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Predictive Maintenance For It Equipment Using Machine Learning Models, Rashed Khalid Alshirawi
Predictive Maintenance For It Equipment Using Machine Learning Models, Rashed Khalid Alshirawi
Theses
Predictive maintenance was recently introduced as a paradigm shift in the field of smart manufacturing, and it allows organizations to predict equipment failures and streamline maintenance planning based on the data-driven insights. The present work addresses the concept of the implementation of a decision tree-based predictive maintenance model based on AI4I 2020 Predictive Maintenance Dataset, a simulated environment of an actual industry through sensor data including temperature, torque, rotational velocity, and toolwear. The study uses a quantitative approach that is based on positivism philosophy, which is the focus on objectivity, empirical validation, and statistical testing of the model performance. Synthetic …
Predicting Residential Real Estate Prices In Dubai Using Integrated Data Analytics: A Machine Learning Approach, Mohammad Nasser
Predicting Residential Real Estate Prices In Dubai Using Integrated Data Analytics: A Machine Learning Approach, Mohammad Nasser
Theses
Dubai’s rapidly evolving real estate market attracts substantial global investment yet remains characterized by information asymmetry, pricing volatility, and fragmented data sources. This study examines how integrated data analytics and ensemble machine learning can be used not primarily for precise price prediction, but rather to identify and quantify the key behavioral and transactional drivers of residential property prices in Dubai. A unified dataset was constructed by integrating over 100,000 residential property transactions from the Dubai Land Department with macroeconomic indicators, web-scraped listing attributes, and sentiment measures derived from online reviews and social media discussions. Supervised learning models including Linear Regression, …
Predictive Analysis Of Residential Property In Urban Areas, Hamad Adnan Alzarooni
Predictive Analysis Of Residential Property In Urban Areas, Hamad Adnan Alzarooni
Theses
This paper aims to determine the predictive modelling of residential property prices in urban Scotland with the help of an integrated framework in which machine learning methods are applied in combination with detailed socio-economic, health, housing, and geographic indicators. Conventional valuation methods tend to be based on the concept of few structural variables and ignore the effect of multidimensional variables in determining spatial variation in housing markets. To cope with this, the study uses Linear Regression, random forest, Multi-layer perceptron, and XGBoost models, which are assisted by a broad range of feature engineering, outlier management, and data preprocessing. Compared with …
Ai-Powered Mobile Phone Activity Insights: Developing Predictive Models For Smarter Decision-Making, Maryam Al Ali
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 …
Predicting Athlete Performance Using Machine Learning Models, Mohammad Abdulbasit Mohammad Alabdulla
Predicting Athlete Performance Using Machine Learning Models, Mohammad Abdulbasit Mohammad Alabdulla
Theses
The purpose of this research is to develop and assess multi-modal machine learning for robust performance analysis to predict athletic performance and evaluate injury risk. The study employed Data Analytics approach, where composite features, i.e., Training Stress and Recovery Score,were modelled to characterize training-recovery correlations. It had four different regression models (MLR, RFR, SVR, DNN) and four different classification models (Logistic Regression, RFC, SVM, DNN). DNN exhibited an increased degree of effectiveness in the forecasting Synthetic Performance Score (R2 =0.9998). Notably, the Random Forest Classifier (RFC) turned out to be the most valid predictor of injuries risk (F1-Score 0.7736; AUC-ROC …
Optimizing Waste Management And Recycling Patterns Using Data Analytics, Mohammad Omar Almarri
Optimizing Waste Management And Recycling Patterns Using Data Analytics, Mohammad Omar Almarri
Theses
The increasing pace of urbanisation and consumption has increased the burden of waste generation in the world and it is a huge burden on the current waste management systems. The United Arab Emirates (UAE) is a region that is intensifying this challenge through the accelerated urbanization, high sustainability targets, and the necessity of effective recycling policies. The thesis that is being examined explores the ways in which the data analytics can be utilized to streamline waste management and recycling trends, emphasizing the enhancement of the collection process, forecasting the trends in the waste generation as well as facilitating the use …
