Digital Commons Network^™

Rafid: A Lightweight Approach To Radio Frequency Interference Detection In Time Domain Using Lstm And Statistical Analysis, Luke A. Smith, Vishesh Kumar Tanwar, Maciej Jan Zawodniok, Sanjay Kumar Madria

Electrical and Computer Engineering Faculty Research & Creative Works

Recently, the utilization of Radio Frequency (RF) devices has increased exponentially over numerous vertical platforms. This rise has led to an abundance of Radio Frequency Interference (RFI) continues to plague RF systems today. The continued crowding of the RF spectrum makes RFI efficient and lightweight mitigation critical. Detecting and localizing the interfering signals is the foremost step for mitigating RFI concerns. Addressing these challenges, we propose a novel and lightweight approach, namely RaFID, to detect and locate the RFI by incorporating deep neural networks (DNNs) and statistical analysis via batch-wise mean aggregation and standard deviation (SD) calculations. RaFID investigates the …

Machine Learning Framework For Real-World Electronic Health Records Regarding Missingness, Interpretability, And Fairness, Jing Lucas Liu

Theses and Dissertations--Computer Science

Machine learning (ML) and deep learning (DL) techniques have shown promising results in healthcare applications using Electronic Health Records (EHRs) data. However, their adoption in real-world healthcare settings is hindered by three major challenges. Firstly, real-world EHR data typically contains numerous missing values. Secondly, traditional ML/DL models are typically considered black-boxes, whereas interpretability is required for real-world healthcare applications. Finally, differences in data distributions may lead to unfairness and performance disparities, particularly in subpopulations.

This dissertation proposes methods to address missing data, interpretability, and fairness issues. The first work proposes an ensemble prediction framework for EHR data with large missing …

Detection Of Grape Clusters In Images Using Convolutional Neural Network, Mohammad Osama Shahzad, Anas Bin Aqeel, Waqar Shahid Qureshi

Articles

Convolutional Neural Networks and Deep Learning have revolutionized every field since their inception. Agriculture has also been reaping the fruits of developments in mentioned fields. Technology is being revolutionized to increase yield, save water wastage, take care of diseased weeds, and also increase the profit of farmers. Grapes are among the highest profit-yielding and important fruit related to the juice industry. Pakistan being an agricultural country, can widely benefit by cultivating and improving grapes per hectare yield. The biggest challenge in harvesting grapes to date is to detect their cluster successfully; many approaches tend to answer this problem by harvest …

Effective Systems For Insider Threat Detection, Muhanned Qasim Jabbar Alslaiman

Browse all Theses and Dissertations

Insider threats to information security have become a burden for organizations. Understanding insider activities leads to an effective improvement in identifying insider attacks and limits their threats. This dissertation presents three systems to detect insider threats effectively. The aim is to reduce the false negative rate (FNR), provide better dataset use, and reduce dimensionality and zero padding effects. The systems developed utilize deep learning techniques and are evaluated using the CERT 4.2 dataset. The dataset is analyzed and reformed so that each row represents a variable length sample of user activities. Two data representations are implemented to model extracted features …

Liquid Tab, Nathan Hulet

Williams Honors College, Honors Research Projects

Guitar transcription is a complex task requiring significant time, skill, and musical knowledge to achieve accurate results. Since most music is recorded and processed digitally, it would seem like many tools to digitally analyze and transcribe the audio would be available. However, the problem of automatic transcription presents many more difficulties than are initially evident. There are multiple ways to play a guitar, many diverse styles of playing, and every guitar sounds different. These problems become even more difficult considering the varying qualities of recordings and levels of background noise.

Machine learning has proven itself to be a flexible tool …

Spatio-Temporal Deep Learning Approaches For Addressing Track Association Problem Using Automatic Identification System (Ais) Data, Md Asif Bin Syed

Graduate Theses, Dissertations, and Problem Reports (ETD)

In the realm of marine surveillance, track association constitutes a pivotal yet challenging task, involving the identification and tracking of unlabelled vessel trajectories. The need for accurate data association algorithms stems from the urge to spot unusual vessel movements or threat detection. These algorithms link sequential observations containing location and motion information to specific moving objects, helping to build their real-time trajectories. These threat detection algorithms will be useful when a vessel attempts to conceal its identity. The algorithm can then identify and track the specific vessel from its incoming signal. The data for this study is sourced from the …

