Digital Commons Network^™

Using Machine Learning Methods To Evaluate Undergraduates’ Executive Function In The Context Of Game-Based Measurement, Ming Chen

Dissertations, Theses, and Capstone Projects

Traditional performance-based measurements have faced criticism due to their potential to induce test fatigue and their limited ecological validity. In contrast, game-based assessments have demonstrated the capacity to engage participants effectively in tests and enhance ecological validity by replicating real-world scenarios. Nevertheless, conventional statistical approaches may prove insufficient in capturing the intricate, non-linear associations between participants' gaming performance and their cognitive capabilities. Leveraging the potential of machine learning (ML) techniques, which can model complex problems, offers a promising avenue for estimating students' performance in video games. This dissertation aims to explore various ML methods for the validation of three game-based …

Predicting Groundwater Withdrawals Using Machine Learning With Limited Metering Data: Assessment Of Training Data Requirements, Dawit Asfaw, Ryan G. Smith, Sayantan Majumdar, Katherine Grote, Bin Fang, B. B. Wilson, V. Lakshmi, J. J. Butler

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

The future of major aquifer systems supporting irrigated agriculture is threatened due to unsustainable groundwater pumping. Metering of pumping is key for implementing robust groundwater management, but metering is limited in most aquifers. Although machine learning methods have been used to estimate pumping over certain regions, these studies have not fully demonstrated the data quantity and input parameter requirements to accurately estimate regional groundwater pumping. This study determined the data quantity required and identified relevant features to develop Random Forests-based annual groundwater pumping estimates (2008–2020) over the Kansas High Plains aquifer. We predicted pumping at two spatial scales, i.e., point …

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 …

The Research University, Industry And Artificial Intelligence, Hamlet Isakhanli

Khazar Journal of Humanities and Social Sciences

Scientific research in cooperation between science and industry occurs mainly in research universities. This article begins with a brief history of research universities, a discussion of their nature and form. Their evolution and development in Europe, the USA and China are recalled; the special importance of Graduate Schools is underlined. Cooperation between the worlds of university and industry is studied using the example in the USA of Silicon Valley, a world leader in its field and initiated by Stanford University. Presented alongside the positive American experience, is the rather negative experience of the ``Russian syndrome''. The development of semiconductor, transistor …

A Comparative Of Detecting Physical And Cyber Attacks On Drones Using Machine Learning And Deep Learning Techniques, Khattab M Ali Alheeti, Sara Abbas Rafa, Maha Mahmood, Aythem Khairi Kareem, Mohammad Aljanabi, Ahmed Adil Nafea

Baghdad Science Journal

Multitudes of Unmanned Aerial Vehicles (UAVs) are generally embraced in military and civilian applications. Yet, this physical-cyber method is intimidated by cyber-attacks. Recently, Machine Learning (ML) based attacks detection approaches have been effectively embraced to detect cyber-attacks. This paper presented the Intrusion Detection Security (IDS) approach. The proposed approach investigates UAVs' cyber and physical attributes under normal process and attack circumstances. Two types of cyber-attacks have been classified: Denial-of-service (DoS) and replay. This study developed IDS approaches established on ML and Deep Learning (DL) prototypes, including Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naive Bayes (NB), and One Dimensional Convolutional …

Enhance Telecom-Related International Indicators Using Machine Learning, Omar Alnemer

Theses

This thesis investigates the enhancement of the Network Readiness Index (NRI) through the application of machine learning methodologies to refine indicator weighting, clustering, and dimensionality. The study builds on research promoting data-driven methods to address the limitations of equal-weighted indices in capturing indicator importance and interdependence. The research aims to evaluate how unsupervised and supervised learning methods can optimize the interpretation of NRI data. The study focuses on three primary research questions: (1) How can PCA be used to reduce the dimensionality of the NRI without significant loss of information? (2) Can k-means clustering reveal meaningful groups of countries with …

Peeking Inside The Black Box: Demystifying Artificial Intelligence, David J. Mulder

Faculty Work Comprehensive List

No abstract provided.

