Open Access
Issue |
MATEC Web Conf.
Volume 312, 2020
9th International Conference on Engineering, Project, and Production Management (EPPM2018)
|
|
---|---|---|
Article Number | 04003 | |
Number of page(s) | 9 | |
Section | Integration of Engineering Management and Project Management | |
DOI | https://doi.org/10.1051/matecconf/202031204003 | |
Published online | 03 April 2020 |
- Y.H. Chiang, L. Tao, F.K.W. Wong, Causal relationship between construction activities, employment and GDP: The case of Hong Kong, Habitat International 46:1-12 (2015) [CrossRef] [Google Scholar]
- R.E. Saks, Job creation and housing construction: Constraints on metropolitan area employment growth, Journal of Urban Economics 64(1):178-195 (2008) [CrossRef] [Google Scholar]
- H. Jiang, X.H. Jin, C. Liu, The effects of the late 2000s global financial crisis on Australia’s construction demand, Construction Economics and Building 13(3):65-79 (2013) [CrossRef] [Google Scholar]
- H. Li, V. Li, Forecasting house rental levels: Analytical rent model versus neural network, Journal of Urban Planning and Development 122(4):118-127 (1996) [CrossRef] [Google Scholar]
- A. Hepşen, M. Vatansever, Forecasting future trends in Dubai housing market by using Box-Jenkins autoregressive integrated moving average, International Journal of Housing Markets and Analysis 4(3):210-223 (2011) [CrossRef] [Google Scholar]
- K.C. Lam, C.Y. Yu, C.K. Lam, Support vector machine and entropy based decision support system for property valuation, Journal of Property Research 26(3):213-233 (2009) [CrossRef] [Google Scholar]
- S. Bond, J. Hopkins, The Impact of Transmission Lines on Residential Property Values: Results of a Case Study in a Suburb of Wellington, NZ. Pacific Rim Property Research Journal, 6(2):52-60 (2000) [CrossRef] [Google Scholar]
- R.B. Abidoye, A.P.C. Chan, Modeling property values in Nigeria using artificial neural network, Journal of Property Research 34(1):1-18 (2017) [CrossRef] [Google Scholar]
- S. McGreal, A. Adair, D. McBurney, D. Patterson, Neural networks: the prediction of residential values, Journal of Property Valuation and Investment 16(1):57-70 (1998) [CrossRef] [Google Scholar]
- N. Nghiep, C. Al, Predicting housing value: A comparison of multiple regression analysis and artificial neural networks, Journal of Real Estate Research 22(3):313-336 (2001) [Google Scholar]
- H. Mallick, M.K. Mahalik, Factors determining regional housing prices: evidence from major cities in India, Journal of Property Research 32(2):123-146 (2015) [CrossRef] [Google Scholar]
- R.B. Abidoye, A.P.C. Chan, A survey of property valuation approaches in Nigeria, Property Management 34(5):364-380 (2016) [CrossRef] [Google Scholar]
- G. Shmueli, O.R. Koppius, Predictive analytics in information systems research, MIS Quarterly 35(3):553-572 (2011) [CrossRef] [Google Scholar]
- R.B. Abidoye, A.P.C. Chan, Improving property valuation accuracy: a comparison of hedonic pricing model and artificial neural network, Pacific Rim Property Research Journal 24(1):71-83 (2018) [CrossRef] [Google Scholar]
- Olatunji, The impact of oil price regimes on construction cost in Nigeria, Construction Management and Economics 28(7):747-759 (2010) [CrossRef] [Google Scholar]
- K'Akumu, Construction statistics review for Kenya, Construction Management and Economics 25(3):315-326 (2007) [CrossRef] [Google Scholar]
- Olaleye, Property market nature and the choice of property portfolio diversification strategies: The Nigeria experience, International Journal of Strategic Property Management 12:35–51 (2008) [CrossRef] [Google Scholar]
- K.M. Woosnam, K.D. Aleshinloye, N. Maruyama, Solidarity at the Osun Osogbo Sacred Grove: A UNESCO world heritage site, Tourism Planning and Development 13(3):274-291 (2016) [CrossRef] [Google Scholar]
- P. Cortez, A. Cerdeira, F. Almeida, T. Matos, J. Reis, Modeling wine preferences by data mining from physicochemical properties, Decision Support Systems, 47(4):547-553 (2009) [CrossRef] [Google Scholar]
- Oshodi, O.A. Ejohwomu, I.O. Famakin, P. Cortez, Comparing univariate techniques for tender price index forecasting: Box-Jenkins and neural network model, Construction Economics and Building 17(3):109-123 (2017) [CrossRef] [Google Scholar]
- J. Tinoco, A. Gomes Correia, P. Cortez, Jet grouting column diameter prediction based on a data-driven approach, European Journal of Environmental and Civil Engineering 1-21 (2016) [Google Scholar]
- M.S. El-Abbasy, A. Senouci, T. Zayed, F. Mirahadi, L. Parvizsedghy, Artificial neural network models for predicting condition of offshore oil and gas pipelines, Automation in Construction 45:50-65 (2014) [CrossRef] [Google Scholar]
- T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.), New York: Springer-Verlag (2008) [Google Scholar]
- R Core Team., R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing (2015) [Google Scholar]
- P. Cortez, Data mining with neural networks and support vector machines using the R/rminer tool. In: P. Perner, ed. Proceedings of the 10th Industrial Conference on Data Mining - Advances in Data Mining: Applications and Theoretical Aspects, July 2010, Berlin, Germany. Berlin: Springer 572–583 (2010) [Google Scholar]
- G. Zhang, B.E. Patuwo, M.Y. Hu, Forecasting with artificial neural networks: The state of the art, International Journal of Forecasting 14:35–62 (1998) [CrossRef] [Google Scholar]
- P. Cortez, A tutorial on the rminer R package for data mining tasks. Teaching Report, Department of Information Systems, ALGORITMI Research Centre, Engineering School, University of Minho, Guimaraes, Portugal (2015) [CrossRef] [Google Scholar]
- S. Dreiseitl, L. Ohno-Machado, Logistic regression and artificial neural network classification models: a methodology review, Journal of Biomedical Informatics, 35(5-6):352-359 (2002) [Google Scholar]
- P. Cortez, M.J. Embrechts, Using sensitivity analysis and visualization techniques to open black box data mining models, Information Sciences 225:1-17 (2013) [CrossRef] [Google Scholar]
- K.C. Lam, C.Y. Yu, K.Y. Lam, An artificial neural network and entropy model for residential property price forecasting in Hong Kong, Journal of Property Research 25(4):321-342 (2008) [CrossRef] [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.