Computer Sciences Commons^™

A Deep Learning Model For Early Diagnosis Of Systemic Lupus Erythematosus From Facial Images, Shourav Bikash Dey

All Graduate Theses, Dissertations, and Other Capstone Projects

Systemic Lupus Erythematosus (SLE) poses significant challenges due to its complex and varied symptoms making diagnosis extremely challenging and time consuming. Symptoms of SLE often mimics other autoimmune or physical conditions and around 5 million people worldwide suffers from this condition, as reported by the Lupus Foundation of America during their study in 2019. However, diagnosis is much more difficult in developing countries with backdated clinical technology and setup therefore, making it virtually unknown the exact number of SLE patient count worldwide. Among all the heterogeneous symptoms presented by SLE, Butterfly Malar Rash (BMR) is one of the symptoms that …

Quantitative Verification For Massive Linear Systems, Qing Liu

School of Computing: Dissertations, Theses, and Student Research

The verification of linear systems has been an active area of research for decades. Reachability analysis is a key component in verification problems. It involves computing the system’s reachable set, the set of reachable states in the state space from a given set of initial states. Most verification methods primarily focus on qualitative verification, which answers whether or not a system may violate specified safety conditions. This paper extends this qualitative verification to quantitative verification by introducing a novel approach, employing probabilistic stars (Probstars) to compute reachable sets, which augment traditional star sets by integrating Gaussian-distributed random variables with …

Multispectral Deep Neural Network Fusion Method For Low-Light Object Detection, Keval Thaker, Sumanth Chennupati, Nathir Rawashdeh, Samir A. Rawashdeh

Michigan Tech Publications

Despite significant strides in achieving vehicle autonomy, robust perception under low-light conditions still remains a persistent challenge. In this study, we investigate the potential of multispectral imaging, thereby leveraging deep learning models to enhance object detection performance in the context of nighttime driving. Features encoded from the red, green, and blue (RGB) visual spectrum and thermal infrared images are combined to implement a multispectral object detection model. This has proven to be more effective compared to using visual channels only, as thermal images provide complementary information when discriminating objects in low-illumination conditions. Additionally, there is a lack of studies on …

Student Attitudes And Intentions To Use Continuous Authentication Methods Applied To Mitigate Impersonation Attacks During E-Assessments, Andrea E. Green

CCAC Theses and Dissertations

No solution can ultimately eliminate cheating in online courses. However, universities reserve funding for authentication systems to minimize the threat of cheating in online courses. Most higher education institutions use a combination of authentication methods to secure systems against impersonation attacks during online examinations. Authentication technologies ensure that an online course is protected from impersonation attacks. However, it is important that authentication methods secure systems against impersonation attacks with minimal disruption during an examination. Authentication methods applied to secure e-assessments against impersonation attacks may impact a student’s attitude and intentions to use the e-examination system.

In this regard, the research …

Heed The Warning Signs: The Effectiveness Of Message Popup Warnings For Deterring The Spread Of Misinformation, Hollis Greenberg

CCAC Theses and Dissertations

As false news can propagate to others rapidly, social media platforms employ multiple methods to combat misinformation. Debunking techniques are warning features embedded into a platform’s interface that alert readers of misinformation. These warnings have two goals: to “debunk” false information and to prevent the further spread of misinformation. Researchers have evaluated the effectiveness of debunking techniques to understand how users increase their awareness of misinformation, and what users do with the information given in warning messages. Message popup warnings are a newer and understudied type of debunking technique.

The overarching research question of this study was: Are message popup …

Empirical Assessment Of Remote Workers’ Cyberslacking And Computer Security Posture To Assess Organizational Cybersecurity Risks, Ariel Luna

CCAC Theses and Dissertations

No abstract provided.

