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  1. 1
    A novel hybrid classification model of genetic algorithms, modified k-Nearest Neighbor and developed backpropagation neural network

    A novel hybrid classification model of genetic algorithms, modified k-Nearest Neighbor and developed backpropagation neural network by Salari, Nader, Shohaimi, Shamarina, Najafi, Farid, Nallappan, Meenakshii, Karishnarajah, Isthrinayagy

    Published 2014
    “…Third, using a modified k-Nearest Neighbor method as well as an improved method of backpropagation neural networks, the classification process was advanced based on optimum arrays of the features selected by genetic algorithms. …”
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  2. 2
    Determining the preprocessing clustering algorithm in radial basis function neural network

    Determining the preprocessing clustering algorithm in radial basis function neural network by S.L. Ang, H.C. Ong, H.C. Law

    Published 2008
    “…Three types of method used in this study to find the centres include random selections, K-means clustering algorithm and also K-median clustering algorithm. …”
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  3. 3
    A novel framework for identifying twitter spam data using machine learning algorithms

    A novel framework for identifying twitter spam data using machine learning algorithms by Maziku, Susana Boniphace, Abdul Rahiman, Amir Rizaan, Muhammed, Abdullah, Abdullah @ Selimun, Mohd Taufik

    Published 2020
    “…The feature selection is introduced to select the most important from the extracted high-dimensional best features and feed the selected features into the minimum Redundancy and Maximal Relevance algorithm and apply random forest for classification. …”
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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
    “…Five state-of-the-art FS methods are used to evaluate the effectiveness of proposed methods in this work. …”
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  5. 5
    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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  6. 6
    Improved cuckoo search based neural network learning algorithms for data classification

    Improved cuckoo search based neural network learning algorithms for data classification by Abdullah, Abdullah

    Published 2014
    “…Specifically, 6 benchmark classification datasets are used for training the hybrid Artificial Neural Network algorithms. …”
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  7. 7
    Integrated Features by Administering the Support Vector Machine of Translational Initiations Sites in Alternative Polymorphic Context

    Integrated Features by Administering the Support Vector Machine of Translational Initiations Sites in Alternative Polymorphic Context by Nanna Suryana, Herman, Burairah, Hussin

    Published 2012
    “…Many algorithms and methods have been proposed for classification problems in bioinformatics. …”
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  8. 8
    Condition diagnosis of bearing system using multiple classifiers of ANNs and adaptive probabilities in genetic algorithms

    Condition diagnosis of bearing system using multiple classifiers of ANNs and adaptive probabilities in genetic algorithms by Wulandhari, Lili A., Wibowo, Antoni, Desa, Mohammad I.

    Published 2014
    “…The mANNs are used to provide several best initial weights which are optimized by APGAs.The set optimized weights from APGAs, afterward, are used as the best weights for condition diagnosis. …”
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  9. 9
    Software metrics selection model for predicting maintainability of object-oriented software using genetic algorithms

    Software metrics selection model for predicting maintainability of object-oriented software using genetic algorithms by Bakar, Abubakar Diwani

    Published 2016
    “…The latest effort to solve this selection problem is the development of the metrics selection model that uses genetic algorithm (GA). …”
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  10. 10
    An improved genetic-fuzzy system for classification and data analysis

    An improved genetic-fuzzy system for classification and data analysis by Lahsasna, A., Seng, W.C.

    Published 2017
    “…In the second variant classifier, we further improve the first variant classifier by enhancing the selection method of the antecedent conditions of the rules generated in the initial population of genetic algorithm. …”
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  11. 11
    Analysis Of Failure In Offline English Alphabet Recognition With Data Mining Approach

    Analysis Of Failure In Offline English Alphabet Recognition With Data Mining Approach by Munnian, Ruthrakumar

    Published 2019
    “…Classification analysis was initially performed on all seven classifier’s algorithms at 10-fold dross validation mode. …”
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  12. 12
    Attacks detection in 6G wireless networks using machine learning

    Attacks detection in 6G wireless networks using machine learning by Saeed, Mamoon M., Saeed, Rashid A., Gaid, Abdulguddoos S. A., Mokhtar, Rania A., Khalifa, Othman Omran, Ahmed, Zeinab E.

