Digital Commons Network^™

Characterizing Language Use In Online Accessibility Discussion Forums, Nithiya Venkatraman, Anand Ravi Aiyer, Yash Prakash, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

Discussion forums are one of the favored platforms for knowledge sharing. Given their popularity, copious research exists on understanding the linguistic and behavioral characteristics of forum conversations, so as to inform the design of many downstream applications including discourse visualization, sentiment analysis, and question answering. However, prior investigations have mainly focused on general forums designed primarily for sighted users, and as such the applicability of their findings to dedicated accessibility discussion forums frequented by blind screen reader users remains unanswered. To bridge this knowledge gap and facilitate the development of better-informed assistive technologies for blind people, we investigated language use …

An Analytical Review Of Preprocessing Techniques In Bengali Natural Language Processing, Sovon Chakraborty, Protiva Das, Shakib Mahmud Dipto, Md Aktaruzzaman Pramanik, Jannatun Noor

Computer Science Faculty Publications

Research in Bengali Natural Language Processing (BNLP) is rapidly expanding. Despite being one of the most widely spoken languages in the world, BNLP research remains insufficient, particularly in Bengali speech recognition. The languages rich morphology, agglutinative structure, and diverse dialects make text and speech processing especially challenging. However, these challenges can be addressed with effective preprocessing techniques. Various organizations in Bangladesh and West Bengal are integrating Natural Language Processing (NLP) into their services, but without a thorough understanding of preprocessing, these implementations remain incomplete. Applying proper preprocessing techniques to the Bengali language will serve as a foundation for developing robust …

The Great Scrape: The Clash Between Scraping And Privacy, Daniel J. Solove, Woodrow Hartzog

Faculty Scholarship

Artificial intelligence (AI) systems depend on massive quantities of data, often gathered by “scraping”—the automated extraction of large amounts of data from the internet. A great deal of scraped data contains people’s personal information. This personal data provides the grist for AI tools such as facial recognition, deep fakes, and generative AI. Although scraping enables web searching, archiving of records, and meaningful scientific research, scraping for AI can also be objectionable and even harmful to individuals and society.

Organizations are scraping at an escalating pace and scale, even though many privacy laws are seemingly incongruous with the practice. In this …

State Preparation Of Lattice Field Theories Using Quantum Optimal Control, Jack Y. Araz, Siddhanth Bhowmick, Matt Grau, Thomas J. Mcentire, Felix Ringer

Physics Faculty Publications

We explore the application of quantum optimal control (QOC) techniques to state preparation of lattice field theories on quantum computers. As a first example, we focus on the Schwinger model, quantum electrodynamics in 1 + 1 dimensions. We demonstrate that QOC can significantly speed up the ground state preparation compared to gate-based methods, even for models with long-range interactions. Using classical simulations, we explore the dependence on the interqubit coupling strength and the device connectivity, and we study the optimization in the presence of noise. While our simulations indicate potential speedups, the results strongly depend on the device specifications. In …

Gamified Mhealth System For Evaluating Upper Limb Motor Performance In Children: Cross-Sectional Feasibility Study, Md Raihan Mia, Sheikh Iqbal Ahamed, Samuel Nemanich

Computer Science Faculty Research and Publications

Background: Approximately 17% of children in the United States have been diagnosed with a developmental or neurological disorder that affects upper limb (UL) movements needed for completing activities of daily living. Gold-standard laboratory assessments of the UL are objective and precise but may not be portable, while clinical assessments can be time-intensive. We developed MoEvGame, a mobile health (mHealth) gamification software system for the iPad, as a potential advanced technology to assess UL motor functions.