Establishing The Foundation To Robotize Complex Welding Processes Through Learning From Human Welders Based On Deep Learning Techniques, Rui Yu

Theses and Dissertations--Electrical and Computer Engineering

As the demand for customized, efficient, and high-quality production increases, traditional manufacturing processes are transforming into smart manufacturing with the aid of advancements in information technology, such as cyber-physical systems (CPS), the Internet of Things (IoT), big data, and artificial intelligence (AI). The key requirement for integration with these advanced information technologies is to digitize manufacturing processes to enable analysis, control, and interaction with other digitized components. The integration of deep learning algorithm and massive industrial data will be critical components in realizing this process, leading to enhanced manufacturing in the Future of Work at the Human-Technology Frontier (FW-HTF).

This …

A Flexible Photonic Reduction Network Architecture For Spatial Gemm Accelerators For Deep Learning, Bobby Bose

Theses and Dissertations--Electrical and Computer Engineering

As deep neural network (DNN) models increase significantly in complexity and size, it has become important to increase the computing capability of specialized hardware architectures typically used for DNN processing. The major linear operations of DNNs, which comprise the fully connected and convolution layers, are commonly converted into general matrix-matrix multiplication (GEMM) operations for acceleration. Specialized GEMM accelerators are typically employed to implement these GEMM operations, where a GEMM operation is decomposed into multiple vector-dot-product operations that run in parallel. A common challenge that arises in modern DNNs is the mismatch between the matrices used for GEMM operations and the …

The Significance Of Machine Learning And Deep Learning Techniques In Cybersecurity: A Comprehensive Review, Maad M. Mijwil, Israa Ezzat Salem, Marwa M. Ismaeel

Iraqi Journal for Computer Science and Mathematics

People in the moderneraspendmost of their lives in virtual environments that offer a range of public and private servicesand social platforms. Therefore, these environments need to be protected from cyber attackers that can steal data or disruptsystems. Cybersecurity refers to a collection of technical, organizational, andexecutive means for preventing the unauthorized use or misuse of electronic informationand communication systems to ensure the continuityof their work, guarantee the confidentialityand privacy of personal data, and protect consumers from threats and intrusions. Accordingly, this articleexplores the cybersecurity practices that protect computer systemsfrom attacks, hacking,and data thefts and investigates the role of artificial intelligence …

Deep Learning Approaches For Automatic Colorization, Super-Resolution, And Representation Of Volumetric Data, Sudarshan Devkota

Graduate Thesis and Dissertation 2023-2024

This dissertation includes a collection of studies that aim to improve the way we represent and visualize volume data. The advancement of medical imaging has revolutionized healthcare, providing crucial anatomical insights for accurate diagnosis and treatment planning. Our first study introduces an innovative technique to enhance the utility of medical images, transitioning from monochromatic scans to vivid 3D representations. It presents a framework for reference-based automatic color transfer, establishing deep semantic correspondences between a colored reference image and grayscale medical scans. This methodology extends to volumetric rendering, eliminating the need for manual intervention in parameter tuning. Next, it delves into …

Open-Vocabulary Scene Graph Generation With Compositional Prompt-Tuning, Mirza Tanzim Sami

All ETDs from UAB

Scene graphs are explicit data structures that express the semantics of an image. It is semantically richer than simple image encodings from Convolutional Neural Networks (CNNs) because objects (big or small) are represented as distinct nodes and the relationship between them are expressed as labeled edges in a graph structure. Thus, scene graphs can express not only the visual context of an image but also the relationship context between pairs of objects. This added context information makes scene graphs ideal for several downstream visual language (VL) tasks such as Visual Question Answering (VQA), image captioning, text to image retrieval, and …

From Machine Learning To Artificial General Intelligence: A Roadmap And Implications, Omar Ibrahim Obaid

Mesopotamian Journal of Big Data

The prospect of developing artificial general intelligence (AGI) with the same comprehensive capabilities as the human mind presents humanity both tremendous opportunities and dire risks. This paper explores the potential applications and implications of AGI across diverse domains including science, healthcare, education, security, and the economy. However, realizing AGI's benefits requires proactive alignment of its goals and values with those of humanity through responsible governance. As AGI approaches and possibly surpasses human-level intellectual abilities, we must grapple with complex ethical issues surrounding autonomy, consciousness, and disruptive societal impacts. The exact timeline for achieving AGI remains uncertain, but its emergence will …

