Search Results - (( data reduction methods algorithm ) OR ( data optimization means algorithm ))
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1
Efficient classifying and indexing for large iris database based on enhanced clustering method
Published 2018“…The proposed method can be used to perform global search and exhibits quick convergence rate while optimizing the initial clustering centers of the K-means algorithm. …”
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Sauvola Segmentation and Support Vector Machine-Salp Swarm Algorithm Approach for Identifying Nutrient Deficiencies in Citrus Reticulata Leaves
Published 2024“…In the next phase, the datasets are optimized using the Salp Swarm Algorithm (SSA), which improves classification accuracy. …”
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Improved K-means clustering and adaptive distance threshold for energy reduction in WSN-IoTs
Published 2025“…This study introduces an enhanced energy aware clustering approach that combines an improved K-Means algorithm with an adaptive distance threshold to optimize relay node selection and cluster formation. …”
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Optimal planning of photovoltaic distributed generation considering uncertainties using monte carlo pdf embedded MVMO-SH
Published 2021“…A hybrid population – based stochastic optimization method named MVMO-SH algorithm is proposed to optimize PVDG locations and sizes in the grid system network. …”
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Chemometrics analysis for the detection of dental caries via ultraviolet absorption spectroscopy / Katrul Nadia Basri
Published 2023“…Dimension reduction algorithm such as LDA and CNN were applied on the spectra to reduce the number of variables to be trained. …”
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Classification of labour pain using electroencephalogram signal based on wavelet method / Sai Chong Yeh
Published 2020“…The training and parameters selection of the machine learning algorithms are conducted using EEG data collected from ten subjects in the laboratory. …”
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7
Customer mobile behavioral segmentation and analysis in telecom using machine learning
Published 2021“…Unsupervised machine learning algorithm K-means was used to cluster the data, and these results were analyzed and labeled with labels and descriptions. …”
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PID controller based on bird mating optimizer for vibration cancellation of horizontal flexible plate
Published 2022“…Then, the linear autoregressive with exogenous (ARX) model is selected as a model structure for model development using system identification method via bird mating optimizer (BMO) algorithm. …”
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Analysis and decentralised optimal flow control of heterogeneous computer communication network models
Published 1993“…The generalised exponential (GE) distributional model with known first two moments has been used to represent general interarrival and transmission time distributions as various users have various traffic characteristics. A new method of general model reduction using the Norton' s theorem for general queueing networks in conjunction with the universal maximum entropy algorithm is proposed for the analysis of xix large general closed queueing networks. …”
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10
Economic analysis of rehabilitation approaches for water distribution networks: comparative study between Egypt and Malaysia
Published 2021“…Additionally, the data from Malaysia suggest two additional cost-effective approaches:“zoning network” and “genetic algorithm.” …”
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Optimized clustering with modified K-means algorithm
Published 2021“…Among the techniques, the k-means algorithm is the most commonly used technique for determining optimal number of clusters (k). …”
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A near-optimal centroids initialization in K-means algorithm using bees algorithm
Published 2009“…This creates problem for novice users especially to those who have no or little knowledge on the data.Trial-error attempt might be one of the possible preference to deal with this issue.In this paper, an optimization algorithm inspired from the bees foraging activities is used to locate near-optimal centroid of a given data set.Result shows that propose approached prove it robustness and competence in finding a near optimal centroid on both synthetic and real data sets.…”
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Data clustering using the bees algorithm
Published 2007“…K-means clustering involves search and optimization. …”
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14
Pid-aco vibration controller with magnetorheological damper for wind turbine tower / Mahmudur Rahman
Published 2019“…At first, appropriate dynamic model is estimated using finite difference method (FDM) and system identification process. The FDM dynamic model is found 100% fit to estimated data with reasonably good value of mean squared error (MSE) and Cross Signature Assurance Criterion (CSAC). …”
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15
An improvement of stochastic gradient descent approach for mean-variance portfolio optimization problem
Published 2021“…Furthermore, the applicability of SGD, Adam, AdaMax, Nadam, AMSGrad, and AdamSE algorithms in solving the mean-variance portfolio optimization problem is validated.…”
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Optimization of the hidden layer of a multilayer perceptron with backpropagation (bp) network using hybrid k-means-greedy algorithm (kga) for time series prediction
Published 2012“…The proposed KGA model combines greedy algorithm withk-means++ clustering in this research to assist users in automating the finding of the optimal number of new-ons inside the hidden layer of the BP network. …”
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Improved stochastic gradient descent algorithm with mean-gradient adaptive stepsize for solving large-scale optimization problems
Published 2023“…SGD uses random or batch data sets to compute gradient in solving optimization problems. …”
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Clustering chemical data set using particle swarm optimization based algorithm
Published 2008“…In this study, Particle Swarm Optimization (PSO) based clustering algorithm is exploited to optimize the results of other clustering algorithm such as K-means. …”
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An improved data classification framework based on fractional particle swarm optimization
Published 2019“…The proposed algorithm is tested and verified for optimization performance comparison on ten benchmark functions against six existing established algorithms in terms of Mean of Error and Standard Deviation values. …”
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20
Feature and Instances Selection for Nearest Neighbor Classification via Cooperative PSO
Published 2014“…The reduction method contains two techniques, namely features reduction and data reduction which are commonly applied to a classification problem. …”
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