Computer Sciences Commons^™

Visualizing And Evaluating Binary Classifier Performance With Contingency Space, Colin D. Kehoe, Azim Ahmadzadeh

Undergraduate Research Symposium

Traditional metrics for evaluating binary classifiers, such as Accuracy, F1 Score, and True Skill Statistic (TSS), often obscure the underlying tradeoffs between true positive and true negative performance—particularly in imbalanced or high-stakes domains. This poster introduces the Contingency Space, a two-dimensional representation of classifier behavior defined by true positive rate (TPR) and true negative rate (TNR). Within this space, scalar performance metrics become geometric surfaces, revealing how scores vary across the entire landscape of possible classifier outputs.

We present a Python package that implements this framework, enabling users to map model predictions into the Contingency Space, visualize metric surfaces …

Efficient Self-Supervised Representation Learning For Large-Scale Time Series Classification, Kevin Garcia

Theses and Dissertations

Recently, there has been a significant advancement in designing Self-Supervised Learning (SSL) frameworks for time series data to reduce the dependency on data labels. Among these works, hierarchical contrastive learning-based SSL frameworks, which learn representations by contrasting data embeddings at multiple resolutions, have gained considerable attention. Due to their ability to gather more information, they exhibit better generalization in various downstream tasks. However, when the time series data length is significant long, the computational cost is often significantly higher than that of other SSL frameworks. In this paper, to address this challenge, we propose an efficient way to train hierarchical …

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 …

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.

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.

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 …

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.

Dogs Emotion System- Poster, Muhammad Anas Baig

ICT

This project is all about a deep learning-based “Dog Emotion System” that can figure out how dogs are feeling just by looking at their faces. We used a balanced set of 4,000 dog images with four different emotion categories and followed the CRISP-DM process to build it. The model was trained from scratch using a Convolutional Neural Network (CNN) without any pre-existing models. It is deployed using Steamlit, where people can upload pictures of their dogs and get their emotional state predicted in real time. The goal of this tech is to make it easier for pet owners to understand …

Detecting Fake News Using Ai, Gustavo Lambert, Lucas Schultz

ICT

This is a document that presents a strategic analysis of the project “Detecting Fake News using AI”. Developed as part of the BSc (Hons) in Computing in IT at CCT College Dublin. The goal is to analyse the potential advantages, exploiting the viability and the impact of applying Artificial intelligence to check, verify and alert about misinformation found and to answer the question “How can Artificial Intelligence be leveraged to accurately detect and combat fake news while ensuring data privacy and compliance with regulations?” and “To what extent can AI-driven misinformation detection help reduce the spread of fake news on …

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 …

Development Of A Deep Learning Model For Synthetic Vs. Real Image Classification Synthetic Vs. Real Image Classification, Bernardo Gandara, Ignacio Varela

ICT

The advancement of generative AI technologies has made it increasingly difficult to distinguish synthetic images from authentic ones. This capstone project addresses the challenge by developing a binary image classification model using deep learning techniques to differentiate AI-generated images from real photographs. Guided by the CRISP-DM methodology, we employed the DeepGuardDB dataset, consisting of 13,000 balanced image samples, evenly split between real and synthetic sources. We implemented and compared three Convolutional Neural Network (CNN) architectures through transfer learning, standardising input pipelines and integrating custom classification heads. Following a performance evaluation across multiple metrics, the best-performing model was selected for further …

Data Driven Public Transport Planning In Dublin : A Clustering And Forecasting Approach, Magdalena Burtinik Urueta, Mirae Yu

ICT

Dublin faces increasing traffic congestions with over 76% of Irish residents relying on private cars for daily transport, well above the EU average (MacCarthaigh, 2022). This contributes to increased greenhouse gas emissions, challenging Ireland’s goals to reduce emissions by 55% by 2030. This project proposes a data-driven approach to identifying current transport accessibility gaps and forecasting future population growth across Dublin to support sustainable infrastructure development. Using Ireland’s Census data, an unsupervised method was applied to cluster EDs based on similarities in population dynamics. Forecasts were generated in 5-year intervals, revealing key growth corridors across Dublin using a clustered VAR …

Data-Driven Public Transport Planning For Dublin: A Clustering And Forecasting Approach, Magdalena Burtinik Urueta, Mirae Yu

ICT

Dublin has been experiencing severe traffic congestion due to rapid economic and population growth, with residents losing an average of 158 hours per year in traffic during rush hour (Europe Data, 2025). A 2022 European Commission study found that 76% of Irish people use a car as their primary mode of transport on a typical day—an 8% increase from 2019, compared to the EU average of 47% (MacCarthaigh, 2022).

