Computer Sciences Commons^™

Dynamic Resource Optimization For Energy-Efficient 6g-Iot Ecosystems, James Adu Ansere, Mohsin Kamal, Izaz Ahmad Khan, Muhammad Naveed Aman

School of Computing: Faculty Publications

The problem of energy optimization for Internet of Things (IoT) devices is crucial for two reasons. Firstly, IoT devices powered by renewable energy sources have limited energy resources. Secondly, the aggregate energy requirement for these small and low-powered devices is translated into significant energy consumption. Existing works show that a significant portion of an IoT device’s energy is consumed by the radio sub-system. With the emerging sixth generation (6G), energy efficiency is a major design criterion for significantly increasing the IoT network’s performance. To solve this issue, this paper focuses on maximizing the energy efficiency of the radio sub-system. In …

Integrated Neutrosophic Best-Worst Method For Comprehensive Analysis And Ranking Of Flood Risks: A Case Study Approach From Aswan, Egypt, Safa Al-Saeed, Nabil M. Abdelaziz

Neutrosophic Systems with Applications

This paper presents a comprehensive framework for the analysis and ranking of flood risks with respect to regional variations and data uncertainties under a Neutrosophic environment. The research introduces a novel approach to flood risk mitigation and management, bringing together the scientifically robust Best-Worst Method (BWM) and single-valued Neutrosophic set for the first time. The unique application of a single-valued Neutrosophic set allows us to better illustrate and manage uncertainty, imprecision, and vagueness in data. Additionally, we employ BWM, a multi-factor decision-making method, for discerning and ranking the most influential flood risk factors. Together, the integrated methodologies provide a balanced, …

Pruning Ghsom To Create An Explainable Intrusion Detection System, Thomas Michael Kirby

Theses and Dissertations

Intrusion Detection Systems (IDS) that provide high detection rates but are black boxes lead
to models that make predictions a security analyst cannot understand. Self-Organizing Maps
(SOMs) have been used to predict intrusion to a network, while also explaining predictions through
visualization and identifying significant features. However, they have not been able to compete with
the detection rates of black box models. Growing Hierarchical Self-Organizing Maps (GHSOMs)
have been used to obtain high detection rates on the NSL-KDD and CIC-IDS-2017 network traffic
datasets, but they neglect creating explanations or visualizations, which results in another black
box model.
This paper offers …

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.

Personalized Health Care In A Data-Driven Era: A Post–Covid-19 Retrospective, Arnob Zahid, Ravishankar Sharma

All Works

No abstract provided.

Exploration Of Feature Selection Techniques In Machine Learning Models On Hptlc Images For Rule Extraction, Bozidar-Brannan Kovachev

Honors Theses

Research related to Biology often utilizes machine learning models that are ultimately uninterpretable by the researcher. It would be helpful if researchers could leverage the same computing power but instead gain specific insight into decision-making to gain a deeper understanding of their domain knowledge. This paper seeks to select features and derive rules from a machine learning classification problem in biochemistry. The specific point of interest is five species of Glycyrrhiza, or Licorice, and the ability to classify them using High-Performance Thin Layer Chromatography (HPTLC) images. These images were taken using HPTLC methods under varying conditions to provide eight …

Monolithic Multiphysics Simulation Of Hypersonic Aerothermoelasticity Using A Hybridized Discontinuous Galerkin Method, William Paul England

Theses and Dissertations

This work presents implementation of a hybridized discontinuous Galerkin (DG) method for robust simulation of the hypersonic aerothermoelastic multiphysics system. Simulation of hypersonic vehicles requires accurate resolution of complex multiphysics interactions including the effects of high-speed turbulent flow, extreme heating, and vehicle deformation due to considerable pressure loads and thermal stresses. However, the state-of-the-art procedures for hypersonic aerothermoelasticity are comprised of low-fidelity approaches and partitioned coupling schemes. These approaches preclude robust design and analysis of hypersonic vehicles for a number of reasons. First, low-fidelity approaches limit their application to simple geometries and lack the ability to capture small scale flow …

Development And Evaluation Of An Automated Tactical Tillage Tool To Control Weeds In Row-Crop Production Systems, Grace Mccormick Friday

Theses and Dissertations

Weed control is an integral part of a successful overall production strategy in row- cropping systems and has the potential to reduce or eliminate yield losses that negatively affect profitability. Timely and correctly selected herbicide applications are the major keys for effective weed control in a majority of instances. However, there are negative factors that contribute to ineffectiveness and weed escape issues that currently lack viable options for management. Sparsely populated late-season weeds that emerge after lay-by herbicide applications and weeds that have become tolerant and resistant to traditional herbicide chemistries are of greatest concern. Historically, these weeds would have …

Beyond Algorithms: A User-Centered Evaluation Of A Feature Recommender System In Requirements Engineering, Oluwatobi Lasisi

