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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	Ki, D. and Lee, S. (2021) Analyzing the effects of Green View Index of neighborhood streets on walking time using Google Street View and deep learning , Landscape and Urban Planning, 205, p. -

	Ki, D., & Lee, S. (2021) Analyzing the Effects of Green View Index of Neighborhood Streets on Walking Time Using Google Street View and Deep Learning , Landscape and Urban Planning, 25, 1392. Available at: https://doi.org/1.116/j.landurbplan.22.1392

	Kido, D, Fukuda, T and Yabuki, N (2021) Assessing future landscapes using enhanced mixed reality with semantic segmentation by deep learning , Advanced Engineering Informatics, Volume 48, April 2021(https://doi.org/10.1016/j.aei.2021.101281), p. 101281

	Kido, D., Author, 1 and Author, 2 (2021) Assessing future landscapes using enhanced mixed reality with semantic segmentation by deep learning , Advanced Engineering Informatics, 48, p. -

	Kumke, J.M. (1981) Oxalis Leaf Movements Driven by Two Separate Systems , Journal of Interdisciplinary Cycle Research, 12(1), p87-96. Available at https://doi.org/10.1080/09291018109359728 (Accessed 20 September 2021)

	Li, Z. et al. (2022) A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects , IEEE Transactions on Neural Networks and Learning Systems, 33(12), pp. 6999-7019. DOI: 10.1109/TNNLS.2021.3084827

	Liu, C., Shen, J., Ren, Y., & Zheng, H. (2021) Pipes of AI-Machine Learning Assisted 3D Modeling Design , Proceedings of the 2020 DigitalFUTURES: The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020) (pp. 17-26). Singapore: Springer

	Liu, Y. Et Al. (2021) Exploration on Machine Learning Layout Generation of Chinese Private Garden in Southern Yangtze. , Proceedings of the 221 DigitalFUTURES, pp 35-44.

	Liu, Y., Fang, C., Yang, Z., Wang, X., Zhou, Z., Deng, Q., & Liang, L. (2022) Exploration on machine learning layout generation of Chinese private garden in Southern Yangtze , 3rd International Conference on Computational Design and Robotic Fabrication, CDRF 2021. (pp.35-44)

	Lu, Y. Et Al. (2021) Deep Multi-task Learning for Cooperative Noma: System Design and Principles. , IEEE Journal on Selected Areas in Communications, 39(1), pp 61-78.

	Luo, Z., Sun, C., Dong, Q., & Qi, X. (2022) Key control variables affecting interior visual comfort for automated louver control in open-plan office--a study using machine learning , Building and Environment, 207, 108565 https://doi.org/10.1016/j.buildenv.2021.108565

	Madhura Jayaratne, Damminda Alahakoon & Daswin de Silva. (2021) Unsupervised skill transfer learning for autonomous robots using distributed Growing Self Organizing Maps , Robotics and Autonomous Systems, 144, 103835

	Mai, N. D., Lee, B. G., & Chung, W. Y. (2021) Affective Computing on Machine Learning-Based Emotion Recognition Using a Self-Made EEG Device , Sensors, 21(15), Article 5135. https://doi.org/10.3390/s21155135

	Mehta, NM and Devarakonda, MVD (2018) Machine learning, natural language programming, and electronic health records: The next step in the artificial intelligence journey? , Journal of Allergy and Clinical Immunology, 141, pp. 2019-2021.e1

	Mokhtar, S., Beveridge, M., Cao, Y. & Drori, I. (2021) Pedestrian Wind Factor Estimation in Complex Urban Environments , 13th Asian Conference on Machine Learning

	Mpungose, C. B. (2021) Lecturers reflections on use of Zoom video conferencing technology for e-learning at a South African university in the context of coronavirus , African Identities, 1-17. https://www.tandfonline.com/doi/abs/10.1080/14725843.2021.1902268

	Newton D. (2021) Generative Deep Learning in Architectural Design , Technol Archit Des 2019; 3: 176–189.

	Nicholas P (2021) Machining and machine learning: extending architectural digital fabrication through AI , Machining and machine learning: extending architectural digital fabrication through AI

	Niese ND (2012) Life Cycle Evaluation under Uncertain Environmental Policies Using a Ship-Centric Markov Decision Process Framework , Ann Arbor, Michigan, United States: University of Michigan, 2012, http://deepblue.lib.umich. edu/handle/2027.42/96130 (accessed 27 January 2021).

	Nourkojouri, H., Shafavi, N. S., Tahsildoost, M., & Zomorodian, Z. S. (2021) Development of a machine-learning framework for overall daylight and visual comfort assessment in early design stages , Journal of Daylighting, 8(2), 270-283. https://doi.org/10.15627/jd.2021.21

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