Search Results - (( parametric classification using algorithm ) OR ( pattern classification parallel algorithm ))
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Improvement of land cover mapping using Sentinel 2 and Landsat 8 imageries via non-parametric classification
Published 2020“…Nevertheless, AC is not required for LCM if the original multi-spectral image is used. The last phase involves developing a new fusion algorithm using SVM and Fuzzy K-Means Clustering (FKM) algorithms for Sentinel 2 data to enhance LCM accuracy. …”
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EMG motion pattern classification through design and optimization of neural network
Published 2012“…A back-propagation neural network with Levenberg-Marquardt training algorithm has been utilized for the classification of EMG signals. …”
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Proceeding Paper -
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EMG motion pattern classification through design and optimization of Neural Network
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Probabilistic ensemble fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms
Published 2015“…To further augment the ARTMAP's pattern classification ability, multiple ARTMAPs were optimized via genetic algorithm and assembled into a classifier ensemble. …”
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Validation on an enhanced dendrite cell algorithm using statistical analysis
Published 2017“…In this study, we evaluated the performance of the enhanced algorithm called dendrite cell algorithm using sensitivity, specificity, false positive rate, and accuracy and validated the result using parametric and non parametric statistical significant tests. …”
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Robust tweets classification using arithmetic optimization with deep learning for sustainable urban living
Published 2024“…In this view, this research develops an arithmetic optimization algorithm with deep learning based tweets classification (AOADL-TC) approach for sustainable living. …”
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Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining.
Published 2005“…The accuracy for rules and classification resulted from the TIP method are compared with other methods such as Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) from Rough Set, Genetic Algorithm (GA), Johnson reducer, HoltelR method, Multiple Regression (MR), Neural Network (NN), Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5); all other classifiers that are mostly used in the classification tasks. …”
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Fpga-Based Accelerator For The Identification Of Finger Vein Pattern Via K-Nearest Centroid Neighbors
Published 2016“…In recent years, finger vein recognition has emerged as a promising biometric technology due to the fact that each person in this world has unique finger vein pattern. Over the past few years, various finger vein recognition algorithms and techniques have been proposed by researchers and scholars. …”
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Data Analysis and Rating Prediction on Google Play Store Using Data-Mining Techniques
Published 2022“…This study aims to predict the ratings of Google Play Store apps using decision trees for classification in machine learning algorithms. …”
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Electromygraphy (EMG) signal based hand gesture recognition using Artificial Neural Network (ANN)
Published 2011“…ANNs are particularly useful for complex pattern recognition and classification tasks. The capability of learning from examples, the ability to reproduce arbitrary non-linear functions of input, and the highly parallel and regular structure of ANNs make them especially suitable for pattern recognition tasks. …”
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Proceeding Paper -
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Generalized derivations and automorphisms of some classes of algebras
Published 2019“…In the Leibniz algebra case, we had eight subspaces too, with different structures, it includes two one-parametric sets as well. Furthermore, by using the two one-parametric sets, two invariant functions are de- fined. …”
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Application of Decision Trees in Athlete Selection: A Cart Algorithm Approach
Published 2023“…The focus of this study is on the use of DTs, employing the Classification and Regression Trees (CART) algorithm, in the initial screening of athletes. …”
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Balancing Exploitation And Exploration Search Behavior On Nature-Inspired Clustering Algorithms
Published 2018“…The experimental results are also thoroughly evaluated and verified via non-parametric statistical analysis. Based on the obtained experimental results, the OGC, DPSO, and VDEO frameworks achieved an average enhancement up to 24.36%, 9.38%, and 11.98% of classification accuracy, respectively. …”
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Hybrid harmony search-artificial intelligence models in credit scoring
Published 2019“…This is believed to be due to the different approaches of both classifiers in capturing data pattern for classification. In terms of computational time, compared to GS-tuned models and the respective HS hybrids, the proposed hybrid MHS-SVM and MHS-RF have reported time improvement of more than 50%, while the parallel computation have saved up approximately 80% of the computational time. …”
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Novel voice activity detection based on cepstrum moments
Published 2010“…In this paper we propose a novel statistical VAD algorithm using Cepstrum coefficients and their moments as features for classification. …”
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An optimized variant of machine learning algorithm for datadriven electrical energy efficiency management (D2EEM)
Published 2024“…It is a well-established fact that machine learning is outperforming in terms of prediction and classification. Therefore, in this study a new optimized variant of machine learning algorithms is presented. …”
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Image Splicing Detection With Constrained Convolutional Neural Network
Published 2019“…It is shown that CNN with constrained convolution algorithm can be used as a general image splicing detection task.…”
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Oil palm mapping over Peninsular Malaysia using Google Earth Engine and machine learning algorithms
Published 2020“…In this study, 30 m Landsat 8 data were processed using a cloud computing platform of Google Earth Engine (GEE) in order to classify oil palm land cover using non-parametric machine learning algorithms such as Support Vector Machine (SVM), Classification and Regression Tree (CART) and Random Forest (RF) for the first time over Peninsular Malaysia. …”
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