Attention-based hybrid deep learning model with CSFOA optimization and G-TverskyUNet3+ for Arabic sign language recognition.

IF 3.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Ahmed A Mohamed, Abdullah Al-Saleh, Sunil Kumar Sharma, Ghanshyam Tejani
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引用次数: 0

Abstract

Arabic sign language (ArSL) is a visual-manual language which facilitates communication among Deaf people in the Arabic-speaking nations. Recognizing the ArSL is crucial due to variety of reasons, including its impact on the Deaf populace, education, healthcare, and society, as well. Previous approaches for the recognition of Arabic sign language have some limitations especially in terms of accuracy and their capability to capture the detailed features of the signs. To overcome these challenges, a new model is proposed namely DeepArabianSignNet, that incorporates DenseNet, EfficientNet and an attention-based Deep ResNet. This model uses a newly introduced G-TverskyUNet3+ to detect regions of interest in preprocessed Arabic sign language images. In addition, employing a novel metaheuristic algorithm, the Crisscross Seed Forest Optimization Algorithm, which combines the Crisscross Optimization and Forest Optimization algorithms to determine the best features from the extracted texture, color, and deep learning features. The proposed model is assessed using two databases, the variation of the training rate was 70% and 80%; Database 2 was exceptional, with an accuracy of 0.97675 for 70% of the training data and 0.98376 for 80%. The results presented in this paper prove that DeepArabianSignNet is effective in improving Arabic sign language recognition.

基于CSFOA优化和G-TverskyUNet3+的基于注意的阿拉伯手语识别混合深度学习模型
阿拉伯手语(ArSL)是一种视觉手册语言,便于阿拉伯语国家聋哑人之间的交流。由于各种原因,认识到ArSL是至关重要的,包括它对聋哑人、教育、医疗保健和社会的影响。以前的识别阿拉伯手语的方法有一些局限性,特别是在准确性和捕捉符号的详细特征的能力方面。为了克服这些挑战,提出了一种新的模型,即DeepArabianSignNet,它结合了DenseNet, EfficientNet和基于注意力的Deep ResNet。该模型使用新推出的G-TverskyUNet3+来检测预处理的阿拉伯手语图像中感兴趣的区域。此外,采用一种新颖的元启发式算法——纵横交错种子森林优化算法,该算法结合纵横交错优化算法和森林优化算法,从提取的纹理、颜色和深度学习特征中确定最佳特征。采用两个数据库对模型进行评估,训练率的变异率分别为70%和80%;数据库2是例外,70%的训练数据准确率为0.97675,80%的训练数据准确率为0.98376。本文的研究结果证明了DeepArabianSignNet在提高阿拉伯语手语识别方面的有效性。
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来源期刊
Scientific Reports
Scientific Reports Natural Science Disciplines-
CiteScore
7.50
自引率
4.30%
发文量
19567
审稿时长
3.9 months
期刊介绍: We publish original research from all areas of the natural sciences, psychology, medicine and engineering. You can learn more about what we publish by browsing our specific scientific subject areas below or explore Scientific Reports by browsing all articles and collections. Scientific Reports has a 2-year impact factor: 4.380 (2021), and is the 6th most-cited journal in the world, with more than 540,000 citations in 2020 (Clarivate Analytics, 2021). •Engineering Engineering covers all aspects of engineering, technology, and applied science. It plays a crucial role in the development of technologies to address some of the world''s biggest challenges, helping to save lives and improve the way we live. •Physical sciences Physical sciences are those academic disciplines that aim to uncover the underlying laws of nature — often written in the language of mathematics. It is a collective term for areas of study including astronomy, chemistry, materials science and physics. •Earth and environmental sciences Earth and environmental sciences cover all aspects of Earth and planetary science and broadly encompass solid Earth processes, surface and atmospheric dynamics, Earth system history, climate and climate change, marine and freshwater systems, and ecology. It also considers the interactions between humans and these systems. •Biological sciences Biological sciences encompass all the divisions of natural sciences examining various aspects of vital processes. The concept includes anatomy, physiology, cell biology, biochemistry and biophysics, and covers all organisms from microorganisms, animals to plants. •Health sciences The health sciences study health, disease and healthcare. This field of study aims to develop knowledge, interventions and technology for use in healthcare to improve the treatment of patients.
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