Digital Commons Network^™

A Simple, Interpretable Machine Learning Model Based On Clinical Factors Accurately Predicts Incident Dysplasia Or Malignancy In Barrett's Esophagus, Ashwin Rao, Jasmine Haydel, Samuel Ma, Aaron P Thrift, Theresa Nguyen-Wenker, Hashem B El-Serag

Faculty, Staff and Students Publications

Purpose: Identifying patients likely to develop dysplasia or malignancy is critical for effective surveillance in patients with Barrett's Esophagus (BE). However, current predictive models are limited. We evaluated the performance of machine learning (ML) models in predicting incident dysplasia or malignancy in a cohort of veteran patients with BE.

Methods: We analyzed data from 598 patients newly diagnosed with non-dysplastic BE (NDBE), BE indefinite for dysplasia (BE-IND), and BE with non-persistent low-grade dysplasia (LGD) at the Michael DeBakey Veterans Affairs Medical Center from November 1990 to January 2019 with follow-up through January 2024. Progressors were patients who developed persistent LGD, …

Machine Learning Based Medical Ultrasound Image Classification And Grad-Cam Interpretation, Victoria C. Hemphill

All Theses

This work takes a step in creating a diagnostic tool for the classification decision process of Achilles tendinopathy using ultrasound images. An attention-based multiple instance learning model is developed to classify the images. Typically, doctors capture multiple ultrasound images of the Achilles tendon during a study to determine a complete diagnosis. Multiple instance models adopt this behavior by providing a single label for a set of instances (images). The images are grouped into ”bags” at the study level and passed into the model. The MIL model then uses its attention property to assign an importance score to each image to …

Potential Impact Of Artificial Intelligence In Cardiovascular Medicine, Katherine Konczak, Elizabeth G. Dieter, Robert S. Dieter Ii, Robert S. Dieter Md, Rvt, Alm, Aravinda Nanjundappa Md

Marshall Journal of Medicine

Artificial intelligence (AI), particularly machine learning (ML), is increasingly transforming cardiovascular medicine by enhancing diagnostic accuracy, imaging analysis, risk prediction, and treatment planning. AI applications in imaging, such as echocardiography, cardiac computed tomography (CT), and magnetic resonance imaging (MRI), improve detection, automate measurements, and reduce variability. Deep learning models interpret electrocardiograms (ECGs) with high accuracy in electrophysiology, aiding early diagnosis of arrhythmias and inherited syndromes. In cardio-oncology, AI identifies cardiovascular risk markers in cancer patients and monitors therapy-related cardiotoxicity. AI-driven tools also support real-time procedural guidance and individualized therapy using electronic health records and imaging data. Despite promising outcomes, concerns …

Gene Regulatory Network Prediction Using Machine Learning, Deep Learning, And Hybrid Approaches, Sai Teja Mummadi, Md Khairul Islam, Victor Busov, Hairong Wei

Michigan Tech Publications

Construction of gene regulatory networks (GRNs) is essential for elucidating the regulatory mechanisms underlying metabolic pathways, biological processes, and complex traits. In this study, we developed and evaluated machine learning, deep learning, and hybrid approaches for constructing GRNs by integrating prior knowledge and large-scale transcriptomic data from Arabidopsis thaliana, poplar, and maize. Among these, hybrid models that combined convolutional neural networks and machine learning consistently outperformed traditional machine learning and statistical methods, achieving over 95% accuracy on the holdout test datasets. These models not only identified a greater number of known transcription factors regulating the lignin biosynthesis pathway but also …

A Survey Of Image Forensics: Exploring Forgery Detection In Image Colorization, Saurabh Agarwal, Deepak Sharma, Nancy Girdhar, Cheonshik Kim, Ki-Hyun Jung

School of Engineering, Computing and Mathematics

In today’s digital era, the rapid evolution of image editing technologies has brought about a significant simplification of image manipulation. Unfortunately, this progress has also given rise to the misuse of manipulated images across various domains. One of the pressing challenges stemming from this advancement is the increasing difficulty in discerning between unaltered and manipulated images. This paper offers a comprehensive survey of existing methodologies for detecting image tampering, shedding light on the diverse approaches employed in the field of contemporary image forensics. The methods used to identify image forgery can be broadly classified into two primary categories: classical machine …

Comparative Study Of Machine Learning Models For Predicting The Market Value Of Professional Football Players, Álvaro Salvador López

Master's Theses or Doctor of Nursing Practice

The market value of professional football players is a critical factor in decision-making for clubs, agents, and analysts. Accurate player valuation impacts transfers, contract negotiations, and financial planning. In recent years, data-driven approaches have emerged to support traditional scouting with predictive analytics. This thesis presents a comparative study of machine learning models to estimate the market value of football players based on historical performance and personal attributes.

