Jauro, Fatsuma and Abdullahi, Usman Ali and Abdulsalami, Aminu Onimisi and Ibrahim, Adamu Abubakar and Abdullahi, Mohammed and Chiroma, Haruna (2024) Modified symbiotic organisms search optimization for automatic construction of convolutional neural network architectures. Intelligent Systems with Applications, 22. pp. 1-20. ISSN 2667-3053
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Abstract
Convolutional Neural Networks (ConvNets) have demonstrated impressive capabilities in image classification; however, the manual creation of these models is a labor-intensive and time-consuming endeavor due to their inherent complexity. This research introduces an innovative approach to Convolutional Neural Network (ConvNet) architecture generation through the utilization of the Symbiotic Organism Search ConvNet (SOS_ConvNet) algorithm. Leveraging the Symbiotic Organism Search optimization technique, SOS_ConvNet evolves ConvNet architectures tailored for diverse image classification tasks. The algorithm’s distinctive feature lies in its ability to perform non-numeric computations, rendering it adaptable to intricate deep learning problems. To assess the effectiveness of SOS_ConvNet, experiments were conducted on diverse datasets, including MNIST, FashionMNIST, CIFAR-10, and the Breast Cancer dataset. Comparative analysis against existing models showcased the superior performance of SOS_ConvNet in terms of accuracy, error rate, and parameter efficiency. Notably, on the MNIST dataset, SOS_ConvNet achieved an impressive 0.31 % error rate, while on Fashion-MNIST, it demonstrated a competitive 6.7 % error rate, coupled with unparalleled parameter efficiency of 0.24 million parameters. The model excelled on CIFAR-10 and BreakHis datasets, yielding accuracies of 82.78 % and 89.12 %, respectively. Remarkably, the algorithm achieves remarkable accuracy while maintaining moderate model size.
| Item Type: | Article (Journal) |
|---|---|
| Uncontrolled Keywords: | Convolutional neural network Neural architecture search Symbiotic organism search And deep learning |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software |
| Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): | Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology |
| Depositing User: | Dr Adamu Abubakar |
| Date Deposited: | 19 Jun 2024 11:24 |
| Last Modified: | 19 Jun 2024 11:24 |
| URI: | http://irep.iium.edu.my/id/eprint/112662 |
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