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Machine Learning-Based Regression For Magnetic Field Prediction From Odmr Spectral Data, Jesse B. Hernandez

Electronic Theses, Projects, and Dissertations

Optically Detected Magnetic Resonance (ODMR) using nitrogen-vacancy (NV) centers in diamond enables sensitive, room-temperature magnetic field sensing, but real ODMR spectra are often noisy and difficult to analyze with traditional peak-fitting methods. This thesis investigates whether machine learning can reliably predict magnetic field strength directly from ODMR spectra, and compares four model families under a single regression task: a random forest, an artificial neural network (ANN), a one-dimensional convolutional neural network (1D-CNN), and a Transformer.

Training data were generated from an NV-ensemble simulation calibrated to real measurements provided by the Ulsan National Institute of Science and Technology (UNIST), spanning 0 …

Artificial Intelligence, Machine Learning, And Deep Learning Approaches For Attention-Deficit/Hyperactivity Disorder Diagnosis: Protocol For A Scoping Review, Benedict Onochie Ibe, Amir Aly, Shaymaa S Al-Juboori, Emmanuel Ifeachor, Rohit Shankar

School of Engineering, Computing and Mathematics

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) affects over 366 million adults and 139 million children worldwide, yet diagnosis remains fundamentally subjective, relying on clinical interviews, behavioral observations, and rating scales that yield inconsistent results across practitioners and settings. Artificial intelligence (AI), machine learning (ML), and deep learning (DL) offer a paradigm shift toward objective, data-driven diagnosis by detecting complex patterns across neuroimaging, electrophysiology, and digital biomarkers that elude conventional assessment. Although AI-based ADHD research has grown exponentially, no comprehensive synthesis examines the full spectrum of data modalities, validation practices, and clinical translation readiness. This gap limits our understanding of which approaches are …

A Deep-Learning Based Biomarker Of Systemic Cellular Senescence Burden To Predict Mortality And Health Outcomes, Shangshu Zhao, Chia-Ling Kuo, Eric J Lenze, Julie L Wetherell, Laura Haynes, Perla El-Ahmad, Richard Fortinsky, George Kuchel, Trevor Harris, Breno S Diniz

2020-Current year OA Pubs

The accumulation of senescent cells is a recognized hallmark of biological aging and is associated with the onset of multiple chronic medical conditions. Senescent cells exhibit a distinct secretory profile, known as the senescence-associated secretory phenotype (SASP), which can propagate cellular senescence to neighboring and distant tissues. Measuring SASP factors in blood serves as a practical proxy for cellular senescence burden and may help track disease states and intervention outcomes. We developed and validated a composite SASP Score by integrating large-scale population proteomics data with a semi-supervised deep learning framework. The analytical workflow included: (1) selection of biologically curated SASP …

Viability Assessment Of Bovine Embryos: A Public Dataset And Deep Learning Baselines, Erfan Khayyati

All Graduate Theses and Dissertations, Fall 2023 to Present

Improving the success rates of cattle breeding is essential for sustainable agriculture, global food security, and high-quality livestock production. Currently, determining whether a lab-grown bovine embryo is healthy enough for a successful pregnancy requires highly trained experts to manually evaluate days of continuous time-lapse video footage. This process is not only incredibly time-consuming but also highly subjective; human reviewers often suffer from visual fatigue when tracking subtle, microscopic cellular changes over a seven-day period, leading to significant disagreement among even top experts on an embryo’s true potential. Furthermore, assessing bovine embryos is notoriously difficult due to their dark, lipid-dense cellular …

Multispecies Weed Detection Using Unmanned Aerial Vehicles And Deep Learning Object Detection Models In Utah Forage Crop Corn Field, Utsav Bhandari

All Graduate Theses and Dissertations, Fall 2023 to Present

Weeds cost global agriculture over $32 billion annually and reduce crop yields by nearly one-third. Current weed control relies heavily on spraying herbicides uniformly across entire fields, leading to herbicide-resistant weeds, environmental harm, and increasing costs for farmers. A smarter approach is detecting weeds from the air and spraying only where they are present. This could dramatically reduce herbicide use while protecting crop yields. This thesis developed a complete system for identifying weeds in corn fields using drone-captured images and artificial intelligence. Working in commercial forage corn fields in Cache Valley, Utah, high-resolution aerial images were collected and used to …

