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Systematic Evaluation Of Regression Models For The Prediction Of Influenza Cases, Mukul Kandwal
Systematic Evaluation Of Regression Models For The Prediction Of Influenza Cases, Mukul Kandwal
Theses
This study systematically evaluates machine learning models to forecast influenza outbreaks, aiming to enhance public health preparedness through accurate predictions. Laboratory-confirmed influenza case data from the New York State Department of Health (2009–2023) were used to train and test five regression models—XGBoost, Neural Network (MLP regressor), K-Nearest Neighbors (KNN), Random Forest, and Support Vector Regression (SVR)—with preprocessing techniques including differencing and phasing. A leave-one-season-out cross-validation framework and Symmetric Mean Absolute Percentage Error (SMAPE) were employed to rigorously assess performance. Key findings reveal that the Neural Network (MLP regressor) outperformed other models, achieving the lowest SMAPE score (23.7), underscoring the critical …
Design And Application Of Molecularly Imprinted Polymers For The Detection Of Bpa, Pfoa, And Caffeine, Finbarr Buckley
Design And Application Of Molecularly Imprinted Polymers For The Detection Of Bpa, Pfoa, And Caffeine, Finbarr Buckley
Theses
Molecular Imprinted Polymers (MIPs) are polymer based solid phase extraction (SPE)recognition systems. MIPs were formulated using Acrylic Acid (AA) and Ethylene glycoldimethacrylate (EGDMA) to selectively target the following compounds Bisphenol A (BPA),Perfluorooctanoic acid (PFOA) and caffeine. This study presents novel MIPs for each targetqualitatively analysed with Raman Spectroscopy with Surface Enhanced Raman Spectroscopy(SERS) substrates. SERS can greatly enhance Raman signals allowing for detection of lowconcentration solutions. BPA, a possible carcinogen and hormone disruptor, is a very commonreagent in manufacturing as is known to leech into environmental and consumer products.PFOA, a constituent of polyfluoroalkyl substances (PFAS), can cause kidney and thyroiddisease, …
The Influence Of The Emerging Italian Instrumental Style Of The Early Seventeenth Century On Adam Jarzębski’S Canzoni E Concerti (1627), Ellen Bolger
Theses
Adam Jarzębski’s Canzoni e Concerti (1627) is an important source of instrumental chamber music from early seventeenth-century Poland. Jarzębski was a member of the royal musical establishment at the court of Sigismund III Vasa, where he worked alongside Italian musicians recruited by the court. The influence of the emerging Italian stile moderno can be seen in the 27 works of Canzoni e Concerti, which mirror the style and structure of instrumental works by Italian composers such as Dario Castello and Biagio Marini. The changing ideas on music theory and musical expression which originated in Italy are reflected in Jarzębski’s use …
Future Scenarios Of Adoption Sustainable Materials By Construction Firms In The United Arab Emirates, Mona Albeshr
Future Scenarios Of Adoption Sustainable Materials By Construction Firms In The United Arab Emirates, Mona Albeshr
Theses
The construction industry accounts for 39% of greenhouse gas emissions, and change is required, especially with the Net Zero initiative. UAE construction companies must evaluate their current positions and update their strategies to support government regulations. Building futuristic communities is crucial for expanding the built environment within the country; however, the types of materials used should account for two aspects: sustainability and climate resilience, and both must be considered whenever these construction companies formulate their strategic response. Hence, scenario planning significantly provides valuable insights to decision-makers in construction companies’ strategic departments to envision four plausible scenarios of the external environment …
Dubai’S Future Mobility: Systems Thinking & Scenarios, Mohammed Mazloum Hanafy
Dubai’S Future Mobility: Systems Thinking & Scenarios, Mohammed Mazloum Hanafy
Theses
Transportation is one of the essential pillars of any smart city. The future of transportation in Dubai faces enormous hurdles in urban mobility, including a growing population density, congestion, and environmental issues. Without careful planning and policy actions, these problems would certainly worsen in the future. Thus, the purpose of this thesis is to investigate the future of urban mobility in Dubai and to suggest solutions for constructing a sustainable, efficient, and equitable transportation system by employing future foresight tools. Dubai has emerged in recent years as a global center for technological innovation, altering its built environment and reevaluating its …
