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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
    A numerical method for frequent pattern mining

    A numerical method for frequent pattern mining by Mustapha, Norwati, Nadimi-Shahraki, Mohammad-Hossein, Mamat, Ali, Sulaiman, Md. Nasir

    Published 2009
    “…In this paper, an efficient numerical method for mining frequent patterns is proposed. This method is based on prime number characteristics to generate all frequent patterns by using maximal frequent ones. …”
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  3. 3
    Development of an intelligent system using Kernel-based learning methods for predicting oil-palm yield.

    Development of an intelligent system using Kernel-based learning methods for predicting oil-palm yield. by Md. Sap, Mohd. Noor, Awan, A. Majid

    Published 2005
    “…At the core of our system is a method for unsupervised partitioning of data for finding spatio-temporal patterns in climate data using kernel methods which offer strength to deal with complex data non-linearly separable in input space. …”
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  4. 4
    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
    “…Therefore, this thesis aims to solve the feature selection problem in EMG signals classification and improve the classification performance of EMG pattern recognition system. …”
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  5. 5
    Optimized feature construction methods for data summarizations of relational data

    Optimized feature construction methods for data summarizations of relational data by Sze, Florence Sia Fui

    Published 2014
    “…The summarized data will then be fed to any classification algorithm to perform the classification task. …”
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  6. 6
    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
    “…The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. …”
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  7. 7
    Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach

    Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach by Qayyum A., Saeed Malik A., Saad N.M., Iqbal M., Abdullah M.F., Rasheed W., Abdullah T.A.B.R., Bin Jafaar M.Y.

    Published 2023
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  8. 8
    FACE CLASSIFICATION FOR AUTHENTICATION APPROACH BY USING WAVELET TRANSFORM AND STATISTICAL FEATURES SELECTION

    FACE CLASSIFICATION FOR AUTHENTICATION APPROACH BY USING WAVELET TRANSFORM AND STATISTICAL FEATURES SELECTION by DAWOUD JADALAH, NADIR NOURAIN

    Published 2011
    “…In the last method, the Modified K-Means Algorithm was used to remove the non-face regions in the input image. …”
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  9. 9
    A framework for predicting oil-palm yield from climate data

    A framework for predicting oil-palm yield from climate data by Awan, A. Majid, Md. Sap, Mohd. Noor

    Published 2006
    “…At the core of our system is a method for unsupervised partitioning of data for finding spatio-temporal patterns in climate data using kernel methods which offer strength to deal with complex data. …”
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  10. 10
    Spectral Estimation And Supervised Classification Technique For Real Time Electromyography Pattern Recognition

    Spectral Estimation And Supervised Classification Technique For Real Time Electromyography Pattern Recognition by Burhan, Nuradebah

    Published 2018
    “…Electromyography (EMG) signal is a biomedical signal which measures physical activity of human muscle.It has been acknowledged to be widely used in rehabilitation or recovery application system assisting physiotherapist to monitor a patient’s physical strength,function,motion and overall well-being by addressing the underlying physical issues.In application system associated with rehabilitation,a signal processing and classification techniques are implemented to classify EMG signal obtained.For real time application in the rehabilitation, the classification is crucial issue.The success of the signal classification depends on the selection of the features that represent a raw EMG signal in the signal processing.Therefore,a robust and resilient denoising method and spectral estimation technique have been acknowledged as necessary to distinguish and detect the EMG pattern.The present study was undertaken to determine the characteristic of EMG features using denoising method and spectral estimation technique for assessing the EMG pattern based on a supervised classification algorithm.In the study,the combination of time-frequency domain (TFD) and time domain (TD) were identified as the preferred denoising method and spectral estimation techniques.In the first part of study, the recorded EMG signal filtered the contaminated noise by using wavelet transform (WT) approach which implemented discrete wavelet transform (DWT) method of the wavelet-denoising signal. …”
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  11. 11
    Application Of Multi-Layer Perceptron Technique To Detect And Locate The Base Of A Young Corn Plant

