Peerapongpat Singkibud

Rajamangala University of Technology Isan

Papers

Total Citations

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About

Peerapongpat Singkibud is a researcher at the forefront of applying artificial intelligence to complex dynamical systems, with a particular focus on biomedical and robotic applications. His most cited work introduces a novel "swarming Morlet wavelet neural network" (MWNN) procedure to solve the mathematical robot system (MRS), a framework he developed to model and analyze the spread of positive coronavirus cases. By partitioning the system into infected (I) and robot (R) classes, Singkibud demonstrates how AI-driven neural networks can offer precise, adaptive solutions for epidemic modeling. This innovative approach, published in 2022 and garnering 6 citations, showcases his ability to bridge computational intelligence with real-world health challenges. His research stands out for its interdisciplinary nature, merging wavelet theory, swarm optimization, and neural networks to tackle nonlinear problems. Singkibud’s work not only advances the field of mathematical robotics but also opens new pathways for using AI in pandemic response, making him a promising voice in the intersection of control systems, epidemiology, and machine learning.