Digital Commons Network^™

Investigation Of Automated 3d Scanning Strategies For Large-Scale Polishing Applications, Casey Egan

Open Access Master's Theses

In automated scanning for industrial refinishing applications, large workpieces often exceed the reach of a single robot base position, and may require the environment to be scanned from multiple locations and stitched to a common reference frame. This thesis compares two robotic scanning workflows for such an object: a full scan that reconstructs the object from a single robot base position, and a segmented scan occurring across multiple simulated robot base positions that are stitched together through fiducial-based localization. The results from both workflows are compared for measurement accuracy of the object's bounding box against known dimensions, position of the …

Pafex: Compiler-Based Floating-Point Exception Detection For Gpu Kernels With Papispecific Software-Defined Events, Samin Islam, Shirley V. Moore, Christoph Q. Lauter

Graduate Student Papers (CS)

As high-performance computing becomes progressively heterogeneous, the dependence upon vendor specific tools for numerical correctness has become an impediment to portability. Although modern GPUs comply with the IEEE 754 standard, the lack of practical native hardware support to raise and handle exceptions (special values like ±∞ or NaN) is a well-known architectural limitation. To embed portable numerical correctness across heterogeneous systems, we propose an architecture agnostic prototype based on LLVM-compiler infrastructure. This framework detects floating-point exceptions in GPU kernels at the Intermediate Representation (IR) level, instrumenting both device code and host code, strictly complying with the 2019 IEEE 754 standard. …

Implications Of Quantum Computing For Enterprise Cybersecurity And Data Integrity, Manikantha Varaprasad Inakollu

Computer Science and Engineering Faculty Publications

Quantum computing represents a paradigm shift in computational capabilities that poses both unprecedented threats and opportunities for enterprise cybersecurity. This research examines the implications of quantum computing advancement on current cryptographic systems, data protection mechanisms, and organizational security frameworks. Through analysis of quantum computing developments from 2019-2024 and surveys of 280 cybersecurity professionals across various industries, this study identifies critical vulnerabilities in existing encryption standards and explores emerging quantum-resistant solutions. The findings reveal that approximately 78% of enterprises remain unprepared for quantum threats, with current RSA and ECC encryption systems facing potential compromise within the next 10-15 years. The research …

Personality Exceptions: Enhancing Character In Narrative Planners With Internal Memory, Elinor Rubin-Mcgregor

University of Kentucky Doctoral Dissertations

Stories and narratives are powerful tools for forging connections between people, as they induce emotions shared through their audience and help build empathy and understanding through sharing unique perspectives. Interactive stories can do all this and more, as they can be used to better educate and engage their audience. But in such stories, the characters must have consistent personality and human-like emotions in order for the audience to relate to them. Current implementations of personality in interactive narratives either portrays characters with static personality that feel less realistic, or else require a great deal of additional work from the author …

Formalizing Asymmetric Control-Telemetry Separation In Distributed Industrial Control Systems, Andrew Manison

College of Graduate Studies: Theses & Dissertations

Distributed industrial control systems often place control and telemetry traffic on the same communication substrate even though the two workloads impose different requirements. Control paths need bounded request-response latency and predictable acknowledgement semantics, whereas telemetry paths benefit from scalable publish-subscribe fanout and tolerance for consumer-side delay. This thesis argues that, for the tested class of mixed workloads on shared commodity infrastructure, these communication roles should be separated architecturally rather than forced through a single protocol. To evaluate that claim, the thesis formalizes an asymmetric control- telemetry pattern and instantiates it in the Asymtra framework using gRPC for synchronous control and …

Machine Learning Acceleration Of Micro-Credentialing, Knowledge Gap Remediation, And Micro-Recruiting Of Stem Assessments: From Learner Data To Hardware, Paul Amoruso

Graduate Studies Theses and Dissertations 2026

The growing demand for a skilled STEM workforce calls for adaptive, data-driven education that personalizes learning while aligning competencies with industry needs. This work introduces a multi-layered framework that integrates artificial intelligence (AI), data mining and hardware-aware optimization to support this goal. At its core, the AchieveUp system applies generative AI-assisted skill tagging to digitized assessments, enabling multi-semester tracking of student progress. Analysis across 14 semesters shows that underperforming students were found to benefit most from attending score clarification sessions, a relationship validated by an R² value of 0.83, indicating strong predictive validity between attendance and skill acquisition. To remediate …

Deceptive Design And Disability: How Dark Patterns Affect Users With Visual Impairments, Konstantin Metz