Leveraging Ai In Traffic Monitoring For Improved Accident Prediction In Uae, Mahra Alattar
Leveraging Ai In Traffic Monitoring For Improved Accident Prediction In Uae, Mahra Alattar
Theses
This paper investigates how Machine Learning (ML) models can be used in traffic surveillance and predict accidents in the United Arab Emirates (UAE). In spite of large infrastructure developments and stringent traffic laws, traffic accidents continue to be a major problem with an increase in fatalities and injuries. There is a gap in effective detection and responses since traditional monitoring and forecasting techniques are unable to capture the intricate, nonlinear, and time-dependent character of traffic patterns. This thesis fills that void by utilizing sophisticated the potential use of Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Deep Neural Network (DNN), …
Predictive Modelling Of Long-Term Outcomes In Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Using Machine Learning, Neha Malik
Theses
Myalgic Encephalomyelitis/Chronic Fatigue Syndrome is a complex chronic illness characterized by debilitating, heterogeneous symptoms. The condition exhibits highly variable long-term outcomes, posing significant challenges for patient management, research, and clinical practice. Currently, there is a lack of tools for predicting individual patient trajectories, creating a substantial prognostic gap that is compounded by the prevailing research focus on diagnostics over prognosis. This study addresses that gap by investigating whether multidimensional baseline patient-reported outcome measures (PROMs), analysed using advanced and explainable machine learning methods, can meaningfully predict heterogeneous 12-month outcomes in ME/CFS, and by examining what patterns of predictability themselves reveal about …
Supervised Learning For Predicting Mental Health And Burnout In Healthcare Workers, Afra Alfalasi
Supervised Learning For Predicting Mental Health And Burnout In Healthcare Workers, Afra Alfalasi
Theses
This study aims to predict and analyze burnout among healthcare workers using supervised machine learning techniques and Exploratory Data Analysis (EDA). Leveraging the Healthcare Workforce Mental Health Dataset, the research identifies key demographic, occupational, and psychological factors most strongly associated with burnout. The methodology involves data preprocessing, feature selection, and model training using algorithms such as logistic regression, decision trees, random forests, and gradient boosting. Model performance will be evaluated through standard metrics, including accuracy, precision, recall, and ROC-AUC. The expected outcome is a predictive framework that highlights high-impact burnout predictors and generates actionable insights to support early intervention and …
Chatgpt As A Mental Health Ally: A Study On College Students’ Adoption Of Ai For Therapy, Alya Albastaki
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, …
Early Diabetes Prediction Using Machine Learning: A Comparative Study Of Classification Models, Ahmed Abdelqadir Bawazir
Early Diabetes Prediction Using Machine Learning: A Comparative Study Of Classification Models, Ahmed Abdelqadir Bawazir
Theses
The current dissertation investigates the application of machine learning in diabetes prediction at early stages through a comparison of the performances of three classification models, including Logistic Regression, Decision Tree, and Random Forest. Inspired by the increased and spread cases of diabetes worldwide, the research objective is to facilitate early diagnosis by providing interpretable and accurate predictive models. Based on Pima Indians Diabetes Dataset containing 768 clinical records, the research implemented data preprocessing including KNN imputation and outlier processing, feature scaling, and formation of interaction features. Quantitative and comparative approach was made to train and test the models based on …
Adaptive Reuse Of Underutilized Strip Malls: Creating Mixed-Use Continuum Care Campuses For Seniors, Julie Le Nguyen
Adaptive Reuse Of Underutilized Strip Malls: Creating Mixed-Use Continuum Care Campuses For Seniors, Julie Le Nguyen
Theses
As suburban commercial landscapes continue to decline, underutilized strip malls present an opportunity to reimagine aging-supportive environments through adaptive reuse. This thesis investigates how these sites can be transformed into mixed-use senior housing and continuum-care campuses through adaptive reuse strategies. Using a comparative case study methodology, the research examines a range of precedents—including senior housing retrofits, dementia villages, and community-based mixed-use developments—to identify key spatial, programmatic, and environmental principles that support aging populations. A five-part evaluative framework assesses project performance across site integration and green space, social and intergenerational engagement, programmatic and service integration, mixed-use activation, and cognitive and environmental …
