Computer Sciences Commons^™

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 …

Applying Machine Learning Methods To Generate Understandings Of Differential Item Functioning In A Flu Knowledge Assessment, William L. Romine, Tanvi Banerjee, Derrick Cox

Computer Science and Engineering Faculty Publications

Current influenza trends, including the severity of the 2025 flu season and the prevalence of H5 bird flu in livestock, necessitate efforts to better understand how to educate students about its transmission. Although validated assessments of influenza knowledge exist, these have not been evaluated for affective and demographic biases. We explore differential item functioning (DIF) effects in four items focused on specific aspects of flu transmission derived from a validated influenza knowledge assessment. In doing so, we introduce and utilize a machine learning framework for exploration of DIF which offers greater flexibility than traditional statistical approaches in terms of studying …

Extraction Of Patient Subtypes Using Llm Generated Knowledge Graphs Integrated With A Transformer Architecture, Benjamin Holmes, Cogan Shimizu

Computer Science and Engineering Faculty Publications

Extracting patient subpopulations (clinically relevant cohorts of individuals who share overlapping symptoms, risk factors, or diagnostic criteria) from unstructured medical notes is an ongoing challenge due to the variability of clinical language and the complex nature of patient conditions. We demonstrate a pipeline that combines named entity recognition (NER), transformer embeddings, guided dimensionality reduction, and LLM-mediated knowledge graph integration to enhance patient extraction. The approach begins with NER using the UMLS metathesaurus [1] to extract clinical terms, followed by transformation into vector embeddings using a biomedical transformer. These embeddings are augmented with structured knowledge graph representations generated through an LLM-driven …

Ontology-Based Data Organization For The Enslaved.Org Project, Cogan Shimizu, Pascal Hitzler

Computer Science and Engineering Faculty Publications

The men, women, and children forced into slavery in the Atlantic world came from diverse African societies with long histories of political, economic, and cultural development. They were taken from the trading centers of the Hausa city-states, the farming and artisanal communities of Senegambia, the Kongo and Mbundu polities of West Central Africa, and many other regions. They carried with them agricultural expertise, metallurgical skills, medical knowledge, religious traditions, and oral histories that helped sustain communities in the face of displacement and enslavement.Enslavement did not erase this intellectual and cultural inheritance, nor did it render its victims passive numbers in …

Ontology Population Using Llms, Sanaz Saki Norouzi, Adrita Barua, Antrea Christou, Nikita Gautam, Andrew Eells, Pascal Hitzler, Cogan Shimizu

Computer Science and Engineering Faculty Publications

No abstract provided.

Application Of Large Language Model Methods In Scientific And Technical Intelligence Practice, Bolin Hua, Yingze Wang

Journal of Scientific Information Research

[Purpose/significance]With the strong ability to process large-scale datasets and outstanding performance in various natural language processing tasks, large language models (LLMs) have excelled across multiple industries.Since scientific and technical intelligence primarily relies on textual data, LLMs are naturally well-suited for this field, ushering in a new wave of transformative changes. [Method /process]This article discusses the advantages of LLMs from five perspectives: low-dimensional dense vector representations of text, large-scale pre-trained models,fine-tuning and prompt learning, high-quality large-scale training data, and human alignment techniques. [Result/conclusion]LLMs have extensive applications in tasks such as intelligence identification, intelligence tracking, intelligence evaluation, and intelligence prediction, resulting in …

Extraction Of Fine-Grained Research Methods In The Field Of Information Science, Jiayi Hao, Yuzhuo Wang, Chengzhi Zhang

Journal of Scientific Information Research

[Purpose/significance]Research methods in information science are one of the critical research directions in this field. Constructing a fine-grained research method corpus and extracting research method entities can help scholars quickly understand the research methods in this field, explore the evolution of methods and their future development trends, and lay the foundation for the service and application of the research method corpus in the subsequent digital wave. [Method/process]Firstly, based on academic articles published in the Journal of the China Society for Scientific and Technical Information from 2000 to 2023, this study randomly selected 50 articles and manually annotated the research methodology …

Digital Infrastructure Development Through Digital Infrastructuring Work: An Institutional Work Perspective, Adrian Yeow, Wee-Kiat Lim, Samer Faraj

