Digital Commons Network^™

Passive Communication Across Diverse Swarm Formations And Scales Utilizing Wake Signatures For Messaging And Object Inference, Bryan Varela

Open Access Master's Theses

Passive wake signatures in fluid flows can support perception and low-rate communication in swarms. In cluttered or contested underwater environments, conventional acoustic, radio, and optical links can be power-hungry, intermittent, or undesirable when low observability is required. Wake-mediated cues offer a local, directional channel that does not require line of sight because each agent naturally sheds coherent vortices that persist downstream and can be sampled by followers with only a small number of probes.

This study evaluates whether sparse downstream probes are sufficient to infer agent attributes and decode simple messages from wakes generated by established source shapes. Computational fluid …

Making Words Count: Computational Linguistics In Management Research, Joseph J. Simpson, Richard A. Hunt, David M. Townsend, Judy Rady, Elham Asgari, Mohammad Rady, Daniel J. Beal

International Business and Entrepreneurship Faculty Publications

In recent years, a rapidly growing number of management scholars are using computational linguistics (CL) to analyze vast corpora of naturally occurring organizational language. Yet the rapid adoption of CL methods has outpaced the development of shared standards for linking theoretical constructs, linguistic data, and computational measurement. We synthesize 353 articles published in leading management journals for 2013–2025, identifying four recurring challenges shaping the credibility of CL-based research: misalignment between constructs and textual corpora, unnecessary model complexity, the opacity of large language model workflows, and insufficient transparency in reporting. Building on these patterns, we develop four integrative design principles—construct-language fit, …

Optimizing Ucs Prediction Models Through Xai-Based Feature Selection In Soil Stabilization, Ahmed Mohammed Awad Mohammed, Omayma Husain, Mosab Hamdan, Abdalmomen Mohammed, Abdullah Ansari, Atef Badr, Abubakar Elsafi, Abubakr Siddig

Research Outputs: 2025-Present

Unconfined Compressive Strength (UCS) is a key parameter for the assessment of the stability and performance of stabilized soils, yet traditional laboratory testing is both time and resource intensive. In this study, an interpretable machine learning approach to UCS prediction is presented, pairing five models (Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), CatBoost, and K-Nearest Neighbors (KNN)) with SHapley Additive exPlanations (SHAP) for enhanced interpretability and to guide feature removal. A complete dataset of 12 geotechnical and chemical parameters, i.e., Atterberg limits, compaction properties, stabilizer chemistry, dosage, curing time, was used to train and test the models. …

Applied Machine Learning In Mineral Prospectivity Mapping In The South Pass-Granite Mountains Of Wyoming, Carl D. York

Graduate Theses/Dissertations

The South Pass-Granite Mountains, comprised of Archean to Tertiary rocks embedded with an Archean greenstone belt in Central Wyoming, have historically been mined for gold, iron, steel, and silver. Rare earth elements have been of increasing interest in the region and thus, several datasets have become available. Knowledge driven and data driven models are used in mineral prospectivity mapping with input data including geological mapping, geochemical data, geophysical (USGS Critical Mineral magnetic and gravity data), radiometric (U, Th, K) data and multispectral remote sensing data. Mineral prospectivity has seen a rapid improvement in recent years using advancements in machine learning …

Machine-Learning- And Iot-Based Approach For Predicting Water Quality Using Data Classification And Explainable Ai Technique, M. Samir Abou El-Seoud, Omar H. Karam 2, Hosam El-Sofany 3

Computer Science

Maintaining water quality is crucial for both public health and environmental sustainability. This study proposes a machine-learning- (ML-) and Internet-of-Things- (IoT-) based approach to water quality prediction that utilizes explainable artificial intelligence (XAI) and data classification techniques. The proposed approach integrates IoT devices to enhance real-time data collection, facilitating continuous monitoring and early anomaly detection. Ten different ML classifiers—Decision Trees, K-Nearest Neighbors, XGBoost, Naïve Bayes, Logistic Regression, AdaBoost, Random Forests, Support Vector Machines, Voting, and Multi-Layer Perceptron—were evaluated to find out which one works best for predicting water quality. We used three distinct approaches for feature selection: analysis of variance …

