Digital Commons Network^™

Ai-Powered Continuous Health Monitoring For Chronic Disease Management In Malaysia: Real-Time Risk Prediction Using Wearable Sensors And Digital Biomarkers, Umi Najiah Ahmad Razimi, Yong Yoke Leng, Nor Hapiza Mohd Ariffin, Mohammed Hazim Alkawaz

Journal of Informatics and Web Engineering

Chronic Non-Communicable Diseases (NCDs), such as diabetes mellitus, hypertension, and cardiovascular disease, make up a significant portion of the mortality cases in Malaysia, as well as impose consistent demands for costs related to health care expenditure. According to the cost reports at the national level, major NCDs (cardiovascular disease, diabetes, and cancer) are responsible for RM 9.65 billion annually as direct healthcare cost [3]. However, the current approach to managing chronic diseases focuses heavily on periodic assessments in clinic setting at weekly or monthly intervals. Here, a predictive analysis framework based on AI technology, leveraging continuous monitoring with patient data …

Intelligent Telemedicine Systems For Contactless Heart Rate Estimation Using Deep Learning-Based Rppg, Do Duc Thinh, Nguyen Duc Manh, Nguyen Thi Bich Ngoc, Vu Van Huan

Journal of Informatics and Web Engineering

Over the last ten years, telemedicine has undergone significant developments, from simple communication-based healthcare to smart and data-driven solutions. The solutions are now powered by Artificial Intelligence (AI), Machine Learning (ML), and Internet of Medical Things (IoMT) technologies. Physiological monitoring is traditionally done with contact-based sensors. The sensors include Electrocardiogram (ECG) and pulse oximeters. The sensors have various disadvantages, including hardware costs, difficulty in continuous usage, and discomfort for patients. Photoplethysmography (rPPG), a remote method of physiological monitoring, is a breakthrough technology. The method is used to estimate physiological signals, including Heart Rate (HR), from video streams captured by standard …

Data Governance Maturity, Ai Integration, And Equity In Colorado K-12 Public Schools, John R. Curtin

Electronic Theses and Dissertations

Colorado's 179 K-12 public school districts operate as autonomous governance units, each responsible for securing and managing student data assets that span health, financial, residential, and academic records. The accelerating integration of artificial intelligence (AI) and machine learning (ML) tools into administrative workflows, productivity software, and instructional platforms has fundamentally altered the risk landscape for student data, yet governance frameworks at the state, district, and school levels have not kept pace. This dissertation investigates whether Colorado's decentralized educational governance structure is institutionally capable of producing equitable, secure, and sustainable data governance outcomes in the AI era.

Drawing on Institutional Theory …

Evaluation And Distillation Of Source Code Generation Tasks By Large Language Models, Danny Brahman

Electronic Theses and Dissertations

Large Language Models (LLMs) are predominantly assessed based on their common sense reasoning, language comprehension, and logical reasoning abilities. While models trained in specialized domains like mathematics or coding have demonstrated remarkable advancements in logical reasoning, there remains a significant gap in evaluating their code generation capabilities. Existing benchmark datasets fall short in pinpointing specific strengths and weaknesses, impeding targeted enhancements in models’ reasoning abilities to synthesize code.

To bridge this gap, this thesis introduces two novel contributions: CodeEval and CodeQual. CodeEval is an innovative, pedagogical benchmarking method that mirrors the evaluation processes encountered in academic programming courses. It comprises …

Medical Image Integrity Protection Through U-Net Based Roi Segmentation And Hybrid Integer Wavelet–Quadtree Embedding, Muna M. Jawad, Rasha F. Nadhim, Noor A. Yousif, Ashwaq T. Hashim

Journal of Intelligent Informatics, Networking, and Cybersecurity

For decades, secure techniques in medical images have focused on securing the sensitive information through limiting direct access to the images themselves. Nonetheless, these methods tend to cause distortion on the images that can affect the diagnostic value and cause loss of crucial information. There arises the fundamental problem of secure embedding of medical data in such a way that does not affect the diagnostic integrity of the image. To overcome this limitation, a secure yet hidden data-hiding mechanism is introduced that is exploited U-Net-based deep learning for accurate Region of Interest (ROI) segmentation. Therefore U-Net architecture is used for …

Data-Driven Qoe Inference For Htttp Adaptive Streaming, Tisa Selma

Thesis/ Dissertation Defenses

In HTTP adaptive streaming, accurately inferring user Quality of Experience (QoE) remains challenging due to dynamic network conditions, content variations, end-to-end encryption, intrusive advertisement interruptions, and diverse user engagement behaviors. Conventional approaches, such as ITU-T P.1203, fall short in capturing real-time user perception, accounting for ad related disruptions, or adapting to model drift over time. This dissertation therefore aims to develop a multimodal face emotion recognition (FER) based inference model that predicts QoE under encryption and advertisement conditions while being interpretable, robust, resilient to drift, and highly accurate. To achieve these objectives, a unified framework is designed and implemented. The …

