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Articles 481 - 510 of 10019
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Improving Obsolescence Management By Enhancing Supplier-Customer Collaboration, Mora Issa
Improving Obsolescence Management By Enhancing Supplier-Customer Collaboration, Mora Issa
Theses
There are currently many legacy facilities operating in the Middle East & Africa region that have plans in place to extend the plant life. Oil prices have been steadily going up, leading to increases in production, and driving economic growth. In order to extend the life of these older facilities, the sites are budgeting to replace critical parts accordingly. Obsolescence is an inevitable part of every product lifecycle and, with the new wave of technology and AI in the market, facilities face the challenge of increasing profitability in the fast-paced economy. The number of more efficient upgrades available presents an …
Analysis Of Team Size Effects On Technical Debt And Code Quality For Open Source Projects, Tyler Borrelli
Analysis Of Team Size Effects On Technical Debt And Code Quality For Open Source Projects, Tyler Borrelli
Theses
Little research has been done to analyze the relationships between the amount of Technical Debt in an open source software project and the number of developers. This study seeks to identify the effects that team size and number of contributors have on Technical Debt and overall software quality. The results of the study observed after accounting for effects from code size that there was a negative correlation for issues with software quality, for example code smells or complexity, and the number of authors on a per file basis. This suggests that increasing the developer count most likely will not negatively …
Predicting Students At Risk Of Dropping Out In The College Of Medicine At Mohammed Bin Rashid University Of Medicine And Health Sciences. A Machine Learning Approach To Understand Attrition Factors Over The Past Seven Years, Moza Sulaiman Al-Zaabi
Theses
This master’s thesis investigates student attrition within the College of Medicine at Mohammed Bin Rashid University of Medicine and Health Sciences (MBRU) by applying the CRISP-DM methodology to analyse a dataset of 19 variables and around 2,000 records from Student Admission and Academic Performance sources. Although the dataset was relatively small, several machine learning models were developed to predict students at risk of dropping out, including Logistic Regression, Decision Trees, Support Vector Machines (SVM), and Artificial Neural Networks (ANN). Of these models, the ANN model, particularly when combined with Principal Component Analysis (PCA), achieved the highest performance with an accuracy …
Ai-Driven Anomaly Detection In Cybersecurity, Mohamed Almansoori
Ai-Driven Anomaly Detection In Cybersecurity, Mohamed Almansoori
Theses
India has witnessed a huge level of digitization in the last two decades which has greatly impacted its economy and society. But the increasing use of technology has also intensified the number and level of threats to cyberspace in the country. Signature-based detection systems that have dominated the cybersecurity field for years have not been up to the challenge of modern attackers who use more complex and diverse attack methods. These challenges are well understood and this research focuses on improving the cybersecurity anomaly detection in India during the period between 2003 and 2022 using advanced AI and machine learning. …
Money Laundering Transactions Chronology Analysis Using Artificial Intelligence, Marwan Alawadhi
Money Laundering Transactions Chronology Analysis Using Artificial Intelligence, Marwan Alawadhi
Theses
Money laundering is one of the most significant threats for the financial systems of the world, as it promotes other crimes and destabilizes the economy. This thesis presents an enhanced analysis of AI and ML on financial transaction data for the identification of patterns that relate to money laundering. It uses techniques like the Isolation Forest for the identification of outliers and clustering techniques like K-means and DBSCAN for identification of unusual transaction patterns across the four high risk countries that include United States, United Kingdom, Japan and Italy. The preprocessed dataset included different types of transactions like cross-border transactions, …
A Summer Class Exploring Computer Science With Educators Of Deaf And Hard Of Hearing Students, Meghan L. Mcsherry, Becca A. Leininger, Maria L. Johnson, Annmarie P. Thomas, Susan Outlaw, Douglas C. Orzolek
A Summer Class Exploring Computer Science With Educators Of Deaf And Hard Of Hearing Students, Meghan L. Mcsherry, Becca A. Leininger, Maria L. Johnson, Annmarie P. Thomas, Susan Outlaw, Douglas C. Orzolek
Journal of Science Education for Students with Disabilities
The purpose of this research project was to design, deliver, and informally assess the content and methodology of an introductory course focused on the block-based coding language, Scratch, for educators of Deaf and Hard of Hearing students (N=20). Prior work by The Playful Learning Lab examined various STEM resources and their effect on student perception of STEM topics and content retention when utilized for Deaf and Hard of Hearing K-12 students. Previous research has shown that computer science and programming resources and curricula available today are not fully accessible for Deaf and Hard of Hearing K-12 students. Educators and teachers …
Student Library News - December 2024, Rit Libraries
Student Library News - December 2024, Rit Libraries
RIT Digital Archives
No abstract provided.
