Digital Commons Network^™

Anomaly Detection In Electrical Systems Using Machine Learning And Statistical Analysis, Mustafa I. Al-Karkhi, Grzegorz Rzadkowski, Latif Ibraheem, Muhammad Aqib

Terra Joule Journal

The reliability of electrical systems is essential for uninterrupted power supply across residential, industrial, and commercial sectors. This research utilizes the various techniques of ML in identifying all sorts of abnormal electrical power variables, including voltage fluctuations, line faults, and frequency variations that emanate from several causes like equipment failures and environmental ones. Voltage and current measurements were pre-processed along with other additional parameters such as frequency and THD, then visualized to assure its quality and relevance for training the ML models. The work implemented the algorithms of Random Forest, Decision Tree, XGBoost, SVM, and Logistic Regression-the Random Forest Classifier …

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, …

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 …

Scalable Targeting Of Social Protection: When Do Algorithms Out-Perform Surveys And Community Knowledge?, Emily Aiken, Anik Ashraf, Joshua E. Blumenstock, Raymond P. Guiteras, Ahmed Mushfiq Mobarak

Discussion Papers

Innovations in big data and algorithms are enabling new approaches to target interventions at scale. We compare the accuracy of three different systems for identifying the poor to receive benefit transfers — proxy means-testing, nominations from community members, and an algorithmic approach using machine learning to predict poverty using mobile phone usage behavior — and study how their cost-effectiveness varies with the scale and scope of the program. We collect mobile phone records from all major telecom operators in Bangladesh and conduct community-based wealth rankings and detailed consumption surveys of 5,000 households, to select the 22,000 poorest households for $300 …

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 …

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 …

Irnet: Immunotherapy Response Prediction Using Pathway Knowledge-Informed Graph Neural Network, Yuexu Jiang, Manish Sridhar Immadia, Duolin Wang, Shuai Zeng, Yen On Chan, Jing Zhou, Dong Xu, Trupti Joshi

Biomedical Sciences

Introduction: Immune checkpoint inhibitors (ICIs) are potent and precise therapies for various cancer types, significantly improving survival rates in patients who respond positively to them. However, only a minority of patients benefit from ICI treatments.

Objectives: Identifying ICI responders before treatment could greatly conserve medical resources, minimize potential drug side effects, and expedite the search for alternative therapies. Our goal is to introduce a novel deep-learning method to predict ICI treatment responses in cancer patients.

Methods: The proposed deep-learning framework leverages graph neural network and biological pathway knowledge. We trained and tested our method using ICI-treated patients’ data from several …

Developing Machine Learning Models And Graphene-Based Flexible Humidity And Temperature Sensors For Machine-Learning-Assisted Sensing, Seth Hajian

Dissertations

Flexible sensor technology has recently gained tremendous momentum in both academic research and industrial applications, transitioning from conceptual frameworks to practical implementations across diverse fields. This remarkable advancement can be attributed to several converging factors, including the maturation of nanomaterial science, the advancements of machine learning algorithms, and the critical demand for intelligent sensing solutions in healthcare, environmental monitoring, and industrial automation. The growing emphasis on personalized medicine and real-time health monitoring, accelerated by global health challenges, has further highlighted the necessity for accurate, cost-effective, and adaptable sensing platforms. This dissertation presents the fulfillment of three interconnected research projects focused …

Modeling Depressive Patterns In Italian Discourse: Insights From Natural Language Processing, Annapia Borraccino

Dissertations, Theses, and Capstone Projects

Depression does not always speak in obvious ways. It often hides in silence, in brief pauses, and in the quiet texture of language. While the prosodic and linguistic characteristics of depression have been extensively studied in English and other languages, they remain underexplored in Italian, with this being a first focused analysis. This study investigates how depression manifests in the speech of Italian speakers, using transcriptions from a clinical corpus. Drawing on emotional, structural, and cognitive-linguistic features, including first person verb use, sentence complexity, verb tense, and negation, the study evaluates how these markers differ between individuals with and without …

Benchmarking Machine Learning Models In Lesion-Symptom Mapping For Predicting Language Outcomes In Stroke Survivors, Deepa Tilwani, Christian O'Reilly, Nicholas Riccardi, Valerie L. Shalin, Dirk B. Den Ouden, Julius Fridriksson, Svetlana V. Shinkareva, Amit P. Sheth, Rutvik H. Desai

