Computer Sciences Commons^™

Andrews University Pre-Professional Students Preparedness For A Future With Artificial Intelligence, Zachary Alignay

Honors Theses

Artificial Intelligence technology has advanced considerably over the past four years. With such rapid technological development, the question has to be asked if students are adequately educated on the implications and abilities of artificial intelligence. Are Andrews University pre-professional students prepared for future careers with artificial intelligence? To approach this question, a survey of students across multiple perspectives was conducted to sample if there was a consensus, or lack thereof, on the perception of ethics regarding artificial intelligence, to ask students how using artificial intelligence has changed their education, what purposes it can be used or cannot be used, personal …

Subject Analysis Ex Machina: Developing A Subject Heading Recommendation Service For Jmu Libraries, Steven W. Holloway

Libraries

Results of a 2022 evaluation of ANNIF, open-source software designed to generate controlled vocabulary subject headings, using James Madison University Libraries resources.

A Design Science Approach To Investigating Decentralized Identity Technology, Janelle Krupicka

Cybersecurity Undergraduate Research Showcase

The internet needs secure forms of identity authentication to function properly, but identity authentication is not a core part of the internet’s architecture. Instead, approaches to identity verification vary, often using centralized stores of identity information that are targets of cyber attacks. Decentralized identity is a secure way to manage identity online that puts users’ identities in their own hands and that has the potential to become a core part of cybersecurity. However, decentralized identity technology is new and continually evolving, which makes implementing this technology in an organizational setting challenging. This paper suggests that, in the future, decentralized identity …

Implementation Of Python Based High Voltage Tests For Gem Detectors, John Paul Hernandez

Aerospace, Physics, and Space Science Student Publications

The Compact Muon Solenoid, CMS, and other detectors at LHC are in the process of being upgraded for the HL-LHC (High-Luminosity Large Hadron Collider) which will produce more than 5 times the particle interactions than of the current LHC. One upgrade to CMS is the introduction of new GEM detectors (Gaseous Electron Multiplier), GE2/1 and ME0 shown at right are new detectors to CMS and therefore must be tested thoroughly prior to being installed.

Ai’S Carbon Shadow: An Exploration Of The Energy Dilemma And Earth’S Future, Olga Roussos

Student Publications and Presentations

As artificial intelligence continues to advance, more processing power and energy usage are required. This increase in energy consumption by AI leads to increased carbon emissions in the atmosphere as well as rapid resource depletion. The effects of AI’s growing energy consumption can have severe negative impacts on global climate, biodiversity, sustainability, and other aspects of the environment. This research aims to highlight the volume of energy that artificial intelligence uses, the expected environmental effects of artificial intelligence’s expansion, as well as the potential innovative solutions and sustainable practices that can be implemented to decrease energy usage and preserve the …

A Research Proposal Investigating The Geopolitical And Economic Impact Resulting From The Development Of Artificial General Intelligence (Agi), Tobia Donadon, Kristen Migliano

Student Publications and Presentations

Artificial General Intelligence (AGI) represents the next step in the development of AI, a huge step in the quest for artificial intelligence. AGI aims to create machines capable of understanding, learning, and applying knowledge across various tasks, much like humans.

This research proposal focuses on advancements and potential effects, with an objective to bridge the gap between narrow AI applications and the broader cognitive capabilities of AGI. This study explores the transformative potential of AGI while addressing the societal impacts. The research looks into the relationship between the development of AGI and geopolitical affairs.

Drawing on recent discoveries in mathematical …

Sign-A-Mander: A Mobile App That Enhances Asl Learning With Computer Vision, Sandrine Adap

Honors Theses

Several machine learning researchers have developed algorithms recognizing American Sign Language (ASL), but few have applied the algorithms to real-world situations, such as with portable ASL learning applications. This project develops a beta version of a mobile application designed to allow beginner ASL learners to practice basic ASL vocabulary and receive feedback about their signing accuracy. Building on Dongxu Li et al.’s I3D sign language recognition algorithm and 2000-word dataset, the app seeks to determine whether the I3D algorithm can sufficiently recognize a user’s motions when recorded from a mobile device and accurately classify whether or not the user signed …

Use Of Deep Learning In Content-Based Image Retrieval (Cbir), Angelina Das

ATU Scholars Symposium

In the world of computer vision and data retrieval, a crucial task is finding images within a database based on their visual content. This is known as content-based image retrieval (CBIR). As the number of digital images explodes across fields like online shopping, healthcare, and social media, the need for powerful and precise CBIR systems becomes ever more critical. Early CBIR methods depended on features crafted by hand, like color distributions, texture descriptions, and shape characteristics. However, these techniques often have difficulty capturing the true meaning of an image and might not handle very large datasets effectively. With the rise …

