Intelligent real-time Arabic sign language classification using attention-based inception and BiLSTM

Abdul, Wadood; Alsulaiman, Mansour; Amin, Syed Umar; Faisal, Mohammed; Muhammad, Ghulam; Albogamy, Fahad R.; Bencherif, Mohamed A.; Ghaleb, Hamid

doi:10.1016/j.compeleceng.2021.107395

Cited by 41 publications

(33 citation statements)

References 10 publications

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“…The layout of the inception sparse structure allows obtaining more feature information while avoiding the increase in the number of parameters. We refer to the inception optimization process from V1 to V4 and found the one-dimensional convolutional dimensionality reduction method [ 32 – 34 ]. We are building the initial temporal convolutional network, where the expansion of parameters is exacerbated by exponentially growing expansion factors in the temporal convolutional layers to expand the network.…”

Section: Methodsmentioning

confidence: 99%

Logistics Optimization Strategy Based on Deep Neural Framework

Liu

Zhu

2022

Computational Intelligence and Neuroscience

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We propose a logistics optimization method based on improved graph convolutional networks to address the current problem of low product delivery rate and untimely product delivery during the peak period of e-commerce activities. Our method can learn excellent planning strategies from previous data and can give the best logistics strategy in time during the peak logistics period, which improves the product delivery rate and delivery time of logistics and greatly enhances the return on investment. First, we add a tensor rotation module to the graph convolution layer to better capture the global features of logistics nodes. Then we add inception structures in the temporal convolution layer to build multiscale temporal convolution filters to obtain temporal information of logistics nodes in different time-aware domains and reduce arithmetic power. Finally, we cooperate with e-commerce platforms to adopt logistics data as the experimental database. The experimental results show that our method greatly accelerates the logistics planning speed, improves the product delivery rate, ensures the timely delivery of products, and increases the return on investment.

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Section: Methodsmentioning

confidence: 99%

Logistics Optimization Strategy Based on Deep Neural Framework

Liu

Zhu

2022

Computational Intelligence and Neuroscience

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“…The initial and most prioritized goal [ 15 ] for the development of human action recognition is to narrow the communication gap between the active speech-impaired community and the general public [ 24 ]. Action recognition is to narrow the communication gap between the active speech impaired community and the regular public members [ 25 ]. This will enable different community members to live in harmony thanks to the greater ease of sharing feelings and personal expression.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The recommendations from the research were that further studies focus on using comparative analysis for the selection of suitable sign language recognition models. Finally, an Arabic sign language classification system was proposed by [ 25 ]; their proposed model consisted of a Convolutional Neural Network integrated with an attention mechanism for retrieving spatial features and bio-inspired deep learning with Long Short-Term Memory (BI-LSTM). The BI-LSTM was used for temporal feature extraction.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The most challenging element is developing a suitable model for solving continuous sign problems that are signer-independent [ 35 , 36 ]. There is considerable variation in duration, speed, and background from one signer to another, which poses a challenge to the development of a model with high accuracy and that is continuous from one model to another [ 25 ]. Different researchers have provided various recommendations for further research into CNN-related models.…”

Section: Literature Reviewmentioning

confidence: 99%

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Sign and Human Action Detection Using Deep Learning

2022

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Human beings usually rely on communication to express their feeling and ideas and to solve disputes among themselves. A major component required for effective communication is language. Language can occur in different forms, including written symbols, gestures, and vocalizations. It is usually essential for all of the communicating parties to be fully conversant with a common language. However, to date this has not been the case between speech-impaired people who use sign language and people who use spoken languages. A number of different studies have pointed out a significant gaps between these two groups which can limit the ease of communication. Therefore, this study aims to develop an efficient deep learning model that can be used to predict British sign language in an attempt to narrow this communication gap between speech-impaired and non-speech-impaired people in the community. Two models were developed in this research, CNN and LSTM, and their performance was evaluated using a multi-class confusion matrix. The CNN model emerged with the highest performance, attaining training and testing accuracies of 98.8% and 97.4%, respectively. In addition, the model achieved average weighted precession and recall of 97% and 96%, respectively. On the other hand, the LSTM model’s performance was quite poor, with the maximum training and testing performance accuracies achieved being 49.4% and 48.7%, respectively. Our research concluded that the CNN model was the best for recognizing and determining British sign language.

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“…The information in Bidirectional Long-Short Term Memory (BiLSTM) is stored both forward and backward of the neural network [22]. The LSTM model is given an encoded sequence of Inception model characteristics.…”

Section: A Bidirectional Long Short-term Memory (Bilstm)mentioning

confidence: 99%

KHO-XAI: Krill herd optimization and Explainable Artificial Intelligence framework for Network Intrusion Detection Systems in Industry 4.0

Sivamohan

2022

Preprint

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Industry 4.0 enable novel business cases, such as client-specific production, real-time monitoring of process condition and progress, independent decision making and remote maintenance, to name a few. However, they are more susceptible to a broad range of cyber threats because of limited resources and heterogeneous nature. Such risks cause financial and reputational damages for businesses, well as the theft of sensitive information. The higher level of diversity in industrial network prevents the attackers from such attacks. Therefore, to efficiently detect the intrusions, a novel intrusion detection system known as Bidirectional Long Short-Term Memory based Explainable Artificial Intelligence framework (BiLSTM-XAI) is developed. Initially, the preprocessing task using normalization and standardization is performed to enhance the data quality for detecting network intrusions. Subsequently, the significant features are selected from the databases using the Krill herd optimization (KHO) algorithm. The proposed BiLSTM-XAI approach provides better security and privacy inside the industry networking system by detecting intrusions very precisely. In this, we utilized SHAP and LIME explainable AI algorithms to further improve the prediction accuracy. The experimental setup is made by MATLAB 2016 software using Honeypot and NSL-KDD datasets as input. The analysis result reveals that the proposed method achieves superior performance in detecting intrusions with a classification accuracy of 98.2%.

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Intelligent real-time Arabic sign language classification using attention-based inception and BiLSTM

Cited by 41 publications

References 10 publications

Logistics Optimization Strategy Based on Deep Neural Framework

Logistics Optimization Strategy Based on Deep Neural Framework

Sign and Human Action Detection Using Deep Learning

KHO-XAI: Krill herd optimization and Explainable Artificial Intelligence framework for Network Intrusion Detection Systems in Industry 4.0

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