Physical Sciences and Mathematics Commons^™

Ocr Post-Processing Using Large Language Models, Mahdi Hajiali

UNLV Theses, Dissertations, Professional Papers, and Capstones

Optical Character Recognition (OCR) technology transforms textual visuals into an electronically readable, non-graphical format of the text. This allows the editing and other text manipulation of the content by language technology software such as machine translation, text comprehension, query-answering systems, and search engines. While Optical Character Recognition (OCR) systems continually progress towards greater precision, several complications persist when dealing with low-resolution source images or those with multicolored backgrounds. Consequently, the text derived from OCR necessitates additional refinement to optimize accuracy, beneficial for various subsequent applications. It is recognized that the character accuracy of OCR-generated text may influence certain natural language …

Facial Expression Recognition Using Convolutional Neural Networks (Cnns) And Generative Adversarial Networks (Gans) For Data Augmentation And Image Generation, Shekhar Singh

UNLV Theses, Dissertations, Professional Papers, and Capstones

Facial expressions play a crucial role in human communication, serving as a powerful means to convey emotions. However, classifying facial expressions using artificial intelligence (AI) can be challenging, especially with small datasets and images. Facial Expression Recognition (FER) is an active area of research, with Convolutional Neural Networks (CNNs) being widely employed for classification. In this research, we propose a CNN-based approach for FER that utilizes both original and augmented datasets to enhance classification accuracy. Experimental results on the FER2013 dataset show test accuracies of 63.39% and 64.59% for the original and augmented datasets, respectively, in a seven-class classification task. …

Dea2uth: A Decentralized Authentication And Authorization Scheme For Secure Private Data Transfer, Phillipe Austria

UNLV Theses, Dissertations, Professional Papers, and Capstones

The sharing of private information is a daunting, multifaceted, and expensive undertaking. Furthermore, identity management is an additional challenge that poses significant technological, operational, and legal obstacles. Present solutions and their accompanying infrastructures rely on centralized models that are susceptible to hacking and can hinder data control by the rightful owner. Consequently, blockchain technology has generated interest in the fields of identity and access control. This technology is viewed as a potential solution due to its ability to offer decentralization, transparency, provenance, security, and privacy benefits. Nevertheless, a completely decentralized and private solution that enables data owners to control their …

Information-Theoretic Model Diagnostics (Infomod), Armin Esmaeilzadeh

UNLV Theses, Dissertations, Professional Papers, and Capstones

Model validation is a critical step in the development, deployment, and governance of machine learning models. During the validation process, the predictive power of a model is measured on unseen datasets with a variety of metrics such as Accuracy and F1-Scores for classification tasks. Although the most used metrics are easy to implement and understand, they are aggregate measures over all the segments of heterogeneous datasets, and therefore, they do not identify the performance variation of a model among different data segments. The lack of insight into how the model performs over segments of unseen datasets has raised significant challenges …

Enhanced Iot-Based Electrocardiogram Monitoring System With Deep Learning, Jian Ni

UNLV Theses, Dissertations, Professional Papers, and Capstones

Due to the rapid development of computing and sensing technologies, Internet of Things (IoT)-based cardiac monitoring plays a crucial role in providing patients with cost-efficient solutions for long-term, continuous, and pervasive electrocardiogram (ECG) monitoring outside a hospital setting. In a typical IoT-based ECG monitoring system, ECG signals are picked up by sensors located on the edge, and then uploaded to the remote cloud servers. ECG interpretation is performed for the collected ECGs in the cloud servers and the analysis results can be made instantly available to the patients as well as their healthcare providers.In this dissertation, we first examine the …

High Clearance Collision-Free Paths, Barun Thapa

UNLV Theses, Dissertations, Professional Papers, and Capstones

Path Planning is one of the widely investigated research areas in computational geometry and robotics. Given a set of polygonal obstacles inside a rectangular box, and start & goal points, the path planning problem is to construct a collision-free path connecting the start point to the goal point. We review existing well known algorithms for solving the path planning problem. We propose new approaches for constructing a collision-free path with high clearance from obstacles. The main idea of the proposed algorithm is the appropriate generation free-region nodes which can be processed to construct high clearance paths. Neighbors of free-region nodes …