Objective: This feasibility study examines whether MoEvGame can assess children’s whole-limb movement, fine motor skills, manual dexterity, and bimanual coordination. The specific aims were to …

Smartphone Based Non Invasive Real Time White Blood Cell Counter Leveraging Blue Light And Static Magnetic Field, Nafi Us Sabbir Sabith, Masud Rabbani, Kazi Shafiul Alam, Sheikh Iqbal Ahamed

Computer Science Faculty Research and Publications

White blood cells (WBCs), also known as leukocytes, are one of the most significant parts of the immune system. They generate antibodies, protect the body from illnesses, and heal wounds. Accurate estimation of WBCs is key for diagnosing cancer, infections, leukemia, lymphoma, and other diseases. However, the widely used Complete Blood Count (CBC) test presents challenges, including prick anxiety, discomfort, and logistical inconvenience to patients. This study introduces a ubiquous White Blood Cell counting system, UbiWhite, a novel smartphone-based, non-invasive system for real-time WBC counting from fingertip videos. Our system uses optical and magnetic techniques to provide accurate WBC counts …

A Survey On Non-Invasive Computing: Neurological-Hematological Framework For Early Infection And Stroke Detection With Future Directions, Masud Rabbani, Nafi Us Sabbir Sabith, Sheikh Iqbal Ahamed

Computer Science Faculty Research and Publications

This paper introduces a novel, non-invasive tool for building a holistic neurological-hematological diagnostic framework that can be used for early detection of infection and stroke. We proposed and developed a multimodal sensing approach, where an ear canal–based acoustic system can be used to identify brain activities and a smartphone-based facial video analysis platform to estimate white blood cell (WBC) and hemoglobin (Hb) levels. Our ear-based EEG methodology achieved a remarkable 96% classification accuracy from the low-frequency level (>30hz), with regression models with mean R2 scores above 0.96 across all EEG bands. For hematological diagnostics, the system predicted WBC counts …

Digital Health Using Data Science, Padmapriya Velupillai Mekandan, Paramita Basak Upama, Masud Rabbani, Amity Ali, Sheikh Iqbal Ahamed

Computer Science Faculty Research and Publications

The digitization of healthcare has led to an unprecedented growth in health-related data, offering new opportunities to transform clinical decision-making, disease prediction, and patient engagement. However, extracting actionable insights from diverse data sources such as electronic health records, wearable devices, and mobile health apps requires a fusion of domain knowledge in healthcare and technical expertise in data science. This paper presents a structured, interdisciplinary framework for digital health that emphasizes practical strategies for data acquisition, preprocessing, feature engineering, machine learning, and ethical data use. The model promotes a holistic understanding of digital health challenges and opportunities, preparing future professionals to …

Integrating Data Management Plans Into The Unified Architecture Framework Standards Views, Cansu Yalim, Holly A. H. Handley

Engineering Management & Systems Engineering Faculty Publications

System Architecting translates an operational concept into a model of the system to be realized. There is a need for a Data Management Plan (DMP) to be included in the overall system engineering process with the advent of Digital Engineering. Data longevity, accessibility, and integrity can all be improved throughout the system's lifecycle by a well-defined DMP. System engineers use an architecture framework to arrange the system data into several sets of viewpoints. Incorporating a DMP at this point specifies the procedures for gathering, storing, retrieving, and maintaining data to ensure that all interested parties have access to current, correct …

Cyberattacks On Port Infrastructures: A Decade Of Trends, Incidents, And Mitigation Strategies (2011-2024), Minodora Badea, Olga Bucovetchi, Adrian V. Gheorghe, Gabriel Raicu

Engineering Management & Systems Engineering Faculty Publications

Port infrastructures are critical to global trade, handling over 80% of the world's cargo by volume. However, their increasing reliance on digital technologies has exposed them to a wide range of cyber threats. This paper provides a comprehensive analysis of cyberattacks targeting port infrastructures from 2011 to the present. We examine the types of attacks, geographical distribution, notable incidents, and underlying vulnerabilities. Additionally, we discuss mitigation strategies and future directions for enhancing cybersecurity in the maritime sector. Our findings highlight the urgent need for robust regulatory frameworks, advanced technological solutions, and collaborative efforts to safeguard critical port operations.

