Digital Commons Network^™

Hybrid Machine Learning And Comparison Error Minimization For Frequency Domain-Based Rapid State Estimation In Structures Subjected To High-Rate Boundary Change, James Scheppegrell

Theses and Dissertations

Dynamic forces and evolving structural boundary conditions pose challenges for various structural systems such as aircraft, orbital infrastructure, and energy harvesting devices. The design, evaluation, and functionality, of such systems can be aided through the collection and analysis of data. However, real-time decision-making for systems experiencing high-rate changes can pose unique challenges, if assessments are to be made accurately and rapidly enough to be relevant. In cases where the systems are well-defined and thoroughly understood, monitoring the frequency response can be instrumental in determining the state of structures subjected to high-rate structural boundary condition changes. This study focuses on investigating …

Cross-Layer Design And Optimization Of Analog In-Memory Computing Systems, Md Hasibul Amin

Theses and Dissertations

There has been a rapid growth in the computational demands of machine learning (ML) workloads in recent days. Conventional von Neumann architectures are not capable of keeping up with the high cost of data movement between the processor and memory, well-known as memory wall problem. In-memory computing (IMC) has been focused as a solution by the researchers, where the computation is performed inside the memory devices such as SRAM, MRAM, RRAM etc. Most commonly, the memory devices are arranged in a crossbar setting where the matrixvector multiplication (MVM) operation is performed through intrinsic parallelism of analog computations. The conventional IMC …

A Data-Driven Recommendation System For Selecting The Appropriate Mode Of Learning And Instructional Tools Based On Course Characteristics, Ayisha Manzoor

Theses

The rapid transformation of educational delivery methods during the COVID-19 pandemic required institutions to transition between online, hybrid, and offline learning approaches, creating both challenges and opportunities for educators and students. While online and hybrid learning modes ensured continuity, their effectiveness across different course types remained uncertain. This thesis addresses this gap by developing a datadriven recommendation framework that predicts Course Learning Outcome (CLO) achievement scores using regression, and recommends the most appropriate learning mode (online, hybrid, or offline) along with instructional tools based on course characteristics. This study analyzed 100 undergraduate and postgraduate courses from the College of Information …

Machine Learning To Predict Stroke Risk From Routine Hospital Data: A Systematic Review, William Heseltine-Carp, Megan Courtman, Daniel Browning, Aishwarya Kasabe, Michael Allen, Adam Streeter, Emmanuel Ifeachor, Martin James, Stephen Mullin

School of Engineering, Computing and Mathematics

Purpose: Stroke remains a leading cause of morbidity and mortality. Despite this, current risk stratification tools such as CHA₂DS₂-VASc and QRISK3 are of limited accuracy, particularly in those without a diagnosis of atrial-fibrillation. Hence, there is a need for more accurate stroke risk prediction models. Machine-learning (ML) may provide a solution to this by leveraging existing routine hospital databases to build accurate stroke risk prediction models and identify novel risk factors for stroke. Aims: In this systematic review we appraise current research using ML to predict stroke risk from routine hospital data. Based on these findings …

An Artificial Intelligence-Based System For Detecting Meta Fake Profiles Via Gradient Boosting And Multilayer Perception, Ruaa A. Al-Falluji, Marwan Ali Albahar, Ahmad Mousa Altamimi

Mesopotamian Journal of CyberSecurity

The rise of metaverse platforms has renewed interest in detecting fake profiles, which pose a significant threat to digital ownership and asset transactions within these virtual environments. If digital ownership is not guaranteed, platforms risk missing the point of the metaverse. Current supervised learning techniques for fake profile detection often struggle to maintain acceptable accuracy and interpretability in practice. To address this problem, this study investigates the application of two machine learning models, multilayer perceptron and gradient boosted trees, for detecting fake profiles, with model evaluation performed via two Explainable AI (XAI) techniques, Local Interpretable Model-agnostic Explanations (LIME) and SHapley …

Machine Learning Methods For Hypervelocity Fragment Flyout Characterization, Katharine Larsen

