Work

Fractal and chaotic map-enhanced grey wolf optimization for robust fire detection in deep convolutional neural networks

Year: 2025

Type: article

Abstract: This paper introduces a novel approach to enhancing the architecture of deep convolutional neural networks, addressing issues of self-design. The proposed strategy leverages the grey wolf optimizer and the multi-scale fractal chaotic map search scheme as fundamental components to enhance exploration and exploitation, thereby improving the classification task. Several experiments validate the method, demonstrating an impressive 87.37% accuracy across 95 random trials, outperforming 23 state-of-the-art classifiers in the study's nine datasets. This work underscores the potential of chaotic/fractal and bio-inspired paradigms in advancing neural architecture.

Source: Scientific Reports

DOI: 10.1038/s41598-025-95519-4

Authors: Yassine Bouteraa, Mohammad Khishe

Institutions: Prince Sattam Bin Abdulaziz University, University of Sfax, Applied Science Private University, Saveetha University

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,690

Funder: Prince Sattam bin Abdulaziz University