Leveraging Deep Learning Recurrence And Attention Mechanisms For Flood Forecasting And Assessment, Elnaz Heidari

All Dissertations

Predicting how much water will flow in rivers and streams is important for managing floods, water supply, and the environment. Traditionally, government agencies have used complex models, such as the National Water Model (NWM), which simulate how much water moves through landscapes using physical laws and real-world data. However, recent advances in Artificial Intelligence (AI) have enabled new ways to make these predictions. This research explored whether AI-based models could predict river discharge more accurately. These AI models learn patterns from past data instead of relying only on physical rules. To find out how well they work, the AI models …

Microstructural Shielding And Advanced Fatigue Crack Analysis In The Presence Of Deformation Twinning, Seyed Ali Asli

All Dissertations

Design methods to mitigate fatigue failures have remained relatively stagnant, as engineers have relied on existing continuum-level models and adhered to the damage tolerance design methodology. Although damage-tolerant design has led to increased safety, extended component in-service life, and better life predictions, it still relies on maintenance routines to identify dangerous cracks. This research aims to test the hypothesis that bundles of deformation twin boundaries will increase the resistance to fatigue crack growth (FCG). Slip and twinning are two dominant mechanisms of plastic deformation in metals. Slip is more common in materials with face-centered cubic (FCC) crystal structures at room …

A Patch-Level Framework For Urban Vegetation Water Demand Estimation Using Remote Sensing And Deep Learning, Jesus Daniel Pereyra Manriquez

Open Access Theses & Dissertations

Urban water management in semi-arid regions requires an improved understanding of how vegetation and climatic conditions influence landscape water demand. Existing approaches often lack an integrated, spatially consistent framework to quantify this relationship at fine scales. This study proposes a patch-level framework to estimate relative landscape water demand by integrating vegetation coverage, vegetation condition, and atmospheric demand. Vegetation coverage is derived from high-resolution imagery obtained from the National Agriculture Imagery Program (NAIP) using a U-Net segmentation model with a MobileNetV2 backbone. A patch-based representation is used to ensure spatial consistency across the study area. Seasonal vegetation dynamics are captured using …

Dynamic Analysis Of The Role Of Border Area Teachers In Implementing The Deep Learning Approach Through Visual Mapping Assisted By Cousal Loop Diagram (Cdl), Siprianus Jewarut, Usman Usman, Rikardus Sehajun, Pramatatya Resindra Widya

Jurnal Pendidikan Geografi: Kajian, Teori, dan Praktek dalam Bidang Pendidikan dan Ilmu Geografi

Bengkayang is one of the districts in Kalimantan Province. This regency is very special because it is located right on the border between Indonesia and Serikin Sarawak, Malaysia. The condition of the border area certainly leaves various challenges due to the area's location quite far from the city center and a fairly large area. This situation presents a very significant challenge in efforts to improve the quality of education and human resources of the community in the Bengkayang region. Based on the actual conditions in the field, various limitations were found, ranging from the lack of availability of educational facilities …

The Data-Schema Bottleneck: Benchmarking 16 Deep Learning Architectures For Real-Time Starlink Leo Telemetry Management, Tanmoy Debnath, Sourabhi Debnath, Miroslaw Narbutt, Maumita Bhattacharya

Research Outputs: 2025-Present

Effective management of Low Earth Orbit (LEO) satellite networks depends on data pipelines capable of predicting link-layer quality metrics (e.g., round-trip time (RTT), throughput (TP), and jitter etc.) at timescales suitable for real-time distributed data routing, handover, and congestion management. This study systematically benchmarks sixteen deep learning (DL) architectures representing four inductive-bias families to evaluate two formally stated hypotheses using 30 days of real Starlink telemetry comprising 417 million observations. Firstly, the Spectral Alignment Hypothesis (Research Question (RQ) 1) investigates whether architectures possessing inductive biases that explicitly decompose the quasi-periodic orbital structure of LEO dynamics systematically outperform those processing the …

