Search results for "Mining"
showing 10 items of 1730 documents
Urban monitoring using multi-temporal SAR and multi-spectral data
2006
In some key operational domains, the joint use of synthetic aperture radar (SAR) and multi-spectral sensors has shown to be a powerful tool for Earth observation. In this paper, we analyze the potentialities of combining interferometric SAR and multi-spectral data for urban area characterization and monitoring. This study is carried out following a standard multi-source processing chain. First, a pre-processing stage is performed taking into account the underlying physics, geometry, and statistical models for the data from each sensor. Second, two different methodologies, one for supervised and another for unsupervised approaches, are followed to obtain features that optimize the urban rela…
A practical methodology to perform global sensitivity analysis for 2D hydrodynamic computationally intensive simulations
2021
Sensitivity analysis is a commonly used technique in hydrological modeling for different purposes, including identifying the influential parameters and ranking them. This paper proposes a simplified sensitivity analysis approach by applying the Taguchi design and the ANOVA technique to 2D hydrodynamic flood simulations, which are computationally intensive. This approach offers an effective and practical way to rank the influencing parameters, quantify the contribution of each parameter to the variability of the outputs, and investigate the possible interaction between the input parameters. A number of 2D flood simulations have been carried out using the proposed combinations by Taguchi (L27…
A probabilistic condensed representation of data for stream mining
2014
Data mining and machine learning algorithms usually operate directly on the data. However, if the data is not available at once or consists of billions of instances, these algorithms easily become infeasible with respect to memory and run-time concerns. As a solution to this problem, we propose a framework, called MiDEO (Mining Density Estimates inferred Online), in which algorithms are designed to operate on a condensed representation of the data. In particular, we propose to use density estimates, which are able to represent billions of instances in a compact form and can be updated when new instances arrive. As an example for an algorithm that operates on density estimates, we consider t…
Adaptive Responses in Hepatic and Intestinal Cholesterogenesis Following Ileal Resection in the Rat
1974
Supervised Analysis for Phenotype Identification: The Case of Heart Failure Ejection Fraction Class
2021
Artificial Intelligence is creating a paradigm shift in health care, with phenotyping patients through clustering techniques being one of the areas of interest. Objective: To develop a predictive model to classify heart failure (HF) patients according to their left ventricular ejection fraction (LVEF), by using available data from Electronic Health Records (EHR). Subjects and methods: 2854 subjects over 25 years old with a diagnosis of HF and LVEF, measured by echocardiography, were selected to develop an algorithm to predict patients with reduced EF using supervised analysis. The performance of the developed algorithm was tested in heart failure patients from Primary Care. To select the mo…
Correction: Mechanical properties of provisional dental materials: A systematic review and meta-analysis.
2018
Provisional restorations represent an important phase during the rehabilitation process, knowledge of the mechanical properties of the available materials allows us to predict their clinical performance. At present, there is no systematic review, which supports the clinicians’ criteria, in the selection of a specific material over another for a particular clinical situation. The purpose of this systematic review and meta-analysis was to assess and compare the mechanical properties of dimethacrylates and monomethacrylates used in fabricating direct provisional restorations, in terms of flexural strength, fracture toughness and hardness. This review followed the PRISMA guidelines. The searche…
Aspects Concerning SVM Method’s Scalability
2008
In the last years the quantity of text documents is increasing continually and automatic document classification is an important challenge. In the text document classification the training step is essential in obtaining a good classifier. The quality of learning depends on the dimension of the training data. When working with huge learning data sets, problems regarding the training time that increases exponentially are occurring. In this paper we are presenting a method that allows working with huge data sets into the training step without increasing exponentially the training time and without significantly decreasing the classification accuracy.