Hyperparameter optimization ResNet by improved Beluga Whale Optimization
Huan Liu, Shizheng Qu, Shuai Zhang, Yingxin Zhang, Yanqiu Li
TL;DR
This paper introduces a new model called EBWO-ResNet that improves the accuracy of maize disease identification using an enhanced optimization algorithm.
Contribution
The novel EBWO algorithm improves the Beluga Whale Optimization and is integrated into ResNet for better performance in hyperparameter tuning.
Findings
The EBWO algorithm outperforms five other swarm intelligence algorithms in three engineering problems.
The EBWO-ResNet model achieves 96.3% accuracy in maize disease identification, surpassing other models by 0.2-1.5 percentage points.
Abstract
The parameter values of neural networks will directly affect the performance of the network, so it is very important to choose the appropriate parameter tuning method to improve the performance of the neural network. In this paper, the improved beluga whale optimization hyperparameter optimization ResNet model is used to construct a new model, EBWO-ResNet. Firstly, in order to solve the problem that the initial population of the original beluga whale optimization is not rich enough, the Tent chaotic map is introduced into the beluga whale optimization, and a new algorithm EBWO is constructed. Secondly, in order to solve the problems of low accuracy and difficult parameter tuning of ResNet, the EBWO algorithm was integrated into ResNet to construct a new model EBWO-ResNet. Finally, in order to verify the effectiveness of the EBWO algorithm, the EBWO algorithm was applied to three…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
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Taxonomy
TopicsSmart Agriculture and AI · Water Quality Monitoring Technologies · Vehicle License Plate Recognition