Saleh Akram Naife

Md Abrar Jahin, Saleh Akram Naife, Anik Kumar Saha et al.

Supply chain risk assessment (SCRA) is pivotal for ensuring resilience in increasingly complex global supply networks. While existing reviews have explored traditional methodologies, they often neglect emerging artificial intelligence (AI) and machine learning (ML) applications and mostly lack combined systematic and bibliometric analyses. This study addresses these gaps by integrating a systematic literature review with bibliometric analysis, examining 1,903 articles (2015-2025) from Google Scholar and Web of Science, with 54 studies selected through PRISMA guidelines. Our findings reveal that ML models, including Random Forest, XGBoost, and hybrid approaches, significantly enhance risk prediction accuracy and adaptability in post-pandemic contexts. The bibliometric analysis identifies key trends, influential authors, and institutional contributions, highlighting China and the United States as leading research hubs. Practical insights emphasize the integration of explainable AI (XAI) for transparent decision-making, real-time data utilization, and blockchain for traceability. The study underscores the necessity of dynamic strategies, interdisciplinary collaboration, and continuous model evaluation to address challenges such as data quality and interpretability. By synthesizing AI-driven methodologies with resilience frameworks, this review provides actionable guidance for optimizing supply chain risk management, fostering adaptability, and informing future research in evolving risk landscapes.

Md Abrar Jahin, Saleh Akram Naife, Fatema Tuj Johora Lima et al.

Domestic violence is commonly viewed as a gendered issue that primarily affects women, which tends to leave male victims largely overlooked. This study presents a novel, data-driven analysis of male domestic violence (MDV) in Bangladesh, highlighting the factors that influence it and addressing the challenges posed by a significant categorical imbalance of 5:1 and limited data availability. We collected data from nine major cities in Bangladesh and conducted exploratory data analysis (EDA) to understand the underlying dynamics. EDA revealed patterns such as the high prevalence of verbal abuse, the influence of financial dependency, and the role of familial and socio-economic factors in MDV. To predict and analyze MDV, we implemented 10 traditional machine learning (ML) models, three deep learning models, and two ensemble models, including stacking and hybrid approaches. We propose a stacking ensemble model with ANN and CatBoost as base classifiers and Logistic Regression as the meta-model, which demonstrated the best performance, achieving $95\%$ accuracy, a $99.29\%$ AUC, and balanced metrics across evaluation criteria. Model-specific feature importance analysis of the base classifiers identified key features influencing their decision-making. Model-agnostic explainable AI techniques, such as SHAP and LIME, provided both local and global insights into the decision-making processes of the proposed model, thereby increasing transparency and interpretability. Statistical validation using paired $t$-tests with 10-fold cross-validation and Bonferroni correction ($α= 0.0036$) confirmed the superior performance of our proposed model over alternatives. Our findings challenge the prevailing notion that domestic abuse primarily affects women, emphasizing the need for tailored interventions and support systems for male victims.