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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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	Sarvepalli, Sarat Kumar (2015) Deep Learning in Neural Networks: The Science behind an Artificial Brain , doi.org/10.13140/RG.2.2.22512.71682

	Shrikant Pandey, SP, Hindoliya, DH and Ritu Mod, RM (2012) Artificial neural networks for predicting indoor temperature using roof passive cooling techniques in buildings in different climatic conditions , Applied Soft Computing, vol.12,issue 3, pp. 1214-1226

	Simonovsky, M., & Komodakis, N. (2018) Graphvae: Towards generation of small graphs using variational autoencoders , Artificial Neural Networks and Machine Learning-ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part I 27 (pp. 412-422)

	Simonovsky, M., & Komodakis, N. (2018) Masked label prediction: Unified message passing model for semi-supervised classification , Artificial Neural Networks and Machine Learning-ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part I 27 (pp. 412-422)

	Skavara, M.E. (2009) Learning Emergence , Adaptive Cellular Automata Façade Trained by Artificial Neural Networks, Master of Science in Adaptive Architecture & Computation Thesis, Bartlett UCL

	Skavara, M.E. (2009) Learning Emergence, Adaptive Cellular Automata Façade Trained by Artificial Neural Networks , Master of Science in Adaptive Architecture & Computation Thesis, Bartlett UCL

	Stanley, K, D B D'Ambrosio, and J Gauci (2007) A hypercube-based encoding for evolving large-scale neural networks , Artificial Life, 185-212

	Stanley, K, D B D'Ambrosio, and J Gauci (2007) A hypercube-based encoding for evolving large-scale neural networks , Artificial Life, 185-212

	S¸ ahin M, Oguz Y and Büyüktümtürk F (2015) Approximate and three-dimensional modeling of brightness levels in ¢ interior spaces by using artificial neural networks , J Electr Eng Technol 2015; 10: 1822–1829

	T. Kazanasmaz, M. Gunaydin, and S. Binol (2009) Artificial neural networks to predict daylight illuminance in office buildings , Build. Environ., vol. 44, pp. 1751–1757

	Tarassenko, L., Hayton, P., Cerneaz, N. and Brady, M. (1995) Novelty detection for the identification of masses in mammograms , Fourth International Conference on Artificial Neural Networks, Cambridge, pp. 442–447

	Tayfur, G. (2014) Soft computing in water resources engineering: Artificial neural networks, fuzzy logic and genetic algorithms , WIT Press

	Tu, J.V. (1996) Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. , Journal of clinical epidemiology,, 49(11), pp. 1225-1231.

	Walczak, S. & Cerpa, N. (2003) Artificial Neural Networks , Encyclopedia of Physical Science and Technology (pp. 631–645). https://doi.org/10.1016/B0-12-227410-5/00837-1

	Wong, S. L., K. K. W. Wan, and T. N. T. Lam (2010) Artificial Neural Networks for Energy Analysis of Office Buildings with Daylighting , Applied Energy 87 (2): 551–57. doi:10.1016/j.apenergy.2009.06.028

	Wong, S., Wan, K. and Lam, T. (2010) Artificial neural networks for energy analysis of office buildings with daylighting. , Applied Energy, 87(2), pp. 551-557

	Yetkin, O. and Sorguç, A.G. (2019) Design Space Exploration of Initial Structural Design Alternatives via Artificial Neural Networks , Architecture in the Age of the 4th Industrial Revolution, Proceedings of the 37th eCAADe and 23rd SIGraDi Conference, 11-13 September 2019, Vol. 1, pp. 55-60. https://www.doi.org/10.5151/proceedings-ecaadesigradi2019_308

	Yetkin, O., and A. G. Sorguç (2019) Design Space Exploration of Initial Structural Design Alternatives via Artificial Neural Networks , Architecture in the Age of the 4th Industrial Revolution [Proceedings of the 37th eCAADe and 23rd SIGraDi Conference], Porto, Portugal, 11–13 September 2019, edited by J.P. Sousa, J.P. Xavier, and G. Henriques, 55–60. CUMINCAD

	(1997) Understanding Natural and the Artificial Worlds- The Sciences of the Artificial , MIT Press, 1-24

	(1997) Optimized process planning by generative simulated annealing , Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 11, 219–235

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