Mathematics And Mental Health: An Interdisciplinary Analysis Of Ai In Counselor Education, Jennifer M. Hightower, Catrina A. May

Journal of Counselor Preparation and Supervision

Although use of Artificial Intelligence (AI) in mental health care has become increasingly common, many professional counselors remain under informed about foundational components of AI. Fundamental issues associated with AI, including model bias and the Black Box Problem, must be considered as these tools become integrated into the counseling profession. This manuscript provides an interdisciplinary, theoretical analysis of AI use in counseling and counselor education grounded in foundational knowledge of AI and the American Counseling Association’s recommendations for the ethical integration of AI (Butler et al., 2023). The paper summarizes these recommendations, establishes accessible definitions of AI terms, explains the …

Improving Arabic Coffee Production Through Disease Detection Based Ai Frameworks, A.S. Balobaid, R. Alarabi, S.M. Abd Elazim

Yanbu Journal of Engineering and Science

Coffee is the second most traded commodity globally after oil and represents a major source of income in many countries. Kingdom of Saudi Arabia has a great interest in growing and expanding of coffee in line with the Saudi Green Initiative as implementation of Vision 2030 AD. This expansion is accompanied by a greater spread of insect pests and the emergence of diseases affecting coffee. Recently, machine learning technologies have been beneficial in the agricultural era in detection and classification of fruit and tree diseases. The study initially focuses on kind of disease that appears in the leafy area of …

Retracted: Iot Flow Parameters Classification Based On Machine Learning Techniques, El-Sayed M. El-Kenawy, Marwa M. Eid, Ban Salman Shukur, Amel Ali Alhussan, Doaa Sami Khafaga

Iraqi Journal for Computer Science and Mathematics

In recent years, there has been a highly remarkable convergence of artificial intelligence (AI) and the Internet of Things (IoT), which has made rapid progress in smart city initiatives by developing smart devices for such cities. Since these devices are increasingly diversified, they require a resilient communication network to demonstrate high performance in managing consistent traffic flows. A machine learning model intended for identifying network parameters from diverse devices, in addition to proposing modifications meant for network performance enhancement, is developed in this study. In relation to packet data as a network traffic parameter, employing gateway devices can facilitate its …

Machine Learning-Enhanced Optimization For The Real-Time, Secure, And Efficient Operation Of The Energy-Water-Hydrogen Nexus, Mostafa Goodarzi

Graduate Thesis and Dissertation post-2024

Given the escalating urgency of climate change, our research addresses the imperative need to curb carbon emissions, particularly from the electricity sector, which accounts for a quarter of total emissions in the U.S. We propose integrated systems, such as the energy-water nexus (EWN) and an innovative concept known as the energy-water-hydrogen (EWH) nexus, which integrates renewable energy sources (RESs) with green hydrogen production through water electrolysis. These concepts align seamlessly with the nation’s commitment to global climate agreements, including the Paris Agreement. Furthermore, green hydrogen—a clean and efficient energy source produced via water electrolysis powered by renewable energy—emerges as a …

Spectroscopy Pre-Trained Transformer (Specpt): A Universal Spectroscopic Analysis And Redshift Measurement Framework, Rohan Pattnaik

Theses

Spectroscopic surveys are essential for measuring galaxy redshifts and probing the physical processes driving galaxy evolution. As datasets grow in scale and complexity, traditional analysis methods face increasing limitations, motivating the development of scalable, data-driven alternatives. This thesis presents SpecPT, a transformer-based deep learning framework for general-purpose spectroscopic analysis, with a focus on redshift estimation. The model is first trained on DESI Early Data Release spectra from the Bright Galaxy and Emission Line Galaxy samples, jointly performing spectral reconstruction and redshift regression while learning a latent representation that captures the intrinsic properties of galaxies. SpecPT is then extended to a …

Research On Autonomous Deviation Correction Of Tunnel Boring Machines And Parameters Based On Machine Learning, Zhang Jun, Li Maopeng

Journal of China & Foreign Highway

In order to solve the problem of realizing the autonomous deviation correction of tunnel boring machines (TBMs ), a TBM deviation correction control method that integrated the random forest (RF) algorithm with the genetic algorithm (GA) was proposed based on actual engineering data.The method combined a prediction model with an optimization model,using target deviation values as input to invert and output the required TBM deviation correction parameter values,thereby further improving the automation level of TBM deviation correction.By comparing it with the actual data,the feasibility of the model was verified.The results show that the RF algorithm-based prediction model achieves an R2 …

