Digital Commons Network^™

Determination Of Gait Events From 2d Video Using Long Short-Term Memory Neural Networks, Marion Mundt, Steffi Colyer, Jacqueline Alderson

ISBS Proceedings Archive

The purpose of this study was to automatically identify the key gait events, foot-strike and foot-off, from 2D video data. Markerless motion capture and pose estimation have become accepted tools in many biomechanics applications to automatically analyse 2D videos of human movement. However, the accurate detection of gait events from various camera views is still a challenge. We trained a long short-term memory neural network to identify foot-strike and foot-off events in walking and running trials captured from nine different camera views based on 2D pose estimation keypoint labels. We achieved a detection accuracy of 86.3-96.1% (F1 score 76.2-92.5%). These …

Early Detection Of Pipeline Natural Gas Leakage From Hyperspectral Imaging By Vegetation Indicators And Deep Neural Networks, Pengfei Ma, Tarutal Ghosh Mondal, Zhenhua Shi, Mohammad Hossein Afsharmovahed, Kevin Romans, Liujun Li, Ying Zhuo, Genda Chen

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

The timely detection of underground natural gas (NG) leaks in pipeline transmission systems presents a promising opportunity for reducing the potential greenhouse gas (GHG) emission. However, existing techniques face notable limitations for prompt detection. This study explores the utility of Vegetation Indicators (VIs) to reflect vegetation health deterioration, thereby representing leak-induced stress. Despite the acknowledged potential of VIs, their sensitivity and separability remain understudied. In this study, we employed ground vegetation as biosensors for detecting methane emissions from underground pipelines. Hyperspectral imaging from vegetation was collected weekly at both plant and leaf scales over two months to facilitate stress detection …

Toward Concurrent Identification Of Human Activities With A Single Unifying Neural Network Classification: First Step, Andrew Smith, Musa Azeem, Chrisogonas O. Odhiambo, Pamela J. Wright, Hanim E. Diktas, Spencer Upton, Corby K. Martin, Brett Froeliger, Cynthia F. Corbett, Homayoun Valafar

Clinical Research Faculty Publications

The characterization of human behavior in real-world contexts is critical for developing a comprehensive model of human health. Recent technological advancements have enabled wearables and sensors to passively and unobtrusively record and presumably quantify human behavior. Better understanding human activities in unobtrusive and passive ways is an indispensable tool in understanding the relationship between behavioral determinants of health and diseases. Adult individuals (N = 60) emulated the behaviors of smoking, exercising, eating, and medication (pill) taking in a laboratory setting while equipped with smartwatches that captured accelerometer data. The collected data underwent expert annotation and was used to train a …

Enhancing Adult Learner Success In Higher Education Through Decision Tree Models: A Machine Learning Approach, Emily Barnes, James Hutson, Karriem Perry

Faculty Scholarship

This article explores the use of machine learning, specifically Classification and Regression Trees (CART), to address the unique challenges faced by adult learners in higher education. These learners confront socio-cultural, economic, and institutional hurdles, such as stereotypes, financial constraints, and systemic inefficiencies. The study utilizes decision tree models to evaluate their effectiveness in predicting graduation outcomes, which helps in formulating tailored educational strategies. The research analyzed a comprehensive dataset spanning the academic years 2013–2014 to 2021–2022, evaluating the predictive accuracy of CART models using precision, recall, and F1 score. Findings indicate that attendance, age, and Pell Grant eligibility are key …

Multi-Case Study Of Left-Flank Boundaries Within Supercells, Peyton B. Stevenson

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

This study investigates the prevalence and significance of forward-flank convergence boundaries (FFCBs) and left-flank convergence boundaries (LFCBs) in shaping the structure and intensity of supercells, using observational data from various field projects. Unlike previous research focusing on individual cases, this study examines a diverse range of cases to provide comprehensive insights into the relationship between these boundaries and supercell characteristics such as intensity, longevity, and tornadogenesis. By analyzing high-resolution surface data, the research addresses the frequency, location, and intensity of these boundaries, and their impact on pseudo vertical vorticity, pseudo convergence, and density gradients. A total of 228 boundary identifications …

