Digital Commons Network^™

Application Of Multiple Data Augmentation Techniques To Improve Training With Synthetic Sar Data In Common Cnn, Stephanie M.V. Saich

Browse all Theses and Dissertations

To address the issues of limited target data in the Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) problem set, synthetic data is often used to aid in filling the gap. This paper covers an in depth look at the use of colorization, dynamic range adjustment, and target extraction as data augmentation techniques to improve the accuracy of deep learning networks trained on synthetic SAR data. The use of multiple different data augmentations combine to dramatically improve the accuracy of a common Convolutional Neural Network (CNN) over the use of standard synthetic data. A comparison of increasing fraction of measured …

Pneumonia Detection With Limited And Imbalanced Data Using Energy-Based Out-Of-Distribution Technique, Jasbin Karki

Browse all Theses and Dissertations

The automated detection of pneumonia through chest X-ray presents a critical challenge in medical diagnostics, particularly due to the restrictions of limited and imbalanced chest X-ray data for training AI models. Traditional methods that depend on softmax confidence scores can be overconfident even when generating erroneous outputs especially when they are processing completely new inputs, leading to unreliable diagnostic results. This research addresses challenges in AI models which aim to develop a robust pneumonia detection system using an Energy-Based Out-of-Distribution (OOD) technique that can work effectively even with limited and imbalanced data. The study focused on creating a more reliable …

Towards Algorithm Selection For Efficient Search-Based Software Engineering, Niranjana Deshpande

Theses

In Search-Based Software Engineering (SBSE), metaheuristic search algorithms are used to construct software by combining components that fulfill diverse user and business requirements. These search algorithms typically employ randomization to accurately and efficiently evaluate numerous component combinations using limited computational resources. As a result, several search algorithms have been proposed to address SBSE problems, each with varying solution quality guarantees and computational resource usage. Recent research has demonstrated that search algorithms exhibit complementary behavior, that is, different search algorithms outperform each other on specific problem instances in terms of computational resource usage and solution quality. Problematically, current SBSE approaches do …

Machine Learning Framework For Detecting Ldap Injection Attacks, Rahul Nair

Theses

LDAP injection attacks have posed a growing danger to web application security, utilizing directory service vulnerabilities to obtain unauthorized access or tamper with sensitive information in recent times. In this thesis, a comprehensive method is developed for detecting and preventing LDAP injection attacks by utilizing a role-based access control approach to generate a unique dataset. Various categories and variations of LDAP queries that mimic real attack situations, as well as legitimate benign queries representing typical LDAP operations, are included in the dataset to further classify them based on user roles. Four different machine learning algorithms, including XGBoost, Logistic Regression, Support …

Machine Learning, Fuzzy Logic And Zero Trust-Based Quishing Prevention Solution For Android Smartphones, Felix William

Theses

QR code phishing, or "quishing," exploits the widespread adoption of QR codes to embed malicious URLs that lead users to phishing sites or initiate malware downloads. Unlike traditional phishing methods, QR phishing is uniquely deceptive, leveraging the inherent trust and convenience associated with QR codes while bypassing standard detection mechanisms. This thesis presents the SEQR scanner, a novel solution tailored to detect and prevent QR phishing attacks, specifically focusing on Android platforms. By integrating multiple detection mechanisms—machine learning prediction, fuzzy logic algorithm, and third-party blacklisting APIs—the solution effectively identifies malicious QR codes with precision. A zero-trust-based mechanism is implemented alongside …

Changes In Psychiatric Diagnosis Associated With Sars-Cov-2 Infection And Predicting The Development Of New Psychiatric Illness In Covid Patients By Using Machine Learning Approach: A Study Using The Us National Covid Cohort Collaborative (N3c), Asif Rahman

Graduate Theses, Dissertations, and Problem Reports (ETD)

The enduring impact of COVID-19 extends beyond acute illness, with potential long-term psychiatric consequences raising significant concern among healthcare professionals and researchers alike. Emerging evidence suggests a multifaceted relationship between COVID-19 and the development of different psychiatric illnesses like Schizophrenia Spectrum and Psychotic Disorders (SSPD), Depression, Bipolar disorder, Personality disorder, Trauma, and a range of other mental health conditions. Considering these emerging connections, our study endeavors to rigorously assess the associations between COVID-19 and various psychiatric illnesses while simultaneously employing machine learning techniques to predict the development of new psychiatric disorders in individuals affected by the virus. Leveraging the extensive …