Doctoral Dissertations and Master's Theses

Resulting from breakup events, such as collisions and explosions, hypervelocity fragments create potential hazards for both terrestrial and on-orbit environments, such as terrestrial weapons explosions and satellite breakup events, respectively. To avoid unnecessary damage, an accurate understanding or characterization of hypervelocity fragmentation events is vital. Currently, publicly available two-line elements collected from on-orbit breakup events are limited, excluding pre-detonation parent body conditions, such as orientation, and information of smaller fragments. The uncertainty of these datasets varies between each collected set. Therefore, the overall goal of this work is to employ machine learning to estimate distribution characteristics of a space debris …

Enhancing Iot Security Using Lightweight Machine Learning Algorithms: A Comprehensive Approach Using Ensemble Learning, Feature Selection, And Federated Transfer Learning, Khawlah Harahsheh

Electrical & Computer Engineering Theses & Dissertations

The rapid expansion of the Internet of Things (IoT) has introduced significant security vulnerabilities due to the resource-constrained nature of IoT devices and their exposure to cyber threats. Traditional security solutions are often infeasible due to the high computational and storage demands they impose. This dissertation presents a lightweight, AI-driven security framework that enhances IoT network resilience by integrating feature selection, ensemble learning, and federated transfer learning while maintaining data privacy and minimizing computational overhead.

The proposed framework consists of three primary components: Feature Selection for Intrusion Detection, which optimizes performance by reducing redundant data and improving detection accuracy with …

Unveiling Risk Strategies In The Online Food Delivery Platforms: Evidence From Risk Disclosures And Reprisk Index, Maryam Mahdikhani, Jiyoon An, Arim Park

Atlantic Marketing Journal

Online Food Delivery Platforms (OFDPs) have faced unique challenges related to uncertainty and global risk in recent decades. To address these risks, this study utilized machine learning (ML) techniques, including Latent Dirichlet Allocation (LDA) and Word2Vec, to extract and cluster OFDP-related risk factors. The analysis was conducted using unstructured text data from the annual reports of two prominent OFDP companies—Delivery Hero (DH) and Grubhub (GH)—from 2017 to 2020. We also examined the multidimensional RepRisk Index (RRI) for these companies and developed a two-dimensional matrix to evaluate preparedness and responsiveness across various risk categories in the OFDP sector. The results indicate …

Harnessing Sociodemographic And Anthropometric Insights To Predict Type 2 Diabetes Risk: A Machine Learning Approach, Rico Kurniawan, Rezki Yunanda, Okky Assetya Pratiwi

Jurnal Biostatistik, Kependudukan, dan Informatika Kesehatan

The complexity of diabetes makes early diagnosis and effective management challenging for healthcare settings. This study developed and evaluated models using machine learning algorithms to predict the risk of diabetes. The data were primarily sourced from the Indonesia Family Life Survey (IFLS). Diabetes status was identified using A1c whole blood sample values (A1c WBS ³5.7%). Sociodemographic and anthropometric factors were set as predictors, most of which were significantly correlated with diabetes status. Discrete machine learning algorithms such as decision tree, k-nearest neighbors (KNN), random forest (RF), naïve Bayer, support vector machine (SVM), and neural network (MLP) were applied to construct …

Air Traffic Trends And Uav Safety: Leveraging Automatic Dependent Surveillance–Broadcast Data For Predictive Risk Mitigation, Prasad Pothana, Paul Snyder, Sreejith Vidhyadharan, Michael Ullrich, Jack Thornby

Petroleum Engineering Student Publications

With the significant potential of Unmanned Aircraft Vehicles (UAVs) extending throughout various fields and industries, their proliferation raises concerns regarding potential risks within the national airspace system (NAS). To enhance the safe and efficient integration of UAVs into airport environments, this paper presents an analysis of temporal statistical patterns in flight traffic, the predictive modeling of future traffic trends using machine learning, and the identification of optimal time windows for UAV operations within airports. The framework was developed using historical Automatic Dependent Surveillance–Broadcast (ADS-B) data obtained from the OpenSky Network. Historical flight data from Class B, C, and D airports …

