Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 39 for “"Spatiotemporal Data"”.
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Multi-label/multi-class Deep Learning Classification Of Spatiotemporal Data
… being able to detect useful information in the data. The other part of the process lies in the machine learning algorithm, the network model. In order to be able to capture the spatiotemporal characteristics of the data, selecting Convolutional Neural Network and Long Short Term Memory …
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Multi-Label/Multi-Class Deep Learning Classification of Spatiotemporal Data
… being able to detect useful information in the data. The other part of the process lies in the machine learning algorithm, the network model. In order to be able to capture the spatiotemporal characteristics of the data, selecting Convolutional Neural Network and Long Short Term Memory …
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Information Analysis of Spatiotemporal Data Stream–Models, Algorithms and Evaluations
The Spatiotemporal data stream has been widely used in different applications for system surveillance, prediction, and optimization. In the past decade, the advancement of sensing and data storage technologies has made spatiotemporal data more achievable and enlarges spatiotemporal data’s scale …
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Towards interactive analytics over voluminous spatiotemporal data using a distributed, in-memory framework
The proliferation of heterogeneous data sources, driven by advancements in sensor networks, simulations, and observational devices, has reached unprecedented levels. This surge in data generation and the demand for proper storage has been met with extensive research and development in distributed …
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Learning from multi-modal spatiotemporal data: machine learning approaches to advance resilience in smart grids
… operators. The rapid growth of technology and data storage allowed the deployment of sensing devices across the electric grid. Such technologies present a golden opportunity to tackle many of the electric grid's challenges. Despite that, such technologies presented many challenges …
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Predictive Analysis of Arizona Dust Storms (1996–2023): GIS, Artificial Intelligence (MaxEnt), and Spatiotemporal Data
… species might live. MaxEnt uses presence-only data which makes it especially useful in environments where reliable absence data is hard to find or unavailable. This study analyzes if the combination of MaxEnt within Geographic Information Systems (ArgGIS Pro) and spatiotemporal data can provide …
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Exploring the effects of uncertainty in hydrologic modeling and reconciling spatiotemporal data gaps at the continental scale
Spatiotemporally continuous estimates of the hydrologic cycle are often generated through hydrologic modeling, reanalysis, or remote sensing methods, and commonly applied as a supplement to, or a substitute for, in-situ measurements when observational data are sparse or unavailable. Increased …
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Spatial and Spatiotemporal Modeling of Epidemiological Data
… focuses on modeling approach for spatial and spatiotemporal data with epidemiological applications. Chapter one gives the general overview of spatial and spatiotemporal data and challenges in the statistical analysis of spatial and spatiotemporal data, and motivation and objectives of the …
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Spatiotemporal Indexing With the M-Tree
… in motion, and to support queries on this spatiotemporal data in real time. Because the M-Tree has been proven as an index for spatial network databases, we have selected it to be enhanced as a spatiotemporal index. We present modifications to the tree which allow trajectory reconstruction …
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A framework for building spatiotemporal applications in Java
… attention during the past decade explores how spatiotemporal data can be efficiently used in applications and stored in databases. Spatial data includes the locations or positions, and possibly also the size and orientation of physical objects. Temporal data is time-stamped, i.e. every piece of …
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Ungoverned spaces and armed civil conflicts: the predicament of developing nations
… The study uses geo-referenced violent events data as a measure of violence and spatiotemporal data for law enforcement agencies (LEAs), social services, and economic infrastructure as measures of state authority. All data is specific to Uganda. Using multi-regression models (negative binomial …
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Mining periodicity and object relationship in spatial and temporal data
… sensor networks, and online social media, spatiotemporal data is now widely collected from smartphones carried by people, sensor tags attached to animals, GPS tracking systems on cars and airplanes, RFID tags on merchandise, and location-based services offered by social media. While such …
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Exploring AI Applications in Low-Dose CT Machines to Generate Better Lung Cancer Spatiotemporal Statistics: San Diego County Case Study
… (AI) discipline in radiological settings. Spatiotemporal data is an integral part of Geographic Information Systems (GIS). The International Agency for Research on Cancer (IARC) and the Center for Disease Control and Prevention show that the current lung cancer spatiotemporal data …
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Robust Prediction of Large Spatio-Temporal Datasets
… order complexity for processing large scale spatiotemporal data. However, STRE has been shown sensitive to outliers or anomaly observations. In our design, the St-RSTP model assumes that the measurement error follows Student's t-distribution, instead of a traditional Gaussian distribution. To …
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Investigating the impact of microbial interactions with geologic media on geophysical properties
… in microbial growth and biofilms causes spatiotemporal heterogeneity in the elastic properties of porous media; (5) SP signatures associated with the injection of groundwater into an in-situ biological PRB are dominated by diffusion potentials induced by the injections. The results …
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Representation learning on heterogeneous spatiotemporal networks
… information, online e-commerce, etc., handle big data that can be structured into Spatiotemporal Heterogeneous Networks (SHNs), thereby making efficient analysis of these networks extremely vital. In recent years, representation learning models have proven to be quite efficient in capturing …
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Toward Robust and Generalizable Spatiotemporal Modeling for Tasks beyond Forecasting and Classification
In spatiotemporal data mining, building models that are robust and generalizable across complex, non-ideal conditions is crucial for real-world deployment. While many existing methods perform well on benchmark datasets, they often assume clean, stationary, and uniformly sampled data, limiting their …
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Modelling the Transmission of Dengue Fever Based on Spatial and Temporal Patterns
… host, and vector interactions result in complex spatiotemporal patterns in dengue disease. Moreover, it has been previously indicated that the dengue fever epidemic is due to several climatic, social, environmental, and biological factors, and these factors vary from place to place and with time. …
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Dynamic modeling and analysis of oscillatory bioreactors
… by comparing transient responses to experimental data. Dynamic simulation can be rather inefficient and ineffective for analyzing bioreactor model. Bifurcation analysis is found to be a powerful tool for obtaining a more efficient and complete characterization of the model behavior. Dynamic …
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