Data Science Commons^™

Identifying And Forecasting Key Drivers Of Greenhouse Gas Emissions In Ireland's Residential Sector Multivariate Time Series Analysis., Sallam Noor Aldeen Salman

ICT

This study evaluates advanced time-series forecasting models to predict greenhouse gas emissions (GHGE) in Ireland's residential sector. LSTM, XGBoost, and ARIMA models were tested alongside feature selection methods including PCA, XGBoost-based importance, and Granger causality. Urban population growth and electricity consumption emerged as the most significant predictors. While LSTM struggled due to limited data, XGBoost showed strong predictive performance (MAPE ~9%), and ARIMA with key features achieved the highest accuracy (MAPE 3.78%). Forecasts indicate a declining GHGE trend, offering actionable insights for environmental planning and policy in the residential sector.

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

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 …

Tour Demand Forecasting In Ireland: Development And Evaluation Of Classical, Deep Learning, And Hybrid Models, Ruben Elias Charleston Montfort

ICT

Tourism plays a significant role in global economies by supporting employment, infrastructure, and national development. As international travel continues to grow, accurate tourism demand forecasting has become increasingly important for effective planning and decision-making. In Ireland, tourism is a key economic sector attracting millions of visitors annually. For tour operators such as Irish Day Tours, reliable demand forecasting is essential for optimizing logistics, resource allocation, marketing strategies, and customer satisfaction. Advances in machine learning and deep learning techniques offer new opportunities to improve forecasting accuracy and support data-driven decision-making within the tourism industry.

‘Waves Of Imagination’ Unconditional Spectogram Diffusion Using Diffusion Architecture., Rahul Vanukuri

Computer Science and Engineering Theses - Archive

The swift evolution of wireless communication technologies,particularly in the field of rf signals or in CBRS bands,demands increasingly sophisticated signal processing techniques to ensure efficient transmission, reception, and spectrum management.Traditional approaches to signal generation and reconstruction, although effective in controlled environments, often struggle to cope with the challenges presented by real-world noisy conditions, hardware constraints, and limited access to large-scale datasets. In response to these limitations, this thesis explores the application of diffusion models—a class of generative models known for their ability to produce high-fidelity samples—to the domain of spectrogram generation for communication signals.

Different from conventional strategies to simulate …

System Dynamics With Insight Maker, Steven D'Alessandro, Fons Wijnhoven

Research outputs 2022 to 2026

This book offers a practical, model-driven pathway for reasoning about uncertain futures in business and public policy using system dynamics with Insight Maker. It begins by motivating why historical data alone often fail to predict social change, and it introduces the core language of system dynamics—stocks, flows, feedbacks, delays, and auxiliary variables—alongside the complementary use of agent-based modeling. Through business-relevant cases (e.g., park management trade-offs, epidemic–economy interactions, and industry competition), the book demonstrates how non-linear structure generates counter-intuitive dynamics, why scenario analysis is essential, and how to translate causal loop diagrams into stock-and-flow simulations. Readers are guided step-by-step to build, …

Assessing Deep Learning Models And Hyperparameter Optimization For Stable Time-Series Electricity Load Forecasting, Sukma Patrya, Aji Prasetya Wibawa, Aripriharta Aripriharta

Knowledge Engineering and Data Science

Long-term electricity load forecasting plays an important role in ensuring system reliability, optimizing energy management, and making operational plans in face of continuously rising electricity demands. This study suggests a complete deep learning method for univariate forecasting of future electricity loads based on climatology and electricity consumption data for the period between 2019 and 2023. The initial dataset was cleaned, normalized, and partitioned chronologically into train/test datasets. Four train/test split cases (20/80, 40/60, 60/40, 80/20) were considered to explore the impact of different levels of historical data availability on the performance of the suggested framework from data-poor to data-rich situations. …

Predicting Superconducting Critical Temperature From Composition-Derived Features: A Transparent Linear And Regularized Regression Study, Md Ahiduzzaman