Split Learning For Distributed Collaborative Training Of Deep Learning Models In Health Informatics, Zhijun Yin, Xiaoqian Jiang, Bradley A Malin

Faculty, Staff and Student Publications

Deep learning continues to rapidly evolve and is now demonstrating remarkable potential for numerous medical prediction tasks. However, realizing deep learning models that generalize across healthcare organizations is challenging. This is due, in part, to the inherent siloed nature of these organizations and patient privacy requirements. To address this problem, we illustrate how split learning can enable collaborative training of deep learning models across disparate and privately maintained health datasets, while keeping the original records and model parameters private. We introduce a new privacy-preserving distributed learning framework that offers a higher level of privacy compared to conventional federated learning. We …

The Application Of Data Analytics Technologies For The Predictive Maintenance Of Industrial Facilities In Internet Of Things (Iot) Environments, Thomas Rieger

School of Engineering, Computing and Mathematics Theses

In industrial production environments, the maintenance of equipment has a decisive influence on costs and on the plannability of production capacities. In particular, unplanned failures during production times cause high costs, unplanned downtimes and possibly additional collateral damage. Predictive Maintenance starts here and tries to predict a possible failure and its cause so early that its prevention can be prepared and carried out in time. In order to be able to predict malfunctions and failures, the industrial plant with its characteristics, as well as wear and ageing processes, must be modelled. Such modelling can be done by replicating its physical …

Towards A Novel Approach For Smart Agriculture Predictability, Rima Grati, Myriam Aloulou, Khouloud Boukadi

All Works

No abstract provided.

Breast Density Classification Using Deep Learning, Conrad Thomas Testagrose

UNF Graduate Theses and Dissertations

Breast density screenings are an accepted means to determine a patient's predisposed risk of breast cancer development. Although the direct correlation is not fully understood, breast cancer risk increases with higher levels of mammographic breast density. Radiologists visually assess a patient's breast density using mammogram images and assign a density score based on four breast density categories outlined by the Breast Imaging and Reporting Data Systems (BI-RADS). There have been efforts to develop automated tools that assist radiologists with increasing workloads and to help reduce the intra- and inter-rater variability between radiologists. In this thesis, I explored two deep-learning-based approaches …

Champions For Social Good: How Can We Discover Social Sentiment And Attitude-Driven Patterns In Prosocial Communication?, Raghava Rao Mukkamala, Robert J. Kauffman, Helle Zinner Henriksen

Research Collection School Of Computing and Information Systems

The UN High Commissioner on Refugees (UNHCR) is pursuing a social media strategy to inform people about displaced populations and refugee emergencies. It is actively engaging public figures to increase awareness through its prosocial communications and improve social informedness and support for policy changes in its services. We studied the Twitter communications of UNHCR social media champions and investigated their role as high-profile influencers. In this study, we offer a design science research and data analytics framework and propositions based on the social informedness theory we propose in this paper to assess communication about UNHCR’s mission. Two variables—refugee-emergency and champion …

Computer Vision In Adverse Conditions: Small Objects, Low-Resoltuion Images, And Edge Deployment, Raja Sunkara

Masters Theses

"Computer vision based on deep learning is an essential field that plays a significant role in object detection, image classification, semantic segmentation, instance segmentation, and other applications. However, these models face significant challenges in adverse conditions, such as small objects, low-resolution images, and edge deployment. These challenges limit the accuracy and efficiency of computer vision algorithms, making it difficult to obtain reliable results.

The primary objective of this thesis is to assess the performance of deep learning- based computer vision models in challenging conditions and provide viable solutions to overcome the obstacles. The study will specifically address three key challenges, …

Predicting Incident Cardiovascular Disease Among African-American Adults: A Deep Learning Approach To Evaluate Social Determinants Of Health In The Jackson Heart Study, Matthew C. Morris, Hamidreza Moradi, Maryam Aslani, Mario Sims, David Schlundt, Chrystyna D. Kouros, Burel Goodin, Crystal Lim, Kerry Kinney

