KAYNAKÇA
Akdoğan, A. (2011). Kamu maliyesi. Ankara: Gazi Kitapevi.
Androutsopoulou, A., vd. (2019). Transforming the communication between citizens and government through AI-guided chatbots. Government Information Quarterly, 36(2), 358–367.
Bagshaw, K. B. (2021). PERT and CPM in project management with practical examples. American Journal of Operations Research, 11, 215-226.
Chen, H., Chiang, R. H. L., ve Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. Management Information Systems Quarterly, 36(4), 1165-1188. https://doi.org/10.2307/41703503
Sayfa 206
Chui, M., vd. (2018). Applying artificial intelligence for social good. McKinsey Global Institute.
Dhar, R., ve Stein, R. (2017). Seven methods for transforming corporate data into actionable business intelligence. NJ: Prentice Hall.
DonVito, P. A. (1969). The essentials of a planning programming budgeting system. California: The RAND Corporation.
Dwivedi, Y. K., vd. (2019). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Edizdoğan, N., Çetinkaya, Ö., ve Gümüş, E., (2016). Kamu maliyesi. Bursa: Ekin Yayınevi.
Edizdoğan, N., ve Çetinkaya, Ö. (2019). Kamu bütçesi. Bursa: Ekin Yayınevi.
Engin, Z., ve Treleaven, P. (2019). Algorithmic government: Automating public services and supporting civil servants in using data science technologies. The Computer Journal, 62(3), 448-460.
Erdem, M., Tatlıoğlu, İ., ve Şenyüz, D. (2021). Kamu maliyesi. Bursa: Ekin Yayınevi.
Fausett, L., (1994). Fundamentals of natural networks: Architecture, algorithms and applications. New Jersey: Prentice Hall.
Gantz, J., ve Reinsel, D. (2018). The digital universe in 2020: Big data, bigger digital shadows, and biggest growth in the far east. IDC Analyze the future. www.emc.com/leadership/digital-universe/index.htm. (Erişim Tarihi: 04.08.2023)
Li, A. vd. (2023). Deep anomaly detection under labeling budget constraints. InInternational Conference on Machine Learning, 19882-19910. https://doi.org/10.48550/arxiv.2302.07832
Marotta, G., Krahnhof, P., ve Au, C. D. (2022). A critical analysis of budgeting processes from the pharmaceutical industry and beyond.Journal of Applied Finance & Banking,12(3), 35-53.
Mitchell, T. (2019). Machine learning & pattern recognition series. McGraw-Hill.
Mullainathan, S., ve Spiess, J. (2017). Machine learning: An applied econometric approach. Journal of Economic Perspectives, 31(2), 87-106.
Nabiyev, V. V., (2005). Yapay zeka: Problemler, yöntemler ve algoritmalar. Ankara: Seçkin Yayıncılık.
OECD. (2018). OECD best practices for performance budgeting. https://one.oecd.org/document/GOV/PGC/SBO(2018)7/en/pdf, (Erişim Tarihi: 04.08.2023).
Pehlivan, O. (2016). Kamu maliyesi. Trabzon: Celepler Matbaacılık.
Sayfa 207
Puron-Cid, G. (2014). Factors for a successful adoption of budgetary transparency innovations: A questionnaire report of an open government initiative in Mexico. Government Information Quarterly, 31, S49–S62.
Robinson, M. ve Last, D. (2009). A basic model of performance-based budgeting. IMF: Technical Notes and Manuals.
Russell, S., vd. (2010). Artificial intelligence: a modern approach. Prentice Hall.
Sheth, A. (2014). Transforming big data into smart data: deriving value via harnessing volume, variety, and velocity using semantic techniques and technologies. 2014 IEEE 30th International Conference on Data Engineeri.
Thierer, A. D., Castillo O’Sullivan, A., ve Russell, R. (2017). Artificial intelligence and public policy. Arlington: Mercatus Research, Mercatus Center at George Mason University.
Uluatam, Ö. (2003). Kamu maliyesi. Ankara: İmaj Yayınevi.
Valle-Cruz, D. (2019). A review of artificial intelligence in government and its potential from a public policy perspective. InProceedings of the 20th Annual International Conference on Digital Government Research, 91-99.
Valle-Cruz, D., Fernandez-Cortez, V., ve Gil-Garcia, J. R. (2022). From E-budgeting to smart budgeting: Exploring the potential of artificial intelligence in government decision-making for resource allocation. Government Information Ouarterly, 39(2), 1-19.
Valle-Cruz, D., Gil-Garcia, J. R., ve Fernandez-Cortez, V. (2020).Towards smarter public budgeting? understanding the potential of artificial ıntelligence techniques to support decision making in government. The 21st Annual International Conference on Digital Government Research.232-242. doi:10.1145/3396956.3396995