Physical Sciences and Mathematics Commons^™

High Frequency Dc-Dc Converter Design Optimization, Modeling And Control Based-Wbg Technology., Salah Ahmed Abdullah Eltief

Electronic Theses and Dissertations

The performance of DC-DC power converters is a cornerstone of modern electric vehicle (EV) powertrains, directly governing overall system efficiency, size, cost, and reliability. This dissertation presents a comprehensive performance analysis and optimization of DC-DC converter topologies to determine the most suitable design for high voltage EV applications. The evaluation rigorously compares efficiency, power losses, and physical size under a range of harsh operating conditions. A primary objective is to leverage Wide Bandgap (WBG) semiconductors, specifically Silicon Carbide (SiC), to push the performance boundaries of power conversion. While SiC devices are known for their superior material properties, a clear understanding …

Archaeological Predictive Meta-Modeling In Pre-Columbian Mexico, Peter Stamm

Electronic Theses and Dissertations

Archaeological Predictive Modeling stands firmly as an important tool for Archaeologists to predict undiscovered sites from civilizations all across the globe. While powerful, this methodology is not without its own set of qualms. Striking a balance between pure a data-driven approach while also observing leading expert theories can be a complicated task. Going further, deciding on the specific domain of features to emphasize or overlook can be a challenge within itself, as one misstep can drastically change the output of model, sometimes for the worst. In addition, creating models that can expose their reasoning process can be rather difficult to …

“The Role Of Machine Learning In Social Media Content Moderation”, Bethaney A. Mallory-Smothers

Science University Research Symposium (SURS)

The majority of the world's over one billion active users depend on large-scale machine learning based social media platforms to personalize content through various methods of curation. Recommendation systems use many types of machine learning model including collaborative filtering, content-based filtering and deep learning to find what a specific user is likely to be interested in, and therefore increase user engagement. As beneficial as this is to the user experience, it has also generated a significant amount of concern about both bias in recommendations and the dissemination of false information on the web, as well as issues related to the …

Csc36000 - Modern Distributed Computing Assignment, Saptarashmi Bandyopadhyay

Open Educational Resources

This assignment covers standard performance metrics for Distributed Systems and the basics of Multiprocessing for CSC36000 - Modern Distributed Computing at the City College of New York CUNY. It is an interactive coding assignment intended to be executed in a Python notebook.

Multi-Label Classification Of Acoustic And Electronic Drum Sounds Using Machine Learning, Sean Perman

Electronic Theses and Dissertations

This paper presents a system for multi-class classification of drum sounds using audio signal processing and machine learning techniques. The project utilizes a diverse dataset of both acoustic and electronic drum samples and extracts ten distinct audio features to capture the timbral and temporal characteristics of each sound. The methodology includes signal preprocessing, feature extraction, and the application of supervised classification algorithms to distinguish between multiple drum classes. Experimental evaluations demonstrate that the selected features significantly enhance classification accuracy across a varied dataset. These findings underscore the effectiveness of combining traditional audio processing with modern machine learning, offering promising applications …

Enhancing Multi-Step Stock Price Forecasting With Social Media Sentiment And Engagement Metrics, Damilare Olaniyan

Electronic Theses and Dissertations

This thesis investigates whether social media sentiment can improve the accuracy of stock price prediction beyond traditional historical data. While financial markets have long relied on structured numerical indicators, the growing influence of public discourse on platforms like Twitter has introduced new opportunities for extracting market-relevant signals from unstructured text. The study focuses on four major technology firms and combines sentiment features derived from Twitter with historical stock prices in a hybrid machine learning framework. Engagement-weighted sentiment, linguistic complexity, and polarity intensity were extracted using natural language processing techniques and incorporated into classification and regression models. Results show that including …

Collaborative Federated Learning For Robots In Heterogeneous Environments, Karlan Schneider

Electronic Theses and Dissertations

This research investigates the performance of Federated Averaging (FedAvg) in simulated Federated Learning (FL) scenarios with varying degrees of environmental heterogeneity among robotic agents. The study explores the impact of data heterogeneity on both the convergence of FedAvg and the fairness of learning, with regard to consistency of performance across agents. Experiments were conducted with simulated robots trained to perform a target collection task, where a subset of agents encountered an unfamiliar environment. The results demonstrate that while FedAvg exhibits resilience to the introduction of new environmental data, it struggles to ensure both convergence and fairness in heterogeneous settings. Specifically, …

Optimizing Option Market Clearing, Juan Andrés Malaver Alvarado

Electronic Theses and Dissertations

Modern options markets clear each strike in isolation, leaving cross-strike arbitrage unexploited. This thesis applies a payoff-dominant clearing mechanism to realized trades—roughly 2 000 Cboe VIX option executions from June–November 2016—after classifying each trade’s side and bundling by expiration. Three optimization formulations are tested: a fractional linear program (LP), a mixed-integer LP, and a pure integer program. On a 10-core laptop every bundle solves in < 0.5 s. The LP captures the greatest surplus, yet the integer models recover nearly as much while filling whole contracts and holding only modest margin. Results reveal persistent, albeit small, inefficiencies in executed trades and demonstrate that an integral cross-strike auction could operate in real time. The accompanying C/Gurobi code is modular and readily extendable to early-exercise options. Trade-level evidence thus supports redesigning exchange clearing to consider the complete option book.

