Digital Commons Network^™

Exploring System Identification Of Non-Linear Dynamics Using The Weighted Composition Operator And The Liouville Operator, Chukwuebuka Amagwula

USF Tampa Graduate Theses and Dissertations

System identification is the process of determining mathematical models that describe the dynamics of a system from data. Dynamic Mode Decomposition (DMD) and Sparse Identification of Nonlinear Dynamical Systems (SINDy) are two distinct approaches used for this purpose.

DMD identifies dominant spatiotemporal modes and eigenvalues that describe the evo lution of a system. It assumes a near-linear representation of dynamics and is closely linked to the Koopman operator, making it ideal for analyzing fluid flows, oscillatory systems, and modal structures. The DMD method uses time series data where each data point is referred to as a snapshot and represents the …

Leveraging Machine Learning For Accurate Prediction Of Nba Player Salaries, Ye Cheng, Yan Song, Mingqi Wang

Iraqi Journal for Computer Science and Mathematics

Basketball players in the NBA are renowned for their talent, athleticism, and commitment to the game. NBA players may make enormous sums of money; however, they vary greatly. Rookie agreements begin at a lower price and go up following performance. NBA players’ pays are influenced by several factors. Because they influence games and the success of the club, exceptional players fetch larger compensation. This study employs Machine Learning (ML) techniques, including Lasso Regression and Random Forest Regression (RFR) models to analyze wage trends, enhanced by the Slime Mould Algorithm (SMA) and Artificial Rabbit Optimization (ARO) for accuracy. The goal is …

Machine Learning Techniques For Optimizing The Efficiency And Costs Of Drill Steel In Sandstone And Granodiorite: A Case Study In Peru, Marco Cotrina, Jairo Marquina, Jose Mamani, Solio Arango, Eusebio Antonio, Eduardo Noriega, Teofilo Donaires, Dominga Cano

Journal of Sustainable Mining

This research aims to optimize the efficiency and costs of drilling steel in sandstone and granodiorite rocks using machine learning techniques in a Peruvian mine. Predictive models, including random forest (RF), XGBoost (XGB), decision trees (DT), and artificial neural networks (ANN), were applied, along with optimization algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), and simulated annealing (SA). A dataset of 705 entries was analyzed, focusing on drill bit wear, percussion and rotation pressures, and cost per meter drilled. Model performance was evaluated using R², RMSE, MAE, and MAPE. The ANN model …

Personal Bankruptcy: A Bibliometric Analysis And Future Research Directions, Aqilah Nadiah Md Sahiq, Saida Farhanah Sarkam, Putri Aliah Mohd Hidzir, Nurwahida Yaakub, Shafinar Ismail, Nurbaity Sabri, Hanifiyah Yuliatul Hijriah, Himmatul Kholidah

The Indonesian Capital Market Review

Personal bankruptcy is a pertinent topic to discuss due to the surge in personal bankruptcy cases around the world and it is an important indicator of household financial problems nationally. However, only few comprehensive reviews have been conducted to date. Hence, this study provides a bibliometric review of 210 studies on personal bankruptcy authored by 496 scholars. Bibliographical data were extracted from the Scopus database and analyzed it using the Bibliometrix-R software. Based on the citation analysis metrics, we revealed the most influential articles, journals, authors, and institutions. Using the network and conceptual structure analysis, we identified three underlying research …

Causal Neuro-Symbolic Artificial Intelligence: Synergy Between Neuro-Symbolic And Causal Artificial Intelligence, Utkarshani Jaimini

Theses and Dissertations

Understanding and reasoning about cause and effect is innate to human cognition. In everyday life, humans continuously engage in causal reasoning and hypothetical retrospection to make decisions, plan actions, and interpret events. This cognitive ability allows us to ask questions such as: “What caused this situation?”, “What will happen if I take this action?”, or “What would have happened had I chosen differently?” This intuitive capacity to form mental models of the world, infer causal relationships, and reason about alternative scenarios, particularly counterfactuals, is central to our intelligence and adaptability. In contrast, current machine learning (ML) and artificial intelligence (AI) …

A Comparative Study Of Machine Learning And Deep Learning Models In Binary And Multiclass Classification For Intrusion Detection Systems, Ayesha Alharthi, Meera Alaryani, Sanaa Kaddoura

