Digital Commons Network^™

Using Hybrid Machine Learning Models For Stock Price Forecasting And Trading., Ahmed Khalil

Theses and Dissertations

Trading stocks of publicly traded companies in stock markets is a challenging topic since investors are researching what tools can be used to maximize their profits while minimizing risks, which encouraged all researchers to research and test different methods to reach such a goal. As a result, the use of both fundamental analysis and technical analysis started to evolve to support traders in buying and selling stocks. Recently, the focus increased on using Machine learning models to predict stock prices and algorithmic trading as currently there is a huge amount of data that can be processed and used to forecast …

Development, Validation, And Diagnostic Performance Of A Novel Radiomic Model For Predicting Prostate Cancer Recurrence, Linda M. Huynh

Theses & Dissertations

Multi-parametric magnetic resonance imaging (MP-MRI)-derived radiomics have been shown to capture sub-visual patterns for the quantitative characterization of prostate cancer (PC) phenotypes. The present dissertation seeks to develop, evaluate, and compare the performance of an MRI-derived radiomic model for the prediction of PC recurrence following definitive treatment with radical prostatectomy (RP).

MP-MRI was obtained from 339 patients who had a minimum of 2 years follow-up following RP at three institutions. The prostate was manually delineated as the region of interest and 924 radiomic features were extracted. All features were evaluated for stability via intraclass correlation coefficient (ICC) and image normalization …

Comparative Analysis Of Surrogate Models For The Dissolution Of Spent Nuclear Fuel, Dayo Awe

Electronic Theses and Dissertations

This thesis presents a comparative analysis of surrogate models for the dissolution of spent nuclear fuel, with a focus on the use of deep learning techniques. The study explores the accuracy and efficiency of different machine learning methods in predicting the dissolution behavior of nuclear waste, and compares them to traditional modeling approaches. The results show that deep learning models can achieve high accuracy in predicting the dissolution rate, while also being computationally efficient. The study also discusses the potential applications of surrogate modeling in the field of nuclear waste management, including the optimization of waste disposal strategies and the …

Artificial Intelligence In Landscape Architecture: A Survey Of Theory, Culture, And Practice, Phillip J. Fernberg

All Graduate Theses and Dissertations, Fall 2023 to Present

This dissertation explores the role of artificial intelligence (AI) in shaping the landscape architecture profession. It looks at how AI has evolved in the field, its current influence, and its potential to change research, teaching, and professional practice. The research includes a detailed review of existing literature to identify trends in AI applications and gaps in knowledge. It also examines landscape architects' attitudes towards AI, revealing a mix of enthusiasm for its benefits and concerns about its impact on creativity and design processes, and proposes new ways of thinking about and working with AI. The work brings a unique perspective …

Techniques To Overcome Energy Storage Limitations In Electric Vehicles, Matthew J. Hansen

All Graduate Theses and Dissertations, Fall 2023 to Present

Electric vehicles are becoming increasingly popular, battery limitations (cost, size, and weight) complicate electric vehicle adoption. While important research on battery development is ongoing, this dissertation discusses two main approaches to overcome those limitations within the existing battery technology paradigm. Those thrusts are: improving battery health through an optimal charging strategy and minimizing necessary battery size through dynamic wireless power transfer. In this dissertation, relevant literature is discussed, with opportunities for further development considered. Within the two thrusts, three objectives sharpen the focus of the research presented here. First, a planning tool is defined for a battery electric bus fleet. …

Semantic Segmentation Of Point Cloud Sequences Using Point Transformer V3, Marion Sisk

Master's Theses

Semantic segmentation of point clouds is a basic step for many autonomous systems including automobiles. In autonomous driving systems, LiDAR sensors are frequently used to produce point cloud sequences that allow the system to perceive the environment and navigate safely. Modern machine learning techniques for segmentation have predominately focused on single-scan segmentation, however sequence segmentation has often proven to perform better on common segmentation metrics. Using the popular Semantic KITTI dataset, we show that by providing point cloud sequences to a segmentation pipeline based on Point Transformer v3, we increase the segmentation performance between seven and fifteen percent when compared …

Application Of High-Deflection Strain Gauges To Characterize Spinal-Motion Phenotypes Among Patients With Clbp, Spencer Alan Baker