Ai-Driven Solutions For Electricity Fraud Detection: A Data-Centric Approach, Fatemeh Alimoradi, Zahra Alimoradi, S. Mohammadali Zanjani, Ghazanfar Shahgholian

NJF Intelligent Engineering Journal

Electricity fraud detection presents a significant challenge for power distribution companies, as non-technical losses resulting from fraudulent activities adversely affect revenue and operational efficiency. This study presents a machine learning-driven approach for accurately identifying fraudulent electricity consumption patterns. A comprehensive analysis of electricity usage data is conducted using multiple classification models, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), a Stacking Model integrating SVM, KNN, Random Forest, and Gradient Boosting, as well as a Weighted Model leveraging confidence-based prediction adjustments. Model performance is evaluated using key metrics, including accuracy, precision, recall, F1-score, AUC-ROC, and confusion matrices. The results indicate that …

Application Of Ann Artificial Network In Slope Behavior Evaluation Using Machine Learning Technique, Marziyeh Tourani, Hadi Bahadori

NJF Intelligent Engineering Journal

In geotechnical engineering, evaluating slope stability remains a significant challenge due to inherent soil variability and environmental uncertainty. This study explores the application of Artificial Neural Networks (ANN) in predicting the factor of safety (FoS) of slopes using dimensionless input variables. An initial dataset of 349 samples was refined to 259 validated cases after removing outliers and incomplete entries. The input parameters were transformed into nondimensional forms c / γh, (tan(⌀)) / (tan (β)), and p / γh based on fluid mechanics principles to improve generalization and reduce dimensional bias. A feedforward ANN model …

Ai-Driven Biomedical Perspectives On Mental Fatigue In The Post-Covid-19 Era: Trends, Research Gaps, And Future Directions, Saba Parveen, Md Belal Bin Heyat, Umair Tariq, Faijan Akhtar, Hafiz Muhammad Zeeshan, Seth Christopher Yaw Appiah, Shang-Ming Zhou, Huang Lei

School of Nursing and Midwifery

Mental fatigue is a complex condition arising from various neurological processes and influenced by external factors such as stress and cognitive demands. This comprehensive review elucidates the primary neurological mechanisms underlying mental fatigue, particularly emphasizing how it was elevated or otherwise affected during the COVID-19 pandemic. We explore the intricate relationship between prolonged cognitive tasks, chronic stress, and the development of mental fatigue, emphasizing the impacts that mental fatigue has on mental health across diverse populations. Utilizing advanced artificial intelligence techniques, including machine learning and deep learning, this study identifies and quantifies the patterns of mental fatigue. The innovative approach …

A Comprehensive Review Of Frugal Artificial Intelligence: Challenges, Applications, And The Road To Sustainable Ai, Nancy Girdhar, Aditya Raj, Deepak Sharma, Vinay Singh, Antoine Doucet, Matthias Renz

School of Engineering, Computing and Mathematics

Artificial Intelligence (AI) has demonstrated its transformative impact in creating learning models, processing extensive datasets, and executing intricate calculations rapidly. Nevertheless, achieving optimal performance with AI models demands substantial investment in powerful and expensive high-end hardware. The learning models running on the hardware are complex, requiring massive data and huge training time. However, the race to achieve higher accuracy and computational limitations of AI further poses a threat to the environment and thus provides motivation to develop AI technology that is cost-effective, scalable, and suitable for resource-constrained environments, Frugal Artificial Intelligence, or Frugal AI. The objective of this paper is …

A Machine Learning Approach To Detect Pores In Laser Powder Bed Fusion Additive Manufacturing, Jose Galarza, Jose Barron Jr., Luis Jimenez, Tamer Oraby, Jianzhi Li, Farid Ahmed

Manufacturing & Industrial Engineering Faculty Publications

Real-time detection of pores in the Laser Powder Bed Fusion (LPBF) metal Additive Manufacturing (AM) process is proposed in this study and can be utilized for in-situ process monitoring and quality control. The average light emission data from the process captured by an optical tomography camera can be integrated into a defect detection module to characterize defects after the deposition of a layer. The light emission contains information on the process zone which could be extracted with the appropriate data techniques. In this paper, we proposed a machine-learning approach that utilizes the mean light intensity data from the melt-pool monitoring …