Predictive Maintenance In Naval Vessel Propulsion Systems For Enhanced Marine Operations Using A Bigmm-Hmm Framework With Divergence-Based Clustering, Farshid Javadnejad, Hyoshin John Park, Samuel Kovacic, Andres Sousa-Poza

Engineering Management & Systems Engineering Faculty Publications

This study introduces a BiGMM-HMM Integration Framework designed to improve predictive maintenance strategies for naval vessel propulsion systems, addressing the need for efficient and reliable operation in marine engineering applications. The framework effectively manages multimodal sensor data by leveraging a unique combination of Gaussian Mixture Models (GMMs) and Hidden Markov Models (HMMs) in a bidirectional architecture. It analyses the dynamic interactions between sensors and subsystems. Two preprocessing methods are evaluated: Method 1 focuses on subsystem interactions, employing divergence-based root cause analysis to identify key sensor variables by clustering of sensors and subsystems. In contrast, Method 2 processes the entire dataset …

Maritime Industry Cybersecurity Threats In 2025: Advanced Persistent Threats (Apts), Hacktivism And Vulnerabilities, Minodora Badea, Olga Bucovetchi, Adrian V. Gheorghe, Mihaela Hnatiuc, Gabriel Raicu

Engineering Management & Systems Engineering Faculty Publications

Background: The maritime industry, vital for global trade, faces escalating cyber threats in 2025. Critical port infrastructures are increasingly vulnerable due to rapid digitalization and the integration of IT and operational technology (OT) systems. Methods: Using 112 incidents from the Maritime Cyber Attack Database (MCAD, 2020-2025), we developed a novel quantitative risk assessment model based on a Threat-Vulnerability-Impact (T-V-I) framework, calibrated with MITRE ATT&CK techniques and validated against historical incidents. Results: Our analysis reveals a 150% rise in incidents, with OT compromise identified as the paramount threat (98/100 risk score). Ports in Poland and Taiwan face the …

Optimizing Port Logistics Through Generative Ai: Revolutionizing Efficiency And Resilience In The Maritime Industry, Minodora Badea, Olga Bucovetchi, Adrian V. Gheorghe, Gabriel Raicu

Engineering Management & Systems Engineering Faculty Publications

The maritime industry faces growing challenges in optimizing port logistics due to increasing trade volumes, environmental regulations, and supply chain disruptions. This comprehensive literature review examines the transformative role of artificial intelligence (AI), with particular focus on generative AI, in enhancing efficiency and resilience in port operations. Through systematic analysis of 23 peer-reviewed studies published between 2021-2025, this review synthesizes advancements in real-time data integration, machine learning, digital twins, IoT, and autonomous systems that collectively improve operational decision-making, risk management, and environmental sustainability. Key findings reveal that machine learning applications achieve 90% effectiveness ratings in operational optimization, while predictive analytics …

A Governance-Centric Framework For Strengthening Healthcare Cybersecurity: A Systems Perspective, Sujatha Alla, Sai Gireesh Komaragiri, Teresa Duvall, Satluk Karahan, Nagesh Bheesetty, Vijay Kumar Chattu

Engineering Management & Systems Engineering Faculty Publications

Healthcare systems face unprecedented security and privacy challenges due to increasing digitization and interconnectedness. This paper provides a comprehensive analysis of these challenges by examining various cyberattacks, defensive mechanisms, and governance frameworks within modern healthcare infrastructure. The research systematically categorizes prevalent security threats, such as ransomware, insider threats, and data breaches, identifying vulnerabilities specific to healthcare systems. Furthermore, the study evaluates current defensive strategies, including encryption techniques, access control systems, and intrusion detection tools, assessing their effectiveness against complex cyber threats. A key focus is placed on governance structures and their role in cybersecurity resilience. The research explores how regulatory …

