№2, 2024
The implementation of artificial intelligence has the scope to revolutionize e-government services by enhancing the quality of life for citizens, enhancing operational efficiency, and enabling groundbreaking applications. There is a possibility that this can be achieved through technical improvements. A wide range of artificial intelligence technologies and services are offered by Amazon Web Services, which has the potential to alter how e-government services are provided. The purpose of this abstract is to study the revolutionary potential of AI by utilizing AWS and to highlight the benefits and possibilities that it provides to the field of e-government. The utilization of artificial intelligence in conjunction with Amazon web services has the potential to significantly enhance the quality of e-government services by reducing inefficiencies, enabling the development of creative applications, and enabling the customization of experiences. With the assistance of the artificial intelligence tools and services offered by Amazon web services, the public sector can take advantage of the revolutionary power of artificial intelligence while also ensuring that responsible and ethical practices are followed (pp.71-81).
Alqudah, M. A. (2021a). Investment Artificial Intelligence in Decision-making Processes in the Jordanian Ministry of Interior. International Journal of Innovations in Engineering Research and Technology, 8(10), 40–53.
Alqudah, M. A. (2021b). Towards the governance of government data using artificial intelligence. Available at SSRN 3992303.
Alqudah, M. A., & Muradkhanli, L. (2021a). Artificial Intelligence in Electric Government; Ethical Challenges and Governance in Jordan. Electronic Research Journal of Social Sciences and Humanities, 3, 65–74.
Alqudah, M. A., & Muradkhanli, L. (2021b). E-government in Jordan and studying the extent of the e-government development index according to the United Nations report. International Journal of Multidisciplinary: Applied Business and Education Research, 2(4), 365–375.
Alqudah, M. A., Muradkhanli, L., Muradkhanli, Z., & Salameh, A. A. (2023). Using Artificial Intelligence Applications For E-Government Services As Iris Recognition. 2023 IEEE 17th International Conference on Application of Information and Communication Technologies (AICT), 1–7. https://doi.org/10.1109/AICT59525.2023.10313183
Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 2053951715622512.
Dubé, L., Du, P., McRae, C., Sharma, N., Jayaraman, S., & Nie, J.-Y. (2018). Convergent innovation in food through big data and artificial intelligence for societal-scale inclusive growth. Technology Innovation Management Review, 8(2).
Fei, J., & Wang, T. (2019). Adaptive fuzzy-neural-network based on RBFNN control for active power filter. International Journal of Machine Learning and Cybernetics, 10(5), 1139–1150.
Li, Y.-H., Huang, P.-J., & Juan, Y. (2019). An efficient and robust iris segmentation algorithm using deep learning. Mobile Information Systems, 2019.
Machine Learning and Artificial Intelligence - Amazon Web Services. (n.d.).
Marda, V. (2018). Artificial intelligence policy in India: a framework for engaging the limits of data-driven decision-making. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180087.
Mitrou, L. (2018). Data Protection, Artificial Intelligence and Cognitive Services: Is the General Data Protection Regulation (GDPR)‘Artificial Intelligence-Proof’? Artificial Intelligence and Cognitive Services: Is the General Data Protection Regulation (GDPR)‘Artificial Intelligence-Proof.
Phung, V. H., & Rhee, E. J. (2019). A high-accuracy model average ensemble of convolutional neural networks for classification of cloud image patches on small datasets. Applied Sciences, 9(21), 4500.
Salamah, A. A., Hassan, S., Aljaafreh, A., Zabadi, W. A., AlQudah, M. A., Hayat, N., Al Mamun, A., & Kanesan, T. (2022). Customer retention through service quality and satisfaction: using hybrid SEM-neural network analysis approach. Heliyon, 8(9), e10570.
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117.
Silva Araújo, V. J., Guimarães, A. J., de Campos Souza, P. V., Rezende, T. S., & Araújo, V. S. (2019). Using resistin, glucose, age and BMI and pruning fuzzy neural network for the construction of expert systems in the prediction of breast cancer. Machine Learning and Knowledge Extraction, 1(1), 466–482.
Sun, T. Q., & Medaglia, R. (2019). Mapping the challenges of Artificial Intelligence in the public sector: Evidence from public healthcare. Government Information Quarterly, 36(2), 368–383.
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.
Wong, C., Guo, Z. X., & Leung, S. Y. S. (2013). Optimizing decision making in the apparel supply chain using artificial intelligence (AI): from production to retail. Elsevier.
Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. ArXiv Preprint ArXiv:1409.1556.
.jpg)
