Digital Commons Network^™

Design And Evaluation Of A Supervised Machine Learning-Based Intrusion Detection System., Tarquin Qazi

ICT

This project involves designing and evaluating a supervised machine-learning-based intrusion detection system (IDS) using the CIC-IDS2017 dataset. The project is structured around the cross-industry standard process for data mining (CRISP-DM) framework. The dataset comprises over 2.8 million labelled instances and was cleaned and reduced from 78 to 63 features via redundancy analysis. These data were used to train and tune three supervised classifiers: Logistic Regression algorithm as a linear baseline, a Decision Tree algorithm and a Random Forest algorithm. Hyperparameter optimisation was conducted using RandomizedSearchCV with 3-fold cross-validation, optimising for the F1-macro scoring metric.

While the random forest performed similarly …

Predicting Co₂ Emissions From Cattle-Driven Deforestation In The Amazon Rainforest Using Machine Learning, Daniel Ambrosio, Suelen Rocha

ICT

Deforestation has become a major environmental issue worldwide, especially in the Amazon Rainforest, which is known as one of the world’s largest carbon sinks. This region has been significantly impacted by cattle farming, contributing to increased CO₂ emissions and land degradation. As Brazil is one of the world’s largest beef producers and exporters, the environmental impact of livestock production has attracted increasing attention from organisations, governments, and sustainability analysts.

This project aims to analyse the relationship between cattle farming, deforestation, and CO₂ emissions in the Amazon using Machine Learning. Using the CRISP-DM framework, the project explored each stage of the …

Integer-Valued Time Series Model Via Copula-Based Bivariate Skellam Distribution, Mohammed Alqawba, Norou Diawara, Mame Mor Sene

Mathematics & Statistics Faculty Publications

Time series analysis is crucial for modeling and forecasting diverse real-world phenomena. Traditional models typically assume continuous-valued data; however, many applications involve integer-valued series, often including negative integers. This paper introduces an approach that combines copula theory with the bivariate Skellam distribution to handle such integer-valued data effectively. Copulas are widely recognized for capturing complex dependencies among variables. By integrating copulas, our proposed method respects integer constraints while modeling positive, negative, and temporal dependencies accurately. Through simulation and an empirical study on a real-life example, we demonstrate that our class of models performs well. This approach has broad applicability in …

The Sub-Lethal Effects Of Trematode Parasites On The Migratory Behavior Of Lesser Scaup (Aythya Affinis), Scott Alan Herman

Graduate Research Theses & Dissertations

The introduction of the invasive freshwater faucet snail (Bithynia tentaculata) and its associated parasites in the Upper Mississippi River and Great Lakes Region provides the opportunity to investigate how anthropogenic stressors impact the life history of lesser scaup (Aythya affinis) using the Mississippi Flyway. To understand the life histories and interactions between lesser scaup and faucet snails we examined historical records, research, and ecology textbooks. Our literature review identified critical knowledge gaps in lesser scaup literature surrounding population dynamics, reproduction, and migration. Specifically, the sub-lethal effects of invasive trematodes on the migratory behavior of lesser scaup in the Upper Mississippi …

Temporal Variational Graph Autoencoder For Influenza Evolution, Soumya Chauhan

2026 Research Poster Competition

Frequent mutations in influenza virus surface proteins can increase infectivity while evading human and vaccine immunity, causing seasonal epidemics. The CDC annually evaluates thousands of virus strains to predict mutated sequences likely to be dominant in the next season, which creates a need for methods that better capture how viral mutations evolve over time. In this study, we represent influenza protein sequences as a network-like graph, creating connections if sequences are collected a week apart and only differ by one mutation. This method explicitly considers time information as part of the evolution, while other existing methods analyze mutated sequences without …

Uav-Based Sensor Integration For In-Situ Lake Water Quality Monitoring, Alec Campbell

2026 Research Poster Competition

Current water quality assessment methods rely heavily on manual sampling and laboratory analysis, which are time-consuming and difficult to perform in remote or hazardous environments. In surface water monitoring, spatial and temporal resolution is often constrained by labor, access, and logistical limitations of manual sampling programs. This research aims to develop a drone-based system for real-time, in situ lake water quality monitoring. To address these challenges, the project focuses on integrating multiple water quality sensors, including oxidation-reduction potential (ORP), pH, electrical conductivity (EC), turbidity, and a multispectral spectrometer, into a compact, UAV-compatible design capable of collecting spatially referenced data. Methods: …