This thesis presents a comparative study of two independently developed machine learning systems designed to predict the market value of football players for the 2020–2021 season. Both systems were trained using real data …

Implementing A Health Recommendation System From Wearable Data, Priti Pramanick

Doctoral Theses

The rising interest in personalized health monitoring has created a demand for intelligent systems that not only evaluate an individual’s health status but also offer actionable recommendations. This dissertation presents a data-driven approach to assess overall health by calculating a weekly health score using multi-dimensional data sources such as sleep patterns, nutrition, cardiovascular activity, fitness levels, and metabolic parameters. The system integrates and processes data stored in MongoDB using Python, applies scoring logic tailored to each health domain, and aggregates them into a unified health score. Addi- tionally, the system generates a detailed summary and leverages a language model to …

What Drives Weight Status Among Female University Students? A Machine Learning Analysis Of Sociodemographic, Dietary, And Lifestyle Determinants, Radwan Qasrawi, Abir Ajab, Leila Cheikh Ismail, Ayesha Al Dhaheri, Sharifa Alblooshi, Razan Abu Ghoush, Stephanny Vicuna Polo, Malak Amro, Suliman Thwib, Ghada Issa, Haleama Al Sabbah

All Works

Background: Obesity and underweight are increasingly common among young adult women, often resulting from complex interactions between diet, lifestyle, and socioeconomic factors. This study addresses that gap by applying machine learning to a wide range of behavioral, dietary, and demographic data. The main research question asks: What are the key factors influencing weight status among female university students, and how accurately can machine learning models identify them? We hypothesize that different factors are significantly associated with underweight, overweight, and obesity, and that machine learning can reliably detect these patterns. The aim is to identify the strongest predictors and support more …

Detecting Android Malware Based On Static Analysis Using Classification And Modified Clustering Techniques, Abdullah Allawi Al-Sraratee, Ahmed Habeeb Al-Azawei

Journal of Intelligent Informatics, Networking, and Cybersecurity

Because Android malware harms internet security, prior research proposes several different approaches to detect it accurately. However, such proposed models depend on numerous number of features to attain high accuracy. This could lead to high computation cost and potential overfitting. Furthermore, manual data labeling is labor-intensive, requiring significant human effort and skills. This research aims to: 1) extend previous literature on Android malware detection, 2) improve the accuracy of Android malware detection based on a low number of features, and 3) modify a clustering technique to group data into two different clusters to address the issue of unlabeled data. To …

Real-Time Fraud Detection Using Big Data, Abdulla Matar Alketbi

Theses

In today’s digital world, fraud detection has become an important part of financial security. This study explores and compares the performance of different machine learning models in identifying fraudulent transactions using the IEEE-CIS Fraud Detection dataset. Techniques such as Random Forest, Gradient Boosting, Deep Neural Networks, and Logistic Regression were evaluated. The dataset was pre-processed using SMOTE to balance the classes and improve model sensitivity to fraud cases. Each performance of the model was assessed using accuracy, precision, recall, and F1-score. The Random Forest model achieved the highest overall performance with an F1-score of 99.23

Predicting Sleep And Sleep Stage In Children Using Actigraphy And Heartrate Via A Long Short-Term Memory Deep Learning Algorithm: A Performance Evaluation, Robert Weaver Med, Phd, James White, Olivia Finnegan, Hongpeng Yang, Zifei Zhong, Keagan Kiely, Catherine Jones, Yan Tong, Srihari Nelakuditi, Rahul Ghosal, David E. Brown, Russell R. Pate Ph.D., Gregory J. Welk, Massimiliano De Zambotti, Yuan Wang, Sarah Burkart, Elizabeth L. Adams Phd, Bridget Armstrong, Michael Beets Med, Mph, Phd

