| dc.description.abstract | This research paper conducts a comprehensive
review of the performance of data classification through
the utilization of modern Convolutional Neural Network
(CNN) architectures. Encompassing prominent designs
such as GoogLeNet, MobileNet, VGG16, AlexNet,
ResNet, and DenseNet, this study evaluates their
effectiveness on established benchmark datasets. The
analysis highlights ResNet's exceptional accuracy as a
frontrunner in deep and efficient architecture, while
DenseNet displays competitive performance on CIFAR-
10 and CIFAR-100 with reduced parameters. This
investigation underscores the adaptability of
architectures to specific tasks, with ResNet excelling in
intricate feature extraction tasks, DenseNet optimizing
parameter efficiency. The continuous exploration of
novel CNN architectures persists, driven by the pursuit of
heightened classification precision and the evolving
landscape of datasets and computational capabilities,
propelling the advancement of effective models across
classification domains. | en_US |
