Physical Sciences and Mathematics Commons^™

Detecting Ai Generated Text Using Neural Networks, Jesus Guerrero

Masters Theses

For humans, distinguishing machine generated text from human written text is men- tally taxing and slow. NLP models have been created to do this more effectively and faster. But, what if some adversarial changes have been added to the machine generated text? This thesis discusses this issue and text detectors in general.

The primary goal of this thesis is to describe the current state of text detectors in research and to discuss a key adversarial issue in modern NLP transformers. To describe the current state of text detectors a Systematic Literature Review was done on 50 relevant papers to machine-centric …

Tempers Rising: The Effect Of Heat On Spite, Jake C. Cosgrove

Master's Theses

The relationship between heat and harmful outcomes is well documented, with research connecting various adverse economic outcomes to the climate. In the presence of increasing global warming and climate change, understanding why the climate leads to negative economic outcomes is essential for forming peaceful institutions of the future. We study how behavioral economic outcomes change in the presence of heat through a lab experiment involving 1,110 observations conducted in five different countries. This paper specifically focuses on the social preference outcome of spite. We find that increased time exposure to the treatment effect of heat is required to elicit an …

Landslide Forecast In Taiwan Based On Machine Learning In The Gis Field, Yi Shen

Honors Capstones

Landslides can pose a significant risk to life, property, and infrastructure in mountainous regions, and can be triggered by various factors, including intense rainfall, earthquakes, and water level changes. Machine learning is commonly used to forecast landslides, based on statistical relationships between past landslides and multiple variables to create a general forecasting model. However, these models often require large amounts of data to achieve accurate results. This project aims to use only a few variables but take advantage of both their spatial distribution and temporal trends to improve the accuracy of landslide forecasts. This approach is tested in Taiwan, a …

Automatic Identification Of Jetting Behavior In 3d Printing With Binary Classification And Anomaly Detection, Alexander Chandy

Honors Scholar Theses

Consistently jetting different materials from the print head of a 3D printer is a key, yet challenging task in manufacturing processes. By using active machine learning, we can efficiently predict complex diagrams that illustrate the region of printing conditions under which “desirable jetting”, “jetting”, and “no jetting” of ink occurs for different substances. However, labeling the images of printed ink droplets that are fed to the active learning model can be time intensive. Therefore, it is ideal to use computer vision to automate the classification of this image data. This classification can be broken down into two steps. In the …

Eddy Current Defect Response Analysis Using Sum Of Gaussian Methods, James William Earnest

Theses and Dissertations

This dissertation is a study of methods to automatedly detect and produce approximations of eddy current differential coil defect signatures in terms of a summed collection of Gaussian functions (SoG). Datasets consisting of varying material, defect size, inspection frequency, and coil diameter were investigated. Dimensionally reduced representations of the defect responses were obtained utilizing common existing reduction methods and novel enhancements to them utilizing SoG Representations. Efficacy of the SoG enhanced representations were studied utilizing common Machine Learning (ML) interpretable classifier designs with the SoG representations indicating significant improvement of common analysis metrics.

Enhancing Earthquake Detection Through Machine Learning- An Application To The 2017 Mw 8.2 Tehuantepec Earthquake In Mexico, Marc Adrian Garcia

Open Access Theses & Dissertations

The Tehuantepec seismic gap, located off the southern shore of Oaxaca and Chiapas, Mexico, was previously thought to be an aseismic zone due to no significant event in 100 years. The September 8, 2017 (M8.2) Tehuantepec earthquake disproved this idea and added many questions surrounding the Mexican subduction zone. Specifically, the earthquake did not occur at the subduction megathrust. It ruptured the subducting plate below the megathrust and appeared to stop at the megathrust. Following this event, as well as the September 19, 2017 (M7.1) Morelos-Puebla earthquake, researchers from the University of Texas at El Paso (UTEP), Universidad Autónoma Cuidad …

The Search For Heavily Obscured Active Galactic Nuclei In The Local Universe, Ross Silver

