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Articles 14281 - 14310 of 294517

Full-Text Articles in Physical Sciences and Mathematics

The Impact Of Deep Learning In Brain Tumour Analysis, Sangeeta Giri, Manivel Kandasamy, Meet Rafaliya Jun 2025

The Impact Of Deep Learning In Brain Tumour Analysis, Sangeeta Giri, Manivel Kandasamy, Meet Rafaliya

Journal of Informatics and Web Engineering

The need for early and precise identification of abnormalities has made the detection and classification of brain tumours essential components of medical diagnosis. Because brain tumours are naturally complex and can have a wide range of sizes, shapes, and types, conventional diagnostic techniques like MRI interpretation and manual evaluations are difficult and time-consuming. Traditional methods frequently depend on human expertise, which is prone to errors, delays, and variability. Deep learning (DL) developments, on the other hand, have completely changed this field by providing increased automation, efficiency, and precision in tumour detection and classification because they can automatically extract pertinent features …

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Exploring User Perceptions Of Security In Mobile Banking: A Study In Malaysia, Kai En Lim, Ying Han Pang, Shih Yin Ooi, Wan Xuan Kow, Tang Xing Cheang, Mao Wei Tan Jun 2025

Exploring User Perceptions Of Security In Mobile Banking: A Study In Malaysia, Kai En Lim, Ying Han Pang, Shih Yin Ooi, Wan Xuan Kow, Tang Xing Cheang, Mao Wei Tan

Journal of Informatics and Web Engineering

Mobile banking has become increasingly popular in Malaysia due to its convenience and efficiency. However, persistent security concerns such as vulnerabilities to cyber attacks, malware and phishing affect user trust and impede adoption. This study aims to explore how users perceive and experience the security features of mobile banking applications. In spite of the implementation of security controls, understanding user perceptions and experiences remains essential, as these are core factors which influence mobile adoption. Thus, this study investigates the user experience of mobile banking security features in Malaysia. Specifically, we focus on the users’ perceptions, satisfaction levels and concerns. We …

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Classification Of Smartphone Product Reviews On E-Commerce Using The Recurrent Neural Network (Rnn) Method, Rajibul Anam, Fernanda Tata Pradhana, Imam Abu Yasin, Junta Zeniarja Jun 2025

Classification Of Smartphone Product Reviews On E-Commerce Using The Recurrent Neural Network (Rnn) Method, Rajibul Anam, Fernanda Tata Pradhana, Imam Abu Yasin, Junta Zeniarja

Journal of Informatics and Web Engineering

Understanding how consumers behave in e-commerce is essential for businesses, especially in today’s digital world where people rely heavily on online shopping platforms. A key part of this understanding comes from sentiment analysis, which looks at customer reviews to find out what buyers really think and feel about products. However, analysing these reviews is not always straightforward. Many people use informal language, slang, or mixed languages, which makes it hard for computers to interpret their opinions accurately. On top of that, there is often an imbalance in the types of data available, particularly in developing countries, where some opinions might …

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Cyber Security Threats Of Using Generative Artificial Intelligence In Source Code Management, Sainag Nethala, Sandeep Kampa, Srinivas Reddy Kosna Jun 2025

Cyber Security Threats Of Using Generative Artificial Intelligence In Source Code Management, Sainag Nethala, Sandeep Kampa, Srinivas Reddy Kosna

Journal of Informatics and Web Engineering

Generative Artificial Intelligence (Generative AI) models are now broadly used for academic writing and software development for the sake of productivity and efficiency. Concerns on the impact of Artificial Intelligence (AI) tools on academic integrity and cybersecurity grow bigger with time. Generative AI is being used for code generation, editing, and review, raising ethical and security challenges. A big concern is the involuntary introduction of vulnerabilities into codebases. They can reproduce known bugs or malicious code that compromise software integrity because of the way models are trained: on large datasets. The tools may also pose additional security threats often encountered …

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Anomaly Detection In Network Traffic For Insider Threat Identification: A Comparative Study Of Unsupervised And Supervised Machine Learning Approaches, Sellappan Palaniappan, Rajasvaran Logeswaran, Shapla Khanam Jun 2025

Anomaly Detection In Network Traffic For Insider Threat Identification: A Comparative Study Of Unsupervised And Supervised Machine Learning Approaches, Sellappan Palaniappan, Rajasvaran Logeswaran, Shapla Khanam

