Digital Commons Network^™

Blood-Based Transcriptomic Biomarkers Are Predictive Of Neurodegeneration Rather Than Alzheimer's Disease, Artur Shvetcov, Shannon Thomson, Jessica Spathos, Ann-Na Cho, Heather M Wilkins, Shea J Andrews, Fabien Delerue, Timothy A Couttas, Jasmeen Kaur Issar, Finula Isik, Simranpreet Kaur, Eleanor Drummond, Carol Dobson-Stone, Shantel L Duffy, Natasha M Rogers, Daniel Catchpoole, Wendy A Gold, Russell H Swerdlow, David A Brown, Caitlin A Finney

Faculty, Staff and Student Publications

Alzheimer's disease (AD) is a growing global health crisis affecting millions and incurring substantial economic costs. However, clinical diagnosis remains challenging, with misdiagnoses and underdiagnoses being prevalent. There is an increased focus on putative, blood-based biomarkers that may be useful for the diagnosis as well as early detection of AD. In the present study, we used an unbiased combination of machine learning and functional network analyses to identify blood gene biomarker candidates in AD. Using supervised machine learning, we also determined whether these candidates were indeed unique to AD or whether they were indicative of other neurodegenerative diseases, such as …

Malfe—Malware Feature Engineering Generation Platform, Avinash Singh, Richard Adeyemi Ikuesan, Hein Venter

All Works

The growing sophistication of malware has resulted in diverse challenges, especially among security researchers who are expected to develop mechanisms to thwart these malicious attacks. While security researchers have turned to machine learning to combat this surge in malware attacks and enhance detection and prevention methods, they often encounter limitations when it comes to sourcing malware binaries. This limitation places the burden on malware researchers to create context-specific datasets and detection mechanisms, a time-consuming and intricate process that involves a series of experiments. The lack of accessible analysis reports and a centralized platform for sharing and verifying findings has resulted …

Classification Of Chronic Pain Using Fmri Data: Unveiling Brain Activity Patterns For Diagnosis, Rejula V, Anitha J, Belfin Robinson

Turkish Journal of Electrical Engineering and Computer Sciences

Millions of people throughout the world suffer from the complicated and crippling condition of chronic pain. It can be brought on by several underlying disorders or injuries and is defined by chronic pain that lasts for a period exceeding three months. To better understand the brain processes behind pain and create prediction models for pain-related outcomes, machine learning is a potent technology that may be applied in Functional magnetic resonance imaging (fMRI) chronic pain research. Data (fMRI and T1-weighted images) from 76 participants has been included (30 chronic pain and 46 healthy controls). The raw data were preprocessed using fMRIprep …

Longboard Classification Using Machine Learning, Tuan (Kevin) Le, Evans Sajtar, Mckenzie Lamb

Annual Student Research Poster Session

There are several techniques a rider can choose from that they can perform being distributed along the long-board ride. This research aims to create a machine-learning model that can efficiently classify these techniques at different periods of time using raw acceleration data. This paper presents the complete workflow of the application. This application involves analytical geometry, multidimensional calculus, and linear algebra and can be used to visualize and normalize time-invariant object paths. This model focuses on displacement data calculated from raw acceleration data and gyro sensor data from a smartphone application called "Physics Toolbox Sensor Suite". We extracted features from …

Wearable Sensor-Based Walkability Assessment At Ferry Terminal Using Machine Learning: A Case Study Of Mokpo, Korea, Jungyeon Choi, Hwayoung Kim

Journal of Marine Science and Technology–Taiwan

Walkability assessments are becoming more popular, as walking offers numerous health, environmental, and economic benefits to communities. However, previous studies on ferry terminal walkability assessment have been inadequate. This study aimed to develop a wearable sensor system to automatically assess walkability at ferry terminals without conducting surveys. We applied seven machine learning (ML) classifiers to detect different walking environments, including flat ground (FG), downhill slope (DS), uphill slope (US), and uneven surface (UE). The ML models were evaluated across different combinations of classes: 2-class (FG vs. UE), 3-class (U) (FG vs. US vs. UE), 3-class (D) (FG vs. DS vs. …

