Digital Commons Network^™

Predicting Customer Churn In B2b Consulting Services, Mediya Alfalasi

Theses

The aim of this research was to develop a machine learning model that can predict customer churn in the consulting industry. The goal was to help businesses identify customers who might leave, allowing them to take action before it happens. To achieve this, a dataset was created by combining different data sources to reflect real-world customer behaviors. After cleaning the data and addressing issues like missing values and data imbalance, four machine learning algorithms—XGBoost, Neural Network, Random Forest, Logistic Regression, and Support Vector Machines (SVM)—were used to build the prediction models. The findings showed that XGBoost performed the best, achieving …

Enhancement Of Firefighter Mental Performance: A Review, Nicole A. Ness

Theses

Background: The life-or-death nature of firefighting exposes those who respond to calls to an increased number of stressors that can impact performance and overall well-being. To combat this, the mental element of firefighting can be optimized through psychological skills training programs and mindfulness training. Other tactical populations, such as the police force, military, and emergency medicine units have found success in implementing these interventions, Literature suggests a similar effect may be observed in firefighters because of their similar demands; although, little research exists regarding the effects of mental performance programs on firefighter populations specifically.

Methods: A review of existing literature …

Enhancing Waste Management And Circular Economy Practices In Higher Education Institutions In Uae: A Study On Current Policies And Strategic Framework, Muna Ali

Theses

This study investigates the integration of Circular Economy (CE) principles within higher education institutions (HEIs) in UAE as a method to address waste challenges in UAE. Despite the announcement of UAE 2021-2031 Circular Economy Policy, and the nationwide continues promotion of sustainability initiatives, HEI’s lack the availability of tailored policies and procedures as well as guiding framework to address their waste challenges and adapt CE within their institutions. To assess this problem, a survey has been distributed to 45 CAA accredited public and private universities in UAE. A qualitative approach has been used to analyze the survey responses. The survey …

An Integrated Decision Support System (Dss) For Sustainable Supplier Selection, Evaluation, And Benchmarking Using A Fis And Molp Approach, Saleem Ghannam

Theses

Applications of sustainable supplier selection criteria in supply chain management are less developed compared to other evaluation methods. This study focuses on three main dimensions of sustainable supplier performance: economic, environmental, and social criteria. The research aims to identify significant criteria within each dimension that are crucial for the sustainable supplier selection process. These criteria will be utilized to develop a hybrid decision support system that integrates a fuzzy inference system with multi-objective linear programming. This comprehensive model will evaluate and benchmark supplier sustainable performance, providing an overall assessment of supplier performance. The proposed model offers a holistic approach to …

Prediction Of Vessel Arrival Time To Optimize Berth Allocation In Ports Using Machine Learning Methods, Anfal Mansour Albloushi

Theses

80% of the world trade is carried out through the sea, which shows the importance of maintaining transportation efficiency in the maritime industry. The vessel arrival time and berth allocation pose a significant challenge in the marine field's day-to-day operation, especially when a high number of vessels are waiting at the anchorage to deliver the goods on time. This increases the pressure on the responsible stakeholders and good owners as some cargo must be delivered urgently. Artificial intelligence and machine learning play a vital role in improving the operation of different fields; utilizing such technologies in the maritime industry will …

Born Of A Nightmare: The Aesthetics And Gameplay Of Lucah, Marigold Parson

Student Work

Lucah: Born of a Dream creates a queer narrative of trauma, repression, and self-creation not only through story or representation, but through its aesthetic and gameplay. By fusing a scratchy, childish visual style with ritualized, intimate combat, and religious and dreamlike symbolism, the game creates what queer scholars describe as “queer resistance”(Ruberg and Phillips 2018). This is experienced through the player as much as the character. Drawing on frameworks from queer game studies, including Pozo’s discussion of haptic and bodily game aesthetics (Pozo 2018) and Dalby’s account of queer orientations in play (Dalby 2024), as well as takeaways from Codex …

Hypnosis And Mindfulness Audio Recordings For Reducing Fatigue In Individuals With Multiple Sclerosis: A Randomized Controlled Study, Mark P. Jensen, Susan Robles, Michael G. Nash, Susanne May, Dwan M. Ehde, Melissa A. Day, Owen Gottlieb, Laurence I. Sugarman, Kevin N. Alschuler

Articles

Background

Fatigue is a common problem in individuals with multiple sclerosis (MS).

Objective

The objective was to evaluate the effects on fatigue of having 4 weeks of

access to audio recordings of therapeutic hypnosis (HYP) and mindfulness meditation

(MM) practices.

