Work

Interactive Mobile English Translation Proficiency Model Based on Particle Swarm Optimisation and Neural Network for Teaching

Year: 2025

Type: article

Abstract: This paper studies an interactive mobile English translation ability analysis model based on particle swarm optimisation (PSO) and neural networks (NNs) and explores its application potential in mobile translation teaching. By integrating the global search capability of PSO algorithm and the powerful learning capability of NN, the model aims to optimise the translation quality assessment process and improve the accuracy and efficiency of translation capability analysis. By training NNs to recognise language features, style and accuracy in translated texts, and fine-tuning NN parameters with PSO algorithm, this paper constructs a model that can effectively evaluate and interactively improve mobile English translation ability. The results show that the interactive mobile English translation ability analysis model based on PSO and NN has significant teaching application value, and brings new possibilities to the field of translation education, especially mobile learning scenarios.

Source: International Journal of Interactive Mobile Technologies (iJIM)

DOI: 10.3991/ijim.v19i08.54043

Authors: Yunhui Li, Mei Huang, Huafeng Dong

Institutions: Fuzhou Preschool Education College, Chongqing Industry Polytechnic University

Language: English

Topic: Educational Technology and Pedagogy

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: gold

Estimated APC paid: $280