Digital Commons Network^™

Predictive Analytics In Bronchopulmonary Dysplasia: Past, Present, And Future, Bryan G Mcomber, Alvaro G Moreira, Kelsey Kirkman, Sebastian Acosta, Craig Rusin, Binoy Shivanna

Faculty, Staff and Students Publications

Bronchopulmonary dysplasia (BPD) remains a significant complication of prematurity, impacting approximately 18,000 infants annually in the United States. Advances in neonatal care have not reduced BPD, and its management is challenged by the rising survival of extremely premature infants and the variability in clinical practices. Leveraging statistical and machine learning techniques, predictive analytics can enhance BPD management by utilizing large clinical datasets to predict individual patient outcomes. This review explores the foundations and applications of predictive analytics in the context of BPD, examining commonly used data sources, modeling techniques, and metrics for model evaluation. We also highlight bioinformatics' potential role …

Stemnesscore: An R Package To Estimate The Stemness Of Glioma Cancer Cells At Single-Cell Resolution, Necla Koçhan, Yavuz Oktay, Gökhan Karakülah

Turkish Journal of Biology

Background/aim: Glioblastoma is the most heterogeneous and the most difficult-to-treat type of brain tumor and one of the deadliest among all cancers. The high plasticity of glioma cancer stem cells and the resistance they develop against multiple modalities of therapy, along with their high heterogeneity, are the main challenges faced during treatment of glioblastoma. Therefore, a better understanding of the stemness characteristics of glioblastoma cells is needed. With the development of various single-cell technologies and increasing applications of machine learning, indices based on transcriptomic and/or epigenomic data have been developed to quantitatively measure cellular states and stemness. In this study, …

Physicochemical Differences Between Camelid Single-Domain Antibodies And Mammalian Antibodies, Nazli Eda Eski̇er, Doğa Eski̇er, Esi̇n Fi̇ruzan, Si̇bel Kalyoncu

Turkish Journal of Biology

Background/aim: In recent years, single-domain antibodies, also known as nanobodies, have emerged as an alternative to full immunoglobulin Gs (IgGs), due to their various advantages including increased solubility, faster clearance, and cheaper production. Nanobodies are generally derived from the variable domain of the camelid heavy-chain-only immunoglobulin Gs (hcIgGs). Due to the high sequence homology between variable heavy chains of camelids (VHHs) and humans (VHs), hcIgGs are ideal candidates for nanobody development. However, further examination is needed to understand the structural differences between VHs and VHHs. This analysis is essential for nanobody engineering to mitigate potential immunogenicity while preserving stability, functionality, …

Improving The Quality Of Micronutrient Food Composition Data In Sub-Saharan Africa Using Predictive Analytics, Junior Mbuyamba Muka

Theses and Dissertations

High-quality food composition data are indispensable for decision making in several health, agricultural, and nutrition-related activities. Analytical data are the most accurate type of food composition data (FCD). Yet, most food composition databases (FCDBs) compiled in Sub-Saharan Africa only contain a small amount of analytical data collected from foods consumed in this region. Most of the data are either borrowed / copied from FCDBs collected in other parts of the world, calculated, imputed, or presumed data. Even with these alternative types, FCDBs in Sub-Saharan Africa have not reached a satisfactory level of completeness, as they still have missing data for …

Predicting Speaking Proficiency With Fluency Features Using Machine Learning, Ethan D. Erickson

Theses and Dissertations

This study investigates the interplay between temporal fluency measures, self-assessment, and language proficiency scores in novice- to intermediate- level language learners of Spanish and French. Analyzing data from 163 participants, the research employs both traditional linear regression and advanced XGBoost machine learning models. Findings demonstrate a moderate positive correlation between self-assessment and Oral Proficiency Interview by Computer (OPIc) scores, underscoring the dependable self-awareness of learners. Notably, XGBoost performs as well as linear regression in predicting OPIc scores and has more potential, underlining the efficacy of advanced methodologies. The study identifies Mean Length of Utterance (MLU) as a crucial predictor, highlighting …

