Digital Commons Network^™

Biodegradation Of Common Post-Consumer Plastic, Laura Krebs

Theses

Plastic waste is widespread throughout the Laurentian Great Lakes watershed. During environmental exposure, plastic undergoes abiotic and biotic degradation. The presence of novel plastic substrates coupled with rapid microbial turnover may lead to selection for or evolution of metabolic pathways and enzymes with enhanced biodegradation capabilities. This study investigated the biodegradation potential of bacteria isolated from debris accumulation hotspots - stormwater retention ponds, storm drains, and tributaries - in the Lake Ontario watershed within Rochester, New York. Bacterial isolates were exposed to commonly littered plastics for 240 d: cellulose acetate from smoked cigarette filters, high-density polyethylene from takeout shopping bags, …

Rise Of Social Media Hacking: Ai-Based Ip Tracking For Uae Law Enforcement, Mounikha Naarrayen Chakravarthula

Theses

Social media has evolved into a critical channel for communication, expression, and public influence, but it has also become a prevalent avenue for cybercrime, particularly in digitally advanced nations such as the United Arab Emirates (UAE). The rising complexity of online offences, coupled with anonymisation tools and cross-border digital behaviour, has made the attribution of social-media-based cyber incidents increasingly challenging for law enforcement. In this context, artificial intelligence (AI) offers the potential to strengthen digital investigations by providing intelligent, scalable, and evidence-driven attribution capabilities. This research develops an AI-assisted Internet Protocol (IP) attribution framework tailored specifically for UAE law enforcement …

The Role Of Ai And Predictive Policing In Crime Prevention, Saif Salem Mohammad Hassan Abdulla

Theses

The current thesis examines the application of Artificial Intelligence (AI) in the arena of predictive policing and crime forecasting using an integrated case study based on the empirical approach supported by a narrative review of literature. Due to the growing use of digital data by the law-enforcement agencies, AI techniques, including machine learning and spatio-temporal modelling, are implemented to detect patterns of crimes, predict high-risk areas, and assist law-enforcement decision-making. Though these technologies have the potential to make the processes of accuracy and resource allocation better, they also bring up the issue of the fairness, transparency, and disproportionate effects on …

Defaking Deepfakes: Designing And Evaluating Ai-Powered Digital Media, Saniat Javid Sohrawardi

Theses

The rapid spread of generative AI has revolutionized media production, creating new challenges for information integrity as convincing deepfakes proliferate. Journalists play a critical role in upholding credible public information, yet existing deepfake detection technologies often overlook their specific workflows and requirements. This dissertation addresses these needs by identifying what journalists require from detection tools and evaluating usability in realistic scenarios through the following works: Journalists' Needs and Tool Design: Through qualitative user studies, we uncover journalists' preferences for tools that provide transparent, explainable evidence and context-aware analysis integrated into news verification routines. These insights drive the design of DeFake, …

Predictive Modeling Of Tokenized Real Estate Prices Using Machine Learning, Mohammed Alteneiji

Theses

In this paper, we propose a machine learning-based model to predict the prices of tokenized real-estate assets, combining blockchain data, real-estate data, and sentiment data. Tokenization refers to partitioning a physical asset into fractional tokens on a blockchain, where each token represents a fraction of that asset. These include token liquidity, trading volume, platform activity, and the on-chain activity of investors. These new variables require data-based analytics that are capable of accounting for more complex relationships than customary real estate valuation approaches. To address this, we propose a multimodal dataset that incorporates on-chain data (token supply, number of wallet-holders, transaction …

An Interpretable Machine Learning Framework For Detecting Phishing Urls Based On Lexical Features, Khalid Alrokhaimi

Theses

Phishing attacks represent one of the most significant and persistent threats in the cybersecu- rity landscape, with attackers increasingly using sophisticated URL manipulation techniques to deceive users and steal sensitive information. Traditional detection methods, which rely primarily on blacklists and heuristic rules, struggle to identify zero-day phishing URLs that have not yet been catalogued in security databases. This research addresses this critical gap by developing an interpretable machine learning framework for detecting phishing URLs usingclexical, structural, content-based, and domain metadata features. The study employs a comprehensive dataset of 11,430 labeled URLs (5,715 legitimate and 5,715 phishing) with 87 extracted features, …

