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  1. 1
    Parallel distributed genetic algorithm development based on microcontrollers framework

    Parallel distributed genetic algorithm development based on microcontrollers framework by Krishnan P.S., Kiong T.S., Koh J.

    Published 2023
    Conference paper
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  2. 2
    MULTIPLE DRONES PATH OPTIMIZATION ALGORITHM FOR 3D SPACE PERFORMANCE USING CENTRALIZED VISUALIZATION PLATFORM

    MULTIPLE DRONES PATH OPTIMIZATION ALGORITHM FOR 3D SPACE PERFORMANCE USING CENTRALIZED VISUALIZATION PLATFORM by Teng, Chu Yao

    Published 2019
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    Final Year Project Report / IMRAD
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  3. 3
    Task-state EEG signal classification for spatial cognitive evaluation based on multiscale high-density convolutional neural network

    Task-state EEG signal classification for spatial cognitive evaluation based on multiscale high-density convolutional neural network by Wen, Dong, Li, Rou, Tang, Hao, Liu, Yijun, Wan, Xianglong, Dong, Xianling, Saripan, M. Iqbal, Lan, Xifa, Song, Haiqing, Zhou, Yanhong

    Published 2022
    “…Besides, the multi-dimensional conditional mutual information method was used to extract the frequency band features of the EEG data. …”
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  4. 4
    Prediction of ADHD from a small dataset using an adaptive EEG theta/beta ratio and PCA feature extraction

    Prediction of ADHD from a small dataset using an adaptive EEG theta/beta ratio and PCA feature extraction by Sase, Takumi, Othman, Marini

    Published 2022
    “…EEG Theta/beta ratio (TBR) is conventionally used as a biomarker in childhood Attention-Deficit/Hyperactivity Disorder (ADHD) prediction and treatment. …”
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    Proceeding Paper
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  5. 5
    Prediction of ADHD from a small dataset using an adaptive EEG Theta/Beta Ratio and PCA feature extraction

    Prediction of ADHD from a small dataset using an adaptive EEG Theta/Beta Ratio and PCA feature extraction by Sase, Takumi, Othman, Marini

    Published 2022
    “…EEG Theta/beta ratio (TBR) is conventionally used as a biomarker in childhood Attention-Deficit/Hyperactivity Disorder (ADHD) prediction and treatment. …”
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    Proceeding Paper
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  6. 6
    The behaviours of convergents in θ-expansions: computational insights based on θ-expansions algorithm using the maple software

    The behaviours of convergents in θ-expansions: computational insights based on θ-expansions algorithm using the maple software by Muhammad, K. N., Kamarulhaili, H., Asbullah, M. A., Sapar, S. H.

    Published 2024
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  7. 7
    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
    “…Using test data contaminated by eye blink artifacts and public dataset from EEGLAB, it was shown that these methods performed better in identifying artifactual components than did existing thresholding methods. …”
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    Thesis
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  8. 8
    Fault Detection and Identification in Quadrotor System (Quadrotor Robot)

    Fault Detection and Identification in Quadrotor System (Quadrotor Robot) by Chan, Shi Jing, Pebrianti, Dwi

    Published 2016
    “…PD controller used the Phi, Theta, Psi, x, y and z as a reference to adjust the attitude and position of the Quadrotor. …”
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    Conference or Workshop Item
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  9. 9
    Discrete wavelet packet transform for electroencephalogram based valence-arousal emotion recognition

    Discrete wavelet packet transform for electroencephalogram based valence-arousal emotion recognition by Olakunle, Oyenuga Wasiu

    Published 2015
    “…In order to evaluate the results of this study, a benchmark EEG dataset was used to implement the emotion classification. In the first experiment, the entropy features of the theta, alpha, beta, and gamma bands through the 10 EEG channels Fp1, Fp2, F3, F4, T7, T8, P3, P4, O1, and O2 were extracted using DWPT and Radial Basis Function-Support Vector Machine (RBF-SVM) was used as the classifier. …”
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    Thesis
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  10. 10
    Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions

    Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions by Saraswati, Galuh Wilujeng

    Published 2017
    “…The project has also implemented the optimization process onto the proposed ANFIS model through the hybrid of Genetic Algorithm on the fuzzy membership function of the ANFIS model. …”
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    Thesis
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  11. 11
    Electrodynamics of olfactory induced signals: an electro-encephalographic study

    Electrodynamics of olfactory induced signals: an electro-encephalographic study by Qureshi, Sitwat, Jahangir, Muhammad, Rehman, Rizwan, Siddiqui, Imran, Abbas, Syed Zeeshan, Che Abdullah, Che Azurahanim

    Published 2020
    “…Independent component analysis algorithm is used for potential of the scalp electrode. …”
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  12. 12
    Enhancement of filter design and EEG power ratio features in IQ pattern analysis / N. H. R. Azamin ...[et al.]

    Enhancement of filter design and EEG power ratio features in IQ pattern analysis / N. H. R. Azamin ...[et al.] by Azamin, N. H. R., Jahidin, A. H., Megat Ali, M. S. A., Taib, M. N.

    Published 2017
    “…Hence, this paper propose an improved extraction algorithm based on the use of high-order equiripple filters. …”
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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
    Complexity Analysis of EEG in Patients With Social Anxiety Disorder Using Fuzzy Entropy and Machine Learning Techniques

    Complexity Analysis of EEG in Patients With Social Anxiety Disorder Using Fuzzy Entropy and Machine Learning Techniques by Al-Ezzi, A., Al-Shargabi, A.A., Al-Shargie, F., Zahary, A.T.

    Published 2022
    “…The main objective of this study is to analyze the electroencephalogram (EEG) complexity of 88 SAD subjects, subdivided into 4 balanced groups (22 severe, 22 moderate, 22 mild, and 22 healthy controls (HCs) using Fuzzy Entropy measure (FE) and machine learning algorithms. …”
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