Computer Engineering Commons^™

Risk Detection Of Gestational Diabetes In Early Pregnancy Depend On Artificial Neural Network, Sanaa Hammad Dhahi

Al-Esraa University College Journal for Engineering Sciences

Early prediction of gestational diabetes mellitus (GDM) risk is particularly important because it may enable more effective interventions and reduce cumulative maternal and fetal injury. The aim of this study is to develop machine learning (ML) models for early prediction of GDM using widely available variables, facilitating early intervention, and enabling the application of prediction models in settings where more complex tests are not accessible. In this paper, an artificial neural network (ANN) was used to detect the risk of gestational diabetes. A global dataset was used to collect clinical and experimental data from pregnant women and people with diabetes …

Impact Of Merging Basalt Fibers With Various Types Of Concrete And The Factors Affecting Them: A Review, Adil M. Jabbar, Lubna S. Dankha

Al-Esraa University College Journal for Engineering Sciences

This paper reviews effecting of blending basalt fibers into concreteon mechanical properties, ductility, and toughness. Based on concretecomponents, the perfect basalt fiber percentage can differ and is influencedby factors like fiber length, w/c ratio, and age. The beneficial basalt fiberspercentage is lower than that of steel fibers. Even with a low concentrationof basalt fibers, concrete tensile and flexural strength can be improved moreeffectively than a higher amount of steel fibers. As the amount of basaltfiber increases, the mix workability decreases. Additionally, the flow abilitymay drop slightly as the length of the fibers increases. Adding basalt fiber toconcrete can minimize drying …

How Cloud, Edge, And Mist Computing Affects Resource Management In Vanets, Andrew Trombly, Izzat Alsmadi

Masters Theses (Archived)

With public interest in automated vehicles as well as self-aware and responsive smart cities the demand for fast and efficient communication and computation will push our current infrastructure beyond its limits. Vehicular networks
(VANETs) look to solve this issue but suffer from extremely dynamic topologies
with unpredictable times and locations of high network and computational de-
minds from highly mobile units. This thesis looks into how current scholarly
works attempt resource management in VANETs and simulate VANETs with
cloud, edge, or mist computing architectures. The data of are compared and
discussed to drive a data-driven understanding and conversation of architecture …

Control Of Fully-Actuated Aerial Manipulators And Omni-Directional Multirotors, Riley M. Mccarthy

Mechanical Engineering ETDs

This thesis details the system modeling, design, control, simulation, construction, and
testing of both a fully-actuated and omni-directional multirotor aerial system created
for the primary purpose of performing active tasks with their environment. This work
verifies the capabilities of both systems through empirical testing, and demonstrates
how through the use of new control methods and physical designs multirotors can
expand their purpose from passive inspection based tasks to active contact based
tasks. These systems take advantage of newly implemented control allocation features present in the PX4 flight control software, version 1.14. The use of which makes designing controllers for such …

Ai’S Next Frontier — Dermatology? How It May Help Close The Health Gap For Dark-Skinned Patients, Gretchen B. Smail

Capstones

This project examines how engineers are creating artificial intelligence systems to help with dermatology diagnosis. Researchers believe that these systems can help close the health care gap in lower income and Black and brown communities, but dermatologists of color worry that these systems are not being trained to recognize skin conditions on patients of color.

Terahertz Permittivity Parameters Of Monoclinic Single Crystal Lutetium Oxyorthosilicate, Sean Knight, Steffen Richter, Alexis Papamichail, Megan Stokey, Rafał Korlacki, Vallery Stanishev, Philipp Kühne, Mathias Schubert, Vanya Darakchieva

Department of Electrical and Computer Engineering: Faculty Publications

The anisotropic permittivity parameters of monoclinic single crystal lutetium oxyorthosilicate, Lu₂SiO₅ (LSO), have been determined in the terahertz spectral range. Using terahertz generalized spectroscopic ellipsometry (THz-GSE), we obtained the THz permittivities along the a, b, and c^⋆ crystal directions, which correspond to the ε_a; ε_b, and ε_c? on-diagonal tensor elements. The associated off diagonal tensor element ε_ac? was also determined experimentally, which is required to describe LSO’s optical response in the monoclinic a–c crystallographic plane. From the four tensor elements obtained in the model fit, we calculate the …

Survey Of Transfer Learning Approaches In The Machine Learning Of Digital Health Sensing Data, Lina Chato, Emma Regentova

Electrical & Computer Engineering Faculty Research

Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to data privacy, security, and data format standardization. Furthermore, there is a risk of bias and discrimination in machine learning models. Thus, developing an accurate prediction model from scratch can be an expensive and complicated task that often requires extensive experiments and complex computations. Transfer learning methods have emerged as a feasible solution to address these …

