ABSTRACT
The rapid and continuous rise of sophisticated cyberattacks has driven the adoption of Deep Learning models in Intrusion Detection Systems (IDS). While these models deliver superior detection performance compared to traditional approaches, their “black-box” nature poses a significant barrier to real-world deployment. Network administrators often struggle to trust system-generated alerts when they cannot understand the reasoning behind them. This paper proposes a comprehensive solution to address this challenge by integrating Explainable Artificial Intelligence (XAI) techniques into IDS. Specifically, we apply SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations) to Deep Neural Network (DNN) models trained on benchmark datasets, namely NSL-KDD and UNSW-NB15. The results demonstrate not only the model’s high effectiveness in detecting various types of cyberattacks but also its ability to provide detailed explanations at both global and local levels. These insights enable network administrators to better analyze the root causes of alerts, thereby improving the reliability and transparency of cybersecurity defense systems.

ABSTRACT
Kampala City, Uganda’s capital, faces severe and escalating traffic congestion due to rapid urbanisation, population growth, and rising vehicle ownership, leading to substantial economic losses, increased emissions, and degraded quality of life. Traditional reactive traffic management systems are inadequate for addressing dynamic urban mobility patterns in resource constrained environments. This study develops an intelligent traffic congestion prediction framework using machine learning classification. A dataset of 500 observations from 15 major road segments is processed through rigorous preprocessing, domain-informed feature engineering (including capacity utilisation and flow efficiency), and ensemble classifiers to categorise congestion into four actionable severity levels: Low, Medium, High, and severe. Experimental evaluation on a stratified test set shows that XGBoost outperforms Random Forest, achieving 84.0% overall accuracy and the highest precision (0.808%), recall (0.840%), and F1-score (0.820%). Feature importance analysis highlights capacity utilisation, vehicle density, and flow efficiency as the dominant predictors, consistent with fundamental traffic flow theory. The proposed system establishes a scalable, interpretable foundation for proactive congestion management in developing cities. With future enhancements in real-world data integration and temporal-spatial modelling, it holds strong potential to support adaptive traffic control, incident response, and data-driven urban planning in Kampala and similar contexts.

ABSTRACT
Aceh, located in the western region of Indonesia, possesses a rich cultural and historical legacy shaped by diverse ethnic influences that have formed the Acehnese Malay identity. Among its significant cultural assets is Rumoh Aceh, a traditional residential architecture that embodies local values but is increasingly threatened by modernization and changing housing practices. This study extends a doctoral research project that applies Gadamerian Hermeneutics, particularly the concept of the “fusion of horizons” introduced by Hans-Georg Gadamer, to reinterpret the architectural meaning of Rumoh Aceh within contemporary contexts. The research aims to develop a contextual design framework for simple housing based on the embedded values of traditional architecture. The findings identify four key design principles derived from Rumoh Aceh: (1) spirituality and symbolism, reflected in elements such as qibla orientation, odd-numbered staircases, and floral ornamentation as markers of identity; (2) environmental adaptation, including cross ventilation, elevated structures, and gable roofs suitable for tropical climates and disaster mitigation; (3) socio-cultural sustainability, emphasizing spatial arrangements that support family interaction, reception, and public–private boundaries; and (4) spatial flexibility, enabling multifunctional and land-efficient design while preserving cultural values. These principles were subsequently operationalized in the design of simple residential housing in Banda Aceh City and Aceh Besar Regency. The study contributes to bridging traditional architectural knowledge and contemporary housing needs by offering an evidence-based and culturally grounded guideline for sustainable simple housing development.

ABSTRACT
In order to find coherent clusters of facilities based on geographic coordinate, user/inspector ratings, and the range of services provided, this study applies unsupervised machine learning to 6,520 health facility records from throughout Uganda. Three stable clusters that correspond with broad regional divisions in Uganda were produced by K Means following systematic cleaning, robust imputation of missing ratings, removal of obvious geographic outliers, feature scaling, and objective selection of the number of clusters using the Silhouette method: a Central/Eastern cluster with high facility density and diversity of services, a Northern cluster with a higher proportion of government-operated facilities and fewer specialized services, and a Southern/Western cluster with more balanced ownership and a relative strength in maternity and laboratory services. We also used centroid analysis, a multilabel binarization procedure to extract the prevalence of critical services, and the distribution of care systems (GOVT, PNFP, and PFP) to further characterize the clusters. The clusters’ remarkable correlation to established regional borders was validated spatially using an administrative district shapefile. The findings are examined in light of Uganda’s health planning requirements, and suggestions for focused action, more data gathering, and additional modelling are made.