Engineering Commons^™

Machine Learning-Based Prediction Of Heavy-Duty Diesel Engine Emissions And Comparative Analysis Against Statistical Models, Abdullah Ahmad Khan

Graduate Theses, Dissertations, and Problem Reports (ETD)

The increasing stringency of emissions regulations for heavy-duty (HD) diesel engines necessitates the development of accurate, efficient, and general predictive models for engine-out emissions under steady-state operating conditions. Traditional statistical approaches, while computationally efficient, often fail to capture the inherent nonlinear relationships between the engine operating parameters and emissions formation. In contrast, machine learning (ML) techniques offer a promising alternative by learning the complex patterns directly from experimental data. The present thesis summarizes a comprehensive study on the prediction of steady-state, HD diesel engine emissions using ML models, along with a comparative analysis against conventional statistical modeling approaches.

A high-quality …

Ai-Driven Prediction And Reconstruction Of Missing Cased-Hole Logs For Improved Well System Understanding, Samuel Avilez Martinez

Graduate Theses, Dissertations, and Problem Reports (ETD)

Well logging is a fundamental technique in formation evaluation providing continuous, real-time measurements of geological and petrophysical properties within a well. Through the systematic analysis of well logs, engineers and geoscientists can accurately determine critical formation characteristics, including porosity, permeability, lithology, and fluid composition. Well logging is fundamental for making informed decisions and reducing uncertainties in the exploration and development of oil and gas reservoirs.

Despite its significance, the acquisition of reliable well log data in oil and gas wells is often compromised by various operational, mechanical, and formation-related challenges, as well as pressure, fluid, and equipment constraints. In high-risk …

Bridging Physics-Based Modeling And Machine Learning To Predict Material Behavior: Applications In Fatigue Crack Growth And Dielectric Property Characterization, Ansan Pokharel

Graduate Theses, Dissertations, and Problem Reports (ETD)

This dissertation integrates physics-based modeling with machine learning (ML) to predict how materials behave under complex thermal and mechanical conditions. A key innovation of this work is the use of finite element analysis (FEA) to supplement experimental data. This approach creates more diverse and representative synthetic datasets, helping to reduce the limitations and biases that arise when training ML models solely on experimental measurements. The research focuses on two applications: improving the prediction of fatigue properties in superalloys and estimating temperature-dependent, high-frequency dielectric properties relevant to microwave-based chemical processing.

In the first study, low-cycle fatigue experiments were performed on the …

Security-Oriented Voice Authentication Using Machine Learning, Ifeoluwa Stella Elegbe

College of Graduate Studies: Theses & Dissertations

This study develops and evaluates a machine learning and deep learning-based voice authentication system for secure identity verification. As traditional authentication methods such as passwords, PINs, and security tokens continue to face challenges, including identity theft, forgetting, and unauthorized access, voice biometrics offers a more secure, convenient, and user-friendly alternative, especially for remote, hands-free, and accessibility-focused applications. The study adopts a closed-set speaker identification framework, where the system determines the most likely speaker from a predefined group of enrolled users. A structured methodology is implemented, beginning with audio preprocessing and feature extraction. Key acoustic features, including Mel-Frequency Cepstral Coefficients (MFCCs), …

Explainable Multi-Horizon Wind Power Forecasting Via Aquila-Optimized Machine Learning Models, Mostafa A. Abdelnaby, Nahla B. Abdel-Hamid, Eman M. El-Gendy, Mahmoud M. Saafan

Mansoura Engineering Journal

The inherently unpredictable nature of wind energy necessitates the development of sophisticated forecasting models to ensure grid stability and optimal distribution. In this research, we propose a novel and systematic approach to wind power forecasting (WPF) across diverse timescales. This approach leverages the power of various machine learning (ML) models, metaheuristic hyperparameter optimization, and utilizes explainable artificial intelligence (XAI). The developed methodology is based on the Aquila optimizer (AO), capable of automatically adjusting different ML models for four different time periods (30 minutes, 6 hours, 24 hours, and 36 hours) on the data collected from the Gabal El-Zayt wind power …

Machine Learning Modeling Of In-Situ Effective Thermal Conductivities And Ablation Behaviors Of Ceramic Matrix Composites Under Hydrogen Combustion Environments For Gas Turbine Engines, Jayanta Bhusan Deb