Development Of A Machine Learning Framework For An Irrigation Decision Support System, Eric Wilkening, Derek M. Heeren, Yeyin Shi, Laila A. Puntel, Guillermo R. Balboa, Abia Katimbo, Kuan Zhang, Precious Nneka Amori, Bruno Lena

Department of Agricultural and Biological Systems Engineering: Faculty Publications

Currently used irrigation scheduling techniques often require significant human intervention and are time consuming, particularly for variable rate irrigation. This research aimed to develop a conceptual framework that incorporates disparate data sources leveraging both mechanistic and machine learning (ML) approaches. The overarching objective is to utilize the growing availability of data and technology to manage irrigation more precisely, which will minimize negative impacts of irrigation on our water resources. An initial irrigation machine learning model was proposed and tested in this study as a key component of an edge-cloud computing and federated learning decision-making framework. The specific objectives were (1) …

Machine Learning And Clinical Eeg Data For Multiple Sclerosis: A Systematic Review, Badr Mouazen, Ahmed Bendaouia, El Hassan Abdelwahed, Giovanni De Marco

Manufacturing & Industrial Engineering Faculty Publications

Multiple Sclerosis (MS) is a chronic neuroinflammatory disease of the Central Nervous System (CNS) in which the body’s immune system attacks and destroys the myelin sheath that protects nerve fibers, leading to a wide range of debilitating symptoms and causing disruption of axonal signal transmission. Accurate prediction, diagnosis, monitoring and treatment (PDMT) of MS are essential to improve patient outcomes. Recent advances in neuroimaging technologies, particularly electroencephalography (EEG), combined with machine learning (ML) techniques — including Deep Learning (DL) models — offer promising avenues for enhancing MS management. This systematic review synthesizes existing research on the application of ML and …

Machine Learning Based Medical Ultrasound Image Classification And Grad-Cam Interpretation, Victoria C. Hemphill

All Theses

This work takes a step in creating a diagnostic tool for the classification decision process of Achilles tendinopathy using ultrasound images. An attention-based multiple instance learning model is developed to classify the images. Typically, doctors capture multiple ultrasound images of the Achilles tendon during a study to determine a complete diagnosis. Multiple instance models adopt this behavior by providing a single label for a set of instances (images). The images are grouped into ”bags” at the study level and passed into the model. The MIL model then uses its attention property to assign an importance score to each image to …

Intelligent Multi-Layer Optical Network Design And Network Softwarization, Boyang Hu

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

The growing demand for high-capacity, low-latency services has placed significant pressure on the design and operation of optical transport networks. Multi-layer optical network design—which coordinates the physical layer with higher-layer protocols—has emerged as a critical strategy to enhance resource efficiency, service flexibility, and fault resilience. Enabled by advancements in software-defined networking (SDN) and network softwarization, intelligent multi-layer architectures allow for adaptive, cross-layer control of routing, grooming, and protection mechanisms, ultimately reducing both capital and operational expenditures.

This dissertation investigates the intelligent design and simulation of multi-layer optical networks through the integration of SDN, machine learning, and high-fidelity physical-layer modeling. We …

Advancing The Utility Of Unmanned Aerial Systems (Uas)-Based Imaging Techniques In Broadacre Agriculture: A Multimodal Case Study On Table Beets, Mohammad Shahriar Saif

Theses

Efficient and sustainable food production and management are growing concerns in the context of an ever-increasing global population. It is in this context that the integration of remote sensing with advanced imaging technologies presents transformative opportunities for data-driven decision-making in agriculture. This study therefore explores the use of unmanned aerial systems (UAS) equipped with multispectral, hyperspectral, and LiDAR sensors for the non-destructive monitoring of table beet (Beta vulgaris) crop traits, with a specific focus on root yield estimation and foliar disease assessment. Table beet, a subterranean crop of increasing commercial and nutritional importance, poses unique challenges for above-canopy sensing due …

Discovery Of High-Performance Cathode Materials For Protonic Ceramic Fuel Cells, Liang Han

All Dissertations

Environmental pollution and rapid energy consumption have become common problems in global development and will continue to grow with the world population. PCFCs use proton-conducting ceramics as electrolytes, with low activation energy and high ionic conductivity at intermediate temperatures, enabling them to operate at intermediate-temperature conditions, which can effectively solve the problems of poor stability and high cost of exotic materials of traditional solid oxide fuel cells. However, as the operating temperature decreases, the electrocatalytic activity of the cathode decreases significantly, seriously affecting PCFC’s performance. Therefore, developing high-performance cathode material suitable for working under intermediate-temperature conditions has become the key …