Machine Learning For Recession Prediction, Ethan Reusser

Williams Honors College, Honors Research Projects

Macroeconomic predictions present challenges in machine learning due to the rarity of economic recessions, the constantly-changing matter of global markets, and severe class imbalance in historical data. This project focuses on predicting the onset of United States economic recessions within a 12-month window using Python and Jupyter Notebook. A machine learning pipeline was developed utilizing multiple models: Logistic Regression, Random Forest, XGBoost, and Long Short-Term Memory (LSTM) neural networks. For class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied strictly to training data, paired with Platt scaling for calibration on thresholds. The resulting models were evaluated in the 2005 …

Beyond The Binary: Navigating Ai’S Role In Palliative Ethics-A Review, Reebu Sara Varghese, Tijo Cherian

ASEAN Journal on Science and Technology for Development

Palliative care is being influenced by artificial intelligence, especially when it comes to data-related aspects. In this context, care can be enhanced in terms of the quality of its results and the level of its efficiency, particularly with the help of technological tools such as artificial intelligence, which is capable of managing different types of information in the field of health care. Such characteristics have the potential to improve the quality of patient care while at the same time reducing the workload of healthcare professionals in palliative care. However, there are considerable ethical issues that need to be addressed with …

Human Perceptions Of Social Intelligence In Large Language Models, Derek Koehl

Dissertations

This dissertation investigates how users form perceptions of social intelligence and trust when interacting with large language model chatbots. Three experimental studies manipulated chatbot agreeableness, goal orientation, and simulated short-term memory, measuring perceived social intelligence and trust. The primary finding is an asymmetric effect of personality: disagreeable chatbot behavior produced large reductions in perceived social intelligence and trust, whereas agreeable behavior produced negligible gains above a warm baseline, consistent with negativity bias in social cognition. Goal orientation did not independently enhance perceptions but functioned as a contextual moderator. Simulated memory produced medium-to-large increases in perceived social intelligence without reliably increasing …

Modeling Sealed Deck Construction In Collectible Card Games Using Learning-To-Rank Approach, Michael Dinh Nguyen

Master's Projects

Artificial intelligence has demonstrated strong performance in complex decision-making domains such as chess and Go, motivating research into its application for games with even richer rules and combinatorial complexity. In collectible card games like Magic: The Gathering, deck construction from a constrained card pool is a critical and challenging task that requires evaluating card strength, synergy, and resource balance. This project explores whether machine learning, specifically learning-to-rank (LTR), can effectively model these human decision processes to construct competitive decks in a sealed format. The results found here can also be applicable to other areas of note, such as sports drafting …

Measuring The Efficiency Of The Arts And Culture Sector Relative To Gdp: A State-Level Analysis Of The United States, Ameerkhan Jaffar Khan Khader Khan

Selected Full-Text Master Theses 2021-

Arts and cultural production contributed $1.17 trillion to United States gross domestic product in 2023, 4.2% of the national total, but that contribution is spread very unevenly across states, and the official statistics describe how large the sector is rather than how efficiently it operates (Bureau of Economic Analysis, 2024). This study asks how efficiently each state convert growth in arts and culture employment into growth in value added, how many inputs the efficiency model can carry before it stops distinguishing 51 observations, and whether observable state characteristics explain the differences found. The analysis uses 2023 state-level data for all …

Machine Learning-Based Intrusion Detection System For Iot Networks Using The Rt-Iot 2022 Dataset, Bukunmi Ebenezer Afolabi

Theses, Dissertations and Capstones

The rapid expansion of the Internet of Things (IoT) has transformed modern computing by enabling seamless connectivity among heterogeneous devices across diverse application domains. However, this increased interconnectivity has significantly enlarged the attack surface of IoT networks, exposing them to a wide range of sophisticated cyber threats. Conventional security mechanisms often lack the capability to detect emerging attacks in real time, thereby necessitating the development of intelligent Intrusion Detection Systems (IDS) capable of accurately identifying malicious network activities. This study developed and evaluated a machine learning-based intrusion detection framework for multiclass IoT attack detection using the RT-IoT2022 dataset. The dataset …