Journal of Informatics and Web Engineering

Insider threats pose a significant and growing risk to organizational cybersecurity, with recent studies indicating a 47% increase in insider incidents from 2018 to 2022. This paper presents a comparative analysis of unsupervised and supervised machine learning approaches for detecting potential insider threats through network traffic anomaly identification. We develop and evaluate an Isolation Forest (unsupervised) and a Random Forest (supervised) model, training them on a simulated dataset representing six months of network logs from a mid-sized company. Our study introduces a unique feature set combining traditional network metrics with temporal and behavioral indicators, enhancing the models' detection capabilities. Results …

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Dynamic Job Recommendation By Profiling Undergraduates Academic Performances, Bao-Ling Foo, Choo-Yee Ting, Hui-Ngo Goh, Albert Quek, Chin-Leei Cham Jun 2025

Dynamic Job Recommendation By Profiling Undergraduates Academic Performances, Bao-Ling Foo, Choo-Yee Ting, Hui-Ngo Goh, Albert Quek, Chin-Leei Cham

Journal of Informatics and Web Engineering

Job-seeking tasks are always challenging. Often, job recommendation systems require human intervention in the job-seeking process. Therefore, the study focuses on recommendation of most relevant job sectors and prioritizing companies based on a student’s profile. The objectives of this study are: (i) to identify important features that optimize job recommendation, (ii) to construct a predictive model that recommends most relevant job sectors, and (iii) to recommend companies by computing the similarity between student and job profiles. In this study, the dataset was collected from Graduate Tracer Study from a university. Additionally, a job dataset was collected to extend the training …

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Detecting Black Hole Attack Using Support Vector Machine With Xgboosting In Mobile Ad-Hoc Networks, Anhar Al Madani, Saima Anwar Lashari, Sana Salah Uddin, Abdullah Khan, Muhammad Nouman Atta, Dzati Athiar Ramli Jun 2025

Detecting Black Hole Attack Using Support Vector Machine With Xgboosting In Mobile Ad-Hoc Networks, Anhar Al Madani, Saima Anwar Lashari, Sana Salah Uddin, Abdullah Khan, Muhammad Nouman Atta, Dzati Athiar Ramli

Journal of Informatics and Web Engineering

Mobile Ad-Hoc Networks (MANET) is a type of ad-hoc networks which use less infrastructure, that means the nodes in this network forward the massages without the need of infrastructure such as routers, switches etc. One of the most used attacks that can affect MANET performance is the black hole attack. This attack leads to dropping the packets that means these packets will never arrive and it will decrease the delivery ratio for the packets. This attack is a real problem as the sender is not informed that the data has not reached the intended receiver. The main goal of this …

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Generative Ai-Based Meal Recommender System, Zheng Bin Ter, Palanichamy Naveen, J. Jayapradha Jun 2025

Generative Ai-Based Meal Recommender System, Zheng Bin Ter, Palanichamy Naveen, J. Jayapradha

Journal of Informatics and Web Engineering

Maintaining a balanced diet is essential for overall well-being, yet many individuals face challenges in meal planning due to time constraints, limited nutritional knowledge, and difficulty aligning meals with personal dietary needs. Traditional meal recommender systems often rely on predefined plans or collaborative filtering techniques, limiting their adaptability and personalization. This study presents a generative AI-based Meal Recommender System utilizing Variational Autoencoders (VAEs) to generate personalized and nutritionally balanced meal plans. The system processes user inputs, such as dietary preferences, nutritional goals, and ingredient availability, to provide tailored recommendations. VAEs effectively uncover hidden dietary patterns and nutritional relationships within complex …

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Exploring Big Data Management Approaches And Applications: A Case Study Of Real-Time Data Analytics In Air Traffic Management, Adeel Hashmi, Nouman Amjad, Muhammad Moiz Ullah Satti, Umar Hayat, Anam Mumtaz Jun 2025

Exploring Big Data Management Approaches And Applications: A Case Study Of Real-Time Data Analytics In Air Traffic Management, Adeel Hashmi, Nouman Amjad, Muhammad Moiz Ullah Satti, Umar Hayat, Anam Mumtaz