Machine Learning-Assisted Multiscale Simulation And Design Optimization Of Composite Jackets Under Low-Velocity Impacts, Masoud Mohammadi

Graduate Theses, Dissertations, and Problem Reports (ETD)

This research presents the development of a data-driven machine learning-assisted approach to simulate and optimize the design of composite materials subjected to low-velocity impacts. It focuses on a specific type of hybrid composite comprised of fiberglass and Kevlar fabrics stitched with Kevlar threads. The study begins by creating a multiscale finite element simulation for the composite under a low-velocity impact, which operates across three scales: microscale, mesoscale, and macroscale. This simulation accepts various inputs, such as different layer configurations and orientations, and provides impact outputs including maximum load and energy absorption capacity and displacement at failure.

Python and MATLAB scripts …

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

College of Graduate Studies: Theses & 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. …

Bringing Gans To Medieval Times: Manuscript Translation Models, Tonilynn M. Holtz

College of Graduate Studies: Theses & Dissertations

The Generative Adversarial Networks (GAN) recently emerged as a powerful framework for producing new knowledge from existing knowledge. These models aim to learn patterns from input data then use that knowledge to generate output data samples that plausibly appear to belong to the same set as the input data. Medieval manuscripts study has been an important research area in the humanities field for many decades. These rare manuscripts are often times inaccessible to the general public, including students in scholars, and it is of a great interest to provide digital support (including, but not limited to translation and search) for …

Machine Learning Assisted Optimization For Calculation And Automated Tuning Of Antennas, Lauren Linkous

Theses and Dissertations

The Antenna Calculation and Autotuning Tool (AntennaCAT) software suite represents a significant advancement in the field of antenna design by automating the entire design, CAD, simulation, and optimization process compatible with several EM simulation software suites. It is the first comprehensive implementation of machine learning in this context. In particular, this work includes the capability to create and export structured datasets from the aforementioned EM software for iterative improvement and includes an expandable selection of optimizers.

Use Of Machine Learning Approaches To Predict Transition Of Retention In Care Among People Living With Hiv In South Carolina: A Real-World Data Study, Ruilie Cai, Xueying Yang Ph.D., Yunqing Ma, Hao H. Zhang, Bankole Olatosi Ph.D., Sharon Weissman, Xiaoming Li Ph.D., Jiajia Zhang Ph.D.

Faculty Publications

Maintaining retention in care (RIC) for people living with HIV (PLWH) helps achieve viral suppression and reduce onward transmission. This study aims to identify the best machine learning model that predicts the RIC transition over time. Extracting from the enhanced HIV/AIDS reporting system, this study included 9765 PLWH from 2005 to 2020 in South Carolina. Transition of RIC was defined as the change of RIC status in each two–year time window. We applied seven classifiers, such as Random Forest, Support Vector Machine, eXtreme Gradient Boosting and Long–short–term memory, for each lagged response to predict the subsequent year's RIC transition. Classification …

Assessing The Condition Of Pavements (Road Surfaces) Using Computer Vision & Machine Learning, Syed Ibrahim Hassam

Doctoral

Regular inspections of pavements are conducted by civil infrastructure departments to evaluate the surface condition. Pavement surfaces are subject to deterioration caused by several factors such as traffic, weather, and sunlight. This deterioration becomes evident through various distresses, including potholes, rutting, cracking, bleeding, patching, and ravelling, which gradually affects the surface layer over time. It is essential to assess the condition of pavements as it not only ensures their usability but also maximises public safety. Effective pavement maintenance requires substantial resources and capital investment to carry out the most suitable maintenance treatments at the optimal time. Furthermore, the outcomes of …

Assessing The Potential Of Uav-Based Multispectral And Thermal Data To Estimate Soil Water Content Using Geophysical Methods, Yunyi Guan, Katherine R. Grote

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Knowledge of the soil water content (SWC) is important for many aspects of agriculture and must be monitored to maximize crop yield, efficiently use limited supplies of irrigation water, and ensure optimal nutrient management with minimal environmental impact. Single-location sensors are often used to monitor SWC, but a limited number of point measurements is insufficient to measure SWC across most fields since SWC is typically very heterogeneous. To overcome this difficulty, several researchers have used data acquired from unmanned aerial vehicles (UAVs) to predict the SWC by using machine learning on a limited number of point measurements acquired across a …

Descriptive Statistical Analysis Of Experimental Data For Wettability Alteration With Smart Water Flooding In Carbonate Reservoirs, Muhammad Ali Buriro, Mingzhen Wei, Baojun Bai, Ya Yao

