Physical Sciences and Mathematics Commons^™

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 …

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 …

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 …

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 …

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 …

A Survey On Securing Iot Ecosystems And Adaptive Network Vision, Tejaswini Goli, Yoohwan Kim

Computer Science Faculty Research

The rapid growth of Internet-of-Things (IoT) devices and the large network of interconnected devices pose new security challenges and privacy threats that would put those devices at high risk and cause harm to the affiliated users. This paper emphasizes such potential security challenges and proposes possible solutions in the field of IoT Security, mostly focusing on automated or adaptive networks. Considering the fact that IoT became widely adopted, the intricacies in the security field tend to grow expeditiously. Therefore, it is necessary for businesses to adopt new security protocols and to the notion of automated network security practices driven by …

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 …

Static Malware Detection Using Deep Neural Networks On Portable Executables, Piyush Aniruddha Puranik

UNLV Theses, Dissertations, Professional Papers, and Capstones

There are two main components of malware analysis. One is static malware analysis and the other is dynamic malware analysis. Static malware analysis involves examining the basic structure of the malware executable without executing it, while dynamic malware analysis relies on examining malware behavior after executing it in a controlled environment. Static malware analysis is typically done by modern anti-malware software by using signature-based analysis or heuristic-based analysis.

This thesis proposes the use of deep neural networks to learn features from a malware’s portable executable (PE) to minimize the occurrences of false positives when recognizing new malware. We use the …

Application Of Machine Learning In Cancer Research, Mandana Bozorgi

UNLV Theses, Dissertations, Professional Papers, and Capstones

This dissertation revisits the problem of five-year survivability predictions for breast cancer using machine learning tools. This work is distinguishable from the past experiments based on the size of the training data, the unbalanced distribution of data in minority and majority classes, and modified data cleaning procedures. These experiments are also based on the principles of TIDY data and reproducible research. In order to fine-tune the predictions, a set of experiments were run using naive Bayes, decision trees, and logistic regression.

Of particular interest were strategies to improve the recall level for the minority class, as the cost of misclassification …

A Machine Learning Approach To Predict First-Year Student Retention Rates At University Of Nevada, Las Vegas, Aditya Rajuladevi

UNLV Theses, Dissertations, Professional Papers, and Capstones

First-year student retention rates for a four-year institution refers to the percentage of First-time Full-time students from the previous fall who return to the same institution for the following fall. First-year retention rates act as an important indicator of the student satisfaction as well as the performance of the university. Moreover, universities with low retention rates may face a decline in the admissions of talented students with a notable loss of tuition fees and contributions from alumni. Therefore, it is important for universities to formulate strategies to identify students who are at risk of not being retained and take necessary …