Data Science Commons^™

Enhancing Insider Threat Detection Through A Hybrid Approach Using Different Artificial Intelligence Techniques., Jose Roberto Da Silva Dure

ICT

Insider threats pose significant challenges in cybersecurity due to their origin from individuals with legitimate access. Traditional defenses often fail to detect malicious behavior embedded within normal activities. This study proposes a hybrid artificial intelligence framework that integrates unsupervised anomaly detection, supervised and ensemble learning, and deep learning to enhance insider threat detection. Using the CERT 4.1 dataset, features encompassing temporal, behavioral, network, and psychometric aspects were engineered. Anomaly detection models informed supervised and ensemble classifiers, while a multi-input deep learning architecture captured sequential and contextual patterns. Performance evaluation using ROC-AUC, precision, recall, F1-score, and cost-sensitive analysis demonstrates that the …

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 …

Enhancing Fake News And Rumor Detection Using Metadata, Context, And User Interaction With A Hybrid Gcn-Transformer., Lucas Romulo Zuin Gigli

ICT

The increasing influence of social media has significantly impacted how news spreads among users, making the detection of misinformation, including fake news and rumours, a critical task. Previous research has explored content-based and metadata-driven approaches, while recent advancements have leveraged Graph Neural Networks (GNNs) such as GCNs, GATs, and SAGE to analyse user interactions and propagation patterns. This thesis investigates the effectiveness of a Hybrid Graph-Transformer Convolutional Neural Network (GCN-Transformer) for fake news detection, combining graph-based learning with Transformer attention mechanisms. Through in-depth data analysis and advanced detection techniques, this model aims to enhance predictive performance and mitigate the spread …

Efficient Evaluation Of Nala In Convlstm For High-Dimensional Time-Series Traffic-Flow Forecasting ., Sergej Sisov

ICT

Accurate traffic congestion prediction is essential for effective urban mobility and energy-efficient transport planning. This study evaluates the impact of optimizers on ConvLSTM models for short-term traffic forecasting using the METR-LA dataset, incorporating weather and event data as exogenous features. Two optimizers—Adam and Nesterov-accelerated Lookahead (NALA)—are compared under a frozen-T evaluation to prevent temporal leakage. Data preprocessing included five-minute alignment of weather data and event integration based on sensor proximity. Results show that the best-performing model (Variant C-2), integrating traffic, weather, and event features with reduced dropout and NALA optimization, achieved an MAE of 5.22 and RMSE of 8.79, outperforming …

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 …

Discovering Latent Themes: Mixed-Methods Comparative Analysis Of Topic Extraction And Clustering., Laura Byrne

ICT

This study investigates whether clustering and topic modeling can uncover themes within the 167 verses of the King James Version of the Book of Esther. A standardized preprocessing pipeline was applied, and TF-IDF and sentence-embedding feature spaces were used to evaluate topic extraction (NMF, GSDMM, BERTopic, Top2Vec) and clustering (HDBSCAN, DBSCAN, Gaussian Mixtures, Agglomerative) using coherence, cluster validity, lexical distinctiveness, and cross-model similarity metrics. NMF provided the most interpretable topics, GSDMM yielded compact low-overlap topics, BERTopic offered entity-centered groupings, and Top2Vec highlighted broad thematic regions. Cross-model analysis revealed overlapping motifs, notably decree and banquet themes, but clusters and topics did …

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 …

Exogenous Variables In Time Series Forecasting During Economic Volatility: A Cross-Sector And Cross-Crisis Evaluation., Orla Kavanagh

ICT

Economic disruptions pose challenges for time series forecasting, but also offer opportunities to evaluate the value of exogenous information. This study investigates the role of external indicators during the COVID-19 pandemic and the 2025 U.S. tariff shock, using data from Dell (technology) and Boeing (aerospace/manufacturing). ARIMA, SARIMA, and LSTM models were tested with candidate exogenous variables. Results indicate that the benefit of external signals is highly context-dependent: sector, disruption type, and forecast horizon all influence effectiveness. LSTM models generally excel at longer horizons with exogenous inputs, while ARIMA-based models perform better short-term. Findings emphasize that careful variable selection, ongoing evaluation, …

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.

