Design tactics for tailoring transformer architectures to cybersecurity challenges
Sign Up to like & getrecommendations! Published in 2024 at "Cluster Computing"
DOI: 10.1007/s10586-024-04355-0
Abstract: In the rapidly evolving landscape of cyber threats, effective defense strategies are crucial for safeguarding sensitive information and critical systems. Deep learning methods, notably the Transformer architecture, have shown immense potential in addressing cybersecurity challenges.… read more here.
Keywords: architectures cybersecurity; design tactics; transformer architectures; design ... See more keywords
Incorporating convolutional and transformer architectures to enhance semantic segmentation of fine-resolution urban images
Sign Up to like & getrecommendations! Published in 2024 at "European Journal of Remote Sensing"
DOI: 10.1080/22797254.2024.2361768
Abstract: ABSTRACT Though convolutional neural networks (CNN) exhibit promise in image semantic segmentation, they have limitations in capturing global context information, resulting in inaccuracies in segmenting small object features and object boundaries. This study introduces a… read more here.
Keywords: information; convolutional transformer; segmentation; transformer architectures ... See more keywords
Satellite Detection and Characterization Using Alternate Transformer Architectures for Spectro-Spatial RF Signal Analysis
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Aerospace and Electronic Systems"
DOI: 10.1109/taes.2025.3610819
Abstract: With the continuous increase in satellite launches and the growing complexity of radio frequency signals, traditional methods struggle to fully exploit the rich data derived from passive frequency scanning. In this article, we propose an… read more here.
Keywords: transformer architectures; spectro spatial; detection characterization;
AI support for colonoscopy quality control using CNN and transformer architectures
Sign Up to like & getrecommendations! Published in 2024 at "BMC Gastroenterology"
DOI: 10.1186/s12876-024-03354-0
Abstract: Background Construct deep learning models for colonoscopy quality control using different architectures and explore their decision-making mechanisms. Methods A total of 4,189 colonoscopy images were collected from two medical centers, covering different levels of bowel… read more here.
Keywords: quality; quality control; transformer architectures; model ... See more keywords