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  1. 1
    Swarm negative selection algorithm for electroencephalogram signals classification

    Swarm negative selection algorithm for electroencephalogram signals classification by Sahel Ba-Karait, Nasser Omer, Shamsuddin, Siti Mariyam, Sudirman, Rubita

    Published 2009
    “…The SNS classification model use negative selection and PSO algorithms to form a set of memory Artificial Lymphocytes (ALCs) that have the ability to distinguish between normal and epileptic EEG patterns. …”
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  2. 2
    Classification of labour pain using electroencephalogram signal based on wavelet method / Sai Chong Yeh

    Classification of labour pain using electroencephalogram signal based on wavelet method / Sai Chong Yeh by Sai , Chong Yeh

    Published 2020
    “…This study proposed an integrated system for EEG signals pre-processing by using machine learning algorithms in the identification of artifactual components during the process of Wavelet-ICA. …”
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  3. 3
    Eeg-Based Person Identification Using Multi-Levelwavelet Decomposition With Multi-Objective Flower Pollination Algorithm

    Eeg-Based Person Identification Using Multi-Levelwavelet Decomposition With Multi-Objective Flower Pollination Algorithm by Yahya Alyasseri, Zaid Abdi Alkareem

    Published 2020
    “…Therefore, the most relevant ones for person identification can be identified and then use a smaller number of electrodes. Second, the EEG signals must be processed to obtain efficient EEG features because there are several noises can corrupt the original EEG signal during the recording time. …”
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  4. 4
    Detection of the onset of epileptic seizure signal from scalp EEG using blind signal separation

    Detection of the onset of epileptic seizure signal from scalp EEG using blind signal separation by Moghavvemi, M., Mehrkanoon, S.

    Published 2009
    “…BSS algorithm is used to demix the EEG signal into signals with independent features. …”
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  5. 5
    EEG-and MRI-based epilepsy source localization using multivariate empirical mode decomposition and inverse solution method

    EEG-and MRI-based epilepsy source localization using multivariate empirical mode decomposition and inverse solution method by Khosropanah, Pegah

    Published 2018
    “…Since MEMD method is a data-driven method which meets the criteria to be applied for EEG processing, therefore this method was employed to extract EEG epileptic spike features. …”
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  6. 6
    Multivariate EEG signal processing techniques for the aid of severely disabled people

    Multivariate EEG signal processing techniques for the aid of severely disabled people by Ibrahimy, Muhammad Ibn, Ibrahimy, Ahmad Ibn

    Published 2022
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  7. 7
    EEG Different Frequency Sound Response Identification using Neural Network and Fuzzy Techniques

    EEG Different Frequency Sound Response Identification using Neural Network and Fuzzy Techniques by Wan Daud, Wan Mohd Bukhari, Sudirman , R, Koh, A. C, Safri, N.M, Mahmood, N.H

    Published 2010
    “…Principle Component algorithm, Discrete Wavelet Transform and Fast Fourier Transform, are applied onto the raw EEG signal to extract useful information and specific characteristics from the EEG signals. …”
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  8. 8
    Home buyer assistant using artificial bee colony algorithm / Muhammad Izzat Azri Azman

    Home buyer assistant using artificial bee colony algorithm / Muhammad Izzat Azri Azman by Azman, Muhammad Izzat Azri

    Published 2017
    “…The assistant will provide suitable house recommendation to a home buyer based on particular information house specification such as house price, locations neighbourhood and surrounding information. This project used Artificial Bee Colony Algorithms (ABC) by adapting the food foraging behaviour of bee in honey bee and find a suitable house for home buyer based on their requirement. …”
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  9. 9
    Flowshop scheduling using artificial bee colony (ABC) algorithm with varying onlooker bees approaches

    Flowshop scheduling using artificial bee colony (ABC) algorithm with varying onlooker bees approaches by Mohd Pauzi, Nur Fazlinda

    Published 2015
    “…The research also analyzes the performance of the ABC algorithm using three different onlooker bee approaches. …”
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  10. 10
    Optimisation of Surface Roughness in 3D Printing Using the Bees Algorithm

    Optimisation of Surface Roughness in 3D Printing Using the Bees Algorithm by Kamaruddin, Shafie, Ridzuan, Arman Hilmi, Sukindar, Nor Aiman

