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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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	Kazanasmaz, T., Günaydin, M. & Binol, S. (2009) Artificial neural networks to predict daylight illuminance in office buildings , Building and Environment, 44(8), 1751–1757

	Kazanasmaz, T., Murat, G., And Selcen, B. (2009) Artificial Neural Networks to Predict Daylight Illuminance in Office Buildings , Building and Environment 44.8 (29), 1751-1757. Available at: https://doi.org/1.116/j.buildenv.28.11.12.

	Kim P (2017) Matlab Deep Learning , With Machine Learning, Neural Networks and Artificial Intelligence. NY: Apress, 2017, p. 130.

	Kim, M., & Park, H.-J. (2023) Application of artificial neural networks using sequential prediction approach in indoor airflow prediction , Journal of Building Engineering, 69, 106319. https://doi.org/10.1016/j.jobe.2023.106319

	Kristiansen, T., Jamil, F., Hameed, I.A., Hamdy, M., (2022) Predicting annual illuminance and operative temperature in residential buildings using artificial neural networks , Building and Environment, 217, 109031 https://doi.org/10.1016/j.buildenv.2022.109031

	Lawe, S and Wang, R (2016) Optimization of Traffic Signals Using Deep Learning Neural Networks , Kang, B and Bai, Q (eds), Australasian Joint Conference on Artificial Intelligence, Springer, Cham, pp. 403-415

	Lu, C., S. Li and Z. Lu (2022) Building energy prediction using artificial neural networks: A literature survey , Energy and Buildings, 262, p. 111718

	López G and Gueymard CA (2007) Clear-sky solar luminous efficacy determination using artificial neural networks , SolEnergy 2007; 81: 929–939

	Morfidis, K. and Kostinakis, K. (2018) Approaches to the rapid seismic damage prediction of r/c buildings using artificial neural networks , Engineering Structures, 165, pp. 120-141

	Nadia D, Roman, Facundo, Bre, Victor D, Fachinotti and Roberto, Lamberts (2020) Application and characterization of metamodels based on artificial neural networks for building performance simulation: A systematic review , Energy and Buildings, 217, p. 109972

	Nolfi, Stefano and Domenico Parisi (2003) Evolution of Artificial Neural Networks , Handbook of Brain Theory and Neural Networks, 2nd Edition, edited by Michael A. Arbib, 418-421. Cambridge, Massachusetts: MIT Press

	Oleinik, A (2019) What Are Neural Networks Not Good at? On Artificial Creativity , Big Data & Soc., 6, pp. 1-13

	Pijanowski, B, Tayyebi, A, Delavar, M and Yazdanpanah, MJ (2009) Urban expansion simulation using geospatial information system and artificial neural networks , International Journal of Environmental Research, 3(4), pp. 493-502

	Pittarello M, Scarpa M, Ruggeri AG, et al (2021) Artificial neural networks to optimize zero energy building (zeb) projects from the early design stages , Appl Sci 2021; 11: 5377

	Platon R, Dehkordi VR and Martel J (2015) Hourly prediction of a building’s electricity consumption using case-based reasoning, artificial neural networks and principal component analysis , Energy Build 2015; 92: 10–18

	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

	Roe, BP, Yang, HJ, Zhu, J, Liu, Y, Stancu, I and McGregor, G (2005) Boosted decision trees as an alternative to artificial neural networks for particle identification , Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 543(2), pp. 577-584

	S. Janjai and P. Plaon (2011) Estimation of sky luminance in the tropics using artificial neural networks: Modeling and performance comparison with the CIE model , Appl. Energy, vol. 88, no. 3, pp. 840–847

	Sahin, M., Oguz, Y. & Büyüktümtürk, F. (2015) Approximate and three-dimensional modeling of brightness levels in interior spaces by using artificial neural networks , Journal of Electrical Engineering and Technology, 10(4), 1822–1829

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