Comparative Analysis Of Leaflet Materials, Stent Materials, And Stent Cell Density For Bileaflet Transcatheter Mitral Valve Design, Joseph Chibuike Nwokeafor, Joshua D. Hofmeister, Breandan B. Yeats, Lakshmi Prasad Dasi, Charanjit S. Rihal, Juan A. Crestanello, Leigh Griffiths, Mohamad A. Alkhouli, Hoda Hatoum

Michigan Tech Publications

Background: The biomechanical performance of bileaflet transcatheter mitral valves (TMVs) depends on complex interactions between leaflet material behavior and stent design. However, the contributions of leaflet materials and constitutive models, stent materials, and stent geometry to valve function and durability remain poorly understood. Methods: A parametric finite element study was conducted using a CAD model of a bileaflet TMV subjected to physiological pressure loading. Five leaflet material models were evaluated: 3 glutaraldehyde-fixed tissues—bovine pericardium (BP; FBP1, FBP2) and porcine pericardium (PP; FPP)—and 2 unfixed tissues—bovine (UBP) and porcine pericardium (UPP). BP was modeled as a linear elastic (FBP1) and Ogden …

Enhanced Uav Surveillance With Rf-Based Drone Identification Using Transfer Learning, Prajoy Podder, Maciej Jan Zawodniok, Sanjay Madria

Electrical and Computer Engineering Faculty Research & Creative Works

With the development of technology and the decrease in costs, drones are now becoming easily accessible to the public. As the accessibility of this technology continues to grow, the concerns of security and surveillance increase, and to ensure a sense of security, the need to have reliable drone detection and identification systems is more urgent than ever. Besides, many civilian applications have been found for drones, which play a huge role in modern security and warfare. Unauthorized drones can be very dangerous regarding security issues, as they can be used for spying, smuggling, or even attacks against critical infrastructure. We …

A Study On Culturally Sensitive Ai-Based Conversational Interfaces To Enhance Digital Service Accessibility For Senior Citizens In The Uae, Salma Eisa Alkhyeli

Thesis/ Dissertation Defenses

The UAE is steadfast in its digitalization process due to the Vision 2031, but the aging population encounters several barriers in its efforts to use essential services like subsidized food and fodder markets through an online platform. This thesis developed and validated an AI-based conversational system that focused on helping the elderly group to acquire digital services. The challenges here are associated with lack of knowledge on digital interfaces, language barrier because of dialects and lack of digital literacy in a majority of older adults. The Study addressed these challenges by considering the diverse dialect, cultural and technological needs of …

Next-Generation Dns Rpz For Automated Threat Intelligence, Risk-Aware Filtering, And User-Centric Security, Jinu S, Kishore V. Krishnan, Rajarshi Middya, Annasamy Bagubali

Journal of Cybersecurity Education, Research and Practice

The Domain Name System (DNS) remains a critical attack vector exploited by adversaries for command-and-control (C2) communication, data exfiltration, and phishing campaigns. DNS Response Policy Zones (RPZ) have emerged as an effective defense mechanism by enabling the redirection or blocking of queries to malicious domains. However, current RPZ implementations encounter significant challenges related to scalability, adaptability, and user awareness, often resulting in static policies, delayed updates, and a high incidence of false positives. To address these limitations, this paper proposes an enhanced RPZ framework that integrates adaptive threat intelligence, machine learning-driven dynamic policy updates, and user-context-aware security controls. The proposed …

Describing Hidden Curriculum In An Undergraduate Computing Context, Joseph R. Teahen, Briana C. Bettin, Leo Ureel

Michigan Tech Publications

Hidden Curriculum (HC) is the set of essential knowledge, skills, and norms students are expected to know, but never explicitly taught. HC is disproportionately experienced across identities and communities. In computing education, most research addresses immediately identifiable HC within the researcher's context. While such work is important, without proper HC descriptive studies, we could miss more subtle HC that affects student success. This work presents results from interviews with undergraduate computing faculty, students, and peer mentors on their HC experiences. The results demonstrate several categories of HC including development tools, professional skills, institutional navigation, social well-being, and physical well-being. These …

The Intersection Between Mindfulness And Cybersecurity: A Tool To Reduce Burnout And Improve Operational Effectiveness, Ivo Ricardo Dias Rosa