Methods

A total of 333 individuals with MS and fatigue were randomly assigned to

one of the three treatment conditions for 28 weeks: (1) access to therapeutic HYP audio

recordings, (2) access to MM audio recordings, or (3) no access to recordings

(treatment as usual or TAU). Fatigue impact (primary outcome) and other outcomes

were assessed at 4, 16, and …

Active Learning Methodologies And Applications, Pradeep Bajracharya

Theses

Active learning (AL) is a Machine Learning (ML) strategy that offers a promising solution to challenges posed by the data-intensive tasks by selectively querying the most informative data, reducing the need for extensive labeled datasets. This approach is particularly valuable for fields like cardiac research and scientific simulations, where data labeling becomes expensive and often impractical. Deep Active Learning (DAL) extends this concept to deep learning models, which are typically data hungry, where the focus is more to optimize data selection in discrete search spaces. Conversely, Explorative Active Learning (EAL), is tailored for exploration of continuous search spaces, employing machine …

How Smart City Growth Principles Can Be Adopted In The Uae To Solve The Local Housing Problems?, Shaima Abdulla Alblooki

Theses

Shaikh Zayed Housing Program “SZHP” is the governmental program that is responsible for local housing in the UAE. The program’s main aim is to offer a variety of housing services such as loans, grants, and ready-to-move houses. However, both the continuously growing urban population and liveability challenges are impeding the program to meet its goal. As mentioned on SZHP’s official website “We should continue to explore new ways to establish informed dialogue with different groups of society and to ensure adequate housing for national families, in achieving stability and community development by meeting future needs” (SZHP, 2022 ). Accordingly, this …

Fake News Detection In Arabic Media Using Machine Learning And The Afnd Dataset, Hussain Ali Bahri

Theses

The proliferation of fake news in digital media poses a significant challenge to information credibility, particularly in linguistically diverse regions such as the Arab world. This study addresses the critical problem of detecting fake news in Arabic media by leveraging advanced natural language processing (NLP) techniques and the AFND dataset, which contains over 600,000 news articles categorized into "credible," "not credible," and "undecided" classes. The research focuses on developing a robust system using AraBERT, a transformer-based model specifically pre-trained for Arabic text. Key contributions of this work include a comprehensive preprocessing pipeline tailored to Arabic linguistic complexities, incorporating stopword removal, …

Predictive Policing - Leveraging Cctv Data And Ai For Crime Hotspots, Maitha Ahmad

Theses

This thesis explores fine-grained sentiment analysis in the context of police social media posts, leveraging hybrid approaches to manage the rapid flow of big data and mitigate its negative impact on youth. As social media becomes a primary medium for public interaction, understanding sentiment within police-related posts is crucial for law enforcement agencies to gauge public opinion and address community concerns effectively. The rapid dissemination of information and the potential for negative sentiments to spread swiftly pose significant challenges, particularly for young audiences. The research begins with a comprehensive review of existing sentiment analysis techniques and their applicability to large-scale …

Structure/Property/Processing Of A 3d Printed Self-Healing Polymer Blend Based On A Thermoplastic Healing Agent, Vincent Mei

Theses

Self-healing polymers can regain mechanical performance following damage, offering increased material durability and sustainability. This thesis establishes structure/property/processing relationships for a 3D printable extrinsically self-healing polymer based on a UV polymerizable thermosetting resin system blended with a low temperature thermoplastic healing agent. This work serves as the first example of a vat polymerization 3D printed soft, low Tg low-melt thermoplastic extrinsically self-healing polymer blend. This development enables high resolution fabrication of complex geometries with self-healing functionality. This strategy of imbuing self-healing properties onto vat polymerization resins will enable functionality in many application spaces including aerospace, biomedical, soft robotics, coatings, and …

Automated Prioritization And Routing Of It Support Tickets Using Machine Learning, Mohamed Almarzooqi

Theses

The increasing complexity of IT service management underscores the need for efficient and scalable solutions to manage IT tickets. This thesis explores the application of machine learning techniques for automated prioritization & routing of IT tickets. So by leveraging publicly available dataset from Kaggle containing IT ticket the study aims to reduce manual intervention, improve resolution times, & optimize resource allocation within IT environments. The study will evaluate various machine learning algorithms, including SVM, Random Forest, & ensemble models, for their ability to classify and prioritize tickets accurately. to accurately classify and prioritize tickets. Advanced data preprocessing techniques like TF-IDF …

Predicting Airbnb Prices In Dubai Using Machine Learning, Hamad Younus Altamimi

Theses

The Airbnb rental market in Dubai is dynamic and full of varying factors like location, equipment, and demand time shortages, which makes it challenging to have fair pricing for the different levels of Airbnb properties in Dubai. The goal of the project was to construct a powerful machine-learning model for the forecast of the Airbnb rental price in Dubai The method used by the project was systematic from the beginning to the end stages, which included the data collection from Kaggle, the data preprocessing which involved the handling of missing values, outlier elimination, data type conversion, and exploratory data analysis …