Survey: A Study On Image Encryption Using Dna In Bioinformatics, Rana M. Zaki, Zaed S. Mahdi, Matheel E. Abdulmunim

Journal of Soft Computing and Computer Applications

One area of study between computer science and biology is bioinformatics, which deals with methods for collecting, processing, storing, and evaluating biological data. Sequences of RiboNucleic Acid (RNA), DeoxyriboNucleic Acid (DNA), and proteins make up biological data, which has a wide range of uses in domains such as feature extraction, data segmentation, data security, and more. In cryptography, DNA sequences are used as data carriers, enhancing the unique properties of biomolecules. This approach involves using DNA sequences to enhance the security of confidential data that must be transmitted over networks or stored securely. Several DNA-based security techniques have been developed, …

New Feature Selection Using Principal Component Analysis, Zaid Mundher Radeef, Soukaena Hassan Hashem, Ekhlas Khalaf Gbashi

Journal of Soft Computing and Computer Applications

Dimensionality reduction techniques streamline machine learning by reducing data complexity, improving model accuracy, and cutting computational costs. They remove noise and irrelevant features, making models faster and more efficient. These techniques also enhance data visualization and interpretation by condensing data into manageable, insightful dimensions. Ultimately, dimensionality reduction leads to simpler, more interpretable models without sacrificing critical information, making it a cornerstone of efficient data analysis and machine learning applications. Theoretically, feature extraction tends to create new features that encapsulate more information by combining multiple existing features, resulting in more concentrated and informative features. In contrast, feature selection involves choosing a …

Development Of A Hybrid Methodology Of Deep Learning And Machine Learning For Lung Nodule Detection In Medical Computed Tomography Images, Zaed S. Mahdi, Rana M. Zaki, Alaa Kadhim Farhan, Negar Majma

Journal of Soft Computing and Computer Applications

Deep learning and machine learning play an important role in the medical field, helping doctors make accurate, fast and effective diagnosis. Despite the progress achieved in the use of modern technologies in detecting cancerous nodes, current studies still suffer from some challenges and limitations that must be addressed to obtain high efficiency in identifying cancerous nodes. These challenges include using image pre-processing, combining deep learning and machine learning techniques, and constantly adapting to clinical changes, in order to address this. A hybrid methodology has been proposed for detecting cancerous nodules in the lung in medical Computed Tomography (CT) images. It …

Pushing The Boundaries Of Large Language Models: Innovations And Limitations In Nlp, Finance, And Mathematics, A M Muntasir Rahman

Dissertations

Large Language Models (LLMs) have emerged as transformative tools across a spectrum of domains, yet their practical deployment reveals a blend of remarkable potential and notable limitations. This research explores innovative methodologies to extend the capabilities of LLMs while addressing critical challenges in their evaluation and application. By leveraging rule-based approaches, the in-context learning capabilities of LLMs, and human-in-the-loop validation across three focused studies, this research introduces robust strategies for dataset synthesis, model enhancement, and model assessment in three distinct domains: natural language processing, financial sentiment analysis, and mathematical reasoning

The first study proposes an efficient data augmentation framework, EASE, …

First-Principles Study Of Ferroelectric Properties And Co2 Reduction Reaction Capabilities In Two-Dimensional Monolayers And Heterostructures, Mo Li

Dissertations

Two-dimensional (2D) materials hold significant potential for CO₂ reduction reactions (CO₂RR) due to their high surface-to-volume ratio. However, achieving high selectivity for desired products and overcoming limitations posed by scaling relationships remain challenging. Recent studies suggest that ferroelectric (FE) materials with switchable out-of-plane polarization (OOP) can effectively tune the adsorption behavior, thermodynamics, and kinetics of CO₂RR, offering promising solutions to these challenges. Using density functional theory (DFT) and the Berry phase approach, this work expands the family of 2D ferroelectrics by theoretically identifying Y₂CO₂, Y₂CS₂, and Sc …

