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Recent Trends of Machine Learning Predictions using Open Data: A Systematic Review

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, particula...

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Main Authors: Ismail, Norismiza, Yusof, Umi Kalsom
Format: Article
Language:English
Published: Universiti Utara Malaysia Press 2022
Subjects:
QA75 Electronic computers. Computer science
Online Access:https://repo.uum.edu.my/id/eprint/28742/1/JICT%2021%2003%202022%20337-381.pdf
https://doi.org/10.32890/jict2022.21.3.3
https://repo.uum.edu.my/id/eprint/28742/
https://e-journal.uum.edu.my/index.php/jict/article/view/14433
https://doi.org/10.32890/jict2022.21.3.3
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spelling my.uum.repo.287422023-02-08T01:17:31Z https://repo.uum.edu.my/id/eprint/28742/ Recent Trends of Machine Learning Predictions using Open Data: A Systematic Review Ismail, Norismiza Yusof, Umi Kalsom QA75 Electronic computers. Computer science 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. Universiti Utara Malaysia Press 2022 Article PeerReviewed application/pdf en cc4_by https://repo.uum.edu.my/id/eprint/28742/1/JICT%2021%2003%202022%20337-381.pdf Ismail, Norismiza and Yusof, Umi Kalsom (2022) Recent Trends of Machine Learning Predictions using Open Data: A Systematic Review. Journal of Information and Communication Technology, 21 (03). pp. 337-381. ISSN 2180-3862 https://e-journal.uum.edu.my/index.php/jict/article/view/14433 https://doi.org/10.32890/jict2022.21.3.3 https://doi.org/10.32890/jict2022.21.3.3
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutional Repository
url_provider http://repo.uum.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Ismail, Norismiza
Yusof, Umi Kalsom
Recent Trends of Machine Learning Predictions using Open Data: A Systematic Review
description 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.
format Article
author Ismail, Norismiza
Yusof, Umi Kalsom
author_facet Ismail, Norismiza
Yusof, Umi Kalsom
author_sort Ismail, Norismiza
title Recent Trends of Machine Learning Predictions using Open Data: A Systematic Review
title_short Recent Trends of Machine Learning Predictions using Open Data: A Systematic Review
title_full Recent Trends of Machine Learning Predictions using Open Data: A Systematic Review
title_fullStr Recent Trends of Machine Learning Predictions using Open Data: A Systematic Review
title_full_unstemmed Recent Trends of Machine Learning Predictions using Open Data: A Systematic Review
title_sort recent trends of machine learning predictions using open data: a systematic review
publisher Universiti Utara Malaysia Press
publishDate 2022
url https://repo.uum.edu.my/id/eprint/28742/1/JICT%2021%2003%202022%20337-381.pdf
https://doi.org/10.32890/jict2022.21.3.3
https://repo.uum.edu.my/id/eprint/28742/
https://e-journal.uum.edu.my/index.php/jict/article/view/14433
https://doi.org/10.32890/jict2022.21.3.3
_version_ 1758580948370194432
score 13.211869

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