An Integrated Yolov8 Based System For Real Time Vehicle Tracking And Identification Using Cctv Footage, Riya Jain, Piyush Kumar, Ashwani Kumar Dubey, Angela Amphawan, Neo Tse Kian

Journal of Informatics and Web Engineering

Vehicle identification and tracking play a critical role in security and surveillance, supporting the efforts of law enforcing authorities to prevent crime, monitor traffic, and enforce the vehicle rules and regulations. This research presents a proof-of-concept system that employs YOLOv8 for real time vehicle detection and car type classification using live or recorded CCTV footage. YOLOv8 was chosen after rigorous comparison, it was chosen for its ease of use and efficient image training capabilities, making it well suited for rapid prototyping that the concept needed. The system is designed to extract key vehicle features, including colour, model of the vehicle, …

Enhancing Air Traffic Management Using Spatio-Temporal Deep Learning Predictions, Madan Singh, Azween Abdullah

Journal of Informatics and Web Engineering

Modern air traffic management faces growing challenges, particularly at busy airports that must handle high flight volumes in limited airspace corridors. The study is especially pertinent for such airports, where daily operations must navigate intricate air traffic networks. To overcome these challenges, innovative approaches are needed to predict and manage congestion effectively which remains a major challenge. The paper presents a Spatio-temporal analytical framework for air traffic flow forecasting, utilizing a dual deep-learning architecture that combines network topology evaluation with time-series analysis. The model contains two major components. The first component employs Graph Neural Networks (GNNs) to model spatial relationships …

Design Of A Cnn–Nlp Based Visual Chatbot For E-Commerce Systems, Sidra Kubra, Salma Jahan Nisha, Nbhan D. Salih, Amalina Ibrahim, Wan Noorshahida Mohd Isa

Journal of Informatics and Web Engineering

E-commerce platforms have limitations to assist interactive and highly personalized experiences using conventional text or voice-based chatbots, particularly in context-aware areas where product discovery and visual identification are necessary. Hence, we propose to integrate an image as a visual-based chatbot. We present a conceptual framework that combines Convolutional Neural Networks (CNN) for image recognition with Natural Language Processing (NLP) for dynamic, context-aware search in a chatbot. The CNN model enables the recognition of products from user-uploaded images, while the NLP component processes and generates appropriate responses to enhance the shopping experience by providing suggestions. This hybrid system will be developed …

Ai-Driven Intrusion Detection System For Network Security, Zhi Lin Sarah Teoh, Wee How Khoh, Hui Yen Yap, Pin Shen Teh

Journal of Informatics and Web Engineering

In the evolving landscape of cybersecurity, Intrusion Detection Systems (IDS) play a vital role in safeguarding computer networks against malicious activity. Traditional signature-based IDS approaches are increasingly ineffective in detecting novel, complex, or zero-day attacks due to their reliance on predefined rules. To overcome these limitations, this study proposes an AI-driven IDS that integrates both classical machine learning and modern deep learning techniques. The framework introduces and compares three models such as Support Vector Machine (SVM), Convolutional Neural Network (CNN), and a hybrid CNN-SVM model. The system is designed to analyze network traffic patterns and classify them as either benign …

Deep Learning Model For Burn Injury Assessment With Enhanced Diagnosis Rate, C. Pabitha, K. Revathi, R. Krishna Priya

Journal of Informatics and Web Engineering

The skin is considered as the largest organ of our body specifically satisfies the environment. As a result, the skin is vulnerable to various injuries, especially burns. Burns are linked to high rates of morbidity and mortality and can be fatal. Effective diagnosis supported by precise evaluation of the burn area and the depth of wound is critical for enabling efficacy in clinical settings. The characteristics of a burn wound on the skin include: the wound is abnormal, the skin is infected, there is discomfort, the skin is tight, and there are raised areas of skin. To properly diagnose burn …

Enhancing The Performance Of Brain-Computer Interfaces Via A Spatially-Aware Framework, Ahmed Ayman Eloraby

Theses and Dissertations

Electroencephalography (EEG)-based brain–computer interfaces (BCIs) provide a non-invasive pathway for communication, with the P300 speller being one of the most established paradigms. Despite its practicality, EEG signals present inherent challenges, including low signal-to-noise ratio, high dimensionality, and limited spatial resolution. These characteristics complicate the extraction of discriminative spatial patterns, which are critical for accurate P300 detection and character-level decoding.

