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.


INTRODUCTION
Articial Intelligence (AI) has been around for some decades in several theoretical forms and complicated systems; however, only recent advances in computational powers and big data have enabled AI to achieve outstanding results in an ever-growing number of domains.For example, AI have tremendously advanced the areas of computer vision [1], medical applications [2], natural language processing [3], reinforcement learning [4], and several other domains.AI can be defined as the ability of a computer to imitate the intelligence of human behavior while improving its own performance.AI is not only robotics, rather an intelligent behavior of an autonomous machine that describes the brain of the machine and not its body; it can drive a car, play a game, and perform diverse sophisticated jobs.AI is a _eld that falls at the intersections of several other domains, including Machine Learning [5], Deep Learning [6], Natural Languages Processing [3], Context Awareness [7], and Data Security and Privacy [8]. Figure 1 illustrates the intersections and relationship of the AI _eld with related _elds.
Machine Learning (ML) is the ability of an algorithm to learn from prior data in order to produce a smart behavior and make correct decisions in various situations that it hasnever faced before.ML algorithms are enabled by training a computational model, which is the process of exposing an algorithm to a large dataset (e.g., citizens' demographics) in order to predict future behaviors (e.g., employment rates).The process of learning from prior datasets is known as a supervised learning.Unlike traditional ML algorithms, Deep Learning, a subfield of ML, has emerged to outcome the limitations of prior ML algorithms.Deep learning can be de_ned as a mapping function that maps raw input data (e.g., a medical image) to the desired output (e.g., diagnosis) by minimizing a loss function using some optimization approach, such as stochastic gradient descent (SGD) [9].Deep learning algorithms, inspired by the neural networks in the human brain, are built with a large number of hierarchical articial neural networks that map the raw input data (inserted at the input layer) to the desired output (produced at the output layer) through a large number of layers (known as hidden layers), and thus the name deep learning.The hidden layers are responsible for the actual mapping process, which is a series of simple but nonlinear mathematical operations (i.e., a dot product followed by a nonlinear process).The main advantage of deep learning is that it does not require feature engineering.Despite the fact that deep learning has improved the state-of-art results in several domains, it is still evident that e-government applications face several challenges regarding adapting deep learning [10].First, given the recent and rapid advances in the deep learning domain, it is becoming more dif_cult to _nd experts of this technology who are capable of developing ef_cient and reliable AI applications, especially in third world countries.Second, the development lifecycle of AI projects, specially deep learning, has introduced a new set of development challenges.In particular, traditional software development focuses on meeting a set of required functional and non-functional requirements; in contract, deep learning development focuses on optimizing a speci_c metric based on a large set of parameters, which is done in a unsystematic search approach.Third, integrating AI and deep learning applications in e-government services requires strong policies and measures on data security and privacy.However, there are still challenges that hinder the creation of concrete standards for data security and privacy, including citizen-government trust, transparency, and other technical dif_culties related to developing and implementing secure systems.

II.
EXISTING SYSTEM Recently, many countries have adopted egovernment services in various departments and many autonomous applications .While there are several studies conducted for enhancing egovernment services, only a few of them address utilizing recent advances in AI and deep learning in the automation of e-government services.Therefore, there is still an urgent need to utilize state-ofthe-art AI techniques and algorithms to address e-government challenges and needs.In contrast, implementing egovernment applications still faces several challenges, including the following: Trust: trusting online services depends heavily on a couple of factors including, the citizens trust in the government itself, the quality of the online services, and the personal believes (e.g., there still a large number of citizens who prefer to handle paper applications rather than web services).Lack of experts: implementing highquality online services requires the establishment of the right team of experts that covers all involved practice areas from web development to security and privacy.Inaccessibility: several third world countries still face significant issues on accessing the internet and its services.Security: state-of-the-art security measures are required to secure egovernment applications and the citizen's privacy. III.

PROPOSED SYSTEM In this paper author describing concept to automate government services with Artificial Intelligence technology such as Deep Learning algorithm called Convolution Neural Networks (CNN).
Government can introduce new schemes on internet and peoples can read news and notifications of such schemes and then peoples can write opinion about such schemes and this opinions can help government in taking better decisions.To detect public opinions about schemes automatically we need to have software like human brains which can easily understand the opinion which peoples are writing is in favour of positive or negative.To build such automated opinion detection author is suggesting to build CNN model which can work like human brains.This CNN model can be generated for any services and we can make it to work like automated decision making without any human interactions.To suggest this technique author already describing concept to implement multiple models in which one model can detect or recognize human hand written digits and second model can detect sentiment from text sentences which can be given by human about government schemes.In our extension model we added anothermodel which can detect sentiment from person face image.Person face expressions can describe sentiments better than words or sentences.So our extension work can predict sentiments from person face images.

V. IMPLEMENTATION USER:
Generate In this paper, we introduced the definitions of artificial intelligence and e-government, briey discussed the current state of e-government indices around the world, and then proposed our solutions to advance the current state of egovernment, considering the Gulf Countries as a case study.We proposed a framework for management of government information resources that help manage the e-government lifecycle end-to-end.Then, we proposed a set of deep learning techniques that can help facilitate and automate several e-government services.
After that, we proposed a smart platform for AI development and implementation in egovernment.
The overarching goal of this paper is to introduce new frameworks and platform to integrate recent advances in AI techniques in the e-government systems and services to improve the overall trust, transparency, and eficiency of e-government.