Engineering Commons^™

A Hybrid Ai-Based Framework For Real-Time Epileptic Seizure Detection In Intracranial Eeg Signals Using Brain-Computer Interfaces, Ahmed, M. Salaheldin Dr., Manal Abdel Wahed Prof., Neven Saleh

Future Engineering Journal

Epilepsy is a serious neurological disorder that can significantly impact an individual's quality of life. This study proposes a novel method for the detection and mitigation of epileptic seizures through the integration of artificial intelligence (AI) and a brain-computer interface (BCI) system. Statistical features were extracted from intracranial electroencephalography (IEEG) signals using a multi-resolution decomposition technique and used to train five classification algorithms: support vector machines (SVM), k-nearest neighbors (KNN), Naïve Bayes (NB), artificial neural networks (ANN), and long short-term memory (LSTM). The model demonstrated strong performance, achieving classification accuracies of 98.28% (SVM), 93.88% (KNN), 95.73% (NB), 95.73% (ANN), and …

Developing Machine Learning Models And Graphene-Based Flexible Humidity And Temperature Sensors For Machine-Learning-Assisted Sensing, Seth Hajian

Dissertations

Flexible sensor technology has recently gained tremendous momentum in both academic research and industrial applications, transitioning from conceptual frameworks to practical implementations across diverse fields. This remarkable advancement can be attributed to several converging factors, including the maturation of nanomaterial science, the advancements of machine learning algorithms, and the critical demand for intelligent sensing solutions in healthcare, environmental monitoring, and industrial automation. The growing emphasis on personalized medicine and real-time health monitoring, accelerated by global health challenges, has further highlighted the necessity for accurate, cost-effective, and adaptable sensing platforms. This dissertation presents the fulfillment of three interconnected research projects focused …

Aggregator Zone Selection For Ev Smart Controls Based-On Ml Clustering Of Grid Strength, Distance, And Charging Homogeneity, Rosemary E. Alden, Sam H. Lowe Ii, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Smart electric vehicle (EV) charging control methods from a central utility hub often require communication infrastructure over a large service area of electric power distribution systems with a large number of nodes. Industry standards such as Open Charge Point Protocol (OCPP) 2.1 have evolved to include topologies for local controllers to the individual chargers, i.e. EV aggregator zones. A machine learning (ML) application of k-means clustering is proposed to establish zones for coordination of EV charging based on grid strength and EV owner decision-making to charge per day. Very large-scale distribution networks including the IEEE 123 and 8500 benchmark feeders …

Hybrid Fea And Meta-Modeling For De Optimization Of A Pm Stator-Excited Motor With A Reluctance Rotor, Oluwaseun A. Badewa, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper presents an innovative method for designing high-performance electric motors by integrating machine learning (ML) based meta-modeling with a differential evolution (DE) optimization algorithm. The approach utilizes finite element analysis (FEA) data to train the ML meta-model, allowing for efficient optimization of high-power-density machines, such as the reluctance rotor and permanent magnet (PM) stator combined excitation motor, which is characterized by nonlinearities. The meta-modeling process employs an Artificial Neural Network (ANN) with 3 hidden layers and uses the motor’s geometrical variables as inputs. The accuracy of the meta-model in capturing the nonlinear relationships between design parameters, core losses, and …

Optimizing Concrete Strength: How Nanomaterials And Ai Redefine Mix Design, Dan Huang, Guangshuai Han, Ziyang Tang

Physics and Engineering Science

Nanomaterials and supplementary cementitious materials (SCMs) are typically used together in efforts to enhance the performance of concrete and mitigate the environmental impact of concrete construction. However, the complex interactions between nanomaterials, SCMs, and cement make concrete mix design a challenging, iterative, and labor-intensive process, often relying on trial-and-error experimentation. Machine learning (ML) offers an opportunity to better understand the influence of input parameters and to accelerate the optimization of mix designs through data-driven insights. This study proposes an open-source and easy-to-access framework, Canopy, to support the concrete research community in optimizing mix design. Using a dataset collected from the …

