Chama ESSAIOUAD
Papers
1
Total Citations
8
H-Index
1
About
Dr. Chama Essaïouad is a rising researcher in artificial intelligence and algorithmic optimization, with a primary focus on path planning and search algorithm efficiency. Her most cited work, "Exploring Maze Navigation: A Comparative Study of DFS, BFS, and A* Search Algorithms" (2024, 8 citations), provides a rigorous empirical evaluation of three fundamental search strategies—Depth-First Search, Breadth-First Search, and A*—using Python and the Pmaze simulation environment. By systematically comparing these algorithms on path cost and computational complexity, Essaïouad offers clear guidance for selecting appropriate search methods in constrained navigation problems. This contribution is particularly valuable for students and practitioners in robotics, game development, and autonomous systems, where efficient path planning is critical. Though early in her career, her work demonstrates a strong command of algorithmic analysis and practical implementation, establishing a foundation for future studies in heuristic search and real-world navigation challenges.
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Top Papers
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