Ibraheem
Papers
2
Total Citations
10
H-Index
2
About
Ibraheem is a researcher specializing in power system forecasting and mobile robotics optimization. His work integrates advanced computational techniques such as fuzzy time series, artificial neural networks, and wavelet transform models to enhance near real-time load forecasting for electrical power systems—a critical area for ensuring reliable energy distribution in increasingly complex grids. In his 2023 study, he demonstrated how these hybrid models can improve prediction accuracy, addressing the challenge of no inventory buffer between generation and consumption. Additionally, Ibraheem has contributed to robotics by comparing obstacle-avoiding path planning algorithms, specifically ant colony optimization and teaching learning-based optimization, in a 2016 study. His research, while still early in citation impact with 5 citations per paper, showcases a dual focus on energy system reliability and autonomous navigation—both vital for smart infrastructure. Ibraheem’s work reflects a commitment to solving real-world engineering problems through data-driven optimization, making him a promising voice in applied computational intelligence.
Research Focus
Key Achievements
Top Papers
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