Theses and Dissertations

Several studies have applied recommender technologies to support requirements engineering activities. As in other application areas of recommender systems (RS), many studies have focused on the algorithms’ prediction accuracy, while there have been limited discussions around users’ interactions with the systems. Since recommender systems are designed to aid users in information retrieval, they should be assessed not just as recommendation algorithms but also from the users’ perspective. In contrast to accuracy measures, user-related issues can only be effectively investigated via empirical studies involving real users. Furthermore, researchers are becoming increasingly aware that the effectiveness of the systems goes beyond recommendation …

Secure And Efficient Federated Learning, Xingyu Li

Theses and Dissertations

In the past 10 years, the growth of machine learning technology has been significant, largely due to the availability of large datasets for training. However, gathering a sufficient amount of data on a central server can be challenging. Additionally, with the rise of mobile networking and the large amounts of data generated by IoT devices, privacy and security issues have become a concern, resulting in government regulations such as GDPR, HIPAA, CCPA, and ADPPA. Under these circumstances, traditional centralized machine learning methods face a problem in that sensitive data must be kept locally for privacy reasons, making it difficult to …

Tornado Outbreak False Alarm Probabilistic Forecasts With Machine Learning, Kirsten Reed Snodgrass

Theses and Dissertations

Tornadic outbreaks occur annually, causing fatalities and millions of dollars in damage. By improving forecasts, the public can be better equipped to act prior to an event. False alarms (FAs) can hinder the public’s ability (or willingness) to act. As such, a probabilistic FA forecasting scheme would be beneficial to improving public response to outbreaks.

Here, a machine learning approach is employed to predict FA likelihood from Storm Prediction Center (SPC) tornado outbreak forecasts. A database of hit and FA outbreak forecasts spanning 2010 – 2020 was developed using historical SPC convective outlooks and the SPC Storm Reports database. Weather …

An Analysis Of Text-Based Machine Learning Models For Vulnerability Detection, Kollin Ryne Napier

Theses and Dissertations

With an increase in complexity of software, developers rely more on reuse and dependencies in their source code via code snippets. As a result, it is becoming harder to identify and mitigate vulnerabilities. Although traditional analysis tools are still utilized, machine learning models are being adopted to expand efforts and combat such threats. Given the possibilities towards usage of such models, research in this area has introduced various approaches which vary in usability and prediction. In generalizing models to a more natural language approach, researchers have opted to train models on source code to identify existing and potential vulnerabilities. Exploratory …

Understanding Data Mining And Its Relation To Information Systems, Malak Alammari

Publications and Research

This research project aims to enrich an Open Educational Resource (OER) textbook on Introduction to Information Systems/Technology with a focus on data mining and its relation to hardware and software components of information systems. The study will address the following research questions: (1) What is data mining? and (2) How does data relate to the hardware and software components of information systems? To answer these questions, the researcher will conduct research to ascertain the current state of data mining and its relevance in the field of information systems/technology. The results of the research will be incorporated into an existing OER …

Making The Transition To Post-Quantum Cryptography, J. Simon Richard

The Downtown Review: An Interdisciplinary Journal Written and Peer-Reviewed by Mandel Honors College Students at Cleveland State University

Without intervention, quantum computing could threaten the security of a large portion of our internet in the near future. However, solutions exist. This paper, which is intended for a general audience, provides a wider context for our current state of quantum-preparedness amid the transition from classical cryptosystems to post-quantum cryptosystems—cryptographic algorithms that can resist the attacks of quantum computers. It will also submit a possible way forward inspired by the actions taken around the globe to prevent the millennium (or Y2K) bug.

Transformer-Based Feature Fusion Approach For Multimodal Visual Sentiment Recognition Using Tweets In The Wild, Fatimah Alzamzami, Abdulmotaleb El Saddik

Computer Vision Faculty Publications

We present an image-based real-time sentiment analysis system that can be used to recognize in-the-wild sentiment expressions on online social networks. The system deploys the newly proposed transformer architecture on online social networks (OSN) big data to extract emotion and sentiment features using three types of images: images containing faces, images containing text, and images containing no faces/text. We build three separate models, one for each type of image, and then fuse all the models to learn the online sentiment behavior. Our proposed methodology combines a supervised two-stage training approach and threshold-moving method, which is crucial for the data imbalance …