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Smart water flooding is a promising eco-friendly method for enhancing oil recovery in carbonate reservoirs. The optimal salinity and ionic composition of the injected water play a critical role in the success of this method. This study advances the field by employing machine learning and data analytics to streamline the determination of these critical parameters, which are traditionally reliant on time-intensive laboratory work. The primary objectives are to utilize data analytics to examine how smart water flooding influences wettability modification, identify key parameter ranges that notably alter the contact angle, and formulate guidelines and screening criteria for successful lab design. …

Enhancing Accessibility : A Pilot Study For Context-Aware Image-Caption To American Sign Language (Asl) Translation, Oluwafolahanmi Adedamola Aluko

Theses

Since the advent of Artificial Intelligence (AI), and the explosion of Generative AI, much work has been done around image captioning, providing wildly accurate captions from still images, and more recently videos [3]. Alt-Text has improved dramatically with efforts to enhance context and sentiment captured during automated captioning. This thesis attempts to expand on a section on Natural Language Processing (NLP) and its relevance to promoting accessibility and context-awareness in the premise of the American Sign Language (ASL) application. This thesis explores what it means to give full context about a caption or image to the end user, including sentiments, …

Kafka In The Age Of Ai And The Futility Of Privacy As Control, Daniel Solove, Woodrow Hartzog

Faculty Scholarship

Despite writing more than a century ago, Franz Kafka captured the core problem of digital technologies—how individuals are rendered powerless and vulnerable. Over the past fifty years, and especially in the twenty-first century, privacy laws have been sprouting up around the world. These laws are often based heavily on an Individual Control Model that aims to empower individuals with rights to help them control the collection, use, and disclosure of their data.

In this Article, we argue that although Kafka starkly shows us the plight of the disempowered individual, his work also paradoxically suggests that empowering the individual isn’t the …

Platform-Independent Estimation Of Human Physiological Time From Single Blood Samples, Yitong Huang, Rosemary Braun

Mathematics Sciences: Faculty Publications

Abundant epidemiological evidence links circadian rhythms to human health, from heart disease to neurodegeneration. Accurate determination of an individual's circadian phase is critical for precision diagnostics and personalized timing of therapeutic interventions. To date, however, we still lack an assay for physiological time that is accurate, minimally burdensome to the patient, and readily generalizable to new data. Here, we present TimeMachine, an algorithm to predict the human circadian phase using gene expression in peripheral blood mononuclear cells from a single blood draw. Once trained on data from a single study, we validated the trained predictor against four independent datasets with …

Leveraging Artificial Intelligence To Improve Data Configuration & Accuracy In Modern Flight Management Systems, Sreeram Chittayil

International Journal of Aviation, Aeronautics, and Aerospace

AI (Artificial intelligence) can automate the process of generating optimized flight routes using real-time data, such as AIRAC (Aeronautical Information Regulation and Control), significantly reducing the time needed for flight management tasks. While AIRAC data typically takes up to 28 days to refresh, AI could condense this process to just minutes, enhancing operational efficiency and ensuring pilots have timely and accurate flight information. The research includes practical experiments, prototype code, and visual case studies to demonstrate AI's role in optimizing FMS functions while addressing issues related to data input errors and human intervention. Key findings from trials show the algorithm's …

Identifying The Origins Of Business’ Data Breaches Utilizing Covert Timing Channels, Gayle L. Frisbie

Master's Theses and Doctoral Dissertations

Cybersecurity events and data breaches are on the rise and are very costly to businesses. Businesses rely on connectivity and information systems to conduct business, yet those same information systems can be breached and the organization's data exposed. Today, there is a heavy reliance of organizations upon network connections to connect the entire organization in order to conduct business efficiently and from multiple locations. Covert timing channels are a cybersecurity attack method in which malicious actors embed privileged information into normal network traffic without authorization. Malicious actors, by carefully manipulating timing patterns in covert timing channels, can create a hidden …

Intrusive Traumatic Re-Experiencing Domain: Functional Connectivity Feature Classification By The Enigma Ptsd Consortium, Benjamin Suarez-Jimenez, Amit Lazarov, Sigal Zilcha-Mano, Xi Zhu, Yoojean Kim, Jacklynn M. Fitzgerald

Psychology Faculty Research and Publications

Background

Intrusive traumatic re-experiencing domain (ITRED) was recently introduced as a novel perspective on posttraumatic psychopathology, proposing to focus research of posttraumatic stress disorder (PTSD) on the unique symptoms of intrusive and involuntary re-experiencing of the trauma, namely, intrusive memories, nightmares, and flashbacks. The aim of the present study was to explore ITRED from a neural network connectivity connectivity perspective.