All Dissertations

Active galactic nuclei (AGN) are supermassive black holes (SMBHs) in the center of galaxies that accrete surrounding gas and emit across the entire electromagnetic spectrum. They are the most energetic persistent emitters in the Universe, capable of outshining their host galaxies despite their emission originating from a region smaller than our Solar System. AGN were some of the first sources discovered that helped teach us that there were galaxies outside of our own, and they proved the existence of black holes. Moreover, AGN can give us valuable insights into other branches of astrophysics. For example, they can be used to …

Emotion Classification And Intensity Prediction On Tweets, Sharath Chander Pugazhenthi

Theses and Dissertations

The task of finding an emotion associated with the text from individuals on a social media platform has become very crucial as it influences the current state of mind of a particular individual in real life. It also helps one to understand social behavior at a given point in time. Microblogging platforms like Twitter serves as a powerful tool for expressing one’s thoughts. Several work have been done in classifying the emotion associated with it. The thesis comprises of a system that first classifies the tweet into one of the four emotions - anger, joy, sadness, and fear with good …

Distance Correlation Based Feature Selection In Random Forest, Jose Munoz-Lopez

Electronic Theses, Projects, and Dissertations

The Pearson correlation coefficient is a commonly used measure of correlation, but it has limitations as it only measures the linear relationship between two numerical variables. In 2007, Szekely et al. introduced the distance correlation, which measures all types of dependencies between random vectors X and Y in arbitrary dimensions, not just the linear ones. In this thesis, we propose a filter method that utilizes distance correlation as a criterion for feature selection in Random Forest regression. We conduct extensive simulation studies to evaluate its performance compared to existing methods under various data settings, in terms of the prediction mean …

A Study Of Various Data Sizes Using Machine Learning, Sochaeta Koeum

Electronic Theses, Projects, and Dissertations

Social media is a great domain for news consumption; however, it is referred to as a double-edged sword. While it is user-friendly and low-cost, social media is the reason why fake news can spread rapidly, which is detrimental to society, businesses, and many consumers. Therefore, fake news detection is an emerging field. However, some challenges have restricted other researchers from developing a universal machine learning model that is fast, efficient, and reliable to stop the proliferation because of the lack of resources available, such as large-sized datasets. The goal of this culminating experience project is to explore how varying datasets …

Enhancing Basic Geology Skills With Artificial Intelligence: An Exploration Of Automated Reasoning In Field Geology, Perry Ivan Quinto Houser

Open Access Theses & Dissertations

This thesis explores the use of Artificial Intelligence, specifically semantics, ontologies, and reasoner techniques, to improve field geology mapping. The thesis focuses on two use cases: 1) identifying a geologic formation based on observed characteristics; and 2) predicting the geologic formation that might be expected next based upon known stratigraphic sequence. The results show that the ontology was able to correctly identify the geologic formation for the majority of rock descriptions, with higher search results for descriptions that provided more detail. Similarly, the units expected next were correctly given and if incorrect, would provide a flag to the field geologist …

Development Of A Cost-Constrained Intelligent Prosthetic Knee With Real-Time Machine Learning, Predictive Stumble Control, Lucas Jonathan Galey

Open Access Theses & Dissertations

The field of biomechatronics is evolving quickly with advances in computer science, biology, and electrical and mechanical engineering. Coupled with increased interests in machine learning (ML) across all industry sectors, there are opportunities to leverage advanced analytics in uniquely complex problems. This study aimed to deploy real-time ML predictions in a novel microprocessor-controlled prosthetic knee (MPK) device capable of identifying and responding to stumble-events to reduce amputee fall prevalence. Innately, stumbling is a chaotic event. Current MPKs operate by detecting gait characteristics and reacting to preprogrammed states. While these systems are beneficial in significant ways, such as energy expenditure and …

Analyzing Software Maintenance Through Machine Learning And Mining Software Repositories Approaches, Sayed Mohsin Reza

