Comparative Analysis of AI and Machine Learning Applications in Modern Database

Using artificial intelligence (AI) and machine learning (ML) in contemporary database systems is the topic of discussion in this article. It examines a variety of scientific publications that investigate the breakthroughs that have been generated by artificial intelligence in a variety of fields, including agriculture, healthcare, military, and cloud computing. In this assessment, the transformational potential of artificial intelligence is highlighted within the context of improving data management, predictive analytics, decision-making, and automation. In addition, the debate discusses important obstacles, such as concerns about ethical issues, scalability issues, computing needs, and data security. The objective of this in-depth assessment is to provide insights into the continuous progress of AI-powered database systems as well as the consequences that these systems will have in the future.

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Publication Details

Journal
Engineering and Technology Journal
Published
2025-04-05
DOI
https://doi.org/10.47191/etj/v10i03.21
Primary Topic
Data Mining Algorithms and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Comparative Analysis of AI and Machine Learning Applications in Modern Database

Hajar Maseeh Yasin, Redeer Avdal Saleh
Engineering and Technology Journal
Data Mining Algorithms and Applications
article

Comparative Analysis of AI and Machine Learning Applications in Modern Database

Hajar Maseeh Yasin, Redeer Avdal Saleh
article en

Abstract

Using artificial intelligence (AI) and machine learning (ML) in contemporary database systems is the topic of discussion in this article. It examines a variety of scientific publications that investigate the breakthroughs that have been generated by artificial intelligence in a variety of fields, including agriculture, healthcare, military, and cloud computing. In this assessment, the transformational potential of artificial intelligence is highlighted within the context of improving data management, predictive analytics, decision-making, and automation. In addition, the debate discusses important obstacles, such as concerns about ethical issues, scalability issues, computing needs, and data security. The objective of this in-depth assessment is to provide insights into the continuous progress of AI-powered database systems as well as the consequences that these systems will have in the future.

Engineering and Technology JournalVol. 10(03)
Chengdu University of Information Technology (CN)
Openalex Percentile: Top 92%
Data Mining Algorithms and Applications
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Comparative Analysis of AI and Machine Learning Applications in Modern Database — Hajar Maseeh Yasin, Redeer Avdal Saleh · Engineering and Technology Journal (2025) | TGRS Research Map | TGRS