ESP Journal of Engineering & Technology Advancements |
© 2023 by ESP JETA |
Volume 3 Issue 4 |
Year of Publication : 2023 |
Authors : Amit Mangal |
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Amit Mangal, 2023. Revolutionizing Project Management with Generative AI, ESP Journal of Engineering & Technology Advancements 3(4): 53-60.
Project management is essential for achieving organizational goals, especially in today's dynamic business environment. As projects become more intricate, the demand for intelligent tools to aid project managers in decision-making and resource allocation has grown. Generative Artificial Intelligence (AI) holds promise in transforming project management by automating tasks, generating insights, and facilitating decision-making. This research paper explores the application of generative AI in project management, examining its potential impact on project outcomes, and discussing relevant techniques, benefits, challenges, real-world use cases, current trends, and future directions.
[1] Ashish, V. (2017). Attention is all you need. Advances in neural information processing systems, 30, I. https://cir.nii.ac.jp/crid/1370849946232757637
[2] Balkrishna, A., Pathak, R., Kumar, S., Arya, V., & Singh, S. K. (2023). A comprehensive analysis of the advances in Indian Digital Agricultural architecture. Smart Agricultural Technology, 5, 100318. https://www.sciencedirect.com/science/article/pii/S2772375523001478
[3] Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence, 35(8), 1798-1828. https://ieeexplore.ieee.org/abstract/document/6472238/
[4] Gaudillo, J., Rodriguez, J. J. R., Nazareno, A., Baltazar, L. R., Vilela, J., Bulalacao, R., ... & Albia, J. (2019). Machine learning approach to single nucleotide polymorphism-based asthma prediction. PloS one, 14(12), e0225574. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0225574
[5] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27. https://proceedings.neurips.cc/paper_files/paper/2014/hash/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html
[6] Hassani, H., Huang, X., Silva, E., & Ghodsi, M. (2020). Deep learning and implementations in banking. Annals of Data Science, 7, 433-446. https://link.springer.com/article/10.1007/s40745-020-00300-1
[7] Kamrani, Z., Tajeddin, Z., & Alemi, M. (2023). Scaffolding principles of content-based science instruction in an international elementary school. Pedagogies: An International Journal, 1-25. https://www.tandfonline.com/doi/abs/10.1080/1554480X.2023.2222716
[8] Karras, T., Aila, T., Laine, S., & Lehtinen, J. (2017). Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196. https://arxiv.org/abs/1710.10196
[9] Kerzner, H. (2017). Project management: a systems approach to planning, scheduling, and controlling. John Wiley & Sons. https://books.google.co.ke/books?hl=en&lr=&id=xlASDgAAQBAJ&oi=fnd&pg=PR19&dq=
[10] Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114. https://arxiv.org/abs/1312.6114
[11] Larsen, A. B. L., Sønderby, S. K., Larochelle, H., & Winther, O. (2016, June). Autoencoding beyond pixels using a learned similarity metric. In International conference on machine learning (pp. 1558-1566). PMLR. https://proceedings.mlr.press/v48/larsen16
[12] Lo Piano, S. (2020). Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward. Humanities and Social Sciences Communications, 7(1), 1-7. https://www.nature.com/articles/s41599-020-0501-9.
[13] Mohibbullah, M., Gain, A. K., & Ahsan, M. N. (2021). Examining local institutional networks for sustainable disaster management: Empirical evidence from the South-West coastal areas in Bangladesh. Environmental Science & Policy, 124, 433-440. https://www.sciencedirect.com/science/article/abs/pii/S146290112100201X
[14] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI blog, 1(8), 9. https://blocksml.com/genaipapers/Language%20Models%20are%20Unsupervised%20Multitask%20Learners.pdf
[15] Sharma, A., Sharma, V., Jaiswal, M., Wang, H. C., Jayakody, D. N. K., Basnayaka, C. M. W., & Muthanna, A. (2022). Recent trends in AI-based intelligent sensing. Electronics, 11(10), 1661. https://www.mdpi.com/2079-9292/11/10/1661
[16] Sheikhalishahi, M., Abdolhossein Zadeh, S., Naeimi, S., & Sardardabadi, A. (2022). Improving Earned Value Management and Earned Schedule by Statistical Quality Control Charts Considering the Dependence between Cost and Schedule. Journal of Quality Engineering and Production Optimization, 7(1), 177-198. https://jqepo.shahed.ac.ir/article_3779.html
[17] Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press. https://www.sciencedirect.com/science/article/abs/pii/S0956053X14002128
[18] Tavana, M., Azadmanesh, A., Nasr, A. K., & Mina, H. (2022). A multicriteria-optimization model for cultural heritage renovation projects and public-private partnerships in the hospitality industry. Current Issues in Tourism, 25(22), 3709-3734. https://www.tandfonline.com/doi/abs/10.1080/13683500.2021.2015299
[19] Wang, C., Zhang, X., Wang, M., Lim, M. K., & Ghadimi, P. (2019). Predictive analytics of the copper spot price by utilizing complex network and artificial neural network techniques. Resources policy, 63, 101414. https://www.sciencedirect.com/science/article/abs/pii/S0301420719300200
[20] Wang, Y., Huang, X., Yu, Q., & Lai, Y. (2023, November). Emerging Techniques for Online Learning Analytics. In International Conference on Technology in Education (pp. 109-118). Singapore: Springer Nature Singapore. https://link.springer.com/chapter/10.1007/978-981-99-8255-4_10
[21] Zeng, X., & Li, J. (2019). WEEE management in USA. In Waste Electrical and Electronic Equipment (WEEE) Handbook (pp. 521-540). Woodhead Publishing. https://www.sciencedirect.com/science/article/abs/pii/B9780081021583000197
[22] Amit Mangal, 2021. "Evaluating Planning Strategies for Prioritizing the most viable Projects to Maximize Investment Returns " ESP Journal of Engineering & Technology Advancements 1(2): 69-77.
