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In this paper, we conduct a large-scale study on the crackability, correlation, and security of ${\sim}145$<alternatives><inline-graphic xlink:href="ji-ieq1-2481884.gif"/> </alternatives> million real world passwords, which were leaked from several popular Internet services and applications. To the best of our knowledge, this is the largest empirical study that has been...
The accurate prediction of crude oil output plays an important role in the development of oilfield planning. This paper proposes a least squares support vector machine model based on the optimization of particle swarm algorithm (PSO-LSSVM) to predict the crude oil output. Each pair of penalty factor and kernel function parameter was taken as a particle, which follows the optimal particle in the current...
Accurate prediction of the crude oil output decline rate is crucial to ensure the stability of oil field. This paper presents a new method that utilizes the neural networks optimized by Genetic Algorithm(GA) to dynamically predict the crude oil output decline rate. Firstly, choose the best weights for neural network by the GA's survival of the fittest mechanism. Next, learn the rules of production...
We describe a novel appearance model with optimal combined features to produce the accurate vessel segmentation. It starts with investigating a set of multi-scale vessel features, followed by a weighed approach to optimally combine different features. Then the optimally combined features advantage the appearance model to reveal more detailed information of vessel. The novelty of the work lies in the...
Based on the coring well and well logging data, according to three methods, including M-N value, curve superposition and curve characteristic value, which are often be used on lithology identification, three different well logging curve parameters set was collected, joining with SVM, lithology identification was fulfilled, after that, to selecting the best well logging parameters set that suitable...
In text classification, term weighting methods design appropriate weights to the given terms to improve the text classification performance. Traditional algorithm of term weighting only considers about tf (term frequency), idf (inverse document frequency) and so on, and this approach simply thinks low frequency terms are important, high frequency terms are unimportant, so it designs higher weights...
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