Digital Commons Network^™

Comparing Pca-Based Machine Learning Algorithms For Covid-19 Classification Using Chest X-Ray Images, Hussein Ahmed Ali, Walid Hariri, Nadia Smaoui Zghal, Dalenda Ben Aissa

Baghdad Science Journal

The rapid spread of the COVID-19 pandemic has strained global healthcare systems, necessitating efficient diagnostic methods. While Polymerase Chain Reaction (PCR) and antigen tests are common, they have limitations in speed and precision. Enhancing the accuracy of imaging techniques, especially Chest X-rays (CXR) and Computerized Tomography (CT) scans, is crucial for detecting COVID-19-related lung abnormalities. CXR, being cost-effective and accessible, is preferred over CT scans, but accurate diagnosis often requires technological support. To address this, an extensive dataset of CXR images categorized into five classes is available on Kaggle. Processing such data involves steps like grayscale conversion, image intensity adjustment, …

Iegenes: A Machine Learning Method For Selecting Differentially Expressed Genes In Cancer Studies, Xiao-Lei Xia, Shang-Ming Zhou, Yunguang Liu, Na Lin, Ian Overton

School of Nursing and Midwifery

Gene selection is crucial for cancer classification using microarray data. In the interests of improvingcancer classification accuracy, in this paper, we developed a new wrapper method called ieGENES for geneselection. First we proposed a parsimonious kernel machine regularization (PKMR) model by usingridge regularization in kernel machine driven classification to tackle multi-collinearity for the sake of stableestimates in high-dimensional settings. Then the ieGENES algorithm was developed to optimally identifyrelevant genes while iteratively eliminating redundant ones based on leave-one-out cross-validation accuracy.In particular, we developed a new methodology to optimally update model parameters upon gene removal.The ieGENES algorithm was evaluated on six cancer …

A Review Of Racial Differences And Disparities In Ecg, Jianwei Zheng, Chizobam Ani, Islam Abudayyeh, Yunfan Zheng, Cyril Rakovski, Ehsan Yaghmaei, Omolola Ogunyemi

Mathematics, Physics, and Computer Science Faculty Articles and Research

The electrocardiogram (ECG) is a widely used, non-invasive tool for diagnosing a range of cardiovascular conditions, including arrhythmia and heart disease-related structural changes. Despite its critical role in clinical care, racial and ethnic differences in ECG readings are often underexplored or inadequately addressed in research. Variations in key ECG parameters, such as PR interval, QRS duration, QT interval, and T-wave morphology, have been noted across different racial groups. However, the limited research in this area has hindered the development of diagnostic criteria that account for these differences, potentially contributing to healthcare disparities, as ECG interpretation algorithms largely developed from major …

A Comprehensive Survey Of Data-Driven Solutions For Lorawan: Challenges And Future Directions, Poonam Maurya, Abhishek Hazra, Preti Kumari, Troels Bundgaard Sørensen, Sajal K. Das

Computer Science Faculty Research & Creative Works

Long-range Wide-area Network (LoRaWAN) is an innovative and prominent communication protocol in the domain of Low-power Wide-area Networks (LPWAN), known for its ability to provide long-range communication with low energy consumption. However, the practical implementation of the LoRaWAN protocol, operating at the Medium Access Control layer and specially built to work upon the LoRa physical layer, presents numerous research challenges, including network congestion, interference, optimal resource allocation, collisions, scalability, and security. To mitigate these challenges effectively, the adoption of cutting-edge data-driven technologies such as Deep Learning (DL) and Machine Learning (ML) emerges as a promising approach. Interestingly, very few existing …

Predicting Diabetes Self-Management Education Engagement: Machine Learning Algorithms And Models, Xiangxiang Jiang, Gang Lv, Minghui Li, Jing Yuan, Z Kevin Lu

Faculty Publications

Introduction Diabetes self-management education (DSME) is endorsed by the American Diabetes Association (ADA) as an essential component of diabetes management. However, the utilization of DSME remains limited in the USA. This study aimed to investigate current DSME participation among the older population and to identify comprehensive factors of DSME engagement through employing various machine learning (ML) models based on a US nationally representative survey linked to claims data.

