Big Data in E-government: Classification and Prediction using Machine Learning Algorithms

Abstract

Many countries have used big data to develop their institutions, such as Estonia in policing, India in health care, and the development of agriculture in the United Kingdom, etc. Data is very important as it is no longer oil that is the most valuable resource in the world, but data. This research examines ways to develop the Iraqi state institutions by using the big data of one of its institutions (electronic civil registry) (ECR) using the mining and analysis of this data. The pre-processing and analysis of this data are carried out depending on the needs of each institution and then using Machine Learning (ML) techniques. Its use has shown remarkable results in many areas, especially in data analysis, classification and forecasting. We applied five ML algorithms that are Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN), Random Forest (RF), and Naive Bayes (NB) carried out in the Orange Data Mining tool. According to the simulation results of the proposed system, the accuracy of the classifications was around 100%, 99%, and 100% for the military department by the SVM classifier, the social welfare department by the RF classifier, and the statistics-planning department by the SVM classifier, respectively.

Webpage Classification Using Ensemble Machine Learning

“…A machine learning model's ability to predict is based on the given data in many domains like the huge growth in social media, and the massive number of users has lured attackers to distribute harmful content through fake accounts [16], and governments started to use webpages for better government services delivered to citizens because of these e-services. [17] algorithms in machine learning [18] split into four categories according to the kind of input data and predicted outputs:…”