A Survey Of Eeg And Machine Learning-Based Methods For Neural Rehabilitation, Jaiteg Singh, Farman Ali, Rupali Gill, Babar Shah, Daehan Kwak

All Works

One approach to therapy and training for the restoration of damaged muscles and motor systems is rehabilitation. EEG-assisted Brain-Computer Interface (BCI) may assist in restoring or enhancing ‘lost motor abilities in the brain. Assisted by brain activity, BCI offers simple-to-use technology aids and robotic prosthetics. This systematic literature review aims to explore the latest developments in BCI and motor control for rehabilitation. Additionally, we have explored typical EEG apparatuses that are available for BCI-driven rehabilitative purposes. Furthermore, a comparison of significant studies in rehabilitation assessment using machine learning techniques has been summarized. The results of this study may influence policymakers’ …

Spoken Language Processing And Modeling For Aviation Communications, Aaron Van De Brook

Doctoral Dissertations and Master's Theses

With recent advances in machine learning and deep learning technologies and the creation of larger aviation-specific corpora, applying natural language processing technologies, especially those based on transformer neural networks, to aviation communications is becoming increasingly feasible. Previous work has focused on machine learning applications to natural language processing, such as N-grams and word lattices. This thesis experiments with a process for pretraining transformer-based language models on aviation English corpora and compare the effectiveness and performance of language models transfer learned from pretrained checkpoints and those trained from their base weight initializations (trained from scratch). The results suggest that transformer language …

Machine-Learning-Based Thermal Conductivity Prediction For Additively Manufactured Alloys, Uttam Bhandari, Yehong Chen, Huan Ding, Congyuan Zeng, Selami Emanet, Paul R. Gradl, Shengmin Guo

Faculty Publications

Thermal conductivity (TC) is greatly influenced by the working temperature, microstructures, thermal processing (heat treatment) history and the composition of alloys. Due to computational costs and lengthy experimental procedures, obtaining the thermal conductivity for novel alloys, particularly parts made with additive manufacturing, is difficult and it is almost impossible to optimize the compositional space for an absolute targeted value of thermal conductivity. To address these difficulties, a machine learning method is explored to predict the TC of additive manufactured alloys. To accomplish this, an extensive thermal conductivity dataset for additively manufactured alloys was generated for several AM alloy families (nickel, …

Faster, Cheaper, And Better Cfd: A Case For Machine Learning To Augment Reynolds-Averaged Navier-Stokes, John Peter Romano Ii

Mechanical & Aerospace Engineering Theses & Dissertations

In recent years, the field of machine learning (ML) has made significant advances, particularly through applying deep learning (DL) algorithms and artificial intelligence (AI). The literature shows several ways that ML may enhance the power of computational fluid dynamics (CFD) to improve its solution accuracy, reduce the needed computational resources and reduce overall simulation cost. ML techniques have also expanded the understanding of underlying flow physics and improved data capture from experimental fluid dynamics.

This dissertation presents an in-depth literature review and discusses ways the field of fluid dynamics has leveraged ML modeling to date. The author selects and describes …

Real-Time Product Structural Validation For Fused Filament Fabrication, Yanzhou Fu

Theses and Dissertations

Fused filament fabrication (FFF) is one of the fastest-growing, most promising, and widely-used Additive manufacturing (AM) technologies, as its capabilities of largely reducing material waste, feasible and flexible designing complicated structure or geometry product. Except for the advantages mentioned above, the unique affordability has made FFF a popular technique for small-scale 3D printing. However, its widescale adaption for industrial applications still faces significant challenges, especially for Industry 4.0, due to the process’s intrinsic uncertainties. During the printing process, even a trivial bonding gap or material void dramatically influences the final product’s structural quality and mechanical properties. Therefore, a defect detection …

A Machine Learning Approach That Measures Ph Using Acidocest Mri Of Iopamidol, Tianzhe Li, Julio Cárdenas-Rodríguez, Priya N Trakru, Mark D Pagel

