Digital Commons Network^™

Unveiling The Interplay Of Electronic And Phononic Excitations In Laser-Induced Oxygen Activation On Ru(0001), Xiangrui Wang, Jiamin Wang, Paul Spiering, Liping Liu, Jörg Meyer, Jerry L. Larue, Hongliang Xin

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Understanding laser-induced dynamics on metal surfaces poses significant challenges due to the intricate interplay between electronic and phononic degrees of freedom, which evolve on distinct timescales. In this study, we introduce a machine learning-accelerated approach to molecular dynamics simulations that incorporates anisotropic electronic friction, providing deeper insights into these complex processes. Our framework extends the accessible time and length scales for nonadiabatic dynamics simulations, enabling a detailed investigation of the laser-induced activation of oxygen on the Ru(0001) surface. Statistical analysis reveals that strong electronic excitation dominates the first 800 fs after laser exposure. Beyond this timescale, energy deposited by electronic …

Artificial Intelligence In Head And Neck Cancer: Towards Precision Medicine, Jacob Hagen, Logan Hornung, William Barham, Supratik Mukhopadhyay, Adam Bess, Kevin Contrera, Devraj Basu, Vlad Sandulache, Guillaume Spielmann, Sagar Kansara

Faculty, Staff and Students Publications

Over the past 20 years, the capabilities of artificial intelligence (AI) have gained significant interest. While AI has been implemented to various degrees in several disciplines, its unique applications in head and neck cancer (HNC) remain underdeveloped. This narrative review examines the existing body of literature regarding the use of AI in HNC. Studies to date have demonstrated AI’s utility across multiple phases of the HNC treatment continuum. Despite its promise, integrating AI into clinical practice faces several challenges, including concerns about system integrity, generalizability, privacy, and bias. In this review, we address these challenges and offer insights into future …

Artificial Intelligence In Head And Neck Cancer: Towards Precision Medicine, Jacob Hagen, Logan Hornung, William Barham, Supratik Mukhopadhyay, Adam Bess, Kevin Contrera, Devraj Basu, Vlad Sandulache, Guillaume Spielmann, Sagar Kansara

School of Medicine Faculty Publications

Over the past 20 years, the capabilities of artificial intelligence (AI) have gained significant interest. While AI has been implemented to various degrees in several disciplines, its unique applications in head and neck cancer (HNC) remain underdeveloped. This narrative review examines the existing body of literature regarding the use of AI in HNC. Studies to date have demonstrated AI’s utility across multiple phases of the HNC treatment continuum. Despite its promise, integrating AI into clinical practice faces several challenges, including concerns about system integrity, generalizability, privacy, and bias. In this review, we address these challenges and offer insights into future …

Integrating Identity-Based Identification Against Adaptive Adversaries In Federated Learning, Jakub Kacper SzeląG, Ji-Jian Chin, Lauren Ansell, Sook-Chin Yip

School of Engineering, Computing and Mathematics

Federated Learning (FL) has recently emerged as a promising paradigm for privacy-preserving, distributed machine learning. However, FL systems face significant security threats, particularly from adaptive adversaries capable of modifying their attack strategies to evade detection. One such threat is the presence of Reconnecting Malicious Clients (RMCs), which exploit FL’s open connectivity by reconnecting to the system with modified attack strategies. To address this vulnerability, we propose the integration of Identity-Based Identification (IBI) as a security measure within FL environments. By leveraging IBI, we enable FL systems to authenticate clients based on cryptographic identity schemes, effectively preventing previously disconnected malicious clients …

Intelligence Architectures And Machine Learning Applications In Contemporary Spine Care, Rahul Kumar, Conor Dougherty, Kyle Sporn, Akshay Khanna, Puja Ravi, Pranay Prabhakar, Nasif Zaman