Theses and Dissertations

Chronic low back pain (CLBP) is a nonspecific and persistent ailment that entails many physiological, psychological, social, and economic consequences for individuals and societies. Although there is a plethora of treatments available to treat CLBP, each treatment has varying efficacy for different patients, and it is currently unknown how to best link patients to their ideal treatment. However, it is known that biopsychosocial influences associated with CLBP affect the way that we move. It has been hypothesized that identifying phenotypes of spinal motion could facilitate an objective and repeatable method of determining the optimal treatment for each patient. The objective …

Multi-Method Approach To Assess The Impact Of Off-Fault Deformation During Fault Evolution, Christ Faviana Ramos Sanchez

Masters Theses

Scaled physical experiments using analog materials that simulate deformation processes in the Earth’s crust provide direct observations of deformation during strike-slip fault evolution. Carefully scaled experiments allow us to directly observe millions of years of deformation within hours and document the behavior of experimental faults. My thesis is composed of three distinct chapters that use scaled physical experiments to help us better understand partitioning of deformation on and off of strike-slip faults within the Earth’s crust. For my first project, I built a workflow that allows the measurement of off-fault deformation manifested as vertical motions along faults. When combined with …

Automated Identification And Mapping Of Interesting Mineral Spectra In Crism Images, Arun M. Saranathan

Doctoral Dissertations

The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) has proven to be an invaluable tool for the mineralogical analysis of the Martian surface. It has been crucial in identifying and mapping the spatial extents of various minerals. Primarily, the identification and mapping of these mineral spectral-shapes have been performed manually. Given the size of the CRISM image dataset, manual analysis of the full dataset would be arduous/infeasible. This dissertation attempts to address this issue by describing an (machine learning based) automated processing pipeline for CRISM data that can be used to identify and map the unique mineral signatures present in …

Data To Science With Ai And Human-In-The-Loop, Gustavo Perez Sarabia

Doctoral Dissertations

AI has the potential to accelerate scientific discovery by enabling scientists to analyze vast datasets more efficiently than traditional methods. For example, this thesis considers the detection of star clusters in high-resolution images of galaxies taken from space telescopes, as well as studying bird migration from RADAR images. In these applications, the goal is to make measurements to answer scientific questions, such as how the star formation rate is affected by mass, or how the phenology of bird migration is influenced by climate change. However, current computer vision systems are far from perfect for conducting these measurements directly. They may …

Enabling Privacy And Trust In Edge Ai Systems, Akanksha Atrey

Doctoral Dissertations

Recent advances in mobile computing and the Internet of Things (IoT) enable the global integration of heterogeneous smart devices via wireless networks. A common characteristic across these modern day systems is their ability to collect and communicate streaming data, making machine learning (ML) appealing for processing, reasoning, and predicting about the environment. More recently, low network latency requirements have made offloading intelligence to the cloud undesirable. These novel requirements have led to the emergence of edge computing, an approach that brings computation closer to the device with low latency, high throughput, and enhanced reliability. Together, they enable ML-powered information processing …

Gt-Ches And Dycon: Improved Classification For Human Evolutionary Systems, Joseph S. Johnson

Theses and Dissertations

The purpose of this work is to rethink the process of learning in human evolutionary systems. We take a sober look at how game theory, network theory, and chaos theory pertain specifically to the modeling, data, and training components of generalization in human systems. The value of our research is three-fold. First, our work is a direct approach to align machine learning generalization with core behavioral theories. We made our best effort to directly reconcile the axioms of these heretofore incompatible disciplines -- rather than moving from AI/ML towards the behavioral theories while building exclusively on AI/ML intuition. Second, this …

Molecular Understanding And Design Of Deep Eutectic Solvents And Proteins Using Computer Simulations And Machine Learning, Usman Lame Abbas

Theses and Dissertations--Chemical and Materials Engineering

Hydrophobic deep eutectic solvents (DESs) have emerged as excellent extractants. A major challenge is the lack of an efficient tool to discover DES candidates. Currently, the search relies heavily on the researchers’ intuition or a trial-and-error process, which leads to a low success rate or bypassing of promising candidates. DES performance depends on the heterogeneous hydrogen bond environment formed by multiple hydrogen bond donors and acceptors. Understanding this heterogeneous hydrogen bond environment can help develop principles for designing high performance DESs for extraction and other separation applications. This work investigates the structure and dynamics of hydrogen bonds in hydrophobic DESs …

