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Articles 901 - 930 of 17307
Full-Text Articles in Computer Sciences
Developments On Abbreviations Towards Machine Reading Comprehension, Sing Choi
Developments On Abbreviations Towards Machine Reading Comprehension, Sing Choi
UNLV Theses, Dissertations, Professional Papers, and Capstones
Machine reading comprehension is a critical step in development of applications that require the semantic understanding of human speech-to-text driven work. Many devices such as smart home appliances like the Amazon Echo Dot, Google Home, or smart assistants like Apple Siri or Microsoft Cortana are examples of these applications. The comprehension task involves a deeper understanding and recognition of named entities such as person names, locations, medicals codes, quantities, abbreviations, and acronyms in speech or text data. In this dissertation, we explore and extend the different approaches and techniques in modern research that tackles the problem of recognition and definition …
Elevating Education: Leveling Up Individual Learning Plans, Maximum Mgrdich-Ararat Sirabian
Elevating Education: Leveling Up Individual Learning Plans, Maximum Mgrdich-Ararat Sirabian
UNLV Theses, Dissertations, Professional Papers, and Capstones
This three-article dissertation investigated the effectiveness, implementation quality, and automation of Individual Learning Plans (ILPs) in promoting college and career readiness. Article 1 analyzed High School Longitudinal Study of 2009 data and found that ILPs did not significantly guide course alignment. Article 2 examined ILP implementation across Nevada high schools, revealing inconsistent quality, limited standardization, and few culturally responsive practices. These findings informed the creation of a new high-quality ILP framework. Article 3 employed a convergent parallel mixed methods design to assess an automated ILP prototype based on this framework. Participants in the automated group reported significantly higher scores in …
An Edge Computing Device Optimized And Transfer Learning Enhanced Deep Learning Model For Detecting Wildfire Flame And Smoke, Giovanny Vazquez
An Edge Computing Device Optimized And Transfer Learning Enhanced Deep Learning Model For Detecting Wildfire Flame And Smoke, Giovanny Vazquez
UNLV Theses, Dissertations, Professional Papers, and Capstones
The integration of autonomous unmanned aerial vehicles (UAVs) with edge computing technology and deep learning (DL)-based object detection offers a groundbreaking solution for real-time wildfire detection, enabling rapid data processing directly on devices and minimizing response delays in critical scenarios. However, although showing early promise, performance is often constrained by limited training data and edge computing devices that lack graphics processing unit (GPU) acceleration. This thesis seeks to address these limitations in two stages.First, this work explores the transformative potential of Transfer Learning (TL) to enhance wildfire object detection model accuracy while also investigating TL’s impact, for DL-based object detection …
Predicting Battery Levels Of Sensor Nodes Using Reinforcement Learning In Harsh Underground Mining Environments, Manish Anand Yadav, Mohamed Elmahallawy, Sanjay Madria, Samuel Frimpong
Predicting Battery Levels Of Sensor Nodes Using Reinforcement Learning In Harsh Underground Mining Environments, Manish Anand Yadav, Mohamed Elmahallawy, Sanjay Madria, Samuel Frimpong
Computer Science Faculty Research & Creative Works
Underground mining is a hazardous environment, with frequent accidents leading to significant loss of life each year. To enhance safety, sensor nodes monitor key environmental factors such as temperature, toxic gases, and miners' locations, as well as transmit critical messages. Miners interact with these sensors, which track their movements, enabling their location to be determined even without GPS signals. Therefore, predicting the battery life of these sensors is essential for: (i) rerouting miners during emergencies, (ii) ensuring timely maintenance, and most importantly (iii) identifying sensors that need energy harvesting to maintain vital communication within the mine. In this work, we …
Memory-Augmented Llm Agent For Predicting Locomotion Modes In Construction Activities, Ehsan Ahmadi
