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Bala Modi

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Open access 2026

Integrated Security System Framework Using Blockchain and Digital Forensics Technology

The proliferation of cybersecurity incidents and large-scale data breaches has exposed significant weaknesses in traditional security systems, particularly regarding data integrity, transparency, chain of custody, and forensic readiness. Centralised architectures suffer from single points of failure, log-tampering risks and limited cross-organisational trust. This paper designed, developed and evaluated an Integrated Security System Framework combining blockchain-style integrity mechanisms with digital forensics methodology. It was implemented as a four-layer system: a React presentation layer, two Node.js/Express API services, a Python-based business logic layer, and a data layer that includes an Ethereum-compatible (Hardhat) deployment path together with off-chain filesystem storage. The evaluated core comprises a custom Python blockchain (SHA-256 Proof-of-Work) with Python classes providing contract-style policy enforcement and audit logging. A parallel suite of four Solidity smart contracts was also implemented, but those contracts were not exercised when producing the quantitative results reported here; that distinction is stated explicitly throughout. The design was evaluated by executing the public implementation directly and repeatedly against a functional correctness workload of 100 events and up to 30 evidence items across five independent runs. Six further security and stress tests examined varied file sizes, concurrent access, malformed transactions, unauthorised access, direct tampering and off-chain tampering. These tests located and fixed three real defects, most significantly a concurrency defect that corrupted the blockchain under twenty simultaneous writers; after the fix, chain validity held under 500 concurrent writes. The evaluation also surfaced an unresolved limitation: no identity-authentication mechanism is implemented, so accountability guarantees depend on honest caller identity claims. All evaluation categories scored above 90 percent on the framework's disclosed self-referential scoring formula, and forensic risk-scoring, audit logging, custody tracking and tampering detection functioned as designed within their tested scope. The findings support the feasibility of integrating blockchain-style integrity with digital forensics for evidence management under controlled evaluation conditions, and they underline the necessity of repeated, adversarially minded testing before stronger operational claims are made.

Kawu Saidu Bappah, Bala Modi, Umar Abdullahi · International journal of re... · 0 citations

Review Open access 2026

An Explainable Stacking Ensemble Model for Predicting Childhood Malnutrition among Under-Five Children in Nigeria

Childhood malnutrition remains a major public health challenge in Nigeria, contributing to child morbidity, mortality, impaired cognitive development, and poor long-term socioeconomic outcomes. Although machine learning has shown promise for malnutrition prediction, many models operate as black boxes, limiting their interpretability and adoption in clinical and public health decision-making. This study developed an explainable stacking ensemble model for classifying and predicting childhood malnutrition among children Under-five in Nigeria, using the 2023–24 Nigeria Demographic and Health Survey (NDHS). Data from 9,374 children with valid anthropometric measurements were analysed, with malnutrition status determined using WHO Child Growth Standards based on height-for-age, weight-for-age, and weight-for-height z-scores. Following preprocessing and a multi-method, multi-phenotype feature selection process combining Recursive Feature Elimination, Mutual Information, and Random Forest Feature Importance, twenty-two socioeconomic, demographic, geographic, maternal, and child-health variables were retained for modelling. A stacking ensemble comprising Random Forest, XGBoost, LightGBM, CatBoost, and Support Vector Machine as base learners, with Logistic Regression as meta-learner, was developed using mother-level stratified group cross-validation to prevent clustering-related data leakage. Within-fold SMOTE addressed class imbalance, and Youden's J statistic guided threshold optimisation. Model interpretability was achieved using SHAP, providing both global and individual-level explanations. The stacking ensemble outperformed all individual base learners, achieving a ROC-AUC of 0.750, F1-score of 0.662, recall of 72.6%, precision of 61.0%, and accuracy of 69.9% at the optimal threshold of 0.40. SHAP analysis identified household wealth index, maternal education, child age, and geographic location as the most influential predictors of malnutrition risk. These findings demonstrate that integrating ensemble learning with explainable artificial intelligence provides an accurate, transparent, and practical decision-support framework for early identification of at-risk children, supporting evidence-based nutrition interventions and public health policy in Nigeria.

Z. Ahmed, Bala Modi, Ali Ahmad Aminu et al. · International journal of re... · 0 citations