Adaptive Multi-Label Classification On Drifting Data Streams, Martha Roseberry

Theses and Dissertations

Drifting data streams and multi-label data are both challenging problems. When multi-label data arrives as a stream, the challenges of both problems must be addressed along with additional challenges unique to the combined problem. Algorithms must be fast and flexible, able to match both the speed and evolving nature of the stream. We propose four methods for learning from multi-label drifting data streams. First, a multi-label k Nearest Neighbors with Self Adjusting Memory (ML-SAM-kNN) exploits short- and long-term memories to predict the current and evolving states of the data stream. Second, a punitive k nearest neighbors algorithm with a self-adjusting …

Towards Machine Learning-Based Control Of Autonomous Vehicles In Solar Panel Cleaning Systems, Farima Hajiahmadi

Theses and Dissertations

This thesis presents a machine learning (ML)-based approach for the intelligent control of Autonomous Vehicles (AVs) utilized in solar panel cleaning systems, aiming to mitigate challenges arising from uncertainties, disturbances, and dynamic environments. Solar panels, predominantly situated in dedicated lands for solar energy production (e.g., agricultural solar farms), are susceptible to dust and debris accumulation, leading to diminished energy absorption. Instead of labor-intensive manual cleaning, robotic cleaners offer a viable solution. AVs equipped to transport and precisely position these cleaning robots are indispensable for efficient navigation among solar panel arrays. However, environmental obstacles (e.g., rough terrain), variations in solar panel …

Reinforcement Learning: Applying Low Discrepancy Action Selection To Deep Deterministic Policy Gradient, Aleksandr Svishchev

Electronic Theses and Dissertations

Reinforcement learning (RL) is a subfield of machine learning concerned with agents learning to behave optimally by interacting with an environment. One of the most important topics in RL is how the agent should explore, that is, how to choose actions in order to rate their impact on long-term reward. For example, a simple baseline strategy might be uniformly random action selection. This thesis investigates the heuristic idea that agents will learn faster if they explore by factoring the environment’s state into their decision and intentionally choose actions which are as different as possible from what they have previously observed. …

Assessing Interatomic Potentials For Molecular Dynamics Simulation Of Soybean Oil Pyrolysis, Tanner Garrett Rust

MSU Graduate Theses

The world today relies on hydrocarbon combustion for many reasons, including its high energy density that provides ease of transportation. However, hydrocarbons sourced from fossil fuels are not expected to last forever. Biodiesel, a renewable alternative, has many attractive benefits but comes with other downsides. Biodiesel can gel in cold environments and may leave residue in an engine. Pyrolysis of biodiesel has shown promise in addressing these common detriments. Inducing pyrolysis on biodiesel feedstock (commonly soybean oil in the USA) would be an attractive option presuming it continues to produce fossil fuel analogs similar to biodiesel pyrolysis. Herein, Langevin molecular …

Predicting Speaking Proficiency With Fluency Features Using Machine Learning, Ethan D. Erickson

Theses and Dissertations

This study investigates the interplay between temporal fluency measures, self-assessment, and language proficiency scores in novice- to intermediate- level language learners of Spanish and French. Analyzing data from 163 participants, the research employs both traditional linear regression and advanced XGBoost machine learning models. Findings demonstrate a moderate positive correlation between self-assessment and Oral Proficiency Interview by Computer (OPIc) scores, underscoring the dependable self-awareness of learners. Notably, XGBoost performs as well as linear regression in predicting OPIc scores and has more potential, underlining the efficacy of advanced methodologies. The study identifies Mean Length of Utterance (MLU) as a crucial predictor, highlighting …

Learning Mortality Risk For Covid-19 Using Machine Learning And Statistical Methods, Shaoshi Zhang

Electronic Thesis and Dissertation Repository

This research investigates the mortality risk of COVID-19 patients across different variant waves, using the data from Centers for Disease Control and Prevention (CDC) websites. By analyzing the available data, including patient medical records, vaccination rates, and hospital capacities, we aim to discern patterns and factors associated with COVID-19-related deaths.

