This research paper’s primary objective revolves around addressing the critical issue of handling imbalanced data sets, particularly in the context of image classification tasks with an uneven ...

Weather image classification is a critical component of the vision systems in autonomous driving systems (ADSs), facilitating accurate decision-making across diverse driving conditions. Adverse ...

Furthermore, a multilayer feature fusion block based on the channel–spatial attention algorithm is integrated into the CNN to capture more discriminative features from arbitrary-size images.

This paper investigates the application of Convolutional Neural Networks (CNNs) in MNIST handwritten digit recognition, with a particular focus on optimizing the ResNet-18 model. By introducing ...

In this work, therefore, we applied 1D CNN and 3D CNN for time-series, coarse resolution (1km) FY-3C image classification in extensive area land cover mapping of a part of Eastern and North-East ...

Unlike CNN, graph convolutional networks (GCNs) can well handle the intrinsic manifold structures of hyperspectral images (HSIs). However, the existing GCN-based classification methods do not fully ...

With the widespread application of deep learning and Convolutional Neural Networks (CNN) in image classification, how to effectively improve model performance and reduce its complexity has become a ...

The classification of human actions has become an important topics in recent researches. Typically the function of recognition human action is converted to the function of classifying the image that ...

To address the shortcomings of classical chaotic time series in image encryption algorithms in terms of low complexity, fewer control parameters, and limited range of value domains, this paper ...

Our experiments demonstrate that this model outperforms both pre-trained and scratch DCNN models in terms of classification accuracy for augmented data. Specifically, our approach shows superior ...