CCX Research

Being able to understand and characterize the digital infrastructure development (DID) process has become even more pressing today due to the rapid advent and implementation of new digital infrastructure (DI) in organizations as well as since the COVID-19 crisis. While information systems (IS) research has begun to recognize the institutional nature of such digital infrastructures, there remains a gap in our understanding of how such developments unfold from an institutional perspective. Through our field study of a digital infrastructure development project involving the implementation of an enterprise-wide electronic medical record system at a large US medical facility, we show how …

Why Ai Monitoring Faces Resistance And What Healthcare Organizations Can Do About It: An Emotion-Based Perspective, Karl Werder, Lan Cao, Eun Hee Park, Balasubramaniam Ramesh

Information Technology & Decision Sciences Faculty Publications

Continuous monitoring of patients' health facilitated by artificial intelligence (AI) has enhanced the quality of health care, that is, the ability to access effective care. However, AI monitoring often encounters resistance to adoption by decision makers. Healthcare organizations frequently assume that the resistance stems from patients' rational evaluation of the technology's costs and benefits. Recent research challenges this assumption and suggests that the resistance to AI monitoring is influenced by the emotional experiences of patients and their surrogate decision makers. We develop a framework from an emotional perspective, provide important implications for healthcare organizations, and offer recommendations to help reduce …

Signal-Based Error Handling: Case Study Using The Bathymetric Attributed Grid Library, Anthony R. Papetti

Honors Theses and Capstones

No abstract provided.

Design And Analysis Of Facial Recognition Algorithms For Home Monitoring, Nathaniel F. Bernich

Honors Theses and Capstones

Facial recognition "in the wild" has posed a challenge in the field of computer vision. Though facial recognition algorithms are generally proficient at recognizing faces up close, subjects at awkward angles and greater distances from the camera make monitoring areas with this software a practical challenge. At UNH's Cognitive Assistive Robotics Lab (CARL), overcoming the weak areas of face recognition is essential to the task of home monitoring. The CARL research team is implementing a suite of robotics and computer vision technologies to monitor patients with Alzheimer's dementia in their homes. This necessitates a reliable and effective facial recognition pipeline …

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 …

A Trust-By-Learning Framework For Secure 6g Wireless Networks Under Native Generative Ai Attacks, Md Shirajum Munir, Sravanthi Proddatoori, Manjushree Muralidhara, Trinidad Mario Dena, Walid Saad, Zhu Han, Sachin Shetty

Center for Secure and Intelligent Critical Systems (CSICS) Publications

Sixth-generation (6G) wireless networks will become vulnerable due to native generative AI (GenAI)-driven intelligent poisoning attacks in both the radio unit and the core network. In particular, network parameters and metrics in cross-layer design pose fundamentally uncertain conditions and can be compromised through the native GenAI mechanism, which leverages data augmentation and reconstruction capabilities. This work investigates the capabilities of native GenAI to create novel poisoning attacks in wireless networks, while investigating their impact through uncertainty-informed root analysis. Then, detected attacks are mitigated by developing a trustworthy service aggregation in the wireless network. First, a joint decision problem is formulated …

Future Of Bse Days 2025: Growing A Regenerative Bse, Derek M. Heeren, Santosh Pitla, Jennifer R. Keshwani, Mark Stone

Department of Agricultural and Biological Systems Engineering: Presentations and White Papers

The Future of BSE Days 2025: Growing a Regenerative BSE brought together over 150 faculty, staff, students, and partners to envision the next quarter-century of the Department of Biological Systems Engineering. The event emphasized regeneration—not only of resources and ecosystems, but also of ideas, learning models, and relationships. Across seven major sessions—three Spark Talks and four Pillar Workshops—participants explored how BSE can thrive amid technological disruption, demographic change, and societal transformation.

Key Outcomes

Redefining Impact: This session challenged participants to evolve from counting outputs to valuing relationships, collaboration, and community well-being.

