Analyzing the Relationship between Contractor’s Qualification Measures and Project Quality in Research Projects: a Case Study

1. Ostadi, B., Aghdasi, M., Albadvi, A., and Rezaie, K. (2010). “The Exploration of the Relationship and Interaction between the Preparation Stage of BPR Projects and Quality Management Systems (QMSs) Using Concepts of RBVF Model and Dynamic Capabilities (Technical note)”. J. of industrial engineering, Vol. 44, No. 2, 117-125.

2. Zegordi, S., Bagheri, S., and Attarian, J. (2010). “Statistical analysis of relationship between key success factors of Six Sigma in Iranian companies”. J. of industrial engineering, Vol. 44, No. 1, 49-62.

3. Fasanghari, M. and Keramati, A. (2011). “Customer Churn Prediction Using Local Linear Model Tree for Iranian Telecommunication Companies”. J. of industrial engineering, Vol. 45, Special Issue, 25-37.

4. Kar, S., Das, S., and Ghosh, P. K. (2014). “Applications of neuro fuzzy systems: A brief review and future outline”. Applied Soft Computing, Vol. 15, 243-259.

5. Shi, H. and Li, W. (2008). “Application of PSO-based Neural Network in Quality Assessment of Construction Project”. Proc., Int. Conf. on MultiMedia and Information Technology, 54-57.

6. Yang, R. and Wang, X. (2010). “The Evaluation of Construction Quality Based on BP neural network”. Proc. of Int. Conf. on Mechanic Automation and Control Engineering, 1582-1585.

7. Yang, B., Yao, L., and Huang, H. Z. (2007). “Early Software Quality Prediction Based on a Fuzzy Neural Network Model”. Proc. of Int. Conf. on Natural Computation, 760-764.

8. Koo, C. W., Hong, T. H., Hyun, C. T., and Koo, K. J. (2010). “A CBR-based hybrid model for predicting a construction duration and cost based on project characteristics in multi-family housing projects”. Canadian J. of Civil Engineering, Vol. 37, No. 5, 739-752.

9. Pewdum, W., Rujirayanyong, T., and Sooksatra, V. (2009). “Forecasting final budget and duration of highway construction projects”. Engineering, Construction and Architectural Management, Vol. 16, No. 6, 544-557.

10. Iranmanesh, S. H., Mirseraji, G. H., and Shahmiri, S. (2009). “An Emotional Learning based Fuzzy Inference System (ELFIS) for improvement of the completion time of projects estimation”. Proc. of Int. Conf. on Computers & Industrial Engineering, 470-475.

11. Chou, J. S., Tai, Y., and Chang, L. J. (2010). “Predicting the development cost of TFT-LCD manufacturing equipment with artificial intelligence models”. Int. J. of Production Economics, Vol. 128, 339-350.

12. Ji, Z. and Li, Y. (2009). “The Application of RBF Neural Network on Construction Cost Forecasting”. Proc. of Int. Workshop on Knowledge Discovery and Data Mining, 32-35.

13. Xiaokang, H. and Mei, L. (2010). “Research on construction cost control based upon BP neural network and theory of constraint”. Proc. of Int. Conf. on Management and Service Science, 1-4.

14. Papatheocharous, E. and Andreou, A. S. (2009). “Hybrid Computational Models for Software Cost Prediction: An Approach Using Artificial Neural Networks and Genetic Algorithms”. In Filipe, J. and Cordeiro, J. (Eds.), Enterprise Information Systems (Vol. 19, 87-100). Berlin, Heidelberg: Springer Berlin Heidelberg.

15. Lee, A., Cheng, C. H., and Balakrishnan, J. (1998). “Software development cost estimation: Integrating neural network with cluster analysis”. Information & Management, Vol. 34, 1-9.

16. Cheng, M. Y., Tsai, H. C., and Hsieh, W. S. (2009). “Web-based conceptual cost estimates for construction projects using Evolutionary Fuzzy Neural Inference Model”. Automation in Construction, Vol. 18, No. 2, 164-172.

17. Li, Y. F., Xie, M., and Goh, T. N. (2009). “A study of the non-linear adjustment for analogy based software cost estimation”. Empirical Software Engineering, Vol. 14, 603-643.

18. Iranmanesh, S. H. and Zarezadeh, M. (2008). “Application of Artificial Neural Network to Forecast Actual Cost of a Project to Improve Earned Value Management System”. World Congress on Science, Engineering and Technology, 240-243.

19. Attarzadeh, I. and Ow, S. H. (2010). “A novel soft computing model to increase the accuracy of software development cost estimation”. Proc. of Int. Conf. on Computer Engineering and Technology, 603-607.

20. Attarzadeh, I. and Ow, S. H. (2010). “Proposing a new software cost estimation model based on artificial neural networks”. Proc. of Int. Conf. on Computer Engineering and Technology, 487-491.

21. Kazemifard, M., Zaeri, A., Ghasem-Aghaee, N., Nematbakhsh, M. A., and Mardukhi, F. (2011). “Fuzzy Emotional COCOMO II Software Cost Estimation (FECSCE) using Multi-Agent Systems”. Applied Soft Computing, Vol. 11, No. 2, 2260-2270.

22. Xin-zheng, W. and Li-ying, X. (2010). “Application of Rough Set and Neural Network in Engineering Cost Estimation”. Proc. of Int. Conf. on Management and Service Science, 1-4.

23. Yao-Ji, J., Qiang, M., and Qian, Z. (2009). “A Study on Risk Evaluation of Real Estate Project Based on BP Neural Networks”. Proc. of Int. Conf. on E-Business and Information System Security, 1-4.

24. Zhang, C., Li, Y., Liu, M., and Liu, Z. (2009). “An Enhanced Approach for Investment Risk Forecasting of Electric Power Projects”. Proc. of Int. Workshop on Computer Science and Engineering, 29-32.

25. Hu, Y., Zhang, X., Sun, X., Liu, M., and Du, J. (2009). “An Intelligent Model for Software Project Risk Prediction”. Proc. of Int. Conf. on Information Management, Innovation Management and Industrial Engineering, 629-632.

26. Liu, Z. and Xiong, F. (2007). “The Model and Application of the Investment Risk Comprehensive Evaluation about the Electric Power Project Based on BP Neural Network”. Proc. of Int. Conf. on Wireless Communications, Networking and Mobile Computing, 5188-5191.

27. Fugui, D. and Li, N. (2008). “The Project Risk Analysis Model Based on Neural Network”. Proc. of Int. Conf. on Wireless Communications, Networking and Mobile Computing, 1-3.

28. Abdollahzade, M. (2008). Price forecasting in Iran local market using neuro-fuzzy model. M.Sc. Thesis, University of Tehran, Tehran.