Graduate Studies Theses and Dissertations 2026

Dark patterns are deceptive interface designs that steer users toward unintended actions, often at financial or privacy cost. This thesis examines how those that are vision impaired are at greater risk of these dark patterns given their visual acuity deficit. We conducted semi structured interviews with nine participants across common contexts including e-commerce checkout, subscription and cancellation flows, software download pages, and mobile permission prompts. The data show recurring exposure to dark patterns such as hidden or de-emphasized fees, pre-selected add-ons, deceptive advertising and fake download buttons along with unequal consent paths, repeated permission nagging, and cancellation friction. Participants reported …

Handwriting Recognition In Vr, Dominique Mosley

EWU Masters Thesis Collection

Virtual Reality (VR) is slowly becoming more popular for more than just entertainment. VR can be found in educational, office, and even healthcare settings to help discover more intuitive ways to teach, collaborate, and treat patients. Outside of the virtual world, these environments typically rely on writing for communicating or note-taking. Currently, VR input forces users to rely on clunky on-screen keyboards which disrupts the user’s immersion and breaks the flow of natural interaction. This thesis explores the potential of VR as a learning platform by combining it with artificial intelligence (AI). It aims to develop a VR-enhanced handwriting practicing …

Watch Your Step, Mind The Crowd: Socially Compliant Navigation In Unstructured Environments, Jack Vice

Graduate Studies Theses and Dissertations 2026

Autonomous mobile robots increasingly operate in complex real-world environments characterized by uneven terrain, dynamic obstacles, and human presence. This dissertation addresses the fundamental challenge of developing adaptive navigation strategies that enable robots to safely and effectively traverse unstructured outdoor environments including construction sites, agricultural fields, and disaster areas while maintaining social compliance around people. We advance this capability through five complementary contributions that collectively target dynamic, unstructured, human-occupied environments. First, we demonstrate evolutionary optimization of legged mobility for uneven terrain. Second, we propose using a spatiotemporal autoencoder to learn the temporal dynamics of moving people and objects. Third, we introduce …

Swarm Of One: Self-Organization In Multicellular Robots, Trevor Riley Smith

Graduate Theses, Dissertations, and Problem Reports (ETD)

Designing robots traditionally relies on centralized, top-down methods that constrain adaptability and resiliency, producing systems optimized for specific tasks. However, such systems often fail in complex environments, such as extraterrestrial, disaster, or natural settings, where designers cannot foresee every scenario a robot may encounter. Achieving autonomy in these contexts requires robots that are resilient and capable of adapting to the unknown. Multicellular organisms exhibit this adaptability through self-organization, where complex structures and behaviors emerge from local interactions among their cells. Such organisms can be viewed simultaneously as a single entity and a swarm of cooperating cells, i.e., a Swarm-of-One.

This …

Semantic-Weighted Lidar–Camera Slam For Robust Mapping In Vegetation-Rich Environments, David Aigbovboise Akhihiero

Graduate Theses, Dissertations, and Problem Reports (ETD)

Many LiDAR odometry and SLAM methods are lightweight and effective, but they assume that all LiDAR point returns are equally reliable. In practice, this assumption is often violated in vegetation rich or semi transparent environments, where in a single beam footprint, LiDAR beams may intersect multiple scattering surfaces like leaves, branches or glass. These ambiguities in returns produces probabilistic or biased range measurements that violate the assumptions of most geometric registration frameworks and can lead to drift and inconsistency.

To address this challenge, this dissertation proposes a semantic-weighted LiDAR-camera SLAM framework that integrates class dependent weights into a front-end LiDAR …

Improving Efficiency In Noma Schemes Having Inter-User Interference Using Mechanism Design, Zory Marantz

Publications and Research

Modern wireless systems utilize non-orthogonal multiple access to increase their rate capacities; however, the efficiency of the individual utility defined in bits per Joule has yet to be considered. Multiple variations of non-orthogonal multiple access have the interference of the signal-to-interference-plus-noise ratio as a function of the received power from multiple other users due to code implementations that are non-orthogonal or non-ideal cancellation in successive-interference-cancellation methods. Game theoretic concepts are used to improve user bits-per-Joule performance. Previous solutions increment transmit power and are not based on closed form systematic methods. The mechanism design presented here led to a non-cooperative Nash …

Dataless Neural Networks For Boolean Satisfiability And Network Optimization, Andrew Evan Gautier

Graduate Theses, Dissertations, and Problem Reports (ETD)