A Robust And Efficient Hybrid Classification Model For Early Diagnosis Of Chest X-Ray Images Of Covid-19, Abeer M. Shanshool, Mariam Bouchakwa, Ikram Amous

Baghdad Science Journal

There has been a COVID-19 pandemic since December 2019, and successful medical treatment for COVID-19 patients requires rapid and accurate diagnosis. Fighting the COVID-19 pandemic requires an automated system that uses deep transfer learning to diagnose the virus on chest X-ray (CXR). CXR are frequently utilized in healthcare because they offer the potential for rapid and accurate disease diagnosis. Automated computer-aided diagnosis (CAD) systems incorporate ML or deep learning to enhance efficiency and accuracy, hence reducing future problems. Numerous AI systems based on deep learning can be employed for diagnosis; among the most widely used is the CNN, which was …

Dynamic Gene Attention Focus (Dygaf): Enhancing Biomarker Identification Through Dual-Model Attention Networks, Md Khairul Islam, Himanshu Wagh, Hairong Wei

Michigan Tech Publications

The DyGAF model, which stands for Dynamic Gene Attention Focus, is specifically designed and tailored to address the challenges in biomarker detection, progression reporting of pathogen infection, and disease diagnostics. The DyGAF model introduced a novel dual-model attention-based mechanism within neural networks, combined with machine learning algorithms to enhance the process of biomarker identification. The model transcended traditional diagnostic approaches by meticulously analyzing gene expression data. DyGAF not only identified but also ranked genes based on their significance, revealing a comprehensive list of the top genes essential for disease detection and prognosis. In addition, KEGG pathways, Wiki Pathways, and Gene …

Artificially Intelligent Maxwell's Demon For Optimal Control Of Open Quantum Systems, Paolo A. Erdman, Robert Czupryniak, Bibek Bhandari, Andrew N. Jordan, Frank Noé, Jens Eisert, Giacomo Guarnieri

Mathematics, Physics, and Computer Science Faculty Articles and Research

Feedback control of open quantum systems is of fundamental importance for practical applications in various contexts, ranging from quantum computation to quantum error correction and quantum metrology. Its use in the context of thermodynamics further enables the study of the interplay between information and energy. However, deriving optimal feedback control strategies is highly challenging, as it involves the optimal control of open quantum systems, the stochastic nature of quantum measurement, and the inclusion of policies that maximize a long-term time- and trajectory-averaged goal. In this work, we employ a reinforcement learning approach to automate and capture the role of a …

Enhanced Detection Of Apt Vector Lateral Movement In Organizational Networks Using Lightweight Machine Learning, Mathew Nicho, Oluwasegun Adelaiye, Christopher D. Mcdermott, Shini Girija

All Works

The successful penetration of government, corporate, and organizational IT systems by state and nonstate actors deploying APT vectors continues at an alarming pace. Advanced Persistent Threat (APT) attacks continue to pose significant challenges for organizations despite technological advancements in artificial intelligence (AI)-based defense mechanisms. While AI has enhanced organizational capabilities for deterrence, detection, and mitigation of APTs, the global escalation in reported incidents, particularly those successfully penetrating critical government infrastructure has heightened concerns among information technology (IT) security administrators and decisionmakers. Literature review has identified the stealthy lateral movement (LM) of malware within the initially infected local area network (LAN) …

Role Of Eye-Tracking Technology And Software Algorithms In Enhancing Adhd Detection And Diagnosis: A Systematic Literature Review, Lauren E. Perkins

Honors College Theses

This systematic literature review explores the role of eye-tracking technology and software algorithms in enhancing the detection and diagnosis of ADHD. ADHD, a neurodevelopmental disorder affecting both children and adults, is traditionally diagnosed through behavioral assessments, which may lack objectivity. Recent studies suggest that eye-tracking, specifically focusing on saccades, fixations, and blink rates, offers the potential for more accurate and objective measures of ADHD. The review examines clinical trials, observational studies, and machine learning research to assess the correlation between ADHD and eye movement patterns. Results indicate that individuals with ADHD exhibit distinct eye movement patterns, which can be quantified …