SKMC Student Presentations and Publications

The rapid evolution of artificial intelligence (AI) and machine learning (ML) technologies has initiated a paradigm shift in contemporary spine care. This narrative review synthesizes advances across imaging-based diagnostics, surgical planning, genomic risk stratification, and post-operative outcome prediction. We critically assess high-performing AI tools, such as convolutional neural networks for vertebral fracture detection, robotic guidance platforms like Mazor X and ExcelsiusGPS, and deep learning-based morphometric analysis systems. In parallel, we examine the emergence of ambient clinical intelligence and precision pharmacogenomics as enablers of personalized spine care. Notably, genome-wide association studies (GWAS) and polygenic risk scores are enabling a shift from …

A Polar Turbulence Invariant Map With Applicability To Realisable Machine Learning Turbulence Models, James G. Wnek, Christopher Schrock, Eric M. Wolf, Mitch Wolff

Mechanical and Materials Engineering Faculty Publications

Invariant maps are a useful tool for turbulence modelling, and the rapid growth of machine learning-based turbulence modelling research has led to renewed interest in them. They allow different turbulent states to be visualised in an interpretable manner and provide a mathematical framework to analyse or enforce realisability. Current invariant maps, however, are limited in machine learning models by the need for costly coordinate transformations and eigendecomposition at each point in the flow field. This paper introduces a new polar invariant map based on an angle that parametrises the relationship of the principal anisotropic stresses, and a scalar that describes …

Deep Learning-Driven Proteomics Analysis For Gene Annotation In The Renin-Angiotensin System, Mortaza Eivazi, Kamran Hosseini, Shahin Alipanahi, Huijing Xia, Luke Restivo, Ayushi Patel, Mahdieh Gozali, Tahereh Ebrahimi, Amy Scarborough, Vahideh Tarhriz, Eric Lazartigues

School of Medicine Faculty Publications

The renin-angiotensin system (RAS) is central to cardiovascular diseases such as hypertension and cardiomyopathy, yet the functions of many RAS genes remain unclear. This study developed a multi-label deep learning model to systematically annotate RAS gene functions and elucidate their roles in biological pathways. A total of 39,463 RAS-related publications from PubMed and PMC were processed into text format. Feature matrices were generated using TF-IDF and token processing, followed by dimensionality reduction via Principal Component Analysis (PCA). A Multi-Layer Perceptron (MLP) was applied for multi-label classification, with performance evaluated using Precision, F1-Score, Ranking Loss, and ROC-AUC metrics. The model outperformed …

Artificial Intelligence In Head And Neck Cancer: Towards Precision Medicine, Jacob Hagen, Logan Hornung, William Barham, Supratik Mukhopadhyay, Adam Bess, Kevin Contrera, Devraj Basu, Vlad Sandulache, Guillaume Spielmann, Sagar Kansara

Faculty Publications

Over the past 20 years, the capabilities of artificial intelligence (AI) have gained significant interest. While AI has been implemented to various degrees in several disciplines, its unique applications in head and neck cancer (HNC) remain underdeveloped. This narrative review examines the existing body of literature regarding the use of AI in HNC. Studies to date have demonstrated AI’s utility across multiple phases of the HNC treatment continuum. Despite its promise, integrating AI into clinical practice faces several challenges, including concerns about system integrity, generalizability, privacy, and bias. In this review, we address these challenges and offer insights into future …

Physics-Based Machine Learning Framework For Predicting Structure-Property Relationships In Ded-Fabricated Low-Alloy Steels †, Atiqur Rahman, Md Hazrat Ali, Asad Waqar Malik, Muhammad Arif Mahmood, Frank Liou

Mechanical and Aerospace Engineering Faculty Research & Creative Works

The Directed Energy Deposition (DED) process has demonstrated high efficiency in manufacturing steel parts with complex geometries and superior capabilities. Understanding the complex interplays of alloy compositions, cooling rates, grain sizes, thermal histories, and mechanical properties remains a significant challenge during DED processing. Interpretable and data-driven modeling has proven effective in tackling this challenge, as machine learning (ML) algorithms continue to advance in capturing complex property structural relationships. However, accurately predicting the prime mechanical properties, including ultimate tensile strength (UTS), yield strength (YS), and hardness value (HV), remains a challenging task due to the complex and non-linear relationships among process …