Towards Characterizing And Quantifying Interpretability Of Knowledge Graph Embedding Models, Narayanan Asuri Krishnan
Towards Characterizing And Quantifying Interpretability Of Knowledge Graph Embedding Models, Narayanan Asuri Krishnan
Theses
Knowledge graphs are structured representations of real-world information, where entities are connected by edges with labels known as predicates. These graphs contain logical patterns, known as inference patterns, such as symmetry, transitivity and composition, to name a few. To leverage the information contained within a knowledge graph, embedding methods have been developed to encode entities and predicates as numerical vectors in a multi-dimensional space. These embeddings aim to capture the semantic and structural information of the graph, including the inference patterns. However, the sub-symbolic nature of these embeddings present significant challenges in terms of interpretability. Additionally, each embedding model utilizes …
Understanding Vaccines: A 3d Animation And Print Materials For Clinical Trial Patients, Shannon Gregory
Understanding Vaccines: A 3d Animation And Print Materials For Clinical Trial Patients, Shannon Gregory
Theses
This project covers the importance of patient education, and the decisions that go into making patient facing materials. The topic of this piece is “Understanding Vaccines” and will explore several key aspects: what vaccines are, how they work, their important role in public health, and how to best communicate with patient audiences. Given the growing challenges in vaccine education, this piece will address common misconceptions and emphasize the importance of clear, accessible communication. The goal of this piece is to educate, but also to encourage individuals to make well-informed decisions regarding vaccination. The artwork created for this thesis was developed …
Fake News Detection On X (Twitter), Sultan Mayoof Almutaiwei
Fake News Detection On X (Twitter), Sultan Mayoof Almutaiwei
Theses
The most important innovation globally is the internet, which is used by a huge number of people all around the world. People use the internet for different purposes. By using different social media platforms people share content and news. Thus, some people use these internet platforms to share false news or fake news among people. Propaganda against a person, group, organization, or political party may be present in this news. For an individual, it is difficult to distinguish between false and true news over the internet. Therefore, machine learning is the smart solution that can identify fake news on the …
Perceived Stress, Psychological Wellbeing, And Sleep Quality Of Passport Control Officers, Roudha Abdulla Almazrouei
Perceived Stress, Psychological Wellbeing, And Sleep Quality Of Passport Control Officers, Roudha Abdulla Almazrouei
Theses
Research on the stress experienced by officers has identified several key findings that illustrate the diverse nature of their workplace stressors. Research specifically addressing the stressors encountered by passport control officers is limited when compared to studies on other law enforcement roles. This study aims to assess stress levels among passport control officers, identify psychological health impacts, determine the intersection of personal and occupational stress, and investigate the effects of shift work on stress intensity and related psychological issues. A mixed methodology incorporating qualitative and quantitative was used. The sample included 166 participants with a job title of passport control …
Space-Time Analysis Of The Sea Level Variability Of The Arabian Sea Using Satellite Altimetry Data, Saleha Saeed Binishaq
Space-Time Analysis Of The Sea Level Variability Of The Arabian Sea Using Satellite Altimetry Data, Saleha Saeed Binishaq
Theses
The ongoing effects of climate change are contributing to a gradual rise in sea levels, which has become a significant environmental concern. It creates a serious threat to coastal areas, especially in the Gulf Region, which includes various cities bordering the Arabian Gulf, the Red and the Arabian Sea. This phenomenon has the potential to significantly influence the socioeconomics of the region by altering coastal marine ecosystems and can pose serious infrastructure sustainability challenges. This research focuses on analyzing the sea-level variability in the Arabian Sea using satellite altimetry data from 1993 to 2023. The analysis was conducted along a …
The Role Of Ai In Digital Propaganda And Its Influence On Public Political Perception. A Case Study Of The Uae Citizen, Maitha Salem Mohamed Alshamsi
The Role Of Ai In Digital Propaganda And Its Influence On Public Political Perception. A Case Study Of The Uae Citizen, Maitha Salem Mohamed Alshamsi
