Digital Commons Network^™

Developing Deep Learning Methods For Cognitive Impairment Detection Based On Non-Invasive Data, Muath Alsuhaibani

Electronic Theses and Dissertations

Cognitive impairment detection is on the rise to help reduce the burden of healthcare costs on institutions and individuals. Mild Cognitive Impairment (MCI) is an early stage of cognitive decline progressing to Alzheimer’s disease (AD) or AD-related Dementia (ADRD). Detecting the early stages of AD/ADRD is crucial for early interventions among older adults to mitigate cognitive decline over time. However, the current diagnostic methods are often costly and/or invasive, such as MRI and PET scans. Thus, the search for non-invasive and cost-effective screening tools for the early detection of cognitive impairment using speech, language, visual, and motor data is growing. …

Investigation Of Dependency Parsing Techniques For Digital Document Analysis Through Deep Learning Approach, Rekah D Ms

Theses and Dissertations

Digital document dependency parsing is a significant task in natural language processing. Dependency parsing supports verification of the grammatical correctness of a sentence besides enabling extraction of relevant documents. Inappropriate extraction of features may result in falsely parsing a document, leading to decreased accuracy. Machine learning methods have been employed to perform feature extraction. However, selecting pertinent features was never achieved which minimizes the time consumption and overhead.

Hence, novel machine learning and deep learning techniques have been designed in our work for accurate and computationally efficient digital document analytics through dependency parsing. Four different contributions have been proposed for …

Multi-Layered Framework For Llm Hallucination Mitigation In High-Stakes Applications: A Tutorial, Sachin Hiriyanna, Wenbing Zhao

Electrical and Computer Engineering Faculty Publications

Large language models (LLMs) now match or exceed human performance on many open-ended language tasks, yet they continue to produce fluent but incorrect statements, which is a failure mode widely referred to as hallucination. In low-stakes settings this may be tolerable; in regulated or safety-critical domains such as financial services, compliance review, and client decision support, it is not. Motivated by these realities, we develop an integrated mitigation framework that layers complementary controls rather than relying on any single technique. The framework combines structured prompt design, retrieval-augmented generation (RAG) with verifiable evidence sources, and targeted fine-tuning aligned with domain truth …

Investigating Information Extraction And Language Models In Medical Domain Text Processing, Pouyan Nahed

UNLV Theses, Dissertations, Professional Papers, and Capstones

This dissertation demonstrates that carefully adapted language-model pipelines can transform unstructured clinical-trial and pharmacological prose into reliable, low-latency structured data. Four interconnected studies support this claim.Tri-AL platform. An open-source dashboard ingests all 440 k+ ClinicalTrials.gov records—including every historical revision—into a normalized schema and parses the 20 GB XML archive over 10x faster than a BeautifulSoup baseline, while exposing hooks for demographic analytics and supporting integration of user-defined modules. Clinical trial summarization. An encoder–decoder model is trained on 57k description–summary pairs to condense clinical trials into a few sentences. ROUGE evaluation shows a 20% improvement over the baseline, while graph-based evaluation …

Quantified Possibility Neutrosophic Soft Set Based Decision Support System For Enhanced Accuracy In Sustainable Supplier Selection Decision Analysis, Neha Andaleeb Khalid, Muhammad Saeed

Neutrosophic Systems with Applications

To deal with the concepts of vulnerability, ambiguity, and indeterminacy that are typical in complex decision analysis contexts, neutrosophic set-like structures are frequently used. Although neutrosophic sets are particularly good at handling indeterminate circumstances, indeterminacy plays a part in making the decision making process imprecise and ambiguous. This paper develops a more advanced technique to improve the accuracy of decision analysis problems: the Quantified Possibility Neutrosophic Soft Set Decision Support System (Q^t PNSSDSS). In order to reduce the element of indeterminacy present in conventional neutrosophic sets, the proposed DSS is based on the Quantified Neutrosophic Set, which employs a …

Some Properties Of Neutrosophic Cubic Hypersoft Sets, Lubna Nayab, Muhammad Gulistan, Fawad Hussain

Neutrosophic Systems with Applications

This study addresses the limitations of NCS in managing uncertainties associated with multiple attributes and their further bifurcation. To address this challenge, we propose a generalization of the neutrosophic cubic soft set, introducing the concept of "neutrosophic cubic hyper soft set." Within this framework, we define internal and external neutrosophic cubic hypersoft sets, along with operations such as P-intersection, P-union, P-restricted union, P-extended intersection, P-OR operator, P-AND operator, R-intersection, R-union, R-restricted union, R-extended intersection, R-OR operator, R-AND operator, complement, and relative complement of neutrosophic cubic hyper soft sets. The study explores and presents relevant results, demonstrating the enhanced capability of …

