Road traffic accidents (RTAs) are a major cause of injuries and fatalities worldwide. In recent years, there has been a growing global interest in analysing RTAs, specifically concerned with analysing and modelling accident data to better understand and assess the causes and effects of accidents. This study analysed the performance of widely used machine learning classifiers using a real-life RTA dataset from Gauteng, South Africa. The study aimed to assess prediction model designs for RTAs to assist transport authorities and policymakers. It considered classifiers such as naïve Bayes, logistic regression, k-nearest neighbour, AdaBoost, support vector machine, random forest, and five missing data methods. These classifiers were evaluated using five evaluation metrics: accuracy, root-mean-square error, precision, recall, and receiver operating characteristic curves. Furthermore, the assessment involved parameter adjustment and incorporated dimensionality reduction techniques. The empirical results and analyses show that the RF classifier, combined with multiple imputations by chained equations, yielded the best performance when compared with the other combinations.

Increased road traffic congestion is due to different factors, such as population and economic growth, in different cities globally. On the other hand, many households afford personal vehicles, contributing to the high volume of cars. The primary purpose of this study is to perform a comparative analysis of ensemble methods using road traffic congestion data. Ensemble methods are capable of enhancing the performance of weak classifiers. The comparative analysis was conducted using a real-world dataset and bagging, boosting, stacking and random forest ensemble models to compare the predictive performance of the methods. The ensemble prediction models are developed to predict road traffic congestion. The models are evaluated using the following performance metrics: accuracy, precision, recall, f1-score, and the misclassification cost viewed as a penalty for errors incurred during the classification process. The combination of AdaBoost with decision trees exhibited the best performance in terms of all performance metrics. Additionally, the results showed that the variables that included travel time, traffic volume, and average speed helped predict vehicle traffic flow on the roads. Thus, the model was developed to benefit transport planners, researchers, and transport stakeholders to allocate resources accordingly. Furthermore, adopting this model would benefit commuters and businesses in tandem with other interventions proffered by the transport authorities.

With the rapid advancement of internet-connected systems and artificial intelligence (AI), AI models have become integral to cybersecurity, particularly in identifying and mitigating cyberattacks. While these AI-based methods are widely utilised, they often generate results that lack interpretability, posing challenges for security experts, developers, educators and others who are involved in understanding the rationale behind specific outcomes. This interpretability gap has driven the emergence of explainable AI (XAI), a growing field focused on enhancing transparency by providing insights and visualisations that make AI decisions understandable to humans. XAI enhances the interpretability of AI-generated outcomes, enabling security experts to better understand and prioritise potential threats. Moreover, it helps to improve the efficiency and accuracy of threat assessments, thereby strengthening overall cybersecurity operations. To explore XAI’s role in this cybersecurity domain, a systematic literature review was conducted to offer a comprehensive analysis of its applications in cybersecurity. Adhering to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a structured search was performed on the Scopus database, identifying 565 peer-reviewed studies. After a thorough selection process, 67 relevant papers were analysed. The findings reveal various AI subsets and XAI techniques employed for cybersecurity, challenges associated with XAI applications and future research directions in this domain. This study contributes significantly to the existing body of knowledge as it deepens understanding of XAI’s role in cybersecurity. Furthermore, it offers valuable insights for advancing research and practical implementations. This work helps to bridge the gap between AI’s technical complexity and its applicability in real-world cybersecurity scenarios.

The increased prevalence of mental health issues in the workplace affects employees’ well-being and organisational success, necessitating proactive interventions such as employee assistance programmes, stress management workshops, and tailored wellness initiatives. Artificial intelligence (AI) techniques are transforming mental health risk prediction using behavioural, environmental, and workplace data. However, the “black-box” nature of many AI models hinders trust, transparency, and adoption in sensitive domains such as mental health. This study used the Open Sourcing Mental Illness (OSMI) secondary dataset (2016–2023) and applied four ML classifiers, Random Forest (RF), xGBoost, Support Vector Machine (SVM), and AdaBoost, to predict workplace mental health outcomes. Explainable AI (XAI) techniques, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), were integrated to provide both global (SHAP) and instance-level (LIME) interpretability. The Synthetic Minority Oversampling Technique (SMOTE) was applied to address class imbalance. The results show that xGBoost and RF achieved the highest cross-validation accuracy (94%), with xGBoost performing best overall (accuracy = 91%, ROC AUC = 90%), followed by RF (accuracy = 91%). SHAP revealed that sought_treatment, past_mh_disorder, and current_mh_disorder had the most significant positive impact on predictions, while LIME provided case-level explanations to support individualised interpretation. These findings show the importance of explainable ML models in informing timely, targeted interventions, such as improving access to mental health resources, promoting stigma-free workplaces, and supporting treatment-seeking behaviour, while ensuring the ethical and transparent integration of AI into workplace mental health management.