Computer Sciences Commons^™

Detection And Classification Of Diabetic Retinopathy Using Deep Learning Models, Aishat Olatunji

Electronic Theses and Dissertations

Healthcare analytics leverages extensive patient data for data-driven decision-making, enhancing patient care and results. Diabetic Retinopathy (DR), a complication of diabetes, stems from damage to the retina’s blood vessels. It can affect both type 1 and type 2 diabetes patients. Ophthalmologists employ retinal images for accurate DR diagnosis and severity assessment. Early detection is crucial for preserving vision and minimizing risks. In this context, we utilized a Kaggle dataset containing patient retinal images, employing Python’s versatile tools. Our research focuses on DR detection using deep learning techniques. We used a publicly available dataset to apply our proposed neural network and …

An Soa-Based Approach Of Adaptive E-Tutoring Systems, Parth Hetalkumar Mistry

Electronic Theses and Dissertations

The educational technology landscape continually evolves, and e-tutoring systems are pivotal in modern pedagogy. Traditional e-tutoring methods often need help with adaptability and user-friendliness across various devices and platforms. To address these challenges, this research introduces a novel approach that leverages service-oriented architecture (SOA) principles, enhancing scalability and flexibility. The SOA configuration streamlines communication between system components, optimizing question delivery and response evaluation. Additionally, the research contributes adaptive interfaces that intelligently engage users based on their device configurations and preferences, offering facial, vocal, or textual interactions. These interfaces ensure a consistent and tailored learning experience across PCs, laptops, and mobile …

City Guarding With Cameras Of Bounded Field Of View, Mohammad Hashemi

Electronic Theses and Dissertations

We study two problems related to the City Guarding and the Art Gallery problems. 1. Given a city with k rectangular buildings, we prove that 3k+1 cameras of 180◦ field of view (half-sphere guards) are always sufficient to guard the free space (the ground, walls, roofs, and the sky). This answers a conjecture of Daescu and Malik (CCCG, 2020). 2. Given k orthogonally convex polygons of total m vertices in the plane, we prove that (m/2)+k+1 cameras of 180◦ field of view are always sufficient to guard the free space (avoiding all the polygons). This answers another conjecture of Daescu …

Automatic Construction Of Ontology With Public-Domain Datasets For Personalized Tutoring With Eca, Asim Jamal

Electronic Theses and Dissertations

E-tutoring systems have transformed remote learning and personalized education, offering potent tools for tailored instruction. The core of a personalized tutor lies in its robust ontology and knowledge base, working seamlessly to deliver captivating educational experiences. These two integral components collaborate to empower the tutor to discern learners’ needs, adapt content accordingly, and provide tailored guidance. This study introduces an automated approach for constructing an ontology utilizing publicly accessible datasets, aiming to enhance personalized tutoring through Embodied Conversational Agents (ECA). The objective is to improve the tutoring encounter by delivering bespoke, domain-specific knowledge to learners. The approach harnesses natural language …

Xlnet4rec: Modeling User’S Long-Term And Short-Term Interests In E-Commerce Recommender Systems, Namarta Vij

Electronic Theses and Dissertations

In e-commerce, a sequential recommender system is often used to predict the item that the user is likely to select next. This prediction can be used to create a recommender system to assist the user in making selections. However, when the user’s interests evolve over time, it becomes challenging to make such personalized recommendations. A more accurate recommender system thus needs to effectively interpret and adapt to a user’s changing interests by considering user’s long-term and short-term interests. Many attention-based methods focus on a user’s last clicked item to learn short-term interests. However, this approach may not consistently represent the …

Knowledge Informed Fake News Detection Using Large Language Models, Jess Joseph Joseph Benny

Electronic Theses and Dissertations

The spread of false or misleading information as news has been a significant threat to governments, organizations and the economy for a long time. However, it has become more prevalent and influential in recent years due to the growing popularity of social media, which is now the primary source of information for more than half of the world’s population. Detecting fake news used to rely mostly on statistical and linguistic analysis of texts, but with the advancement of AI and computer-assisted writing tools, fake news authors can now deceive statistical models. Therefore, more sophisticated methods that use document representations from …

