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Full-Text Articles in Engineering
A Novel Spatiotemporal Prediction Method Of Cumulative Covid-19 Cases, Junzhe Cai
A Novel Spatiotemporal Prediction Method Of Cumulative Covid-19 Cases, Junzhe Cai
Department of Computer Science and Engineering: Dissertations, Theses, and Student Research
Prediction methods are important for many applications. In particular, an accurate prediction for the total number of cases for pandemics such as the Covid-19 pandemic could help medical preparedness by providing in time a sufficient supply of testing kits, hospital beds and medical personnel. This thesis experimentally compares the accuracy of ten prediction methods for the cumulative number of Covid-19 pandemic cases. These ten methods include two types of neural networks and extrapolation methods based on best fit linear, best fit quadratic, best fit cubic and Lagrange interpolation, as well as an extrapolation method from Revesz. We also consider the …
Decaf: A New Event Detection Logic For The Purpose Of Fusing Delineated-Continuous Spatial Information, Kerry Q. Hart
Decaf: A New Event Detection Logic For The Purpose Of Fusing Delineated-Continuous Spatial Information, Kerry Q. Hart
Department of Computer Science and Engineering: Dissertations, Theses, and Student Research
Geospatial information fusion is the process of synthesizing information from complementary data sources located at different points in space and time. Spatial phenomena are often measured at discrete locations by sensor networks, technicians, and volunteers; yet decisions often require information about locations where direct measurements do not exist. Traditional methods assume the spatial phenomena to be either discrete or continuous, an assumption that underlies and informs all subsequent analysis. Yet certain phenomena defy this dichotomy, alternating as they move across spatial and temporal scales. Precipitation, for example, appears continuous at large scales, but it can be temporally decomposed into discrete …
Adaptive Interpolation Algorithms For Temporal-Oriented Datasets, Jun Gao
Adaptive Interpolation Algorithms For Temporal-Oriented Datasets, Jun Gao
Department of Computer Science and Engineering: Dissertations, Theses, and Student Research
Spatiotemporal datasets can be classified into two categories: temporal-oriented and spatial-oriented datasets depending on whether missing spatiotemporal values are closer to the values of its temporal or spatial neighbors. We present an adaptive spatiotemporal interpolation model that can estimate the missing values in both categories of spatiotemporal datasets. The key parameters of the adaptive spatiotemporal interpolation model can be adjusted based on experience.