Deep Learning Image Analysis To Isolate And Characterize Different Stages Of S-Phase In Human Cells, Kevin A. Boyd, Rudranil Mitra, John Santerre, Christopher L. Sansam

SMU Data Science Review

Abstract. This research used deep learning for image analysis by isolating and characterizing distinct DNA replication patterns in human cells. By leveraging high-resolution microscopy images of multiple cells stained with 5-Ethynyl-2′-deoxyuridine (EdU), a replication marker, this analysis utilized Convolutional Neural Networks (CNNs) to perform image segmentation and to provide robust and reliable classification results. First multiple cells in a field of focus were identified using a pretrained CNN called Cellpose. After identifying the location of each cell in the image a python script was created to crop out each cell into individual .tif files. After careful annotation, a CNN was …

Differentiation Of Human, Dog, And Cat Hair Fibers Using Dart Tofms And Machine Learning, Laura Ahumada, Erin R. Mcclure-Price, Chad Kwong, Edgard O. Espinoza, John Santerre

SMU Data Science Review

Hair is found in over 90% of crime scenes and has long been analyzed as trace evidence. However, recent reviews of traditional hair fiber analysis techniques, primarily morphological examination, have cast doubt on its reliability. To address these concerns, this study employed machine learning algorithms, specifically Linear Discriminant Analysis (LDA) and Random Forest, on Direct Analysis in Real Time time-of-flight mass spectra collected from human, cat, and dog hair samples. The objective was to develop a chemistry- and statistics-based classification method for unbiased taxonomic identification of hair. The results of the study showed that LDA and Random Forest were highly …

Phenotyping Cotton Compactness Using Machine Learning And Uas Multispectral Imagery, Joshua Carl Waldbieser

Theses and Dissertations

Breeding compact cotton plants is desirable for many reasons, but current research for this is restricted by manual data collection. Using unmanned aircraft system imagery shows potential for high-throughput automation of this process. Using multispectral orthomosaics and ground truth measurements, I developed supervised models with a wide range of hyperparameters to predict three compactness traits. Extreme gradient boosting using a feature matrix as input was able to predict the height-related metric with R2=0.829 and RMSE=0.331. The breadth metrics require higher-detailed data and more complex models to predict accurately.

Moisture Effects On Visible Near-Infrared And Mid-Infrared Soil Spectra And Strategies To Mitigate The Impact For Predictive Modeling, Francis Hettige Chamika Anuradha Silva

Theses and Dissertations

Instrumental disparities and soil moisture are two of the key limitations in implementing spectroscopic techniques in the field. This study sought to address these challenges through two objectives. The first objective was to assess Visible-near infrared (VisNIR) and mid-infrared (MIR) spectroscopic approaches and explore the feasibility of transferring calibration models between laboratory and portable spectrometers. The second objective addressed the challenge of soil moisture and its impact on spectra. The portable spectrometers demonstrated comparable performance to their laboratory-based counterparts in both regions. Spiking with extra-weight, was the most effective calibration transfer method eliminating disparities between instruments. The samples were rewetted …

Overcoming Foreign Language Anxiety In An Emotionally Intelligent Tutoring System, Daneih Ismail

College of Computing and Digital Media Dissertations

Learning a foreign language entails cognitive and emotional obstacles. It involves complicated mental processes that affect learning and emotions. Positive emotions such as motivation, encouragement, and satisfaction increase learning achievement, while negative emotions like anxiety, frustration, and confusion may reduce performance. Foreign Language Anxiety (FLA) is a specific type of anxiety accompanying learning a foreign language. It is considered a main impediment that hinders learning, reduces achievements, and diminishes interest in learning.

