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在大学中使用高性能计算技术驱动的多媒体融合模型英语翻译英语翻译
Lin Shi1, Minne DuJiang1, Ping Gao1
1Xi'an University of Architecture and Technology, Xi'an, China.
PeerJ. Computer science
|October 23, 2023
概括
本研究介绍了大学英语翻译的多媒体框架,集成大数据和人工智能. 提出的递归神经网络模型显著提高了翻译准确性和记忆力.
科学领域:
- 教育技术的教育技术
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 多媒体集成正在改变大学英语翻译教学.
- 现有的教材对于教师开发独特的教学风格提出了挑战.
- 需要创新的方法来加强英语翻译教育.
研究的目的:
- 提出一个多媒体综合英语翻译框架.
- 将大数据技术与多媒体教学模式相结合.
- 为了提高大学英语翻译教学的有效性.
主要方法:
- 开发了一个结合大数据技术和多媒体教学的框架.
- 从数据和教学过程的角度探索框架集成.
- 实现并测试了一种由殖民地算法优化的递归神经网络算法.
主要成果:
- 拟议的框架显示了翻译准确性的显著改进.
- 该方法显示,学习材料的保留率显著增加.
- 模拟结果验证了开发方法的有效性.
结论:
- 多媒体框架有效地解决了大学英语翻译方面的挑战.
- 大数据和人工智能的整合提高了教学和学习成果.
- 拟议的算法为改善翻译教育提供了一个有希望的解决方案.
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