Journal of Cybersecurity Education, Research and Practice

Abstract: This paper offers a conceptual discussion of how mindfulness, understood as present moment awareness and deliberate attention regulation, can support cybersecurity professionals. Drawing on a narrative synthesis of workplace mindfulness, burnout, and high pressure decision making literature, we map plausible self regulation mechanisms to typical cyber defense tasks. Rather than presenting new empirical data, we develop an explanatory framework linking attention, reactivity, and recovery to decision quality, team communication, and adherence to incident playbooks. We focus on two connected outcomes: reducing burnout in roles with sustained cognitive and emotional demands, and improving operational effectiveness during critical situations such as …

Cyber-Ready Libraries, Building Digital Fortresses For Tomorrow, Adeyinka B. Tella, Oluchi Precious Ogbonna Dr, Adebola Aderemi Olatoye Mrs

Journal of Cybersecurity Education, Research and Practice

Background and Purpose: Libraries are evolving into highly networked information ecosystems in this age of fast digital transformation, which increases their susceptibility to cybersecurity risks. The study "Cyber-Ready Libraries: Building Digital Fortresses for Tomorrow" looks into how prepared libraries are to face cyber threats and considers methods for creating information systems that are safe, robust, and ready for the future. To safeguard digital assets and user data, the study aims to assess existing cybersecurity practices in library settings and offer a roadmap for combining technological, human, and governance solutions.

Design/Method: The existing literature, case studies, and policy frameworks pertaining …

Parent Cultural Wealth: How Beliefs, Curriculum Familiarity, And Community Awareness Facilitate Home-Based Cs Conversations, Nicol Howard, Leiny Y. Garcia, Jean Ryoo, Julie Flapan, Michelle Choi, Paula Nazario, Wei Wei

Mathematics, Physics, and Computer Science Faculty Articles and Research

Parents are critical influences on children's computer science (CS) pathways, yet families’ involvement remains understudied. Guided by parental involvement and community cultural wealth frameworks, this work-in-progress examined survey data from 53 parents representing families historically underrepresented in computing within California (71% women; 54% Hispanic/Latin/x/e). Findings revealed that CS school curriculum familiarity and awareness of CS community events significantly predicted parent-child CS conversations, while parent CS confidence and attitudes did not, suggesting that curriculum transparency and community connections can promote equitable involvement. Future work will integrate qualitative interviews to examine how families draw upon their cultural wealth.

A Comprehensive Review Of Optimization Techniques For Healthcare Using Cancer Datasets, Dina Tbaishat, Mohammad Tubishat, Sharif Naser Makhadmeh, Mohammed Azmi Al-Betar

All Works

This study presents a systematic review of metaheuristic optimization techniques applied to healthcare problems using cancer datasets. A structured search of recently published peer-reviewed literature was carried out, focusing on five major application areas: feature selection, classification, image segmentation, hyperparameter tuning, and early detection. For each eligible study, the optimization strategy, dataset characteristics, data modality, learning model, validation protocol, and reported outcomes are provided. The reviewed works were organized into a taxonomy of original, modified, and hybridized algorithms, and a descriptive analysis was performed to assess algorithm prevalence and dataset utilization. The findings highlight that feature selection remains the most …

Edge Ai And Iot-Based Intelligent Smart Irrigation System For Sustainable Agriculture, Afra Alshamsi

Thesis/ Dissertation Defenses

Traditional agriculture faces several challenges, including excessive water consumption, inefficient irrigation practices, and the increasing demand for sustainable food production. The rapid advancement of the Internet of Things (IoT) and Artificial Intelligence (AI) provides opportunities to develop intelligent agricultural systems that improve resource management and support precision farming. This thesis presents an Edge AI and IoT-based smart irrigation system designed for real-time environmental monitoring, automated irrigation control, and intelligent irrigation prediction to support the optimal growth of arugula plants. The proposed system continuously monitors key cultivation parameters, including soil moisture, temperature, humidity, light intensity, pH, nutrient levels (NPK), and water …

Rehabvr: A Virtual Reality System For Upper-Body Orthopaedic Physical Therapy Rehabilitation, Winnie Brenda Wanjiru Waiya

Computer Science Senior Theses

Physical therapy is a central component of rehabilitation for musculoskeletal conditions, yet adherence to prescribed treatment remains persistently poor. Jack et al. identified pain, boredom, and insufficient feedback as key barriers to treatment adherence in physiotherapy outpatient settings,¹ and Rucinski et al. confirmed that non-adherence rates in orthopaedic populations remain between 50 and 70%, with patients who disengage facing elevated risk of reoperation, progressive functional decline, and poor clinical outcomes.² According to the World Health Organization, approximately 1.71 billion people globally live with musculoskeletal conditions,³ with shoulder pain specifically carrying a community prevalence ranging from 0.67 to 55.2% worldwide and …