Research design and methods Data from the Medicare Current Beneficiary Survey were employed, and this study included data on US Medicare beneficiaries with diabetes from 2017 to 2019. Comprehensive variables following the …

Machine Learning-Enhanced Surface-Enhanced Spectroscopic Detection Of Polycyclic Aromatic Hydrocarbons In The Human Placenta, Oara Neumann, Yilong Ju, Andres B Sanchez-Alvarado, Guodong Zhou, Weiwu Jiang, Bhagavatula Moorthy, Melissa A Suter, Ankit Patel, Peter Nordlander, Naomi J Halas

Faculty, Staff and Students Publications

The detection and identification of polycyclic aromatic hydrocarbons (PAHs) and their derivatives, polycyclic aromatic compounds (PACs), are essential for environmental and health monitoring, for assessing toxicological exposure and their associated health risks. PAHs/PACs are the most dangerous chemicals found in tobacco smoke, and cigarette use during pregnancy can convey these molecules to the developing fetus through the placenta. This exposure is associated with many negative health outcomes, from premature birth to sudden infant death syndrome and adverse neurodevelopmental disorders. This study demonstrates the use of surface-enhanced Raman and surface-enhanced infrared absorption spectroscopies for direct detection of PAHs/PACs in human placental …

Systematic Evaluation Of Regression Models For The Prediction Of Influenza Cases, Mukul Kandwal

Theses

This study systematically evaluates machine learning models to forecast influenza outbreaks, aiming to enhance public health preparedness through accurate predictions. Laboratory-confirmed influenza case data from the New York State Department of Health (2009–2023) were used to train and test five regression models—XGBoost, Neural Network (MLP regressor), K-Nearest Neighbors (KNN), Random Forest, and Support Vector Regression (SVR)—with preprocessing techniques including differencing and phasing. A leave-one-season-out cross-validation framework and Symmetric Mean Absolute Percentage Error (SMAPE) were employed to rigorously assess performance. Key findings reveal that the Neural Network (MLP regressor) outperformed other models, achieving the lowest SMAPE score (23.7), underscoring the critical …

A Deep Learning-Based System For Automatic Detection Of Emesis With High Accuracy In Suncus Murinus, Zengbing Lu, Yimeng Qiao, Xiaofei Huang, Dexuan Cui, Julia Y H Liu, Man Piu Ngan, Luping Liu, Zhixin Huang, Zi-Tong Li, Lingqing Yang, Aleena Khalid, Yingyi Deng, Sze Wa Chan, Longlong Tu, John A Rudd

Children’s Nutrition Research Center Staff Publications

Quantifying emesis in Suncus murinus (S. murinus) has traditionally relied on direct observation or reviewing recorded behaviour, which are laborious, time-consuming processes that are susceptible to operator error. With rapid advancements in deep learning, automated animal behaviour quantification tools with high accuracy have emerged. In this study, we pioneere the use of both three-dimensional convolutional neural networks and self-attention mechanisms to develop the Automatic Emesis Detection (AED) tool for the quantification of emesis in S. murinus, achieving an overall accuracy of 98.92%. Specifically, we use motion-induced emesis videos as training datasets, with validation results demonstrating an accuracy of 99.42% for …

Artificial Intelligence (Ai) In Pharmacy, Giang Nguyen, Elizabeth Sartschev, John Reyes, Allie Honigford, Marisa Petrunich, Kiley Devoll, Brianna Lu, Joshua Honaker, T'Bony M. Jewell

Pharmacy and Wellness Review

Artificial Intelligence (AI) has transformed the pharmaceutical field by enabling computer software systems to learn and perform human behavior. Specifically, AI has revolutionized chronic diabetes management through continuous glucose monitoring, showcasing its immense potential in healthcare. However, alongside its transformative impact, AI’s increasing role in healthcare has prompted concerns over privacy and its premature integration. Despite these challenges, AI offers limitless opportunities to improve medication management and treatment regimens, driving advancements across various domains. From improving CT imaging to enhancing adenoma detection in colonoscopies and facilitating medication adherence, AI’s impact on healthcare is profound. Furthermore, AI plays a pivotal role …

Applicable And Generalizable Machine Learning For Intelligent Welding In Automotive Manufacturing, Peng Edward Wang, Xijia Zhao

