Mipa-Resgcn: A Multi-Input Part Attention Based Residual Graph Convolution Framework for Sign Language Recognition Article Swipe

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Neelma Naz , Hasan Sajid , Sara Ali , Osman Hasan , Muhammad Khurram Ehsan ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.2139/ssrn.4460028

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Computer Science

Artificial Intelligence

Sign Language

Theoretical Computer Science

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Mathematics

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Residual Computer science Sign (mathematics) Graph Convolution (computer science) Artificial intelligence Sign language Natural language processing Speech recognition Theoretical computer science Algorithm Linguistics Mathematics Artificial neural network Philosophy Mathematical analysis

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Type: preprint
Language: en
Landing Page: https://doi.org/10.2139/ssrn.4460028
OA Status: green
References: 70
Related Works: 10
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DOI: https://doi.org/10.2139/ssrn.4460028

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Title: Mipa-Resgcn: A Multi-Input Part Attention Based Residual Graph Convolution Framework for Sign Language Recognition

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Language: en

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Publication year: 2023

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Publication date: 2023-01-01

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Authors: Neelma Naz, Hasan Sajid, Sara Ali, Osman Hasan, Muhammad Khurram Ehsan

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Landing page: https://doi.org/10.2139/ssrn.4460028

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Concepts: Residual, Computer science, Sign (mathematics), Graph, Convolution (computer science), Artificial intelligence, Sign language, Natural language processing, Speech recognition, Theoretical computer science, Algorithm, Linguistics, Mathematics, Artificial neural network, Philosophy, Mathematical analysis

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cited_by_percentile_year
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sustainable_development_goals[0].id	https://metadata.un.org/sdg/4
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