IEEE

Enhancing CNN-Based Motion Sickness Detection using EEG Signals Weighted by Motion Sickness Dose Value

Abstract: With the advancement of autonomous driving technologies, passengers increasingly engage in non-driving activities. However, these activities are often limited by motion sickness (MS), which ...
IEEE

Early Radar Work Mode Recognition Using Hybrid CNN-Transformer and Reinforcement Learning

In this paper, a novel approach is proposed for early recognition of Radar Work Mode, which integrates a hybrid CNN-Transformer architecture and a Reinforcement Learning strategy. The model processes ...
IEEE

Multilevel Multimodal CNN–Transformer Fusion Network for Land Use and Land Cover Classification

Abstract: In remote sensing (RS), convolutional neural networks (CNNs) are well-recognized for their spatial–spectral feature extraction capabilities, whereas vision transformers (ViTs), which ...
IEEE

Self-Supervised Monocular Depth Estimation Using Hybrid CNN-VMamba Architecture

Abstract: Self-supervised monocular depth estimation (MDE) typically employs convolutional neural networks (CNNs) or Transformers to predict scene depth. However, CNNs struggle with long-range ...
IEEE

Automated Deep Learning System for Early Detection of Stroke Using CNN Architecture

Abstract: Strokes are a major cause of disability worldwide, with ischemic and hemorrhagic strokes accounting for the majority of cases. In India, stroke remains the second most common cause of ...