Computer Sciences Commons^™

A Generative Neural Network For Discovering Near Optimaldynamic Inductive Power Transfer Systems, Md Shain Shahid Chowdhury Oni

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

An urgent need is to electrify transportation to lower carbon emissions into the atmosphere. Wireless charging makes electrical vehicles (EVs) more convenient and cheaper because energy is transferred to the vehicle without the need to plug it in. Dynamic wireless charging is particularly interesting, where the vehicle does not need to stop to receive the energy. This technology requires the EV and the roadway to include coils of wire, where the roadway coil is energized as the vehicle passes over it to induce an electrical current in the EV coil through electromagnetic induction. However, the problem of designing the two …

Coding Bootcamps - Perceptions And Outcomes, Logan L. Hendricks

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

This thesis is focused on gathering, aggregating and analysing data related to software development coding bootcamps. It comprises of three major research initiatives: A coding bootcamp outcomes meta-analysis, a study on perspectives regarding white-label coding bootcamps, and the data analysis of a survey gathering long-term outcomes of coding bootcamp and certificate program graduates.

The first study aggregates graduate outcome data from the three main organizations that review coding bootcamp outcomes: CourseReport.com, SwitchUp.com and the Council on Integrity in Results Reporting (CIRR). The purpose of this meta-review is to establish a baseline dataset which is immediately utilized in my further research. …

Deep Learning With Attention Mechanisms In Breast Ultrasound Image Segmentation And Classification, Meng Xu

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Breast cancer is a great threat to women’s health. Breast ultrasound (BUS) imaging is commonly used in the early detection of breast cancer as a portable, valuable, and widely available diagnosis tool. Automated BUS image analysis can assist radiologists in making accurate and fast decisions. Generally, automated BUS image analysis includes BUS image segmentation and classification. BUS image segmentation automatically extracts tumor regions from a BUS image. BUS image classification automatically classifies breast tumors into benign or malignant categories. Multi-task learning accomplishes segmentation and classification simultaneously, which makes it more appealing and practical than an either individual task. Deep neural …

Adversarial Swarming: A Groundwork For Multi-Drone Independent Interception Exercises Through Ma-Poca In Unity, Johnathan D. Kunz

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

As drones become more popular and easier to use, air spaces are becoming more congested. Airports, hospitals, and similar structures require controlled, safe airspaces and drones are increasingly a threat. Locally controlled airspace requires efficient removal of airborne threats to continue sensitive operations. Many methods have been investigated for removing drones from contested airspace. Generally these methods involve ground-based signal disruption, physical contact, or drone interception of a single intruder. In this work we present a drone interception model with a low-cost, low-capability group of short-range drones intercepting an incoming drone.

Wearable Sensor Gait Analysis For Fall Detection Using Deep Learning Methods, Haben Girmay Yhdego

Electrical & Computer Engineering Theses & Dissertations

World Health Organization (WHO) data show that around 684,000 people die from falls yearly, making it the second-highest mortality rate after traffic accidents [1]. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. In light of the recent widespread adoption of wearable sensors, it has become increasingly critical that fall detection models are developed that can effectively process large and sequential sensor signal data. Several researchers have recently developed fall detection algorithms based on wearable sensor data. However, real-time fall detection remains challenging because of the wide …

Decision Support Issues In Automated Driving Systems, William N. Caballero, David Ríos Insua, David Banks

Faculty Publications

Machine learning and computational processing have advanced such that automated driving systems (ADSs) are no longer a distant reality. Many automobile manufacturers have developed prototypes; however, there exist numerous decision support issues requiring resolution to ensure mass ADS adoption. In the coming decades, it is likely that production ADSs will only be partially autonomous. Such ADSs operate within predetermined conditions and require driver intervention when they are violated. Since forecasts of their 20-year market penetration are relatively low, ADSs will likely operate in heterogeneous traffic characterized by vehicles of varying autonomy levels. Under these conditions, effective decision support must consider …

Supporting Account-Based Queries For Archived Instagram Posts, Himarsha R. Jayanetti

Computer Science Theses & Dissertations

Social media has become one of the primary modes of communication in recent times, with popular platforms such as Facebook, Twitter, and Instagram leading the way. Despite its popularity, Instagram has not received as much attention in academic research compared to Facebook and Twitter, and its significant role in contemporary society is often overlooked. Web archives are making efforts to preserve social media content despite the challenges posed by the dynamic nature of these sites. The goal of our research is to facilitate the easy discovery of archived copies, or mementos, of all posts belonging to a specific Instagram account …

