http://hdl.handle.net/20.500.12283/347 2026-04-03T23:20:23Z 2026-04-03T23:20:23Z Akol, Joseph http://hdl.handle.net/20.500.12283/4679 2026-02-06T07:24:48Z 2025-01-01T00:00:00Z

Gait recognition as a biometric signal, investigating skeletal trajectory extraction and classification approaches Akol, Joseph Gait recognition, a non-intrusive biometric method effective at a distance, serves as a practical alternative to traditional identification techniques like fingerprints or facial recognition, particularly in forensic and surveillance contexts. This study aims to design and compare 2D and 3D models for gait recognition with the general objective of analyzing their comparative performance. Specific objectives include: (1) identifying a robust gait dataset recorded across various environmental conditions and camera angles, (2) extracting 2D and 3D skeletal keypoints using OpenPose and MediaPipe, respectively, (3) developing and evaluating the accuracy of 2D and 3D gait recognition models, and (4) analyzing their performance under varying environmental conditions. The research addresses the effectiveness of dataset utilization, comparative model accuracy, and key performance influencers. Results show the 2D model excels in occlusion handling, achieving a 0% false positive rate (FPR) for unknown instances, while the 3D model better generalizes to unseen sequences at higher thresholds, with a 74.55% FPR. However, the 2D model struggles with single-frame performance (80% FPR), and the 3D model exhibits significant bias and poor occlusion handling (100% FPR). Recommendations include enhancing dataset diversity, incorporating temporal features via LSTM layers, and improving 3D keypoint robustness through occlusion augmentation. This study provides a replicable framework, offering insights into model trade-offs and advancing gait recognition for resource constrained environments, forensic applications, and academic research. Dissertation

2025-01-01T00:00:00Z Ntanda, Nassar http://hdl.handle.net/20.500.12283/4667 2026-01-06T12:06:17Z 2025-01-01T00:00:00Z

Diagnosing foot and mouth disease and lumpy skin disease in cattle using computer vision Ntanda, Nassar Cattle farming is a major source of income for many people in the Sub-Harran region, and the livestock sector played a vital role in supporting the region’s economy and food security [2]. However, the prevalence of infectious cattle diseases, particularly Lumpy Skin Disease (LSD) and Foot and Mouth Disease (FMD), posed serious threats to food security, farmer livelihoods, and animal productivity. Addressing the impacts of these diseases required early and accurate diagnosis [1]. This study presents a computer vision-based diagnostic model that utilizes deep learning techniques to detect and classify FMD and LSD in cattle. The proposed system captured the visible signs of infection using a Convolutional Neural Network (CNN) trained on a dataset of annotated cattle images [3]. The model achieved high levels of accuracy and robustness under diverse environmental conditions and was capable of distinguishing between healthy cattle and those infected with FMD or LSD. To improve accessibility, the trained model was integrated into a mobile application, enabling rural farmers and veterinary practitioners to perform real-time diagnosis without the need for specialized equipment. The study also addressed challenges related to image data collection in the Sub-Harran region, such as variations in cattle breeds, lighting conditions, and disease presentation. Data augmentation and transfer learning techniques were employed to improve the model’s generalization. The model’s performance will be evaluated using standard metrics, including accuracy, precision, recall, and F1-score, with results indicating strong potential for practical deployment [4]. Overall, this study will offer a scalable solution for improving cattle disease management through the application of artificial intelligence, with implications for enhancing disease surveillance, reducing economic losses, and improving animal health in resource-constrained settings. Dissertation

2025-01-01T00:00:00Z Kisakye, Diana Michelle http://hdl.handle.net/20.500.12283/4655 2026-01-05T09:48:09Z 2025-01-01T00:00:00Z

An Enhanced Text Steganography Technique (SHA-Logic): Based on Conditional Logic and SHA-3 (Secure Hash Algorithm 3) Kisakye, Diana Michelle An Enhanced Text Steganography Technique (SHA-Logic): Based on Conditional Logic and SHA-3 (Secure Hash Algorithm 3) Dissertation

2025-01-01T00:00:00Z Musanje, Richard Nyangoma, Joan http://hdl.handle.net/20.500.12283/4624 2025-12-17T08:03:54Z 2025-01-01T00:00:00Z

A luganda crop disease diagnosis feedback tool Musanje, Richard; Nyangoma, Joan A luganda crop disease diagnosis feedback tool Dissertation

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