Search Results - parallel selective ((models algorithm) OR (means algorithm))
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Hyper-heuristic approaches for data stream-based iIntrusion detection in the Internet of Things
Published 2022“…Here, the memory consumption can be reduced by enabling a feature selection algorithm that excludes nonrelevant features and preserves the relevant ones. the algorithm is developed based on the variable length of the PSO. …”
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New CFAR algorithm and circuit development for radar receiver
Published 2020“…Therefore, the MSS-CA-CFAR is chosen to implement by practical digital circuit and there is another important feature in the MSS-CFAR algorithm that is parallel processing since the spike selection process is done at the same time with summing of samples process that makes this algorithm much less in processing time from any other algorithm using the same environment. …”
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Extreme air pollutant data analysis using classical and Bayesian approaches
Published 2015“…MTM algorithm is an extension of MH algorithm, designed to improve the convergence of MH algorithm by performing parallel computation. …”
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Parallel algorithms for numerical simulations of EHD ion-drag micropump on distributed parallel computing systems
Published 2014“…To implement the parallel algorithms a distributed parallel computing laboratory using easily available low cost computers is setup. …”
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Parallel algorithms on some numerical techniques using PVM platform on a cluster of workstations
Published 2002“…In this paper, a few parallel algorithms are explained in solving one dimensional heat model problem using Parallel Virtual Machine (PVM). …”
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Automated plant recognition system based on multi-objective parallel genetic algorithm and neural network
Published 2014“…To conclude, multi objective parallel genetic algorithm can automatically tune feed forward neural network to classify the dataset with a good classification rate.…”
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A Parallel-Model Speech Emotion Recognition Network Based on Feature Clustering
Published 2023“…To address this issue, we proposed a novel algorithm called F-Emotion to select speech emotion features and established a parallel deep learning model to recognize different types of emotions. …”
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A parallel-model speech emotion recognition network based on feature clustering
Published 2023“…To address this issue, we proposed a novel algorithm called F-Emotion to select speech emotion features and established a parallel deep learning model to recognize different types of emotions. …”
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Fpga Implementation Of Metaheuristic Optimization Algorithm
Published 2022“…The Discrete SKF was then modelled using behavioural modelling to produce the Binary SKF which was then implemented onto FPGA. …”
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FPGA implementation of metaheuristic optimization algorithm
Published 2023“…The Discrete SKF was then modeled using behavioral modeling to produce the Binary SKF which was then implemented onto FPGA. …”
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OPTIMIZATION OF HYBRID-FUZZY CONTROLLER FOR SERVOMOTOR CONTROL USING A MODIFIED GENETIC ALGORITHM
Published 2011“…In this thesis, a new optimization GA-based algorithm that emanates from modification of conventional GA to reduce the iterations number and the duration time, namely, semi-parallel operation genetic algorithm (SPOGA) is proposed. …”
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Small-scale helicopter system identification model using recurrent neural networks
Published 2010“…We have selected a Nonlinear AutoRegressive with eXogenous Inputs Series- Parallel (NARXSP) network model which identifies the dynamics model of an unmanned aerial helicopter from real flight data. …”
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Hybrid harmony search-artificial intelligence models in credit scoring
Published 2019“…With the parallelization of MHS hybrid models, the computational time is effectively reduced, with RF hybrid models faster than SVM hybrid models. …”
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Effective software fault localization based on complex network theory / Abubakar Zakari
Published 2019“…This has led researchers to adopt approaches such as one bug-at-a-time debugging approach (OBA) and parallel debugging approach. However, using OBA debugging approach increases software time-to-delivery and potentially leads to more faults during regression testing, while utilizing k-mean clustering algorithm with Euclidean distance metric to group failed tests based on their execution profile similarity in parallel debugging approach is claimed to be problematic and inappropriate. …”
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Effect of model simplification through manual reduction in number of surfaces on room acoustics simulation
Published 2019“…Parallel to this aim, the objective is to examine the effects of the change in model detail level on selected acoustical parameters in six OPOs. …”
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A trade-off criterion for bi-objective problem in solving hybrid flow shop scheduling with energy efficient (EE-HFS) using multi-objective dragonfly algorithm (MODA)
Published 2024“…The (EE-HFS) optimization has been carried out using the Multi-Objective Dragonfly Algorithm (MODA). The optimization result was compared with well-established algorithms, the Pareto Envelope-based Selection Algorithm II (PESA2), Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D), and new algorithms, Multi-Objective Grasshopper Optimization Algorithm (MOGOA) and Multi-Objective Ant Lion Optimizer (MOALO). …”
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Development of a multi-objective optimization model for transport and environment in a closed-loop automotive supply chain
Published 2014“…In the last stage, an extended Gravitational Search Algorithm (GSA) as a parallel search algorithm and high convergence rate into high quality final solutions is used to solve the proposed mathematical model and to achieve the Pareto set of solution. …”
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Hybrid ANN and Artificial Cooperative Search Algorithm to Forecast Short-Term Electricity Price in De-Regulated Electricity Market
Published 2019“…Therefore, this research proposes a hybrid method for electricity price forecasting via artificial neural network (ANN) and artificial cooperative search algorithm (ACS). In parallel, a feature selection technique based on the combination of mutual information (MI) and neural network (NN) is developed in this study to select the input variables subsets, which have substantial impact on forecasting of electricity price. …”
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