Quantum Chemical Methods And Multiscale Modeling In Computer-Aided Drug Design, Hunter W. La Force

Chemistry Theses and Dissertations

Computer-Aided Drug Design (CADD) leverages a diverse toolkit of computational methods to accelerate the discovery and development of novel therapeutics. Among these, quantum chemical calculations provide unparalleled accuracy in understanding molecular interactions, albeit at a higher computational cost. This accuracy is crucial for identifying and quantifying fundamental interactions that dictate drug efficacy and selectivity, as exemplified by our investigation of ruthenium polypyridyl complexes. These metal-based compounds are model systems for studying covalent coordination bonds between ruthenium and its ligands and noncovalent interactions with DNA and protein targets. Local Mode Vibrational Theory emerges as a powerful lens within this framework, enabling …

Hybrid Graph-Recurrent Architecture For Citation Recommendation Via Future Embedding Forecasting, Mohammad Ausaf Ali Haqqani

Computer Science and Engineering Theses and Dissertations

The rapid expansion of scientific literature has intensified the challenge of identifying relevant citations, particularly for newly published or under-cited papers. Traditional citation recommendation systems typically model static relationships or respond to past citation activity, offering limited predictive power for emerging works. In response, this thesis presents a temporal modeling framework for citation recommendation that anticipates future scholarly relevance by forecasting the latent representations of academic papers.

Building on prior work that utilized Temporal Graph Networks (TGNs) to model dynamic citation flows, we propose Graph-Time, a hybrid architecture that integrates a Graph Transformer with a GRU-based time series predictor. The …

Simulating 3d Humanoid Ragdoll Physics Using Velocity Verlet Integration, Pin Constraints, And Rigid Body Collision Systems, Son D. Nguyen

Programming Theses and Dissertations

Ragdoll physics simulates realistic character collapse with physical realism by responding to environmental forces rather than using predefined animations.

Ml Playground: Data Modification/Preprocessing And Model Simulation Tool, Marco D. Cerrato

Electronic Theses, Projects, and Dissertations

There is a heavy reliance on programming when it comes to learning machine learning (ML). This often creates barriers for students and newcomers unfamiliar with coding. While the lessons you learn in the classroom provide essential foundational understanding, some technical or practical aspects of ML—such as data preprocessing, feature engineering, and model tuning—are best learned through hands-on interaction. ML Playground was developed to act as a proof-of-concept application to address this gap by offering a browser-based, graphical user interface that lets users engage with core ML workflows without writing code. Designed with educational accessibility in mind, the application allows users …

Extending Feature-Based Detection For Artificial Intelligence, Kayla Ahrndt

SPARK Symposium Presentations

AI text generation is rapidly developing, and, as a result, it is becoming increasingly difficult to differentiate it from human written text. Our base study by Leon Fröhling et al. proposed a feature-based detection model trained on GPT2, GPT3, and Grover data, as well as human-generated text. Our work extends their research by training a modified model with four neural networks on word embeddings, select features from the original study, as well as updated data (GPT3, GPT4, and Grover).

Mashed Potato Gravy Boat And Cream Cheese Fish: Modifying A 3d Printer To Print With Unconventional Materials, Aahanaa Tibrewal

Mathematics, Statistics, and Computer Science Honors Projects

3D printing is growing beyond plastics into fields like food and construction, bringing rapid additive manufacturing to various industries and consumers. However, high costs and the need for specialized knowledge limit access for many. My project aimed to modify a low-cost 3D printer to print with paste-like materials using commonly available parts and simple processes. I tested the modification with clay, mashed potatoes, and cream cheese, and found that it successfully worked with all three. This modification has three key benefits: it allows users to print with unconventional materials, helps researchers create low-cost proof of concepts, and contributes to the …

Investigating Key Structures In Protective Scenes For Llms, Eben M. Weisman

University Honors Theses

This research delves into the realm of "protective scenes" within Large Language Models (LLMs), exploring their impact on bias mitigation, deception, and context preservation. The study investigates the use of roleplay prompting human-like behavior and reasoning in LLMs, focusing on the Character-LLM framework's concept of protective scenes with graduated levels of protection. By combining insights from psychology, cognitive science, and computational analysis, this research aims to develop a framework for understanding how protective scenes influence roleplay performance in LLMs, ultimately contributing to the development of more reliable and ethical AI systems.

