A framework for automating e-government services based on artificial intelligence

Sarabadani, Ali; Saffarie, Mahrdad; RahseparFard, Kheirollah

doi:10.22091/jemsc.2024.9256.1171

A framework for automating e-government services based on artificial intelligence

Document Type : Original Article

Authors

¹ Phd student of Information Technology (IT) Engineering, e-commerce , Department of Computer Engineering and Information Technology, Faculty of Technology and Engineering, Qom University

² Assistant Prof. Department of Computer Engineering and Information Technology,Faculty of Technology and Engineering, Qom University

10.22091/jemsc.2024.9256.1171

Abstract

Electronic government refers to the provision of continuous government information and services to people through the Internet or other digital methods. The electronic government revolution has the ability to transform the public sector and re-establish the relationship between the government and citizens, a revolution that would have been almost ineffective without the element called artificial intelligence. One of the biggest developments of the last few years can be called the launch of e-government by artificial intelligence all over the world. In this paper, we address the challenges of e-government systems and propose a framework that uses artificial intelligence. In the next step, we present an intelligent e-government database architecture that supports the development and implementation of e-government artificial intelligence applications. The main goal is to use reliable artificial intelligence techniques in advancing the current state of e-government. Services to minimize processing time, reduce costs and improve citizen satisfaction.

Keywords

Main Subjects

Electronic Government (e-Government)

References

-Zhang, K., Zuo, W., Chen, Y., Meng, D., & Zhang, L. (2017). Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE transactions on image processing, 26(7), 3142-3155. https://doi.org/10.48550/arXiv.1608.03981

-Zhang, Y. D., Zhang, Y., Hou, X. X., Chen, H., & Wang, S. H. (2018). Seven-layer deep neural network based on sparse autoencoder for voxelwise detection of cerebral microbleed. Multimedia Tools and Applications, 77, 10521-10538. DOI: https://doi.org/10.1007/s11042-017-4554-8

-Venugopalan, S., Xu, H., Donahue, J., Rohrbach, M., Mooney, R., & Saenko, K. (2014). Translating videos to natural language using deep recurrent neural networks. arXiv preprint arXiv:1412.4729. https://doi.org/10.48550/arXiv.1412.4729

-Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., ... & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. nature, 529(7587), 484-489. DOI: https://doi.org/10.1038/nature16961

-Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern recognition and machine learning (Vol. 4, No. 4, p. 738). New York: springer.

DOI: https://doi.org/10.1016/j.ejor.2011.02.001

-LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521 (7553), 436-444. Google Scholar Google Scholar Cross Ref Cross Ref, 25. https://doi.org/10.1016/j.ecolind.2024.111704

-Abowd, G. D., Dey, A. K., Brown, P. J., Davies, N., Smith, M., & Steggles, P. (1999). Towards a better understanding of context and context-awareness. In Handheld and Ubiquitous Computing: First International Symposium, HUC’99 Karlsruhe, Germany, September 27–29, 1999 Proceedings 1 (pp. 304-307). Springer Berlin Heidelberg. (DOI): https://doi.org/10.22059/IMJ.2016.61711

-Jajodia, S., & van Tilborg, H. C. (Eds.). (2011). Encyclopedia of Cryptography and Security: L-Z. Springer. DOI: https://doi.org/10.1016/j.eswa.2019.06.039

-Mellouli, S., Luna-Reyes, L. F., & Zhang, J. (2014). Smart government, citizen participation and open data. Information Polity, 19(1-2), 1-4. doi.org/10.4102/sajems.v21i1.1717

-Kankanhalli, A., Charalabidis, Y., & Mellouli, S. (2019). IoT and AI for smart government: A research agenda. Government Information Quarterly, 36(2), 304-309. https://doi.org/10.1016/j.giq.2019.02.003

-United Nation E-Government Survey. Accessed: Jul. 2019. [Online]. Available: https://publicadministration.un.org/egovkb/en-us/Reports/UNE-Government-Survey-2018. https://doi.org/10.1016/j.giq.2019.02.003

-NEOM. Accessed: Aug. 2019. [Online]. Available: https://www.neom.com/

-Arab Digital Technologies for Development Report. Accessed: Jul. 2019. [Online]. Available: https://sdg.iisd.org/news/escwa-reviews-applicationof-digital-technologies-for-sdgs-in-arab-region/ https://doi.org/10.1016/j.giq.2019.02.003

-K. S. Soliman and J. F. Affisco, ‘‘E-government: A strategic operations management framework for service delivery,’’ Bus. Process Manage. J., to be published. DOI: https://doi.org/10.1108/14637150610643724

-Shi, B., Bai, X., & Yao, C. (2016). An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE transactions on pattern analysis and machine intelligence, 39(11), 2298-2304. DOI: https://doi.org/10.1108/14637150610643724

-Hussein, D. M. E. D. M. (2018). A survey on sentiment analysis challenges. Journal of King Saud University-Engineering Sciences, 30(4), 330-338. DOI: https://doi.org/10.1108/14637150610643724

-Arabic Products Reviews Dataset. Accessed: May 2019. [Online]. Available: https://github.com/hadyelsahar/large-arabic-sentimentanalysis-resouces. DOI: https://doi.org/10.1108/14637150610643724