The use of Geographic Information Systems has increased considerably since the eighties and nineties. As one of their most demanding applications we can mention shortest paths search. Several studies about shortest path search show the feasibility of using graphs for this purpose. Dijkstra’s algorithm is one of the classic shortest path search algorithms. This algorithm is not well suited for shortest path search in large graphs. This is the reason why various modifications to Dijkstra’s algorithm have been proposed by several authors using heuristics to reduce the run time of shortest path search. One of the most used heuristic algorithms is the A* algorithm, the main goal is to reduce the run time by reducing the search space. This article proposes a modification of Dijkstra’s shortest path search algorithm in reduced graphs. It shows that the cost of the path found in this work, is equal to the cost of the path found using Dijkstra’s algorithm in the original graph. The results of finding the shortest path, applying the proposed algorithm, Dijkstra’s algorithm and A* algorithm, are compared. This comparison shows that, by applying the approach proposed, it is possible to obtain the optimal path in a similar or even in less time than when using heuristic algorithms.

In the context of the information age, a land administration system must be technologically driven to manage land information and data transparently. This ensures the registration and protection of land rights for people. In this study, we present a Blockchain Land Registration system designed as a tool for enhancing land administration in South Saharan Africa (SSA). Drawing inspiration from Namibia, we have developed a user interface comprising a homepage/landing page, a users’ registration form, a login form that incorporates MetaMask authentication prompts, and an authenticated dashboard for landowners and purchasers. Design Science was employed as the methodology for this proposal. Being technical design research for solving a land administration problem (that of inefficient land registration), the technical solution identified involves system design, the development of blockchain integration and testing, and development aspects. Based on this approach, blockchain was conceptualised as an “artefact” that could be investigated as a technical solution to address the challenges posed by inefficient land registration. This study provides a comprehensive roadmap for the conceptualisation, development, validation, and deployment of a blockchain-based land titles registry suitable for SSA countries. It also explores a discussion on the practical and policy implications of blockchain in land administration in SSA countries.

Learning analytics has become an essential component of evidence-based decision-making in higher education, yet many predictive models continue to face critical limitations, including weak generalizability, narrow contextual alignment, and limited integration of established student retention theories. This study advances the field by developing a theoretically grounded and ensemble learning model to predict student retention at the International University of Management (IUM) in Namibia. Using a large-scale dataset of 11,090 anonymised student records (2021–2023) comprising academic, behavioural, demographic, and engagement variables, the study applies a suite of machine learning techniques including Decision Tree, Random Forest, Support Vector Machine, Artificial Neural Network, and Gradient Boosting and an optimized stacking ensemble architecture. The dataset was divided into 80% training and 20% testing subsets, and the models were evaluated using 10-fold cross-validation to ensure stability and generalizability. Predictive performance was assessed using accuracy, precision, recall, and F1-score, with the final ensemble (DT-ANN-RF-SVM) achieving 94.6% accuracy, outperforming all individual models. The study makes three key contributions: (1) it operationalizes constructs from Tinto’s Student Integration Theory, Bean and Metzner’s Attrition Model, and Astin’s Theory of Student Involvement into measurable learning analytics variables; (2) it demonstrates how ensemble learning can overcome methodological limitations prevalent in prior work; and (3) it offers a reproducible institutional framework for applying analytics-driven retention strategies. The results highlight the value of theoretically informed machine learning models in identifying at-risk students early and enabling tailored support interventions. The findings provide actionable insights for institutional policy, pedagogical design, and future research on adaptive learning analytics in higher education.

Pest control has been an important undertaking in maximising yields from crop farming. Manual methods of combating pests have shown limited success, prompting the need for more modern, innovative approaches. This paper presents the use of AI and IoT for pest control. The developed prototype employs AI to analyse images captured by a camera. The analysis focuses on detecting and identifying pests, which then triggers the irrigation process for the specific pesticide required. The prototype has been developed using the Design Science Research Methodology. The prototype was implemented on a Raspberry Pi with a camera and a water pump. The camera continuously captures images, which are processed by a Machine Learning model tasked with detecting and identifying pests. Once a pest is identified, the water pump is then activated to spray the appropriate pesticide based on the identified pest. The study's results demonstrate the potential for controlling pests using AI and IoT, as the prototype was able to efficiently identify pests and trigger the spraying of the pesticide. Future work will examine the full implementation of a prototype in a crop field, with additional functionality such as weather monitoring and predicting future pest infestations based on historical data.