Digital Commons Network^™

From Fully-Supervised Single-Task To Semi-Supervised Multi-Task Deep Learning Architectures For Segmentation In Medical Imaging Applications, S M Kamrul Hasan

Theses

Medical imaging is routinely performed in clinics worldwide for the diagnosis and treatment of numerous medical conditions in children and adults. With the advent of these medical imaging modalities, radiologists can visualize both the structure of the body as well as the tissues within the body. However, analyzing these high-dimensional (2D/3D/4D) images demands a significant amount of time and effort from radiologists. Hence, there is an ever-growing need for medical image computing tools to extract relevant information from the image data to help radiologists perform efficiently. Image analysis based on machine learning has pivotal potential to improve the entire medical …

Comparing The Performance Of Different Machine Learning Models In The Evaluation Of Solder Joint Fatigue Life Under Thermal Cycling, Jason Scott Ross

Dissertations and Theses

Predicting the reliability of board-level solder joints is a challenging process for the designer because the fatigue life of solder is influenced by a large variety of design parameters and many nonlinear, coupled phenomena. Machine learning has shown promise as a way of predicting the fatigue life of board-level solder joints. In the present work, the performance of various machine learning models to predict the fatigue life of board-level solder joints is discussed. Experimental data from many different solder joint thermal fatigue tests are used to train the different machine learning models. A web-based database for storing, sharing, and uploading …

Evaluation Of Precision Livestock Technology And Human Scoring Of Nursery Pigs In A Controlled Immune Challenge Experiment, Eduarda M. Bortoluzzi, Mikayla J. Goering, Sara J. Ochoa, Aaron J. Holliday, Jared M. Mumm, Catherine E. Nelson, Hui Wu, Benny E. Mote, Eric T. Psota, Ty B. Schmidt, Majid Jaberi-Douraki, Lindsey E. Hulbert

Department of Animal Science: Faculty Publications

The objectives were to determine the sensitivity, specificity, and cutoff values of a visual-based precision livestock technology (NUtrack), and determine the sensitivity and specificity of sickness score data collected with the live observation by trained human observers. At weaning, pigs (n = 192; gilts and barrows) were randomly assigned to one of twelve pens (16/pen) and treatments were randomly assigned to pens. Sham-pen pigs all received subcutaneous saline (3 mL). For LPS-pen pigs, all pigs received subcutaneous lipopolysaccharide (LPS; 300 µg/kg BW; E. coli O111:B4; in 3 mL of saline). For the last treatment, eight pigs were randomly …

Evaluation Of Precision Livestock Technology And Human Scoring Of Nursery Pigs In A Controlled Immune Challenge Experiment, Eduarda M. Bortoluzzi, Mikayla J. Goering, Sara J. Ochoa, Aaron J. Holliday, Jared M. Mumm, Catherine E. Nelson, Hui Wu, Benny Mote, Eric T. Psota, Ty B. Schmidt, Majid Jaberi-Douraki, Lindsey E. Hulbert

Department of Animal Science: Faculty Publications

The objectives were to determine the sensitivity, specificity, and cutoff values of a visual-based precision livestock technology (NUtrack), and determine the sensitivity and specificity of sickness score data collected with the live observation by trained human observers. At weaning, pigs (n = 192; gilts and barrows) were randomly assigned to one of twelve pens (16/pen) and treatments were randomly assigned to pens. Sham-pen pigs all received subcutaneous saline (3 mL). For LPS-pen pigs, all pigs received subcutaneous lipopolysaccharide (LPS; 300 μg/kg BW; E. coli O111:B4; in 3 mL of saline). For the last treatment, eight pigs were randomly …

Machine Learning Models Interpretability For Malware Detection Using Model Agnostic Language For Exploration And Explanation, Ikuromor Mabel Ogiriki

Theses and Dissertations

The adoption of the internet as a global platform has birthed a significant rise in cyber-attacks of various forms ranging from Trojans, worms, spyware, ransomware, botnet malware, rootkit, etc. In order to tackle the issue of all these forms of malware, there is a need to understand and detect them. There are various methods of detecting malware which include signature, behavioral, and machine learning. Machine learning methods have proven to be the most efficient of all for malware detection. In this thesis, a system that utilizes both the signature and dynamic behavior-based detection techniques, with the added layer of the …

Normalization And Generalization In Deep Learning, Griffin Hurt

Theses

In this thesis, we discuss the importance of data normalization in deep learning and its relationship with generalization. Normalization is a staple of deep learning architectures and has been shown to improve the stability and generalizability of deep learning models, yet the reason why these normalization techniques work is still unknown and is an active area of research. Inspired by this uncertainty, we explore how different normalization techniques perform when employed in different deep learning architectures, while also exploring generalization and metrics associated with generalization in congruence with our investigation into normalization. The goal behind our experiments was to investigate …