Faculty, Staff and Student Publications

Tumor acidosis is an important biomarker for aggressive tumors, and extracellular pH (pHe) of the tumor microenvironment can be used to predict and evaluate tumor responses to chemotherapy and immunotherapy. AcidoCEST MRI measures tumor pHe by exploiting the pH-dependent chemical exchange saturation transfer (CEST) effect of iopamidol, an exogenous CT agent repurposed for CEST MRI. However, all pH fitting methodologies for acidoCEST MRI data have limitations. Herein we present results of the application of machine learning for extracting pH values from CEST Z-spectra of iopamidol. We acquired 36,000 experimental CEST spectra from 200 phantoms of iopamidol prepared at five concentrations, …

Machine Learning Approach To Activity Categorization In Young Adults Using Biomechanical Metrics, Nathan Q. C. Holland

Mechanical & Aerospace Engineering Theses & Dissertations

Inactive adults often have decreased musculoskeletal health and increased risk factors for chronic diseases. However, there is limited data linking biomechanical measurements of generally healthy young adults to their physical activity levels assessed through questionnaires. Commonly used data collection methods in biomechanics for assessing musculoskeletal health include but are not limited to muscle quality (measured as echo intensity when using ultrasound), isokinetic (i.e., dynamic) muscle strength, muscle activations, and functional movement assessments using motion capture systems. These assessments can be time consuming for both data collection and processing. Therefore, understanding if all biomechanical assessments are necessary to classify the activity …

On The Prediction Of The Mechanical Properties Of Limestone Calcined Clay Cement: A Random Forest Approach Tailored To Cement Chemistry, Taihao Han, Bryan K. Aylas-Paredes, Jie Huang, Ashutosh Goel, Narayanan Neithalath, Aditya Kumar

Materials Science and Engineering Faculty Research & Creative Works

Limestone calcined clay cement (LC3) is a sustainable alternative to ordinary Portland cement, capable of reducing the binder's carbon footprint by 40% while satisfying all key performance metrics. The inherent compositional heterogeneity in select components of LC3, combined with their convoluted chemical interactions, poses challenges to conventional analytical models when predicting mechanical properties. Although some studies have employed machine learning (ML) to predict the mechanical properties of LC3, many have overlooked the pivotal role of feature selection. Proper feature selection not only refines and simplifies the structure of ML models but also enhances these models' prediction performance and interpretability. This …

Accelerating Training Of Mlips Through Small-Cell Training, Jason A. Meziere, Yu Luo, Yi Xia, Laurent Karim Béland, Mark R. Daymond

Mechanical and Materials Engineering Faculty Publications and Presentations

While machine-learned interatomic potentials have become a mainstay for modeling materials, designing training sets that lead to robust potentials is challenging. Automated methods, such as active learning and on-the-fly learning, construct reliable training sets, but these processes can be resource-intensive. Current training approaches often use density functional theory (DFT) calculations that have the same cell size as the simulations that the potential is explicitly trained to model. Here, we demonstrate an easy-to-implement small-cell training protocol and use it to model the Zr-H system. This training leads to a potential that accurately predicts known stable Zr-H phases and reproduces the α-β …

Utilizing Speech Analysis To Differentiate Progressive Supranuclear Palsy From Parkinson’S Disease, Kyurim Kang, Adonay S Nunes, Mansi Sharma, A J Hall, Ram Kinker Mishra, Jose Casado, Rylee Cole, Marc Derhammer, Gregory Barchard, Bijan Najafi, Ashkan Vaziri, Anne-Marie Wills, Alexander Pantelyat

Center on Aging Staff Publications

Introduction: Distinguishing Parkinson's disease (PD) from Progressive supranuclear palsy (PSP) at early disease stages is important for clinical trial enrollment and clinical care/prognostication.