All Works

Network infrastructure evolution has significantly expanded the attack surface, leading to increasingly complex and sophisticated cybersecurity threats. Traditional rule-based intrusion detection systems (IDS) often fail to detect emerging attack vectors, prompting the need for intelligent, data-driven approaches. This study evaluates and compares the performance of machine learning (ML) and deep learning (DL) models for network intrusion detection. Two publicly available datasets were utilized: a binary-labeled software-defined networking (SDN) dataset and a multiclass industrial control system dataset based on the IEC 60870-5-104 protocol. Preprocessing steps included normalization, label encoding, and a 70:10:20 train-validation-test split. Seven models, Random Forest, Decision Tree, K-Nearest …

Approaches To Enhancing Multiple Hypothesis Testing Methods With Side-Information, Siyu Zheng

Theses and Dissertations

Lesion-symptom mapping (LSM) studies offer insight into the brain areas involved in various aspects of cognition. This is commonly done via behavioral testing in patients with a naturally occurring brain injury or lesions (e.g., strokes or brain tumors). This results in high-dimensional observational data where lesion status (present/absent) is non-uniformly distributed, with some voxels having lesions in very few (or no) subjects. In this situation, mass univariate hypothesis tests have severe power heterogeneity where many tests are known a priori to have little to no power. Additionally, high-dimensional observational data can be grouped according to brain anatomical structure.

In this …

Network And Tree-Based Approaches To Data-Driven Modeling Of Complex Climate Systems, Adam M. Giammarese

Theses

The guiding thread throughout this thesis work is data-driven modeling of Earth's climate. We investigate a variety of settings within the field of complex climate systems, such as gridded, high-resolution reanalysis data, lower-resolution climate indices, and incomplete paleoclimate records with missing values. We begin with reanalysis data of high spatial and temporal resolution, and use climate network analysis to uncover hidden trends in the data in regards to the Amazon rainforest and its changing role in the global climate system. We introduce a novel formulation of k-nearest neighbors climate networks to study changing trends at a local level, and apply …

Milpbench: A Large-Scale Benchmark Test Suite For Mixed Integer Linear Programming Problems, Huigen Ye, Yaoyang Cheng, Hua Xu, Zhiguang Cao, Hanzhang Qin

Research Collection School Of Computing and Information Systems

Mixed-integer linear programming (MILP) is a cornerstone of optimization with applications across numerous domains. However, the development and evaluation of MILP-solving algorithms are hindered by existing benchmark datasets, which are often limited in scale, lack diversity, and are poorly structured, making them inadequate for systematic testing across different solving approaches, especially for machine learning (ML)-based methods. To address these issues, we introduce MILPBench, a large-scale benchmark suite comprising 100,000 MILP instances organized into 60 well-categorized classes. Using structural properties and embedding similarity metrics, we developed a novel classification framework to ensure both intra-class homogeneity and inter-class diversity. In addition to …

Process-Grounded Knowledge-Infused Learning And Decision Making, Kaushik Roy

Theses and Dissertations

This dissertation introduces process-grounded knowledge-infused learning and reasoning, a novel framework for integrating domain-expertise-based process knowledge into the learning and reasoning mechanisms of artificial intelligence systems. This approach is designed to produce controlled, transparent, and reliable predictions in critical tasks such as medical diagnosis and recommendation. By focusing on the case study of mental illness diagnosis and recommendation—where decision-making must be grounded in processes such as disorder-specific diagnostic criteria—this work demonstrates methods to embed structured decision-making directly into the system architecture during both training and inference. This integration facilitates end-to-end training and reasoning while ensuring that outputs strictly adhere to …

Performance Comparison Of Quantum And Classical Machine Learning Models For Chronic Kidney Disease Prediction, Parama Sridevi, Paramita Basak Upama, Masud Rabbani, Sheikh Iqbal Ahamed

Computer Science Faculty Research and Publications

In this study, we develop and compare quantum and classical machine learning-based chronic kidney disease prediction models. We used the "Chronic_Kidney_Disease Data Set" of the UCI Machine Learning Repository. We performed data preprocessing and applied feature engineering techniques to select the best features. We developed two quantum machine learning-based models and two classical machine learning-based models. We used a hybrid classical-quantum environment for building quantum machine learning models. Finally, we compared the performances of all four models. We found that the Quantum Support Vector Machine performs best among the quantum models. The model’s accuracy was 95% with a k-fold cross-validation …

Identification Accuracy Of Cost-Effective Visible (Vis) To Near-Infrared (Nir) Sensor As7265x On Plastic Waste In The Philippines, Ma. Tricia Jasmin M. Ocho, Chloe Lizanne R. Bravo, Jennika Love R. Loyola, Kierra Jamielle V. Taroma, Leianne Nina Arabelle E. Lampayan

