Digital Commons Network^™

Nucleate Boiling Under Different Gravity Values: Numerical Simulations & Data-Driven Techniques., Sandipan Banerjee

Electronic Theses and Dissertations

Nucleate boiling is important in nuclear applications and cooling applications under earth gravity conditions. Under reduced gravity or microgravity environment, it is significant too, especially in space exploration applications. Although multiple studies have been performed on nucleate boiling, the effect of gravity on nucleate boiling is not well understood. This dissertation primarily deals with numerical simulations of nucleate boiling using an adaptive Moment-of-Fluid (MoF) method for a single vapor bubble (water vapor or Perfluoro-n-hexane) in saturated liquid for different gravity levels. Results concerning the growth rate of the bubble, specifically the departure diameter and departure time have been provided. The …

Beyond Accuracy In Machine Learning., Aneseh Alvanpour

Electronic Theses and Dissertations

Machine Learning (ML) algorithms are widely used in our daily lives. The need to increase the accuracy of ML models has led to building increasingly powerful and complex algorithms known as black-box models which do not provide any explanations about the reasons behind their output. On the other hand, there are white-box ML models which are inherently interpretable while having lower accuracy compared to black-box models. To have a productive and practical algorithmic decision system, precise predictions may not be sufficient. The system may need to have transparency and be able to provide explanations, especially in applications with safety-critical contexts …

Machine Learning Methods For Statistical Analysis And Representation Learning On Neuroimaging Data, Fan Yang

Computer Science and Engineering Dissertations - Archive

With the recent advance and widespread adoption of imaging technological innovations, clinical practitioner and scientists can easily acquire and store a large amount of various neuroimaging modalities, such as Diffusion Tensor Imaging (DTI), Magnetic Resonance Imaging (MRI), resting-state functional MRI (rs-fMRI) and Positron Emission Tomography (PET), etc. These novel imaging data sources cover a rich amount of factors that influence patients' cognitive health, offer an objective view of patients at unprecedented multi-resolution for the understanding of brain structure and function, and have the significant potential to improve healthcare by aiding better decision-making in diagnosing, monitoring and treating diseases. Machine Learning …

Volcano Infrasound: Progress And Future Directions, Jacob F. Anderson, Jeffrey B. Johnson

Geosciences Faculty Publications and Presentations

Over the past two decades (2000–2020), volcano infrasound (acoustic waves with frequencies less than 20 Hz propagating in the atmosphere) has evolved from an area of academic research to a useful monitoring tool. As a result, infrasound is routinely used by volcano observatories around the world to detect, locate, and characterize volcanic activity. It is particularly useful in confirming subaerial activity and monitoring remote eruptions, and it has shown promise in forecasting paroxysmal activity at open-vent systems. Fundamental research on volcano infrasound is providing substantial new insights on eruption dynamics and volcanic processes and will continue to do so over …

Evaluating The Behaviour Of Centrally Perforated Unreinforced Masonry Walls: Applications Of Numerical Analysis, Machine Learning, And Stochastic Methods, Mohsen Khaleghi, Javid Salimi, Visar Farhangi, Mohammad Javad Moradi, Moses Karakouzian

Civil and Environmental Engineering and Construction Faculty Research

The presence of openings greatly affects the response of unreinforced masonry (URM) walls. This topic greatly attracts the attention of many researchers. Perforated unreinforced masonry (PURM) walls under in-plane loads through the truss discretization method (TDM) along with several machine learning approaches such as Multilayer perceptron (MLP), Group of Method Data Handling (GMDH), and Radial basis function (RBF) are described in this paper. A new method named Multi-pier (MP) that is fast and accurate, is used to determine the behavior of PURM walls. The results of the MP method are expressed as a ratio of lateral load-bearing capacity and initial …

Deep Learning In Sports Prediction, Fan Lee

Computer Science Honors Theses

Sports prediction has always been an interesting problem in the entertainment industry. Many data scientists have come out different methods on this problem. We hope to see how well a neural network model can predict an individual game outcome and the final ranking on NBA data. We examined the possibility of different unbiased deep learning models can perform as well as other mathematics methods. We were also looking for what types of data are more influential for the models. Then, we can make some assumptions on our models and the other sports prediction methods.

