Discussion of “Improvement in Estimating Durations for Building Projects Using Artificial Neural Network and Sensitivity Analysis” by Su-Ling Fan, I-Cheng Yeh, and Wei-Sheng Chi
This article is a reply.
VIEW THE ORIGINAL ARTICLEPublication: Journal of Construction Engineering and Management
Volume 148, Issue 11
Get full access to this article
View all available purchase options and get full access to this article.
References
Abu-Mostafa, Y. S. 1995. “Hints.” Neural Comput. 7 (4): 639–671. https://doi.org/10.1162/neco.1995.7.4.639.
Afram, A., F. Janabi-Sharifi, A. S. Fung, and K. Raahemifar. 2017. “Artificial neural network (ANN) based model predictive control (MPC) and optimization of HVAC systems: A state of the art review and case study of a residential HVAC system.” Energy Build. 141 (Apr): 96–113. https://doi.org/10.1016/j.enbuild.2017.02.012.
Alwosheel, A., S. van Cranenburgh, and C. G. Chorus. 2018. “Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis.” J. Choice Modell. 28 (Sep): 167–182. https://doi.org/10.1016/j.jocm.2018.07.002.
Baum, E. B., and D. Haussler. 1989. “What size net gives valid generalization?” Neural Comput. 1 (1): 151–160. https://doi.org/10.1162/neco.1989.1.1.151.
Ding, H., P. M. Feng, W. Chen, and H. Lin. 2014. “Identification of bacteriophage virion proteins by the ANOVA feature selection and analysis.” Mol. Biosyst. 10 (8): 2229–2235. https://doi.org/10.1039/C4MB00316K.
Jiang, C., P. Huang, J. Lessan, L. Fu, and C. Wen. 2019. “Forecasting primary delay recovery of high-speed railway using multiple linear regression, supporting vector machine, artificial neural network, and random forest regression.” Can. J. Civ. Eng. 46 (5): 353–363. https://doi.org/10.1139/cjce-2017-0642.
Lin, H., and W. Chen. 2011. “Prediction of thermophilic proteins using feature selection technique.” J. Microbiol. Methods 84 (1): 67–70. https://doi.org/10.1016/j.mimet.2010.10.013.
Mabel, M. C., and E. Fernandez. 2008. “Analysis of wind power generation and prediction using ANN: A case study.” Renewable Energy 33 (5): 986–992. https://doi.org/10.1016/j.renene.2007.06.013.
Methaprayoon, K., C. Yingvivatanapong, W. J. Lee, and J. R. Liao. 2007. “An integration of ANN wind power estimation into unit commitment considering the forecasting uncertainty.” IEEE Trans. Ind. Appl. 43 (6): 1441–1448. https://doi.org/10.1109/TIA.2007.908203.
MK, A. N., and M. Ashiq V. 2020. “Role of energy use in the prediction of emissions and economic growth in India: Evidence from artificial neural networks (ANN).” Environ. Sci. Pollut. Res. 27 (19): 23631–23642. https://doi.org/10.1007/s11356-020-08675-7.
Nawi, N. M., W. H. Atomi, and M. Z. Rehman. 2013. “The effect of data pre-processing on optimized training of artificial neural networks.” Procedia Technol. 11 (Jan): 32–39. https://doi.org/10.1016/j.protcy.2013.12.159.
Nayak, S. C., B. B. Misra, and H. S. Behera. 2014. “Impact of data normalization on stock index forecasting.” Int. J. Comput. Inf. Syst. Ind. Manage. Appl. 6 (2014): 257–269.
Salman, S., and X. Liu. 2019. “Overfitting mechanism and avoidance in deep neural networks.” Preprint, submitted January 19, 2019. https://arxiv.org/abs/1901.06566.
Sola, J., and J. Sevilla. 1997. “Importance of input data normalization for the application of neural networks to complex industrial problems.” IEEE Trans. Nucl. Sci. 44 (3): 1464–1468. https://doi.org/10.1109/23.589532.
Verma, C., V. Stoffová, Z. Illés, S. Tanwar, and N. Kumar. 2020. “Machine learning-based student’s native place identification for real-time.” IEEE Access 8 (Jul): 130840–130854. https://doi.org/10.1109/ACCESS.2020.3008830.
Were, K., D. T. Bui, Ø. B. Dick, and B. R. Singh. 2015. “A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape.” Ecol. Indic. 52 (May): 394–403. https://doi.org/10.1016/j.ecolind.2014.12.028.
Yang, L., and A. Shami. 2020. “On hyperparameter optimization of machine learning algorithms: Theory and practice.” Neurocomputing 415 (Nov): 295–316. https://doi.org/10.1016/j.neucom.2020.07.061.
Yuan, H., G. Yang, C. Li, Y. Wang, J. Liu, H. Yu, H. Feng, B. Xu, X. Zhao, and X. Yang. 2017. “Retrieving soybean leaf area index from unmanned aerial vehicle hyperspectral remote sensing: Analysis of RF, ANN, and SVM regression models.” Remote Sens. 9 (4): 309. https://doi.org/10.3390/rs9040309.
Information & Authors
Information
Published In
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Jul 31, 2021
Accepted: Feb 7, 2022
Published online: Sep 13, 2022
Published in print: Nov 1, 2022
Discussion open until: Feb 13, 2023
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.