Adaptive Model Selection In Stock Market Prediction: A Modular And Scalable Big Data Analytics Approach, Mohammadehsan Akhavanpour

Electronic Theses and Dissertations

In today's globalized economy, financial markets are more interconnected than ever, generating vast amounts of data from thousands of sources every second. The need to accurately analyze and interpret this data is crucial for investors, analysts, and researchers alike. Traditional models for market prediction are limited by their ability to adapt to the real-time nature and 'big data' dimensions of these complex financial datasets. To address these challenges, this thesis proposes and implements a novel framework that combines Apache Kafka with a microservices framework. This framework offers a scalable, real-time solution for financial market prediction that effectively manages the 5Vs …

Reinforcement Learning: Applying Low Discrepancy Action Selection To Deep Deterministic Policy Gradient, Aleksandr Svishchev

Electronic Theses and Dissertations

Reinforcement learning (RL) is a subfield of machine learning concerned with agents learning to behave optimally by interacting with an environment. One of the most important topics in RL is how the agent should explore, that is, how to choose actions in order to rate their impact on long-term reward. For example, a simple baseline strategy might be uniformly random action selection. This thesis investigates the heuristic idea that agents will learn faster if they explore by factoring the environment’s state into their decision and intentionally choose actions which are as different as possible from what they have previously observed. …

Advanced Deep Learning Multivariate Multi-Time Series Framework For A Novel Covid-19 Dataset, Swastik Bagga

Electronic Theses and Dissertations

This thesis introduces an innovative framework aimed at addressing the complexities of predicting outcomes in multivariate multi time series datasets in regression analysis. By applying this framework to a novel COVID-19 dataset, it enhances predictive analytics by providing accurate forecasts for epidemic trends at regional or provincial levels, going beyond national-level analysis. The framework incorporates advanced data preprocessing, feature selection, engineering, encoding, and model architecture, effectively capturing intricate variable interactions and temporal dependencies. This makes it a powerful tool for tackling multivariate multi time series regression challenges, offering valuable insights for informed decision-making. Predicting outcomes in such datasets is challenging …

Mitigating The Shortcomings Of Language Models: Strategies For Handling Memorization & Adversarial Attacks, Aly Kassem

Electronic Theses and Dissertations

Deep learning models have recently achieved remarkable progress in Natural Language Processing (NLP), specifically in classification, question-answering, and machine translation. However, NLP models face challenges related to security and privacy. Security-wise, even small perturbations in the input can significantly impact a model's prediction. This highlights the importance of generating natural adversarial attacks to analyze the weaknesses of NLP models and bolster their robustness through adversarial training (AT). Conversely, Large Language Models (LLMs) are trained on vast amounts of data, which may include sensitive information. If exposed, this poses a risk to personal privacy. LLMs can memorize portions of their training …

Context-Driven Behavior: Improved Contextual Reasoning For Context-Aware Agents, Christian L. Wilson

Electronic Theses and Dissertations

Over the last three decades, a considerable amount of research has been dedicated to improving an artificial agent's ability to recognize and deal effectively with context. In this paper, I discuss a framework for a novel form of contextual reasoning. Unlike existing contextual reasoning frameworks, which allow an agent to apply its contextual knowledge after it is operating in an instance of a known context, the model I discuss allows an agent to reason about context proactively. With a proactive model, an agent forecasts the future contexts it will encounter, then takes steps to ensure its behaviors are appropriate for …

A Bridge Between Graph Neural Networks And Transformers: Positional Encodings As Node Embeddings, Bright Kwaku Manu

Electronic Theses and Dissertations

Graph Neural Networks and Transformers are very powerful frameworks for learning machine learning tasks. While they were evolved separately in diverse fields, current research has revealed some similarities and links between them. This work focuses on bridging the gap between GNNs and Transformers by offering a uniform framework that highlights their similarities and distinctions. We perform positional encodings and identify key properties that make the positional encodings node embeddings. We found that the properties of expressiveness, efficiency and interpretability were achieved in the process. We saw that it is possible to use positional encodings as node embeddings, which can be …