Persuasive Pathways In Digital Apologies: The Role Of Persuasion Routes In Engagement With Youtuber Apology Videos, Darrin Majocha

Theses

This study explores how viewers process YouTube apology videos and how those processing methods relate to their judgments of the creator and their willingness to continue engaging with the channel. Guided by the Elaboration Likelihood Model, a single-session online survey experiment randomly assigned 413 adult participants to watch one of five publicly available apology videos from well-known YouTubers. After viewing, participants reported the extent to which they engaged in central versus peripheral processing, rated the creator's credibility and the apology's sincerity, and indicated their intentions to engage. Central processing was positively associated with engagement intentions, whereas peripheral processing was weakly …

Predicting Violent Crime Hotspots, Mohammad Alhammadi

Theses

This thesis presents a systematic literature review and an empirical demonstration focused on predicting violent-crime hotspots. Drawing on 50 studies published between 2010 and 2025, the review synthesises methodological developments across hotspot mapping, spatio-temporal modelling, risk terrain analysis, and machine-learning approaches. The review highlights a clear evolution from retrospective density maps to more dynamic, data-driven techniques, while also identifying persistent challenges related to data bias, temporal granularity, environmental context, fairness, and operational implementation. To complement the review, the thesis applies kernel density estimation (KDE) and three ensemble machine-learning models—Random Forest, Gradient Boosting, and XGBoost—to 769,680 geocoded violent-crime incidents recorded in …

Potential And Low-Cost Football Talents For Uae Clubs Based On Data-Driven Analysis, Rashid Alqemzi

Theses

The use of data analytics in professional football has changed the way clubs view, value and invest in players. Meanwhile, elite European teams have consistently exploited the data-driven strategies that offer them a competitive edge, but in up-and-coming football markets, such as the UAE Pro League, nepotism has remained strong, with reputation-based and agentdriven recruitment being the transformation choice of even the biggest profile moves. This work explores whether predictive analytics can discover hidden high-quality football talent from ”hotbeds” of talent including Brazil and Argentina, in a way that matches the economic capabilities of clubs in the UAE, given the …

Ai-Powered Multimodal Tour Guide: Enhancing Cultural Tourism With Image Recognition, And Personalized Storytelling, Abdulla Ibrahim Aljawi

Theses

This thesis presents the design and evaluation of an AI-powered multimodal tour guide that uses image recognition and personalised storytelling to enhance cultural heritage experiences. Traditional approaches to learning about monuments rely on static plaques, generic tour content, or manual web searches, which limit personalisation, interactivity, and accessibility. To address these limitations, the study implemented a “Multimodal Monument Explorer” that allows users to upload a photo of a landmark or describe it in natural language and then receive rich, context-aware explanations in both text and audio form. The system integrates a persistent vector database of monument images, OpenCLIP-based visual embeddings, …

Event-Driven Traffic Management, Abdulla Humaid Alhosani

Theses

Traffic congestion during peak hours and large public events is a persistent challenge in urban areas, affecting mobility, economic productivity, and quality of life. While many cities are moving towards smart, data-driven traffic management, the practical effectiveness of predictive models for event-driven traffic control remains uncertain. This thesis presents an offline, data-driven feasibility study that investigates whether ma- chine learning and time-series models can predict traffic volume patterns under different conditions, including weather and the presence of events. Using a historical traffic dataset with derived trend variables, the study applies exploratory data analysis (EDA) and two predictive approaches: ARIMA for …

Ai-Powered Mobile Phone Activity Insights: Developing Predictive Models For Smarter Decision-Making, Maryam Al Ali

Theses

This study investigates how artificial intelligence can enhance telecom network management by forecasting internet usage, predicting congestion, and identifying user behavior patterns from mobile phone activity data. The study made use of anonymized logs for calls, SMS and internet, and put up a multi-model analytical pipeline, which was composed of time-series forecasting (ARIMA, LSTM), clustering (K-Means), and classification (XGBoost), to perform the analysis. Among the time-series methods, ARIMA ranked first in the forecast performance (RMSE=0.31) and gave LSTM a convincing defeat in the case of this particular short and stable dataset. Based on K-Means segmentation, users were sorted into five …

