Open Access
| Issue |
BIO Web Conf.
Volume 89, 2024
The 4th Sustainability and Resilience of Coastal Management (SRCM 2023)
|
|
|---|---|---|
| Article Number | 09003 | |
| Number of page(s) | 8 | |
| Section | Environmental and Hazard Mitigation | |
| DOI | https://doi.org/10.1051/bioconf/20248909003 | |
| Published online | 23 January 2024 | |
- D. Onalo, S. Adedigba, O. Oloruntobi, F. Khan, L. A. James, and S. Butt, “Data-driven model for shear wave transit time prediction for formation evaluation,” J. Pet. Explor. Prod. Technol., vol. 10, no. 4, pp. 1429–1447, 2020, doi: 10.1007/s13202-020-00843-2. [CrossRef] [Google Scholar]
- A. K. A. Mohammed and M. K. Dhaidan, “Prediction of Well Logs Data and Estimation of Petrophysical Parameters of Mishrif Formation, Nasiriya Field, South of Iraq Using Artificial Neural Network (ANN),” Iraqi J. Sci., vol. 64, no. 1, pp. 253–268, 2023, doi: 10.24996/ijs.2023.64.1.24. [CrossRef] [Google Scholar]
- A. Al Ghaithi and M. Prasad, “Machine learning with artificial neural networks for shear log predictions in the volve field norwegian north sea,” SEG Tech. Progr. Expand. Abstr., vol. 2020-Octob, pp. 450–454, 2020, doi: 10.1190/segam2020-3427540.1. [Google Scholar]
- N. Hou et al., “Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost,” J. Transl. Med., vol. 18, no. 1, pp. 1–14, 2020, doi: 10.1186/s12967-020-02620-5. [CrossRef] [Google Scholar]
- C. X. Lv, S. Y. An, B. J. Qiao, and W. Wu, “Time series analysis of hemorrhagic fever with renal syndrome in mainland China by using an XGBoost forecasting model,” BMC Infect. Dis., vol. 21, no. 1, pp. 1–13, 2021, doi: 10.1186/s12879-021-06503-y. [CrossRef] [Google Scholar]
- K. Kawano et al., “Prediction of mortality risk of health checkup participants using machine learning-based models: the J-SHC study,” Sci. Rep., vol. 12, no. 1, pp. 1–8, 2022, doi: 10.1038/s41598-022-18276-8. [CrossRef] [Google Scholar]
- Y. Dong and C. Y. J. Peng, “Principled missing data methods for researchers (Expectation Maximaization explained),” Springerplus, vol. 2, no. 1, pp. 1–17, 2013. [CrossRef] [PubMed] [Google Scholar]
- A. Kumar Dubey, A. Kumar, V. García-Díaz, A. Kumar Sharma, and K. Kanhaiya, “Study and analysis of SARIMA and LSTM in forecasting time series data,” Sustain. Energy Technol. Assessments, vol. 47, no. August 2020, p. 101474, 2021, doi: 10.1016/j.seta.2021.101474. [CrossRef] [Google Scholar]
- A. Li et al., “A generalized framework for population based training,” Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., pp. 1791–1799, 2019, doi: 10.1145/3292500.3330649. [Google Scholar]
- A. Raghu, J. Lorraine, S. Kornblith, M. McDermott, and D. Duvenaud, “Meta-Learning to Improve Pre-Training,” Adv. Neural Inf. Process. Syst., vol. 28, no. NeurIPS, pp. 23231–23244, 2021. [Google Scholar]
- S. Seo and K. Obermayer, “Dynamic hyperparameter scaling method for LVQ algorithms,” IEEE Int. Conf. Neural Networks - Conf. Proc., pp. 3196–3203, 2006, doi: 10.1109/ijcnn.2006.247304. [Google Scholar]
- M. Khodak et al., “Federated Hyperparameter Tuning: Challenges, Baselines, and Connections toWeight-Sharing,” Adv. Neural Inf. Process. Syst., vol. 23, no. NeurIPS, pp. 19184–19197, 2021. [Google Scholar]
- J. N. Van Rijn and F. Hutter, “Hyperparameter importance across datasets,” Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., pp. 2367–2376, 2018, doi: 10.1145/3219819.3220058. [Google Scholar]
- K. Uyar, Ş. Taşdemir, and I. A. Özkan, “The analysis and optimization of CNN Hyperparameters with fuzzy tree model for image classification,” Turkish J. Electr. Eng. Comput. Sci., vol. 30, no. 3, pp. 961–977, 2022, doi: 10.3906/ELK-2107-130. [CrossRef] [Google Scholar]
- K. E. Hoque and H. Aljamaan, “Impact of hyperparameter tuning on machine learning models in stock price forecasting,” IEEE Access, vol. 9, pp. 163815–163830, 2021, doi: 10.1109/ACCESS.2021.3134138. [CrossRef] [Google Scholar]
- D. Jia, R. Wang, C. Xu, and Z. Yu, “QIM: Quantifying hyperparameter importance for deep learning,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 9966 LNCS, pp. 180–188, 2016, doi: 10.1007/978-3-319-47099-3_15. [Google Scholar]
- Y. Mai, Z. Sheng, H. Shi, and Q. Liao, “Using Improved XGBoost Algorithm to Obtain Modified Atmospheric Refractive Index,” Int. J. Antennas Propag., vol. 2021, 2021, doi: 10.1155/2021/5506599. [Google Scholar]
- Y. Xie, C. Zhu, W. Zhou, Z. Li, X. Liu, and M. Tu, “Evaluation of machine learning methods for formation lithology identification: A comparison of tuning processes and model performances,” J. Pet. Sci. Eng., vol. 160, no. October 2017, pp. 182–193, 2018, doi: 10.1016/j.petrol.2017.10.028. [CrossRef] [Google Scholar]
- J. L. Peugh and C. K. Enders, “Missing data in educational research: A review of reporting practices and suggestions for improvement,” Rev. Educ. Res., vol. 74, no. 4, pp. 525–556, 2004, doi: 10.3102/00346543074004525. [CrossRef] [Google Scholar]
- V. Arel-Bundock and K. J. Pelc, “When Can Multiple Imputation Improve Regression Estimates?,” Polit. Anal., vol. 26, no. 2, pp. 240–245, 2018, doi: 10.1017/pan.2017.43. [CrossRef] [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.