Exposing Gan-Generated Faces Using Deep Neural Network, Hui Guo

Legacy Theses & Dissertations (2009 - 2024)

Generative adversarial network (GAN) generated high-realistic human faces are visually challenging to discern from real ones. They have been used as profile images for fake social media accounts, which leads to high negative social impacts.In this work, we explore a universal physiological cue of the eye, namely the pupil shape consistency, to identify GAN-generated faces reliably. We show that GAN-generated faces can be exposed via irregular pupil shapes. This phenomenon is caused by the lack of physiological constraints in the GAN models. We demonstrate that such artifacts exist widely in high-quality GAN-generated faces. We design an automatic method to segment …

A Comprehensive Survey For Non-Intrusive Load Monitoring, Efe İsa Tezde, Eray Yildiz

Turkish Journal of Electrical Engineering and Computer Sciences

Energy-saving and efficiency are as important as benefiting from new energy sources to supply increasing energy demand globally. Energy demand and resources for energy saving should be managed effectively. Therefore, electrical loads need to be monitored and controlled. Demand-side energy management plays a vital role in achieving this objective. Energy management systems schedule an optimal operation program for these loads by obtaining more accurate and precise residential and commercial loads information. Different intellegent measurement applications and machine learning algorithms have been proposed for the measurement and control of electrical devices/loads used in buildings. Of these, nonintrusive load monitoring (NILM) is …

Structure-Aware Visualization Retrieval, Haotian Li, Yong Wang, Aoyu Wu, Huan Wei, Huamin. Qu

Research Collection School Of Computing and Information Systems

With the wide usage of data visualizations, a huge number of Scalable Vector Graphic (SVG)-based visualizations have been created and shared online. Accordingly, there has been an increasing interest in exploring how to retrieve perceptually similar visualizations from a large corpus, since it can benefit various downstream applications such as visualization recommendation. Existing methods mainly focus on the visual appearance of visualizations by regarding them as bitmap images. However, the structural information intrinsically existing in SVG-based visualizations is ignored. Such structural information can delineate the spatial and hierarchical relationship among visual elements, and characterize visualizations thoroughly from a new perspective. …

Automated Identification Of Libraries From Vulnerability Data: Can We Do Better?, Stefanus A. Haryono, Hong Jin Kang, Abhishek Sharma, Asankhaya Sharma, Andrew E. Santosa, Ming Yi Ang, David Lo

Research Collection School Of Computing and Information Systems

Software engineers depend heavily on software libraries and have to update their dependencies once vulnerabilities are found in them. Software Composition Analysis (SCA) helps developers identify vulnerable libraries used by an application. A key challenge is the identification of libraries related to a given reported vulnerability in the National Vulnerability Database (NVD), which may not explicitly indicate the affected libraries. Recently, researchers have tried to address the problem of identifying the libraries from an NVD report by treating it as an extreme multi-label learning (XML) problem, characterized by its large number of possible labels and severe data sparsity. As input, …

The Quest For New Music: A Recommendation Algorithm For Spotify Users, Ian Curtis

Honors Projects

Music is one of the rare forms of communication that can be understood on a profound level by anyone; it has the power to cause significant emotional effects, to spark inspiration, to ignite change, to spread knowledge, and more, even regardless of song language. A popular subject of research in music pertains to recommendations; determining a song a listener would enjoy is not an easy task. Moreover, certain factors may influence a user's satisfaction with recommended songs and their likelihood to continue using a service. Focusing on the major streaming service Spotify, we build a K-Means clustering algorithm to recommend …

Design And Evaluation Of A Machine Learning Based Model For Optimization Of Residential Battery Energy Storage System Scheduling For Cost And Emissions Reductions, Kevin George

Theses

Usage of battery energy storage system (BESS) to facilitate demand response (DR) programs such as time of use (TOU) pricing can reduce utility bills for residential customers. However, using such a system for minimizing electric bills under these rate structures has the potential to cause an increase in emissions from the grid system. The increased emissions were majorly due to bulk energy storage of electricity produced by off-peak generators with higher emission rates and excess energy consumption due to battery inefficiency. BESS operating to optimize competing objectives to minimize utility cost and minimize CO2 emissions requires complex models that require …

