The PI+CI is a reset compensator that has been shown to be effective in a number of practical applications. In this work, a simple PI+CI tuning method for integrating systems with time delay is proposed. It is a direc...
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This paper presents a mathematical technique for modeling the generation of Grid-solutions employing a Case based reasoning system (CBR). Roughly speaking, an intelligent system that tries to be adapted to highly dyna...
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Apart from the well-established facility of searching for research articles, the modern academic search engines also provide information regarding the scientists themselves. Until recently, this information was limite...
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The industry that designs and promotes advertising products in television channels is constantly growing. For effective market analysis and contract validation, various commercial tracker systems are employed. However...
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The provision of publicly available Open Data targets to provide transparency in several public sector decisions and actions. However, this information is served massively and in different forms - mostly due to differ...
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This paper proposes a new version of Particle Filter, called Articulated Particle Filter - ArPF -, which has been specifically designed for an efficient sampling of hierarchical spaces, generated by articulated object...
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Although the field has led to promising early results, the use of crowdsourcing as an integral part of science projects is still regarded with skepticism by some, largely due to a lack of awareness of the opportunitie...
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We propose a generalized data driven constraint for support vector machines exemplified by classification of paired observations in general and specifically on the human ear canal. This is particularly interesting in ...
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Feature selection is an active research in machine learning. The main idea of feature selection is to choose a subset of available features, by eliminating features with little or no predictive information, and featur...
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ISBN:
(纸本)9789898565334
Feature selection is an active research in machine learning. The main idea of feature selection is to choose a subset of available features, by eliminating features with little or no predictive information, and features strongly correlated. There are many approaches for feature selection, but most of them can only work with crisp data. Until our knowledge there are not many approaches which can directly work with both crisp and low quality (imprecise and uncertain) data. That is why, we propose a new method of feature selection which can handle both crisp and low quality data. The proposed approach integrates filter and wrapper methods into a sequential search procedure with improved classification accuracy of the features selected. This approach consists of steps following: (1) Scaling and discretization process of the feature set;and feature pre-selection using the discretization process (filter);(2) Ranking process of the feature pre-selection using a Fuzzy Random Forest ensemble;(3) Wrapper feature selection using a Fuzzy Decision Tree technique based on cross-validation. The efficiency and effectiveness of the approach is proved through several experiments with low quality datasets. Approach shows an excellent performance, not only classification accuracy, but also with respect to the number of features selected.
In this paper, we propose a secure code update protocol for TPM-equipped sensor nodes, which enables these nodes to prove their trustworthiness to other nodes using efficient attestation protocols. As main contributio...
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