Combinatorial optimization problems (COPs) require searching over a finite solution space subject to constraints, with the goal of satisfying an objective function. They arise in operations research, scheduling, resource allocation, circuit design, and many other fields. Many problems in combinatorial optimization (including satisfiability and network design) are NP-hard. Traditionally, researchers have built approximate solvers that return near- optimal solutions efficiently by developing increasingly sophisticated heuristics and meta- heuristics. Deep learning has provided new opportunities for improving combinatorial solvers by leveraging neural guidance to prune the search space. Traditional neural networks have distinct drawbacks in this context: separate training and …

Data Augmentation And The Reliability Of Conformal Prediction For Uncertainty Quantification In Medical Imaging, Rizwan Ahamed

Graduate Theses, Dissertations, and Problem Reports (ETD)

The safe clinical deployment of deep learning models for high-stakes medical imaging tasks requires more than high average accuracy; it requires demonstrable, per-case reliability. Uncertainty quantification (UQ) provides the missing signal that tells a clinician when a model prediction can be trusted and when a case should be escalated for expert review. Among UQ approaches, conformal prediction (CP) is especially attractive because it produces prediction sets that are guaranteed, under the assumption of exchangeability, to contain the true label with a user chosen probability, and it does so without assumptions about the model or the data distribution. This report first …

Optimal Power Flow Control In The Iraqi Power Grid Using Artificial Intelligence Algorithms For Carbon Emission Reduction, Mohammed Shakir Juber

Al-Esraa University College Journal for Engineering Sciences

Iraq’s power sector remains in a protracted and severe crisis characterized by a significant mismatch between supply and demand, high levels of losses during transmission and distribution, as well as an increasing challenge to the resources base being largely unfavourably. These inefficiencies impose heavy costs on the national economy in excess of 40 billion annually and they also reinforce greenhouse gas emissions, and exacerbate environmental issues. To deal with these problems, we proposed in this paper that a new hybrid AI tool should be developed for the solution of multi-objective optimization problem based on purpose Genetic Algorithm (GA) merger with …

Applying Machine Learning Techniques For Early Detection Of Cyber Attacks On Iot Devices, Noor Adnan Allamy

Al-Esraa University College Journal for Engineering Sciences

This research designs, implements, and evaluates a machine learning-based framework for the early detection of cyber attacks targeting Internet of Things (IoT) devices, with a specific focus on the context and challenges present in Iraq. The study conducts a comparative analysis of three supervised learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Networks (DNN)—using a combination of benchmark datasets (NSL-KDD, CIC-IDS-2017, Bot-IoT) and a synthesized dataset adapted to simulate the Iraqi threat landscape. Key performance metrics, including accuracy, precision, recall, and F1-score, were used for evaluation. The proposed Random Forest model demonstrated superior performance, achieving an accuracy …

Ai-Enhanced Heat Transfer Optimization In Magnetic Bio-Nanofluids, Hasan Attyah Shaboot

Al-Esraa University College Journal for Engineering Sciences

This study presents a hybrid Artificial Intelligence–Computational Fluid Dynamics (AI-CFD) framework for optimizing heat transfer in magnetic bio-nanofluids subjected to external magnetic fields. Magnetic bio-nanofluids, composed of biocompatible base fluids containing superparamagnetic nanoparticles, exhibit tunable thermal and flow behavior, making them promising for biomedical and micro-cooling applications. Conventional optimization methods based on experiments or brute-force CFD are computationally expensive and limited in exploring the full design space. To overcome these challenges, an Artificial Neural Network (ANN) surrogate model was developed to predict two key performance indicators, the Nusselt number and the friction factor, with high accuracy (R² > 0.997). The surrogate …

The Role Of The Scenario In Formulating The Contemporary Architectural Text, Rounaq Arif Mohsin, Abbas Ali Hamza, Bashar Shamil Alkhafaji

Al-Esraa University College Journal for Engineering Sciences

In order to create an integrated architectural text, which is a complicated art that blends beauty and practical elements, a rigorous design procedure is necessary. In this situation, the scenario’s role becomes crucial in developing the current architectural text and directing architectural approaches. In order to better understand how the scenario influences the design process and the interactions between various architectural aspects, this project intends to explore and analyze the impact of the scenario in developing and refining architectural text formulation approaches.