Real–Time Semantic Segmentation For Railway Anomalies Analysis, Paul Stanik Iii

UNLV Theses, Dissertations, Professional Papers, and Capstones

In the past few years, computer vision has made huge jumps due to deep learning which leverages increased computational power and access to data. The computer vision community has also embraced transparency to accelerate research progress by sharing open datasets and open source code. Access to large scale datasets and benchmark challenges propelled and opened the field. The autonomous vehicle community is a prime example. While there has been significant growth in the automotive vision community, not much has been done in the rail domain. Traditional rail inspection methods require special trains that are run during down time, have sensitive …

Identification Of Factors Contributing To Traffic Crashes By Analysis Of Text Narratives, Cristian D. Arteaga-Sanchez

UNLV Theses, Dissertations, Professional Papers, and Capstones

The fatalities, injuries, and property damage that result from traffic crashes impose a significant burden on society. Current research and practice in traffic safety rely on analysis of quantitative data from crash reports to understand crash severity contributors and develop countermeasures. Despite advances from this effort, quantitative crash data suffers from drawbacks, such as the limited ability to capture all the information relevant to the crashes and the potential errors introduced during data collection. Crash narratives can help address these limitations, as they contain detailed descriptions of the context and sequence of events of the crash. However, the unstructured nature …

Jiapi: A Type Checker Generator For Statically Typed Languages, Benjamin Cisneros Merino

UNLV Theses, Dissertations, Professional Papers, and Capstones

Type systems are a key characteristic in the context of the study of programming languages. They frequently offer a simple, intuitive way of expressing and testing the fundamental structure of programs. This is especially true when types are used to provide formal, machine-checked documentation for an implementation. For example, the absence of type errors in code prior to execution is what type systems for static programming languages are designed to assure, and in the literature, type systems that satisfy this requirement are referred to as sound type systems. Types also define module interfaces, making them essential for achieving and maintaining …

Reachability And Turn Constraint Paths, Sabrina Wallace

UNLV Theses, Dissertations, Professional Papers, and Capstones

Problems dealing with the development of efficient algorithms for constructing collision-free paths have been explored extensively. We review existing algorithms for constructing collision-free paths under turn-angle constraints. We examine the problem of computing collision-free paths in the presence of polygonal obstacles. We present an algorithm for identifying the placement of a source vertex so that the maximum number of obstacle vertices can be reached via the shortest path tree under turn-angle requirements. We also present some experimental results on the construction of collision-free paths in the presence of polygonal obstacles.

Hyperspectral Image Analysis Of Food For Nutritional Intake, Shirin Nasr Esfahani

UNLV Theses, Dissertations, Professional Papers, and Capstones

The primary object of this dissertation is to investigate the application of hyperspectral technology to accommodate for the growing demand in the automatic dietary assessment applications. Food intake is one of the main factors that contribute to human health. In other words, it is necessary to get information about the amount of nutrition and vitamins that a human body requires through a daily diet. Manual dietary assessments are time-consuming and are also not precise enough, especially when the information is used for the care and treatment of hospitalized patients. Moreover, the data must be analyzed by nutritional experts. Therefore, researchers …

Radiomic Features To Predict Overall Survival Time For Patients With Glioblastoma Brain Tumors Based On Machine Learning And Deep Learning Methods, Lina Chato

UNLV Theses, Dissertations, Professional Papers, and Capstones

Machine Learning (ML) methods including Deep Learning (DL) Methods have been employed in the medical field to improve diagnosis process and patient’s prognosis outcomes. Glioblastoma multiforme is an extremely aggressive Glioma brain tumor that has a poor survival rate. Understanding the behavior of the Glioblastoma brain tumor is still uncertain and some factors are still unrecognized. In fact, the tumor behavior is important to decide a proper treatment plan and to improve a patient’s health. The aim of this dissertation is to develop a Computer-Aided-Diagnosis system (CADiag) based on ML/DL methods to automatically estimate the Overall Survival Time (OST) for …