This thesis addresses these challenges through the development of AttenPCA-Net, a deep learning framework that integrates spatially-aware processing with dimensionality reduction. Principal Component Analysis (PCA) is employed to reduce redundancy and noise while preserving the most informative components of the EEG …

Edge Ai And Iot-Based Intelligent Smart Irrigation System For Sustainable Agriculture, Afra Alshamsi

Thesis/ Dissertation Defenses

Traditional agriculture faces several challenges, including excessive water consumption, inefficient irrigation practices, and the increasing demand for sustainable food production. The rapid advancement of the Internet of Things (IoT) and Artificial Intelligence (AI) provides opportunities to develop intelligent agricultural systems that improve resource management and support precision farming. This thesis presents an Edge AI and IoT-based smart irrigation system designed for real-time environmental monitoring, automated irrigation control, and intelligent irrigation prediction to support the optimal growth of arugula plants. The proposed system continuously monitors key cultivation parameters, including soil moisture, temperature, humidity, light intensity, pH, nutrient levels (NPK), and water …

Streamlined Biomedical Image Processing Pipelines, Jiehyun Kim

Graduate Doctoral Dissertations

This dissertation focuses on advancing carotid artery analysis through a series of visualizations and deep learning tools for calcified plaque assessment and related biomedical imaging tasks. Accurate plaque evaluation is essential, but current workflows depend on slow, clinician-dependent manual review. To address these limitations, this work introduces the CACTAS framework, a set of tools and methods that enable fast and reliable plaque segmentation for clinicians.

The first study, the CACTAS-Tool, provides a web-based labeling tool that enables clinicians to label plaque directly in three dimensions through a streamlined one-click interface. This tool significantly reduces the effort required to generate high-quality …

Mri-Based Deep Learning Radiomics Model For Automated Classification Of Disc Degeneration In The Lumbar Spine, Shiv Patil, Om Gandhi, Mert Karabacak, Matthew Carr, Konstantinos Margetis

Student Papers, Posters & Projects

Disc degeneration in the lumbar spine is a major cause of low back pain (LBP). The accurate grading of disc degeneration on magnetic resonance imaging (MRI) is critical for clinical management and patient selection for spine surgery. This study aims to develop and evaluate machine learning (ML) models that combine features from deep learning (DL) and radiomics for the automated prediction of Pfirrmann grade (PG), a measure of disc degeneration, using multi-parametric lumbar spine MRI. Sagittal T1, T2, and T2 SPACE MRIs of 218 patients with LBP were acquired from the SPIDER dataset. For each intervertebral disc and available sequence, …

Deep-Learning Endomicroscope With Large Field-Of-View And Depth-Of-Field For Real-Time In Vivo Imaging Of Epithelial Cancer Hallmarks, Huayu Hou, Jimin Wu, Jinyun Liu, Vivek Boominathan, Argaja Shende, Karthik Goli, Jennifer Carns, Richard A Schwarz, Ann M Gillenwater, Preetha Ramalingam, Mila P Salcedo, Kathleen M Schmeler, Tomasz S Tkaczyk, Jacob T Robinson, Ashok Veeraraghavan, Rebecca R Richards-Kortum

Faculty, Staff and Students Publications

In vivo microscopy (IVM) has shown great promise to improve early detection of epithelial precancer, but it suffers from fundamental trade-offs that limit the resolution, field-of-view (FOV) and depth-of-field (DOF). Here, we present PrecisionView, a compact, deep learning-enabled endomicroscope that breaks these constraints and achieves 20 mm2 FOV and 500 µm DOF with 4 µm resolution, representing approximately 5× increase in FOV and 8× larger DOF compared to conventional IVM with similar resolution. PrecisionView integrates a deep learning-optimized phase mask and real-time reconstruction, enabling rapid in vivo assessment of two key hallmarks of cancer: epithelial cell nuclear morphology and subsurface …