To explore features linked to COVID-19 mortality, we employ different techniques such as Filter, Wrapper, and Embedded methods for feature selection. Furthermore, we apply various machine learning methods, including support vector machines, decision trees, random forests, logistic regression, K-nearest neighbours, na¨ıve Bayes methods, and artificial neural networks, to uncover underlying …

Cm-Ii Meditation As An Intervention To Reduce Stress And Improve Attention: A Study Of Ml Detection, Spectral Analysis, And Hrv Metrics, Sreekanth Gopi

Master of Science in Computer Science Theses

Students frequently face heightened stress due to academic and social pressures, particularly in de- manding fields like computer science and engineering. These challenges are often associated with serious mental health issues, including ADHD (Attention Deficit Hyperactivity Disorder), depression, and an increased risk of suicide. The average student attention span has notably decreased from 21⁄2 minutes to just 47 seconds, and now it typically takes about 25 minutes to switch attention to a new task (Mark, 2023). Research findings suggest that over 95% of individuals who die by suicide have been diagnosed with depression (Shahtahmasebi, 2013), and almost 20% of students …

Phenotyping Cotton Compactness Using Machine Learning And Uas Multispectral Imagery, Joshua Carl Waldbieser

Theses and Dissertations

Breeding compact cotton plants is desirable for many reasons, but current research for this is restricted by manual data collection. Using unmanned aircraft system imagery shows potential for high-throughput automation of this process. Using multispectral orthomosaics and ground truth measurements, I developed supervised models with a wide range of hyperparameters to predict three compactness traits. Extreme gradient boosting using a feature matrix as input was able to predict the height-related metric with R2=0.829 and RMSE=0.331. The breadth metrics require higher-detailed data and more complex models to predict accurately.

Overcoming Foreign Language Anxiety In An Emotionally Intelligent Tutoring System, Daneih Ismail

College of Computing and Digital Media Dissertations

Learning a foreign language entails cognitive and emotional obstacles. It involves complicated mental processes that affect learning and emotions. Positive emotions such as motivation, encouragement, and satisfaction increase learning achievement, while negative emotions like anxiety, frustration, and confusion may reduce performance. Foreign Language Anxiety (FLA) is a specific type of anxiety accompanying learning a foreign language. It is considered a main impediment that hinders learning, reduces achievements, and diminishes interest in learning.

Detecting FLA is the first step toward reducing and eventually overcoming it. Previously, researchers have been detecting FLA using physical measurements and self-reports. Using physical measures is direct …

Characterizing Silicate Materials Via Raman Spectroscopy And Machine Learning: Implications For Novel Approaches To Studying Melt Dynamics, Blake O. Ladouceur

Doctoral Dissertations

Silicate melt characteristics impose dramatic influence over igneous processes that operate, or have operated on, differentiated bodies: such as the Earth and Mars. Current understanding of these melt properties, such as composition, primarily comes from investigations on their volcanic byproducts. Therefore, it is imperative to innovate on modalities capable of constraining melt information in environments where a reliance on laboratory methods is severed. Recent investigations have turned to Raman Spectroscopy and amorphous volcanics as a suitable pairing for exploring these ideas. Silicate glasses are a proxy for igneous melts; and Raman spectroscopy is a robust analytical technique capable of operating …

Evaluation Of Evapotranspiration Estimates Using An Existing Hybrid Machine Learning Model In A Natural And A Managed Dryland Site, Katya Esquivel Herrera

Open Access Theses & Dissertations

Evapotranspiration (ET) is a critical component of the hydrologic cycle, encompassing both evaporative water loss from surfaces and transpiration through plant stomata. The environmental factors influencing ET include water and energy availability, atmospheric capacity for water uptake, and various meteorological variables. ET serves as a unique climate variable linking water, energy, and carbon cycles. In agroecosystems, accurate ET quantification is vital for optimizing water use efficiency, irrigation management, and crop yield. Traditional methods for ET estimation involve direct measurements and indirect models, with both presenting limitations.