Understanding The Role Of Tacit And Explicit Knowledge Hiding In Organizations, Darren Wiggins

CCAC Theses and Dissertations

Knowledge Hiding (KHi) is the deliberate act of withholding knowledge from others, driven by distrust. This distrust stems from three key factors: rationalized hiding, evasive hiding, and playing dumb. The latter two, evasive hiding and playing dumb, are particularly detrimental as they foster a cycle of mutual distrust within the workplace. To counteract this, organizations have significantly invested in promoting Tacit Knowledge (TK) and Explicit Knowledge (EK) sharing. These initiatives aimed to facilitate knowledge transfer, foster collaboration, enhance problem-solving capabilities, and strengthen social and interpersonal relationships.

Recent studies highlighted the importance of understanding the attributes linked to TK and EK. …

Using Ontological Methods To Compare Cybersecurity Maturity Model Certification 2.0 And Cobit 19, Aaron Marshall Ramey

CCAC Theses and Dissertations

Cybersecurity frameworks developed by a variety of organizations and implemented by a much larger collection of organizations differ in their focus and application. Whether designed by a private or government organization, the primary goal is to provide a framework to assess and reduce risk. The Department of Defense (DoD) has recently implemented the second version of the Cybersecurity Maturity Model Certification (CMMC 2.0). In some situations, compliance with CMMC 2.0 has already become mandatory for the Defense Industrial Base (DIB). Compliance will soon be required for all Large Businesses (LB) and Small Businesses (SB) within the DIB. While COBIT 19 …

Combating Disinformation On Social Media Networks With Media And Information Literacy Training For Social Media Network Users, Oscar Kwok Chao Ho

CCAC Theses and Dissertations

In the Internet age, social media networks (SMNs), such as Facebook (FB), Instagram (IG), and Twitter (TW), have gained popularity and become an essential part of human life. SMNs provide ease of connection to family, friends, and communities; however, they increase the chances social media network users (SMN users) will disclose private information (PI), causing critical harm to SMN users’ information privacy (IP). Furthermore, SMN users are exposed to significant amounts of disinformation, misinformation, or fake news, which they share without realizing the information is untrustworthy.

The goal of this developmental research was to investigate, examine, and understand the effects …

Enhancing Sentiment Analysis In Niche Domains: Introducing Diverse Datasets And Evaluating Model Performance In Car Dealership And Board Game Reviews, Kimon Andreou

CCAC Theses and Dissertations

The field of Natural Language Processing (NLP) has witnessed significant advancements in recent decades, with text classification emerging as a critical task, particularly in sentiment analysis applications. However, a constant challenge within sentiment analysis research is the scarcity of diverse and specialized labeled datasets. The present dissertation addresses this gap by developing two novel, labeled textual datasets sourced from niche areas: BoardGameGeek.com's top 250 board game reviews and TrustPilot.com's car dealership reviews

The main goal of this dissertation is to enrich sentiment analysis methodologies by providing unique datasets and insights into the performance of current models within specialized domains. By …

A Technique For Visualization Of Multivariate Categorical Data, Janice James

CCAC Theses and Dissertations

Multivariate Categorical Data (MCD) plays a significant role in many industries, and the ability to understand the data is critical for insight and decision making. Visualization is a key tool for understanding the data. This dissertation designed and implemented a novel technique for visualizing MCD called Pivoting Parallel Charts (PPC). The design of PPC was informed by studying several existing MCD visualization techniques.

PPC visualizes MCD as a sequence of parallel axes with affixed bar charts. A user-specified axis, called the pivot, acts as the crucial point of consideration for all data relationships. The bar charts are color-coded by the …

Development Of The Passphrase Alleviating Abstraction, Remembering, And Strength (Palabras) Method, Juan Manuel Madrid

CCAC Theses and Dissertations

The currently most used method for computer authentication is the password because it is simple to implement, and users are familiar with it. However, passwords are vulnerable to attacks that can be mitigated by increasing the complexity of the chosen password, particularly in length. One possible approach to increasing the complexity of passwords is by using passphrases. Passphrases can be easier to remember than a standard password, improving memorability. They can reduce the loss of work time and productivity related to forgotten passwords. To achieve the required balance between complexity and memorability, the concept of passphrase categories can be applied, …

Creation Of A Digital Storage System For Genome Sequencing Metadata, Jacquelin W. Olexa

