Physical Sciences and Mathematics Commons^™

Machine Learning-Based Gps Jamming And Spoofing Detection, Alberto Squatrito

Doctoral Dissertations and Master's Theses

The increasing reliance on Global Positioning System (GPS) technology across various sectors has exposed vulnerabilities to malicious attacks, particularly GPS jamming and spoofing. This thesis presents an analysis into detection and mitigation strategies for enhancing the resilience of GPS receivers against jamming and spoofing attacks. The research entails the development of a simulated GPS signal and a receiver model to accurately decode and extract information from simulated GPS signals. The study implements the generation of jammed and spoofed signals to emulate potential threats faced by GPS receivers in practical settings. The core innovation lies in the integration of machine learning …

Lmcrot: An Enhanced Protein Crotonylation Site Predictor By Leveraging An Interpretable Window-Level Embedding From A Transformer-Based Protein Language Model, Pawel Pratyush, Soufia Bahmani, Suresh Pokharel, Hamid D. Ismail, Dukka Bahadur

Michigan Tech Publications, Part 2

MOTIVATION: Recent advancements in natural language processing have highlighted the effectiveness of global contextualized representations from Protein Language Models (pLMs) in numerous downstream tasks. Nonetheless, strategies to encode the site-of-interest leveraging pLMs for per-residue prediction tasks, such as crotonylation (Kcr) prediction, remain largely uncharted. RESULTS: Herein, we adopt a range of approaches for utilizing pLMs by experimenting with different input sequence types (full-length protein sequence versus window sequence), assessing the implications of utilizing per-residue embedding of the site-of-interest as well as embeddings of window residues centered around it. Building upon these insights, we developed a novel residual ConvBiLSTM network designed …

Designing For Deployable, Secure, And Generic Machine Learning Systems, Li-Yun Wang

Dissertations and Theses

Machine learning systems have catalyzed numerous image-centric applications owing to the significant achievements of machine learning algorithms and models. While these systems have showcased the efficacy of machine learning models, certain challenges persist, such as machine learning system design and security vulnerabilities inherent in deep neural networks. Moreover, the deployment of deep neural network models remains a significant hurdle. This dissertation introduces a multimedia prototyping framework tailored for visual analytical applications, improving the reusability of video analysis software tools with minimal performance overhead. Furthermore, we present novel image-processing techniques designed to bolster the robustness of deep neural networks and propose …

The Mathematical And Historical Significance Of The Four-Color Theorem, Brock Bivens

Scholars Day Conference

Computers becoming more readily used in mathematics.

Hello, World., Elliot Cetinski, Evan Chartock, Olivia Cross, Kiran Drew, Kaya Eller, Ben Little, Joey Nolan, Spencer Toth, Sophie Wahl-Taylor, Sadie Walker, Destiny Young, Annie Zulick

Theater and Dance Presentations

This project works to theatrically represent the current state of Artificial Intelligence (AI), as well as its benefits and drawbacks, in the style of the Living Newspaper. Originating from a Great Depression-era job program, the Living Newspaper sought to take headlines and present them onstage for a poignant and contemporary social critique. This work does the same, melding different angles of the AI debate into a single production that emphasizes the rapidly progressing state of modern AI technology and the need for humans to consider the impacts such technologies will have. Furthermore, it asks the audience to question their position …

Combining Empirical And Physics-Based Models For Solar Wind Prediction, Rob Johnson, Soukaina Filali Boubrahimi, Omar Bahri, Shah Muhammad Hamdi

Computer Science Faculty and Staff Publications

Solar wind modeling is classified into two main types: empirical models and physics-based models, each designed to forecast solar wind properties in various regions of the heliosphere. Empirical models, which are cost-effective, have demonstrated significant accuracy in predicting solar wind at the L1 Lagrange point. On the other hand, physics-based models rely on magnetohydrodynamics (MHD) principles and demand more computational resources. In this research paper, we build upon our recent novel approach that merges empirical and physics-based models. Our recent proposal involves the creation of a new physics-informed neural network that leverages time series data from solar wind predictors to …

Predicting Ffar4 Agonists Using Structure-Based Machine Learning Approach Based On Molecular Fingerprints, Zaid Anis Sherwani, Syeda Sumayya Tariq, Mamona Mushtaq, Ali Raza Siddiqui, Mohammad Nur-E-Alam, Aftab Ahmed, Zaheer Ul-Haq