Computational Approaches To Understanding Subduction Zone Geodynamics, Surface Heat Flow, And The Metamorphic Rock Record, Buchanan C. Kerswell

Boise State University Theses and Dissertations

Pressure-temperature (PT) estimates from exhumed high-pressure (HP) metamorphic rocks and global surface heat flow observations evidently encode information about subduction zone thermal structure and the nature of mechanical and chemical processing of subducted materials along the interface between converging plates. Previous work demonstrates the possibility of decoding such geodynamic information by comparing numerical geodynamic models with empirical observations of surface heat flow and the metamorphic rock record. However, ambiguous interpretations can arise from this line of inquiry with respect to thermal gradients, plate coupling, and detachment and recovery of subducted materials. This dissertation applies a variety of computational techniques to …

Intraday Algorithmic Trading Using Momentum And Long Short-Term Memory Network Strategies, Andrew R. Whitinger Ii

Undergraduate Honors Theses

Intraday stock trading is an infamously difficult and risky strategy. Momentum and reversal strategies and long short-term memory (LSTM) neural networks have been shown to be effective for selecting stocks to buy and sell over time periods of multiple days. To explore whether these strategies can be effective for intraday trading, their implementations were simulated using intraday price data for stocks in the S&P 500 index, collected at 1-second intervals between February 11, 2021 and March 9, 2021 inclusive. The study tested 160 variations of momentum and reversal strategies for profitability in long, short, and market-neutral portfolios, totaling 480 portfolios. …

New Accurate, Explainable, And Unbiased Machine Learning Models For Recommendation With Implicit Feedback., Khalil Damak

Electronic Theses and Dissertations

Recommender systems have become ubiquitous Artificial Intelligence (AI) tools that play an important role in filtering online information in our daily lives. Whether we are shopping, browsing movies, or listening to music online, AI recommender systems are working behind the scene to provide us with curated and personalized content, that has been predicted to be relevant to our interest. The increasing prevalence of recommender systems has challenged researchers to develop powerful algorithms that can deliver recommendations with increasing accuracy. In addition to the predictive accuracy of recommender systems, recent research has also started paying attention to their fairness, in particular …

Predicting Gross Metropolitan Product Worldwide Using Statistical Learning Models, Socio-Economic, And Satellite Imagery Data, Simin Joshaghani

Boise State University Theses and Dissertations

Gross metropolitan product (GMP) is one the most critical indicators for determining a metropolitan area’s economic performance. While GMP data currently exists for major cities in the US and OECD countries, the rest of the world is a blind spot. This study aims at estimating the GMP of 1289 cities in non-US and OECD countries, where no official city-level statistics are produced. We perform this estimation through multiple machine learning models, using night-time lights satellite imagery, and other publicly available data. We analyze eight spatial databases and four cross-sectional datasets and derive a feature vector of covariates through various techniques, …

Characterization And Mitigation Of False Information On The Web, Anu Shrestha

Boise State University Theses and Dissertations

Social media and Web sources have made information available, accessible, and shareable any time and anywhere nearly without friction. This information can be truthful, falsified, or can only be the opinion of the writer as users in such platforms are both information creators and consumers. In any case, it has the power to affect the decision of an individual, the beliefs of the society, activities, and the economy of the whole country. Thus, it is imperative to identify false information and mitigate the effects of false information that are ubiquitous across the Web and social media. Therefore, the main goal …

Machine Learning Based Aerodynamic Shape Optimization, Noe Martinez Jr.

