Engineering Commons^™

Design And Fabrication Of 3d Bioprinted Scaffold: Experimental And Machine Learning Methods, Mohan K. Dey

LSU Doctoral Dissertations

The development of reliable hydrogel-based scaffolds for extrusion bioprinting remains limited by the poor structural fidelity of low-viscosity bioinks and the lack of robust, high-throughput quality evaluation methods. This dissertation can resolve such challenges by developing the combination of optimized hydrogel formulations, cryogenic-assisted bioprinting, and artificial intelligence (AI)-based scaffold evaluation to the use of tissue engineering and preclinical cancer modelling applications. To assess the rheological behavior, printability, mechanical properties, and biocompatibility of the alginate – gelatin (Alg–Gel) system, a novel system of hydrogel was developed and characterized using Alg–Gel hydrogel system. The 7% alginate, 8% gelatin mixture was found to …

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 …

Explainable Machine Learning For Biomedical Diagnostics: Optical Imaging And Eeg Signal Analysis, Fozia Rajbdad

LSU Doctoral Dissertations

The growing convenience of complex biomedical data begins new roads for better disease detection and functional identification via artificial intelligence (AI). Nevertheless, conventional analysis methods often rely on basic metrics that drop sensitive biotic differences, and various AI systems are difficult to infer, limiting their clinical reliability and practical use. There is a growing need for explainable, physiologically relevant computational models that can extract key biomarkers from diverse biomedical data sources. This dissertation addresses this problem by obtaining explainable machine learning and deep learning procedures for studying biomedical signals and optical imaging data.

This dissertation is divided into two parts; …

Feasibility Of Using Untreated And Treated Rice Straw Fibers As A Modifier For Hot Mix Asphalt, Aditya Chowdhury

Civil Engineering Theses

Flexible pavements are subject to progressive deterioration driven by repeated traffic loading, environmental exposure, and material degradation, which causes distress. This study presents an integrated research framework that combines laboratory performance evaluation of rice straw fiber (RSF)-modified hot mix asphalt with machine learning-based pavement distress prediction to advance sustainable pavements. Three plant-produced HMA mixtures were modified with untreated and alkali-treated RSF at dosage levels of 0.2%, 0.4%, and 0.6% by total mixture weight. FTIR spectroscopic analysis confirmed that alkali treatment induced significant chemical modifications to the fiber surface, increasing hydroxyl group availability and enhancing fiber–binder adhesion. Alkali-treated RSF consistently outperformed …

Comprehensive Study On Explainable Artificial Intelligence For Enhanced Decision-Making, Eman Taher, Wessam H. El-Behaidy, Doaa S. Elzanfaly

Computer Science

Artificial Intelligence (AI) models are often criticized for their black-box nature, particularly in high-stakes domains such as finance, healthcare, and business decision-making, where transparency, accountability, and trust are essential. As machine learning advances with more complex architectures, including deep neural networks and ensemble models, the need for explainability becomes increasingly critical. This study focuses on Explainable Artificial Intelligence (XAI) as a key approach to enhance interpretability in classification, regression, and clustering tasks that serve as the foundation of data-driven analytical systems. XAI methods such as SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and Integrated Gradients provide a means …

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 …

Analysis Of Dynamic Difficulty Scaling Ai In Video Games, Tyrese W. Walker

Honors Program: Senior Projects (Public)

Video games are a popular form of interactive media and, since their inception, have become one of the largest forms of media consumed. As such, they have evolved greatly from their humble beginnings into much more complex experiences, and adjusting the difficulty level to suit the needs of the player has become common practice. While there are simple ways to do so, the best games often feature a dynamic difficulty-scaling system that adapts to the player. Classics like Resident Evil and Left 4 Dead are excellent examples of innovative dynamic scaling design. By analyzing the strongest elements of these works, …

