NATURENGS MTU Journal of Engineering and Natural Sciences Malatya Turgut Ozal University, Год журнала: 2024, Номер unknown

Опубликована: Окт. 25, 2024

Image classification is a critical area of research with widespread applications across various disciplines, including computer vision, pattern recognition, and artificial intelligence. Despite the advancements in Convolutional Neural Networks (CNNs), which have revolutionized field by providing powerful tools for image classification, many studies encountered challenges achieving optimal performance. These often arise from complex nature CNN architectures multitude hyperparameters that require fine-tuning. Among models, AlexNet has been widely recognized its contributions to deep learning, yet there remains significant potential improvement through optimization hyperparameters. In this study, WF-AlexNET designed enhance performance architecture optimizing first convolutional layer using Equilibrium Optimization (EO) algorithm. The EO algorithm, was employed fine-tune filter size, number, stride, padding parameters, are crucial effective feature extraction. proposed method rigorously tested on multi-class weather dataset evaluate accuracy robustness. Experimental results demonstrate significantly outperforms standard model, 10.5% increase mean validation 6.51% test accuracy. Furthermore, approach compared against other prominent architectures, VGG-16, GoogleNet, ShuffleNet, MobileNet-V2, VGG-19. consistently exhibited superior multiple metrics, F1-score maximum accuracy, highlighting efficacy addressing associated hyperparameter CNNs.

Язык: Английский