Analyzing The Hidden Impact Of Work Stress On Mental Health, Shaikha Aljabry
Analyzing The Hidden Impact Of Work Stress On Mental Health, Shaikha Aljabry
Theses
Occupational stress and deteriorating mental health constitute an increasing issue in the global community, and the problem of how job demands, personal resources, and cognitive processes interrelate to determine employee well-being is hardly known. This dissertation will study these dynamics by incorporating theory-driven Structural Equation Modeling (SEM) with data-driven Machine Learning (ML) methods that offer explanatory and predictive information. The study examines the influences of job demands, personal resources, effort–reward imbalance (ERI), emotional labor, cognitive appraisal, and social support on the stress and mental health outcomes of a sample of 5,000 employees working in various industries and regions of the …
The Environmental Impact Of Rapid Construction In Australia, Ahmad Alsubousi
The Environmental Impact Of Rapid Construction In Australia, Ahmad Alsubousi
Theses
This research examines the degrading effect of the high-rate of construction in Australia especially on the generation of waste, effectiveness of policies to be applied regarding waste and sustainability implication. The research utilizes regression analysis, clustering, and forecasting based on ARIMA with data provided by National Waste Report (2006 07 to 2022 23) to analyse the magnitude and the factors affected by construction and demolition (C&D) waste that ended up being the major factor of overall waste production in Australia. Findings depict an observable upward trend in C&D waste along with landfill use and variations in the performance of waste …
Symbiotic Furniture Design: A Single Chair Integrating Epiphytes With Taiwanese Window Grille Structures, Kuan-Wen Lin
Symbiotic Furniture Design: A Single Chair Integrating Epiphytes With Taiwanese Window Grille Structures, Kuan-Wen Lin
Theses
Plants have been integrated into residential and interior environments for centuries, yet research on the psychological effects and interactive potential of plants in furniture design is still relatively limited. Traditional planting methods, such as individual potted plants or large green walls, often require additional floor or wall space, which is not ideal for environments with limited living space. Therefore, this study investigates how plants can be integrated into furniture design to enhance the effect of interior greenery and user engagement, and to redefine the relationship between furniture and the natural environment. This research explores a furniture design that incorporates epiphytic …
Predicting Mental Health Risk In Remote Workers: A Machine Learning Approach, Majed Al Mheiri
Predicting Mental Health Risk In Remote Workers: A Machine Learning Approach, Majed Al Mheiri
Theses
This dissertation explores the application of machine learning in mental health risk prediction of remote workers, which is increasingly becoming a significant issue, with the practise of flexible working reshaping organisational practises. It is based on the theoretical framework that has been used previously such as Job Demands Resources model and Stress-Strain model which emphasise the role of workload, support systems and personal resources in determining the well-being of employees. The context of the study indicates the growing rate of remote and hybrid employment, and the associated increase in the number of issues associated with stress and isolation, as well …
Defaking Deepfakes: Designing And Evaluating Ai-Powered Digital Media, Saniat Javid Sohrawardi
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, …
Microgrid Approach With Data-Driven Monitoring To Enhance The Resilience Of Water Distribution Systems, Binod Ale Magar
Microgrid Approach With Data-Driven Monitoring To Enhance The Resilience Of Water Distribution Systems, Binod Ale Magar
Theses
Existing centralized water supply systems are critically threatened by the joint effects of climate and socioeconomic changes, extreme weather events, and physical degradation due to aging, and their changeability. The uncertain and changing drivers pose challenges for water supply systems in providing sustainable water services during disruptions. The energy field dealing related issues has endorsed the microgrid approach with a decentralized energy supply with dispersed local energy sources. The application of energy microgrids has demonstrated their resiliency for sustainable energy supply. For improved resiliency of a water distribution system, this study investigated the application of a microgrid approach and the …
