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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	Fayyad, U.M., Piatetsky-Shapiro, G. & Smyth, P. (1996) From Data Mining to Knowledge Discovery: An Overview , AI Magazine, Vol. 17, no.2, pp. 37-54

	Fayyad, U.M. (1996) Advances in Knowledge Discovery and Data Mining , eds. U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. California: AAAI Press and MIT Press

	Fayyad, UM, Piatetsky-Shapiro, G and Smyth, P (1996) From Data Mining to Knowledge Discovery: An Overview , AI Magazine, 17(2), pp. 37-54

	Fazio, P., HE, H. S., Hammad, A., Horvat M. (2007) IFC-based framework for evaluating total performance of building envelopes , Journal of architectural engineering, 1, 44-53

	Fazio, P., Zemeureanu, R. and Kowalski, A. (1989) Select-HVAC: knowledge-based system as an advisor to configure HVAC systems , CAD March 1989

	Feder, G, Donovan, S and Carter, Y (2000) Guidelines for Prevention of Falls in People over 65 , British Medical Journal, 321, p. 1007-1011

	Federle, MJ and Bassler, BL (2003) Interspecies communication in bacteria , Journal of Clinical Investigation, 112(9), p. 1291-1299

	Fedra, K. and R. Reitsma (1990) Decision support and geographic information systems , Scholten and Stillwell (eds.) Geographic information systems for urban and Regional Planning, Kluwer, Dordrecht, the Netherlands, p.177-188

	Fei, L. and Sujan, D. (2013) Origami Theory and Its Applications: A Literature Review , International Journal of Social, Behavioral, Educational, Economic and Management Engineering, 7, p. 113-117

	Fei, M. A., Fr, A., Kfy, B., Yg, A., Cz, A., Dan, G. A. (2019) The spatial coupling effect between urban public transport and commercial complexes: A network centrality perspective - ScienceDirect , Sustainable Cities and Society, 50, 101645-101645.Fotheringham, A. S., Yang, W., Kang, W. (2017). Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265.Harris, P., Fotheringham, A. S., Crespo, R., Charlton, M. (2018). Inference in multi-scale geographically weighted regression. WILEY, 43(3), 399.Jia, J., Zhang, X. (2021). A human-scale investigation into economic benefits of urban green and blue infrastructure based on big data and machine learning: A case study of Wuhan. Journal of Cleaner Production, 316, 128321.Oshan, T. M., Li, Z., Kang, W., Wolf, L. J., Fotheringham, A. S. (2019). MGWR: A Python Implementation of Multiscale Geographically Weighted Regression for Investigating Process Spatial Heterogeneity and Scale. International Journal of Geo-Information, 8(6), 269.Rifkin, J. (2013). The Third Industrial Revolution. International Study Reference, 6(1), 8-11.Yu H, Fotheringham A S, Li Z, et al. (2019). Inference in multi-scale geographically weighted regression. Geographical Analysis, 52(1), 87-106

	Feigenbaum, E. A. and McCorduck, P. (1983) The Fifth Generation. Aritificial Intelligence and Japans Computer Challenge to the World , Reading, MA: Addison-Wesley

	Feiner, S., A. Webster, T. Krueger, B. MacIntyre, and E. Keller (1995) Architectural Anatomy , Presence: Teleoperators and Virtual Environments, 4(3), p. 318-325

	Felbrich, B., Wulle, F., Allgaier, C., Menges, A. and Verl, A. (2018) A novel rapid additive manufacturing concept for architectural composite shell construction inspired by the shell formation in land snails , Bioinspiration & biomimetics, 13(2), p. 26010

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