Residential housing price index forecasting via neural networks
X Xu, Y Zhang - Neural Computing and Applications, 2022 - Springer
During the past decade, the housing market in China has witnessed rapid growth and the
significance of forecasting related to housing prices has undoubtedly elevated, which has …
significance of forecasting related to housing prices has undoubtedly elevated, which has …
Machine learning-based forecasts of residential property prices in Hangzhou city, Zhejiang province, China
B Jin, X Xu - Neural Computing and Applications, 2024 - Springer
The Chinese real estate market has grown at such a quick rate over the last few decades, up
to the current falling patterns that began at the end of 2021. This difficulty has made it more …
to the current falling patterns that began at the end of 2021. This difficulty has made it more …
AI-based shear capacity of FRP-reinforced concrete deep beams without stirrups
M AlHamaydeh, G Markou, N Bakas… - Engineering …, 2022 - Elsevier
The presented work utilizes Artificial Intelligence (AI) algorithms, to model and interpret the
behavior of the fiber reinforced polymer (FRP)-reinforced concrete deep beams without …
behavior of the fiber reinforced polymer (FRP)-reinforced concrete deep beams without …
Pre-owned housing price index forecasts using Gaussian process regressions
B Jin, X Xu - Journal of Modelling in Management, 2024 - emerald.com
Purpose The purpose of this study is to make property price forecasts for the Chinese
housing market that has grown rapidly in the last 10 years, which is an important concern for …
housing market that has grown rapidly in the last 10 years, which is an important concern for …
Improving performance of mass real estate valuation through application of the dataset optimization and Spatially Constrained Multivariate Clustering Analysis
S Sisman, AC Aydinoglu - Land Use Policy, 2022 - Elsevier
Mass real estate valuation is a multidimensional and complex matter because it depends on
many constant and time-varying factors. It is desirable to have high level of model …
many constant and time-varying factors. It is desirable to have high level of model …
Composite property price index forecasting with neural networks
X Xu, Y Zhang - Property Management, 2023 - emerald.com
Purpose The Chinese housing market has gone through rapid growth during the past
decade, and house price forecasting has evolved to be a significant issue that draws …
decade, and house price forecasting has evolved to be a significant issue that draws …
A general framework of high-performance machine learning algorithms: application in structural mechanics
G Markou, NP Bakas, SA Chatzichristofis… - Computational …, 2024 - Springer
Data-driven models utilizing powerful artificial intelligence (AI) algorithms have been
implemented over the past two decades in different fields of simulation-based engineering …
implemented over the past two decades in different fields of simulation-based engineering …
New fundamental period formulae for soil-reinforced concrete structures interaction using machine learning algorithms and ANNs
The importance of designing safe and economic structures in seismically active areas is of
great importance. Thus, developing tools that would help in accurately predicting the …
great importance. Thus, developing tools that would help in accurately predicting the …
Forecasting energy consumption of a public building using transformer and support vector regression
J Huang, S Kaewunruen - Energies, 2023 - mdpi.com
Most of the Artificial Intelligence (AI) models currently used in energy forecasting are
traditional and deterministic. Recently, a novel deep learning paradigm, called 'transformer' …
traditional and deterministic. Recently, a novel deep learning paradigm, called 'transformer' …
Automation of negative infrastructural externalities assessment methods to determine the cost of land resources based on the development of a “thin client” model
E Bykowa, M Skachkova, I Raguzin, I Dyachkova… - Sustainability, 2022 - mdpi.com
This article discusses the need to transform real estate valuation methods. It is associated
with the problems of obtaining unreliable results affecting the subsequent adoption of …
with the problems of obtaining unreliable results affecting the subsequent adoption of …