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Edge computing based english translation model using fuzzy semantic optimal control technique

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  • Author(s): Wang, Na
  • Source:
    PLOS One ; volume 20, issue 6, page e0320481 ; ISSN 1932-6203
  • Document Type:
    article in journal/newspaper
  • Language:
    English
  • Additional Information
    • Contributors:
      El-Fiqi, Heba; This research was funded by 2021 Teacher education curriculum Reform research project of Henan Province: Research on the Application of Temperament Theory in the Teaching of Primary Education Major; The annual general project of the 14th five-year Plan of Education science in Henan Province: Research on the Cultivation Mode of Primary Education Specialty from the Perspective of Temperament; Henan Education Department project: Research on the Application of Temperament Theory in Normal College Teaching
    • Publication Information:
      Public Library of Science (PLoS)
    • Publication Date:
      2025
    • Collection:
      PLOS Publications (via CrossRef)
    • Abstract:
      People’s need for English translation is gradually growing in the modern era of technological advancements, and a computer that can comprehend and interpret English is now more crucial than ever. Some issues, including ambiguity in English translation and improper word choice in translation techniques, must be addressed to enhance the quality of the English translation model and accuracy based on the corpus. Hence, an edge computing-based translation model (FSRL-P2O) is proposed to improve translation accuracy by using huge bilingual corpora, considering Fuzzy Semantic (FS) properties, and maximizing the translation output using optimal control techniques with the incorporation of Reinforcement Learning and Proximal Policy Optimisation (PPO) techniques. The corpus data is initially gathered, and necessary preprocessing and feature extraction techniques are made. The preprocessed sentences are given as input to the fuzzy semantic similarity phase, which aims to avoid uncertainties by measuring the semantic resemblance between two linguistic elements, such as phrases, words, or sentences involved in a translation using the Jaccard similarity coefficient. The fuzzy semantic resemblance component’s training estimates the degree of overlap or similarity between two sentences, such as calculating the percentage of characters and length of the longest matching sequence of characters. The suggested Reinforcement learning and PPO can address specific uncertainty causes in machine translation assessment, like out-of-domain data and low-quality references. In addition to simple word-level comparison, it permits a more complex grasp of the semantic link. Reinforcement Learning (RL) and Proximal Policy Optimisation (PPO) techniques are implemented as optimal control techniques to optimize the translation procedures and enhance the quality and precision of generated translations. RL and PPO aim to improve a machine translation system’s translation policy depending on a predetermined reward signal or quality parameter. The ...
    • Accession Number:
      10.1371/journal.pone.0320481
    • Online Access:
      https://doi.org/10.1371/journal.pone.0320481
      https://dx.plos.org/10.1371/journal.pone.0320481
    • Rights:
      http://creativecommons.org/licenses/by/4.0/
    • Accession Number:
      edsbas.69B24A8