Digital Commons Network^™

A Framework For Filtering Irrelevant Notams, Roudha Abdulrahman

Theses

The Notice to Airmen (NOTAM) system is essential for aviation safety, giving critical information on risks and operating limitations. However, the volume and complexity of NOTAM data complicate interpretation, which could compromise safety and efficiency. This study tries to address these issues by creating a predictive algorithm for analyzing NOTAM data and predicting whether to keep or remove them. The methodology combines effective machine learning algorithms to reveal insights that improve safety and operational efficiency. Using a dataset of NOTAM entries, multiple prediction algorithms are tested to forecast possible problems and enable proactive risk management. The study's findings help to …

Improving Automatic Refactoring Candidate Identification, Ryan Devoe

Theses

Extract method refactoring is pivotal for enhancing code readability, maintainability, and modularity by segmenting complex code into clearer, isolated methods. Identifying opportunities for such refactorings necessitates a deep understanding of the codebase’s evolution and its intricate relationships. Current methodologies utilize developer commit messages, advanced graph analysis, and diverse machine learning approaches to automate this identification process. This research delves into the application of deep learning-based Large Language Models (LLMs) to tackle the complexities inherent in extract method refactoring. We introduce innovative approaches, including the use of LLMs to cluster code blocks based on complex patterns and dependencies, and the analysis …

Evolutionary Neural Network For Optimized Clock Tree Synthesis, Patrick Jeffery

Theses

Clock Tree Synthesis (CTS) is a complex and in depth process that, in modern designs, would take an individual months if not years to get a working design. Tools such as Cadence Innovus and Synopsys ICC provide excellent support for CTS and can be used to create well optimized trees; allowing the user to edit the tree’s generation down to a single buffer. However, even these tools can fall short of a perfectly optimized route and oftentimes need a capable user to direct them in the right direction just to get a functioning clock tree. The aim of this work …

Predicting Student Attrition In Higher Education Institutions In The Uae Using Machine Learning, Dezzil M. Castelino

Theses

UAE has made significant progress in the field of education, including Higher Education, by attracting students from all around the world to various colleges and universities. Student dropout or attrition is a major issue that is faced by Higher Education Institutions (HEI). Hence, we need effective techniques to identify student data that affects attrition. This research aims to explore the use of machine learning algorithms to predict student attrition at Rochester Institute of Technology- Dubai (RIT Dubai). In this research, the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology was used as it is widely used in projects based on …

An Analysis Of Machine Learning Hardware From Tenstorrent, Matthew Stephen Doerner

Theses

Machine learning is growing at an exponential rate in industry today, with contemporary machine learning models having parameter counts in the high hundred millions to trillions paired with a huge computing requirement. As the field continues to grow and the models get larger and more computationally expensive, there is a greater need for specialized hardware that can accelerate the inference and training computations that are required of these machine learning models. The design of a custom hardware device specifically made to accelerate machine learning models is very intricate, with software and hardware development that needs to work together at the …

Continual Learning For An Ever Evolving And Intelligent Malware Classification System, Mohammad Saidur Rahman

Theses

Malware classification poses unique challenges for continual learning (CL) systems, driven by the daily influx of new samples and the evolving nature of malware threats that exploit new vulnerabilities. Antivirus vendors encounter hundreds of thousands of unique software pieces daily, encompassing both malicious and benign files. Over its operational life, a malware classifier can accumulate more than a billion samples. Training malware classification system with only new samples and classes leads to catastrophic forgetting (CF), where the system forgets previously learned data distribution. While retraining with all old and new samples effectively combats CF, it is computationally expensive and necessitates …

Driver’S Accident Behavioral Analytics Using Ai, Mohamad Amin Obaid

Theses

This comprehensive dissertation constitutes a significant contribution to the ongoing global discourse on road safety. Through a judicious utilization of advanced data analysis techniques, with a particular emphasis on machine learning applications, this research endeavors to address and bridge crucial gaps in our comprehension of multifaceted aspects related to road safety. Specifically, the study aims to delve into the intricacies of accident severity factors, driver characteristics, vehicle attributes, and the complex dynamics of road conditions. By systematically exploring these dimensions, the research endeavors to unearth more nuanced and precise relationships that influence accident outcomes. Moreover, a particular focus is dedicated …

Bitcoin Short-Term Price Prediction Using Time Series Analysis, Alanood Alkamali

Theses

This thesis explores the application of Autoregressive Integrated Moving Average (ARIMA) model to predict Bitcoin prices, a prominent and volatile cryptocurrency. The research falls within the context of financial forecasting, focusing specifically on the cryptocurrency markets. The primary research question is: “Can time series analysis be used to predict the future price of Bitcoin?” To answer this question, historical Bitcoin daily price data from 17/09/2014 to 17/09/2023 was obtained from Kaggle and analyzed. The study employs ARIMA modeling techniques to capture the autocorrelation, seasonality, and trend present in Bitcoin price time series. As a prerequisite for ARIMA modeling, the data …