Faculty Publications

Children's ambulatory sleep is commonly measured via actigraphy. However, traditional actigraphy measured sleep (e.g., Sadeh algorithm) struggles to predict wake (i.e., specificity, values typically < 70) and cannot predict sleep stages. Long short-term memory (LSTM) is a machine learning algorithm that may address these deficiencies. This study evaluated the agreement of LSTM sleep estimates from actigraphy and heartrate (HR) data with polysomnography (PSG). Children (N = 238, 5–12 years,52.8% male, 50% Black 31.9% White) participated in an overnight laboratory polysomnography. Participants were referred be-cause of suspected sleep disruptions. Children wore an ActiGraph GT9X accelerometer and two of three consumer wearables(i.e., Apple Watch Series 7, Fitbit Sense, Garmin Vivoactive 4) on their non-dominant wrist during the polysomnogram. LSTM estimated sleep versus wake and sleep stage (wake, not-REM, REM) using raw actigraphy and HR data for each 30-s epoch. Logistic regression and random forest were also estimated as a benchmark for performance with which to compare the LSTM results. A 10-fold cross-validation technique was employed, and confusion matrices were constructed. Sensitivity and specificity were calculated to assess the agreement between research-grade and consumer wearables with the criterion polysomnography. For sleep versus wake classification, LSTM outperformed logistic regression and random forest with accuracy ranging from 94.1to 95.1, sensitivity ranging from 94.9 to 95.9 across different devices, and specificity ranging from 84.5 to 89.6. The addition of HR improved the prediction of sleep stages but not binary sleep versus wake. LSTM is promising for predicting sleep and sleep staging from actigraphy data, and HR may improve sleep stage prediction.

Towards Leveraging Social Media Data For Fostering Collaborations Among Non-Profits, Monazil Chowdhury

LSU Doctoral Dissertations

Nonprofit organizations serve a crucial role in tackling a wide range of significant social, environmental, and economic issues. But it is often hard to get a clear picture of their work because their information is spread out and it is difficult to see how they are collaborating. To address this issue we developed a web-based tool to collect scattered data—from a variety of sources, such as the IRS, social media, and the Census, into one easy-to-use resource. The tool begins by taking IRS records and geocoding each nonprofit’s physical address With its coordinates. It then retrieves census tract information from …

A Machine Learning Approach To Quantitative X-Ray Diffraction Analysis, Spencer Snow Chandler

Theses and Dissertations

X-ray Powder Diffraction (XRPD) is a powerful method in material sciences that gives insights into the atomical and crystallographic structure of a material, revealing information into the material's properties and suitability for industrial and scientific application. In geology, XRPD analysis is frequently leveraged to identify and quantify the present mineral phases in an unknown mixture. Despite it's widespread use, interpreting XRPD patterns requires highly-specialized knowledge, making the analysis largely dependent upon the background experience of the analyst. To assist experts, computational methods have been developed over the years. Some of these techniques involve fitting diffraction patterns using pseudo-Voigt functions, which …

The Feasibility Of Passively Tracking Children’S Tv Viewing And Mobile Device Use In Naturalistic Settings, Teresia M O'Connor, Tatyana Garza, Uzair Alam, Anil Kumar Vadathya, Jennette P Moreno, Alicia Beltran, Samah Haidar, Nimah Haidar, Sheryl O Hughes, Debbe Thompson, Salma M A Musaad, Tom Baranowski, Jason A Mendoza, Joseph Young, Akane Sano, Ashok Veeraraghavan

Faculty, Staff and Students Publications

Research on children's technology and digital media (TDM) is hampered by a lack of robust approaches for assessing TDM use. This study assessed the feasibility of passively measuring children's TV screens and mobile devices (TDM) in a naturalistic setting. In the three-day feasibility study, FLASH-TV was set up on one to two TVs the child (5-12 year olds) typically used in the home (n=20). Children's mobile device use was assessed with either the Chronicle App or ScreenTime screenshots. Parents completed three TDM diaries. An exit interview with the parent explored their perceptions of the assessments and the child's TDM use …

