novel hybrid algorithms that combine neural networks—such as Gudermannian, Morlet wavelet, and Levenberg–Marquardt backpropagation networks—with global optimization techniques like genetic algorithms and interior-point schemes. These methods have been applied to solve challenging mathematical models, most notably a robotic system designed to detect and analyze positive coronavirus cases. His most cited work (2023, 33 citations) provides a reliable stochastic framework for this robotic model, while subsequent studies (2024, 8 citations; 2022, 6 citations) extend the approach to fractional-order dynamics and swarming wavelet networks. By integrating AI-driven solvers with epidemiological and robotic systems, Sabir’s work demonstrates a powerful, scalable methodology for real-world problem-solving, making his research highly relevant for students and researchers in computational intelligence and mathematical biology.