Table 1 Comparison Analysis of Related Works.
Author and Year | Methods | Objective | Findings |
|---|---|---|---|
Yang et al.18, 2022 | Neural Machine Translation (NMT) | Increases the efficiency level in providing services to users | Improves the effective range of NMT systems |
Zhang19, 2023 | Deep learning (DL) | To detect grammatical errors in a sentence | Increases the accuracy of the error detection process |
Abbaszade et al.20 , 2021 | Quantum natural language processing (Q-NLP) | Aiming to develop the automatic language processing system | Designed Q-NLP improves the feasibility and performance level of the systems |
Zhao and Liu21, 2023 | Recurrent context model | Aim of the method is to encode the large content which is presented in a sentence | Significantly improves the performance range of NMT systems |
Zhao et al.22, 2021 | Word-region alignment (WRA) approach | Actual semantic correlation between visual and textual modalities for MNMT is analyzed | WRA approach increases the accuracy of the translation process |
Tian et al.23, 2022 | Transfer network-based French-to-English machine translation (MT) | To identify the defects which are presented in a content | Increases the accuracy of data translation which enhances the effectiveness of MT systems |
Shen and Qin24, 2021 | A new deep neural network (DNN) | To reduce the time of processing for translating the data | Increases the translation accuracy that improves the efficiency of machine translation systems |
Li et al.25, 2022 | Fast gradient method (FGM) | To extract the linguistic information for the translation process | Maximizes the reliability level of the systems |
Zhang et al.26, 2022 | Statistical machine translation (SMT) using deep learning (DL) algorithms | To create the automatic translation process | Provides high-quality translation content for the users |
Rajalakshmi et al.27, 2023 | Vision-based hybrid deep neural network | Identification to help the speech and hearing handicapped | Generalization and deployment in the actual world necessitate more study |
Pham and Pham 28, 023 | Data augmentation method for English-Vietnamese NMT systems | Creating synthetic parallel data translation process | Provides an effective data translation system for the users |
Unanue et al.29, 2022 | Sentence embedding technique | Creating the automated translation technique in NMT | Increases the performance range of the systems |
Rajalakshmi et al.30, 2022 | Hybrid Neural Network Architecture | To develop the sign language recognition systems | Ensures the high detection accuracy |
Li et al.31, 2021 | Latent feature encoder (LFE) | Produces relevant latency features for the translation process | Model improves the overall quality of service (QoS) and performance range of the systems |
Natarajan and Elakkiya32 | Generative adversarial network | To regulate the training and photo realistic high-quality videos for understanding sign language | 28.7 peak signal to noise ratio value, 0.92 structural similarity score, 8.72 average inception score |