Seeking Structure In Complex Systems: From Feature Analysis To Space-Time Causal Discovery With Earth Science Applications, Jeffrey J. Nichol

Computer Science ETDs

Complex systems are difficult to study because of their many interacting parts, emergent phenomena, and feedback loops. These systems underpin all life on Earth. We need improved tools for seeking an understanding of them. This body of research presents my investigations into data-driven methods for understanding complex systems, including my invention of a novel causal discovery meta-algorithm for space-time gridded data. I demonstrated machine learning feature importance and causal discovery capabilities for comparing simulated and observed climate data. I developed a new benchmark for modeling space-time dynamics of locally driven phenomena and examined a prominent causal discovery algorithm. Finding that …

Benford's Law In Basic Rnn And Long Short-Term Memory And Their Associations, Farshad Ghassemi Toosi

Department of Computer Science Publications

Benford's Law describes the distribution of numerical patterns, specifically focusing on the frequency of the leading digit in a set of natural numbers. It divides these numbers into nine groups based on their first digit, with the largest category comprising numbers beginning with 1, followed by those starting with 2, and so on. Each neuron within a neural network (NN) is associated with a numerical value called a weight, which is updated according to specific functions. This research examines the Degree of Benford's Law Existence (DBLE) across two language model methodologies: (1) recurrent neural networks (RNNs) and (2) long short-term …

From Imaging To Computational Domains For Physics-Driven Molecular Biology Simulations: Hindered Diffusion In Platelet Masses, Catherine House, Ziyi Huang, Kaushik Shankar, Sandra Young, Meghan Roberts, Scott Diamond, Maurizio Tomaiuolo, Timothy Stalker, Lu Lu, Talid Sinno

Cardeza Foundation for Hematologic Research

When formed in vivo, murine hemostatic thrombi exhibit a heterogeneous architecture comprised of distinct regions of densely and sparsely packed platelets. In this study, we utilize high-resolution electron microscopy alongside machine learning and physics-based simulations to investigate how such clot microstructure impacts molecular diffusivity. We used Serial Block Face - Scanning Electron Microscopy (SBF-SEM) to image select volumes of hemostatic masses formed in a mouse jugular vein, producing high-resolution 2D images. Images were segmented using machine learning software (Cellpose), whose training was augmented by manually segmented images. The segmented images were then utilized as 2D computational domains for Lattice Kinetic …

Exploring System Identification Of Non-Linear Dynamics Using The Weighted Composition Operator And The Liouville Operator, Chukwuebuka Amagwula

USF Tampa Graduate Theses and Dissertations

System identification is the process of determining mathematical models that describe the dynamics of a system from data. Dynamic Mode Decomposition (DMD) and Sparse Identification of Nonlinear Dynamical Systems (SINDy) are two distinct approaches used for this purpose.

DMD identifies dominant spatiotemporal modes and eigenvalues that describe the evo lution of a system. It assumes a near-linear representation of dynamics and is closely linked to the Koopman operator, making it ideal for analyzing fluid flows, oscillatory systems, and modal structures. The DMD method uses time series data where each data point is referred to as a snapshot and represents the …

Machine Learning Techniques For Optimizing The Efficiency And Costs Of Drill Steel In Sandstone And Granodiorite: A Case Study In Peru, Marco Cotrina, Jairo Marquina, Jose Mamani, Solio Arango, Eusebio Antonio, Eduardo Noriega, Teofilo Donaires, Dominga Cano

Journal of Sustainable Mining

This research aims to optimize the efficiency and costs of drilling steel in sandstone and granodiorite rocks using machine learning techniques in a Peruvian mine. Predictive models, including random forest (RF), XGBoost (XGB), decision trees (DT), and artificial neural networks (ANN), were applied, along with optimization algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), and simulated annealing (SA). A dataset of 705 entries was analyzed, focusing on drill bit wear, percussion and rotation pressures, and cost per meter drilled. Model performance was evaluated using R², RMSE, MAE, and MAPE. The ANN model …

