Physical Sciences and Mathematics Commons^™

A Deep Learning Based Three-Stage Fusion Neural Network With Composite Adaptive Momentum Optimizer For Bangla Cyberbully Identification, Mahin Montasir Afif, Abdullah Al Noman, Kazi Abdullah Jarif, Md Emamul Arefin Islam, Sathya Narayana Sharma K, Mahfujur Rahman, Dipta Gomes, Kazi Tanvir, Md Obaidur Rahaman, Syed Mohammed Shamsul Islam

Research outputs 2022 to 2026

Cyberbullying in Bangla social media is an increasingly critical issue, posing severe risks to mental health and online safety. Despite the growing need for automated detection systems, challenges such as limited linguistic resources and the nuanced characteristics of the Bangla language hinder model performance. In this study, we propose TriB-FNN, a novel and explainable three-stage fusion neural network, designed to effectively capture cyberbully in Bangla social media texts. The model integrates Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (Bi-LSTM), and Gated Recurrent Units (GRUs) in a hierarchical pipeline. To optimize training, we employ a Composite Adaptive Momentum (CAM) Optimizer, …

Trustworthy And Explainable Malware Threat Intelligence Through Social Media Analytics And Nature-Inspired Optimization, Feras Al-Obeidat, Muhammad Saad Rashad, Muhammad Amin, Waqas Ali, Bilal Khan, Sajid Anwar

All Works

The convergence of media analytics, Cyber threat Intelligence (CTI) and trustworthy artificial intelligence has become essential for modern cybersecurity systems operating over large-scale, heterogenous data sources. In particular, Social Media Intelligence (SOCMINT) and Open Source Intelligence (OSINT) provide high-volume, real-time signals that complement structured CTI frameworks for early-stage malware and adversarial threat detection. However, integrating these unstructured and dynamic sources with Structured Threat Information Expression (STIX) remains challenging due to its hierarchical complexity, semantic redundancy, and computational overhead in resource-constrained environments. This paper proposes an explainable and optimized intelligence pipeline (BERT-STIX) that unifies SOCMINT, OSINT, and STIX-based CTI using deep …

Efficient Heterogeneous Photo-Fenton Degradation Of Azo Dyes Using The Nanocomposite Mgfe2o4/Mos2, Zahra Zeinali, Leila Ershadi Afshar, Naz Chaibakhsh, Farnaz Isapour

Turkish Journal of Chemistry

In the present work, a facile hydrothermal route was employed to synthesize the nanocomposite MgFe2O4/MoS2, which acted as a heterogeneous photo-Fenton catalyst for the degradation of a synthetic diazo dye, Acid Blue 113 (AB113). The nanocatalyst produced underwent analysis using multiple characterization techniques. The parameters influencing the oxidative reaction were statistically modeled and optimized using response surface methodology. Optimal degradation efficiency for AB113 (94.92%) was attained at pH 8.79 using 10 mg of MgFe2O4/MoS2 nanocatalyst, 1.6 mL of H2O2, and a reaction duration of 27 min. The synergistic effect resulting from the formation of electron-hole pairs renders the combined photocatalysis …

Evaluation Of Biological Components In Decision-Making In Forage Allocation, J E. Skiles, G M. Van Dyne

IGC Proceedings (1977-2023)

Forage allocation to mixtures of large herbivores is accomplished by minimizing the difference between the available herbage and the required forage for animals on rangeland. Availability is determined in part by the plant's physiological tolerance to grazing or its allowable-use factor and the animal preference for that plant, measured in part by the proper-use factor. The mix of large herbivores on the rangeland must also be considered. The grazing requirements for animals are dependent on animal preference for plant species and on the total forage intake rates. This paper presents an analysis of these concepts and published information. The concept …

Gradient Based Optimization Methods For Robust Learning And Biomedical Signal Modeling, Jarrod Mau

All Graduate Theses and Dissertations, Fall 2023 to Present

This dissertation explores how modern artificial intelligence techniques can be used to better understand complex biological data. Specifically, it develops new machine learning based methods and applies them to two important biomedical problems: analyzing brain signals and studying protein behavior.