Unmanned Aerial Vehicle (Uav)-Based High-Throughput Phenotyping For Maize Improvement, Eric T. Rodene

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Modern breeding programs rely heavily on efficiently screening large numbers of genotypes for agronomic traits, such as disease resistance, drought tolerance, and yield. Identifying the genetic loci or genes associated with these traits using GWAS or functional analyses will benefit future plant breeding efforts seeking to incorporate these traits into new crop varieties, whether through conventional breeding or gene editing techniques. Unmanned aerial vehicle (UAV)-based image data has been increasingly used for this task, as it allows entire test plots to be quickly and cost-effectively phenotyped. In my research, I have developed methods to improve the accuracy of machine learning …

Efficient Machine Learning On Scientific Data Using Bayesian Optimization, Rui Xin

Theses and Dissertations

Deep Learning is pivotal in advancing data analysis across various scientific fields, from genomics to materials discovery. Despite its widespread use, efficiently learning from limited data and operating under resource constraints remains a significant challenge, often limiting its full potential in environments where data is scarce or resources are restricted. This dissertation explores Active Learning and Automated Machine Learning (AutoML) powered by Bayesian Optimization to enhance the efficiency of machine learning across multiple disciplines. It focuses on algorithm optimization and data management through three interconnected studies. In the first study, we investigate how data management technique - active learning helps …

Reliable Detection Of Generalized Convulsive Seizures Using An Off-The-Shelf Digital Watch: A Multisite Phase 2 Study, Yash Shashank Vakilna, Xiaojin Li, Jaison S Hampson, Yan Huang, John C Mosher, Yuri Dabaghian, Xi Luo, Blanca Talavera, Sandipan Pati, Masel Todd, Ryan Hays, Charles Akos Szabo, Guo-Qiang Zhang, Samden D Lhatoo

Faculty, Staff and Student Publications

Objective: The aim of this study was to develop a machine learning algorithm using an off-the-shelf digital watch, the Samsung watch (SM-R800), and evaluate its effectiveness for the detection of generalized convulsive seizures (GCS) in persons with epilepsy.

Methods: This multisite epilepsy monitoring unit (EMU) phase 2 study included 36 adult patients. Each patient wore a Samsung watch that contained accelerometer, gyroscope, and photoplethysmographic sensors. Sixty-eight time and frequency domain features were extracted from the sensor data and were used to train a random forest algorithm. A testing framework was developed that would better reflect the EMU setting, consisting of …

Label-Free Surface-Enhanced Raman Spectroscopy Coupled With Machine Learning Algorithms In Pathogenic Microbial Identification: Current Trends, Challenges, And Perspectives, Jia Wei Tang, Quan Yuan, Xin Ru Wen, Muhammad Usman, Alfred Chin Yen Tay, Liang Wang

Research outputs 2022 to 2026

Infectious diseases caused by microbial pathogens remain a primary contributor to global health burdens. Prompt control and effective prevention of these pathogens are critical for public health and medical diagnostics. Conventional microbial detection methods suffer from high complexity, low sensitivity, and poor selectivity. Therefore, developing rapid and reliable methods for microbial pathogen detection has become imperative. Surface-enhanced Raman Spectroscopy (SERS), as an innovative non-invasive diagnostic technique, holds significant promise in pathogenic microorganism detection due to its rapid, reliable, and cost-effective advantages. This review comprehensively outlines the fundamental theories of Raman Spectroscopy (RS) with a focus on label-free SERS strategy, reporting …

