Search Results - (( java implementation swarm algorithm ) OR ( pattern classification bee algorithm ))
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1
Application of the bees algorithm to the selection features for manufacturing data
Published 2007“…The Bees Algorithm is employed to select an optimal set of features for a particular pattern classification task. …”
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2
Neural Network Training Using Hybrid Particle-move Artificial Bee Colony Algorithm for Pattern Classification
Published 2017“…The performance of the HPABC algorithm was investigated on four benchmark pattern-classification datasets and the results were compared with other algorithms. …”
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3
Neural network training using hybrid particle-move artificial bee colony algorithm for pattern classification
Published 2017“…In this work, we aimed to highlight the performance of the Hybrid Particle-move Artificial Bee Colony (HPABC) algorithm by applying it on the ANNT application.The performance of the HPABC algorithm was investigated on four benchmark pattern-classification data sets and the results were compared with other algorithms.The results obtained illustrate that HPABC algorithm can efficiently be used for ANNT.HPABC outperformed the original ABC and PSO as well as other state-of-art and hybrid algorithms in terms of time, function evaluation number and recognition accuracy.…”
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4
Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection
Published 2022“…The simulation is implemented with iFogSim and java programming language. …”
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5
Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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6
Design And Implementation Of Human Crowd Density Estimation System With Energy Harvesting In Wireless Sensor Network Platform
Published 2017“…These factors are then integrated into the proposed H-CDE algorithm. The H-CDE algorithm and its crowd classification yielded an average of 71.2 % accuracy in identifying the level of crowd density, which is the best compared to other algorithms found in the literature. …”
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