Computer Engineering Commons^™

A Beginner’S Guide To Artificial Intelligence (Ai) And Generative Ai (Gen Ai) For Small Businesses, Stanley Mierzwa, Iassen Christov

Center for Cybersecurity

Generative Artificial Intelligence (GenAI) guide for small businesses.

Support and funding for this effort was received from the United States Small Business Administration (Contract 73351023C0016) for this activity and research. Formulated and ultimately created as a result of this grant was the New Jersey Cybersecurity Regional Cluster (NJCRC), which included the partners of NTouch-BCT Strategies, Covenant Business Concepts, and the Kean University Center for Cybersecurity. As part of the free cybersecurity risk assessments offered to small businesses in the community, organizations were introduced to GenAI through a demonstration, with the key information provided in this booklet.

Artificial Intelligence In Decision-Making: Literature Review, Najm A. Kh. Alhatimi Aleessawi, Leila Djaghrouri

Journal of the Association of Arab Universities for Research in Higher Education مجلة اتحاد الجامعات العربية للبحوث في التعليم العالي

In the fast-changing world of artificial intelligence (AI), the relationship between technology and decision-making has become a central area of study. Over the past five years, numerous papers have been published examining how AI methods are applied to decision-making processes across various industries. This article aims to highlight the key potential of artificial intelligence to enhance decision-making. It does so by systematically reviewing the literature on the role of AI in improving decision-making, particularly studies published between 2020 and 2024. The review consolidates the main findings from articles in renowned databases such as Google Scholar, Scopus, and IEEE Xplore, offering …

Efficacy Of Immersive Virtual Reality Gameplay In Environmental Attitude Change: The Case Of Abandoned Offshore Oil Platforms In Santa Barbara, Arun Prasad Srinivasan Manoharan

Master's Theses

Public perception plays an important role in shaping conservation policies and decisions, especially in contested environmental spaces. Offshore oil platforms, historically viewed as environmental hazards, have been found to serve as marine habitats that support diverse marine life. However, public perception remains largely negative, influenced by concerns over pollution from past oil spill accidents. Traditional environmental education methods, such as lectures and documentaries, often fail to engage audiences effectively or shift entrenched opinions. This study explores the efficacy of immersive Virtual Reality (VR) gameplay in changing environmental attitudes, specifically in the context of abandoned offshore oil platforms in Santa Barbara, …

Are Emojis The New Words? A Sentiment Analysis Of Social Media Brand Conversations, Yashodhan Karulkar, Dev T. Vora, Siddharth Vaddepalli, Yash Thakur

Journal of International Technology and Information Management

Emojis have become an increasingly important aspect of consumer-brand interactions in the Indian subcontinent. However, the impact of emoji use on brand image and mental health remains underexplored, particularly in emerging economies like India, where structured research on this topic is limited. To address this gap, the present study analyzes over 4,600 consumer tweets related to 19 prominent brands across eleven industries. Using VADER sentiment analysis, the research develops a metric to assess consumer sentiment and brand engagement in relation to emoji usage. The findings indicate that effective integration of emojis contributes to positive consumer sentiment and enhanced brand engagement. …

Modeling Of Analog-To-Digital Converter In Signal Processing, Ravshan Aliev, A.U. Djalilov

Chemical Technology, Control and Management

This article is devoted to the study of the modeling process of analog-to-digital converters (ADCs) that process signals, one of the main parts of control system elements and devices. As we know, ADCs are an important part of modern control systems. During the research, the main stages of analog signal conversion were analyzed, i.e. discretization, quantization, coding. A classification of analog-to-digital conversion methods was made and the advantages and disadvantages of each were identified. Also, the characteristics and parameters of ADC were studied, their impact on ADCs performance was evaluated, and it was determined that certain characteristics should be taken …

Machine Learning And Shap Interpretability For Chronic Disease Understanding, Nnaemeka Charles Igwe, Khandaker Mamun Ahmed

