Search Results - (( its implications _ algorithm ) OR ( based application ((svm algorithm) OR (new algorithm)) ))*
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Using the bees algorithm to optimise a support vector machine for wood defect classification
Published 2007“…This paper describes a new application of the Bees Algorithm to the optimization of a Support Vector Machine (SVM) for the problem of classifying defects in plywood. …”
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Comparison of Logistic Regression, Random Forest, SVM, KNN Algorithm for Water Quality Classification Based on Contaminant Parameters
Published 2024“…This research provides new insights into the application of machine learning algorithms for water quality management as well as guidance for optimal algorithm selection.…”
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LS-SVM Hyper-parameters Optimization Based on GWO Algorithm for Time Series Forecasting
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Detection and classification of conflict flows in SDN using machine learning algorithms
Published 2021“…The EFDT and hybrid DT-SVM algorithms were designed and deployed based on DT and SVM algorithms to achieve improved performance. …”
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An application of genetic algorithm and least squares support vector machine for tracing the transmission loss in deregulated power system
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Mutable Composite Firefly Algorithm for Microarray-Based Cancer Classification
Published 2024“…Thus, a swarm-based hybrid approach is proposed for cancer classification with a new variant of the Firefly Algorithm (FA) and Correlation-based Feature Selection (CFS) filter. …”
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A fuzzy approach for early human action detection / Ekta Vats
Published 2016“…However, the employability of these algorithms depends on the desired application and its requirements. …”
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Classification models for higher learning scholarship award decisions
Published 2018“…Five algorithms were employed to develop a classification model in determining the award of the scholarship, namely J48, SVM, NB, ANN and RT algorithms. …”
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Spectral discrimination and index development of roofing materials and conditions using field spectroscopy and worldview-3 satellite image
Published 2016“…Comparatively, overall accuracy obtained from GA, SVM and RF algorithms are fairly high in percentage with GA and SVM both produced 96.3%, while RF yield 97.53% accuracy. …”
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Mental stress classification based on selected electroencephalography channels using correlation coefficient of Hjorth parameters
Published 2023“…Leveraging features from the time, frequency, and time–frequency domains of these channels, and employing machine learning algorithms, notably RLDA, SVM, and KNN, our approach achieved a remarkable accuracy of 81.56% with the SVM algorithm outperforming existing methodologies. …”
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Improvement on rooftop classification of worldview-3 imagery using object-based image analysis
Published 2019“…The accuracy of each algorithm was evaluated using LibSVM, Bayes network, and Adaboost classifier. …”
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Named entity recognition using a new fuzzy support vector machine.
Published 2008“…Some of the Machine learning algorithms used in NER methods are, support vector machine(SVM), Hidden Markov Model, Maximum Entropy Model (MEM) and Decision Tree. …”
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Face Recognition Approach using an Enhanced Particle Swarm Optimization and Support Vector Machine
Published 2019“…In this study, a new hybrid technique based on the combination of "Accelerated PSO" and "OPSO-SVM" is introduced for face recognition applications. …”
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