Ensemble Learning Models For Large-Scale Time Series Forecasting In Supply Chain, Minjuan Zhang

Dissertations

Machine learning and AI techniques are transforming supply chain forecasting, driven by the expanding availability of data assets. These advanced methods offer powerful opportunities to optimize management processes, reduce operational costs, and enhance strategic decision-making, which is crucial for enterprise success. However, conventional statistical approaches, such as Autoregressive Integrated Moving Average Models (ARIMA), dynamic regression, and Unobserved Component Models (UCMs)—which have long dominated time series forecasting—often fall short in accuracy and scalability. These traditional models face limitations in batch processing, handling large-scale data, addressing uncertainty-induced disruptions, and synchronizing demand-supply scenarios.

To address these challenges, a novel class of AI-powered ensemble …

Machine Learning Methods For Pattern Recognition Analysis Of Genomic And Molecular Data, Kuang Du

Dissertations

While immune therapies achieve remarkable success in treating various cancers, only a subset of patients achieves a durable clinical response, and many exhibit innate or acquired resistance. Precision medicine aims to tailor treatments to individual patients based on specific biological markers, ensuring that each patient receives the therapy most likely to be effective. Predictive biomarkers and gene signatures offer potential for more personalized treatment strategies by identifying patients likely to benefit. Recent studies suggest that gene signatures, comprising sets of genes, hold predictive value for certain clinical variables. Typically derived from biological expert knowledge, these signatures demonstrate substantial predictive potential, …

Knowledge Diffusion In Networks Of Artificial Learners, Ehsan Beikihassan

Dissertations

The dissertation draws inspiration from the topic of peer learning in the social sciences and the study of information dissemination and knowledge diffusion in network science. In particular, it introduces and studies a setting involving a population or network of artificial learners, with the objective of optimizing aggregate performance measures under constraints on training resources. In this context, natural knowledge diffusion processes in networks of interacting artificial learners are studied. The term "natural" refers to processes that emulate human peer learning, where the internal state and learning processes of students remain largely opaque, and the main degree of freedom lies …

Interactive Visualization Workflows For Mitigating Analytical Uncertainty, Kaustav Bhattacharjee

Dissertations

This dissertation takes a process-centric and stakeholder-first perspective for handling analytical uncertainty: the form of uncertainty that confronts data analysts' insight-generation processes in high-consequence decision-making scenarios. The cost of an incorrect decision when data is used for movie recommendations as opposed to when personal data is used to drive insights or when data-driven modeling is used to drive real-time decisions for maintaining the health of a grid are vastly different in terms of consequences. This dissertation looks at analytical uncertainty in two real-world scenarios: i) how sensitive information leakage can be prevented during the open data release process with data …

Crowd-Sourced Learning For Computer Graphics Applications, Yunhao Zhang

Dissertations

Computer Graphics (CG) revolves around virtual content creation using computational methods, spanning applications from games to visual effects. Typically, the creation of CG content is led by expert practitioners who guide computational algorithms towards satisfactory results. Thus, creating CG content often requires manual iterations encompassing algorithm design, parameter tuning, and aesthetic feedback. This work investigates how to leverage crowd-sourcing to streamline such creation processes, focusing on animation and simulation. In animation, a novel crowd-sourcing framework is proposed for combat animation, enabling users to analyze motion similarities, and retrieve matching motions using novel crowd-sourced motion features. Such features enable quantifying previously …

Shalom In Social Media Marketing Shalom In Social Media Marketing, Jill R. Risner, Thomas Betts

University Faculty Publications and Creative Works

This paper will examine social media and the ways in which it currently does and does not contribute to shalom through an examination of both the platforms themselves as well as the content shared through them. As Christ’s ambassadors in the world and the primary funders of social media, marketers have power and an obligation to consider social media’s impact on shalom in the world and to use it in ways that contribute to shalom. This paper will provide several recommendations of how marketers can do this including posting content that intentionally contributes to shalom, engaging on platforms that support …