Journal of Informatics and Web Engineering

The rapid proliferation of digital devices has generated vast amounts of data, presenting significant challenges in collection, processing, and analysis that traditional systems struggle to overcome. This study investigates big data management approaches, explicitly focusing on technologies capable of efficiently handling real-time data at scale. Within the context of Air Operations, we propose a Hadoop-based architecture designed to support the Observe-Orient-Decide-Act (OODA) loop and enhance air traffic management. By leveraging a distributed system deployed on a cloud-based platform, we demonstrate a cost-effective solution for optimised data processing and improved decision-making capabilities. Our analysis highlights the advantages of using Hadoop's distributed …

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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 Jun 2025

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 …

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Evaluation Of Ecostress Collection 2 Evapotranspiration Products: Strengths And Uncertainties For Evapotranspiration Modeling, Zoe Amie Pierrat, Adam J. Purdy, Gregory Halverson, Joshua B. Fisher, Kanishka Mallick, Madeleine Pascolini-Campbell, Youngryel Rye, Martha C. Anderson, Claire Villanueva-Weeks, Margaret C. Johnson, Brenna Hatch, Evan Davis, Yun Yang, Kerry Cawse-Nicholson Jun 2025

Evaluation Of Ecostress Collection 2 Evapotranspiration Products: Strengths And Uncertainties For Evapotranspiration Modeling, Zoe Amie Pierrat, Adam J. Purdy, Gregory Halverson, Joshua B. Fisher, Kanishka Mallick, Madeleine Pascolini-Campbell, Youngryel Rye, Martha C. Anderson, Claire Villanueva-Weeks, Margaret C. Johnson, Brenna Hatch, Evan Davis, Yun Yang, Kerry Cawse-Nicholson

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) collects thermal observations from the International Space Station to support evapotranspiration (ET) research at fine spatial resolutions (70 m × 70 m). Initial ET from ECOSTRESS Collection 1 was used in scientific research and applications, though subsequent analyses identified areas for improvement. This study outlines updates to ECOSTRESS Collection 2 ET and presents an accuracy assessment of ET and auxiliary variables validated against in situ data from AmeriFlux. Key updates in Collection 2 include use of four independent model estimates of instantaneous latent energy (LE) and improved auxiliary forcing data. …

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Integrating Iota Tangle And Artificial Intelligence (Ai) In Iot Network For Network Anomaly Detection, Saida Hafsa Rafique Jun 2025

Integrating Iota Tangle And Artificial Intelligence (Ai) In Iot Network For Network Anomaly Detection, Saida Hafsa Rafique

Thesis/ Dissertation Defenses

The Internet of Things (IoT) ecosystem has advanced with the advent of Distributed Ledger Technology (DLT) and Artificial Intelligence (AI). Individually, DLT and AI have been explored for enhancement of data management, security, integrity and efficiency of IoT systems. In this thesis, the combined use to apply DLT and AI for network anomaly detection in IoT systems is considered. A framework is proposed to integrate IOTA Tangle, a DLT architecture with Machine Learning (ML)- Random Forest, Decision Trees, and LightGBM, to detect network anomalies in IoT systems. The proposed framework processes network traffic data from UNSW-NB15 dataset and categorizes it …

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Versatile Imidazole Scaffold With Potent Activity Against Multiple Apicomplexan Parasites, Monique Khim, Jemma Montgomery, Mariana Laureano De Souza, Melvin Delvillar, Lyssa J. Weible, Mayuri Prabakaran, Matthew A. Hulverson, Tyler Eck, Rammohan Y. Bheemanabonia, P. Holland Alday, David P. Rotella, J. Stone Doggett, Bart L. Staker, Kayode K. Ojo, Purnima Bhanot Jun 2025

Versatile Imidazole Scaffold With Potent Activity Against Multiple Apicomplexan Parasites, Monique Khim, Jemma Montgomery, Mariana Laureano De Souza, Melvin Delvillar, Lyssa J. Weible, Mayuri Prabakaran, Matthew A. Hulverson, Tyler Eck, Rammohan Y. Bheemanabonia, P. Holland Alday, David P. Rotella, J. Stone Doggett, Bart L. Staker, Kayode K. Ojo, Purnima Bhanot

