Digital Commons Network^™

Training Neural Networks With A Self-Adaptive Ant Colony Algorithm, Ashraf M. Abdelbar, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

ACOR is a well-established ant colony optimization algorithm that has been applied to neural network training. We present an approach for the dynamic adaptation of the ACOR algorithm's search intensification/diversification parameter q, based on using several pre-specified parameter configurations, which we call personalities. Before an ant begins to generate a candidate solution, it stochastically adopts a personality based on the relative past success of the different personalities. The success of a personality is measured, in turn, by the relative quality of previous solutions generated by ants adopting that personality. The premise of our approach is that some personalities will be …

University Of Akron Commuter Rideshare Service (Uacrs), Nicholas Szijarto

Williams Honors College, Honors Research Projects

At the University of Akron, a large number of students commute to and from the campus. A majority of them use their own personal vehicles every day. I propose a web-based application that would benefit the University by offering ridesharing services for commuting students and faculty. This would be made with an Angular frontend, a Spring Boot Java backend, and a MySQL database. I would hope to allow this application to have users register and then input their schedules of travel, including starting point, destination, and the time of this transfer. The program could suggest groups of students to travel …

Automatic Scoring Cornhole Board System, Eric Diffendal, Jonah Harsh, Connor Lengel, Brett Sukie

Williams Honors College, Honors Research Projects

The objective is to create a self-scoring cornhole board that can detect and calculate each team's score based on the bags thrown each round and to be created at a low cost/eventually being sold at the current cost of a normal board. When playing cornhole, the game is simple: throw a bag on the board; however, the scores are variable (deduct and add) across each round. The most common issue when playing cornhole is miscalculations of the scores and forgetting the correct scores. Thus, this invention will make gameplay easy for all to play.

Skinrisk Ai, Spencer Simms

Williams Honors College, Honors Research Projects

SkinRisk AI is an exploration of the opportunities for implementing machine learning (ML) and artificial intelligence (AI) in the medical technology field, specifically in the early detection of skin cancer. This project presents the design, development, and evaluation of a mobile application that allows users to capture images of skin lesions and receive a machine learning assisted risk assessment. The system combines a convolutional neural network (CNN) for image analysis with an intuitive mobile app built using Flutter, FastAPI, and Supabase to deliver real time screening.

Motivated by the rising skin cancer rates and importance of early detection, SkinRisk AI …

Connecting Worlds: Travelmate’S Bidding System For Personalized Travel Experiences, Suman Khadka

Williams Honors College, Honors Research Projects

TravelMate is a web platform that connects travelers with local guides through a personalized trip posting system along with real time bidding from freelancer guides. This platform utilizes Next.js for a responsive frontend, and Supabase for managing database and backend RESTful API, using PostgREST - a thin API layer on top of Postgres. This platform is ideal for anyone looking to explore new places with a freelancer guide and gain cultural insights from a local expert who understands the place deeply.

Autonomous Landscape Exploration Rover Using Imaging Surface Navigation (Alexis), Noah Jones, Dominic Salupo

Williams Honors College, Honors Research Projects

The objective is to develop a small-form-factor rover prototype that can be used to prove out a novel traversal method for use on extraterrestrial surfaces. The novel traversal method being proposed is LIDAR/CV-enhanced navigation, provided by a detachable flight vehicle that can communicate with the rover. On planets with thin atmospheres, cold gas thrusters or similar may be needed, but for the scope of this project more traditional flight/propulsion methods will be used.

Silent Sabotage: Identifying And Preventing Cyber Attacks From Inside Actors, Autumn Groen

Williams Honors College, Honors Research Projects

Cyber-attacks are becoming increasingly common and damaging as technology advances each year. Many businesses cannot afford the latest security technologies, and even with the highest security measures, there can still be room for employee error or insider threats that are not taken into account. It is crucial to keep these factors in mind when securing a business network of any size or financial standing.This project will aim to simulate a business environment by first building a small network with three routers and a switch, and implementing some of the common best practices for network hardening from credible organizations like NIST …

User Interface For Custom Car Infotainment Systems, Dylan Miller

Williams Honors College, Honors Research Projects

The infotainment system is often considered one of the most functional and luxurious aspects of modern cars, containing functions that are useful to drivers in ways that range from convenient to safety-enhancing. However, modern infotainment systems can have some drawbacks such as making it more difficult to repair the vehicles they are in, helping to artificially limit the lifespan of the vehicles they are in, and not being present in most vehicles more than 15 years old. The software described in this paper, OpenQarUI, seeks to be a part of a solution to these problems. It is a piece of …