Open Access Theses & Dissertations

The rapid growth of software systems demands meticulous planning and maintenance to accommodate the evolution of the code base over extended periods. Without maintenance, software systems will become more complex, low in quality, and hence unsustainable. Software engineers who perform maintenance often strive to optimize code quality or minimize code smells in a timely manner. Several techniques have been used to detect code quality or code smells as a part of software maintenance. Most of these techniques are based on heuristics, which create detection rules using a few metrics. These approaches have reasonable accuracy but do not work in cross-project …

The Identification Of Rogue Access Points Using Channel State Information, Irene Mcginniss

Theses, Dissertations and Culminating Projects

Today's wireless networks (Wi-Fi) handle more significant numbers of connections, deploy efficiently, and provide increased reliability and high speeds at low cost. The ability of rogue access points (RAPs) to mimic legitimate APs makes them the most critical threat to wireless security. APs are found in coffee shops, supermarkets, stadiums, buses, trains, airports, hospitals, theaters, and shopping malls.

Rogue access points (RAP) are unauthorized devices that connect to legitimate access points and networks and bypass authorized security procedures. RAP detection has been attempted using hardware and software-based solutions requiring the developing of dedicated tools or beacon frame modification. (Arisandi, 2021). …

Examining Model Complexity's Effects When Predicting Continuous Measures From Ordinal Labels, Mckade S. Thomas

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Many real world problems require the prediction of ordinal variables where the values are a set of categories with an ordering to them. However, in many of these cases the categorical nature of the ordinal data is not a desirable outcome. As such, regression models treat ordinal variables as continuous and do not bind their predictions to discrete categories. Prior research has found that these models are capable of learning useful information between the discrete levels of the ordinal labels they are trained on, but complex models may learn ordinal labels too closely, missing the information between levels. In this …

Achieving Causal Fairness In Recommendation, Wen Huang

Graduate Theses and Dissertations

Recommender systems provide personalized services for users seeking information and play an increasingly important role in online applications. While most research papers focus on inventing machine learning algorithms to fit user behavior data and maximizing predictive performance in recommendation, it is also very important to develop fairness-aware machine learning algorithms such that the decisions made by them are not only accurate but also meet desired fairness requirements. In personalized recommendation, although there are many works focusing on fairness and discrimination, how to achieve user-side fairness in bandit recommendation from a causal perspective still remains a challenging task. Besides, the deployed …

Improving Classification In Single And Multi-View Images, Hadi Kanaan Hadi Salman

Graduate Theses and Dissertations

Image classification is a sub-field of computer vision that focuses on identifying objects within digital images. In order to improve image classification we must address the following areas of improvement: 1) Single and Multi-View data quality using data pre-processing techniques. 2) Enhancing deep feature learning to extract alternative representation of the data. 3) Improving decision or prediction of labels. This dissertation presents a series of four published papers that explore different improvements of image classification. In our first paper, we explore the Siamese network architecture to create a Convolution Neural Network based similarity metric. We learn the priority features that …

A Machine Learning Approach For Predicting Clinical Trial Patient Enrollment In Drug Development Portfolio Demand Planning, Ahmed Shoieb

Masters Theses

One of the biggest challenges the clinical research industry currently faces is the accurate forecasting of patient enrollment (namely if and when a clinical trial will achieve full enrollment), as the stochastic behavior of enrollment can significantly contribute to delays in the development of new drugs, increases in duration and costs of clinical trials, and the over- or under- estimation of clinical supply. This study proposes a Machine Learning model using a Fully Convolutional Network (FCN) that is trained on a dataset of 100,000 patient enrollment data points including patient age, patient gender, patient disease, investigational product, study phase, blinded …

Inaugural Artificial Intelligence For Public Health Practice (Ai4php) Retreat: Ontario, Canada, Jacqueline K. Kueper, Laura C. Rosella, Richard G. Booth, Brent D. Davis, Sarah Nayani, Maxwell J. Smith, Dan Lizotte