Business Inferences And Risk Modeling With Machine Learning; The Case Of Aviation Incidents, Burak Cankaya, Kazim Topuz, Aaron M. Glassman

Publications

Machine learning becomes truly valuable only when decision-makers begin to depend on it to optimize decisions. Instilling trust in machine learning is critical for businesses in their efforts to interpret and get insights into data, and to make their analytical choices accessible and subject to accountability. In the field of aviation, the innovative application of machine learning and analytics can facilitate an understanding of the risk of accidents and other incidents. These occur infrequently, generally in an irregular, unpredictable manner, and cause significant disruptions, and hence, they are classified as "high-impact, low-probability" (HILP) events. Aviation incident reports are inspected by …

Data Augmentation For Neutron Spectrum Unfolding With Neural Networks, James Mcgreivy, Juan J. Manfredi, Daniel Siefman

Faculty Publications

Neural networks require a large quantity of training spectra and detector responses in order to learn to solve the inverse problem of neutron spectrum unfolding. In addition, due to the under-determined nature of unfolding, non-physical spectra which would not be encountered in usage should not be included in the training set. While physically realistic training spectra are commonly determined experimentally or generated through Monte Carlo simulation, this can become prohibitively expensive when considering the quantity of spectra needed to effectively train an unfolding network. In this paper, we present three algorithms for the generation of large quantities of realistic and …

Efficient Cloud-Based Ml-Approach For Safe Smart Cities, Niveshitha Niveshitha

Browse all Theses and Dissertations

Smart cities have emerged to tackle many critical problems that can thwart the overwhelming urbanization process, such as traffic jams, environmental pollution, expensive health care, and increasing energy demand. This Master thesis proposes efficient and high-quality cloud-based machine-learning solutions for efficient and sustainable smart cities environment. Different supervised machine-learning models for air quality predication (AQP) in efficient and sustainable smart cities environment is developed. For that, ML-based techniques are implemented using cloud-based solutions. For example, regression and classification methods are implemented using distributed cloud computing to forecast air execution time and accuracy of the implemented ML solution. These models are …

Machine Learning Algorithms Improve Modis Gpp Estimates In United States Croplands, D. Menefee, T. O. Lee, K. C. Flynn, J. Chen, M. Abraha, J. Baker, A. Suyker

School of Natural Resources: Faculty Publications

No abstract provided.

Time Series Forecasting For Stock Market Prices, Albert Zhou

Senior Honors Projects

No abstract provided.

A Machine Learning Approach To Deepfake Detection, Delaney Conrad

All Undergraduate Theses and Capstone Projects

The ability to manipulate videos has been around for decades but a process that once would take time, money, and professionals, can now be created by anyone due to the rapid advancement of deepfake technology. Deepfakes use deep learning artificial intelligence to make fake digital content, typically in the form of swapping a person’s face in a video or image. This technology could easily threaten and manipulate individuals, corporations, and political organizations, so it is essential to find methods for detecting deepfakes. As the technology for creating deepfakes continues to improve, these manipulated videos are becoming increasingly undetectable. It is …

Improving Cervical Spine Fracture Detection In Ct Imaging With Yolov5 Data Augmentation Strategies, Salem Ghanem Ali Al-Ali

Theses

Cervical spine fractures are the most common type of spinal injury, with over 17,730 spinal cord injuries occurring annually in the United States alone. The prevalence of spinal fractures is highest among the elderly, and these fractures are often difficult to detect due to the possibility of coexisting degenerative illnesses and osteoporosis. Computed Tomography (CT) is increasingly being used for the imaging diagnosis of adult spine fractures, replacing traditional radiography. An accurate and timely diagnosis is critical to preventing neurological degeneration and paralysis following trauma. Machine learning algorithms, such as the YOLO series developed by Ultralytics, have emerged as valuable …

Graph-Based Acoustic Clustering And Classification, Justin Youngho Sunu

CGU Theses & Dissertations

The rapid growth of audio data collection in various domains necessitates advanced techniquesfor efficient analysis and classification. This dissertation proposes new approaches for categorizing acoustic data, using both unsupervised and semi-supervised learning methods. Starting with raw audio, we preprocess the signal to segment it into time windows, each of which we consider as an independent data point. We use the short-time Fourier transform to describe the signal in a given time window as a set of Fourier coefficients. We interpret the resulting frequency signature as a high-dimensional feature description of each data point. We then develop a graph-based approach for …

Detecting Overlapping Gene Regions Using The U-Net Attention Mechanism, Samuel Lemma

