Physical Sciences and Mathematics Commons^™

Survival Predictions From Classification Algorithms – Concepts And Application To Graft And Patient Survival After Kidney Transplantation, Antje Jahn

SAML-25 Workshop on Statistical and Machine Learning

Clinical prediction models are developed to predict long-term patient outcomes following medical interventions. One example motivating this research is the prediction of graft and patient survival after kidney transplantation, using data from the German organ transplantation registry. A practical issue in this context is to deal with incomplete information due to right-censoring, which arises when patients are lost to follow-up or enter the study at different times, resulting in varying durations of observation. This is particularly relevant in the registry data, where follow-up is frequently incomplete or irregular. While traditional survival analysis methods handle censoring by modeling the hazard function, …

Pathology’S Place In Understanding The Bias And Inequalities In Women’S Healthcare, Andrea Heaney, Eugene Hickey, Emma Murphy

SAML-25 Workshop on Statistical and Machine Learning

Women’s healthcare is a complex, multifaceted issue with both historic and implicit biases, along with biological differences between men and women. With the advancement of AI tools in healthcare and the potential for biased data to create biased models, it is vital to consider how women are represented in data. Previously conducted semi-structured semantic interviews with clinicians were analysed via Braun and Clark’s method of thematic analysis. The analysis of these interviews yielded the following themes: Gender Influencing Health, Pregnancy, Social Factors, General Health, Treatment, Training, and Research. These themes highlight that context is key to understanding the biases in …

Impact Of Spatial Diversity And Subject Variability On Wifi-Based Human Activity Recognition, Amany Elkelany, Robert J. Ross, Susan Mckeever

SAML-25 Workshop on Statistical and Machine Learning

In recent years, WiFi-based Human Activity Recognition (HAR) has gained substantial attention due to the ubiquity of WiFi infrastructure and advancements in wireless communication. Unlike camera-based systems that raise privacy concerns or wearable sensors that require user compliance, WiFi-based HAR provides a noninvasive and practical alternative that operates seamlessly with existing infrastructure. WiFi-based HAR leverages fluctuations in wireless signals, particularly Channel State Information (CSI), to passively detect and classify human activities. WiFi-based HAR models often achieve high accuracy in a single environment but suffer significant performance drops when applied to new environments due to variations in spatial settings, human movement, …

Optimising Ai For Chemical Imaging: Benchmarking Performance Against Foundation Models For Task-Specific Applications In Histopathology, Rahul Suresh, Mohd Rifqi Rafsanjani, Karin Jirstrom, Arman Rahman, William M. Gallagher, Aidan Meade

SAML-25 Workshop on Statistical and Machine Learning

The integration of chemical imaging with artificial intelligence presents a compelling route toward fully digital, label-free histopathology, yet it also introduces notable challenges. While deep learning models from domains like machine vision, digital pathology, and remote sensing are readily accessible, they frequently struggle to generalize effectively to chemical imaging data, as highlighted in recent research [1]. Additionally, although foundational pathological models show potential for advancing AI-based histopathological diagnostics and prognostics, our preliminary assessments suggest they may fall short in addressing the broad spectrum of classification tasks encountered in clinical settings. In this presentation, we highlight some recent published work from …

Automated Approaches To Interpreting And Explaining Machine Learning Models, Tilak Chandrashekar, Tarry Singh, Maged Shaban

SAML-25 Workshop on Statistical and Machine Learning

This paper proposes a novel framework for automating ML model interpretation and explainability across different applications with an emphasis on transparency, trust, and human-centric decisionmaking assistance. Although ML models, particularly advanced structures such as deep neural networks, possess superior predictive powers, their interpretability tends to be obscure, and thus their application in sensitive or regulated domains is impeded. Current XAI techniques, though promising, tend to be post-hoc and are not scalable for real-time or large-scale deployments. This study addresses such concerns by presenting an automated, modular pipeline where interpretation techniques are embedded in the process of developing the ML model. …

Tracking The Kinetics Cellular Glycolysis And Glutaminolysis Pathways Using Vibrational Spectroscopy, Combined With Multivariate Statistical And Machine Learning Approaches For Data Mining, Zohreh Mirveis, Nithin Patil, Hugh Byrne