Memory-Augmented Llm Agent For Predicting Locomotion Modes In Construction Activities, Ehsan Ahmadi
LSU Doctoral Dissertations
The construction industry faces significant challenges, including labor shortages, high physical demands, and safety risks, necessitating advanced assistive technologies like exoskeletons to enhance worker efficiency and reduce injuries. However, effective exoskeleton control in dynamic construction environments requires accurate locomotion prediction, a task complicated by the diversity of activities and reliance on supervised learning methods that struggle to generalize. This study investigates a multimodal approach to locomotion prediction, leveraging speech commands and visual data from smart glasses to enable adaptive and safe human-exoskeleton interaction. The research unfolds in two stages: the first develops a framework to evaluate the zero-shot capability and …
Real-World Implementation Of A Noninvasive, Ai-Augmented, Anemia-Screening Smartphone App And Personalization For Hemoglobin Level Self-Monitoring, Robert G. Mannino, Julie Sullivan, Jennifer K. Frediani, Paul George, Jeremy Whitson, James Tumlin, L. Andrew Lyon, Erika A. Tyburski, Wilbur A. Lam
Real-World Implementation Of A Noninvasive, Ai-Augmented, Anemia-Screening Smartphone App And Personalization For Hemoglobin Level Self-Monitoring, Robert G. Mannino, Julie Sullivan, Jennifer K. Frediani, Paul George, Jeremy Whitson, James Tumlin, L. Andrew Lyon, Erika A. Tyburski, Wilbur A. Lam
Engineering Faculty Articles and Research
Anemia, characterized by low blood hemoglobin (Hgb) levels, afflicts >2 billion individuals worldwide. Here, we report real-world data generated by a smartphone app that noninvasively screens for anemia using only “fingernail selfies.” App data for anemia screening were obtained from >1.4 million uses across the United States enabling geographic mapping of Hgb levels. Of those, 9,061 users also self-reported complete blood count Hgb levels for comparison, resulting in accuracy and performance that match gold standard laboratory testing and a sensitivity and specificity of 89% and 93%, respectively, when using an anemia cutoff of 12.5 g/dL. Geotagged data enabled construction of …
Computational Complexity Of Soundness Verification For Neural Networks, Scott Sirri
Computational Complexity Of Soundness Verification For Neural Networks, Scott Sirri
McKelvey School of Engineering Graduate Student Theses & Dissertations
Neural networks are an increasingly ubiquitous tool in systems of varying complexity across a range of domains. While these tools can be used to learn and predict complex functions, their opaque nature limits the scope of their acceptable applications. In particular, a lack of performance guarantees means that they are unsuitable for safety-critical applications such as self-driving cars and scheduling systems. Neural networks trained to solve NP-complete problems, in particular, are unlikely to be able to solve the problem exactly. However, a weaker soundness guarantee may be sufficient for some systems, e.g., that positive instances of the problem may be …
Development Of Interactive Games On An Affordable Braille Display, Daniel Tsivkovski, Dylan Ravel, Maryam Etezad
Development Of Interactive Games On An Affordable Braille Display, Daniel Tsivkovski, Dylan Ravel, Maryam Etezad
Student Scholar Symposium Abstracts and Posters
Developing an affordable and STEM learning-focused Braille display addresses a significant disparity in the market for Braille displays, where most fail to provide a cost-effective, accessible, and education-oriented solution. This research aims to bridge this gap through innovative hardware and software development, offering a comprehensive learning experience to elementary school children (K-6) who are blind/visually impaired. The hardware features a piezo-electric tactile display that displays up to six Braille characters at once or a shape in an 8x8 pin array configuration. The educational software includes a user-friendly website packed with engaging STEM activities specifically designed for blind/visually impaired children. The …
Praxly: An Online Ide For The Praxis Cs Test Pseudocode, Benjamin Saupp
Praxly: An Online Ide For The Praxis Cs Test Pseudocode, Benjamin Saupp
James Madison Undergraduate Research Journal (JMURJ)
No abstract provided.
