Open Access
Issue
BIO Web Conf.
Volume 97, 2024
Fifth International Scientific Conference of Alkafeel University (ISCKU 2024)
Article Number 00058
Number of page(s) 11
DOI https://doi.org/10.1051/bioconf/20249700058
Published online 05 April 2024
  • Campo, M.D., Neural Architecture. 2022: Gordon Goff. [Google Scholar]
  • Skansi, S., Introduction to Deep Learning: from logical calculus to artificial intelligence. 2018: Springer. [Google Scholar]
  • Casas, I., Networks, Neural. 2020. [Google Scholar]
  • Mohandes, S.R., X. Zhang, and A. Mahdiyar, A comprehensive review on the application of artificial neural networks in building energy analysis. Neurocomputing, 2019. 340: p. 55–75. [CrossRef] [Google Scholar]
  • Hedges, V., Introduction to Neuroscience. 2022: Michigan State University. 5. [Google Scholar]
  • Haines, D.E. and G.A. Mihailoff, Fundamental neuroscience for basic and clinical applications Ebook. 2017: Elsevier Health Sciences. 3. [Google Scholar]
  • Tang, J., et al., Bridging biological and artificial neural networks with emerging neuromorphic devices: fundamentals, progress, and challenges. Advanced Materials, 2019. 31(49): p. 1902761. [CrossRef] [Google Scholar]
  • Chen, Z., et al., An Overview of In Vitro Biological Neural Networks for Robot Intelligence. Cyborg and Bionic Systems, 2023. 4: p. 0001. [CrossRef] [Google Scholar]
  • Efimenko, M., A. Ignatev, and K. Koshechkin, Review of medical image recognition technologies to detect melanomas using neural networks. BMC bioinformatics, 2020. 21(11): p. 1–7. [CrossRef] [PubMed] [Google Scholar]
  • Gogate, M.R., New paradigms and future critical directions in heterogeneous catalysis and multifunctional reactors. Chemical Engineering Communications, 2017. 204(1): p. 1–27. [CrossRef] [Google Scholar]
  • Tamke, M., P. Nicholas, and M. Zwierzycki, Machine learning for architectural design: Practices and infrastructure. International Journal of Architectural Computing, 2018. 16(2): p. 123–143. [CrossRef] [Google Scholar]
  • Liu, S., et al., Application of artificial neural networks in construction management: Current status and future directions. Applied Sciences, 2021. 11(20): p. 9616. [CrossRef] [Google Scholar]
  • Gao, X. and P. Pishdad-Bozorgi, A framework of developing machine learning models for facility lifecycle cost analysis. Building Research & Information, 2020. 48(5): p. 501–525. [CrossRef] [Google Scholar]
  • Banaei, M., et al., Walking through architectural spaces: The impact of interior forms on human brain dynamics. Frontiers in human neuroscience, 2017: p. 477. [CrossRef] [PubMed] [Google Scholar]
  • Cho, D., Y.-W. Tai, and I.S. Kweon, Deep convolutional neural network for natural image matting using initial alpha mattes. IEEE Transactions on Image Processing, 2018. 28(3): p. 1054–1067. [Google Scholar]
  • Krishna, R., Computer vision: Foundations and applications. Reference Book, 2017. 213. [Google Scholar]
  • Trach, R., Y. Trach, and M. Lendo-Siwicka, Using ANN to predict the impact of communication factors on the rework cost in construction projects. Energies, 2021. 14(14): p. 4376. [CrossRef] [Google Scholar]
  • Hong, W.-K. and T.-A. Le, ANN-based optimized design of doubly reinforced rectangular concrete beams based on multi-objective functions. Journal of Asian Architecture and Building Engineering, 2023. 22(3): p. 1413–1429. [CrossRef] [Google Scholar]
  • Hong, W.-K. and T.D. Pham, An AI-based auto-design for optimizing RC frames using the ANN-based Hong-Lagrange algorithm. Journal of Asian Architecture and Building Engineering, 2023 : p. 1–13. [CrossRef] [Google Scholar]
  • Attoue, N., I. Shahrour, and R. Younes, Smart building: Use of the artificial neural network approach for indoor temperature forecasting. Energies, 2018. 11(2): p. 395. [CrossRef] [Google Scholar]
  • Zhang, H., et al., Artificial Neural Network for Predicting Building Energy Performance: A Surrogate Energy Retrofits Decision Support Framework. Buildings, 2022. 12(6): p. 829. [CrossRef] [Google Scholar]
  • Ghamari, H., et al., Neuroarchitecture assessment: an overview and bibliometric analysis. European Journal of Investigation in Health, Psychology and Education, 2021. 11(4): p. 1362–1387. [CrossRef] [PubMed] [Google Scholar]
  • Wang, S., et al., The Embodiment of Architectural Experience: A Methodological Perspective on Neuro-Architecture. Frontiers in human neuroscience, 2022: p. 236. [Google Scholar]
  • Higuera-Trujillo, J.L., C. Llinares, and E. Macagno, The cognitive-emotional design and study of architectural space: A scoping review of neuroarchitecture and its precursor approaches. Sensors, 2021. 21(6): p. 2193. [CrossRef] [PubMed] [Google Scholar]
  • Huang, W. and H. Zheng. Architectural drawings recognition and generation through machine learning. in Proceedings of the 38th annual conference of the association for computer aided design in architecture, Mexico City, Mexico. 2018. [Google Scholar]
  • del Campo, M., A. Carlson, and S. Manninger. 3D graph convolutional neural networks in architecture design. in Proceedings of the ACADIA Conference. 2020. ACADIA Online+ Global. [Google Scholar]
  • Bolojan, D. and E. Vermisso. Deep Learning as heuristic approach for architectural concept generation. in ICCC. 2020. Association for Computational Creativity (ACC). [Google Scholar]
  • Alani, M. and B. Al-Kaseem, Fill in the blanks: Deep convolutional generative adversarial networks to investigate the virtual design space of historical islamic patterns. Architecture in the Age of Disruptive Technologies: Transformations and Challenges, Robert Gordon University, 2021: p. 614–621. [Google Scholar]
  • Eroğlu, R. and L. Gül, Architectural Form Explorations through Generative Adversarial Networks: Predicting the potentials of StyleGAN. 2022. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.