Machine Learning For Nonprofit Organizations, Justin Holzer

Journal of Nonprofit Innovation

Nonprofit organizations are often looking for ways to increase efficiency while operating with limited resources. With data more readily available than in the recent past, machine learning provides powerful tools for unlocking valuable insights and enabling nonprofits to do more with limited resources. This paper explores various machine learning techniques to enhance nonprofit operations with an overview of machine learning approaches, including supervised, unsupervised, semi-supervised, and reinforcement learning, and their potential benefits for nonprofit organizations. A practical use-case of donor prediction for fundraising is presented to demonstrate how supervised learning can be employed to identify potential repeat donors. The neural …

Addressing Social Inequalities Using Ai, Big Data, And Machine Learning, Erica L. Jensen, Lakell Archer, Sumaya Ali

Journal of Nonprofit Innovation

No abstract provided.

Assessment And Prediction Of Meteorological Drought Using Machine Learning Algorithms And Climate Data, Khalid En-Nagre, Mourad Aqnouy, Ayoub Ouarka, Syed Ali Asad Naqvi, Ismail Bouizrou, Jamal Eddine Stitou El Messari, Aqil Tariq, Walid Soufan, Wenzhao Li, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Monitoring drought in semi-arid regions due to climate change is of paramount importance. This study, conducted in Morocco’s Upper Drâa Basin (UDB), analyzed data spanning from 1980 to 2019, focusing on the calculation of drought indices, specifically the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) at multiple timescales (1, 3, 9, 12 months). Trends were assessed using statistical methods such as the Mann-Kendall test and the Sen’s Slope estimator. Four significant machine learning (ML) algorithms, including Random Forest, Voting Regressor, AdaBoost Regressor, and K-Nearest Neighbors Regressor, were evaluated to predict the SPEI values for both three …

Automated Flood Prediction Along Railway Tracks Using Remotely Sensed Data And Traditional Flood Models, Abdul Rashid Zakaria, Thomas Oommen, Pasi Lautala

Michigan Tech Publications

Ground hazards are a significant problem in the global economy, costing millions of dollars in damage each year. Railroad tracks are vulnerable to ground hazards like flooding since they traverse multiple terrains with complex environmental factors and diverse human developments. Traditionally, flood-hazard assessments are generated using models like the Hydrological Engineering Center–River Analysis System (HEC-RAS). However, these maps are typically created for design flood events (10, 50, 100, 500 years) and are not available for any specific storm event, as they are not designed for individual flood predictions. Remotely sensed methods, on the other hand, offer precise flood extents only …

Feature Extraction From Vibration Signature Required From Railroad Bearing Onboard Condition Monitoring Sensor Modules, Kevin Quaye, Ping Xu, Dimah Dera, Heinrich D. Foltz, Constantine Tarawneh, Alberto Diaz

Mechanical Engineering Faculty Publications

From 2013 to 2022, 1671 derailments have been reported by the Federal Railroad Administration (FRA), 8.2% of which were due to journal bearing defects. The University Transportation Center for Railway Safety (UTCRS) designed an onboard monitoring system that tracks vibration waveforms over time to assess bearing health through three analysis levels. However, the speed of the bearing, a fundamental parameter for these analyses, is often acquired from Global Positioning System (GPS) data, which is typically not available at the sensor location. To solve this issue, this paper proposes to employ Machine Learning (ML) algorithms to extract the speed and other …

Effects Of Single-Session Practice Structure On Motor Skill Acquisition And Alpha And Beta Eeg Oscillations, Aubrey Porter, Ronald Victor Croce, Wayne Smith

Faculty Publications

Background: Although it is known that practicing a motor skill updates the associated internal model, it is still unclear as to how cortical oscillations linked with the motor skill change under differing practice schedules. The current study investigated α- and β-power changes associated with motor skill acquisition. Objectives: Firstly, we investigated the behavioral effects of practice on motor learning and retention during repetitive (RP) and variable (VP) practice schedules on an anticipation timing task. Secondly, we investigated changes in cortical α (10 - 13 Hz) and β (15 - 30 Hz) event-related synchronization and desynchronization (ERS/ERD) under RP and VP …