Understanding Societal Values Of Chatgpt, Yidan Tang

McKelvey School of Engineering Graduate Student Theses & Dissertations

As Large language models (LLMs) become increasingly pervasive in various domains, it is crucial to ensure that their outputs adhere to societal values and ethical considerations. In this thesis, we investigate the alignment of ChatGPT, a recent state-of-the-art large language model developed by OpenAI, with societal values. Specifically, we define the problem of societal values of LLMs and assemble a representative collection of 7 datasets covering 4 topics related to societal values. In-context learning techniques are applied and appropriate prompts are designed. The performance of each dataset is measured using a standardized evaluation system focused on accuracy. We then display …

Evaluating The Problem Solving Abilities Of Chatgpt, Fankun Zeng

McKelvey School of Engineering Graduate Student Theses & Dissertations

This thesis addresses the need for a fair evaluation of language models' problem solving abilities by presenting a unified evaluation framework for ChatGPT on 16 problem solving datasets (e.g., NaturalQA, HellaSwag, MMLU, etc.). We evaluate the model's performance using F1, exact match, and quasi-exact match metrics and find that ChatGPT is highly accurate in solving tasks that require commonsense and knowledge. However, we also identify truncated text bias and few-shot scenarios as challenges that may impact ChatGPT's performance. Our research highlights the importance of standardizing datasets and developing a unified evaluation system for the fair evaluation of language models. Overall, …

Sustainable Grain Transportation In Ukraine Amidst War Utilizing Knarm And Knowwheregraph, Yinglun Zhang, Antonina Broyaka, Jude Kastens, Allen M. Featherstone, Cogan Shimizu, Pascal Hitzler, Hande Küçük Mcginty

Computer Science and Engineering Faculty Publications

In this work, we propose a sustainable path-finding application for grain transportation during the ongoing Russian military invasion in Ukraine. This application is to build a suite of algorithms to find possible optimal paths for transporting grain that remains in Ukraine. The application uses the KNowledge Acquisition and Representation Methodology(KNARM) and the KnowWhereGraph to achieve this goal. Currently, we are working towards creating an ontology that will allow for a more effective heuristic approach by incorporating the lessons learned from the KnowWhereGraph. The aim is to enhance the path-finding process and provide more accurate and efficient results. In the future, …

Neutrosophic Mcdm Methodology For Evaluation Onshore Wind For Electricity Generation And Sustainability Ecological, Karam M. Sallam, Ali Wagdy Mohamed

Neutrosophic Systems with Applications

The conviction in the necessity of renewable energy has been prompted by both the constantly increasing need for power production and the ecological issues of recent years. When it comes to generating power in a sustainable manner, wind is among the most essential renewable energy sources. This research attempts to present a structural technique for assessing the performance of operational onshore wind facilities with respect to the three dimensions of sustainability. The energy production, environmental effect, and practicability of onshore wind facilities are all dependent on accurate assessments. Wind resources, site accessibility, environmental impact, permitting and regulatory requirements, turbine technology, …

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 …

Neutrosophic Mcdm Methodology For Evaluation Onshore Wind For Electricity Generation And Sustainability Ecological, Karam M. Sallam, Ali Wagdy Mohamed

Neutrosophic Systems with Applications

The conviction in the necessity of renewable energy has been prompted by both the constantly increasing need for power production and the ecological issues of recent years. When it comes to generating power in a sustainable manner, wind is among the most essential renewable energy sources. This research attempts to present a structural technique for assessing the performance of operational onshore wind facilities with respect to the three dimensions of sustainability. The energy production, environmental effect, and practicability of onshore wind facilities are all dependent on accurate assessments. Wind resources, site accessibility, environmental impact, permitting and regulatory requirements, turbine technology, …

Quantification Of Various Types Of Biases In Large Language Models, Sudhashree Sayenju

Doctor of Data Science and Analytics Dissertations

Natural Language Processing (NLP) systems are included everywhere on the internet from search engines, language translations to more advanced systems like voice assistant and customer service. Since humans are always on the receiving end of NLP technologies, it is very important to analyze whether or not the Large Language Models (LLMs) in use have bias and are therefore unfair. The majority of the research in NLP bias has focused on societal stereotype biases embedded in LLMs. However, our research focuses on all types of biases, namely model class level bias, stereotype bias and domain bias present in LLMs. Model class …

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 …

Head And Neck Tumor Histopathological Image Representation With Pre- Trained Convolutional Neural Network And Vision Transformer, Ranny Rahaningrum Herdiantoputri, Daisuke Komura, Tohru Ikeda, Shumpei Ishikawa