Satellite Reorientation Using Reinforcement Learning Under Unknown Attitude Failure, Matthew Willoughby
Satellite Reorientation Using Reinforcement Learning Under Unknown Attitude Failure, Matthew Willoughby
Doctoral Dissertations and Master's Theses
This study presents a reinforcement learning (RL) approach for reestablishing communication with deep-space satellites under unknown attitude determination and control system (ADCS) failures. When traditional fault-tolerant control methods cannot restore signal, the proposed RL controller acts as a last-resort measure by autonomously reorienting the satellite’s antenna toward Earth while charging the battery via solar panels. A generic reward function, designed for the RL-based method, enables the controller to adapt to diverse failure scenarios, including severe actuator noise, misalignment, and complete actuator failure. Simulations are conducted in the Basilisk environment and trained with the tonic framework and demonstrate ranging capabilities of …
Soft Modular Robots: From Modular Tensegrity Structures To Bioinspired Sea Robots, Luyang Zhao
Soft Modular Robots: From Modular Tensegrity Structures To Bioinspired Sea Robots, Luyang Zhao
Dartmouth College Ph.D Dissertations
The rapid advancement of robotics necessitates systems capable of adapting to complex, unstructured environments. Soft robots, with their flexibility and compliance, excel in delicate interactions, making them ideal for medical applications and search-and-rescue missions. Modular robots, on the other hand, offer reconfigurability, enabling diverse task-specific adaptations in dynamic settings. Despite their individual advantages, the integration of soft and modular robotics remains underexplored. This proposal aims to develop soft modular robots that combine the adaptability of soft robotics with the versatility of modularity. These systems will be capable of autonomously transitioning between locomotion, manipulation, and infrastructure assembly across land, water, and …
A Hierarchical Soft Computational Model For Optimizing Agricultural Uavs: Recruiting Neutrosophic Theory And Tree Soft Sets, Mona Mohamed, Nurhan Alaa, Bilal Arain, Karam M. Sallam
A Hierarchical Soft Computational Model For Optimizing Agricultural Uavs: Recruiting Neutrosophic Theory And Tree Soft Sets, Mona Mohamed, Nurhan Alaa, Bilal Arain, Karam M. Sallam
Neutrosophic Systems with Applications
Precision agriculture is being transformed by Unmanned Aerial Vehicles (UAVs), which make it possible for yield optimization, targeted spraying, and sophisticated crop monitoring. With an emphasis on their operational capabilities, economic feasibility, and environmental implications, this research explores the revolutionary potential of UAV technology in contemporary farming systems. Practically speaking, the procedure of opting UAVs for agricultural applications is complicated by several competing aspects, inherent uncertainties, and differing stakeholder agendas. This paper suggests a new hybrid decision framework that combines Tree Soft Sets (TrSS), Neutrosophic theory, and Multi-Criteria Decision-Making (MCDM) to methodically handle these issues. Hence, the robust hybrid model …
Towards Explainable And Robust Nlp: Neutrosophic Probability Augmentation In Text Classification, Nabil M. Abdel-Aziz, Mahmoud Ibrahim, Khalid A. Eldrandaly
Towards Explainable And Robust Nlp: Neutrosophic Probability Augmentation In Text Classification, Nabil M. Abdel-Aziz, Mahmoud Ibrahim, Khalid A. Eldrandaly
Neutrosophic Systems with Applications
The rapid growth of textual data necessitates advanced text classification models. However, traditional methods struggle with ambiguity and uncertainty in natural language, reducing classification reliability. To address this, we integrate neutrosophic logic, which explicitly models truth, indeterminacy, and falsity, into a DistilBERT-based text classification framework. Additionally, we employ data augmentation using synonym replacement to enhance generalization. Our approach is evaluated on the AG News dataset, classifying articles into four categories: World, Sports, Business, and Science/Technology. By incorporating neutrosophic attributes, the proposed framework assesses text quality, mitigates uncertainty, and improves robustness against ambiguous inputs. Experimental results demonstrate an accuracy of 94.10%, …
Designing An Interactive Exit Ticket System To Enhance Learning In Engineering Education, Tapanga Witt
Designing An Interactive Exit Ticket System To Enhance Learning In Engineering Education, Tapanga Witt
McNair Scholars Manuscripts
Within Grand Valley State University's (GVSU) Padnos College of Engineering, many first-year Engineering courses consistently see pass rates below 70%, signaling a need for stronger academic support. This is an issue across many universities; students are underprepared and face a lot of challenges with workload, pace, and independence due to the transition from high school to college (Flanigan, 2024). For this project, I designed and developed a website-based exit ticket system to support student reflection and instructor feedback. Using UX design principles, the platform offers a weekly self-reflection form that recommends resources like tutoring, office hours, or study groups. Gamification …
Controlling A Mobile Inverted Pendulum And Optimizing Leaning Angle To Apply Force Using Reinforcement Learning, Aryan Mediratta
Controlling A Mobile Inverted Pendulum And Optimizing Leaning Angle To Apply Force Using Reinforcement Learning, Aryan Mediratta
2025 Spring Honors Capstone Projects - Archive