Adaptive Learning Models: This discussion introduced design studios, micro-credentials, …

Examining The Disclosure Of Sensitive Information Through Mobile Applications: A Privacy Calculus And Warning Experiment On Location-Based Services, Dwayne A. Ford

CCAC Theses and Dissertations

Smartphones and mobile applications have become all but ubiquitous in society. These applications provide a plethora of functions both in standalone and network configurations. Many of these popular applications utilize Location-Based Services (LBS) to deliver value to the user. Whether for navigation, transportation or social interactions, sharing information is essential when using these applications. While LBS applications provide various benefits, the sharing of location data also creates significant privacy risks. In many cases, users are unaware of the real risks and continue to share their location data in exchange for the benefits the application provides.

The problem identified in this …

A Hybrid Deep Learning Model For Iot Network Anomaly Detection, Yonas Getachew Mulissa

CCAC Theses and Dissertations

The rapid expansion of Internet of Things (IoT) networks has heightened the need for intelligent, automated Anomaly Detection (AD) systems to identify sophisticated and evolving cyber threats. This study designed, implemented, and evaluated a broad range of deep learning models—including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNNs) (Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional Gated Recurrent Unit (BiGRU), Bidirectional Long Short-Term Memory (BiLSTM)), Transformer-based architectures, Autoencoders, and hybrid combinations—to address the challenge of multiclass anomaly classification in IoT traffic. Using two benchmark datasets, IoT-DS-2 and CIC-IoT-2023, we conducted extensive experiments to assess classification performance, training efficiency, and …

Development Of A Phishing Risk Exposure Taxonomy On Mobile Devices In The Healthcare Industry, Christopher P. Collins

CCAC Theses and Dissertations

Phishing emails accessed on mobile devices present substantial risks to healthcare organizations when employees often operate under high cognitive load and with limited cybersecurity training. Despite widespread security awareness initiatives, healthcare workers continue to engage with phishing content on mobile platforms, posing threats to organizational data. Given the high value of healthcare data and the increasing sophistication of phishing schemes targeting healthcare professionals, there is a pressing need to enhance their ability to recognize phishing indicators on mobile devices.

This study developed and validated a Healthcare Workers Phishing Risk Exposure (HWPRE) taxonomy, designed to classify healthcare workers based on their …

An Evaluation Of Data Protection And Privacy Issues Introduced By Byod In Financial Institutions, Andy Miguel Santana

CCAC Theses and Dissertations

The idea of “bring your own device” (BYOD) allows organizational employees to conduct their tasks or processes on their own personal devices, has increased organizational efficiency significantly while allowing employees more flexibility. However, this approach also introduces major concerns about the security of organizational data as employees take their devices everywhere with them, opening more opportunities for unauthorized access to important data. Another major concern is the privacy of employee personal data. As many organizations implement BYOD, employees worry that with organizational monitoring and device management, their personal data is at risk as well. The problem this study tackles is …

Architectural Technical Debt Migrating Object Oriented Systems To Modular Architectures, Lionel Standridge

CCAC Theses and Dissertations

Architectural Technical Debt (ATD), a subset of Technical Debt (TD), arises when outdated architectural decisions present significant challenges to the maintenance and evolution of legacy object-oriented monolithic systems. These systems tend to have tightly coupled components and rigid dependencies, making it difficult to scale, adapt, and modernize. This dissertation investigated strategies for managing ATD during the transition from monolithic architectures to modular systems. By identifying the root causes of ATD in a legacy objectoriented system and evaluating various decomposition strategies, this research proposed a framework to guide practitioners in reducing ATD and improving a system’s modularity. Using quantitative metrics, the …

Enhanced Network Anomaly Detection Using Machine Learning Models, Ousmane Barry

CCAC Theses and Dissertations

This dissertation investigates enhanced network anomaly detection using Machine Learning (ML) models. The study addresses two distinct classification problems: binary classification and multiclass classification. In the binary classification task, network traffic data is categorized as either "normal" or "abnormal," where abnormal includes all non-normal traffic. Leveraging the balanced nature of the dataset, this study develops optimized models that achieve consistently high classification performance. Key metrics, including precision, recall, and F1 scores, are used to ensure robust evaluation and reliable detection across all classes.