Faculty Scholarship

This review paper examines the application and challenges of machine learning (ML) in intelligent welding processes within the automotive industry, focusing on resistance spot welding (RSW) and laser welding. RSW is predominant in body-in-white assembly, while laser welding is critical for electric vehicle battery packs due to its precision and compatibility with dissimilar materials. The paper categorizes ML applications into three key areas: sensing, in-process decision-making, and post-process optimization. It reviews supervised learning models for defect detection and weld quality prediction, unsupervised learning for feature extraction and data clustering, and emerging generalizable ML approaches like transfer learning and federated learning …

Retracted: A Review Of Breast Cancer Histological Image Classification: Challenges And Limitations, Israa Faisal Jassam, Abdulrahman Abbas Mukhlif, Ahmed Adil Nafea, Mustafa Adnan Tharthar, Ahmed Isam Khudhair

Iraqi Journal for Computer Science and Mathematics

This paper comprehensively reviews the classification of breast cancer histological images. The paper discusses the research objectives, methodologies used, and conclusions drawn, as well as suggestions for the future. The study is based on the ICIAR 2018 database, which is considered one of the largest databases available to support this research. The paper also addresses major challenges such as lack of data, variation in tissue preparation, class imbalance, and computational requirements. Advanced techniques such as deep learning (DL), transfer learning and data augmentation are explored, along with innovative models such as convolutional neural networks (CNNs) and generative adversarial networks (GANs). …

On The Books In South Carolina: Mining For Jim Crow Laws, Kate F. Boyd, Vandana Srivastava, Lance Dupre, Christopher Frear, Nitin Gupta, Bobby Donaldson

Faculty and Staff Publications

On the Books in South Carolina: Mining for Jim Crow Laws is a collections-as-data and machine learning project by the University of South Carolina Libraries (USC), sub awarded by the University of North Carolina at Chapel Hill (UNC), and made possible by The Andrew W. Mellon Foundation, for the period of May 2022 - December 2024. Following UNC’s steps from their first year of the grant, the USC project created a text corpus of South Carolina state legislature acts passed in the period from Reconstruction through the Civil Rights Movement (1868-1968). The USC team then utilized machine learning techniques to …

Uav-Based Phytoforensics: Hyperspectral Image Analysis To Remotely Detect Explosives Using Maize (Zea Mays), Paul V. Manley, Stephen M. Via, Joel G. Burken

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Remnant explosive devices are a deadly nuisance to both military personnel and civilians. Traditional mine detection and clearing is dangerous, time-consuming, and expensive. And routine production and testing of explosives can create groundwater contamination issues. Remote detection methods could be rapidly deployed in vegetated areas containing explosives as they are known to cause stress in vegetation that is detectable with hyperspectral sensors. Hyperspectral imagery was employed in a mesocosm study comparing stress from a natural source (drought) to that of plants exposed to two different concentrations of Royal Demolition Explosive (RDX; 250 mg kg−1, 500 mg kg−1). Classification was accomplished …

Constructing A Parameterized Stimuli Space Suitable To Induce Controlled Changes In Human And Machine Perceptual Systems, Andrew Frankel

Dissertations, Theses, and Capstone Projects

Traditional computer vision applications focus on objective tasks, such as object detection, classification, or segmentation. Much of human experience is inherently subjective, such as our personal response to artwork. Individualized experiences are challenging to replicate in a controlled laboratory environment, as those experiences depend on the unique history and internal models of the observer. This work presents a technique to construct a 2-dimensional parameterized stimuli space suitable to induce controlled changes in the perceptual systems of both human and machine observers. Our contributions are threefold:

1. Using a balanced dataset of 8,800 fine art images spanning 11 art movements and 88 …

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The Influence Of A Deep Learning Tool On The Performance Of Oral And Maxillofacial Radiologists In The Detection Of Apical Radiolucencies, Manal Hamdan, Sergio E. Uribe, Lyudmila Tuzova, Dmitry Tuzoff, Zaid Badr, Andre Mol, Donald A. Tyndall

School of Dentistry Faculty Research and Publications

Objectives

This study aimed to assess the impact of a deep learning model on oral radiologists’ ability to detect periapical radiolucencies on periapical radiographs. The secondary objective was to conduct a regression analysis to evaluate the effects of years of experience, time to diagnose, and specialty.

