EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 370 (2026) EPJ Web Conf., 370 (2026) 01031 References
Open Access
Issue
EPJ Web Conf.
Volume 370, 2026
International Conference on Advanced Physics: Innovations for a Sustainable Future (IEMPHYS-26)
Article Number 01031
Number of page(s) 9
DOI https://doi.org/10.1051/epjconf/202637001031
Published online 29 May 2026
  1. H. Kerzner, Project Management: A Systems Approach to Planning, Scheduling, and Controlling (John Wiley &Sons, 2017) [Google Scholar]
  2. Project Management Institute, A Guide to the Project Management Body of Knowledge (PMBOK® Guide) (Project Management Institute, 2021) [Google Scholar]
  3. C. Bredillet, Project Management: A Research Perspective, Int. J. Proj. Manag. 34, 1 (2016) [Google Scholar]
  4. J. Smith, Time Management in R&D Projects: Challenges and Solutions, Res. Manag. Rev. 25, 22 (2020) [Google Scholar]
  5. S. Marcelino-Sadaba, L. F. Gonzalez-Jaen, A. Perez-Ezcurdia, Project Management and Artificial Intelligence: A Review, Int. J. Proj. Manag. 32, 1 (2014) [Google Scholar]
  6. K. Davis, The Role of Artificial Intelligence in Project Management, J. Proj. Manag. 34, 45 (2019) [Google Scholar]
  7. H. H. Hussain, Time Management Tools and Techniques for project management, Socio-econ. Res. Bull. 2014, 55 (2014) [Google Scholar]
  8. H. Sarwar, M. M. Rahman, A Systematic Short Review of Machine Learning and Artificial Intelligence Integration in Current Project Management Techniques, in Proceedings of the Conference, 262-270 2024. doi: https://doi.org/10.1109/seai62072.2024.10674089 [Google Scholar]
  9. M. S. Khatun, Munira, Strategic data management and innovation digital transformation in banking: a systematic review of trends, technologies, and challenges, Emerg. Technol. Cybersec. Financ. Innov. 2, 78 (2025). doi: https://doi.org/10.71292/sdmi.v2i01.12 [Google Scholar]
  10. S. Zhang, X. Li, A comparative study of machine learning regression models for predicting construction duration, J. Asian Archit. Build. Eng. 2023. doi: https://doi.org/10.1080/13467581.2023.2278887 [Google Scholar]
  11. A. Oikonomidis, C. Catal, A. Kassahun, Hybrid Deep Learningbased Models for Crop Yield Prediction, Appl. Artif. Intell. 36 2022. doi: https://doi.org/10.1080/08839514.2022.2031823 [Google Scholar]
  12. J. Smith, L. Jones, Agile Methodologies in IT Project Management, J. Inf. Technol. 2022 [Google Scholar]
  13. Y. Wang, L. Zhang, S. Chen, Predictive Analytics in Project Management: A Review, J. Proj. Manag. 2021 [Google Scholar]
  14. J. Smith, A. Lee, Chatbots in Project Management: Efficiency through Automation, J. Bus. Res. 2023 [Google Scholar]
  15. A. Kumar, R. Singh, P. Sharma, Impact of Project Management on Construction Efficiency, Int. J. Proj. Manag. 2021 [Google Scholar]
  16. C. Lee, H. Kim, S. Park, Healthcare Project Management: Enhancing Patient Care, J. Healthc. Manag. 2023 [Google Scholar]
  17. S. Choudhury, S. Saha, Machine Learning Applications in Project Scheduling, Int. J. Proj. Manag. 2022 [Google Scholar]
  18. R. Miller, J. Thompson, K. Davis, AI and Team Collaboration: A New Paradigm, Proj. Manag. J. 2022 [Google Scholar]
  19. B. Sekeroglu et al., Comparative Evaluation and Comprehensive Analysis of Machine Learning Models for Regression Problems, Data Intell. 4, 620 2022 [Google Scholar]
  20. R. Chopra, S. Goswami, Unveiling R&D project timelines with AI, in Proceedings of the IEEE Delhi Section Flagship Conference (DELCON), 1-7 2024. doi: https://doi.org/10.1109/DELCON64804.2024.10866495 [Google Scholar]
  21. Y. Wang et al., Deep Learning for Project Time Prediction, IEEE Trans. Eng. Manag. 2020 [Google Scholar]