Visual Interpretability Of Multimodal Tissue Perfusion Classification Using Grad-Cam And Saliency Maps, Metehan Zorluoglu

UNLV Theses, Dissertations, Professional Papers, and Capstones

Accurate identification of the tissue perfusion phase from hand images can aid doctors in decision-making with non-invasive techniques. The present study proposes a multimodal deep learning model for classifying the tissue perfusion phase using infrared, thermal, and visible spectrum images of the human hand. The proposed model consists of various preprocessing techniques such as manipulation, homography alignments, and masking. The significant contribution of this thesis is the interpretability analysis of deep learning models, achieved through the analysis of saliency maps and the Gradient-weighted Class Activation Mapping (Grad-CAM) methods. The purpose of this method is to find out how the convolutional …

Stridevision: Automated Detection Of Running Form Deviations From 2d Pose Estimation And Machine Learning, Paulina Eguibar Ortega

Honors Theses

Running gait analysis plays a critical role in injury prevention and performance optimization, however, existing approaches often rely on specialized laboratory equipment or wearable sensors with limited interpretability. Recent advances in computer vision, particularly 2D human pose estimation, enable markerless motion analysis from standard video. However, progress remains constrained by the lack of publicly available datasets designed for running form analysis.

In this work, we introduce a preliminary dataset and benchmark for stride-level running gait analysis. The dataset consists of 73 treadmill running videos from 15 participants with varying experience levels, annotated with over 4,600 stride-level labels across multiple biomechanical …

Perceptual Evaluation Of Deep Learning Models For Hdr Reconstruction Under Severe Overexposure, Alireza Rabbanifar

Theses

Overexposure in imaging systems leads to irreversible loss of scene information due to sensor saturation, resulting in degraded perceptual quality and limited dynamic range. While recent deep learning approaches have shown promising results in correcting mild to moderate overexposure, their performance under severe overexposure and their perceptual quality in HDR image generation remain less understood. In this work, a comparative evaluation of four representative models—Multi-Scale Exposure Correction (MSEC), Illumination-Adaptive Transformer (IAT), LEDiff, and X2HDR—is conducted across multiple scenes and levels of overexposure, with particular emphasis on severe overexposure conditions. The evaluation focuses on the perceptual quality of the reconstructed HDR …

Automated Arctic Coastal Boundary Extraction From Multispectral Satellite Imagery, Harshavardhini Bagavathyraj

Open Access Theses & Dissertations

Arctic coastlines are changing rapidly as permafrost thaw and coastal erosion accelerate under a warming climate, creating a need for accurate and scalable methods to monitor landscape change. Two geomorphically important indicators of this change are the shoreline, which represents the land--water boundary, and the bluff edge, which marks the transition from vegetated tundra to the coastal slope or cliff. However, delineating these features from high-resolution imagery remains challenging because Arctic scenes are spatially extensive and visually complex. In this study, we address these challenges by developing deep learning methods for automated shoreline and bluff-edge extraction from high-resolution satellite imagery …

Bayesian Deep Learning For Photovoltaic Power Forecasting: A Probabilistic Framework For Uncertainty Quantification And Grid Reliability Optimization, Pablo Abraham Bustamante

Open Access Theses & Dissertations

The global energy landscape is undergoing a profound transformation, driven by the urgent need to decarbonize power systems, enhance energy security, and meet growing electricity demands. Solar photovoltaic (PV) power has emerged as a critical component of future energy infrastructure due to its abundance, scalability, and cost-effectiveness. However, PV generation is inherently variable and weather-dependent, introducing significant uncertainty into grid operations and complicating the task of balancing supply and demand. Accurate forecasting of PV power generation-particularly on day-ahead and hour-ahead horizons-has become a strategic necessity for grid stability, economic efficiency, and environmental sustainability. PV output is influenced by numerous factors, …