Leveraging Large-Language Models As Collaborative Reasoning Partners To Enhance Scientific Workflow, Douglas B. Craig

Wayne State University Dissertations

The rise of Large Language Models (LLMs) has transformed artificial intelligence, offering advanced capabilities in text generation, natural language understanding, and multi-modal interactions. However, their use as standalone tools or as perceived repositories of static knowledge has limited their potential in real-world applications, especially in critical domains like healthcare and scientific research, where transparency, explainability, and accountability are paramount. This research addresses these limitations by conceptualizing LLMs as reasoning engines within a hybrid framework that integrates retrieval-augmented generation (RAG) and case-based reasoning (CBR) within a note-taking application.

The study introduces a novel system, LmRaC, designed to enhance the reliability, explainability, …

Advancing Road Safety Through Software-Defined Vehicles, Raef Abdallah

Wayne State University Dissertations

Road traffic accidents are a significant global concern, claiming approximately 1.3 million lives annually and causing non-fatal injuries to 20–50 million people, many of which result in long-term disabilities. They are the leading cause of death for individuals aged 5–29 and impose a substantial economic burden, costing most countries 3% of their Gross Domestic Product (GDP). In addition to car accidents, other types of road incidents, such as collisions involving tall vehicles and overpasses, also pose significant risks. These accidents result in numerous fatalities and cause millions of dollars in damages annually. Environmental conditions like wet or icy roads and …

Advancing Generative Ai In 3d And 4d Spaces, Hasan Iqbal

Wayne State University Dissertations

Generative Artificial Intelligence (AI) has transformed how we synthesize, edit, and manipulate complex data, opening new possibilities for immersive digital content. This dissertation, titled “Advancing Generative AI in 3D and 4D Spaces,” investigates the capabilities and limitations of state-of-the-art generative models in static 3D scenes and time-aware 4D environments. In the 3D setting, scene editing is hindered by multi-view inconsistency and the high computational cost of per-scene retraining. To address these issues, the work introduces Free-Editor, a training free approach that utilizes an Edit Transformer to propagate a single edited view across all camera perspectives without additional optimisation, delivering prompt …

Rapid Inference Of Atmospheric Feature Parameters From Light Curves Using Bayesian Neural Networks, Eugenio A. Diaz

Honors Undergraduate Theses

Mapping atmospheres using rotationally modulated light curves offers insights into cloud structures and dynamics. Current retrieval methods, primarily based on Markov Chain Monte Carlo (MCMC) techniques like Aeolus, can infer atmospheric features but are computationally prohibitive for large datasets. This project proposes a neural network (NN) framework for the rapid, variational inference of atmospheric structure from light curves, particularly those of brown dwarfs. The primary approach focuses on training a Bayesian NN (BNN) to perform regression, predicting the spot parameters that describe the object's surface brightness map. Given the scarcity of suitable observational training data, the BNN is trained on …

Fuel Consumption Prediction Using Bayesian Neural Networks, Syarifah Diana Permai, Jurike V. Moniaga, Zener Sukra Lie, Ferry Jie

Research outputs 2022 to 2026

Transportation is one of the necessities of life. Because humans need transportation to move from one location to another. Transportation requires fuel. On the other hand, fuel consumption is important and must be controlled. This is because fuel can come from both renewable and non-renewable energy sources, depending on the type and process of its formation. Several factors influence the fuel efficiency of a car, including the type of engine, vehicle weight, aerodynamics, driving habits, and other vehicle conditions. This research aims to predict car fuel consumption and identify the factors that affect fuel consumption. Several Machine Learning and Statistical …

Pfedrag: A Personalized Federated Retrieval-Augmented Generation System With Depth-Adaptive Tiered Embedding Tuning, Hangyu He, Xin Yuan, Kai Wu, Ren Ping Liu, Wei Ni