Data Science and Data Mining

We study prediction of superconducting critical temperature (Tc) from 81 composition-derived descriptors across 21,263 materials. To keep the analysis transparent and repro- ducible, we focus on linear models: Ordinary Least Squares (OLS), Ridge, Lasso, and Elastic Net (ENet). All models share a single evaluation protocol (5-fold cross-validation with standardized inputs) and are compared on RMSE, MAE, and R2. On this feature set, OLS attains the best cross-validated performance (RMSE = 17.6 K, MAE = 13.3 K , R2 = 0.735), with Lasso/ENet essentially tied next (RMSE ≈ 17.7 K , R2 ≈ 0.734); Ridge underperforms (RMSE = 18.9 K , …

Comparative Analysis Of Lasso, Ridge, And Elastic Net For Variable Selection In High-Dimensional Maize Data, Md Ahiduzzaman

Data Science and Data Mining

In high-dimensional genomic data analysis, traditional linear regression techniques often struggle due to the presence of a large number of predictor variables relative to observations. Penalized regression methods such as LASSO, Ridge, and Elastic Net have emerged as effective solutions by imposing regularization, which helps in managing multicollinearity and enhancing prediction accuracy. This study applies these techniques to the Maize dataset to model the time to male flowering, selecting relevant genetic markers as predictors. Our findings suggest that Elastic Net is particularly effective for high-dimensional data with correlated variables, achieving a balance between prediction accuracy and variable selection. The results …

Handwritten Digit Recognition Using Machine Learning Classifiers, Md Ahiduzzaman

Data Science and Data Mining

This project explores and compares the performance of various machine learning classifiers for handwritten digit recognition using the MNIST dataset. The classifiers include Logistic Regression, k-Nearest Neighbors, and Convolutional Neural Networks. Each classifier is evaluated based on accuracy, precision, recall, F1-score, and confusion matrix analysis.

Cmc Thesis Chatbot, Luis Gomez

CMC Senior Theses

This GitHub repo is a senior thesis for Claremont McKenna College; it is a thesis about theses. The project is an interactive RAG-based chatbot that helps students, researchers, and faculty explore Claremont McKenna College senior theses. The goal was to create a domain-specific chatbot to show that it is possible to combat the limitations of AI, including hallucinations, outdated data, and lack of domain expertise. The website link is:

CMCThesisChatbot

Analyzing Political Sentiment On Micro-Blogging Data: A Lexicon And Machine Learning Approach To The 2024 U.S. Presidential Election, Ava Grey

CMC Senior Theses

This paper explores the trends in sentiment towards U.S. presidential candidates Kamala Harris and Donald Trump through micro-blogging social media text during the five months leading up to the election. Two datasets of varying sizes and origins were used to contextualize and validate analysis findings. The analyses include both a lexicon-based approach and a machine learning predictive method. Common sentiment analysis techniques like term frequency, term frequency inverse, various lexicons, and n-grams were utilized during the lexicon approach. During the modeling, a random forest was utilized in addition to the methods used during the lexicon approach. Results showed that overall …

Neural Correlates Of Attentional Biases In Dietary Choice: Role Of Childhood Socioeconomic Status, Justine Jamie N. Gotico

CMC Senior Theses

Childhood poverty has been shown to increase adult risk for obesity above and beyond its direct effects on adult socioeconomic status (SES). One proposed mechanism of these effects is by shifting behavioral patterns of dietary consumption and choice, for example by increasing rapid attention to high-calorie unhealthy foods. Yet, whether such neural mechanisms can explain observed differences in dietary behavior based on childhood SES remains an open question. Here we used event-related potentials (ERPs) to examine early attentional correlates of low childhood SES during a dietary choice task, based on research suggesting that early attentional biases toward high-calorie foods emerge …

Analyzing Patterns In Chicago Motor Vehicle Crashes Using Time-Series Techniques, Christina Trotta

