Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing *** Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Lan...
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Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing *** Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Language(JSL)for ***,existing JSL recognition systems have faced significant performance limitations due to inherent *** response to these challenges,we present a novel JSL recognition system that employs a strategic fusion approach,combining joint skeleton-based handcrafted features and pixel-based deep learning *** system incorporates two distinct streams:the first stream extracts crucial handcrafted features,emphasizing the capture of hand and body movements within JSL ***,a deep learning-based transfer learning stream captures hierarchical representations of JSL gestures in the second ***,we concatenated the critical information of the first stream and the hierarchy of the second stream features to produce the multiple levels of the fusion features,aiming to create a comprehensive representation of the JSL *** reducing the dimensionality of the feature,a feature selection approach and a kernel-based support vector machine(SVM)were used for the *** assess the effectiveness of our approach,we conducted extensive experiments on our Lab JSL dataset and a publicly available Arabic sign language(ArSL)*** results unequivocally demonstrate that our fusion approach significantly enhances JSL recognition accuracy and robustness compared to individual feature sets or traditional recognition methods.
In this work, a novel methodological approach to multi-attribute decision-making problems is developed and the notion of Heptapartitioned Neutrosophic Set Distance Measures (HNSDM) is introduced. By averaging the Pent...
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In this internet era, cloud computing and there are various problems in the cloud computing, where the consumers as well as the service providers facing in their day to day cloud activities. Job scheduling problem pla...
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Previous articles on unsupervised skeleton-based action recognition primarily focused on strategies for utilizing features to drive model optimization through methods like contrastive learning and reconstruction. Howe...
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This paper proposes A dynamic switching strategy based on Dijkstra algorithm and A ∗ algorithm. By setting a threshold, the dynamic switching algorithm according to the distance can improve the efficiency of path plan...
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This paper tackles the complexity of detecting abnormal behavior by focusing on temporal patterns, such as social force and optical flow, rather than spatial patterns. The CycleGAN system trains on normal behaviors, e...
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Vehicle cloud computing (VCC) is a recent area of study that blends vehicular networks with cloud computing, offering networking and sensor capabilities to vehicles for interaction with other vehicles and roadside inf...
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With the rapid development of deep learning, various semantic communication models are emerging, but the current semantic communication models still have much room for improvement in the coding layer. For this reason,...
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With the rapid development of deep learning, various semantic communication models are emerging, but the current semantic communication models still have much room for improvement in the coding layer. For this reason, a joint-residual neural networks (Joint-ResNets) framework based on the joint control of shallow neural networks (SNNs) and deep neural networks (DNNs) is proposed to cope with the problems in semantic communication coding. The framework synergizes SNNs and DNNs based on their shared utility, and uses variable weight \begin{document}$\alpha$\end{document} term to control the ratio of SNNs and DNNs to fully utilize the simplicity of SNNs and the richness of DNNs. The article details the construction of the Joint-ResNets framework and its canonical use in classical semantic communication models, and illustrates the control mechanism of the variable weight \begin{document}$\alpha$\end{document} term in the Joint-ResNets framework and its importance in balancing the model complexity between SNNs and DNNs. The article takes the task-oriented communication model in the device edge collaborative reasoning system as an example for experimentation and analysis. The experimental validation shows that DNNs and SNNs can be combined in a more effective way to standardize semantic coding, which improves the overall predictive performance, interpretability, and robustness of semantic communication models, and this framework is expected to bring new breakthroughs in the field of semantic communication.
Autonomous vehicles (AVs) increasingly rely on vehicle-to-everything (V2X) networks for communication. However, due to the devices' heterogeneity, they are more susceptible to attacks like distributed denial of se...
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This study presents a three-dimensional inverted pendulum system using three symmetrically arranged vertical reaction wheels for balance control and an additional horizontal reaction wheel for direction control. The d...
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