Digital Commons Network^™

Deep Learning Approach For Accurate Segmentation Of Oil Spills In Marine Systems, Mohamed Elsheref

LSU New Orleans Theses and Dissertations

Oil spills present critical environmental hazards, threatening marine ecosystems and necessitating fast, accurate detection for effective mitigation. Synthetic Aperture Radar (SAR) imagery has been instrumental in detecting oil spills, but manual interpretation is often inefficient and prone to errors. This study addresses the limitations of manual methods by proposing a deep learning approach for automated oil spill detection and segmentation.

Utilizing a novel transfer learning-based semantic segmentation model, this research focuses on detecting oil slicks on the sea surface with higher accuracy and efficiency. The model leverages pre-trained networks and incorporates U-Net variants, including UNet++ and MultiResUNet, to optimize spatial …

Draft Final 2022 Insufficiently Reclaimed Sites Sampling Bres No. 37 – Josephine Shaft Site Evaluation Summary Report, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Artificial Intelligence And Machine Learning In Cancer Pain: A Systematic Review, Vivian Salama, Brandon Godinich, Yimin Geng, Laia Humbert-Vidan, Laura Maule, Kareem A Wahid, Mohamed A Naser, Renjie He, Abdallah S R Mohamed, Clifton D Fuller, Amy C Moreno

Faculty, Staff and Student Publications

Background/objectives: Pain is a challenging multifaceted symptom reported by most cancer patients. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and pain management in cancer.

Methods: A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms: "Cancer," "Pain," "Pain Management," "Analgesics," "Artificial Intelligence," "Machine Learning," and "Neural Networks" published up to September 7, 2023. AI/ML models, their validation and performance were summarized. Quality assessment was conducted using PROBAST risk-of-bias andadherence to TRIPOD guidelines.

Results: Forty four studies from 2006 to 2023 were included. Nineteen studies used …

Learning To Represent Temporal Dynamics And Generative Factors For Intelligent Visual Navigation, Sahand Khoshdel

All Theses

Visual navigation systems are crucial in various applications, including autonomous driving, unmanned aerial systems (UAS), and industrial automation. For these systems to operate efficiently in dynamic environments, they must not only interpret complex surroundings but also anticipate changes over time. Temporal prediction—forecasting environmental changes like moving obstacles or shifting lighting conditions—enables navigation systems to act proactively, enhancing both safety and performance. This dissertation investigates representation learning methods both as a backbone feature extractor for RL agents as well as a proxy for systems oriented for Explainable AI (XAI). Two main projects are presented as case studies to achieve the aforementioned …

Informing Management Of American Shad (Alosa Sapidissima) In The Carolinas: Evaluating Novel Fish Passage Approaches And Acoustic Tracking Methods, Margaret Gaither

All Theses

Acoustic telemetry is a valuable tool for fisheries scientists to study the movements and migratory behaviors of aquatic organisms. A critical assumption of this method is that tagged fish exhibit similar behaviors and survival rates as their untagged counterparts. To investigate this, we conducted a 12-week laboratory study on adult American Shad (Alosa sapidissima) using gastroesophageal insertion of acoustic transmitters. We implanted 80 fish with dummy transmitters and monitored tag retention and survival. Our analysis, employing a mixed effects Cox’s proportional hazards model, revealed no significant differences in survival between tagged and control fish, regardless of sex. Additionally, …

Multiple Cut Systems Of Cannabis Sativa L. For Micropropagation Without The Use Of Cytokinins, Molly Mckay

All Theses

Micropropagation is a technique used to propagate clean, high-quality clonal stock plants using plant growth regulators (PGR) in a single harvest batch system. This two-part study uses an in vitro multiple harvest system “hedging” combined with the fed- batch media process, to observe (1) the effects of modified physical state and (2) LED light quality to improve shoot production and quality. In Experiment 1, four genotypes of Cannabis sativa (‘Cherry 1’,‘BaOx’,‘T1’,‘Peach’) were observed in three different physical states: stationary agar (A), stationary Oasis® infused with liquid (OIL_s) and agitated Oasis® infused with liquid (OIL_a). Fifteen …

Striking A Balance: Market Shock & Responses In Automotive Components Manufacturing, Emma Lane Mcgahey