An Empirical Investigation Into The Impact Of Automated Grading, Alex James St. Aubin

UNLV Theses, Dissertations, Professional Papers, and Capstones

Context: Computer Science enrollment has seen increases in recent years. At the University of Nevada, Las Vegas we have seen an average year to year growth rate of 17.33% in the spring and 13.71% in the fall over the past 10 years in our entry level programming course. These enrollment increases have led to considerable additional costs for grading course material.Objective: The goal of this study is to determine the impact of automatic grading systems on students. If automatic grading is at least as effective as manual grading in practice, it may reduce cost under the context of at least …

Material Handling With Embodied Loco-Manipulation, Jean Chagas Vaz

UNLV Theses, Dissertations, Professional Papers, and Capstones

Material handling is an intrinsic component of disaster response. Typically, first responders, such as firefighters and/or paramedics, must carry, push, pull, and handle objects, facilitating the transportation of goods. For many years, researchers from around the globe have sought to enable full-sized humanoid robots to perform such essential material handling tasks. This work aims to tackle current limitations of humanoids in the realm of interaction with common objects such as carts, wheelbarrows, etc. Throughout this research, many methods will be applied to ensure a stable Zero Moment Point (ZMP) trajectory to allow a robust gait while loco-manipulating a cart. The …

Machine Learning Analysis Of Single Nucleotide Polymorphism (Snp) Data To Predict Bone Mineral Density In African American Women, Erick Githua Wakayu

UNLV Theses, Dissertations, Professional Papers, and Capstones

Osteoporosis is a debilitating disease in which an individual’s bones weaken, making bones fragile and more susceptible to fracture. While commonly found amongst postmenopausal Caucasian and Asian women based on previous studies, those of African descent (African American/Black) have largely been ignored when it comes to osteoporotic studies, especially when it comes to Genome Wide Association Studies (GWAS). From GWA studies, we gain access to single nucleotide poly-morphisms (SNPs) that may contribute to certain illnesses, such as osteoporosis. With low Bone Mineral Density (BMD) being one of the primary markers of potential osteoporosis, it is prudent that proper research is …

From Language Comprehension Towards General Ai, Binay Dahal

UNLV Theses, Dissertations, Professional Papers, and Capstones

Language comprehension or more formally, natural language understanding is one of the major undertakings in Artificial Intelligence. In this work, we explore a few of the problems in language understanding using fixed deep learning models. Specifically, first, we look into question generation. Asking questions relates to the cognitive ability of language comprehension and context understanding. For that reason, making progress in question generation is significant. We introduce a novel task called “question generation with masked target answer” and propose various models and present the baseline result for the task. Next, we extend on the question generation task and develop a …

A Human-Embodied Drone For Dexterous Aerial Manipulation, Dongbin Kim

UNLV Theses, Dissertations, Professional Papers, and Capstones

Current drones perform a wide variety of tasks in surveillance, photography, agriculture, package delivery, etc. However, these tasks are performed passively without the use of human interaction. Aerial manipulation shifts this paradigm and implements drones with robotic arms that allow interaction with the environment rather than simply sensing it. For example, in construction, aerial manipulation in conjunction with human interaction could allow operators to perform several tasks, such as hosing decks, drill into surfaces, and sealing cracks via a drone. This integration with drones will henceforth be known as dexterous aerial manipulation.