The first part of the work introduces a new machine learning approach designed to improve how computers classify structured data. Traditional neural networks are powerful but can sometimes generalize poorly. This research proposes a method that combines the flexibility of neural networks with the reliability of ensemble techniques, leading to more robust and accurate predictions across different types of datasets. …

Constraint-Aware Metaheuristic Optimization For Experimental Design, Benjamin N. Fuller

All Graduate Theses and Dissertations, Fall 2023 to Present

Designing experiments becomes much more challenging when many variables and strict constraints are involved, as is common in modern science and engineering. This thesis introduces a new computational and mathematical framework that efficiently searches for optimal experiments in complex, high-dimensional spaces where traditional methods fail. By combining geometric techniques with flexible optimization algorithms like particle swarm optimization, our methods handle difficult constraints while scaling to real-world problems. Built in the high-performance Julia programming language and released as open-source software, this work bridges advanced theory with practical tools, offering researchers a powerful and accessible way to design better experiments under realistic …

Benefits Of Traffic Reprofiling For Delay Sensitive Networking, Jiaming Qiu

McKelvey School of Engineering Graduate Student Theses & Dissertations

Deterministic networking systems, such as Time-Sensitive Networking (TSN) and Deterministic Networking (DetNet), require strict end-to-end delay guarantees while efficiently utilizing limited network resources. Conventional approaches typically focus on fixed traffic profiles, which can lead to suboptimal resource utilization in scheduling or admission control problems. This dissertation investigates traffic reprofiling—the proactive reshaping of traffic arrival patterns—as a complementary mechanism for improving both resource efficiency and delay performance under strict service guarantees. The dissertation consists of three parts. The first part studies bandwidth minimization under hard delay constraints for Service Curve Earliest Deadline First (SCED) schedulers. We show that traffic reprofiling can …

Differentiable Objectives For 3d Scene Relighting Via Gradient Descent On Olat Basis Coefficients, Anson Savage

Theses and Dissertations

Designing effective lighting is an iterative and often time-consuming process. This work contributes to automatic lighting design research by presenting a render-engine agnostic optimization routine: gradient descent on RGB multipliers of one-light-at-a-time (OLAT) basis images. We compare several objective functions to accomplish lighting tasks and show that our method is capable of quickly and effectively exploring different lighting styles using either text prompts or reference images. We also present several datasets specific to lighting tasks and show that fine-tuning on these datasets can improve performance.

Editorial: Intelligent Systems And The Next Wave Of Digital Innovation, Hairulnizam Mahdin

Journal of Informatics and Web Engineering

The field of artificial intelligence (AI), machine learning and intelligent automation has become pervasive in our modern digital world. It extends from business and public services to environmental management and even into people's daily experiences with technology. In this special issue "Intelligent Systems and the Next Wave of Digital Innovation," published in the Journal of Informatics and Web Engineering, reviews several studies to explore the increasing role of intelligent systems in current society and their importance. Some of the more significant areas of discussion are how to formalize the expectations for explainable AI, evaluating face recognition models in the real …

A Bioinspired Approach For Adaptive Solid-Solid Phase Change Material Coatings With Optimized Surface Features For Passive Thermal Regulation, Rajae Bousselham, Zhiying Xiao, Mingjiang Tao, Sergio Granados-Focil, Adriana Hera, Steven Van Dessel

Chemistry

The necessity to reduce global energy consumption calls for innovative strategies in building thermal management. Passive thermal regulation, particularly through bio-inspired designs, offers a promising avenue by mimicking nature's efficient control of optical properties. This research introduces a novel, climate-responsive coating that integrates optimized bio-inspired surface features with a solid-solid phase change material (SS-PCM) to dynamically manage solar absorptivity without adding additional thickness, enabling both heating and cooling as needed. Drawing on the photonic architectures of the Saharan silver ant and Morpho Didius butterfly, we employed a modeling and multi-objective optimization framework to tailor these surface features. Simulations reveal that …

Detecting And Repairing Conflicting Constraints In Co-Trained Physics-Informed Neural Networks For Composite Curing Processes, Cooper J. Evans

Dissertations, Master's Theses and Master's Reports

Composite materials have become a critical component of modern manufacturing, especially in the automotive and aerospace industries. The curing process for these composites has been modeled using a variety of partial differential equations representing the heat transfer and composite curing kinetics. Optimizing the applied temperature profile is critical for maximizing the efficiency and capacity of composite part manufacturers. Constraints must be placed on the inputs and outputs of the model, including but not limited to, the applied temperature profile, part temperature, and final degree of cure. Conflicting sets of constraints are easy to unknowingly impose due to the highly coupled …