Cyber Threat Intelligence For Smart Grids Using Knowledge Graphs, Digital Twins, And Hybrid Machine Learning In Scada Networks, Nabeel Al-Qirim, Munir Majdalawieh, Anoud Bani-Hani, Hussam Al Hamadi

All Works

In the SCADA (Supervisory Control and Data Acquisition) network of a smart grid, the network switch is connected to multiple Intelligent Electronic Devices (IEDs) that are based on protective relays. False-Data Injection Attacks (FDIA), Remote-Tripping Command Injection (RTCI), and System Reconfiguration Attacks (SRA) are three types of cyber-attacks on SCADA networks, resulting in single-line-to-ground (SLG) fault, IED-relay failure, and circuit-breaker open issues occur. The existing cyber threat intelligence (CTI) approaches of grids are unable to provide visualization of cyber-attacking grid effects. To understand the full effect of the attacks, there is a need for a knowledge-graph method-based digital-twin cyber-attack visualization …

Sexual Health In The Era Of Artificial Intelligence: A Scoping Review Of The Literature, Elia Abou Chawareb, Brian H. Im, Sherry Lu, Muhammed A.M. Hammad, Tiffany R. Huang, Henry Chen, Faysal A. Yafi

Student Papers, Posters & Projects

INTRODUCTION: Artificial Intelligence (AI) has witnessed significant growth in the field of medicine, leveraging machine learning, artificial neuron networks, and large language models. These technologies are effective in disease diagnosis, education, and prevention, while raising ethical concerns and potential challenges. However, their utility in sexual medicine remains relatively unexplored.

OBJECTIVE: We aim to provide a comprehensive summary of the status of AI in the field of sexual medicine.

METHODS: A comprehensive search was conducted using MeSH keywords, including "artificial intelligence," "sexual medicine," "sexual health," and "machine learning." Two investigators screened articles for eligibility within the PubMed and MEDLINE databases, with …

On Large Language Models In National Security Applications, William N. Caballero, Phillip R. Jenkins

Faculty Publications

The overwhelming success of GPT-4 in early 2023 highlighted the transformative potential of large language models (LLMs) across various sectors, including national security. This article explores the implications of LLM integration within national security contexts, analyzing their potential to revolutionize information processing, decision-making, and operational efficiency. Whereas LLMs offer substantial benefits, such as automating tasks and enhancing data analysis, they also pose significant risks, including hallucinations, data privacy concerns, and vulnerability to adversarial attacks. Through their coupling with decision-theoretic principles and Bayesian reasoning, LLMs can significantly improve decision-making processes within national security organizations. Namely, LLMs can facilitate the transition from …

Spatio-Temporal Graph Neural Networks For Streamflow Prediction In The Upper Colorado Basin, Akhila Akkala, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi, Pouya Hosseinzadeh, Ayman Nassar

Computer Science Student Research

Streamflow prediction is vital for effective water resource management, enabling a better understanding of hydrological variability and its response to environmental factors. This study presents a spatio-temporal graph neural network (STGNN) model for streamflow prediction in the Upper Colorado River Basin (UCRB), integrating graph convolutional networks (GCNs) to model spatial connectivity and long short-term memory (LSTM) networks to capture temporal dynamics. Using 30 years of monthly streamflow data from 20 monitoring stations, the STGNN predicted streamflow over a 36-month horizon and was evaluated against traditional models, including random forest regression (RFR), LSTM, gated recurrent units (GRU), and seasonal auto-regressive integrated …

Machine Learning For Time Series Prediction In Psychotherapy, Ian Goodwin

Undergraduate Honors Theses

The need for quality mental health care is great, but many clinics face limited resources as they work to help clients. Predictions of a client’s future mental state during a course of therapy have the potential to inform decisions by individual therapists and counseling clinics. In this paper, we use archival OQ-45 data from a large university counseling center to examine the ability of many simple, rule-of-thumb predictive methods, as well as more complex methods (ridge regression, random forests, and LSTMs), to predict the next value in a time series of distress scores based on the previous scores. Learned inference …