Our recent work integrated the worker’s experience into …

Calculating The Learning Rate Of A Neural Network Using A Genetic Algorithm, Eric Miller

UNLV Theses, Dissertations, Professional Papers, and Capstones

In the field of Computer Science, neural networks and genetic algorithms have become very popular tools in solving complex problems. Because of this growing popularity, there has been several attempts to combine the two concepts. Some of these attempts focused on using genetic algorithms to determine the best architecture, starting weights, or feature selection, to name of few of the applications. While a lot of the research that is available focuses on solving more than one element of the neural network design or is looking to use genetic algorithms to replace a part of the traditional neural network, such as …

Exploring The Latent Space Of Image Captioning Networks, Mikian J. Musser

UNLV Theses, Dissertations, Professional Papers, and Capstones

State-of-the-art image captioning models can successfully produce a diverse set of accurate captions. Previous research has focused on improving caption diversity while maintaining a high level of fidelity. We shift the focus from accuracy and diversity to controllability. We use a modified version of the traditional encoder-decoder network that allows the model to produce a meaningful and structured latent space. We then explore the latent space using several latent cartographic methods: lerp, slerp, analogy completion, attribute vector rotation, and interpolation graphs. Additionally, we discuss different categories of latent space and provide modifications for each of the cartographic methods. Finally, we …

Forecasting Pedestrian Trajectory Using Deep Learning, Arsal Syed

UNLV Theses, Dissertations, Professional Papers, and Capstones

In this dissertation we develop different methods for forecasting pedestrian trajectories. Complete understanding of pedestrian motion is essential for autonomous agents and social robots to make realistic and safe decisions. Current trajectory prediction methods rely on incorporating historic motion, scene features and social interaction to model pedestrian behaviors. Our focus is to accurately understand scene semantics to better forecast trajectories. In order to do so, we leverage semantic segmentation to encode static scene features such as walkable paths, entry/exits, static obstacles etc. We further evaluate the effectiveness of using semantic maps on different datasets and compare its performance with already …

Rotten With Prediction, Serena Raquel Hicks

UNLV Theses, Dissertations, Professional Papers, and Capstones

This project focuses on the relationship between religion and technology as it is portrayed in Science Fiction (SF). This thesis explores the SF genre rhetorically by examining the 2002 movie Minority Report (MR), which signaled the importance of surveillance and the need to predict future crimes following 9/11. The events of 9/11 played a significant role in post 9/11 SF films, which reflect and critique our communal and cultural values. 9/11 created a new relationship between the U.S justice system, predictive technologies (PTs), and data gathering. Through the Bush Doctrine of “preemptive action,” the U.S government attempted to use Dataism, …

Comparing A New Algorithm For The Traveling Salesman Problem To Previous Deterministic And Stochastic Algorithms, Edward Friesema

UNLV Theses, Dissertations, Professional Papers, and Capstones

The challenges of drone navigation have driven many advances in the development of autonomous systems. Unmanned Autonomous Vehicles(UAVs) operate in a rapidly changing flight space and have to balance a complex set of constraints and objectives. Many of these objectives can be represented in variations of the classic Traveling Salesman Problem. Numerous approximate solutions to TSP have been proposed over the years, but these approaches have difficulty when adding new constraints that require rapid recalculation of the solution. Either they are fast but do not provide solutions that are close to the optimum, or they provide excellent solutions but they …

Effect Of Boundary Approximation On Visibility, Samridhi Jha

UNLV Theses, Dissertations, Professional Papers, and Capstones

The problem of simplifying a complex shape with simpler ones is an important research area in computer science and engineering. In this thesis, we investigate the effect on the visibility properties of polygons when their boundaries are approximated to make them simpler. We present two algorithms for approximating a restricted class of polygons called 1.5 D terrain. We also present experimental investigations on the performance of reviewed and proposed approximation algorithms.

An Introduction To Federated Learning And Its Analysis, Manjari Ganapathy

UNLV Theses, Dissertations, Professional Papers, and Capstones

With the onset of the digital era, data privacy is one of the most predominant issues. Decentralized learning is becoming popular as the data can remain within local entities by maintaining privacy. Federated Learning is a decentralized machine learning approach, where multiple clients collaboratively learn a model, without sharing raw data. There are many practical challenges in solving Federated Learning, which include communication set up, data heterogeneity and computational capacity of clients. In this thesis, I explore recent methods of Federated Learning with various settings, such as data distributions and data variability, used in several applications. In addition, I, specifically, …

A Hyperelastic Porous Media Framework For Ionic Polymer-Metal Composites And Characterization Of Transduction Phenomena Via Dimensional Analysis And Nonlinear Regression, Zakai J. Olsen

