基于学生意图的教育聊天机器人系统的开发,在对称卷积方法上采用自适应和专心的DTCN
Atul Kathole1, Suvarna Patil1, Dr Devyani Jadhav2
1Department of Computer Engineering, Dr. D. Y. Patil Institute of Technology, Dr. D. Y. Patil Dnyan Prasad University, Pimpri, Pune 411018, India.
MethodsX
|August 18, 2025
概括
本研究介绍了一个智能教育聊天机器人,它使用深度学习和一种新的优化算法来快速准确地回答学生的学术询问,为学生和管理人员节省时间.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 教育技术的教育技术
背景情况:
- 在机构网站上访问学术信息对学生来说是耗时的.
- 当前的聊天机器人平台往往提供无关的输出和不准确的用户意图预测.
- 需要一个自动化,智能化的系统来提供快速而精确的学术援助.
研究的目的:
- 开发一个基于学生意图的自动化教育聊天机器人.
- 整合深度学习和高级优化算法,以实现准确的意图识别.
- 通过提供及时和相关的学术信息来提高学生的参与度.
主要方法:
- 预处理学生查询和通用聊天机器人数据.
- 使用BERT,TransformerNet和Text CNN将文本数据表示为向量.
- 采用基于平均值的驾驶培训 - 巴纳克尔交配优化器 (ADT-BMO) 进行特征选择和优化.
- 使用具有对称卷积的自适应深度神经网络 (AA-DTCN-SC) 进行意图识别.
- 使用ADT-BMO优化AA-DTCN-SC模型参数.
主要成果:
- 与基线模型 (DTCN,RNN,Bi-LSTM) 相比,开发的聊天机器人表现出更高的性能.
- 绩效改进率为4.44% (DTCN),3.3% (RNN),10.59% (Bi-LSTM) 和11.9% (未指定的基线).
- ADT-BMO算法有效地优化特征权重和模型参数,以提高准确性.
- 该系统自动生成对学生查询的上下文相关的响应.
结论:
- 这项研究提出了一个功能良好,可扩展和节省时间的教育聊天机器人.
- 聊天机器人通过快速,精确和相关的学术援助来提高学生的参与度.
- 由ADT-BMO优化的新型AA-DTCN-SC模型有效地解决了教育环境中现有的聊天机器人平台的局限性.
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