Detecting FLA is the first step toward reducing and eventually overcoming it. Previously, researchers have been detecting FLA using physical measurements and self-reports. Using physical measures is direct …

Predictive Machine Learning And Its Future In Professional Basketball, Zachary Harmon

Honors College Theses

Artificial Intelligence (AI) is an ever-evolving field, transforming various aspects of contemporary life. From language models to immersive gaming experiences, AI technologies have become integral to our daily existence. Among the most promising arenas for AI integration is the world of sports. This research delves into the application of machine learning models to predict NBA game outcomes, shedding light on the profound impact of machine learning in the realm of professional basketball. Beyond the scope of game prediction, this study explores the broader implications, such as optimizing the selection of televised games, assisting players in showcasing their skills, and much …

Lung Cancer Growth Forecasting And Classification Models By Machine Learning Using A Novel 2d Co-Culture High Throughput Device, Adam Adrien Germain

Bioengineering Dissertations - Archive

Co-culture devices have commonly been used during in vitro experimentation to create therapeutic testing devices for lung cancer that more closely mimic the in vivo environment to gain better insights into new therapies and the reasons for inconsistency for current therapies. Moreover, AI (artificial intelligence) in the form of convolutional neural networks (CNN) and convolutional long-short term memory neural networks (convLSTM) are being used to classify, diagnose, and predict drug outcomes from the visual data provided by clinical imaging modalities, such as CT-scans, MRIs, or histopathological slides among others, or from numerical and textual data gathered from the genetic testing …

An Intelligent Multi-Modal Framework Towards Assessing Human Cognition, Ashish Jaiswal

Computer Science and Engineering Dissertations - Archive

Cognition is the mental process of acquiring knowledge and understanding through thought, experience, and senses. Fatigue is a loss in cognitive or physical performance due to physiological factors such as insufficient sleep, long work hours, stress, and physical exertion. It adversely affects the human body and can slow reaction times, reduce attention, and limit short-term memory. Hence, there is a need to monitor a person's state to avoid extreme fatigue conditions that can result in physiological complications. However, tools to understand and assess fatigue are minimal. This thesis primarily focuses on building an experimental setup that induces cognitive fatigue (CF) …

Are Bond Returns Predictable With Real-Time Macro Data?, Dashan Huang, Fuwei Jiang, Kunpeng Li, Guoshi Tong, Guofu Zhou

Research Collection Lee Kong Chian School Of Business

We investigate the predictability of bond returns using real-time macro variables and consider the possibility of a nonlinear predictive relationship and the presence of weak factors. To address these issues, we propose a scaled sufficient forecasting (sSUFF) method and analyze its asymptotic properties. Using both the existing and the new method, we find empirically that real-time macro variables have significant forecasting power both in-sample and out-of-sample. Moreover, they generate sizable economic values, and their predictability is not spanned by the yield curve. We also observe that the forecasted bond returns are countercyclical, and the magnitude of predictability is stronger during …

Development Of A Machine Learning System For Irrigation Decision Support With Disparate Data Streams, Eric Wilkening

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

In recent years, advancements in irrigation technologies have led to increased efficiency in irrigation applications, encompassing the adoption of practices that utilize data-driven irrigation scheduling and leveraging variable rate irrigation (VRI). These technological improvements have the potential to reduce water withdrawals and diversions from both groundwater and surface water sources. However, it is vital to recognize that improved application efficiency does not necessarily equate to increased water availability for future or downstream use. This is particularly crucial in the context of consumptive water use, which refers to water consumed and not returned to the local or sub-regional watershed, representing a …

Duties For Datasets, Jerrold Tsin Howe Soh

Research Collection Yong Pung How School Of Law

Machine learning (ML) systems are increasingly being deployed in contexts, such as law, medicine and finance, where system errors present serious and foreseeable risks. As ML system behaviour is largely determined by their training inputs, should dataset providers owe duties of care to victims? Using the ImageNet dataset and the Generative Pre-trained Transformer (GPT) models as case studies, this chapter argues that the conventional approach of centralising duties on system providers alone yields insufficient safeguards. Dataset-specific duties should also be considered to incentivise precaution in the preparation of crucial ML input. The chapter analyses how dataset duties may be encompassed …

Impact Of Weather Factors On Airport Arrival Rates: Application Of Machine Learning In Air Transportation, Robert W. Maxson, Dothang Truong, Woojin Choi

Publications

Weather is responsible for approximately 70% of air transportation delays in the National Airspace System, and delays resulting from convective weather alone cost airlines and passengers millions of dollars each year due to delays that could be avoided. This research sought to establish relationships between environmental variables and airport efficiency estimates by data mining archived weather and airport performance data at ten geographically and climatologically different airports. Several meaningful relationships were discovered from six out of ten airports using various machine learning methods within an overarching data mining protocol, and the developed models were tested using historical data.