Methods

This study used an annotated dataset and a beta version of a deep learning model (Denti.AI). The testing subset comprised 68 intraoral periapical radiographs confirmed with cone-beam computed tomography for the presence/absence of apical radiolucencies. Four oral radiologists participated in a cross-over reading scenario, analysing the radiographs under 2 conditions: initially without AI …

Exploring Key Factors Influencing Depressive Symptoms Among Middle-Aged And Elderly Adult Population: A Machine Learning-Based Method, Ngoc Doan Thu Tran, Yi Zhen Tan, Sapphire Lin, Fang Zhao, Yee Sien Ng, Dong Ma, Jeonggil Ko, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

Objective: This paper aims to investigate the key factors, including demographics, socioeconomics, physical wellbeing, lifestyle, daily activities and loneliness that can impact depressive symptoms in the middle-aged and elderly population using machine learning techniques. By identifying the most important predictors of depressive symptoms through the analysis, the findings can have important implications for early depression detection and intervention. Participants: For our cross-sectional study, we recruited a total of 976 volunteers, with a specific focus on individuals aged 50 and above. Each participant was requested to provide their demographic, socioeconomic information and undergo several physical health tests. Additionally, they were asked …

Predicting Individual Long-Term Prognosis Of Spatial Neglect Based On Acute Stroke Patient Data, Lisa Röhrig, Daniel Wiesen, Dongyun Li, Chris Rorden, Hans-Otto Karnath

Faculty Publications

One of the most pressing questions after a stroke is whether an individual patient will recover in the long term. Previous studies demonstrated that spatial neglect—a common cognitive deficit after right hemispheric stroke—is a strong predictor for poor performance on a wide range of everyday tasks and for resistance to rehabilitation. The possibility of predicting long-term prognosis of spatial neglect is therefore of great relevance. The aim of the present study was to test the prognostic value of different imaging and non-imaging features from right hemispheric stroke patients: individual demographics (age, sex), initial neglect severity and acute lesion information (size, …

An Analytical Comparison Of The Behavior Of Machine Learning And Deep Learning In Stock Market Prediction, Hasanen S. Abdullah, Nada Hussain Ali, Ammar Hussein Jassim, Syed Hamid Hussain

Baghdad Science Journal

Machine learning is considered a powerful technique in many applications such as classification, clustering, recognition and prediction. Deep learning is a modern, vital and superior machine learning that gives stunning performance, especially with huge data. Stock market price prediction is the process of determining the future value of a prospect of a financial instrument traded in the market, to gain a great profit a successful prediction must be conducted, in order to achieve that machine learning is used, in this article, two approaches are proposed to predict the stock market prices and movement using two datasets, the first approach employs …

Robust Cluster Prediction Across Data Types Validates Association Of Sex And Therapy Response In Gbm, David L Gibbs, Gino Cioffi, Boris Aguilar, Kristin A Waite, Edward Pan, Jacob Mandel, Yoshie Umemura, Jingqin Luo, Joshua B Rubin, David Pot, Jill Barnholtz-Sloan

Faculty, Staff and Students Publications

Background: Previous studies have described sex-specific patient subtyping in glioblastoma. The cluster labels associated with these "legacy data" were used to train a predictive model capable of recapitulating this clustering in contemporary contexts.

Methods: We used robust ensemble machine learning to train a model using gene microarray data to perform multi-platform predictions including RNA-seq and potentially scRNA-seq.

Results: The engineered feature set was composed of many previously reported genes that are associated with patient prognosis. Interestingly, these well-known genes formed a predictive signature only for female patients, and the application of the predictive signature to male patients produced unexpected results. …

Development Of Hybrid Machine Learning In Patient Diagnosis Classification Using The Xrp Model (Extraction, Reduction & Prediction), Hendra Nusa Putra, Sarjon Defit, Gunadi Widi Nurcahyo

Baghdad Science Journal

This study, carried out over six months at an Indonesian hospital, explores the benefits of standardizing medical record data and integrating health information systems for healthcare delivery. Utilizing a quantitative research approach, it focuses on the impact of precise data mining extraction on data analysis and the advantages of an integrated system for accessing patient records. Advanced data mining methods were employed for feature extraction, selection, and dataset reduction to enhance data classification accuracy. Findings revealed a direct correlation between the accuracy of data extraction and the reliability of data classification, highlighting the significant role of dataset reduction in improving …

The Impact Of Artificial Intelligence On Data Privacy: A Risk Management Perspective, Norman Mooradian, Patricia C. Franks, Amitabh Srivastav

