Digital Commons Network^™

Transparency And Authority Concerns With Using Ai To Make Ethical Recommendations In Clinical Settings, Jeffrey Byrnes, Michael Robinson

Philosophy Faculty Articles and Research

In response to recent proposals to utilize artificial intelligence (AI) to automate ethics consultations in healthcare, we raise two main problems for the prospect of having healthcare professionals rely on AI-driven programs to provide ethical guidance in clinical matters. The first cause for concern is that, because these programs would effectively function like black boxes, this approach seems to preclude the kind of transparency that would allow clinical staff to explain and justify treatment decisions to patients, fellow caregivers, and those tasked with providing oversight. The other main problem is that the kind of authority that would need to be …

Ethical Aspects Of Utilising Artificial Intelligence In Clinical Settings, Jeffrey Byrnes, Michael Robinson

Philosophy Faculty Articles and Research

In response to recent proposals to utilize artificial intelligence (AI) to automate ethics consultations in healthcare, we raise two main problems for the prospect of having healthcare professionals rely on AI-driven programs to provide ethical guidance in clinical matters. The first cause for concern is that, because these programs would effectively function like black boxes, this approach seems to preclude the kind of transparency that would allow clinical staff to explain and justify treatment decisions to patients, fellow caregivers, and those tasked with providing oversight. The other main problem is that the kind of authority that would need to be …

Measuring And Improving Api Usability And Quality: A Comprehensive Framework And Empirical Study, Sultan Alanazy

Computer Science and Engineering Theses and Dissertations

Cloud computing provides on-demand access to flexible computing resources, enabling rapid application deployment without substantial infrastructure investment. Application Programming Interfaces (APIs) play an important role in ensuring the success of cloud applications. The primary users of APIs are the extensive community of application programmers who search, read, and understand APIs before integrating them into their applications or systems. In addition, developers often turn to online API support when seeking help. Problems in such support can result in incorrect API usage and integration problems. There is an urgent need to measure API usability and support issues to identify, characterize, and assess …

Optimization Of The Starch Chitosan-Based Flocculant Crosslinked By Ethylene Glycol Dimethacrylate On Removing Dypro 19 Textile Dye From Wastewater, Asep Nurohmat Majalis, Putri Ramadhani, Hendris Hendarsyah Kurniawan, Axel Dimaz Sanusi Pasaribu, Hafiizh Prasetia, Fitri Yuliani, Andreas Andreas, Hartati Hartati

Karbala International Journal of Modern Science

Dyes used in industry, especially textile dyes, are one of the water pollutants that receive much attention because they are potentially toxic, carcinogenic, mutagenic, and generally challenging to decompose naturally. Textile dyes from wastewater can be removed through coagulation-flocculation. However, conventional coagulation-flocculation based on Fe and Al salts and synthetic polymers often leaves residual pollution. In this research, the performance of the new biopolymer-based flocculant, namely starch-ethylene glycol dimetacrylate-chitosan (SEC), which can act as coagulants and flocculants in solid-liquid separation of textile dyes, has been optimized using response surface methodology (RSM) approach. The influences of several independent variables, such …

Noise-Enhanced Network Science, Reyhaneh Abdolazimi

Dissertations - ALL

Graphs are a versatile and powerful data structure used to model complex relationships in diverse domains such as social networks, biological systems, and transportation networks. In graphs, entities are represented by nodes, and interactions or relationships between them are represented by edges. For example, in the World Wide Web, web pages are considered vertices, and if there is a link from one page to another page on the web, there will be a directed edge between those pages in its graph data structure. These structures allow us to analyze and solve important problems like community detection (identifying closely related clusters …

Development Of A Climbing Performance Analysis Tool Using Computer Vision, Guy Ludford

The Plymouth Student Scientist

Indoor bouldering, a rapidly growing sport in the UK and globally, has seen a significant rise in participation, paralleled by an increase in the use of fitness apps and wearable trackers. Despite this growth, tracking and recording metrics for indoor climbing remains a challenge. This paper proposes an automated tool utilising computer vision and a gym-wide camera system to collect data on routes climbed and attempts made, without the need for manual logging. This tool aims to provide climbers with detailed performance analysis and support climbing gyms in optimising route setting and member engagement. Initial market research and discussions with …

Privacy Protection In Machine Learning Via Exploitation Of Constrained Adversarial Evasion, Brian Testa

