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Instant Sign Language Recognition by WAR Strategy Algorithm Based Tuned Machine Learning

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dc.citedby1
dc.contributor.authorAbd Al-Latief S.T.en_US
dc.contributor.authorYussof S.en_US
dc.contributor.authorAhmad A.en_US
dc.contributor.authorKhadim S.M.en_US
dc.contributor.authorAbdulhasan R.A.en_US
dc.contributor.authorid58590896700en_US
dc.contributor.authorid16023225600en_US
dc.contributor.authorid55390963300en_US
dc.contributor.authorid58590009300en_US
dc.contributor.authorid56905439800en_US
dc.date.accessioned2025-03-03T07:41:45Z
dc.date.available2025-03-03T07:41:45Z
dc.date.issued2024
dc.description.abstractSign language serves as the primary means of communication utilized by individuals with hearing and speech disabilities. However, the comprehension of sign language by those without disabilities poses a significant challenge, resulting in a notable disparity in communication across society. Despite the utilization of numerous effective Machine learning techniques, there remains a minor compromise between accuracy rate and computing time when it comes to sign language recognition. A novel sign language recognition system is presented in this paper with an exceptionally accurate and expeditious, which is developed upon the recently devised metaheuristic WAR Strategy optimization algorithm. Following the preprocessing, both of spatial and temporal features has been extracted using the Linear Discriminant Analysis (LDA) and Gray-level cooccurrence matrix (GLCM) methods. Afterward, the WAR Strategy optimization algorithm has been adopted in two procedures, first in optimizing the extracted set of features, and second to fine-tune the hyperparameters of six standard machine learning models in order to achieve precise and efficient sign language recognition. The proposed system was assessed on sign language datasets of different languages (American, Arabic, and Malaysian) containing numerous variations. The proposed system attained a recognition accuracy ranging from 93.11% to 100% by employing multiple optimized machine learning classifiers and training time of 0.038?10.48 s. As demonstrated by the experimental outcomes, the proposed system is exceptionally efficient regarding time, complexity, generalization, and accuracy. ? The Author(s) 2024.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s44227-024-00039-8
dc.identifier.epage361
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85203236322
dc.identifier.spage344
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85203236322&doi=10.1007%2fs44227-024-00039-8&partnerID=40&md5=a257a502d7aa4e26b07e6fb1abe9e54a
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36271
dc.identifier.volume12
dc.pagecount17
dc.publisherSpringer Science and Business Media B.V.en_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleInternational Journal of Networked and Distributed Computing
dc.titleInstant Sign Language Recognition by WAR Strategy Algorithm Based Tuned Machine Learningen_US
dc.typeArticleen_US
dspace.entity.typePublication
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