Faculty And Staff Library News - December 2024, Rit Libraries
Faculty And Staff Library News - December 2024, Rit Libraries
RIT Digital Archives
No abstract provided.
Comparative Analysis Of Artificial Intelligence And Statistical Models For Li-Ion Battery Cells State Estimation In Electric Vehicles, Rasha H.A. Tabasha
Comparative Analysis Of Artificial Intelligence And Statistical Models For Li-Ion Battery Cells State Estimation In Electric Vehicles, Rasha H.A. Tabasha
Theses
This graduate paper presents a comparative study of statistical modeling and Artificial Intelligence (AI) approaches for estimating essential parameters of lithium-ion (Li-ion) batteries in electric vehicles (EVs). The key parameters estimated in this study are voltage, current, and temperature of Li-ion battery cells in EVs. A precise state estimation is required to ensure safety operation, to optimize the Battery Management System (BMS), and to enhance the battery life. Variations in EVs batteries parameters may occur due to different reasons such as sensor faults or attacks. As a result, obtaining an accurate estimation is essential to maintain EV battery efficiency. Using …
Lost Roots, Graziela Larroca
Lost Roots, Graziela Larroca
Theses
Lost Roots is an interactive 2.5D exploration game where players unite spirits by growing an underground network of roots. With a limited amount of roots to grow, players must unlock memories of the deceased and navigate through a cemetery in order to advance. The game serves as a unique take on the idea of purgatory, where the soul is essentially stuck and the player must find their own way out. The player first awakens underneath a tombstone with no memories of the past. Their only ability is to navigate through the dirt as a singular root, navigating around obstacles to …
Near Real-Time Center Of Gravity Estimation Of An Unmanned Aerial Vehicle, Carola Vasquez
Near Real-Time Center Of Gravity Estimation Of An Unmanned Aerial Vehicle, Carola Vasquez
Theses
As UAV technology advances and finds broader applications across industries, real-time estimation of the center of gravity (CG) becomes crucial for ensuring safe and efficient flight. Accurate CG estimation is vital not only for UAVs but also for manned aircraft, as it significantly influences stability and control by indicating the point where the entire weight is evenly distributed. Variations in CG, resulting from changes in altitude, attitude, speed, fuel consumption, and payload, directly impact stability and may lead to compromised performance and increased risk of accidents if not monitored. Existing methods focus on regression analysis, multi-accelerometer measurements, and pitching moment …
Design Of A Custom Embedded Replay Control Board For A Vintage Porsche, Christopher Heltz
Design Of A Custom Embedded Replay Control Board For A Vintage Porsche, Christopher Heltz
Theses
Many industries rely on decades old software and electronics to run machines that are essential for America’s industry. These companies operate on the sentiment of “if it’s broke, don’t fix it”. This works great until something breaks, then these same companies are faced with a tough decision. Either replace the old software and electronics with modern solutions or fix the original electronics. Both options have their challenges, the first costs much more in the short term and will require many man hours to design a new solution however the latter will require finding the original documentation or having trained individuals …
Scalable Access-Pattern Aware I/O Acceleration And Multi-Tiered Data Management For Hpc And Ai Workloads, Avinash Maurya
Scalable Access-Pattern Aware I/O Acceleration And Multi-Tiered Data Management For Hpc And Ai Workloads, Avinash Maurya
Theses
The exponential growth of data-intensive scientific simulations and deep learning workloads presents significant challenges for high-performance computing~(HPC) systems. These workloads generate massive data volumes at unprecedented velocities, straining the capabilities of existing memory hierarchies, I/O subsystems, and scheduling mechanisms. This dissertation addresses critical challenges in data management and workload scheduling to enhance performance, scalability, and resource efficiency in next-generation HPC and Artificial Intelligence~(AI) applications. Firstly, we develop advanced data and memory management strategies to alleviate I/O bottlenecks in deep memory hierarchies of modern HPC environments. We significantly mitigate data movement overhead by exploiting application-specific data access patterns and optimizing data …
Making Social Interactions Accessible For The Blind And Low-Vision People By Detecting Emotions Through Vocal Tone, Facial Expressions, And Body Language, Gautham Krishna
Making Social Interactions Accessible For The Blind And Low-Vision People By Detecting Emotions Through Vocal Tone, Facial Expressions, And Body Language, Gautham Krishna
Theses
