http://hdl.handle.net/1893/35 This community contains the ePrints and eTheses produced by Computing Science and Mathematics staff and students. Sat, 14 Nov 2026 20:24:02 GMT 2026-11-14T20:24:02Z http://hdl.handle.net/1893/38448 Title: The case for model-informed vaccine development Author(s): PF Bai, Jane; Beigmohammadi, Fatemeh; Craig, Morgan; Dalton, Mackenzie; Desikan, Rajat; Easlick, Terry; Gazeau, Sonia; Heffernan, Jane; Kirpichnikova, Anna; Padmanabhan, Pranesh; Sachs, Jeffrey R; Sher, Anna; Skibinski, David; Smith, Amber M Abstract: This perspective paper illustrates how model-informed vaccine development (MIVD) impacts vaccine discovery and development decisions, saving time and resources. It discusses the benefits of mechanistic and other mathematical models within MIVD. We outline key applications, opportunities, challenges, and future steps for MIVD as a call for the continued integration of mathematical modelling during vaccine development. Sat, 24 Oct 2026 00:00:00 GMT http://hdl.handle.net/1893/38448 2026-10-24T00:00:00Z http://hdl.handle.net/1893/38444 Title: Foundation Models in Biomedical Imaging: Turning Hype into Reality Corresponding Author Author(s): Muneer, Amgad; Zhang, Kai; Hamdi, Ibraheem; Qureshi, Rizwan; Waqas, Muhammad; Fouad, Shereen; Ali, Hazrat; Anwar, Syed Muhammad; Wu, Jia Abstract: Foundation models (FMs) are driving a prominent shift in biomedical imaging from task-specific models to unified backbone models for diverse tasks. This opens an avenue to integrate imaging, pathology, clinical records, and genomics data into a composite system. However, this vision contrasts sharply with modern medicine's trajectory toward more granular sub-specialization. This tension, coupled with data scarcity, domain heterogeneity, and limited interpretability, creates a gap between benchmark success and real-world clinical value. We argue that the immediate role of FMs lies in augmenting, not replacing, clinical expertise. To separate hype from reality, we introduce REAL-FM (Real-world Evaluation and Assessment of Foundation Models), a multi-dimensional framework for assessing data, technical readiness, clinical value, workflow integration, and responsible AI. Using REAL-FM, we find that while FMs excel in pattern recognition, they fall short in causal reasoning, domain robustness, and safety. Clinical translation is hindered by scarce representative data for model training, unverified generalization beyond oversimplified benchmark settings, and a lack of prospective outcome-based validation. We further examine FM reasoning paradigms, including sequential logic, spatial understanding, and symbolic domain knowledge. We envision that the path forward lies not in a monolithic medical oracle, but in coordinated subspecialist AI systems that are transparent, safe, and clinically grounded. Sat, 01 Aug 2026 00:00:00 GMT http://hdl.handle.net/1893/38444 2026-08-01T00:00:00Z http://hdl.handle.net/1893/38420 Title: Explaining solutions for decision support via Partial Solutions Author(s): Catalano, Giancarlo Antonino Pasquale Ignazio Abstract: Optimisation problems arise across numerous domains, with metaheuristics being widely adopted due to their flexibility and ease of use. Although the metaheuristic framework is relatively simple, the search process is sufficiently complex that it is effectively a black box. Users are often forced to accept results at face value despite the risks of faulty implementations, non-determinism, and unintended biases. To address these limitations, this research introduces an explainability framework for metaheuristics by extracting human-interpretable insights from generated solutions. Central to the proposed methodology is the novel concept of Partial Solutions—defined and implemented as part of this work—which represent understandable sub-structures within metaheuristic solutions associated with high fitness. To find these patterns, we use a multi-objective search method that leverages information relating to their complexity, fitness, and structure. The resulting patterns offer trade-offs between objectives, and each can be post-processed into an explanation. To quantify the “structure” of Partial Solutions, we developed two novel linkage learning methods and a new linkage evaluation procedure, which offers a superior trade-off between accuracy and execution time compared with existing methods. The utility and domain-agnostic nature of this framework are demonstrated and evaluated across several distinct applications. First, the methodology was applied to a complex industrial optimisation problem posed by British Telecom (BT), yielding insightful explanations. Second, the framework was adapted for a recommender system model, successfully generating explanations that satisfy stakeholder objectives. Furthermore, the same underlying methodology was utilised to construct transparent models for predicting solution fitness. To conclude, Partial Solutions are a flexible tool for explainability. They are shown to recover known patterns within benchmark problems, and to be applicable and insightful in large and complex problems such as British Telecom’s Staff Rostering. Our methodology can also be used beyond metaheuristics; for example, we apply it to a recommender system to produce domain-specific explanations. Finally, Partial Solutions can be used to predict fitness: we show that decision trees based on Partial Solutions are competitive with traditional approaches. We conclude the thesis with reflections on whether our objectives have been achieved, and possible future work. Partial Solutions may be used to implement efficient and more understandable metaheuristics, and the applicability of the explanations produced may be expanded beyond combinatorial optimisation. Mon, 15 Jun 2026 00:00:00 GMT http://hdl.handle.net/1893/38420 2026-06-15T00:00:00Z http://hdl.handle.net/1893/38410 Title: An Explainable Estimation Model for Financial Time Series Author(s): Takahashi, Osamu; Cuyt, Annie; O'Hare, Anthony; Lee, Wen-shin; Ferrara, Massimiilinao Editor(s): Vantaggi, Barbara; Coletti, Giulianella; Denoeux, Thierry; Laurent, Anne; Petturiti, Davide; Miranda, Enrique; Medina, Jesus; Bouchon-Meunier, Bernadette; Yager, Ronald R Abstract: Recent developments in artificial intelligence have led to powerful machine learning approaches to model fluctuating dynamics in financial time series, even in the presence of non-stationary behavior. However, such methods often suffer from limited interpretability while uncertainty, arising from complex data fluctuations, remains difficult to quantify. We present a method that decomposes financial data into different frequency components and trends, thereby enabling explicit identification of cyclical patterns and long-term trends. Concurrently, noise components are separated from signal components. Its effectiveness and its high predictive accuracy have been demonstrated in the interpretation of some financial time series. Mon, 15 Jun 2026 00:00:00 GMT http://hdl.handle.net/1893/38410 2026-06-15T00:00:00Z