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Mostrando ítems 11-20 de 191
Artículo
Ameva: An autonomous discretization algorithm
(ScienceDirect, 2009-04)
This paper describes a new discretization algorithm, called Ameva, which is designed to work with supervised learning algorithms. Ameva maximizes a contingency coefficient based on Chi-square statistics and generates a ...
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Artículo
Spectrum-Based Fault Localization in Model Transformations
(ACM, 2018)
Model transformations play a cornerstone role in Model-Driven Engineering (MDE), as they provide the essential mechanisms for manipulating and transforming models. The correctness of software built using MDE techniques ...
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Automated analysis of feature models: Quo vadis?
(Springer, 2018)
Feature models have been used since the 90's to describe software product lines as a way of reusing common parts in a family of software systems. In 2010, a systematic literature review was published summarizing the advances ...
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Early Evaluation of Mobile Applications’ Resource Consumption and Operating Costs
(IEEE Computer Society, 2020)
The explosive growth of the mobile application market in recent years has led to a large concomitant mobile software industry whose components are, in many cases, startups and small-size software providers. The success ...
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ARIEX: Automated ranking of information extractors
(Elsevier, 2016)
Information extractors are used to transform the user-friendly information in a web document into structured information that can be used to feed a knowledge-based system. Researchers are interested in ranking them to ...
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CarGene: Characterisation of sets of genes based on metabolic pathways analysis
(Inderscience, 2011)
The great amount of biological information provides scientists with an incomparable framework for testing the results of new algorithms. Several tools have been developed for analysing gene-enrichment and most of them ...
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An Experimental Review on Deep Learning Architectures for Time Series Forecasting
(World Scientific, 2021)
In recent years, deep learning techniques have outperformed traditional models in many machine learning tasks. Deep neural networks have successfully been applied to address time series forecasting problems, which is a ...
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Lean requirements traceability automation enabled by model-driven engineering
(PeerJ, 2022)
Background: The benefits of requirements traceability, such as improvements in software product and process quality, early testing, and software maintenance, are widely described in the literature. Requirements traceability ...
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Hybridizing humans and robots: An RPA horizon envisaged from the trenches
(Elsevier, 2022)
After the initial hype on RPA, companies have more realistic expectations of this technology. Its current mature vision relegates the end-to-end robotic automation to a less suitable place and considers the human-robot ...