Effects Of Single-Session Practice Structure On Motor Skill Acquisition And Alpha And Beta Eeg Oscillations, Aubrey Porter, Ronald Victor Croce, Wayne Smith

Faculty Publications

Background: Although it is known that practicing a motor skill updates the associated internal model, it is still unclear as to how cortical oscillations linked with the motor skill change under differing practice schedules. The current study investigated α- and β-power changes associated with motor skill acquisition. Objectives: Firstly, we investigated the behavioral effects of practice on motor learning and retention during repetitive (RP) and variable (VP) practice schedules on an anticipation timing task. Secondly, we investigated changes in cortical α (10 - 13 Hz) and β (15 - 30 Hz) event-related synchronization and desynchronization (ERS/ERD) under RP and VP …

Detection Of Aflatoxin B1 In Chili Powder Using Attenuated Total Reflectance–Fourier Transform Infrared Spectroscopy, Thin Thin Sein, Urairat Mongmonsin, Patutong Chatchawal, Molin Wongwattanakul, Oranee Srichaiyapol, Rungrueang Pattanakul, Patcharaporn Tippayawat

Journal of Food and Drug Analysis

Aflatoxin B₁, a major global food safety concern, is produced by toxigenic fungi during crop growing, drying, and storage, and shows increasing annual prevalence. This study aimed to detect aflatoxin B₁ in chili samples using ATR–FTIR coupled with machine learning algorithms. We found that 83.6% of the chili powder samples were contaminated with Aspergillus and Penicillium species, with aflatoxin B₁ levels ranging from 7.63 to 44.32 µg/kg. ATR–FTIR spectroscopy in the fingerprint region (1800−400 cm^-1) showed peak intensity variation in the bands at 1587, 1393, and 1038 cm^-1, which are mostly related …

A Meta-Ensemble Predictive Model For The Risk Of Lung Cancer, Sideeqoh Oluwaseun Olawale-Shosanya, Olayinka Olufunmilayo Olusanya, Adeyemi Omotayo Joseph, Kabir Oluwatobi Idowu, Oyelade Babatunde Eriwa, Adedeji Oladimeji Adebare, Morufat Adebola Usman

Al-Bahir

The lungs play a vital role in supplying oxygen to every cell, filtering air to prevent harmful substances, and supporting defense mechanisms. However, they remain susceptible to the risk of diseases such as infections, inflammation, and cancer that affect the lungs. Meta-ensemble techniques are prominent methods used in machine learning to enhance the accuracy of classifier learning systems in making predictions. This work proposes a robust predictive model using a meta-ensemble method to identify high-risk individuals with lung cancer, thereby taking early action to prevent long-term problems benchmarked upon the Kaggle Machine Learning practitioners' Lung Cancer Dataset. Three machine learning …

Multisite Pragmatic Cluster-Randomized Controlled Trial Of The Concern Early Warning System, Sarah C Rossetti, Patricia C Dykes, Chris Knaplund, Sandy Cho, Jennifer Withall, Graham Lowenthal, David Albers, Rachel Lee, Haomiao Jia, Suzanne Bakken, Min-Jeoung Kang, Frank Y Chang, Li Zhou, David W Bates, Temiloluwa Daramola, Fang Liu, Jessica Schwartz-Dillard, Mai Tran, Syed Mohtashim Abbas Bokhari, Jennifer Thate, Kenrick D Cato

Faculty, Staff and Students Publications

Importance: Late predictions of hospitalized patient deterioration, resulting from early warning systems (EWS) with limited data sources and/or a care team's lack of shared situational awareness, contribute to delays in clinical interventions. The COmmunicating Narrative Concerns Entered by RNs (CONCERN) Early Warning System (EWS) uses real-time nursing surveillance documentation patterns in its machine learning algorithm to identify patients' deterioration risk up to 42 hours earlier than other EWSs.

