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Improved GART neural network model for pattern classification and rule extraction with application to power systems
Published 2023Subjects:Article -
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Development of a scaled conjugate gradient algorithm for significant RF neural signal processing
Published 2025“…This study aims to improve the classification of RF neural data patterns using SCG. EEG neural data was captured in sessions before, during and after RF exposure. …”
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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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Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition
Published 2004“…The algorithm enhances the recognition ability of the system compared to manual extraction and labeling of pattern classes. …”
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Identifying melanoma characteristics using directional imaging algorithm and convolutional neural network on dermoscopic images / Mohammad Asaduzzaman Rasel
Published 2024“…Multiple deep-learning models are proposed for segmentation. Several imaging, computer vision, and pattern recognition algorithms are employed to describe five dermoscopic features. …”
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A new model for iris data set classification based on linear support vector machine parameter's optimization
Published 2020“…Classification is a data analysis that extracts a model which describes an important data classes. …”
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Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network
Published 2017“…General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). …”
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Heart sound diagnosis using nonlinear ARX model / Noraishah Shamsuddin
Published 2011“…A Lipschitz method and Leven berg Marquardt algorithm is used to determine the model order number and train the network respectively. …”
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Photogrammetric low-cost unmanned aerial vehicle for pothole detection mapping / Shahrul Nizan Abd Mukti
Published 2022“…The study set four main objectives to achieve its aim: (1) To analyse RGB and multispectral sensor calibration, (2) To evaluate the optimal flight parameters for pothole modelling production using RGB imagery, (3) To investigate various classifier algorithms and band combinations for pothole region areas using multispectral imagery and (4) To validate geometric information from the extracted pothole. …”
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An Improved Network Intrusion Detection Method Based On CNN-LSTM-SA
Published 2025“…Using the NSL-KDD dataset for evaluation, the proposed method demonstrates superior performance compared to conventional algorithms and related deep learning techniques, achieving higher precision, recall, F1 scores and overall accuracy in both binary and multi-class classification tasks. …”
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Classification of stock market index based on predictive fuzzy decision tree
Published 2005“…In particular, predictive FDT algorithm is based on the concept of degree of importance of attribute contributing to the classification. …”
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The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN
Published 2021“…The performance of BCIs primarily depends on the efficacy of the feature extraction and feature selection techniques, as well as the classification algorithms employed. …”
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Opposition Based Competitive Grey Wolf Optimizer For EMG Feature Selection
Published 2020“…Four state-of-the-art algorithms include particle swarm optimization, flower pollination algorithm, butterfly optimization algorithm, and CBGWO are used to examine the effectiveness of proposed methods in feature selection. …”
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Data mining for structural damage identification using hybrid artificial neural network based algorithm for beam and slab girder / Meisam Gordan
Published 2020“…In the modeling phase, amongst all DM algorithms, the applicability of machine learning, artificial intelligence and statistical data mining techniques were examined using Support Vector Machine (SVM), Artificial Neural Network (ANN) and Classification and Regression Tree (CART) to detect the hidden patterns in vibration data. …”
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Hybrid Models Of Fuzzy Artmap And Qlearning For Pattern Classification
Published 2015“…To overcome the opaqueness issue, a Genetic Algorithm (GA) is used to extract fuzzy if-then rules from QFAM. …”
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Dorsal Hand Vein Pattern Recognition: A Comparison between Manual and Automatic Segmentation Methods
Published 2023“…Various techniques for dorsal hand vein (DHV) pattern extraction have been introduced using small datasets with poor and inconsistent segmentation. …”
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Dorsal Hand Vein Pattern Recognition: A Comparison between Manual and Automatic Segmentation Methods
Published 2023“…Objectives: Various techniques for dorsal hand vein (DHV) pattern extraction have been introduced using small datasets with poor and inconsistent segmentation. …”
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Deep plant: A deep learning approach for plant classification / Lee Sue Han
Published 2018“…They look for the procedures or algorithms that maximize the use of leaf databases for plant predictive modelling, but this results in leaf features which are liable to change with different leaf data and feature extraction techniques. …”
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