https://www.um.edu.mt/library/oar/handle/123456789/143806 2026-05-22T10:52:08Z https://www.um.edu.mt/library/oar/handle/123456789/145972 Title: Enhancing spatial feature development from imagery using computer vision aided by GIS Abstract: This dissertation addresses the challenge of detecting and validating spatial features of differing geometric complexity. It focuses on buildings and swimming pools extracted from high-resolution satellite imagery and orthophotos for GIS applications. Conventional object detection workflows often lack mechanisms to reconcile computer vision outputs with authoritative spatial data. This limitation reduces their reliability for urban and environmental planning. The research is motivated by the need to integrate deep learning–based detection with GIS-based spatial validation to improve confidence and interpretability. The methodology uses the YOLOv11 object detection framework trained on approximately 1,000 manually annotated images. Both single-class and multi-class configurations are evaluated. Due to limitations in ArcGIS Pro’s native deep learning toolbox, inference is performed externally. Detection outputs are then reintroduced into the GIS environment. A novel GIS–CV integration pipeline is implemented using the arcpy library. Post-inference spatial refinement is applied using Intersection over Union (IoU) and Dice coefficient analysis. Authoritative planning basemap polygons are used to enable confidence reweighting. After spatial validation, the single-class swimming pool model achieved a mAP@0.5 of 0.78. It obtained a precision of 0.85 and a recall of 0.75 after 122 epochs. The runtime for this model was 0.289 hours. The building detection model achieved a mAP@0.5 of 0.45 after 100 epochs. It recorded a precision of 0.698 and a recall of 0.626, with a runtime of 0.254 hours. Pool mAP@0.5 increased from 0.74 to 0.78, while building mAP@0.5 increased from 0.439 to 0.45. A multi-class model detecting buildings, pools, and vegetation achieved an overall mAP@0.5 of 0.475. This model recorded a precision of 0.54 and a recall of 0.489. This main contribution is a custom GIS–CV pipeline with a novel post-inference validation framework. This approach enhances detection reliability and supports scalable integration of computer vision outputs into operational GIS workflows. Description: M.Sc. ICT(Melit.) 2026-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/145970 Title: A tool to support the diagnosis of Alzheimer’s disease Abstract: Alzheimer’s disease is a progressive neurodegenerative disorder affecting millions of individuals worldwide. Currently, there is no cure, making early recognition critical, as timely interventions can help slow functional deterioration and maintain quality of life. This dissertation aimed to develop a tool to support the diagnosis of Alzheimer’s disease. The tool is designed to complement existing assessment methods rather than replace them, supporting practitioners in their diagnostic process. The objectives were achieved by developing a Convolutional Neural Network (CNN) model to classify MRI scans into four stages of Alzheimer’s disease, creating a prototype web application to evaluate whether the integration of the model with it is feasible, and conducting interviews with domain experts to inform the tool’s features and functionalities. The prototype was then refined and re-evaluated with expert feedback. The resulting web application allows authorised medical specialists to log in, manage patient information, upload MRI scans, predict the stage of Alzheimer’s disease, and access comprehensive reports that include both current and past scans for comparison. This tool demonstrates the potential of integrating artificial intelligent assisted imaging analysis into clinical workflows to support more informed and efficient diagnostic decisions. Domain experts evaluated the tool as aesthetically pleasing, easy to follow, clear, and straightforward to use. Description: M.Sc.(Melit.) 2026-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/144337 Title: A framework to support test tool design and acquisition Abstract: Software testing is an important facet of software delivery, supported by tools intended to improve the efficiency and effectiveness of testing. Industry experience and academic research show that tool adoption can be problematic; tools are acquired but not used, or are used but do not deliver. The research problem this thesis addresses is how to design tools that better match the needs of testers to operate in an increasingly complex socio‐technical environment. Industry practitioners’ and experts’ experiences with tools were explored, through in‐depth interviews, workshops and surveys. It was found that testers experienced frustrations arising from tools which, while offering attractive interfaces, did not provide quality in use necessary to meet testers’ needs. In this work, this is referred to as the ‘illusion of usability’. This illusion arises from a superficial understanding of usability as being focused on the user interface, working with a limited persona set, and focusing narrowly on usability, without considering the other attributes that make up quality in use. Furthermore, finding that testers do not conform to the stereotype of IT workers, and cannot be represented in tool design by a simple, small set of personas or archetypes, it was decided to apply an HCI lens to the problem, with the research question “How can HCI techniques help with the design of test tools?” In answering this question, this work proposes an empirically grounded framework (idea‐t), which supports decision making in both design and acquisition of tools through a set of heuristics, guidelines and activities. The idea‐t framework (“Influencing the Design, Evaluation and Acquisition of Tools for Testing”) emerged following a series of studies and was iteratively reviewed and validated through five industry case studies. Learning was carried forward from each case study and applied to the framework. The five formative case studies iteratively informed the development of the framework, while also providing evidence of its effectiveness in the process. Participants reported benefits including new insights and improved communication within their teams. A final retrospective analysis evaluated the framework by examining a backlog of customer issues raised on a commercial tool; it was found that potentially 40% of issues could have been mitigated by the idea‐t framework. Expert reviews were also carried out to assess the latest version of the framework, where experts from testing, test tool development, and HCI provided positive feedback on the framework’s efficacy, and suggestions for its practical application. Description: Ph.D.(Melit.) 2026-01-01T00:00:00Z