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 system's effectiveness is evaluated by various metrics such as accuracy, Fuzzy semantic similarity, Bi-Lingual Evaluation Understudy (BLEU), and National Institute of Standards and Technology score (NIST). Thus, the proposed system achieves higher quality and translation accuracy of the text that has been translated and produces higher semantic similarity.