Theses
The study investigates the role of artificial intelligence (AI) in digital propaganda and its influence on public political perception, using the United Arab Emirates as a case study. It aims to understand how AI-generated content shapes political opinions by analyzing the relationships between digital propaganda exposure, AI content interaction, social media engagement and media literacy. In addition, the study uses the Framing Theory (1974) by Erving Goffman. It posits that individuals interpret information and events through cognitive frameworks or "frames" that organize and influence their understanding. It implies that AI amplifies specific frames by customizing content and targeting audiences, which …
User Perceptions Of Ai Services In Abu Dhabi Government: An Empirical Study Of Satisfaction And Acceptance Factors On The Tamm Platform, Muneera Ali Zaher Alsulaimani
User Perceptions Of Ai Services In Abu Dhabi Government: An Empirical Study Of Satisfaction And Acceptance Factors On The Tamm Platform, Muneera Ali Zaher Alsulaimani
Theses
Artificial Intelligence (AI) is transforming how governments deliver services by enabling automation, personalization, and data-driven decisionmaking. In the United Arab Emirates, particularly in Abu Dhabi Emirate, the TAMM platform exemplifies this transformation, integrating AI to streamline citizen-government interaction. Understanding the factors that influence the user’s satisfaction of Powered AI services in the public sector is crucial for effective policy implementation and user engagement. This study employs a sequential explanatory mixed-methods approach, grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT), extended with User Satisfaction as a fifth construct. Quantitative data were collected from 137 TAMM platform users …
The Role And The Limitation Of Ai Implementations In Pcb Defect Detection, Hamad Saeed Mohammed Alyammahi
The Role And The Limitation Of Ai Implementations In Pcb Defect Detection, Hamad Saeed Mohammed Alyammahi
Theses
This research explores the transformative impact of Artificial Intelligence (AI) on quality control in Printed Circuit Board (PCB) fabrication, explicitly focusing on enhancing defect detection processes. As PCB designs become increasingly complex and dense, traditional inspection methods, such as manual inspection and Automated Optical Inspection (AOI), need to be revised to meet the precision and efficiency demands of modern electronics manufacturing. Manual inspection is often slow and labor-intensive, while AOI needs help with accuracy in detecting subtle defects in intricate designs, leading to potential errors in quality assurance. This study investigates AI-driven techniques to overcome these challenges, leveraging deep learning …
A Comparative Analysis Of Machine Learning Models For Predictive Process Monitoring Using Objectcentric Event Logs, Buthaina Hasan Aidaros Alhebshi
A Comparative Analysis Of Machine Learning Models For Predictive Process Monitoring Using Objectcentric Event Logs, Buthaina Hasan Aidaros Alhebshi
Theses
The increasing complexity of business processes drives the need for advanced predictive monitoring solutions capable of handling multi-entity interactions. Traditional case-centric event logs fall short of capturing these complexities, leading to the emergence of Object-Centric Event Logs (OCEL). Despite the growing interest in OCELs, their structural complexity and impact on predictive performance remain unexplored. This thesis investigates how process complexity influences the performance of machine learning models in predictive process monitoring using OCEL. A framework combining Graph Neural Networks and graph embedding-based models is applied across three predictive tasks: next activity, next timestamp, and remaining time prediction. The evaluation covers …
An Inclusive Learning Metaverse: A Virtual Reality Approach To Supporting Dyslexic Children, Reem Salem Mohammed Salem
An Inclusive Learning Metaverse: A Virtual Reality Approach To Supporting Dyslexic Children, Reem Salem Mohammed Salem
Theses
Dyslexia is one of the most common learning difficulties worldwide, often requiring tailored interventions targeting phonological awareness, reading comprehension, and cognitive support. This study explores the design and evaluation of a metaverse-based virtual reality (VR) application to support dyslexic children in the UAE. Using a usercentered, explanatory sequential mixed-methods approach, the research involved three phases: a stakeholder survey with therapists, educators, and parents (N = 151), development of a multi-featured VR prototype, and evaluation by six dyslexia experts. The survey identified key priorities such as progress tracking, adaptive learning, accessibility, working memory, and attention, along with concerns about VR unfamiliarity, …