Ais26s: Ai In Biomedicine, Shiqian Shen

Paul English Applied Artificial Intelligence (AI) Institute Publications

This presentation explores the role of artificial intelligence in advancing biomedical research and clinical practice from the perspective of a physician-scientist. Dr. Shiqian Shen discusses current challenges in neuroscience and medicine, including limitations in human observation, data analysis, and decision-making across complex biological systems. The talk highlights how AI-driven approaches such as computer vision, neural signal processing, and large-scale data modeling can improve the understanding of disease mechanisms, enhance experimental workflows, and enable more precise, patient-centered care. Specific applications include automated classification of neural activity, behavioral analysis in animal models, and multimodal data integration. A key concept introduced is “shadow …

Ai Institute Summer Camp Academic Preview Webinar: Curriculum, Research, And Outcomes, Paul English Applied Artificial Intelligence Institute

Paul English Applied Artificial Intelligence (AI) Institute Publications

This webinar presents an academic preview of the AI Institute Summer Camp hosted by the Paul English Applied Artificial Intelligence Institute at the University of Massachusetts Boston. The session introduces the program’s curriculum, structure, and student outcomes, providing insight into a hybrid learning model that combines faculty-led lectures, hands-on labs, and guided project development. The webinar highlights the program’s five-week structure, covering topics such as machine learning, neural networks, computer vision, speech and language processing, and generative AI. Participants learn how students engage in real-world AI applications, complete portfolio-ready projects, and develop research and presentation skills. This session is designed …

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.

Physics-Based And Data-Driven Low-Order Modeling Of Centrifugal Compressors, Chase Oliphant

Theses and Dissertations

Centrifugal compressors serve as critical components across many applications, yet their design and performance prediction present significant challenges. While computational fluid dynamics provides high-fidelity predictions of compressor behavior, its prohibitive computational cost renders it impractical for preliminary design phases where rapid exploration of the geometric and operating space is essential. Conversely, existing low-order models have enabled efficient performance estimation but suffer from limited accuracy and poor interpretability, particularly when applied to novel geometries or off-design operating conditions. This dissertation addresses these limitations through three complementary contributions that bridge the gap between high-fidelity CFD and preliminary design tools. The first contribution …

Predictive Analytics In Oncology And Ophthalmology: Machine Learning Applications For Diabetic Retinopathy And Breast Cancer, Ali Abidalkareem

Electronic Theses and Dissertations

The convergence of artificial intelligence and healthcare represents one of the most transformative developments in modern medicine, with deep learning technologies emerging as powerful tools for addressing complex diagnostic challenges. This dissertation develops and validates machine learning frameworks that address critical challenges in medical diagnosis through innovative approaches to data augmentation, feature learning, and classification, focusing on two fundamental problems: Diabetic Retinopathy (DR) severity classification using multi-model convolutional neural networks (CNNs), and breast cancer stage identification using microRNA (miRNA) gene expression biomarkers. For diabetic retinopathy classification, this work proposes an ensemble deep learning framework that integrates Diffusion-based data augmentation for …

Ais26s: Genai And Llms For Cybersecurity, Political Sciences, Transportation Security, And Resiliency, Latifur Khan

Paul English Applied Artificial Intelligence (AI) Institute Publications

This presentation examines the role of generative artificial intelligence (GenAI) and large language models (LLMs) in addressing complex challenges across cybersecurity, political science, and transportation security. Dr. Latifur Khan discusses how advanced AI methods can be applied to threat detection, data analysis, and decision-making in high-risk and data-intensive environments. The talk highlights interdisciplinary applications of LLMs, emphasizing their ability to extract insights from large-scale data, improve system resilience, and support intelligent infrastructure. Emerging research directions and practical implications for real-world deployment are also discussed.

Aiw26s: Machine Learning Of Structured Data, Moumita Saha

Paul English Applied Artificial Intelligence (AI) Institute Publications

This workshop introduces the fundamentals of machine learning for structured data, focusing on tabular datasets and real-world applications. Participants explore key concepts such as data types, data preprocessing, feature engineering, and supervised learning methods. The session covers commonly used models, including linear regression, logistic regression, decision trees, and neural networks, along with evaluation metrics such as RMSE, accuracy, and confusion matrices. By the end of the workshop, participants will have gained a practical understanding of how to build, interpret, and evaluate machine learning models for structured data.