Dissertations - ALL

Deep neural networks are extensively applied to real-world tasks. In many cases, the data feeding these tasks are human generated content, which emphasizes the criticality of privacy and data protection. The diverse modalities of this user data provide fertile ground for 3rd parties to monetize a user’s data. This work considers two such modalities: user speech when interacting with smart speaker voice assistants (VAs) and images shared with online service providers. In these cases, the user would like to support some form of machine learning (ML) inference without allowing others. A user interacting with a smart speaker would like the …

Harmonic Impedance Modeling And Oscillation Analysis Of Modular Multilevel Converter, Yuhong Wang, Wensheng Chen, Shilin Gao, Jianquan Liao, Yangfan Cheng

Journal of System Simulation

Abstract: To facilitate rapid analysis of the oscillation stability mechanism in modular multilevel converter-based high voltage direct current (MMC-HVDC) systems and streamline the simulation process for determining MMC impedance characteristics, a simplified mathematical simulation model for MMC closed-loop impedance is developed using the harmonic state space method. This model considers various control strategies and includes both AC-side and DC-side impedance models. By applying a Nyquist criterion-based impedance analysis method, the stability mechanisms on the AC and DC sides of the MMC are examined. In addition, a data-driven oscillation stability analysis method is also proposed, leveraging a global sensitivity algorithm based …

Multimodal Representation Learning Frameworks For Modeling Progression And Heterogeneity In Alzheimer’S Disease, Sayantan Kumar

McKelvey School of Engineering Graduate Student Theses & Dissertations

Alzheimer’s Disease (AD) is the leading cause of dementia, characterised by cognitive and functional impairments that disrupt daily activities. Different clinical modalities such as neuroimaging biomarkers, cognitive assessments, fluid biomarkers and genetic data provide unique and complementary information, contributing to a more comprehensive understanding of disease progression and heterogeneity in disease characteristics. With recent advancements in computational capabilities, particularly in deep learning, multimodal representation learning frameworks aim to integrate diverse clinical modalities into a cohesive framework, capturing the most significant patterns within each modality. Existing data-driven multimodal representation learning frameworks in AD research have two major limitations. First, AD progresses …

Reimagining The Machine Vision Pipeline In Cyber Physical Systems For Trustworthiness And Efficiency, Adith Boloor

McKelvey School of Engineering Graduate Student Theses & Dissertations

Cyber-physical systems (CPS), including autonomous vehicles, drones, and mobile robots, rely on intricate sensors, actuators, and machine learning algorithms to perceive the physical world and execute actions within their surroundings. In the context of vision-driven CPS, achieving this demands processing a substantial volume of visual data captured by on-board cameras. The data is subsequently channeled through digital processors and harnessed by deep neural networks for tasks such as image classification, object detection, and depth perception. This data-centric, machine-vision-infused CPS fosters intelligent decision-making, thereby enhancing overall system performance. Trustworthiness, encompassing the robustness of the entire machine-vision pipeline, and system-level efficiency are …

Multimodal Representation Learning Frameworks For Modeling Progression And Heterogeneity In Alzheimer’S Disease, Sayantan Kumar

McKelvey School of Engineering Graduate Student Theses & Dissertations

Alzheimer’s Disease (AD) is the leading cause of dementia, characterised by cognitive and functional impairments that disrupt daily activities. Different clinical modalities such as neuroimaging biomarkers, cognitive assessments, fluid biomarkers and genetic data provide unique and complementary information, contributing to a more comprehensive understanding of disease progression and heterogeneity in disease characteristics. With recent advancements in computational capabilities, particularly in deep learning, multimodal representation learning frameworks aim to integrate diverse clinical modalities into a cohesive framework, capturing the most significant patterns within each modality. Existing data-driven multimodal representation learning frameworks in AD research have two major limitations. First, AD progresses …

Reimagining The Machine Vision Pipeline In Cyber Physical Systems For Trustworthiness And Efficiency, Adith Boloor

McKelvey School of Engineering Graduate Student Theses & Dissertations

Cyber-physical systems (CPS), including autonomous vehicles, drones, and mobile robots, rely on intricate sensors, actuators, and machine learning algorithms to perceive the physical world and execute actions within their surroundings. In the context of vision-driven CPS, achieving this demands processing a substantial volume of visual data captured by on-board cameras. The data is subsequently channeled through digital processors and harnessed by deep neural networks for tasks such as image classification, object detection, and depth perception. This data-centric, machine-vision-infused CPS fosters intelligent decision-making, thereby enhancing overall system performance. Trustworthiness, encompassing the robustness of the entire machine-vision pipeline, and system-level efficiency are …