DLSU Senior High School Research Congress

In the Philippines, plastic waste persists as a significant issue caused by the widespread use of single-use plastics and the lack of recycling systems. The lack of proper systems made waste segregation and identification inefficient and disorganized. Past studies explain the use of VIS/NIR in plastic segregation and potential of spectral frequencies. However, there is limited investigation of the AS7265x sensor’s consistency when integrated with machine learning to further improve detection reliability; thus, the study evaluates the effectiveness of the AS7265x triad spectroscopy sensor, incorporated with machine learning algorithms. An experimental setup was created using the AS7265x sensor and Arduino …

Classification Of Human Trust In Ai Using Brain Activity Data, Danushka Bandara, Ruhuan Liao, Fatima Chowdhury, Leslie Abbott

Northeast Journal of Complex Systems (NEJCS)

Trust plays a crucial role in human-computer interaction, particularly in scenarios involving artificial intelligence (AI) systems. This study explores the feasibility of using functional near-infrared spectroscopy (fNIRS) data to classify trust levels in human-AI interaction scenarios. A total of 18 participants completed an image classification task with an AI team member while their hemodynamic responses were recorded using fNIRS. Preprocessing of fNIRS data involved motion artifact removal, filtering, and normalization. Exploratory analysis identified significant associations between hemodynamic responses in the prefrontal cortex and trust levels. An across-subject binary trust classification model was developed using machine learning techniques, achieving an F1 …

Predicting Energy Consumption Trends In The Philippines (2025-2030) Using Machine Learning Models, Christine Angeline F. Vermudo, Hannah Ashley A. Mendoza, Angelica Nicole B. Constantino, Rhian Consuelo C. Ledesma

DLSU Senior High School Research Congress

Energy security remains a significant concern for the Philippines amid rising demand, growing population, and continuous modernization. With the history of the Philippines’ problems in energy, attention to energy is necessary. With this, the study aims to forecast the Philippines' possible annual energy consumption from 2025 to 2030, considering population growth as a potential variable influencing the Philippines’ projected energy consumption trends. The study employs a machine learning approach using the linear regression model, which was the model with the best performance in forecasting future energy consumption based on MATLAB’s Linear Regression Learner app. Prior to forecasting, historical data from …

Enhancing Software Effort Estimation: A Comparative Analysis Of Machine Learning Models With Correlation-Based Feature Selection, Muhammad Abid, Momina Liaqat Ali

Sustainable Machine Intelligence Journal

Software effort estimation is a critical process in the management of software projects, essential for predicting the resources and time required for application development. Accurate estimation is paramount for delivering projects on time and computational resources. This paper investigates techniques that utilize machine learning models to ameliorate software effort estimation. We employ four popular machine learning models including the Linear Regression, Gradient Boosting, Random Forest, and Decision Tree and evaluate their performance across five diverse climate-related datasets: Antarctica Hotpoints/Climate Change NASA, Crop Production and Climate Change, Global Warming on Earth (Global Temp), Climate Change, and Climate Treasure (Marine). A key …

A Comprehensive Systematic Review Of Machine Learning Applications In Assessing Land Use/Cover Dynamics And Their Impact On Land Surface Temperatures, Rasool Vahid, Mohamed Aly

Environmental Dynamics Faculty Publications and Presentations

In a world experiencing rapid urbanization, the phenomenon of land surface temperature (LST) variation has invited substantial attention due to its profound impact on the environment and human well-being. Changes in land use and land cover (LULC) within urban areas significantly influence the dynamics of LST and are a major driver of urban eco-environmental change. The complex connections between LULC dynamics, LST, and climate change are investigated in this systematic review, with a focus on the combined effects of these variables and the use of Machine Learning (ML) techniques. The data in this study, based on peer-reviewed publications from the …

Machine Learning: Neural Networking With Relu And Optimization, Aidan Redmond Brownell

Undergraduate Theses, Capstones, and Recitals

At its core, learning is an algorithmic process: it begins with input data, undergoes a series of transformations or computations, and yields an output intended to solve a specific task. This output is then compared against a target or desired result, and the internal mechanisms are updated based on how well the output aligns with expectations. While this feedback-driven process occurs almost effortlessly in humans, it is a far more structured, deliberate, and computationally intensive undertaking for machines.