Research outputs 2022 to 2026

Large Language Models (LLMs) can undergo hallucinations in specialized domains, and standard Retrieval-Augmented Generation (RAG) often falters due to general-purpose embeddings ill-suited for domain-specific terminology. Though domain-specific fine-tuning enhances retrieval, centralizing data introduces privacy risks. The use of federated learning (FL) can alleviate this to some extent, but faces challenges of data heterogeneity, poor personalization, and expensive training data generation. We propose pFedRAG, a novel Personalized Federated RAG framework, which enables efficient collaborative fine-tuning of embedding models to address these challenges. The key contribution is a new Depth-Adaptive Tiered Embedding (DATE) architecture, which comprises a Global Shared Layer, combined using …

Zero Day Ransomware Detection With Pulse: Function Classification With Transformer Models And Assembly Language, Matthew Gaber, Mohiuddin Ahmed, Helge Janicke

Research outputs 2022 to 2026

Finding automated AI techniques to proactively defend against malware has become increasingly critical. The ability of an AI model to correctly classify novel malware is dependent on the quality of the features it is trained with and the authenticity of the features is dependent on the analysis tool. Peekaboo, a Dynamic Binary Instrumentation tool defeats evasive malware to capture its genuine behaviour. The ransomware Assembly instructions captured by Peekaboo, follow Zipf's law, a principle also observed in natural languages, indicating Transformer models are particularly well-suited to binary classification. We propose Pulse, a novel framework for zero day ransomware detection with …

Topo-Vm-Unetv2: Encoding Topology Into Vision Mamba Unet For Polyp Segmentation, Diego Adame, Jose Angel Nunez, Fabian Vazquez Jr., Nayeli Gurrola, Huimin Li, Haoteng Tang

Computer Science Faculty Publications

Convolutional neural network (CNN) and Transformer-based architectures are two dominant deep learning models for polyp segmentation. However, CNNs have limited capability for modeling long-range dependencies, while Transformers incur quadratic computational complexity. Recently, State Space Models such as Mamba have been recognized as a promising approach for polyp segmentation because they not only model long-range interactions effectively but also maintain linear computational complexity. However, Mamba-based architectures still struggle to capture topological features (e.g., connected components, loops, voids), leading to inaccurate boundary delineation and polyp segmentation. To address these limitations, we propose a new approach called Topo-VM-UNetV2, which encodes topological features into …

Procedural Terrain Generation: Noise Functions, Modern Methods, And Style Transfer, Hunter Barton

EWU Masters Thesis Collection

Procedural terrain generation, the algorithmic creation of digital terrain, finds use in multiple types of digital media. As the capabilities of modern computation increase, the ability to create more and more realistic terrains fully procedurally at scale improves. Modern methods of procedural generation have also overlapped with these advances, most notably advances in hardware. To account for this, a survey was done of modern methods for procedural terrain generation. Smooth procedural noise functions are one of the backbones of procedural terrain generation. Perlin noise, value noise, and fractal noise were explored in-depth. These noise functions were also tested for capabilities …

Blockchain-Based Trust Model For Inter-Domain Routing, Qiong Yang, Li Ma, Sami Ullah, Shanshan Tu, Hisham Alasmary, Muhammad Waqas

Research outputs 2022 to 2026

Border Gateway Protocol (BGP), as the standard inter-domain routing protocol, is a distance-vector dynamic routing protocol used for exchanging routing information between distributed Autonomous Systems (AS). BGP nodes, communicating in a distributed dynamic environment, face several security challenges, with trust being one of the most important issues in inter-domain routing. Existing research, which performs trust evaluation when exchanging routing information to suppress malicious routing behavior, cannot meet the scalability requirements of BGP nodes. In this paper, we propose a blockchain-based trust model for inter-domain routing. Our model achieves scalability by allowing the master node of an AS alliance to transmit …

Joint 3d Beamforming-And-Trajectory Design For Uav-Satellite Uplink Covert Communication, Jihong Yu, Yuting Cai, Shihao Yan, Yun Li, Jingjing Wang, Jiahao Liu, Jianping An

Research outputs 2022 to 2026

In this paper,we study uplink covert communication in a space-air system,where an unmanned aerial vehicle (UAV) transmits sensitive data to a Geosynchronous Earth Orbit (GEO) satellite while preventing the transmission action from being discovered by a warden. We derive the optimal decision threshold of the warden. We investigate the 3-dimensional (3D) beamformer and 3D trajectory design for the transmitter UAV against this optimum warden to maximize the covert transmission rate in the presence of imperfect channel state information and uncertain noise. Due to the non-convex structure and dependence between beamforming vectors and locations of the transmitter UAV,we develop a decoupling …