Gpr-185 A Multimodal Approach To Quiz Generation: Leveraging Rag Models For Educational Assessments, Mourya Teja Kunuku

C-Day Computing Showcase

Crafting quiz questions that effectively assess students’ understanding of lectures and course materials, such as textbooks, poses significant challenges. Recent AI-based quiz generation efforts have predominantly concentrated on static resources, like textbooks and slides, often overlooking the dynamic and interactive elements of live lectures—contextual cues, discussions, and interactions—that contribute to the learning experience. In this work, we propose a Retrieval-Augmented Generation (RAG) model that processes multimodal inputs by combining text, audio, and video to produce quizzes that capture a fuller context. Our method incorporates Whisper for audio transcription and utilizes a Large Vision-Language Model (LVLM) to extract essential visual data …

Tubarr: A Self-Hosted Video Archiver, Joshua Sheputa

Honors Program Theses and Projects

Over the past few years, I’ve been slowly expanding my homelab, a personal setup for me to experiment with software, hardware, and deployment configurations. One driving force behind this is my desire to digitize and save anything that is important to me or that I want easy access to. I started with my family’s old CD collection, then moved on to any Blu-rays movie discs we had. Eventually, I started thinking about what else I care about and don’t want to lose track of. This brought me to the thought that any YouTube video that I may want to revisit …

Schedulability Analysis Of Multi-Phase Limited-Preemption Tasks, Benjamin Standaert

McKelvey School of Engineering Graduate Student Theses & Dissertations

This work addresses hard real-time systems, in which tasks must be scheduled so that they are guaranteed to meet deadlines. In particular, when tasks execute across multiple domains with high preemption costs, the combined cost of these preemptions can cause the system to become unschedulable. The number of preemptions must therefore be bounded to limit the overall task execution time, while ensuring that task blocking times are small enough to allow the system to be schedulable. Prior work introduces the Multi-Phase Secure model, which describes a more exact version of this scenario, and an algorithm to determine schedulability of sporadic …

Safety And Optimality Monitors For Learning-Enabled Systems Using Conformal Prediction, Jackson Cox

McKelvey School of Engineering Graduate Student Theses & Dissertations

The use of machine learning to create data-driven plant models and controllers has led to an increased need for safety and optimality monitors for model-based systems. System plant models are subject to uncertainty due to learning constraints such as unseen data and overfitting or physical constraints such as unknown dynamics and noise. This uncertainty is detrimental to safety-critical systems and must be properly regulated. To curb this uncertainty, we create prediction sets using the guarantees provided by Conformal Prediction. With a user-specified high probability, these prediction sets contain the true plant system states for an entire prediction horizon, which we …

The Chinese Room And Creating Consciousness: How Recent Strides In Ai Technology Revitalize A Classic Debate, Thomas Held

Departmental Honors & Graduate Capstone Projects

Since 1950, when Alan Turing first posed the question of whether a machine could think, the possibility of artificial consciousness has sparked intense and ongoing debate, and strong positions have been staked out on each side of the argument. On the one hand, the historically popular functionalist school of thought claims that any system capable of producing suitably “conscious” behavior in a given environment should be considered conscious. On the other hand, John Searle’s famous “Chinese Room” argument insists that this cannot be the case, and that consciousness is in all likelihood not artificially reproducible. However, both positions have issues—the …

Artificial Intelligence-Based Methodologies For Early Diagnostic Precision And Personalized Therapeutic Strategies In Neuro-Ophthalmic And Neurodegenerative Pathologies, Rahul Kumar, Ethan Waisberg, Joshua Ong, Phani Paladugu, Dylan Amiri, Jeremy Saintyl, Jahnavi Yelamanchi, Robert Nahouraii, Ram Jagadeesan, Alireza Tavakkoli

SKMC Student Presentations and Publications

Advancements in neuroimaging, particularly diffusion magnetic resonance imaging (MRI) techniques and molecular imaging with positron emission tomography (PET), have significantly enhanced the early detection of biomarkers in neurodegenerative and neuro-ophthalmic disorders. These include Alzheimer's disease, Parkinson's disease, multiple sclerosis, neuromyelitis optica, and myelin oligodendrocyte glycoprotein antibody disease. This review highlights the transformative role of advanced diffusion MRI techniques-Neurite Orientation Dispersion and Density Imaging and Diffusion Kurtosis Imaging-in identifying subtle microstructural changes in the brain and visual pathways that precede clinical symptoms. When integrated with artificial intelligence (AI) algorithms, these techniques achieve unprecedented diagnostic precision, facilitating early detection of neurodegeneration and …