Flair- Flow-Level Anomaly Intrustion Recognition, Joseph Dumond

2026 Research Poster Competition

Industrial control systems (ICS) and industrial Internet of Things (IIoT) networks support critical infrastructure such as power generation, water treatment, and manufacturing. As these systems become more connected, they are increasingly exposed to cyber threats. However, many existing intrusion detection approaches rely on detailed packet inspection or supervised machine learning techniques that require labeled attack data and extensive tuning, making them difficult to deploy in real industrial environments.ICS networks typically generate highly regular and predictable communication patterns due to periodic control logic and deterministic device behavior. Because of this structure, abnormal or malicious activity often appears as a disruption to …

P4 Driven Data Plane Analytics For Industrial Control Network Security, Haden Fowler

2026 Research Poster Competition

Industrial control systems manage critical infrastructure such as power grids, water treatment plants, and manufacturing facilities. These systems rely on specialized network protocols to send measurements and commands between sensors, controllers, and operator workstations. Protecting these networks from cyberattacks is essential because a successful intrusion could disrupt services that millions of people depend on daily. Most current security monitoring approaches analyze copies of network traffic after it has already passed through the system. This delay means that malicious commands may reach their targets before any alarm is raised. More importantly, when alerts do occur, operators often lack the evidence needed …

Artificial Elections: How Artificial Intelligence Can Affect Elections And Decide Our Future, Ryan D. Granger

Honors Undergraduate Theses

This thesis examines the growing role of artificial intelligence (AI) in democratic elections, highlighting both its transformative potential and its associated risks. Drawing on a qualitative analysis of existing literature, the study explores how AI is increasingly integrated into political campaigns, election administration, and voter engagement. Key benefits include enhanced data analysis, personalized political messaging, and improved efficiency in campaign operations. AI also supports real-time fact-checking and more accurate vote tabulation, which can strengthen transparency and trust in electoral processes. However, the thesis emphasizes that these advantages are accompanied by significant challenges. AI technologies enable the rapid creation and dissemination …

From Lap To Map: How Musical Scale, Place, And Play Drive The Interconnected Mario Kart World, Cameron Cummins

Honors Undergraduate Theses

With their deserts, castles, and ghost houses, the environments of Super Mario games are colorful, whimsical, and charming, but why are they so compelling, and what happens when our analysis of these environments extends beyond individual levels to expansive game worlds? Drawing on Cresswell’s theory of place (2014) and recent work on musical place-building in Mario Kart 8 (Heazlewood-Dale, 2024), I propose a spectrum between localized and globalized scale in games. As game environments become increasingly globalized, the music may be similarly altered to account for this shift in scale. Consequently, players may then encounter a broader, less musically congruent …

Type Ii Diabetes Treatment Comparison Via Compartment Modeling, Abigail M. Collins

Theses and Dissertations

Type II diabetes mellitus affects one in ten adults worldwide, yet the effects of treatment type and adherence level on developing complications and quality of life have not been well characterized at the population level, and mathematical modeling offers a structured way to examine these dynamics. This thesis adapts the Boutayeb et al. (2004) model to incorporate dynamic treatment types and levels of adherence, producing nine scenarios in which complication development rate and complication recovery rate differed, to compare peak complications and quality of life across treatment and adherence conditions. Using a system of ordinary differential equations and compartment modeling, …

Evaluating The Role Of Power Electronics In Enhancing Renewable Energy And Smart Grid Performance: A Review, Muhammad Irfan Habib

ASEAN Journal on Science and Technology for Development

The review article demonstrates the importance of power electronics in propelling the increase of renewables and increasing the efficiency of contemporary electrical grids. In particular, it highlights the need for bidirectional converters for effective energy management in vehicle-to-grid (V2G) systems with two-way power transfer. The paper examines the series of complications offered by the significantly large integration of power electronic converter-interfaced renewables, such as those related to system steadiness, power quality, and resignation bottlenecks. Also, it recognizes a mix of new technologies, such as wide bandgap (WBG) semiconductors like silicon carbide (SiC) and gallium nitride (GaN), that deliver superior performance …