Brain Tumor Classification Using Deep Learning: Custom Cnn Vs. Resnet50, Rayen Bentemessek

ICT

The project presents a deep learning solution to classify brain tumors through MRI images. Following the CRISP-DM framework, two Convolutional Neural Network (CNN) models were developed and evaluated, a custom CNN designed from scratch and a pretrained ResNet50 that was transfer learned and fine-tuned. Both models were assessed using standard performance metrics such as accuracy, precision, recall and F1-score. Despite the higher test accuracy achieved by the custom CNN, further interpretability indicated inconsistent attention to the actual tumor regions also known as shortcut learning. On the other hand, ResNet50 showed more reliable and clinically relevant focus which supported its selection …

Player Transfer Market In European Football Using Machine Learning To Analyse The Evolution Of The European Football., Pablo Lopes De Souza Oliveira

ICT

The study uses machine learning to analyse the European football transfer market from 2015 to 2025, revealing patterns in transfer fees and market values influenced by player attributes, highlighting the potential of data-driven insights.

Predicting Early Customer Inactivity In The Banking Sector Using Machine Learning: A Churn Prevention, Ivana Mc Fadden

ICT

Customer churn, when customers stop using a company’s services, is a challenge for the banking sector (Singh et al., 2023). High churn rates often signal poor customer experiences, resulting in revenue losses and increased costs to obtain new clients. Goyal and Srivastava (2015) stress that fostering loyalty through exceptional service and understanding customer needs is important for long-term retention.

This project aims to predict early customer inactivity, an indicator of churn, by using machine learning. Early identification of at-risk customers will allow banks to apply targeted interventions, reduce acquisition costs, and improve customer satisfaction (Singh et al., 2023). By analysing …

Using Machine Learning To Predict Credit Card Fraud, Pedro Henrique Das Chagas Morais

ICT

Credit card fraud poses a significant challenge to financial institutions, leading to substantial financial losses and declining customer trust. This project develops and evaluates machine learning models to detect fraudulent credit card transactions using a large, realistic synthetic dataset. Following data preprocessing, exploratory analysis, and class-balancing using SMOTE, four supervised models—Logistic Regression, Decision Tree, Random Forest, and XGBoost—were trained and compared. Performance was assessed using metrics suited to imbalanced classification, including AUC, Recall, Precision, F1-score, and Average Precision. Results show that XGBoost, particularly after hyperparameter optimisation, delivered the strongest performance (AUC 0.99, Recall 0.83, AP 0.70), outperforming other models and …

Fake News Detection Using Machine Learning Models., Anne Higgins

ICT

This project investigates the use of machine learning to detect fake news, addressing the societal and political challenges posed by the widespread dissemination of false and misleading information. Using automated classification techniques, the project analyses news content to predict the likelihood of intentional deception. The methodology follows the CRISP-DM framework, encompassing data preparation, model development, and evaluation. By leveraging machine learning, the study aims to support organisations, governments, and digital platforms in mitigating misinformation, while also considering ethical, interpretability, and strategic implications. The findings provide actionable insights for enhancing content moderation and reducing the influence of disinformation in digital media …

Data Analysis For Maintenance Reliability., Romulo Menezes Santos

ICT

This project explores the application of predictive maintenance (PdM) in modern manufacturing, highlighting its strategic role in reducing downtime, optimising resources, and improving operational efficiency. Traditional reactive and preventive maintenance approaches are insufficient for Industry 4.0 environments, where machine failures can cause significant financial and operational losses. By leveraging sensor data, historical performance records, and machine learning techniques, PdM enables early detection of potential equipment failures, allowing maintenance teams to act proactively. The approach not only enhances operational reliability and safety but also supports strategic decision-making, cost control, and competitive advantage in globalised manufacturing contexts.

Comparative Evaluation Of Ai-Generated Synthetic Data And Real-World Data Performance In Predictive Analytics., Corey Louise Hughes

ICT

There are growing restraints when it comes to Real World Data (RWD), these include topics such as privacy regulations, ethical concerns, and the cost of collecting the data, and they have drove an interest in AI-generated synthetic data as a potential alternative in predictive analytics. This project examines the possibilities of synthetic data and if it can act as a reliable substitute for RWD in predictive modelling. This project uses Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) to generate synthetic reproductive health data and evaluates its predictive performance against RWD using linear regression and key metrics, including Mean Absolute …

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 …

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.

Csc36000 - Modern Distributed Computing Assignment 2, Saptarashmi Bandyopadhyay

Open Educational Resources

This assignment is designed to help the student identify and mitigate common errors in Distributed Computing such as race conditions and reaching consensus, as well as reflecting on how Distributed Computing concepts apply to their class project.

Improving Chatbot Interactions Through Ai-Driven Hate Speech Detection: Evolving To A Safer Digital Environment-Poster, Rata Gheorghita, Wellington Mariano

ICT

This project aims to explore how Machine Learning can contribute to a better digital interaction, mainly focusing on environments such as online chats, social media, and customer support as they are now an imperative part of daily communication. With this, concerns around hate speech in digital conversations is critical (Council of Europe, 2024). This study focus on the development of a Hate Speech Language Detection Chatbot using machine learning techniques. The key purpose of the chatbot is to monitor and detect harmful content in real time, reducing the need for manual intervention. The creation and implementation of such a tool …