SDSU Data Science Symposium

Non-communicable diseases (NCDs), such as diabetes, are major global health concerns influenced by various health parameters and lifestyle choices. Traditional methods struggle to efficiently predict and manage these conditions due to the complexity and diversity of medical data. There is a need to leverage machine learning algorithms and modern computational tools to accurately predict diabetes, improve diagnosis, and provide actionable insights for better healthcare outcomes. In this project we study the application of machine learning methods for predicting NCDs such as diabetes. Moreover, we leverage hyperparameter tuning techniques for model development and SHapley Additive exPlanation (SHAP) for results interpretations and …

Design And Implementation Of Uvm-Based Verification Framework For Deep Learning Accelerators, Randa Ahmed Hussein Aboudeif

Theses and Dissertations

Recent advancements in deep learning (DL) have made hardware accelerators, known as deep learning accelerators (DLAs), a preferred solution for numerous high-performance computing (HPC) applications, including speech recognition, computer vision, and image classification. DLAs are composed of hundreds of parallel processing engines to speed up computations and can gain access to pre-trained networks from the cloud or through on-chip memory to implement the DNN inference process. DLA verification is becoming an important and challenging phase. The verification process is required to handle the complex DLA design. Moreover, the reliability of DLAs is critical for assessment as they are involved in …

Interfaces Gráficas Y Género: Impacto En Discursos Normativos, Marco V. Ferruzca, Paulo C. Portilla, Juan Villegas, Román A. Mora

GDI. Revista de investigación de Género, Diseño e Innovación

El diseño de interfaces gráficas de usuario generalmente considera una reflexión sobre propiedades vinculadas a aspectos visuales como el color, las imágenes, la tipografía, etc. Asimismo, algunos aspectos del usuario se ponen también a consideración como su edad, cultura, experiencia, género, entre otros. Sin embargo, la noción de género sólo se reduce a un binarismo para definir si la interfaz gráfica encaja en la categoría mujer u hombre. Muy pocos investigadores se han detenido a profundizar en el discurso heteronormativo que puede desprenderse de la interpretación de significado de una interfaz gráfica como consecuencia de la interrelación entre usuariogénerocomputadora. El …

An Evaluation Of Reinforcement Learning Algorithms In Video Game Development, Isaac Lockwood

Masters Theses & Specialist Projects

Reinforcement Learning (RL) has demonstrated substantial promise for creating adaptive, responsive AI in complex environments such as video games. Yet despite growing academic interest, industry adoption remains limited due to computational overhead, reward-design challenges, and unpredictable AI behaviors. This thesis investigates how RL algorithms—specifically Advantage Actor-Critic (A2C), Deep Q-Network (DQN), and Proximal Policy Optimization (PPO)—can be applied to three different genres of video games. Those being first-person shooter (fps), fighting, and strategy.

Through a combination of scenario-based experimentation and comprehensive analysis, this work explores the feasibility and design considerations crucial for integrating RL-driven AI into commercial games. Key factors examined …

Ai In Higher Ed, Where Are We Now?: Insights From The 2025 Educause Ai Landscape Study, Angela Neria, Jeff Burns

Posters

Curious about how higher education is really using AI? Wondering what’s next for AI policies, workforce impacts, and leadership strategies? The 2025 EDUCAUSE AI Landscape Study has the answers! Based on fresh data from institutions across higher ed, this study highlights key trends, challenges, and opportunities in AI adoption. Stop by our poster session to get a quick snapshot of where AI stands today—and where it’s headed. Let’s talk about what these findings mean for PSU and the future of AI in higher education!

Securing Iac:Comparing Checkov, Terrascan, And Tfsec On Aws And Azure, Maliha Binte Ruhul Amin, David White

Academic Poster Collection

Securing IaC:Comparing Checkov, Terrascan, and Tfsec on AWS and Azure

Comparison Of Kafka Operators: Strimzi Vs Koperator Vs Confluent: A Comprehensive Industry Analysis, Ankit Anthony, Gary Clynch

Academic Poster Collection

Comparison of Kafka Operators: Strimzi vs Koperator vs Confluent: A Comprehensive Industry Analysis

Analysis Of Impact Of Configuration Choice Upon Azure Service Bus Performance, Brian Skehan, Mary Rose Donnelly

Academic Poster Collection

Analysis of impact of configuration choice upon Azure Service Bus performance.