Curation And Analysis Of Ai Ready Environmental Justice Datasets : A Proof-Of-Concept Study, Paridhi Parajuli

Theses

Equity and Environmental Justice (EEJ) advocates for unbiased distribution of environmental impacts across communities, regardless of social and economic characteristics. After extreme events like natural disasters, EEJ gains importance due to evident disparities in impact among communities. Addressing these injustices requires comprehensive datasets and analytical methods for quantification and resolution. While AI and advanced data analysis offer promising solutions, creating AI-ready EEJ datasets is challenging due to heterogeneity in the data surrounding EEJ. In this work, we focus on curating novel datasets for EEJ targeting a few recent extreme events - Maui Wildfire, Hurricane Harvey, and Hurricane Ida. We demonstrate …

Ambient Temperature Modelling With Ecostress And Private Weather Stations, Gaurav Khatri

Theses

This thesis explores the development and application of a novel data architecture for predicting ambient temperatures across US cities, focusing on integrating multi-source data i.e. ECOSTRESS land surface temperatures, urban surface properties, and crowdsourced weather data. The methodology is designed for scalability and adaptability across different urban regions, employing rigorous data quality control to enhance prediction accuracy. The validation of this model across diverse urban settings, demonstrated through rigorous RMSE comparisons and spatial mapping, validates its superiority over traditional models. Through experiments in diverse climatic conditions in Madison, Wisconsin, and Las Vegas, Nevada, the study assesses the model’s generalizability and …

Predictive Methods And Data Pattern Analysis For Reducing Car Plate Theft, Noor Alzayani

Theses

The project titled " Predictive Methods and Data Pattern Analysis for Reducing Car Plate Theft" seeks to provide innovative solutions to combat car plate theft. The project contains data of 6500 thieves who have stolen number plates and were involved in various other types of criminal activities by putting that number plate in their vehicle. The data collected for the present project is from the emirates of Dubai. It contains information related to thieves age, education, nationality, type of crime committed, area in which crime is committed, number of crimes committed, timing of crime, residential status of criminal, visa status, …

Towards Algorithm Selection For Efficient Search-Based Software Engineering, Niranjana Deshpande

Theses

In Search-Based Software Engineering (SBSE), metaheuristic search algorithms are used to construct software by combining components that fulfill diverse user and business requirements. These search algorithms typically employ randomization to accurately and efficiently evaluate numerous component combinations using limited computational resources. As a result, several search algorithms have been proposed to address SBSE problems, each with varying solution quality guarantees and computational resource usage. Recent research has demonstrated that search algorithms exhibit complementary behavior, that is, different search algorithms outperform each other on specific problem instances in terms of computational resource usage and solution quality. Problematically, current SBSE approaches do …

Prediction Of Unplanned 30-Day Readmission Of Heart Failure Patients Using Lstm, Jesdin Raphael

Theses

In the context of heart failure, a leading cause of hospitalization in the United States, approximately 15\% of patients discharged within 30 days face readmission, contributing to escalated healthcare costs and compromised clinical outcomes. This study leverages a wealth of personal information extracted from Emergency Health Records (EHRs) spanning two decades. This research aims to enhance the predictive capabilities of re-hospitalization for cardiac patients by creating a two-stage LSTM model while incorporating features into regression and time series datasets. A critical aspect of this study involves ensuring the integration of all relevant features across both datasets. Including constant data elements, …

Knowledge Integration For Human-In-The-Loop Machine Learning, Ervine Zheng

Theses

Machine learning has evolved into advanced techniques for vision, language, and applications in different areas. However, human expertise is still essential in providing meaningful interpretations of the semantics for tasks in knowledge-rich domains, such as medicine, science, and security intelligence. It is beneficial to incorporate human knowledge into a machine learning system, and we consider human-in-the-loop an increasingly important area for future research. Human-in-the-loop machine learning aims to develop a reliable prediction model with minimal costs by integrating human knowledge and experience. In this thesis, we propose human-in-the-loop methods to address knowledge-rich data understanding challenges for different machine-learning tasks. Specifically, …

Bearings Fault Classification Using Machine Learning And Dashboard For Bearing Signals Vibration, Ameirah Mohamed

Theses

Bearings are crucial components for the mechanical system that allows relative motion between two parts. Bearings primarily used to reduce the friction on the component. However, Bearings can fail for several reasons such corrosions, fatigue and electrical damage ..etc .This research aims to develop an accurate prediction model and visualization dashboard to avoid the costly downtimes, high repair cost and enhance the maintenance experience for workers and engineers. The data used in this research is CASE WESTERN RESRVE UNIVERSITY (CWRU) bearing data set. The data set contains signals vibration readings of bearings that can used for bearings fault detection. The …