Optimizing Keyboard Layouts For English Text, David Sommerfield

Research & Creative Achievement Day

QWERTY has been the de facto layout for English text input since its invention in 1874. Its continued usage has led to concerns about its ergonomic shortcomings. Previous attempts at layout creation have usually relied on manual observations of typing data rather than a predictive model. To address this issue, we propose a methodology that incorporates both corpus data from 22 million English websites and 8,228 hours of real-world typing data from participants. The corpus data is processed into bigrams and their number of occurrences. The typing data is preprocessed to exclude user-made typos, and then each bigram is tabulated …

Binder, Tyler A. Peaster, Lindsey M. Davenport, Madelyn Little, Alex Bales

ATU Scholars Symposium

Binder is a mobile application that aims to introduce readers to a book recommendation service that appeals to devoted and casual readers. The main goal of Binder is to enrich book selection and reading experience. This project was created in response to deficiencies in the mobile space for book suggestions, library management, and reading personalization. The tools we used to create the project include Visual Studio, .Net Maui Framework, C#, XAML, CSS, MongoDB, NoSQL, Git, GitHub, and Figma. The project’s selection of books were sourced from the Google Books repository. Binder aims to provide an intuitive interface that allows users …

Jsper (Just Stablediffusion Plus Easy Retraining), Adam Rusterholz, Meghan Finn, Zach Zolliecoffer, Zach Judy

ATU Scholars Symposium

JSPER is an an AI art generation Web Application that is both flexible and accessible. Our goal is to enable anyone to create and use their own customized art models, regardless of technical skill level. These models can be trained on almost anything, from a person, to an animal, to a specific object, or even style. The user only has to upload a handful of images of their subject. Then, training settings get optimized at the push of a button to match the type of subject the user is training. After training, their customized model can be used to generate …

Optimizing Campus Chat-Bot Experience Using Puaa: Integrating Large Language Model (Llm) Into University Ai Assistants, Sijan Panday, Zurab Sabakhtarishvili, Clayton Jensen

ATU Scholars Symposium

The advent of large language models (LLMs) such as Chat-GPT and Bard marks a significant milestone in knowledge acquisition, offering a streamlined alternative to the traditionally labor-intensive process of navigating through multiple checkpoints on the web. This emerging trend in LLMs renders the prevalent rule-based chatbots, commonly utilized by universities, increasingly outdated and subpar. This research project proposes integrating LLM technology into university websites, specifically targeting the needs of students seeking information about their institutions by introducing PUAA (Personal University AI Assistant). Our approach involves using the Retrieval-Augmented Generation (RAG) framework, leveraging the capabilities of the LlamaIndex in conjunction with …

Techniques To Detect Fake Profiles On Social Media Using The New Age Algorithms – A Survey, A K M Rubaiyat Reza Habib, Edidiong Elijah Akpan

ATU Scholars Symposium

This research explores the growing issue of fake accounts in Online Social Networks [OSNs]. While platforms like Twitter, Instagram, and Facebook foster connections, their lax authentication measures have attracted many scammers and cybercriminals. Fake profiles conduct malicious activities, such as phishing, spreading misinformation, and inciting social discord. The consequences range from cyberbullying to deceptive commercial practices. Detecting fake profiles manually is often challenging and causes considerable stress and trust issues for the users. Typically, a social media user scrutinizes various elements like the profile picture, bio, and shared posts to identify fake profiles. These evaluations sometimes lead users to conclude …

Spoton, Corey A. Naegle, Caleb Mcclure, Chase M. Tallon, Holden J. O'Neal

ATU Scholars Symposium

SpotOn is a project developed to solve problems with owners losing their pets. The project is in short a solar-powered dog harness with GPS capability with its own application for mobile devices.

Enhancing R2l Intrusion Detection Using Decision Trees, Stephen Sommer

Research & Creative Achievement Day

In the age of advancing technology, artificial intelligence, and big data, Remote to Local (R2L) attacks are increasingly threatening cloud computing environments, heightening concerns about security and privacy. Intrusion detection systems (IDS) using Artificial Intelligence play a role in safeguarding data integrity within databases by swiftly identifying and isolating suspicious records. Furthermore, machine learning techniques enhance the effectiveness of these IDS by continuously adapting to new attack patterns and improving accuracy. This research investigates the use of Decision Tree, a Machine Learning Algorithm for enhancing Remote to Local (R2L) intrusion detection capabilities, utilizing the KDD Cup 1999 dataset and the …

Genetic Association In Entylia Carinata Using Random Forest Classification, Caden J. Harper

