Data Science Commons^™

Implementation Of Time Series And Neural Networks For Forecasting Agricultural Prices In The Irish Market: A Comparative Analysis Of Milk, Beef, And Potatoes., César Augusto Núñez

ICT

Agricultural price volatility represents a central challenge for the Irish agri-food sector, affecting the stability of producers, cooperatives, and policymakers. This study aimed to compare three predictive approaches applied to strategic commodities such as milk, beef, and potatoes: a traditional statistical time series model (SARIMA) and two deep learning architectures (RNN and LSTM). Using historical price series collected over a decade, the models were developed and evaluated following a rigorous methodological process that included data preparation, algorithm training, and validation of results using performance metrics widely used in time series research. The findings show that the SARIMA model was most …

Enhancing Uk Electricity Price Forecasting Using Deep Learning., Stephen Cooke

ICT

Accurate short-term electricity price forecasting (EPF) is crucial for efficient operation of the UK’s multi-layered power market, impacting generators, traders, the ESO, and policymakers. Prices are highly volatile and non-linear due to renewables, demand fluctuations, and market coupling across Day-Ahead, Intraday, and Balancing Mechanism venues. Traditional statistical models often fail under such dynamics, while machine learning and deep learning approaches—particularly LSTM, GRU, and hybrid architectures—effectively capture temporal dependencies and exogenous drivers. Empirical evidence shows that these models outperform classical baselines, enabling more accurate scheduling, risk management, and financial savings.

Evaluating The Effectiveness Of Variational Mode Decomposition(Vmd)-Enhanced Hybrid Models For Forecasting Volatile Price And Indicator Time Series., Danny Allen

ICT

Forecasting volatile time series, such as commodity prices and market volatility indices, is critical for financial and operational decision-making. This study evaluates the impact of Variational Mode Decomposition (VMD) on hybrid forecasting models combining ARIMA, GARCH, and LSTM. Using gold, platinum, and VIX datasets, models were assessed via MAE, RMSE, and computational time. Results show that VMD improves ARIMA forecasts, and achieves optimal performance when combined with ARIMA-LSTM using performance weighting reducing MAE by 0.62-3.81 and reducing RMSE by 1.48-6.20. Conversely, VMD integration with ARIMA-GARCH and LSTM decreased accuracy. While VMD enhances predictive performance, it increases computational time substantially, increasing …

Exploring The Determinants Of Life Expectancy At Birth: Predicting And Forecasting Global Health Trends Using Statistical, Machine Learning, And Deep Learning Models., Emma Rath

ICT

Accurate life expectancy forecasting is essential for health policy planning, yet research comparing statistical, machine learning, and deep learning approaches under real-world constraints remains limited. This study evaluates ARIMA/ARIMAX, tree-based, and neural network models using Irish and global datasets, considering small samples, missing data, and COVID-19 shocks. ARIMAX with lagged socioeconomic variables outperformed LSTM and other ML/DL methods. Income-based stratification improved predictive accuracy and interpretability, with SHAP analysis highlighting GDP per capita for developed countries and school enrolment and trade indicators for developing contexts. Results provide practical guidance for policymakers and establish limits for model complexity under constrained health data.

Improving The Completeness Of Food Composition Databases Using Predictive Analysis., Carla Arenhart

ICT

This study investigates the use of machine learning regression models to impute missing micronutrient values in Food Composition Databases (FCDBs), focusing on the FAO/INFOODS dataset. A cascading prediction methodology leverages nutrient interdependencies to systematically estimate missing values. Four models—Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machines (GBM), and Deep Neural Networks (DNN)—were evaluated using MAE, MSE, RMSE, and R². RF and GBM achieved the highest predictive accuracy for protein, phosphorus, calcium, and magnesium, demonstrating that ML-based predictive analytics can provide a more reliable alternative to traditional imputation methods. These findings support improved dietary assessments, nutritional research, and data-driven …

Hybrid Mixtures Of Factor Analyzers For High Dimensional Data, Kazeem Abiodun Kareem