Gans And Synthetic Financial Data: Calculating Var*, David E. Allen, Leonard Mushunje, Shelton Peiris

Research outputs 2022 to 2026

Generative Adversarial Neural nets (GANs) are a new branch of machine learning techniques. A GAN learns to generate new data from the training data set. We examine the characteristics of the fake financial data using GANs trained on samples of daily S&P 500 and FTSE 100 index values. GANs feature two competing neural networks in a game theoretic context. The Generator net generates pseudo data that is presented to the discriminator net which then attempts to distinguish between the real and the fake data. This facilitates unsupervised learning on the dataset. The generative network generates data sets, while the discriminative …

Toward Privacy-Preserving Data Sharing - An Australian Healthcare Perspective, Kimley Foster, Nectarios Costadopoulos, Arash Mahboubi, Sabih Ur Rehman, Md Zahidul Islam

Research outputs 2022 to 2026

The rise of big data has brought increased urgency to the importance of privacy-preserving data sharing in healthcare. In Australia, health records exist in various databases; however data sharing is limited. While many consumers and healthcare professionals recognise the advantages of sharing data for research and health care services, misgivings about privacy and security persist. This study examined current perspectives on data sharing, investigating the trust level in privacy preserving data sharing tools and techniques among healthcare professionals and organisations, and their openness to adopting technology for secure data sharing. We incorporated participants from various healthcare professions across Australia. We …

Towards Robust Multimodal Land Use Classification: A Convolutional Embedded Transformer, Muhammad Zia Ur Rehman, Syed Mohammed Shamsul Islam, Anwaar Ulhaq, David Blake, Naeem Janjua

Research outputs 2022 to 2026

Multisource remote sensing data has gained significant attention in land use classification. However, effectively extracting both local and global features from various modalities and fusing them to leverage their complementary information remains a substantial challenge. In this paper, we address this by exploring the use of transformers for simultaneous local and global feature extraction while enabling cross-modality learning to improve the integration of complementary information from HSI and LiDAR data modalities. We propose a spatial feature enhancer module (SFEM) that efficiently captures features across spectral bands while preserving spatial integrity for downstream learning tasks. Building on this, we introduce a …

System Dynamics With Insight Maker, Steven D'Alessandro, Fons Wijnhoven

Research outputs 2022 to 2026

This book offers a practical, model-driven pathway for reasoning about uncertain futures in business and public policy using system dynamics with Insight Maker. It begins by motivating why historical data alone often fail to predict social change, and it introduces the core language of system dynamics—stocks, flows, feedbacks, delays, and auxiliary variables—alongside the complementary use of agent-based modeling. Through business-relevant cases (e.g., park management trade-offs, epidemic–economy interactions, and industry competition), the book demonstrates how non-linear structure generates counter-intuitive dynamics, why scenario analysis is essential, and how to translate causal loop diagrams into stock-and-flow simulations. Readers are guided step-by-step to build, …

Genai’S Impact On Global It Management: A Multi-Expert Perspective And Research Agenda, Yogesh K. Dwivedi, Laurie Hughes, Mohammad S. Al-Ahmadi, Vincent Dutot, Syed Q. Ahmed, Shahriar Akter, Rahul De’, Keyao Li, Nitish Singh, Paul Walton

Research outputs 2022 to 2026

Generative AI (GenAI) is disrupting global IT management and challenging established practice. The increasing use of GenAI technology is redefining localization, transforming existing workforce roles, outsourcing strategy, and team dynamics. Simultaneously, GenAI’s security complexities have prompted the rethinking of existing risk frameworks to meet a new set of challenges from GenAI enhanced cyber threats. This article explores these complex and converging factors, providing a roadmap to address GenAI’s significant impact on global IT management. We advocate the responsible adoption of GenAI and importance of building resilient, value-driven, globally consistent IT ecosystems able to adapt to the significant challenges and opportunities …

Panoscu: A Simulation-Based Dataset For Panoramic Indoor Scene Understanding, Mariia Khan, Yue Qiu, Yuren Cong, Jumana Abu-Khalaf, David Suter, Bodo Rosenhahn