Computational String Theory Using Machine Learning, Giorgi Butbaia

Doctoral Dissertations

Low-energy couplings of certain superstring theories are related to the masses of the particles. One of the most promising superstring theories is $\mathcal{N}=1$ supersymmetric $E_8\times E_8$ heterotic string theory. In a certain embedding of the standard model into $E_8\times E_8$ gauge group of the heterotic string, the particle spectrum consists of $(\mathbf{27})^3$, $(\overline{\mathbf{27}})^3$, $(\mathbf{1})^3$ and $\mathbf{1}\cdot \mathbf{27}\cdot\overline{\mathbf{27}}$ couplings. While the $(\overline{\mathbf{27}})^3$ couplings are quasi-topological, the $(\mathbf{27})^3$ explicitly depend on the Ricci-flat metric on Calabi-Yau.

In this dissertation, we explore recently developed techniques for calculating the normalized Yukawa couplings on various Calabi-Yau manifolds. In Chapter 2, we describe a novel architecture, …

Trends And Predictive Modeling Of Real Estate Prices In Major Saudi Arabia Cities, Meshal S. Aldahas

Theses and Dissertations

his research examines historical trends and explanatory modeling of real estate prices in major Saudi cities, with a focus on Riyadh, Jeddah, and Dammam. Using a mixed-methods approach, the study integrates quantitative data from 2010–2023, including housing and macroeconomic indicators, with qualitative insights drawn from over 320 survey responses that captured consumer sentiment on affordability, job security, and housing policies. A combination of descriptive statistics, ARIMA and Exponential Smoothing techniques was applied to detect long-term patterns, seasonal variations, and market shocks. Predictive modeling was conducted using Linear Regression, Decision Trees, and Neural Networks, with results showing that job security consistently …

Low-Resource Ecoacoustic Audio Classification, Enis Berk Coban

Dissertations, Theses, and Capstone Projects

Ecoacoustic monitoring via machine learning enables scalable analysis but is often constrained by labeled data scarcity, particularly in remote regions like the Arctic. This thesis confronts low-resource ecoacoustic audio classification by developing and evaluating complementary machine learning methodologies. We introduce EDANSA, the first publicly available, expert- labeled Arctic dataset of its kind, curated via novel active learning, alongside a baseline CNN. We systematically evaluate transfer learning, showing general audio embeddings effectively bootstrap classifiers for challenging Arctic sounds, significantly outperforming direct label mapping. Optimizing label utility, we investigate standard data augmentation and introduce novel audio data valuation via Shapley values, revealing …

Novel Machine Learning Methods For Quasar Variability And Strong Gravitational Lensing, Joshua Fagin

Dissertations, Theses, and Capstone Projects

Machine learning methods are well suited to handle the unprecedented data volume expected from upcoming wide-field surveys. The European Space Agency's Euclid telescope was launched in July 2023 and is expected to observe billions of galaxies at high resolution, including tens of thousands of strongly lensed galaxies. The Rubin Observatory Legacy Survey of Space and Time (LSST) will monitor tens of millions of quasars throughout its ten-year lifetime, thousands of which will be strongly lensed. This flood of data will enable measuring lens galaxy mass distributions and substructure, as well as probing quasar accretion disk structure and black hole properties …

The “Founder’S Gaze”: How The Fourth Amendment Is A Surveillance Technology That Enables Ai To Scale Control Over The Subaltern, Diego H. Alcalá Laboy

Michigan Journal of Race and Law

Much has been written about the rise of artificial intelligence and machine learning applications and how the current Fourth Amendment law has been unable to mitigate the privacy harm that these tools produce. This article explores how the development and usage of AI and machine learning models is dependent on the originalism principles of Fourth Amendment Law. Utilizing Critical Surveillance Studies and Anticolonial Theory, I posit that the Fourth Amendment is a surveillance technology that categorizes conduct, persons, and places to impose the material conditions for the subjugation of historically minoritized communities within the United States. Furthermore, this article explores …

Exact And Approximate Conformal Inference For Multi-Output Regression, Chancellor Johnstone, Eugene Ndiaye