Comparative Analysis Of Mysql And Mongodb In A High-Concurrency System, Gabriel Solares, Cormac Keogh

Academic Poster Collection

Comparative Analysis of MySQL and MongoDB in a High-Concurrency System

Analysis Of Functional Programming Languages For Use In Serverless Lambda Functions On Aws Platform, William Spain, Gary Clynch

Academic Poster Collection

Analysis of functional programming languages for use in serverless lambda functions on AWS platform

A Comparison Of Terraform And Bicep Quality Attributes, Colm O'Hara, Kevin Bayliss

Academic Poster Collection

A Comparison of Terraform and BICEP Quality Attributes

Comparing Kubernetes And Nomad For Hosting .Net Legacy Workloads In Azure, Luke Osbourne, Omar Portillo

Academic Poster Collection

Comparing Kubernetes and Nomad for hosting .NET legacy workloads in Azure

Evaluating Kubernetes Security Mechanisms For Dos Prevention: A Comparative Analysis Of Multi-Layer Protection Strategies, Sergej Dikun, Omar Portillo

Academic Poster Collection

Evaluating Kubernetes Security Mechanisms for DOS Prevention: A Comparative Analysis of Multi-layer Protection Strategies

Improving Iac Script Quality: Evaluating Static Analysis Tools And Establishing Best Practices, Isha Rai, Kevin Bayliss

Academic Poster Collection

Improving IaC Script Quality: Evaluating Static Analysis Tools and Establishing Best Practices

Comparative Cost And Capacity Analysis Of Managed Service And Non-Managed Service Api Gateway Architectures On Leading Cloud Platforms, George Brown, Cormac Keogh

Academic Poster Collection

Comparative Cost and Capacity Analysis of Managed Service and Non-Managed Service API Gateway Architectures on Leading Cloud Platforms

The Irish Food Manufacturing Industry’S Preparedness For Nis2 Cybersecurity, Niall Mcgowan, Mary Rose Donnelly

Academic Poster Collection

The Irish Food Manufacturing Industry’s Preparedness for NIS2 Cybersecurity

A Comparison Of Aks And K3s On Vmss, Craig Dillon, Omar Portillo

Academic Poster Collection

A Comparison of AKS and K3s on VMSS

Comparison Of Asynchronous Architectural Patterns On Aws & Gcp Using Terraform, Alan Mcgee, Mary Rose Donnelly

Academic Poster Collection

Comparison of Asynchronous Architectural Patterns on AWS & GCP Using Terraform

Event-Based Histogram Of Gradients For Lane Detection, Ganesh Gupta

Computer Science and Engineering Theses - Archive

In the rapidly evolving landscape of autonomous driving technology, lane detection systems stand as fundamental guardians of vehicular safety. The National Highway Traffic Safety Administration identifies unintentional lane departures as responsible for approximately one-third of all road accidents—a sobering statistic that underscores the critical importance of robust lane detection methodologies. This thesis embarks on an academic exploration at the intersection of neuromorphic engineering and computer vision, examining how the distinctive properties of event-based cameras might be harnessed to enhance lane detection capabilities under challenging environmental conditions. Unlike conventional frame-based imaging sensors that capture entire scenes at fixed intervals, event-based cameras …

Training Data Privacy In Machine Learning: A Systematization Of Attacks And Defenses, Mohammad Sufyaan Saeed

Computer Science and Engineering Theses - Archive

Training and deploying Machine Learning (ML) models introduce significant data confidentiality risks, as modern models can inadvertently memorize and leak information about their training data. While attacks such as membership inference and model inversion are well studied, the literature remains fragmented, with inconsistent threat models and unclear relationships across attack classes and defenses. This work presents a Systematization of Knowledge (SoK) that unifies the landscape of training-data privacy attacks and defenses, aligning them with the NIST Adversarial Machine Learning (AML) taxonomy to enable standardized threat modeling and comparison. Our analysis shows that, despite significant progress in characterizing attack vectors, defenses …

