Papers
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2
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About
Dr. Sarah Madi is a leading researcher in machine learning and intelligent systems, with a primary focus on optimizing classification algorithms for real-world robotics applications. Her most-cited work, "A multi-stage genetic algorithm for instance selection dedicated to k nearest neighbours classification: application to robot wall following problem" (2022, 2 citations), tackles a fundamental challenge in computational intelligence: the trade-off between accuracy and efficiency in the k-nearest neighbors (KNN) algorithm. While KNN is renowned for its high accuracy and rapid learning phase, its classification speed suffers when handling large datasets. Dr. Madi’s key contribution lies in developing a novel multi-stage genetic algorithm for instance selection, which systematically eliminates redundant and erroneous data points without sacrificing predictive performance. This approach not only accelerates KNN classification but also demonstrates practical utility in autonomous robotics, specifically for wall-following navigation tasks. Her work bridges theoretical optimization with tangible engineering solutions, offering a scalable framework for real-time decision-making in resource-constrained environments. With growing interest in efficient AI, Dr. Madi’s research continues to influence the design of lightweight, high-performance classifiers for mobile robotics and embedded systems.
Research Focus
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Top Papers
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