Faculty Publications

It is common in machine learning to estimate a response y given covariate information x . However, these predictions alone do not quantify any uncertainty associated with said predictions. One way to overcome this deficiency is with conformal inference methods, which construct a set containing the unobserved response with a prescribed probability. Unfortunately, even with a one-dimensional response, conformal inference is computationally expensive despite recent encouraging advances. In this paper, we explore multi-output regression, delivering exact derivations of conformal inference p-values when the predictive model can be described as a linear function of y . Additionally, we introduce a multivariate …

Multi-Fidelity Machine Learning Modeling For Aerodynamic Response Prediction Of Aerospace Vehicles, Ethan S. Jackman

Theses and Dissertations

Hypersonic vehicle design requires understanding complex aerodynamic phenomena across the full flight regime. This study presents a novel MF surrogate modeling methodology that enables the prediction the full field response across a vehicle’s surface. A Space-Filling Curve (SFC) is used to convert unstructured data into 1D vectors. The a Convolutional Autoencoder is used with transfer learning to reduce the dimensionality of the data. An Emulator-Embedded Neural Network (E2NN) combines multi-fidelity data for fast, accurate predictions. A benchmark analytical example and hypersonic application are used to evaluate the methodology. Using various numbers of samples and sampling strategies it is found that …

Harnessing Graphs For Knowledge Representation In Natural Language Processing, Uras Varolgunes

Dissertations

This work proposes innovative methods for integrating domain-specific knowledge into natural language processing tasks through the use of graphs, aiming to enhance the performance of models across various domains, including finance and healthcare. Several novel approaches are proposed that fuse graph structures with modern deep learning techniques, addressing the challenges of missing word embeddings, label prediction, and graph representation learning for large language models.

First, a powerful embedding method built on top of the recent advances in latent graph learning is introduced to address the critical problem of word embedding imputation. Second, a graph-enhanced label attention model designed for medical …

Sugar: A Sequence Unfolding Based Transformer Model For Group Activity Recognition, Yash U. Gondkar

Graduate Masters Theses

Large Language Models have improved significantly in the past couple of years due to the adoption of transformers. However, transformers still find it challenging to process videos due to limited context size caused by their quadratic computing cost. Therefore, we studied a booming field in machine learning which powers applications like social scene analysis and video surveillance systems called Group Activity Recognition (GAR). We found that recent models were able to achieve more than 90% accuracy on popular datasets like the Volleyball dataset, however, it turned out that even they relied on transformers.

Therefore, in this work, we developed a …

The Research University, Industry And Artificial Intelligence, Hamlet Isakhanli

Khazar Journal of Humanities and Social Sciences

Scientific research in cooperation between science and industry occurs mainly in research universities. This article begins with a brief history of research universities, a discussion of their nature and form. Their evolution and development in Europe, the USA and China are recalled; the special importance of Graduate Schools is underlined. Cooperation between the worlds of university and industry is studied using the example in the USA of Silicon Valley, a world leader in its field and initiated by Stanford University. Presented alongside the positive American experience, is the rather negative experience of the ``Russian syndrome''. The development of semiconductor, transistor …

A Comparative Of Detecting Physical And Cyber Attacks On Drones Using Machine Learning And Deep Learning Techniques, Khattab M Ali Alheeti, Sara Abbas Rafa, Maha Mahmood, Aythem Khairi Kareem, Mohammad Aljanabi, Ahmed Adil Nafea

Baghdad Science Journal

Multitudes of Unmanned Aerial Vehicles (UAVs) are generally embraced in military and civilian applications. Yet, this physical-cyber method is intimidated by cyber-attacks. Recently, Machine Learning (ML) based attacks detection approaches have been effectively embraced to detect cyber-attacks. This paper presented the Intrusion Detection Security (IDS) approach. The proposed approach investigates UAVs' cyber and physical attributes under normal process and attack circumstances. Two types of cyber-attacks have been classified: Denial-of-service (DoS) and replay. This study developed IDS approaches established on ML and Deep Learning (DL) prototypes, including Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naive Bayes (NB), and One Dimensional Convolutional …