Research & Creative Achievement Day

The goal of this research was to identify locations in the genome of the Entylia carinata, known as the treehopper, that are associated with anomalous behavior exhibited by the species. Treehoppers are phytophagous insects and are shown to feed, reproduce, and rear their young on specific aster species. Observation has shown that the insects will disregard potential mates in close proximity in favor of those that originate from the same plant species as themselves. This behavior suggests genetic separation in the species based on plant nativity and warrants genetic analysis. Machine learning offers an effective genetic association technique due to …

Anomaly Detection With Spiking Neural Networks (Snn), Shruti Bhandari, Vyshnavi Gogineni

ATU Scholars Symposium

Abstract:

Anomaly detection, the identification of rare or unusual patterns that deviate from normal behavior, is a fundamental task with wide-ranging applications across various domains. Traditional machine learning techniques often struggle to effectively capture the complex temporal dynamics present in real-world data streams. Spiking Neural Networks (SNNs), inspired by the spiking nature of biological neurons, offer a promising approach by inherently modeling temporal information through precise spike timing. In this study, we investigate the use of Spiking Neural Networks (SNNs) for detecting anomalies or unusual patterns in data. We propose an SNN model that can learn what constitutes normal …

Innovating Inventory And Alert Systems With Object Tracking, Juan Harmse, Esther Peden

Campus Research Month

Security system users require safeguarding inventory from potential theft while reducing manual tracking of physical objects. Our contribution harnesses the power of artificial intelligence and computer vision with YOLO to automate the process of tracking inventory items. The system sends alerts to the inventory manager when it detects particular events. Our approach was evaluated with KernProf profiling, interference, and orientation tests. The results were overall positive in these testing areas.

Finding Combinatorial Patterns In Real Valued Omics Data, Kenneth Smith

Dissertations

Precision medicine is a healthcare approach which tailors disease prevention and treatment to an individual, based on their genetics, environment, lifestyle, and physiological state. These factors interact to produce biological changes that can be measured to produce data called omics, and include genomics, lipidomics, and proteomics. Despite the abundance of omics data and analysis techniques, researchers still struggle to identify biological findings that replicate across data sets and translate into clinical applications. In this dissertation, we employ combinatorial optimization techniques to improve upon three steps in the precision medicine analysis pipeline: 1) data cleaning, 2) community detection, and 3) feature …

Accessing Advanced National Supercomputing And Storage Resources For Computational Research, Ramazan Aygun

All Things Open

This presentation will cover ACCESS (Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support), and Kennesaw State University's involvement in Open Science Data Federation program as a data origin to help researchers and educators with or without supporting grants to utilize the nation’s advanced computing systems and services. ACCESS, a program established and funded by the National Science Foundation, is an ecosystem with capabilities for new modes of research and further democratizing participation. The presentation covers how to apply for allocations on ACCESS. The last part of the presentation will briefly explain Open Science Data Federation and Kennesaw State University's involvement as …

Providing Beginners With Interactive Exploration Of Error Messages In Clojure, John Walbran, Elena Machkasova

Undergraduate Research Symposium 2024

Programmers are imperfect, and will often make mistakes when programming and create a program error, for example, attempting to divide by zero. When a computer tries to run a program with an error, the program will halt and present the details of the error to the user in the form of an error message. These error messages are often very jargon-heavy, and are not designed to be palatable to a novice programmer. This creates significant friction for new programmers trying to learn programming languages. This work is a part of an ongoing project (called Babel) led by Elena Machkasova in …

Enhancing Evolutionary Computation: Optimizing Phylogeny-Informed Fitness Estimation Through Strategic Modifications, Chenfei Peng, Nic Mcphee

Undergraduate Research Symposium 2024

In evolutionary computation, programs are developed using evolution's basic principles, such as selection, mutation, and recombination, to iteratively improve problem solutions towards optimal outcomes in a reasonable amount of time. To save time and be more efficient, we are currently exploring a modified version of phylogeny-informed fitness estimation. The original version evaluates each individual program on a subset of the training cases and estimates the performance everywhere else according to its parent's performance. Our approach involves comprehensive evaluation of promising programs across all training cases, increasing computational investment where the sub-sampled results indicated potential gains. This method led to our …

Algorithmic Approaches For Object Tracking And Facial Detection Using Drones, Kareem Shahatta, Peter Savarese, Gina Egitto, Jongwook Kim

Computer Science Student Work

Drones are unmanned aerial vehicles that have a variety of uses in many fields such as package delivery and search operations. Tello is a small, programmable drone designed for educational purposes. We developed algorithms using DJI Tello Py, an open-source Application Programming Interface, to command the movements of Tello for tracking a target object (i.e., human). Our algorithms utilize digital image processing techniques on Tello's live video stream to optimize the number of movements Tello needs to reach its target. Our poster presentation will explain our approaches to implement object-tracking and facial detection for Tello, discuss lessons we learned, and …

