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  1. 1
    Mussels wandering optimization algorithmn based training of artificial neural networks for pattern classification

    Mussels wandering optimization algorithmn based training of artificial neural networks for pattern classification by Abusnaina, Ahmed A., Abdullah, Rosni

    Published 2013
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  2. 2
    Formulating new enhanced pattern classification algorithms based on ACO-SVM

    Formulating new enhanced pattern classification algorithms based on ACO-SVM by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2013
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  3. 3
    A derivative-free optimization method for solving classification problem

    A derivative-free optimization method for solving classification problem by Shabanzadeh, Parvaneh, Abu Hassan, Malik, Leong, Wah June

    Published 2010
    “…One of the most promising ways to data classification is based on methods of mathematical optimization. …”
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  4. 4
    An ensemble learning method for spam email detection system based on metaheuristic algorithms

    An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

    Published 2015
    “…Feature subset selection and classifier ensemble learning are familiar techniques with high ability to optimize above problems. Recently, various techniques based on different algorithms have been developed. …”
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  5. 5
    New learning algorithm based on Hidden Markov Model (HMM) as stochastic modelling for pattern calssification

    New learning algorithm based on Hidden Markov Model (HMM) as stochastic modelling for pattern calssification by Balakrishnan, Malarvili, Ting, Chee Ming, Shaikh Salleh, Sheikh Hussain

    Published 2009
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  6. 6
    A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem

    A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem by Mohd Pozi, Muhammad Syafiq

    Published 2016
    “…There are two methods in dealing with imbalanced classification problem, which are based on data or algorithmic level. …”
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  7. 7
    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
    “…Four state-of-the-art algorithms include particle swarm optimization, flower pollination algorithm, butterfly optimization algorithm, and CBGWO are used to examine the effectiveness of proposed methods in feature selection. …”
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  8. 8
    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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  9. 9
    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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  10. 10
    Detecting lung cancer region from CT image using meta-heuristic optimized segmentation approach

    Detecting lung cancer region from CT image using meta-heuristic optimized segmentation approach by Shakeel, Pethuraj Mohamed, Mohd Aboobaider, Burhanuddin, Salahuddin, Lizawati

    Published 2022
    “…In this paper, the butterfly optimization algorithm-based K-means clustering (BOAKMC) method is introduced for reducing CT image segmentation uncertainty. …”
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  11. 11
    A New Probabilistic Output Constrained Optimization Extreme Learning Machine

    A New Probabilistic Output Constrained Optimization Extreme Learning Machine by Wong S.Y., Yap K.S., Li X.C.

    Published 2023
    “…Benchmarking; Classification (of information); Constrained optimization; Decision making; Electric power systems; Iterative methods; Knowledge acquisition; Learning algorithms; Pattern recognition; Probability; Confidence threshold; Decision making process; Extreme learning machine; Machine learning approaches; Pattern classification problems; Post-processing procedure; Power system applications; Probabilistic output; Machine learning…”
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  12. 12
    A genetic algorithm based fuzzy inference system for pattern classification and rule extraction

    A genetic algorithm based fuzzy inference system for pattern classification and rule extraction by Wong S.Y., Yap K.S., Li X.

    Published 2023
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  13. 13
    Ant system-based feature set partitioning algorithm for classifier ensemble construction

    Ant system-based feature set partitioning algorithm for classifier ensemble construction by Abdullah, , Ku-Mahamud, Ku Ruhana

    Published 2016
    “…Ensemble method is considered as a new direction in pattern classification. …”
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  14. 14
    Diabetic retinopathy detection using fusion of textural and optimized convolutional neural network features / Uzair Ishtiaq

    Diabetic retinopathy detection using fusion of textural and optimized convolutional neural network features / Uzair Ishtiaq by Uzair , Ishtiaq

    Published 2024
    “…Combining Local Binary Patterns (LBP) based texture features and deep learning features resulted in the creation of the fused features vector which was then optimized using Binary Dragonfly Algorithm (BDA) and Sine Cosine Algorithm (SCA). …”
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  15. 15
    Classification of Agarwood using ANN

    Classification of Agarwood using ANN by M. S., Najib, N. A., Mohd Ali, M. N., Mat Arip, M., Abd Jalil, M. N., Taib

    Published 2012
    “…The network developed based on three layers feed forward network and the back propagation learning algorithm was used in executing the network training. …”
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  16. 16
    STATISTICAL FEATURE LEARNING THROUGH ENHANCED DELAUNAY CLUSTERING AND ENSEMBLE CLASSIFIERS FOR SKIN LESION SEGMENTATION AND CLASSIFICATION

    STATISTICAL FEATURE LEARNING THROUGH ENHANCED DELAUNAY CLUSTERING AND ENSEMBLE CLASSIFIERS FOR SKIN LESION SEGMENTATION AND CLASSIFICATION by Adil H., Khan, Dayang Nurfatimah, Awang Iskandar, Jawad F., Al-Asad, SAMIR, EL-NAKLA, SADIQ A., ALHUWAIDI

    Published 2021
    “…Moreover, skin lesion images are clustered based on fused color, pattern and shape based features. …”
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  17. 17
    Heart sound diagnosis using nonlinear ARX model / Noraishah Shamsuddin

    Heart sound diagnosis using nonlinear ARX model / Noraishah Shamsuddin by Shamsuddin, Noraishah

    Published 2011
    “…The best network architecture for modeling the heart sounds is 2-4-1. As for classification, the features are extracted and selected from the modeled signals and their distinctive patterns are used as inputs to the classifier. …”
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  18. 18
    Enhancing land cover classification in remote sensing imagery using an optimal deep learning model

    Enhancing land cover classification in remote sensing imagery using an optimal deep learning model by Motwake, Abdelwahed, Hassan Abdalla Hashim, Aisha, Obayya, Marwa, Eltahir, Majdy M.

    Published 2023
    “…The current study presents an Improved Sand Cat Swarm Optimization with Deep Learning-based Land Cover Classification (ISCSODL-LCC) approach on the RSIs. …”
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  19. 19
    A Comparative Performance Analysis of Malware Detection Algorithms Based on Various Texture Features and Classifiers

    A Comparative Performance Analysis of Malware Detection Algorithms Based on Various Texture Features and Classifiers by Ahmed I.T., Hammad B.T., Jamil N.

    Published 2025
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  20. 20
    Classification of agarwood using ANN / Muhammad Sharfi Najib ...[et al.]

    Classification of agarwood using ANN / Muhammad Sharfi Najib ...[et al.] by Najib, Muhammad Sharfi, Md Ali, Nor Azah (Dr.), Mat Arip, Mohd Nasir, Jalil, Abd Majid, Taib, Mohd Nasir

    Published 2012
    “…The network developed based on three layers feed forward network and the back propagation learning algorithm was used in executing the network training. …”
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