Retracted: Robust Security System: A Novel Facial Recognition Optimization Using Coronavirus-Inspired Algorithm And Machine Learning, Saif Mohanad Kadhim, Johnny Koh Siaw Paw, Yaw Chong Tak, Shahad Thamear Abd Al-Latief

Iraqi Journal for Computer Science and Mathematics

Facial recognition has become an invaluable and rapidly advancing technology that plays a crucial role in various daily applications. From identity authentication to video surveillance, mobile payment, and even law enforcement and security measures. Despite the remarkable progress, facial recognition is still a dynamic research field and confronts several challenges. One of the main challenges is the high variability in facial images due to factors like facial expressions, lighting conditions, aging, and the presence of accessories. Additionally, the computational complexity and the time concerns surrounding face recognition systems have raised considerations that need to be addressed. This research presents a …

Mfgat: Map-Free Trajectory Prediction With Graph Attention Networks For Autonomous Vehicles, Zehra Gunindi

UNLV Theses, Dissertations, Professional Papers, and Capstones

Accurate trajectory prediction is a key component for ensuring safe and efficient navigation of autonomous vehicles in complex traffic scenarios. While traditional methods rely heavily on high-definition (HD) maps, these approaches face significant challenges, including high costs, limited availability, and susceptibility to rapid obsolescence. This thesis proposes an end-to-end, map-free trajectory prediction model that leverages Graph Attention Networks (GAT) to dynamically capture spatial-temporal interactions among road agents, eliminating the need for HD maps.The research introduces UNLVTraj, a novel LiDAR-based dataset collected around the University of Nevada, Las Vegas campus, specifically along Cottage Grove Street, Harmon Avenue, and Maryland Parkway. This …

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 …

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 …

Estimating Pedestrian Crossing Times At Scramble Crossings Via Machine Learning And Agent-Based Modeling, Sho Takami

Honors Capstones

Scramble crosswalks differ from conventional crosswalks in their ability for pedestrians to cross diagonally. This research compares the average crossing times and investigates the walking behaviors that pedestrians adopt to produce the speediest times in the two crosswalk configurations. Identification of the most efficient set of walking behaviors is done through an agent-based model, whereas producing polynomials relating crossing times to the most prominent walking behaviors is done through regression algorithms in machine learning. With the combination of these two approaches, it is revealed that pedestrians must adopt a relaxed walking style to make each crosswalk configuration efficient. Additionally, between …

Data-Driven Dynamic Decision-Making Using Discrete Optimization And Supervised Machine Learning, Navid Rashedi

Dartmouth College Ph.D Dissertations

In recent years, the operations research community has developed data-driven optimization techniques to solve complex combinatorial problems with the aid of machine learning. This thesis contributes to these efforts by combining machine learning with optimization to expedite online decision-making, with applications in transportation and healthcare.

In the domain of airline operations recovery, the focus is on the aircraft recovery process—repairing disrupted schedules by minimizing overall disruption costs. Traditional exact methods are too time-consuming, while heuristic approaches often yield poor solution quality and lack generalizability across varying formulations. To address these challenges, this research employs supervised machine learning to identify near-optimal …

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 …

Hybrid Fea And Meta-Modeling For De Optimization Of A Highly Saturated Spoke Ipm, Oluwaseun A. Badewa, Marcelo Silva, Rosemary E. Alden, Pedram Asef, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper introduces a novel approach for high performance electric motor design that combines machine learning (ML)-based meta-modeling with a differential evolution (DE) optimization algorithm. The method leverages finite element analysis (FEA) results to train the ML meta-model, enabling efficient design optimization for high-power density cored machines, such as spoke interior permanent magnet motors (IPM), which exhibit complex nonlinearities and saturation effects. This hybrid ML-DE framework seeks to provide an alternative for physics-based electric motor design and optimization, offering significant reductions in computational effort while maintaining accuracy. The meta-model’s accuracy in capturing the nonlinear relationships between design parameters, core losses, …

