Buscar
Mostrando ítems 101-110 de 215
Ponencia
![Con acceso al texto completo Icon](/themes/idUS//images/acceso/opened_access.png)
A Method Based on AHP to Define the Quality Model of QuEF
(Springer, 2011)
QuEF is a framework to analyze and evaluate the quality of ap proaches based on Model-Driven Web Engineering (MDWE). In this frame work, the evaluation of an approach is calculated in terms of a set of informa tion needs ...
Artículo
![Con acceso al texto completo Icon](/themes/idUS//images/acceso/opened_access.png)
Trinity: On Using Trinary Trees for Unsupervised Web Data Extraction
(IEEE Xplore, 2014-06)
Web data extractors are used to extract data from web documents in order to feed automated processes. In this article, we propose a technique that works on two or more web documents generated by the same server-side template ...
Ponencia
![Con acceso al texto completo Icon](/themes/idUS//images/acceso/opened_access.png)
FM fact label: a configurable and interactive visualization of feature model characterizations
(ACM: Association for Computing Machinery, 2022)
Recognizing specific characteristics of feature models (FM) can be challenging due to the different nature and domains of the models. There are several metrics to characterize FMs. However, there is no standard way to ...
Ponencia
![Con acceso al texto completo Icon](/themes/idUS//images/acceso/opened_access.png)
An Elasticity Framework for Smart Contracts
(IEEE Computer Society, 2021)
Smart contracts provide computation facilities to blockchains, enabling many application scenarios where au tomatic analysis and complex transactions can be performed. However, in situations where the flow of information ...
Artículo
![Con acceso al texto completo Icon](/themes/idUS//images/acceso/opened_access.png)
Reasoning on the usage control security policies over data artifact business process models
(ComSIS Consortium, 2022)
The inclusion of security aspects in organizations is a crucial aspect to ensure compliance with both internal and external regulations. Business process models are a well-known mechanism to describe and automate the ...
Ponencia
![Con acceso al texto completo Icon](/themes/idUS//images/acceso/opened_access.png)
Robustness Testing of a Machine Learning-based Road Object Detection System: An Industrial Case
(IEEE Computer Society, 2022)
artifi-cial intelligence (AI), methods have been proposed and evaluated in academia to assess the reliability of these systems. In the context of computer vision, some approaches use the generation of images altered by ...
Ponencia
![Con acceso al texto completo Icon](/themes/idUS//images/acceso/opened_access.png)
Integrating Deep-Web Information Sources
(Springer, 2010)
Deep-web information sources are difficult to integrate into automated business processes if they only provide a search form. A wrapping agent is a piece of software that allows a developer to query such information ...
Artículo
![Con acceso al texto completo Icon](/themes/idUS//images/acceso/opened_access.png)
MOMIC: A multi-omics pipeline for data analysis, integration and interpretation
(MDPI, 2022)
Background and Objectives: The burst of high-throughput omics technologies has given rise to a new era in systems biology, offering an unprecedented scenario for deriving meaningful biological knowledge through the ...
Artículo
![Con acceso al texto completo Icon](/themes/idUS//images/acceso/opened_access.png)
Improving models for environmental applications of LiDAR: Novel approaches based on soft computing
(IOS Press, 2016)
This work proposes novel methodologies to improve the use of Light Detection And Ranging (LiDAR) for environ mental purposes, especially for thematic mapping (LiDAR only or fused with other remote sensors) and the estimation ...
Ponencia
![Con acceso al texto completo Icon](/themes/idUS//images/acceso/opened_access.png)
Embedded Temporal Feature Selection for Time Series Forecasting Using Deep Learning
(Springer Link, 2023-10)
Traditional time series forecasting models often use all available variables, including potentially irrelevant or noisy features, which can lead to overfitting and poor performance. Feature selection can help address this ...