Automating E-Government Services With Artificial Intelligence

Artificial Intelligence (AI) has recently advanced the state-of-art results in an ever-growing number of domains. However, it still faces several challenges that hinder its deployment in the e-government applications–both for improving the e-government systems and the e-government-citizens interactions. In this paper, we address the challenges of e-government systems and propose a framework that utilizes AI technologies to automate and facilitate e-government services. Specifically, we first outline a framework for the management of e-government information resources. Second, we develop a set of deep learning models that aim to automate several e-government services. Third, we propose a smart e-government platform architecture that supports the development and implementation of AI applications of e-government. Our overarching goal is to utilize trustworthy AI techniques in advancing the current state of e-government services in order to minimize processing times, reduce costs, and improve citizens’ satisfaction.

[1]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[2]  A. Ho Reinventing Local Governments and the E‐Government Initiative , 2002 .

[3]  John F. Affisco,et al.  E-government: a strategic operations management framework for service delivery , 2006, Bus. Process. Manag. J..

[4]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Gregory D. Abowd,et al.  Towards a Better Understanding of Context and Context-Awareness , 1999, HUC.

[6]  Xiang Bai,et al.  An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Gülçin Büyüközkan,et al.  Analysis of e-Government Strategies with Hesitant Fuzzy Linguistic Multi-Criteria Decision Making Techniques , 2019, Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making.

[8]  Ricardo Santa,et al.  The role of trust in e-Government effectiveness, operational effectiveness and user satisfaction: Lessons from Saudi Arabia in e-G2B , 2019, Gov. Inf. Q..

[9]  Hong Chen,et al.  Seven-layer deep neural network based on sparse autoencoder for voxelwise detection of cerebral microbleed , 2017, Multimedia Tools and Applications.

[10]  Hans J Schnoll E-Government: Information, Technology, and Transformation , 2014 .

[11]  Yannis Charalabidis,et al.  IoT and AI for Smart Government: A Research Agenda , 2019, Gov. Inf. Q..

[12]  Mete Yildiz,et al.  E-government research: Reviewing the literature, limitations, and ways forward , 2007, Gov. Inf. Q..

[13]  Radford M. Neal Pattern Recognition and Machine Learning , 2007, Technometrics.

[14]  Subhashini Venugopalan,et al.  Translating Videos to Natural Language Using Deep Recurrent Neural Networks , 2014, NAACL.

[15]  Rajash Rawal,et al.  Understanding e-government in Europe : issues and challenges , 2009 .

[16]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[17]  Léon Bottou,et al.  Large-Scale Machine Learning with Stochastic Gradient Descent , 2010, COMPSTAT.

[18]  Erik Brynjolfsson,et al.  Big data: the management revolution. , 2012, Harvard business review.

[19]  Jean Damascène Mazimpaka,et al.  The public value of E-Government - A literature review , 2019, Gov. Inf. Q..

[20]  Doaa Mohey El Din Mohamed Hussein,et al.  A survey on sentiment analysis challenges , 2016, Journal of King Saud University - Engineering Sciences.

[21]  Cynthia Dwork,et al.  Differential Privacy , 2006, ICALP.

[22]  Gregory A. Porumbescu,et al.  Engendering inclusive e-government use through citizen IT training programs , 2019, Gov. Inf. Q..

[23]  Miftachul Huda,et al.  Tactical Steps for e-Government Development , 2018 .