Attention Deficit Hyperactivity Disorder And Metacognitive Awareness Of Learning, Kara R. Muscha
Attention Deficit Hyperactivity Disorder And Metacognitive Awareness Of Learning, Kara R. Muscha
Theses
The present study examined knowledge updating abilities in individuals with and without ADHD when forced to metacognitively evaluate the effectiveness of different study strategies. Participants with and without ADHD were randomly assigned to either a spontaneous or a forced knowledge updating condition, with those in the latter condition being asked to keep track of encoding strategy-recall outcome results, but no additional instructions for those in the spontaneous condition. I expected that healthy control participants would show greater knowledge updating abilities than those with ADHD, the participants in the forced knowledge updating condition would show greater knowledge updating abilities than participants …
Evaluating Bias In Facial Recognition Datasets : A Study On Representation And Classification Fairness, Milyonta Williams
Evaluating Bias In Facial Recognition Datasets : A Study On Representation And Classification Fairness, Milyonta Williams
Theses
Facial recognition technology is widely used in security and identity verification but exhibits biases that disproportionately impact certain demographic groups. This thesis examines racial and skin tone biases in commonly used facial recognition datasets, assessing their influence on classification models. Using manual classification via the Fitzpatrick scale and numerical skin tone representations from LAB color values, we identified significant dataset imbalances, with lighter skin tones (Fitzpatrick Types 1 and 2) overrepresented and darker tones (Types 4-6) underrepresented. Model evaluation using Random Forest, SVM, and XGBoost showed an overall accuracy of 72\%, but classification for Asian individuals was notably weaker (0.45 …
Modifying The L-Tryptophan Affinity Of The Ribosomal Arrest Peptide Tnac, Alexis Orlando Delgado Castillo
Modifying The L-Tryptophan Affinity Of The Ribosomal Arrest Peptide Tnac, Alexis Orlando Delgado Castillo
Theses
Since creating biosensors from zero has proven a more challenging enterprise than expected, resorting into naturally occurring regulatory systems already characterized represents a more accurate, rapid, and attractive option. Various previous strategies have sought to exploit and modify regulatory elements that sense for different molecules, including monosaccharides (Tang et al., 2008), secondary metabolites (Flachbart et al., 2021), and other amino acids (Della Corte et al., 2020). This study aims to leverage current scientific knowledge and advances concerning the structure and function of the ribosomal arrest peptide TnaC. With this research I integrated the TnaC ribosome-arresting peptide into a new biosensor …
Aviation Weather Forecasting Utilizing An Artificial Neural Network, Joshua Mote
Aviation Weather Forecasting Utilizing An Artificial Neural Network, Joshua Mote
Theses
Weather forecasting is critical to minimize risk and maximize efficiency for flying operations and is challenging due to the uncertainty involved in atmospheric changes. Operational area weather is becoming more critical with the growing reliance on air domain for transportation. This study investigates whether the forecasting accuracy using a Hybrid Long-Short Term Memory (LSTM) Artificial Neural Network (ANN) outperforms the San Antonio International Airport (KSAT) Terminal Aerodrome Forecast (TAF) by integrating diverse data sources – regional Meteorological Aerodrome Report (METAR), TAF, Avian Advisory System (AHAS) Data and geomagnetic activity K Index Data. When tested on one year of historical data, …
Improving Satellite Needs Working Group Assessment Process Through Automation And Process Consolidation, Raj Dangol
Improving Satellite Needs Working Group Assessment Process Through Automation And Process Consolidation, Raj Dangol
Theses
The increasing complexity of modern software ecosystems, presents significant challenges to system resilience, maintainability and automation. This work presents a proof-of-concept implementation of an automation system to address these challenges for a large scale fragmented software systems. Succinctly we address these challenges in the context of Satellite Needs Working Group (SNWG), which is responsible for collecting and communicating federal agencies' Earth observation needs to National Aeronautics and Space Administration (NASA). Multiple automation scripts and applications exist to support the SNWG's assessment cycle, encompassing the areas of data validation, visualization, report generation, and user management. However, the dispersion of these tools …