Objective: To test our a priori hypothesis that patients with care teams informed by the CONCERN EWS intervention have a lower mortality rate and shorter length of stay (LOS) than the patients …

How To Propagate Uncertainty Via Ai Algorithms, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Any data processing starts with measurement results. Measurement results are never absolutely accurate. Because of this measurement uncertainty, the results of processing measurement results are, in general, somewhat different from what we would have obtained if we knew the exact values of the measured quantities. To make a decision based on the result of data processing, we need to know how accurate is this result, i.e., we need to propagate the measurement uncertainty through the data processing algorithm. There are many techniques for uncertainty propagation. Usually, they involve applying the same data processing algorithm several times to appropriately modified data. …

Leveraging Machine Learning To Analyze Semantic User Interactions In Visual Analytics, Dong Hyun Jeong, Bong Keun Jeong, Soo Yeon Ji

Management and Decision Sciences

In the field of visualization, understanding users’ analytical reasoning is important for evaluating the effectiveness of visualization applications. Several studies have been conducted to capture and analyze user interactions to comprehend this reasoning process. However, few have successfully linked these interactions to users’ reasoning processes. This paper introduces an approach that addresses the limitation by correlating semantic user interactions with analysis decisions using an interactive wire transaction analysis system and a visual state transition matrix, both designed as visual analytics applications. The system enables interactive analysis for evaluating financial fraud in wire transactions. It also allows mapping captured user interactions …

Impact Of Operational Ladar Occlusions On Point Cloud Instance Segmentation, Andrew D. Gibson

Theses and Dissertations

Data exploitation techniques are the enabler for technological advancements in military ISR applications of ladar ISR. By identifying instances of military objects in observed scenes, point cloud deep learning models can unlock new standards of real-time information delivery to warfighters. Although current deep learning training datasets do not include real-world collection occlusions consistent with military applications, this research characterizes SPT model performance by adding occlusions to the DALESObjects dataset via artificial flyby simulations.We find that a baseline model trained on unoccluded data suffers performance degradation on both semantic and instance segmentation tasks when evaluated on occluded data, but that the …

Forecasting Future Research Trends In The Construction Engineering And Management Domain Using Machine Learning And Social Network Analysis, Gasser G. Ali, Islam H. El-Adaway, Muaz O. Ahmed, Radwa Eissa, Mohamad Abdul Nabi, Tamima Elbashbishy, Ramy Khalef

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Construction Engineering and Management (CEM) is a broad domain with publications covering interrelated subdisciplines and considered a key source of knowledge sharing. Previous studies used scientometric methods to assess the current impact of CEM publications; however, there is a need to predict future citations of CEM publications to identify the expected high-impact trends in the future and guide new research efforts. To tackle this gap in the literature, the authors conducted a study using Machine Learning (ML) algorithms and Social Network Analysis (SNA) to predict CEM-related citation metrics. Using a dataset of 93,868 publications, the authors trained and tested two …

On The Ubiquity, Properties And Evolution Of Small-Scale Magnetic Flux Ropes In The Heliosphere, Hameedullah Farooki

Dissertations

The solar wind is a plasma constantly blowing out from the Sun with a large-scale magnetic field having significant local complexity at small scales. Small-scale magnetic flux ropes (SMFRs), plasma structures with twisted field lines, are an important element of this complexity. This dissertation contributes several studies that further our understanding of SMFRs. The first study applies machine learning to measurements from Wind labeled by the presence of SMFRs and magnetic clouds (MCs). MCs were distinguished from non-MFRs with an AUC of 94% and SMFRs with an AUC of 89% and had distinctive plasma properties, whereas SMFRs appeared to be …

Computational Microscopy For Biomedical Imaging With Deep Learning Assisted Image Analysis, Yuwei Liu

