The availability of structural image data has significantly increased in recent years due to the widespread use of improved imaging techniques. The increase in the amount of data has required the ...

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 ...

In recent years, convolutional neural networks (CNNs) have been impressive due to their excellent feature representation abilities, but it is difficult to learn long-distance spatial structures ...

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 ...

python image deep-neural-networks deep-learning code resnet convolutional-neural-networks cnn-classification inception-resnet-v2 deepfakes deepfake efficientnet deepfake-detection deepfake-dataset ...

A CNN-based image classifier built with PyTorch and FastAPI. Features model training, evaluation metrics, visualizations, and a web interface for real-time predictions. Includes comprehensive ...

This research presents a comprehensive comparative analysis of various pre-trained backbone models and machine learning techniques for output layers in convolutional neural networks (CNNs) applied to ...

An autonomous driving system requires efficient image recognition to interpret the environment, detect obstacles, and make real-time decisions. This study compares Convolutional Neural Networks (CNNs) ...

In recent years, vision transformer (ViT) has achieved remarkable breakthroughs in fine-grained visual classification (FGVC) because of its self-attention mechanism that excels in extracting ...

Auroral image classification has long been a focus of research in auroral physics. However, current methods for automatic auroral classification typically assume that only one type of aurora is ...

Glioma classification is critical for early diagnosis and treatment planning, yet manual MRI-based diagnosis is time-consuming and prone to errors. This study proposed a CNN-based approach optimized ...