Process-Grounded Knowledge-Infused Learning And Decision Making, Kaushik Roy

Theses and Dissertations

This dissertation introduces process-grounded knowledge-infused learning and reasoning, a novel framework for integrating domain-expertise-based process knowledge into the learning and reasoning mechanisms of artificial intelligence systems. This approach is designed to produce controlled, transparent, and reliable predictions in critical tasks such as medical diagnosis and recommendation. By focusing on the case study of mental illness diagnosis and recommendation—where decision-making must be grounded in processes such as disorder-specific diagnostic criteria—this work demonstrates methods to embed structured decision-making directly into the system architecture during both training and inference. This integration facilitates end-to-end training and reasoning while ensuring that outputs strictly adhere to …

Two Essays In Finance: Can Machines Better Predict Insider Trading? And Csr And Firm Value Following Dividend Cuts: International Evidence, Solmaz Batebi

Theses and Dissertations

This dissertation consists of two essays. In the first paper, I find that machine learning (ML) models predict the likelihood and magnitude of insider trading significantly better than linear models such as OLS and logistic regression. I use ML models, including LASSO, Random Forest, and eXtreme Gradient Boosting, optimizing model parameters through Bayesian hyperparameter tuning to identify the best configuration. Additionally, I apply SHAP values to better understand the determinants of insider trading. I also use Gaussian Thompson Sampling (GTS) to explore the sources of insiders’ market-timing capabilities. I find that ML models can boost the R² for models predicting …

A Visual-Omics Foundation Model To Bridge Histopathology With Spatial Transcriptomics, Weiqing Chen, Pengzhi Zhang, Tu N Tran, Yiwei Xiao, Shengyu Li, Vrutant V Shah, Hao Cheng, Kristopher W Brannan, Keith Youker, Li Lai, Longhou Fang, Yu Yang, Nhat-Tu Le, Jun-Ichi Abe, Shu-Hsia Chen, Qin Ma, Ken Chen, Qianqian Song, John P Cooke, Guangyu Wang

Faculty, Staff and Student Publications

Artificial intelligence has revolutionized computational biology. Recent developments in omics technologies, including single-cell RNA sequencing and spatial transcriptomics, provide detailed genomic data alongside tissue histology. However, current computational models focus on either omics or image analysis, lacking their integration. To address this, we developed OmiCLIP, a visual-omics foundation model linking hematoxylin and eosin images and transcriptomics using tissue patches from Visium data. We transformed transcriptomic data into 'sentences' by concatenating top-expressed gene symbols from each patch. We curated a dataset of 2.2 million paired tissue images and transcriptomic data across 32 organs to train OmiCLIP integrating histology and transcriptomics. Building …

Causal Neuro-Symbolic Artificial Intelligence: Synergy Between Neuro-Symbolic And Causal Artificial Intelligence, Utkarshani Jaimini

Theses and Dissertations

Understanding and reasoning about cause and effect is innate to human cognition. In everyday life, humans continuously engage in causal reasoning and hypothetical retrospection to make decisions, plan actions, and interpret events. This cognitive ability allows us to ask questions such as: “What caused this situation?”, “What will happen if I take this action?”, or “What would have happened had I chosen differently?” This intuitive capacity to form mental models of the world, infer causal relationships, and reason about alternative scenarios, particularly counterfactuals, is central to our intelligence and adaptability. In contrast, current machine learning (ML) and artificial intelligence (AI) …

Approaches To Enhancing Multiple Hypothesis Testing Methods With Side-Information, Siyu Zheng

Theses and Dissertations

Lesion-symptom mapping (LSM) studies offer insight into the brain areas involved in various aspects of cognition. This is commonly done via behavioral testing in patients with a naturally occurring brain injury or lesions (e.g., strokes or brain tumors). This results in high-dimensional observational data where lesion status (present/absent) is non-uniformly distributed, with some voxels having lesions in very few (or no) subjects. In this situation, mass univariate hypothesis tests have severe power heterogeneity where many tests are known a priori to have little to no power. Additionally, high-dimensional observational data can be grouped according to brain anatomical structure.