Using Ml To Understand The Factors Impacting Diabetes In Diabetic Patients, Marwa Ahmed Almarzooqi

Theses

Diabetes, a well-known medical condition since ancient times, has become a prevalent and significant health concern in recent decades. The rising incidence of diabetes has necessitated early diagnosis and effective treatment. Machine learning (ML) innovations have revolutionized disease prediction and decision-making by utilizing massive datasets. This study aims to develop and compare machine learning (ML) models for diabetes prediction using a preprocessed dataset of 532 instances obtained from Kaggle. Important variables included in the data set are Pregnancies, Glucose, Blood Pressure, Skin Thickness, Insulin, BMI, Diabetes Pedigree Function, Age, and Outcome. The correlation analysis revealed a strong positive association between …

A Morphological Analysis Of High Redshift Galaxy Mergers Using Machine Learning, Caitlin Rose

Theses

Galaxy mergers play an important role in the formation and evolution of galaxies. However, identifying mergers can be difficult, especially at high redshift, due to effects such as: cosmological surface brightness dimming, poor resolution of images, the shifting of optical light to the infrared, and the inherently more irregular morphologies of younger galaxies. The advent of JWST and new deep, high-resolution near-infrared NIRCam images from the Cosmic Evolution Early Release Science Survey (CEERS) will help mitigate some of these problems to better detect high redshift merger features. Simultaneously, sophisticated machine learning analysis techniques have the po- tential to more accurately …

Study To Improve The Employee Experiences And Reducing The Employee Attrition, Mahra Essa Mohammed Belarai Alfalasi

Theses

Employee attrition is a significant issue that occurs in every company today, regardless of external environment changes. According to the definition of attrition, the number of employees gradually decreases due to retirement, death, and resignations (Marais, 2022). Attrition can occur when a well-trained and well-adjusted talented person leaves the company for any reason, leaving a gap in the workplace (BasuMallick, 2021). It is extremely difficult for HR employees to fill the gap that has been created. For today's managers, minimizing turnover rates is a major concern, and modern HR managers do this in several ways, the employee's decision was motivated …

Early Detection Of Autism Spectrum Disorder (Asd) Using Machine Learning Techniques, Mahra Musabbah Bin Beyat Alfalasi

Theses

ASD is a neurological disorder that affects over 1 in 44 chilren and this rate has increased. The diagnosis process can be timely and costly. This will make it difficult for the patients to adhere to the prescribed treatments and will hinder the progress of the patient. This project is focused on increasing the efficiency of this diagnosis process through machine learning techniques. The proposed datasets are; ASD Screening Data for Adult, ASD screening Data for Children and ASD Screening Data for Adolescent. These datasets are a categorical, continuous, and binary data type. They have 21 common attributes and a …

Customised Investment Optimization Using Genetic Algorithms, Maythaa Al Ali, Shaikha Al Dossari

Theses

Portfolio selection is an important part of fund management as it contributes to investors’ economic growth. Investing is investing money to obtain an additional or specific advantage over money. Investment involves not only profit (return), but also risk that the investor bears. The higher the return an investor expects, the higher the risk the investor takes. Proper portfolio construction can minimize the level of risk to the expected value of an individual stock portfolio. Equity portfolio optimization, therefore, plays an important role in setting an investor’s investment portfolio strategy. In recent decades, there have been great advances in financial mathematics. …

Towards A Reliable Machine Learning-Based Model Designed For Translating Sign Language Videos To Text, Maitha Essa Mohammad Ahli

Theses

Communication serves key roles in building relationships through sharing feelings, passing information, and connecting with others. Communication among the hearing impaired remains a significant stumbling block in today’s society since their communication means demands for an interpreter each moment. Various researchers agree that successful communication calls for the involvement of all individuals in a conversation and thus, deaf and hearing-impaired people require precise and welcoming communication to promote their working and learning relationships. Sign Language Recognition (SLR) is a critical and auspicious approach to promoting communication among hearing-impaired people. Sign languages greatly benefit from Machine Learning based translation techniques since …

Quantifying The Nexus Of Climate, Economy, And Health: A State-Of-The-Art Time Series Approach, Kameron Blair Kinast

Theses

Extreme weather events pose significant threats to human life, the economy, agriculture, and various other socio-economic aspects. This thesis presents a comprehensive analysis of the patterns of climate factors and their impact on the economy and human health using state-of-the-art and emerging statistical machine learning techniques. This research consists of two parts: exploring and comparing the effectiveness of statistical models with respect to climate time series forecasting and analyzing the effects on the economy and human health. The study employs a predominantly computational approach, leveraging R, Python, and Julia to demonstrate the role of statistical computing in understanding climate change …