    Published 2023
    “…The second stage involves the feature selection approach. Correlation Feature Selection algorithm (CFS) is used to implement the suggested hybrid strategy. …”
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  13. 13
    Application of Decision Trees in Athlete Selection: A Cart Algorithm Approach

    Application of Decision Trees in Athlete Selection: A Cart Algorithm Approach by Riska Wahyu, Romadhonia, A'yunin, Sofro, Danang, Ariyanto, Dimas Avian, Maulana, Junaidi Budi, Prihanto

    Published 2023
    “…The focus of this study is on the use of DTs, employing the Classification and Regression Trees (CART) algorithm, in the initial screening of athletes. …”
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  14. 14
    Development of compound clustering techniques using hybrid soft-computing algorithms

    Development of compound clustering techniques using hybrid soft-computing algorithms by Salim, Naomie, Shamsuddin, Siti Mariyam, Salleh @ Sallehuddin, Roselina, Alwee, Razana

    Published 2006
    “…One of most used compound selection method is cluster-based compound selection, which involves subdividing a set of compounds into clusters and choosing one compound or a small number of compounds from each cluster. …”
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  15. 15
    Assessing the efficacy of machine learning algorithms for syncope classification: A systematic review

    Assessing the efficacy of machine learning algorithms for syncope classification: A systematic review by Goh, Choon-Hian, Ferdowsi, Mahbuba, Gan, Ming Hong, Kwan, Ban-How, Lim, Wei Yin, Tee, Yee Kai, Rosli, Roshaslina, Tan, Maw Pin *

    Published 2024
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  16. 16
    A Multi-Criteria Decision-Making Approach for Targeted Distribution of Smart Indonesia Card (KIP) Scholarships

    A Multi-Criteria Decision-Making Approach for Targeted Distribution of Smart Indonesia Card (KIP) Scholarships by Komang, Aryasa

    Published 2025
    “…Meanwhile, in the classification stage, the C5.0 algorithm achieved the highest accuracy of 97.27% from a total of 551 data points, with 80% used as training data and 20% as testing data. …”
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  17. 17
    Finite impulse response optimizers for solving optimization problems

    Finite impulse response optimizers for solving optimization problems by Tasiransurini, Ab Rahman

    Published 2019
    “…Simulated Kalman filter (SKF) algorithm is one of the algorithms under this classification. …”
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  18. 18
    Finite impulse response optimizers for solving optimization problems

    Finite impulse response optimizers for solving optimization problems by Ab Rahman, Tasiransurini

    Published 2019
    “…Simulated Kalman filter (SKF) algorithm is one of the algorithms under this classification. …”
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  19. 19
    Predicting noise-induced hearing loss (NIHL) in TNB workers using GDAM algorithm

    Predicting noise-induced hearing loss (NIHL) in TNB workers using GDAM algorithm by Rehman Gillani, Syed Muhammad Zubair

    Published 2012
    “…Previously, several modifications were suggested to improve the convergence rate of Gradient Descent Back-propagation algorithm such as careful selection of initial weights and biases, learning rate, momentum, network topology, activation function and ‘gain’ value in the activation function. …”
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  20. 20
    Comparative Analysis Using Bayesian Approach To Neural Network Of Translational Initiation Sites In Alternative Polymorphic Contex

    Comparative Analysis Using Bayesian Approach To Neural Network Of Translational Initiation Sites In Alternative Polymorphic Contex by Herman, Nanna Suryana, Husin, Nurul Arneida, Hussin, Burairah

    Published 2012
    “…The objectives of this paper are to develop useful algorithms and to build a new classification model for the case study.The first approach of neural network includes training on algorithms of Resilient Backpropagation,Scaled Conjugate Gradient Backpropagation and Levenberg-Marquardt.The outputs are used in comparison with Bayesian Neural Network for efficiency comparison.The results showed that Resilient Backpropagation have the consistency in all measurement but performs less in accuracy.In second approach,the Bayesian Classifier_01 outperforms the Resilient Backpropagation by successfully increasing the overall prediction accuracy by 16.0%.The Bayesian Classifier_02 is built to improve the accuracy by adding new features of chemical properties as selected by the Information Gain Ratio method,and increasing the length of the window sequence to 201.The result shows that the built model successfully increases the accuracy by 96.0%.In comparison,the Bayesian model outperforms Tikole and Sankararamakrishnan (2008) by increasing the sensitivity by 10% and specificity by 26%. …”
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