Anis Ladgham
Papers
1
Total Citations
2
H-Index
1
About
Anis Ladgham is a researcher whose work sits at the intersection of computational intelligence and image processing, with a particular focus on advancing segmentation techniques through metaheuristic optimization. His most-cited paper, "Multi-level fractional order PSO new paradigm algorithm for image segmentation" (2016), introduces a novel hybrid approach that combines fractional calculus with particle swarm optimization to enhance multi-level thresholding for complex images. This work, which has garnered 2 citations, addresses critical challenges in pre-processing for fields such as robotics, autonomous systems, computer vision, and medical imaging. Ladgham’s contributions are notable for pushing the boundaries of how fractional-order dynamics can improve convergence and accuracy in segmentation algorithms. While his citation count is modest, his research represents a meaningful step in the ongoing effort to automate and refine image characterization—a cornerstone of modern AI-driven visual systems. For students and researchers exploring the synergy between swarm intelligence and fractional calculus, Ladgham’s work offers a compelling case study in algorithmic innovation for real-world imaging tasks.
Research Focus
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Top Papers
- 1