This study examines the impact of artificial intelligence (AI)-driven insights on sustainable Human Resource Management (HRM) performance in higher education institutions. It explores the mediating roles of HRM practices optimization and decision-making enhancement, as well as the moderating effects of organizational culture, AI adoption readiness, and ethical considerations. By incorporating innovative insights into artificial intelligence technologies, this research contributes to career and HRM literature by revealing how AI transforms workforce dynamics in academic institutions. Data was collected via a quantitative survey from 215 participants, including administrators, AI and data science experts, data analysts, human resource management professionals, institutional leaders, IT staff, and policymakers from higher education institutions in the UAE. Using non-probability quota sampling. Data analysis was conducted through descriptive statistics, t-tests, correlation analysis, and structural equation modeling (SEM) using Partial Least Squares SEM (PLS-SEM) in SmartPLS software. Findings confirm that AI-driven insights significantly enhance sustainable HRM performance. Both HRM practices optimization and decision-making enhancement mediate this relationship. While ethical considerations, organizational culture, and AI adoption readiness directly influence HRM sustainability, they do not significantly moderate AI’s impact, suggesting that individual engagement with AI technologies plays a more pivotal role than broader organizational factors. This research uniquely integrates the Resource-Based View (RBV) and Technology Acceptance Model (TAM) to examine AI adoption in HRM within higher education—an area previously underexplored. It underscores the importance of HRM optimization and decision-making in maximizing AI’s benefits. The findings offer strategic insights for higher education institutions seeking to enhance HRM sustainability through effective AI integration.

The utilization of blockchain technology is increasingly emerging as a catalyst for significant changes across multiple industries, including the domain of Islamic finance. This study examines the influence of blockchain technology on the factors that contribute to the successful adoption of blockchain in Islamic banks located in the United Arab Emirates (UAE). The present study employs a cross-sectional survey methodology, encompassing a sample of 344 banking professionals. The investigation utilizes Partial Least Squares Structural Equation Modeling (PLS-SEM) as a statistical technique to examine the association between several crucial variables, namely Trust, Financial Transfers, Operating Expenses, Safety and Security, and the effective implementation of blockchain technology. The results indicate that these variables have a major impact on the effectiveness of implementing blockchain technology, confirming its ability to boost the efficiency of transactions, decrease expenses, and enhance security while adhering to Shariah law. This work makes a vital contribution to the scholarly discourse around the deployment of technology in the context of Islamic banking. In particular, it emphasizes blockchain technology's part in fostering innovation within the sector and fostering a culture of compliance with the sector's ethical and operational standards.

Thyroid disorders are among the most prevalent endocrine conditions worldwide and exert far–reaching effects on metabolic, cardiovascular and neurological systems. Early diagnosis remains challenging because the clinical manifestations of hypo– and hyperthyroidism are non– specific, symptoms evolve slowly and laboratory thresholds vary across populations. Meanwhile, underdiagnosis and delayed treatment contribute to morbidity, healthcare costs and reduced quality of life. Machine learning (ML) offers an opportunity to integrate demographic, clinical and laboratory data to support clinicians in identifying dysfunction at an early stage. We reanalyse a publicly available thyroid disease cohort consisting of 377 euthyroid cases and 61 cases spanning clinical and subclinical hyper– and hypothyroidism. Despite the modest sample size and severe class imbalance (smallest class n = 7), our methodologically rigorous approach provides a transparent baseline for future work. After rigorous preprocessing—including missing–value imputation, one– hot encoding and standardisation—we evaluated six classical classifiers (logistic regression, decision tree, random forest, support vector machine, k-nearest neighbours and naïve Bayes) using stratified nested cross–validation (outer five–fold for performance estimation; inner cross–validation for hyperparameter tuning). Class imbalance is mitigated through inverse class weighting and synthetic minority oversampling (SMOTE/SMOTE–NC) applied only to training folds to prevent leakage. Hyperparameters are tuned via random search, and the best models are calibrated via Platt scaling and isotonic regression. Performance is summarised with mean ± standard deviation across folds for accuracy, macro–precision, macro–recall, macro–F1, micro– and macro–area under the receiver operating characteristic curve, Brier score and expected calibration error. The random forest achieves the highest macro–area under the ROC curve at 0.99±0.01 and balanced accuracy on this specific dataset, while logistic regression and support vector machines yield competitive performance. However, given the small sample size and severe class imbalance, these results should be validated on larger, independent cohorts before clinical deployment. Statistical comparisons using DeLong and McNemar tests confirm that the random forest significantly outperforms other models at the α = 0.05 level. Rather than proposing a new model architecture, this work contributes a transparent, leakage-aware and statistically grounded baseline for classical machine learning on this dataset. We conclude by discussing methodological choices, limitations and ethical considerations, and by situating our findings within the context of recent work.