Deepfake Audio Detection, Rashed Alfalasi

Theses

The rise of deepfake audio technology has introduced a serious threat to information credibility, personal security, and media integrity. This thesis investigates the application of machine learning techniques for detecting synthetic audio through the analysis of acoustic features, including Mel-Frequency Cepstral Coefficients (MFCCs), spectral centroid, chroma_stft, and zero-crossing rate. The dataset used in this study was sourced from Kaggle and contains labeled samples of real and fake audio clips. The research aimed to train and evaluate multiple machine learning models—Support Vector Machines (SVM), Random Forest, XGBoost, Logistic Regression, and Neural Networks—to determine the most effective approach for deepfake audio classification. …

Analyzing Airline Customer Experience Challenges And Their Impact On Dubai's Tourism Sector, Suhail Alfalasi

Theses

The paper explores whether there is a connection between operational performance, customer sentiment, and digital administrative complexity among major regional Middle Eastern airlines, such as Flag Carriers (e.g., Emirates) and Low-Cost carriers (LCCs) (e.g., Air Arabia). With the use of a highly detailed dataset of customer review and operations data, the study proves that a large service paradox is present in which, despite the high Net Promoter Scores (NPS: 48.004) of the carriers, which are positively reinforced by delivering world-class soft products, the loyalty is constantly disrupted by low-frequency but high-severe operational delays (Delay_Minutes). Since it has been analyzed that …

Beyond Detection: A Batch-Based Ai Framework For Temporal And Event-Correlated Trend Analysis Of Misinformation On Social Media, Vishnu Tejas Vijayaraghavan

Theses

The rapid spread of misleading information on social media influences public behaviour and complicates crisis communication. Although transformer models such as BERT accurately detect misinformation at the post level, most studies analyse posts in isolation and overlook howmisinformation fluctuates over time or responds to major events. This study addresses that gap by developing an end-to-end analytical workflow that integrates BERT-based classification with temporal aggregation, topic clustering, anomaly detection, and event alignment. The analysis uses 10,700 COVID-19–related tweets (6,420 training, 2,140 validation, and 2,140 testing). Because timestamps were unavailable, synthetic timestamps were assigned using an evenly spaced date range between 1 …

Chatgpt As A Mental Health Ally: A Study On College Students’ Adoption Of Ai For Therapy, Alya Albastaki

Theses

This research investigates college students’ use of ChatGPT for mental health support, addressing a population with high unmet mental health needs due to barriers like accessibility and cost. Through a mixed-methods study, which included a survey of 126 students and sentiment analysis of 1,200+ social media posts, the research examined adoption prevalence, gender influences, and perceived benefits and limitations. Survey findings show 40.5% of students use ChatGPT for mental health, especially those with self-reported challenges. Female students reported higher adoption, linked to greater mental health needs and openness to supplementary support. Key benefits included 24/7 access, anonymity, and low cost, …

Assessing Large Language Models As An Interpretive Layer In Marketing Mix Modeling: Implications For Marketing Analytics, Mohammad Bin Haider

Theses

Marketing mix modelling (MMM) remains a core technique for guiding budget allocation, yet its outputs are often difficult for non-technical planners to interpret and govern. At the same time, large language models (LLMs) offer new possibilities for translating complex model artefacts into narrative guidance, but raise concerns about hallucination, reproducibility, and alignment with model-risk governance. This thesis examines whether an open-source MMM framework can be engineered as a repro- ducible, governance-ready pipeline and then augmented with a tightly constrained LLM interpretive layer. The empirical setting is a multi-brand, multi-country retail portfolio with several years of digital marketing and outcome data …

Desiccation Tolerance In Tetradesmus Green Algae, Kristen Patten

Theses

In the face of the global climate change threat, understanding the adaptations that organisms have evolved to handle environmental variation is of particular interest to scientists. With climate change impacting global water availability and increasing the risk of drought for many traditional agricultural areas in the United States (IPCC, 2014, IPCC, 2021, and Kuwayama et al., 2019), desiccation tolerance in vegetative states is one adaptation that is currently receiving a lot of attention. Green algae are particularly useful organisms for understanding this adaptation due to their ubiquity across environments, which has given rise to independently evolved organisms displaying different levels …