Preserving Platform Innovation With A Narrow Exception: Section 230(C)(1) Immunity For Algorithmic Recommendations, John "Jack" Di Masi

American University Business Law Review

This Comment examines Section 230(c)(1)’s immunity for online platforms that use machine-learning algorithms to recommend third party content and proposes a narrow exception for truly “bad-actor” cases. It reviews Section 230’s text, as well as its early cases, showing that Congress intended broad protection for editorial functions such as content organization. It then traces how recommendation algorithms evolved and how courts treat algorithmic sorting as a neutral tool. This Comment addresses conflicting interpretations and defends broad immunity for routine curation. This Comment argues that immunity should be stripped only after a balancing test under a three-factor “bad-actor” framework—one that evaluates …

Assessment Of Carbon Capture Potential In Rubber Plantations Via Landsat 9 Imagery Analysis, Ade Fitria, Rahmawaty Rahmawaty, Razali Razali, Jamin Saputra, Seca Gandaseca

Baghdad Science Journal

Indonesia is a significant greenhouse gas (GHG) emitter, contributing 12.3% of carbon dioxide (CO₂) of total emissions. Carbon dioxide (CO₂), a major GHG, is increasing in the Earth’s atmosphere. The CO₂ absorption can be increased through rubber plantations because rubber plants, such as forest plants, can process CO₂ as a carbon source for photosynthesis. This research aims to analyze carbon uptake in relation to tree density, biomass of rubber vegetation, and soil organic carbon (SOC) content, as well as to map the distribution of carbon potential using remote sensing. This research was conducted in …

Stem-Fit And Soil-Fit: Integrated Plant And Soil Nitrogen-Hormone Sensing With Machine Learning-Based Forecasting For Next-Generation Precision Agriculture, Nafize I. Hossain, Mohammad Solaiman, A.K.M. Ahsanul Habib, Md Al Mahmud Hossain Al Hadi, Shawana Tabassum

Electrical Engineering Faculty Publications and Presentations

Inefficient fertilizer application in agriculture leads to reduced crop productivity, nutrient losses, and reduced crop resilience, highlighting the urgent need for real-time monitoring of plant–soil nutrient and stress dynamics. This research aims to develop and validate a multiplexed sensing platform for real-time, in-situ measurement of key soil nutrients (Soil-FIT) and plant phytohormones (Stem-FIT) involved in nitrogen signaling and stress regulation. The proposed sensor suite integrates 3D-printed modules for continuous monitoring of nitrate, ammonium, and pH in both soil and plant sap, along with salicylic acid (SA), indole-3-acetic acid (IAA), methyl jasmonate (MeJA), and ethylene (ET) in plant sap. The sensors, …

The Crucial Role Of Machine Learning Models In Predicting Current Childhood Asthma: Model Comparison, Calibration, And Shap-Based Interpretation, Aditya Chakraborty, A. K.M. Raquibul Bashar

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Background: Asthma is one of the most prominent chronic diseases in children and one of the most challenging ailments to diagnose in infants and preschoolers in the United States. Predictive models can be instrumental in improving early diagnosis, personalized treatment strategies, and disease progression. By utilizing nationalized data, this study focuses on building and comparing high-performing analytical predictive models based on the relevant risk factors and identifying the most influential predictors.