Real-Time Defect Detection And Classification In Robotic Assembly Lines: A Machine Learning Framework, Fadi El Kalach, Mojtaba Farahani, Thorsten Wuest, Ramy Harik

Faculty Publications

Manufacturing systems have witnessed a significant transformation with the introduction of Industry 4.0, introducing new capabilities with the emergence of new technologies. One such instance is the proliferation of sensors enabling the generation and acquisition of vast amounts of data, leading to advancements in Artificial Intelligence (AI) for manufacturing. One field profiting from this is that of Time Series Analytics (TSC) which includes forecasting and classification. TSC can be crucial for fault detection and diagnosis in manufacturing systems. However, there are still challenges in utilizing manufacturing datasets to train and deploy classification algorithms for real time classification. As such this …

Machine Learning For Leadership In Energy And Environmental Design Credit Targeting: Project Attributes And Climate Analysis Toward Sustainability, Ali Mansouri, Mohsen Naghdi, Abdolmajid Erfani

Michigan Tech Publications

Achieving Leadership in Energy and Environmental Design (LEED) certification is a key objective for sustainable building projects, yet targeting LEED credit attainment remains a challenge influenced by multiple factors. This study applies machine learning (ML) models to analyze the relationship between project attributes, climate conditions, and LEED certification outcomes. A structured framework was implemented, beginning with data collection from the USGBC (LEED-certified projects) and US NCEI (climate data), followed by preprocessing steps. Three ML models—Decision Tree (DT), Support Vector Regression (SVR), and XGBoost—were evaluated, with XGBoost emerging as the most effective due to its ability to handle large datasets, manage …

Enhancing Tree Species Mapping In Arkansas’ Forests Through Machine Learning And Satellite Data Fusion: A Google Earth Engine–Based Approach, Abdullah Al Saim, Mohamed Aly

Geosciences Faculty Publications and Presentations

Arkansas' subtropical climate nurtures extensive forested regions, particularly within the Ozark-St. Francis and Ouachita National Forests. Despite this, the state lacks an up-to-date high-resolution map detailing the distribution of tree species within its forests. This study harnesses the power of machine learning, specifically the Random Forest (RF), Gradient Tree Boosting (GTB), Support Vector Machine (SVM), and K-Nearest Neighbors (K-NN) classifiers, within the Google Earth Engine (GEE) framework. These classifiers are applied to classify tree species distribution in Arkansas' forests by integrating data from various sources, including Sentinel-1/-2, Landsat-8, and the National Agriculture Imagery Program (NAIP). The study evaluates the classification …

Time-Series Forecasting In Smart Manufacturing Systems: An Experimental Evaluation Of The State-Of-The-Art Algorithms, Mojaba A. Farahani, Fadi El Kalach, Austin Harper, M.R. Mccormick, Ramy Harik, Thorsten Wuest

Faculty Publications

Time-Series Forecasting (TSF) is a growing research area across various domains including manufacturing. Manufacturing can benefit from Artificial Intelligence (AI) and Machine Learning (ML) innovations for TSF tasks. Although numerous TSF algorithms have been developed and proposed over the past decades, the critical validation and experimental evaluation of the algorithms hold substantial value for researchers and practitioners and are missing to date. This study aims to fill this research gap by providing a rigorous experimental evaluation of the state-of-the-art TSF algorithms on thirteen manufacturing-related datasets with a focus on their applicability in smart manufacturing environments. Each algorithm was selected based …

Comparison Of Deep Learning And Traditional Machine Learning Models For Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers, Kesheng Wang Phd, Ma, Bs, Donald A. Adjeroh, Wei Fang, Suzy M. Walter, Danqing Xiao, Ubolrat Piamjariyakul, Chun Xu

Faculty Publications

Mild cognitive impairment (MCI) is a clinical condition characterized by a decline in cognitive ability and progression of cognitive impairment. It is often considered a transitional stage between normal aging and Alzheimer’s disease (AD). This study aimed to compare deep learning (DL) and traditional machine learning (ML) methods in predicting MCI using plasma proteomic biomarkers. A total of 239 adults were selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort along with a pool of 146 plasma proteomic biomarkers. We evaluated seven traditional ML models (support vector machines (SVMs), logistic regression (LR), naïve Bayes (NB), random forest (RF), k-nearest neighbor …