Exploring Denitrification In Wetland Nitrogen Cycling And Biostimulant-Based Agricultural Strategies To Improve Ecosystem Health, Alexander Bechtel
Exploring Denitrification In Wetland Nitrogen Cycling And Biostimulant-Based Agricultural Strategies To Improve Ecosystem Health, Alexander Bechtel
Theses
The overuse of fertilizers has doubled the amount of reactive nitrogen in the environment. Agricultural runoff then carries these pollutants into the waterways eventually causing severe ecological damage such as the Gulf of Mexico dead zone. Nitrogen plays an important role in all life on this planet, here we describe two complementary projects that investigate ways to mitigate nitrogen pollution. The first project investigates denitrification rates in soil after the Len Small Levee breached in 2016 which reconnected the Dogtooth Bend Floodplain with the Mississippi briefly. The denitrification process that began in the hydrologically activated soils was hypothesized to be …
Echo’S Repertoire: Performing Science Fiction And Trans Care Models, Juniper Blue
Echo’S Repertoire: Performing Science Fiction And Trans Care Models, Juniper Blue
Theses
Echo’s Repertoire is a mixture of a passion piece and a call for more work with performing science fiction. It focuses on interpersonal communication within trans studies and the experiences of transgender individuals and myself. Echo’s Repertoire is also an example of the Lyrical Model proposed by Awkward-Rich as a new model for trans affirming speech. The show is laid out non-linearly and follows along with Echo as he starts to build new relationships with the advice of his spaceship ai, Eep, who is a previous version of himself prior to transformation (transition).
Relationship Between Semi-Aquatic Mammal Occupancy And Particulate Carbon, Nitrogen, And Phosphorus Dynamics In Freshwater Ecosystems, Madison H. Stokoski
Relationship Between Semi-Aquatic Mammal Occupancy And Particulate Carbon, Nitrogen, And Phosphorus Dynamics In Freshwater Ecosystems, Madison H. Stokoski
Theses
Semi-aquatic mammals such as beavers (Castor canadensis) and muskrats (Ondatra zibethicus) can influence carbon (C), nitrogen (N) and phosphorus (P) dynamics in freshwater systems. While the effect of beavers, in particular, are typically attributed to their dam and lodge building behaviors, these animals do not always build these structures. It’s possible that other, non-damming behaviors (e.g. foraging and burrowing) may influence the availability of nutrients as well. To assess this possibility, we surveyed 54 lentic and lotic waterbodies across Southern Illinois and investigated the possibility that beaver and muskrat occupancy improved our ability to explain variation in nutrient availability across …
Evaluating Streambank And In-Stream Erosion As Acontributors To Water Quality In Two Illinois Agricultural Watersheds, Chibueze Chigozie Iguegbe
Evaluating Streambank And In-Stream Erosion As Acontributors To Water Quality In Two Illinois Agricultural Watersheds, Chibueze Chigozie Iguegbe
Theses
This study investigates its contribution to sediment and phosphorus (P) export in two agriculturally dominated watersheds in south central Illinois—Lost Creek and East Fork Shoal Creek (EFS). Using cross-section surveys, and erosion pin measurements over a two-year monitoring period, we quantified bank erosion rates, sediment yields, and associated phosphorus loads. Hydrologic data from the Illinois State Water Survey (ISWS), alongside local precipitation and soil data, were utilized in the interpretation of erosion dynamics in the study watersheds. Key findings from this study show spatial and temporal variability in erosion rates, which is driven by seasonal flow events, riparian vegetation type, …
Comprehensive Drought Analysis: Machine Learning-Based Meterological Drought Forecasting And Pca-Driven Agricultural Drought Monitoring., Bishal Poudel
Theses
Drought is a long-term natural disaster that affects many aspects of human life from health, water supply to ecosystems and agriculture. Drought’s occurrence and intensity has increased in recent years because of global warming, which urges for proper and accurate monitoring and forecasting of drought. For better understanding of droughts, this study uses two approaches. In first part, the study uses the historical temperature and precipitation data from period of 1960 to 2021 as input features for three different machine learning models – Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest (RF). The research focuses on calculating …