Faculty Research, Scholarly, and Creative Activity

Purpose: The purpose of this paper is to increase the artificial intelligence (AI) ethical literacy of information governance professionals by explaining how AI intensifies familiar data privacy issues by virtue of its dependency on data and ability to create new personal information, to explicate emerging privacy enhancing methods and to show their continuity with existing privacy and information governance principles. Design/methodology/approach: The paper uses an interdisciplinary design research methodology that extends current governance frameworks by combining information science, information governance and applied ethics concepts. Three information sources were referenced: 1) academic papers; 2) standards and best practices published by governmental …

Fake News Detection On X (Twitter), Sultan Mayoof Almutaiwei

Theses

The most important innovation globally is the internet, which is used by a huge number of people all around the world. People use the internet for different purposes. By using different social media platforms people share content and news. Thus, some people use these internet platforms to share false news or fake news among people. Propaganda against a person, group, organization, or political party may be present in this news. For an individual, it is difficult to distinguish between false and true news over the internet. Therefore, machine learning is the smart solution that can identify fake news on the …

Landslide Susceptibility Assessment Of The Wanzhou District: Merging Landslide Susceptibility Modelling (Lsm) With Insar-Derived Ground Deformation Map, Chao Zhou, Lulu Gan, Ying Cao, Yue Wang, Samuele Segoni, Xuguo Shi, Mahdi Motagh, Ramesh P. Singh

Mathematics, Physics, and Computer Science Faculty Articles and Research

The prevalent catalog-based Landslide Susceptibility Modelling (LSM) operates under the assumption that future landslide occurrences mirror past and current patterns. Due to growing urban expansion and climate change, certain landslides follow new patterns of occurrence, disrupting the foundational assumption of catalog-based LSM and leading to constraints in the effectiveness of traditional susceptibility maps. Here, to address this problem, we proposed a method to produce more accurate and dynamic landslide susceptibility maps by coupling advanced Ensemble Machine Learning (EML) and Multi-Temporal Interferometric SAR (MT-InSAR). The Wanzhou District in Three Gorges Reservoir area of China is considered as the test site. The …

Enhancing Smartphone Authentication By Integrating Decision-Making Model With Touch Pressure, Finger Location Data, And Advanced Cybersecurity Techniques, Maytham M. Hamood, Moceheb Lazam Shuwandy, Rawan Adel Fawzi Alsharida

Iraqi Journal for Computer Science and Mathematics

Smartphone authentication methods face significant challenges in achieving high accuracy, robustness, and usability within cybersecurity applications. Traditional methods, such as passwords and biometric recognition, often lack adaptability and are prone to high false-positive rates, impacting security and user acceptance. This study presents a novel hybrid approach incorporating machine learning (ML) and the Analytic Hierarchy Process (AHP) in a framework to facilitate decision-making abilities and improve smartphone authentication. A novel dataset was constructed based on 3D touch sensor data (pressure levels and spatial dynamics) collected from 20 participants performing tasks per task over sessions, where AHP was used to rank/choose relevant …

Current Ai Applications And Challenges In Oral Pathology, Zaizhen Xu, Alice Lin, Xiaoyuan Han

Pacific Faculty Work

Artificial intelligence (AI), particularly through machine learning (ML) and deep learning (DL) techniques such as convolutional neural networks (CNNs) and natural language processing (NLP), has shown remarkable promise in image analysis and clinical documentation in oral pathology. In order to explore the transformative potential of artificial intelligence (AI) in oral pathology, this review highlights key studies demonstrating current AI’s improvement in oral pathology, such as detecting oral diseases accurately and streamlining diagnostic processes. However, several limitations, such as data quality, generalizability, legal and ethical considerations, financial constraints, and the need for paradigm shifts in practice, are critically examined. Addressing these …

Supplementary Files For: "Structure Identification For High-Dimensional Data In The Vicinity Of Bear Lake", Ben Shaw, Haley Burger, Brennan Bean, Kevin Moon

Browse all Datasets

This report focuses on seven water quality measurements taken at 43 different depths on the Bear Lake for the months of June - November in the years 2018 - 2023. These measurements create a high-dimensional dataset on which we apply state-of-the-art machine learning (ML) techniques to look for low-dimensional structure in the data. A similar effort was made for weather measurements taken near the lake. Our analysis revealed that water quality measurements tend to cluster (i.e., group together) by year, while weather measurements tend to cluster by time of the year. This suggests that the structure observed in the water …