A Machine Learning Framework For Contractor Scope Matching Using Profile Characteristics And Work Package Vector Similarity, Pedro Fonseca, Sergei Chuprov

Computer Science Faculty Publications

This paper presents a machine learning-based framework for predicting contractor performance in the construction industry by integrating contractor profile information with work package characteristics. The proposed approach addresses procurement challenges arising from the misalignment between contractor expertise and assigned scopes, a key contributor to cost overruns and schedule delays. Unlike existing models that rely on aggregated project-level indicators, the framework enables scope-aware performance prediction by leveraging the Work Breakdown Structure (WBS) to capture similarity among work packages. Contractor profiles are encoded using historical performance data, while WBS elements are vectorized to quantify scope similarity and contextualize predictions. Experimental results demonstrate …

Developing An Assessment Framework To Support Critical Evaluation Of Ai-Powered Academic Search Engines, Huajin Wang

FORCE 2026

As AI-powered tools are emerging in all aspects of research, it’s challenging for researchers, students, and librarians alike to assess the benefit, usefulness, and risks associated with a new AI-powered tool or AI-enhanced features of an existing one. To address this challenge, Carnegie Mellon University Libraries is developing a Tool Evaluation Framework to help users understand how AI-powered tools work, initially focused on AI-enhanced academic search engines. By evaluating criteria such as mechanisms for document retrieval, processes for content generation, and the quality of outputs, this framework guides users to carefully consider the strengths and weaknesses of a tool before …

Genai In Qualitative Data Analysis: Framework-Guided Prompt Engineering In Library Research Practice, Debby R. Wegener

FORCE 2026

As Generative AI (GenAI) tools become increasingly integrated across the research lifecycle, researchers need practical, reproducible methods for the responsible use of these technologies. This presentation will demonstrate a systematic approach to using GenAI for qualitative data analysis through a case study of thematic coding in a library website usability study at the Singapore Institute of Technology.

Drawing on prompt engineering frameworks like CLEAR, this session will illustrate how structured prompts can maintain academic rigour and enhance the reliability of GenAI-assisted analysis. The presentation will walk through the complete workflow, that is, from initial data preparation and tool selection to …

Biologically Informed Negative Samplingfor Antibody Chain Pairing Classification, Ishita Singh

Computer Science Senior Theses

Antibody heavy and light chain (H/L) pairing is fundamental to antigen recognition and stability. While single-cell sequencing preserves native pairing information, widely used bulk repertoire and spatial transcriptomics platforms do not, motivating the need for efficient ML methods to infer H/L pairing. Training a binary classifier for this task faces the methodological challenge of a lack of true biological negatives, since natural selection eliminates B cells with incompatible H/L pairs.

In this thesis, I introduce a biologically informed negative sampling strategy for H/L pairing classification, drawing on known V-gene biases in heavy and light chain pairing. Pseudo-negatives are constructed by …

Mimic: A Multimodal Dataset For Affective Incongruity In Video, Ethan M. Baird

Computer Science Senior Theses

This thesis investigates the computational challenges of constructing a database for affective incongruity, exploring the difficulties in automating the collection of contradictory affective states. Capturing these incongruities is essential for advancing Vision-Language Models (VLMs) and sentiment analysis, which struggle to interpret signals deviating from basic emotional archetypes. Curating non-congruent affect provides the data necessary for models to navigate complex social contexts, which is critical for applications like automated Audio Description (AD) for the visually impaired and nuanced Human-Computer Interaction (HCI). To capture these signals, two distinct methodologies were employed. The first utilized a tripartite decomposition of video data, isolating textual, …

Llzk-Symex: Building A Circuit Verification Framework For The Llzk Zero-Knowledge Intermediate Language, Nicolás Iair Schaievitch

Computer Science Senior Theses

The rising adoption of ZK (Zero-Knowledge) technology has led to a plethora of DSLs (Domain Specific Languages) for facilitating the development of ZK circuits. At the same time, there has also been an increase in the number of compilation targets for these languages, with different cryptographic foundations and performance optimizations. LLZK, by Veridise, aims to unify the ecosystem by providing a single IR (Intermediate Representation) for the different frontend DSLs, that would then also allow choosing between different backends. Given the high stakes (particularly financial) that a lot of ZK circuits are under, together with their high level of complexity, …

Capturing Large Language Model Similarity Through Spectral Analysis, Ishan Verma Prasad