Pharmacy Faculty Articles and Research

Free Fatty Acid Receptor 4 (FFAR4), a G-protein-coupled receptor, is responsible for triggering intracellular signaling pathways that regulate various physiological processes. FFAR4 agonists are associated with enhancing insulin release and mitigating the atherogenic, obesogenic, pro-carcinogenic, and pro-diabetogenic effects, normally associated with the free fatty acids bound to FFAR4. In this research, molecular structure-based machine-learning techniques were employed to evaluate compounds as potential agonists for FFAR4. Molecular structures were encoded into bit arrays, serving as molecular fingerprints, which were subsequently analyzed using the Bayesian network algorithm to identify patterns for screening the data. The shortlisted hits obtained via machine learning protocols …

Using Data Mining To Analyze Job Reviews, Nicholas Bornkamp, Tony Breitzman

STEM Student Research Symposium Posters

Job review websites like Glassdoor are not always clear on how well the company operates, especially as viewed from differing levels of employment. For instance, a middle or upper manager from Amazon may have an overall positive review of the company with minor issues about it, but someone who works in the warehouse may have a mixed experience. To solve this issue and determine any correlation between employee level and their review, data mining techniques were utilized such as website scraping and neural network training to develop a model that analyzes employee reviews.

Self-Sovereign Digital Identities, Maryam M. Ahmed, Bijayata Shrestha, Nick Ivanov

STEM Student Research Symposium Posters

In today's digital landscape, the dominance of internet giants over our digital identities raises concerns regarding user control and privacy. These companies aggregate vast amounts of data from diverse sources, ranging from online interactions to personal information shared on their platforms. This centralized control impedes individual autonomy and privacy. To address these challenges, we propose Self-Sovereign Digital Identities (SSDIs) as a solution. SSDIs empower individuals with control over their online identity information, encompassing ownership, security, privacy, and portability. By decentralizing identity management, SSDIs offer users autonomy and enhance privacy protection. Moreover, we introduce Sans-Chain Smart Contracts, a novel approach to …

Utilizing Machine Learning To Predict Workplace Violence In Hospitals, Aiden Touhill, Carter Profico, Avery Bobbitt, Joe Dipietro, Christopher Duym, Anthony Ung, Jack Myers

STEM Student Research Symposium Posters

Random forest machine learning models are a form of classification model, which attempts to sort data into one of two predefined categories. When trained on a set of data from a hospital, where each entry is listed as either conditions for workplace violence or not, a random forest model can begin to classify new data as it comes in. We developed a way to automatically poll hospital systems for the required data needed to make a prediction on the potential for workplace violence at any one given moment. Our team was unable to gain access to real hospital data, so …

Missionlog R - Air Force Mission History Report Management System With Encryption & Database Integration, Matthew Bachrach, Sam Jeffery, Michael Lim, Joe Johnston, Jack Healy, Andrew Siciliano, Jack F. Myers

STEM Student Research Symposium Posters

MissionLog R is a mobile and web application designed to allow for the filing, confirmation, and finalization of a mission history report for the US Air Force. The mobile application is used by filing members to fill out their report on their issued iPad. The form has been fitted with data validation and ghost data so the user knows what type of input that specific field is looking for. The web application can be used by the filing member to import their report from their iPad if no WiFi was available when they filled out the report. Then, the confirming …

Blueberry Drone Ai: Smart Farming Of Blueberries Using Artificial Intelligence And Autonomous Drones, Robert Czarnota, Anthony Segrest, Anthony Thompson, Harper Zappone, Hieu Nguyen, Nguyen Thanh, Ik Jae Lee, Lori Green, Tuan Le

STEM Student Research Symposium Posters

This project seeks to assist blueberry growers in New Jersey with preventing blueberry scorch disease. Plants can’t be cured of scorch, so they have to be removed to prevent the disease from spreading to other bushes. This project aims to use object detection and classifier machine learning models in order to detect scorch disease with photos from intelligent drones. Images are first tiled, then processed through and convolutional neural network that detects scorch symptoms. Lastly, a fully connected neural network is implemented to make a final prediction.