Methods: We analyzed cross-sectional BRFSS Asthma Call-Back Survey data (2011-2020; N = 9,813) and randomly split participants into training and testing sets. An XGBoost model (hyperparameters tuned via grid …

Combined Effects Of Cumulative Triglyceride-Glucose And Blood Pressure On Stroke In Middle-Aged And Older Chinese Adults: A Longitudinal Analysis, Xinyue Wang, Wen Zheng, Xiangqian Jin, Hongzhi Liu, Jiheng Hao, Liyong Zhang, Yuanyuan Gu, Haifeng Hou, Wei Wang

Research outputs 2022 to 2026

Background: The triglyceride-glucose (TyG) index has increasingly been recognised an indicator for stroke risk. We aimed to explore the relationship between TyG and blood pressure (BP) and the combined effects of cumulative TyG (CumTyG) and cumulative BP (CumBP) on stroke risk in a cohort of Chinese middle-aged and older adults. Methods: We enrolled 5138 participants from the 2011–2018 waves of the China Health and Retirement Longitudinal Study. Cumulative TyG and BP exposures were recorded between 2011 and 2015, and stroke incidence was then followed up until 2018. We conducted a cross-lagged panel analysis to address the temporal and directional relationships …

Mid-Infrared Spectroscopy And Machine Learning For Chlorogenic Acid Quantification In Coffee, Deborah Herdt, Felix Wühler, Thomas Kunz, Victoria Schiwek, Sarah Kühnemuth, Brian Gibson, Matthias Rädle

Research Outputs: 2025-Present

To predict chlorogenic acid (CGA) concentration in coffee during roasting, a machine learning algorithm was applied to mid-infrared (MIR) data. A total of 44 roasting samples between 140 and 220 °C, along with an unroasted control, were dry-heated in an eddy current roaster and subsequently ground and measured. CGA concentrations were predicted from MIR spectroscopy data using a multilayer perceptron (MLP) regressor and validated against a high-performance liquid chromatography with diode array detector reference. The algorithm performed spectral preprocessing and selected relevant wavenumber regions. The MLP-based model achieved a high coefficient of determination, outperforming classic peak evaluation, indicating that automated …

Stall Detection In Hydraulic Excavator Operations Using Heuristics And Machine Learning: A Case Study, Mateo Fernando Montenegro Defaz, Kwame Awuah-Offei, Yixiang Gao

Mining Engineering Faculty Research & Creative Works

This work aims to develop a reliable algorithm for stall detection during excavator digging by analyzing key operational variables such as velocity and angular displacements from machine monitoring data. The work develops and validates a heuristic algorithm to detect stalling events and trains a support vector machine classification algorithm to distinguish between "normal" digging cycles and cycles with stalling. This work is a novel attempt at using a classification algorithm to categorize digging cycles into normal and those with stalling events based on machine monitoring data alone. The developed classification algorithm achieved a sensitivity of 100%, indicating it correctly identified …

Untrained Position-Encoded Multilayer Perceptron Network For Structured Illumination Microscopy Reconstruction, Sahil Sharma, Leonidas Zimianitis, Krishnendu Samanta, Balpreet Singh Ahluwalia, Joby Joseph, Dushan N. Wadduwage

Computer Science Faculty Publications

Structured Illumination Microscopy (SIM) enables super-resolution imaging by encoding high-frequency spatial information through patterned light. While traditional Fourier-based reconstruction methods are prone to artifacts under suboptimal conditions, recent deep learning approaches often require large training datasets and lack adaptability across different imaging setups. In this work, we present Position Encoded Multi-Layer Perceptron (PEM) network that leverages implicit neural representations (INRs) and SIM forward-model-driven modeling to reconstruct super-resolved images without any training data. PEM-SIM represents each spatial coordinate as a combination of sinusoidal functions across multiple frequencies, enabling rich encoding of fine spatial detail. A forward model grounded in SIM image …

Geospatial Analysis Of Sinkhole Density And Characteristics In West Virginia, Marissa Shannon Loftus

Graduate Theses, Dissertations, and Problem Reports (ETD)

Creation of a sinkhole map of West Virginia provides useful data to help identify areas susceptible to sinkhole related hazards, as well as allowing for detailed analysis of factors controlling karst development. Newly available, statewide LiDAR-derived digital elevation data have made mapping sinkholes over larger spatial extents feasible. For this study, sinkholes were mapped for all areas of West Virginia that are underlain by carbonate rocks; then the distribution and characteristics were evaluated overall and in four focus areas (Berkeley and Jefferson counties, Pendleton County, Greenbrier County, and Monroe County). These areas correspond to broader physiographic regions, with Berkeley, Jefferson, …