Turnstile File Transfer: A Unidirectional System For Medium-Security Isolated Clusters, Mark Monnin, Lori L. Sussman

Journal of Cybersecurity Education, Research and Practice

Data transfer between isolated clusters is imperative for cybersecurity education, research, and testing. Such techniques facilitate hands-on cybersecurity learning in isolated clusters, allow cybersecurity students to practice with various hacking tools, and develop professional cybersecurity technical skills. Educators often use these remote learning environments for research as well. Researchers and students use these isolated environments to test sophisticated hardware, software, and procedures using full-fledged operating systems, networks, and applications. Virus and malware researchers may wish to release suspected malicious software in a controlled environment to observe their behavior better or gain the information needed to assist their reverse engineering processes. …

Finding Diffs Of Pull Request Commits, Chalinee Karawek, John Businge

Undergraduate Research Symposium Posters

Github is a social network that allows developers' projects to be forked for various uses, such as developing the existing repository or use the code to steer development into a new direction. As more forks are made, the more bugs that can occur due to new pull requests not synchronizing with the upstream repository. PaReco is a clone detection tool by Ramkisoen et al. (Ramkisoen et al. 2022) that identifies the changes inside the files inside a pull request by performing diff on every file. However, in this study, we take a different approach to identifying the changes inside the …

Chronic Kidney Disease Android Application, Paul Le

Computer Science and Engineering Senior Theses

Chronic kidney disease is increasingly recognized as a leading public health problem over the world that affects more than 10 percent of the population worldwide, where electrolytes and wastes can build up in your system. Kidney failure might not be noticeable until more advanced stages where it may then become fatal if not for artificial filtering or a transplant. As a result, it is important to detect kidney disease early on to prevent it from progressing to kidney failure. The current main test of the disease is a blood test that measures the levels of a waste product called creatine …

Adaptive Traction, Power And Torque Control Strategies And Optimization In An All-Electric Powertrain, Aymane Hidara

Theses and Dissertations

Electric and hybrid-electric vehicles lean heavily on intricate control algorithms to provide smooth, reliable, and secure operations under any driving conditions. Three distinct supervisory control strategies have been developed, each aiming to improve reliability and vehicle performance of a dual-motor electric vehicle equipped with an all-wheel-drive, fully electric powertrain. These algorithms are adept at dynamically modulating and constraining the torque provided to the wheels, leveraging two autonomous permanent magnet electric drive units. This study utilizes a vehicle model jointly provided by MathWorks and General Motors in partnership with industry sponsors. The these strategies were implemented in the model and enhanced …

Safety-Aware Autonomous Robot Navigation, Mapping And Control By Optimization Techniques, Tingjun Lei

Theses and Dissertations

The realm of autonomous robotics has seen impressive advancements in recent years, with robots taking on essential roles in various sectors, including disaster response, environmental monitoring, agriculture, and healthcare. As these highly intelligent machines continue to integrate into our daily lives, the pressing imperative is to elevate and refine their performance, enabling them to adeptly manage complex tasks with remarkable efficiency, adaptability, and keen decision-making abilities, all while prioritizing safety-aware navigation, mapping, and control systems. Ensuring the safety-awareness of these robotic systems is of paramount importance in their development and deployment. In this research, bio-inspired neural networks, nature-inspired intelligence, deep …

Timeseries Forecasting Of U.S. Housing Price Index Using Machine Learning And Deep Learning Models, Krishna Chaitanya Nunna

Dissertations

Time series forecasting is a promising technique for various applications which predicts future values or patterns by taking historical data as base. Forecasting future trends is very beneficial for different industries to make valuable decisions and strategies. One such industry is housing market; it has biggest influence on U.S. economy. Housing price index (HPI) is a one of the crucial economic indices published by various government funded and private agency to benefit several industries and individuals for better analysis of future trends of housing market.