Uncertainty Propagation And Sensitivity Analysis For Constrained Optimization Of Nuclear Waste Vitrification, Lagrande Gunnell, Xiaonan Lu, John D. Vienna, Dong-Sang Kim, Brian J. Riley, John Hedengren

Faculty Publications

The vitrification of high-level waste (HLW) by heating a mixture of glass-forming chemicals (GFCs) with the waste can be improved using a constrained optimization problem. This study explores how different uncertainty propagation (UP) methods implemented with the optimization process can affect the glass formulation of nuclear waste glasses. UP is the effort of propagating uncertain inputs through a system to understand and quantify output distributions. Uncertainty intervals are crafted from output distributions to inform the optimization algorithm. UP is often implemented with Monte Carlo (MC) sampling for large nonlinear systems, which can be difficult to implement within a constrained optimization …

Mxml (Exploring The Relationship Between Measurement And Machine Learning): Survey Of The Measurement Community, Yi Zheng, Sijia Huang, Steven Nydick, Susu Zhang

Chinese/English Journal of Educational Measurement and Evaluation | 教育测量与评估双语期刊

The field of educational measurement (hereafter shortened as measurement) has seen rapid growth in applications of machine learning (ML) recently. However, it is imperative to examine potential gaps between ML and the fundamental principles of measurement. The MxML project seeks to shed light on how to close the gaps between the two to harness the power of ML to serve measurement practices. Phase 1 of the project was a systematic review of the recent 10 years of measurement literature, in which we provided a snapshot of the literature in (1) areas of measurement where ML is discussed, (2) types …

Smart Resolutions: Exploring The Role Of Artificial Intelligence In Alternative Dispute Resolution, Nadia Ahmad

Cleveland State Law Review

Artificial intelligence (AI) has had a significant impact on most industries, including the legal landscape. Effective AI incorporation has removed barriers to access to justice, resolving issues such as backlogged court systems and inadequate resources for pursuing claims. Similarly, the expediency and cost-effectiveness associated with alternative dispute resolution (ADR) have established ADR as a mainstay in most countries to enhance legal accessibility. It was inevitable that AI and ADR were integrated to efficiently deliver justice.

Most automated ADR systems implemented thus far have been with human intervention. As machine learning develops, there are efforts to completely automate ADR, and this …

A Human-In-The-Loop Framework For Scalable And Interpretable Event Triaging In Large-Scale Systems, Ibrahim Khaled Al-Agha

Doctoral Dissertations

This dissertation presents a comprehensive and scalable framework for real-time fault detection and event triage in industrial systems, addressing critical challenges such as class imbalance, ambiguous feature boundaries, and the prioritization of complex, high-dimensional event data. The proposed framework integrates advanced methodologies, including micro-batch processing, retrospective divergence-based event detection (DB-RED), association rule mining (ARM), clustering, and Dempster-Shafer Theory (DST) for conflict resolution. Together, these components enable the systematic stratification of events into actionable priority levels, ensuring robust and interpretable decision-making in real-time environments. DB-RED forms the cornerstone of the framework, leveraging KL-divergence and PE-divergence metrics to detect subtle and transient …

Forecasting And Assessment Of Air Quality Dynamics In Northeast India Using Machine Learning Models, Kumar Shubham, Gopikrishnan T, Anshuman Singh

The Philippine Agricultural Scientist

This study investigated air quality dynamics in Northeast India, a region with unique terrestrial features, including the Eastern Himalayas. While air quality varies across districts, pollution impacts the entire area. Northeast India’s rich ecology is crucial for Himalayan climate regulation. Robust air quality monitoring and pollution control are essential to preserve environmental balance. This work focused on forecasting emissions of aerosols, SO₂, NO₂, CO, HCHO, O₃, and CH₄ primarily associated with human activities. Utilizing data from the Tropospheric Monitoring Instrument (TROPOMI) satellite instrument from 2019 to 2023, a 9-mo forecast was conducted using …