Knowledge Integration For Human-In-The-Loop Machine Learning, Ervine Zheng

Theses

Machine learning has evolved into advanced techniques for vision, language, and applications in different areas. However, human expertise is still essential in providing meaningful interpretations of the semantics for tasks in knowledge-rich domains, such as medicine, science, and security intelligence. It is beneficial to incorporate human knowledge into a machine learning system, and we consider human-in-the-loop an increasingly important area for future research. Human-in-the-loop machine learning aims to develop a reliable prediction model with minimal costs by integrating human knowledge and experience. In this thesis, we propose human-in-the-loop methods to address knowledge-rich data understanding challenges for different machine-learning tasks. Specifically, …

Prediction Of Unplanned 30-Day Readmission Of Heart Failure Patients Using Lstm, Jesdin Raphael

Theses

In the context of heart failure, a leading cause of hospitalization in the United States, approximately 15\% of patients discharged within 30 days face readmission, contributing to escalated healthcare costs and compromised clinical outcomes. This study leverages a wealth of personal information extracted from Emergency Health Records (EHRs) spanning two decades. This research aims to enhance the predictive capabilities of re-hospitalization for cardiac patients by creating a two-stage LSTM model while incorporating features into regression and time series datasets. A critical aspect of this study involves ensuring the integration of all relevant features across both datasets. Including constant data elements, …

Accelerating Machine Learning Inference For Satellite Component Feature Extraction Using Fpgas., Andrew Ekblad

Theses and Dissertations

Running computer vision algorithms requires complex devices with lots of computing power, these types of devices are not well suited for space deployment. The harsh radiation environment and limited power budgets have hindered the ability of running advanced computer vision algorithms in space. This problem makes running an on-orbit servicing detection algorithm very difficult. This work proposes using a low powered FPGA to accelerate the computer vision algorithms that enable satellite component feature extraction. This work uses AMD/Xilinx’s Zynq SoC and DPU IP to run model inference. Experiments in this work centered around improving model post processing by creating implementations …

Addressing The Analytical And Computational Challenges Using Machine Learning In Biomedical Research, Yizhuo Wang

Dissertations and Theses (Open Access)

In the contemporary healthcare field, professionals are confronted with an ever-growing volume of clinical data stored in electronic health records, alongside the genomic data stemming from laboratory experiments. As a response to this deluge of data, the application of machine learning (ML) techniques is gaining popularity since ML techniques have demonstrated an exceptional proficiency in processing big data and deciphering complex nonlinear patterns that are intrinsic to biomedical research.

My research leverages ML's capabilities to address the computational challenges spanning diverse areas, including adaptive clinical trial designs, survival analysis, and high-dimensional genetic data analysis. Specifically, Chapter 2 focused on the …

Evaluation Of Evapotranspiration Estimates Using An Existing Hybrid Machine Learning Model In A Natural And A Managed Dryland Site, Katya Esquivel Herrera

Open Access Theses & Dissertations

Evapotranspiration (ET) is a critical component of the hydrologic cycle, encompassing both evaporative water loss from surfaces and transpiration through plant stomata. The environmental factors influencing ET include water and energy availability, atmospheric capacity for water uptake, and various meteorological variables. ET serves as a unique climate variable linking water, energy, and carbon cycles. In agroecosystems, accurate ET quantification is vital for optimizing water use efficiency, irrigation management, and crop yield. Traditional methods for ET estimation involve direct measurements and indirect models, with both presenting limitations.