For multiclass classification, only classes present in both training and test datasets are included to ensure meaningful …

Empirical Assessment Of Cybersecurity Competencies Through Human-Generative Artificial Intelligence (Genai) Teaming, Dariusz Witko

CCAC Theses and Dissertations

The critical shortage of skilled cybersecurity professionals, with over 750,000 unfilled positions in the United States (U.S.), combined with rising practitioner burnout and the complexity of modern cyber threats, poses significant risks to national security. As Generative Artificial Intelligence (GenAI) emerges as a potential tool to support cybersecurity operations, its role in assisting human analysts with high-demand tasks offers both opportunities and challenges. While GenAI can improve efficiency, it also introduces adversarial risks if manipulated by malicious actors. This study investigated how human-GenAI collaboration can address these challenges, focusing on the fundamental cybersecurity knowledge, skills, and task completion required for …

Empirical Analysis Of Political Districting Splitability Via Uniform Spanning Trees In Polynomial Time, Brooke C. Feinberg

Scripps Senior Theses

This work expands a recently proven conjecture that a polynomial fraction of all uniform spanning trees (USTs) are splittable into k balanced partitions on grid graphs to real-world political districting plans. We investigate whether similar structural properties hold for the planar dual graphs of U.S. counties (cnty) and tracts (t), using Wilson’s algorithm to generate uniform random spanning trees and Breadth- First Search (BFS) to check for splitability into balanced partitions. Our empirical findings suggest that real-world districting plans can be split into 2-balanced, connected partitions in a fraction of polynomial time. This result highlights the potential for scalable redistricting …

A Vision Transformer Based Assistive System For Dermatological Diagnosis In Systemic Lupus Erythematosus, Syeda Lamima Farhat

All Graduate Theses, Dissertations, and Other Capstone Projects

Systemic Lupus Erythematosus (SLE) is a complex and often underdiagnosed autoimmune disease that affects multiple organs and presents with a wide range of symptoms-ranging from fatigue and joint pain to life-threatening organ damage. One of its most visible and diagnostically significant indicators is the Butterfly Malar Rash (BMR), a distinctive facial rash that often resembles other common dermatological conditions like rosacea, acne, eczema, and fifth disease. This overlap can lead to misdiagnosis or delayed detection, especially in busy clinical environments. To assist dermatologists in distinguishing BMR from similar facial rashes, this study explores the development of an AI-powered image classification …

Evaluating Aspect-Based Sentiment Analysis In Healthcare Drug Reviews Across Machine Learning, Deep Neural Networks, And Transformer Models, Eun Soo Park

All Graduate Theses, Dissertations, and Other Capstone Projects

Sentiment analysis has become a critical area of research in Natural Language Processing (NLP), enabling insights from unstructured text. Within this field, Aspect-Based Sentiment Analysis (ABSA) plays a practical role in domains such as healthcare, where patients drug reviews often contain diverse opinions across multiple aspects, including overall comments, perceived benefits, and side effects. However, aspect-level classification remains challenging due to class imbalance, subtle sentiment expression, and the limitations of traditional models. This research investigates the performance of three modeling paradigms: traditional machine learning (SVM, SVC, and XGBoost), deep learning (CNN-BiLSTM), and transformer-based approaches (DistilBERT sentence-pair classification). Using the UCI …

Identifying All Matches Of A Rigid Object In An Input Image Using Visible Triangles, Abdullah N. Arslan

Faculty Publications

It has been suggested that for objects identifiable by their corners, every triangle formed by these corner points can serve as a reference for detecting other corner points. This approach enables effective rigid object detection, including partial matches. However, when there are many corner points, the implementation becomes impractical due to excessive memory requirements. To overcome this, we propose a new algorithm that leverages Delaunay triangulation, considering only the triangles generated by the Delaunay triangulation to reduce the complexity of the original approach. Our algorithm is significantly faster and requires significantly less memory, offering a viable solution for large problem …

Predicting Lung Cancer Severity Using Machine Learning Algorithms: Enhanced By Statistical Analysis, Esin Bilgin

Theses, Dissertations and Culminating Projects

Cancer is a serious and severe cause seen in every region of the world and severely affects the quality of life and life span. Among the various types of cancer, lung cancer is one of the most critical, having a fatal impact on life. While medical imaging techniques, laboratory results, and biomarkers play a significant role in diagnosis and prognosis, clinical studies are also crucial in monitoring the progression of cancer and identifying diagnostic and prognostic factors. The findings demonstrate satisfactory accuracy, and the analysis incorporates statistical data with machine learning techniques. These findings play a pivotal role in supporting …