Enhanced Content-Based Fake News Detection Methods With Context-Labeled News Sources, Duncan Arnfield

Electronic Theses and Dissertations

This work examined the relative effectiveness of multilayer perceptron, random forest, and multinomial naïve Bayes classifiers, trained using bag of words and term frequency-inverse dense frequency transformations of documents in the Fake News Corpus and Fake and Real News Dataset. The goal of this work was to help meet the formidable challenges posed by proliferation of fake news to society, including the erosion of public trust, disruption of social harmony, and endangerment of lives. This training included the use of context-categorized fake news in an effort to enhance the tools’ effectiveness. It was found that term frequency-inverse dense frequency provided …

Geo-Location Informed Team Formation Using Gnn, Karan Saxena

Electronic Theses and Dissertations

Establishing a competent team is crucial to the success of a project and is influenced by skill distribution and geographic proximity. A team not only benefits from the shared knowledge amongst the team members derived from geographic closeness but also affects the outcome of the project the team is assigned to perform. A team benefits by sharing resources among each member, collaborating efficiently on a given task, brainstorming on an idea more effectively and saving time and money for both the team members and the organization. This thesis uses a neural-based multi-label classifier after a spatial team formation that uses …

Enhancing Search Engine Results: A Comparative Study Of Graph And Timeline Visualizations For Semantic And Temporal Relationship Discovery, Muhammad Shahiq Qureshi

Electronic Theses and Dissertations

In today’s digital age, search engines have become indispensable tools for finding information among the corpus of billions of webpages. The standard that most search engines follow is to display search results in a list-based format arranged according to a ranking algorithm. Although this format is good for presenting the most relevant results to users, it fails to represent the underlying relations between different results. These relations, among others, can generally be of either a temporal or semantic nature. A user who wants to explore the results that are connected by those relations would have to make a manual effort …

Matches Made In Heaven Or Somewhere: Personalized Query Refinement Gold Standard Generation Using Transformers, Yogeswar Lakshmi Narayanan

Electronic Theses and Dissertations

The foremost means of information retrieval, search engines, have difficulty searching into knowledge repositories, e.g., the web, because they are not tailored to the users' differing information needs. User queries are, more often than not, under-specified or contain ambiguous terms that also retrieve irrelevant documents. Query refinement is the process of transforming users' queries into new refined versions without semantic drift to enhance the relevance of search results. Prior query refiners have been benchmarked on ad-hoc web retrieval datasets following weak assumptions that users' input queries improve gradually within a search session. Existing methods also have employed additional metadata, such …

Many-To-One: Transformer-Based Unsupervised Anomaly Detection And Localization On Industrial Images, Naga Jyothirmayee Dodda

Electronic Theses and Dissertations

Anomaly detection is of utmost importance in the realm of industrial defect identification, particularly when employing computer vision-based inspection mechanisms within quality control systems. This research introduces the Many-to-One (M2O) framework, which relies on a multi-level transformer encoder combined with a single transformer decoder, which forms many-to-one relation in the framework for detecting and localizing anomalies. The rise of Industry 4.0 and electric vehicles has increased interest in this area. Although previous research has made significant contributions, challenges still exist in this area. It is crucial to develop models that can generalize well and overcome time complexity problems that affect …

Anomaly Detection In Large Datasets: A Case Study In Loan Defaults, Rayhaan Pirani

Electronic Theses and Dissertations

Given the rise in loan defaults, especially after the COVID-19 pandemic, it is necessary to predict if customers might default on a loan for risk management. This thesis proposes an early warning system architecture using anomaly detection based on the unbalanced nature of loan default data in the real world. Most customers do not default on their loans; only a tiny percentage do, resulting in an unbalanced dataset. We aim to evaluate potential anomaly detection methods for their suitability in handling unbalanced datasets. We conduct a comparative study on different anomaly detection approaches on four balanced and unbalanced datasets. We …

An End-To-End Hybrid Approach To Automatic And Semi-Automatic Image Annotation, Roisul Islam Rumi