Reliability-Oriented Spatiotemporal Machine Learning For High-Impact Power Outage Event Prediction, Marwa Gamal

Mansoura Engineering Journal

Power outages have become an increasing concern for modern power systems due to their impact on infrastructure reliability, economic activities, and public safety. The growing frequency of extreme weather events and the rising demand for electricity have made it more difficult to anticipate high-impact outage events. One of the main challenges in this context is the complex interaction between temporal patterns and geographic variations, which traditional methods often fail to capture effectively. This study develops a machine learning framework that combines temporal characteristics with geographic information to improve the prediction of high-impact power outages. Temporal features such as seasonal patterns, …

The Performance Of Machine Learning Algorithms In Predicting Under-Five Anaemia In Tanzania, Henry Ofori Duah, Derrick Nyantakyi Owusu, Godwin G. Duah, Paul Shidende

College of Nursing Faculty Research and Publications

Aim

Anaemia in children under five remains a major public health concern, contributing significantly to global child morbidity and mortality. With the rise of machine learning (ML), novel opportunities exist to model and predict anaemia more effectively. This study aimed to evaluate and compare the performance of five ML algorithms in predicting anaemia among under-five children in Tanzania.

Methods

We conducted a secondary data analysis using the 2017 Tanzania Malaria Indicator Survey (TMICS). The dataset (n = 5906) was randomly split into training (70%) and test (30%) sets. Five ML algorithms, Linear Discriminant Analysis (LDA), Logistic Regression, Random Forest (RF), …

Performance Analysis Of Sparse Neural Networks In Brain Abnormality Detection, Megan Danh

Honors Undergraduate Theses

Neuroimages have held the capability of revealing to medical professionals patterns for brain abnormalities since their development. However, more recently, these professionals and researchers are looking to use neural networks to identify these brain abnormalities through neuroimages for early detection that would allow more effective treatment. Neuroimage datasets, specifically functional magnetic resonance imaging (fMRI), are extremely large in size. This would result in their processing and training to be computationally expensive, even with smaller neural networks. Fortunately, recent pruning methods have recently emerged, where network weights and neurons are pruned to reduce computational cost without compromising too much accuracy. By …

Assessment Of Temperature Correction Factors For Falling Weight Deflectometer Deflections, Pablo Orosa Iglesias, Jin Li, Bongsuk Park, Seonghwan Cho, John E. Haddock

JTRP Technical Reports

This study presents the development of an improved temperature correction methodology for falling weight deflectometer (FWD) deflections in full-depth asphalt pavements. The objective was to address the limitations of existing correction methods by integrating viscoelastic pavement behavior, structural properties, and realistic thermal gradients. Field temperature data from instrumented sites in Indiana were used to train a machine learning model capable of predicting pavement temperature profiles in flexible pavements. These predictions informed a series of finite element simulations that captured pavement responses to FWD loading under a range of thermal and structural conditions. Temperature correction factors were then derived and modeled …

Evaluating Multi-Year Transferability Of Single- And Multi-Class Machine Learning For Remote Monitoring Of Beaver-Engineered Landscapes, Harris J. Sloan

Graduate Student Theses, Dissertations, & Professional Papers

Long-term satellite image archives enable ecological monitoring at large scales, but manual extraction of fine-grained features (e.g., beaver dams) remains time-consuming. Machine learning models may automate these tasks, but their temporal transferability for multiannual monitoring is still poorly resolved.

We evaluated the performance of two complementary neural-network frameworks across a 100,000-hectare watershed in Montana USA, using a decade of satellite imagery (2014-2024): (i) a single-class ResNet-50 beaver dam detector, and (ii) a multi-class ResU-Net landcover classifier.