Graduate Studies Theses and Dissertations 2026

Through the integration of experimental characterization, machine learning, deep learning, numerical modeling, and physics-informed artificial intelligence, this dissertation explores the thermal performance of polymer-derived ceramic matrix composites (CMCs) for hydrogen combustion environments. The polymer infiltration and pyrolysis (PIP) process was used to create Yttria-stabilized zirconia (YSZ)-fiber-reinforced ceramic matrix composites, which were then experimentally assessed under hydrogen torch and hydrogen combustion conditions typical of next-generation gas turbine and aerospace propulsion systems. Front- and back-surface temperature measurements were used to continually monitor thermal reactions, and post-test microstructural characterization and numerical simulations were carried out to evaluate material integrity and heat-transfer behavior. To …

Skeleton-Based Human Action Recognition Using Spatio-Temporal Latent Features With A Gcn Model, Avazjon Marakhimov, Kabul Khudaybergenov, Mominov Zakhriddin

Chemical Technology, Control and Management

Owing to its resilience to visual noise and viewpoint variations, skeleton-based analysis has become a cornerstone of human action recognition research. Despite its practical significance, existing methodologies often suffer from a reliance on single-stream skeletal representations, which fail to encompass the full complexity of action features. This study introduces Latent Features for Human Action Recognition (LFHAR), a novel architecture designed to overcome these limitations by utilizing diverse spatio-temporal latent representations for improved feature extraction. The approach applies graph-based transformations to individual skeletal frames in temporal sequences, then arranges the derived graph features into spatio-temporal matrices. Evaluation of standard datasets demonstrates …

Evaluating Traffic Safety And Geometric Characteristics Using Machine Learning Ensemble Techniques: A Case Study Of Egyptian Rural Multi-Lane Divided Roads, Sania R. Elagamy, Ahmed N. Awaad, Usama E. Shahdah, Sherif M. El-Badawy, Marwa E. Elbany, Eman K. Ali

Mansoura Engineering Journal

Crash prediction models are essential for evaluating traffic safety by analyzing crash occurrence, frequency, or severity. Recently, machine learning techniques have gained prominence in statistical regression modeling and data analysis. This study assesses the effectiveness of machine learning in predicting crashes on Egyptian rural multi-lane divided roads using limited regional data. Supervised machine learning ensemble techniques were applied to predict crash-prone segments (classification) and estimate the total number of crashes per segment (regression). A comparative analysis aims to identify the most suitable method. The Synthetic Minority Oversampling Technique (SMOTE) addressed data imbalance, while K-means Clustering (KC) enhanced regression model accuracy, …

Machine Learning For Anomaly Detection In Neural Network Security And Srf Cavities, Hal Ferguson

Electrical & Computer Engineering Theses & Dissertations

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications.

First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves …

Microgrid Assessment And Ml-Based Power System Faults Detection Leveraging Real-Time Co-Simulation, Diego Normando Gandara Mendez

Open Access Theses & Dissertations

The rapid growth of distributed energy resources (DERs) and the increasing reliance on data-driven decision making have reshaped the operational challenges of modern electric power systems. As microgrids become more prominent in distribution networks, utilities require methods that unify planning, control, and real-time situational awareness to ensure resilient operation under faulted or uncertain conditions. The goal of this MSEE thesis is to design and validate a latency-aware ML framework for rapid, reliable fault detection in distribution grids. To achieve the goal of the thesis, there are three specific objectives. Objective 1 evaluates optimized microgrid configurations under varying DER levels and …

Integrating Machine Learning With An Fps Aim Trainer For Optimal Sensitivity Finding, Sharan Krishna

Computer Science and Software Engineering

First-person shooter (FPS) games often demand high levels of skill in aiming, which leads players to look for external tools to improve their performance. This is where the concepts of aim training and aim trainers come in, becoming an easily accessible outside source for players to strengthen their performance with custom scenarios outside a set game. While many aim trainers exist, they offer limited insight into player performance metrics or adaptability to varying aiming styles. Furthermore, most existing aim trainers lack a standardized way of correlating aim skill with real-world performance or personalized feedback. This aim trainer addresses these limitations …

Microstrip Antenna Design Based On Ai And Machine Learning, Waleed Mohamed Sha Moulavi

Theses

Microstrip patch antennas (MPAs) rely on precise impedance matching for efficient power transfer between the antenna and feed line. This is often achieved using a number of different techniques, one of which is the quarter-wavelength transformer (QWT). While commercial electromagnetic (EM) solvers offer robust optimization capabilities, they often operate as "black boxes" without providing physical insights into parameter interdependencies. Furthermore, this thesis focuses on the specific scenario where the antenna input impedance is purely real. To address the lack of explicit design relationships for these specific conditions, this thesis develops and comparatively evaluates artificial intelligence (AI) models for QWT width …