Despite the thriving research in the field of accessibility, there are several niche area where there is a need for specific solutions. One such area is social interaction. Blind and Low-Vision people (BLV) are limited in identifying emotions through verbal communication alone, which is often biased and unreliable in certain environments. Prior research illustrates the importance of body language in expressions and articulates the mechanism behind human emotions. Drawing inspiratoin from such research and various studies that were focused on emotion recognition systems, our research focused on identifying a portable accessible solution for BLV that classified emotion based on multi-modal …
Impact Of Coolant Temperature On Boiling Chamber Performance For Cooling Electronic Chips, Nooruldeen Mustafa
Impact Of Coolant Temperature On Boiling Chamber Performance For Cooling Electronic Chips, Nooruldeen Mustafa
Theses
Data centers in their quest for ever-increasing processing power are generating more heat than ever before. This heat, if not effectively managed, can lead to equipment failure, reduced performance, and significant financial losses. The development of efficient cooling systems is therefore a pressing need. While numerous two-phase techniques have been developed to meet this escalating heat dissipation demand, their compatibility with free-to-air cooling, especially at higher temperatures, remains a challenge. To address this issue, we propose the use of boiling chambers employing subcooled boiling and submerged condensation mechanisms, an innovative solution that promises effective heat dissipation. This study delves into …
Smart Credit Card Fraud Detection Using Machine Learning, Aisha Bin Sulaiman, Rowdha Masood Falaknaz
Smart Credit Card Fraud Detection Using Machine Learning, Aisha Bin Sulaiman, Rowdha Masood Falaknaz
Theses
This thesis addresses the increasing issue of fraud resulting from technological advancements affecting both customers and fraudsters, while law enforcement agencies face challenges of inadequate case prioritization; data overload and slow investigative analysis. The central objective of this thesis revolves around strategies to counter these challenges, emphasizing the urgency of rapid response to major alert cases that can lead to widespread impacts. The authors' experience in financial fraud detection and credit card crime investigation within the police department has inspired this research, guiding the approach toward practical solutions for everyday operational challenges. The significance of this thesis lies in its …
Utilizing Machine Learning For Categorizing Disaster-Related Tweets, Ahmad Alfalasi
Utilizing Machine Learning For Categorizing Disaster-Related Tweets, Ahmad Alfalasi
Theses
This study presents the development of a machine-learning model that recognizes tweets about disasters in real-time. The fast-paced development of social media, especially Twitter, has drastically changed the field of news transmission during crises. This change creates new challenges for the management of the volume, velocity, and veracity of data that are generated in such situations. The present tools do not have the capabilities of real-time processing of the huge volume of data; hence, new solutions are required. The model we are proposing addresses this problem by using advanced machine learning methods, such as natural language processing, to filter and …
Heat Transfer During Air-Water Two Phase Flow, Saumitra K. Deshpande
Heat Transfer During Air-Water Two Phase Flow, Saumitra K. Deshpande
Theses
The effects of air injection on the heat transfer coefficient for the liquid flow in two-phase in microchannels are studied. Different manifolds and microchannel configurations are used for studying the effects of air injection. The manifold has a uniform gap of 400μm between the heater chip and the cover. A manifold is designed and manufactured to provide different air water flow patterns for different combinations of liquid and air flow rates. The effect of these flow rates on the heat transfer coefficient are studied. The flow rate for air injected is kept very small with respect to the flow rate …
Modeling Impact Of Simulated Myocardial Perfusion On Cardiac Ablation For Arrhythmia, Brendan Cappon
Modeling Impact Of Simulated Myocardial Perfusion On Cardiac Ablation For Arrhythmia, Brendan Cappon
Theses
Bioheat transfer is the study of heat transfer applied in anatomy and physiology, which is relevant to maintaining homeostatic conditions from metabolic activity, thermal exposures, and various treatments and diagnostic methods. Blood flow significantly impacts heat transfer throughout the body, facilitating the diffusion of heat. Models have been developed to quantify bioheat transfer and the effect of blood flow through tissue for many biological functions and medical procedures, one of which is radiofrequency ablation for cardiac arrhythmia. Significant improvements to models and assumptions are still required as success rates for this procedure remain low for various arrhythmia types. The research …
Development And Optimization Of Electric-Double-Layer Gated Transistors Based On Low-Dimensional Materials: Gate-All-Around Inas Nanowire Arrays And Graphene Channels, John Wyatt Morrell
Development And Optimization Of Electric-Double-Layer Gated Transistors Based On Low-Dimensional Materials: Gate-All-Around Inas Nanowire Arrays And Graphene Channels, John Wyatt Morrell