    Application Of Multi-Layer Perceptron Technique To Detect And Locate The Base Of A Young Corn Plant by Morshidi, Malik Arman

    Published 2007
    “…Another structure of MLP trained using backpropagation algorithm is used to detect and locate the base of the young corn tree using the skeleton of the segmented image. …”
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  12. 12
    Adaptive Similarity Component Analysis in Nonparametric Dynamic Environment

    Adaptive Similarity Component Analysis in Nonparametric Dynamic Environment by Sojodishijani, Omid

    Published 2011
    “…Data arrives from operational field in a stream model and similarity-based classification algorithms must identify them with acceptable performance. …”
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  13. 13
    Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition

    Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition by Ali Adlan, Hanan Hassan

    Published 2004
    “…The algorithm enhances the recognition ability of the system compared to manual extraction and labeling of pattern classes. …”
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  14. 14
    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
    “…In this research the BCI competition data-set has been processed through 5 optimized detection methods. Wavelet transform (WT), student’s two-sample t-statistic (T-Test) and support vector machines (SVM) used in designing the algorithms. …”
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  15. 15
    Transformer-Based Model for Malicious URL Classification

    Transformer-Based Model for Malicious URL Classification by Do N.Q., Selamat A., Lim K.C., Krejcar O., Ghani N.A.M.

    Published 2024
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  16. 16
    Normal and hypoacoustic infant cry signal classification using time-frequency analysis and general regression neural network

    Normal and hypoacoustic infant cry signal classification using time-frequency analysis and general regression neural network by Hariharan, Muthusamy, Dr., Sindhu, R, Sazali, Yaacob, Prof. Dr.

    Published 2012
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  17. 17
    Efficient gear fault feature selection based on moth‑flame optimisation in discrete wavelet packet analysis domain

    Efficient gear fault feature selection based on moth‑flame optimisation in discrete wavelet packet analysis domain by Ong, Pauline, Tieh, Tony Hieng Cai, Lai, Kee Huong, Lee, Woon Kiow, Ismon, Maznan

    Published 2019
    “…Lastly, the MFO-selected features were used as the input for a support vector machine (SVM) diagnostic model to identify fault patterns. …”
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  18. 18
    Identification model for hearing loss symptoms using machine learning techniques

    Identification model for hearing loss symptoms using machine learning techniques by Nasiru Garba Noma

    Published 2014
    “…The model is implemented using both unsupervised and supervised machine learning techniques in the form of Frequent Pattern Growth (FP-Growth) algorithm as feature transformation method and multivariate Bernoulli naïve Bayes classification model as the classifier. …”
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  19. 19
    Classification of compressive strength grades for lightweight aggregate concrete with palm oil fuel ash (POFA) using k-Nearest Neighbour (k-NN)

    Classification of compressive strength grades for lightweight aggregate concrete with palm oil fuel ash (POFA) using k-Nearest Neighbour (k-NN) by Mohamad Hushnie, Haron, Nur Azzimah, Zamri, Khairunisa, Muthusamy

    Published 2023
    “…To address these shortcomings, a technique to classify the compressive strength grades for lightweight aggregate concrete containing POFA using a machine learning algorithm has been developed. In terms of method, concrete mixtures consisting of POFA, cement, sand, superplasticizer and water were prepared and tested to determine the compressive strength. …”
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  20. 20
    Classification of compressive strength grades for lightweight aggregate concrete with palm oil fuel ash (POFA) using kNearest Neighbour (k-NN)

    Classification of compressive strength grades for lightweight aggregate concrete with palm oil fuel ash (POFA) using kNearest Neighbour (k-NN) by Mohamad Hushnie, Haron, Nur Azzimah, Zamri, Khairunisa, Muthusamy

    Published 2023
    “…To address these shortcomings, a technique to classify the compressive strength grades for lightweight aggregate concrete containing POFA using a machine learning algorithm has been developed. In terms of method, concrete mixtures consisting of POFA, cement, sand, superplasticizer and water were prepared and tested to determine the compressive strength. …”
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