Search Results - (( model validation using algorithm ) OR ( based information swarm algorithm ))*
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Time-varying water temperature modelling of steam distillation pilot plant using NARX-based binary particle swarm optimisation structure selection / Najidah Hambali … [et al.]
Published 2017“…This study reports a nonlinear modelling for a time-varying process of water temperature by utilising a Binary Particle Swarm Optimisation (BPSO) algorithm based on Nonlinear Auto-Regressive with eXogenous input (NARX) structure. …”
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NARX modelling for steam distillation pilot plant using binary particle swarm optimisation technique / Najidah Hambali
Published 2019“…Prior to analysing the efficiency of SDPP nonlinear models, the NARX-based BPSO model was checked based on the model validation. …”
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Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi
Published 2019“…Despite attempts to solve the data clustering issues, there are also many variants of modified algorithms in traditional information clustering that attempt to solve issues such as clustering algorithms based on condensation. …”
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Intelligent image noise types recognition and denoising system using deep learning / Khaw Hui Ying
Published 2019“…An ensemble of these algorithms is an intelligent and adaptive solution, producing a clean output, while preserving significant pixel information. …”
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Intelligence Integration Of Particle Swarm Optimization And Physical Vapour Deposition For Tin Grain Size Coating Process Parameters
Published 2016“…Additionally,analysis of variance (ANOVA) was used to determine the significant factors influencing resultant TiN coating grain size.Based on that,a quadratic polynomial model equation was developed to represent the process variables and coating grain size.Then,in order to optimize the coating process parameters,genetic algorithms (GAs) were combined with the RSM quadratic model and used for optimization work.Finally,the models were validated using actual testing data to measure model performances in terms of residual error and prediction interval (PI).The result indicated that for RSM,the actual coating grain size of validation runs data fell within the 95% (PI) and the residual errors were less than 10 nm with very low values, the prediction accuracy of the model is 96.09%.In terms of optimization and reduction the experimental data,GAs could get the best lowest value for grain size then RSM with reduction ratio of ≈6%, ≈5%, respectively.…”
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Join query enhancement processing (jqpro) with big rdf data on a distributed system using hashing-merge join technique
Published 2021“…Then, a processing framework for RDF queries was introduced and the benchmark was used for performance evaluation. Finally, the validation was conducted by standard statistical analysis to validate and compare the performance of the JQPro model with current models. …”
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Evolutionary low frequency steering vibration control towards human spine
Published 2014“…The vertebrae dynamic model is identified based on grey-box modelling technique. …”
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Integrated examination and analysis model for improving mobile cloud forensic investigation
Published 2022“…FEDO models the Linearly Decreasing Weight (LDW) strategy based Particle Swarm Optimization (PSO) algorithm on the case-specific evidence to improve the searching capability of the investigation across the massive MCC environment. …”
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Optimized techniques for landslide detection and characteristics using LiDAR data
Published 2018“…The segmentation process was optimized using Fuzzy-based Segmentation Parameter. Also, six techniques: Ant Colony Optimization (ACO), Gain Ratio (GR), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), Random forest (RF), and Correlation-based Feature Selection (CFS) were used for the feature selection. …”
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Improved particle swarm optimization by fast annealing algorithm
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Document clustering based on firefly algorithm
Published 2015“…Document clustering is widely used in Information Retrieval however, existing clustering techniques suffer from local optima problem in determining the k number of clusters.Various efforts have been put to address such drawback and this includes the utilization of swarm-based algorithms such as particle swarm optimization and Ant Colony Optimization.This study explores the adaptation of another swarm algorithm which is the Firefly Algorithm (FA) in text clustering.We present two variants of FA; Weight- based Firefly Algorithm (WFA) and Weight-based Firefly Algorithm II (WFAII).The difference between the two algorithms is that the WFAII, includes a more restricted condition in determining members of a cluster.The proposed FA methods are later evaluated using the 20Newsgroups dataset.Experimental results on the quality of clustering between the two FA variants are presented and are later compared against the one produced by particle swarm optimization, K-means and the hybrid of FA and -K-means. …”
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Review of Multi-Objective Swarm Intelligence Optimization Algorithms
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An Adaptive Switching Cooperative Source Searching And Tracing Algorithms For Underwater Acoustic Source Localization
Published 2019“…In order to optimize search space exploration and to maintain inter-robot communication connectivity at swarm level, a dispersion algorithm based on attraction and repulsion force is proposed. …”
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Improved opposition-based particle swarm optimization algorithm for global optimization
Published 2021“…An efficient algorithm must have symmetry of information between participating entities. …”
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