Material quality assessment of silk nanofibers based on swarm intelligence

TL;DR

This paper introduces a novel texture analysis method using autonomous agents inspired by swarm intelligence, specifically applied to silk nanofiber quality assessment, demonstrating improved classification performance.

Contribution

The paper presents a new artificial crawler-based texture analysis approach that incorporates agent interactions and signatures, pioneering its application to silk fibroin scaffold characterization.

Findings

Combining minimum and maximum signatures improves classification accuracy.

The approach effectively characterizes silk fibroin scaffolds.

Swarm intelligence methods enhance texture analysis performance.

Abstract

In this paper, we propose a novel approach for texture analysis based on artificial crawler model. Our method assumes that each agent can interact with the environment and each other. The evolution process converges to an equilibrium state according to the set of rules. For each textured image, the feature vector is composed by signatures of the live agents curve at each time. Experimental results revealed that combining the minimum and maximum signatures into one increase the classification rate. In addition, we pioneer the use of autonomous agents for characterizing silk fibroin scaffolds. The results strongly suggest that our approach can be successfully employed for texture analysis.