Search Results - (( evolution optimization bat algorithm ) OR ( pattern extraction path algorithm ))

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  1. 1
    Multi-Swarm bat algorithm

    Multi-Swarm bat algorithm by Taha A.M., Chen S.-D., Mustapha A.

    Published 2023
    “…In this study a new Bat Algorithm (BA) based on multi-swarm technique called the Multi-Swarm Bat Algorithm (MSBA) is proposed to address the problem of premature convergence phenomenon. …”
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  2. 2
    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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  3. 3
    Quality of service and energy efficient aware (QEEA) scheduling algorithm for long term evolution (LTE) network / Nurulanis Mohd Yusoff

    Quality of service and energy efficient aware (QEEA) scheduling algorithm for long term evolution (LTE) network / Nurulanis Mohd Yusoff by Mohd Yusoff, Nurulanis

    Published 2017
    “…Basically, the QEEA is based on the Time Domain (TD) and Frequency Domain (FD) scheduling where it is dependent on the QoS requirements to allocate resources. The proposed algorithm is compared against other scheduling algorithms, namely, the Channel and QoS Aware (CQA), Priority Set Scheduler (PSS), Proportional Fair (PF), Maximum Throughput (MT) and Blind Average Throughput (BAT). …”
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  4. 4
    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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  5. 5
    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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  6. 6
    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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  7. 7
    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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  8. 8
    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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  9. 9
    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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  10. 10
    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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