Methods

Data were collected from 9 sites taking part in the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) PTSD Consortium (n = 584) and included itemized PTSD symptom scores and resting-state functional connectivity (rsFC) data. We assessed the …

Privacy-Preserving Topological Data Analysis Using Homomorphic Encryption, Dominic Gold

Electronic Theses and Dissertations 2020 - Present

Computational tools grounded in algebraic topology, known collectively as topological data analysis (TDA), have been used for dimensionality-reduction to preserve salient and discriminating features in data. This faithful but compressed representation of data through TDA’s flagship method, persistent homology (PH), motivates its use to address the complexity, depth, and inefficiency issues present in privacy-preserving, homomorphic encryption (HE)-based machine learning (ML) models, which permit a data provider (often referred to as the Client) to outsource computational tasks on their encrypted data to a computationally-superior but semi-honest party (the Server). This work introduces efforts to adapt the well-established TDA-ML pipeline on encrypted …

Topological Machine Learning With Unreduced Persistence Diagrams, Nicole Juliana Abreu

Electronic Theses and Dissertations 2020 - Present

A common topological data analysis approach used in the experimental sciences involves creating machine learning pipelines that incorporate discriminating topological features derived from persistent homology (PH) of data samples, encoded in persistence diagrams (PDs) and associated topological feature vectors. Often the most computationally demanding step is computing PH through an algorithmic process known as boundary matrix reduction. In this work, we introduce several methods to generate topological feature vectors from unreduced boundary matrices. We compared the performance of classifiers trained on vectorizations of unreduced PDs to vectorizations of fully-reduced PDs across several benchmark ML datasets. We discovered that models trained …

Ai Computation Of L1-Norm-Error Principal Components With Applications To Training Dataset Curation And Detection Of Change, Kavita Varma

Electronic Theses and Dissertations 2020 - Present

The aim of this dissertation is to achieve a thorough understanding and develop an algorithmic framework for a crucial aspect of autonomous and artificial intelligence (AI) systems: Data Analysis. In the current era of AI and machine learning (ML), ”data” holds paramount importance. For effective learning tasks, it is essential to ensure that the training dataset is accurate and comprehensive. Additionally, during system operation, it is vital to identify and address faulty data to prevent potentially catastrophic system failures. Our research in data analysis focuses on creating new mathematical theories and algorithms for outlier-resistant matrix decomposition using L1-norm principal component …

Data-Driven Attractor Identification Via Computational Homology And Machine Learning, Paul Tatasciore

Electronic Theses and Dissertations 2020 - Present

Dynamical systems play a pivotal role across various scientific domains, encompassing disciplines from physics to biology and engineering. The long-term behavior of these systems hinges on the structure of their attractors, with many exhibiting multistability characterized by multiple minimal attractors. Understanding the structure of these attractors and their corresponding basins is a central theme in dynamical systems theory. In recent years, machine learning algorithms have emerged as potent tools for clustering, prediction, and modeling complex data. By harnessing the capabilities of neural networks along with techniques from topological data analysis, in particular persistence homology, we can construct surrogate models of …

A Novel Assessment Of Whole-Mount Gleason Grading In Prostate Cancer To Identify Candidates For Radical Prostatectomy: A Machine Learning-Based Multiomics Study, Jing Ning, Clemens P Spielvogel, David Haberl, Karolina Trachtova, Stefan Stoiber, Sazan Rasul, Vojtech Bystry, Gabriel Wasinger, Pascal Baltzer, Elisabeth Gurnhofer, Gerald Timelthaler, Michaela Schlederer, Laszlo Papp, Helga Schachner, Thomas Helbich, Markus Hartenbach, Bernhard Grubmüller, Shahrokh F Shariat, Marcus Hacker, Alexander Haug, Lukas Kenner

Faculty, Staff and Student Publications

Purpose: This study aims to assess whole-mount Gleason grading (GG) in prostate cancer (PCa) accurately using a multiomics machine learning (ML) model and to compare its performance with biopsy-proven GG (bxGG) assessment.

