Skip to content
Log in
Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

Author

Prema Kirubakaran

View on Semantic Scholar

5 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access Aug 2026

Prediction of Geriatric Patients Readmission Using XGBoost

Hospital readmission among geriatric patients is a critical indicator of healthcare quality, imposing significant burdens on both patients and healthcare systems. This study proposes an Extreme Gradient Boosting (XGBoost)-based predictive framework for identifying 30-day hospital readmission risk in patients aged 65 ye...

Aisha Almustapha, Prema A. Kirubakaran, Ridwan Koladapo et al. · 0 citations
Review Open access Sep 2026

Machine Learning Techniques for Intrusion Detection Systems in 5G and Beyond Networks: A Systematic Literature Review

The paper concludes that no single model is universally best; rather, technique selection should depend on the type of attack, the quality of validation, the amount of latency, the scalability, privacy, and explainability, to determine the most appropriate approach.

Elijah Abayomi Olaniyi, T. Atoyebi, Ridwan Kolapo et al. · 0 citations
Review Open access Sep 2026

Machine Learning-Based Enterprise Security Information and Event Management Systems: A Systematic Literature Review

-Large firms utilize Security Information and Event Management (SIEM) systems that allow them to gather, normalize, correlate, and analyze security events that, in turn, come from endpoints, networks, identity platforms, cloud services, databases, and applications. Even if traditional rule-based SIEM is indeed useful f...

Joshua Ahuose Omoighe, T. Atoyebi, Ridwan Kolapo et al. · 0 citations
Review Open access Sep 2026

Deep Learning for Image Steganalysis: A Structured Comparative Review of Dataset, Domain, and Robustness Coverage

Closing structural gaps in deep learning steganalysis requires a unified architecture combining multi-source training, dual-domain coverage, and pixel-level adversarial testing, representing an essential open challenge for practical deployment.

Asma'u Shehu, Prema A. Kirubakaran, Bilkisu L. Muhammad-Bello et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.