Research outputs 2022 to 2026

Panoramic images offer a comprehensive spatial view that is crucial for indoor robotics tasks such as visual room rearrangement, where an agent must restore objects to their original positions or states. Unlike existing 2D scene change understanding datasets, which rely on single-view images, panoramic views capture richer spatial context, object relationships, and occlusions—making them better suited for embodied artificial intelligence (AI) applications. To address this, we introduce Panoramic Scene Change Understanding (PanoSCU), a dataset specifically designed to enhance the visual object rearrangement task. Our dataset comprises 5,300 panoramas generated in an embodied simulator, encompassing 48 common indoor object classes. PanoSCU …

Novel Digital Twin Deployment Approaches: Local And Distributed Digital Twin, Shahid Rauf, Fazal Muhammad, Akhtar Badshah, Hisham Alasmary, Muhammad Waqas, Sheng Chen

Research outputs 2022 to 2026

The Digital Twin (DT) technology is considered as a backbone in the Industrial 4.0 revolution as it is playing a vital role in the digitization of various industries. A DT is a virtual representation of a physical entity, thus having the ability to simulate real data generated at physical space to optimize, estimate, control, monitor and forecast states/configurations. Despite enormous benefits, DT technology has several implementation challenges. Although deploying DT on edge or cloud platforms yields a plethora of services, its implementation in both spaces faces certain limitations. These limitations include latency, data communication overload, transmission energy consumption, privacy concerns, …

Protocol For An Integrative Meta-Analysis Of The Application Of Machine Learning Algorithms In The Prediction Of Chronic Disease Risks And Outcomes, Ebenezer Afrifa-Yamoah, Emmanuel Peprah-Yamoah, Enoch Odame Anto, Victor Opoku-Yamoah, Eric Adua

Research outputs 2022 to 2026

Background: Precise risk prediction of chronic diseases is essential for effective preventive care and management. Machine learning (ML) is a promising avenue to enhance chronic disease risk prediction; however, a comprehensive assessment of ML performance across various chronic diseases, populations, and health settings is needed. Methods: This meta-analysis aims to synthesize evidence on the performance of ML techniques for predicting the risks and outcomes of chronic diseases. A literature search was conducted through PubMed, Web of Science, Scopus, Science Direct, Medline, and Embase. Studies applying ML techniques to predict chronic disease risks or outcomes and reporting performance metrics were included. …

Artificial Intelligence And Digital Technologies In Finance: A Comprehensive Review, Soudeh Pazouki, Mohamad Jamshidi, Mirarmia Jalali, Arya Tafreshi

Finance Faculty Publications

This study explores the transformative impact of artificial intelligence (AI) and digital technologies on the financial technology (FinTech) industry, highlighting their role in fostering business growth, operational efficiency, and enhanced customer engagement. AI-driven strategies have unlocked new avenues for streamlining workflows, boosting productivity, and expanding financial inclusion by reaching underrepresented populations. However, these advancements also pose challenges, including navigating complex regulatory frameworks and adapting to the rapidly evolving technological landscape. This paper delves into the macroeconomic effects of AI, examining its influence on labor markets, consumer behavior, and organizational success. Furthermore, the paper discusses blockchain applications and their potential to …

Augmenting, Not Replacing: The Role Of Llms In Human-Centric Formal Re: Supplemental Material, Sonora Halili, Paola Spoletini, Alicia M. Grubb

Computer Science: Faculty Publications

This repository contains the supplemental information for the RE'25 paper entitled "Augmenting, Not Replacing: The Role of LLMs in Human-Centric Formal RE" and the Smith College Departmental Honors Thesis entitled "The LTL Whisperer: Prompting AI to Explain Temporal Logic: Supplemental Information". This work investigates how and to what extent generative AI with large language models (LLMs) can assist practitioners and novices in interpreting formal requirements expressed in Linear Temporal Logic (LTL).

A View Of The Restaurant Script Through The Lens Of Hierarchical Planning, Jamie C. Macbeth, Mark Roberts, Boming Zhang, Sharmin Badhan, Molly Neu, Tanush Garg, Yining Hua, Manushaqe Muco, Mackie Zhou

Computer Science: Faculty Publications

The flexible nature of human cognition and of the structures it uses is well known, as is the difficulty of building cognitive systems that exhibit transfer and use the same structures for radically different tasks. In this paper, we perform a close examination of Schank-Abelsonian scripts, picking apart the goal- and plan- oriented nature of low-level acts and high-level reasoning inherent in them. We then view scripts through the lens of hierarchical planning systems and construct the well-known restaurant script as a hierarchical goal network planning domain. These are evidence in support of a claim that some, if not all, …