All Theses

This thesis examines the effects of extreme market shocks on supply chain dynamics within the automotive industry. Through an analysis of demand data from an automotive manufacturer to its component suppliers (January 2018 to May 2024), the study investigates the relationship between market shocks and supply chain responses, providing insights into how auto components inventory management handles downstream responses to market shocks. With supporting public data—from FRED, BLS, and the U.S. Census Bureau resources—we explore two primary relationships: the impact of market shocks on the Average Standard Deviation of Demand (SDO) and the effect of demand variability on expedited pricing …

Uptake Of Contaminants Of Emerging Concerns (Cecs) By Plants Irrigated With Recycled Waters, Abid Hussain

UNLV Theses, Dissertations, Professional Papers, and Capstones

The increasing demand for water in arid and semi-arid regions necessitates the use of recycled water (RW) for irrigation. RW is a viable alternative to conventional irrigation water at most locations, but it can be a major route for contaminants of emerging concerns (CECs) to be taken up by the tissues of food crops, thereby leading to their subsequent entry into the food chain. CECs include pharmaceuticals, personal care products, and industrial chemicals that are generally present at low concentrations in environmental waters, hence their common designation as trace organic compounds (TOrCs), and CECs also include microbiological constituents such as …

Using Neighborhood Information To Improve Fiber Direction Estimation From Neuroimaging Data, Anjan Mandal

UNLV Theses, Dissertations, Professional Papers, and Capstones

Accurately estimating neuronal fiber directions is crucial in neuroimaging analysis. The Ball-and-Stick Model (BSM), introduced by Behrens et al. (2003), and widely used in software tools like FSL, remains one of the most popular models for this purpose. BSM analyzes each voxel individually, based solely on its signal information.In this dissertation, we propose modifications to BSM, that incorporate signal information from neighboring voxels in the estimation process, potentially improving the accuracy of the estimates. Additionally, within the estimation process, we introduce two novel proposal distributions to enhance the efficiency of the Markov chain Monte Carlo sampling procedure on the simplex …

Advancing Biofabrication Methodologies To Develop Biomimetic Tissue Scaffolds For Musculoskeletal Applications, Nashaita Yezdi Patrawalla

Theses and Dissertations

Musculoskeletal injuries impose a significant global burden, affecting millions and generating an economic toll exceeding $14 billion. Tissue engineering has gained traction as a promising strategy offering potential for functional restoration of damaged musculoskeletal tissues. This research aims to advance biofabrication techniques for generation of biomimetic tissue scaffolds, by optimizing scaffold design and modulating fabrication process parameters to generate scaffolds for musculoskeletal regeneration with application-specific tailored properties. In this realm, collagen-based biomaterials have been extensively investigated for tendon and ligamentous applications, and in combination with bioceramics incorporated into the collagen framework to produce composite scaffolds that better recapitulate the native …

Intent Visualization In Human-Agent Teams, Rahul Tushar Mehta

Theses and Dissertations

Despite advances in autonomous systems, effective collaboration between humans and intelligent agents remains a significant challenge, particularly in shared-control scenarios. This study investigates how intent visualization affects human-agent collaboration in telecollaboration scenarios, examining its impact on team performance, operator trust, and workload. Using a custom simulation environment and Wizard-of-Oz methodology, we conducted an experiment with 13 participants who completed exploration tasks under two conditions: a baseline interface and an enhanced interface with intent visualization. Results showed that while intent visualization did not significantly improve objective performance metrics, it led to a 22.6\% increase in explicit disagreements between operators and the …

Domain-Specific Machine Learning Approaches For Geospatial Problems, Shine Bedi

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

This dissertation explores novel algorithms for complex geospatial problems at the intersection of environmental, social, and computational sciences. Emphasizing the unique challenges of the geospatial domain, particularly the deviation from the independent and identical distribution (IID) assumption, the research spans various methodologies across different domains, demonstrating the benefits of specialized approaches in spatial analysis.

First, we show that machine learning techniques can be effectively used in environmental modeling, which often has severe class imbalance challenges. Using artificial neural networks (ANN), support vector machines (SVM), and extreme gradient boosting (XGB) and techniques to address class imbalance provides insights into groundwater quality …

Deep Learning Vision-Based Bridge Inspection With Resource-Constrained Unmanned Aircraft Systems, Ji Young Lee

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Bridge inspection is critical for ensuring structural integrity, extending the service life of infrastructure, and minimizing maintenance costs. As bridges age and endure increasing loads, regular inspections help detect early signs of wear, such as cracks or corrosion, that could impact safety and performance. However, traditional inspection methods are labor-intensive, requiring significant time, specialized equipment, and manual access to challenging areas, which can lead to costly disruptions. Additionally, reliance on human inspectors introduces subjectivity, with assessments varying by individual expertise. These factors highlight the inefficiencies and safety risks in current inspection practices, underscoring the need for more objective, efficient solutions. …