Enhancing Water Scarcity Resilience In Egypt Through Machine Learning-Driven Phenological Crop Mapping And Water Use Efficiency Analysis, Surendra Maharjan, Wenzhao Li, Shahryar Fazli, Aqil Tariq, Rejoice Thomas, Cyril Rakovski, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Agriculture forms the backbone of Egypt’s economy, with the Nile Valley and Delta serving as key production zones for crops like wheat, rice, and clover. However, the sector faces mounting pressure from water scarcity, as it depends almost entirely on the Nile for irrigation, making it necessary to map major crops for assessing Water Use Efficiency (WUE) and informing agricultural planning. In this study, we used machine learning (ML) techniques—specifically Support Vector Machine (SVM) to time-series phenological data and optical indices (Enhanced Vegetation Index (EVI), Bare Soil Index (BSI), Land Surface Water Index (LSWI), Normalized Difference Vegetation Index (NDVI), and …

The Prevalence And Prediction Of Uncontrolled Type 2 Diabetes Mellitus In The Lower South Of Thailand, Akemat Wongpairin, Apiradee Lim, Haris Khurram

Journal of Health Research

Background: Type 2 diabetes (T2DM) is a major health issue globally, particularly in middle-income countries. This study assessed the prevalence and factors linked to uncontrolled T2DM and proposed a machine learning model for early prediction in southern Thailand.

Methods: Data from 60,903 T2DM patients (2016–2023) were obtained from Thailand's National Health Data Center. Multiple logistic regression identified associated factors, and machine learning models were compared for prediction.

Results: Uncontrolled T2DM prevalence was 53.5%, highest among patients ≤44 years (69.5%) and in Pattani (61.6%), Satun (58.1%), and Narathiwat (56.7%). Risk factors included being female, younger age, early or long disease duration, …

Multi-Label Classification Of Acoustic And Electronic Drum Sounds Using Machine Learning, Sean Perman

Electronic Theses and Dissertations

This paper presents a system for multi-class classification of drum sounds using audio signal processing and machine learning techniques. The project utilizes a diverse dataset of both acoustic and electronic drum samples and extracts ten distinct audio features to capture the timbral and temporal characteristics of each sound. The methodology includes signal preprocessing, feature extraction, and the application of supervised classification algorithms to distinguish between multiple drum classes. Experimental evaluations demonstrate that the selected features significantly enhance classification accuracy across a varied dataset. These findings underscore the effectiveness of combining traditional audio processing with modern machine learning, offering promising applications …

Enhancing Multi-Step Stock Price Forecasting With Social Media Sentiment And Engagement Metrics, Damilare Olaniyan

Electronic Theses and Dissertations

This thesis investigates whether social media sentiment can improve the accuracy of stock price prediction beyond traditional historical data. While financial markets have long relied on structured numerical indicators, the growing influence of public discourse on platforms like Twitter has introduced new opportunities for extracting market-relevant signals from unstructured text. The study focuses on four major technology firms and combines sentiment features derived from Twitter with historical stock prices in a hybrid machine learning framework. Engagement-weighted sentiment, linguistic complexity, and polarity intensity were extracted using natural language processing techniques and incorporated into classification and regression models. Results show that including …

Intelligent Intrusion Detection In Clustered Wireless Sensor Networks: A Dynamic Clustering And Machine Learning-Based Approach, Abdullah R. Abdulwahhab, Mohd Fadzli Mohd Salleh, Muhammad Firdaus Akb, Mohammed Najm Abdullah

Iraqi Journal for Computer Science and Mathematics

Traditional Intrusion Detection Systems (IDS) designed for more conventional network infrastructures are often ill-equipped to handle the unique challenges WSNs pose, leading to significant gaps in security and resilience. This paper introduces an Intelligent Intrusion Detection System (IIDS) explicitly tailored for clustered WSNs to address these critical challenges. The proposed IIDS integrates dynamic clustering with advanced machine learning algorithms to create a robust and adaptive security solution capable of real-time threat detection and mitigation. The dynamic clustering mechanism is designed to continuously monitor and respond to changes in sensor node network topology and energy levels, ensuring that energy consumption is …

Revised Unsteady Drag Force And Its Impact On Particle Dynamics At High Reynolds Numbers, Ahmad Talaei, Timothy J. Garrett

Physics Student Research

The accurate modeling of particle motion in viscous fluids at high Reynolds numbers remains a fundamental challenge, particularly under unsteady flow conditions where nonlinear effects dominate. This study introduces a revised unsteady drag formulation, extending its applicability beyond previous studies. By integrating classical solutions of the Navier–Stokes equations, the new formulation overcomes the limitations of the Maxey–Riley–Gatignol (MRG) equation, addressing non-physical memory effects associated with Basset drag and extending its applicability to higher Reynolds numbers.