Dissertations, Master's Theses and Master's Reports

Factor analysis is a powerful tool for modeling latent structures in high-dimensional data, traditional approaches assume a single global structure, limiting their ability to capture heterogeneity. The Mixture of Factor Analyzers (MFA) extends classical factor analysis by modeling data as a mixture of Gaussian-distributed local subspaces, effectively uncovering cluster-specific latent structures. However, MFA relies on Gaussian mixtures, making it sensitive to outliers and ill-suited for heavy-tailed data. The Mixture of $t$-Factor Analyzers (M$t$FA) addresses these limitations by incorporating multivariate $t$-distributions, improving robustness. Despite their advantages, both MFA and M$t$FA face significant computational challenges in high-dimensional settings, particularly due to costly …

Demand Forecasting And Inventory Optimization In Mid-Sized Grocery Retail Using Machine Learning: A Data-Driven Approach To Minimizing Stock-Outs And Waste., Dragos Andrei Ungureanu

ICT

Mid-sized grocery retailers face a persistent challenge in balancing on-shelf availability with minimizing spoilage of perishable goods. This dissertation addresses this issue by developing a data-driven forecasting and inventory simulation framework within Microsoft Fabric, leveraging scalable data ingestion, Spark-based processing, and advanced machine learning. Using multi-year transactional data enriched with holiday schedules, promotions, and macroeconomic indicators, the study compares classical ARIMA models with XGBoost to capture complex demand patterns. Rigorous hyperparameter tuning in a distributed environment demonstrates that XGBoost outperforms baseline models in terms of MAE and MAPE, particularly during promotion-driven spikes. Inventory simulations based on these forecasts reduce stock-outs …

Comparing Custom And Transfer-Learning Cnn Models For Chest X-Ray Classification: Evaluating Performance And Scalability With Kubernetes Orchestration., Christopher Anich

ICT

Chest radiography (CXR) is the primary imaging tool for respiratory diseases, but interpretation can be time-consuming and requires expertise. This study compares a small custom CNN (Victorio) with three transfer-learning models (MobileNetV2, EfficientNet-B0, ResNet-50) for three-class CXR classification (normal, pneumonia, tuberculosis) and evaluates deployment on a Kubernetes cluster. Using a balanced dataset of ~13,500 CXRs, models were trained with ImageNet pre-training and from scratch. Transfer-learning models achieved near-perfect accuracy (99–100%) and AUROC, while the custom CNN reached 87–91%. Explainability analyses confirmed models focused on relevant pulmonary regions. Kubernetes deployment showed low latency, with most delays due to service overhead rather …

Data-Driven Decarbonisation Strategy For Residential Buildings: A Q-Learning-Based Simulation Approach., Erdenechimeg Tserendorj

ICT

This study presents an AI-driven framework for optimising energy retrofit interventions in Irish residential buildings, combining Q-Learning (QL), surrogate models (ANN, RF), and a Genetic Algorithm (GA) optimiser. Q-Learning simulated sequential retrofit decisions, generating a dataset used to train surrogate models for predicting Building Energy Rating (BER) and CO₂ outcomes. The GA explored retrofit combinations to maximise BER improvement, CO₂ reduction, and financial returns. The framework identified strategies achieving up to 928 kWh/m² BER reduction and 1,563 kg CO₂ reduction per building, with strong ROI and payback within 15 years. The modular, interpretable pipeline demonstrates the practical value of reinforcement …

Chess Evaluation And Player Profiling Using Convolutional Neural Networks (Cnns) And Spatial Recognition., Joel D’Mello

ICT

This thesis explores the feasibility of employing data analytics techniques in chess, with the purpose of profiling player styles and building a comprehensive chess analytics platform. The data set consists of over 20,000 anonymized games, and therefore, the study involved feature engineering, classification and visual analytics, in order to gain more insight into player decision making in chess. The data pre-processing part of analysis involved parsing Portable Game Notation (PGN) files, and feature engineering positional characteristics - material imbalance, pawn structure, king safety, and piece mobility - along with quantifying the sample using Average Centipawn Loss (ACPL) using Stockfish. ACPL …