Resilient Power System Load Frequency Control, Ghadeer Ahmed Almahri

Theses

Standard frequency and voltage levels need to be maintained when the electric power system is running in order to keep it in a satisfactory state of operation. Between AC power generation and use, there should be a balance between active and reactive power. However, due to variations in active and reactive power requirements, the frequency and voltage provided may fluctuate from their rated values. Any unwelcome change in frequency or voltage can have an immediate effect on how the power system operates and may even cause harm to linked devices. These parts are intended to operate at certain rated frequencies …

Predicting Bus Ridership In Dubai Using Advanced Machine Learning Models, Fatma Almheiri

Theses

In rapidly urbanizing cities, the public transport systems have to be efficient and reliable with proper demand forecasting to guarantee customer satisfaction. This paper explores how evidence-based machine learning tools can be used to forecast weekly bus ridership in Dubai, using a dataset of more than 10 million trip-level boarding observations between June 2013 and July 2014. It was aggregated to 25,195 route-week observations, and preprocessed with temporal feature engineering, lag variables and rolling statistics. Three predictive models have been tested: Linear Regression (as a baseline), Random Forest and XGBoost (as an ensemble method). Mean Absolute Error (MAE), Root Mean …

Internet Of Things (Iot) In Developing Smart City Traffic Management Systems In Dubai, Ayesha Ahmed Alnuaimi

Theses

Traffic congestion is an ever-present problem affecting most cities, negatively affecting the economy, the environment, and people’s quality of life. Dubai is one of the rapidly growing cities in terms of urbanization and the usage of technologies, and it is aware of the ability of IoT to design intelligent city traffic control systems to resolve this problem. Consequently, this study sought to assess the implication of IoT in improving traffic management systems within the Emirate of Dubai, gleaning data concerning the factors that have a direct impact on the implementation of the IoT, the challenges, and barriers met during the …

Machine Learning And Artificial Intelligence Models And Techniques In Modern Power Grid Communication Architecture, Majid Jamal Majid Salim Almheiri

Theses

Smart grid is evolving in different phases and artificial intelligence (AI) models are also the part of it. In the recent developments of Smart grid system there are research areas which are left unaddressed specifically in privacy techniques, scalability, more efficient data processing techniques. This Thesis highlights the research gaps on smart grid development across various integration phases. In the initial phase of Smart grid development, challenges included scalability issues in Machine-to-Machine (M2M) communication, communication delays affecting voltage stabilization and high computational costs in implementing Genetic Algorithm (GA)-based grid state estimation are covered in this review report. Review is further …

2025-2026 Graduate Catalog And Course Descriptions, Rochester Institute Of Technology

Graduate Catalog and Course Descriptions

No abstract provided.

2025-2026 Undergraduate Catalog And Course Descriptions, Rochester Institute Of Technology

Undergraduate Catalog and Course Descriptions

No abstract provided.

Optimizing Wind Wing Wall Ventilation In High-Rise Buildings Within Dense Urban Hot And Humid Climate., Arushi Bhatia

Theses

This study investigates how wing walls can increase natural ventilation in high-rise structures, particularly in hot, humid locations such as Mumbai, India. Using wind flow simulations and computational fluid dynamics (CFD) modeling, the study investigates how wind direction and the installation of wing walls affect airflow throughout the building. The study examines four scenarios, each analyzing how airflow varies when a building is located near surrounding structures of differing heights. It uses comprehensive CFD simulations to assess how alternative wing wall depth design can affect ventilation, as well as how wall-to-window ratio variation can contribute to airflows. Following these simulations, …

From Factories To Homes: A Comparative Analysis Of Adaptive Reuse In New England Industrial Buildings For Multi-Family Residential Conversion, Jeremy Weeden

Theses

The adaptive reuse of industrial buildings offers a promising path toward sustainable urban development, particularly in New England. This study investigates the feasibility and energy efficiency of converting an older, unused industrial building into a multi-family residential structure by evaluating three distinct wall assemblies that preserves the exterior brick facade: existing structural brick, a typical adapted wall assembly with interior 2x4 wood construction, and a high-efficiency adapted wall assembly with an additional interior 2x6 wood construction. Through a detailed case study analysis focusing on the building’s core and shell, this research aims to identify the most effective wall assembly for …

Comparative Analysis Of Earth-Bermed And Conventional; Single-Family, Residential Homes In The Hudson Valley Region, Beacon, New York, Michael G. Patchen