Accelerating The Degradation Of Biodegradable Mulch Films In Soil And Compost Environments, Harshal Jayesh Kansara

Theses

The incomplete degradation of biodegradable mulch films (BMFs) in agricultural soils poses environmental challenges, with persistent plastic residues impacting soil health. This dissertation investigates the use of bioaugmentation with P. guariconensis to enhance BMF degradation under laboratory, raised bed, and field conditions. The research focuses on optimizing microbial delivery methods, evaluating the efficacy of drip and spray bioaugmentation techniques, and assessing mass loss as a primary metric for degradation. Laboratory experiments established proof-of-concept by demonstrating enhanced carbon mineralization, weight loss, and fragmentation of BMFs under bioaugmented conditions. These findings were translated to field-scale trials, where bioaugmentation treatments consistently outperformed untreated …

Timeless Poetry, Xuning Li

Theses

Timeless Poetry reimagines classical Chinese poetry through contemporary digital tools, transforming ancient verse into immersive visual experiences. Using Cinema 4D and Blender for 3D modeling and ink-wash–inspired rendering, the project reconstructs poetic landscapes as cinematic-style environments that invite viewers to encounter traditional imagery through modern visual language. Postcards—chosen for their nostalgic and emotional resonance—serve as both a physical anchor and an interactive trigger: when scanned, each card reveals an AR visuals that reveal the scene in motion. This hybrid design bridges past and present, showing how technology can enhance, rather than replace, cultural heritage by offering new entry points for …

Into The Arms Of Earth, Alex Paat

Theses

This thesis investigates the construction of a visual language illuminating the Biblical theme of duality-union. Section 1 provides an overview of duality-union across Scripture, investigates the portrayal of duality-union in the Biblical theme of Heaven and Earth, and summarizes criteria for a successful visual language surrounding this concept. Section 2 outlines and justifies the building blocks of this visual language, which include clay, neon, a gestural building process, arboreal imagery, references to the mandorla shape, an optimistic aesthetic influenced by midcentury children's book illustration, and found objects. Section 3 provides a description and assessment of the seven pieces that make …

Ecoviz: Experience The Future Of Environmental Insight Through Motion And Visuals, Ronger Xu

Theses

The project titled ‘ECOVIZ’ investigates the potential of motion-based visualization to transform the way environmental data is understood and emotionally experienced. Through an interactive visual system that merges 3D motion design with environmental datasets, the project explores how abstract ecological information can be translated into intuitive and immersive experiences. ECOVIZ examines the ways in which data visualization, when paired with motion, depth, and spatial interaction, can foster a deeper and more meaningful engagement with climate-related information, bridging the gap between scientific data and public comprehension. ‘ECOVIZ’ transforms environmental statistics into dynamic, visually expressive forms that mirror the rhythms, fluctuations, and …

Exploring Human Perception And Cognition In Expressing And Understanding Mechanical Designs, Yan-Ting Chen

Theses

Effective communication of mechanical designs through technical drawings requires geometric accuracy, efficiency, and an understanding of human perception and cognition. Although advances in computer-aided design (CAD) software have improved drawing precision and automation, current tools often overlook the spatial reasoning processes that users employ to interpret these representations. This study seeks to improve the accuracy and efficiency of human-computer interaction in CAD environments by examining how individuals perceive and interpret three-dimensional mechanical components within the context of technical drawings. A key focus is the identification of canonical and optimal views that align with intuitive human understanding. Through a series of …

The World Is Beautiful, Chelsea Demott Wildey

Theses

My thesis, The World is Beautiful is a short, digitally animated film that follows the journey of a jaded employee working at a dystopian fishery. The intention of this piece was to tell a well-paced, coherent story that also served as an allegory to spark verbal discourse in an audience. As a surface level story, the film portrays a working protagonist who has been forced to remain seated at her desk by a mechanical chair. She, presumably, has been monotonously pressing icons on a screen for her entire life until something catches her eye and prompts her to break free …