Du Undergraduate Showcase: Research, Scholarship, And Creative Works, Caitlyn Aldersea, Justin Bravo, Sam Allen, Anna Block, Connor Block, Emma Buechler, Maria De Los Angeles Bustillos, Arianna Carlson, William Christensen, Olivia Kachulis, Noah Craver, Kate Dillon, Muskan Fatima, Angel Fernandes, Emma Finch, Colleen Cassidy, Amy Fishman, Andrea Francis, Stacia Fritz, Simran Gill, Emma Gries, Rylie Hansen, Shannon Powers, Jacqueline Martinez, Zachary Harker, Ashley Hasty, Mykaela Tanino-Springsteen, Kathleen Hopps, Adelaide Kerenick, Colin Kleckner, Ci Koehring, Elijah Kruger, Braden Krumholz, Maddie Leake, Lyneé Alves, Seraphina Loukas, Yatzari Lozano Vazquez, Haley Maki, Emily Martinez, Sierra Mckinney, Audrey Mitchell, Kipling Newman, Audrey Ng, Megan Lucyshyn, Andrew Nguyen, Stevie Ostman, Casandra Pearson, Alexandra Penney, Julia Gielczynski, Tyler Ball, Anna Rini, Christina Rorres, Simon Ruland, Helayna Schafer, Emma Sellers, Sarah Schuller, Claire Shaver, Kevin Summers, Isabella Shaw, Madison Sinar, Claudia Pena, Apshara Siwakoti, Carter Sorensen, Madi Sousa, Anna Sparling, Alexandra Revier, Brandon Thierry, Dylan Tyree, Maggie Williams, Lauren Wols

DU Undergraduate Research Journal Archive

DU Undergraduate Showcase: Research, Scholarship, and Creative Works

When Ai Moves Downstream, Frances S. Grodzinsky, Keith W. Miller, Marty J. Wolf

School of Computer Science & Engineering Faculty Publications

After computing professionals design, develop, and deploy software, what is their responsibility for subsequent uses of that software “downstream” by others? Furthermore, does it matter ethically if the software in question is considered to be artificial intelligent (AI)? The authors have previously developed a model to explore downstream accountability, called the Software Responsibility Attribution System (SRAS). In this paper, we explore three recent publications relevant to downstream accountability, and focus particularly on examples of AI software. Based on our understanding of the three papers, we suggest refinements of SRAS.

Big Ideas In Sports Analytics And Statistical Tools For Their Investigation, Benjamin S. Baumer, Gregory J. Matthews, Quang Nguyen

Statistical and Data Sciences: Faculty Publications

Sports analytics—broadly defined as the pursuit of improvement in athletic performance through the analysis of data—has expanded its footprint both in the professional sports industry and in academia over the past 30 years. In this article, we connect four big ideas that are common across multiple sports: the expected value of a game state, win probability, measures of team strength, and the use of sports betting market data. For each, we explore both the shared similarities and individual idiosyncracies of analytical approaches in each sport. While our focus is on the concepts underlying each type of analysis, any implementation necessarily …

Exploring The Antikythera Mechanism Through Augmented Reality., Tiffany Hatzidimitriu

Honors College Theses

The Antikythera mechanism is the oldest known analogue computer. Discovered off the coast of the Greek island of Antikythera, the Antikythera mechanism predicts and displays the position of astronomical bodies as well as astronomical phenomena. Due to its complexity, visualization of the gears movement can be very difficult. For my project I created an interactive Augmented Reality (AR) model of the Antikythera mechanism using Unity game engine. I hope this AR application exposes people to the Antikythera mechanism and its inner workings.

Integrating Ai Into Culinary Medicine: A Revolution In Nutrition And Home Cooking, Emeka Ikeakanam, Evan Curry, Terrence Mchugh, Jason Walker

Research Day

Introduction

With the growing popularity of the emerging field of culinary medicine, there is a growing understanding of the culinary barriers needed to be overcome to adopt healthier eating habits. Lack of confidence, low skills, and lack of time are some of the most common barriers that prevent individuals from cooking at home. However, integrating AI can offer personalized support for home cooking and help individuals overcome these barriers. AI-powered meal planning and recipe suggestions can guide healthy and nutritious food choices that cater to their dietary needs and preferences. Additionally, AI can modify recipes to accommodate individual health conditions …

Atlantic Zambezi Global, Dabana Intenque

Honors Program Theses and Projects

The purpose of “Atlantic Zambesi Global” is to offer an online marketplace that does not exist in my home country. People in Guinea struggle when they want something that does not exist in the region. The current way they get the product they want: 1. Either by someone getting the product for them 2. By traveling to get the product themselves. I created an online delivery system that will help people get their basic needs products at home and spend less.

The Process Of Using Unity To Create A 2d Video Game, Sean Tammelleo

Honors Program Theses and Projects

No abstract provided.