Digital Trading Platform Selection Under Neutrosophic Numbers And Multi-Criteria Decision-Making Methodology: An Illustrative Example, Eman Sayed, Karam M. Sallam, Ibrahim Alrashdi

Neutrosophic Systems with Applications

Uncertainty, vagueness, and incomplete information are pervasive in real-world decision-making scenarios, particularly in multi-criteria decision-making (MCDM) contexts involving expert judgments. To address these challenges, this study introduces a comprehensive decision-making framework that integrates Triangular Neutrosophic Numbers (TNNs) with the Weighted Aggregated Sum Product Assessment (WASPAS) method. The framework begins by capturing expert evaluations using TNNs, which effectively represent the degrees of truth, indeterminacy, and falsity inherent in subjective assessments. These evaluations are then transformed into crisp values using a novel score function that preserves the embedded uncertainty. The resulting decision matrices are aggregated into a unified structure to ensure consistency …

Enhanced Global Oil Spill Dataset From 1967 To 2023 Based On Text-Form Incident Information, Yiming Liu, Zhuoli Yin, Hua Cai

The School of Sustainability Engineering and Environmental Engineering (SEE) Faculty Publications

Knowing how much oil was released into the environment in each incident is critical to studying the environmental, ecological, and economic impacts of oil spills. However, the release amounts (RAs) for numerous oil spill incidents remain unavailable in a structured format for large-scale analysis. The most extensive global oil spill database, managed by NOAA’s Office of Response and Restoration, only documents the worst-case scenario estimations in the machine-readable dataset, while more accurate values are embedded within unstructured incident descriptions and subsequent updates. To enhance the dataset with more accurate RAs, we developed a framework to extract the actual RAs from …

Exploring The Role Of Accessibility In Enhancing User Experience: Trends, Challenges, And Opportunities Across Digital Platforms, Adiba Khan

Harrisburg University Dissertations and Theses

Accessibility was a fundamental aspect of enhancing user experience (UX) across digital platforms, ensuring equitable access to information and services for a diverse range of users, including individuals with visual, auditory, cognitive, or motor impairments. As digital products became increasingly central to education, commerce, and communication, the need for inclusive design grew. This study explored the intersection of accessibility and UX, with a specific focus on how accessibility-driven design improved usability, user satisfaction, engagement, and the overall quality of digital interactions. Grounded in Agile project management principles, the research examined how early integration of accessibility into the software development lifecycle …

Enhancing Emotional Accuracy And Behavioral Novelty In Robot Behaviors: A Generative-Discriminative Approach, Rista Baral

Boise State University Theses and Dissertations

Recent advancements in language modeling have improved robotic emotion expressiveness, yet several challenges remain. Many existing robotic expression models rely on fixed rules and static frameworks, which limit their ability to capture the dynamic nature of emotional expression. Additionally, these systems often struggle to balance emotional accuracy with generating novel and varied behaviors. These limitations underscore the need for method capable of delivering both emotionally accurate and diverse robot behaviors.

In this thesis, we addresses these challenges by introducing a new framework for expressive behavior generation in robots. We develop a generative model that generates robot behavior sequences that align …

How Developers Use Type-System Related Programming Language Features, Samuel W. Flint

School of Computing: Dissertations, Theses, and Student Research

Optional type annotations are a popular feature of programming languages that allow developers to omit explicit type information in code while, in some cases, retaining many of the benefits of static typing, such as in-code documentation, improved detection of type errors, or enforcement of code properties. However, how developers use and understand optional type annotations is not clear. The focus of this dissertation is to understand the use and comprehension of optional type annotations.