Reinforcement Learning is a Machine Learning paradigm that involves simulating learning through rewards and penalties in intelligent systems. This technique is often employed in robotics when traditional control methods are insufficient or when human intuition does not provide a good solution on how to control robot systems, This project involves training a Segway-style Mobile Inverted Pendulum (MIP) robot to balance and push a box forward. The BeagleBone Blue board is used that includes a built-in Inertial Measurement Unit (IMU) and encoder ports. These sensors enable the system to measure its current state. The goal is to find the optimal leaning …
Tutortech: A Web App For A Smarter And More Efficient Tutoring System, Smarika Pathak
Tutortech: A Web App For A Smarter And More Efficient Tutoring System, Smarika Pathak
2025 Spring Honors Capstone Projects - Archive
The Computer Science and Engineering (CSE) department faces challenges with managing its tutoring services, especially tracking attendance, booking sessions, and overall management of the tutoring system - all of which severely limits the ability for tutors to connect and engage with students. To help overcome these issues, TutorTech, a web-based application that provides improved management of the tutoring system and supports more engaging learning experiences between students and tutors was designed. Through this project, the aim was to optimize the TutorTech search capabilities - assisting students to find tutors based on skills, while also considering the effect of user interface …
Air And Missile Defense Threat Scenario Variation To Reduce Pretest Sensitization, Video Games As A Case Study, Julie Renee Szekerczes
Air And Missile Defense Threat Scenario Variation To Reduce Pretest Sensitization, Video Games As A Case Study, Julie Renee Szekerczes
All-Inclusive List of Electronic Theses and Dissertations
This study uses fixed and variable video game types to measure pretest sensitization as a proxy for repeated and varied threat test scenarios in system performance testing of air and missile defense systems. The pretest sensitization phenomenon exists when repeated exposure to a test condition influences the participant's response. Research shows air and missile defense development correlates with video games, resulting in similar interfaces and computer operating environments. Department of Defense acquisition test and evaluation results must reflect system performance without prior knowledge of the threat scenarios confounding the results. System performance results inform acquisition decisions, such as further funding …
Using Gaussian Process Regression To Learn Thermodynamic Equations Of State With Uncertainty Quantification, Austen T. Lee
Using Gaussian Process Regression To Learn Thermodynamic Equations Of State With Uncertainty Quantification, Austen T. Lee
Chemical Engineering Undergraduate Honors Theses
This study investigates the use of derivative-informed Gaussian Process (GP) models to estimate thermodynamic behavior across temperature and density by building a Helmholtz-based equation of state. Argon, a stable monatomic gas, was chosen as a case study within the vapor region. The GP model was trained using values of experimentally measurable properties found by taking first and second derivatives of the original potential function. Results show that while the GP model offered uncertainty quantification and informed thermodynamic behavior, it predicted values that deviated from the ground truth depending on the property. The model exhibited high confidence in regions with substantial …
Cross-Dataset Fairness Evaluation Of Transformer-Based Sentiment Models, Sara Zuiran
Cross-Dataset Fairness Evaluation Of Transformer-Based Sentiment Models, Sara Zuiran
Theses and Dissertations
With the growing exploration of Natural Language Processing (NLP) systems in decision-making environments, it is essential to evaluate technical and ethical aspects of the dataset and the NLP model to improve fairness. To assess fairness, the thesis examines demographic imbalances in sentiment classification models by evaluating transformer-based models fine-tuned on the Stanford Sentiment Treebank version 2 dataset (SST-2) against the demographically annotated Comprehensive Assessment of Language Model dataset (CALM). This work identifies performance disparities in sentiment prediction across demographic groups by examining sensitive attributes such as gender and race. The study evaluates both the RoBERTa and MentalBERT transformer models using …
Implementation Of Residual Tandem Neural Networks For Photonic Inverse Design, Ponthea A. Zahraii
Implementation Of Residual Tandem Neural Networks For Photonic Inverse Design, Ponthea A. Zahraii
Electrical Engineering and Computer Science (MS) Theses
Deep-learning approaches can greatly benefit the modeling and design of nanophotonic and optical structures. Traditional full-wave simulations are time and resource-intensive, which can act as a bottleneck in photonic design. On the other hand, deep-learning approaches for designing the response of nanophotonic geometries can be computationally inexpensive and produce accurate and efficient results. In this project, we specifically investigate the case of optical forces near meta-structures. We propose using an inverse design approach with residual blocks to account for the deep nature of this architecture and inherently address the non-uniqueness problem. A tandem approach, which consists of two interconnected models, …
Reconfigurable Python Autopilot Software For Rc Aircraft, Kate Doiron
Reconfigurable Python Autopilot Software For Rc Aircraft, Kate Doiron
Honors Theses
No abstract provided.