This project proposes a data-driven approach to identifying current transport accessibility gaps and forecasting future population growth across Dublin to support sustainable infrastructure development. Using Ireland’s Census data, an unsupervised method …

Predicting Early Hospital Readmissions For Diabetic Patients Using Machine Learning, Amanda Ferraz, Leonardo Oliveira

ICT

This project applies machine learning to predict whether diabetic patients will be readmitted to a hospital within 30 days of discharge. Early readmissions are a costly and critical issue in healthcare, often signalling gaps in post-discharge care and risk management. Diabetic patients face unfair high readmission rates compared to the general population. According to the CDC Diabetes Report Card 37.3 million people in the U.S. or 11.3% of the population had diabetes as of 2019 (CDC, 2021). Our goal here is to develop a binary classification model capable of flagging high risk patient (< 30-day readmission) based on their clinical, demographic, and administrative data. This lets healthcare institutions to take measures,

Strategic Analysis Of Employment Permit Statistics And Predictive Analytics For Workforce Planning In Ireland- Poster, Amy Souza, Thaynna Vieira

ICT

This project analyses employment permit trends in Ireland from 2020 to 2025. It aims to help recruitment agencies and job seekers with data driven insights to enhance hiring placement. Forecasting permit demand by sector to help improve workforce planning and policy decisions.

Strategic Analysis Of Employment Permit Statistics And Predictive Analytics For Workforce Planning In Ireland, Amy Souza, Thaynna Vieira

ICT

By analysing historical employment permit data from Enterprise.gov.ie (Enterprise.gov.ie, 2024), this project has the aim to use Data Analytics and Machine Learning to make predictions of employment permits trends across sectors and companies, providing insights to optimize workforce planning for Recruitment Agencies and guide international job seekers requiring work visas. The insights gained are intended to enhance strategic recruitment practices and empower job seekers to make informed career decisions in Ireland’s competitive labour market.

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 …

Brain Tumor Classification Using Deep Learning- Poster, Rayen Bentemessek

ICT

The project presents deep learning solutions to classify brain tumors through MRI images. Two Convolutional Neural Network (CNN) models were developed, a custom CNN designed from scratch and a pretrained ResNet50 that was transfer learned and fine-tuned.

Both models were implemented following CRISP-DM methodology from data understanding to deployment, and they were evaluated using different metrics such as accuracy, precision, recall and F1-score.

Key Highlights: •The custom CNN model achieved higher accuracy but failed to locate tumors. •ResNet50 provided a good performance while balancing explainability through Grad-CAM. •Model was deployed through Gradio to demonstrate a real-world use of the solution.

Development Of A Deep Learning Model For Synthetic Vs. Real Image Classification Synthetic Vs. Real Image Classification-Poster, Bernardo Gandara, Ignacio Varela

ICT

This project develops a deep learning model to classify images as either AI-generated or real, addressing the growing challenge of synthetic media detection. Using the DeepGuardDB dataset and guided by the CRISP-DM methodology, we implemented and compared three Convolutional Neural Networks (CNNs) architectures via transfer learning. The best-performing model was further optimised using hyperparameter tuning and fine-tuning techniques The resulting model achieved strong accuracy and generalisation, making it a promising candidate for real-time deployment and practical use across diverse industries.

Dogs Emotion System, Muhammad Anas Baig

ICT

For our capstone project, we built a machine learning model that can look at pictures of dogs and figure out how they’re feeling, like if they’re happy, sad, or just chill. The idea came from how important pets are in people’s lives these days and how cool it would be to actually understand their emotions better using tech. This system will allow users to upload images of dogs, which are then analysed by a trained model to classify the dog's emotional states such as happy, sad, or neutral. We followed the CRISP-DM process to build it, which basically means we …

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.

Development And Evaluation Of Machine Learning Models For Early Pediatric Sepsis Prediction, Ancita M. Andrade

Browse all Theses and Dissertations

Sepsis is a leading cause of pediatric mortality, claiming more lives in the United States annually than all childhood cancers combined. Early identification in Emergency Departments (EDs) remains challenging, as the current Phoenix criteria establishes an updated international consensus definition for sepsis, however is not designed for use as a screening tool. This study aimed to develop predictive models identifying pediatric patients at risk of sepsis within 24 hours of admission. Multiple tree-based and deep learning models were trained utilizing clinical and laboratory data from the initial four hours of presentation. Both the LightGBM and LSTM architectures demonstrated superior performance, …