Hardware Accelerators And Their Use In Computer Vision, Cameron Villone

Theses

The evolution of technology is impressive. Before digital, there was analog; before software, there needed hardware. This evolution is natural as we try and optimize technology for our needs. The shift to digital was fueled by the space saved from using digital systems compared to analog. When it came to software, the ability to use generic hardware in the forms of cen- tral processing units; CPUs, Graphics Processing Units; GPUs, and Random Access Memory; RAM allowed for complex software solutions to be able to run on many different devices with- out much need for translations. With software development getting so …

Machine Learning Based Framework For Smart Contract Vulnerability Detection In Ethereum Blockchain, Qusai Omar Mustafa Hasan

Theses

Abstract

Blockchain technology is a disruptive technology that revolutionized digital payments and transactions of digital assets. Blockchain transactions operate using smart contracts which are automated software code that facilitates transactions between parties without the need for intermediary systems. Smart contracts have become an increasingly popular means of conducting transactions and executing code in a decentralized manner. As it can be written in various languages which have their flaws in terms of logic and vulnerabilities, also the immutability and autonomy of smart contracts also make them vulnerable to various security threats. Security for smart contracts is essential as exploiting bad logic …

Quantum Acceleration Of Linear Regression For Artificial Neural Networks, Martin A. Hoffnagle

Theses

Through proofs and small scale implementations, quantum computing has shown potential to provide significant speedups in certain applications such as searches and matrix calculations. Recent library developments have introduced the concept of hybrid quantum-classical compute models where quantum processor units could be used as additional hardware accelerators by classical computers. While these developments have opened the prospect of applying quantum computing to machine learning tasks, there are still many limitations of near and midterm quantum computing. If implemented carefully, the advantages of quantum algorithms could be used to accelerate current machine learning models. In this work, a hybrid quantum-classical model …

Investigating The Impact Of Baselines On Integrated Gradients For Explainable Ai, Ajay Shewale

Theses

Deep Neural Networks have rapidly developed over the last few years, demonstrating state-of-the-art performances on various machine learning tasks such as image classification, natural language processing, and speech recognition. Despite their remarkable performance, deep neural networks are often criticized for their need for more interpretability, which makes it difficult to comprehend their decision-making process and get insights into their workings. Explainable AI has emerged as an important area of study that aims to overcome this issue by providing understandable explanations for deep neural network predictions. In this thesis, we focus on one of the explainability methods called Integrated Gradients (IG) …

An Analysis Of Heterogeneous Quantization Schemes For Neural Networks, Anusha Holavanahali

Theses

Quantization of neural network models is becoming a necessary step in deploying artificial intelligence (AI) at the edge. The quantization process reduces the precision of model parameters, thereby lowering memory and computational costs. However, in doing so, this process also limits the model’s representational capacity, which can alter both its performance on nominal inputs (clean accuracy) as well as its robustness to adversarial attacks (adversarial accuracy). Few researchers have explored these two metrics simultaneously in the context of quantized neural networks, leaving several open questions about the security and trustworthiness of AI algorithms implemented on edge devices. This research explores …

Investigating The Importance Of Non-Textual Caption Properties From Deaf And Hard Of Hearing Viewers' Perspective, Akhter Al Amin

Theses

Deaf and hard-of-hearing (DHH) viewers rely on captioning to perceive auditory information while watching live television programming. Captioning ensures that viewers can receive textual and non-textual information that is crucial to comprehend the content of the video. However, no research has investigated how non-textual properties of captioning, such as caption placement and the presentation of current speakers, influence DHH viewers’ ability to comprehend video content and their judgement of a captioned video’s quality. Thus, this work aims to understand the effect of non-textual properties of captioning on DHH viewers’ live captioned video watching experiences; these findings can inform a better …

House Price Prediction Using Machine Learning Model, Abdulla Alfalasi

Theses

Data Analytics and Machine Learning play an important role in extracting insights and patterns from datasets. The primary objective of using the above techniques for real world problems is to understand the intricacies of the problem, which is impossible with the help of manual human effort. The below chart shows the price trend (per sq. ft) over the past 10 years in Dubai, with a steady rise during the years from 2014. The 2020 pandemic plummeted the global prices due to the lack of demand and travel restrictions, which is clearly depicted in the chart. But at the very end …

Using Decision-Tree Analysis For Predicting Wildfires Impact On Vegetation In Australia, Saif Al-Khuraisat

Theses

This research aims to use machine learning algorithm to predict wildfires impact on green landscape based on weather conditions while exploring factors that could influence the possibility. By utilizing the generated results, it could help decision-makers and authorities to come-up with best possible strategy to prepare for and respond to wildfires. Being prepare will work toward the economy best interest by saving unintended expenditure in time of crises also saves the cash liquidity along the economy on the long-term. For example, back in East cost of United States of America in Florida, in 1998, the estimated economical losses because of …