Computer Vision In Soccer: Yolov11 Analytics Engine For Quantifying Game Strategy, Connor S. Maurer
Computer Vision In Soccer: Yolov11 Analytics Engine For Quantifying Game Strategy, Connor S. Maurer
Data Science Undergraduate Honors Theses
Single-shot object detection capabilities significantly reduce computational overhead for real-time computer vision in sports analytics at 60 FPS. YOLO11’s lightweight CNN gives promising accuracy while meeting the low-latency demand of dynamic soccer matches. As data-driven approaches take over the sport of soccer, efficient player tracking systems become critical for informing coach’s strategies. I prototype the ETL (Extract, Transform, Load) process of data collected from a single- shot detection program and evaluate its viability for estimating player fatigue. YOLO11 detects players, the ball, and other characteristics, with the output transformed by homography to estimate the positions in the real world. These …
Towards Mitigation Of The Light Network Load Performance Penalty Of The Network Link Outlier Factor (Nlof), Jaime Merin Guzman
Towards Mitigation Of The Light Network Load Performance Penalty Of The Network Link Outlier Factor (Nlof), Jaime Merin Guzman
Open Access Theses & Dissertations
Detecting and localizing faults in communication networks is critical to maintaining reliable and efficient network operations. The Network Link Outlier Factor with Most Likely Link (NLOF: MLL) algorithm has demonstrated its potential to automate this task but suffers from significant performance degradation under low network load conditions, where limited network flow data reduces its ability to localize faults. This thesis proposes and evaluates the performance of a synthetic traffic generation algorithm to be used with NLOF:MLL. This algorithm strategically injects synthetic flows that supplement the insufficient real network flows, thereby improving NLOF:MLL's performance under low-load conditions. Specifically, we select network …
Improving Home Security Through User Centered Device Positioning, Lalith Nadipalli
Improving Home Security Through User Centered Device Positioning, Lalith Nadipalli
Theses and Dissertations
In today’s world, where technology is advancing rapidly and security threats are becoming more complex, the need for effective home safety measures is more critical than ever. Homeowners are increasingly turning to a variety of smart devices, such as smoke detectors, carbon monoxide detectors, and security cameras, to protect their living spaces against potential dangers like burglary, fire, and environmental hazards. These devices offer essential protection, acting as both early warning systems and visual surveillance tools. However, their effectiveness largely hinges on how well they are placed within the home. Proper placement of these safety devices ensures that they provide …
Key-Based Authentication Scheme For Evtol Drones Using Chebyshev Chaotic Maps, Eduardo A. Hernandez Escobar
Key-Based Authentication Scheme For Evtol Drones Using Chebyshev Chaotic Maps, Eduardo A. Hernandez Escobar
Master's Theses
The development of electric Vertical Take-Off and Landing (eVTOL) drones signifies a substantial advancement in urban air mobility, ready to transform transportation models in densely populated regions. These advanced drones, distinguished by their capacity to function in limited spaces and their minimized environmental impact, are set to transform individual, shipping, emergency services, and public safety activities. Nonetheless, like any transformational technology, the implementation of eVTOL systems presents many challenges, especially in the realm of cybersecurity. Adding many devices and entities to an eVTOL network increases the risk of privacy and security attacks. This paper proposes a key-based authentication scheme that …
A Hierarchical Soft Computational Model For Optimizing Agricultural Uavs: Recruiting Neutrosophic Theory And Tree Soft Sets, Mona Mohamed, Nurhan Alaa, Bilal Arain, Karam M. Sallam
A Hierarchical Soft Computational Model For Optimizing Agricultural Uavs: Recruiting Neutrosophic Theory And Tree Soft Sets, Mona Mohamed, Nurhan Alaa, Bilal Arain, Karam M. Sallam
Neutrosophic Systems with Applications