Graphic Design For Visual Narratives: The Semiotics Of Diegetic Retrofuturism, Brooke Luke Ballard

Theses

While semiotics, diegesis, and retrofuturism have all been discussed at length in academia, it is uncommon to find the synthesis of all three. When these concepts are combined, it forms what I argue is a new genre of graphic design now prevalent enough in contemporary media to warrant definition and analysis. This thesis first explores the theories of visual semiotics, narrative diegesis, and retrofuturistic aesthetics individually before uniting them as a new design genre. Several candidates for this design classification will then be examined in case studies that meet the criteria of 1) containing a visual language that a modern …

Startup Success Forecasting Through Machine Learning: A Comprehensive Analysis Of It Startups, Khaled Abdulla Alhassani

Theses

Lately, startups attracted significant attention from investors throughout the previous years. This raised several questions concerning startups and what they possibly define as them. It could refer to collective individuals who focus on innovative ideas with a reproducible and scalable business model; others refer to it as a newly established business. Nevertheless, all these definitions lead to a predictive question. Will these startups face success?

This study explores startup success prediction methods, focusing on forecasting information technology startup (SIT) insights using Machine Learning (ML) models such as Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbor (k-NN), …

Extremism Governance In The United Arab Emirates: A Case Study Using Qualitative Policy Framework, Haya Saleh Almansoori

Theses

This thesis investigates how the United Arab Emirates governs extremism using various models of governance and Gareth Morgan’s metaphorical organizational models. This research goes beyond a focus on security and looks at extremism as a governance and policy problem related to social cohesion, institutional trust, and legitimacy. The research utilizes a qualitative case study design through analyzing federal laws and national strategies, as well as institutional frameworks to understand how authority, coordination and meaning are organized in the UAE’s governance of extremism.

The results show that the UAE’s governance of extremism is characterized as a hybrid governance model with hierarchical …

Isolation And Characterization Of Plant Growth Promoting Rhizobacteria From Soils In The Uae And Their Effect On Plant Salt Tolerance, Layla Zayed Almazrouei

Theses

Plants are constantly challenged by environmental stresses that restrict their growth and productivity. In the United Arab Emirates (UAE), soil salinity is a critical barrier to agriculture, particularly for tomato (Solanum lycopersicum), which is highly sensitive to moderate salinity levels (>2.5 dS m⁻¹). This study explored the potential of plant growth–promoting rhizobacteria (PGPR), especially actinobacteria, to enhance salt tolerance in tomato. The objective was to evaluate the effects of rhizosphere-competent (RC) and non-rhizosphere-competent (NRC) PGPR on plant physiology and yield in saline sandy soils. Actinobacterial strains were isolated from the tomato rhizosphere and screened to produce 1-aminocyclopropane-1-carboxylic …

Enhancing The Acceptability Of Decision-Making System Using Xai Case: Crime Profiling System, Mohamed Saeed Al Shamsi

Theses

In the current world, we need to place more emphasis on how easily interpretable, accurate, and acceptable data analysis results are, given that essential operations in law enforcement, among other sectors, are backed up by the use of complex computing systems. Crime profiling systems that use crime data for profiling encounter major problems because they depend on algorithm-based methods. These methods can be ambiguous and inaccurate, leading to low public acceptability. The study investigates major problems with Complex Crime profiling systems (CPS) because their unexplained algorithms result in system performance issues and public scepticism. XAI provides a solution to handle …

Core-Scale Study Of Miscible Co2 Foam–Oil Interactions, Kuvonchbek Abdirakhmonov

Theses

Foam is currently the most effective means for gas mobility control in a variety of geo-energy applications, including enhanced oil recovery (EOR), carbon capture, utilization and storage (CCUS), and aquifer/soil remediation. This study investigates the mobility control of miscible CO₂ foam in the presence of oil.

The primary objective is to quantify the impact of oil on CO₂ foam behavior under miscible conditions, specifically examining foam stability, strength, and flow regimes as influenced by oil composition and reservoir permeability. While most oils destabilize foam, few studies explore the coarsening mechanisms of CO₂ foam in the presence of miscible oils, which …