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 ...

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 ...

Electrical treeing, a significant degradation process in XLPE insulated cables, presents a substantial risk as it gradually weakens the insulation, ultimately leading to failure. Thus, understanding ...

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) ...

Music services are currently very popular and accessible to anyone, particularly through internet platforms. However, this widespread accessibility necessitates careful consideration, as music and ...

The overall accuracy of 99% underscores the models’ effectiveness in classification tasks, suggesting their potential for practical implementation in dental healthcare settings. Additionally, the ...

Depression profoundly deteriorates the quality of life. Timely and accurate diagnosis remains challenging. While MRI shows promise in depression detection, existing methods lack the diagnostic ...

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 ...

Dune images typically display intricate details and relatively uniform spectral characteristics, making them a unique challenge for image segmentation tasks. Due to the limitations of traditional ...

As urban road damage issues become increasingly severe, automated road damage detection systems are becoming critically important. This paper proposes a CNN-YOLO-based road damage detection model ...

Detecting healthy arecanut leaves, yellow leaf disease in arecanut, and differentiating these from other types of leaves using deep learning involves designing an advanced neural network model for ...

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 ...