Research

Research & publications

Peer-reviewed and working-paper research across social welfare, financial inclusion, and the use of Artificial Intelligence (AI) and Machine Learning (ML) in agriculture and education.

Published & peer-reviewed

Journal publication

Peer-reviewed · 2026 Development · Springer Nature

Extended Family System and Welfare Care in Post-COVID Nigeria: A Case Study of Warri, Nigeria

This study examines extended family welfare structures in post-COVID Nigeria, focusing on intergenerational care patterns, economic stress, and household resilience.

Journal: Development (Springer Nature, Society for International Development)
Authors: Chimezie Anajama, Warrence Oghenevwegba, Ojonugwa Wada
DOI: 10.1057/s41301-026-00494-6

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In progress

Working paper

Working paper · In progress

Predicting Digital Payment Adoption Across Economies: A Machine Learning Framework with Explainable AI

This study develops a machine learning framework to predict digital payment adoption across diverse economies using the World Bank Global Findex dataset, with explainable AI techniques used to identify key socio-economic drivers of adoption.

Focus: ML, Explainable AI, Financial Inclusion
Methods: Classification models with SHAP-based interpretability

Other publications

Self-archived on Zenodo

2023 AgriTech

Revolutionizing AgriTech: Integrating Automated Machinery for Sustainable Farming Practices

This study explores how mechanized farming can sustainably transform rural agriculture in Nigeria, with emphasis on productivity, profitability, and sustainable farming practice.

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2025 AI · Remote sensing

Transforming Agricultural Pest Management: The Effect of Artificial Intelligence and Remote Sensing

This research examines how AI and remote sensing technologies support precision pest management, including early detection, predictive analytics, and sustainable agricultural decision-making.

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2025 ML · Education

Design and Development of a Decision Support System for Predicting Student Academic Performance

This study applies ML to predict student outcomes and support academic decision-making through an interactive prediction system.

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