Using Deep Learning And Two-Photon Excitation Fluorescence Microscopy To Predict Breast Cancer Recurrence And Response To Chemotherapy, Nicholas Powell

Graduate Theses and Dissertations

Accurate prediction of breast cancer recurrence plays a critical role in guiding treatment decisions, particularly regarding the use of systemic therapy for patients. While genomic assays such as the Oncotype DX Recurrence Score offer valuable prognostic information, they are limited in accessibility and application. The work presented in this thesis explores an imaging-based approach for the prediction of breast cancer recurrence that combines multiphoton microscopy (MPM) with deep learning to classify individual breast cancer biopsy samples by risk of recurrence. Genomic differences in tumors with different recurrence potentials may translate to distinct optical and structural information that can be captured …

Computation-Driven Design And Synthesis Of Molecular Sensors For Surface-Enhanced Raman Spectroscopy In Liquids, Junhu Zhou

Dartmouth College Ph.D Dissertations

The reliable detection of molecules in liquid environments is a continuing challenge in chemical sensing and biological diagnostics. While surface-enhanced Raman spectroscopy (SERS) is well-recognized for its high sensitivity and molecular specificity, conventional platforms – based on colloidal nanoparticles or patterned substrates – have shortcomings. These conventional configurations struggle with signal reproducibility, non-uniform hotspot distribution, and, critically, restricted accessibility for large targets such as proteins and exosomes.

This thesis develops a computation-guided approach to developing next-generation SERS sensors capable of overcoming these persistent obstacles. At the heart of the study is the design and fabrication of metal-insulator-metal (MIM) nanoparticles, where …

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 …

Developing End-To-End Imitation Learning For Asteroid Proximity Operations, Patrick David Quinn

Theses and Dissertations

Asteroid exploration remains a popular topic in the scientific community, however hurdles still exist for controlling spacecraft within the asteroid environment. Communication delays often require the usage of limited onboard computing hardware for navigation. Additionally, long mission timelines must be accommodated with highly efficient fuel use. Considering these issues, it is apparent that any guidance, navigation, and control (GNC) system in these spacecraft should emphasize both computational and fuel efficiency in its design. Furthermore, the integration of a robust state estimation system is necessary for the successful deployment of such systems. The development of a controller aiming to address these …

Flowermd: A Flexible Library Of Organic Workflows And Extensible Recipes For Molecular Dynamics And Machine-Learned Coarse-Grained Simulations Of Isotropic And Anisotropic Systems, Marjan Albooyeh

Boise State University Theses and Dissertations

Molecular dynamics (MD) simulations are essential tools for understanding and predicting material behavior at the atomic and molecular scale. While numerous open-source software packages exist for different stages of MD simulations, assembling a seamless, end-to-end workflow for complex multi-step simulations remains a significant challenge. In this work, we develop FlowerMD, a flexible and extensible Python-based software package that automates molecular simulation workflows, improving both reproducibility and ease of use.

FlowerMD provides modular components that simplify the end-to-end execution of MD workflows, from system initialization and force field application to simulation execution. It also automates multi-step simulation workflows through Recipes—predefined, modular …

Graph Based Deep Reinforcement Learning Aided By Transformers For Multi-Agent Cooperation, Michael S. Elrod

All Theses

Mission planning for a fleet of cooperative autonomous drones in applications that involve serving distributed target points, such as disaster response, environmental monitoring, and surveil- lance, is challenging, especially under partial observability, limited communication range, and uncertain environments. Traditional path-planning algorithms struggle in these scenarios, particu- larly when prior information is not available. To address these challenges, I propose an innovative framework that integrates Graph Neural Networks (GNNs), Deep Reinforcement Learning (DRL), and transformer-based mechanisms for enhanced multi-agent coordination and collective task ex- ecution. My approach leverages GNNs to model agent-agent and agent-goal interactions through adaptive graph construction, enabling efficient …