Prevalence Of Autism Spectrum Characteristics In Students Taking Undergraduate Computing Courses, Rachel Michaela Mettenbrink

School of Computing: Dissertations, Theses, and Student Research

The incidence rate of autism spectrum condition (ASC) has increased significantly in recent decades, as awareness of the condition and its impacts increases amongst clinicians, parents, and the general population. Medical literature has proposed that there may be a relationship between ASC and participation in the computing field. This study tests for the prevalence of autism spectrum condition traits measured by delivering the Autism Spectrum Quotient (AQ) to a population of undergraduate computer science students. We examine the relationships between AQ scores and students taking undergraduate computer science classes, sex, socioeconomic status, and parents in the computing industry. Additionally, we …

Multi-Criteria Decision-Making Approach Based On Correlation Coefficient For Multi-Polar Interval-Valued Neutrosophic Soft Set, Hamza Naveed, Saalam Ali

Neutrosophic Systems with Applications

The correlation coefficient between two factors is crucial in statistical computation, indicating the extent and evolution of the appropriate link. The precision of applicability evaluations frequently relies on the thoroughness and caliber of data obtained from a certain dataset. Statistical research sometimes entails data marked by intrinsic trade-offs and uncertainty. This study seeks to present m-polar interval-valued neutrosophic soft sets (mPIVNSSs) through the integration of m-polar fuzzy sets with interval-valued neutrosophic soft sets. The suggested mPIVNSS structure is a significantly generalized version of m-polar neutrosophic soft sets and serves as a substantial extension of interval-valued neutrosophic soft sets. In this …

Debtor Eligibility Prediction Using Deep Learning With Chatbot-Based Testing, Reski Noviania, Enny Itje Sela, Luther Alexander Latumakulita, Steven R. Sentinuwo

Knowledge Engineering and Data Science

Predicting debtor eligibility is essential for effective risk management and minimizing lousy credit risks. However, financial institutions face challenges such as imbalanced data, inefficient feature selection, and limited user accessibility. This study combines Recursive Feature Elimination (RFE) and Deep Learning (DL) to improve prediction accuracy. It integrates a chatbot interface for user-friendly testing. RFE effectively identifies critical features, while the DL model achieves a validation accuracy of 97.62%, surpassing previous studies with less comprehensive methodologies. The chatbot's novel design not only ensures accessibility but also enhances user engagement through flexible input options, such as approximate values, enabling non experts to …

Deep Learning Approach For Dental Anomalies X-Ray Imaging Using Yolov8, Amelia Ritahani Ismail, Md Salim Sadman Taseen

Knowledge Engineering and Data Science

Dental X-ray imaging is a critical diagnostic tool for identifying various dental anomalies. However, manual interpretation is time-consuming, prone to human error, and requires specialized expertise. Deep learning models, particularly object detection frameworks like YOLO, have demonstrated promising results in automating medical image analysis. This study aims to develop and evaluate a YOLOv8-based deep learning model for automated detection and classification of 14 dental anomaly categories, including Caries, Crowns, Fillings, Implants, and Periapical lesions. The proposed approach addresses limitations in previous YOLO versions by leveraging anchor-free detection and enhanced feature extraction for improved accuracy. The model was trained on a …

A Hierarchical Density-Based Spatial Clustering Of Applications With Noise (Hdbscan) Approach For Identifying Potential Villages In Buleleng Regency, Dina Nur Amalina, Achmad Fauzan

Knowledge Engineering and Data Science

Buleleng Regency, located in Bali Province, possesses diverse village potential, including agricultural production and tourist attractions. However, this potential has not been fully optimized. Therefore, it is important to enhance village potential by clustering villages based on their specific characteristics to identify and prioritize those requiring special attention. This approach aims to promote equitable village development and reduce poverty levels. This study clusters villages in Buleleng Regency based on their potential using the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) method. The data utilized in this study comprises village potential data obtained from the Buleleng Regency Statistics Office …

Classification Of Anxiety Levels Entering The World Of Work In Final Year Students Using The Neighbor Weighted K-Nearest Neighbor Method, Awang Hendrianto Pratomo, Muhammad Fahmi Adam, Dessyanto Boedi Prasetyo

