Rima Daoudi

Papers

Total Citations

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About

Rima Daoudi is a researcher whose work bridges artificial intelligence and biomedical engineering, with a particular focus on computational methods for disease diagnosis. Her most cited paper, "An Immune-Inspired Approach for Breast Cancer Classification" (2013), has garnered 9 citations, showcasing her early contributions to applying nature-inspired algorithms—specifically artificial immune systems—to medical data analysis. This work exemplifies her broader research interests in machine learning, pattern recognition, and bio-inspired computing for healthcare applications. Daoudi’s approach leverages the adaptive and self-learning capabilities of immune system models to improve the accuracy and robustness of cancer classification, a critical step toward more reliable computer-aided diagnosis. While her citation count reflects a niche but growing impact, her work is notable for its innovative integration of immunological principles with computational intelligence, offering a fresh perspective on tackling complex biomedical classification problems. For students and researchers exploring the intersection of AI and medicine, Daoudi’s research demonstrates how unconventional computational paradigms can yield meaningful solutions in clinical decision support.