Theses and Dissertations

The coefficient of pressure distribution for various 2D airfoil geometries were found using source – vortex panel methods. The data obtained in these simulations was used in multiple machine learning models which would predict the airfoil geometry from a given coefficient of pressure distribution. The neural networks employed were fully connected feedforward networks with Levenberg – Marquardt backpropagation and one model employed Bayesian Regularization. A novel tool for optimizing airfoil shape for a given coefficient of pressure distribution was created which performed well during testing. These models serve as the first step in minimizing the conflict between aerodynamic and stealth …

Hardware Isolation Approach To Securely Use Untrusted Gpus In Cloud Environments For Machine Learning, Lucas D. Hall

Theses and Dissertations

Machine Learning (ML) is now a primary method for getting useful information out of the immense volumes of data being generated and stored in society today. Useful data is a commodity for training ML models and those that need data for training are often not the owners of the data leading to a desire to use cloud-based services. Deep learning algorithms are best suited to run on a graphical processing unit (GPU) which presents a specific problem since the GPU is not a secure or trusted piece of hardware in the cloud computing environment.

In this paper, we will analyze …

Deep Learning Object-Based Detection Of Manufacturing Defects In X-Ray Inspection Imaging, Juan C. Parducci

Mechanical & Aerospace Engineering Theses & Dissertations

Current analysis of manufacturing defects in the production of rims and tires via x-ray inspection at an industry partner’s manufacturing plant requires that a quality control specialist visually inspect radiographic images for defects of varying sizes. For each sample, twelve radiographs are taken within 35 seconds. Some defects are very small in size and difficult to see (e.g., pinholes) whereas others are large and easily identifiable. Implementing this quality control practice across all products in its human-effort driven state is not feasible given the time constraint present for analysis.

This study aims to identify and develop an object detector capable …

New Debiasing Strategies In Collaborative Filtering Recommender Systems: Modeling User Conformity, Multiple Biases, And Causality., Mariem Boujelbene

Electronic Theses and Dissertations

Recommender Systems are widely used to personalize the user experience in a diverse set of online applications ranging from e-commerce and education to social media and online entertainment. These State of the Art AI systems can suffer from several biases that may occur at different stages of the recommendation life-cycle. For instance, using biased data to train recommendation models may lead to several issues, such as the discrepancy between online and offline evaluation, decreasing the recommendation performance, and hurting the user experience. Bias can occur during the data collection stage where the data inherits the user-item interaction biases, such as …

Parallel Construction Of Random Forest On Gpu, Kennedy Senagi, Nicolas Jouandeau

All Peer-Reviewed Publications

There is tremendous growth of data generated from different industries, i.e., health, agriculture, engineering, etc. Consequently, there is demand for more processing power. Compared to computer processing units, general-purpose graphics processing units (GPUs) are rapidly emerging as a promising solution to achieving high performance and energy efficiency in various computing domains. Multiple forms of parallelism and complexity in memory access have posed a challenge in developing Random Forest (RF) GPU-based algorithm. RF is a popular and robust machine learning algorithm. In this paper, coarse-grained and dynamic parallelism approaches on GPU are integrated into RF(dpRFGPU). Experiment results of dpRFGPU are compared …

Development Of A Machine Learning-Based Financial Risk Control System, Zhigang Hu

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

With the gradual end of the COVID-19 outbreak and the gradual recovery of the economy, more and more individuals and businesses are in need of loans. This demand brings business opportunities to various financial institutions, but also brings new risks. The traditional loan application review is mostly manual and relies on the business experience of the auditor, which has the disadvantages of not being able to process large quantities and being inefficient. Since the traditional audit processing method is no longer suitable some other method of reducing the rate of non-performing loans and detecting fraud in applications is urgently needed …

Modeling And Analysis Of Subcellular Protein Localization In Hyper-Dimensional Fluorescent Microscopy Images Using Deep Learning Methods, Yang Jiao

UNLV Theses, Dissertations, Professional Papers, and Capstones

Hyper-dimensional images are informative and become increasingly common in biomedical research. However, the machine learning methods of studying and processing the hyper-dimensional images are underdeveloped. Most of the methods only model the mapping functions between input and output by focusing on the spatial relationship, whereas neglect the temporal and causal relationships. In many cases, the spatial, temporal, and causal relationships are correlated and become a relationship complex. Therefore, only modeling the spatial relationship may result in inaccurate mapping function modeling and lead to undesired output. Despite the importance, there are multiple challenges on modeling the relationship complex, including the model …