[23] Amit Mangal, 2022. "Envisioning the Future of Professional Services: ERP, AI, and Project Management in the Age of Digital Disruption"ESP Journal of Engineering & Technology Advancements 2(4): 71-79.
[24] Amit Mangal, 2023. An Analytical Review of Contemporary AI-Driven Hiring Strategies in Professional Services, ESP Journal of Engineering & Technology Advancements 3(3): 52-63.
[25] S. E. V. S. Pillai and W. -C. Hu, "Misinformation Detection Using an Ensemble Method with Emphasis on Sentiment and Emotional Analyses," 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA), Orlando, FL, USA, 2023, pp. 295-300, doi: 10.1109/SERA57763.2023.10197706
[26] Muthukumaran Vaithianathan, Mahesh Patil, Shunyee Frank Ng, Shiv Udkar, 2023. "Comparative Study of FPGA and GPU for High-Performance Computing and AI" ESP International Journal of Advancements in Computational Technology (ESP-IJACT) Volume 1, Issue 1: 37-46. [PDF]
[27] Bhat, V. Gojanur, and R. Hegde. 2015. 4G protocol and architecture for BYOD over Cloud Computing. In Communications and Signal Processing (ICCSP), 2015 International Conference on. 0308-0313. Google Scholar. [Link]
[28] Aparna Bhat, Rajeshwari Hegde, “Comprehensive Study of Renewable Energy Resources and Present Scenario in India,” 2015 IEEE International Conference on Engineering and Technology (ICETECH), Coimbatore, TN, India, 2015. [Link]
[29] Piyush Ranjan, 2022.”Fundamentals Of Digital Transformation In Financial Services: Key Drivers and Strategies”, International Journal of Core Engineering & Management, Volume 7, Issue 3, PP 41-50, [Link]
[30] Ayyalasomayajula, M. M. T., Chintala, S., & Sailaja, A. (2019). A Cost-Effective Analysis of Machine Learning Workloads in Public Clouds: Is AutoML Always Worth Using? International Journal of Computer Science Trends and Technology (IJCST), 7(5), 107–115.
[31] Chintala, S. ., & Ayyalasomayajula, M. M. T. . (2019). Optimizing Predictive Accuracy With Gradient Boosted Trees In Financial Forecasting. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 10(3), 1710–1721. https://doi.org/10.61841/turcomat.v10i3.14707
[32] Empowering Rules Engines: AI and ML Enhancements in BRMS for Agile Business Strategies. (2022). International Journal of Sustainable Development through AI, ML and IoT, 1(2), 1-20. https://ijsdai.com/index.php/IJSDAI/article/view/36
[33] Chanthati, Sasibhushan Rao. (2022). A Centralized Approach To Reducing Burnouts in the It Industry Using Work Pattern Monitoring Using Artificial Intelligence. International Journal on Soft Computing Artificial Intelligence and Applications. Sasibhushan Rao Chanthati. Volume-10, Issue-1, PP 64-69.[LINK]
[34] Chanthati, Sasibhushan Roa. (2021). A segmented approach to encouragement of entrepreneurship using data science. World Journal of Advanced Engineering Technology and Science. https://doi.org/10.30574/wjaets.2024.12.2.0330. [Link]
[35] Kalla, Dinesh and Smith, Nathan and Samaah, Fnu and Polimetla, Kiran, Facial Emotion and Sentiment Detection Using Convolutional Neural Network (January 2021). Indian Journal of Artificial Intelligence Research (INDJAIR), Volume 1, Issue 1, January-December 2021, pp. 1–13, Article ID: INDJAIR_01_01_001, Available at SSRN: https://ssrn.com/abstract=4690960
[36] Piyush Ranjan, 2022.”Fundamentals Of Digital Transformation In Financial Services: Key Drivers and Strategies”, International Journal of Core Engineering & Management, Volume 7, Issue 3, PP 41-50, [Link]
Generative AI, Project Management, Insights.