Optional type annotations are examined through four lenses: first, by examining the evolution of usage in a statically typed programming language (Kotlin, the default language for …

Designing A User-Centered Bias System To Enhance Media Literacy And Factual Accuracy, Anjali Majan

Theses and Dissertations

In an era of rapid news consumption, readers often struggle to detect bias and misinformation. This study examined whether interface design can support more critical engagement with news. We developed a progressive disclosure interface that encouraged users to reflect as they read by gradually revealing bias and factual cues. Participants were assigned to either Progressive Disclosure or Ground News. The experiment involved two phases. In the intervention phase, participants used an interface with support features. In the assessment phase, they completed tasks without the tool. We evaluated their performance using five measures: bias recognition accuracy, bias shift, factuality judgment, overlap …

Genwriter: Reducing Gender Cues In Biographies Through Text Rewriting, Shweta Soundararajan, Sarah Jane Delany

Conference papers

Gendered language is the use of words that indicate an individual’s gender. Though useful in certain context, it can reinforce gender stereotypes and introduce bias, particularly in machine learning models used for tasks like occupation classification. When textual content such as biographies contains gender cues, it can influence model predictions, leading to unfair outcomes such as reduced hiring opportunities for women. To address this issue, we propose GenWriter, an approach that integrates Case-Based Reasoning (CBR) with Large Language Models (LLMs) to rewrite biographies in a way that obfuscates gender while preserving semantic content. We evaluate GenWriter by measuring gender bias …

Systems Statistical Engineering – Hierarchical Fuzzy Constraint Propagation, Hengameh Fakhravar

Engineering Management & Systems Engineering Theses & Dissertations

Driven by the growing need in the 21st century for integrating rigorous statistical analysis into engineering research, there is a movement to develop an integrated statistical engineering science within statistics and quality communities (Hoerl & Snee, 2010; Anderson-Cook et al., 2012). Systems Statistical Engineering research seeks to integrate the Causal Bayesian hierarchical modeling (Pearl, 2009) and cybernetic control theory within Beer’s Viable System Model (1972, 1979, 1985) and the Complex Systems Governance framework (Keating, 2014; Keating & Katina, 2015, 2016) to produce multivariate systemic models for robust dynamic systems mission performance. Cotter & Quigley (2018) set forth the Bayesian systemic …

The Effect Of Transformer Based Pre-Trained Language Models On Biomedical Relation Extractions, Eman Saad, Sherif Kishk, Amr Ali-Eldin, Ahmed I. Saleh

Mansoura Engineering Journal

Biomedical relation extraction represents a critical advancement in healthcare research. As the volume of biomedical publications continues to grow exponentially, efficient extraction of entity relationships has become essential for accelerating knowledge discovery, drug development, and precision medicine initiatives. The field has evolved from focusing on simple binary relations, such as Protein-Protein Interactions (PPI), which are fundamental to understanding cellular processes and therapeutic development, to addressing more complex multi-class classification challenges like Drug-Drug Interactions (DDI) and Chemical-Protein Interactions (CPI).Pre-trained language models have become the cornerstone of modern approaches of extracting biomedical relations. Models like BioBERT, PubMedBERT, and SciBERT, along with general-purpose …

Type-2 Neutrosophic Set With Mcdm Methodology For Fire Safety Estimation In Healthcare Services, Eman Sayed

Neutrosophic Systems with Applications

Fire safety represents a critical priority in healthcare facilities, where complex infrastructures and the vulnerability of patients present significant challenges to evacuation and emergency response. Traditional fire risk assessment methods often fall short in addressing the linguistic variability, uncertainty, inconsistency, and indeterminacy inherent in expert evaluations. While fuzzy and Neutrosophic approaches have been applied in broader healthcare decision-making contexts, no existing study has utilized Type-2 Neutrosophic Numbers Sets (T2NNs) for prioritizing hospital departments based on fire risk. To address this gap, this study introduces a novel multi-criteria decision-making (MCDM) framework that integrates T2NNs for expert modeling, the Entropy method for …

Mediating The Exquisite: Aesthetic Affordances Of The Network Arts, Seth Adams

Journal of Network Music and Arts

This paper argues that the Network Arts afford uniquely nuanced aesthetic interactions among humans and between humans and machines. After defining these moments under a new concept I call exquisite musicking, I investigate how distributed networks of human and nonhuman actors create experiences that challenge or complicate traditional notions of agency, ensemble, aesthetics, and synchrony. Using Actor-Network Theory as a lens of inquiry, I explore the exquisite affordances of network-based musicking, discussing how pitch, timbre, and texture are co-created in networked performances. I also examine how exquisite musicking could serve as a fruitful concept in music education, particularly in pedagogical …

Visionarium, July 2025, Full Issue

International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM

No abstract provided.

