Intelligent Detection System Based on Recurrent Neural Network Machine Translation for Typical Errors in English Translation

The most important and widely used intelligent recognition translation technique that has developed with the advancement of modern intelligent recognition is machine translation. English translation technologies emerged in recent years as a result of technological developments; however, the accuracy of translation offered by intelligent recognition technology aren’t guaranteed. Consequently, this research developed an intelligent recognition approach based on Recurrent Neural Network Machine Translation (RNN-MT) to improve the logic of English translation. With its many characteristics, including ease, machine translation based on the RNN-MT technique and various similar algorithms is adequate for translating text into English. The intelligent recognition framework for English translation using the RNN-MT improves sentence flow and has the potential to address some translation problems while providing a coherent translation in context. The results shown that the suggested method outperforms existing models, such as Machine English Translation Errors based on Multifeatured Fusion (METE-MF), Recurrent Neural network with double directions (Double-RNN), and Radio Magnetic Pronunciation Recording Devices (RMPRD), in terms of identification accuracy, attaining $\mathbf{9 9. 9 3 \%}$ respectively.