Search Results - (( processes classification using algorithm ) OR ( pattern classification modified algorithm ))
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1
Modified word representation vector based scalar weight for contextual text classification
Published 2024“…In addition, a contextual text classification experiment is conducted using benchmarked datasets to assess the performance of the modified word vectors in the targeted classification task. …”
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2
Classification of herbs plant diseases via hierarchical dynamic artificial neural network
Published 2010“…Basically, in this study, image processing and pattern classification are going to be used to implement a machine vision system that could identify and classify the visual symptoms of herb plants diseases. …”
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3
Classification of herbs plant diseases via hierachical dynamic artificial neural network after image removal using kernel regression framework
Published 2011“…Basically, in this study, image processing and pattern classification are going to be used to implement a machine vision system that could identify and classify the visual symptoms of herb plants diseases. …”
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4
Enhanced grey wolf optimisation algorithm for feature selection in anomaly detection
Published 2022“…Thus, not all features in the dataset can be used in the classification process since some features may lead to low performance of classifier. …”
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5
Modern fuzzy min max neural networks for pattern classification
Published 2019“…Among these algorithms, Fuzzy Min Max (FMM) neural network algorithm has been proven to be one of the premier neural networks for undertaking the pattern classification problems. …”
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6
A hybrid approach for artificial immune recognition system / Mahmoud Reza Saybani
Published 2016“…The components of the AIRS2 algorithm that pose problems will be modified. This thesis proposes three new hybrid algorithms: The FRA-AIRS2 algorithm uses fuzzy logic to improve data reduction capability of AIRS2 and to solve the linearity problem associated with resource allocation of AIRS. …”
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7
Classification Of Hand Movements Based On Discrete Wavelet Transform And Enhanced Feature Extraction
Published 2019“…The extracted features are then fed into the machine learning algorithm for classification process. Four popular machine learning algorithms include k-nearest neighbor (KNN), linear discriminate analysis (LDA), Naïve Bayes (NB) and support vector machine (SVM) are used in evaluation. …”
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8
Binary whale optimization algorithm with logarithmic decreasing time-varying modified sigmoid transfer function for descriptor selection problem
Published 2023“…In cheminformatics, choosing the right descriptors is a crucial step in improving predictive models, particularly those that use machine learning algorithms. Recently, researchers in cheminformatics have been lured to swarm intelligence to optimize the process of discovering relevant descriptors in the wrapper feature selection. …”
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9
Modified Piecewise Linear Mapping Contrast Enhancement And Local Otsu Segmentation Methods For Hep-2 Cell Images
Published 2019“…This study analyses the importance of both methods, which could possibly improve the HEP-2 classification process. The pre-processing is capable to provide a better quality cell features in an image through contrast enhancement process, while segmentation which segregates important features could improve the classification performances. …”
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10
Design Of Feature Selection Methods For Hand Movement Classification Based On Electromyography Signals
Published 2020“…Therefore, this thesis aims to solve the feature selection problem in EMG signals classification and improve the classification performance of EMG pattern recognition system. …”
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11
Finger Vein Recognition Using Pattern Map As Feature Extraction
Published 2012“…Every fmger vein image is then transformed into pattern map images from a pattern matching process between an input finger vein image and the pattern templates. …”
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12
A framework of modified adaptive neuro-fuzzy inference engine
Published 2012“…The Takagi-Sugeno-Kang (TSK) type fuzzy inference system was chosen and constructed by an automatic generation of clusters as well as membership functions and minimal rules through the use of hybrid fuzzy clustering and the modified apriori algorithms respectively. …”
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13
FACE CLASSIFICATION FOR AUTHENTICATION APPROACH BY USING WAVELET TRANSFORM AND STATISTICAL FEATURES SELECTION
Published 2011“…In the last method, the Modified K-Means Algorithm was used to remove the non-face regions in the input image. …”
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Pattern generation through feature values modification and decision tree ensemble construction
Published 2013“…The method modifies feature values of some patterns with the values of other patterns to generate different patterns for different classifiers. …”
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16
A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification
Published 2010“…The first stage consists of a modified fuzzy min-max (FMM) neural-network-based pattern classifier, while the second stage consists of a genetic-algorithm (GA)-based rule extractor. …”
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17
A hybrid-based modified adaptive fuzzy inference engine for pattern classification
Published 2011“…The Neuro-Fuzzy hybridization scheme has become of research interest in pattern classification over the past decade. The present paper proposes a hybrid Modified Adaptive Fuzzy Inference Engine (MAFIE) for pattern classification. …”
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18
Quantitative spasticity assessment model of neurological disorder patients / AA Puzi … [et al.]
Published 2023“…The cues from the MMG signals pattern will be used to select the sampling features for the development of the classification algorithm model. …”
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19
Finger-Vein Biometric Identification Using Convolutional Neural Network
Published 2016“…For network training, we have modified and applied the stochastic diagonal Levenberg-Marquardt algorithm, which results in a faster convergence time. …”
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20
A Hybrid Rough Sets K-Means Vector Quantization Model For Neural Networks Based Arabic Speech Recognition
Published 2002“…That is, to use training speech patterns to generate classification rules that can be used later to classify input words patterns. …”
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