Painting With Ai: Enhancing Creativity And Understanding In The Arts Classroom, Erica Blum

International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM

In the context of contemporary art and design education, the rapid advancement of generative artificial intelligence has introduced profound shifts in both creative practice and pedagogical responsibility. Art and design professors now encounter students who approach AIdriven tools with a mixture of skepticism and curiosity, often perceiving such technologies as antithetical to authentic artistic development or as expedient alternatives to disciplined practice. This article articulates an instructional approach that leverages Adobe Photoshop’s generative capabilities—specifically neural filters, generative fill, and the manipulation of alpha channels— to position artificial intelligence as a collaborative, rather than substitutive, element within digital art-making. The 2025 …

Bridging Tradition And Technology: Ai As A Catalyst For Heritage Preservation And Humanities Research, Kholoud Ghaith

International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM

It is an empirical study examining how AI can be the driving force for cultural heritage and humanities conservation. As the AI tools that digitize, analyze, and conserve cultural resources have become in recent years, they have led to digital transformations in machine learning, digital twinning, and natural language processing. These technologies answer the industry's most challenging problems, such as the loss of historical material, access restrictions to heritage sites, and the labor-intensive nature of traditional conservation techniques. In implementing AI in heritage, institutions can document more accurately, keep records predictively, and offer more interesting public experiences via rich digital …

Writing As Curation: Empowering Authorial Agency In Ai-Assisted Composition Through Style Prompting And Quotation Glosses, Daniel Plate

International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM

As artificial intelligence writing tools become increasingly prevalent in educational settings, students often report feeling alienated from AI-generated content, describing it as lacking their "authentic voice." This article presents a pedagogical approach that addresses this challenge by repositioning students as curators of their authorial identities rather than passive consumers of AI-generated text. Through a dual curation methodology combining quotation glosses with style prompting, students develop multiple authorial voices for different rhetorical situations while maintaining agency over their writing process. The approach builds on classical rhetorical traditions while leveraging contemporary AI capabilities, creating a framework where students collaborate with AI tools …

Ai-Powered Storytelling And Interactive Timelines For Arts And Humanities Education, Bhavya Raoulji

International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM

The integration of artificial intelligence (AI) in education has opened avenues for transformative teaching and learning experiences, particularly in the arts and humanities. This submission explores the development of two AI-driven classroom tools: Chabot and interactive timelines. These tools immerse students in storytelling and historical exploration, fostering critical thinking, creativity, and interdisciplinary collaboration. The Chabot simulates conversations with historical figures or cultural narrators, providing AI-generated responses enriched with contextual details and multimedia content. The interactive timeline offers dynamic visualizations of historical events, enhanced by AI-generated summaries, audio clips, and visual aids. These tools integrate project-based learning methodologies, promoting critical thinking, …

From Industrial Automation To Intelligent Automation: The Impact Of Iiot On Process Control - A Review?, Ali S. Allahloh, Mohammad Sarfraz, Duraid Y. Mohammed, Nadeen Khaleel Ibrahim, Hayder Hussein Thary

Iraqi Journal for Computer Science and Mathematics

The Industrial Internet of Things (IIoT) is reshaping process control, turning torrents of plant data into near-instant insight. Field evidence already shows unplanned-downtime cuts of 55%, maintenance-budget savings of 40%, and gains in overall-equipment-effectiveness of roughly 15%; nevertheless, fewer than 30% of facilities have pushed beyond pilot scale to full, always-on predictive maintenance. To clarify why, this review screens 312 Scopus records published between 2010 and 2023, then subjects 62 peer-reviewed studies and a broad set of industry reports to deep content analysis, bibliometric mapping, and thematic clustering.The results reveal four tightly connected research fronts—IIoT/Industry 4.0 core technologies, classical process-control …

Intelligent Control Of Reducing Energy Consumption And Water Loss In Smart Home Systems, Hakimjon Nasriddinovich Zaynidinov, Damira Farxodovna Hodjaeva, Dhananjay Singh

Technical science and innovation

The article presents an innovative approach to developing an intelligent control system for managing temperature and water level in smart home systems. The proposed method integrates an adaptive PID controller with fuzzy logic algorithms, enabling dynamic adjustment of the PID controller coefficients in real time. The mathematical model of the system incorporates heat balance equations, differential heat transfer relations, and nonlinear models of fluid loss. The adaptive control algorithms developed within the study allow the system to effectively respond to changes in input data.