Journal of Dentistry Indonesia

Image representation via machine learning is an approach to quantitatively represent histopathological images of head and neck tumors for future applications of artificial intelligence-assisted pathological diagnosis systems. Objective: This study compares image representations produced by a pre-trained convolutional neural network (VGG16) to those produced by a vision transformer (ViT-L/14) in terms of the classification performance of head and neck tumors. Methods: W hole-slide images of five oral t umor categories (n = 319 cases) were analyzed. Image patches were created from manually annotated regions at 4096, 2048, and 1024 pixels and rescaled to 256 pixels. Image representations were …

Comparing Igneous Geochemical Data From Hawaii And Southern California Via Machine Learning, Miro Manestar

MS in Computer Science Project Reports

Bi-plots are commonly used in geochemical analyses. However, their use can become cumbersome in the case of multi-variate analyses. Therefore, this thesis explores the application of unsupervised machine learning techniques, specifically PCA and K-Means, to analyze large geochemical data sets from two distinct regions, Hawaii and the \acrfull{prb} in Southern California. The IBM Foundational Methodology for Data Science was utilized to ensure proper data preparation and analysis. PCA provided dimensionality reduction, revealing which features correlated most strongly with variances within the data. K-Means clustering allowed for deeper interpretation of the data. The analysis yielded valuable insights into the composition and …

Instaanalytica, Sapna Maduraimuthu

Masters Projects

This project aims to analyze social media trends using Instagram as the primary source of data extraction and provides insights such as understanding engagement rates, top hashtags, optimal posting times, and post rankings based on engagement rates and sentiment scores thus the users can understand their audience and improve their posting strategies. Firstly, the profile trend chart and area chart based on the average engagement rate is generated to obtain day, week, or month engagement rates. Secondly, text preprocessing is done before generating a word cloud displaying the most frequently occurring words in the captions. The size of each word …

Analyzing And Computing Complete Solution For Dots And Boxes Game, Carl Mcaninch

Undergraduate Theses and Capstone Projects

This thesis improves a process that analyzes all the states of a game of Dots and Boxes. We use retrograde analysis and simulations to create a solution that provides significant performance improvements over our previous best solution. Expanding upon a previous 4x4 solution using rotations, reflections, better optimization, and cloud computing to limit the processing time and gather more data efficiently. We compute a file and the number of states associated with each file and process every state starting with a completely filled board. We optimized the data for cloud computing by running simulations to find the most efficient number …

Co-Designing Assistive Technology With And For Persons Living With Dementia, Dympna O'Sullivan, Jonathan Turner, Siobhan O'Neill, Micheal Wilson, Julie Doyle

Conference papers

Dementia is a chronic and progressive neurodegenerative illness, which can lead to significant difficulties in a person’s capacity to perform activities of daily living (ADLs) and engage in meaningful activities. There is an acute need, which digital health technologies can potentially fulfil, to provide proactive support for persons living with dementia (PLwD) and their caregivers. However, there is limited involvement of PLwD in the design of technology that could be used to support their personal plans for independent living at home. In this paper, we describe how we are employing a co-design methodology to support engagement in an assistive technology …

Blind Fighter – A Video Game For The Visually Impaired, Avery Wayne Harrah

ATU Scholars Symposium

Blind Fighter is a video game made to be playable by anyone, regardless of any visual impairments the player may have. The game relies on auditory queues to allow players to understand what is happening in the game without ever having to see the screen. The project’s goal is to serve as a proof of concept that video games can be made inclusive with a few additions during development, without sacrificing overall quality. To do this, the game features full graphics in addition to testing many strategies for visually impaired players, including direction-based audio, unique sound effects for each game …

Crime Prediction Using Machine Learning: The Case Of The City Of Little Rock, Zurab Sabakhtarishvili, Sijan Panday, Clayton Jensen

ATU Scholars Symposium

Crime is a severe problem in the city of Little Rock, Arkansas. In this study, we aim to develop a machine-learning model to predict criminal activities in the city and provide insights into crime patterns. We will analyze publicly available crime datasets from Little Rock Police Department from January 2017 to March 2023 to identify trends and patterns in crime occurrence. We used data cleaning and exploratory data analysis techniques, such as figured-based visualizations, to prepare the data for machine learning. We will employ the Neural Prophet, a time-series machine learning model, to predict daily crime counts. The model will …

Reworking Of The Arkansas Tech Human Resources Employee Records Software, Dalton J. George, Brayan Bonilla-Chavez, John Modica, Angelina Das

ATU Scholars Symposium

Evisions Argos is a real-time reporting tool used by Arkansas Tech in many record-keeping departments. Reworking HR's software using this tool, security and database access concerns were negated, as Argos is already connected to the University's backend. Using Argos, we have made ATU HR's employee records software more user friendly and built a system that can be pushed to production for use by the university. As a secondary portion to this final project, we developed a proof-of-concept web application using the MEAN (Mongo, Express, Angular, Node) stack. This gave us the opportunity to produce a full-stack application from scratch as …

The Ozark Getaway, Houston Barber, Marcus Gasca, Evan Reece Matlock, Avery Wayne Harrah

ATU Scholars Symposium

Website designed for usage of renting AirBnb houses outside of the original website under the specific owner.