Precision agriculture is being transformed by Unmanned Aerial Vehicles (UAVs), which make it possible for yield optimization, targeted spraying, and sophisticated crop monitoring. With an emphasis on their operational capabilities, economic feasibility, and environmental implications, this research explores the revolutionary potential of UAV technology in contemporary farming systems. Practically speaking, the procedure of opting UAVs for agricultural applications is complicated by several competing aspects, inherent uncertainties, and differing stakeholder agendas. This paper suggests a new hybrid decision framework that combines Tree Soft Sets (TrSS), Neutrosophic theory, and Multi-Criteria Decision-Making (MCDM) to methodically handle these issues. Hence, the robust hybrid model …
A Machine Learning Framework For Packet Anomaly Detection In Smartgrid Substation Networks, Sowmya Bandari
A Machine Learning Framework For Packet Anomaly Detection In Smartgrid Substation Networks, Sowmya Bandari
School of Computing: Dissertations, Theses, and Student Research
The increasing reliance on Smart Grid Substation Networks for efficient electricity distribution has amplified cybersecurity vulnerabilities, particularly within Supervisory Control and Data Acquisition (SCADA) systems. The IEC 60870-5-104 (IEC-104) protocol, widely adopted for communication between Remote Terminal Units (RTUs) and Human-Machine Interfaces (HMIs), lacks inherent encryption and authentication mechanisms, rendering it susceptible to sophisticated cyberattacks. Threats such as False Data Injection Attacks (FDIAs), command injection, covert attacks and replay attacks pose significant risks by manipulating grid control signals, potentially leading to undetected operational disruptions, cascading failures, or system-wide instability. Conventional signature-based Intrusion Detection Systems (IDS) often fail to identify zero-day …
Expressive And Interpretable User Engagement Prediction Using Multivariate Survival Processes, Akshay Aravamudan
Expressive And Interpretable User Engagement Prediction Using Multivariate Survival Processes, Akshay Aravamudan
Theses and Dissertations
The ability to characterize how information diffuses online is of paramount importance to stakeholders that are interested in tasks such as proposing solutions for mitigating and countering dis/misinformation, predicting user engagement of content in social media, planning marketing campaigns to roll-out products and planning dissemination of political campaign messaging among others. One such facet of learning the dynamics of information diffusion is the ability to predict user engagement or the popularity of a single piece of information as it spreads through an online medium. Existing works in this regard mainly either obfuscate user level information or utilize frameworks that are …
Physics Embedded Neural Network: A Novel Data-Free Numerical Method For Solving Computational Physics Problems, Pawan Gaire
Physics Embedded Neural Network: A Novel Data-Free Numerical Method For Solving Computational Physics Problems, Pawan Gaire
Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–
A novel approach for solving partial differential equations (PDEs) using neural networks for scientific computing is introduced. The proposed approach, referred to as physics-embedded neural network (PENN), features a unique architecture that incorporates the PDE and boundary conditions information directly within the final fully-connected layer of the feed-forward neural network (NN). The key aspect of PENN is the parallel numerical embedding of a differential equation associated with physical problems within the activation function of the network’s final layer. This integration leads to a new class of computational solvers competitive with classical methods like the Finite Element Method (FEM) and capable …
Generative Ai For 3d Printed Antenna Design, Jennifer Ann Chavez
Generative Ai For 3d Printed Antenna Design, Jennifer Ann Chavez
Open Access Theses & Dissertations
This research explores the integration of generative artificial intelligence (AI) with a physics-informed particle swarm optimizer (PSO) to develop 3D printable microstrip patch antennas. A neural network was trained on a dataset of microstrip patch antenna geometries and their corresponding performance metrics: return loss and gain. The PSO used a fitness function prioritizing low return loss in potential antennas, eventually yielding novel antenna geometries with parasitic components. 3D printing constraints were also hard coded into the framework, thus preventing any geometries being generated that cannot be fabricated. When simulated using Ansys HFSS, the AI generated microstrip patch antennas exceeded the …