In this …

Leveraging Lime For Trustworthy Apple Quality Assessment, İsmail Kırbaş, Ahmet Çifci

Platform: A Journal of Engineering

This study explores the integration of machine learning (ML) models and explainable artificial intelligence (XAI) techniques to enhance the accuracy and transparency of apple quality assessment. Leveraging the Apple Quality dataset from Kaggle, we evaluated the performance of random forest (RF), logistic regression (LR), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) across accuracy, precision, recall, and F1-score metrics. Among these models, RF demonstrated superior performance, achieving an accuracy of 87.6%, underscoring its robustness in managing complex feature interactions. A novel contribution of this study lies in employing the local interpretable model-agnostic explanations (LIME) method to elucidate model predictions, …

Estimating Order Picking Distances Using Machine Learning, Hector J. Carlo, Carlos A. Morel-Figueroa, Victoria Méndez-González, Yamilette Boscio-Sánchez, Alex Demel-Pacheco

International Material Handling Research Colloquium

No abstract provided.

Identification Accuracy Of Cost-Effective Visible (Vis) To Near-Infrared (Nir) Sensor As7265x On Plastic Waste In The Philippines, Ma. Tricia Jasmin M. Ocho, Chloe Lizanne R. Bravo, Jennika Love R. Loyola, Kierra Jamielle V. Taroma, Leianne Nina Arabelle E. Lampayan

DLSU Senior High School Research Congress

In the Philippines, plastic waste persists as a significant issue caused by the widespread use of single-use plastics and the lack of recycling systems. The lack of proper systems made waste segregation and identification inefficient and disorganized. Past studies explain the use of VIS/NIR in plastic segregation and potential of spectral frequencies. However, there is limited investigation of the AS7265x sensor’s consistency when integrated with machine learning to further improve detection reliability; thus, the study evaluates the effectiveness of the AS7265x triad spectroscopy sensor, incorporated with machine learning algorithms. An experimental setup was created using the AS7265x sensor and Arduino …

Classification Of Human Trust In Ai Using Brain Activity Data, Danushka Bandara, Ruhuan Liao, Fatima Chowdhury, Leslie Abbott

Northeast Journal of Complex Systems (NEJCS)

Trust plays a crucial role in human-computer interaction, particularly in scenarios involving artificial intelligence (AI) systems. This study explores the feasibility of using functional near-infrared spectroscopy (fNIRS) data to classify trust levels in human-AI interaction scenarios. A total of 18 participants completed an image classification task with an AI team member while their hemodynamic responses were recorded using fNIRS. Preprocessing of fNIRS data involved motion artifact removal, filtering, and normalization. Exploratory analysis identified significant associations between hemodynamic responses in the prefrontal cortex and trust levels. An across-subject binary trust classification model was developed using machine learning techniques, achieving an F1 …

Enhancing Software Effort Estimation: A Comparative Analysis Of Machine Learning Models With Correlation-Based Feature Selection, Muhammad Abid, Momina Liaqat Ali

Sustainable Machine Intelligence Journal

Software effort estimation is a critical process in the management of software projects, essential for predicting the resources and time required for application development. Accurate estimation is paramount for delivering projects on time and computational resources. This paper investigates techniques that utilize machine learning models to ameliorate software effort estimation. We employ four popular machine learning models including the Linear Regression, Gradient Boosting, Random Forest, and Decision Tree and evaluate their performance across five diverse climate-related datasets: Antarctica Hotpoints/Climate Change NASA, Crop Production and Climate Change, Global Warming on Earth (Global Temp), Climate Change, and Climate Treasure (Marine). A key …

Proactive Risk Identification In New Zealand Infrastructure Projects: A Machine Learning Approach To Classifying Project Risk, Dr Tirth Patel, Assoc. Prof. Eric Scheepbouwer, Dr. Jacobus Daniel Van Der Walt

CIB Conferences

In the construction industry, proactive identification and management of challenges such as delays, budget constraints, and regulatory hurdles are essential for effective risk management and successful project delivery. Traditional methods, relying on manual analysis and expert judgment, are often time-consuming, inconsistent, and subjective. This study proposes a data-driven framework to automate the identification and classification of challenges within project descriptions using Natural Language Processing (NLP) and machine learning (ML) techniques. Utilizing the Pacificon dataset, comprising 254,923 infrastructure projects across New Zealand, the study defines challenge categories through regular expressions and implements a machine learning pipeline with TfidfVectorizer and Logistic Regression. …