A Focus On Student Education: Determining Student Attitudes Towards Transparent Autograding And Developing Artificially Intelligent Tools To Help Students Succeed, Andra Rice

All Graduate Theses and Dissertations, Fall 2023 to Present

This thesis is composed of two parts both relating to helping students succeed. First, the focus is on determining how we can help students feel more comfortable using an AI tool that can provide them immediate feedback. Second, machine learning algorithms are explored in relation to tracking student tasks to encourage healthy study habits.

The development of effective autograders is key for scaling assessment and feedback. While AI based autograding systems for open-ended response questions have been found to be beneficial for providing immediate feedback, autograders are not always liked, understood, or trusted by students. Our research tested the effect …

Integrating Uas And Satellite Data To Improve Classification Of Plant Functional Types In A Forest Landscape, Sky Mae Gennette

Boise State University Theses and Dissertations

Remotely sensed land-use/land-cover (LULC) data products are an important tool for understanding landscape processes at all scales. The types of inference researchers can derive from remote sensing data depend on the quality and characterization of these LULC products. Currently, gaps in the detail of widely available vegetation cover products limit inference about processes at the level of plant functional type (PFT). Advanced remote sensing tools, machine learning, and data fusion can be employed to contribute novel methods to produce detailed, accurate vegetation cover products. Here, we combine high resolution Unoccupied Aerial Systems (UAS) multispectral, structural and textural data with large-scale …

Comparative Analysis Of Sequential And Non-Sequential Modeling Techniques For Ddos Attack Detection With Explainable Ai, Vincent Agbenyeavu

Theses and Dissertations

Cybersecurity is known today as one of the greatest challenges of the modern era. Among the various types of cyber-attacks that threaten our security, the Distributed Denial of Service (DDoS) attack is among some of the most common, effective, and well-recognized attack strategies. Since this form of attack is meant to disrupt the availability factor covertly, it can be detrimental to the targeted machines and difficult to discover. Because of that, there have been several approaches, as well as solutions that have been devised to detect it as accurately and efficiently as possible. In this study, four sequential data modeling …

Low-Cost Monitoring And Fingerprinting Of High-Powered Electric Systems, Kwabena Buamono Aboagye-Otchere

Theses and Dissertations

Electric motors are vital to industry, transport, and energy, yet their maintenance challenges persist. While traditional reactive maintenance leads to costly downtime and safety risks, predictive maintenance, especially through IoT and machine learning offers early fault detection and operational efficiency. However, this shift introduces security concerns due to unintended magnetic emissions from motors. These emissions, though useful for non-intrusive monitoring, can be exploited to eavesdrop on sensitive industrial processes. This dissertation explores the dual nature of magnetic emissions: their value in motor diagnostics and their potential as a security vulnerability. It demonstrates how emissions can identify motors, monitor health, and …

Computational Learning Across Biological And Industrial Systems: Bayesian And Segmentation Models For Epigenomic And Manufacturing Data, Prosenjit Roy

All Theses

In this thesis, we made a complete dual-domain investigation using a machine learning approach into two different scientific areas which were epigenetic analysis of seal species and pore identification in the work of additive manufacturing. The study showcased the flexibility and strength of modern computational tools in addressing complicated issues in various biological and industrial systems. We further investigated the epigenetics by using the Bayesian Neural Network and other machine learning methods to conduct a study on the DNA methylation pattern within three pinniped species (the northern elephant seal, Hawaiian monk seal, and Weddell seal) with perfect precision on species …

Non-Blackbox Robust Design Of Machine Learning In Networks, Venkat Sai Suman Lamba Karanam

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Networked systems have become increasingly complex, with newer communication technologies and standards being added every day. Machine Learning (ML) and Artificial Intelligence (AI) paradigms have been adopted in networks to not only solve many fundamental problems, but also to allow seamless integration of components comprising them. The saying “let’s not reinvent the wheel” in ML/AI adoption implies that model architecture design be left for pure ML/AI researchers, while network researchers focus on input preprocessing (e.g. formatting the packet data to be fed to a model), hyperparameter fine-tuning and a trial-and-error approach to find the “best” result. …