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This thesis addresses these three problems by proposing a constraint solver based framework that can be applied to these problems and experimentally evaluates its scalability and feasibility.
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Instead, we adopt an overall molar composition-based framework that can robustly model phase appearance or disappearance.
Here we present a GIS-based framework that can help decision-makers to prioritize the spatial allocation of policy interventions at different spatial scales or administrative levels.
In this work, we propose Kuaa, a workflow-based framework that can be used for designing, deploying, and executing machine learning experiments in an automated fashion.
The main contribution of our research work is the design of a novel text analytics-based framework that can extract latent semantics from the textual descriptions of projects to predict the fund raising outcomes of these projects.
Narwaria and Lin [24] propose a FR ML-based framework that can be used both for image and video quality assessment.
The main purpose of this study was to quantitatively assess and determine the relationship between a series of inferences in an interpretive argument-based framework that can potentially threaten the validity of the test.
Here, we introduce a graph-based framework that can accommodate non-equilibrium mechanisms.
We first present a theoretically based framework that describes the mechanisms by which neighborhoods can lead to adverse health outcomes.
This paper introduces a 3D convolutional neural networks (CNN) based SVQA framework that can model not only local spatio-temporal information but also global temporal information with cubic difference video patches as input.
The STAR model is based on a Bayesian framework that can accommodate both linear and nonlinear explanatory variables as well as spatial and temporal autocorrelations.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com