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Bibliometric Analysis of Math and Artificial Intelligence Research

Year 2025, , 149 - 160, 15.01.2025
https://doi.org/10.34248/bsengineering.1516593

Abstract

This study conducts a comprehensive bibliometric analysis to explore the landscape of research in mathematics and artificial intelligence (AI). Using Scopus as the primary data source, we identify key publications and trends in these fields. Through VOSviewer, we visualize networks of keywords and collaborations among researchers and institutions. The analysis reveals the prominence of topics such as AI and mathematics in academic discourse, as well as the central role played by countries like the United States, the United Kingdom, and China in research collaboration. Limitations include potential biases in data sources and the reliance on keywords for analysis. Future research could integrate alternative metrics and qualitative analyses to provide a more nuanced understanding of research trends and impact.

References

  • De Aggarwal, C. C., Aggarwal, L. F., Lagerstrom-Fife. 2020. Linear algebra and optimization for machine learning. Springer International Publishing. Vol. 156:97-120.
  • Dydak, J. 2023. Artificial Intelligence And Teaching Of Linear Algebra. University of Tennessee, Knoxville, USA. 2-42.
  • Golchi, S., Willard, J. 2023. Estimating the Sampling Distribution of Test-Statistics in Bayesian Clinical Trials. arXiv preprint.
  • Ismail, A., Wediawati, B. 2023. Understanding the Fundamentals of Machine Learning and AI for Digital Business. Asadel Publisher. 20-35.
  • Kirby, A. 2023. "Exploratory Bibliometrics: Using VOSviewer as a Preliminary Research Tool" Publications MDPI. 11(1): 1-14.
  • Lin, W. C., Hsiao, C. H., Huang, W. T., Yao, K. C., Lee, Y. D., Jian, J. L., Hsieh, Y. 2024. Network Reconfiguration Framework for CO2 Emission Reduction and Line Loss Minimization in Distribution Networks Using Swarm Optimization Algorithms. Sustainability, 16(4): 1493.
  • Liu, F. L. F. 2024. Financial Statement Analysis Based on RNN-RBM Model. Journal of Electrical Systems, 20(1), 106-123.
  • Mejia, C., Wu, M., Zhang, Y., Kajikawa, Y. 2021. Exploring topics in bibliometric research through citation networks and semantic analysis. Frontiers in Research Metrics and Analytics, 6: 742311.
  • Nawaz, N. 2019. Artificial Intelligence Face Recognition for applicant tracking system. International Journal of Emerging Trends in Engineering Research, 7(12): 895 – 901.
  • Pal, T., Kaushik, M. 2023. Aircraft parameter estimation using a novel hybrid Luus–Jaakola/Hooke–Jeeves neural-network based optimization technique. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 237(9): 2196-2208.
  • Pap, E. 2021. Mathematical Foundation of Artificial Intelligence. Artificial Intelligence: Theory and Applications, 3-30 .
  • Pitkow, X., Angelaki, D. E. 2017. Inference in the brain: statistics flowing in redundant population codes. Neuron, 94(5): 943-953.
  • Senaratne, A., Seneviratne, L. 2023. Embedded to Interpretive: A paradigm shift in knowledge discovery to represent dynamic knowledge. In AAAI Spring Symposium on Challenges Requiring the Combination of Machine Learning and Knowledge Engineering 3433. 102.
  • Wang, Q., Zhou, D., Guan, Q., Li, Y., Yang, J. 2018. A privacy-preserving classifier in statistic pattern recognition. In Cloud Computing and Security: 4th International Conference, ICCCS 2018, Springer International Publishing. Haikou, China. 4: 496-507.
  • Torres Tello, J. W. 2022. Optimization of AI models as the Main Component in Prospective Edge Intelligence Applications Doctoral thesis. University of Saskatchewan. Faculty of Engineering. Canada. 50-65.
  • Tsiamis, A., Ziemann, I., Matni, N., Pappas, G. J. 2023. Statistical learning theory for control: A finite-sample perspective. IEEE Control Systems Magazine, 43(6), 67-97.
  • Tyagi, A. K., Chahal, P. 2022. Artificial intelligence and machine learning algorithms. In Research anthology on machine learning techniques, methods, and applications. IGI Global. 421-446.
  • Wylie, R., Kamel, M. S. 1997. Model transformations in simulation and planning: behavior preserving model simplifications. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 27(4), 424-435.
  • Zhenpeng, Y. 2024. Application of Artificial Intelligence in Computer Network Technology in the Age of Big Data. Journal of Artificial Intelligence Practice, 7(1): 11-16.

