A COMPARATIVE STUDY OF CONVOLUTIONAL NEURAL NETWORKS AND CYBERNETIC APPROACHES ON CIFAR-10 DATASET
Keywords:
CIFAR-10, Convolutional Neural Networks, Cybernetic Approaches, Image Classification, Deep Learning, Machine Learning, Computer Vision, Performance EvaluationAbstract
In this paper, a comparison is made between Convolutional Neural Networks (CNNs) and cybernetic approaches to image classification using the CIFAR-10 dataset. The primary objective of the study is to assess the performance of these two methods based on several factors, such as accuracy, precision, recall, F1-score, computational efficiency, and training time. The paper offers an overview of the CIFAR-10 dataset and image preprocessing techniques, followed by a discussion of the architecture, layers, and training techniques of CNNs. Additionally, various types of cybernetic approaches are described, including their advantages and limitations. The paper also delves into the experimental setup, implementation of both methods, and performance evaluation metrics. Based on the results, CNNs surpass cybernetic approaches in terms of accuracy, precision, recall, and F1-score, though with longer training time and higher computational requirements. Finally, the paper concludes with a summary of the findings, their implications, and potential future research directions.
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