Mosquito Classification And Explainability From Image Data Via Deep Learning Techniques, Farhat Binte Azam

USF Tampa Graduate Theses and Dissertations

According to the World Health Organization (WHO), mosquitoes are the deadliest animals on Earth, responsible for more human deaths annually than any other species. Mosquito-borne illnesses continue to pose severe risks to global health. In 2015 alone, there were an estimated 214 million malaria cases worldwide. Similarly, a 2016 report from the Centers for Disease Control and Prevention (CDC) revealed that Puerto Rico’s Department of Health received over 62,500 suspected cases of Zika, with 29,345 confirmed positive cases. In 2019, Southeast Asia experienced its worst dengue outbreak in recorded history. Of the approximately 4,500 mosquito species distributed across 34 genera, …

Development Of Advanced Algorithms For Autonomous Vehicle Navigation, Liridon Hoti, Besnik Qehaja, Jeton Sllamniku

UBT International Conference

This paper addresses the development of advanced algorithms for autonomous vehicle navigation, focusing on the creation of an intelligent platform capable of safely planning and managing vehicle movement without human intervention. The main objective is to design a functional and reliable system by applying modern artificial intelligence techniques, optimization methods, and sensor data processing (sensor fusion). The study begins with a review of the existing literature on autonomous navigation, identifying key approaches such as Machine Learning, Simultaneous Localization and Mapping (SLAM), and Path Planning. It then presents the development methods for the autonomous navigation system using machine learning through simulation, …

Detecting Polar Ring Galaxies Via Deep Learning, Fawad Kirmani, Anathavishnu S. Unnii, Varsha P. Kulkarni, Kyle Lackey, John R. Rose

Faculty Publications

Polar ring galaxies (PRGs) are peculiar galaxies that show a ring of stars, gas, and dust oriented roughly over the poles of the central ‘host’ galaxy (i.e. roughly orthogonal to the disc of the host galaxy). The formation models for these rings involve mergers or tidal interactions of the host galaxy with another galaxy. Although the identified PRGs look different from each other, they all have a ring that is not in the same plane as the disc of the host galaxy. Unlike in galaxies such as our Milky Way, where stars form in spiral arms, the rings exemplify an …

Csc36000 - Modern Distributed Computing Assignment, Saptarashmi Bandyopadhyay

Open Educational Resources

This assignment covers standard performance metrics for Distributed Systems and the basics of Multiprocessing for CSC36000 - Modern Distributed Computing at the City College of New York CUNY. It is an interactive coding assignment intended to be executed in a Python notebook.

Cyberbullying Defensive Strategy In Social Media Sessions Via Machine Learning And Cyber Deception, Mohammad Shafiqul Islam

Masters Theses

Cyberbullying poses a significant challenge on social media, where traditional detection systems struggle with the nuanced and dynamic nature of online abuse. This thesis proposes an integrated framework that combines large language model (LLM)-based detection with generative decoy responses to enable real-time protection for victims on platforms like Instagram and WhatsApp. Using models such as Mistral 7B, GPT-3.5 Turbo, and Phi-3 Mini, prompt-based one-shot learning achieved 87% detection accuracy and a 5% F1-score improvement over zero-shot approaches, demonstrating robust identification of text-based bullying. A novel synthetic dataset of 98 multi-turn conversations, designed with diverse subtypes and evaluated for realism, addressed …

Detecting Electrical Submersible Pump (Esp) Failures And Estimating Run Life Using Artificial Neural Networks, Mostafa Ahmed Sobhy

Theses and Dissertations

Electric Submersible Pumps (ESPs) are one of the important artificial lift methods for sustaining production in mature and high-water-cut wells; but may suffer frequent failures due to mechanical, electrical, hydraulic, chemical, and operational failures. These failures can yield substantial deferred production and intervention costs. Plenty of ESP installations are fitted with downhole sensors. Yet, it is observed that the current industry practice underutilizes the wealth of available sensor and operational data and lacks standardized, explainable failure-type identification and classification.