Methods: We recruited 21 participants with PSP(n = 11) or PD(n = 10) with reliable caregivers. Standardized passage reading, counting, and sustained phonation were recorded on the BioDigit Home tablet (BioSensics LLC, Newton, MA USA), and speech features from the assessments were analyzed using the BioDigit Speech platform (BioSensics LLC, Newton, MA USA). An independent t-test was performed to compare each speech feature between PSP and PD participants. We also performed Spearman's correlations to evaluate associations between …

A Machine Learning Approach For Dyslexia Detection Using Turkish Audio Records, Tuğberk Taş, Muhammed Abdullah Bülbül, Abas Haşi̇moğlu, Yavuz Meral, Yasi̇n Çalişkan, Gunay Budagova, Mücahi̇d Kutlu

Turkish Journal of Electrical Engineering and Computer Sciences

Dyslexia is a learning disorder, characterized by impairment in the ability to read, spell, and decode letters. It is vital to detect dyslexia in earlier stages to reduce its effects. However, diagnosing dyslexia is a time-consuming and costly process. In this paper, we propose a machine-learning model that predicts whether a Turkish-speaking child has dyslexia using his/her audio records. Therefore, our model can be easily used by smart phones and work as a warning system such that children who are likely to be dyslexic according to our model can seek an examination by experts. In order to train and evaluate, …

Stepwise Dynamic Nearest Neighbor (Sdnn): A New Algorithm For Classification, Deni̇z Karabaş, Derya Bi̇rant, Peli̇n Yildirim Taşer

Turkish Journal of Electrical Engineering and Computer Sciences

Although the standard k-nearest neighbor (KNN) algorithm has been used widely for classification in many different fields, it suffers from various limitations that abate its classification ability, such as being influenced by the distribution of instances, ignoring distances between the test instance and its neighbors during classification, and building a single/weak learner. This paper proposes a novel algorithm, called stepwise dynamic nearest neighbor (SDNN), which can effectively handle these problems. Instead of using a fixed parameter k like KNN, it uses a dynamic neighborhood strategy according to the data distribution and implements a new voting mechanism, called stepwise voting. Experimental …

Pca-Clf: A Classifier Of Prostate Cancer Patients Into Patients With Indolent And Aggressive Tumors Using Machine Learning, Yashwanth Karthik Kumar Mamidi, Tarun Karthik Kumar Mamidi, Md Wasi Ul Kabir, Jiande Wu, Md Tamjidul Hoque, Chindo Hicks

School of Medicine Faculty Publications

A critical unmet medical need in prostate cancer (PCa) clinical management centers around distinguishing indolent from aggressive tumors. Traditionally, Gleason grading has been utilized for this purpose. However, tumor classification using Gleason Grade 7 is often ambiguous, as the clinical behavior of these tumors follows a variable clinical course. This study aimed to investigate the application of machine learning techniques (ML) to classify patients into indolent and aggressive PCas. We used gene expression data from The Cancer Genome Atlas and compared gene expression levels between indolent and aggressive tumors to identify features for developing and validating a range of ML …

Legalbench: A Collaboratively Built Benchmark For Measuring Legal Reasoning In Large Language Models, Neel Guha, Julian Nyarko, Daniel E. Ho, Christopher Ré, Adam Chilton, Aditya Narayana, Alex Chohlas-Wood, Austin Peters, Brandon Waldon, Daniel Rockmore, Diego A. Zambrano, Dmitry Talisman, Enam Hoque, Faiz Surani, Frank Fagan, Galit Sarfaty, Gregory M. Dickinson, Haggai Porat, Jason Hegland, Jessica Wu, Joe Nudell, Joel Niklaus, John Nay, Jonathan H. Choi, Kevin Tobia, Margaret Hagan, Megan Ma, Michael A. Livermore, Nikon Rasumov-Rahe, Nils Holzenberger, Noam Kolt, Peter Henderson, Sean Rehaag, Sharad Goel, Shang Gao, Spencer Williams, Sunny Gandhi, Tom Zur, Varun Iyer, Zehua Li

All Papers

The advent of large language models (LLMs) and their adoption by the legal community has given rise to the question: what types of legal reasoning can LLMs perform? To enable greater study of this question, we present LegalBench: a collaboratively constructed legal reasoning benchmark consisting of 162 tasks covering six different types of legal reasoning. LegalBench was built through an interdisciplinary process, in which we collected tasks designed and hand-crafted by legal professionals. Because these subject matter experts took a leading role in construction, tasks either measure legal reasoning capabilities that are practically useful, or measure reasoning skills that lawyers …