Multi-Objective Optimization Of Energy Costs And Ev Battery Health In V2g Enabled Homes, Dzifa M. Hodey

Theses and Dissertations--Computer Science

Electric vehicles (EVs) and rooftop solar photovoltaic (PV) systems are increasingly being integrated into residential settings, creating new opportunities for vehicle-to-grid (V2G) and vehicle-to-home (V2H) operations. In these systems, the EV battery functions as a controllable energy storage unit that can charge from the grid or PV and discharge energy to supply household load or export to the grid for a profit. By intelligently scheduling this bidirectional power exchange, households can reduce electricity costs and enhance PV utilization. Realizing these benefits requires optimization strategies that balance cost reduction with EV battery health preservation. However, existing V2G/V2H studies largely emphasize cost …

Soft-Constrained Variants Of T-Distributed Stochastic Neighbor Embedding For Global Structure Preservation, Joseph A. Balderas

Mathematics Dissertations

Dimensionality reduction (DR) is a fundamental tool in data science and machine learning that transforms high-dimensional data into a low-dimensional representation while preserving important structural properties of the original data. Among modern DR methods, t-distributed stochastic neighbor embedding (t-SNE) has become one of the most widely used techniques for visualization due to its strong ability to preserve local neighborhood structure and produce visually separated clusters. However, despite its popularity, t-SNE is well known to struggle with preserving global structure of data, often producing embeddings in which distances between clusters and neighborhoods do not accurately reflect relationships in the high-dimensional space. …

Multipacking On Graphs And Euclidean Metric Space, Sk Samim Islam

Doctoral Theses

A multipacking in an undirected graph G = (V,E) is a set M ⊆ V such that for every vertex v ∈ V and for every integer r ≥ 1, the ball of radius r around v contains at most r vertices of M, that is, there are at most r vertices in M at a distance at most r from v in G. The multipacking number of G is the maximum cardinality of a multipacking of G and is denoted by mp(G). The MULTIPACKING problem asks whether a graph contains a multipacking of size at least k. For more …

Game-Theoretically Optimal Strategies In Baseball, William Michael Melville

Theses and Dissertations

Baseball players and their managers make numerous decisions that can have a significant effect on the result of the game. The effectiveness of their decision-making strategies depend on the strategies played by their opponents. For example, throwing a first-pitch fastball down the middle is a good strategy against a hitter who always takes the first pitch, but it is a risky strategy against a hitter who likes to swing at the first pitch and who is good at hitting fastballs. Additionally, any payoff a player receives from playing a strategy comes at the expense of their opponent. A batter cannot …

A (Mini) Mathematics Research Experience In A Math Teachers’ Circle Session, Michelle Manes, Linda Venenciano, Seanyelle Yagi

Journal of Math Circles

We describe a single Math Teachers’ Circle session during which a group of teachers collectively engaged in a “mini mathematics research experience.” This provides a model for providing research experiences for mathematics teachers through content-based professional development programs. We conjecture that these experiences will have many of the same benefits as research experiences for teachers in lab sciences.

Editorial: Ai-Enhanced Computing And Digital Transformation, Hairulnizam Mahdin

Journal of Informatics and Web Engineering

Artificial intelligence (AI), machine learning (ML), and advanced computing techniques are now central to the digital transformation journey, reshaping industries, academia, and society. This special issue of the Journal of Informatics and Web Engineering on AI-Enhanced Computing and Digital Transformation brings together contributions that reflect both technical innovation and societal applications. The featured articles span optimization methods, software quality improvements, data augmentation techniques, intelligent mobile applications, blockchain-based governance systems, and disaster management platforms. Together, they illustrate how computational advances not only strengthen efficiency and accuracy but also enable resilience in the face of global challenges. From a technical standpoint, metaheuristic …

Improving Co-Decoding Based Security Hardening Of Code Llms Leveraging Knowledge Distillation, Dong Li, Shanfu Shu, Meng Yan, Zhongxin Liu, Chao Liu, Xiaohong Zhang, David Lo