Forecasting Hourly Police Call For Service Volumes: A Comparative Analysis Of Statistical, Machine Learning And Neural Network Models For Operational Planning., Patrick Duggan

ICT

This study investigates hourly call volume forecasting for New Orleans police service data, comparing statistical, tree-based, and deep learning approaches. ARIMA models represent traditional methods, XGBoost and Random Forest serve as tree-based ensembles, and Gated Recurrent Units (GRUs) provide deep learning alternatives, with a naive seasonal model as a baseline. Models are evaluated using a practical, expanding time-block framework simulating real operational deployment. Results show GRUs achieve the highest accuracy (R² = 0.74, MAPE = 13.5%), with XGBoost performing similarly, while ARIMA underperforms. Additionally, a Random Forest model offers interpretability, identifying key factors that drive forecasting performance, providing actionable insights …

Enhancing Financial Fraud Detection Using Explainable Deep Learning Models On Simulated Big Data Architectures: A Comparative Analysis With Traditional Methods., Aoife Yang

ICT

Financial fraud poses a growing global challenge, driven by the rapid expansion of digital banking, e-commerce, and mobile payments. Traditional rule-based and early machine learning systems struggle to detect novel and sophisticated fraud patterns in real time. This research investigates the integration of deep learning, explainable artificial intelligence (XAI), and big data technologies to enhance financial fraud detection. A scalable data pipeline is proposed to process large volumes of transactional data, improve detection accuracy, and provide interpretable insights for stakeholders. The study highlights the potential of combining advanced AI techniques with explainability to strengthen the transparency, effectiveness, and trustworthiness of …

Predicting S&P Corporate Credit Ratings Using Financial Ratios And Machine Learning: An Analysis Of European Non-Financial Companies., Gabriele Frattaroli

ICT

This study investigates the prediction of multi-class S&P corporate credit ratings for European non-financial firms from 2010 to 2024 using a machine learning framework grounded in financial fundamentals. To ensure robustness and generalizability, the analysis excluded the Year variable, which was identified as a source of data leakage. After this correction, non-linear ensemble models demonstrated a clear advantage over linear baselines. The top-performing Random Forest model achieved a weighted F1-score of approximately 0.60, more than doubling the performance of the Logistic Regression benchmark used as a baseline (0.26), with most misclassifications concentrated in adjacent rating categories. This indicates that while …

Exploring Workforce Attrition In Early Childcare Sector Using Data Segmentation, Machine Learning And Artificial Neural Networks., Anna Fontani-Tankovska

ICT

This research presents a framework of workforce attrition exploration based on integration of data segmentation, feature selection and classification models. K-prototypes clustering and generation-based segmentation are applied to create data subsets processed using logistic regression, random forest, naïve Byes, and artificial neural networks. By segmenting data, this study aims at enhancing models classification capacity. To address data imbalance and improve models ability to identify minority class, synthetic minority oversampling technique (SMOTE) and adaptive synthetic sampling approach (ADASYN) are applied, with average recall rate improving from 0.39 to 0.61 (SMOTE) and 0.50 (ADASYN). Feature selection using Recursive Feature Elimination with SVC …

Predicting Monthly Weather Anomalies In Ireland: A Comparative Study Of Machine Learning And Deep Learning Models., Fiona Behan

ICT

Abstract

This study investigates the application of statistical, machine learning, and deep learning methods to detect monthly weather anomalies in Ireland between 1960-2024. Climate variability is intensifying globally, increasing the urgency for accurate detection of unusual weather events. Using publicly available data from Met Eireann provided by the CSO PxStat Open Data Portal, the research applied comprehensive preprocessing including Bayesian Ridge iterative imputation, temporal and seasonal feature engineering, and ensemble statistical anomaly labelling based on z-score, interquartile range, and rolling residual analysis. Four models were developed and evaluated: Isolation Forest, XGBoost, Long Short Term Memory networks, and Gated Recurrent Units, …