UNLV Theses, Dissertations, Professional Papers, and Capstones

Ionic polymer-metal composites (IPMC) are smart materials that exhibit large deformation in response to small applied voltages, and conversely generate detectable electrical signals in response to mechanical deformations. The study of IPMC materials is a rich field of research, and an interesting intersection of material science, electrochemistry, continuum mechanics, and thermodynamics. Due to their electromechanical and mechanoelectrical transduction capabilities, IPMCs find many applications in robotics, soft robotics, artificial muscles, and biomimetics. This study aims to investigate the dominating physical phenomena that underly the actuation and sensing behavior of IPMC materials. This analysis is made possible by developing a new, hyperelastic …

A Fortified Extension Of The Aes And Its Implementation, Ashby Mullin

UNLV Theses, Dissertations, Professional Papers, and Capstones

With the advancement of quantum computing (QC), the integrity of cryptography has been called into question. For example, two QC algorithms have been developed that can break asymmetric encryption (i.e., Grover’s, Shor’s), which also poses a threat to symmetric encryption. Asymmetric encryption efforts addressing this threat include lattice-based cryptography, which uses lattice problems to reduce efficiency of cryptanalysis. Symmetric encryption security can be bolstered by increasing the key length, allowing for additional permutations a key could have; known as keyspace. This thesis seeks to expand the keyspace of symmetric encryption in order to create more possibilities. This fortification to the …

The Processj C++ Runtime System And Code Generator, Alexander Christian Thomason

UNLV Theses, Dissertations, Professional Papers, and Capstones

ProcessJ is a modern Process-Oriented language that builds on previous work from other languages like occam and occam-pi. However, the only readily-available runtime system is built on top of the Java Virtual Machine (JVM). This is not a choice made intentionally, but simply out of a lack of other implementations -- until now. This thesis introduces the new C++-based runtime system for ProcessJ, coupled with a new C++ code generator for the ProcessJ compiler. This thesis later examines the implementation details of the runtime system, including the components that make it up. We also examine the ability to cooperatively schedule …

A Divide And Conquer With Semi-Global Failover For Software Defined Networks, Kasra Goravanchi

UNLV Theses, Dissertations, Professional Papers, and Capstones

Nowadays, many service providers need to provide many other functions than just a network connectivity. They also need to provide network functions such as network address translation, firewall, encryption, Domain Name Service (DNS), caching, routing and many other services. Usually these functions come with the hardware at the user or customer’s premises. This can increase the revenue of the revenue, but also can cost a lot and also be extremely difficult to maintain. Moreover, it is important to be able to configure the network and later modify the configuration to create fault tolerance and to prepare the system for future …

Analysis Of Blockchain-Based Storage Systems, Phillipe Austria

UNLV Theses, Dissertations, Professional Papers, and Capstones

Increasing storage needs has driven cloud storage to become a prevalent choice to store data for both business and personal use. Cloud service providers, such as Google, offer advantages over storing personal hard drives; however, customers lose privacy and require trust in the provider to act honestly with their data. This thesis explores an alternative to cloud storage using Blockchain technology. I focus on Sia, a blockchain-based storage platform that allows users to rent storage from other users.

In this study, I evaluate the security, performance and costs between the Sia and traditional cloud storage. I assessed security based on …

An Investigation Into Multi-View Error Correcting Output Code Classifiers Applied To Organ Tissue Classification, Daniel Alvarez

UNLV Theses, Dissertations, Professional Papers, and Capstones

Large amounts of data is being generated constantly each day, so much data that it is difficult to find patterns in order to predict outcomes and make decisions for both humans and machines alike. It would be useful if this data could be simplified using machine learning techniques. For example, biological cell identity is dependent on many factors tied to genetic processes. Such factors include proteins, gene transcription, and gene methylation. Each of these factors are highly complex mechanism with immense amounts of data. Simplifying these can then be helpful in finding patterns in them. Error-Correcting Output Codes (ECOC) does …