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In this paper, comparative analysis of various machine learning (ML) algorithms and neural network (NN) for cross-language translation and security applications is discussed in detail. The development of the Internet as a medium for international communication and the ever-growing demands for secure communication has highlighted the need for translation and security. The following are 20 recent studies that employ sophisticated ML and NN techniques to tackle these challenges, which are reviewed and analyzed systematically. Here, we briefly discuss major aspects of our analysis, such as translation accuracy, processing speed, data security, and applicability for different domains. In this comparative research, our objectives are to focus on the advantages, drawbacks, and possible further advancements in the interaction of machine learning and neural network in cross-language translation and security. The results contribute to the knowledge of researchers and practitioners for the purposes of improving the means of communication technology as well as the protection of digital platforms using advanced AI methods.

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Evaluating the Efficacy of Machine Learning and Neural Networks in Cross-Language Translation and Security Applications

  • 会议论文

  • 发表日期：

    2026年

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  • Aziza Zokirjon,
  • Eshnazarova Shokhruza,
  • Gaurav Aggarwal,
  • Danish Ather,
  • Naina Chaudhary,
  • Balvinder Shukla

摘要

In this paper, comparative analysis of various machine learning (ML) algorithms and neural network (NN) for cross-language translation and security applications is discussed in detail. The development of the Internet as a medium for international communication and the ever-growing demands for secure communication has highlighted the need for translation and security. The following are 20 recent studies that employ sophisticated ML and NN techniques to tackle these challenges, which are reviewed and analyzed systematically. Here, we briefly discuss major aspects of our analysis, such as translation accuracy, processing speed, data security, and applicability for different domains. In this comparative research, our objectives are to focus on the advantages, drawbacks, and possible further advancements in the interaction of machine learning and neural network in cross-language translation and security. The results contribute to the knowledge of researchers and practitioners for the purposes of improving the means of communication technology as well as the protection of digital platforms using advanced AI methods.