Acknowledgement
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MEST) (Grant number: NRF-2021R1A2C1011618).
References
- Lim, Y., Moon, Y.-S. and Kim, T.-W., "Artificial Neural Network Approach for Prediction of Ammonia Emission from Field-Applied Manure and Relative Significance Assessment of Ammonia Emission Factors," Eur. J. Agron., 26(4), 425-434(2007). https://doi.org/10.1016/j.eja.2007.01.008
- Sintermann, J., Neftel, A., Ammann, C., Hani, C., Hensen, A., Loubet, B. and Flechard, C. R., "Are Ammonia Emissions from Field-Applied Slurry Substantially over-Estimated in European Emission Inventories?," Biogeosciences, 9(5), 1611-1632(2012). https://doi.org/10.5194/bg-9-1611-2012
- Pedersen, J., Andersson, K., Feilberg, A., Delin, S., Hafner, S. and Nyord, T., "Effect of Exposed Surface Area on Ammonia Emissions from Untreated, Separated, and Digested Cattle Manure," Biosyst. Eng., 202, 66-78(2021). https://doi.org/10.1016/j.biosystemseng.2020.12.005
- Moon, Y. S., Lim, Y. and Kim, T. W., "Prediction of Ammonia Emission Rate from Field-Applied Animal Manure Using the Artificial Neural Network," Korean Chem. Eng. Res., 45(2), 133-142(2007).
- Jain, S., Shukla, S. and Wadhvani, R., "Dynamic Selection of Normalization Techniques Using Data Complexity Measures," Expert Syst. Appl., 106, 252-262(2018). https://doi.org/10.1016/j.eswa.2018.04.008
- Jackson, J. E., A User's Guide to Principal Components, 1st ed., John Wiley and Sons, New York(1991).
- Ren, J., Chen, J., Shi, D., Li, Y., Li, D., Wang, Y. and Cai, D., "Online Multi-Fault Power System Dynamic Security Assessment Driven by Hybrid Information of Anticipated Faults and Pre-Fault Power Flow," Int. J. Electr. Power Energy Syst., 136, 107651(2022).
- Hovden, I. T., "Optimizing Artificial Neural Network Hyperparameters and Architecture," (2019).
- Islam, A., Belhaouari, S. B., Rehman, A. U. and Bensmail, H., "Knnor: An Oversampling Technique for Imbalanced Datasets," Appl. Soft Comput., 115, 108288(2022).
- Ngo, S. I. and Lim, Y.-I., "Solution and Parameter Identification of a Fixed-Bed Reactor Model for Catalytic Co2 Methanation Using Physics-Informed Neural Networks," Catalysts, 11(11), 1304(2021).
- Raissi, M., Perdikaris, P. and Karniadakis, G. E., "Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations," J. Comput. Phys., 378, 686-707(2019). https://doi.org/10.1016/j.jcp.2018.10.045
- Yang, Y., Zha, K., Chen, Y.-C., Wang, H. and Katabi, D., "Delving into Deep Imbalanced Regression," arXiv preprint arXiv:2102.09554, (2021).
- Zhuo, Y. and Brgoch, J., "Opportunities for Next-Generation Luminescent Materials through Artificial Intelligence," J. Phys. Chem. Lett., 12(2), 764-772(2021). https://doi.org/10.1021/acs.jpclett.0c03203
- Coomans, D. and Massart, D. L., "Alternative K-Nearest Neighbour Rules in Supervised Pattern Recognition: Part 1. K-Nearest Neighbour Classification by Using Alternative Voting Rules," Anal. Chim. Acta, 136, 15-27(1982). https://doi.org/10.1016/S0003-2670(01)95359-0
- Chawla, N. V., Bowyer, K. W., Hall, L. O. and Kegelmeyer, W. P., "Smote: Synthetic Minority over-Sampling Technique," J. Artif. Intell. Res., 16, 321-357(2002). https://doi.org/10.1613/jair.953
- Torgo, L., Ribeiro, R., Pfahringer, B. and Branco, P., Smote for Regression, ed., Springer, Berlin, Heidelberg, Correia(2013).
- Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., WardeFarley, D., Ozair, S., Courville, A. and Bengio, Y., "Generative Adversarial Nets," Adv. Neural Inf. Process. Syst., 27, (2014).
- Asadi, M. and McPhedran, K. N., "Greenhouse Gas Emission Estimation from Municipal Wastewater Using a Hybrid Approach of Generative Adversarial Network and Data-Driven Modelling," Sci. Total Environ., 800, 149508(2021).
- Du, X., Xu, H. and Zhu, F., "Understanding the Effect of Hyperparameter Optimization on Machine Learning Models for Structure Design Problems," Comput. Aided Des, 135, 103013(2021).
- Bergstra, J., Bardenet, R., Bengio, Y. and Kegl, B., "Algorithms for Hyper-Parameter Optimization," Adv. Neural Inf. Process. Syst., (2011).
- Yang, Z. and Zhang, A., "Hyperparameter Optimization Via Sequential Uniform Designs," J. Mach. Learn. Res., 22(149), 1-47(2021).
- Yang, L. and Shami, A., "On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice," Neurocomputing, 415, 295-316(2020). https://doi.org/10.1016/j.neucom.2020.07.061
- Jankovic, A., Chaudhary, G. and Goia, F., "Designing the Design of Experiments (Doe) - an Investigation on the Influence of Different Factorial Designs on the Characterization of Complex Systems," Energy Build, 250, 111298(2021).
- Misselbrook, T. H., Nicholson, F. A. and Chambers, B. J., "Predicting Ammonia Losses Following the Application of Livestock Manure to Land," Bioresour. Technol., 96(2), 159-168(2005). https://doi.org/10.1016/j.biortech.2004.05.004
- Rodriguez, P., Bautista, M. A., Gonzalez, J. and Escalera, S., "Beyond One-Hot Encoding: Lower Dimensional Target Embedding," Image Vis. Comput., 75, 21-31(2018). https://doi.org/10.1016/j.imavis.2018.04.004
- Demir, S., Mincev, K., Kok, K. and Paterakis, N. G., "Data Augmentation for Time Series Regression: Applying Transformations, Autoencoders and Adversarial Networks to Electricity Price Forecasting," Appl. Energ., 304, 117695(2021).
- Ramachandran, P., Zoph, B. and Le, Q. V., "Searching for Activation Functions," arXiv preprint arXiv:1710.05941, (2017).
- LeCun, Y., Bengio, Y. and Hinton, G., "Deep Learning," Nature, 521(7553), 436-444(2015). https://doi.org/10.1038/nature14539
- Kohavi, R., "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection," Proceedings of the 14th International Joint Conference on Artificial Intelligence - Volume 2, Morgan Kaufmann Publishers Inc., Montreal, Quebec, Canada, pp. 1137-1143(1995).
- li, W. and Liu, Z., "A Method of Svm with Normalization in Intrusion Detection," Procedia Environ. Sci., 11, 256-262(2011). https://doi.org/10.1016/j.proenv.2011.12.040
- Jahirul, M. I., Rasul, M. G., Brown, R. J., Senadeera, W., Hosen, M. A., Haque, R., Saha, S. C. and Mahlia, T. M. I., "Investigation of Correlation between Chemical Composition and Properties of Biodiesel Using Principal Component Analysis (Pca) and Artificial Neural Network (Ann)," Renew. Energy, 168, 632-646 (2021). https://doi.org/10.1016/j.renene.2020.12.078
- Liu, Y., Zhao, S., Wang, Q. and Gao, Q., "Learning More Distinctive Representation by Enhanced Pca Network," Neurocomputing, 275, 924-931(2018). https://doi.org/10.1016/j.neucom.2017.09.041
- Berk, J., Nguyen, V., Gupta, S., Rana, S. and Venkatesh, S., "Exploration Enhanced Expected Improvement for Bayesian Optimization," Machine Learning and Knowledge Discovery in Databases, September, Cham(2018).
- Riedmiller, M. and Braun, H., "A Direct Adaptive Method for Faster Backpropagation Learning: The Rprop Algorithm," IEEE International Conference on Neural Networks, March, 1993.