Open Access
Cross-Lingual Information Retrieval Systems
Michael Johnson
1
,
Ayşe Yılmaz
2
,
Hans Müller
3
,
Yuki Tanaka
4
,
Elena Petrova
5
,
Ayşe Yılmaz
2
,
Hans Müller
3
,
Yuki Tanaka
4
,
Elena Petrova
5
1
Massachusetts Institute of Technology, USA
2
Istanbul Technical University, Turkey
3
Technical University of Munich, Germany
4
University of Tokyo, Japan
5
Moscow State University, Russia
Abstract
This study investigates this research area using advanced computational methods. We propose a novel framework that combines advanced algorithms with modern techniques to achieve improved performance. The experimental results demonstrate significant improvements, achieving up to 40% enhancement in overall efficiency. Our approach integrates multiple data sources to create comprehensive predictive models. The findings suggest that significant improvements can be achieved with the proposed approach.
Keywords
machine learning,deep learning,neural networks,artificial intelligence,data science
How to Cite
Johnson, M., Yılmaz, A., Müller, H., Tanaka, Y., & Petrova, E. (2022). Cross-Lingual Information Retrieval Systems. Nivo Light - International Journal of Research & Innovation, 4(4), 66–71. https://doi.org/10.28051/ojstest-150
References
Smith, J., & Brown, A. (2018). Deep learning applications in urban computing. Journal of Smart Cities, 15(3), 234-251.
Wang, L., Chen, H., & Liu, X. (2017). Energy optimization algorithms for intelligent buildings. Energy and Buildings, 142, 45-58.
García, M., Rodriguez, P., & Martinez, S. (2018). Sustainable urban development: A comprehensive review. Sustainability Science, 13(2), 189-205.
Tanaka, K., & Yamamoto, H. (2016). Smart grid technologies and renewable energy integration. IEEE Transactions on Smart Grid, 7(4), 1892-1901.
Anderson, R., Williams, T., & Davis, M. (2017). Machine learning for demand response systems. Applied Energy, 201, 112-125.