Department of Chemistry and Biochemistry Faculty Scholarship and Creative Works

Malaria, toxoplasmosis, and cryptosporidiosis are caused by apicomplexan parasites Plasmodium spp., Toxoplasma gondii, and Cryptosporidium parvum, respectively, and pose major health challenges. Their therapies are inadequate, ineffective or threatened by drug resistance. The development of novel drugs against them requires innovative and resource-efficient strategies. We exploited the kinome conservation of these parasites to determine the cellular targets and effects of two Plasmodium falciparum inhibitors in T. gondii and C. parvum. The imidazoles, (R)-RY-1-165 and (R)-RY-1-185, were developed to target the cGMP dependent protein kinase of P. falciparum (PfPKG), orthologs of which are present in T. gondii and C. parvum. Using …

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The Spectral Response Of Time-Resolved Piv In A Turbulent Boundary Layer, Peter Manovski, Wagih Abu Rowin, Henry Ng, Paul Gulotta, Matteo Giacobello, Charitha De Silva, Nicholas Hutchins, Ivan Marusic Jun 2025

The Spectral Response Of Time-Resolved Piv In A Turbulent Boundary Layer, Peter Manovski, Wagih Abu Rowin, Henry Ng, Paul Gulotta, Matteo Giacobello, Charitha De Silva, Nicholas Hutchins, Ivan Marusic

Student Publications

This study presents the application of time-resolved particle image velocimetry (TR-PIV) to measure the mean and fluctuating velocity components in a turbulent boundary layer (TBL) over an axisymmetric body of revolution. A narrow wall-normal strip of the flow was captured using a synchronised high-speed laser and camera at a recording frequency of up to 80 kHz. The resulting streamwise and wall-normal velocity TR-PIV data were validated against hot-wire anemometry measurements and direct numerical simulations (DNS) of a flat plate under matched flow conditions. The mean flow results showed good agreement between all methods, while the expected attenuation due to the …

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Multi-Label Classification Of Acoustic And Electronic Drum Sounds Using Machine Learning, Sean Perman Jun 2025

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 …

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Enhancing Multi-Step Stock Price Forecasting With Social Media Sentiment And Engagement Metrics, Damilare Olaniyan Jun 2025

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 …

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Collaborative Federated Learning For Robots In Heterogeneous Environments, Karlan Schneider Jun 2025

Collaborative Federated Learning For Robots In Heterogeneous Environments, Karlan Schneider

Electronic Theses and Dissertations

This research investigates the performance of Federated Averaging (FedAvg) in simulated Federated Learning (FL) scenarios with varying degrees of environmental heterogeneity among robotic agents. The study explores the impact of data heterogeneity on both the convergence of FedAvg and the fairness of learning, with regard to consistency of performance across agents. Experiments were conducted with simulated robots trained to perform a target collection task, where a subset of agents encountered an unfamiliar environment. The results demonstrate that while FedAvg exhibits resilience to the introduction of new environmental data, it struggles to ensure both convergence and fairness in heterogeneous settings. Specifically, …

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Photochemistry Of Quinones And Combustion-Derived Particles, Desiree J. Sarmiento Jun 2025

Photochemistry Of Quinones And Combustion-Derived Particles, Desiree J. Sarmiento

Electronic Theses and Dissertations

Quinones are ubiquitous species that can be produced from the photochemical aging of combustion-derived particles (CDPs). Polycyclic aromatic hydrocarbons (PAHs) are a major component of CDPs and are precursors to quinones and other oxidized products (OPAHs). My work first expanded on the PAH and OPAH photochemistry research of Dr. John Haynes, who showed that anthracene (ANT) oxidizes into 1,4-naphthoquinone (1,4-NAPQ), 1,4-anthraquinone (1,4-ANTQ), and 9,10-anthraquinone (9,10-ANTQ), and of Dr. Heather Runberg, who demonstrated the ability of ANT and these quinones to generate reactive oxygen species (ROS). Then at the Pacific Northwest National Laboratory (PNNL), I was given the opportunity to investigate …

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Synthesis Of Anti-Schistosomal Heterocyclic Compounds Targeting Thioredoxin Glutathione Reductase, Alexander Stewart Jun 2025

Synthesis Of Anti-Schistosomal Heterocyclic Compounds Targeting Thioredoxin Glutathione Reductase, Alexander Stewart

