Other Computer Sciences Commons^™

Enhanced Content-Based Fake News Detection Methods With Context-Labeled News Sources, Duncan Arnfield

Electronic Theses and Dissertations

This work examined the relative effectiveness of multilayer perceptron, random forest, and multinomial naïve Bayes classifiers, trained using bag of words and term frequency-inverse dense frequency transformations of documents in the Fake News Corpus and Fake and Real News Dataset. The goal of this work was to help meet the formidable challenges posed by proliferation of fake news to society, including the erosion of public trust, disruption of social harmony, and endangerment of lives. This training included the use of context-categorized fake news in an effort to enhance the tools’ effectiveness. It was found that term frequency-inverse dense frequency provided …

Enhancing Search Engine Results: A Comparative Study Of Graph And Timeline Visualizations For Semantic And Temporal Relationship Discovery, Muhammad Shahiq Qureshi

Electronic Theses and Dissertations

In today’s digital age, search engines have become indispensable tools for finding information among the corpus of billions of webpages. The standard that most search engines follow is to display search results in a list-based format arranged according to a ranking algorithm. Although this format is good for presenting the most relevant results to users, it fails to represent the underlying relations between different results. These relations, among others, can generally be of either a temporal or semantic nature. A user who wants to explore the results that are connected by those relations would have to make a manual effort …

Proposing A Measure Of Ethicality For Humans And Ai, Alejandro Jorge Napolitano Jawerbaum

Electronic Theses and Dissertations

Smarter people or intelligent machines are able to make more accurate inferences about their environment and other agents more efficiently than less intelligent agents. Formally: ‘Intelligence measures an agent’s ability to achieve goals in a wide range of environments.’ (Legg, 2008)

In this dissertation we extend this definition to include ethical behaviour and we will offer a mathematical formalism and a way to estimate how ethical an action is or will be, both for a human and for a computer, by calculating the expected values of random variables. Formally, we propose the following measure of ethicality, which is computable, or …

A Data-Driven Multi-Regime Approach For Predicting Real-Time Energy Consumption Of Industrial Machines., Abdulgani Kahraman

Electronic Theses and Dissertations

This thesis focuses on methods for improving energy consumption prediction performance in complex industrial machines. Working with real-world industrial machines brings several challenges, including data access, algorithmic bias, data privacy, and the interpretation of machine learning algorithms. To effectively manage energy consumption in the industrial sector, it is essential to develop a framework that enhances prediction performance, reduces energy costs, and mitigates air pollution in heavy industrial machine operations. This study aims to assist managers in making informed decisions and driving the transition towards green manufacturing. The energy consumption of industrial machinery is substantial, and the recent increase in CO2 …

An Investigation Into Machine Learning Techniques For Designing Dynamic Difficulty Agents In Real-Time Games, Ryan Adare Dunagan

Electronic Theses and Dissertations

Video games are an incredibly popular pastime enjoyed by people of all ages world wide. Many different kinds of games exist, but most games feature some elements of the player overcoming some challenge, usually through gameplay. These challenges are insurmountable for some people and may turn them off to video games as a pastime. Games can be made more accessible to players of little skill and/or experience through the use of Dynamic Difficulty Adjustment (DDA) systems that adjust the difficulty of the game in response to the player’s performance. This research seeks to establish the effectiveness of machine learning techniques …

Novel Approach For Non-Invasive Prediction Of Body Shape And Habitus, Emma Young

Electronic Theses and Dissertations

While marker-based motion capture remains the gold standard in measuring human movement, accuracy is influenced by soft-tissue artifacts, particularly for subjects with high body mass index (BMI) where markers are not placed close to the underlying bone. Obesity influences joint loads and motion patterns, and BMI may not be sufficient to capture the distribution of a subject’s weight or to differentiate differences between subjects. Subjects in need of a joint replacement are more likely to have mobility issues or pain, which prevents exercise. Obesity also increases the likelihood of needing a total joint replacement. Accurate movement data for subjects with …

