A Review Study on Machine Learning Approaches on Coronavirus Big Data
| المصدر: | مجلة كلية المأمون |
|---|---|
| الناشر: | كلية المأمون الجامعة |
| المؤلف الرئيسي: | Shanshool, Abeer M. (Author) |
| مؤلفين آخرين: | Ahmed H. Salman (Co-Author), Hamad, Amaal Ghazi (Co-Author), Saeed, Enas Mohammed Hussein (Co-Author) |
| المجلد/العدد: | ع37 |
| محكمة: | نعم |
| الدولة: |
العراق |
| التاريخ الميلادي: |
2022
|
| الصفحات: | 431 - 459 |
| DOI: |
10.36458/1253-000-037-016 |
| ISSN: |
1992-4453 |
| رقم MD: | 1315447 |
| نوع المحتوى: | بحوث ومقالات |
| اللغة: | الإنجليزية |
| قواعد المعلومات: | EcoLink, IslamicInfo, HumanIndex |
| مواضيع: | |
| رابط المحتوى: |
الناشر لهذه المادة لم يسمح بإتاحتها. |
| المستخلص: |
Information has the ability for protecting against unexpected events and controlling crises like CoronaVirus Disease COVID-19 pandemic. Because this pandemic has spread so quickly worldwide, only technology-driven management of data could give reliable information in order to help handle the situation. The goal of this research is to look at the potential of the technologies that are related to big data to control and regulate the transmission of COVID-19. To collect the important aspects, a systematic review was conducted using Preferred Reporting Items for Systematic Reviews and Meta-Analyses PRISMA criteria. The thirty-two most relevant documents for the qualitative analyses have been indicated in the present work. This research also identifies 10 potential data sources and 8 essential big data applications for studying virus infection trends, virus associations, transmission patterns, and differences of genetic modifications. Also, it looks at some of the drawbacks of big data, such as privacy concerns, unethical data use, and data exploitation. The research's results will offer fresh information to administrators and policymakers, allowing them to establish data-driven strategies for addressing and managing the COVID-19 epidemic. |
|---|---|
| ISSN: |
1992-4453 |
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