Surveying Subterranean Biodiversity Of The Tongass National Forest Using Traditional Survey Approaches And Environmental Dna (Edna) Metabarcoding, Jared P. Higgs
Theses
The Tongass National Forest (TNF) is a large temperate rainforest in southeastern Alaska, much of which is underlain by karst. Timber harvest, among other threats, can have detrimental effects on karst habitats; however, the subterranean fauna of this region is understudied and lacks a baseline for monitoring. I spent three field seasons conducting biological assessments of caves in the TNF using two approaches: traditional specimen-based sampling and eDNA metabarcoding. To improve the reference sequence database, I generated 213 novel sequences from invertebrate specimens collected from 35 caves. I also collected 125 water samples from 39 sites for environmental DNA (eDNA) …
Utilization Of Artificial Intelligence To Predict Surface Energy Budget, Joseph Bonucchi
Utilization Of Artificial Intelligence To Predict Surface Energy Budget, Joseph Bonucchi
Theses
Estimating heat and moisture exchange between the land and atmosphere has several important practical applications, including water resource management, air pollution forecasting, and atmospheric propagation modeling. Turnkey systems for measuring the surface energy budget typically cost between $5,000 and over $50,000. This study explores the use of artificial intelligence (AI) to predict sensible heat flux (SHF) and latent heat flux (LHF) using inexpensive surface meteorology data and downwelling solar radiation measurements as inputs. Observations from Amer- iFlux sites were used to train several AI models—Convolutional Neural Networks (CNNs), One Dimensional Transformers (1D Transformers), Artificial Neural Net- works (ANNs), and Long …
Information Capacity For Time-Varying Adversarial Networks, Raphael Thorp
Information Capacity For Time-Varying Adversarial Networks, Raphael Thorp
Theses
This thesis investigates the capacity of time-varying adversarial networks where intelligent adversaries can corrupt transmissions on restricted subsets of network edges that change availability over time. Building upon the work of Beemer et al. on adversarial network coding with restricted adversaries, we analyze networks whose topology evolves temporally, motivated by applications in space networking and delay-tolerant systems. We introduce different adversarial modes that capture different temporal aspects of corruption behavior and establish their hierarchical relationship with respect to network capacity. The analysis employs time-expanded graph representations and develops recursive counting methods for computing expected network capacity under random time assignments, …
Evaluating Sequential Inference Via Poisson And Change-Point Frameworks, Samuel Johnson
Evaluating Sequential Inference Via Poisson And Change-Point Frameworks, Samuel Johnson
Theses
Sequential testing reduces sample size and costs compared to fixed-sample approaches by allowing decision making during data collection. This thesis investigates sequential methods for Poisson processes and change-point detection. Wald’s sequential probability ratio test (SPRT) and efficiency measures are developed. Practical applications of the SPRT are studied, from detecting noisy sources to classification algorithms. The MaxSPRT is derived, and its critical value algorithm is optimized. The MaxSPRT, a Bayesian SPRT, Wilk’s generalized likelihood ratio test, and the Cash statistic are examined. Various change-point algorithms are surveyed, including Shewhart control charts, cumulative sum methods, homogeneity tests, and multiple change-point approaches. These …
Blockchain Poisoning Attacks In Federated Learning, Mohammad Raihan Uddin
Blockchain Poisoning Attacks In Federated Learning, Mohammad Raihan Uddin
Theses
Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, thereby preserving privacy. However, conventional FL depends on a centralized aggregator, exposing it to poisoning and backdoor attacks that threaten model integrity. This thesis introduces B-ZkFed, a blockchain-based and zero-knowledge proof (ZKP)-enhanced FL framework designed to ensure verifiable trust, transparency, and robustness. The blockchain layer decentralizes aggregation through smart contracts, while the ZKP module allows clients to cryptographically prove the correctness of updates without revealing private data. A multi-layer adaptive defense combining gradient-norm filtering, model-similarity analysis, and robust aggregation mitigates poisoning threats under both IID and …