2020-Current year OA Pubs

The present study sought to leverage machine learning approaches to determine whether social determinants of health improve prediction of incident cardiovascular disease (CVD). Participants in the Jackson Heart study with no history of CVD at baseline were followed over a 10-year period to determine first CVD events (i.e., coronary heart disease, stroke, heart failure). Three modeling algorithms (i.e., Deep Neural Network, Random Survival Forest, Penalized Cox Proportional Hazards) were used to evaluate three feature sets (i.e., demographics and standard/biobehavioral CVD risk factors [FS1], FS1 combined with psychosocial and socioeconomic CVD risk factors [FS2], and FS2 combined with environmental features [FS3]) …

Deep Learning-Based Dose Prediction For Automated, Individualized Quality Assurance Of Head And Neck Radiation Therapy Plans, Mary P Gronberg, Beth M Beadle, Adam S Garden, Heath Skinner, Skylar Gay, Wenhua Cao, Christine Chung, David T Fuentes, Clifton D Fuller, Rebecca M Howell, Anuja Jhingran, Tze Yee Lim, Barbara Marquez, Raymond Mumme, Adenike M Olanrewaju, Christine B Peterson, Ivan Vazquez, Thomas J Whitaker, Zachary Wooten, Ming Yang, Laurence E Court

Faculty, Staff and Student Publications

PURPOSE: This study aimed to use deep learning-based dose prediction to assess head and neck (HN) plan quality and identify suboptimal plans.

METHODS AND MATERIALS: A total of 245 volumetric modulated arc therapy HN plans were created using RapidPlan knowledge-based planning (KBP). A subset of 112 high-quality plans was selected under the supervision of an HN radiation oncologist. We trained a 3D Dense Dilated U-Net architecture to predict 3-dimensional dose distributions using 3-fold cross-validation on 90 plans. Model inputs included computed tomography images, target prescriptions, and contours for targets and organs at risk (OARs). The model's performance was assessed on …

Remote Sensing Approach For Terramechanics Applications Utilizing Machine And Deep Learning, Jordan J. Ewing

Dissertations, Master's Theses and Master's Reports

Terrain traversability is critical for developing Go/No Go maps, significantly impacting a mission's success. To predict the mobility of a vehicle over a terrain, one must understand the soil characteristics. In situ measurements performed by soldiers in the field are the current method of collecting this information, which is time-consuming, are only point measurements, and can put soldiers in harm's way. Therefore, this study investigates using remote sensing as an alternative approach to characterize terrain properties.

This approach will explore the relationships between electromagnetic radiation and soil types with varying properties. Optical, thermal, and hyperspectral sensors will be used to …

Prostate Cancer Malignancy Detection And Localization From Mpmri Using Auto-Deep Learning As One Step Closer To Clinical Utilization, Weiwei Zong, Eric Carver, Simeng Zhu, Eric Schaff, Daniel Chapman, Joon K. Lee, Hassan Bagher-Ebadian, Benjamin Movsas, Winston Wen, Tarik Alafif, Xiangyun Zong

Radiation Oncology Articles

Automatic diagnosis of malignant prostate cancer patients from mpMRI has been studied heavily in the past years. Model interpretation and domain drift have been the main road blocks for clinical utilization. As an extension from our previous work we trained on a public cohort with 201 patients and the cropped 2.5D slices of the prostate glands were used as the input, and the optimal model were searched in the model space using autoKeras. As an innovative move, peripheral zone (PZ) and central gland (CG) were trained and tested separately, the PZ detector and CG detector were demonstrated effective in highlighting …

Investigating Applications Of Deep Learning For Diagnosis Of Post Traumatic Elbow Disease, Hugh James

McKelvey School of Engineering Graduate Student Theses & Dissertations

Traumatic events such as dislocation, breaks, and arthritis of musculoskeletal joints can cause the development of post-traumatic joint contracture (PTJC). Clinically, noninvasive techniques such as Magnetic Resonance Imaging (MRI) scans are used to analyze the disease. Such procedures require a patient to sit sedentary for long periods of time and can be expensive as well. Additionally, years of practice and experience are required for clinicians to accurately recognize the diseased anterior capsule region and make an accurate diagnosis. Manual tracing of the anterior capsule is done to help with diagnosis but is subjective and timely. As a result, there is …

Semantic Segmentation Based Deep Learning Approaches For Weed Detection, Puranjit Singh

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Global increase in herbicide use to control weeds has led to issues such as evolution of herbicide-resistant weeds, off-target herbicide movement, etc. Precision agriculture advocates Site Specific Weed Management (SSWM) application to achieve precise and right amount of herbicide spray and reduce off-target herbicide movement. Recent advancements in Deep Learning (DL) have opened possibilities for adaptive and accurate weed recognitions for field based SSWM applications with traditional and emerging spraying equipment; however, challenges exist in identifying the DL model structure and train the model appropriately for accurate and rapid model applications over varying crop/weed growth stages and environment. In our …

Advances In Nlp Algorithms On Unstructured Medical Notes Data And Approaches To Handling Class Imbalance Issues, Hanna Lu

Computational and Data Sciences (PhD) Dissertations

This dissertation is composed of three research projects, each addressing one aspect of the text classification tasks on unstructured medical notes.