Surveying The Role Of Visual Analytics In Human-Machine Teaming, Naga Datha Saikiran Battula

Theses

Humans and machines both possess their unique capabilities and have their strengths and weaknesses, which can be complementary to one another and allow them to achieve a common goal. Teaming in the modern era involves text prompts, voice commands, gesture recognition, touch interfaces, and the latest visualization techniques that allow parties/agents to interact. Communication through visualization plays a vital role in allowing robust insights to be gained through a glance. Using visualization as a medium between humans and machines can increase the communication bandwidth. Human-machine teaming has witnessed much progress, with many theories and practical examples emerging. In the report, …

Impact Of Urban Development On Uv Exposure: A Clustering And Machine Learning Assessment, Taufik Roni Sahroni Mr., Verdi Yasin, Lulut Alfaris, Reza Ariefka, Ruben Cornelius Siagian, Mohammad Alfin Karim, Nana Rahdiana, Ade Suhara

Journal of Environmental Science and Sustainable Development

The relocation of Indonesia's capital city is anticipated to promote inclusive economic growth while embracing cultural diversity. However, this transition may affect ultraviolet (UV) radiation exposure patterns. The study investigated variations in UV exposure in the IKN region, focusing on urban development factors such as land use and population density that affect public health, sun protection, and skin cancer prevention. The research hypothesized that UV radiation is significantly correlated with these factors. UV Index data from 2010-2023, a hierarchical clustering method, identifies complex data patterns without determining the number of clusters. XGBoost, a machine learning model, was used for handling …

Ai-Assisted Academia: Unveiling Doctoral Students' Perspectives On Dissertation In Practice Innovation, Jennifer J. Lesh, Jévaughn J. Lancaster

Faculty and Staff Publications & Presentations

This action research study explores 73 doctoral students' perceptions of using Generative Artificial Intelligence (GAI) throughout their research journey in one educational doctorate (Ed.D) program. The first phase employed surveys, while the second incorporated semi-structured focus group interviews based on the survey data from a diverse sample of students across educational disciplines currently enrolled in the university's educational leadership doctoral program. In the study's first phase, the survey quantified educators' familiarity with, attitudes towards, perceived challenges, ethical considerations, and benefits of using GAI in doctoral research. The exploration of GAI in this practitioner-inspired doctoral program has uncovered essential insights into …

Optimizing Vgg16 Deep Learning Model With Enhanced Hunger Games Search For Logo Classification, Mohammed Hussain, Thaer Thaher, Mohamed Basel Almourad, Majdi Mafarja

All Works

Accurate classification of logos is a challenging task in image recognition due to variations in logo size, orientation, and background complexity. Deep learning models, such as VGG16, have demonstrated promising results in handling such tasks. However, their performance is highly dependent on optimal hyperparameter settings, whose fine-tuning is both labor-intensive and time-consuming. Swarm intelligence algorithms have been widely adopted to solve many highly nonlinear, multimodal problems and have succeeded significantly. The Hunger Games Search (HGS) is a recent swarm intelligence algorithm that has shown good performance across various applications. However, the standard HGS still faces limitations, such as restricted population …

Text-To-Text Generative Approach For Enhanced Complex Word Identification, Patrycja Śliwiak, Syed Afaq Ali Shah

Research outputs 2022 to 2026

This paper presents a novel approach for solving the Complex Word Identification (CWI) task using the text-to-text generative model. The CWI task involves identifying complex words in text, which is a challenging Natural Language Processing task. To our knowledge, it is a first attempt to address CWI problem into text-to-text context. In this work, we propose a new methodology that leverages the power of the Transformer model to evaluate complexity of words in binary and probabilistic settings. We also propose a novel CWI dataset, which consists of 62,200 phrases, both complex and simple. We train and fine-tune our proposed model …