Enhance Telecom-Related International Indicators Using Machine Learning, Omar Alnemer

Theses

This thesis investigates the enhancement of the Network Readiness Index (NRI) through the application of machine learning methodologies to refine indicator weighting, clustering, and dimensionality. The study builds on research promoting data-driven methods to address the limitations of equal-weighted indices in capturing indicator importance and interdependence. The research aims to evaluate how unsupervised and supervised learning methods can optimize the interpretation of NRI data. The study focuses on three primary research questions: (1) How can PCA be used to reduce the dimensionality of the NRI without significant loss of information? (2) Can k-means clustering reveal meaningful groups of countries with …

Mathematics And Mental Health: An Interdisciplinary Analysis Of Ai In Counselor Education, Jennifer M. Hightower, Catrina A. May

Journal of Counselor Preparation and Supervision

Although use of Artificial Intelligence (AI) in mental health care has become increasingly common, many professional counselors remain under informed about foundational components of AI. Fundamental issues associated with AI, including model bias and the Black Box Problem, must be considered as these tools become integrated into the counseling profession. This manuscript provides an interdisciplinary, theoretical analysis of AI use in counseling and counselor education grounded in foundational knowledge of AI and the American Counseling Association’s recommendations for the ethical integration of AI (Butler et al., 2023). The paper summarizes these recommendations, establishes accessible definitions of AI terms, explains the …

Retracted: Iot Flow Parameters Classification Based On Machine Learning Techniques, El-Sayed M. El-Kenawy, Marwa M. Eid, Ban Salman Shukur, Amel Ali Alhussan, Doaa Sami Khafaga

Iraqi Journal for Computer Science and Mathematics

In recent years, there has been a highly remarkable convergence of artificial intelligence (AI) and the Internet of Things (IoT), which has made rapid progress in smart city initiatives by developing smart devices for such cities. Since these devices are increasingly diversified, they require a resilient communication network to demonstrate high performance in managing consistent traffic flows. A machine learning model intended for identifying network parameters from diverse devices, in addition to proposing modifications meant for network performance enhancement, is developed in this study. In relation to packet data as a network traffic parameter, employing gateway devices can facilitate its …

Modeling The Importance Of Life Exposure Factors On Memory Performance In Diverse Older Adults: A Machine Learning Approach, Evan Fletcher, Marianne Chanti-Ketterl, Emily Hokett, Yi Lor, Umesh Venkatesan, Ruijia Chen, Omonigho M. Bubu, Rachel Whitmer, Paola Gilsanz, Zvinka Z. Zlatar

Moss-Magee Rehabilitation Papers

INTRODUCTION: Many health life exposure factors (LEFs) influence cognitive decline and dementia incidence, but their relative importance to episodic memory (an early indicator of cognitive decline) among diverse older adults is unclear. We used machine learning to rank LEFs for memory performance in a large and diverse US cohort.

METHODS: Kaiser Healthy Aging and Diverse Life Experiences (KHANDLE) and Study of Healthy Aging in African Americans (STAR), participants underwent neuropsychological testing and answered questionnaires about multiple LEFs. XGBoost and Shapley Additive exPlanation values ranked the importance of factors influencing cross-sectional episodic memory in the full sample and by sex and …

Machine Learning-Enhanced Optimization For The Real-Time, Secure, And Efficient Operation Of The Energy-Water-Hydrogen Nexus, Mostafa Goodarzi

Graduate Thesis and Dissertation post-2024

Given the escalating urgency of climate change, our research addresses the imperative need to curb carbon emissions, particularly from the electricity sector, which accounts for a quarter of total emissions in the U.S. We propose integrated systems, such as the energy-water nexus (EWN) and an innovative concept known as the energy-water-hydrogen (EWH) nexus, which integrates renewable energy sources (RESs) with green hydrogen production through water electrolysis. These concepts align seamlessly with the nation’s commitment to global climate agreements, including the Paris Agreement. Furthermore, green hydrogen—a clean and efficient energy source produced via water electrolysis powered by renewable energy—emerges as a …

