Search Results - (( based selection method algorithm ) OR ( pattern extraction based algorithm ))

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  1. 1
    Fingerprint verification using clonal selection algorithm / Farah Syadiyah Shamsudin

    Fingerprint verification using clonal selection algorithm / Farah Syadiyah Shamsudin by Shamsudin, Farah Syadiyah

    Published 2017
    “…There will be two processes involved, which are feature extraction using minutiae-based method and also the implementation of the proposed algorithm, CSA. …”
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  2. 2
    Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion

    Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion by Zafar, R., Dass, S.C., Malik, A.S.

    Published 2017
    “…In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. …”
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  3. 3
    Opposition Based Competitive Grey Wolf Optimizer For EMG Feature Selection

    Opposition Based Competitive Grey Wolf Optimizer For EMG Feature Selection by Too, Jing Wei, Abdullah, Abdul Rahim

    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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  4. 4
    Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network

    Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network by Zafar, R., Kamel, N., Naufal, M., Malik, A.S., Dass, S.C., Ahmad, R.F., Abdullah, J.M., Reza, F.

    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). MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. …”
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  5. 5
    Non-invasive pathological voice classifications using linear and non-linear classifiers

    Non-invasive pathological voice classifications using linear and non-linear classifiers by Hariharan, Muthusamy

    Published 2010
    “…Three feature extraction methods are proposed based on the time-domain energy variations, Mel frequency cepstral coefficients combined with singular value decomposition and wavelet packet and entropy features. …”
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  6. 6
    Static image of hand gesture for numerical sign language recognition system using backfrofagation neural network / Erman Ibrahim

    Static image of hand gesture for numerical sign language recognition system using backfrofagation neural network / Erman Ibrahim by Ibrahim, Erman

    Published 2007
    “…A study on the research and development of the previous project based on pattern recognition has been done as a result selected; method, theory and techniques will be gathered in order to perform a hand gesture for sign language recognition system. …”
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  7. 7
    A guided hybrid k-means and genetic algorithm models for children handwriting legibility performance assessment / Norzehan Sakamat

    A guided hybrid k-means and genetic algorithm models for children handwriting legibility performance assessment / Norzehan Sakamat by Sakamat, Norzehan

    Published 2021
    “…The instruments combine's observations, feature extraction methods and clustering methods which are expected to produce predictive results of high agreement with human experts based on evaluation of selected individually handwritten alphabets. …”
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  8. 8
    Feature extraction: hand shape, hand position and hand trajectory path

    Feature extraction: hand shape, hand position and hand trajectory path by Bilal, Sara Mohammed Osman Saleh, Akmeliawati, Rini

    Published 2011
    “…The performance of recognition system fIrst depends on the process of getting effIcient features to represent pattern characteristics [1]. There is no algorithm which shows how to select the representation or choose the features [2] so the selection of features will depend on the application. …”
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  9. 9
    Image clustering comparison of two color segmentation techniques

    Image clustering comparison of two color segmentation techniques by Subramaniam, Kavitha Pichaiyan

    Published 2010
    “…There are many algorithm for analysing clustering each having its own method to do clustering. …”
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  10. 10
    EEG Eye State Identification based on Statistical Feature and Common Spatial Pattern Filter

    EEG Eye State Identification based on Statistical Feature and Common Spatial Pattern Filter by Woon, W.C., Yahya, N., Badruddin, N.

    Published 2019
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  11. 11
    EEG Eye State Identification based on Statistical Feature and Common Spatial Pattern Filter

    EEG Eye State Identification based on Statistical Feature and Common Spatial Pattern Filter by Woon, W.C., Yahya, N., Badruddin, N.

    Published 2019
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  12. 12
    Computer-aided acute leukemia blast cells segmentation in peripheral blood images

    Computer-aided acute leukemia blast cells segmentation in peripheral blood images by Madhloom, H.T., Kareem, S.A., Ariffin, H.

    Published 2015
    “…The first aim is to segment the leukemic cells by mean of color transformation and mathematical morphology. The method also introduces an approach to split overlapping cells using the marker-controlled watershed algorithm based on a new marker selection scheme. …”
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  13. 13
    Self learning neuro-fuzzy modeling using hybrid genetic probabilistic approach for engine air/fuel ratio prediction

    Self learning neuro-fuzzy modeling using hybrid genetic probabilistic approach for engine air/fuel ratio prediction by Al-Himyari, Bayadir Abbas

    Published 2017
    “…An enhanced hybrid Genetic Algorithm based Fuzzy Bayesian classifier (GA-FBC) was proposed to help the NFS in the rule extraction. …”
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  14. 14
    Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining.

    Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining. by Saeed, Walid

    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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  15. 15
    The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN

    The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN by Rashid, Mamunur, Bari, Bifta Sama, Hasan, Md Jahid, Mohd Azraai, Mohd Razman, Rabiu Muazu, Musa, Ahmad Fakhri, Ab. Nasir, Anwar, P. P. Abdul Majeed

    Published 2021
    “…The common spatial pattern (CSP) has been applied to extract the features from the MI response, and the effectiveness of random forest (RF)-based feature selection algorithm has also been investigated. …”
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  16. 16
    EEG-Based Person Authentication Modelling Using Incremental Fuzzy-Rough Nearest Neighbour Technique

    EEG-Based Person Authentication Modelling Using Incremental Fuzzy-Rough Nearest Neighbour Technique by Liew, Siaw Hong

    Published 2016
    “…The correlation-based feature selection (CFS) method was used to select representative WPD vector subset to eliminate redundancy before combining with other features. …”
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  17. 17
    Design Of Feature Selection Methods For Hand Movement Classification Based On Electromyography Signals

    Design Of Feature Selection Methods For Hand Movement Classification Based On Electromyography Signals by Too, Jing Wei

    Published 2020
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  18. 18
    Channel selection for common spatial pattern Based on energy calculation of motor imagery EEG signal

    Channel selection for common spatial pattern Based on energy calculation of motor imagery EEG signal by Hilman, Fauzi, Mohd Ibrahim, Shapiai, Noor Akhmad, Setiawan, Jafreezal, Jaafar, Mahfuzah, Mustafa

    Published 2017
    “…One of the popular features extraction methods for recognizing motor imagery EEG signal is Common Spatial Pattern (CSP). …”
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  19. 19
    P300 detection of brain signals using a combination of wavelet transform techniques

    P300 detection of brain signals using a combination of wavelet transform techniques by Motlagh, Farid Esmaeili

    Published 2012
    “…Meanwhile the new approaches in channels selection methods help the algorithms for convenient online usage.…”
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  20. 20
    Liver segmentation on CT images using random walkers and fuzzy c-means for treatment planning and monitoring of tumors in liver cancer patients

    Liver segmentation on CT images using random walkers and fuzzy c-means for treatment planning and monitoring of tumors in liver cancer patients by Moghbel, Mehrdad

    Published 2017
    “…This is followed by the clustering of the liver tissues using particle swarm optimized spatial FCM algorithm. Then, these tissues are classified into tumors and blood vessels by an AdaBoost classification method based on tissue features extracted utilizing first, second and higher order image features selected by a minimal-redundancy maximalrelevance feature selection approach. …”
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