Unlocking Green Potential In Indonesian Small Medium Enterprises: Challenges And Opportunities For Sustainable Vermicelli Production, Togar W. S. Panjaitan, Karina Agustin, Prayonne Adi, Jani Rahardjo, I Nyoman Sutapa

ASEAN Journal on Science and Technology for Development

Due to limited initiatives and support, the transition to green manufacturing among small and medium enterprises (SMEs) in developing countries remains challenging. Through a critical literature review and case study of the Indonesian vermicelli industry, this study highlights the importance of collaboration and strong policies to address this. Other findings also reveal that the energy intensity and emissions in the sector exceed best practice benchmarks, with the cooking and drying stages contributing significantly to emissions due to high energy consumption. Lack of government regulations, company awareness, and understanding of applicable practices and technologies creates challenges in green manufacturing. The study …

Hybrid Model For Phishing Website Detection Using Transfer Learning, Atul Dubal, Mansi Subhedar, Santosh Dhamala, Manasi Patil

ASEAN Journal on Science and Technology for Development

The rapid digitization of human activities has intensified reliance on internet-based platforms, creating fertile ground for cybercriminal exploits such as phishing. Despite advancements in detection mechanisms, phishing attacks continue to evolve, leveraging sophisticated visual mimicry to deceive users. This paper proposes a robust vision-based phishing detection system using ensemble deep learning to analyse webpage screenshots. The framework integrates transfer learning with pre-trained VGG16 and DenseNet121 models, extracting complementary low-level texture features (edges, gradients) and high-level hierarchical patterns (logos, layouts). These features are fused through a custom classifier with dropout regularization to mitigate overfitting. A balanced dataset of 3,000 webpage screenshots …

Ai-Assisted Hybrid Ga–Pso Channel Allocation Under 3gpp Tr 38.901 Uma For Efficient 5g Radio Resource Management, Sharada Narsingrao Ohatkar

ASEAN Journal on Science and Technology for Development

The increased traffic and heterogeneity in the 5G/B5G network require efficient radio resource management (RRM). However, the existing methods, such as GA and PSO, have poor convergence speed and require proper initial conditions. Additionally, learning-based methods have high computational complexity. Hence, in this paper, a novel AI-assisted Hybrid Channel Allocation (AI–HCA) framework is proposed by using a support vector regression (SVR)-based predictive initialization method and GA-PSO optimization. Simulation results using the 3GPP UMa channel model show that the proposed method has a 28% reduction in call blocking probability (CBP), a 15-25% enhancement in spectral efficiency (SE), and a 18-25% enhancement …

Modeling The Impact Of Projected Water Demand On Groundwater Flow And Transport In The Mahomet Aquifer, Christabel Chinonye Abugu

Theses and Dissertations

The Mahomet Aquifer is a vital groundwater resource in east-central Illinois, supporting municipal, agricultural, and industrial water use. With projected changes in water demand driven by population growth and future socioeconomic conditions, assessing the long-term response of the aquifer is critical for sustainable groundwater management. Increased pumping to meet future demand may contribute to groundwater-level decline, intensified cones of depression, and altered groundwater-flow paths relevant to contaminant migration. This study addressed two research questions: (1) how projected changes in groundwater demand affect groundwater levels in the Mahomet Aquifer, and (2) how projected demand influences groundwater-flow paths relevant to contaminant migration. …

Gain-Delay Decoupling In Human Stimulus Frequency Otoacoustic Emissions Reveals Constraints On Cochlear Nonlinear Amplification, Yoshita Sharma, Arturo Moleti, Renata Sisto, Teresa Botti, Hansapani Rodrigo, Sri Mishra

School of Mathematical & Statistical Sciences Faculty Publications

Purpose: The nonlinear cochlear amplifier, driven by outer hair cells, underlies the remarkable sensitivity and frequency selectivity of the mammalian auditory system. Stimulus frequency otoacoustic emissions (SFOAEs) provide a noninvasive window into these active cochlear processes, yet the relationship between emission gain and delay across stimulus levels remains incompletely understood. This study examined the level dependence of SFOAEs in normal-hearing human listeners to characterize cochlear nonlinear response properties. We tested how emission gain and delay vary with stimulus level and estimated the frequency of the apical–basal transition associated with the breakdown of the approximate local scaling symmetry.