Areas Of Same Cardinal Direction, Periyandy Thunendran

Electronic Theses and Dissertations

Cardinal directions, such as North, East, South, and West, are the foundation for qualitative spatial reasoning, a common field of GIS, Artificial Intelligence, and cognitive science. Such cardinal directions capture the relative spatial direction relation between a reference object and a target object, therefore, they are important search criteria in spatial databases. The projection-based model for such direction relations has been well investigated for point-like objects, yielding a relation algebra with strong inference power. The Direction Relation Matrix defines the simple region-to-region direction relations by approximating the reference object to a minimum bounding rectangle. Models that capture the direction between …

A Programmatic Geographic Information Systems Analysis Of Plant Hardiness Zones, Andrew Bowen

Electronic Theses and Dissertations

The Plant Hardiness Zone Map consists of thirteen geographical zones that describe whether a plant can survive based on average annual minimal temperatures. As climate change progresses, minimum temperatures in all regions are expected to change. This work programmatically evaluates predicted future climate projection data and converts it to United States Department of Agriculture-defined hardiness zones. Through the next 80 years, hardiness zones are projected to move poleward; in effect, colder zones will lose area and warmer zones will gain area globally. Some implications include changes in crop growing degree days, which could alter crop productivity, migration and settlement of …

Differentiate Metasploit Framework Attacks From Others, Gina Liu Ajero

Electronic Theses and Dissertations

Metasploit Framework is a very popular collection of penetration testing tools. From auxiliaries such as network scanners and mappers to exploits and payloads, Metasploit Framework offers a plethera of apparatuses to implement all the stages of a penetration test. There are two versions: both a free open-source community version and a commercial professional version called Metasploit Pro. The free version, Metasploit Framework, is heavily used by cyber crimininals to carry out illegal activities to gain unauthorized access to targets.

In this paper, I conduct experiments in a virtual environment to discover whether attacks originated from Metasploit Framework are marked with …

Reduced Fuel Emissions Through Connected Vehicles And Truck Platooning, Paul D. Brummitt

Electronic Theses and Dissertations

Vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication enable the sharing, in real time, of vehicular locations and speeds with other vehicles, traffic signals, and traffic control centers. This shared information can help traffic to better traverse intersections, road segments, and congested neighborhoods, thereby reducing travel times, increasing driver safety, generating data for traffic planning, and reducing vehicular pollution. This study, which focuses on vehicular pollution, used an analysis of data from NREL, BTS, and the EPA to determine that the widespread use of V2V-based truck platooning—the convoying of trucks in close proximity to one another so as to reduce air drag …

Frequency Analysis Of Trabecular Bone Structure, Daniel Parada San Martin

Electronic Theses and Dissertations

Medical data is hard to obtain due to privacy laws making research difficult. Many databases of medical data have been compiled over the years and are available to the scientific community. These databases are not comprehensive and lack many clinical conditions. Certain type of medical conditions are rare, making them harder to obtain, or are not present at all in the aforementioned databases. Due to the sparsity or complete lack of data regarding certain conditions, research has stifled. Recent developments in machine learning and generative neural networks have made it possible to generate realistic data that can overcome the lack …

Local-Global Results On Discrete Structures, Alexander Lewis Stevens

Electronic Theses and Dissertations

Local-global arguments, or those which glean global insights from local information, are central ideas in many areas of mathematics and computer science. For instance, in computer science a greedy algorithm makes locally optimal choices that are guaranteed to be consistent with a globally optimal solution. On the mathematical end, global information on Riemannian manifolds is often implied by (local) curvature lower bounds. Discrete notions of graph curvature have recently emerged, allowing ideas pioneered in Riemannian geometry to be extended to the discrete setting. Bakry- Émery curvature has been one such successful notion of curvature. In this thesis we use combinatorial …