Traditional And Machine-Learning Equalization Techniques For Bandwidth-Limited Short-Reach Optical Communication Channels, Abdullah Khawatmi

Theses

This thesis investigates equalization techniques for bandwidth-limited short-reach optical communication systems, with a focus on Visible Light Communication (VLC) and Step-Index Plastic Optical Fiber (SI-POF) links. Commercial light-emitting diodes and photodiode receivers impose severe bandwidth constraints, inter-symbol interference, and noise sensitivity, which fundamentally limit achievable data rates. The work addresses these impairments through systematic evaluation of traditional digital signal processing–based equalizers and modern machine-learning-based post-equalization methods. The primary aim of this thesis is to enhance the achievable data rate and reliability of commercial short-reach optical links while maintaining practical computational complexity. Specifically, the objectives are to (i) design and experimentally …

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 …

Nlp-Based Comparative Assessment Of Clauses Quantifying Risk Transfer In Construction Contracts, Hoda M. Elshamy

Theses and Dissertations

Employers in the construction industry mostly deviate from standard contract forms such as FIDIC and NEC, by introducing alterations to the contract conditions that shift a great portion of risks from the client to the contractor. Originally, these risks were distributed more equitably between all contracting parties in the standards forms. The imbalances in the contractual conditions create fertile ground for conflicts, and if not resolved, will escalate to disputes during the project execution phase, leading to significant cost overruns and time delays. The contractors, therefore, attempt to restore the original balance through making amendments to the communicated contract, through …

Harnessing Ml And Iiot For Traceability In Continuous Production Systems: A Conceptual Framework, Kholoud M. Abdelaal

Theses and Dissertations

In the era of rapid technological advancement, the manufacturing sector faces increasing pressure to leverage emerging technologies to enhance operational efficiency and minimize waste. In this context, traceability plays a pivotal role, as it provides complete visibility of processes and products throughout manufacturing systems, enabling them to identify areas for improvement and take corrective actions accordingly. Additionally, traceability ensures compliance, supports product recalls, provides a clear understanding of the system’s performance, and enables fact-driven decision-making in multiple aspects of the manufacturing system. Although the broad spectrum of traceability applications in batch production-based plants, traceability remains challenging to achieve in continuous …

Areosense, Josephine Turney

Williams Honors College, Honors Research Projects

The AeroSense growing system is designed to make indoor aeroponic gardening easy and accessible for everyone. By combining sensors, automation, and AI, the system can monitor and adjust humidity, lighting, and nutrient levels to help plants thrive without requiring expert knowledge. The goal is to create a self-regulating garden that takes the guesswork out of growing fresh herbs and vegetables at home. The project involves building a working aeroponic prototype equipped with misters, pumps, and environmental sensors, all managed by a Raspberry Pi. Using computer vision and machine learning, AeroSense will be able to assess plant health and respond automatically …

A Hybrid Object Detection And Temporal Attention Framework For Intelligent Video Surveillance, Sudheer Reddy Bandi, Eswari Vanaparthi, Yakshini Edapalli, Roshan Kavuri

Mansoura Engineering Journal

The rapid growth of urban areas has greatly heightened the need for smart video surveillance systems that can automatically process extensive amounts of CCTV footage. Traditional surveillance methods largely depend on human monitoring, which is not only inefficient but also susceptible to human mistakes, especially in intricate and crowded environments. To tackle these issues, this paper introduces a combined object detection and temporal attention for intelligent video surveillance that concurrently analyzes spatial and temporal data from video streams. The proposed system analyzes real-time CCTV footage utilising a multi-pathway frame extraction technique that includes slow, fast, and full-frame sampling to capture …

Process-Guided Learning Via Data-Driven Modeling And Controller Synthesis, Benton Clark