Recent years have witnessed the integration of remote sensing and machine learning (ML) algorithms …

Prediction Of Unplanned 30-Day Readmission Of Heart Failure Patients Using Lstm, Jesdin Raphael

Theses

In the context of heart failure, a leading cause of hospitalization in the United States, approximately 15\% of patients discharged within 30 days face readmission, contributing to escalated healthcare costs and compromised clinical outcomes. This study leverages a wealth of personal information extracted from Emergency Health Records (EHRs) spanning two decades. This research aims to enhance the predictive capabilities of re-hospitalization for cardiac patients by creating a two-stage LSTM model while incorporating features into regression and time series datasets. A critical aspect of this study involves ensuring the integration of all relevant features across both datasets. Including constant data elements, …

Knowledge Integration For Human-In-The-Loop Machine Learning, Ervine Zheng

Theses

Machine learning has evolved into advanced techniques for vision, language, and applications in different areas. However, human expertise is still essential in providing meaningful interpretations of the semantics for tasks in knowledge-rich domains, such as medicine, science, and security intelligence. It is beneficial to incorporate human knowledge into a machine learning system, and we consider human-in-the-loop an increasingly important area for future research. Human-in-the-loop machine learning aims to develop a reliable prediction model with minimal costs by integrating human knowledge and experience. In this thesis, we propose human-in-the-loop methods to address knowledge-rich data understanding challenges for different machine-learning tasks. Specifically, …

Exact Models, Heuristics, And Supervised Learning Approaches For Vehicle Routing Problems, Zefeng Lyu

Doctoral Dissertations

This dissertation presents contributions to the field of vehicle routing problems by utilizing exact methods, heuristic approaches, and the integration of machine learning with traditional algorithms. The research is organized into three main chapters, each dedicated to a specific routing problem and a unique methodology. The first chapter addresses the Pickup and Delivery Problem with Transshipments and Time Windows, a variant that permits product transfers between vehicles to enhance logistics flexibility and reduce costs. To solve this problem, we propose an efficient mixed-integer linear programming model that has been shown to outperform existing ones. The second chapter discusses a practical …

Deep Reinforcement Learning For The Design Of Structural Topologies, Nathan Brown

All Dissertations

Advances in machine learning algorithms and increased computational efficiencies have given engineers new capabilities and tools for engineering design. The presented work investigates using deep reinforcement learning (DRL), a subset of deep machine learning that teaches an agent to complete a task through accumulating experiences in an interactive environment, to design 2D structural topologies. Three unique structural topology design problems are investigated to validate DRL as a practical design automation tool to produce high-performing designs in structural topology domains.

The first design problem attempts to find a gradient-free alternative to solving the compliance minimization topology optimization problem. In the proposed …

Damage Detection With An Integrated Smart Composite Using A Magnetostriction-Based Nondestructive Evaluation Method: Integrating Machine Learning For Prediction, Christopher Nelon

All Dissertations

The development of composite materials for structural components necessitates methods for evaluating and characterizing their damage states after encountering loading conditions. Laminates fabricated from carbon fiber reinforced polymers (CFRPs) are lightweight alternatives to metallic plates; thus, their usage has increased in performance industries such as aerospace and automotive. Additive manufacturing (AM) has experienced a similar growth as composite material inclusion because of its advantages over traditional manufacturing methods. Fabrication with composite laminates and additive manufacturing, specifically fused filament fabrication (fused deposition modeling), requires material to be placed layer-by-layer. If adjacent plies/layers lose adhesion during fabrication or operational usage, the strength …

Human-Centric Smart Cities: A Digital Twin-Oriented Design Of Interactive Autonomous Vehicles, Oscar G. De Leon-Vazquez

Theses and Dissertations

Autonomous vehicle (AV) technology is introduced as a solution to improve transportation safety by eliminating traffic accidents caused by human error, which is the leading cause of 90% of accidents. One key feature of AVs is sensing and perceiving their surrounding environment through processing observations collected from the environment. The perception system is essential for an AV to make informed decisions and safely navigate the environment. This study presents an image semantic segmentation algorithm developed in the area of computer vision to improve AV perception. The U-Net-based algorithm is trained and validated using a synthetically generated dataset in a simulation …