Model-Based Testing Of Smart Home Systems Using Efsm, Cefsm, And Fsmapp, Afnan Mohammed Albahli

Electronic Theses and Dissertations

Smart Home Systems (SHS) are some of the most popular Internet of Things (IoT) applications. In 2021, there were 52.22 million smart homes in the United States and they are expected to grow to 77.1 million in 2025 [71]. According to MediaPost [74], 69 percent of American households have at least one smart home device. The number of smart home systems poses a challenge for software testers to find the right approach to test these systems. This dissertation employs Extended Finite State Machines (EFSMs) [6, 24, 105], Communicating Extended Finite State Machines (EFSMs) [68] and FSMApp [10] to generate reusable …

Humanizing Computational Literature Analysis Through Art-Based Visualizations, Alexandria Leto

Electronic Theses and Dissertations

Inequalities in gender representation and characterization in fictional works are issues that have long been discussed by social scientists. This work addresses these inequalities with two interrelated components. First, it contributes a sentiment and word frequency analysis task focused on gender-specific nouns and pronouns in 15,000 fictional works taken from the online library, Project Gutenberg. This analysis allows for both quantifying and offering further insight on the nature of this disparity in gender representation. Then, the outcomes of the analysis are harnessed to explore novel data visualization formats using computational and studio art techniques. Our results call attention to the …

Local Feature Selection For Multiple Instance Learning With Applications., Aliasghar Shahrjooihaghighi

Electronic Theses and Dissertations

Feature selection is a data processing approach that has been successfully and effectively used in developing machine learning algorithms for various applications. It has been proven to effectively reduce the dimensionality of the data and increase the accuracy and interpretability of machine learning algorithms. Conventional feature selection algorithms assume that there is an optimal global subset of features for the whole sample space. Thus, only one global subset of relevant features is learned. An alternative approach is based on the concept of Local Feature Selection (LFS), where each training sample can have its own subset of relevant features. Multiple Instance …

The Design, Development, And Determination Of A Virtual Reality Classroom, Victoria Alexxis Reddington

Electronic Theses and Dissertations

The COVID-19 pandemic has radically changed the way students learn and engage with their peers and instructors. Likewise, instructors have had to quickly transform their course materials to suit the online classroom format. Results from a survey of students and instructors at the University of Denver revealed that perceived levels of learning and collaboration were lessened with the transition to online learning. Moreover, the sense of presence in an educational atmosphere with other individuals was reported to be significantly stronger in a real physical classroom, as compared to an online classroom. This thesis therefore seeks to provide a new, alternative …

Exploring Information For Quantum Machine Learning Models, Michael Telahun

Electronic Theses and Dissertations

Quantum computing performs calculations by using physical phenomena and quantum mechanics principles to solve problems. This form of computation theoretically has been shown to provide speed ups to some problems of modern-day processing. With much anticipation the utilization of quantum phenomena in the field of Machine Learning has become apparent. The work here develops models from two software frameworks: TensorFlow Quantum (TFQ) and PennyLane for machine learning purposes. Both developed models utilize an information encoding technique amplitude encoding for preparation of states in a quantum learning model. This thesis explores both the capacity for amplitude encoding to provide enriched state …

Applications Of Digital Remote Sensing To Quantify Glacier Change In Glacier And Mount Rainier National Parks, Brianna Clark

Electronic Theses and Dissertations

Digital remote sensing and geographic information systems were employed in performing area and volume calculations on glacial landscapes. Characteristics of glaciers from two geographic regions, the Intermountain Region (between the Rocky Mountain and Cascade Ranges) and the Pacific Northwest, were estimated for the years 1985, 2000, and 2015. Glacier National Park was studied for the Intermountain Region whereas Mount Rainier National Park was representative of the glaciers in the Pacific Northwest. Within the thirty year period of the study, the glaciers in Glacier National Park decreased in area by 27.5 percent while those on Mount Rainier only decreased by 5.7 …