React Native Photo & Video Streaming/Processing Api Integration, Anamaria Oharciuc, William Carr, Sushanth Ambati, Ryan Blaisdell, Kyle Reed, Jack F. Myers

STEM Student Research Symposium Posters

RunSignup is a software company specializing in event management technology. Event organizers, known as Race Directors, utilize their platform to create and manage events, organize media albums, stream their events to the public, and more. In order to allow Race Directors to complete these actions in real-time at their events, RunSignup asked our team to develop a mobile app. With the app, Race Directors would be able to upload and stream photos to the event’s photo albums as soon as they are taken, and they could livestream the event to YouTube directly from their mobile device. Even if the device …

Drone-Based Bug Detection In Orchards With Nets: A Novel Orienteering Approach, Francesco Betti Sorbelli, Federico Coró, Sajal K. Das, Lorenzo Palazzetti, Cristina M. Pinotti

Computer Science Faculty Research & Creative Works

The Use of Drones for Collecting Information and Detecting Bugs in Orchards Covered by Nets is a Challenging Problem. the Nets Help in Reducing Pest Damage, But They Also Constrain the Drone's Flight Path, Making It Longer and More Complex. to Address This Issue, We Model the Orchard as an Aisle-Graph, a Regular Data Structure that Represents Consecutive Aisles Where Trees Are Arranged in Straight Lines. the Drone Flies Close to the Trees and Takes Pictures at Specific Positions for Monitoring the Presence of Bugs, But its Energy is Limited, So It Can Only Visit a Subset of Positions. to …

Bookworm: A Home Library Aid, Johnny Quach

Student Academic Conference

This project involved the design and development of a website intended to allow users to create a personal inventory of their purchased media with a primary focus on books. The goal of the project was to create a website that is both user-friendly, convenient, and both personally and professionally useful. The project began with an analysis of existing personal inventory websites and apps which identified many different aspects that are included in the finished website, as well as aspects that fell short. Utilizing Python and Flask, the design includes a number of easy-to-use features, such as the ability to create …

Reduction Of E-Waste With Multi Generational Clustering Of Obsolete Computers, Tyler Sather, Judah Nava

Student Academic Conference

The rise of E-waste is a growing concern as there are many toxic materials or toxic chemicals created with mishandling of the waste. Even in the school system we see obsolete computers systems laying around in closets collecting dust ready to be sent to the landfill. Our aim is to see if by clustering these old systems together we can give them new life thus reducing the need to add them to the worlds e-waste problem. Often these obsolete systems have lower powered CPUs which makes them less desirable for computing, if we cluster these systems together we can potentially …

Artificial Intelligence: Unveiling Potential, Navigating Challenges, Kavivarma Kandasamy

Student Academic Conference

Artificial intelligence (AI) has rapidly transformed from science fiction to a tangible reality, permeating various aspects of our lives. This report delves into the current state of AI, exploring its foundational concepts, technical advancements, and diverse applications. By analyzing academic literature, industry reports, and real-world case studies, the report sheds light on AI's potential to revolutionize fields like healthcare, scientific research, and entertainment. However, the report also acknowledges the ethical considerations and potential risks associated with advanced AI. By examining areas like safety concerns and responsible development practices, the report aims to foster a comprehensive understanding of AI's impact on …

Traversing Through A Graph, Andrew Selvig

Student Academic Conference

Software such as Google Maps or flight path routes utilize algorithms which find the most optimal path between two points or locations. These optimal routes are based on existing paths between nodes (locations). These kinds of software have made our lives much more convenient when we want to go somewhere. However, there are situations in which nodes and their paths have not been established. Nonetheless, there are two points that need to be connected with barriers in between. ;The purpose of this project is to tap into a way to create and traverse nodes between two points while refusing to …

Creating With Generative Ai, Gudrun Hall, Zeitun Abdinoor

Student Academic Conference

CustomGPT is a framework that utilizes advanced natural language processing techniques to generate customized versions of ChatGPT, adapting conversational agents to various contexts, domains, or individual preferences. This project demonstrates how ChatGPT serves as a powerful tool for creating chatbots with actionable features and dynamic mobile games. By showcasing its versatility in empowering users to innovate and drive creative content generation, we highlight ChatGPT's potential to shape the future of interactive mobile experiences.

Assigning A Dynamic Personality To Ai, Austin Jeral

Student Academic Conference

In today's world where artificial intelligence capabilities are advancing the potential for custom, and tailor made chatbots are similarly advancing. Such artificial intelligence chatbots could be particularly useful in many fields from the entertainment industry in the form of video game characters with dynamic dialog that stay true to their characters, to the world of psychology where such chatbots may be useful in helping budding psychologists train and be exposed to a vast array of different personalities that they may not be exposed to. Such artificial intelligence chatbots require however to have a believable and dynamic personality. Personality is a …

Effects Of Sensory Processing Impairments On Social Language Skills In Children With Autism Spectrum Disorder, Sara Stier