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have been widely adopted by developers in software development. However, the massive pretraining code data is not rigorously filtered, allowing LLMs to learn unsafe coding patterns. Several prior studies have demonstrated that code LLMs tend to generate code with potential vulnerabilities. The widespread adoption of intelligent programming assistants poses a significant threat to the software development process. Existing approaches to mitigating this risk primarily involve constructing secure data that are free of vulnerabilities and then retraining or fine-tuning the models. However, such an effort is resource intensive and requires significant manual supervision. When the model parameters …

Sugar: A Sequence Unfolding Based Transformer Model For Group Activity Recognition, Yash U. Gondkar

Graduate Masters Theses

Large Language Models have improved significantly in the past couple of years due to the adoption of transformers. However, transformers still find it challenging to process videos due to limited context size caused by their quadratic computing cost. Therefore, we studied a booming field in machine learning which powers applications like social scene analysis and video surveillance systems called Group Activity Recognition (GAR). We found that recent models were able to achieve more than 90% accuracy on popular datasets like the Volleyball dataset, however, it turned out that even they relied on transformers.

Therefore, in this work, we developed a …

Tools To Design Algorithms For Implementing Control Over Quantum Computers, Shilpa Narashimhan, Jihan Abou Halloun, Kip Nieman, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Quantum computers (QCs) may find future applications within control systems that operate manufacturing processes. For application within control engineering, quantum algorithm development must be led by control engineers. However, control engineers may face challenges in designing quantum algorithms for control engineering problems. In this work, we provide several path-finding studies that leverage engineering tools such as optimization, encryption, and computational "short-cuts" toward making algorithm design for QC easier for control engineers.

Investigating The Influence Of Experimental Parameters On The Formation Of Gold Nanoparticles: A Green Synthesis And Design Of Experiments Approach, Isaac Opeyemi Subuloye

Master's Theses

This thesis examines the green synthesis of gold nanoparticles using catechin, a naturally occurring plant-derived compound, as both a reducing and capping agent. The synthesis process was guided by a structured Design of Experiments (DOE) methodology to ensure systematic investigation and optimization. A two-phase experimental approach was adopted. First, a full factorial screening design was used to evaluate the effects of catechin concentration, NaOH concentration, and pre-reaction time. This was followed by an optimization phase using a face-centered composite design to identify conditions that produce nanoparticles with ideal characteristics. The nanoparticles were analyzed using UV-visible spectroscopy and dynamic light scattering …

Aquatic Habitat Representation For Robust Water Resources Management And Fish Conservation Decision-Making, Gregory C. Goodrum

All Graduate Theses and Dissertations, Fall 2023 to Present

We rely on rivers to provide water supply, flood control, and hydroelectricity for people and to sustain aquatic ecosystems. The development of dams and reservoirs to provide water for people did not consider environmental impacts to rivers, leading to widespread decline of freshwater species and habitat. Improving environmental outcomes of water management is needed to protect and restore aquatic ecosystems. However, rivers are complex and variable, making them difficult to represent as objectives for water management. This dissertation explores approaches to improve aquatic habitat representation and environmental objectives for water resources management and river conservation and restoration.

Chapter 2 uses …

Removal Of Heavy Metal Iron(Ii) Ions From Wastewater Using An Ultrasonic System With Climbazole-Alcohol, Melek Gökmen Karakaya, Bahdi̇şen Gezer, Abdullah Menzek, Özlem Gündoğdu Aytaç

Turkish Journal of Chemistry

Climbazole (CBZ) is an antifungal active pharmaceutical ingredient often used in antidandruff products. In this study, the ketone group in the racemic CBZ molecule was reduced to synthesize CBZ-alcohol, named 1-(4-chlorophenoxy)-1-(1H-imidazol-1-yl)-3,3-dimethylbutan-2-ol, and the optimum adsorption conditions were investigated for removing iron(II) (Fe2+) ions from wastewater by adsorption from aqueous solutions using the economical and environmentally friendly ultrasonic method. The parameters and levels used in the study were designed using response surface methodology and model equations were derived to optimize the results. The independent variables selected were the initial pH (1, 3, and 5), adsorption time (30, 45, and 60 min), …

Exploiting Compiler-Introduced Vulnerabilities In C: A Cross-Compiler And Cross-Architecture Analysis Of Undefined Behavior, Erik Mccutchen

Master's Theses

Compilers are a critical component in generating secure software across engineering disciplines. However, languages like C that permit undefined behavior introduce a fundamental tension between the compiler’s interpretation of undefined behavior and the security of the generated code. This tension can result in security vulnerabilities that, from the programmer's perspective, are ``created'' by the compiler. The widespread use of these languages, combined with the complexity of modern optimizations and limited developer visibility into compiler behavior, makes these vulnerabilities both pervasive and difficult to detect.