Undergraduate Theses, Professional Papers, and Capstone Artifacts

As the field of computational genomics continues to expand in both potential and application, it is now more imperative than ever to ensure that massive genetic sequencing datasets are properly stored in an accessible manner. This project sought to establish a practical, user-friendly, secure system for a genomics research lab (the Good Lab; thegoodlab.org) at the University of Montana. A MySQL database and connected web application was ruled the best configuration to maximize utility and accessibility for the lab’s researchers. Building the logical framework for the database, creating the server, and sourcing data occurred over several months. The dataset ranged …

The Process Of Video Game Development: Watermelon Willy, Jade Westgor

University Honors Program Senior Projects

Developing a video game requires and provides skills that can be very helpful for computer science students, as well as skills that relate to storytelling/worldbuilding, organization, and artistic expression. This paper goes over the process I have taken in order to design, develop, and evaluate the game Watermelon Willy from the very start to alpha testing. The process started some time ago with the idea being present in my head for a while, however the project started with some basic storyboards to map out some of the fundamental aspects of the game. Coding began with a simple template, which was …

Deterministic And Stochastic Dynamics Of Marine Food Webs, Julian A. Hernandez S.

Theses, Dissertations and Culminating Projects

Ecologists have long been concerned with understanding the behavior and evolutionary patterns exhibited within complex ecological communities. Under- standing the delicate balance that sustains ecosystems is crucial in determining how these communities evolve over time. Recently, researchers have combined deterministic Lotka-Volterra dynamics with different types of synthetic food webs (cascade, niche and generalized cascade models), and have analyzed the mechanisms behind primary extinction events and the ensuing secondary extinction cascade. These studies also enabled the exploration of the complex interplay of species loss to explain how food web structure influences primary and secondary extinction. We have extended these ideas to …

Infusing Commonsense Via Knowledge Bases In Multipurpose Robotic Task Organization, Rafael Omar Hidalgo

Theses, Dissertations and Culminating Projects

This research explores the innovative integration of commonsense knowledge (CSK) within AI systems, with a particular focus on domestic robotics. At the heart of this study is the Robo- CSK-Organizer, a groundbreaking system that utilizes a classical knowledge base, namely ConceptNet, to enhance robotic decision-making through sophisticated object organization and classification. This system is contrasted with a ChatGPT-based organizer, examining their performance in terms of ambiguity resolution, consistency in object placement, adaptability to task classifications, and crucially, in explainability, a key aspect of XAI (Explainable AI). Through a combination of controlled experiments, quantitative and qualitative analysis, the study demonstrates that …

Feature Matching Methods Comparison With Limited Computing Power, Xu Du

Theses, Dissertations and Culminating Projects

This work presents a comparative analysis of feature-matching techniques implemented on low-end hardware, focusing on their efficiency and performance under various image transformations. The study evaluates several well-established feature matching algorithms, including ORB, AKAZE, BRISK, FAST combined with ORB, and SIFT, for their robustness against rotation, perspective, and scale changes in images. The base image used for experimentation features is the Montclair State University's Red Hawk mascot—a complex, textured subject that presents a substantial challenge for feature matching algorithms. The experiment simulates real-world conditions by applying a series of transformations to the base image and utilizes the default settings of …

Assessing Organizational Investments In Cybersecurity And Financial Performance Before And After Data Breach Incidents Of Cloud Saas Platforms, Munther B. Ghazawneh

CCAC Theses and Dissertations

Prior research indicated that providing inappropriate investment in organizations for Information Technology (IT) security makes these organizations suffer from IT security issues that may cause data breach incidents. Data breaches in cloud Software as a Service (SaaS) platforms lead to the disclosure of sensitive information, which causes disruption of services, damage to the organizational image, or financial losses. Massive data breaches still exist in cloud SaaS platforms which result in data leaks and data theft of customers in organizations.