Intelligent Surveillance For Weapon Threat Detection: Transformer-Based Detection And Segmentation Using Thermal And Rgb Imagery, Akhila Kambhatla

Dissertations

Proactive public-safety surveillance requires deep learning systems that can detect and segment weapons reliably under real-world conditions. This research develops and evaluates a progressive, multi-phase pipeline that moves (i) from RGB object detection to pixel-level semantic segmentation and (ii) from visible-spectrum cameras to thermal imagery to improve robustness under low-light, clutter, and occlusion. Early phases establish strong detection baselines using one-stage and two-stage detectors (YOLOv5 and Faster R-CNN) for multi-class weapon localization, followed by practical optimization through pruning and ensembling to reduce false positives and computational overhead while maintaining real-time feasibility. Building on the limitations of bounding-box localization, the methodology …

Leveraging Convolutional Neural Networks For Through-The-Wall Radar Imaging: Challenges, Impacts, And Future Directions, Tumaini Edgar, Abdulla F. Ally, Abdi T. Abdalla

Tanzania Journal of Engineering and Technology (TJET)

Through-the-wall radar imaging (TWRI) is an essential technology for military and rescue applications; however, its performance in detecting and visualizing high-quality images of targets behind walls is significantly degraded by multipath reflections and signal attenuation. This paper reviews the current state of TWRI and its challenges, and explores the transformative potential of deep learning, particularly convolutional neural networks (CNNs), in addressing these challenges. Peer-reviewed articles published from 2018 to 2024 were analysed to examine CNN applications in addressing TWRI challenges. The analysis reveals that using CNNs, TWRI systems can be more effective by filtering wall distortions, reducing noise, lowering computational …

Predicting Ventilation From Single Breathing Phase Non-Contrast Ct Using Swin Transformers, Yi-Kuan Liu, Hsu-Ting Kuo, Alaa Melek, Richard Castillo, Yevgeniy Vinogradskiy, Lili Zhao, Girish Nair, Edward Castillo

Department of Radiation Oncology Faculty Papers

BACKGROUND: Pulmonary ventilation imaging enables functional avoidance radiotherapy treatment plans by quantifying regional lung function. However, current clinical standards, such as 99𝑚Tc-based single-photon emission computed tomography (SPECT), rely on radioactive tracers, which can introduce imaging deposition artifacts. CT ventilation imaging (CTVI) methods based on both physical models and deep learning approaches currently require multiple CT images as input, such as the inhale/exhale phases of a 4DCT. While the theoretical foundation of physics-based CTVI is built on multi-phase information, the feasibility of single-phase deep learning CTV models has not been determined.

PURPOSE: While deep learning methods have predicted SPECT ventilation from …

Intelligent Deep Learning-Based Sign Language Translation System, Nada Rasem Shahin

Dissertations

The Deaf and Hard of Hearing (DHH) community uses sign language as a primary means of communication. However, the shortage of sign language interpreters and the existence of hundreds of sign languages limit accessibility and inclusion. Sign Language Machine Translation (SLMT) systems present a promising solution for bridging the communication gap between the DHH and the hearing individuals, supporting inclusive societies. In smart cities, such systems play an essential role in improving the quality of life on a community level. In particular, as the population’s well-being is critical, developing intelligent assistive technologies, such as SLMT systems, is necessary to provide …

Development Of Deep Fused Neural Architecture For Ancient Tamil Palm-Leaf Manuscript Recognition, Hariharan P Mr

Theses and Dissertations

Digitizing Tamil palm-leaf manuscripts is important for education, communication, and the preservation of cultural heritage. The complex structure of the Tamil script, the wide range of handwriting styles, and the degradation seen in ancient Tamil palm-leaf manuscripts make these texts very difficult to read and understand. Digital Image Processing (DIP), document analysis techniques, and traditional Optical Character Recognition (OCR) are unable to handle noise, background interference, faded ink, and limited labelled data, motivating the need for robust, effective Deep Learning (DL)- based solutions.

As a prerequisite to understanding and designing effective recognition systems for ancient manuscripts, this thesis first examines …