Improving Clinical Information Extraction From Electronic Health Records: Leveraging Large Language Models And Evaluating Their Outputs, Kriti Bhattarai

McKelvey School of Engineering Graduate Student Theses & Dissertations

Accurate extraction of clinical entities and phenotypes from unstructured electronic health record (EHR) text is crucial for various clinical research tasks, including cohort identification, tracking temporal patterns in disease progression and deciding treatment course. However, this task remains challenging due to the complexity and ambiguity of medical language. This dissertation explores the application of advanced generative pre-trained transformer (GPT) models, such as GPT-4, GPT-3.5-turbo, Llama-3.1, Llama-3 and Flan-T5, for clinical entity and phenotype extraction from EHRs. Building upon these findings, this dissertation also investigates a hybrid approach where integration of external knowledge sources, such as Unified Medical Language System (UMLS) …

Predictive Maintenance Analysis Of Turbofan Engine Sensor Data, Stanley A. Melkumian

The Journal of Purdue Undergraduate Research

Predictive maintenance in aviation and aerospace applications is among the most explored problems in machine learning (ML) and artificial intelligence (AI), and datasets such as NASA’s C-MAPPS turbofan engine degradation simulation data have proven invaluable, helping researchers explore numerous questions on engine performance, maintenance, and failure. The purpose of this study was to extend the current research on predicting the remaining useful life (RUL) of engines and their risk classification. Starting with simple yet under-investigated nonlinear survival and random forest models, the analysis implemented eXtreme Gradient Boosting (XGBoost) and long short-term memory (LSTM) from TensorFlow’s Keras library. For both regression …

Efficient Visual Data Processing Approaches To Improve Resource Utilization, Yeganeh Jalalpour

Dissertations and Theses

With continuous advancements in technology, the volume of visual data being captured, distributed, and consumed across various applications is rapidly increasing. Enhancing the efficiency of visual data processing can improve resource consumption, making the storage, transmission, and use of this growing visual content more effective.

Image and video data constitute a significant share of internet traffic, making video compression a critical factor in enhancing overall data throughput by improving the video compression ratio. Capturing, transferring, and storing raw video data is challenging due to the substantial resources required for both storage and computation. Video compression, however, can significantly mitigate these …

An Examination Of Academic Library Platforms And Systems During Covid-19, Cole Hudson, Paul Gallagher

University Libraries Faculty Publications and Presentations

This paper examines the use and subsequent trajectory of academic library technologies due to the impact of the COVID-19 pandemic. Taking a broad view of technologies, the systems and services discussed will center around resource use because COVID restrictions shuttered many in-person technologies. The two academic libraries compared in this study show a similar pattern of use and signal growth of certain platforms and technologies for the future.

Calculation And Statistical Analysis Of Wins Above Replacement, Joshua Taylor

Departmental Honors & Graduate Capstone Projects

The Wins Above Replacement (WAR) statistic in Major League Baseball is a prominent metric used to estimate player value by quantifying all aspects of play in terms of wins added to a baseball team. We will use R to calculate WAR for all players from 1871 to 2012 and use data from those years to construct multivariate predictive models to attempt to estimate WAR for players from 2013 to 2024. We find strong correlations between predicted and actual WAR values for most models, with the exception of the polynomial predictive model for non-qualified pitchers.

Advanced Models For Linking Process In Data Washing Machine, Bushra Sajid

Theses and Dissertations

Entity Resolution (ER) is a critical process in data integration and quality improvement that identifies and links multiple records referring to the same real-world entity. As data volumes and heterogeneity increase, traditional ER methods face new challenges, prompting research into more advanced techniques. The Proof-of-Concept Data Washing Machine (DWM), developed under the NSF DART Data Life Cycle and Curation research theme, aims to automatically detect and correct data quality errors through unsupervised entity resolution. Recent research focuses on enhancing DWM's effectiveness by replacing rule-based methods with machine learning and deep learning approaches, particularly in the linking process. Deep learning models, …