Knowledge Engineering and Data Science

This study evaluates the accuracy of the Neighbor Weighted K-Nearest Neighbor (NWKNN) method in classifying the anxiety levels of final-year students as they prepare to enter the workforce, particularly in cases of unbalanced data distribution. The system was developed using the prototype method, and NWKNN was applied to classify anxiety levels into low, medium, and high categories. Testing using the Confusion Matrix demonstrated strong performance, achieving an accuracy of 94% based on a dataset of 1009 students, with a 90:10 ratio of training to test data. The results indicate that NWKNN effectively provides classification input values, making it a reliable …

Synthesis Of Photocleavable Molecules For Neurological Applications, Nishal Madujith Egodawaththa Arachchilage Don

Theses and Dissertations

In recent biomedical research, the precise control of molecular dynamics through light activation has emerged as a powerful tool, particularly in the field of neurobiology. This approach involves the use of photocleavable cages or photoprotective groups (PPGs) that are chemically tethered to biologically active molecules such as neurotransmitters or calcium chelators. These cages remain inert until exposed to specific wavelengths of light, typically in the visible range, triggering the release of the active compound with high spatiotemporal precision.

For neurotransmitter systems, such as glutamate (Glu), which plays a critical role in synaptic transmission and memory formation, researchers have developed novel …

Novel, Ultra-Fast Electrochemical Sensors For The Enhanced Detection Of Neurotransmitters And Toxic Heavy Metals In Aqueous Solutions, Noel Manring

Theses and Dissertations

Neurodegenerative diseases (NDDs) are a growing global health concern, affecting millions of people worldwide. NDDs arise from the progressive loss of neuronal cell function, ultimately leading to cell death. Abnormal levels of neurotransmitters, the chemical messengers in the brain, have also been linked to NDDs. Despite various treatments and medications aimed at alleviating physical and mental symptoms, there is currently no cure or means to halt disease progression. While the exact pathogenesis of NDDs remain poorly understood, cognitive decline is largely attributed to brain volume reduction, particularly in the hippocampus. Clinical studies suggest that environmental factors, such as exposure to …

Machine Learning Methods For Quantification Of Glacier Variations Through Satellite Imagery, Robert D. Breininger

Theses and Dissertations

Glaciers around the world have experienced a trend of recession within the past century. Quantification of glacier variations using satellite imagery is of great interest due to the importance of glaciers as freshwater resources and as indicators of climate change. The potential methods to quantify glacier variations with increasing complexity include detecting the terminus location, quantifying the glacier surface area, and measuring glacier volume. Although there are methods in literature designed purposefully for glacier area segmentation that have achieved acceptable results, they are often localized to the region where their training data were acquired and further rely on training sets …

A Hybrid Approach Of Vision Transformers And Cnns For Detection Of Ulcerative Colitis, Syed Abdullah Shah, Imran Taj, Syed Muhammad Usman, Syed Nehal Hassan Shah, Ali Shariq Imran, Shehzad Khalid

All Works

Ulcerative Colitis is an Inflammatory Bowel disease caused by a variety of factors that lead to a serious impact on the quality of life of the patients if left untreated. Due to complexities in the identification procedures of this disease, the treatment timeline and quality can be severely affected, leading to further consequences for the sufferer. The difficulties in identification are due to high patients to healthcare professionals ratio. Researchers have proposed variety of machine/deep learning methods for automated detection of ulcerative colitis, however, several challenges exists including class imbalance problem, comprehensive feature extraction and accurate classification. We propose a …

Human Vs. Ai Counseling: College Students' Perspectives, Mohammad Amin Kuhail, Nazik Alturki, Justin Thomas, Amal K. Alkhalifa

All Works

Transitioning to college life while navigating the complexities of emerging adulthood can be stressful. In some instances, it may even lead to the onset of mental health problems or the exacerbation of existing issues. While therapeutic resources are typically available in tertiary educational contexts, social stigma may lead to service underutilization. Additionally, high student-to-therapist ratios can create bottlenecks to access when such services are sought. Offering an adjunct to traditional campus counseling services, AI chatbots can potentially address such issues. Chatbots can provide flexible, accessible, anonymous, and cost-effective first-line support, improving access and extending traditional treatment methodologies. This study evaluates …

Real-Time Simulation And Workforce Development For Industrial Control Systems With Troy: Testbed For Resilient Operational Systems, Gideon Henry Sutterfield