Communication Sciences and Disorders Faculty Articles and Research

Several decades of research have investigated the neural connections between stroke-induced brain damage and language difficulties. Typically, lesion-symptom mapping (LSM) studies that address this connection have relied on mass univariate statistics, which do not account for multidimensional relationships between variables. Machine learning (ML) techniques, which can capture these intricate connections, offer a promising complement to LSM methods. To test this promise, we benchmarked ML models on structural and functional MRI to predict aphasia severity (N = 238) and naming impairment (N = 191) for a cohort of chronic-stage stroke survivors. We used nested cross-validation to examine performance along …

Course Portfolio: Csce 478/878 Introduction To Machine Learning, Hongzhi Guo

UNL Faculty Course Portfolios

This course portfolio was developed for CSCE 478/878: Introduction to Machine Learning, offered by the School of Computing at the University of Nebraska–Lincoln, as part of the Faculty-led Inquiry into Reflective and Scholarly Teaching (FIRST) project. The course provides an introduction to foundational machine learning concepts, models, and programming tools. It enrolls both undergraduate and graduate students from a wide range of academic backgrounds, including computer science, computer engineering, software engineering, biological systems engineering, chemical engineering, business-related fields, etc. Offered in Spring 2025, the course was peer-reviewed to support continuous improvement in teaching. The primary goals of this portfolio are …

Retracted: Robust Security System: A Novel Facial Recognition Optimization Using Coronavirus-Inspired Algorithm And Machine Learning, Saif Mohanad Kadhim, Johnny Koh Siaw Paw, Yaw Chong Tak, Shahad Thamear Abd Al-Latief

Iraqi Journal for Computer Science and Mathematics

Facial recognition has become an invaluable and rapidly advancing technology that plays a crucial role in various daily applications. From identity authentication to video surveillance, mobile payment, and even law enforcement and security measures. Despite the remarkable progress, facial recognition is still a dynamic research field and confronts several challenges. One of the main challenges is the high variability in facial images due to factors like facial expressions, lighting conditions, aging, and the presence of accessories. Additionally, the computational complexity and the time concerns surrounding face recognition systems have raised considerations that need to be addressed. This research presents a …

Vaccine Hesitancy In Türkiye: A Natural Language Processing Study On Social Media, Semi̇h Sari, Ulya Bayram

Turkish Journal of Electrical Engineering and Computer Sciences

Vaccine hesitancy is a significant public healthcare problem that is threatening everyone worldwide. Vaccine hesitancy has become more ingrained in Turkish society, mainly through social media. Unfortunately, reflections of this hesitancy are preventable deaths or permanent disabilities. Because of the uncontrolled spread of misinformation and disinformation on social media, Türkiye is facing a future health crisis. As a step towards preventing this crisis, our main objective is to use the power of artificial intelligence techniques on Turkish social media posts to detect antivaccine posts. Through this study, it will be possible to raise awareness about the importance of vaccines in …

Corradjust Unveils Biologically Relevant Transcriptomic Correlations By Efficiently Eliminating Hidden Confounders, Stepan Nersisyan, Phillipe Loher, Isidore Rigoutsos

Computational Medicine Center Faculty Papers

Correcting for confounding variables is often overlooked when computing RNA-RNA correlations, even though it can profoundly affect results. We introduce CorrAdjust, a method for identifying and correcting such hidden confounders. CorrAdjust selects a subset of principal components to residualize from expression data by maximizing the enrichment of "reference pairs" among highly correlated RNA-RNA pairs. Unlike traditional machine learning metrics, this novel enrichment-based metric is specifically designed to evaluate correlation data and provides valuable RNA-level interpretability. CorrAdjust outperforms current state-of-the-art methods when evaluated on 25 063 human RNA-seq datasets from The Cancer Genome Atlas, the Genotype-Tissue Expression project, and the Geuvadis …

A Complete Transfer Learning-Based Pipeline For Discriminating Between Select Pathogenic Yeasts From Microscopy Photographs, Ryan A. Parker, Danielle S. Hannagan, Jan H. Strydom, Christopher J. Boon, Jessica Fussell, Chelbie A. Mitchell, Katie L. Moerschel, Aura G. Valter-Franco, Christopher Cornelison

Faculty Articles

Pathogenic yeasts are an increasing concern in healthcare, with species like Candida auris often displaying drug resistance and causing high mortality in immunocompromised patients. The need for rapid and accessible diagnostic methods for accurate yeast identification is critical, especially in resource-limited settings. This study presents a convolutional neural network (CNN)-based approach for classifying pathogenic yeast species from microscopy images. Using transfer learning, we trained the model to identify six yeast species from simple micrographs, achieving high classification accuracy (93.91% at the patch level, 99.09% at the whole image level) and low misclassification rates across species, with the best performing model. …

