Mamba vision models: Automated American sign language recognition
Historically, individuals with hearing impairments have faced significant challenges in effective communication due to the lack of adequate resources. Recent technological advancements have spurred the development of innovative tools aimed at enhancing the quality of life for those with hearing disa...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Franklin Open |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2773186325000143 |
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| author | Ali Salem Altaher Chiron Bang Bader Alsharif Ahmed Altaher Munid Alanazi Hasan Altaher Hanqi Zhuang |
| author_facet | Ali Salem Altaher Chiron Bang Bader Alsharif Ahmed Altaher Munid Alanazi Hasan Altaher Hanqi Zhuang |
| author_sort | Ali Salem Altaher |
| collection | DOAJ |
| description | Historically, individuals with hearing impairments have faced significant challenges in effective communication due to the lack of adequate resources. Recent technological advancements have spurred the development of innovative tools aimed at enhancing the quality of life for those with hearing disabilities. This research focuses on the application of Vision Mamba Models for classifying hand gestures representing the American Sign Language (ASL) alphabet, with a detailed comparative analysis of its performance against 13 deep learning architectures. The Vision Mamba Models, namely, Vision Mamba and Remote Sensing Mamba, were trained on a substantial dataset comprising 87,000 images of ASL hand gestures, and through iterative fine-tuning of their architectural parameters, the models’ accuracy and performance were optimized. Experimental results demonstrated that the Mamba Vision Models outperformed all other models previously examined in this context scoring exceptional accuracy rate that exceeded 99.98%, with less architectural complexity. These findings highlight the potential of deep learning technologies, particularly the Mamba vision models, in advancing assistive technologies, offering a sophisticated and highly accurate tool for interpreting ASL hand gestures and promising improved communication and accessibility for individuals with hearing impairments. |
| format | Article |
| id | doaj-art-bbb7a175866640b4b8be3b7c03665bc4 |
| institution | Kabale University |
| issn | 2773-1863 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Franklin Open |
| spelling | doaj-art-bbb7a175866640b4b8be3b7c03665bc42025-01-31T05:12:49ZengElsevierFranklin Open2773-18632025-03-0110100224Mamba vision models: Automated American sign language recognitionAli Salem Altaher0Chiron Bang1Bader Alsharif2Ahmed Altaher3Munid Alanazi4Hasan Altaher5Hanqi Zhuang6Department of Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, 33431, FL, USA; College of Medicine, Ibn Sina University of Medical and Pharmaceutical Science, Baghdad, Iraq; Corresponding author.Department of Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, 33431, FL, USADepartment of Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, 33431, FL, USA; Department of Computer Science and Engineering, College of Telecommunication and Information, Technical and Vocational Training Corporation, Riyadh, 12464, Saudi ArabiaDepartment of Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, 33431, FL, USA; Electronic Computer Center, Al-Nahrain University, Jadriya, Baghdad, IraqDepartment of Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, 33431, FL, USA; Business Informatics Department, College of Business, King Khalid University, Abha, Saudi ArabiaInformation and Communication Technology Department, Baghdad Institute of Technology, Middle Technical University, Baghdad, IraqDepartment of Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, 33431, FL, USAHistorically, individuals with hearing impairments have faced significant challenges in effective communication due to the lack of adequate resources. Recent technological advancements have spurred the development of innovative tools aimed at enhancing the quality of life for those with hearing disabilities. This research focuses on the application of Vision Mamba Models for classifying hand gestures representing the American Sign Language (ASL) alphabet, with a detailed comparative analysis of its performance against 13 deep learning architectures. The Vision Mamba Models, namely, Vision Mamba and Remote Sensing Mamba, were trained on a substantial dataset comprising 87,000 images of ASL hand gestures, and through iterative fine-tuning of their architectural parameters, the models’ accuracy and performance were optimized. Experimental results demonstrated that the Mamba Vision Models outperformed all other models previously examined in this context scoring exceptional accuracy rate that exceeded 99.98%, with less architectural complexity. These findings highlight the potential of deep learning technologies, particularly the Mamba vision models, in advancing assistive technologies, offering a sophisticated and highly accurate tool for interpreting ASL hand gestures and promising improved communication and accessibility for individuals with hearing impairments.http://www.sciencedirect.com/science/article/pii/S2773186325000143American sign languageDeep learningTransfer learningVision Mamba.Remote sensing mamba |
| spellingShingle | Ali Salem Altaher Chiron Bang Bader Alsharif Ahmed Altaher Munid Alanazi Hasan Altaher Hanqi Zhuang Mamba vision models: Automated American sign language recognition Franklin Open American sign language Deep learning Transfer learning Vision Mamba. Remote sensing mamba |
| title | Mamba vision models: Automated American sign language recognition |
| title_full | Mamba vision models: Automated American sign language recognition |
| title_fullStr | Mamba vision models: Automated American sign language recognition |
| title_full_unstemmed | Mamba vision models: Automated American sign language recognition |
| title_short | Mamba vision models: Automated American sign language recognition |
| title_sort | mamba vision models automated american sign language recognition |
| topic | American sign language Deep learning Transfer learning Vision Mamba. Remote sensing mamba |
| url | http://www.sciencedirect.com/science/article/pii/S2773186325000143 |
| work_keys_str_mv | AT alisalemaltaher mambavisionmodelsautomatedamericansignlanguagerecognition AT chironbang mambavisionmodelsautomatedamericansignlanguagerecognition AT baderalsharif mambavisionmodelsautomatedamericansignlanguagerecognition AT ahmedaltaher mambavisionmodelsautomatedamericansignlanguagerecognition AT munidalanazi mambavisionmodelsautomatedamericansignlanguagerecognition AT hasanaltaher mambavisionmodelsautomatedamericansignlanguagerecognition AT hanqizhuang mambavisionmodelsautomatedamericansignlanguagerecognition |