SAML-25 Workshop on Statistical and Machine Learning

Understanding dynamic metabolic processes within living cells is crucial for gaining insights into cellular function and disease mechanisms. The kinetics of glycolysis and glutaminolysis pathways play significant roles, as alterations in their activity have been linked to various disorders, including cancer and mental health conditions such as bipolar disorder. These pathways therefore hold potential as biomarkers for disease diagnosis and therapy. However, real-time monitoring of their kinetics remains challenging due to the lack of suitable non-invasive techniques. Current gold-standard fluxomics approaches, such as mass spectrometry, are destructive to cells and thus unsuitable for time-resolved studies. In this study, we evaluate …

Benchmarking Energy And Performance Of Parallel Machine Learning Models Using Hardware And Software Power Meters, Urooj Asgher, Tania Malik

SAML-25 Workshop on Statistical and Machine Learning

The growing reliance on machine learning algorithms across domains such as healthcare, transportation, and finance has led to their increased deployment on high-performance computing platforms. While performance optimization remains a central concern, energy efficiency is emerging as a critical design consideration, particularly in light of global sustainability goals. This study presents a comparative analysis of the energy consumption and performance of serial and parallel implementations of four machine learning algorithms, K-means clustering, Ant Colony Optimization, Logistic Regression, and Random Search. Experiments were conducted on an HPC testbed using both hardware-based and software-based power meters to measure energy consumption. The results …

Partitioning Around Medoids On Product Spaces: A Clustering Approach For Cylindrical Data, Yahia Hammami, Houyem Demni, Amor Messaoud, Giovanni C. Porzio

SAML-25 Workshop on Statistical and Machine Learning

Clustering is a common unsupervised task in data analysis and machine learning. It deals with finding clusters of objects that are characterized by the highest similarity within the same cluster and the highest dissimilarity between different clusters. One of the most used algorithms in clustering is the popular Partitioning Around Medoids (PAM), also known as k-medoids [4, 5]. The algorithm imposes the center of clusters to be some of the data points, and it looks for a minimal value of the sum of the dissimilarity to all the objects. One of the recognized properties of such a method is its …

Improving Node Classification For Graphs Withweak Feature Signals: A Similarity-Entropy Aggregation Approach, Brian Daniel Bernhardt, Chiara Marciano, Mario Rosario Guarracino

SAML-25 Workshop on Statistical and Machine Learning

Graph Neural Networks (GNNs) have established themselves as powerful tools for graph-structured data. However, when feature separability among nodes is low, conventional neighborhood aggregation strategies often result in performance degradation due to over-smoothing and noisy information propagation. In this work, we introduce a novel GNN framework that refines the aggregation process by integrating feature similarity and neighborhood entropy into node message passing. Unlike standard models that uniformly aggregate neighbor information, this new model dynamically adjusts neighbor influence, prioritizing nodes with high similarity and low entropy. We evaluate the model on synthetic graphs generated using the Stochastic Block Model (SBM), varying …

Dealing With Large Data Sets: The Data Nugget Subset Selection Approach, Vipin Kumar, Simona Balzano, Giovanni C. Porzio

SAML-25 Workshop on Statistical and Machine Learning

Analysing big data has always been a major issue because its massive volume poses significant challenges for traditional analytical techniques. When the number of instances is extremely large, existing approaches become computationally infeasible due to the complexity of many algorithms, along with memory and time constraints inherent in processing large datasets. In such cases, using a subset of the data is considered a more practical solution, and analyses are typically performed over a simple random sample drawn from the entire dataset. Various subsampling methods have been proposed to address these issues. However, they often fall short in producing representative subsamples …

A Statistical Approach To Portfolio Optimization Using Copula-Garch Models For European Investments, Jegors Fjodorovs

SAML-25 Workshop on Statistical and Machine Learning

This study explores portfolio optimization using copula functions and GARCH models, focusing on the European stock market. Traditional mean-variance methods often miss dynamic dependencies and tail risks. By applying copula-GARCH models—particularly the Student’s t copula with eGARCH—we better capture volatility asymmetries and tail dependencies. Conditional Value at Risk (CVaR) is used to evaluate downside risk across 10,000 simulated portfolios using high-performance computing. Results show that copula- GARCH models, especially eGARCH, consistently outperform traditional methods in risk-adjusted returns, offering improved risk management.

