An Analysis of Machine Learning and Deep Learning Research in Aviation Using Scientific Measurements
Abstract
This study examines the scientific development of machine learning (ML) and deep learning (DL) in the aviation sector using bibliometric analysis methods. Aviation is a highly regulated field in which safety is paramount and large amounts of data are generated. Machine learning- and deep learning-based solutions are utilized in numerous sub-fields, including predictive maintenance, air traffic management, flight safety and risk analysis, management information systems, and aircraft design. However, most of the existing literature consists of review articles focusing on specific application areas rather than examining the intellectual structure, collaboration networks, and development dynamics of the field at a macro level. To address this gap, the study analyzed publications retrieved from the Web of Science database using the search query “(machine learning OR deep learning) AND aviation” with CiteSpace software. The analyses included citation clustering, topic categories, and citation burst detection. In addition, the prominent machine learning and deep learning methods employed in 25 critical publications exhibiting citation bursts were examined in detail. The study provides a comprehensive bibliometric mapping of the field by revealing distinct evolutionary phases, citation bursts, and emerging research fronts in aviation-specific ML/DL applications. The findings identify the most commonly used methods and prominent research themes in aviation and highlight potential directions for future research. By mapping the intellectual structure of the field, identifying critical trends, and revealing emerging research fronts, the study provides a guiding framework for researchers interested in machine learning and deep learning applications in aviation.
Keywords
References
- Basora, L., Olive, X., & Dubot, T. (2019). Recent advances in anomaly detection methods applied to aviation. Aerospace, 6(11), 117. https://doi.org/10.3390/aerospace6110117
- Bishop, C. M. (2006). Pattern recognition and machine learning. Springer. https://doi.org/10.1007/978-0-387-45528-0
- Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359-377. https://doi.org/10.1002/asi.20317
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. http://www.deeplearningbook.org
- Heyne, J., Rauch, B., Le Clercq, P., & Colket, M. (2021). Sustainable aviation fuel prescreening tools and procedures. Fuel, 290, 120004. https://doi.org/10.1016/j.fuel.2021.120004
- Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90. https://doi.org/10.1145/3065386
- Laudon, K. C., & Laudon, J. P. (2020). Management information systems: Managing the digital firm (16th ed.). Pearson.
- LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
Details
Primary Language
English
Subjects
Air Transportation and Freight Services
Journal Section
Research Article
Publication Date
December 26, 2025
Submission Date
October 3, 2025
Acceptance Date
December 22, 2025
Published in Issue
Year 2025 Volume: 5 Number: 2
APA
Evrentuğ, B., & Bilal, Ç. (2025). An Analysis of Machine Learning and Deep Learning Research in Aviation Using Scientific Measurements. Research in Aviation Management (RAM), 5(2), 35-44. https://doi.org/10.5281/zenodo.18059296
AMA
1.Evrentuğ B, Bilal Ç. An Analysis of Machine Learning and Deep Learning Research in Aviation Using Scientific Measurements. Research in Aviation Management (RAM). 2025;5(2):35-44. doi:10.5281/zenodo.18059296
Chicago
Evrentuğ, Burak, and Çiğdem Bilal. 2025. “An Analysis of Machine Learning and Deep Learning Research in Aviation Using Scientific Measurements”. Research in Aviation Management (RAM) 5 (2): 35-44. https://doi.org/10.5281/zenodo.18059296.
EndNote
Evrentuğ B, Bilal Ç (December 1, 2025) An Analysis of Machine Learning and Deep Learning Research in Aviation Using Scientific Measurements. Research in Aviation Management (RAM) 5 2 35–44.
IEEE
[1]B. Evrentuğ and Ç. Bilal, “An Analysis of Machine Learning and Deep Learning Research in Aviation Using Scientific Measurements”, Research in Aviation Management (RAM), vol. 5, no. 2, pp. 35–44, Dec. 2025, doi: 10.5281/zenodo.18059296.
ISNAD
Evrentuğ, Burak - Bilal, Çiğdem. “An Analysis of Machine Learning and Deep Learning Research in Aviation Using Scientific Measurements”. Research in Aviation Management (RAM) 5/2 (December 1, 2025): 35-44. https://doi.org/10.5281/zenodo.18059296.
JAMA
1.Evrentuğ B, Bilal Ç. An Analysis of Machine Learning and Deep Learning Research in Aviation Using Scientific Measurements. Research in Aviation Management (RAM). 2025;5:35–44.
MLA
Evrentuğ, Burak, and Çiğdem Bilal. “An Analysis of Machine Learning and Deep Learning Research in Aviation Using Scientific Measurements”. Research in Aviation Management (RAM), vol. 5, no. 2, Dec. 2025, pp. 35-44, doi:10.5281/zenodo.18059296.
Vancouver
1.Burak Evrentuğ, Çiğdem Bilal. An Analysis of Machine Learning and Deep Learning Research in Aviation Using Scientific Measurements. Research in Aviation Management (RAM). 2025 Dec. 1;5(2):35-44. doi:10.5281/zenodo.18059296