Electronic Theses and Dissertations

This thesis presents a new end-to-end hybrid machine learning (ML) and deep learning (DL) approach for semi-automatic image annotation (SAIA) and automatic image annotation (AIA) in industrial assembly line setups. Image annotation refers to adding descriptive labels or tags to an image to provide information about the objects and features present in the image. On a high level, the proposed system uses the following steps to annotate images. The first step involves using an ML algorithm, Haar cascade, to split an image into smaller regions of interest (ROI) based on the object of interest, in our case, the connectors of …

An Approach Of Lip Synchronization With Facial Expression Rendering For An Eca, Mohammed Maaz Mohammed Shoieb Khan

Electronic Theses and Dissertations

E-Tutoring systems have emerged as effective tools for remote learning and personalized education. However, to foster engaging and interactive experiences, there is a need to enhance the communication and expressiveness of virtual tutors within these systems. This research focuses on integrating facial expression animation and lip-syncing capabilities into an e-tutoring system, aiming to improve the realism and effectiveness of virtual tutor interactions. The study presents a novel approach to animating the facial expressions of the virtual tutor’s avatar using computer graphics, 3D animation, and computer vision techniques. Working with 3D files and using them to interpolate each face vertex are …

Transmission Power Based Congestion Control Using Q-Learning Algorithm In Vehicular Ad Hoc Networks (Vanet), Pooja Chandrasekharan

Electronic Theses and Dissertations

To enhance road safety, Vehicular ad hoc networks (VANETs), an emerging wireless technology used for vehicle-to-vehicle and vehicle-to-infrastructure communication, are essential components to reduce road accidents and traffic congestion in Intelligent Transportation Systems (ITS). It also provides additional services to vehicles and their users. However, vehicles must balance awareness and congestion control in a dynamic environment to efficiently transmit basic safety messages (BSMs) and event-driven warnings. The limited channel capacity makes the reliable delivery of BSMs a challenging problem for VANETs. This paper aims to optimize the performance of VANETs by effectively managing channel load and reducing congestion by maintaining …

A Quantitative Visualization Tool For The Assessment Of Mammographic Risky Dense Tissue Types, Margaret R. Mccarthy

Electronic Theses and Dissertations

Breast cancer is the second most occurring cancer type and is ranked fifth in terms of mortality. X-ray mammography is the most common methodology of breast imaging and can show radiographic signs of cancer, such as masses and calcifcations. From these mammograms, radiologists can also assess breast density, which is a known cancer risk factor. However, since not all dense tissue is cancer-prone, we hypothesize that dense tissue can be segregated into healthy vs. risky subtypes. We propose that risky dense tissue is associated with tissue microenvironment disorganization, which can be quantified via a computational characterization of the whole breast …

Accessible Autonomy: Exploring Inclusive Autonomous Vehicle Design And Interaction For People Who Are Blind And Visually Impaired, Paul D. S. Fink

Electronic Theses and Dissertations

Autonomous vehicles are poised to revolutionize independent travel for millions of people experiencing transportation-limiting visual impairments worldwide. However, the current trajectory of automotive technology is rife with roadblocks to accessible interaction and inclusion for this demographic. Inaccessible (visually dependent) interfaces and lack of information access throughout the trip are surmountable, yet nevertheless critical barriers to this potentially lifechanging technology. To address these challenges, the programmatic dissertation research presented here includes ten studies, three published papers, and three submitted papers in high impact outlets that together address accessibility across the complete trip of transportation. The first paper began with a thorough …

Proposing A Measure Of Ethicality For Humans And Ai, Alejandro Jorge Napolitano Jawerbaum

Electronic Theses and Dissertations

Smarter people or intelligent machines are able to make more accurate inferences about their environment and other agents more efficiently than less intelligent agents. Formally: ‘Intelligence measures an agent’s ability to achieve goals in a wide range of environments.’ (Legg, 2008)

In this dissertation we extend this definition to include ethical behaviour and we will offer a mathematical formalism and a way to estimate how ethical an action is or will be, both for a human and for a computer, by calculating the expected values of random variables. Formally, we propose the following measure of ethicality, which is computable, or …

A Data-Driven Multi-Regime Approach For Predicting Real-Time Energy Consumption Of Industrial Machines., Abdulgani Kahraman

