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Articles 1 - 4 of 4
Full-Text Articles in Physical Sciences and Mathematics
Land-Surface Parameters For Spatial Predictive Mapping And Modeling, Aaron E. Maxwell, Charles Shobe
Land-Surface Parameters For Spatial Predictive Mapping And Modeling, Aaron E. Maxwell, Charles Shobe
Faculty & Staff Scholarship
Land-surface parameters derived from digital land surface models (DLSMs) (for example, slope, surface curvature, topographic position, topographic roughness, aspect, heat load index, and topographic moisture index) can serve as key predictor variables in a wide variety of mapping and modeling tasks relating to geomorphic processes, landform delineation, ecological and habitat characterization, and geohazard, soil, wetland, and general thematic mapping and modeling. However, selecting features from the large number of potential derivatives that may be predictive for a specific feature or process can be complicated, and existing literature may offer contradictory or incomplete guidance. The availability of multiple data sources and …
Using Landsat-Based Phenology Metrics, Terrain Variables, And Machine Learning For Mapping And Probabilistic Prediction Of Forest Community Types In West Virginia, Faith M. Hartley
Using Landsat-Based Phenology Metrics, Terrain Variables, And Machine Learning For Mapping And Probabilistic Prediction Of Forest Community Types In West Virginia, Faith M. Hartley
Graduate Theses, Dissertations, and Problem Reports
This study investigates the mapping of forest community types for the entire state of West Virginia, USA using Global Land Analysis and Discovery (GLAD) Phenology Metrics analysis ready data (ARD) derived from the Landsat time series and digital terrain variables derived from a digital terrain model (DTM). Both classifications and probabilistic predictions were made using random forest (RF) machine learning (ML) and training data derived from ground plots provided by the West Virginia Natural Heritage Program (WVNHP). The primary goal of this study is to explore the use of globally consistent ARD data for operational forest type mapping over a …
Classifying Blood Glucose Levels Through Noninvasive Features, Rishi Reddy
Classifying Blood Glucose Levels Through Noninvasive Features, Rishi Reddy
Graduate Theses, Dissertations, and Problem Reports
Blood glucose monitoring is a key process in the prevention and management of certain chronic diseases, such as diabetes. Currently, glucose monitoring for those interested in their blood glucose levels are confronted with options that are primarily invasive and relatively costly. A growing topic of note is the development of non-invasive monitoring methods for blood glucose. This development holds a significant promise for improvement to the quality of life of a significant portion of the population and is overall met with great enthusiasm from the scientific community as well as commercial interest. This work aims to develop a potential pipeline …
Exploring Cyberterrorism, Topic Models And Social Networks Of Jihadists Dark Web Forums: A Computational Social Science Approach, Vivian Fiona Guetler
Exploring Cyberterrorism, Topic Models And Social Networks Of Jihadists Dark Web Forums: A Computational Social Science Approach, Vivian Fiona Guetler
Graduate Theses, Dissertations, and Problem Reports
This three-article dissertation focuses on cyber-related topics on terrorist groups, specifically Jihadists’ use of technology, the application of natural language processing, and social networks in analyzing text data derived from terrorists' Dark Web forums. The first article explores cybercrime and cyberterrorism. As technology progresses, it facilitates new forms of behavior, including tech-related crimes known as cybercrime and cyberterrorism. In this article, I provide an analysis of the problems of cybercrime and cyberterrorism within the field of criminology by reviewing existing literature focusing on (a) the issues in defining terrorism, cybercrime, and cyberterrorism, (b) ways that cybercriminals commit a crime in …