Towards Advancing Streamflow And Peak Flow Prediction With Machine Learning: Identifying Infrastructure At Risk, Sudan Pokharel

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Due to climate change and its impact, the need for adaptive strategies for natural disaster mitigation and resource management has never been more urgent. Central to this is water resource management, which is essential for sustainable human activities, ecological balance, and the mitigation of natural hazards like floods. Streamflow is a crucial element of water resource management and plays a vital role in planning and building water infrastructure, implementing emergency response plans, supporting flood mitigation initiatives, and regulating agricultural and industrial use. However, accurate prediction of streamflow still remains a challenge due to the complex non-linear and non-stationary interaction between …

Deep Learning Algorithms For Traffic Flow Predictions, Adegoke Ojeniyi, Prince Pal Singh, Ankita Vashisht, Swati Kumari, Karan Karan

AUIQ Technical Engineering Science

Given the growing complexity of urban transportation systems, precise traffic flow forecasting is essential for reducing not only issues of congestion but also, for boosting road safety and enhancing mobility management. This study integrates Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), Long Short-Term Memory (LSTM), and Recurrent Neural Networks (RNN) to present a hybrid deep learning framework for traffic prediction. Of these, the CNN-LSTM model is a reliable option for real-time traffic forecasting since it successfully captures both spatial and temporal dependencies, resulting in superior predictive performance. The dataset used to assess the framework includes 48,120 records from a traffic monitoring …

Investigating The Impact Of Waterhead, Time And Temperature On Dam Displacement: Application Of Computer Aided Models, Maaz Abdullah

AUIQ Technical Engineering Science

Dam displacement is a crucial indicator for assessing the safety of a concrete dam through structural health monitoring. Since the displacement data exhibits a non-linear and complex relationship with influencing factors like waterhead, time and temperature, machine learning models are deployed to accurately predict dam displacement. Furthermore, the limited availability of monitored data in the majority of the dams renders the studies conducted with a large number of observations valueless. In order to address the aforementioned issues, this study proposes a feature selection approach to predict dam displacement by examining the ability of four ensemble machine learning models on different …

Investigating The Differential Effects Of Smote Variants On Class Imbalance And Exploring Their Applicability To A Thalassemia Prediction Model, Hussam Mezher Merdas, Ayad Hameed Mousa

AUIQ Technical Engineering Science

Researchers work around the clock on many datasets provided by various institutions. These researchers strive to come up with highly efficient Artificial Intelligence models. Often, researchers face the problem of imbalance in the distribution of classes in a particular feature in the selected dataset, which creates an Artificial Intelligence model biased towards one class at the expense of another class that is no less important than the first. On the other hand, thalassemia is a disease that affects people of different ages. The degree of disease varies according to the thalassemia class. This study proposes an improved Machine Learning model …

Algorithms For Fast Fire Risk Prediction And Real-Time Data Processing, Mirzoyan Mirzaaxmedovich Kamilov, Tolaniddin Ramziddinovich Nurmukhamedov, Oybek Zokirovich Koraboshev, Bakhodir Saydullayevich Achilov

Chemical Technology, Control and Management

Ensuring fire safety in facilities with high fire risk is one of the pressing problems of modern society. Nowadays, there is a great need for accurate and effective prediction systems for fire prevention and rapid response. Since traditional methods do not provide the ability to quickly analyze and predict in real time, the development of algorithms and modern approaches using modern technologies is of great importance. This article analyzes fire risk prediction algorithms, their principles of operation and effectiveness, and considers methods for assessing and predicting fire risk using Artificial Intelligence (AI), Machine Learning (ML), and Big Data technologies. The …

A Comprehensive Analysis Of Recognition Of Hand Gestures Using Machine Learning, Shivani Shivani, Satinder Bal Gupta