Recent years have witnessed the integration of remote sensing and machine learning (ML) algorithms …

Human-Centric Smart Cities: A Digital Twin-Oriented Design Of Interactive Autonomous Vehicles, Oscar G. De Leon-Vazquez

Theses and Dissertations

Autonomous vehicle (AV) technology is introduced as a solution to improve transportation safety by eliminating traffic accidents caused by human error, which is the leading cause of 90% of accidents. One key feature of AVs is sensing and perceiving their surrounding environment through processing observations collected from the environment. The perception system is essential for an AV to make informed decisions and safely navigate the environment. This study presents an image semantic segmentation algorithm developed in the area of computer vision to improve AV perception. The U-Net-based algorithm is trained and validated using a synthetically generated dataset in a simulation …

Federated Active Learning For Network Intrusion Detection, Matthew D. R. Sauer

Theses and Dissertations

This thesis addresses challenges with detecting attacks on computer networks within a Federated Learning (FL) framework, when labeled instances are few. We explore the integration of active learning (AL) and semi-supervised learning (SSL). AL efficiently uses data that would otherwise be wasted or require substantial time for labeling. SSL provides capacity to train models that have a limited amount of labeled data, by utilizing additional unlabeled data that is available. We show how FL combined with AL or SSL can realize a detection system that adapts and trains quickly to new networks, reducing the total amount of data labeling needed. …

Algorithm Selection Using Edge Ml And Case-Based Reasoning, Rahman Ali, Muhammad Sadiq Hassan Zada, Asad Masood Khatak, Jamil Hussain

All Works

In practical data mining, a wide range of classification algorithms is employed for prediction tasks. However, selecting the best algorithm poses a challenging task for machine learning practitioners and experts, primarily due to the inherent variability in the characteristics of classification problems, referred to as datasets, and the unpredictable performance of these algorithms. Dataset characteristics are quantified in terms of meta-features, while classifier performance is evaluated using various performance metrics. The assessment of classifiers through empirical methods across multiple classification datasets, while considering multiple performance metrics, presents a computationally expensive and time-consuming obstacle in the pursuit of selecting the optimal …

Enhanced Privacy-Enabled Face Recognition Using Κ-Identity Optimization, Ryan Karl

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Facial recognition is becoming more and more prevalent in the daily lives of the common person. Law enforcement utilizes facial recognition to find and track suspects. The newest smartphones have the ability to unlock using the user's face. Some door locks utilize facial recognition to allow correct users to enter restricted spaces. The list of applications that use facial recognition will only increase as hardware becomes more cost-effective and more computationally powerful. As this technology becomes more prevalent in our lives, it is important to understand and protect the data provided to these companies. Any data transmitted should be encrypted …