The Implementation Of Artificial Intelligence In University Classrooms: Perspective And Applications, Erika Grodzki, Gary Carlin, Stefanie Powers, Hung Chum Kao

Faculty and Staff Publications & Presentations

This study examined the integration of Artificial Intelligence (AI) in university classrooms, focusing on its benefits, challenges, and the diverse perspectives of academic faculty. While AI was widely embraced in disciplines like animation and design for enhancing creativity and efficiency, traditional fields remained cautious due to concerns about academic integrity and its impact on critical thinking. By analyzing literature and case studies, the presentation highlighted AI’s transformative potential in higher education, fostering dialogue on its strategic adoption to balance innovation with ethical and pedagogical considerations.

Unmanned Aerial Systems (Uas) Image Preprocessing To Reduce Artifacts And Improve Geometric Registration When Generating Orthophoto Mosaics And 3d Models, Eddie Ironsmith

Electronic Theses and Dissertations

Drones can now be used to quickly collect imagery data in a highly automated way; however, individual images must be combined to form an orthomosaic or 3-Dimentional (3D) model using photogrammetry software. Currently, the existing software may generate erroneous output in the form of artifacts or positional errors caused by homogeneous areas, light reflections, object movement between photos, or sub-optimal algorithms. The goal of this research was to develop preprocessing algorithms that would filter movement (or other time or position-based differences) and areas of homogeneity. The hypothesis is that filtering these parts of the image would reduce artifacts and improve …

A Comprehensive Performance Evaluation Of Proprietary And Open-Source Language Models In Closed And Open-Domain Tasks, Abhilash Kanduri

Theses and Dissertations

As the field of Natural Language Processing (NLP) continues to evolve, evaluating the performance of both proprietary and open-source language models has become increasingly critical. This research provides a comprehensive analysis of proprietary models like GPT-3.5 Turbo, GPT-4, and GPT-4 Turbo, alongside open-source models such as FLAN-T5, GPT-Neo, and GPT-2. By leveraging traditional metrics like ROUGE and BLEU, as well as custom metrics including ReGrAde, Contextual Precision, and Faithfulness, the study evaluates these models across closed-domain tasks (e.g., factual question-answering) and open-domain tasks (e.g., creative writing and brainstorming). The proprietary models excelled in structured, fact-based tasks, while the open-source models …

Analysis Of Cortical Evoked Auditory Response Detection In Adults Using Machine Learning, Pranavi Beerelli

Theses and Dissertations

This study focuses on the use of machine learning (ML) techniques to automate the detection of Cortical Evoked Auditory Responses (CEARs), which are key in understanding how the auditory cortex processes sound stimuli. Traditionally, analyzing these auditory responses has relied on manual interpretation by audiologists, a process that can introduce variability and human error, particularly in complex cases. To address this challenge, the research utilizes advanced deep learning models, including Convolutional Neural Networks (CNNs), Long Short Term Memory (LSTM) networks, and Bidirectional LSTM (BiLSTM) architectures, to analyze Electroencephalography (EEG) data and classify the presence or absence of auditory responses automatically. …

Deep Learning - Based Automated Detection And Classification Of Foreign Materials In Poultry Using Color And Hyperspectral Imaging, Rohini Maram

Theses and Dissertations

This thesis explores the use of Deep learning for detection and classification of small foreign materials (FMs) in poultry meat using color and hyperspectral imagery (HSI). The study employs You only look once (YOLO) object detection models on color images for precise localization, and one-dimensional convolutional neural network (1D CNN), two-dimensional convolutional neural network (2D CNN) was used on HSI (600 – 1700 nm) for classification. Twelve different FMs commonly known as polymers including PVC, PET, LDPE and HDPE, were examined using 52 color and 52 hyperspectral images. Four YOLO models (v5x, v7x, v8x, v10x) were implemented, trained, tested and …

Improved Vector Pruning For Partially Observable Markov Decision Processes, Thomas Jonathan Bowman

Theses and Dissertations

Exact dynamic programming algorithms for planning problems that are represented as partially observable Markov decision processes rely on a subroutine that removes, or ``prunes", dominated vectors from sets of vectors that represent piecewise-linear and convex value functions. The classic vector pruning subroutine solves one linear program per vector, where the number of variables is equal to the size of the state space and the number of constraints is equal to the number of vectors shown so far to be undominated. Thus, its scalability is limited not only by the number of linear programs it solves, but especially by their size. …