RECENT TRENDS OF MACHINE LEARNING PREDICTIONS USING OPEN DATA: A SYSTEMATIC REVIEW

Abstract

Machine learning (ML) prediction determinants based on open data (OD) are investigated in this work, which is accomplished by examining current research trends over ten years. Currently, OD is commonly regarded as the most crucial trend for users to improve their ability to make decisions, particularly to the exponential expansion of social networking sites (SNSs) and open government data (OGD).The purpose of this study was to examine if there was an increase in the usage of OD in ML prediction techniques by conducting a systematic literature review (SLR) of the results of the trends. The papers published in major online scientific databases between 2011 and 2020, including ScienceDirect, Scopus, IEEE Xplore, ACM, and Springer, were identified and analysed. After various selection and Springer, were identified and analysed. After various selection processes, according to SLR based on precise inclusion and exclusion criteria, a total of 302 articles were located. However, only 81 of them were included. The findings were presented and plotted based on the research questions (RQs). In conclusion, this research could be beneficial to organisations, practitioners, and researchers by providing information on current trends in the implementation of ML prediction using OD setting by mapping studies based on the RQs designed, the most recent growth, and the necessity for future research based on the findings.