Research On Autonomous Deviation Correction Of Tunnel Boring Machines And Parameters Based On Machine Learning, Zhang Jun, Li Maopeng

Journal of China & Foreign Highway

In order to solve the problem of realizing the autonomous deviation correction of tunnel boring machines (TBMs ), a TBM deviation correction control method that integrated the random forest (RF) algorithm with the genetic algorithm (GA) was proposed based on actual engineering data.The method combined a prediction model with an optimization model,using target deviation values as input to invert and output the required TBM deviation correction parameter values,thereby further improving the automation level of TBM deviation correction.By comparing it with the actual data,the feasibility of the model was verified.The results show that the RF algorithm-based prediction model achieves an R2 …

Application Of Ann Artificial Network In Slope Behavior Evaluation Using Machine Learning Technique, Marziyeh Tourani, Hadi Bahadori

NJF Intelligent Engineering Journal

In geotechnical engineering, evaluating slope stability remains a significant challenge due to inherent soil variability and environmental uncertainty. This study explores the application of Artificial Neural Networks (ANN) in predicting the factor of safety (FoS) of slopes using dimensionless input variables. An initial dataset of 349 samples was refined to 259 validated cases after removing outliers and incomplete entries. The input parameters were transformed into nondimensional forms c / γh, (tan(⌀)) / (tan (β)), and p / γh based on fluid mechanics principles to improve generalization and reduce dimensional bias. A feedforward ANN model …

Ai-Driven Biomedical Perspectives On Mental Fatigue In The Post-Covid-19 Era: Trends, Research Gaps, And Future Directions, Saba Parveen, Md Belal Bin Heyat, Umair Tariq, Faijan Akhtar, Hafiz Muhammad Zeeshan, Seth Christopher Yaw Appiah, Shang-Ming Zhou, Huang Lei

School of Nursing and Midwifery

Mental fatigue is a complex condition arising from various neurological processes and influenced by external factors such as stress and cognitive demands. This comprehensive review elucidates the primary neurological mechanisms underlying mental fatigue, particularly emphasizing how it was elevated or otherwise affected during the COVID-19 pandemic. We explore the intricate relationship between prolonged cognitive tasks, chronic stress, and the development of mental fatigue, emphasizing the impacts that mental fatigue has on mental health across diverse populations. Utilizing advanced artificial intelligence techniques, including machine learning and deep learning, this study identifies and quantifies the patterns of mental fatigue. The innovative approach …

Assessing Greenhouse Gas Emissions From Michigan’S Drowned River Mouths Using In Situ And Remote Sensing Methods, Jillian A. Greene

Masters Theses

Freshwater estuaries are natural contributors to the carbon cycle including production and emission of methane (CH4) and carbon dioxide (CO2), potent greenhouse gases (GHGs); however, estimates of their contribution to regional and global GHG emissions is largely unconstrained. Few studies have examined the quantification and drivers of lake CH4 and CO2 production in the Great Lakes region and how it may differ in response to anthropogenic development. In this study, CH4 and CO2 emissions were measured from three drowned river mouth estuaries (DRMs) along the eastern shore of Lake Michigan in 2024. The DRMs exist along a latitudinal gradient ranging …

A Comprehensive Review Of Frugal Artificial Intelligence: Challenges, Applications, And The Road To Sustainable Ai, Nancy Girdhar, Aditya Raj, Deepak Sharma, Vinay Singh, Antoine Doucet, Matthias Renz

School of Engineering, Computing and Mathematics

Artificial Intelligence (AI) has demonstrated its transformative impact in creating learning models, processing extensive datasets, and executing intricate calculations rapidly. Nevertheless, achieving optimal performance with AI models demands substantial investment in powerful and expensive high-end hardware. The learning models running on the hardware are complex, requiring massive data and huge training time. However, the race to achieve higher accuracy and computational limitations of AI further poses a threat to the environment and thus provides motivation to develop AI technology that is cost-effective, scalable, and suitable for resource-constrained environments, Frugal Artificial Intelligence, or Frugal AI. The objective of this paper is …