Methods Of Optimizing Storage And Retrieval Of Structured Data, Neelim Haider

Computer Science and Engineering Theses - Archive

Storing and retrieving large amounts of data reliably is becoming more and more important as time goes on. There are high demands to store highly personal information such as social security numbers, bank account information, and residence information to rapidly changing data such as employee information, inventory information, and stock information. Therefore, the ability of a system to store, remove, and update such information efficiently and correctly is critical. There are different types of data that database systems can potentially hold: structured, unstructured, and semistructured data. Various database models have been developed to provide a framework that allows designers to …

Diversity-Driven Xor Secret Sharing: Reliable And Secure Multi-Path Transmission, Richard M. Olu Jordan

Computer Science and Engineering Theses - Archive

The growing reliance on distributed storage and multipath communication sys- tems has intensified the need for security mechanisms that remain robust even when individual nodes or channels are compromised. Secret sharing provides an information- theoretic approach to achieving both confidentiality and availability, and XOR-based constructions in particular offer lightweight and highly structured designs. This thesis develops a unified analytical framework for understanding and evalu- ating XOR-based secret sharing schemes across multiple operational settings, includ- ing plaintext storage, encrypted-data scenarios, and noisy binary symmetric chan- nels (BSCs). Building on a general (t, n) system model, we examine five threshold configurations—(2, 3), …

An Slo-Aware, Multi-Pronged Approach To Enhancing Resource And Energy Efficiency In Server Applications, Ning Li

Computer Science and Engineering Dissertations - Archive

Server applications operating in oversubscribed cloud environments face the dual challenges of meeting strict Quality-of-Service (QoS) requirements and improving resource and energy efficiency. As the number of user connections and workload diversity continue to grow, existing scheduling mechanisms struggle to balance QoS guarantees, fairness, resource efficiency, and power consumption. This dissertation presents a unified, cross-layer framework to address these challenges through three key contributions: AppleS, UTSLO, and REEF.

First, we propose AppleS, a user-space QoS-aware fine-grained I/O scheduling framework that delivers fair and efficient service to concurrent client connections. AppleS introduces a QoS-centric metric that guides admission control and scheduling …

Fair And Sustainable Machine Learning: A Holistic Approach To Data Quality, Efficiency, And Resource-Aware Training, Zahidur Rahim Talukder

Computer Science and Engineering Dissertations - Archive

The increasing reliance on distributed, privacy-sensitive data has driven the emergence of Federated Learning (FL) as a transformative paradigm for collaborative machine learning. By enabling multiple client devices to train a shared global model without transferring raw data, FL offers significant privacy advantages. However, real-world deployments of FL are constrained by critical challenges such as data heterogeneity, client unreliability, and hardware disparities. These factors lead to uneven model convergence, degraded global accuracy, and fairness issues that threaten FL's scalability and inclusivity in diverse environments.

This dissertation investigates these challenges and proposes three novel algorithmic frameworks to advance the state-of-the-art in …

Exploring Emerging Memory Technologies For Enhancing Data Center Applications, Zhen Lin

Computer Science and Engineering Dissertations - Archive

The rapid evolution of memory and storage technologies is fundamentally reshaping the design of operating systems and data management. Emerging devices such as persistent memory, NVMe SSDs, and Compute Express Link (CXL)--enabled hybrid memory modules introduce new opportunities for high-performance, cost-efficient data management, yet they also expose limitations in traditional software abstractions. File systems, originally designed for slow block-based devices, incur excessive overhead on ultra-low-latency media, while block-level caches suffer from metadata and eviction inefficiencies. Moreover, hardware-managed tiering provides transparency but restricts adaptability across workloads. These challenges highlight the need to rethink caching and tiered memory management across multiple system …