The study looks at the idea of a scenario and how it has been used in architecture, as …

Real-Time Deep Learning Detection Of Toraja Carving Motifs Using Yolo11m For Cultural Heritage Preservation, Herman Herman, Farid Wajdi Mufti, Abdul Rachman Manga, Haidawati Nasir

Knowledge Engineering and Data Science

Toraja carvings are an important part of Indonesia’s cultural heritage, rich in symbolic, aesthetic, and philosophical meaning. However, the identification and preservation of carving motifs still rely on subjective, time-consuming manual processes, limiting scalability and inconsistent knowledge transmission. From a Knowledge Engineering and Cognitive Data Science perspective, this challenge highlights the need for mechanisms that can transform visual cultural artifacts into structured, machine-interpretable knowledge. This study investigates the use of the YOLO11m model as a data-driven approach for modeling cultural knowledge through automated detection of three Toraja carving motifs: pa_tedong, pa_kapu_baka, and pa_manu_londongan using original images collected directly from traditional …

Numerical Modeling And Experimental Validation Of A High-Temperature Latent Heat Thermal Energy Storage System Using Metallic Phase Change Materials For Concentrated Solar Power Applications, Fouad Hashim Sharhan

Al-Esraa University College Journal for Engineering Sciences

To ensure reliable performance for the power-block of CSP plants, high power density and near-isothermal discharge must be achieved. A robust means of achieving this is through thermal energy storage (TES). High temperature latent heat storage (LHS) with phase change materials (PCMs) can serve these purposes, however the conventional salts used in PCMs typically suffer from low thermal conductivity that limits power density and increases energy storage charging/discharging times. This manuscript investigates ways to address these limitations using high temperature metallic phase change materials (MPCMs) while also developing a validated numerical framework to aid in their design. To this end, …

Thermal Performance Evaluation Of Solar Cooling Systems For Educational Facilities In Baghdad, Yasser Alawi Mohammed Attia Al-Jubouri

Al-Esraa University College Journal for Engineering Sciences

Schools in Baghdad also suffer from poor thermal comfort, which is a common problem in high-temperature and low-humidity arid climates such as that of the city, where using cooling devices leads to a large energy demand and overloads on the national power grid. Such study offers a thorough assessment of the thermal performance of solar-based cooling systems in an efficient manner to be suitable for educational buildings in Baghdad. The analysis is centred on a solar thermal cooling system with absorption as the heat interaction medium, and an experimental characterisation and mathematical simulation itself are performed. A pilot installation, consisting …

Efficient Task Allocation Technique For Robots In Smart Warehouse Using Center Of Items (Coi) Methodology, ‪Ahmed Yahya‬‏, Mohamed S. Saraya, Ahmed I. Saleh

Mansoura Engineering Journal

Nowadays, online shopping is very important in Daily life. in 2020 During the quarantine during the spread of COVID-19, online orders increased dramatically, as a result of the great progress in fifth-generation technologies and using the Internet of Things (IoT) and industrial Internet of Things (IIoT), industry 4.0 and 5.0, fog computing, cloud computing, robots systems, and drones systems. Smart warehouses picking system becomes a Basic need and can be implemented to improve efficiency in logistics. Smart warehouses are growing more dependent on robot systems to make the process of meeting these requests more flexible and effective. Therefore, the allocation …

Algorithm For Stabilizing The Reference Trajectory Of Self-Tuning Systems With A Reference Model, Isamidin Hakimovich Sidikov, Feruzakhon Botirxon Qizi Sodiqova

Technical science and innovation

The paper addresses the problem of stabilizing self-tuning systems using adaptive control methods based on a reference process model. As the optimality criterion, the functional of maximum speed of response is selected. The algorithm for synthesizing the self-tuning system is based on a relay-linear control law, which possesses the property of invariance to small disturbances. The issue of ensuring the practical stability of the system under adaptive and multiplicative disturbances is examined. An algorithm for the synthesis of a reference trajectory stabilization system has been developed on the basis of a quasi-optimal passive self-tuning system (STS) with a reference model, …

Using Machine Learning To Predict Women At Risk Having A Child With Congenital Heart Defects, Amany M. Abdo Prof., Asmaa M. Mosallam Ms., Laila M. Abdelhamid Assoc.Prof.