Computer Science Publications

The Artificial Intelligence (AI) for Public Health Practice Retreat was a hybrid event held in October 2022 in London, Ontario to achieve three main goals: 1) Identify both the goals of public health practitioners and the tasks that they undertake as part of their practice to achieve those goals that could be supported by AI, 2) Learn from existing examples and the experience of others about facilitators and barriers to AI for public health, and 3) Support new and strengthen existing connections between public health practitioners and AI researchers. The retreat included a keynote presentation, group brainstorming exercises, breakout group …

Automated Classification Of Pectinodon Bakkeri Teeth Images Using Machine Learning, Jacob A. Bahn

MS in Computer Science Project Reports

Microfossil dinosaur teeth are studied by paleontologists in order to better under- stand dinosaurs. Currently, tooth classification is a long, manual, error-ridden process. Deep learning offers a solution that allows for an automated way of classifying images of these microfossil teeth. In this thesis, we aimed to use deep learning in order to develop an automated approach for classifying images of Pectinodon bakkeri teeth. The proposed model was trained using a custom topology and it classified the images based on clusters created via K-Means. The model had an accuracy of 71%, a precision of 71%, a recall of 70.5%, and …

Using Machine Learning To Measure Political Polarization On Social Media, Veronica Cagle

Student Research Submissions

Polarization in the political sphere, seen through combative communication and stalemate, may impose negative social impacts on the population. Attempting to measure political polarization in the masses through self-reported surveys and interviews can present response biases of social desirability. The classification of thought freely written online allows political polarization to be measured in an impartial manner. Reddit is one application that enables users to share opinions and create discussions anonymously; this text can be used to measure the political climate at any given time. Disagreement has grown over the perceived level of polarization in our society. The purpose of my …

The Role Of Machine Learning In Improved Functionality Of Lower Limb Prostheses, Joaquin Dominguez, Richard Kim, Robert Slater

SMU Data Science Review

Lower-limb amputations can cause a plethora of obstacles that lead to a lower quality of life. Implementing machine learning techniques means advanced prosthetics can contribute to facilitating the lives of those that live with lower-limb amputations. Using the publicly available HuGaDB data set, the current study investigates several classification models (random forest, neural network, and Vowpal Wabbit) to predict the locomotive intentions of individuals using lower-limb prostheses. The results of this study show that the neural network model yielded the highest accuracy, comparable precision, and recall scores to the other models. However, the Vowpal Wabbit model's advantage in speed may …

Accelerating Atmospheric Gravity Wave Simulations Using Machine Learning: Kelvin-Helmholtz Instability And Mountain Wave Sources Driving Gravity Wave Breaking And Secondary Gravity Wave Generation, Wenjun Dong, David Fritts, Alan Z. Liu, Hanli Liu, Jonathan Snively

Publications

Gravity waves (GWs) and their associated multi-scale dynamics are known to play fundamental roles in energy and momentum transport and deposition processes throughout the atmosphere. We describe an initial, two-dimensional (2-D), machine learning model – the Compressible Atmosphere Model Network (CAMNet) - intended as a first step toward a more general, three-dimensional, highly-efficient, model for applications to nonlinear GW dynamics description. CAMNet employs a physics-informed neural operator to dramatically accelerate GW and secondary GW (SGW) simulations applied to two GW sources to date. CAMNet is trained on high-resolution simulations by the state-of-the-art model Complex Geometry Compressible Atmosphere Model (CGCAM). Two …

Learning Analytics Through Machine Learning And Natural Language Processing, Bokai Yang

Theses and Dissertations

The increase of computing power and the ability to log students’ data with the help of the computer-assisted learning systems has led to an increased interest in developing and applying computer science techniques for analyzing learning data. To understand and investigate how learning-generated data can be used to improve student success, data mining techniques have been applied to several educational tasks. This dissertation investigates three important tasks in various domains of educational data mining: learners’ behavior analysis, essay structure analysis and feedback providing, and learners’ dropout prediction. The first project applied latent semantic analysis and machine learning approaches to investigate …

