Fuzzy Swin transformer for Land Use/ Land Cover change detection using LISS-III Satellite data

MohanRajan, Sam Navin; Loganathan, Agilandeeswari; Manoharan, Prabukumar; Alenizi, Farhan A.

doi:10.1007/s12145-023-01208-z

Cited by 10 publications

(1 citation statement)

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“…Nguyen et al's research [29] in dynamic semantic communication demonstrates the model's efficiency in handling diverse computational requirements in communication systems. The versatility of Swin transformers is further evidenced in MohanRajan et al's [30] study for land use and cover change detection and Ekanayake et al's work in MRI reconstruction [31], showing its effectiveness in both environmental monitoring and medical imaging.…”

Section: Related Workmentioning

confidence: 98%

Applying Swin Architecture to Diverse Sign Language Datasets

Kumar,

Huang,

Lin

et al. 2024

Electronics

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In an era where artificial intelligence (AI) bridges crucial communication gaps, this study extends AI’s utility to American and Taiwan Sign Language (ASL and TSL) communities through advanced models like the hierarchical vision transformer with shifted windows (Swin). This research evaluates Swin’s adaptability across sign languages, aiming for a universal platform for the unvoiced. Utilizing deep learning and transformer technologies, it has developed prototypes for ASL-to-English translation, supported by an educational framework to facilitate learning and comprehension, with the intention to include more languages in the future. This study highlights the efficacy of the Swin model, along with other models such as the vision transformer with deformable attention (DAT), ResNet-50, and VGG-16, in ASL recognition. The Swin model’s accuracy across various datasets underscore its potential. Additionally, this research explores the challenges of balancing accuracy with the need for real-time, portable language recognition capabilities and introduces the use of cutting-edge transformer models like Swin, DAT, and video Swin transformers for diverse datasets in sign language recognition. This study explores the integration of multimodality and large language models (LLMs) to promote global inclusivity. Future efforts will focus on enhancing these models and expanding their linguistic reach, with an emphasis on real-time translation applications and educational frameworks. These achievements not only advance the technology of sign language recognition but also provide more effective communication tools for the deaf and hard-of-hearing community.

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Section: Related Workmentioning

confidence: 98%