Intelligent model for automated identification of architectural style
https://doi.org/10.31675/1607-1859-2023-25-4-38-44
Abstract
The growing interest to the digital development of the society stipulate studies of its history described by the artifacts of structures of past generations. Since each epoch has its own characteristics, architectural ideas inextricably linking certain years to the architectural style of buildings, have changed.
Purpose: The aim of this work is the software development for the identification the architectural style of architectural a building.
Research findings: For anyone interested in history, regardless of their gender and age, it is sometimes not enough to read historical books in order to determine the architectural style of a building. The artificial intelligence model is proposed to fill this gap.
Practical implications: The proposed model can be used to supplement the practical experience in the field.
About the Authors
P. A. Pylov
Gorbachev Kuzbass State Technical University
Russian Federation
Russian Federation
Petr A. Pylov, Research Assistant, Gorbachev
28, Vesennyaya Str., 650000, Kemerovo
A. V. Dyagileva
Gorbachev Kuzbass State Technical University
Russian Federation
Russian Federation
Anna V. Dyagileva, PhD, A/Professor, Gorbachev
28, Vesennyaya Str., 650000, Kemerovo
E. A. Nikolaeva
Gorbachev Kuzbass State Technical University
Russian Federation
Russian Federation
Evgeniya A. Nikolaeva, PhD, A/Professor, Gorbachev
28, Vesennyaya Str., 650000, Kemerovo
T. A. Shalygina
Tomsk State University of Architecture and Building
Russian Federation
Russian Federation
Tat'yana A. Shalygina, PhD, A/Professor
2, Solyanaya Sq., 634003, Tomsk
References
1. Wolpert D.H. Stacked generalization. Neural Networks. 1992; 5 (2): 241–259. Available: http://dx.doi.org/10.1016/S0893-6080(05)80023-1
2. Kopec D. Classic computer science problems in Python. Manning. Shelter Island. 2019. 206 p. ISBN 9781617295980.
3. Johansson R. Numerical Python. Apress Inc., 2018. 488 p. DOI: 10.1007/978-1-4842-0553-2
4. Wolohan J.T. Mastering large datasets with Python. Manning Press. 2020. 312 p. ISBN 9781617296239.
5. Pratap Dangeti, Allen Yu, Claire Chung. Learning path numerical computing with Python. Packt Publishing. 2018. 682 p. ISBN 1789953634, 9781789953633.
6. Downey A., Elkner J., Meyers Ch. How to think like a computer scientist. Green Tea Press, 2002. 288 p. ISBN 0-9716775-0-6.
Review
For citations:
Pylov P.A., Dyagileva A.V., Nikolaeva E.A., Shalygina T.A. Intelligent model for automated identification of architectural style. Vestnik Tomskogo gosudarstvennogo arkhitekturno-stroitel'nogo universiteta. JOURNAL of Construction and Architecture. 2023;25(4):38-44. (In Russ.) https://doi.org/10.31675/1607-1859-2023-25-4-38-44
Views:
193
Email this article 