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      A Chaotic Neural Network Model for English Machine Translation Based on Big Data Analysis

      research-article
      1 , , 2
      Computational Intelligence and Neuroscience
      Hindawi

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          Abstract

          In this paper, the chaotic neural network model of big data analysis is used to conduct in-depth analysis and research on the English translation. Firstly, under the guidance of the translation strategy of text type theory, the translation generated by the machine translation system is edited after translation, and then professionals specializing in computer and translation are invited to confirm the translation. After that, the errors in the translations generated by the machine translation system are classified based on the Double Quantum Filter-Muttahida Quami Movement (DQF-MQM) error type classification framework. Due to the characteristics of the source text as an informative academic text, long and difficult sentences, passive voice, and terminology translation are the main causes of machine translation errors. In view of the rigorous logic of the source text and the fixed language steps, this research proposes corresponding post-translation editing strategies for each type of error. It is suggested that translators should maintain the logic of the source text by converting implicit connections into explicit connections, maintain the academic accuracy of the source text by adding subjects and adjusting the word order to deal with the passive voice, and deal with semitechnical terms by appropriately selecting word meanings in postediting. The errors of machine translation in computer science and technology text abstracts are systematically categorized, and the corresponding post-translation editing strategies are proposed to provide reference suggestions for translators in this field, to improve the quality of machine translation in this field.

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          The rise of deep learning in drug discovery

          Over the past decade, deep learning has achieved remarkable success in various artificial intelligence research areas. Evolved from the previous research on artificial neural networks, this technology has shown superior performance to other machine learning algorithms in areas such as image and voice recognition, natural language processing, among others. The first wave of applications of deep learning in pharmaceutical research has emerged in recent years, and its utility has gone beyond bioactivity predictions and has shown promise in addressing diverse problems in drug discovery. Examples will be discussed covering bioactivity prediction, de novo molecular design, synthesis prediction and biological image analysis.
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            Temporal data classification and forecasting using a memristor-based reservoir computing system

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              Uniqueness of weak solutions to a Keller–Segel–Navier–Stokes system

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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
                2021
                2 July 2021
                : 2021
                : 3274326
                Affiliations
                1School of Foreign Languages, Chengdu University of Information Technology, Chengdu 610036, China
                2Chengdu Angke Technologies Co., Ltd., Chengdu 610000, China
                Author notes

                Academic Editor: Syed Hassan Ahmed

                Author information
                https://orcid.org/0000-0002-7198-6861
                Article
                10.1155/2021/3274326
                8270720
                34306051
                f5920b5a-0817-4878-82c1-fff99531ed15
                Copyright © 2021 Qianyu Cao and Hanmei Hao.

                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
                : 2 June 2021
                : 21 June 2021
                : 26 June 2021
                Funding
                Funded by: Sichuan Social Science Research Program
                Award ID: SC16WY006
                Categories
                Research Article

                Neurosciences
                Neurosciences

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