Software defect prediction is essential for maintaining code quality in critical domains, yet it remains challenging due to feature redundancy and class imbalance. This study proposes an optimized Pi–Sigma Neural Network (PSNN) framework leveraging Correlation-Based Feature Selection (CBFS) and Min-Max normalization. Utilizing the NASA PROMISE CM1 dataset, a 5-fold stratified cross-validation pipeline was implemented to ensure statistical robustness and prevent data leakage. Experimental results on the CM1 dataset show the refined PSNN achieves high performance (99.80% accuracy on CM1) after aggressive feature reduction to 3–5 features and a precision of 1.000. To address class imbalance, the model achieved a Matthews Correlation Coefficient (MCC) of 0.988 and a G-Mean of 0.990. Comparative analysis shows that the developed PSNN-FS, despite its simplicity, achieves strong performance competitive with more complex architectures on the CM1 dataset.
Barka Piyinkir Ndahi, O. Abisoye, O. Ojerinde et al.· Bulletin of the National Res...· 0 citations
In Nigeria's oil and gas industry, the problems of inconsistent reporting, lack of inter-agency collaboration, and untrustworthy state institutions continue to persist. This paper analyses the effects of blockchain technology on strengthening the processes of reporting and auditing petroleum production, given the current institutional settings in Nigeria. This analysis does not consider blockchain technology to be the best, or the only option. Rather, the focus is on the extent to which the technology can help provide more reliable audit trails, improve real-time inter-agency communication, and build structures to support the verification and authentication of records. The analysis also explains the institutional and technological frameworks within which the proposed improvements can be realized. This research contributes to the field by managing expectations on the contribution of blockchain technology to governance improvements within the existing socio-institutional structures.
O. Ojerinde, J. Mamman· International Journal of Bus...· 0 citations