Makara Journal of Technology

Hand gestures are a natural means of conveying information and thus, there is an increasing interest in utilizing gestures for communication with computers. This study focuses on systematically reviewing different machine learning algorithms while assessing their working mechanisms and accuracy. Articles were analyzed for comparing the performance of K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). In accordance with input data, intricacy of gestures, processing resources, and real-time demands, the study shows that each technique has distinct advantages and disadvantages. RNN showed the best accuracy of …

Machine Learning In Baseball Analytics: Sabermetrics And Beyond, Wenbing Zhao, Vyaghri Seetharamayya Akella, Shunkun Yang, Xiong Luo

Electrical and Computer Engineering Faculty Publications

In this article, we provide a comprehensive review of machine learning-based sports analytics in baseball. This review is primarily guided by the following three research questions: (1) What baseball analytics problems have been studied using machine learning? (2) What data repositories have been used? (3) What and how machine learning techniques have been employed for these studies? The findings of these research questions lead to several research contributions. First, we provide a taxonomy for baseball analytics problems. According to the proposed taxonomy, machine learning has been employed to (1) predict individual game plays; (2) determine player performance; (3) estimate player …

Investigating The Role Of Blood Models In Predicting Rupture Status Of Intracranial Aneurysms, Zonghan Lyu, Mostafa Rezaeitaleshmahalleh, Nan Mu, Jingfeng Jiang

Michigan Tech Publications

Purpose. Selecting patients with high-risk intracranial aneurysms (IAs) is of clinical importance. Recent work in machine learning-based (ML) predictive modeling has demonstrated that lesion-specific hemodynamics within IAs can be combined with other information to provide critical insights for assessing rupture risk. However, how the adoption of blood rheology models (i.e., Newtonian and Non-Newtonian blood models) may influence ML-based predictive modeling of IA rupture risk has not been investigated.Methods and Materials.In this study, we conducted transient CFD simulations using Newtonian and non-Newtonian rheology (Carreau-Yasuda [CY]) models on a large cohort of 'patient-specific' IA geometries (>100) under pulsatile flow conditions to …

Measurement Of Groundwater And Contaminant Fluxes In Fractures Using A Combined System Of Passive Flux Meter And Multiport Sampler, Qasim Raza Khan

Thesis/ Dissertation Defenses

Groundwater and contaminant movement in fractured rock aquifers is highly variable. Its dependence on fracture apertures and orientation as well as fracture network interconnectivity is not well understood. This poses a challenge to the measurement of groundwater and contaminant fluxes, especially when using open-hole techniques, which significantly alter natural flow conditions by connecting different fractures along an open borehole or a well. In this work, the use of Fractured Rock Passive Flux Meter (FRPFM) with invisible tracer and visible dye component to measure groundwater fluxes and identify geometric fracture parameters is explored through laboratory experiments. The invisible tracer component results …

Heavy Ion Testing Of Versal Acap Ai Engines, Caleb S. Price

Theses and Dissertations

With recent developments in artificial intelligence (AI), there has been an ever increasing need for computation power in space. These AI models are traditionally implemented on GPUs. However, these units come at a high cost in terms of weight and power usage. Due to these limitations, many organizations are turning to the Versal ACAP device, a system on chip (SOC) with hundreds of AI engines. This option delivers more computation per watt than a traditional GPU. The desire to use Versal ACAP in space has created a need to perform radiation testing on the device. Extensive testing has been performed …

Using Human Computation And Harmonic Expansions For Characterization And Design Of Grain Boundary Networks, Christopher W. Adair

Theses and Dissertations

Grain Boundary Networks (GBNs) are a high-dimensional feature in polycrystalline microstructural materials that are formed from the crystallographic degrees of freedom. This feature affects multiple material properties such as diffusivity, fracture, elasticity, heat transfer, and others. The link between the crystallography and the material properties is called the structure-property linkage, and allows for simulation and design of materials. While it is possible to use the structure-property linkage to design materials for better performance, the high-dimensional nature of the interconnected grain boundaries preclude the use of many common optimization algorithms for efficiency or local minima considerations. This work shows how the …