Building on prior work, this thesis refines a dataset of C code snippets that exhibit Compiler-Introduced …

Rotating Scatter Mask System Optimization Study For Determining Optimal Image Recreation, Seth L. Grover

Theses and Dissertations

The Rotating Scatter Mask (RSM) system is a radiation imaging technology currently limited by the mask design and governing identification algorithm parameters. To optimize the RSM design, Dakota—an optimization software—was integrated with a ray tracing code that simulates particle interactions with the RSM detector, and with the Locally Competitive Algorithm (LCA), which reconstructs the source image based on the ray tracing code’s Detector Response Matrix (DRM). Since the original ray tracing code was developed in MATLAB, it was translated into Python to improve compatibility with both Dakota and LCA. The Python version of the ray tracing code was then integrated …

A Comprehensive Study Of Oop-Related Bugs In C++ Compilers, Bo Wang, Chong Chen, Junjie Chen, Bowen Xu, Chen Ye, Youfang Lin, Guoliang Dong, Jun Sun

Research Collection School Of Computing and Information Systems

Modern C++, a programming language characterized by its extensive use of object-oriented programming (OOP) features, is widely used for system programming. However, C++ compilers often struggle to correctly handle these sophisticated OOP features, resulting in numerous high-profile compiler bugs that can lead to crashes or miscompilation. Despite the significance of OOP-related bugs, existing studies largely overlook OOP features, hindering their ability to discover such bugs. To assist both compiler fuzzer designers and compiler developers, we conduct a comprehensive study of the compiler bugs caused by incorrectly handling C++ OOP-related features. First, we systematically extract 788 OOP-related C++ compiler bugs from …

Magic: The Gathering Deck Testing And Optimization, Ian B. Watson

Honors Theses

The goal of this project is to provide a tool for players of the trading card game Magic: the Gathering to determine whether or not a given deck is optimally built by outputting relevant information regarding its optimality. This is done through a simulator that plays a one-sided game, recording what cards are played, what turn they are played, and how much mana is left over at the end of every turn. The simulator was tested with both optimized and unoptimized decks to show how it can be used to diagnose both.

A Systemic Approach To Maximize Heterogeneous System Performance, Thomas L. Randall

All Dissertations

Continuous increases in high performance computing (HPC) throughput have served as catalysts for industry and scientific advancement in countless manners that have fundamentally shaped our modern world. Our demands on compute resources continue to scale, but the limitations of Ahmdal’s law and Dennard scaling have proven increasingly difficult to overcome when approached solely through hardware or software design. Furthermore, many HPC applications fail to utilize the collective system’s performance, even on the most advanced supercomputers.

However, the resurgence of AI in the industry has promoted an explosion of hardware and software codesign that have fueled massive improvements in GPU design …

Optimizing Fire Station Placement In Sugar Land, Tx: A Socioeconomic Risk-Based Approach, Alicia Gallemore

Data Science Undergraduate Honors Theses

Fire station placement has a critical role in emergency response efficiency and community safety. Traditional optimization models focus on mainly the minimization of response times and the maximization of coverage. However, this approach may overlook potential socioeconomic disparities that can influence emergency demand. This study seeks to expand upon the existing project of zoning a fire station in Sugar Land, TX, by integrating spatial road network analysis and publicly available census data—including population density, median household income, and age-based vulnerability—into a Maximal Coverage Location Problem (MCLP) framework. Using a road network-based travel time with realistic constraints, the goal is to …

Optimization And Personalization Of Fitness Routines Using Mathematical Models, Tyson M. Kerr

Theses/Capstones/Creative Projects

This project aims to develop a mathematical model for generating personalized workout plans tailored to individual fitness goals, preferences, and constraints. By leveraging optimization techniques such as linear programming and genetic algorithms, the model will address key questions, including how to maximize efficiency and consistency in workout plans and which methodologies are best suited for specific fitness objectives. The model involves data collection on user inputs and exercise attributes, defining objective functions for different goals, and integrating constraints like session duration. The implementation will utilize the Python library PuLP to prototype the model, alongside a user-friendly interface for input and …