  22. E. M. M. Alzeyani, C. Szabó, Comparative Evaluation of Model Accuracy for Predicting Selected Attributes in Agile Project Management, Mathematics 12, 2529 2024 [Google Scholar]
  23. L. Bendada et al., Predicting project duration using a coupled artificial neural network and Taguchi method approach, Stud. Eng. Exact Sci. 5, e5641 2024. doi: https://doi.org/10.54021/seesv5n2-019 [Google Scholar]
  24. X. Liu et al., Ensemble Learning for Critical Path Prediction, Proj. Manag. J. 2021 [Google Scholar]
  25. I. K. Nti et al., Enhancing Flood Prediction using Ensemble and Deep Learning Techniques, in 2021 22nd International Arab Conference on Information Technology (ACIT), Muscat, Oman, 1-9 2021. doi: https://doi.org/10.1109/ACIT53391.2021.9677084 [Google Scholar]
  26. A. A. ForouzeshNejad, F. Arabikhan, S. Aheleroff, Optimizing Project Time and Cost Prediction Using a Hybrid XGBoost and Simulated Annealing Algorithm, Machines 12 2024. doi: https://doi.org/10.3390/machines12120867 [Google Scholar]
  27. I. U. Altuncan, M. Vanhoucke, A hybrid forecasting model to predict the duration and cost performance of projects with Bayesian networks, Eur. J. Oper. Res. 315, 511 2024 [Google Scholar]
  28. Lishner, A. Shtub, Using an Artificial Neural Network for Improving the Prediction of Project Duration, Mathematics 10, 4189 (2022). doi: https://doi.org/10.3390/ma10224189 [Google Scholar]
  29. R. Jones, T. Brown, Explainable AI in Project Management, Comput. Ind. 2020 [Google Scholar]
  30. A. Jadhav, S. K. Shandilya, Reliable machine learning models for estimating effective software development efforts: A comparative analysis, J. Eng. Res. 11, 362 (2023). doi: https://doi.Org/10.1016/j.jer.2023.100150 [Google Scholar]
  31. Taboada et al., Artificial Intelligence Enabled Project Management, Appl. Sci. 2023 [Google Scholar]
  32. M. E. Nenni, F. De Felice, C. De Luca, et al., How artificial intelligence will transform project management in the age of digitization: a systematic literature review, Manag. Rev. Q. 75, 1669 (2025). doi: https://doi.org/10.1007/s11301024-00418-z [Google Scholar]
  33. W. Han et al., Comparison of Machine Learning Algorithms for Software Project Time Prediction, Int. J. Multimed. Ubiquitous Eng. 10, 1 (2015). doi: https://doi.org/10.14257/ijmue.2015.10.9.01 [Google Scholar]
  34. S. Soltan, M. Ashrafi, Predicting project duration and cost, and selecting the best action plan using statistical methods for earned value management, J. Proj. Manag. 5, 157 (2020). doi: https://doi.org/10.5267/jjpm.2020 3 002 [Google Scholar]
  35. M. Danler et al., Quality and Effectiveness of AI Tools for Students and Researchers for Scientific Literature Review and Analysis, Stud. Health Technol. Inform. 2024. doi: https://doi.org/10.3233/shti240038 [Google Scholar]
  36. S. Uddin, S. Yan, H. Lu, Machine learning and deep learning in project analytics: methods, applications and research trends, Prod. Plan. Control 2024. doi: https://doi.org/10.1080/09537287.2024.2320790 [Google Scholar]
  37. D. S. Adamantiadou, L. Tsironis, Leveraging Artificial Intelligence in Project Management: A Systematic Review of Applications, Challenges, and Future Directions, Computers 14, 66 (2025) [Google Scholar]
  38. S. Uddin et al., Integrating machine learning and network analytics to model project cost, time and quality performance, Prod. Plan. Control 35, 1475 (2023) [Google Scholar]
  39. M. Nacchia et al., A Systematic Mapping of the Advancing Use of Machine Learning Techniques for Predictive Maintenance in the Manufacturing Sector, Appl. Sci. 11, 2546 (2021). doi: https://doi.org/10.3390/app11062546 [Google Scholar]
  40. A. Tadimarri et al., Efficiency Unleashed: Harnessing AI for Agile Project Management 2024. [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.