Rescape: Transforming Coral-Reefscape Images For Quantitative Analysis, Zachary Ferris, Eraldo Ribeiro, Tomofumi Nagata, Robert Van Woesik

Ocean Engineering and Marine Sciences Faculty Publications

Ever since the first image of a coral reef was captured in 1885, people worldwide have been accumulating images of coral reefscapes that document the historic conditions of reefs. However, these innumerable reefscape images suffer from perspective distortion, which reduces the apparent size of distant taxa, rendering the images unusable for quantitative analysis of reef conditions. Here we solve this century-long distortion problem by developing a novel computer-vision algorithm, ReScape, which removes the perspective distortion from reefscape images by transforming them into top-down views, making them usable for quantitative analysis of reef conditions. In doing so, we demonstrate the …

A Holistic And Collaborative Behavioral Health Detection Framework Using Sensitive Police Narratives, Martin Keagan Wynne Brown

Dissertations

Identifying behavioral health is paramount for law enforcement officers to provide appropriate follow-up community care. In the current practice, law enforcement offices manually identify these behavioral health cases to allow the designation of the relevant follow-up resources. Police reports generated by officers' response to 911 calls remain an untapped resource for identifying such incidents. Therefore, we advocate for the incorporation of manual annotations from experts, natural language processing (NLP), active learning, advanced machine learning, and ensemble techniques to detect behavioral health cases within police reports. In this dissertation, we develop tools and frameworks to automatically detect behavioral health cases from …

An Intelligent Framework Towards Fully Autonomous Driving Fueled By Smart Roads, Muhammad Jalal Khan

Thesis/ Dissertation Defenses

Autonomous vehicles (AVs) are transforming next-generation autonomous mobility. Such vehicles promise to increase road safety, improve traffic efficiency, reduce vehicle emissions, and enhance mobility. The development of AVs involves the integration of various disciplines and technologies, i.e., sensors, communication, computation, and artificial intelligence (AI), to achieve higher levels of autonomous driving (AD). The main objective of this dissertation is to design and develop a novel approach for achieving higher levels of automation in AD through an end-to-end intelligent framework. This involves addressing the challenges of technological augmentation of road infrastructure to support intelligent transport system (ITS) services, service satisfaction in …

Factors Influencing The Perceptions Of Human-Computer Interaction Curriculum Developers In Higher Education Institutions During Curriculum Design And Delivery, Cynthia Augustine, Salah Kabanda

The African Journal of Information Systems

Computer science (CS) and information systems students seeking to work as software developers upon graduating are often required to create software that has a sound user experience (UX) and meets the needs of its users. This includes addressing unique user, context, and infrastructural requirements. This study sought to identify the factors that influence the perceptions of human-computer interaction (HCI) curriculum developers in higher education institutions (HEIs) in developing economies of Africa when it comes to curriculum design and delivery. A qualitative enquiry was conducted and consisted of fourteen interviews with HCI curriculum developers and UX practitioners in four African countries. …

Kalamazoo Nature Center Mobile Application, Jacob Tebben

Honors Theses

This project aimed to address the challenge of enhancing visitor engagement and information dissemination at the Kalamazoo Nature Center (KNC) through the development of an integrated mobile and desktop application system. This initiative arose due to the limitations posed by traditional mobile applications which often become outdated and need to be updated by a dedicated software team. This project was designed for any user of the KNC desktop app to be able to update content on the mobile app, without the need of a dedicated software team.

The mobile application was designed for visitor use, enabling them to access up-to-date …

5675 Wiredcats Scouting Hub, Sebastian Smiley

Honors Theses

The WiredCats Scouting Hub was created to provide FIRST team 5675 with an application that allows them to make more informed strategic decisions regarding their competitive play. The app synthesizes data from multiple sources, parsing multiple data formats into a single source of truth. It also presents data to users through graphs and charts and provides interactive tables. The application meets the requirements set forth by the leadership of team 5675, effectively providing the capabilities they seek.

Enhancing Health Analytics: Secure And Private Federated Learning Solutions, Nisha Thorakkattu Madathil

Thesis/ Dissertation Defenses

Federated Learning (FL) is a collaborative approach permitting individuals to jointly train a model without sharing their local datasets with others. FL utilizes decentralized data sources to train machine learning models while protecting privacy, has emerged as a promising method. This holds especially true in medical contexts, where the confidentiality of data is critical. FL permits the utilization of heterogeneous datasets from a variety of healthcare organizations while maintaining patient confidentiality. It also plays a crucial role in advancing medical research and healthcare services while adhering to data distribution and compliance requirements. The primary challenges within federated healthcare encompass privacy …