Dissertations

Microscopy plays a crucial role across various scientific fields by enabling structural and functional imaging with microscopic resolution. In biomedicine, microscopy contributes to basic research and clinical diagnosis. Conventionally, optical microscopy derives its contrast from the amplitude of the optical wave and provides visualization of the physical structure of the sample qualitatively. To understand the function at the cellular or tissue level, there is a need to characterize the sample quantitatively and explore contrast mechanisms other than light intensity. Image enhancement or reconstruction from microscopic imaging systems is known as computational microscopy, and it involves the application of computational techniques …

The Next Strike: Pioneering Forward-Thinking Attack Techniques With Rowhammer In Dram Technologies, Nakul Kochar

Theses

In the realm of DRAM technologies this study investigates RowHammer vulnerabilities in DDR4 DRAM memory across various manufacturers, employing advanced multi-sided fault injection techniques to impose attack strategies directly on physical memory rows. Our novel approach, diverging from traditional victim-focused methods, involves strategically allocating virtual memory rows to their physical counterparts for more potent attacks. These attacks, exploiting the inherent weaknesses in DRAM design, are capable of inducing bit flips in a controlled manner to undermine system integrity. We employed a strategy that compromised system integrity through a nuanced approach of targeting rows situated at a distance of two rows …

Predictive Analysis Of Local House Prices: Leveraging Machine Learning For Real Estate Valuation, Joey Hernandez, Danny Chang, Santiago Gutierrez, Paul Huggins

SMU Data Science Review

This paper presents a comprehensive study examining the real estate market potential in the dynamic urban landscapes of Frisco and Plano, Texas. Combining traditional real estate analysis with cutting-edge machine learning techniques, the study aims to predict home prices and assess investment feasibility. Leveraging these findings, the study proposes a strategic focus on predictive modeling and investment potential identification, emphasizing the continual refinement of machine learning models with updated data to accurately forecast changes in the real estate market. By harnessing the predictive power of these models, investors can identify high-growth areas and optimize their investment decisions, thus capitalizing on …

Human-Machine Interactions For Behavioral Health Care, Zhiwei Yu

Theses

About one in four American adults (26%) and 20.3% of children aged 8 to 16 years reported behavioral health concerns in 2023. However, a substantial gap remains between demand for behavioral healthcare services and access to such services. In response, research efforts have been made to advance effective and accessible approaches to provide assessments and treatments for populations with behavioral health needs. Yet, two primary challenges exist in current practice and research: 1) self-reports and manual observation-based assessments are subjective and labor-intensive, as evaluations are limited to the availability of clinical professionals; 2) they only target the general experiences but …

Machine Learning Models For Enhanced Stock Trading Strategies, Alyaa Al Ali

Theses

The purpose of this research is to develop sophisticated machine-learning models that can predict stock price movements, taking into account the multifaceted influences that create volatility in financial markets. To navigate the complexities of market dynamics effectively, investors, financial institutions, and academia rely on precision stock price predictions. This research generates actionable buy and sell signals by using historical stock data, technical market indicators, Linear Support Vector Machines (LSVM), Neural Networks, and Logistic Regression. A distinctive feature of this model is it uses market indicators such as PPO, MACD, RSI Signal, Bollinger Bands, ROC Signal, and DX Signal instead of …

Machine Learning For Intrusion Detection: A Reproducible Baseline Is All You Need, Salma A. Walli, Karam Sallam

Sustainable Machine Intelligence Journal

Ensuring Responsible AI practices is paramount in the advancement of systems founded upon machine learning (ML) principles, particularly in sensitive domains like intrusion detection within cybersecurity. A fundamental aspect of Responsible AI is reproducibility, which guarantees the reliability and transparency of research outcomes. In this paper, we address the critical challenge of establishing reproducible for intrusion detection utilizing ML techniques. Leveraging the NSL-KDD dataset and the Edge-IIoTset, we carry out extensive experiments to evaluate the efficacy of our approach. Our study prioritizes meticulous experiment design and careful implementation setups, aligning with the principles of Responsible AI. Through rigorous experimentation and …