Scholarlink

The paper represents the bibliometric analysis of applications of RNN in computer science, considering growth, trends, and impacts brought by this disruptive technology between 2010 and 2025. The investigation covers the main development trends regarding the publication of papers about RNN research in computer science, significant authors, institutions, and countries based on data provided by Scopus. The analysis shows that RNN research has grown significantly, especially after 2015, due to the increase in computational power, large datasets, and sophisticated neural network architecture. The findings highlight the global nature of RNN research, with substantial contributions from China, the United States, and India. Key research areas include natural language processing, time-series analysis, and speech recognition. This study will be important to the researchers, computer scientists, and policy analysts by indicating how RNNs have the potential to solve current computer science challenges and will also set the future research direction.

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

Exploring Recurrent Neural Networks: Trends, Applications, and Future Perspectives in Computer Science

  • 章节

  • 发表日期：

    2026年

查看全文
  • Faisal Asad Farid Aburub,
  • Suleiman Shelash,
  • Nawaf Alshdaifat,
  • Khaleel Ibrahim,
  • Asokan Vasudevan,
  • J. Bamini,
  • Rajani Balakrishnan,
  • Najah Al-Shanableh,
  • Mazen Alzyoud

摘要

The paper represents the bibliometric analysis of applications of RNN in computer science, considering growth, trends, and impacts brought by this disruptive technology between 2010 and 2025. The investigation covers the main development trends regarding the publication of papers about RNN research in computer science, significant authors, institutions, and countries based on data provided by Scopus. The analysis shows that RNN research has grown significantly, especially after 2015, due to the increase in computational power, large datasets, and sophisticated neural network architecture. The findings highlight the global nature of RNN research, with substantial contributions from China, the United States, and India. Key research areas include natural language processing, time-series analysis, and speech recognition. This study will be important to the researchers, computer scientists, and policy analysts by indicating how RNNs have the potential to solve current computer science challenges and will also set the future research direction.