Neuroimaging-Based Classification Of Ptsd Using Data-Driven Computational Approaches: A Multisite Big Data Study From The Enigma-Pgc Ptsd Consortium, Xi Zhu, Yoojean Kim, Orren Ravid, Xiaofu He, Benjamin Suarez-Jimenez, Sigal Zilcha-Mano, Amit Lazarov, Seonjoo Lee, Chadi G Abdallah, Michael Angstadt, Christopher L Averill, C Lexi Baird, Lee A Baugh, Jennifer U Blackford, Jessica Bomyea, Steven E Bruce, Richard A Bryant, Zhihong Cao, Kyle Choi, Josh Cisler, Andrew S Cotton, Judith K Daniels, Nicholas D Davenport, Richard J Davidson, Michael D Debellis, Emily L Dennis, Maria Densmore, Terri Deroon-Cassini, Seth G Disner, Wissam El Hage, Amit Etkin, Negar Fani, Kelene A Fercho, Jacklynn Fitzgerald, Gina L Forster, Jessie L Frijling, Elbert Geuze, Atilla Gonenc, Evan M Gordon, Staci Gruber, Daniel W Grupe, Jeffrey P Guenette, Courtney C Haswell, Ryan J Herringa, Julia Herzog, David Bernd Hofmann, Bobak Hosseini, Anna R Hudson, Ashley A Huggins, Jonathan C Ipser, Neda Jahanshad, Meilin Jia-Richards, Tanja Jovanovic, Milissa L Kaufman, Mitzy Kennis, Anthony King, Philipp Kinzel, Saskia B J Koch, Inga K Koerte, Sheri M Koopowitz, Mayuresh S Korgaonkar, John H Krystal, Ruth Lanius, Christine L Larson, Lauren A M Lebois, Gen Li, Israel Liberzon, Guang Ming Lu, Yifeng Luo, Vincent A Magnotta, Antje Manthey, Adi Maron-Katz, Geoffery May, Katie Mclaughlin, Sven C Mueller, Laura Nawijn, Steven M Nelson, Richard W J Neufeld, Jack B Nitschke, Erin M O'Leary, Bunmi O Olatunji, Miranda Olff, Matthew Peverill, K Luan Phan, Rongfeng Qi, Yann Quidé, Ivan Rektor, Kerry Ressler, Pavel Riha, Marisa Ross, Isabelle M Rosso, Lauren E Salminen, Kelly Sambrook, Christian Schmahl, Martha E Shenton, Margaret Sheridan, Chiahao Shih, Maurizio Sicorello, Anika Sierk, Alan N Simmons, Raluca M Simons, Jeffrey S Simons, Scott R Sponheim, Murray B Stein, Dan J Stein, Jennifer S Stevens, Thomas Straube, Delin Sun, Jean Théberge, Paul M Thompson, Sophia I Thomopoulos, Nic J A Van Der Wee, Steven J A Van Der Werff, Theo G M Van Erp, Sanne J H Van Rooij, Mirjam Van Zuiden, Tim Varkevisser, Dick J Veltman, Robert R J M Vermeiren, Henrik Walter, Li Wang, Xin Wang, Carissa Weis, Sherry Winternitz, Hong Xie, Ye Zhu, Melanie Wall, Yuval Neria, Rajendra A Morey

Faculty, Staff and Students Publications

Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group.

Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls …

Computer Vision In Osteoporotic Vertebral Fracture Risk Prediction: A Systematic Review, Anthony K Allam, Adrish Anand, Alex R Flores, Alexander E Ropper

Faculty, Staff and Students Publications

Osteoporotic vertebral fractures (OVFs) are a significant health concern linked to increased morbidity, mortality, and diminished quality of life. Traditional OVF risk assessment tools like bone mineral density (BMD) only capture a fraction of the risk profile. Artificial intelligence, specifically computer vision, has revolutionized other fields of medicine through analysis of videos, histopathology slides and radiological scans. In this review, we provide an overview of computer vision algorithms and current computer vision models used in predicting OVF risk. We highlight the clinical applications, future directions and limitations of computer vision in OVF risk prediction.

Neuroimaging-Based Classification Of Ptsd Using Data-Driven Computational Approaches: A Multisite Big Data Study From The Enigma-Pgc Ptsd Consortium, Jacklynn M. Fitzgerald

Psychology Faculty Research and Publications

Background

Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group.

Methods

We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls …

Fuzzycsampling: A Hybrid Fuzzy C-Means Clustering Sampling Strategy For Imbalanced Datasets, Abdullah Maraş, Çi̇ğdem Erol

Turkish Journal of Electrical Engineering and Computer Sciences

Classification model with imbalanced datasets is recently one of the most researched areas in machine learning applications since they induce to the emergence of low-performing machine learning models. The imbalanced datasets occur if target variables have an uneven number of examples in a dataset. The most prevalent solutions to imbalanced datasets can be categorized as data preprocessing, ensemble techniques, and cost-sensitive learning. In this article, we propose a new hybrid approach for binary classification, named FuzzyCSampling, which aims to increase model performance by ensembling fuzzy c-means clustering and data sampling solutions. This article compares the proposed approaches' results not only …