A Deep Bilstm Machine Learning Method For Flight Delay Prediction Classification, Desmond B. Bisandu, Irene Moulitsas

Journal of Aviation/Aerospace Education & Research

This paper proposes a classification approach for flight delays using Bidirectional Long Short-Term Memory (BiLSTM) and Long Short-Term Memory (LSTM) models. Flight delays are a major issue in the airline industry, causing inconvenience to passengers and financial losses to airlines. The BiLSTM and LSTM models, powerful deep learning techniques, have shown promising results in a classification task. In this study, we collected a dataset from the United States (US) Bureau of Transportation Statistics (BTS) of flight on-time performance information and used it to train and test the BiLSTM and LSTM models. We set three criteria for selecting highly important features …

Thermodynamic And Kinetic Modeling Of Electrocatalytic Reactions Using A First-Principles Approach, M. Vasanthapandiyan, Shagun Singh, Fernanda Bononi, Oliviero Andreussi, Naiwrit Karmodak

Chemistry and Biochemistry Faculty Publications and Presentations

The computational modeling of electrochemical interfaces and their applications in electrocatalysis has attracted great attention in recent years. While tremendous progress has been made in this area, however, the accurate atomistic descriptions at the electrode/electrolyte interfaces remain a great challenge. The Computational Hydrogen Electrode (CHE) method and continuum modeling of the solvent and electrolyte interactions form the basis for most of these methodological developments. Several posterior corrections have been added to the CHE method to improve its accuracy and widen its applications. The most recently developed grand canonical potential approaches with the embedded diffuse layer models have shown considerable improvement …

A Probabilistic View Of Protein Stability, Conformational Specificity, And Design, Jacob A. Stern, Tyler J. Free, Kimberlee L. Stern, Spencer Gardiner, Nicholas A. Dalley, Bradley Charles Bundy, Joshua L. Price, David Wingate, Dnnis Della Corte

Faculty Publications

Various approaches have used neural networks as probabilistic models for the design of protein sequences. These "inverse folding" models employ different objective functions, which come with trade-offs that have not been assessed in detail before. This study introduces probabilistic definitions of protein stability and conformational specificity and demonstrates the relationship between these chemical properties and the p(stucture|seq) Boltzmann probability objective. This links the Boltzmann probability objective function to experimentally verifiable outcomes. We propose a novel sequence decoding algorithm, referred to as “BayesDesign”, that leverages Bayes’ Rule to maximize the p(stucture|seq) objective instead of the p(seq|structure) objective common in inverse folding …

Evolutionary Action-Machine Learning Model Identifies Candidate Genes Associated With Early-Onset Coronary Artery Disease, Dillon Shapiro, Kwanghyuk Lee, Jennifer Asmussen, Thomas Bourquard, Olivier Lichtarge

Faculty, Staff and Students Publications

Background Coronary artery disease is a primary cause of death around the world, with both genetic and environmental risk factors. Although genome-wide association studies have linked >100 unique loci to its genetic basis, these only explain a fraction of disease heritability. Methods and Results To find additional gene drivers of coronary artery disease, we applied machine learning to quantitative evolutionary information on the impact of coding variants in whole exomes from the Myocardial Infarction Genetics Consortium. Using ensemble-based supervised learning, the Evolutionary Action-Machine Learning framework ranked each gene's ability to classify case and control samples and identified 79 significant associations. …

Compatibility Of Clique Clustering Algorithm With Dimensionality Reduction, Ug ̆Ur Madran, Duygu Soyog ̆Lu

Applied Mathematics & Information Sciences

In our previous work, we introduced a clustering algorithm based on clique formation. Cliques, the obtained clusters, are constructed by choosing the most dense complete subgraphs by using similarity values between instances. The clique algorithm successfully reduces the number of instances in a data set without substantially changing the accuracy rate. In this current work, we focused on reducing the number of features. For this purpose, the effect of the clique clustering algorithm on dimensionality reduction has been analyzed. We propose a novel algorithm for support vector machine classification by combining these two techniques and applying different strategies by differentiating …

