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models algorithm » mould algorithm (Expand Search), deer algorithm (Expand Search)
first algorithm » firefly algorithm (Expand Search)
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Automated Deep Learning BLACK-BOX Attack for Multimedia P-BOX Security Assessment
Published 2022“…This paper provides a deep learning-based decryptor for investigating the permutation primitives used in multimedia block cipher encryption algorithms.We aim to investigate how deep learning can be used to improve on previous classical works by employing ciphertext pair aspects to maximize information extraction with low-data constraints by using convolution neural network features to discover the correlation among permutable atoms to extract the plaintext from the ciphered text without any P-box expertise. …”
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A kernelization algorithm for d-Hitting Set
Published 2010“…For 3-Hitting Set, an arbitrary instance is reduced into an equivalent one that contains at most 5k2+k elements. This kernelization is an improvement over previously known methods that guarantee cubic-order kernels. …”
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Graph Contraction for Mapping Data on Parallel Computers
Published 1994“…Mapping data to parallel computers aims at minimizing the execution time of the associated application. …”
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The Effects of Data Mining on Small Businesses in Dubai
Published 2011“…While there are numerous studies on the best data mining models and their uses, even on certain industries, this study focuses on the applications more than the algorithms and models and their usefulness for small businesses specifically. …”
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Predicting Plasma Vitamin C Using Machine Learning
Published 2022“…The low R-squared scores obtained by the models are likely to be due to the low resolution of the NHANES data, particularly the dietary data. …”
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Block constrained pressure residual preconditioning for two-phase flow in porous media by mixed hybrid finite elements
Published 2023“…<p dir="ltr">This work proposes an original preconditioner that couples the Constrained Pressure Residual (CPR) method with block preconditioning for the efficient solution of the linearized systems of equations arising from fully implicit multiphase flow models. …”
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A FAMILY OF NORMALIZED LEAST MEAN FOURTH ALGORITHMS
Published 2020“…In this work, a family of normalized least mean fourth algorithms is presented. Unlike the LMF algorithm, the convergence behavior of these algorithms is independent of the input data correlation statistics. …”
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Optimizing Document Classification: Unleashing the Power of Genetic Algorithms
Published 2023“…Additionally, our proposed model optimizes the features using a genetic algorithm. …”
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Intelligent Rapidly-Exploring Random Tree Star Algorithm
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Application of Data Mining to Predict and Diagnose Diabetic Retinopathy
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doctoralThesis -
135
The effects of data balancing approaches: A case study
Published 2023“…In this research, classification algorithms, combined with resampling strategies and dimensionality reduction methods, were investigated to find a prediction model to correctly identify the samples of treated animals. …”
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Adaptive Secure Pipeline for Attacks Detection in Networks with set of Distribution Hosts
Published 2022“…So far none addresses the use of Threat Intelligence (IT) data in Ensemble Learning algorithms to improve the detection process, nor does it work as a function of time, that is, taking into account what happens on the network in a limited time interval. …”
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Evolutionary algorithm for predicting all-atom protein structure
Published 2011“…We present an improved version of a scatter search (SS) algorithm for predicting all-atoms protein structures using a recent energy model. …”
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Capturing outline of fonts using genetic algorithm and splines
Published 2001“…In order to obtain a good spline model from large measurement data, we frequently have to deal with knots as variables, which becomes a continuous, non-linear and multivariate optimization problem with many local optima. …”
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Improvement of Kernel Principal Component Analysis-Based Approach for Nonlinear Process Monitoring by Data Set Size Reduction Using Class Interval
Published 2024“…Generally, RKPCA reduces the number of samples in the training data set and then builds the KPCA model based on this data set. …”
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