Simplified Soil-Based Approach For Maximum Pressure Prediction In Horizontal Directional Drilling Operations, India Clark
Simplified Soil-Based Approach For Maximum Pressure Prediction In Horizontal Directional Drilling Operations, India Clark
Theses
Horizontal Directional Drilling (HDD) is a trenchless pipeline installation method used in areas where traditional trenching is not feasible due to environmental concerns, existing infrastructure, or other constraints. However, HDD presents significant engineering challenges, with design failures often resulting from excessive installation stresses and Hydro Fracture. Hydro Fracture occurs when the pressure of drilling fluid exceeds the soil’s bearing capacity, leading to unintended subsurface fractures. To mitigate this risk, researchers have relied on the Delft Equation to estimate the maximum allowable mud pressure for borehole stability. However, this equation does have notable limitations and assumptions, which may not accurately reflect …
Sensor Fusion For Enhancing Motion Capture : Integrating Optical And Inertial Motion Capture Systems, Hailey Hicks
Sensor Fusion For Enhancing Motion Capture : Integrating Optical And Inertial Motion Capture Systems, Hailey Hicks
Theses
This study aimed to create and evaluate a custom sensor fusion algorithm based on Gauss-Newton optimization that combines Optical Motion Capture (OMC) and Inertial Motion Capture (IMC) measurements to combat the limitations of solely using each system. The goal was to demonstrate how inertial measurement unit (IMU) data can be seamlessly integrated into motion capture data to provide a more efficient and reliable gap filling process for future research. The algorithm takes the first and last frame of OMC data, as quaternions, and fills the rest with IMU gyroscope orientation data to simulate gaps of up to ten minutes. The …
A Deep Learning Multi-Channel Framework For Indoor Image Retrieval, Vidhi M. Bhatia
A Deep Learning Multi-Channel Framework For Indoor Image Retrieval, Vidhi M. Bhatia
Theses
Processing and analyzing multi-source image data is vital across a wide range of fields, including artificial intelligence (AI), deep learning, computer vision, geolocation, autonomous navigation, remote sensing, and forensic investigations. Although conventional image matching methods such as Scale-Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF) have proven effective in controlled setting, they often face challenges when applied to diverse datasets that exhibit substantial variations in viewpoint, scale, lighting conditions, and modality. Recent developments in deep learning, including convolutional neural networks (CNNs), transformers, generative adversarial networks (GANs), and self-supervised learning, have led to the creation of more efficient and precise …
Comparing Role Differences Among Systems Architects, Systems Designers, And Systems Engineers, Kelly X. Campo
Comparing Role Differences Among Systems Architects, Systems Designers, And Systems Engineers, Kelly X. Campo
Theses
The roles of a systems architect (SA), systems designer (SD), and systems engineer (SE) often work together to develop systems, especially as the scale and complexity of engineered systems increase. However, management of these roles can result in ambiguity of task assignments and may reduce accountability for deliverables. Furthermore, there are few published works examining the roles of SAs, SDs, and SEs collectively. This research explores perceptions of the similarities and differences between the roles of SAs, SDs, and SEs by conducting a systematic literature review of 57 sources containing 1064 quotes in addition to a thematic content analysis, analyzed …
Average Heat Flux Measurements In An Additively Manufactured Rotating Detonation Rocket Engine Calorimeter, Matthew Aaron Maybee
Average Heat Flux Measurements In An Additively Manufactured Rotating Detonation Rocket Engine Calorimeter, Matthew Aaron Maybee
Theses
Rotating detonation rocket engines (RDREs) offer benefits over traditional deflagrative engines, including increased engine performance, compact combustion and negligible detrimental thermoacoustic instabilities due to mode-locking. Realizing the benefits of a detonation-based propulsion system will be accompanied by increased thermal loads due to a combination of heat release at elevated pressure and temperature, as well as compact heat release due to detonation. Managing these heat loads necessitates the development of thermal management strategies. As a first step towards establishing the requirements for detonation-based engine thermal management strategies, this work develops an additively manufactured (AM) water-based, axially resolved calorimeter for integration in …