Bibliometric Analysis of Math and Artificial Intelligence Research

Year 2025, , 149 - 160, 15.01.2025
https://doi.org/10.34248/bsengineering.1516593

Abstract

This study conducts a comprehensive bibliometric analysis to explore the landscape of research in mathematics and artificial intelligence (AI). Using Scopus as the primary data source, we identify key publications and trends in these fields. Through VOSviewer, we visualize networks of keywords and collaborations among researchers and institutions. The analysis reveals the prominence of topics such as AI and mathematics in academic discourse, as well as the central role played by countries like the United States, the United Kingdom, and China in research collaboration. Limitations include potential biases in data sources and the reliance on keywords for analysis. Future research could integrate alternative metrics and qualitative analyses to provide a more nuanced understanding of research trends and impact.

References

  • De Aggarwal, C. C., Aggarwal, L. F., Lagerstrom-Fife. 2020. Linear algebra and optimization for machine learning. Springer International Publishing. Vol. 156:97-120.
  • Dydak, J. 2023. Artificial Intelligence And Teaching Of Linear Algebra. University of Tennessee, Knoxville, USA. 2-42.
  • Golchi, S., Willard, J. 2023. Estimating the Sampling Distribution of Test-Statistics in Bayesian Clinical Trials. arXiv preprint.
  • Ismail, A., Wediawati, B. 2023. Understanding the Fundamentals of Machine Learning and AI for Digital Business. Asadel Publisher. 20-35.
  • Kirby, A. 2023. "Exploratory Bibliometrics: Using VOSviewer as a Preliminary Research Tool" Publications MDPI. 11(1): 1-14.
  • Lin, W. C., Hsiao, C. H., Huang, W. T., Yao, K. C., Lee, Y. D., Jian, J. L., Hsieh, Y. 2024. Network Reconfiguration Framework for CO2 Emission Reduction and Line Loss Minimization in Distribution Networks Using Swarm Optimization Algorithms. Sustainability, 16(4): 1493.
  • Liu, F. L. F. 2024. Financial Statement Analysis Based on RNN-RBM Model. Journal of Electrical Systems, 20(1), 106-123.
  • Mejia, C., Wu, M., Zhang, Y., Kajikawa, Y. 2021. Exploring topics in bibliometric research through citation networks and semantic analysis. Frontiers in Research Metrics and Analytics, 6: 742311.
  • Nawaz, N. 2019. Artificial Intelligence Face Recognition for applicant tracking system. International Journal of Emerging Trends in Engineering Research, 7(12): 895 – 901.
  • Pal, T., Kaushik, M. 2023. Aircraft parameter estimation using a novel hybrid Luus–Jaakola/Hooke–Jeeves neural-network based optimization technique. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 237(9): 2196-2208.
  • Pap, E. 2021. Mathematical Foundation of Artificial Intelligence. Artificial Intelligence: Theory and Applications, 3-30 .
  • Pitkow, X., Angelaki, D. E. 2017. Inference in the brain: statistics flowing in redundant population codes. Neuron, 94(5): 943-953.
  • Senaratne, A., Seneviratne, L. 2023. Embedded to Interpretive: A paradigm shift in knowledge discovery to represent dynamic knowledge. In AAAI Spring Symposium on Challenges Requiring the Combination of Machine Learning and Knowledge Engineering 3433. 102.
  • Wang, Q., Zhou, D., Guan, Q., Li, Y., Yang, J. 2018. A privacy-preserving classifier in statistic pattern recognition. In Cloud Computing and Security: 4th International Conference, ICCCS 2018, Springer International Publishing. Haikou, China. 4: 496-507.
  • Torres Tello, J. W. 2022. Optimization of AI models as the Main Component in Prospective Edge Intelligence Applications Doctoral thesis. University of Saskatchewan. Faculty of Engineering. Canada. 50-65.
  • Tsiamis, A., Ziemann, I., Matni, N., Pappas, G. J. 2023. Statistical learning theory for control: A finite-sample perspective. IEEE Control Systems Magazine, 43(6), 67-97.
  • Tyagi, A. K., Chahal, P. 2022. Artificial intelligence and machine learning algorithms. In Research anthology on machine learning techniques, methods, and applications. IGI Global. 421-446.
  • Wylie, R., Kamel, M. S. 1997. Model transformations in simulation and planning: behavior preserving model simplifications. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 27(4), 424-435.
  • Zhenpeng, Y. 2024. Application of Artificial Intelligence in Computer Network Technology in the Age of Big Data. Journal of Artificial Intelligence Practice, 7(1): 11-16.
There are 19 citations in total.

