Search Results - parallel attribute ((optimization algorithm) OR (selection algorithm))
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1
Hybrid metaheuristics for QOS-aware service composition / Hadi Naghavipour
Published 2022“…An absolute majority of base algorithms for this problem were nature-inspired and population-based metaheuristics extended to complementary methods in hybrid solutions. …”
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Thesis -
2
A GPGPU Approach to Accelerate Ant Swarm Optimization Rough Reducts (ASORR) Algorithm
Published 2012“…In order to find a reducts, a such applications of rough set uses a discernibility matrix. Ant Swarm Optimization Rough Reducts (ASORR) algorithm is used in rough reducts calculation for identifying significant attribute set optimally. …”
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3
Optimization of extractive Automatic Text Summarization using Decomposition-based Multi-objective Differential Evolution and parallelization
Published 2024“…The central challenge in Automatic Text Summarization (ATS) is efficiently generating machine-generated text summaries through optimization algorithms, a critical component for systems dealing with textual information processing. …”
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4
Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System
Published 2020“…The proposed method combined the improved teaching-learning-based optimisation (ITLBO) algorithm, improved parallel JAYA (IPJAYA) algorithm, and support vector machine. …”
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Article -
5
Hybrid harmony search-artificial intelligence models in credit scoring
Published 2019“…Then, the two types of features importance computed from RF algorithm are utilized for the attributes explanation. …”
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6
Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining.
Published 2005“…These rules consist of conditions over attribute value pairs called the descriptions, and decision attributes. …”
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7
Prognosis of early cervical carcinoma using gene expression profiling
Published 2015“…Additionally, stochastic proximity embedding (SPE) was applied to reducing the number of attributes by selecting the most informative genes of the input dataset. …”
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Proceeding Paper
