Computer Engineering Commons^™

Evaluating Machine Learning Models On Classification Of Novel Cyber Attacks In The Healthcare Domain, Promise Ehimen

Dissertations, Theses, and Projects

The increasing adoption of the Internet of Medical Things (IoMT) has improved healthcare delivery through connected medical devices while simultaneously expanding the cybersecurity risks facing healthcare organizations. Although machine learning based intrusion detection systems have demonstrated high detection accuracy, their ability to respond reliably to previously unseen cyberattacks remains uncertain. This study investigated how a Neural Network model and a Logistic Regression model classified novel cyberattacks within the IoMT environment. The Neural Network and Logistic Regression models were both trained and tested using a subset of the CICIoMT2024 benchmark dataset. The Neural Network achieved 99.82% test accuracy and a 0.94 …

Evaluating Soil Health And Crop Yield In Louisiana Agricultural Systems: Impacts Of Best Management Practices And Prediction Models, Hector J. Mendoza Lagos

LSU Doctoral Dissertations

The adoption of conservation management practices is critical for improving soil health, enhancing nutrient use efficiency, and sustaining crop productivity in row crop systems in Louisiana. This study evaluated the role of conservation agronomic practices, soil biochemical indicators, and machine learning predictive models to improve soil nutrient dynamics, soil health indicators, microbial communities (MC), and crop productivity on a corn (Zea mays L.) research plot scale and in a commercial forty-hectare cotton (Gassypium hirsutum L.)-corn-soybean (Glycine max L.) rotation system in northeast Louisiana. The objectives of the study were to evaluate soil nutrient dynamics and MCs under …

Machine Learning And Formal Methods In Quantum Chemistry: Theory And Application, Ishna Satyarth

Computer Science and Engineering Theses and Dissertations

In recent years, the progress in inter-disciplinary application of machine learning and artificial intelligence (ML/AI) have truly transformed various fields, from weather forecasting and drug development to medical diagnostics, energy, and sustainability. Computational chemistry uses computational tools to model, predict, analyze, and explain chemical phenomena, while the Quantum chemistry specifically uses techniques based on quantum mechanics (as opposed to classical mechanics or empirical models). Quantum chemistry or Computational chemistry has also observed a momentum in application of ML techniques over the past decade significantly accelerating results and providing valuable insights into vast datasets, often surpassing traditional methods.

This dissertation explores …

Robust Hardware Trojan Detection Leveraging Dual‑Domain Features And Stacked Ensemble Learning, Sefatun-Noor Puspa

All Theses

In Cyber-physical systems rely on sensors, communication, and computing, all powered by integrated circuits (ICs). These ICs are vulnerable to malicious hardware attacks, with hardware Trojans being one of the stealthiest threats. Trojans are malicious implants in the circuitry, which are often inserted during design or fabrication stages. This stealthy addition remains dormant until triggered and might cause functional disruptions or sensitive information leakage once triggered. Traditional IC validation methods, such as functional testing and logic analysis, usually fail to capture these subtle anomalies because hardware Trojans are intentionally designed to mimic normal circuit behavior. They often remain dormant under …

A Low-Cost Motion Classification System For A Stuffed Animal Using An Imu And Machine Learning, Rachel N. Guynes

Honors Theses

One of the many fields that has seen the integration of robots is therapy. Zoomorphic robots (ZR) are designed to look and behave like animals to assist in Animal Assisted Therapy (AAT) practices. Studies show that ZRs can provide benefits similar to working with an actual animal; however, their high cost limits their accessibility. This thesis documents the process of building a real-time, low-cost motion classification system that can be attached to a stuffed animal to make it more interactive. Using a Random Forest (RF) classifier, the system identifies movements with approximately 81.67% accuracy.

Development Of Deep Fused Neural Architecture For Ancient Tamil Palm-Leaf Manuscript Recognition, Hariharan P Mr

Theses and Dissertations

Digitizing Tamil palm-leaf manuscripts is important for education, communication, and the preservation of cultural heritage. The complex structure of the Tamil script, the wide range of handwriting styles, and the degradation seen in ancient Tamil palm-leaf manuscripts make these texts very difficult to read and understand. Digital Image Processing (DIP), document analysis techniques, and traditional Optical Character Recognition (OCR) are unable to handle noise, background interference, faded ink, and limited labelled data, motivating the need for robust, effective Deep Learning (DL)- based solutions.

As a prerequisite to understanding and designing effective recognition systems for ancient manuscripts, this thesis first examines …

Ai Applications In Assessing Risk For Periodontal Disease: A Systematic Review, Grant O. Korte, Claudia M. Tellez Freitas

Annual Research Symposium

Objectives:

The goal of this systematic review is to examine the impact of using artificial intelligence (AI) to predict a patient’s risk level for periodontal disease by analyzing proven systemic health diseases linked to periodontitis. This is being done by primarily focusing on prevention and early diagnosis using machine learning programs that have been proven effective in other fields of periodontitis research.

Methods:

To conduct this study, a comprehensive literature review of journals published after 2020 was performed through four databases: PubMed, Scopus, Web of Science and Dentistry and Oral Science Source. The search was completed in adherence …

Clear Skies, Avery C. Munn

Williams Honors College, Honors Research Projects

Air quality impacts public health, environmental sustainability, and quality of life. However, accurate and easily accessible short-term air quality forecasting is challenging to find. This project, Clear Skies, presents a machine learning–based system for forecasting next-day Air Quality Index (AQI) levels across regions in Ohio. By using historical pollutant data with variables such as temperature, humidity, wind speed, and atmospheric pressure, the system finds relationships that traditional statistical models often don’t show.