Graduate Theses and Dissertations

Digital automation for critical infrastructure (CI) is largely supported by industrial control systems (ICS) and operational technology (OT). The increase in digital automation as time progresses correlates with a rise in targeted cyberattacks at these vulnerable yet precious assets. TROY: Testbed for Resilient Operational sYstems, a highly scalable hybrid IT & OT utilizing virtual machines and a Typhoon-HIL real-time simulation, contributes to cybersecurity for CI by serving as a research and workforce development platform. By using Typhoon-HIL to simulate ICS, the platform can be used to replicate a realistic network environment for a myriad of research functions. The system uses …

Towards Comprehensive And Interpretable Video Understanding, Khoa Vo

Graduate Theses and Dissertations

Video understanding is a critical domain in computer vision, focusing on analysis of sequential visual data to extract meaningful spatiotemporal information for tasks such as action recognition, video captioning, video retrieval, and temporal action localization, etc. Despite significant advancements with spatio-temporal convolutional neural networks and attention-based video models, current methods face limitations, including inadequate representation of main actors, lack of fine-grained modeling of relevant objects, and limited interpretability.
This thesis addresses these challenges by proposing novel approaches that enhance video understanding through modeling interactions among entities (actors and objects) and between entities and the environment, while improving interpretability in the …

Deep Learning Framework For Inverse Problems In Computational Imaging: A Lensless Imaging And Super-Resolution Magnetic Resonance Imaging Case, Arpan Poudel

Graduate Theses and Dissertations

Inverse problems in computer vision involve reconstructing an original scene or image from incomplete, noisy, or indirect measurements. These problems are critical in tasks such as image denoising, deblurring, super-resolution, and lensless imaging, where the goal is to recover high-quality images from degraded or partial measurements. This thesis introduces novel approaches to address two specific real-world inverse problems: (1) image super-resolution in medical imaging and (2) lensless image reconstruction . In the first part of this work, we tackle the problem of image super-resolution in Magnetic Resonance Imaging (MRI). High-resolution MRI scans are often limited by hardware constraints, patient movement, …

Enhancing Smart Grid Security And Resilience Using Programmable Networks, Zheng Hu

Graduate Theses and Dissertations

The security and resilience of smart grids are essential to ensuring reliable and efficient energy distribution, especially as these cyber-physical systems grow more interconnected and complex. Supervisory Control and Data Acquisition (SCADA) systems play a critical role in smart grid operations by enabling essential infrastructure control and real-time monitoring. However, SCADA systems are highly vulnerable to modern cyber threats, which target weaknesses in industrial protocols and real-time data requirements.

This dissertation investigates the potential of programmable network technologies, with a focus on P4 (Programming Protocol-independent Packet Processors) switch, to deliver adaptable, in-network security solutions tailored to the needs of smart …

Evaluation Of Industrial Hemp Cultivar And Biochar Rate To Remediate Heavy-Metal-Contaminated Soil From The Tar Creek Superfund Site, Usa, Dietrich V. Thurston, Kristofor R. Brye, David M. Miller, Philip A. Moore Jr., Donald M. Johnson, Mike Richardson

Crop, Soil and Environmental Sciences Faculty Publications and Presentations

Soil contamination by cadmium (Cd), lead (Pb), and zinc (Zn) at the Tar Creek superfund site in northeast Oklahoma, United States, remains a threat to the environment and local ecosystem. Phytoremediation with industrial hemp (Cannabis sativa L.) and the use of biochar (BC) have been independently shown to be effective methods to remediate heavy-metal-contaminated soils. The objective of this greenhouse study was to evaluate the effects of industrial hemp cultivar (‘Carmagnola’ and ‘Jinma’), biochar rate (0, 2, 5, and 10% by volume), soil contamination level (low, medium, and high), and their interactions on above- (AG) and belowground dry matter …

De-Identification Is Not Enough: A Comparison Between De-Identified And Synthetic Clinical Notes, Atiquer Rahman Sarkar, Yao-Shun Chuang, Noman Mohammed, Xiaoqian Jiang

Faculty, Staff and Student Publications

For sharing privacy-sensitive data, de-identification is commonly regarded as adequate for safeguarding privacy. Synthetic data is also being considered as a privacy-preserving alternative. Recent successes with numerical and tabular data generative models and the breakthroughs in large generative language models raise the question of whether synthetically generated clinical notes could be a viable alternative to real notes for research purposes. In this work, we demonstrated that (i) de-identification of real clinical notes does not protect records against a membership inference attack, (ii) proposed a novel approach to generate synthetic clinical notes using the current state-of-the-art large language models, (iii) evaluated …