In this thesis, a comprehensive Machine Learning (ML) and Deep Learning (DL) framework was introduced for ESPs that simultaneously estimates remaining …

Algorithms For Assessing Soil Salinity Levels Based On Remote Sensing Imagery, Bobomurod Mamitjonovich Tojiboev

Chemical Technology, Control and Management

This article investigates methods for assessing soil salinity levels based on satellite (remote sensing) imagery and their calculation algorithms. Determining the degree of salinity plays a crucial role in the rational use of land resources and increasing agricultural efficiency. The study analyzes indices for determining soil salt content using remote sensing technologies, particularly multispectral images obtained from satellite systems such as Landsat and Sentinel (for example, SI - Salinity Index, NDVI - Normalized Difference Vegetation Index, and others). Furthermore, algorithms are developed based on these indices that enable automatic determination of salinity assessments. Artificial intelligence, machine learning, and geographic information …

An Envisioned And Efficient Design Of Next Generation Decentralized Iot Bot Detection Model, Ahmed Abdullah Almalki

Doctoral Dissertations

The Industrial Internet of Things (IIoT) and Internet of Medical Things (IoMT) are revolutionizing critical infrastructures, but their expansion has also introduced severe cybersecurity vulnerabilities. Traditional IoT Bot Detection Systems (IBDS) struggle to scale in environments characterized by high-dimensional, large-scale, and redundant network traffic. These challenges hinder the development of reliable cloud-based intrusion detection systems. The limitations of static and rulebased methods in detecting evolving IoT botnet attacks—such as those launched by Mirai and Gafgyt—underscore the need for intelligent, adaptive approaches. To address this, the present study proposes a machine learning and deep learning-driven IoT Botnet Detection Model, validated through …

Early Detection Of Oak Wilt Using Unmanned Aerial Vehicles (Uav) & Computer Vision, Muttaki I. Bismoy

Masters Theses

Forests are critical ecosystems, delivering services such as biodiversity conservation, climate regulation, timber production, and recreation. However, they face increasing threats from pathogens like Bretziella fagacearum, which causes Oak Wilt, a lethal disease that disrupts water transport in oak trees, leading to canopy dieback and eventual death. Traditional detection methods rely on manual ground surveys, which are labor-intensive, time-consuming, and prone to error, particularly in early disease stages.

This research presents an automated, scalable, high-precision Oak Wilt detection system using Unmanned Aerial Vehicles (UAVs) combined with deep learning-based computer vision. Expanding on earlier work with a lightweight CNN achieving an …

Design And Development Of Deep Learning Based Generic Platform For Promoting Precision Agriculture, Srilakshmi A

Theses and Dissertations

Precision agriculture also referred as precision farming or smart farming, is an innovative approach to agricultural management that leverages technology and data to optimize various aspects of the farming process. This approach aims to make farming more effective, sustainable, and profitable by affording farmers with the application tools and information they need to make more informed decisions.

Precision agriculture combines elements of agriculture, technology, and data science to enhance crop production, and resource utilization. Precision agriculture techniques can be highly effective in leaf disease detection within crop fields. Machine learning has been developed incredibly across multiple domains and shown it …

Ai Based Early Detection Of Hormonal Imbalance And Poly-Cystic Ovary Syndrome In Young Women, Reka S

Theses and Dissertations

A hormonal disorder, Poly-Cystic Ovary Syndrome (PCOS) usually affects women during the reproductive age. It is characterised by imbalances in hormones, particularly a rise in the female body's androgen level (male hormone) and enlarged ovaries with small cysts. PCOS can cause ovarian cysts, weight gain, acne, excessive hair growth, insulin resistance, and irregular menstrual cycles along with other health problems. While the exact origin of PCOS is uncertain and its symptoms are unclear, diagnosing PCOS in real-world conditions is a difficult task. Therefore, prompt and precise PCOS diagnosis is essential for efficient treatment and for averting long-term issues.

Clinicians typically …

Exploring Longitudinal Stability Of Spatiotemporal Sequential Patterns By Means Of Autoencoder Schemes For Eeg Based Personal Identification, Muhammed E. Oztemel

LSU Doctoral Dissertations

Robust personal identification remains a critical and challenging task in the digital era. Electroencephalography (EEG) offers a unique biometric modality that captures individual brain dynamics through complex neural signals. This dissertation proposes autoencoder (AE) based feature extraction and subject identification through these features. EEG recordings are first transformed into topographic maps to represent spatial brain activity. Consecutive topomaps are then concatenated to capture temporal transitions across frames. Convolutional autoencoders (CAEs) are used to learn spatial and temporal patterns, while domain-adaptive AEs are designed to model evoked potential based responses. Additionally, self-attention mechanism is incorporated to enhance feature representation. To analyze …