Theses and Dissertations--Mechanical and Aerospace Engineering

This work introduces process-guided learning, a framework in which process characteristics and learning algorithms are combined in the modeling process of engineering applications with the aim of uniting traditional and data-driven modeling and control techniques. Traditional engineering methods using simplified analytic models fail to capture the increasing complexities of engineering problems with sufficient accuracy to achieve modern requirements. More advanced modeling and control techniques using high-fidelity models often produce a computational burden that is infeasible for real-time solutions. Data-driven modeling offers a simple and flexible algorithm for producing real-time capable, high-fidelity models, but naive implementations lack the physical constraints of …

Computational Modeling And Machine Learning For The Design Of Polymer-Based Protective Systems Under Dynamic Loading, Jonathan Tate Villada

Open Access Dissertations

Computational modeling and machine learning offer powerful pathways for designing polymer-based protective systems subjected to dynamic loading, particularly for mitigating the early shock-dominated response generated by near-field underwater explosions (UNDEX). As naval, offshore, and submerged infrastructure systems continue to grow in strategic importance, there is an increasing need for lightweight, damage-tolerant protective solutions capable of reducing transmitted pressure, deformation, and energy transfer under extreme impulsive environments. Since large-scale experimental testing under such conditions are costly and limited, validated numerical frameworks provide an efficient means to evaluate polymeric coatings, architected metastructures, and data-driven predictive tools across broad design spaces. The first …

Network-Aware Airline-Specific Flight Delay Prediction Using Tree-Based Ensemble Models, Mary Dufie Afrane

College of Graduate Studies: Theses & Dissertations

Flight delays pose persistent challenges to the efficiency and reliability of air transportation systems, affecting airlines, airports, regulators, and passengers alike. As traffic demand grows and operational environments become increasingly interconnected, accurately predicting both departure and arrival delays has become crucial for effective planning and mitigation. This study presents a network-aware, airline-specific framework for predicting flight delays in U.S. domestic air transportation systems using tree-based ensemble machine learning models. A large-scale dataset of 1.98 million flights, enriched with weather information, is used to develop predictive models for both departure and arrival delays. To capture the structural and operational complexity of …

Buckling Analysis Of Auxetic Composite Laminates And Optimal Design Using Lamination Parameters And Machine Learning, Hans Bendon Maria Tamil Selvan

Mechanical and Aerospace Engineering Theses

Composite materials are widely used as structural panels in aerospace, automotive, and civil engineering applications, where buckling is often a critical failure mode. This thesis focuses on the analysis and design of composite laminates that maximize buckling performance under prescribed stiffness and thickness constraints.

The first part of the study investigates the buckling behavior of auxetic laminates, which exhibit a negative Poisson's ratio. While previous studies suggest that auxetic laminates can achieve higher critical buckling loads than non-auxetic laminates under simply supported boundary conditions with lateral restraint, the influence of other boundary conditions and plate aspect ratios has not been …

Ai-Based Porosity Detection In Babbitt Bore Turning, Kaylee Leggett, Hannah Adams Gemmell, Kaisa Shingleton

Williams Honors College, Honors Research Projects

For this project, an external company reached out to the University of Akron requesting assistance with defect detection during their vertical turning operations. As babbitt is removed in a vertical turning process, it occasionally reveals defects, mainly porosity, which can lead to costly downstream failures of the part. Current inspection techniques involve use of dye penetrant, which is time consuming, labor intensive, unergonomic, and a source of human error. The goal of the project is to create an alternative inspection method using an AI-based machine-learning model. After the turning operation, a camera is deployed to perform an in-place inspection, taking …

Development Of A Signature Model For Predicting Normal Boiling Point Using Machine Learning, Mackenzie Karabin

Williams Honors College, Honors Research Projects

The goal of this project was to utilize the Signature molecular descriptor to construct a predictive model for computing the normal boiling point using a 4856-compound dataset. The final model intends to be applied to screening compounds whose experimental normal boiling point values are not readily available. Compounds were deconstructed into their height-1 atomic Signatures, resulting in 225 unique descriptors for the dataset, which were used as independent variables for creating the model. Machine learning (ML) techniques were employed to evaluate the applicability and accuracy of different ML models for predicting normal boiling point using the Regression Learner App in …

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 …