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1
A near-optimal centroids initialization in K-means algorithm using bees algorithm
Published 2009“…The K-mean algorithm is one of the popular clustering techniques.The algorithm requires user to state and initialize centroid values of each group in advance. …”
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2
Efficient genetic partitioning-around-medoid algorithm for clustering
Published 2019“…These algorithms mostly built upon the partitioning k-means clustering algorithm. …”
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Thesis -
3
An efficient indexing and retrieval of iris biometrics data using hybrid transform and firefly based K-means algorithm title
Published 2019“…The enhanced method combines three transformation methods for analyzing the iris image and extracting its local features. It uses a weighted K-means clustering algorithm based on the improved FA to optimize the initial clustering centers of K-means algorithm, known as Weighted K-means clustering-Improved Firefly Algorithm (WKIFA). …”
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4
Optimization grid scheduling with priority base and bees algorithm
Published 2014“…The main aim of this current research to propose an optimization of the initial scheduler for grid computing using the bees algorithm. …”
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5
Clustering ensemble learning method based on incremental genetic algorithms
Published 2012“…In addition, experiments prove that incremental genetic-based clustering ensemble algorithm speed up to converge into an optimal clustering solution, where pattern ensemble learning method and the cluster partitions produced by the threshold fuzzy c-means clustering algorithm are employed as recombination operator and initial population, respectively.…”
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6
Design of wavelet neural networks based on symmetry fuzzy C-means for function approximation
Published 2013“…In this paper, an enhanced fuzzy C-means algorithm, specifically the modified point symmetry–based fuzzy C-means algorithm (MPSDFCM), was proposed, in order to determine the optimal initial locations for the translation vectors. …”
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Article -
7
Social media mining: a genetic based multiobjective clustering approach to topic modelling
Published 2021“…Although effective, the performance of the k-means clustering algorithm depends heavily on the initial centroids and the number of clusters, k. …”
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Article -
8
An effective and novel wavelet neural network approach in classifying type 2 diabetics
Published 2012“…In this paper, we propose a novel enhanced fuzzy c-means clustering algorithm – specifically, the modified point symmetry-based fuzzy c-means (MPSDFCM) algorithm – in initializing the translation vectors of the WNNs. …”
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9
Application of intelligence based genetic algorithm for job sequencing problem on parallel mixed-model assembly line
Published 2010“…A heuristic algorithm was introduced to generate the initial population for intelligence based genetic algorithm. …”
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Article -
10
The Determination of Pile Capacity Using Artificial Neural-net: An Optimization Approach
Published 2001“…Using the developed algorithm, the safety measures involved are such as reliability index and the probability of failure; instead of only factor of safety if conventional deterministic approach is used. …”
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11
A study on the application of discrete curvature feature extraction and optimization algorithms to battery health estimation
Published 2024“…This study employs two optimization algorithms, namely, particle swarm optimization (PSO) and sparrow optimization algorithm (SSA), in conjunction with least squares support vector machine (LSSVM) to compare the model against three conventional models, namely, Gaussian process regression (GPR), convolutional neural networks (CNN), and long short-term memory (LSTM). …”
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12
Enhancing clustering algorithm with initial centroids in tool wear region recognition
Published 2020“…Nevertheless, the algorithm is sensitive to the initial centroids giving various solution every time the system updating. …”
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13
CUCKOO SEARCH OPTIMIZATION NEURAL NETWORK MODELS FOR FORECASTING LONG-TERM PRECIPITATION
Published 2024“…This paper presents the application of a novel optimization algorithm, Cuckoo Search Optimization (CSO), to train feedforward neural networks to forecast long-term precipitation using three climate models, namely HadCM3, ECHAM5, and HadGEM3‐RA. …”
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Book Chapter -
14
Solving computational algorithm using CLONALNet technique based on artificial clonal selection
Published 2023“…By using this algorithm the steps to obtain the fitness function is optimized and processing time is reduced. …”
Conference paper -
15
A study on the application of discrete curvature feature extraction and optimization algorithms to battery health estimation
Published 2024“…This study employs two optimization algorithms, namely, particle swarm optimization (PSO) and sparrow optimization algorithm (SSA), in conjunction with least squares support vector machine (LSSVM) to compare the model against three conventional models, namely, Gaussian process regression (GPR), convolutional neural networks (CNN), and long short-term memory (LSTM). …”
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16
Social media mining: a genetic based multiobjective clustering approach to topic modelling
Published 2021“…Although effective, the performance of the k-means clustering algorithm depends heavily on the initial centroids and the number of clusters, k. …”
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17
Fuzzy clustering method and evaluation based on multi criteria decision making technique
Published 2018“…After the optimization of the weights by modified version of the Kohonen Network method these weights will be set as the initial centres of the Gustafson-Kessel (GK) algorithm. …”
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Thesis -
18
Modeling flood occurences using soft computing technique in southern strip of Caspian Sea Watershed
Published 2012“…The application of FES optimized by GA on regionalization creates opportunities for further researches which utilizes different types of optimization like Ant Colony Optimization (ACO), ANN’s, Particle Swarm Optimization (PSO) and Imperialist Competitive Algorithm (ICA).…”
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19
Enhanced weight-optimized recurrent neural networks based on sine cosine algorithm for wave height prediction
Published 2021“…The results show that the optimized models outperform the original three benchmarking models in terms of mean squared error (MSE), root mean square error (RMSE), and mean absolute error (MAE). …”
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20
Individual-tree segmentation and extraction based on LiDAR point cloud data
Published 2024“…The objective was to identify the optimal parameters for both algorithms in terms of tree height extraction precision. …”
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