Scalable And Adaptive Agile Framework For Semiconductor Foundry: Advanced Packaging And Heterogeneous Integration Perspective, Pravin Thorat

Harrisburg University Dissertations and Theses

This research addressed the critical requirement for a scalable and adaptive agile framework specifically designed for the unique demands of semiconductor foundries specializing in advanced packaging and heterogeneous integration (HI). The semiconductor industry was encountering growing pressure to innovate and respond quickly to rapidly evolving demands, yet traditional manufacturing processes often struggled to adapt. Existing agile frameworks, mainly developed for the software industry, lacked the necessary adaptations to address the complexities of semiconductor manufacturing, including extended lead times, high capital investment, rigorous quality requirements, and the integration of various technologies. This research gap hindered the ability of semiconductor foundries to …

Fourier-Feature Mlp Toolkit For Gpu-Accelerated Cardiac-Mri 4dcmr Strain Analysis, Aarnav T. Sabale, Marco A. Prado, Craig J. Goergen

Discovery Undergraduate Interdisciplinary Research Internship

This paper explores the embedding of a Fourier-Feature—enhanced multiplayer perceptron(MLP-FEE) at the heart of a newly refactored python workflow for four-dimensional cardiac-MRI strain quantification demonstrating how a single, compact network can outperform traditional convolution and spline-based methods. The original code, capable of orientation normalization, displacement tracking, and finite-difference strain computation, has been translated and consolidated into pytorch. By injecting sinusoidal positional encodings at the network’s input layer supplied a rich set of high-frequency basis functions hence enabling multilayer MLP to resolve gradients that cubic splines and conventional CNNs typically blur or struggle with. Profiling on an Apple-silicon GPU shows interactive …

Mission-To-Designs: Automating Uav Conceptual Design Generation, Nana A. Adjei

All Theses

The conceptual design stage of Unmanned Aerial Vehicles (UAVs) is an exploratory phase where designers are tasked with developing and evaluating a wide range of viable design concepts, considering different geometries and configurations based on mission requirements. The goal is to identify the most suitable design after the exploration for further development and progression to the next phase. Several tools and frameworks have been developed to assist designers in this stage, each offering unique capabilities, but all with the common aim of aiding in the efficient development and evaluation of conceptual designs. This is important in the UAV design process …

Multimedia Forensics: Identification And Verification Of Source Camera, Vehicle Speed Estimation, And Deepfakes Detection, Jiajun Jiang

Electrical & Computer Engineering Theses & Dissertations

This dissertation advances multimedia forensics by addressing three critical research areas that enhance the authenticity verification and analysis of digital media. Multimedia forensics, which encompasses techniques for examining images, videos, audio, and text, faces increasing challenges due to sophisticated editing tools and massive data volumes. In the first study, a fast source camera identification and verification method based on PRNU analysis is proposed for video forensic investigations. By integrating camera rolling and I-frame analysis, this approach achieves a processing speed improvement of at least 15 times over conventional frame-by-frame methods while reducing false positives. The second study focuses on vehicular …

Revealing Building Operating Carbon Dynamics For Multiple Cities, Winston Yap, Abraham Noah Wu, Clayton Miller, Filip Biljecki

Research Collection College of Integrative Studies

Achieving carbon neutrality is a critical yet elusive goal for many cities, hindered by limited understanding of the relationship between building emissions and their surroundings. To address this challenge, we present a generalizable open science framework that integrates building energy-consumption data, multi-modal geospatial inputs and graph deep learning to quantify building operating emissions and their links to urban form and socio-economic factors. Applying this approach to five cities with diverse climates and planning contexts—Melbourne, New York City (Manhattan), Seattle, Singapore and Washington DC—we demonstrate that our models explain 78.4% of the variation in building operating carbon emissions across cities, achieving …

Effective Transformer Networks For Undersampled Magnetic Resonance Image Reconstruction, Tahsin Rahman

Open Access Theses & Dissertations

The proliferation of data-driven tools for solving problems in every possible domain, coupled with rapid advances in computing technology, has led to an arms race of AI development and application research in industry and academia. One field of research that stands to gain immeasurably from this revolution is medical imaging. It is a critical part of modern diagnostics, and advancements in this area can directly benefit the average person by making healthcare more accessible, accurate, and affordable. Breakthroughs in mainstream image processing and computer vision have long fueled development in medical imaging, and it is now common to see cutting …