Computer Science Senior Theses

With the rapid development of open-sourced models on Huggingface, there is a strong need for a way to systematically determine the similarity between models. More strongly, for intellectual property and organization, we need a way to determine the "lineage" of models. We borrow principles from Heavy-Tailed Self-Regularization and Random Matrix Theory to provide an inference-free method to accomplish this. We cluster a corpus of several model families by their spectral fingerprints and demonstrate that each model family occupies a distinct region in weight space. This confirms prior ideas of training setups leaving artifacts on model weights and allows us to …

Rscore - A Tool For Learning And Quantifying Software Infrastructure Resiliency, Selena Yujia Zhou

Computer Science Senior Theses

As LLMs take over writing code, infrastructure resilience has become the central challenge of software development, especially for gaming applications, where latency, availability, and scale demands are extreme. However, holistic software infrastructure is difficult to understand for casual developers because of the multiple layers of abstraction that make up an application, and the lack of structured information from proprietary companies. This thesis explores the creation of rscore, a tool that quantifies software infrastructure resiliency and subsequently educates developers about the architecture of any existing gaming application. The tool generates two graphical models of a game’s early versus current infrastructure and …

Holistic Strategies For Optimizing Municipal It Operations And Cost Efficiency, Joseph Yazdanpanahi

Certified Public Manager® Applied Research

Municipalities work to deliver secure and impactful IT (information technology) services while often working within tight budget constraints. To address this problem, this article offers an experience-based and comprehensive framework to optimize municipal IT operations. It emphasizes leveraging automation, streamlining hardware and software management, and forging strategic partnerships to drive cost savings and efficiency gains. Designed to equip municipal IT leaders with practical tools and insights, this article aims to help modernize IT processes.

Building And Restoring Trust In Deep Learning: From Multimodal Sensing To Generative Synthesis And Model Integrity, Liqun Shan

Doctoral Dissertations

Deep learning has become a foundational technology for modern intelligent systems used in sensing, authentication, media generation, and automated decision-making. As these systems are increasingly deployed in security- and privacy-sensitive settings, ensuring their trustworthiness has become a critical challenge. Yet deep learning models remain vulnerable to spoofed sensory inputs, synthetic media, and malicious behaviors hidden within trained networks. These vulnerabilities undermine reliability and raise serious concerns about whether such systems can be trusted under adversarial and deceptive scenarios. This dissertation investigates how to build and restore trust in deep learning across three tightly connected dimensions: multimodal sensing, generative authenticity, and …

Learning And Predicting The Performance Of Gradual Type System, Mohammad Wahiduzzaman Khan

Doctoral Dissertations

Gradual typing reconciles the complementary strengths of static and dynamic typing by allowing programmers to incrementally introduce type annotations while preserving the flexibility of dynamically typed code. This approach improves reliability, documentation, and tooling support without sacrificing rapid prototyping. To ensure soundness, gradual type systems enforce annotations through runtime checks, typically implemented via cast insertion. Although these checks guarantee correctness, they can introduce substantial and highly variable runtime overhead, making performance prediction and optimization a central challenge for practical adoption. Prior work has largely focused on coarse-grained (macro-level) configurations, where entire modules are either fully typed or untyped. While such …

Scientific Crosstalk: Natural Language Processing, Praveshika Bhandari

Theses and Dissertations

While sentiment analysis has made significant strides in domains such as social media and personal correspondence, its application to formal scientific writings remains under-explored. The crosstalk between emotional expressions in personal and professional communications has also received limited attention despite its potential to reveal insights into the emotional drivers of scientific creativity. Our research introduces a computational framework designed to detect and quantify emotional expressions across various documents over time. Leveraging state-of-the-art transformer models fine-tuned on domain-specific corpora, the framework models emotional tone distribution. Integrating emotion analysis with knowledge graph modeling enables the exploration of emotional trends alongside key scientific …

Olaf: Towards Robust Llm-Based Annotation Framework In Empirical Software Engineering, Mia Mohammad Imran, Tarannum Shaila Zaman

Computer Science Faculty Research & Creative Works

Large Language Models (LLMs) are increasingly used in empirical software engineering (ESE) to automate or assist annotation tasks such as labeling commits, issues, and qualitative artifacts. Yet the reliability and reproducibility of such annotations remain underexplored. Existing studies often lack standardized measures for reliability, calibration, and drift, and frequently omit essential configuration details. We argue that LLM-based annotation should be treated as a measurement process rather than a purely automated activity. In this position paper, we outline the Operationalization for LLM-based Annotation Framework (OLAF), a conceptual framework that organizes key constructs: reliability, calibration, drift, consensus, aggregation, and transparency. The paper …