Search Results - (( pattern machine algorithm ) OR ((( patterns new algorithm ) OR ( patterns acs algorithm ))))*
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Using GA and KMP algorithm to implement an approach to learning through intelligent framework documentation
Published 2023Subjects: “…Knuth-Morris-Pratt (KMP) pattern matching algorithm…”
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Survival versus non-survival prediction after acute coronary syndrome in Malaysian population using machine learning technique / Nanyonga Aziida
Published 2019“…Combinations of feature selection and classification algorithms were used for mortality prediction post ACS. …”
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Hybrid ACO and SVM algorithm for pattern classification
Published 2013“…Support Vector Machine (SVM) is a pattern classification approach originated from statistical approaches. …”
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Advanced flood prediction at forest with rainfall data using various machine learning algorithms
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A Truly Online Learning Algorithm using Hybrid Fuzzy ARTMAP and Online Extreme Learning Machine for Pattern Classification
Published 2023“…Algorithms; Benchmarking; E-learning; Knowledge acquisition; Learning systems; Pattern recognition; Bench-mark problems; Efficient learning; Extreme learning machine; Fuzzy ARTMAP; Generalization performance; Online learning; Online learning algorithms; Online sequential extreme learning machine; Learning algorithms…”
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The Potential of a Classification-based Algorithm to Calculate Calories in Real-Time Via Pattern Recognition
Published 2016“…While the algorithm helped to classify different types of wavelengths produced from the sensor, a classification-based algorithm via Pattern Recognition Method will be used to classify and match the food components. …”
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Frequent Lexicographic Algorithm for Mining Association Rules
Published 2005“…An algorithm called Flex (Frequent lexicographic patterns) has been proposed in obtaining a good performance of searching li-equent patterns. …”
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Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns
Published 2023“…This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. …”
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Using predictive analytics to solve a newsvendor problem / S. Sarifah Radiah Shariff and Hady Hud
Published 2023“…Secondly, in solving every Machine Learning problem, there is no one algorithm superior to other algorithms. …”
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Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns
Published 2023“…This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. …”
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Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns
Published 2023“…This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. …”
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Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns
Published 2023“…This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. …”
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Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns
Published 2023“…This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. …”
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Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns
Published 2023“…This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. …”
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Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns
Published 2023“…This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. …”
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New bio-inspired barnacle optimizers based least-square support vector machine for time-series prediction of pandemic outbreaks
Published 2024“…Firstly, this thesis proposes hybrid optimized machine learning models by combining improved BMO variants with Least Squares Support Vector Machines (LSSVM) to effectively capture intricate temporal patterns. …”
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Neural Network Based Pattern Recognition in Visual Inspection System for Intergrated Circuit Mark Inspection
Published 1998“…Therefore a study was conducted to introduce a new algorithm to inspect integrated circuit package markings. …”
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Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns
Published 2023“…This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. …”
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