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Chiradeja, Pathomthat, Liang, Yijuan, Jettanasen, Chaiyan (2025) Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks. Applied Sciences, 15 (6). doi:10.3390/app15062957

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Reference TypeJournal (article/letter/editorial)
TitleSign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks
JournalApplied Sciences
AuthorsChiradeja, PathomthatAuthor
Liang, YijuanAuthor
Jettanasen, ChaiyanAuthor
Year2025 (March 10)Volume15
Issue6
PublisherMDPI AG
DOIdoi:10.3390/app15062957Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID18152007Long-form Identifiermindat:1:5:18152007:0
GUID0
Full ReferenceChiradeja, Pathomthat, Liang, Yijuan, Jettanasen, Chaiyan (2025) Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks. Applied Sciences, 15 (6). doi:10.3390/app15062957
Plain TextChiradeja, Pathomthat, Liang, Yijuan, Jettanasen, Chaiyan (2025) Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks. Applied Sciences, 15 (6). doi:10.3390/app15062957
In(2025, March) Applied Sciences Vol. 15 (6). MDPI AG

References Listed

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Jiang (2024) CMES Comput. Model. Eng. Sci. A Survey on Chinese Sign Language Recognition: From Traditional Methods to Artificial Intelligence 140, 1
Tao (2024) IEEE Access Sign Language Recognition: A Comprehensive Review of Traditional and Deep Learning Approaches, Datasets, and Challenges 12, 75034
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Murali (2022) Int. J. Eng. Innov. Adv. Technol. Sign language recognition system using convolutional neural network and computer sensor-based 4, 138
Not Yet Imported: - journal-article : 10.1007/s11042-023-17372-9

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Zhu, W. (2024). Quiet Interaction: Designing an Accessible Home Environment for Deaf and Hard of Hearing (DHH) Individuals Through AR, AI, and IoT Technologies. [Doctoral Dissertation, OCAD University].
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Liang (2024) J. Internet Technol. Development of Sensor Data Fusion and Optimized Elman Neural Model-based Sign Language Recognition System 25, 671
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Muthusamy (2023) Int. J. Intell. Eng. Syst. Recognition of Indian Continuous Sign Language Using Spatio-Temporal Hybrid Cue Network 16, 874
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Not Yet Imported: Journal of Sensors - journal-article : 10.1155/2023/9503961

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Gupta, A., Sawan, A., Singh, S., and Kumari, S. (2024, January 14–15). Dynamic Sign Language Recognition with Hybrid CNN-LSTM and 1D Convolutional Layers. Proceedings of the 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India.
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Kumar (2021) J. King Saud Univ. Comput. Inf. Sci. Early estimation model for 3D-discrete indian sign language recognition using graph matching 33, 852
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