All Graduate Theses, Dissertations, and Other Capstone Projects

The current issue of locating, diagnosing, and treating cancer and other diseases linked to specific target genes necessitates the creation of a reliable system for precisely identifying target genes that are initially extracted from a human chromosome. Current methodologies often suffer from overlapping gene regions in the target gene that occurs during the analysis process, which can have a substantial impact on the accuracy of the results. Our recommended approach, which was the appropriate model to apply for this particular problem, is set to enhance the analytical process by utilizing neural networks' U-Net with an attention mechanism. We were able …

Historical And Forecasted Kentucky Specific Slope Stability Analyses Using Remotely Retrieved Hydrologic And Geomorphologic Data, Daniel M. Francis

Theses and Dissertations--Civil Engineering

Hazard analyses of rainfall-induced landslides have typically been observed to experience a lack of inclusion of measurements of soil moisture within a given soil layer at a site of interest. Soil moisture is a hydromechanical variable capable of both strength gains and reductions within soil systems. However, in situ monitoring of soil moisture at every site of interest is an unfeasible goal. Therefore, spatiotemporal estimates of soil moisture that are representative of in-situ conditions are required for use in subsequent landslide hazard analyses.

This study brings together various techniques for the acquisition, modeling, and forecasting of spatiotemporal retrievals of soil …

Quantum Computing And Its Applications In Healthcare, Vu Giang

OUR Journal: ODU Undergraduate Research Journal

This paper serves as a review of the state of quantum computing and its application in healthcare. The various avenues for how quantum computing can be applied to healthcare is discussed here along with the conversation about the limitations of the technology. With more and more efforts put into the development of these computers, its future is promising with the endeavors of furthering healthcare and various other industries.

Data-Driven Strategies For Pain Management In Patients With Sickle Cell Disease, Swati Padhee

Browse all Theses and Dissertations

This research explores data-driven AI techniques to extract insights from relevant medical data for pain management in patients with Sickle Cell Disease (SCD). SCD is an inherited red blood cell disorder that can cause a multitude of complications throughout an individual’s life. Most patients with SCD experience repeated, unpredictable episodes of severe pain. Arguably, the most challenging aspect of treating pain episodes in SCD is assessing and interpreting the patient’s pain intensity level due to the subjective nature of pain. In this study, we leverage multiple data-driven AI techniques to improve pain management in patients with SCD. The proposed approaches …

Enabling Multi-Modal Enhanced Search Using Self-Supervised Learning On Multispectral Images, Meghana Nagarahalli Paramesh

Theses

Advanced Deep Learning techniques have been widely used in Spatial Data Science specifically with Geographic Information Systems (GIS). Petabyte scale data production is occurring due to advances in remote sensing capabilities within the commercial and government sectors. Discovering relevant data for science research and applications can become difficult with large volumes of spatially and temporally diverse data availability. The NASA Commercial Smallsat Data Acquisition (CSDA) Program acquires and archives data from commercial satellite remote sensing vendors. While the underlying observation characteristics may be similar, such as being visible imagery, varying orbital and acquisition strategies result in a disparate data archive. …

Sign Language Recognition Using A Hybrid Machine Learning Model, Peeyusha Shivayogi

Master's Projects

Sign Language is a visual language used by millions of people around the world. American Sign Language (ASL) is one of the most popular sign languages and the third most popular language in the United States. Automatic recognition of ASL signs can help bridge the communication gap between deaf and hearing individuals. In this project, we explore the use of deep learning models for ASL sign recognition, using the MNIST dataset as a benchmark. We preprocessed the data by reshaping the images to the input layer size of the models and normalized the pixel values. We evaluated five popular deep-learning …

Image-Based Malware Detection Using Convolutional Neural Network Techniques, Brandon Palomino

Master's Projects

In this study, we delve into the realm of malware detection and classification, leveraging the capabilities of different Convolutional Neural Networks (CNNs). Our approach involves transforming executable files into image formats and subsequently applying advanced CNN techniques for image recognition. The study emphasizes the use of two distinct CNN architectures: a traditional CNN model and a modified CNN variant known as a convolutional recurrent neural network (CRNN), each with unique structural and functional attributes. To effectively train these models, we adopt a transfer learning strategy, utilizing pre-existing CNN models that have been extensively trained on large-scale image datasets. This methodology …

Chatgpt, Professor Of Law, Tammy Oltz

Faculty Scholarship

In this Paper, I test the ability of ChatGPT to assist with common tasks performed by law professors. To conduct the experiment, I ran four service-related prompts and three teaching-related prompts through the free version of ChatGPT and then analyzed the results. ChatGPT performed very well on the service-related prompts and moderately well on the teaching-related prompts. I conclude that ChatGPT can provide law professors with near-finished products for routine tasks and a solid jumping-off point for those that are more complex.

