Abstract: Plant and leaf diseases have a significant impact on agricultural production, leading to a decrease in crop yield and quality. Effective crop management demands early and precise detection ...
Abstract: Imagined speech-based brain-computer interface (BCI) facilitates brain signal-driven intuitive communication which holds great promise as an effective speech rehabilitation tool, enabling ...
Abstract: Distributed fiber optic sensing technology has been extensively applied in the field of perimeter security. The distributed acoustic sensing (DAS) system driven by a deep learning ...
Abstract: Document image classification has a significant difficulty for the retrieval of digital documents and systems management in recent years. The main goal of this study is to investigate the ...
Abstract: Low Earth Orbit (LEO) satellite constellations have been widely recognized as a key component of sixth-generation (6G) wireless communications due to their great potential in enabling ...
Abstract: One of the most critical neurological conditions is Brain tumors, timely and correct diagnosis is needed for effective treatment. Advances in neuroimaging technology such as MRI, limitations ...
Abstract: With the widespread use of Internet services, the risk of cyber attacks has increased significantly. Existing anomaly-based network intrusion detection systems suffer from slow processing ...
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 ...
Abstract: In India, countless children are reported missing every year, with a significant percentage remaining untraced due to challenges in identification and limited resources. This project ...
Abstract: The extraction of text from masked or partially occluded images, such as a road sign covered by a poster, a tree, or the environment (in which case occlusion may be caused by anything but a ...
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 ...
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 ...
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