Several factors influence the HPI, economical, geographical, and demographic features. Development of traditional time series …

Data Science And The Ethics Of Private Information, Faria R. Promi

Publications and Research

This research focuses on the moral questions linked to modern data technologies like Big Data and the Internet of Things. These technologies can be, but they are also about privacy and keeping data secure. We've looked at what experts say about these concerns and discovered that most agree we need to safeguard people's privacy and data. Our research encourages everyone to use these technologies, finding a balance between technological progress and responsible, ethical behavior. This way, we can enjoy the benefits of these technologies while also respecting individuals' privacy and data rights. The study reviews what experts have said about …

Brain-Inspired Spatio-Temporal Learning With Application To Robotics, Thiago André Ferreira Medeiros

USF Tampa Graduate Theses and Dissertations

The human brain still has many mysteries and one of them is how it encodes information. The following study intends to unravel at least one such mechanism. For this it will be demonstrated how a set of specialized neurons may use spatial and temporal information to encode information. These neurons, called Place Cells, become active when the animal enters a place in the environment, allowing it to build a cognitive map of the environment. In a recent paper by Scleidorovich et al. in 2022, it was demonstrated that it was possible to differentiate between two sequences of activations of a …

A Low-Power Analog Cell For Implementing Spiking Neural Networks In 65 Nm Cmos, John S. Venker, Luke Vincent, Jeff Dix

Computer Science and Computer Engineering Faculty Publications and Presentations

A Spiking Neural Network (SNN) is realized within a 65 nm CMOS process to demonstrate the feasibility of its constituent cells. Analog hardware neural networks have shown improved energy efficiency in edge computing for real-time-inference applications, such as speech recognition. The proposed network uses a leaky integrate and fire neuron scheme for computation, interleaved with a Spike Timing Dependent Plasticity (STDP) circuit for implementing synaptic-like weights. The low-power, asynchronous analog neurons and synapses are tailored for the VLSI environment needed to effectively make use of hardware SSN systems. To demonstrate functionality, a feedforward Spiking Neural Network composed of two layers, …

Pollutant Forecasting Using Neural Network-Based Temporal Models, Richard Pike

Masters Theses & Specialist Projects

The Jing-Jin-Ji region of China is a highly industrialized and populated area of the country. Its periodic high pollution and smog includes particles smaller than 2.5 μm, known as PM2.5, linked to many respiratory and cardiovascular illnesses. PM2.5 concentration around Jing-Jin-Ji has exceeded China’s urban air quality safety threshold for over 20% of all days in 2017 through 2020.

The quantity of ground weather stations that measure the concentrations of these pollutants, and their valuable data, is unfortunately small. By employing many machine learning strategies, many researchers have focused on interpolating finer spatial grids of PM2.5, or hindcasting PM2.5. However, …

Ethical Implications Of Ai-Based Algorithms In Recruiting Processes: A Study Of Civil Rights Violations Under Title Vii And The Americans With Disabilities Act, Vanessa Rodriguez

Cyber Operations and Resilience Program Graduate Projects

This research paper analyzes the ethical implications of utilizing artificial intelligence, specifically AI-based algorithms in business selection and recruiting processes, with a focus on potential violations under Title VII of the Civil Rights Act of 1964 and Title 1 of the Americans with Disabilities Act (ADA). Amazon’s attempt at launching AI recruiting tools is examined. This paper will assess the fairness of AI recruiting practices, considering data collection, potential biases, and accuracy concerns in its implementation process. Additionally, the paper will provide an overview of federal civil rights statutes enforced by the U.S. Equal Employment Opportunity Commission (EEOC) and recent …

Recycled Polycarbonate And Polycarbonate/Acrylonitrile Butadiene Styrene Feedstocks For Circular Economy Product Applications With Fused Granular Fabrication-Based Additive Manufacturing, Alessia Romani, Marinella Levi, Joshua M. Pearce

Electrical and Computer Engineering Publications

Distributed recycling and additive manufacturing (DRAM) holds enormous promise for enabling a circular economy. Most DRAM studies have focused on single thermoplastic waste stream. This study takes three paths forward from the previous literature: 1) expanding DRAM into high-performance polycarbonate/ acrylonitrile butadiene styrene (PC/ABS) blends, 2) extending PC/ABS blend research into both recycled materials and into direct fused granular fabrication (FGF) 3-D printing and 3) demonstrating the potential of using recycled PC/ABS feedstocks for new applications in circular economy contexts. A commercial open source large-format FGF 3-D printer was modified and used to assess the different printability and accuracy of …

Integrating Ai Into Uavs, Huong Quach

Cybersecurity Undergraduate Research Showcase

This research project explores the application of Deep Learning (DL) techniques, specifically Convolutional Neural Networks (CNNs), to develop a smoke detection algorithm for deployment on mobile platforms, such as drones and self-driving vehicles. The project focuses on enhancing the decision-making capabilities of these platforms in emergency response situations. The methodology involves three phases: algorithm development, algorithm implementation, and testing and optimization. The developed CNN model, based on ResNet50 architecture, is trained on a dataset of fire, smoke, and neutral images obtained from the web. The algorithm is implemented on the Jetson Nano platform to provide responsive support for first responders. …