Methods: SFOAEs were …

Workshop Outcomes Report: 2nd International Workshop On Seismic Resilience Of Arctic Infrastructure And Social Systems, Majid Ghayoomi, Daniela Morganti

Faculty Publications

The report provides an overview of the second international workshop on Seismic Resilience of Arctic Infrastructure and Social Systems. The report discusses agenda, workshop activities, interdisciplinary working groups, and results. It ends with several strategic questions and investigation plans that were developed as part of the workshop activities.

Multi-Timescale Monitoring And Modeling For Resilient Smart Grids: Pmu Anomaly Detection And Battery Digital Twins, Muhammad Imran Hossain

Graduate Theses, Dissertations, and Problem Reports (ETD)

Modern power systems face failures at very different timescales. Cyber-physical disturbances may emerge within seconds, while battery degradation develops over hundreds of operating cycles. Both monitoring problems share the same underlying difficulty: power systems produce measurement data in abundance, but reliably labeled examples of abnormal or degraded operation are scarce. Rare grid events are difficult to label, and battery degradation data are heterogeneous across cells, cycling protocols, and chemistries. This thesis addresses these challenges through two complementary domain-informed learning frameworks that constrain representation learning using information specific to each physical problem.

First, at the system level, T-BiGAN, a Transformer-augmented bidirectional …

Energy Partition And Heating Mechanisms In Asymmetric Antiparallel Magnetic Reconnection, Aleksandra Abova-Volkova

Graduate Theses, Dissertations, and Problem Reports (ETD)

Magnetic reconnection is a fundamental plasma physics process whereby a change in magnetic field connectivity drives the conversion of magnetic energy into energy of the surrounding plasma, often explosively. One of the many settings where it occurs is at the dayside magnetopause of Earth and other planets, where the magnetic field, number density, and/or temperature in the plasmas on the two sides upstream of the reconnection site are asymmetric. How the incoming magnetic energy gets partitioned into outgoing energies during asymmetric reconnection remains an outstanding question; of special interest is the fraction that gets converted into internal energy of ions …

High-Resolution Laser Diagnostics For Ion Measurements In Magnetized Plasmas, Katey Stevenson

Graduate Theses, Dissertations, and Problem Reports (ETD)

In this work, we investigate ion heating in two plasma systems (helicon plasmas and flux rope plasmas) using the PHAse Space MApping (PHASMA) experiment. Our studies rely on high-resolution laser diagnostics, specifically laser-induced fluorescence (LIF), to measure ion velocity distribution functions (IVDFs). The temperature and bulk flow of each species are inferred from their respective VDFs. This dissertation is organized into three main parts: the study of ion heating in helicon plasmas, pulsed LIF modeling and diagnostic development, and the study of ion heating in flux rope plasmas. Helicon plasma sources efficiently generate high-density plasmas and are widely used to …

A Digital Calibration Source For 21 Cm Cosmology Telescopes, Kalyani Balkrishna Bhopi

Graduate Theses, Dissertations, and Problem Reports (ETD)

Precise calibration of radio telescope beams and gains is a central requirement for 21 cm intensity mapping experiments, which aim to measure large scale cosmological structure through the redshifted emission line of neutral hydrogen. Bright astrophysical foregrounds dominate the sky at these frequencies, and separating them from the cosmological signal demands precise control over instrumental systematics, particularly the telescope beam and its frequency-dependent response. Existing aerial calibration sources are incoherent broadband emitters, detectable only as total power. They provide no direct phase information and suffer from poor sensitivity in low signal-to-noise regimes.

We present the Precision Emitter for 21cm Array …

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, …

Techniques For Inclusive College Mathematics Classrooms, Christopher Anderson Davis, Josh Hiller, Anil Venkatesh

Journal of Mathematics and Science: Collaborative Explorations

Creating a welcoming and inclusive mathematics classroom is an important responsibility for all college mathematics faculty. But where do we begin? In this paper we present several techniques based on White’s (2024) building inclusive classrooms strategies framework, which the authors have found to be successful in making their own classrooms more inclusive. The techniques include the inclusion of contemporary diverse voices, exploring the history of applications of mathematical concepts, elevating the diverse intellectual history of mathematics, bringing diverse guest speakers into the classroom, and mentoring students as they engage in doing research. For each strategy we provide narrative comments based …