A comprehensive analysis of the membership functions is conducted, along with adaptive tuning of the PID …

A Comprehensive Review Of Software Requirements Dependencies Analysis Techniques, Nahla Mohamed, Sherif Mazen, Waleed Helmy

Iraqi Journal for Computer Science and Mathematics

The software Requirements Prioritization (RP) process is essential for producing a successful software project. Requirements are interdependent in software projects, so handling their dependency during the RP process is mandatory. Many researchers have shown that requirements dependency is challenging for large-scale systems. Extracting requirements dependency is difficult since requirements are documented in natural language. Improper handling of dependencies among requirements while prioritization can cause inaccurate prioritization results and deadlocks, which cause project delays, rework, and redesign. Many techniques have been introduced to automate the dependency analysis process among software requirements, including artificial intelligence (AI) and other logic-based methods such as …

Retracted: A Novel Benchmarking Framework For Selecting The Best Deep Learning Model Diagnosing Covid-19 Based On New Development For Dual Mcdm Methods, Mahmood M. Salih, Yousif Raad Muhsen, M.A. Ahmed, Reem D. Ismael, Moceheb Lazam Shuwandy, Z.T. Al-Qaysi

Iraqi Journal for Computer Science and Mathematics

COVID-19 was diagnosed using deep learning models by a group of studies. Evaluating and benchmarking these models are essential to achieving the most suitable model for diagnosing coronavirus. Objective: In this investigation, we offer an inclusive valuation of several deep learning models to detect the maximum appropriate and active model which gratifies doctors' requirements and assessment criteria. Method: This study combines Fuzzy decision by the opinion score method (FDOSM) and Fuzzy-Weighted Zero-Inconsistency (FWZIC). According to the advantage of Trapezoidal Intuitionistic fuzzy, we developed FWZIC into Trapezoidal Intuitionistic fuzzy named (TrIF-FWZIC) for weighting criteria and FDOSM into Trapezoidal Intuitionistic fuzzy FDOSM …

Syntax-Enhanced Boundary-Aware Named Entity Recognition Model, Chuanming Yu, Bin Deng, Zhengang Zhang

Journal of Scientific Information Research

[Purpose/significance] This study addresses the issue of inadequate perception of entity boundaries in traditional character-level modeling-based named entity recognition models by integrating syntax information containing entity boundary features into the task using a multi-head graph attention network with dense connections. This integration enhances the effectiveness of named entity recognition.

[Method/process] This study proposes a Syntax-enhanced Boundary-aware Named Entity Recognition Model (SynBNER), which utilizes BERT for text semantic representation and integrates syntax information using a dense-connected graph attention network. This integration incorporates implicit entity boundary information from syntax information into word representations, thereby enhancing the model's entity boundary perception capability.

[Result/conclusion] …

Artificial Intelligence In Education, Sally Brown, Jill Woods, Mohamed Hefeida, Jennifer Sano-Franchini, Megan Vendemia, Erin Brock Carlson, Megan Leight, Gangqing Hu, Nicole Fuller

Artificial Intelligence Exhibit

This section explores the intersection of AI and education, including an overview of WVU's AI statement and submissions from WVU faculty, classes, students, and staff on AI related projects.

Building A Long Text Privacy Policy Corpus With Multi-Class Labels, David Stein, Florencia Marotta-Wurgler

Vanderbilt Law School Faculty Publications

Legal text poses distinctive challenges for natural language processing. The legal import of a term may depend on omissions, cross-references, or silence, Further, legal text is often susceptible to multiple valid, conflicting interpretations; as the saying goes: a good lawyer’s answer to any question is “it depends.”This work introduces a new, hand-coded dataset for the interpretation of privacy policies. It includes privacy policies from 149 firms, including materials incorporated by reference. The policies are annotated across 64 dimension that reflect the applicable legal rules and contested terms from EU and US privacy regulation and litigation. Our annotation methodology is designed …

Seconds From Impact: Anticipatory Vehicular Crash Prediction Using Video Vision Transormers, Ryan P. Geisen

Master's Theses

Vehicular collisions represent a significant public health concern, necessitating re search into advanced emergency notification systems. While deep learning has shown promise in accident detection, a research gap persists in applying state-of-the-art transformer architectures to the task of anticipatory, real-time crash prediction from video. This thesis addresses this gap by developing and evaluating a Video Vision Transformer (ViViT) for the binary classification of imminent vehicular collisions. Utilizing a curated dataset of 1,493 unique collision sequences, this study systemati cally investigates the impact of temporal context by comparing the ViViT against a single-frame Vision Transformer (ViT) baseline and conducting comprehensive exper …