WF-AlexNet:AlexNet with Automatically Optimized Hyperparameters for Weather Forecasting

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