Scholarlink

Community Detection (CD) is an important aspect of identifying new groups and relations in E-learning Online Social Networks (OSNs). Community identification is a challenging task due to detecting small or overlapping communities having higher resolution future limit problems which is time-consuming and not suitable for large-scale datasets. To alleviate the limitations of previous methods, this paper presents a Community Enhancement Structure-based Evolutionary Propagation Recurrent Neural Network (CES-EPRNN) algorithm. Initially, the Box-Cox Transformation (BCT) algorithm is used to reduce outliers from the education community dataset in OSN. In addition, The Correlation Exhaustive Feature Selection (CEFS) algorithm proficiently identifies the attributes of the education community. Following that, the proposed CES-EPRNN algorithm determines who belongs to the same academic community in OSN. The extensive experiments demonstrate that our proposed technique outperforms conventional methods on the Education Community Dataset (ECD).

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Enhanced E-Learning Community Detection Using Distributed Overlapping Feature Selection-Based Evolutionary Propagation Recurrent Neural Network

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • A. P. Tharani,
  • V. Sumalatha,
  • S. Meenakshi

摘要

Community Detection (CD) is an important aspect of identifying new groups and relations in E-learning Online Social Networks (OSNs). Community identification is a challenging task due to detecting small or overlapping communities having higher resolution future limit problems which is time-consuming and not suitable for large-scale datasets. To alleviate the limitations of previous methods, this paper presents a Community Enhancement Structure-based Evolutionary Propagation Recurrent Neural Network (CES-EPRNN) algorithm. Initially, the Box-Cox Transformation (BCT) algorithm is used to reduce outliers from the education community dataset in OSN. In addition, The Correlation Exhaustive Feature Selection (CEFS) algorithm proficiently identifies the attributes of the education community. Following that, the proposed CES-EPRNN algorithm determines who belongs to the same academic community in OSN. The extensive experiments demonstrate that our proposed technique outperforms conventional methods on the Education Community Dataset (ECD).