Hybrid Sentiment Analysis Of Police Social Media: Managing Big Data Impact On Youth, Alunood Yousuf Alamiri

Theses

The pervasive influence of social media has reshaped public communication, particularly for institutions like law enforcement agencies that rely on these platforms to engage with the community. As police departments increasingly utilize social media to disseminate information, interact with the public, and manage public relations, the need for effective sentiment analysis becomes crucial. Understanding how police messages are received by the public, especially by the youth, who are among the most active and impressionable users of social media, is essential for maintaining public trust and ensuring effective communication. This thesis, seeks to develop a comprehensive solution for this challenge. The …

Predicting Patient No-Show Rate In Healthcare To Improve Operational Excellence, Saeed Ahmed Al Wari

Theses

The challenge of integrating no-show predictive models into healthcare environments is complex and multifaceted, extending far beyond the distinct technical challenge of building accurate models. This thesis will discuss more on the subject of incorporating machine learning into healthcare. While algorithmic accuracy and model validation are critical, they are a mere sub-system of a much larger operational, technological, ethical, legal and human ecosystem. Predictive models face mass deployment in healthcare systems which are complex networks of stakeholders across strict regulatory frameworks and deeply embedded workflows (AlMuhaideb et al., 2019). However, for these models to result in long term and real …

Mfgat: Map-Free Trajectory Prediction With Graph Attention Networks For Autonomous Vehicles, Zehra Gunindi

UNLV Theses, Dissertations, Professional Papers, and Capstones

Accurate trajectory prediction is a key component for ensuring safe and efficient navigation of autonomous vehicles in complex traffic scenarios. While traditional methods rely heavily on high-definition (HD) maps, these approaches face significant challenges, including high costs, limited availability, and susceptibility to rapid obsolescence. This thesis proposes an end-to-end, map-free trajectory prediction model that leverages Graph Attention Networks (GAT) to dynamically capture spatial-temporal interactions among road agents, eliminating the need for HD maps.The research introduces UNLVTraj, a novel LiDAR-based dataset collected around the University of Nevada, Las Vegas campus, specifically along Cottage Grove Street, Harmon Avenue, and Maryland Parkway. This …

Efficient Eeg Epilepsy Classification And Feature Selections Based On Hellinger Distance, Muhammed Sadiq

Theses and Dissertations

Accurate and efficient detection of epileptic seizures from EEG signals remains a critical challenge due to high-dimensional data, class imbalance, and the limitations of standard classifiers. This thesis introduces two novel models to address these challenges. The first model presents a new classifier based on the Hellinger Distance, specifically designed to enhance discriminative capability and robustness against imbalanced datasets. By integrating the Hellinger Distance Classifier with Particle Swarm Optimization (PSO) for feature selection, this model significantly improves classification performance while reducing computational complexity. Experimental evaluations on the Bonn dataset demonstrate an accuracy of 96.25%, an F1-score of 97.74%, a recall …

Computational Complexity Of Soundness Verification For Neural Networks, Scott Sirri

McKelvey School of Engineering Graduate Student Theses & Dissertations

Neural networks are an increasingly ubiquitous tool in systems of varying complexity across a range of domains. While these tools can be used to learn and predict complex functions, their opaque nature limits the scope of their acceptable applications. In particular, a lack of performance guarantees means that they are unsuitable for safety-critical applications such as self-driving cars and scheduling systems. Neural networks trained to solve NP-complete problems, in particular, are unlikely to be able to solve the problem exactly. However, a weaker soundness guarantee may be sufficient for some systems, e.g., that positive instances of the problem may be …

Identification Of Subtypes Of Post-Stroke And Neurotypical Gait Behaviors Using Neural Network Analysis Of Gait Cycle Kinematics, Andrian Kuch, Nicolas Schweighofer, James M. Finley, Alison Mckenzie, Yuxin Wen, Natalia Sánchez

Physical Therapy Faculty Articles and Research

Gait impairment post-stroke is highly heterogeneous. Prior studies classified heterogeneous gait patterns into subgroups using peak kinematics, kinetics, or spatiotemporal variables. A limitation of this approach is the need to select discrete features in the gait cycle. Using continuous gait cycle data, we accounted for differences in magnitude and timing of kinematics. Here, we propose a machine-learning pipeline combining supervised and unsupervised learning. We first trained a Convolutional Neural Network and a Temporal Convolutional Network to extract features that distinguish impaired from neurotypical gait. Then, we used unsupervised time-series k-means and Gaussian Mixture Models to identify gait clusters. We tested …

