University of New Mexico Digital Repository
Not a member yet
148775 research outputs found
Sort by
Single-Valued Neutrosophic Multiple sets and its Application in Multi-Criteria Decision-Making
Get PDFOptimizing Defective Product Distribution for Sustainability and Profitability in Manufacturing Model under Neutrosophic Environment
No full text
RF Emission from Partial Breakdown Events and Their Classification
Get PDFElectrical breakdown is a core component of Pulse Power both in its utilization and as a failure point in systems. This research is investigating the feasibility of D-dot antennas to detect and potentially identify different forms of partial electrical breakdown in a point-to-plane gap geometry. In this work, the RF emission detected by a D-dot probe during a partial breakdown event is shown to correlate with the presence of light and current using a fast-current transformer and photomultiplier tube. Additionally, the frequency components of the RF signal were measured with the use of a fast Fourier transform. Identifying these frequencies provides the potential to identify forms of partial electrical breakdown solely on the frequency makeup of the RF emissions. This research paired with a future array of D-dot probes could possibly be used to identify the nature of partial breakdowns and their location in a system by utilizing triangulation and frequency component signatures
Nutrition Curriculum Module 9 Carrot Food Detectives
No full texthttps://digitalrepository.unm.edu/chile-plusmod9nc/1000/thumbnail.jp
Neutrosophic-Supported Machine Learning Models for Oral Disease Classification
No full textThis study is presented to investigate the influence of the neutrosophic (NS) domain on the performance of the most common machine learning (ML) models. Specifically, it evaluates the effectiveness of Random Forest (RF), Extra Trees (ET), K-Neighbors (KNN), Gaussian Naive Bayes (GaussianNB), and Decision Tree (DT) classifiers in detecting oral diseases. The NS domain divides any image into three membership components: falsity (��), indeterminacy (��) and truth (��), where T denotes the degree to which each pixel in an image is a member of a particular class or category, F denotes the degree to which the pixel is not a member of that class or category and I denotes the degree of uncertainty or indeterminacy. This domain is herein employed to divide the images into three datasets (T, I, and F). Those three datasets, in addition to the original dataset, one by one, are divided into training and testing datasets with a splitting ratio of 80% and 20%, respectively. Afterwards, the five studied ML models are trained on the training dataset before being assessed on the testing dataset to show their ability to generalize using four performance indicators, such as precision, accuracy, F1-score and recall. According to the experimental results, the I-domain, when compared to the other domains, could considerably increase the performance of four of the five studied ML models, meaning that the NS domain might improve the ML models\u27 performance in categorizing oral diseases more correctly. Among all studied models, the RF classifier with the I-domain has the highest classification accuracy, indicating that it is a strong alternative for oral disease classification
2023/2024 UNMV Business Admin AS Assessment
No full texthttps://digitalrepository.unm.edu/provost_assessment/4224/thumbnail.jp
2023-2024 ASM BBA Accounting Assessment Report
No full texthttps://digitalrepository.unm.edu/provost_assessment/4210/thumbnail.jp