Cumincad : CUMINCAD References : Search Results

CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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	Allen-Dumas, M.R. et al. (2022) Generative adversarial networks for ensemble projections of future urban morphology , Proceedings of the 5th ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities. SIGSPATIAL '22: The 30th International Conference on Advances in Geographic Information Systems, Seattle Washington: ACM, pp. 1-6

	Alsharif, R., Arashpour, M., Golafshani, E., Rashidi, A., Li, H., (2023) Multi-objective optimization of shading devices using ensemble machine learning and orthogonal design of experiments , Energy and Buildings, 283, 112840 https://doi.org/10.1016/j.enbuild.2023.112840

	Ayoub M (2022) Contrasting accuracies of single and ensemble models for predicting solar and thermal performances of traditional vaulted roofs , Sol Energy 2022; 236: 335–355

	Choubin, B., Moradi, E., Golshan, M., Adamowski, J., Sajedi-Hosseini, F., & Mosavi, A. (2019) An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines , The Science of the total environment, 2019-02, Vol.651 (Pt 2), p.2087-2096"

	Hagedorn, R., R. Buizza, T. M. Hamill, M. Leutbecher, and T. N. Palmer (2012) Comparing TIGGE Multimodel Forecasts with Reforecast-Calibrated ECMWF Ensemble Forecasts , Quarterly Journal of the Royal Meteorological Society 138(668): 1814-27. DOI: DOI: 10.1002/qj.1895

	Hölscher, C., Büchner, S. J., Meilinger, T., & Strube, G. (2009) Adaptivity of way- finding strategies in a multi-building ensemble: The effects of spatial structure, task requirements, and metric information , Journal of Environmental Psychology, 29 (208-219). https://doi:10.1016/j.jenvp.2008.05.010

	Johnson, S. (2005) The Architecture Ensemble , Architectural Design, 75(4), 2005, 96-99

	Li, H and Sun, J (2011) Principal component case-based reasoning ensemble for business failure prediction , Information & Management, 48, pp. 220-227

	Martin, CP, Jensenius, AR and Torresen, J (2018) Composing an Ensemble Standstill Work for Myo and Bela , Proceedings of the International Conference on New Interfaces for Musical Expression, pp. 196-197

	Mendes-Moreira, J., Soares, C., Jorge, A. M. & De Sousa, J. F. (2012) Ensemble approaches for regression: A survey , ACM Computing Surveys, 45(1). https://doi.org/10.1145/2379776.2379786

	Park S and Kim H (2021) 3DPlanNet: generating 3D models from 2D floor plan images using ensemble methods , Electronics 2021; 10: 2729

	Rakotomamonjy A., Guigue V., Mallet G. et al. (2005) Ensemble of SVMs for improving brain computer interface P300 speller performances , Hutchison D, Kanade T, Kittler J, et al. (eds) Artificial neural networks: biological inspirations – ICANN 2005 , vol. 3696. Berlin; Heidelberg: Springer, pp. 45–50

	Rakotomamonjy, A, Guigue, V, Mallet, G and Alvarado, V (2005) Ensemble of SVMs for Improving Brain Computer Interface P300 Speller Performances , Artificial Neural Networks: Biological Inspirations - ICANN 2005, pp. 45-50

	Sarmas, E. et al. (2023) Estimating the Energy Savings of Energy Efficiency Actions with Ensemble Machine Learning Models , Applied Sciences (Switzerland) 13(4). doi: 10.3390/app13042749

	Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2014) Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS , Journal of hydrology (Amsterdam), 2014-05, Vol.512, p.332-343

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