Mathematical and computational perspectives on next-generation neural networks for sign language recognition: A systematic review of advances, challenges, and assistive applications

Yahia Said; Mohammad Barr; Yazan A. Alsariera; Ahmed A. Alsheikhy; Yahia Said; Mohammad Barr; Yazan A. Alsariera; Ahmed A. Alsheikhy

doi:10.3934/math.2026156

AIMS Mathematics

2026, Volume 11, Issue 2: 3839-3902. doi: 10.3934/math.2026156

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Review

Mathematical and computational perspectives on next-generation neural networks for sign language recognition: A systematic review of advances, challenges, and assistive applications

1.
Center for Scientific Research and Entrepreneurship, Northern Border University, 73213, Arar, Saudi Arabia
2.
Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 91431, Saudi Arabia
3.
Department of Computer Science, College of Information and Communications Technology, Tafila Technical University, Tafila 66110, Jordan
4.
Department of Computer & Network Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia

Received: 29 September 2025 Revised: 24 December 2025 Accepted: 12 January 2026 Published: 09 February 2026
MSC : 68T07, 68T45

Artificial intelligence (AI) and machine learning (ML) have revolutionized assistive technologies, particularly for individuals with hearing and speech impairments. This systematic review critically examines recent innovations in next-generation neural network architectures for sign language recognition (SLR), emphasizing their mathematical and computational foundations. Following PRISMA guidelines, we analyze state-of-the-art models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and hybrid approaches integrating classical machine learning methods such as support vector machines (SVMs). We explore strategies for feature extraction, data augmentation, multimodal fusion, and optimization, highlighting their roles in improving accuracy, robustness, and real-time adaptability. Persistent challenges include dataset scarcity, limited generalizability, and computational trade-offs. From a mathematical perspective, optimization techniques, probabilistic modeling, and explainable AI frameworks are emerging as key enablers for safe and trustworthy SLR systems. This review identifies research gaps and proposes future directions toward responsible, mathematically grounded, and computationally efficient AI-powered assistive technologies.
- artificial intelligence,
- machine learning,
- mathematical models,
- neural networks,
- sign language recognition,
- multimodal fusion,
- optimization methods,
- explainable AI,
- assistive technologies,
- systematic review
Citation: Yahia Said, Mohammad Barr, Yazan A. Alsariera, Ahmed A. Alsheikhy. Mathematical and computational perspectives on next-generation neural networks for sign language recognition: A systematic review of advances, challenges, and assistive applications[J]. AIMS Mathematics, 2026, 11(2): 3839-3902. doi: 10.3934/math.2026156

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Abstract

Artificial intelligence (AI) and machine learning (ML) have revolutionized assistive technologies, particularly for individuals with hearing and speech impairments. This systematic review critically examines recent innovations in next-generation neural network architectures for sign language recognition (SLR), emphasizing their mathematical and computational foundations. Following PRISMA guidelines, we analyze state-of-the-art models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and hybrid approaches integrating classical machine learning methods such as support vector machines (SVMs). We explore strategies for feature extraction, data augmentation, multimodal fusion, and optimization, highlighting their roles in improving accuracy, robustness, and real-time adaptability. Persistent challenges include dataset scarcity, limited generalizability, and computational trade-offs. From a mathematical perspective, optimization techniques, probabilistic modeling, and explainable AI frameworks are emerging as key enablers for safe and trustworthy SLR systems. This review identifies research gaps and proposes future directions toward responsible, mathematically grounded, and computationally efficient AI-powered assistive technologies.

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