Through experimental comparisons and direct numerical simulations (DNS), the revised equation of motion is compared to the MRG equation, both during steady-state conditions and non-equilibrium …

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

Future Engineering Journal

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

Shape-Based Nanoparticle Classification Using Machine Learning, Caitlin Caitlin Robertson, Hender Lopez

SAML-25 Workshop on Statistical and Machine Learning

The accurate classification of nanoparticles (NPs) based on their shapes is crucial for understanding their physical-chemical properties and predict their bioactivity. Nowadays, synthesis method are able to produce a broad range of shapes, such as spheres, cubes and branched NPs and commonly these NP shapes are only described qualitative. This study presents NP descriptors obtained from NPs contours extracted from electron microscopy images. Descriptors such as Fourier descriptors, aspect ratio, and compactness are then used as input for machine learning classifiers. In particular, XGBoost, Random Forest, and neural networks are explored and the their performances are compared and discussed.

Analyzing Option Chain Bid–Ask Spreads With Machine Learning, Brian Byrne, Qianru Shang

SAML-25 Workshop on Statistical and Machine Learning

This paper investigates the determinants of option bid–ask spreads using machine learning techniques. We analyze a cross-sectional dataset of Apple Inc. (AAPL) call options, focusing on the relative bid–ask spread as the target variable. By comparing linear models with ensemble methods such as Random Forests and XGBoost, we find that nonlinear machine learning methods significantly outperform traditional OLS regression. The most influential factors are moneyness, implied volatility, and time to expiration, while volume and open interest have limited predictive power. Results suggest that spreads are driven by a mix of market microstructure dynamics, capital constraints, and regulatory requirements such as …

A Machine Learning Approach To Improve Prediction In Chemical Exposure Risk Assessment, Michele Marro, Cédric Koller, Hasnaa Chettou, David Vernez

SAML-25 Workshop on Statistical and Machine Learning

Exposure models play a crucial role in predicting chemical exposure in workplaces, offering an essential alternative to measurements, which are resource-intensive and time-consuming and sometimes not possible. Despite their widespread use and continuous development, significant challenges persist, including variability in predictions, limited model updates, and difficulties in accessing the required input data. In this study, we investigate how modern machine learning techniques can contribute to the improvement of exposure models by addressing these limitations. To overcome the frequent lack of data, we explore the use of synthetic datasets generated through existing exposure models. This approach allows for the study of …

Development Of A Machine-Learning-Driven Digital Teaching Assistant That Utilises Student Engagement Data To Improve Access To And Success In K-12 Stem Education, Kelly Shreeve, Kathryn Jessen Eller, Michael Cassidy, Beth Price, Brandy Jackson, Anthony M. Perry, Leo Celi, Ismini Lourentzou, Luk Hendrik

Teaching, Leadership & Professional Practice Faculty Publications

Student engagement is a key predictor of academic achievement and is closely linked to career awareness, interest, and preparedness. Measuring student engagement during STEM learning is challenging for teachers, given the dynamic and ever-changing nature of these learning environments. Even when engagement data can be collected, leveraging this information to refine and personalise instruction requires significant experience and time. To address this, we are developing Scoutlier EngagEd, a digital teaching assistant that embeds in existing Learning Management Systems (LMS) to automatically and invisibly gather multidimensional data on student engagement and performance during STEM learning. These data are being leveraged to …

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

Electrical and Computer Engineering Graduate Research

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

Predicting Consumers’ Purchase Intention Of Browsed Products: A Study Based On Eye‑Tracking, Feiyan Jia, Choon Ling Sia, Yani Shi, Fiona Fui-Hoon Nah, Keng Siau

Research Collection School Of Computing and Information Systems

Predicting consumers’ purchase intention of browsed products enables sellers to implement nuanced promotion strategies to stimulate purchase. But how can we predict consumers’ purchase intention of browsed products? Our research demonstrates that consumers’ eye movement data collected when they browse products can serve this aim. We train and test the prediction model using logistic regression and random forest algorithms. Using data collected in a laboratory experiment, our empirical results show that both algorithms perform much better than a random guess, and the logistic regression performs slightly better than the random forest. Our findings imply that eye movement data enable sellers …