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 ...

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 ...

Hyperspectral images (HSIs) contain rich spatial and spectral information, while light detection and ranging (LiDAR) data can provide elevation details. Effectively fusing HSI and LiDAR data can help ...

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 ...

This study focuses on classification of electrical tree images into Inception, Propagation, and Breakdown stages utilizing state of the art CNN and pre-trained CNNs, including InceptionNet, ResNet, ...

This study presents a novel approach in the application of deep learning for the classification of esophageal squamous cell carcinoma (Escc) using whole-slide images (WSIs). Our methodology uniquely ...

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) ...

Explicit Content Classification in Indonesian Song Lyrics Using the LSTM-CNN Method Abstract: Music services are currently very popular and accessible to anyone, particularly through internet ...

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