Student Academic Conference

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that is commonly characterized by impairments in social communication, language, and other cognitive skills, behavior and emotional challenges, sensory processing issues, and feeding challenges (ASHA, n.d.). Sensory processing refers to the process of the central nervous system receiving input from the senses and integrating this information to generate an appropriate behavioral response (Kojovic et al., 2019). A highly regulated sensory system is a vital piece that is needed in order to be able to communicate wants, needs, and ideas effectively and appropriately (Sensory Issues, n.d.). The main sensory systems that individuals with …

Maximizing Data Optimization: Analysis, Retention, And Website Accessibility, Selena Cade

Culminating Experience Projects

As a graduate assistant at Grand Valley, there is significant involvement with a program that assists middle and high school students with learning about college. As a preface, the information presented in this document will remain anonymous and confidential for the program's benefit.

Students receive a campus tour, eat at the dining halls, and obtain information about college. Before students arrive, a pre-visit survey is sent to gauge their college knowledge. After the visit, a post-visit survey with the same questions is sent out to determine if they gained more knowledge. This data has rarely been viewed in the past …

Optimal Molecular Dynamics System Size For Increased Precision And Efficiency For Epoxy Materials, Khatereh Kashmari, Sagar Patil, Josh Kemppainen, Shankara Gowtham, Gregory Odegard

Michigan Tech Publications, Part 2

Molecular dynamics (MD) simulation is an important tool for predicting thermo-mechanical properties of polymer resins at the nanometer length scale, which is particularly important for efficient computationally driven design of advanced composite materials and structures. Because of the statistical nature of modeling amorphous materials on the nanometer length scale, multiple MD models (replicates) are typically built and simulated for statistical sampling of predicted properties. Larger replicates generally provide higher precision in the predictions but result in higher simulation times. Unfortunately, there is insufficient information in the literature to establish guidelines between MD model size and the resulting precision in predicted …

Data Engineering: Building Software Efficiency In Medium To Large Organizations, Alessandro De La Torre

Whittier Scholars Program

The introduction of PoetHQ, a mobile application, offers an economical strategy for colleges, potentially ushering in significant cost savings. These savings could be redirected towards enhancing academic programs and services, enriching the educational landscape for students. PoetHQ aims to democratize access to crucial software, effectively removing financial barriers and facilitating a richer educational experience. By providing an efficient software solution that reduces organizational overhead while maximizing accessibility for students, the project highlights the essential role of equitable education and resource optimization within academic institutions.

Data-Driven Evidence-Based Syntactic Sugar Design, David Obrien, Robert Dyer, Tien N. Nguyen, Hridesh Rajan

School of Computing: Faculty Publications

Programming languages are essential tools for developers, and their evolution plays a crucial role in supporting the activities of developers. One instance of programming language evolution is the introduction of syntactic sugars, which are additional syntax elements that provide alternative, more readable code constructs. However, the process of designing and evolving a programming language has traditionally been guided by anecdotal experiences and intuition. Recent advances in tools and methodologies for mining open-source repositories have enabled developers to make datadriven software engineering decisions. In light of this, this paper proposes an approach for motivating data-driven programming evolution by applying frequent subgraph …

Scalable Relational Analysis Via Relational Bound Propagation, Clay Stevens, Hamid Bagheri

School of Computing: Faculty Publications

Bounded formal analysis techniques (such as bounded model checking) are incredibly powerful tools for today’s software engineers. However, such techniques often suffer from scalability challenges when applied to large-scale, real-world systems. It can be very difficult to ensure the bounds are set properly, which can have a profound impact on the performance and scalability of any bounded formal analysis. In this paper, we propose a novel approach—relational bound propagation—which leverages the semantics of the underlying relational logic formula encoded by the specification to automatically tighten the bounds for any relational specification. Our approach applies two sets of semantic rules to …

Ai-Powered Learning: Blending Ai With Active Learning In The Information Literacy Classroom, Kevin J. Reagan, Wilhelmina Randtke

Georgia International Conference on Information Literacy

In 2016, the ACRL Framework for Information Literacy in Higher Education launched in response to more voluminous, less-vetted online information, including misinformation and content farms. Subsequently, the ACRL Framework has been widely adopted, and numerous high-quality lesson plans and resources for teaching the frames already exist, including published lesson plans and textbooks. Now, generative AI tools, such as ChatGPT and other chat bots present new challenges for information literacy educators. For instance, in addition to teaching students how to identify issues such as fake news, the information literacy professional has to address topics such as ethical AI use, AI hallucination …

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.

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 …

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 …