Multiparametric Mri Along With Machine Learning Predicts Prognosis And Treatment Response In Pediatric Low-Grade Glioma, Anahita Fathi Kazerooni, Adam Kraya, Komal Rathi, Meen Chul Kim, Arastoo Vossough, Nastaran Khalili, Ariana Familiar, Deep Gandhi, Neda Khalili, Varun Kesherwani, Debanjan Haldar, Hannah Anderson, Run Jin, Aria Mahtabfar, Sina Bagheri, Yiran Guo, Qi Li, Xiaoyan Huang, Yuankun Zhu, Alex Sickler, Matthew R Lueder, Saksham Phul, Mateusz Koptyra, Phillip Storm, Jeffrey Ware, Yuanquan Song, Christos Davatzikos, Jessica Foster, Sabine Mueller, Michael J Fisher, Adam Resnick, Ali Nabavizadeh

Department of Neurosurgery Faculty Papers

Pediatric low-grade gliomas (pLGGs) exhibit heterogeneous prognoses and variable responses to treatment, leading to tumor progression and adverse outcomes in cases where complete resection is unachievable. Early prediction of treatment responsiveness and suitability for immunotherapy has the potential to improve clinical management and outcomes. Here, we present a radiogenomic analysis of pLGGs, integrating MRI and RNA sequencing data. We identify three immunologically distinct clusters, with one group characterized by increased immune activity and poorer prognosis, indicating potential benefit from immunotherapies. We develop a radiomic signature that predicts these immune profiles with over 80% accuracy. Furthermore, our clinicoradiomic model predicts progression-free …

Leveraging Intrinsic Properties For Classification Of Coal Seams Towards Spontaneous Combustion Proclivity And Predicting Susceptibility Using Machine Learning: Smart And Sustainable Mining Approach, Siddhartha Agarwal, Pradeep K. Gautam, Yuhao Zou, Rishabh Dwivedi, D.C. Panigrahi, C. Dagli, Atul Singh

Journal of Sustainable Mining

Mine fires and other hazards caused by spontaneous coal combustion are a pervasive and longstanding issue in Jharia coalfields, India. This study proposes a novel approach to classify coal seams based on their propensity to spontaneous combustion using the intrinsic properties of 30 coal samples from different seams. This method eliminates the need for expensive and time-consuming experimental determinations of susceptibility indices (SI) such as crossing point temperature (CPT), critical air blast (CAB), and differential thermal analysis (DTA). All clustering models, viz. hierarchical, k-means, and multidimensional scaling, aptly classify coal seams into three categories: highly risky, medium risky, and low …

Private Rooms In Peer-To-Peer Accommodation: Employing Machine Learning To Determine Revenue Drivers, Ewa E. Kiczmachowska

European Management Studies

Purpose: The goal of this study was to identify the performance determinants of private rooms offered via peer-to-peer accommodation (P2PA) platforms.

Design/methodology/approach: Based on property level data of Airbnb, the applications of machine learning clustering and artificial neural networks methods were proposed to identify the performance determinants of private room performance defined as yearly revenue.

Findings: The results indicate that the most important revenue determinants were maximum number of guests, number of photos, number of bathrooms, location, and cancellation policy, as well as high availability of the property throughout the year. The highest revenues were achieved by 2–3 …

Crossing The Ai Threshold: Advancing The Avian-S Machine Learning Model For Safety Report Analysis, R. Jordan Hinson, Edward Bynum, Michael Sawyer, Amelia Kinsella, Katherine Berry, Ryan Dlugash

International Symposium on Aviation Psychology - 2025

Voluntary safety reporting programs (VSRPs) can be essential in gaining insights into aviation safety and operations. However, analyzing safety reports can be time-consuming and tedious for subject matter experts (SMEs). AVIAN-S is a novel machine learning model that automatically labels aviation safety reporting data. This model utilizes an aviation-specific training set with over 70,000 samples of manually labeled aviation VSRP data. The model labels safety reports with a set of factors from a human factors taxonomy. Previous iterations of themodel utilized report rationales as the model input (Hinson, et al., 2023a). While utilizing rationales as input is helpful in the …