Electronic Theses and Dissertations

This thesis focuses on methods for improving energy consumption prediction performance in complex industrial machines. Working with real-world industrial machines brings several challenges, including data access, algorithmic bias, data privacy, and the interpretation of machine learning algorithms. To effectively manage energy consumption in the industrial sector, it is essential to develop a framework that enhances prediction performance, reduces energy costs, and mitigates air pollution in heavy industrial machine operations. This study aims to assist managers in making informed decisions and driving the transition towards green manufacturing. The energy consumption of industrial machinery is substantial, and the recent increase in CO2 …

Cannabidiol Tweet Miner: A Framework For Identifying Misinformation In Cbd Tweets., Jason Turner

Electronic Theses and Dissertations

As regulations surrounding cannabis continue to develop, the demand for cannabis-based products is on the rise. Despite not producing the psychoactive effects commonly associated with THC, products containing cannabidiol (CBD) have gained immense popularity in recent years as a potential treatment option for a range of conditions, particularly those associated with pain or sleep disorders. However, due to current federal policies, these products have yet to undergo comprehensive safety and efficacy testing. Fortunately, utilizing advanced natural language processing (NLP) techniques, data harvested from social networks have been employed to investigate various social trends within healthcare, such as disease tracking and …

Evaluating Chatgpt For Recommendation: How Does The Ability To Converse Impact Recommendation?, Kyle Spurlock

Electronic Theses and Dissertations

Recommendation algorithms have become an absolute necessity in the modern world to avoid information overload. However, the interaction between the human and the system is largely superficial and without any real contact. If you are given poor recommendations, you have no choice but to sift through mountains of content on your own until the model learns to accommodate your tastes more. This is bad for business as well as the consumer. Recently, large language models like ChatGPT have seen a significant rise in popularity due to their ease of use and wide range of knowledge. It has now become nearly …

Controllable Language Generation Using Deep Learning, Rohola Zandie

Electronic Theses and Dissertations

The advent of deep neural networks has sparked a revolution in Artificial Intelligence (AI), notably with the creation of Transformer models like GPT-X and ChatGPT. These models have surpassed previous methods in various Natural Language Processing (NLP) tasks. As the NLP field evolves, there is a need to further understand and question the capabilities of these models. Text generation, a crucial part of NLP, remains an area where our comprehension is limited while being critical in research.

This dissertation focuses on the challenging problem of controlling the general behaviors of language models such as sentiment, topical focus, and logical reasoning. …

Extending The Work Of Dt-Fixup: Examining The Effects Of Powernorm And Madgrad Optimization On Dt-Fixup Performance, Prem Shanker Mohan

Electronic Theses and Dissertations

With the introduction of the attention technique, the Bidirectional Encoder Representations from Transformers (BERT) have greatly advanced the study of solving sequence-to-sequence tasks in Natural Language Processing (NLP). When the task-specific annotations are limited, the NLP tasks are commonly performed by pre-training a model using the transformer technique on large-scale general corpora, followed by fine-tuning the model on domain-specific data. Instead of using shallow neural components for fine-tuning, additional transformer layers could be introduced into the architecture. Recent research shows that, by resolving some initialization and optimization issues, these augmented transformer layers could lead to performance gains despite of the …

Reinforcement Learning-Based Data Rate Congestion Control For Vehicular Ad-Hoc Networks, Gnana Shilpa Nuthalapati

Electronic Theses and Dissertations

Vehicular Ad-Hoc Network(VANET) is an emerging wireless technology vital to the Intelligent Transportation System(ITS) for vehicle-to-vehicle and vehicle-to-infrastructure communication. An ITS is an advanced solution that aims to deliver innovative services pertaining to various transportation modes and traffic management. Its objective is to enhance user awareness, promote safety, and enable more efficient and coordinated utilization of transport networks. ITS aims to mitigate traffic problems and improve the safety of transport by preventing unexpected events. When the vehicle density, i.e., the number of vehicles communicating in a wireless channel, increases, the channel faces congestion resulting in unreliable safety applications. Various decentralized …