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p-Index From 2021 - 2026
0.444
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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT)
Hlomani, Hlomani
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Computer Science & IT Engineering

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Interoperability in healthcare: a critical review of ontology approaches and tools for building prescription frameworks Okon, Eunice Chinatu; Sigwele, Tshiamo; Galani, Malatsi; Mokgetse, Tshepiso; Hlomani, Hlomani
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i2.pp366-381

Abstract

Efficient healthcare interoperability is pivotal for delivering high-quality patient care. This research article presents a critical review of ontology based approaches and tools in the development of ontology-based electronic prescriptions (e-prescription), with a focus on enhancing healthcare interoperability. The investigation encompasses two major domains: ontology overview and healthcare interoperability using semantic e-prescription. In the ontology overview, we scrutinize various aspects of ontology development, including the methodologies, languages, tools, and evaluation metrics adopted from literature. Notable comparisons between ontologies and databases are explored. Additionally, we delve into the challenges associated with ontology development and provide a comprehensive summary of methodologies, languages, tools, and evaluation approaches. Healthcare interoperability using semantic e-prescription undertakes a detailed review of e-prescription systems, emphasizing their critical role in healthcare interoperability. A thorough examination of frameworks facilitating semantic e-prescription is presented, offering a nuanced perspective on their contributions and limitations. The section concludes with a concise summary of the key findings from the e-prescription framework review. The article further addresses challenges in healthcare interoperability, including data standardization and system integration issues. To direct continuing research efforts that integrate cutting-edge technologies and interdisciplinary collaborations, future directions and emerging trends are outlined.
Securing cloud data with machine learning: trends, gaps, and performance metrics Ifeoluwa Omogbehin, Blessing; Sigwele, Tshiamo; Semong, Thabo; Maenge, Aone; Nedev, Zhivko; Hlomani, Hlomani
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp44-55

Abstract

The increasing reliance on cloud computing has raised significant concerns about the security of data access control, as traditional models are insufficient in managing the dynamic and large-scale nature of cloud environments. This review evaluates machine learning (ML)-based approaches to improve cloud data security, with a particular focus on advancements in anomaly detection and insider threat prevention. Deep learning (DL) models emerge as the most dominant, utilized by 47% of the studies due to their superior ability to process large datasets and adapt to real-time environments. Random forest models are also prominent, being adopted in 20% of the studies for their strong performance in anomaly detection and categorization. TensorFlow stands out as the most widely used tool, featuring in nearly 37% of the reviewed works, while datasets like Amazon Access and computer emergency response team (CERT) are employed in 20% and 13% of the research, respectively. Anomaly detection and prevention are critical priorities, accounting for 41.2% of the research objectives. However, gaps remain, with 21.7% of the studies noting adversarial vulnerabilities and 13% identifying limitations in dataset diversity. The review recommends further development of ML models to address these challenges, expanding dataset diversity, and improving real-time monitoring techniques to enhance cloud data security.
Co-Authors Galani, Malatsi Ifeoluwa Omogbehin, Blessing Maenge, Aone Mokgetse, Tshepiso Nedev, Zhivko Okon, Eunice Chinatu Semong, Thabo Sigwele, Tshiamo