Investigation Of A Polymer-Based Holographic Grating For Visible Light Dosimetry Using A Bleachable Dye, Saoirse Maher, Denise Denning, Jackie Mccavana Dr., Seán Cournane Dr., Suzanne Martin Dr., Dervil Cody

Conference Papers

Holographic sensors are of interest for a range of sensing tasks because of their high sensitivity, rapid response time, broad dynamic range, lightweight characteristics, and design flexibility. The practical application of holography makes it possible to design optical sensors that are effective in the visible and near-infrared ranges. The objective of this research is to construct a holographic grating that is customised to provide quantitative data in visible light dosimetry applications. Upon exposure to visible light, the proposed holographic grating will produce a measurable change in diffraction efficiency based on the bleaching of the grating material. In the evolution of …

Assessing Maths Modules Online, Blathnaid Sheridan

Case studies: Digital Education

With class sizes increasing and students anxious to know their CA results asap, I began looking at an alternative way of assessing students on their maths modules. Furthermore, in an attempt to ensure better engagement with the content and continuity across maths modules in Years 1 & 2, students are now assessed using online quizzes with immediate results.

Quantifying The Role Of Active Listening And Reassurance In Virtual Health Coach Interactions, Ghulam Hussain, Brian Keegan, Robert Ross

Conference papers

Conversational Agents have the potential to support healthcare through coaching exercise routines, but are still lacking in demonstrating authentic social behaviours to support engagement. To this end, we present a series of experiments that we conducted in order to investigate how automated health care coaches can be more effective when their interaction style is tailored to demonstrate qualities associated with a good bedside manner, namely active listening and reassurance. To test this, we first developed a dataset of 135 dialogue excerpts from three distinct sources, i.e., original, handcrafted and LLMs, the latter two of which were tuned to demonstrate specific …

Quantifying The Role Of Active Listening And Reassurance In Virtual Health Coach Interactions, Ghulam Hussain, Brian Keegan, Robert Ross

Articles

Conversational Agents have the potential to support healthcare through coaching exercise routines, but are still lacking in demonstrating authentic social behaviours to support engagement. To this end, we present a series of experiments that we conducted in order to investigate how automated health care coaches can be more effective when their interaction style is tailored to demonstrate qualities associated with a good bedside manner, namely active listening and reassurance. To test this, we first developed a dataset of 135 dialogue excerpts from three distinct sources, i.e., original, handcrafted and LLMs, the latter two of which were tuned to demonstrate specific …

Evaluating The Ghg Emissions, Land Use, And Water Use Associated With Contemporary Dietary Patterns In The Republic Of Ireland, Daniel Burke, Paul Hynds, Anushree Priyadarshini

Articles

Dietary patterns are intrinsically linked to greenhouse (GHG) emissions, land use, and water use via food production systems. Analysing and comparing contemporary dietary patterns and their environmental impact is critical to identifying which should be promoted to enhance global sustainability. A cross-sectional survey of adult consumption patterns was conducted across Ireland with a representative sample size of 957 respondents. Subsequently, a farm-to-fork life cycle assessment (LCA) was employed via OpenLCA 2.0.4 to assess three primary environmental impacts (global warming, land use, and water use) across the population. Thirteen distinct dietary patterns were analysed: total population, rural, urban, self-reported (omnivore, flexitarian, …

Mathematical Modelling Of Hybrid Photonic Structures For Holographic Sensors, Jack Lyons

Doctoral

This thesis outlines a mathematical framework for modelling the formation of holographic gratings in hybrid photopolymer based nanocomposites with the aim of optimising their holographic recording properties for optical sensing applications. Thus, the second aim of the work is to model the change in optical properties of the grating in response to exposure to a target analyte. This work has been a collaborative research project between the School of Mathematics & Statistics at Technological University Dublin and the Centre for Industrial and Engineering Optics that have done extensive experimental work with holographic gratings recorded in photopolymer materials.

In recent years, …

Techmate: A Toolkit For Advancing Gender Equality In Computing Education, Alina Berry

Academic Posters Collection

To address the issue of gender inequality in computing education.

To inspire and guide institutions to implement change and track progress with easy to follow guidance.

To provide champions with useful and easy to access resources.

