Search Results - pattern extraction ((bees algorithm) OR (((path algorithm) OR (bat algorithm))))

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  1. 1
    Clustering natural language morphemes from EEG signals using the Artificial Bee Colony algorithm

    Clustering natural language morphemes from EEG signals using the Artificial Bee Colony algorithm by Sulaiman, Suriani, Ahmed Yahya, Saba, Mohd Shukor, Nur Sakinah, Ismail , Amelia Ritahani, Zaahirah, Qazi, Yaacob, Hamwira, Abdul Rahman, Abdul Wahab, Dzulkifli, Mariam Adawiah

    Published 2015
    “…This study aims at analyzing EEG signals for the purpose of clustering natural language morphemes using the Artificial Bee Colony (ABC) algorithm. Using as input the features extracted from EEG signals during morphological priming tasks, our experimental results indicate that applying the ABC algorithm on EEG datasets to cluster Malay morphemes produces promising results.…”
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  2. 2
    Clustering natural language morphemes from EEG signals using the Artificial Bee Colony algorithm

    Clustering natural language morphemes from EEG signals using the Artificial Bee Colony algorithm by Sulaiman, Suriani, Ahmed Yahya, Saba, Mohd Shukor, Nur Sakinah, Ismail , Amelia Ritahani, Zaahirah, Qazi, Yaacob, Hamwira Sakti, Abdul Rahman, Abdul Wahab, Dzulkifli, Mariam Adawiah

    Published 2015
    “…This study aims at analyzing EEG signals for the purpose of clustering natural language morphemes using the Artificial Bee Colony (ABC) algorithm. Using as input the features extracted from EEG signals during morphological priming tasks, our experimental results indicate that applying the ABC algorithm on EEG datasets to cluster Malay morphemes produces promising results.…”
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  3. 3
    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
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  4. 4
    Intelligent road recognition system for automous vehicle

    Intelligent road recognition system for automous vehicle by Soon, Adrian Bee Toing

    Published 2013
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  5. 5
    Animal voice recognition for identification (ID) detection system

    Animal voice recognition for identification (ID) detection system by Yeo, Che Yong, Syed Mohamed, Syed Abdul Rahman Al-Haddad, Ng, Chee Kyun

    Published 2011
    “…While the voice pattern classification will be done by using DTW algorithm. …”
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  6. 6
    Dog voice identification (ID) for detection system

    Dog voice identification (ID) for detection system by Yeo, Che Yong, Syed Mohamed, Syed Abdul Rahman Al-Haddad, Ng, Chee Kyun

    Published 2012
    “…While the voice pattern classification will be done by using DTW algorithm. …”
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  7. 7
    Data normalization techniques in swarm-based forecasting models for energy commodity spot price

    Data normalization techniques in swarm-based forecasting models for energy commodity spot price by Yusof, Yuhanis, Mustaffa, Zuriani, Kamaruddin, Siti Sakira

    Published 2014
    “…Data mining is a fundamental technique in identifying patterns from large data sets.The extracted facts and patterns contribute in various domains such as marketing, forecasting, and medical.Prior to that, data are consolidated so that the resulting mining process may be more efficient.This study investigates the effect of different data normalization techniques.which are Min-max, Z-score and decimal scaling, on Swarm-based forecasting models.Recent swarm intelligence algorithms employed includes the Grey Wolf Optimizer (GWO) and Artificial Bee Colony (ABC).Forecasting models are later developed to predict the daily spot price of crude oil and gasoline.Results showed that GWO works better with Z-score normalization technique while ABC produces better accuracy with the Min-Max.Nevertheless, the GWO is more superior than ABC as its model generates the highest accuracy for both crude oil and gasoline price.Such a result indicates that GWO is a promising competitor in the family of swarm intelligence algorithms.…”
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  8. 8
    Artificial Bee Colony With Differential Evolution Algorithm For Feature Extraction And Selection Of Mass Spectrometry Data

    Artificial Bee Colony With Differential Evolution Algorithm For Feature Extraction And Selection Of Mass Spectrometry Data by Mohamed Yusoff, Syarifah Adilah

    Published 2016
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  9. 9
    NN with DTW-FF Coefficients and Pitch Feature for Speaker Recognition

    NN with DTW-FF Coefficients and Pitch Feature for Speaker Recognition by Sudirman, Rubita, Salleh, Sh-Hussain, Salleh, Shaharuddin

    Published 2006
    “…This paper proposes a new method to extract speech features in a warping path using dynamic programming (DP). …”
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  10. 10
    Local DTW coefficients and pitch feature for back-propagation NN digits recognition

    Local DTW coefficients and pitch feature for back-propagation NN digits recognition by Sudirman, R., Salleh, Shahruddin Hussain, Salleh, Sh-Hussain

    Published 2006
    “…This paper presents a method to extract existing speech features in dynamic time warping path which originally was derived from LPC. …”
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  11. 11
    Local DTW Coefficients and Pitch Feature for Back-Propagation NN Digits Recognition

    Local DTW Coefficients and Pitch Feature for Back-Propagation NN Digits Recognition by Sudirman, Rubita, Salleh, Sh-Hussain, Salleh, Shaharuddin

    Published 2006
    “…This paper presents a method to extract existing speech features in dynamic time warping path which originally was derived from LPC. …”
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  12. 12
    Advances in materials informatics: A review

    Advances in materials informatics: A review by Sivan, Dawn, Kumar, K. Satheesh, Aziman, Abdullah, Raj, Veena, Izan Izwan, Misnon, Ramakrishna, Seeram, Jose, Rajan

    Published 2024
    “…Conventional ML models are simple and interpretable, relying on statistical techniques and algorithms to learn patterns and make predictions with limited data. …”
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  13. 13
    A hybrid environment control system combining EMG and SSVEP signal based on brain-computer interface technology

    A hybrid environment control system combining EMG and SSVEP signal based on brain-computer interface technology by Rashid, Mamunur, Bari, Bifta Sama, Norizam, Sulaiman, Mahfuzah, Mustafa, Md Jahid, Hasan, Islam, Md Nahidul, Naziullah, Shekh

    Published 2021
    “…The feature in terms of the common spatial pattern (CSP) has been extracted from four classes of SSVEP response, and extracted feature has been classified using K-nearest neighbors (k-NN) based classification algorithm. …”
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