Knot Flow Classification And Its Applications In Vehicular Ad-Hoc Networks (Vanet), David Schmidt

Electronic Theses and Dissertations

Intrusion detection systems (IDSs) play a crucial role in the identification and mitigation for attacks on host systems. Of these systems, vehicular ad hoc networks (VANETs) are difficult to protect due to the dynamic nature of their clients and their necessity for constant interaction with their respective cyber-physical systems. Currently, there is a need for a VANET-specific IDS that meets this criterion. To this end, a spline-based intrusion detection system has been pioneered as a solution. By combining clustering with spline-based general linear model classification, this knot flow classification method (KFC) allows for robust intrusion detection to occur. Due its …

Using Natural Language Processing To Categorize Fictional Literature In An Unsupervised Manner, Dalton J. Crutchfield

Electronic Theses and Dissertations

When following a plot in a story, categorization is something that humans do without even thinking; whether this is simple classification like “This is science fiction” or more complex trope recognition like recognizing a Chekhov's gun or a rags to riches storyline, humans group stories with other similar stories. Research has been done to categorize basic plots and acknowledge common story tropes on the literary side, however, there is not a formula or set way to determine these plots in a story line automatically. This paper explores multiple natural language processing techniques in an attempt to automatically compare and cluster …

Certification-Driven Testing Of Safety-Critical Systems, Aiman S. Gannous

Electronic Theses and Dissertations

Safety-critical systems are those systems that when they fail they could cause loss of life or significant physical damages. Since software now is an essential component of these types of systems, failures caused by software faults could come from flaws in the software development life-cycle. As a result, challenges unfold in two directions. First, in verifying that the software will not put the system in an unsafe state, and identifying external failures and mitigate them properly. Second, in providing sufficient evidence for an efficient safety certification process. In this study, we propose an approach for testing safety-critical systems called Model-Combinatorial …

Deep Siamese Neural Networks For Facial Expression Recognition In The Wild, Wassan Hayale

Electronic Theses and Dissertations

The variation of facial images in the wild conditions due to head pose, face illumination, and occlusion can significantly affect the Facial Expression Recognition (FER) performance. Moreover, between subject variation introduced by age, gender, ethnic backgrounds, and identity can also influence the FER performance. This Ph.D. dissertation presents a novel algorithm for end-to-end facial expression recognition, valence and arousal estimation, and visual object matching based on deep Siamese Neural Networks to handle the extreme variation that exists in a facial dataset. In our main Siamese Neural Networks for facial expression recognition, the first network represents the classification framework, where we …

Automatic Target Recognition With Deep Metric Learning., Abdelhamid Bouzid

Electronic Theses and Dissertations

An Automatic Target Recognizer (ATR) is a real or near-real time understanding system where its input (images, signals) are obtained from sensors and its output is the detected and recognized target. ATR is an important task in many civilian and military computer vision applications. The used sensors, such as infrared (IR) imagery, enlarge our knowledge of the surrounding environment, especially at night as they provide continuous surveillance. However, ATR based on IR faces major challenges such as meteorological conditions, scale and viewpoint invariance. In this thesis, we propose solutions that are based on Deep Metric Learning (DML). DML is a …

A Longitudinal Study Of Mammograms Utilizing The Automated Wavelet Transform Modulus Maxima Method, Brian C. Toner

Electronic Theses and Dissertations

Breast cancer is a disease which predominatly affects women. About 1 in 8 women are diagnosed with breast cancer during their lifetime. Early detection is key to increasing the survival rate of breast cancer patients since the longer the tumor goes undetected, the more deadly it can become. The modern approach for diagnosing breast cancer relies on a combination of self-breast exams and mammography to detect the formation of tumors. However, this approach only accounts for tumors which are either detectable by touch or are large enough to be observed during a screening mammogram. For some individuals, by the time …

