Open Access
| Issue |
ITM Web Conf.
Volume 79, 2025
International Conference on Knowledge Engineering and Information Systems (KEIS-2025)
|
|
|---|---|---|
| Article Number | 01022 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/itmconf/20257901022 | |
| Published online | 08 October 2025 | |
- C. Fan, M. Chen, X. Wang, J. Wang, B. Huang, A review on data preprocessing techniques toward efficient and reliable knowledge discovery from building operational data. Front. Energy Res. 9, 652801 (2021). https://doi.org/10.3389/fenrg.2021.652801 [CrossRef] [Google Scholar]
- E.A. Felix, S.P. Lee, Systematic literature review of preprocessing techniques for imbalanced data. IET Softw. 13, 375–386 (2019). https://doi.org/10.1049/iet-sen.2018.5193 [Google Scholar]
- F. Qayyum, D.H. Kim, S.J. Bong, S.Y. Chi, Y.H. Choi, A survey of datasets, preprocessing, modeling mechanisms, and simulation tools based on AI for material analysis and discovery. Materials 15, 1428 (2022). https://doi.org/10.3390/ma15041428 [Google Scholar]
- T.A. Alghamdi, N. Javaid, A survey of preprocessing methods used for analysis of big data originated from smart grids. IEEE Access 10, 29149–29171 (2022). https://doi.org/10.1109/ACCESS.2022.3157941 [Google Scholar]
- G.Y. Lee, L. Alzamil, B. Doskenov, A. Termehchy, A survey on data cleaning methods for improved machine learning model performance. arXiv:2103.10003 (2021). https://doi.org/10.48550/arXiv.2109.07127 [Google Scholar]
- Y. Sun, J. Zhang, Distance-based data cleaning: A survey. Data Sci. Eng. 5, 320–335 (2020). https://doi.org/10.48550/arXiv.2011.11176 [Google Scholar]
- C. Li, Preprocessing methods and pipelines of data mining: An overview. J. Phys. Conf. Ser. 1168, 032077 (2019). https://doi.org/10.1088/1742-6596/1168/3/032077 [Google Scholar]
- D. Vargas, J.A. Schneider Aranda, R.D. Santos Costa, P.R. da Silva Pereira, J.L. Victória Barbosa, Imbalanced data preprocessing techniques for machine learning: a systematic mapping study. Knowl. Inf. Syst. 65, 31–57 (2023). https://doi.org/10.1007/s10115-022-01772-8 [Google Scholar]
- A. Tawalkuli, B. Havers, V.M. Gulisano, D. Kaiser, T. Engel, Time-series data preprocessing: A survey and an empirical analysis, In Proceedings of the 2024 International Conference on Big Data (BigData 2024), IEEE, Sorrento, Italy, June 1 (2024), 674–711 [Google Scholar]
- A. Mumuni, F. Mumuni, Automated data processing and feature engineering for deep learning and big data applications: A survey. J. Inf. Intell. 3, 113–153 (2025). https://doi.org/10.1016/j.jiixd.2024.01.002 [Google Scholar]
- M. Trigka, E. Dritsas, A comprehensive survey of deep learning approaches in image processing. Sensors 25, 531 (2025). https://doi.org/10.3390/s25020531 [Google Scholar]
- X. Du, Y. Sun, Y. Song, W. Chi, L. Dong, X. Zhao, Impact of input image resolution on deep learning models for seafloor substrate classification. Remote Sens. 17, 2431 (2025). https://doi.org/10.3390/rs17142431 [Google Scholar]
- G. Li, R. Zhang, D. Qi, H. Ni, Plant-leaf recognition based on sample standardization and transfer learning. Appl. Sci. 14, 8122 (2024). https://doi.org/10.3390/app14188122 [Google Scholar]
- X. Liu, G. Karagoz, N. Meratnia, Analyzing the impact of data augmentation on medical image classification and explainability. Informatics 7, 1 (2024). https://doi.org/10.3390/informatics7010001 [Google Scholar]
- H.J.P. Koresh, Impact of the preprocessing steps in deep learning-based image classifications. Proc. Indian Natl. Sci. Acad. 47, 645–647 (2024). https://doi.org/10.1007/s40009-023-01372-2 [Google Scholar]
- I. Pacal, I. Kunduracioglu, M.H. Alma, M. Deveci, S. Kadry, J. Nedoma, V. Slany, R. Martinek, A systematic review of deep learning techniques for plant disease detection. Artif. Intell. Rev. 57, 304 (2024). https://doi.org/10.1007/s10462-024-10944-7 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