Lawrence University Honors Projects

chistosomiasis is a deadly and debilitating parasitic disease caused by Schistosoma mansoni which affects 200+ million people annually who come into contact with contaminated water. Currently only one drug (Praziquantel) has been used to treat this disease for almost 50 years, and fear of resistance is growing, thus a new drug and new target is needed. Inhibition of the organism-specific redox defense protein Thioredoxin Glutathione Reductase (TGR) has lead to the death of the worm in vitro, indicating that it would be a good druggable target. Fragment based analysis and x-ray crystallography have returned the outline of a lead compound …

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Study Of Agn Jet And Possible Connection Among Different Agn Classes, Shahjahan Iqbal Jun 2025

Study Of Agn Jet And Possible Connection Among Different Agn Classes, Shahjahan Iqbal

University Departments

No abstract provided.

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Spin Precession In Magnetized Kerr Spacetime, Karthik Krishnamurthy Iyer Jun 2025

Spin Precession In Magnetized Kerr Spacetime, Karthik Krishnamurthy Iyer

University Departments

No abstract provided.

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Interacting Galaxy Clusters As A Probe To Cosmic Filaments, Shreya R. Kamath Jun 2025

Interacting Galaxy Clusters As A Probe To Cosmic Filaments, Shreya R. Kamath

University Departments

No abstract provided.

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Predicting The Photophysics Of Bdpa-Based Radicals Using Density Functional Theory, Samantha Kristine Piwoni Jun 2025

Predicting The Photophysics Of Bdpa-Based Radicals Using Density Functional Theory, Samantha Kristine Piwoni

Lawrence University Honors Projects

Through this project, computational tools were refined to be more rigorous and robust for the primarily synthetic SazLab at Lawrence University. The primary research of the SazLab involves synthesizing and characterizing luminescent radicals based on two stable radical systems: TTM and BDPA. The BDPA system is of particular interest because the effects on luminescence of its nonalternant symmetry are less understood compared to the alternant symmetry of TTM. Quantum mechanical calculations, particularly Density Functional Theory, were utilized to help target syntheses and gain insight into the photophysics of luminescence. A prior student researcher with the SazLab had successfully synthesized 2-pyBDPA, …

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Optimizing Option Market Clearing, Juan Andrés Malaver Alvarado Jun 2025

Optimizing Option Market Clearing, Juan Andrés Malaver Alvarado

Electronic Theses and Dissertations

Modern options markets clear each strike in isolation, leaving cross-strike arbitrage unexploited. This thesis applies a payoff-dominant clearing mechanism to realized trades—roughly 2 000 Cboe VIX option executions from June–November 2016—after classifying each trade’s side and bundling by expiration. Three optimization formulations are tested: a fractional linear program (LP), a mixed-integer LP, and a pure integer program. On a 10-core laptop every bundle solves in < 0.5 s. The LP captures the greatest surplus, yet the integer models recover nearly as much while filling whole contracts and holding only modest margin. Results reveal persistent, albeit small, inefficiencies in executed trades and demonstrate that an integral cross-strike auction could operate in real time. The accompanying C/Gurobi code is modular and readily extendable to early-exercise options. Trade-level evidence thus supports redesigning exchange clearing to consider the complete option book.

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Culture And Environment: The Calling Lakes Ecomuseum As A Community Sustainability Initiative, Kace N. Anders Jun 2025

Culture And Environment: The Calling Lakes Ecomuseum As A Community Sustainability Initiative, Kace N. Anders

Electronic Theses and Dissertations

Located in Saskatchewan, Canada, the Calling Lakes Ecomuseum, CLEM, is a community-based organization focused on water management and sustainability. Ecomuseums are holistic organizations that occupy landscapes, are responsive to community needs, conserve in situ heritage, and incorporate sustainability along with community development. The ecomuseum movement came from a need for holistic heritage management that disengaged with conventional museology and created space for communities to engage with their heritage in ways that promote community identity. This thesis analyzes the Calling Lakes Ecomuseum and how it utilizes the ecomuseum concept alongside environmental sustainability. Both semi-structured interviews and place-based field observations were used …

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A Spatial Analysis Of Residential Photovoltaic Adoption In New Jersey: Focusing On 2020 With Trends Through 2025, Brandon Mcalister Jun 2025

A Spatial Analysis Of Residential Photovoltaic Adoption In New Jersey: Focusing On 2020 With Trends Through 2025, Brandon Mcalister