In 2024, the dam detector identified 352 of 404 mapped dams, achieving balanced accuracy = 93.6%, precision = 79.8%, recall = 87.1%, F1 = …

Applying Machine Learning Techniques For Early Detection Of Cyber Attacks On Iot Devices, Noor Adnan Allamy

Al-Esraa University College Journal for Engineering Sciences

This research designs, implements, and evaluates a machine learning-based framework for the early detection of cyber attacks targeting Internet of Things (IoT) devices, with a specific focus on the context and challenges present in Iraq. The study conducts a comparative analysis of three supervised learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Networks (DNN)—using a combination of benchmark datasets (NSL-KDD, CIC-IDS-2017, Bot-IoT) and a synthesized dataset adapted to simulate the Iraqi threat landscape. Key performance metrics, including accuracy, precision, recall, and F1-score, were used for evaluation. The proposed Random Forest model demonstrated superior performance, achieving an accuracy …

Asadh: A Meta Learning-Based Auto-Selective System For Anomaly Detection In Healthcare, Nadia Rashid, Rashid Mehmood, Fahad Alqurashi, Turki Alghamdi

Journal of King Abdulaziz University: Computing and Information Technology Sciences

Anomaly detection plays a crucial role in healthcare, supporting early disease identification, process improvement, and the reliability of medical equipment. Traditional machine learning (ML) and deep learning (DL) models often fail to generalize across heterogeneous medical datasets due to variations in feature distributions, class imbalance, and high dimensionality. To address this, we extend our earlier ASAD framework and propose ASADH (Auto-Selective Anomaly Detection in Healthcare), designed specifically for medical anomaly detection. ASADH applies meta-learning over a unified set of cross-modal meta-features spanning tabular, time-series, and medical image data to characterize each dataset and automatically recommend the most suitable anomaly detection …

Deep Learning Approaches For License Plate Detection In Saudi Arabia, Reemah M. Alhebshi, Abeer Almakky, Lujain Waleed Aljahdali, Raghad Bakr Hawsawi

Journal of King Abdulaziz University: Computing and Information Technology Sciences

The most important and challenging tasks in an intelligent traffic monitoring system are vehicle detection and classification. Conventional approaches are significantly computationally intensive and create limitations when the data-collecting modality alters. This study evaluates the effectiveness of deep learning models in extracting and classifying key vehicle attributes, such as license plates, models, and colors, through four AI-driven components for vehicle analysis. The YOLOv8m model was used for detecting the vehicle license plate and character recognition, achieving accuracy ranging from 95.10% to 99.24%. For vehicle model recognition, the Xception model also showed high performance with a precision of 96.79%. The EfficientNetB3 …

Generative Ai For Method Development In Analytical Chemistry: A New Paradigm In Experimental Design And Optimization, Yasir Fathi Mahmood

Al-Esraa University College Journal for Engineering Sciences

The fast development of artificial intelligence (AI), especially generative AI models, is changing the environment of analytical chemistry. As classical method generation in analytical methods relies on manual trial-and-error methodology as well as statistical methods, generative AI is a new paradigm with automated generation of experimental methodology and optimization. In this paper, the authors discuss the use of generative AI-based technologies, including large language models (LLMs) and neural network-based generators, to create new, efficient, and customized methods of analysis. The paper examines existing applications, technology frameworks, and issues and offers a roadmap with regards to the future incorporation of generative …

Phishing Detection Using Data-Driven Intelligence, Ahood Al Darmaki, Ajmal Khan, Muhammad Rizwan Mughal, Mohammed M. Bait-Suwailam

The Journal of Engineering Research

Since the early days of the Internet, phishing attacks have been a serious threat to the security of users and their personal information on the web. This problem raises the need to find a strict solution to minimize the cyber-attacks. This paper explores the application of machine learning models to detect phishing websites by analyzing multiple datasets containing phishing and legitimate website features. In order to evaluate machine learning models, in this study three publicly available phishing datasets were utilized. These datasets have different sizes and features. The performance of the model was evaluated on the basis of accuracy, precision, …