Premium Wireless, Dakota Burkhart, Andrew Clark, Garrett Kenney, Brandon Monroe

ATU Scholars Symposium

Our work implements an inventory system and appointment system for the Premium Wireless phone company. It includes separate views for employees to manage inventory and view appointments for the day and the near future and allows customers to view all current inventory and set an appointment.

Arkavalley Liquor: Simplifying Restaurant Alcohol Orders, Isaiah A. Kitts, Dayton Drilling, Bradlee Treece, Cameron Lumpkin

ATU Scholars Symposium

The ArkaValley Liquor system is a web-based ordering platform designed to simplify the process of ordering alcohol for local restaurants. Currently, restaurants place orders by emailing the store, which makes it difficult to maintain a paper trail and track order details. With the ArkaValley Liquor system, the ordering process is automated, and all order details are saved in one central location. Each restaurant will have a login, ensuring only authorized individuals can place orders. The system will also provide a record of each restaurant's most recent order, making it easy to reorder if necessary. By using the ArkaValley Liquor system, …

Analysis Of Honeypots In Detecting Tactics, Techniques, And Procedure (Ttp) Changes In Threat Actors Based On Source Ip Address, Carson Reynolds, Andy Green

Symposium of Student Scholars

The financial and national security impacts of cybercrime globally are well documented. According to the 2020 FBI Internet Crime Report, financially motivated threat actors committed 86% of reported breaches, resulting in a total loss of approximately $4.1 billion in the United States alone. In order to combat this, our research seeks to determine if threat actors change their tactics, techniques, and procedures (TTPs) based on the geolocation of their target’s IP address. We will construct a honeypot network distributed across multiple continents to collect attack data from geographically separate locations concurrently to answer this research question. We will configure the …

Analysis Impact Of Intrinsic And Extrinsic Motivation On Job Satisfaction In Logistics Service Sector: An Intelligent Neutrosophic Model, Zenat Mohamed, Mahmoud M. Ismail, Amal F. Abd El-Gawad

Neutrosophic Systems with Applications

The success of every company relies heavily on the happiness of its workforce, and the logistics service sector is no exception. The capacity of logistics suppliers to satisfy the demands of their clients depends on the efficiency and efficacy of operations, which in turn is affected by the level of employee satisfaction. This paper aims to analyze the factors of employee satisfaction in the logistic service industry to achieve productivity and a satisfied workforce. This paper used the multi-criteria decision-making (MCDM) methodology to handle various factors. The SWARA method is an MCDM method used to compute the importance of factors. …

Analysis Impact Of Intrinsic And Extrinsic Motivation On Job Satisfaction In Logistics Service Sector: An Intelligent Neutrosophic Model, Zenat Mohamed, Mahmoud M. Ismail, Amal F. Abd El-Gawad

Neutrosophic Systems with Applications

The success of every company relies heavily on the happiness of its workforce, and the logistics service sector is no exception. The capacity of logistics suppliers to satisfy the demands of their clients depends on the efficiency and efficacy of operations, which in turn is affected by the level of employee satisfaction. This paper aims to analyze the factors of employee satisfaction in the logistic service industry to achieve productivity and a satisfied workforce. This paper used the multi-criteria decision-making (MCDM) methodology to handle various factors. The SWARA method is an MCDM method used to compute the importance of factors. …

The Impact Of Virtual Reality On The Healthcare Industry, Peter Sullivan

Honors Projects in Information Systems and Analytics

Virtual reality (VR) took off in 2013 and has touched many public sectors, from gaming, to business, to healthcare. This study looks at virtual reality's impact has affected the healthcare system, with a focus on its use for medical training, patient recovery, patient pain management, and mental health care. A literature review was conducted on the current state of the industry addressing virtual reality's performance in the field, the perception of experts, and an estimation of financial undertakings. Looking at cost analyses brought a fuller approach to the research. Surveying researchers and workers within the realm of healthcare and VR …