Double Trouble: Applying Deep Learning To Ebs Systems, Noah Reneau, Hidemi Mitani Shen, Nicholas Chandler, Ian Pourlotfali

WWU Honors College Senior Projects

Eclipsing binaries (EB) are fundamental stellar laboratories that can be detected via long-term photometric monitoring. Analyzing the orbital motion of these EBs offers a unique ability to directly measure the parameters of both stars in the system, including masses, radii, and effective temperatures, without relying on theoretical models. Nonetheless, this process is non-trivial, and arriving to a correct solution for a given system can often take significant time. In the ongoing work, we are developing deep learning models capable of providing fast and accurate predictions of these fundamental parameters in these EBs, which will enable the characterization of an increasingly …

A Hybrid Continual Machine Learning Model For Efficient Hierarchical Classification Of Domain-Specific Text In The Presence Of Class Overlap (Case Study: It Support Tickets), Yasmen M. Wahba

Electronic Thesis and Dissertation Repository

In today’s world, support ticketing systems are employed by a wide range of businesses. The ticketing system facilitates the interaction between customers and the support teams when the customer faces an issue with a product or a service. For large-scale IT companies with a large number of clients and a great volume of communications, the task of automating the classification of incoming tickets is key to guaranteeing long-term clients and ensuring business growth.

Although the problem of text classification has been widely studied in the literature, the majority of the proposed approaches revolve around state-of-the-art deep learning models. This thesis …

Ai Applications On Planetary Rovers, Alexis David Pascual

Electronic Thesis and Dissertation Repository

The rise in the number of robotic missions to space is paving the way for the use of artificial intelligence and machine learning in the autonomy and augmentation of rover operations. For one, more rovers mean more images, and more images mean more data bandwidth required for downlinking as well as more mental bandwidth for analyzing the images. On the other hand, light-weight, low-powered microrover platforms are being developed to accommodate the drive for planetary exploration. As a result of the mass and power constraints, these microrover platforms will not carry typical navigational instruments like a stereocamera or a laser …

Chatgpt As Metamorphosis Designer For The Future Of Artificial Intelligence (Ai): A Conceptual Investigation, Amarjit Kumar Singh (Library Assistant), Dr. Pankaj Mathur (Deputy Librarian)

Library Philosophy and Practice (e-journal)

Abstract

Purpose: The purpose of this research paper is to explore ChatGPT’s potential as an innovative designer tool for the future development of artificial intelligence. Specifically, this conceptual investigation aims to analyze ChatGPT’s capabilities as a tool for designing and developing near about human intelligent systems for futuristic used and developed in the field of Artificial Intelligence (AI). Also with the helps of this paper, researchers are analyzed the strengths and weaknesses of ChatGPT as a tool, and identify possible areas for improvement in its development and implementation. This investigation focused on the various features and functions of ChatGPT that …

Reducing Negative Transfer Of Random Data In Source-Free Unsupervised Domain Adaptation, Anthony Wong

Electronic Thesis and Dissertation Repository

In domain adaptation, a model trained on one dataset (source domain) is applied to a different but related dataset (target domain). The most cutting-edge method is unsupervised source-free domain adaptation (SFDA), in which source data, source labels, and target labels are not available during adaptation. This thesis explores a realistic scenario where the target dataset includes some images that are unrelated to the adaptation process. This scenario can occur from errors in data collection or processing. We provide experiments and analysis to show that current state-of-the-art (SOTA) SFDA methods suffer significant performance drops under a specific domain adaptation setup when …

Modeling Daily Fantasy Basketball, Martin Jiang

Master's Theses

Daily fantasy basketball presents interesting problems to researchers due to the extensive amounts of data that needs to be explored when trying to predict player performance. A large amount of this data can be noisy due to the variance within the sport of basketball. Because of this, a high degree of skill is required to consistently win in daily fantasy basketball contests. On any given day, users are challenged to predict how players will perform and create a lineup of the eight best players under fixed salary and positional requirements. In this thesis, we present a tool to assist daily …