Surface Modifications Of Titanium Alloys For Biomedical Applications, Justynne Fabian

Doctoral

Hip implant failure remains a critical concern in healthcare, primarily driven by poor bone integration and bacterial infection. These issues contribute to a rising number of revision surgeries, posing significant challenges that must be addressed, particularly in light of an ageing global population. Titanium surface anodisation emerges as a promising solution for biomedical applications, offering a cost-effective, controllable, and efficient process. The anodisation technique results in the formation of titanium dioxide nanotubes (TiO2 NTs) on the metal surface. These nanostructures offer several advantages, including enhanced mechanical properties, and potential for surface modification through the incorporation of antibacterial and osteoinductive properties. …

Doping Hybrid Photopolymerisable Glass With Bodipy Photosensitiser: An Efficient Approach To Improve Uv-Resistance, Luca Sorridente, Tatsiana Mikulchyk, Metodej Dvoracek, Mikhail A. Filatov, Izabela Naydenova, Kevin P. Murphy

Conference Papers

The development of photosensitive materials with improved performance and extended functionality is crucial for advancing holographic technologies. This study presents the modification and characterization of a recently reported photopolymerisable glass composition, focusing on achieving an optimal balance between maximizing photosensitivity of the material during recording and minimizing the impact of UV light on the performance of the recorded holographic structure. Building on these findings, the ultimate goal is to develop UV-resistant holographic optical elements for wavefront sensing in space applications. The research approach is based on introducing a photosensitiser with weaker UV absorption than the typically used in this material …

Bayesian Networks For Safety-Critical Systems, Joseph Mietkiewicz

Theses

This thesis addresses a operational challenge in modern industrial operations: the increasing complexity of systems and the consequent cognitive burden on operators. As industrial technologies advance, the human-computer interface has become the primary conduit for information flow, playing a pivotal role in operational decision-making. However, the proliferation of data often leads to information overload, potentially compromising rather than enhancing operator performance. This research explores an approach to this pressing issue through the application of Bayesian networks as decision support systems in safety- critical scenarios. Our study employs a multi-faceted approach, combining theoretical modeling with empirical testing. Through collaboration with industry …

Modelling The Formation Of Unslanted Holographic Gratings In Hybrid Photopolymer Media, Jack Lyons, Dana Mackey, Izabela Naydenova

Articles

The theoretical modelling of holographic recording in photopolymers has been an important tool in their optimisation. More complex, hybrid organic/inorganic photopolymers have been developed in pursuit of materials with higher sensitivity, low shrinkage, high dynamic range and environmental stability. Recent attempts to augment the existing models for the redistribution of inorganic nanoparticles in holographic recording were successful but there is still a knowledge gap in regards to modelling optical losses, mutual cross-diffusion, the formation of slanted holographic gratings and polymerization induced shrinkage in hybrid photopolymer media. This paper will describe a novel approach to modelling the formation of unslanted holographic …

Highly Sensitive Diffractive Optical Structures For Detection Of Volatile Organic Compounds, Faolan Radford Mcgovern

Doctoral

From aerospace to agriculture, sensors play a fundamental role in many aspects of modern life. Sensors form an integral part of the complex systems and devices required for the continued functioning and development of services and industries across society. The use of sensors is paramount in areas affecting human health, one such area being the monitoring of indoor air quality, in particular the detection of volatile organic compounds (VOCs). Human contact with VOCs has been associated with many health complications, including skin and eye irritation, cardiovascular damage, and cancers. Optical, electrical, gravimetric, and chemical sensors have been developed for VOC …

Comparing Multivalent Cross-Linked Sodium Alginate/ Montmorillonite Beads Containing Curcumin Or Cayenne Pepper As Drug Carriers And Their Antimicrobial Resistance, Shionainn Traynor, Shubham Sharma

SURE Journal: Science Undergraduate Research Experience Journal

The aim of the following work was to investigate and compare curcumin and cayenne pepper in microbeads composed of sodium alginate (SA) crosslinked with montmorillonite (MMT) to act as drug carriers and their antimicrobial resistance. The microbeads were prepared through ion-exchange in multivalent solutions of CaCl₂, MgCl₂ and AlCl₃. The microbeads were characterized by Scanning Electron Microscopy (SEM), Energy Dispersive X-ray analysis (EDX) and the time required for intercalation of curcumin and cayenne pepper with MMT. Simulated intestinal fluid (pH 7.3) and gastric fluid (pH 1.2) at 37°C were used for comparison in swelling studies. …