Identifying Skills Gaps And Labour Market Trends Using Machine Learning: A Data Analysis Of Tendencies Within The Irish Online Job Market., Stephen Ray

ICT

The Irish labour market is a dynamic system characterised by overlapping and distinct demands. The skills required to maintain competitiveness within today's labour market are constantly evolving in response to the needs of corporations, non-governmental organisations, and capital. Labour market participants, fulfilling specific roles, contribute to a skill-based relationship where supply and demand operates in new and innovative ways. With the advent of the internet, the labour market has shifted towards a digital environment, leading to the rise of online job advertisement platforms that cater to geographically defined regions. These platforms attract vast numbers of users and prospective employees daily …

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 …

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

ICT

Accurate demand forecasting is critical in operational settings where resource allocation and planning decisions depend on anticipated service volumes. Transactional systems that capture timestamped records provide valuable data sources for developing demand forecasts. This study examines hourly call volume forecasting using New Orleans police calls for service data, comparing the performance of statistical models, tree-based methods, and recurrent neural networks.

The research evaluates four primary modelling approaches: ARIMA models representing traditional statistical methods, XGBoost and Random Forest as a tree-based ensemble technique, and Gated Recurrent Units (GRUs) as deep learning alternatives. A naive seasonal model serves as the baseline benchmark. …

Predictive Analysis Of Atlantic Meridional Overturning Circulation Amoc And Its Climatic Implications On Ireland Using Advanced Machine Learning Techniques., Corey Whyte

ICT

The Atlantic Meridional Overturning Circulation (AMOC) plays a key role in regulating climate across the North Atlantic, including Ireland. This study analyses AMOC variability using RAPID, ODYSSEA, and MÉRA datasets to examine relationships between subsurface ocean conditions, sea surface temperatures, and air temperature. ARIMA/SARIMA models identify trends and correlations, while a Long Short-Term Memory (LSTM) neural network forecasts future climate changes. Results show rising sea surface temperatures, moderate links between salinity and temperature, and seasonal air temperature patterns with a slight upward trend. The LSTM model outperforms traditional statistical approaches in capturing complex patterns, highlighting the value of machine learning …

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 …

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, …

Optimizing Lstm Neural Network For Multimodal Multivariate Footfall Prediction., Aws Al Adhami

ICT

Accurate forecasting of pedestrian activity is important for smart city planning, retail analytics, and public service management. This study presents a forecasting framework using Long Short-Term Memory (LSTM) neural networks that integrates multiple data sources, including weather, calendar variables, and engineered temporal features. The model development follows a structured pipeline from baseline univariate models to a fully optimized multivariate model using feature engineering and hyperparameter tuning techniques such as Hyperband and Bayesian optimization. The final model achieved a Mean Absolute Error of 24.05 and explained 97.10% of the variance in unseen data, effectively capturing both regular patterns and short-term fluctuations …

Neural Networks Activation Functions And Hybrid Activations Functions Accuracy And Loss Comparison On Small Dataset Against Large Datasets For Classification Problems., Antonio Felipe Cora Martins

ICT

Even on the era of Big Data, small datasets are the reality of many companies and sectors. Many datasets in rare disease diagnosis, custom manufacturing, military sciences, bioengineering, and disaster events are commonly limited in size, making machine learning predictive modelling difficult. Being the Activation Function choice crucial for Neural Networks learning, it raises the question of their effectiveness in such scenarios. This study compares five standard (single) activation functions (Sigmoid, Tanh, ReLU, Leaky ReLU, ELU) and two hybrid variants (one a mix of ReLU plus Tanh and a Learnable Activation Function with a trainable weight (alpha) that balances ReLU …

Leaf Classification Using Convolutional Neural Networks And Vision Transformers, Louis Wilkie

ICT

This study investigates deep learning techniques for plant image classification using four publicly available datasets: Folio, Flavia, Swedish, and Sugarcane. Three modelling approaches—Convolutional Neural Networks (CNN), Vision Transformers (ViT), and hybrid CNN-ViT ensembles—are evaluated. Models leverage PyTorch implementations and pretrained weights from the Hugging Face hub, with hyperparameter tuning applied to optimize accuracy. Transfer learning with pretrained ViT models achieved the highest performance, attaining 100% accuracy on Folio, Flavia, and Swedish datasets, and 94.07% on Sugarcane. CNN and CNN-ViT models showed comparable results, but pretrained CNN-ViTs were more resource-efficient. The findings highlight the importance of evaluating multiple pretrained architectures to …

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 …

Ripples: An Automated Embedding Generation Algorithm For The Forward-Forward Algorithm, Spencer Connolly

All Graduate Theses, Dissertations, and Other Capstone Projects

The Forward-Forward algorithm (FF) is yet another novel invention by Geoffrey Hinton, the creator of the famous backpropagation algorithm (BP). Since its proposal, many papers have been published exploring its potential, and good progress has been made in increasing its viability. Though FF continually falls short of BP, its purpose is not to replace BP and preliminary research shows that there is plenty of room for growth. In this paper, we present a literature review for FF algorithms applied to Convolution Neural Networks (CNN) for image classification tasks and set the stage for applying FF to more complex datasets. The …