Franklin Tchakounte

Marcellin Atemkeng, Victor Osanyindoro, Rockefeller Rockefeller et al.

One of the critical factors that drive the economic development of a country and guarantee the sustainability of its industries is the constant availability of electricity. This is usually provided by the national electric grid. However, in developing countries where companies are emerging on a constant basis including telecommunication industries, those are still experiencing a non-stable electricity supply. Therefore, they have to rely on generators to guarantee their full functionality. Those generators depend on fuel to function and the rate of consumption gets usually high, if not monitored properly. Monitoring operation is usually carried out by a (non-expert) human. In some cases, this could be a tedious process, as some companies have reported an exaggerated high consumption rate. This work proposes a label assisted autoencoder for anomaly detection in the fuel consumed by power generating plants. In addition to the autoencoder model, we added a labelling assistance module that checks if an observation is labelled, the label is used to check the veracity of the corresponding anomaly classification given a threshold. A consensus is then reached on whether training should stop or whether the threshold should be updated or the training should continue with the search for hyper-parameters. Results show that the proposed model is highly efficient for reading anomalies with a detection accuracy of $97.20\%$ which outperforms the existing model of $96.1\%$ accuracy trained on the same dataset. In addition, the proposed model is able to classify the anomalies according to their degree of severity.

Irene Nandutu, Marcellin Atemkeng, Patrice Okouma et al.

In South Africa, it is a common practice for people to leave their vehicles beside the road when traveling long distances for a short comfort break. This practice might increase human encounters with wildlife, threatening their security and safety. Here we intend to create awareness about wildlife fencing, using drone technology and computer vision algorithms to recognize and detect wildlife fences and associated features. We collected data at Amakhala and Lalibela private game reserves in the Eastern Cape, South Africa. We used wildlife electric fence data containing single and double fences for the classification task. Additionally, we used aerial and still annotated images extracted from the drone and still cameras for the segmentation and detection tasks. The model training results from the drone camera outperformed those from the still camera. Generally, poor model performance is attributed to (1) over-decompression of images and (2) the ability of drone cameras to capture more details on images for the machine learning model to learn as compared to still cameras that capture only the front view of the wildlife fence. We argue that our model can be deployed on client-edge devices to inform people about the presence and significance of wildlife fencing, which minimizes human encounters with wildlife, thereby mitigating wildlife-vehicle collisions.

Abidemi Kuburat Adedeji, Franklin Tchakounte, Sulaiman Oluwasegun Yusuff

Artificial intelligence (AI) is now embedded in educational, civic, and economic systems worldwide. For African primary and secondary education, this creates a double imperative: to prepare a young population (over sixty per cent of Africans are under twenty-five) for AI-mediated labour markets without uncritically importing curricula designed for other linguistic, cultural, and socio-political contexts. The African Union's Continental AI Strategy (2024) and the 2025 Africa Declaration on AI have elevated these questions to the continental agenda. This paper proposes a Pan-African, culturally contextualised, and ethically grounded framework for integrating AI education into African primary and secondary schools. The paper is a structured conceptual synthesis of continental and national policy documents, peer-reviewed scholarship on AI ethics, AI literacy, decolonial pedagogy, and Ubuntu-grounded AI governance. We contribute: (i) a framework of six guiding principles, four curriculum domains, five ethical competencies, and an age-banded progression from lower primary to upper secondary; (ii) a comparative analysis of continental and national policy contexts; (iii) an explicit mapping between global AI-ethics principles and Ubuntu-informed relational ethics; (iv) a planned empirical validation programme combining a Delphi study, teacher surveys across anglophone, francophone, lusophone, and arabophone contexts, and multi-country classroom piloting; and (v) targeted recommendations for policymakers, educators, civil society, and international partners. We argue that an ethical AI curriculum can serve as a transformative tool for equity, innovation, and social justice, and outline a research agenda to embed ethics, resilience, and critical thinking at the core of Africa's digital future.