Theses
Electric-double-layer (EDL) gating is a promising alternative to conventional gating methods for field-effect transistors (FETs), with potential applications spanning current logic, future logic, and neuromorphic computing architectures. This technique employs mobile ions in an electrically insulating but ionically conductive medium to generate strong local electric fields at the gate-to-channel interface, achieving field magnitudes on the order of 1 V/nm. These intense local fields enable significant charge carrier accumulation in the semiconducting channel, positioning EDL gating as a strong candidate for device applications. In this study, we investigate the use of low-dimensional channel materials, including two-dimensional graphene and one-dimensional InAs nanowires, …
Artificial Neurovascular Networks To Simulate Energy Distribution To Spiking Neural Networks, William Bottom
Artificial Neurovascular Networks To Simulate Energy Distribution To Spiking Neural Networks, William Bottom
Theses
Biological neurons are known to be much more energy efficient than modern computers, with the human brain only consuming about 20 Watts. An Artificial Neural Network can take hours or days to train on high-performance and energy-consuming GPUs, so networks that can achieve similar performance at a fraction of the energy cost are very sought after. Spiking Neural Networks, which mimic biological neurons more closely than Artificial Neural Networks (ANNs), are a potential option for the energy problem in Neural Networks. By converting an ANN to an SNN and applying energy-saving methods/architectures, the equivalent SNN network can achieve similar performance …
Leveraging Ai To Improve Service Quality For Better Passenger Satisfaction At Airport, Abdulrhman Alsaleh
Leveraging Ai To Improve Service Quality For Better Passenger Satisfaction At Airport, Abdulrhman Alsaleh
Theses
The aim of this research is to explore the impact of airport service quality on passenger satisfaction level in Dubai International Airport (DXB) and the study employed machine learning approach to establish the services quality dimensions relationship with passenger satisfaction. However, the initial objective is to analyze patterns and make future forecasts that DXB could use to enhance service delivery. The research focuses on key service quality dimensions: The study investigates the causal relationship between five constructs: reliability, service quality tangibility, assurance, responsiveness, and empathy using machine learning models such as regression and classification. The information data applies to the …
Using Machine Learning For Road Accident Severity Prediction And Optimal Rescue Pathways, Mohammad Yousuf Sulaiman Alhashmi
Using Machine Learning For Road Accident Severity Prediction And Optimal Rescue Pathways, Mohammad Yousuf Sulaiman Alhashmi
Theses
Road traffic accidents remain a critical challenge for urban planners, transportation authorities, and public safety stakeholders worldwide. Despite advancements in road infrastructure and traffic management, the frequency and severity of accidents continue to strain emergency response systems and compromise public safety. Accident hotspots, are particularly concerning, as they often exhibit recurring patterns of accidents due to factors such as poor road design, high traffic density, and adverse weather conditions. This thesis addresses the challenge of predicting accident severity and identifying accident-prone areas through a data-driven approach. Using a large dataset of over 7.7 million accident records containing geographical, environmental, and …
An Automated Approach To Detect Cyber Threats With Smart Contracts In Blockchain Systems., Alia Mohammad Ahli
An Automated Approach To Detect Cyber Threats With Smart Contracts In Blockchain Systems., Alia Mohammad Ahli
Theses
Blockchain is becoming one of the main components of the modern world due to its rapid development. Considering its main characteristics as a collaborative distributed database that provides transparency and scalability, blockchain development and application have grown dramatically over the past decade. While the blockchain is a key feature and is used in the industry, it is subject to many risk factors and attacks. Advanced technologies that can successfully detect and mitigate vulnerabilities must be developed for solving these security challenges. This thesis presents the development of a machine learning based approach to discover vulnerabilities in blockchain-based systems. It begins …
Enhancing Medical Cybersecurity: A Machine Learning Solution For Intrusion Detection In Iomt Devices, Afra Yaqoob
Enhancing Medical Cybersecurity: A Machine Learning Solution For Intrusion Detection In Iomt Devices, Afra Yaqoob
Theses
In the past few decades, the healthcare sector has undergone major technological advancements, which have included the adoption of Internet of Medical Things (IoMT), and networked medical devices. These advancements have changed the manner in which patient care is provisioned, enabling for more accurate diagnosis, personalized treatment plans, among other benefits. However, as healthcare providers are growing more reliant on interconnected systems, they have also been exposed to critical cybersecurity vulnerabilities. Medical devices have now become targets for cyberattacks, threatening patient data and its security and endangering patient's health and safety. This thesis examines the affects that inadequate cybersecurity measures, …
