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Journal of Information and Optimization Sciences cover

Journal of Information and Optimization Sciences

(Since 1980)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Journal of Information and Optimization Sciences
Open Access Research Article

Semantic web empowered prediction of cervical cancer through machine learning on cloud platform using SemanticAutoMLCloud 

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pp. 1045–1057Vol. 46Issue 4-AMay 2025DOI: 10.47974/JIOS-1890XML
Received:
16 Oct 2024
Published Online:
31 May 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1890
Pages:
1045–1057

Abstract

Semantic web technologies and machine learning have shown great results in predictive analytics in many areas. Applying machine learning to semantic web data can solve use cases in various domains including the healthcare domain. With rapid growth of data on the web, there is a need for graph database solutions that can be used to store and process semantic web data. This paper proposes an algorithm SemanticAutoMLCloud that further demonstrates the ability with the proposed architecture to integrate semantic web technologies with machine learning using the Amazon SageMaker Autopilot on the AWS cloud platform, which uses Amazon Neptune as a graph database solution to store a large volume of semantic web data. Furthermore, Amazon SageMaker Autopilot training modes, including the Ensembling mode and Hyperparameter Optimization (HPO) mode, and different algorithms of these training modes have been applied to cervical cancer data extracted from the RDF dataset using SPARQL query processing. The XGBoost algorithm of HPO mode outperformed as compared to other algorithms with 99.5% accuracy in the classification of cervical cancer. The purpose of this research is to integrate the machine learning implementation over semantic web data on a cloud platform to solve diverse use cases. This paper proposes a novel architecture that can work on heavy datasets having billions of triples and can be used to process large amounts of data. This paper also illustrates the implementation details of the proposed architecture, which focuses on machine learning.

Keywords

Cloud computingAmazon neptuneMachine learning (ML)Semantic webRDFSPARQL

Subject Classifications

Primary 68P20Secondary 68T99

References

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