Senior Honors Theses and Projects

This project explores time series forecasting of daily traffic crash rates in Chicago from 2018 to 2024, with a focus on understanding how past crash patterns and external conditions influence future risk. The primary research question asks: To what extent does yesterday’s crash rate help predict today’s? Using a combination of Holt-Winters exponential smoothing, Prophet forecasting, and SARIMAX models, we assess the role of autoregression, seasonality, and exogenous variables such as weather and roadway conditions. Daily crash data was cleaned, aggregated, and enriched with engineered features including holiday indicators, weather metrics from O’Hare and Midway airports, and binary flags for …

Embscu, Mariia Khan, Jumana Abu-Khalaf, David Suter, Bodo Rosenhahn, Yue Qiu, Yuren Cong

Research Datasets

This dataset was created for the evaluation of the EmbSCU method, suitable for solving the Scene Change Understanding (SCU) task. The SCU task involves predicting a changed location, describing a change, and generating language instructions for the robotic agent to revert a change. Current datasets, related to scene change understanding, can be divided into scene change detection (SCD) and image difference captioning (IDC) datasets. Unlike existing approaches, EmbSCU facilitates simultaneous change detection, description and language-based rearrangement instruction generation for the agent to revert changes. Although the EmbSCU dataset is simulated, it is highly complex, incorporating 104 unique indoor Ai2Thor rooms. …

M3t, Mariia Khan, Jumana Abu-Khalaf, David Suter, Bodo Rosenhahn, Yue Qiu, Yuren Cong

Research Datasets

For embodied agents, such as robots, tracking objects in their surroundings through visual observation is essential — a task, referred to as Visual Object Tracking (VOT). For instance, during a rearrangement task, a robot may need to track objects, as part of the scene change understanding process, to accurately restore them to their original states. Classic Multiple Object Tracking (MOT) datasets typically focus on tracking moving, single-class object instances in a video from a fixed viewpoint, limiting their applicability to embodied AI tasks. In embodied AI tasks, objects belong to multiple classes, are often static, and are observed from continuously …

A Two-Phase Learning Approach Integrated With Multi-Source Features For Cloud Service Qos Prediction, Fuzan Chen, Jing Yang, Haiyang Feng, Harris Wu, Minqiang Li

Information Technology & Decision Sciences Faculty Publications

Quality of Service (QoS) is a key factor for users when choosing cloud services. However, QoS values are often unavailable due to insufficient user evaluations or provider data. To address this, we propose a new QoS prediction method, Multi-source Feature Two-phase Learning (MFTL). MFTL incorporates multiple sources of features influencing QoS and uses a two-phase learning framework to make effective use of these features. In the first phase, coarse-grained learning is performed using a neighborhood-integrated matrix factorization model, along with a strategy for selecting high-quality neighbors for target users. In the second phase, reinforcement learning through a deep neural network …

Model-Free Organization Of Patient Reported Outcomes Data: Geometrical Rep-Resentation Of The Modified Compartmen-Talization Method, Manasi Sheth, N. Rao Chaganty

Mathematics & Statistics Faculty Publications

There is a recent advancement in the field of mathematics and statistics to understand the geometry or connectedness of the data due to the massive amounts of data being generated. The data provided for analyses are usually very large and need to be organized and minimized in order to make it more useful and meaningful. In biostatistics or medical field, it is important for patients to have access to high-quality, safe and effective and/ or efficacious medical products. It is quite necessary to ascertain that the patients and their care-partners stay at the center of the regulatory decision-making process. In …

A Comparative Analysis Of Preprocessing Filters For Deep Learning-Based Equipment Power Efficiency Classification And Prediction Models, Sang-Ha Sung, Chang-Sung Seo, Michael Pokojovy, Sangjin Kim

Mathematics & Statistics Faculty Publications

The quality of input data is critical to the performance of time-series classification models, particularly in the domain for industrial sensor data where noise and anomalies are frequent. This study investigates how various filtering-based preprocessing techniques impact the accuracy and robustness of a Transformer model that predicts power efficiency states (Normal, Caution, Warning) from minute-level IIoT sensor data. We evaluated five techniques: a baseline, Simple Moving Average, Median filter, Hampel filter, and Kalman filter. For each technique, we conducted systematic experiments across time windows (360 and 720 min) that reflect real-world industrial inspection cycles, along with five prediction offsets (up …