Exploring Recruitment And Retention Of Mathematics Teachers In Southwest And South-Central Virginia, Anthony Dove

Journal of Mathematics and Science: Collaborative Explorations

This study examines the recruitment and retention of secondary mathematics teachers in rural southwest and south-central Virginia public schools from the perspectives of school principals. Guided by three research questions focused on retention, recruitment, and opportunities for improvement, the study employed a constant comparative analysis of semi-structured interviews conducted with principals from 10 rural middle and high schools. Findings indicate that many schools have historically benefited from long-term teacher retention reinforced by strong community ties and supportive school environments. However, anticipated retirements, salary competition within the region, and increasing challenges in recruitment are beginning to place strains on schools. Principals …

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Journal of Mathematics and Science: Collaborative Explorations

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Challenges And Applications Of Fine-Grained Temporal Action Understanding: Modeling Temporal Granularity And Data Efficiency, Halil I. Helvaci

Theses and Dissertations--Electrical and Computer Engineering

Fine-grained Temporal Action Segmentation (TAS) has become a cornerstone of video understanding, offering dense frame-level predictions essential for clinical assessment, surgical skill evaluation, and human-computer interaction. While TAS methods have delivered strong results on coarse-grained benchmarks, two fundamental challenges persist: (1) global attention mechanisms dilute boundary information critical for subsecond precision, a phenomenon we term the temporal granularity bottleneck, and (2) dense frame-level annotation remains prohibitively expensive, with most datasets requiring exhaustive labeling of lengthy untrimmed videos. These challenges are particularly pronounced in medical domains, where sub-second primitives define clinical outcomes while expert annotation remains scarce. In this dissertation, we …

Generative Ai Peer Tutoring To Support Peer-Reviewed Source Identification And Evaluation, Rae Mair, Michelle Kelley, Taylar Wenzel, Andrea C. Borowczak, Yuqing Li, Mike Borowczak

Faculty Scholarship and Creative Works

This chapter explores the design and evaluation of a generative artificial intelligence peer tutor prompt to support college students in identifying and evaluating peer-reviewed sources for academic research. Grounded in literature on peer tutoring, Socratic dialogue, and AI-supported learning, the authors describe an iterative prompt engineering process designed to transform large language models (LLMs) into Socratic-style peer tutors capable of scaffolding student reasoning without completing tasks for them. Five guiding criteria for an effective peer tutor shaped development and evaluation: cognitive congruence, step-by-step guidance, avoiding giving answers, adaptability to student level, metacognitive transparency, and following assignment directions. Across multiple human-centered …

Visual Analytics For Interpretable Quantum Computing, Shaolun Ruan

Dissertations and Theses Collection (Open Access)

Quantum computing has entered a stage of increasing practicality. Many quantum hardware vendors such as IBM, Rigetti, Honeywell, and IonQ now enable experiments on real devices in the Noisy Intermediate-Scale Quantum (NISQ) era. These platforms show computational advantages in domains such as optimization, machine learning, and materials science. However, they remain limited by hardware noise and the absence of human-interpretable information. Existing visual metaphors, such as the Bloch Sphere for single-qubit states or circuit schematics for algorithm design, struggle to convey multi-qubit entanglement or measurement probabilities in ways accessible to human reasoning. Likewise, the rise of variational quantum circuits and …

Improving Pid Control With Bayesian Optimization For Adversarially Robust Federated Learning, Adrian Pena, Sergei Chuprov, Raman Zatsarenko, Leon Reznik

Computer Science Faculty Publications

Federated Learning (FL) often suffers from unstable convergence and reduced robustness under non-IID data and malicious attacks. In this paper, we present FedPIDAvg_tuned, a control-theoretic aggregation framework for improving stability and adversarial robustness in FL. In particular, our approach combines a server-side Proportional–Integral–Derivative (PID) controller with Bayesian optimization to tune controller gains for different data and attack conditions. The PID controller regulates global model updates through feedback on loss dynamics, providing adaptive scaling that improves stability and convergence. The tuned gains are applied within a trust-weighted trimmed-mean mechanism to remove adversarial or outlier updates. Using the Flower framework, we evaluate …