IT security risks and vulnerabilities cost organizations millions of dollars a year as organizations may face an increase in cybersecurity challenges. …

Development Of Cybersecurity Footprint Index For Manufacturing Companies To Assess Organizational Cyber Posture, John A. Del Vecchio

CCAC Theses and Dissertations

With the continued changes in how businesses work, cyber-attack targets are constantly in flux between organizations, individuals, and various aspects of the supply chain of interconnected companies delivering goods and services. As one of the 16 critical infrastructure sectors, manufacturing is known for complex integrated Information Systems (ISs) incorporated heavily into production operations. Many of these ISs are procured and supported by third parties, also called interconnected entities in the supply chain. Disruptions to manufacturing companies would not only have significant financial losses but would also have economic and safety impacts on society. The vulnerabilities of interconnected companies create inherited …

Constructed Language (Conlang) Audio Honing, Ronald B. Oakes

CCAC Theses and Dissertations

An important aspect of a constructed language (conlang) is how it sounds when spoken. This dissertation designs and implements a tool to allow the user to hear how their conlang sounds when spoken. This tool will generate spoken language based on sample text in the constructed language. Further, it will enable the user to manipulate the phonetics of the language and hear how these changes impact the language in its spoken form.

This tool also allows users to assess the preferability of the language’s phonetics using Net Auditory Distance under the framework of Beats-and-Bindings Phonology. It was shown that the …

Studies In Prefix Rewriting, Ashley Marie Suchy

Legacy Theses & Dissertations (2009 - 2024)

In this dissertation, we introduce computational problems with respect to prefix grammars and introduce a new concept called \emph{left-linear phrase-structure grammars}.

(Meta-)Physical Artworks: Digital Augmentation In Art Observation, Macy A. Toppan

Dartmouth College Master’s Theses

Augmented art— the subgenre of art that incorporates physical and digital artwork— is a rapidly growing field driven by advancing technology and a new generation for whom that tech is a given. Yet the presence of media like augmented and virtual reality in exhibition remains a controversial subject. Rather than focusing on the many theoretical debates about whether digital pieces can qualify as "good" art, we study it in practice through the eyes of the casual art observer. This paper highlights the audience in a within-participant study that asked viewers to take in a physical sculpture intentionally built with virtual …

Natural Language Processing And Neurosymbolic Ai: The Role Of Neural Networks With Knowledge-Guided Symbolic Approaches, Emily Barnes, James Hutson

Faculty Scholarship

Neurosymbolic AI (NeSy AI) represents a groundbreaking approach in the realm of Natural Language Processing (NLP), merging the pattern recognition of neural networks with the structured reasoning of symbolic AI to address the complexities of human language. This study investigates the effectiveness of neurosymbolic AI in providing nuanced understanding and contextually relevant responses, driven by the need to overcome the limitations of existing models in handling complex linguistic tasks and abstract reasoning. Employing a hybrid methodology that combines multimodal contextual modeling with rule-governed inferences and memory activations, the research delves into specific applications like Named Entity Recognition (NER), where architectures …

Enhancing Iot Security: Optimizing Anomaly Detection Through Machine Learning, Maria Balega, Waleed Farag, Xin-Wen Wu, Soundarararjan Ezekiel, Zaryn Good

Computer Science Articles

As the Internet of Things (IoT) continues to evolve, securing IoT networks and devices remains a continuing challenge. Anomaly detection is a crucial procedure in protecting the IoT. A promising way to perform anomaly detection in the IoT is through the use of machine learning (ML) algorithms. There is a lack of studies in the literature identifying optimal (with regard to both effectiveness and efficiency) anomaly detection models for the IoT. To fill the gap, this work thoroughly investigated the effectiveness and efficiency of IoT anomaly detection enabled by several representative machine learning models, namely Extreme Gradient Boosting (XGBoost), Support …

A Robust Form Understanding System Using Graph-Based Neural Network, Chavin Chuangchaichatchavarn

Chulalongkorn University Theses and Dissertations (Chula ETD)

