Search Results - (( program implementation function algorithm ) OR ( based classification means algorithm ))
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Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model
Published 2018“…Hence, this situation is believed in yielding of decreasing the classification accuracy. In this article, we present the exploration on the combination of the clustering based algorithm with an ensemble classification learning. …”
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Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms
Published 2005“…In cluster labelling process, a cluster labelling algorithm based on calculation of minimum-distance (MD) between cluster mean and class mean was developed to label the clusters. …”
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An improved data classification framework based on fractional particle swarm optimization
Published 2019“…The proposed classification framework is then evaluated for classification accuracy, computational time and Mean Squared Error on five benchmark datasets against seven existing techniques. …”
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5
Fuzzy C-Mean And Genetic Algorithms Based Scheduling For Independent Jobs In Computational Grid
Published 2006“…Our model presents the method of the jobs classifications based mainly on Fuzzy C-Mean algorithm and mapping the jobs to the appropriate resources based mainly on Genetic algorithm. …”
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An Improved Wavelet Neural Network For Classification And Function Approximation
Published 2011“…Second, the proposed enhanced fuzzy c-means clustering algorithm—specifically, the modified point symmetry-based fuzzy c-means (MPSDFCM) algorithm—was employed in selecting the locations of the translation vectors of the WNN. …”
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8
Pattern Classification of Human Epithelial Images
Published 2016“…Last but not least, from the mean of properties, it will classify into the pattern after ranging the value of mean properties of each of the pattern itself that has been done in classification stage.…”
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Efficient classifying and indexing for large iris database based on enhanced clustering method
Published 2018“…In the current work, the new Weighted K-means algorithm based on the Improved Firefly Algorithm (WKIFA) has been used to overcome the shortcomings in using the Fireflies Algorithm (FA). …”
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Rule-based embedded HMMs phoneme classification to improve Qur’anic recitation recognition
Published 2022“…This paper introduces a Rule-Based Phoneme Duration Algorithm to improve phoneme classification in Qur’anic recitation. …”
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Use of hybrid classification algorithm for land use and land cover analysis in data scarce environment
Published 2013“…Based on the overall accuracy and kappa statistics, hybrid classifier was found to be more preferred classification approach than k-means and SVM. …”
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12
Logic Programming In Radial Basis Function Neural Networks
Published 2013“…The first technique is to encode the logic programming in radial basis function neural networks. …”
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Gas Identi cation by Using a Cluster-k-Nearest-Neighbor
Published 2009“…We find 98.7% of accuracy in the classification of 6 different types of Gas by using K-means cluster algorithm and we find almost the same by using the new clustering algorithm.…”
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Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms
Published 2023“…The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). …”
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Logistic regression methods for classification of imbalanced data sets
Published 2012“…This thesis aims to develop the simple and effective imbalanced classification algorithms by previously improving the algorithms performance of general classifiers i.e. …”
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Projecting image on non-planar surface with zero-th order geometric continuity using simple dual-linear function and manipulation of strict integer implementation in programming la...
Published 2015“…Usage of a projection system to display large screen images is still relevant in the midst of LED-based display increasing popularity.This is due to that the system itself is a mature technology, reliable and cheaper than the LED counterpart.While various methods had addressed the projection problems on curve surface, projecting image on jagged like surface (zero order geometric continuity) has yet to be studied in depth.This paper proposes a method for projecting image on non-planar surface with zero-order geometric continuity property using parametric modeling.The method manipulate linear function by combining two functions into one by taking advantage of computer programs strict implementation of integer variables.The method was applied to grid-based texturing algorithm in order to create the desired zero-continuity effect on the surface.The method was compared with texturing that implement existing curve algorithm to project image on the screen.Visual evaluation results showed that the proposed method fared better compared to existing curve-based projection algorithm.…”
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Design and optimization of Levenberg-Marquardt based Neural Network Classifier for EMG signals to identify hand motions
Published 2013“…Between the Levenberg-Marquardt and scaled conjugate gradient learning algorithms, the aforesaid algorithm shows better classification performance. …”
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Performance of Levenberg-Marquardt neural network algorithm in power quality disturbances classification / Adibah I’zzah Mohamad Kasim
Published 2025“…The classification process incorporated Root Mean Square (RMS) analysis for feature extraction and multilayer perceptron (MLP) neural networks for pattern recognition. …”
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A rule-based image segmentation method and neural network model for classifying fruit in natural environment / Hamirul'aini Hambali
Published 2015“…The new method, named as Adaptive K-means, is developed based on clustering approach. …”
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Extremal region selection for MSER detection in food recognition
Published 2021“…Therefore, this research proposes an Extremal Region Selection (ERS) algorithm to improve MSER detection by reducing the number of irrelevant extremal regions by using unsupervised learning based on the k-means algorithm. …”
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