Growing Through Simulated Realities: Proxy-Tuning Llms With Embodied Multi-Task Planning, Sirui Wang

Dartmouth College Master’s Theses

This thesis presents a method for teaching large language models (LLMs) to perform embodied multi-task planning using simulation data and a lightweight training technique called proxy-tuning. Traditional LLMs are strong in language understanding but struggle with tasks that require reasoning and planning in real-world environments. To address this, we construct a dataset based on VirtualHome, a simulator that models household activities using input commands. We extract individual tasks from the simulator and combine them into multi-task plans, optimizing for coherence and efficiency. These plans are formatted as Q&A pairs to train models to generate step-by-step instructions that complete multiple tasks …

Satellite Reorientation Using Reinforcement Learning Under Unknown Attitude Failure, Matthew Willoughby

Doctoral Dissertations and Master's Theses

This study presents a reinforcement learning (RL) approach for reestablishing communication with deep-space satellites under unknown attitude determination and control system (ADCS) failures. When traditional fault-tolerant control methods cannot restore signal, the proposed RL controller acts as a last-resort measure by autonomously reorienting the satellite’s antenna toward Earth while charging the battery via solar panels. A generic reward function, designed for the RL-based method, enables the controller to adapt to diverse failure scenarios, including severe actuator noise, misalignment, and complete actuator failure. Simulations are conducted in the Basilisk environment and trained with the tonic framework and demonstrate ranging capabilities of …

Improving The Reproducibility Of Deep Learning Software: An Initial Investigation Through A Case Study Analysis, Nikita Ravi, Abhinav Goel, James C. Davis, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

The field of deep learning has witnessed significant breakthroughs, spanning various applications, and fundamentally transforming current software capabilities. However, alongside these advancements, there have been increasing concerns about reproducing the results of these deep learning methods. This is significant because reproducibility is the foundation of reliability and validity in software development, particularly in the rapidly evolving domain of deep learning. The difficulty of reproducibility may arise due to several reasons, including having differences from the original execution environment, incompatible software libraries, proprietary data and source code, lack of transparency, and the stochastic nature in some software. A study conducted by …

Data-Driven Dynamic Decision-Making Using Discrete Optimization And Supervised Machine Learning, Navid Rashedi

Dartmouth College Ph.D Dissertations

In recent years, the operations research community has developed data-driven optimization techniques to solve complex combinatorial problems with the aid of machine learning. This thesis contributes to these efforts by combining machine learning with optimization to expedite online decision-making, with applications in transportation and healthcare.

In the domain of airline operations recovery, the focus is on the aircraft recovery process—repairing disrupted schedules by minimizing overall disruption costs. Traditional exact methods are too time-consuming, while heuristic approaches often yield poor solution quality and lack generalizability across varying formulations. To address these challenges, this research employs supervised machine learning to identify near-optimal …

Evaluating And Applying Coherence Models: Llms As Coherence Evaluators And Neural Entity Grids As Noun Entity Shift Indicators, Elyas A. Amin

Theses and Dissertations

This thesis investigates computational approaches to discourse coherence through two interconnected studies. First, we evaluate the capabilities of Large Language Models (LLMs) as coherence evaluation models across real-world textual data domains. Our experiments with GPT-4o, GPT-3.5, Llama, and Mistral models reveal that while LLMs significantly outperform traditional entity grid methods (55% improvement in F1 scores), their performance varies substantially across domains and coherence levels. Chain-of-thought prompting unexpectedly decreased performance, and fine-tuning experiments demonstrated improved detection of low and high coherence texts but diminished performance on medium coherence cases. In our second study, we explore the practical application of entity grid-based …

Incorporating Media Coverage And The Impact Of Geopolitical Events For Stock Market Predictions With Machine Learning, Vinayaka Gude, Daniel Hsiao

Faculty Publications

This paper explores the impact of the Israel–Palestine conflict on the stock performance of U.S. companies and their public positions on the conflict. In an era where corporate positions on geopolitical issues are increasingly scrutinized, understanding the market implications of such statements is critical. This research aims to capture the complex, non-linear relationships between corporate actions, media coverage, and financial outcomes by integrating traditional statistical techniques with advanced machine learning models. To achieve this, we constructed a novel dataset combining public corporate announcements, media sentiment (including headline and article body tone), and philanthropic activities. Using both classification and regression models, …