In this work, we address the challenge of form understanding in real-world documents affected by OCR noise and layout uncertainty. We introduce TONDFU, a bilingual Thai and English dataset consisting of official documents such as vehicle registrations and utility bills, annotated for entity labelling and entity linking. We also introduce a noisy character feature extractor that captures lexical and spatial patterns to improve the model's robustness against noisy textual content. This feature is integrated with geometric, visual, and semantic features in the graph-based model. Experiments show that the noisy character feature outperforms the frequency histogram baseline, and with pretraining on …

Enhancing 21 U.S.C. §§ 355, 356, And 360 To Encompass Artificial Intelligence-Based Drug Design And Manufacturing Methods, Aj Tsang

Michigan Technology Law Review

Despite newfound attention to how artificial intelligence (AI) may accelerate pharmaceutical development, federal regulators may find that current statutes are ambiguous or silent about their applicability to AI-based drug design and manufacturing methods. This poses a serious problem in the era of Loper Bright and the Major Questions Doctrine. As federal agencies struggle to adjust to courts’ growing demand for Congress to craft clear, explicit, and express delegations of authority, this note develops a statutory framework in which the Food and Drug Administration (FDA) would have more flexibility to regulate the use of AI in advanced drug manufacturing. Guided by …

Analysing Natural Language Processing Techniques: A Comparative Study Of Nltk, Spacy, Bert, And Distilbert On Customer Query Datasets., Patrizia De Camillis

ICT

This study examines the role of sentiment analysis in customer queries, emphasising its impact on brand perception and the risks of poor query management. It compares the performance of NLP models—NLTK, spaCy, BERT, and DistilBERT—on customer query and feedback data. The findings show that BERT and DistilBERT produce similar results, often categorising queries as neutral, indicating their strength in handling diverse sentiments. NLTK and spaCy also share performance patterns. The research offers insights into the capabilities and limitations of these models in sentiment analysis.

Application Of Machine Learning Algorithms To Evaluate The Changes In Energy Consumption In The Leinster Area And Subsequently The Impact On Consumer Behaviour In The Commercial Sector., Maria Dominguez Alvarenga

ICT

This study focuses on predicting electricity consumption through data analytics and ensemble learning methods, addressing fluctuations influenced by external economic factors. Techniques like Gradient Boosting Regressor (GBR) and Random Forest Regressor (RFR) proved effective due to their ability to generalise well with new data. CRISP-DM served as the guiding methodology, supported by robust preprocessing techniques such as winsorisation to handle outliers, feature selection to refine variables, and scaling to standardise data for improved model performance.

The research involved datasets from non-residential clients and data centres, uncovering consumption patterns through visualisations in Tableau. Analysis showed that County Dublin and Kildare were …

An Investigation Into The Role Of Machine Learning And Deep Learning Models As A Means Of Leveraging The Ever-Expanding Volume Of Astronomical Data To Automate Stellar Classification., Gerard Heraghty

ICT

This research investigates the use of machine learning and neural network models for automated stellar classification in large astronomical surveys, addressing challenges posed by the increasing volume of data. Using the MK scheme as the classification standard, the study focused on spectroscopic data and balanced the dataset using SMOTE techniques to handle class imbalances. Various models, including Random Forest, SVM, MLP, and CNN, were trained and compared for classifying MK main and sub-classes. CNN achieved the highest accuracy (93.86%) for main class classification, while SVM excelled at sub-class classification (63.23%) on balanced datasets. However, when tested on real-world SDSS data, …

Applying Neural Networks To Predict Factors Affecting Harmful Algal Blooms For Timely Alerting And Implementing Preventive Measures In Ireland's Marine Ecosystem., Nikolai Potapov

ICT

This study applies neural networks to predict harmful algal blooms (HABs) along the Irish coast, addressing ecological, health, and economic risks. Using primary interviews and secondary data on HAB species like Alexandrium and Karenia mikimotoi, the research incorporated Exploratory Data Analysis and tested three neural models: LSTM, Ensemble Stacking LSTM, and CNN-LSTM. Key factors influencing HABs, such as sea surface temperature and euphotic zone depth, were identified.

Results demonstrate the potential of neural networks to improve HAB prediction and monitoring, despite limitations. Future work aims to enhance model accuracy and integrate them into HAB warning systems.