Research

Adaptive intelligence, applied where it has to hold up

My research centres on machine-learning systems that keep working after the world they were trained on has moved — in financial fraud detection, biometric identity security, healthcare prediction, and educational AI.

Areas of Focus

Research interests

Adaptive Machine Learning & Concept Drift
Financial Fraud Detection & AML Systems
Predictive Analytics & Forecasting
AI for Healthcare & Biomedical Applications
Deep Learning & Neural Architecture Design
Identity Verification & Biometric Security
Big Data Systems & Distributed Computing
Sustainable & Responsible AI
Precision Agriculture & Smart Systems
Educational Data Mining & Learning Analytics

Browse by Category

Explore publications by research area

Adaptive Machine Learning AI for Healthcare AI in Education Big Data Systems Biometric & Identity Security Concept Drift Financial AI Fraud Detection & AML Precision Agriculture Predictive Analytics Responsible & Sustainable AI

Featured Work

Selected publications

View all publications

2024Fraud Detection & AML

Strategies for Combating Synthetic Identity Fraud: The Role of Machine Learning and Behavioural Analysis in Enhancing Financial Ecosystem Security

Okunola Orogun, L. Ogungbe, N. Adegboye, T. Adetuyi, S. Alabi

International Journal of Research in Engineering and Science (IJRES)

Examines machine-learning and behavioural-analysis strategies for detecting synthetic identity fraud and strengthening the security of financial ecosystems.

2024AI in Education

Advancing Educational Equity through Sustainable AI Deployment: Strategies and Innovations for the United Kingdom

Okunola Orogun, L. Ogungbe, A. Ajani, N. Adegboye, O. Ogunsola

European Journal of Contemporary Education and E-Learning

Explores strategies for deploying AI sustainably and equitably within UK higher education, connecting responsible-AI principles to educational-equity outcomes.

The Design of Ensemble Deep Learning Model for the Prediction of Lassa Fever Outbreak using Multiple Data Sources

S.A. Oluwadare, I.P. Adegun, Okunola Orogun

International Journal of Research in Engineering and Science (IJRES)

Presents an ensemble deep-learning model that integrates multiple epidemiological data sources to predict Lassa fever outbreaks, developed under a TETFund National Research Fund grant.