    Published 2025
    “…The focus of this study is to determine the optimum input parameter of the 3D printer using the Bees Algorithm (BA). This study uses the Bees Algorithm to predict the best combination parameters to optimise the surface roughness of parts printed by a fused deposition modelling (FDM) machine. …”
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  11. 11
    Assembly sequence optimization using the bees algorithm

    Assembly sequence optimization using the bees algorithm by Kamaruddin, Shafie, Azmi, Nabilah, Sukindar, Nor Aiman

    Published 2022
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  12. 12
    Classification of left/right hand movement from EEG signal by intelligent algorithms

    Classification of left/right hand movement from EEG signal by intelligent algorithms by Baig, M.Z., Javed, E., Ayaz, Y., Afzal, W., Gillani, S.O., Naveed, M., Jamil, M.

    Published 2015
    “…Algorithms have been implemented on both unprocessed features and processed reduced feature sets. …”
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  13. 13
    Implementation of welch pre-processing in SVM algorithm for improved accuracy on EEG data

    Implementation of welch pre-processing in SVM algorithm for improved accuracy on EEG data by Hariyady Hariyady, Ag Asri Ag Ibrahim, Jason Teo, Muhammad Balya Firjaun Barlaman, Muhammad Aulanas Bitaqwa, Azhana Ahmad, Fouziah Md Yassin, Carolyn Salimun, Ng, Giap Weng

    Published 2024
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  14. 14
    BRAIN ACTIVITIES FOR MOTOR MOVEMENT

    BRAIN ACTIVITIES FOR MOTOR MOVEMENT by ., WAFAA ELSAYED ELBASTY

    Published 2012
    “…The research covers the procedure of designing the BCI algorithm and this consists of three stages firstly recording EEG brain signals, secondly EEG signals pre-processing, Last stage is EEG signals classification. …”
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  15. 15
    An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction

    An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction by Shah, Habib

    Published 2014
    “…Here, three improved learning approaches inspired by artificial honey bee's behavior are used to train MLP. They are: Global Guided Artificial Bee Colony (GGABC), Improved Gbest Guided Artificial Bee Colony (IGGABC) and Artificial Smart Bee Colony (ASBC) algorithm. …”
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  16. 16
    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
    “…Hence, this work aims to develop an algorithm using statistical-CSP feature for eye state classification from EEG signal. …”
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  17. 17
    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
    “…Hence, this work aims to develop an algorithm using statistical-CSP feature for eye state classification from EEG signal. …”
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  18. 18
    Fused multivariate empirical mode decomposition (MEMD) and inverse solution method for EEG source localization

    Fused multivariate empirical mode decomposition (MEMD) and inverse solution method for EEG source localization by Khosropanah, Pegah, Ramli, Abdul Rahman, Lim, Kheng Seang, Marhaban, Mohammad Hamiruce, Ahmedov, Anvarjon

    Published 2018
    “…High accuracy results of proposed algorithm using non-invasive and low-resolution EEG provide the potential of using this work for pre-surgical evaluation towards epileptogenic zone localization in clinics.…”
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  19. 19
    Fused multivariate empirical mode decomposition (MEMD) and inverse solution method for EEG source localization

    Fused multivariate empirical mode decomposition (MEMD) and inverse solution method for EEG source localization by Khosropanah, Pegah, Ramli, Abdul Rahman, Lim, Kheng Seang, Marhaban, Mohammad Hamiruce, Ahmedov, Anvarjon

    Published 2018
    “…High accuracy results of proposed algorithm using non-invasive and low-resolution EEG provide the potential of using this work for pre-surgical evaluation towards epileptogenic zone localization in clinics.…”
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  20. 20
    Multichannel optimization with hybrid spectral- entropy markers for gender identification enhancement of emotional-based EEGs

    Multichannel optimization with hybrid spectral- entropy markers for gender identification enhancement of emotional-based EEGs by Al-Qazzaz, Noor Kamal, Sabir, Mohannad K., Mohd Ali, Sawal Hamid, Ahmad, Siti Anom, Grammer, Karl

    Published 2021
    “…The electroencephalogram (EEG) is a tool that potentially can be used to detect gender differences. …”
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