Journal Articles

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12504/538

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    Adaptive object detection in dynamic road environments using YOLOv11 with continuous learning for real-time obstacle detection
    (Discover Artificial Intelligence, 2026-01-13) Nkalubo, Lenard Byenkya; Nakibuule, Rose; Okila, Nixson
    Real-time object detection in dynamic road environments is crucial for enhancing road safety, infrastructure monitoring, and Intelligent Transportation Systems (ITS). This study presents an adaptive object detection model based on YOLOv11 with continuous learning, designed to detect road obstacles in real-time without frequent retraining autonomously. Unlike existing YOLO-based models such as YOLOv10 and LD-YOLOv10, the proposed model integrates a continuous learning mechanism, enabling it to adapt dynamically to evolving road conditions, including potholes, vehicles, signposts, and other obstacles. The model was trained and evaluated on a dataset collected from diverse urban road environments in Uganda, achieving a mean average precision (mAP) of 0.856, precision of 0.873, and recall of 0.849. It also demonstrates high-speed performance at 78.7 Frames Per Second (FPS), making it suitable for real-time deployment on resourceconstrained edge devices. While this study primarily focuses on model development and optimization for road obstacle detection, future research will explore its potential application in assistive navigation systems for visually impaired individuals. Comparative analysis against state-of-the-art models, including YOLOv10, LD-YOLOv10, and Tiny-DSOD, demonstrates the competitive performance of the proposed model, particularly in detecting small and occluded objects.
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    Optimizing cervical cancer classification using transfer learning with deep gaussian processes and support vector machines
    (Discover Artificial Intelligence, 2024-10-30)  Ahishakiye, Emmanuel;  Kanobe, Fredrick
    Background Cervical cancer is the fourth most frequent cancer in women worldwide. Even though cervical cancer deaths have decreased significantly in Western countries, low and middle-income countries account for nearly 90% of cervical cancer deaths. While Western countries are leveraging the powers of artificial intelligence (AI) in the health sector, most countries in sub-Saharan Africa are still lagging. In Uganda, cytologists manually analyze Pap smear images for the detection of cervical cancer, a process that is highly subjective, slow, and tedious. Machine learning (ML) algorithms have been used in the automated classification of cervical cancer. However, most of the MLs have overfitting limitations which limits their deployment, especially in the health sector where accurate predictions are needed. Methods In this study, we propose two kernel-based algorithms for automated detection of cervical cancer. These algorithms are (1) an optimized support vector machine (SVM), and (2) a deep Gaussian Process (DGP) model. The SVM model proposed uses an optimized radial basis kernel while the DGP model uses a hybrid kernel of periodic and local periodic kernel. Results Experimental results revealed accuracy of 100% and 99.48% for an optimized SVM model and DGP model respectively. Results on precision, recall, and F1 score were also reported. Conclusions The proposed models performed well on cervical cancer detection and classification, and therefore suitable for deployment. We plan to deploy our proposed models in a mobile application-based tool. The limitation of the study was the lack of access to high-performance computational resources.