Theses

This thesis is a comparative analysis of the environmental sustainability between two similar square-foot, single-family residential homes constructed as a bermed shelter and the conventional wood frame in the Hudson Valley Region, Beacon, New York. The research will focus on the two designs' embodied carbon, cost, and energy use. To quantify the environmental impact of these two construction methods, an in-depth understanding of the diverse host climate is necessary. Climate data and the results from the experiments are collected by performing life cycle, cost, and energy analysis, providing insights into the environmental implications of residential construction methods. The methodology will …

Toward Prototypical Vision Clustering, James Chenhao Liang

Theses

Over the past decades, deep learning methods have emerged as the predominant approach in computer vision. Most approaches rely on complex interactions among pixels, involving heavy computations. However, Human vision possesses a unique attention mechanism that selectively focuses on relevant parts of the visual field while disregarding irrelevant information. This can be likened to a clustering approach, in which individual pixel points are decomposed and reorganized into relevant concepts to address various tasks. This dissertation explores the frontier of vision clustering by integrating innovative prototypical learning with advanced Transformer architectures, which heralds a significant paradigm shift applied to vision clustering. …

Modeling Stormwater Driven Plastic Debris In The Lake Ontario Watershed, Jayson A. Kucharek

Theses

Stormwater-driven plastic debris has emerged as a significant contributor to environmental pollution, particularly in urbanized watersheds like the Lake Ontario basin. This study focuses on developing a comprehensive framework to quantify and predict anthropogenic debris (AD) inputs and transport pathways across Monroe County, NY, using stormwater systems as a key vector. Over two years, empirical data on plastic debris were collected from storm drains retrofitted with LittaTrap™ devices across urban and suburban areas. Debris was weighed, categorized by material and use, and paired with spatial and temporal variables such as land use, rainfall, temperature, and wind events. A Support Vector …

Leveraging Machine Learning For Advanced Malware Detection, Khalifa Al Habtoor

Theses

The detection of malware is an important challenge in the field of cybersecurity, which is demanding efficient and accurate solutions for making threats easier. This study is focused on the detection of malware by integrating a framework that uses a Mamdani fuzzy interference system (FIS) and diffusion techniques based on three different deep learning algorithms, which are convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs) for malware detection by using recent dataset. This was done by using their outputs through a voting and routing mechanism. The results of the study show that the fuzzy logic …

Amorfe: An Ai-Integrated Interactive Brainstorming Interface, Ipshita Pal

Theses

Amorfe is an interactive multi-touch digital interface that reimagines brainstorming and collaboration using artificial intelligence (AI). Designed to make ideation more dynamic, the platform allows participants to explore, merge, and refine ideas in real time. AI is a creative catalyst, offering unpredictable outcomes and streamlining the brainstorming process. This makes Amorfe engaging, intuitive, and inclusive, encouraging active participation. The platform’s potential was demonstrated through a hypothetical scenario: “How do we create more green spaces on the RIT campus?” Four participants explored various brainstorming phases through touch interaction, leading to the creation of a prototype simulation video and a promotional video. …

Rental Property Demand And Supply Analysis Using Machine Learning, Ahmed Amiri

Theses

The real estate market in Dubai is famous for its activity and stimulating potential due to the geographical position of the emirate, well-developed transport and legal framework. Timely determination and prediction of rental price is crucial for investors, property owners and managers, tenants and authorities in their decision making to maximize returns and ensure market stability. Traditional approaches are insufficient for the analysis of the temporal and spatial relations between property characteristics and market processes, which require the use of sophisticated machine learning algorithms. This paper aims to predict the rental prices of properties in Dubai using machine learning models …

Social Media Intelligence: Responding To Crime And Violence In The Digital World, Chloe Lynn Sitton

Theses

The integration of social media into everyday life has transformed how crime is committed, communicated, and investigated. This capstone project examines the emerging field of social media intelligence and its growing influence on modern policing, surveillance, and the community. Drawing from sociological theory, law enforcement practice, legal analysis, and community-based research, this project offers a far-reaching interdisciplinary view of how social media is shaping crime and justice in the United States.

Machine-Learning And Game-Theoretic Inspired Strategies For Adversarial Freeze-Tag, Matthew Giacovelli

Theses

In this thesis, we explore three machine-learning and game-theoretic inspired strategies to address the Freeze-Tag Problem with adversarial agents (FTPA). We develop a custom Gymnasium envi- ronment where unfrozen robots collaborate to “tag” and unfreeze immobile frozen robots while avoiding being tagged by the adversaries. The FTPA models scenarios such as restoring disabled relay towers after a cyberattack where different towers vary in priority, repair difficulty, and ad- versarial threats. We evaluate three strategies: a baseline heuristic where unfrozen robots target the nearest untargeted frozen robot, a hierarchical approach that incorporates adversary interac- tions and likelihood of success into decision-making, …