Information Systems

Congenital heart defects (CHD) are heart malformations present at birth, affecting heart function and circulation, and are a leading cause of infant mortality. CHD can result from genetic, environmental, and maternal health factors, making early detection essential. Early diagnosis allows for timely intervention, reducing risks like heart failure or stroke. In countries like Egypt, CHD often remains undiagnosed due to limited healthcare resources. Artificial intelligence (AI) can improve early detection by analyzing risk factors. This study presents a predictive model for CHD using maternal and paternal health factors. Data was collected from 571 families: 260 with a CHD-affected child and …

Exploring Math Word Problem Generation With Llms, Trung Hieu Vuong

Master's Theses

Math Word Problem (MWP) is an important building block for learning math. This type of problem is particularly useful for younger audiences because solving it involves two simultaneous skill sets: reading comprehension and mathematical reasoning. With publicly available large language models (LLMs), generating additional MWPs is readily achievable. While researchers have started using LLMs as MWP facilitators, there still exists a gap in studies about the diversity of MWPs generated by unmodified, publicly accessible LLMs. For that reason, our study focused on two goals: (1) to evaluate the diversity of MWPs generated by publicly available LLMs when provided with examples …

Models And Algorithms Of Control Mechanisms In Information Exchange Processes, Madina M. Fozilova, Dilshoda N. Uchqunova

Chemical Technology, Control and Management

In modern digital systems, efficient and reliable information exchange is essential for the stability of corporate systems. Traditional data management models struggle to detect and eliminate invalid, incomplete data at early stages, resulting in reduced accuracy and system inefficiency. This article proposes an advanced framework for controlling information exchange processes through the development of a Verification and Filtering algorithm. The algorithm operates within a multi-layered conceptual model that includes data input, control, validation, optimization, and decision layers. Acting as the core component, the Verification and Filtering algorithm distinguishes valid from invalid records in real time, ensuring data integrity before storage. …

Supply Chain Network Based On Blockchain And Intelligent Agent, Hiba Hamdi Hassan, Rana Fareed Ghani

Journal of Soft Computing and Computer Applications

In agricultural supply chains, the complexity and indeterminacy pose serious challenges to traceability, reliability and confidence today. This challenge is especially acute in the olive oil industry where adulteration, wrong labeling, and uneven chemical quality threaten the actual well-being of producers and consumers. The project aims to design a blockchain-based hybrid architecture with intelligent agents (FNNs) to enhance transparency, reliability and responsiveness in the olive oil supply chain. The Blockchain component enables a completely open, tamper-proof ledger to be built in a very decentralized way and preserved as an archive of every account of its transactions. The intelligent agents contribute …

Intelligent Extensible Markup Language Encryption Using Type-2 Fuzzy Logic, Faiez Musa Lahmood Alrufaye, Seham Ahmed Hashem

Journal of Soft Computing and Computer Applications

Financial and commercial institutions increasingly rely on Extensible Markup Language (XML) files as a standard means of exchanging data. However, this extensive use has created serious security challenges due to the fact that these files contain sensitive information such as bank card numbers and expiration dates. Relying on traditional full file encryption methods achieves a high degree of security, but it causes problems related to the large file sizes that consume memory and the long encryption and decryption times, which reduces the efficiency of systems when dealing with a large number of daily transactions. Methods based on Type-1 Fuzzy Logic …

Enhanced Generative Convolutional Networks: A Hybrid Algorithm For Refinement Video Classification, Dalal Thair Mahjoub, Hala Bahjat Abdulwahab, Kesra Nermend

Journal of Soft Computing and Computer Applications

Video classification is a vital area of research due to the growing volume of video content in various applications. Accurate category across various resolutions poses challenges, which include adapting to scaling, resizing, and compression. Therefore, this paper introduces an innovative Generative Convolutional Network (GCN) set of rules tailored for multi-resolution video classes. The proposed GCN model utilizes Convolutional Neural Networks (CNNs) combined with generative modeling to enhance the extraction of functions across varying video resolutions, which is crucial for maintaining class robustness in the face of common video adjustments, such as scaling, resizing, and compression. In contrast, traditional fashions frequently …

Review Of Video Steganography By Using Deep Learning Methods: Datasets, Techniques, And Evaluations, Noor Fahem Sahib, Soukaena Hassan Hashem, Ekhlas Falih Naser

Journal of Soft Computing and Computer Applications

The growing prevalence of cyber threats, including fraud and attacks, has intensified the demand for secure methods of safeguarding confidential information exchanged between users. As telecommunications increasingly rely on multimedia data, video steganography has become a prominent technique to address these concerns. By embedding sensitive data within video files, this approach enhances protection against unauthorized access and common internet-based attacks, offering a robust layer of security in an era of escalating digital risks. With the introduction of Deep Learning (DL) steganography methods recently, video steganography can be defined as a rapidly developing subject within information security. This study provides a …