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      Design and Proofreading of the English-Chinese Computer-Aided Translation System by the Neural Network

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      1 , , 2
      Computational Intelligence and Neuroscience
      Hindawi

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          Abstract

          At present, complete machine translation (MT) cannot meet the needs of information communication and cultural exchange, and the speed of complete human translation is too slow. Therefore, if MT is used to assist in the process of English-Chinese translation, it can not only prove that machine learning (ML) can translate English to Chinese but also improve the translation efficiency and accuracy of translators through human-machine cooperation. The research on the mutual cooperation between ML and human translation has an important research significance for translation systems. An English-Chinese computer-aided translation (CAT) system is designed and proofread based on a neural network (NN) model. First, it gives a brief overview of CAT. Second, the related theory of the NN model is discussed. An English-Chinese CAT and proofreading system based on the recurrent neural network (RNN) is constructed. Finally, the translation accuracy and proofreading recognition rate of the translation files of 17 different projects under different models are studied and analyzed. The research results reveal that according to the different translation properties of different texts, the average accuracy rate of text translation under the RNN model is 93.96%, and the mean accuracy of text translation under the transformer model is 90.60%. The translation accuracy of the RNN model in the CAT system is 3.36% higher than that of the transformer model. The English-Chinese CAT system based on the RNN model has different proofreading results for sentence processing, sentence alignment, and inconsistency detection of translation files of different projects. Among them, the recognition rate for sentence alignment and the inconsistency detection of English-Chinese translation is high, and the expected effect is achieved. The design of the English-Chinese CAT and proofreading system based on the RNN can make the translation and proofreading be carried out simultaneously, which greatly improves the efficiency of translation work. Meanwhile, the above research methods can improve the problems encountered in the current English-Chinese translation, provide a path for the bilingual translation process, and have certain promotion prospects.

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          Most cited references31

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          Convolutional neural network: a review of models, methodologies and applications to object detection

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            Overcoming Hurdles in Nanoparticle Clinical Translation: The Influence of Experimental Design and Surface Modification

            Nanoparticles are becoming an increasingly popular tool for biomedical imaging and drug delivery. While the prevalence of nanoparticle drug-delivery systems reported in the literature increases yearly, relatively little translation from the bench to the bedside has occurred. It is crucial for the scientific community to recognize this shortcoming and re-evaluate standard practices in the field, to increase clinical translatability. Currently, nanoparticle drug-delivery systems are designed to increase circulation, target disease states, enhance retention in diseased tissues, and provide targeted payload release. To manage these demands, the surface of the particle is often modified with a variety of chemical and biological moieties, including PEG, tumor targeting peptides, and environmentally responsive linkers. Regardless of the surface modifications, the nano–bio interface, which is mediated by opsonization and the protein corona, often remains problematic. While fabrication and assessment techniques for nanoparticles have seen continued advances, a thorough evaluation of the particle’s interaction with the immune system has lagged behind, seemingly taking a backseat to particle characterization. This review explores current limitations in the evaluation of surface-modified nanoparticle biocompatibility and in vivo model selection, suggesting a promising standardized pathway to clinical translation.
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              Transformer neural network for protein-specific de novo drug generation as a machine translation problem

              Drug discovery for a protein target is a very laborious, long and costly process. Machine learning approaches and, in particular, deep generative networks can substantially reduce development time and costs. However, the majority of methods imply prior knowledge of protein binders, their physicochemical characteristics or the three-dimensional structure of the protein. The method proposed in this work generates novel molecules with predicted ability to bind a target protein by relying on its amino acid sequence only. We consider target-specific de novo drug design as a translational problem between the amino acid “language” and simplified molecular input line entry system representation of the molecule. To tackle this problem, we apply Transformer neural network architecture, a state-of-the-art approach in sequence transduction tasks. Transformer is based on a self-attention technique, which allows the capture of long-range dependencies between items in sequence. The model generates realistic diverse compounds with structural novelty. The computed physicochemical properties and common metrics used in drug discovery fall within the plausible drug-like range of values.
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                Author and article information

                Contributors
                Journal
                Comput Intell Neurosci
                Comput Intell Neurosci
                cin
                Computational Intelligence and Neuroscience
                Hindawi
                1687-5265
                1687-5273
                2023
                22 February 2023
                : 2023
                : 9450816
                Affiliations
                1School of Humanities and Social Sciences, Xi'an Polytechnic University, Xi'an City 710048, China
                2Shaanxi Contemporary Red Culture Training and Education Center, Xi'an City 710061, China
                Author notes

                Academic Editor: Arpit Bhardwaj

                Author information
                https://orcid.org/0000-0002-0467-4040
                Article
                10.1155/2023/9450816
                9977533
                36873384
                c59014d4-440c-4443-a13e-82bc56fdf1c8
                Copyright © 2023 Yutong Liu and Shile Zhang.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 16 June 2022
                : 28 June 2022
                : 26 July 2022
                Categories
                Research Article

                Neurosciences
                Neurosciences

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