Identifying Functional And Non-Functional Software Requirements From User App Reviews And Requirements Artifacts, Dev Jayant Dave

Theses, Dissertations and Culminating Projects

This thesis proposes and evaluates Machine Learning (ML) based data models to identify and isolate software requirements from datasets containing user app review statements. The ML models classify user app review statements into Functional Requirements (FRs), Non-Functional Requirements (NFRs), and Non-Requirements (NRs). This proposed approach consisted of creating a novel hybrid dataset that contains software requirements from Software Requirements Specification (SRS) documents and user app reviews. The Support Vector Machine (SVM), Stochastic Gradient Descent (SGD), and Random Forest (RF) ML algorithms combined with the term frequency-inverse document frequency (TF-IDF) natural language processing (NLP) technique were implemented on the hybrid dataset. …

Machine Learning Classification Of Digitally Modulated Signals, James A. Latshaw

Electrical & Computer Engineering Theses & Dissertations

Automatic classification of digitally modulated signals is a challenging problem that has traditionally been approached using signal processing tools such as log-likelihood algorithms for signal classification or cyclostationary signal analysis. These approaches are computationally intensive and cumbersome in general, and in recent years alternative approaches that use machine learning have been presented in the literature for automatic classification of digitally modulated signals. This thesis studies deep learning approaches for classifying digitally modulated signals that use deep artificial neural networks in conjunction with the canonical representation of digitally modulated signals in terms of in-phase and quadrature components. Specifically, capsule networks are …

Data-Driven Framework For Understanding & Modeling Ride-Sourcing Transportation Systems, Bishoy Kelleny

Civil & Environmental Engineering Theses & Dissertations

Ride-sourcing transportation services offered by transportation network companies (TNCs) like Uber and Lyft are disrupting the transportation landscape. The growing demand on these services, along with their potential short and long-term impacts on the environment, society, and infrastructure emphasize the need to further understand the ride-sourcing system. There were no sufficient data to fully understand the system and integrate it within regional multimodal transportation frameworks. This can be attributed to commercial and competition reasons, given the technology-enabled and innovative nature of the system. Recently, in 2019, the City of Chicago the released an extensive and complete ride-sourcing trip-level data for …

Deep Learning-Based Anomaly Detection For Edge-Layer Devices, Jonathan Hunter

Masters Theses and Doctoral Dissertations

This thesis work proposes a novel DL-based anomaly detection framework for IoT environments, employing higher-capacity embedded devices as a first line of defense for the IoT edge layer. In the proposed framework, embedded devices implement the DL anomaly detection engine at the network gateway and adapt to potential attacks by retraining on incoming network traffic. In order to test the feasibility of this framework, two neural network models, trained on variations of the CICIDS 2018 Intrusion Detection Data Set, are deployed and tested on the Raspberry Pi 4. Model performance metrics, including fit and evaluation time across various batch and …

Machine Learning-Enabled Classification Of Climbers Using Small Data, Nicholas Milburn

Masters Theses and Doctoral Dissertations

Athlete performance scoring within climbing presents interesting challenges as the sport does not have an objective way to assign skill. Assessing skill level is valuable as it can be used to mark training progress and help an athlete choose appropriate climbs to attempt. Machine learning-based methods are popular for complex problems like this. The dataset available was composed of dynamic force data recorded during climbing; however, this dataset came with challenges such as data scarcity, imbalance, and it was temporally heterogeneous. Investigated solutions to these challenges include data augmentation, temporal normalization, conversion of time series to the spectral domain, and …

Dataset Evaluation For Data Trading Using Expected Loss And Homomorphic Encryption, Minsung Joo

Senior Honors Papers / Undergraduate Theses

Supervised machine learning suffers from the ``garbage-in garbage-out" phenomenon where the performance of a model is limited by the quality of the data. While a myriad of data is collected every second, there is no general rigorous method of evaluating the quality of a given dataset. This hinders fair pricing of data in scenarios where a buyer may look to buy data for use with machine learning. In this work, I propose using the expected loss corresponding to a dataset as a measure of its quality, relying on Bayesian methods for uncertainty quantification. Furthermore, I present a secure multi-party computation …