Lucky Oghenechodja Daniel

This chapter explores the proliferation of digital identity systems across Africa, highlighting both their potential to enhance social inclusion and administrative efficiency and their risks to privacy and human rights. It investigates the tension between national security imperatives and the protection of fundamental legal rights, employing a qualitative, descriptive methodology informed by policy documents, academic literature, and case studies from Nigeria, Kenya, and South Africa. The analysis demonstrates that while legal frameworks for data protection are increasingly present, their effectiveness is frequently compromised by national security priorities, infrastructural limitations, and digital literacy gaps. The chapter contends that technocratic or purely legalistic approaches are inadequate and proposes a human-rights-by-design framework emphasizing public trust, ethical AI governance, and gender-inclusive considerations as essential foundations for sustainable policy.

Climate change poses a significant threat to coastal groundwater resources along the African coast, with rising sea levels and shifting precipitation patterns increasing the salinity of vital aquifers. This research investigates the socio-economic and environmental impacts of groundwater salinity through predictive modelling, highlighting its implications on agriculture, freshwater availability, and economic stability. Salinity intrusion, exacerbated by climate variability and anthropogenic activities, jeopardizes food security and public health, amplifying the vulnerability of dependent communities. Through a multidisciplinary approach combining hydrogeological analysis, remote sensing, and economic impact assessments, this study aims to provide actionable insights for sustainable water resource management. By prioritizing resilience strategies tailored to regional conditions, policy-makers can mitigate adverse effects and safeguard livelihoods in these climate-sensitive zones.

This paper investigates an optimized approach to coordinated resource allocation and power control for wireless communication networks. To address limitations in existing methods, such as scalability and fairness, a novel algorithm is implemented that leverages federated learning regression-based decision trees to achieve efficient resource allocation and improved network performance. The system model considers a dense communication network with different user equipment types and incorporates constraints like power limitations and quality of service requirements. Simulation results demonstrate that the implemented approach significantly outperforms existing techniques in terms of throughput, energy efficiency, and latency.

Wind offers an environmentally sustainable energy resource that has seen increasing global adoption in recent years. However, its intermittent, unstable and stochastic nature hampers its representation among other renewable energy sources. This work addresses the forecasting of wind speed, a primary input needed for wind energy generation, using data obtained from the South African Wind Atlas Project. Forecasting is carried out on a two days ahead time horizon. We investigate the predictive performance of artificial neural networks (ANN) trained with Bayesian regularisation, decision trees based stochastic gradient boosting (SGB) and generalised additive models (GAMs). The results of the comparative analysis suggest that ANN displays superior predictive performance based on root mean square error (RMSE). In contrast, SGB shows outperformance in terms of mean average error (MAE) and the related mean average percentage error (MAPE). A further comparison of two forecast combination methods involving the linear and additive quantile regression averaging show the latter forecast combination method as yielding lower prediction accuracy. The additive quantile regression averaging based prediction intervals also show outperformance in terms of validity, reliability, quality and accuracy. Interval combination methods show the median method as better than its pure average counterpart. Point forecasts combination and interval forecasting methods are found to improve forecast performance.