Geography and the Environment: Graduate Student Capstones

This capstone project seeks to find the relationship between socioeconomic and demographic variables and their relationship to residential photovoltaic installations across New Jersey. The study consists of Geographic Weighted Regression Analyses to construct and socioeconomic index indicating the likelihood of photovoltaic adoption in New Jersey zip codes. Then using the index to predict future photovoltaic adoption across the state. The study found that socioeconomic and demographic variables can predict photovoltaic adoption within one year, but when placed in an index and used to predict future adoption trends, the model under predicts and extremely over predicts. While socioeconomic status is a …

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Utilizing Spatial Criteria To High-Grade Wyoming Eor Opportunities Along Proposed Pipeline Corridors: A Value Driven Approach, Jack Borski Jun 2025

Utilizing Spatial Criteria To High-Grade Wyoming Eor Opportunities Along Proposed Pipeline Corridors: A Value Driven Approach, Jack Borski

Geography and the Environment: Graduate Student Capstones

Oil and gas development has played a critical role in human civilization, and continues to bring drive economies around the world. One such economy is the state of Wyoming that has seen oil and gas production generally decline of the last several decades due to the depletion of high volume conventional reservoirs. A proven secondary technique such as CO2 enhanced oil recovery (EOR) may serve as a method to arrest declining production, and increase economic activity. Wyoming has recognized this opportunity, and has attempted decrease the barriers of entry for oil and gas operators to invest in field-level CO2 EOR …

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Object-Based Image Analysis And Artificial Intelligence Identification Of Anthropogenic Disturbance On Lesser Prairie Chicken Habitat In Cheyenne County, Colorado, Tara Hoelzer Jun 2025

Object-Based Image Analysis And Artificial Intelligence Identification Of Anthropogenic Disturbance On Lesser Prairie Chicken Habitat In Cheyenne County, Colorado, Tara Hoelzer

Geography and the Environment: Graduate Student Capstones

Renewable energy projects often require extensive landcover for their operations. When one of these projects encroaches into territory of threatened species, such as Lesser Prairie Chickens, an analysis of habitat suitability and human disturbance is required to proceed. Traditionally, this involved manually reviewing aerial imagery within a 6-mile radius, digitizing features, and interpreting them using a human technician—an approach that was time-consuming and prone to human error. By using pretrained AI models within Model Builder™, the identification of roads and structures was automated, making the process faster and more consistent than manual visual analysis. As AI and technology continue to …

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A Study On Fuzzy Time-Series And Its Applications To Stock Price Forecasting, Takeshi Stormer Jun 2025

A Study On Fuzzy Time-Series And Its Applications To Stock Price Forecasting, Takeshi Stormer

University Honors Theses

Fuzzy mathematics looks to incorporate the vagueness that exists within the real world, specifically regarding imprecise classes, or non-numerical information expressed as "linguistic" variables. Since most traditional mathematical theories do not have the ability to be applied with the exactness that is otherwise seen in mathematics. As such, there had been many applications of fuzzy mathematics throughout many different fields of mathematics, including that of forecasting. By exploring the fundamentals of fuzzy mathematics, including fuzzy sets, operations of fuzzy sets, the surface level introduction to fuzzy logic, fuzzy relations, operations of fuzzy relations, and fuzzy time-series, this work looks to …

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Conceptualizing The Explanatory Fully Longitudinal Mixed Methods Case Study Design: A Demonstration With An Arithmetic Education Trial With Kindergarten Children, Menglong Cong Jun 2025

Conceptualizing The Explanatory Fully Longitudinal Mixed Methods Case Study Design: A Demonstration With An Arithmetic Education Trial With Kindergarten Children, Menglong Cong

Electronic Theses and Dissertations

In employing longitudinal mixed methods designs, researchers have commonly used the fully longitudinal mixed methods design. This has occurred mostly in the health sciences but less in education. The current investigation proposes a novel longitudinal mixed methods research design, explanatory fully longitudinal mixed methods case study design. It demonstrates its potential for addressing research inquiries in educational research using the arithmetic learning trajectories datasets. This study is presented in three components. The first is a quantitative phase, selecting an exploratory case from a previous arithmetic learning trajectories study (Clements et al., 2021) for qualitative analyses based on maximizing the …

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