Machine learning models are evaluated alongside AI techniques to find the environmental factors that influence AQI predictions. This helps reduce the “black box” nature of many AI systems …

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 …

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, …

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 …

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 …

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 …

Effects Of Lossy Compression Data On Machine Learning Models, Max H. Faykus Iii

All Dissertations

Machine learning is a fundamental tool that is incorporated in every field across academia and other industries. Due to the large amount of data needed for training machine learning models, lossy compression plays a crucial role in storing data. Machine learning involves the use of algorithms and models to learn patterns in data. This allows the AI to make decisions without specific programming. On the other hand, compression utilizes encoding and decoding techniques to reduce the size of files. Compression is either lossy or lossless, lossy causes a loss of data while lossless preserves the data. This dissertation will explore …

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 …

Human Activity Recognition And Identification Driven Automated Deep Learning For Time-Series Classification, Justin Alan Gamble

Engineering Management & Systems Engineering Theses & Dissertations

The growing emphasis on Digital Engineering (DE) within the U.S. Department of Defense (DoD) demands advanced methods for leveraging vast time-series data generated by sensor-rich environments. Deep learning models offer promising solutions for complex timeseries classification tasks, however their design and optimization remain highly resource intensive, requiring specialized expertise. This dissertation addresses this challenge by developing and evaluating an Automated Machine Learning (AutoML) framework specifically tailored for the time-series classification task of Human Activity Recognition and Identification (HARI).

A systematic investigation was conducted using the Design Science Research Methodology (DSRM) comparing traditional search strategies of grid search and random search …

Towards Efficient Privacy-Preserving Deep Learning: He-Friendly Structures, Flexible Pruning, He-Efficient Architectures, And Secure Transformer Token Drop, Yifei Cai

Electrical & Computer Engineering Theses & Dissertations

Deep learning (DL) has become a powerful tool for solving complex problems, but developing DL models typically requires vast datasets, high computational resources, and expert knowledge—barriers that limit accessibility. Machine Learning as a Service (MLaaS) addresses this challenge by allowing resource-rich providers to deliver pre-trained DL models as services. However, privacy concerns arise: clients hesitate to share sensitive data, while providers protect their proprietary models. To address this, privacy-preserving MLaaS integrates cryptographic techniques into DL computations, as seen in frameworks like Cryptonets, SecureML, GAZELLE, CrypTFlow2, Cheetah, and BOLT. Among them, Homomorphic Encryption (HE) enables computation on encrypted data but remains …

Hierarchical Reinforcement Learning (Hrl) In Multi-Goal Spatial Navigation With Autonomous Mobile Robots, Brendon Johnson

USF Tampa Graduate Theses and Dissertations

Hierarchical reinforcement learning (HRL) is hypothesized to be able to take advantage of the inherent hierarchy in robot learning tasks with sparse reward schemes, in contrast to more traditional reinforcement learning algorithms. In this research, hierarchical reinforcement learning is evaluated and contrasted with standard reinforcement learning in complex navigation tasks. We evaluate unique characteristics of HRL, including their ability to create sub-goals and the termination function. We constructed experiments to test the differences between PPO and HRL, different ways of creating sub-goals, manual vs automatic sub-goal creation, and the effects of the frequency of termination on performance. These experiments highlight …

Deeppanorf: Deep Prior For Neural 3d Reconstruction From Sparse Panoramas, Edward Du

Master's Theses

Advances in neural field representations have led to a significant improvement in view synthesis quality. However, many current novel view synthesis methods rely on a dense set of input views, which can be impractical and inefficient in real-world applications. We propose DeepPanoRF, a novel method for 360◦ scene reconstruction from a sparse set of input equirectangular panoramas. Built upon K-Planes, a radiance field representation that encodes explicit features on orthogonal feature planes, our method does not directly learn feature grids. Instead, we parameterize the feature grids to enable sparse view reconstruction without pretraining or additional regularization. We implement a custom …

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 …

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 …

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 …

Inference Per Joule: A Performance Metric For Artificial Intelligence In Space Applications, Eduardo Macias Zugasti

Open Access Theses & Dissertations

The use of artificial intelligence (AI) has grown exponentially in recent years. This growth is driven in part by the significant advancements in computing capabilities, which have also increased exponentially. Computers have not only become more powerful but also smaller in size, thanks to the evolution of transistor technology. These developments have enabled AI to become a widely accessible tool, even in recreational activities such as image creation and entertainment videos.

More recently, the use of AI has extended to space applications, where it can enhance and optimize various tasks. However, space conditions pose significant challenges for conventional computers due …

Car Damage Detection Using Deep Learning, Rahul Varma Indukuri Sr.

Electronic Theses, Projects, and Dissertations

Growing vehicle usage has resulted in a notable increase in road accidents, so it is imperative to have effective systems for identifying and evaluating vehicle damage. This work aims to create a computer vision and deep learning-based automated car damage detection system. This project's main goal is to develop a model that, using visual cues, can categorize car photos as either damaged or undamaged.

The algorithm operates in two steps: first, determining whether the picture features an automobile; then, it classifies the state of the car—damaged or undamaged. We thus employ the InceptionV3 model for damage classification and the MobileNet …

Virtual Makeup And Technology Integration, Vishwa Bhatt

Electronic Theses, Projects, and Dissertations

The Virtual Makeup Streamlit application presents an advanced approach to digital cosmetic try-on by allowing users to apply makeup to their facial images in real time. This project uses computer vision and web technologies to create an interactive and user-friendly platform that capitalizes on the increasing popularity of virtual try-on solutions in the cosmetics industry.

At its core, the system uses effective facial detection and semantic segmentation techniques to recognize and separate facial areas such as lips and hair. Techniques such as U-Net and Resnet, and Midepipe are used to create accurate segmentation masks, which are essential for accurate makeup …