The first study investigated the model performance of sequence deep learning models that are widely used in NLP tasks such as RNN, GRU, LSTM, Bi-LSTM, as well as CNN and the novel and more advanced attention-based algorithms such as the Transformer Encoder and BERT-Base. The model performances of these algorithms were evaluated with and without pre-trained word embeddings. The Transformer Encoder model stood out as the best model for all tasks and the CNN model produced comparable performance when …

Learning Graphical Models Of Multivariate Functional Data With Applications To Neuroimaging, Jiajing Niu

All Dissertations

This dissertation investigates the functional graphical models that infer the functional connectivity based on neuroimaging data, which is noisy, high dimensional and has limited samples. The dissertation provides two recipes to infer the functional graphical model: 1) a fully Bayesian framework 2) an end-to-end deep model.

We first propose a fully Bayesian regularization scheme to estimate functional graphical models. We consider a direct Bayesian analog of the functional graphical lasso proposed by Qiao et al. (2019).. We then propose a regularization strategy via the graphical horseshoe. We compare both Bayesian approaches to the frequentist functional graphical lasso, and compare the …

Multiscale Topology Optimization With A Strong Dependence On Complementary Energy, Dustin Dean Bielecki

All Dissertations

A discrete approach introduces a novel deep learning approach for generating fine resolution structures that preserve all the information from the topology optimization (TO). The proposed approach utilizes neural networks (NNs) that map the desired engineering properties to seed for determining optimized structure. This framework relies on utilizing parameters such as density and nodal deflections to predict optimized topologies. A three-stage NN framework is employed for the discrete approach to reduce computational runtime while maintaining physics constraints.

A continuous representation that uses complementary energy (CE) methods to solve a representative element's homogenized properties consists of an embedded structure that is …

Wildfire Spread Prediction Using Attention Mechanisms In U-Net, Kamen Haresh Shah, Kamen Haresh Shah

Master's Theses

An investigation into using attention mechanisms for better feature extraction in wildfire spread prediction models. This research examines the U-net architecture to achieve image segmentation, a process that partitions images by classifying pixels into one of two classes. The deep learning models explored in this research integrate modern deep learning architectures, and techniques used to optimize them. The models are trained on 12 distinct observational variables derived from the Google Earth Engine catalog. Evaluation is conducted with accuracy, Dice coefficient score, ROC-AUC, and F1-score. This research concludes that when augmenting U-net with attention mechanisms, the attention component improves feature suppression …

Optimized Learning Using Fuzzy-Inference-Assisted Algorithms For Deep Learning, Miroslava Barua

Open Access Theses & Dissertations

For years, researchers in Artificial Intelligence (AI) and Deep Learning (DL) observed that performance of a Deep Learning Network (DLN) could be improved by using larger and larger datasets coupled with complex network architectures. Although these strategies yield remarkable results, they have limits, dictated by data quantity and quality, rising costs by the increased computational power, or, more frequently, by long training times on networks that are very large. Training DLN requires laborious work involving multiple layers of densely connected neurons, updates to millions of network parameters, while potentially iterating thousands of times through millions of entries in a big …

Non-Destructive Terrain Evaluation And Modeling For Off-Road Autonomy, Howard Brand

All Dissertations

In recent years, there has been an increased interest in implementing intelligent robotic systems in outdoor environments. Paramount to accomplishing this objective is being able to conduct successful robotic navigation in unprepared outdoor environments. This presents unique challenges in that there is a risk of catastrophic immobilization in terrain regions which, though unoccupied, cannot provide traction support for vehicle mobility. Methods for providing prior knowledge and perception of traction support is therefore an interest and focus of research.

In the advent of ever advancing machine learning models, “learn-as-you-go” approaches have emerged as topics of interest for mobility prediction. These approaches, …