Investigating The Effectiveness Of Traditional Vs Hybrid Time Series Models In Operational Planning., Cristina Priolo

ICT

This study conducts a systematic comparison of traditional and hybrid time series forecasting models for operational planning in a SaaS company, using large multichannel, multilanguage datasets aggregated at six-hour intervals. Classical models (ARIMA, SARIMA, ETS) are evaluated against hybrid models (ARIMA-ANN, SARIMA-ANN, ETS-ANN) to assess their ability to capture both linear and nonlinear patterns. Forecasts over a three-month horizon were evaluated using RMSE, MAE, and MAPE, with hyperparameter optimization applied to all models. Results show that hybrid models, particularly ARIMA-ANN and SARIMA-ANN, outperform traditional models in predicting volatile and high-volume data, while traditional models remain competitive for stable datasets. These …

Streamlining The Data Mining Process Through Ai-Driven Prompt Templates, Mia Montevirgen, Drew Yan, Clara Lu, Laurent Shen, Weihong Ni

Capstone Showcase

With the increasing use and relevancy of AI in the world, this project aims to harness the power of AI, specifically ChatGPT, to streamline the process of data mining workflows. By developing custom prompt templates, this project seeks to utilize OpenAI API to assist with key data mining tasks, including data understanding, importing, and cleaning. This approach aims to increase workflow speed, reproducibility, and accessibility in data mining projects. The effectiveness of these prompt templates is evaluated by applying them to diverse datasets and assessing their impact on accuracy, efficiency, and reproducibility. Overall, the project highlights the potential to use …

T3-Ciders: Train-The-Trainer And Community Building To Increase Cyberinfrastructure Adoption In Cybersecurity Research And Education, Wirawan Purwanto, Mohan Yang, Peng Jiang, Shanan Chappell Moots, Masha Sosonkina, Hongyi Wu

University Administration Publications

T³-CIDERS is a train-the-trainer program to increase the adoption of advanced cyberinfrastructure (CI) and data skills into the fabric of research and education in cybersecurity and cyber-related disciplines. T³-CIDERS trains faculty, researchers, and students as “future trainers” (FTs) with hands-on technical and instructional skills to enable more people to effectively leverage CI in cybersecurity. The program includes a series of technical pre-training modules, a weeklong summer institute, ongoing learning engagements conducted over an academic year; it culminates with the FTs conducting locally tailored CI-infused training events at their respective home institutions. Ultimately, T³-CIDERS aims to build a “CI+cybersecurity” community of …

In-Season Nitrogen Mmanagement: Leveraging Data Visualization For Precision Agriculture, Chathurika Harshani Narayana

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Effective nitrogen management is vital for sustainable agriculture, impacting both crop yield and environmental health. Traditional methods often use fixed application rates set before planting, which do not adapt to changing crop needs during the season. This can lead to over- or under-application, reducing efficiency and sustainability. While modern tools like sensors, satellites, and UAVs provide valuable real-time data on crop and field conditions, integrating and using this data to guide timely nitrogen decisions remains a major challenge. In-season nitrogen management offers a solution by allowing for dynamic adjustments to nitrogen applications, addressing crop needs as they arise. This approach …

Three-Dimensional Spreading Of Magnetic Reconnection Between Non-Parallel Flux Ropes With A Guide Field, Regis John

Graduate Theses, Dissertations, and Problem Reports (ETD)

Magnetic reconnection is a fundamental plasma process that facilitates the rapid conversion of magnetic energy into particle acceleration, plasma flows, and heating. It plays a central role in explosive astrophysical events such as solar flares, where vast amounts of magnetic energy are released on short time scales. A key structure in many reconnection sites is the magnetic flux rope, a column of plasma carrying current threaded by helical magnetic fields, which is frequently involved in or generated by reconnection. Understanding how reconnection unfolds in such flux rope systems is critical for interpreting both space weather phenomena and laboratory plasma dynamics. …