Materials and Methods: A total of 146 patients with PCa recruited in a pilot study of a prospective clinical trial (NCT02659527) were retrospectively included in the side study, all of whom underwent ⁶⁸Ga-PSMA-11 integrated positron emission tomography (PET) / magnetic resonance (MR) before radical prostatectomy (RP) between May 2014 and April 2020. To establish a multiomics ML model, we quantified PET radiomics features, pathway-level genomics …

Harnessing The Potential Of Artificial Intelligence In Managing Viral Hepatitis, Guma Ali, Maad M. Mijwil, Ioannis Adamopoulos, Bosco Apparatus Buruga, Murat Gök, Malik Sallam

Mesopotamian Journal of Big Data

Viral hepatitis continues to be a serious global health concern, impacting millions of people, putting a strain on healthcare systems across the world, and causing significant morbidity and mortality. Traditional diagnostic, prognostic, and therapeutic procedures to address viral hepatitis are successful but have limits in accuracy, speed, and accessibility. Artificial intelligence (AI) advancement provides substantial opportunities to overcome these challenges. This study investigates the role of AI in revolutionizing viral hepatitis care, from early detection to therapy optimization and epidemiological surveillance. A comprehensive literature review was conducted using predefined keywords in the Nature, PLOS ONE, PubMed, Frontiers, Wiley Online Library, …

The Application Of Novel Machine Learning Algorithms To Study Multi-Dimensional Fragmentation Functions Of Hadrons In Jets At Star, Hannah A. Harrison-Smith

Theses and Dissertations--Physics and Astronomy

Hadronization, the process by which colored quarks and gluons shower from high energy collisions and recombine to form stable, experimentally-observable particles, is a fundamental aspect of Quantum Chromodynamics (QCD) that is not yet fully understood. Fragmentation functions, typically measured in electron-positron collisions, encapsulate this hadronization process well for quarks. Studying proton-proton collisions offers direct access to gluon fragmentation that other channels like electron-positron do not. Recent theoretical developments have proposed the study of hadronic showers in groupings called jets, introducing the concept of multi-dimensional jet fragmentation functions. This thesis presents the extraction of collinear and transverse momentum-dependent fragmentation functions for …

Leveraging Ai And Big Data In Low-Resource Healthcare Settings, Ahmed Hussein Ali, Saad Ahmed Dheyab, Abdullah Hussein Alamoodi, Aws Abed Al Raheem Magableh, Yuantong Gu

Mesopotamian Journal of Big Data

Big data and artificial intelligence are game-changing technologies for the underdeveloped healthcare industry because they help optimize the entire supply chain and deliver more exact patient outcome information. Machine learning approaches that have recently seen more growing popularity include deep learning models that have brought revolution within the healthcare system in the previous years due to more complicated data compared to previous years . Machine learning is an essential data analysis procedure to describe efficient and effective methods to extract hidden information from large amounts of data that it would take logical analytics too long to manage. Recent years have …

Mlp And Rbf Algorithms In Finance: Predicting And Classifying Stock Prices Amidst Economic Policy Uncertainty, Bushra Ali, Khder Alakkari, Mostafa Abotaleb, Maad M. Mijwil, Klodian Dhoska

Mesopotamian Journal of Big Data

In the realm of stock market prediction and classification, the use of machine learning algorithms has gained significant attention. In this study, we explore the application of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) algorithms in predicting and classifying stock prices, specifically amidst economic policy uncertainty. Stock market fluctuations are greatly influenced by economic policies implemented by governments and central banks. These policies can create uncertainty and volatility, which in turn makes accurate predictions and classifications of stock prices more challenging. By leveraging MLP and RBF algorithms, we aim to develop models that can effectively navigate these uncertainties and …

Anomaly-Based Ids (Intrusion Detection System) For Cyber-Physical Systems, Ahmad Muter Awaad, Khattab M. Alheeti, Abdul Kream A.H. Najem

Mesopotamian Journal of Big Data

Cyber-physical systems (CPS) are critical infrastructures that integrate physical processes with computational components. The security of CPS is paramount, as any breach can lead to severe consequences. Anomaly-based intrusion detection systems (IDS) have emerged as a promising approach to safeguard CPS against cyber threats. This paper presents an anomaly-based IDS designed specifically for CPS, leveraging machine learning techniques to establish a baseline of normal system behaviour and promptly detect deviations indicative of malicious activities. The proposed system incorporates multiple classification techniques, including KNeighbors, RandomForest, XGB, DecisionTree, SGD, SVM, LGBM, AdaBoost, Bagging, and MLP Classifier, to enhance detection accuracy and robustness. …