Using Machine Learning To Predict Energy Consumption In Commercial Buildings In The Uae, Saif Nasser Aljasmi
Using Machine Learning To Predict Energy Consumption In Commercial Buildings In The Uae, Saif Nasser Aljasmi
Theses
The ever-growing demand for energy necessitates a shift towards efficient management practices. Buildings, responsible for a significant portion of global energy consumption, present a prime opportunity for optimization. Building Energy Management Systems (BEMS) have emerged as a crucial tool in this endeavor, offering a comprehensive platform for monitoring and integrating various building systems. However, a key functionality within BEMS, energy consumption prediction, has faced limitations in accuracy. This stagnation hinders the full potential of BEMS for achieving optimal energy management strategies. Traditional methods for energy prediction often rely on historical data and lack the sophistication to capture the complex and …
Analyzing The Effects Of Economic Factors On Residential Real Estate Market Dynamics Of Dubai: Using Machine Learning Methods, Zarrina Shokirova
Analyzing The Effects Of Economic Factors On Residential Real Estate Market Dynamics Of Dubai: Using Machine Learning Methods, Zarrina Shokirova
Theses
Dubai’s residential real estate market is known for its rapid growth and dynamic nature, making it an intriguing subject for analyzing the impact of various economic factors. This thesis examines how key economic elements such as GDP growth, inflation rates, and foreign investment influence property dynamics in Dubai. By synthesizing current research, this study aims to understand how these economic conditions affect real estate values, demand, and supply. Additionally, modern analytical methods, including decision trees, logistic regression, and random forest models, are employed to predict market trends and uncover significant patterns. This research contributes to both academic understanding and practical …
Mitigating Network Congestion In High-Density Areas Through Advanced Distributed Antenna Systems, Amal Abdulrahman Albeshr
Mitigating Network Congestion In High-Density Areas Through Advanced Distributed Antenna Systems, Amal Abdulrahman Albeshr
Theses
This dissertation investigates the optimization of Distributed Antenna Systems (DAS) to address network performance challenges, enhance user satisfaction, and improve operational efficiencies in high-density environments. The study explores the integration of DAS with smart city infrastructure to support modern urban needs, such as real-time monitoring and IoT device connectivity. Set against the backdrop of growing urbanization and increasing demand for reliable connectivity, this research provides a comprehensive analysis of DAS as a transformative solution for telecommunications and smart city applications. The primary research question guiding this study was: How can Distributed Antenna Systems (DAS) be optimized to enhance network performance …
Temporal Analysis And Prediction Of Air Quality Based On Environmental Data, Mizbah Syed
Temporal Analysis And Prediction Of Air Quality Based On Environmental Data, Mizbah Syed
Theses
Air pollution is one of the most pressing environmental challenges of the modern era, significantly impacting public health, ecosystems, and economic stability. Pollutants such as PM2.5, PM10, and NO₂—largely driven by urbanization, industrialization, and vehicular emissions—have exacerbated global health issues, including respiratory illnesses, cardiovascular diseases, and premature mortality. According to the World Health Organization, air pollution contributes to over 7 million deaths annually, with economic losses amounting to billions of dollars. Beyond its health and financial toll, air pollution worsens climate change, underscoring the urgent need for accurate air quality index (AQI) prediction to enable timely interventions and evidence-based policymaking. …
Parametric Analysis Of Wire Arc Additively Manufactured Low-Carbon Steel Using Response Surface Methodology, Mirzagha Halilov
Parametric Analysis Of Wire Arc Additively Manufactured Low-Carbon Steel Using Response Surface Methodology, Mirzagha Halilov
Theses
As the manufacturing sector continues on its path towards sustainability, additive manufacturing (AM) remains as one of its most promising streams, aiming to reduce material waste while also allowing for increased design complexity. In the extensive area of metal 3D printing, one method that is steadily gaining traction is wire-arc additive manufacturing (WAAM). This method of AM utilizes the energy created by an electric arc formed between a continuous consumable metal wire and a metal base, fusing the two together and ultimately creating a 3D component layer by layer. WAAM has several notable advantages over other metal 3D printing methods …