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data algorithm » jaya algorithm (Expand Search), deer algorithm (Expand Search)
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Variable Selection in Data Analysis: A Synthetic Data Toolkit
Published 2024“…Variable (feature) selection plays an important role in data analysis and mathematical modeling. This paper aims to address the significant lack of formal evaluation benchmarks for feature selection algorithms (FSAs). …”
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Auto-indexing Arabic texts based on association rule data mining. (c2015)
Published 2015“…Our model denotes extracting new relevant words by relating those chosen by the previous classical methods, to new words using data mining rules. …”
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Efficient Dynamic Cost Scheduling Algorithm for Data Batch Processing
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Efficient Dynamic Cost Scheduling Algorithm for Financial Data Supply Chain
Published 2021“…This work investigates the problem of scheduling the processing of tasks with non-identical sizes and different priorities on a set of parallel processors. An iterative dynamic scheduling algorithm (DCSDBP) was developed to address the data batching process. …”
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UniBFS: A novel uniform-solution-driven binary feature selection algorithm for high-dimensional data
Published 2024“…To overcome these challenges, a new FS algorithm named Uniform-solution-driven Binary Feature Selection (UniBFS) has been developed in this study. …”
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Topics in graph algorithms
Published 2003“…This is achieved by implementing some algorithms for the vertex cover problem, and conducting experiments on real data sets. …”
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A multi-class discriminative motif finding algorithm for autosomal genomic data. (c2015)
Published 2016“…The challenge now is to develop fast and accurate computational methods to analyze this huge amount of data. …”
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Mapping realistic data sets on parallel computers
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Efficient Approximate Conformance Checking Using Trie Data Structures
Published 2021“…By encoding the proxy behavior using a trie data structure, we obtain a logarithmically reduced search space for alignment computation compared to a set-based representation. …”
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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“…In this paper, the proposed algorithm selects relevant observations from the original data set by utilizing a class interval technique (i.e. histogram) to maintain a bunch of representative samples from each bin. …”
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Adaptive Secure Pipeline for Attacks Detection in Networks with set of Distribution Hosts
Published 2022“…As a function of the proposed objective, ensembling algorithms applicable to network security have been investigated and evaluated, and a methodology for detecting infected PAGE 2 hosts using ensembling has been developed, based on experiments designed and tested with real datasets. …”
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Automatic keyword extraction from a real estate classifieds data set
Published 2011“…Further, we progress to extract the candidate keywords from the cleansed data set. Finally, we develop a method to automatically extract the keywords and also the key phrases that are formed along with the keywords.…”
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Data reductions and combinatorial bounds for improved approximation algorithms
Published 2016“…Kernelization algorithms in the context of Parameterized Complexity are often based on a combination of data reduction rules and combinatorial insights. …”
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Allocating data to distributed-memory multiprocessors by genetic algorithms
Published 2016“…We present three genetic algorithms (GAs) for allocating irregular data sets to multiprocessors. …”
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Development of an Optimization Algorithm for Internet Data Traffic
Published 2020“…The algorithm monitors data repetitions in IP datagram and prepares a compression code in response of this repetition. …”
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Indexing Arabic texts using association rule data mining
Published 2019“…The model denotes extracting new relevant words by relating those chosen by previous classical methods to new words using data mining rules. …”
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Using genetic algorithms to optimize software quality estimation models
Published 2004“…In the first approach, we assume the existence of several models, and we use a genetic algorithm to combine them, and adapt them to a given data set. …”
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masterThesis