Ifocus: A Framework For Non-Intrusive Assessment Of Student Attention Level In Classrooms, Narayanan Veliyath

Electronic Theses and Dissertations

The process of learning is not merely determined by what the instructor teaches, but also by how the student receives that information. An attentive student will naturally be more open to obtaining knowledge than a bored or frustrated student. In recent years, tools such as skin temperature measurements and body posture calculations have been developed for the purpose of determining a student's affect, or emotional state of mind. However, measuring eye-gaze data is particularly noteworthy in that it can collect measurements non-intrusively, while also being relatively simple to set up and use. This paper details how data obtained from such …

A Transfer Learning Approach For Sentiment Classification., Omar Abdelwahab

Electronic Theses and Dissertations

The idea of developing machine learning systems or Artificial Intelligence agents that would learn from different tasks and be able to accumulate that knowledge with time so that it functions successfully on a new task that it has not seen before is an idea and a research area that is still being explored. In this work, we will lay out an algorithm that allows a machine learning system or an AI agent to learn from k different domains then uses some or no data from the new task for the system to perform strongly on that new task. In order …

Principles And Guidelines For Advancement Of Touchscreen-Based Non-Visual Access To 2d Spatial Information, Hari Prasath Palani

Electronic Theses and Dissertations

Graphical materials such as graphs and maps are often inaccessible to millions of blind and visually-impaired (BVI) people, which negatively impacts their educational prospects, ability to travel, and vocational opportunities. To address this longstanding issue, a three-phase research program was conducted that builds on and extends previous work establishing touchscreen-based haptic cuing as a viable alternative for conveying digital graphics to BVI users. Although promising, this approach poses unique challenges that can only be addressed by schematizing the underlying graphical information based on perceptual and spatio-cognitive characteristics pertinent to touchscreen-based haptic access. Towards this end, this dissertation empirically identified a …

Region Based Gene Expression Via Reanalysis Of Publicly Available Microarray Data Sets., Ernur Saka

Electronic Theses and Dissertations

A DNA microarray is a high-throughput technology used to identify relative gene expression. One of the most widely used platforms is the Affymetrix® GeneChip® technology which detects gene expression levels based on probe sets composed of a set of twenty-five nucleotide probes designed to hybridize with specific gene targets. Given a particular Affymetrix® GeneChip® platform, the design of the probes is fixed. However, the method of analysis is dynamic in nature due to the ability to annotate and group probes into uniquely defined groupings. This is particularly important since publicly available repositories of microarray datasets, such as ArrayExpress and NCBI’s …

Fm Radio Signal Propagation Evaluation And Creating Statistical Models For Signal Strength Prediction In Differing Topographic Environments, Timothy Land

Electronic Theses and Dissertations

Radio wave signal strength and associated propagation models are rarely analyzed across individual geographic provinces. This study evaluates the effectiveness of the Radio Mobile model to predict radio wave signal strength in the Blue Ridge and Valley and Ridge physiographic provinces. A spectrum analyzer was used on 19 FM transmitters to determine model accuracy. Statistical analysis determined the significance between different terrain factors and signal strength. Field signal strength was found to be related to test site elevation, transmitter azimuth, elevation angle, transmitter elevation, path loss, and distance. Using 76 signal strength receiver sites, Ordinary Least Square regression models predicted …

Old English Character Recognition Using Neural Networks, Sattajit Sutradhar

Electronic Theses and Dissertations

Character recognition has been capturing the interest of researchers since the beginning of the twentieth century. While the Optical Character Recognition for printed material is very robust and widespread nowadays, the recognition of handwritten materials lags behind. In our digital era more and more historical, handwritten documents are digitized and made available to the general public. However, these digital copies of handwritten materials lack the automatic content recognition feature of their printed materials counterparts. We are proposing a practical, accurate, and computationally efficient method for Old English character recognition from manuscript images. Our method relies on a modern machine learning …