Survivorship In Virginia Opossums And The Management Implications For Burmese Pythons In Key Largo, Florida, Arya J. Sanjar
Survivorship In Virginia Opossums And The Management Implications For Burmese Pythons In Key Largo, Florida, Arya J. Sanjar
Theses
This study employed a telemetry-based framework, using radio-collared Virginia opossums (Didelphis virginiana), to quantify survival patterns and estimate detection probabilities of python predation events. Between April 2022 and January 2025, 135 adult opossums were monitored using VHF collars across protected habitats within Key Largo. Annual survival was estimated using a staggered-entry Kaplan-Meier model, producing an annual survival rate of 0.17 (SE = 0.089) for females, and male survival was estimated at 0.04 (SE = 0.032) at 10 months since no male lived 10 months after collaring. Cause-specific monthly survival analyses indicated python predation peaked during the late summer months of …
Effect Of Water-Deficit Stress On Cannabis Sativa Production And Secondary Metabolite Levels, Shiksha Sharma
Effect Of Water-Deficit Stress On Cannabis Sativa Production And Secondary Metabolite Levels, Shiksha Sharma
Theses
Cannabis (Cannabis sativa L.) is an emerging high-value crop that belongs to the Cannabaceae family and the genus Cannabis, which is known to produce more than 200 cannabinoids. Although genetic variation is the main factor in cannabinoid production, water-deficit stress is believed to induce its production. The objective of this master’s thesis was to determine the effects of water-deficit stress frequencies and timing on growth, physiology, yield, and cannabinoid concentration in cannabis. Heidi cultivars were planted in a controlled environmental growth unit. One period of water-deficit stress was found to produce the lowest concentration of total CBD and THC. In …
Flood Susceptibility Assessment Through Gis Integrated Analytical Hierarchy Process And Machine Learning Models, Sujan Shrestha
Flood Susceptibility Assessment Through Gis Integrated Analytical Hierarchy Process And Machine Learning Models, Sujan Shrestha
Theses
Flooding is among the most destructive natural disasters globally, and it inflicts severe damage on both natural environments and human-made structures. The frequency of floods has been increasing due to unplanned urbanization, climate change, and changes in land use. Flood susceptibility maps help identify at-risk areas, supporting informed decisions in disaster preparedness, risk management, and mitigation. This study aims to generate a flood susceptibility map for two regions: Davidson County of Tennessee using an integrated geographic information system (GIS) and analytical hierarchical process (AHP), and the Briar Creek watershed of Mecklenburg County, North Carolina using an integrated GIS and machine …
Accelerating The Degradation Of Biodegradable Mulch Films In Soil And Compost Environments, Harshal Jayesh Kansara
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 …
Playa Sediment Bioaccessibility Presents Previously Unknown Risks To Population Health In A Water-Stressed Region Undergoing Lake Desiccation: Salton Sea, Usa, Jordan Jaeger
Theses
Globally, drylands are expanding, and endorheic basins are in rapid decline. Saline lake desiccation exposes lakebed playa likely to become dust, posing potential health risks to surrounding communities. Studies investigating health risks from playa dust exposure often focus on health effects associated with the presence of airborne particulates, but fewer studies investigate health risks associated with dust source chemistry. This study examines dust source particle composition in a desiccating inland sea in a water-stressed agricultural region of California. Specifically, chemical bioaccessibility of bound trace elements uncovers risks to children, adults, and agricultural worker health. Arsenic exposure via sediment ingestion poses …
It Hurts To Become, Ana Maria Joyce
It Hurts To Become, Ana Maria Joyce
Theses
Using the medium of oil painting, this thesis will investigate the ways that memories of my past inform and distort my perception of reality. Using domestic spaces as a framework in combination with my own body as the subject matter, I am creating compositions that redefine the meaning of home as a concept. Rather than a physical place of comfort, home is a cemetery for past selves that I revisit in my mind and grieve, no matter the time that has passed. By expressing this internal decay through physically scraping and sludging paint onto large scale surfaces, I aim to …
Graphic Design For Visual Narratives: The Semiotics Of Diegetic Retrofuturism, Brooke Luke Ballard
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 …