Review: Cell Mechanisms In The Control Of Inflammatory Foundations Of Obesity And Secondary Metabolic Disorders, Kristin Goldstone, Keelin Irwin, Ava O' Meara-Cushen, Cathy Brougham

SURE Journal: Science Undergraduate Research Experience Journal

Review: Obesity is a global threat to human health that is precipitating epidemically, with almost 2 billion adults categorised as overweight or obese in 2016. Ireland has one of the highest reports of obesity in Europe with 60% of Irish adults reported as overweight or obese. Inflammation plays an integral role in disease pathogenesis and is a major hallmark of obesity and obesity-related secondary co-morbidities. Obesity-related inflammation can precipitate several diseases, such breast cancer, insulin resistance, type II diabetes and cardiovascular issues. As obesity puts human health at inherent risk, the regulation of obesity-related inflammation is therefore a necessary component …

Mathematical Modelling Of Disease Outbreak, Favour Christian, Matthew Molloy

SURE Journal: Science Undergraduate Research Experience Journal

Establishing a model framework for more research necessitates a thorough understanding of the causes, distribution, prevalence, and evolution of infectious illnesses. The main mathematical concept used in this modelling simulation is ordinary differential equations (ODEs). The purpose of this study was to investigate the significance of the many criteria linked to a zombie virus spread. The zombie framework provides an accessible and relatively simple representation of the nature of infectious disease spread, allowing for tractable assumptions and the development of more complex situations.

The models are designed around a zombie outbreak in which the zombie virus is spread through a …

Editorial - Sure Journal Vol 6, Anne M. Friel, Eva Campion, Sinead Loughran

SURE Journal: Science Undergraduate Research Experience Journal

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Identification And Profiling Of Socioeconomic And Health Characteristics Associated With Consumer Food Purchasing Behaviours Using Machine Learning, Daniel Burke, Martin Boudou, Jennifer Mccarthy, Majid Bahramian, Courage Krah, Christina Kenny, Paul Hynds, Anushree Priyadarshini

Articles

Food systems and food-related policies influence food consumption, dietary patterns, and human and environmental health. Consumers play a vital role in enhancing health and sustainability through their purchasing choices. To identify and cluster food purchasing behaviours and map relationships, a cross-sectional survey was conducted across Ireland with a sample size of 957 adults. Two-step cluster analysis, generalised linear models, and recursive partitioning and regression trees were used to elucidate adherence to identified food purchasing behavioural clusters. Three clusters (‘food quality’, ‘taste’, and ‘price’) were identified based on food purchasing priorities and statistically categorised. ‘Food quality’ members were significantly less likely …

Identifying Subject Bias In Wifi-Based Human Activity Recognition Evaluation Methods, Amany Elkelany, Robert J. Ross, Susan Mckeever

Conference papers

WiFi-based Human Activity Recognition (HAR) has emerged as a promising approach for monitoring and analysing human activities in a non-intrusive manner, leveraging WiFi signals for activity classification. Despite advancements, existing WiFi-based HAR research lacks consideration of subject (human) bias. This results in learning models performing well on individuals used in the training samples but failing to generalise to new/unseen subjects, in contrast to known good practices in machine learning. In this paper, we address this oversight directly by systematically examining the evaluation methodology for the WiFi-based HAR context. Specifically, we investigate the impact of Leave-One-Subject-Out Cross-Validation (LOSOCV) in a hybrid …

On The Benefits Of Directness In Virtual Characters For Motivational Interviews, Michael O'Mahony, Cathy Ennis, Robert Ross

Conference papers

Understanding the factors influencing successful engagement with Embodied Conversational Agents (ECAs) remains a significant challenge. This understanding could be used to personalise agents to users to improve interactions. Some studies have shown that simulating personalities in healthcare agents can improve effectiveness and engagement. However, it is not yet well understood how variations of agent personality can be leveraged to improve user engagement with Motivational Interviewing (MI) ECAs. Specifically how the balance between agent warmth and directness can be controlled in an MI agent to improve likeability and engagement. We conducted an online Wizard-of-Oz (WoZ) mediated study of two variants of …