Details

Primary Language English
Subjects Pure Mathematics (Other)
Journal Section Research Articles
Authors

Şeyma Bozkurt Uzan 0000-0003-3527-3730

Nesibe Manav Mutlu 0000-0002-7853-6337

İrem Deniz Arberk 0009-0001-5192-6781

Publication Date January 15, 2025
Submission Date July 15, 2024
Acceptance Date December 2, 2024
Published in Issue Year 2025

Cite

APA Bozkurt Uzan, Ş., Mutlu, N. M., & Arberk, İ. D. (2025). Bibliometric Analysis of Math and Artificial Intelligence Research. Black Sea Journal of Engineering and Science, 8(1), 149-160. https://doi.org/10.34248/bsengineering.1516593
AMA Bozkurt Uzan Ş, Mutlu NM, Arberk İD. Bibliometric Analysis of Math and Artificial Intelligence Research. BSJ Eng. Sci. January 2025;8(1):149-160. doi:10.34248/bsengineering.1516593
Chicago Bozkurt Uzan, Şeyma, Nesibe Manav Mutlu, and İrem Deniz Arberk. “Bibliometric Analysis of Math and Artificial Intelligence Research”. Black Sea Journal of Engineering and Science 8, no. 1 (January 2025): 149-60. https://doi.org/10.34248/bsengineering.1516593.
EndNote Bozkurt Uzan Ş, Mutlu NM, Arberk İD (January 1, 2025) Bibliometric Analysis of Math and Artificial Intelligence Research. Black Sea Journal of Engineering and Science 8 1 149–160.
IEEE Ş. Bozkurt Uzan, N. M. Mutlu, and İ. D. Arberk, “Bibliometric Analysis of Math and Artificial Intelligence Research”, BSJ Eng. Sci., vol. 8, no. 1, pp. 149–160, 2025, doi: 10.34248/bsengineering.1516593.
ISNAD Bozkurt Uzan, Şeyma et al. “Bibliometric Analysis of Math and Artificial Intelligence Research”. Black Sea Journal of Engineering and Science 8/1 (January 2025), 149-160. https://doi.org/10.34248/bsengineering.1516593.
JAMA Bozkurt Uzan Ş, Mutlu NM, Arberk İD. Bibliometric Analysis of Math and Artificial Intelligence Research. BSJ Eng. Sci. 2025;8:149–160.
MLA Bozkurt Uzan, Şeyma et al. “Bibliometric Analysis of Math and Artificial Intelligence Research”. Black Sea Journal of Engineering and Science, vol. 8, no. 1, 2025, pp. 149-60, doi:10.34248/bsengineering.1516593.
Vancouver Bozkurt Uzan Ş, Mutlu NM, Arberk İD. Bibliometric Analysis of Math and Artificial Intelligence Research. BSJ Eng. Sci. 2025;8(1):149-60.

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