Fedar+: A Federated Learning Approach To Appliance Recognition With Mislabeled Data In Residential Environments, Ashish Gupta, Hari Prabhat Gupta, Sajal K. Das

Computer Science Faculty Research & Creative Works

With the enhancement of people's living standards and the rapid evolution of cyber-physical systems, residential environments are becoming smart and well-connected, causing a significant raise in overall energy consumption. as household appliances are major energy consumers, their accurate recognition becomes crucial to avoid unattended usage and minimize peak-time load on the smart grids, thereby conserving energy and making smart environments more sustainable. Traditionally, an appliance recognition model is trained at a central server (service provider) by collecting electricity consumption data via smart plugs from the clients (consumers), causing a privacy breach. Besides that, the data are susceptible to noisy labels …

Leveraging Neutrosophic Uncertainty Theory Toward Choosing Biodegradable Dynamic Plastic Product In Various Arenas, Mona Mohamed, Karam M. Sallam

Neutrosophic Systems with Applications

Numerous studies in recent years have documented the negative effects of plastic waste on the environment and human wellness. Due to their widespread usage in daily life, particularly in packaging, and their rising direct or indirect discharge into the environment, plastics are recognized as an emerging environmental hazard. Thus, this point is considered the first problem in this study. As a result, efforts to replace traditional plastics with bioplastics have intensified. However, studies regarding the effects of conventional and bioplastics (BioPs) are also important. Hence, biodegradable polymers for industrial and commercial usage are essential in the present day as an …

Fedar+: A Federated Learning Approach To Appliance Recognition With Mislabeled Data In Residential Environments, Ashish Gupta, Hari Prabhat Gupta, Sajal K. Das

Computer Science Faculty Research & Creative Works

With the enhancement of people's living standards and the rapid evolution of cyber-physical systems, residential environments are becoming smart and well-connected, causing a significant raise in overall energy consumption. As household appliances are major energy consumers, their accurate recognition becomes crucial to avoid unattended usage and minimize peak-time load on the smart grids, thereby conserving energy and making smart environments more sustainable. Traditionally, an appliance recognition model is trained at a central server (service provider) by collecting electricity consumption data via smart plugs from the clients (consumers), causing a privacy breach. Besides that, the data are susceptible to noisy labels …

Leveraging Neutrosophic Uncertainty Theory Toward Choosing Biodegradable Dynamic Plastic Product In Various Arenas, Mona Mohamed, Karam M. Sallam

Neutrosophic Systems with Applications

Numerous studies in recent years have documented the negative effects of plastic waste on the environment and human wellness. Due to their widespread usage in daily life, particularly in packaging, and their rising direct or indirect discharge into the environment, plastics are recognized as an emerging environmental hazard. Thus, this point is considered the first problem in this study. As a result, efforts to replace traditional plastics with bioplastics have intensified. However, studies regarding the effects of conventional and bioplastics (BioPs) are also important. Hence, biodegradable polymers for industrial and commercial usage are essential in the present day as an …

Using Deep Learning Model To Identify Iron Chlorosis In Plants, Munir Majdalawieh, Shafaq Khan, Md. T. Islam

All Works

Iron deficiency in plants causes iron chlorosis which frequently occurs in soils that are alkaline (pH greater than 7.0) and that contain lime. This deficiency turns affected plant leaves to yellow, or with brown edges in advanced stages. The goal of this research is to use the deep learning model to identify a nutrient deficiency in plant leaves and perform soil analysis to identify the cause of the deficiency. Two pre-trained deep learning models, Single Shot Detector (SSD) MobileNet v2 and EfficientDet D0, are used to complete this task via transfer learning. This research also contrasts the architecture and performance …

Product Review Classification Using Machine Learning And Statistical Data Analysis, Kajal Singh

Independent Student Projects and Publications

The aim of the paper is to implement and analyze the machine learning models for product review dataset. The project focuses on binary classification, multi-class classification, and clustering approaches to analyze and categorize product reviews. The performance of the models over each of the five classification tasks is measured by the 5-fold cross-validation scores over the training data.

A Generalization Of The Chomsky-Halle Phonetic Representation Using Real Numbers For Robust Speech Recognition In Noisy Environments, Peter Z. Revesz

School of Computing: Faculty Publications

Speech recognition is difficult when the speech signal is weak or occurs in a noisy environment. This paper presents an efficient and robust method that can reconstruct the standard pronunciation of English phonemes and words given a weak or noisy signal. The reconstruction is based on a novel representation of the reconstruction task as a problem of data retrieval from a database in two different cases: (1) when the phonemes are represented in the database as binary tuples and the input is also a binary tuple from which deletion errors occur, and (2) when the phonemes are represented in the …

Blockchain Security: Double-Spending Attack And Prevention, William Henry Scott Iii

Electronic Theses and Dissertations

This thesis shows that distributed consensus systems based on proof of work are vulnerable to hashrate-based double-spending attacks due to abuse of majority rule. Through building a private fork of Litecoin and executing a double-spending attack this thesis examines the mechanics and principles behind the attack. This thesis also conducts a survey of preventative measures used to deter double-spending attacks, concluding that a decentralized peer-to-peer network using proof of work is best protected by the addition of an observer system whether internal or external.