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  1. 1
    Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad

    Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad by Ahmad, Khairul Adilah

    Published 2018
    “…This reserarch adapted a methodology of computer vision and algorithms that exploit image segmentation, feature extraction and fuzzy classification to guide the research activities. …”
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    Thesis
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  2. 2
    Jogging activity recognition using k-NN algorithm

    Jogging activity recognition using k-NN algorithm by Afifah Ismail

    Published 2022
    “…The finding in this project is the k-NN algorithm is good feature extraction and classifier. …”
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    Academic Exercise
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  3. 3
    Japanese face emotions classification using lip features

    Japanese face emotions classification using lip features by Mohamad Rizon, Mohamed Juhari, Muthukaruppan, Karthigayan, Sazali, Yaacob, Nagarajan, R.

    Published 2009
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  4. 4
    Computational Technique for an Efficient Classification of Protein Sequences With Distance-Based Sequence Encoding Algorithm

    Computational Technique for an Efficient Classification of Protein Sequences With Distance-Based Sequence Encoding Algorithm by Iqbal, M.J., Faye, I., Said, A.M.D., Samir, B.B.

    Published 2017
    “…A statistical metric-based feature selection algorithm is then adopted to identify the reduced set of features to represent the original feature space. …”
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  5. 5
    Investigation of features for classification RFID reading between two RFID reader in various support vector machine kernel function

    Investigation of features for classification RFID reading between two RFID reader in various support vector machine kernel function by Choong, Chun Sern, Ahmad Fakhri, Ab. Nasir, P.P. Abdul Majeed, Anwar, Muhammad Aizzat, Zakaria, Mohd Azraai, Mohd Razman

    Published 2022
    “…The Polynomial-SVM model is capable of delivering a classification accuracy of 84.81 and 20.00% of the error rate in test data by using the function MIN extracted. …”
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  6. 6
    Improving Brain MR Image Classification for Tumor Segmentation using Phase Congruency

    Improving Brain MR Image Classification for Tumor Segmentation using Phase Congruency by Ghazanfar, Latif, Dayang Nur Fatimah, Binti Awg Iskandar, Jaafar, Alghazo, Arfan, Jaffar

    Published 2018
    “…In order to minimize the extracted feature set, we applied the principal Component Analysis algorithm. …”
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  7. 7
    The classification of wink-based eeg signals by means of transfer learning models

    The classification of wink-based eeg signals by means of transfer learning models by Jothi Letchumy, Mahendra Kumar

    Published 2021
    “…This study aimed to explore the performance of different pre-processing methods, namely Fast Fourier Transform, Short-Time Fourier Transform, Discrete Wavelet Transform, and Continuous Wavelet Transform (CWT) that could allow TL models to extract features from the images generated and classify through selected classical ML algorithms . …”
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  8. 8
    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
    “…A novel feature extraction algorithm was developed to extract the feature vectors. …”
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  9. 9
    New Learning Models for Generating Classification Rules Based on Rough Set Approach

    New Learning Models for Generating Classification Rules Based on Rough Set Approach by Al Shalabi, Luai Abdel Lateef

    Published 2000
    “…Recently, different models were used to generate knowledge from vague and uncertain data sets such as induction decision tree, neural network, fuzzy logic, genetic algorithm, rough set theory, and others. …”
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  10. 10
    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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  11. 11
    EMG motion pattern classification through design and optimization of neural network

    EMG motion pattern classification through design and optimization of neural network by Ahsan, Md. Rezwanul, Ibrahimy, Muhammad Ibn, Khalifa, Othman Omran

    Published 2012
    “…The EMG signals obtained for different kinds of hand motions, which further denoised and processed to extract the features. Extracted time and time-frequency based feature sets are used to train the neural network. …”
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  12. 12
    Opposition Based Competitive Grey Wolf Optimizer For EMG Feature Selection

    Opposition Based Competitive Grey Wolf Optimizer For EMG Feature Selection by Too, Jing Wei, Abdullah, Abdul Rahim

    Published 2020
    “…Initially, several useful features are extracted from the EMG signals to construct the feature set. …”
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  13. 13
    EMG motion pattern classification through design and optimization of Neural Network

    EMG motion pattern classification through design and optimization of Neural Network by Md. Rezwanul, Ahsan, Muhammad Ibn, Ibrahimy, Othman Omran, Khalifa

    Published 2012
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  14. 14
    Simulink model of Mel Frequency Cepstral Coefficient analysis for extracting asphyxiated infant cry features

    Simulink model of Mel Frequency Cepstral Coefficient analysis for extracting asphyxiated infant cry features by Mohebbi Zulfikar, Mohd Ali, Wahida, Mansor, Lee, Yoot Kuan, Dr., Azlee, Zabidi

    Published 2012
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  15. 15
    Brain machine interface: classification of mental tasks using short-time PCA and recurrent neural networks

    Brain machine interface: classification of mental tasks using short-time PCA and recurrent neural networks by Hema, Chengalvarayan Radhakrishnamurthy, Paulraj, Murugesapandian, Sazali, Yaacob, Abd Hamid, Adom, Ramachandran, Nagarajan

    Published 2009
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  16. 16
    Modelling semantic context for novelty detection in wildlife scenes

    Modelling semantic context for novelty detection in wildlife scenes by Yong, SP, Deng, JD, Purvis, MP

    Published 2010
    “…Working with wildlife image data, the framework starts with image segmentation, followed by feature extraction and classification of the image blocks extracted from image segments. …”
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  17. 17
    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
    “…The result of the first experiment showed that entropy features extracted using DWPT are better than bandpower features. …”
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  18. 18
    Noise eliminated ensemble empirical mode decomposition scalogram analysis for rotating machinery fault diagnosis

    Noise eliminated ensemble empirical mode decomposition scalogram analysis for rotating machinery fault diagnosis by Atik, Faysal

    Published 2022
    “…Artificial intelligence can be applied in fault feature extraction and classification. It is crucial to use an effective feature extraction method to obtain most of the fault information and a robust classifier to classify those features. …”
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  19. 19
    Diagnosis and treatment recommender system for myocardial infarction using decision tree and Support Vector Machines (SVM) / Wan Marzuqiamrin Wan Mansor

    Diagnosis and treatment recommender system for myocardial infarction using decision tree and Support Vector Machines (SVM) / Wan Marzuqiamrin Wan Mansor by Wan Mansor, Wan Marzuqiamrin

    Published 2025
    “…The prototype functions by initially allowing the user to upload an ECG image which will be processed using SVM for feature extraction and classification. …”
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  20. 20
    Identification Of Flow Blockage Levels In Centrifugal Pump By Machine Learning

    Identification Of Flow Blockage Levels In Centrifugal Pump By Machine Learning by Ng, Woon Li

    Published 2021
    “…Besides, the result of the Linear SVM model is validated by using the classification packages in Google Colaboratory. …”
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