Machine Learning Prediction Of Dod Personal Property Shipment Costs, Tiffany Tucker, Torrey J. Wagner, Paul Auclair, Brent T. Langhals

Faculty Publications

U.S. Department of Defense (DoD) personal property moves account for 15% of all domestic and international moves - accurate prediction of their cost could draw attention to outlier shipments and improve budget planning. In this work 136,140 shipments between 13 personal property shipment hubs from April 2022 through March 2023 with a total cost of $1.6B were analyzed. Shipment cost was predicted using recursive feature elimination on linear regression and XGBoost algorithms, as well as through neural network hyperparameter sweeps. Modeling was repeated after removing 28 features related to shipment hub location and branch of service to examine their influence …

Authorbots, Derek E. Bambauer, Mihai Surdeanu

UF Law Faculty Publications

ChatGPT has exploded into the popular consciousness in recent months, and the hype and concerns about the program have only grown louder with the release of GPT-4, a more powerful version of the software. Its deployment, including with applications such as Microsoft Office, has raised questions about whether the developers or distributors of code that includes ChatGPT, or similar generative pre-trained transformers, could face liability for tort claims such as defamation or false light. One important potential barrier to these claims is the immunity con-ferred by 47 U.S.C. § 230, popularly known as “Section 230.” In this Essay, we make …

Robot Slanderer, Henry H. Perritt Jr.

University of Arkansas at Little Rock Law Review

No abstract provided.

Sequential User Modeling And Recommendation Under Partially Observable Environment, Chunpai Wang

Legacy Theses & Dissertations (2009 - 2024)

A tremendous amount of user data is collected daily due to technological progress that enables us to understand users better. The availability of this data also advances machine learning-based technologies, which aim to learn generic global patterns of user behavior from large volumes of data. User modeling is the process of building user profiles and finding the inherent representation of the user. Precise user modeling is critical for predicting users' future behavior and providing personalized services or products to individuals. Many machine learning models have been explored for user modeling to meet the increasing demand for user-centric technologies. Machine learning …

Learning From Hierarchical And Noisy Labels, Wenting Qi

Legacy Theses & Dissertations (2009 - 2024)

One branch of machine learning algorithms is supervised learning, where the label is crucial for the learning model. Numerous algorithms have been proposed for supervised learning with different classification tasks. However, fewer works question the quality of the training labels. Training a learning model with noisy labels leads to decreased or untruthful performance. On the other hand, hierarchical multi-label classification (HMC) is one of the most challenging problems in machine learning because the classes in HMC tasks are hierarchically structured, and data instances are associated with multiple labels residing in a path of the hierarchy. Treating hierarchical tasks as flat …

Early Diagnosis Of Pancreatic Cancer By Machine Learning Methods Using Urine Biomarker Combinations, İrem Acer, Firat Orhan Bulucu, Semra İçer, Fatma Lati̇foğlu

Turkish Journal of Electrical Engineering and Computer Sciences

The most common type of pancreatic cancer is pancreatic ductal adenocarcinoma (PDAC), which accounts for the vast majority of pancreatic cancers. The five-year survival rate for PDAC due to late diagnosis is 9%. Early diagnosed PDAC patients survive longer than patients diagnosed at a more advanced stage. Biomarkers can play an essential role in the early detection of PDAC to assist the health professional. Machine learning and deep learning methods are used with biomarkers obtained in recent studies for diagnostic purposes. In order to increase the survival rates of PDAC patients, early diagnosis of the disease with a noninvasive test …

Fintech Adoption Dynamics In A Pandemic: An Experience From Some Financial Institutions In Nigeria During Covid-19 Using Machine Learning Approach, Onome Christopher Edo, Egbe Etu Etu, Imokhai Tenebe, Oluwarotimi Samuel Oladele, Solomon Edo, Oladapo Ayodeji Diekola, Joshua Emakhu

Faculty Research, Scholarly, and Creative Activity

The novel coronavirus caused a lifestyle shift, and the acceptance of offsite financial transactions is still a case for financial technology (fintech). Mobile financial transactions continue to be at an all-time low, and financial institutions are developing approaches for financial digitalization acceptability. The present study attempts to understand users’ motivations for fintech adoption. The technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTUAT) were utilized to uncover the rationale behind technology adoption. This study explored the drivers inhibiting the adoption of financial technology in Nigeria during the pandemic. A machine learning (ML) approach was …

Garbage In, Garbage Out: A Theory-Driven Approach To Improve Data Handling In Supervised Machine Learning, Steven J. Hyde, Eric Bachura, Joseph S. Harrison

Boise State University Publications and Presentations

Machine learning (ML) has recently gained momentum as a method for measurement in strategy research. Yet, little guidance exists regarding how to appropriately apply the method for this purpose in our discipline. We address this by offering a guide to the application of ML in strategy research, with a particular emphasis on data handling practices that should improve our ability to accurately measure our constructs of interest using ML techniques. We offer a brief overview of ML methodologies that can be used for measurement before describing key challenges that exist when applying those methods for this purpose in strategy research …