Modelling And Predicting Learners Numeracy Test Results Using Some Regression And Machine Learning Classifiers, Tlhalitshi Volition Montshiwa, Tshegofatso Botlhoko

Journal of Statistics Applications & Probability

The prediction of early childhood numeracy skills development is often studied by determining the learner’s performance in a numeracy test. It is an important study area since numeracy impacts on the learner’s mathematical and statistical abilities later in life. Despite having pros and cons over each other, classification algorithms are often applied in the prediction of early childhood numeracy skills development without justifying the choice of a certain algorithm over others. In this paper, the bi-directional stepwise logistic regression model (SLRM), hierarchical logistic regression model (HLRM), classification and regression tree (CART) and Naïve Bayes (NB) were compared in terms of …

Synthetic Image Generation And The Use Of Virtual Environments For Image Enhancement Tasks, Neil Patrick Del Gallego

Software Technology Dissertations

Deep learning networks are often difficult to train if there are insufficient image samples. Gathering real-world images tailored for a specific job takes a lot of work to perform. This dissertation explores techniques for synthetic image generation and virtual environments for various image enhancement/ correction/restoration tasks, specifically distortion correction, dehazing, shadow removal, and intrinsic image decomposition. First, given various image formation equations, such as those used in distortion correction and dehazing, synthetic image samples can be produced, provided that the equation is well-posed. Second, using virtual environments to train various image models is applicable for simulating real-world effects that are …

On Predicting Esg Ratings Using Dynamic Company Networks, Gary Ang, Zhiling Guo, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Environmental, social and governance (ESG) considerations play an increasingly important role due to the growing focus on sustainability globally. Entities, such as banks and investors, utilize ESG ratings of companies issued by specialized rating agencies to evaluate ESG risks of companies. The process of assigning ESG ratings by human analysts is however laborious and time intensive. Developing methods to predict ESG ratings could alleviate such challenges, allow ESG ratings to be generated in a more timely manner, cover more companies, and be more accessible. Most works study the effects of ESG ratings on target variables such as stock prices or …

Predicting University Campus Parking Demand Using Machine Learning Models, Sohil Paudel, Matthew Vechione, Okan Gurbuz

Civil Engineering Faculty Publications and Presentations

Parking demand at university campuses has been an issue for decades and is gradually increasing each year. With limited capacity, space, and funds to expand parking facilities, there is a dire need to better understand parking behavior on a university campus so that universities can better utilize the limited resources available. One methodology that has been used by Metropolitan Planning Organizations to predict traveler behavior is known as travel demand modeling, where the most common modeling technique is a four-step procedure that utilizes socioeconomic data to predict current and future traffic volumes in a network (e.g., a city). This study …

Using Ml To Understand The Factors Impacting Diabetes In Diabetic Patients, Marwa Ahmed Almarzooqi

Theses

Diabetes, a well-known medical condition since ancient times, has become a prevalent and significant health concern in recent decades. The rising incidence of diabetes has necessitated early diagnosis and effective treatment. Machine learning (ML) innovations have revolutionized disease prediction and decision-making by utilizing massive datasets. This study aims to develop and compare machine learning (ML) models for diabetes prediction using a preprocessed dataset of 532 instances obtained from Kaggle. Important variables included in the data set are Pregnancies, Glucose, Blood Pressure, Skin Thickness, Insulin, BMI, Diabetes Pedigree Function, Age, and Outcome. The correlation analysis revealed a strong positive association between …

Perceptions And Barriers To Adopting Artificial Intelligence In K-12 Education: A Survey Of Educators In Fifty States, Karen Woodruff, James Hutson, Kathryn Arnone

Faculty Scholarship

Artificial Intelligence (AI) is making significant strides in the field of education, offering new opportunities for personalized learning and access to education for a more diverse population. Despite this potential, the adoption of AI in K-12 education is limited, and educators’ express hesitancy towards its integration due to perceived technological barriers and misconceptions. The purpose of this study is to examine the perceptions of K-12 educators in all 50 states of the USA towards AI, policies, training, and resources related to technology and AI, their comfort with technology, willingness to adopt new technologies for classroom instruction, and needs assessment for …